Microalgal Biotechnology: Potential and Production 9783110225020, 9783110225013

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Microalgal Biotechnology: Potential and Production Clemens Posten and Christian Walter (Eds.)

Microalgal Biotechnology: Potential and Production Editors Clemens Posten and Christian Walter

DE GRUYTER

Editors Prof. Dr. Clemens Posten Institute of Life Science Engineering Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany [email protected]

Dr. Christian Walter Institute of Bioprocess Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, Germany [email protected]

The book has 82 figures and 16 tables.

isbn 978-3-11-022501-3 e-isbn 978-3-11-022502-0

Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the internet at http://dnb.dnb.de. © 2012 by Walter de Gruyter GmbH, Berlin/Boston. The publisher, together with the authors and editors, has taken great pains to ensure that all information presented in this work (programs, applications, amounts, dosages, etc.) reflects the standard of knowledge at the time of publication. Despite careful manuscript preparation and proof correction, errors can nevertheless occur. Authors, editors and publisher disclaim all responsibility and for any errors or omissions or liability for the results obtained from use of the information, or parts thereof, contained in this work. Typesetting: Meta Systems GmbH, Wustermark Printing and binding: Hubert & Co., Göttingen Printed in Germany. ∞ Printed on acid-free paper. www.degruyter.com

Preface In recent years, the use of microalgae as phototrophic microorganisms capable of sustainably converting sunlight and CO2 into energy-rich and valuable products became an innovative research area of increasing interest. Microalgae are known to efficiently use the process of water-splitting photosynthesis for the conversion of solar energy into useful chemical energy. They are therefore considered as excellent candidates for the production of biofuels and biopolymers, thus replacing or complementing production processes with heterotrophic microorganisms or traditional crops. The solar-powered synthesis of sustainable products directly from CO2, water and sunlight with microalgae has the potential to provide a renewable source of fuels and high-value commodity chemicals whilst helping to mitigate climate change and is therefore of economic and environmental priority for the public. However, biotechnology driven research with microalgae is still in its infancy. Improvements are required for the molecular processes of sun-to-product conversion as well as for the development of efficient and profitable biomass production systems with positive energy balances. To reach these goals, state-of-the-art research in algae biotechnology projects include a wide range of activities ranging from fundamental approaches, such as screening for suitable algae species or performing systems biology for the identification of potential bottlenecks of production processes, to more applied approaches like the development of crucial technologies for genetic and bioprocess engineering. As an essential add-on, detailed life cycle analyses are widely performed embedded into theoretical and practical feasibility studies to ensure that sustainable bio-refineries with microalgae can be achieved in the future. This book covers a wide range of these exciting developments in the area of algae biotechnology and guides the reader through different aspects of molecular biology and biochemistry research, process development, case studies and life cycle analyses performed by academia but also by spin-up companies. The first two sections – collected in the present volume – focus on the biological potential microalgae can provide and on technical means of algae production. The idea for this book arose from several communities: the microalgae working group of the “DECHEMA”, the collaborative algae research group of the EU-FP7-KBBE “SUNBIOPATH” project and the international “SOLARBIOFUELS” consortium (www.solarbiofuels.org). Members of these groups felt the need to have an updated comprehensive book on modern microalgal biotechnology and decided to activate their personal worldwide networks to contribute. I am delighted that the book has now been published in such an attractive format that a good reception by the readers can be expected and I am sure that this book can provide a valuable guideline for future microalgal biotechnology discussions. Professor Dr. Olaf Kruse, University of Bielefeld

Contents Preface

1 1.1 1.2 1.2.1 1.2.2 1.2.3 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.3.5

2 2.1 2.2 2.2.1 2.2.2 2.3 2.4 2.4.1 2.4.2 2.4.3 2.5 2.5.1 2.5.2 2.5.3

v Clemens Posten Introduction – Discovering Microalgae as Source for Sustainable 1 Biomass 1 All life eminates from the sun! All life originates from the sea! 3 Sustainable microalgal biomass of the third generation Microalgae produce 5 times more biomass per hectare than 3 terrestrial crops Microalgae can be cultivated in arid areas which are not suitable for 4 agriculture Microalgae exhibit high lipid contents over 50 % and high titers of 4 other products 4 The technical challenge 4 Microalgae can use CO2 and sunlight Microalgae can deliver cheap sustainable biomass for bulk chemicals 5 and biofuels 5 Microalgae can be produced nearly everywhere 6 Microalgae do not need pesticides and only little fertilizers 7 Closed photobioreactors as tools of choice The biological potential of microalgae Thomas Friedl, Nataliya Rybalka and Anastasiia Kryvenda 11 Phylogeny and systematics of microalgae: An overview 11 Introduction 16 Diversity and evolution of microalgae 16 Algal diversity 17 Algal evolution 19 Cyanobacteria: The prokaryotic algae Plantae or Archaeplastida supergroup: Green algae, red algae and 22 glaucophytes 22 Viridiplantae: The green algae distributed over two phyla 25 Rhodophyta: Red algae 26 Glaucophytes Chromalveolate algae: The photosynthetic Stramenopiles (heterokont 26 algae) 27 Diatoms (Bacillariophyta; photosynthetic Stramenopiles) Eustigmatophyceae and Xanthophyceae (photosynthetic 29 Stramenopiles) Other photosynthetic Stramenopiles 30

viii 2.5.3.1 2.5.3.2 2.5.3.3 2.6 2.7 2.8

3 3.1 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.4

4 4.1 4.1.1 4.1.2 4.2 4.2.1 4.2.1.1 4.2.1.2 4.2.1.3 4.2.1.4 4.2.1.5 4.2.2 4.2.2.1 4.2.2.2 4.2.2.3 4.2.2.4

Contents

Raphidophyceae 30 Synurophyceae and Chrysophyceae 30 Phaeophyceae 31 Chromalveolate algae: coccolithophorids and haptophyte algae 31 Chromalveolate algae: Dinoflagellates (Dinophyta) 32 Euglenoids (Excavata supergroup) 33 Acknowledgements 33 References 34 Christian Wilhelm and Torsten Jakob Balancing the conversion efficiency from photon to biomass 39 Introduction 39 Definition of important terms 40 Photosynthetic efficiency 40 Growth efficiency (photon to biomass efficiency) 41 Physiological dynamics of processes which control biological energy conversion efficiency 45 45 Absorption Regulation and efficiency of photochemistry 46 Regulation of electron flow 47 Regulation of carbon allocation 48 Conclusions for microalgal biotechnology 50 References 51 Thomas Brück and Daniel Garbe Algae symbiosis with eukaryotic partners 55 Introduction to algae-specific symbiosis 55 55 Importance of algae symbiotic relationships Modes of algae symbiosis with eukaryotes 56 Aquatic systems 58 Algae symbiosis with Cnidaria 58 Symbiont uptake and management 60 Flux of primary metabolites in host and symbiont 60 Optimizing photosynthesis for efficient metabolite exchange 61 Symbiont-derived secondary metabolites Effects of environmental stress on symbiosis 62 Algae symbiosis with Porifera 62 Morphology of sponge–algae associations 63 Symbiont uptake, specificity and transmission 64 Flux of primary metabolites in host and symbiont 64 Symbiont-derived secondary metabolites 65

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Contents

4.2.2.5 4.2.3 4.2.3.1 4.2.3.2 4.2.3.3 4.3 4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 4.3.6 4.3.6.1 4.3.7 4.3.8 4.3.9

65 Effects of environmental stress on symbiosis 66 Algae symbiosis with Mollusca Morphology of mollusc–algae associations 66 Symbiont uptake and maintenance 67 68 Flux of primary metabolites in host and symbiont 68 Terrestrial system 68 Lichens: Ecological pioneers 69 Modes of lichen symbiosis Lichen taxonomy and evolution 69 70 Lichen morphology 71 Symbiotic interactions Lichen growth and propagation 72 73 Lichen propagation Symbiotic benefits for algal photobionts 73 Biotechnological aspects of lichen/mycobiont cultivation Potential of bioactive lichen-derived metabolites 77 References 79

76

Anna Kirchmayr and Christoph Griesbeck 5 Genetic engineering, methods and targets 87 87 5.1 Introduction 87 5.2 Methods in genetic engineering of eukaryotic microalgae 87 5.2.1 Transformation 5.2.1.1 Glass beads and silicon whiskers 87 88 5.2.1.2 Particle bombardment 88 5.2.1.3 Electroporation 5.2.1.4 Agrobacterium tumefaciens-mediated transformation 88 89 5.2.2 Promoters 5.2.3 Gene silencing 91 91 5.2.4 Codon usage 91 5.2.5 Improvement of expression rates and secretion of proteins 93 5.2.6 Selection markers 5.2.7 Reporter genes 94 96 5.3 Examples for biotechnological relevant proteins 96 5.3.1 Proteins expressed in Chlamydomonas reinhardtii 5.3.2 Recombinant proteins in other microalgae 98 5.4 Future prospects/outlook 98 98 5.4.1 Methods for genetic engineering 99 5.4.2 Products from genetically modified microalgae Acknowledgements 100 100 References

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Contents

6 6.1 6.2 6.3

Jean-Paul Cadoret, Alexandre Lejeune, Rémy Michel and Aude Carlier Algenics: Providing microalgal technologies for biological drugs 107 Background and inception of the company 107 Development and optimization of proprietary technologies 108 From proofs of concept to therapeutic product candidates 109 References 109

7 7.1 7.2 7.2.1 7.2.2 7.2.3 7.2.4 7.2.5 7.2.6 7.2.7 7.2.8 7.2.9 7.2.10 7.2.11 7.2.12 7.2.13 7.3 7.4 7.4.1 7.4.2 7.4.2.1 7.4.2.2 7.5 7.5.1 7.6 7.7

8 8.1 8.2 8.3

Technical Means for Algae Production Yusuf Chisti Raceways-based production of algal crude oil 113 Introduction 113 Raceways 114 General configuration 114 Flow in a raceway 115 Power consumption for mixing 118 Paddlewheel design 120 Location 121 Evaporation from raceways 121 122 Temperature variations Culture pH and carbon dioxide demand 124 Oxygen removal 125 Potential for contamination 126 Irradiance variation with depth 126 Local and average values of specific growth rate 128 Raceway capital cost 129 130 Algal crude oil as replacement petroleum Algae biomass production 131 Productivity of biomass and oil 132 Limits to algal biomass productivity 134 Photosynthetic efficiency 135 Why are microalgae more efficient than terrestrial plants? Economics of algal crude oil 137 139 Residual biomass Concluding remarks 141 Nomenclature 142 References 144 Jeff Obbard Cellana LLC: Algae-based products for a sustainable future Introduction 147 147 Cellana technology and demonstration facility Biorefinery approach 148

136

147

Contents

8.4

Prospects References

150 150

F. G. Acién Fernández, J. M. Fernández Sevilla and E. Molina Grima 151 9 Principles of photobioreactor design 9.1 Introduction 151 9.2 Major factors governing the production of microalgae 151 9.3 Open systems 153 9.3.1 Open raceways 153 9.3.1.1 Technical issues 155 9.3.1.2 Scale-up 157 9.3.1.3 Drawbacks 159 9.4 Enclosed photobioreactors 159 9.4.1 Flat-panel photobioreactors 159 9.4.1.1 Technical issues 161 9.4.1.2 Scale-up 166 9.4.1.3 Drawbacks 166 9.4.2 Tubular photobioreactors 167 168 9.4.2.1 Technical issues 9.4.2.2 Scale-up 174 9.5 Summary of major characteristics of large-scale algal cultures systems 177 Acknowledgements 178 References 178 Jérémy Pruvost and Jean-François Cornet 10 Knowledge models for the engineering and optimization of photobioreactors 181 10.1 Introduction 181 10.2 Theoretical background for radiation measurement and 181 handling 10.2.1 Main physical variables 181 10.2.2 Solar illumination 184 10.3 Modeling light-limited photosynthetic growth in photobioreactors 184 184 10.3.1 Overview of the modeling approach 10.3.2 Mass balances 186 187 10.3.3 Stoichiometry of photosynthetic growth 10.3.3.1 Simple stoichiometric equations 187 10.3.3.2 Structured stoichiometric equations 188 10.3.4 Kinetic modeling of photosynthetic growth 189 10.3.5 Energetics of photobioreactors 192

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10.3.6 10.3.6.1 10.3.6.2 10.4 10.4.1 10.4.1.1 10.4.1.2 10.4.1.3 10.4.1.4 10.4.2 10.4.2.1 10.4.2.2 10.4.3 10.5 10.6

Radiative transfer modeling 194 Radiative transfer equation 195 Optical and radiative properties for micro-organisms 201 Illustrations of the utility of modeling for the understanding and optimization of cultivation systems 203 203 Understanding the role of light-attenuation conditions 203 Illuminated fraction γ Achieving maximal productivities with appropriate definition of lightattenuation conditions 204 206 Prediction of biomass concentration and productivity Engineering formula for assessment of maximum kinetic performance 210 in PBRs Solar production 211 Prediction of PBR productivity as a function of radiation 211 conditions 214 Engineering formula for maximal productivity determination Modeling light/dark cycle effects 214 Acknowledgments 217 217 Nomenclature 220 References

Linda Oeschger and Clemens Posten 11 Construction and assessment parameters of photobioreactors 225 11.1 Introduction 11.2 Technical design features 225 226 11.2.1 Material issues 226 11.2.2 Geometric parameters 11.2.3 Hydrodynamic parameters 228 230 11.3 Measured performance criteria 11.4 Mode and stability of operation 231 234 11.5 Conclusion 235 References

12

12.1 12.2 12.3

225

Peter Bergmann, Peter Ripplinger, Lars Beyer and Walter Trösch Autotrophic, industrial cultivation of photosynthetic microorganisms using flue gas as carbon source and Subitec’s flat-panel-airlift (FPA) cultivation system 237 237 Introduction Subitec GmbH and the flat-panel-airlift system 237 239 From laboratory to pilot scale 242 References

Contents

13 13.1 13.2 13.3

Luc Roef, Michel Jacqmain and Mark Michiels Case study: Microalgae production in the self-supported ProviAPT 243 vertical flat-panel photobioreactor system 243 Introduction ProviAPT technology and features 243 Prospects 245 References 245

Alexander Piek 14 Case study: Biomass from open ponds 247 14.1 Introduction 247 247 14.2 Production process 14.2.1 Removal of coarse solids 248 14.2.2 Concentrating the biomass 248 14.2.3 Washing the biomass 249 250 14.2.4 Differences to closed photo-bioreactors 14.3 Energy consumption 250 14.4 Survey of process relevant data 251 References 252 Marco Brocken Case study: Spiral plate technology for totally dewatering algae 253 alive 253 15.1 Introduction 15.2 Separation technology 253 15.2.1 Evodos technology 253 15.2.2 Key design parameters 254 256 15.3 Operational characteristics 258 References 15

Index

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List of contributing authors Peter Bergmann Subitec GmbH, Stuttgart, Germany Chapter 12 Lars Beyer Subitec GmbH, Stuttgart, Germany Chapter 12 Marco Brocken Loosdrecht, The Netherlands e-mail: [email protected] Chapter 15 Thomas Brück Fachgebiet Industrielle Biokatalyse, Fakultät für Chemie, Technische Universität München, Garching, Germany e-mail: [email protected] Chapter 4 Jean-Paul Cadoret Algenics, Pôle Bio Ouest, Saint-Herblain, France e-mail: [email protected] Chapter 6 Aude Carlier Algenics, Pôle Bio Ouest, Saint-Herblain, France Chapter 6 Yusuf Chisti College of Sciences, School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand e-mail: [email protected] Chapter 7 Jean-Francois Cornet Laboratoire de Génie Chimique Biologique, Université Blaise Pascal, Aubière, France e-mail: [email protected] Chapter 10

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List of contributing authors

F. G. Acién Fernández Department of Chemical Engineering, University of Almería, Almería, Spain e-mail: [email protected] Chapter 9 Thomas Friedl Experimental Phycology and Culture Collection of Algae (SAG), Georg-August-Universität Göttingen, Göttingen, Germany e-mail: [email protected] Chapter 2 Daniel Garbe Department of Chemistry, Industrial Biocatalysis, Technische Universität München, Garching, Germany e-mail: [email protected] Chapter 4 Christoph Griesbeck Department of Biotechnology, MCI Management Center Innsbruck, Innsbruck, Austria e-mail: [email protected] Chapter 5 Emilio Molina Grima Biotechnology of Microalgae, Science and Technology Building 2A, University of Almería, Almería, Spain e-mail: [email protected] Chapter 9 Michel Jacqmain Proviron Industries NV, Hemiksem, Belgium Chapter 13 Torsten Jakob Institut für Biologie I, Abteilung Pflanzenphysiologie, Universität Leipzig, Leipzig, Germany e-mail: [email protected] Chapter 3

List of contributing authors

xvii

Anna Kirchmayr Umwelt-, Verfahrens- und Biotechnik, MCI Management Center Innsbruck, Innsbruck, Austria e-mail: [email protected] Chapter 5 Anastasiia Kryvenda Experimental Phycology and Culture Collection of Algae (SAG), Georg August University Göttingen, Göttingen, Germany and Botany Department, Education and Science Center “Institute of Biology”, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine Chapter 2 Alexandre Lejeune Algenics, Pôle Bio Ouest, Saint-Herblain, France Chapter 6 Rémy Michel Algenics, Pôle Bio Ouest, Saint-Herblain, France Chapter 6 Mark Michiels Proviron Industries NV, Hemiksem, Belgium e-mail: [email protected] Chapter 13 Jeff Obbard Department of Civil & Environmental Engineering, The National University of Singapore, Singapore e-mail: [email protected] Chapter 8 Linda Oeschger Institute of Life Science Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected] Chapter 11

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List of contributing authors

Alexander Piek Renewable Resources, GEA Mechanical Equipment, GEA Westfalia Separator Group GmbH, Oelde, Germany e-mail: [email protected] Chapter 14 Clemens Posten Institute of Life Science Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany e-mail: [email protected] Chapter 1, 11 Jeremy Pruvost Université de Nantes, Saint-Nazaire, France e-mail: [email protected] Chapter 10 Peter Ripplinger Subitec GmbH, Stuttgart, Germany e-mail: [email protected] Chapter 12 Luc Roef Proviron Industries NV, Hemiksem, Belgium Chapter 13 Nataliya Rybalka Experimental Phycology and Culture Collection of Algae (SAG), Georg August University Göttingen, Göttingen, Germany and Plant Cell Physiology and Biotechnology, Botanical Institute, Christian Albrechts University of Kiel, Kiel, Germany Chapter 2 J. M. Fernández Sevilla Department of Chemical Engineering, University of Almería, Almería, Spain Chapter 9

List of contributing authors

Walter Trösch Fraunhofer Institute for Interfacial Engineering and Biotechnology, Stuttgart, Germany Chapter 12 Christian Wilhelm Institut für Biologie, Abteilung Pflanzenphysiologie, Universität Leipzig, Leipzig, Germany e-mail: [email protected] Chapter 3

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Clemens Posten

1 Introduction – Discovering Microalgae as Source for Sustainable Biomass 1.1 All life eminates from the sun! All life originates from the sea! From the beginning people worshipped the sun as a life-giving divine entity. So it is not a new insight of modern natural sciences that all life depends on the sun. Then humans developed their mechanistic view to nature. For over 200 years we have known that photosynthesis is the biochemical process which converts the sun’s energy to chemical energy stored in plant biomass accompanied by carbon dioxide fixation and oxygen formation (from water splitting). In fact, photosynthesis produced the oxygen-rich atmosphere of our planet. About 50 % of the world wide oxygen is produced from marine algae, water plants with a simple structure. They occur either as single-celled microalgae with sizes of typically a few micrometer, which can form colonies, or as many-celled macroalgae, up to 60 m long (seaweed, kelp). They are the most important primary biomass producers on earth. Plankton algae are especially productive. Depending on the availability of nutrients, plankton biomass forms 2–6 tons per hectare and year; in case of algal blooms up to 50 tons. Until now humans made only indirect use of microalgae by breathing and by putting fish on the food list. Microalgae form the basis of the marine food chain, with humans on the top and edible fish in between. All of them rely on the valuable compounds of the algae like pigments or polyunsaturated fatty acids. Algae thus represent an important untapped resource, which can be enhanced further by cultivation. Mankind learned to cultivate plants and to domesticate animals. Now with exhausting resources in the sea and with limited land areas, it is time to get direct access to microalgae as a treasure of the sea. How to start with finding suitable strains of the microalgae for technical use? There are estimated 500,000 species of algae on earth. They represent several completely different cellular structures ordered in different phylogenetic lineages. So the term “Alga” represents a natural group but more an artificial definition based on similar morphology like trees and shrubs. Only a small fraction of these, about 220 macroalgae and 15 microalgae mainly the cyanobacterium Arthrospira platensis (Spirulina) and the eukaryotic alga Chlorella vulgaris, are being utilized commercially for foodstuff, animal feed, and cosmetic ingredients. A large number of screening programs have commenced in the last years. Facing the huge diversity of algae and the multiplicity of anticipated applications, the programs restrict themselves to pre-defined algae groups. Most of them focus on growth rate and

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1 Introduction – Discovering Microalgae as Source for Sustainable Biomass

Fig. 1.1: Painterly execution of a Chlamydomonas cell by Anna Nickelsen, Munich (Copyright Jörg Nickelsen, Munich, 2012).

high lipid content under standardized conditions. Direct measurements of biomass yield from light during screening are quite rare. General statements which could help make pre-screening easier such as “small algae cells have typically higher growth yield” are assumed but not proven. However, surprises like the hydrocarbon excreting alga Botryococcus braunii are not excluded. In parallel, biotechnologists go into a deeper understanding of some strains e.g. of the green alga Chlamydomonas reinhardtii employing genetics, proteomics, and metabolomics. In fact, these algae have been completely sequenced and genetic engineering has become a standard approach. The next indispensable step should go further in the direction of process-oriented strain development. This could consider practical issues like robustness against temperature oscillations or high cell densities in reactors, easy harvesting, or not sticking to surfaces. More sophisticated approaches consider antenna reduction to avoid unnecessarily high mutual shading or sharpening product profiles. As for arable crops, higher productivities are expected in exchange for a more or less protected environment in the reactor. While mankind went step by step for millennia to breed high production plants from early grasses and herbs, we do have not even decades to come to a visible success. But also in agriculture, a lot of progress was achieved only in the last decades by making use of new biological and technical means. Whether that succeeds or not for the microalgae has to be proven, but the signals are on “green” to move forward on a sustainable pathway towards “domestication” of microalgae and development of new applications. With the rapid developments in research and the increasing visibility of large open ponds, society has also begun to take more and more note of microalgae.

1.2 Sustainable microalgal biomass of the third generation

3

Headlines like “concentrated green energy” or “gold of the future” should definitely be written with question marks. Geneticists claim “The making of a super plant”, while economists demand “The quest for commercialization”. However, algae have already been adopted by architects designing future urban scenarios, inspired artists and reached even the mind of the youngsters, see a painting in figure 1. Facing an uncertain future with food and energy shortages and increasing climate change, it is especially for the children’s sake that microalgae biotechnology has to become a success.

1.2 Sustainable microalgal biomass of the third generation Microalgae are increasingly becoming the focus of interest in the field of renewable resources. Some authors already classify them as biomass of the third generation – “Biomass 3.0”. Energy crops of the first generation will evoke the food to fuel debate, while conversion of agricultural remainders to biofuel of the second generation is basically limited in amount. What are the specific qualities that make microalgae so interesting?

1.2.1 Microalgae produce 5 times more biomass per hectare than terrestrial crops Many microalgae species can regularly reach specific growth rates of over 3/day which corresponds to doubling times of nearly 5 hours. With increasing culture density, the available light becomes more and more a thermodynamic limit and determines the process. The growth pattern shifts from exponential to linear and has to be discussed in terms of biomass formed per light available. Under lab conditions microalgae can convert the energy of sunlight to more than 5 % (theoretically up to 10 %) to the chemical energy of biomass. This value is higher than the value that can be obtained from agricultural energy crops (less than 1.5 %). In fact, photosynthesis is the same for both cases, but due to their unicellular habitus all cells can be supplied equally well with nutrients and CO2, while higher plants invest in other things like their complex macroscopic structure. In the literature many fantastic values regarding the areal productivity of microalgae in comparison to terrestrial crops have been published. In most cases they are exaggerated and do not mention the specific conditions for which the values are measured. The sometimes quoted 250 t dry mass / hectare / year is for example an extrapolation from lab values obtained under ideal conditions to light conditions in the Sahara, all kind of dust, temperature or logistic problems ignored. While in open ponds the areal productivity does not exceed values of terrestrial crops, a stable production on a real hectare and a real year under strict phototrophic conditions in closed photobioreactors is still an open challenge.

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1 Introduction – Discovering Microalgae as Source for Sustainable Biomass

1.2.2 Microalgae can be cultivated in arid areas which are not suitable for agriculture This is an important point, as only an increase of the land or sea area for the production of biomass can solve the current problems of supplying mankind with food and fuel. Indeed, microalgae can be grown on land, where no other claims exist, thus especially avoiding competition for arable land. In the case of production in open ponds, water to replace water losses from evaporation has to be provided, but it can be brackish water or even sea water. That limits the possible sites for such plants, but for the time being arid areas close to brackish water reservoirs are not limited. In closed photobioreactors evaporation can potentially be avoided. Furthermore, microalgae can be harvested all year-round. This requires some process engineering equipment, but can be developed to industrial scale without classical agricultural work.

1.2.3 Microalgae exhibit high lipid contents over 50 % and high titers of other products Indeed, microalgae can accumulate oils to over 50 % of the cell dry weight. With respect to volume fraction of the living cell and facing the high water content that is of course much less. Due to the simple morphology lacking roots, leaves, stem and complex reproduction organs, the entire algal biomass can be harvested and utilized. This makes the process much more efficient compared to higher plants, where only a few parts (e.g. the seeds) show acceptable oil concentrations. Also pigments like carotinoids can be accumulated to high levels. Furthermore, many species do not form compounds like poorly degradable lignin, what make them easily accessible for energetic or chemical use.

1.3 The technical challenge But what makes microalgal cultivation so difficult that industrial microalgal biomass production is not state of the art? Some people even have problems to “keep the algae out of the pool”.

1.3.1 Microalgae can use CO2 and sunlight That is of course true thanks to photosynthesis and it makes algae not different from higher plants. However, what is politically a big issue is a challenge from the viewpoint of biochemical engineering. Algae can bind about 1.85–2.5 kg CO2 per kg biodrymass, depending on cell composition. While flue gas emissions are available

1.3 The technical challenge

5

from industrial regions, these CO2-sources are remote from possible microalgal production sites. Carbon dioxide from air would in principle do the job, but a high CO2-content of the gas is necessary to overcome the mass transfer limitations between gas phase and fluid phase. Additionally, an increased CO2 partial pressure compared to the atmosphere is necessary to support maximum growth kinetically without carbon limitation. The fact that algae do bind CO2 does not actually mean that they can be used for CO2 sequestration, which is a common misunderstanding. In the best case a carbon neutral life cycle can be assumed. Furthermore, the simple fact that light is not miscible leads to a high need for auxiliary energy for mixing and aeration. The so-called fluctuating-light-effect predicts high productivity only with high mechanical energy input. So up to now no energy-neutral closed photobioreactor has been tested in larger areas in the sense that it needs less auxiliary energy than it gains as chemical energy from microalgae.

1.3.2 Microalgae can deliver cheap sustainable biomass for bulk chemicals and biofuels Despite all advantages microalgal biomass is up to now available only at costs which are several times higher than costs for residual biomass from agriculture. Besides the problem of auxiliary energy, the reactor costs are decisive. While open ponds are fairly achievable, closed reactors are still too expensive to allow for microalgal production cheaply enough for biofuel production, but nevertheless engineers have taken on the challenge to solve this energy-water-productivity nexus with highly promising results.

1.3.3 Microalgae can be produced nearly everywhere Like all living organisms, microalgae have a temperature optimum for growth and product formation. The temperature dependent growth curve is quite narrow for most species investigated so far. In any case cultivation on an industrial scale requires cooling of the reactor – evaporation in open ponds – even in temperate middle latitudes, making temperature control a major problem for the future. Production sites in the Andean highlands would be theoretically ideal with respect to an evenly distributed annual and daily temperature, but may suffer from logistic problems. Light should come as evenly as possible to the algae plant. While in the Sahara the annual irradiation by sun light reaches two to three times the values of temperate latitudes, the sunshine is concentrated in a few hours, making concepts of light dilution more difficult and increases temperature problems as well. So the search for an optimum production site – land or sea based, higher latitude or tropical region, mountain or plane – is not yet over.

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1 Introduction – Discovering Microalgae as Source for Sustainable Biomass

1.3.4 Microalgae do not need pesticides and only little fertilizers Like all living cells microalgae consist of proteins, nucleic acids, lipids and carbohydrates, so nitrogen and phosphate containing fertilizers are of course necessary. The good news is that these compounds can potentially be applied much more effectivly as they cannot drain into the ground water or emit into the atmosphere. Pesticide-resistant microalgae are a quite recently occurring development. Contamination with grassers and predators (e.g. ciliates), bacterial and fungal contamination as well as competing microalgae reduce the yield unacceptably. Here closed photobioreactors are also not really an exception. A way out is the employment of extremophile conditions or the use of pesticides in combination with pesticideresistant production strains as it is obviously foreseen in industry. Starting from the work of Beijerink and Pringsheim more than 100 years ago, see figure 2, and moving to the first technical pond systems of Burlew some 50 years ago, we have ended up now with high rate ponds and a large diversity of concepts for closed photobioreactors. Nevertheless, waiting for nearterm industrial production units for products in the middle and lower price segment, scientific

Fig. 1.2: Portrait of M.W. Beijerink, the pioneer in applying microbiological working rules to microalgae, here rightly hailed by E.G. Pringsheim in his book about pure cultures of microalgae including media and illumination.

1.3 The technical challenge

7

Fig. 1.3: Picture of a growth experiment in microtiter plate to test different media compositions for different strains illustrating the sensitivity of the system.

progress seems to be excruciatingly slow. Much work, which is already done in heterotrophic bioprocesses, has to be made up. But today’s microalgae research is excellently positioned. Media coming from empirically found recipes for use in lab scale for 2 g/L shaking flasks are further optimized to media supporting e.g. 10 g/L biodrymass in large scale under a more economic view with optimally balances compounds. Nowadays that is done with employing highly parallel experiments in microtiter plates (figure 3) and testing the results against stoichiometric and accumulation models, following the ion uptake in a second step using ion chromatography in bench scale, or employing all kinds of -omics to evaluate cell responses.

1.3.5 Closed photobioreactors as tools of choice Designing photobioreactors is no longer a job for engineers or biologists only. It is rather a concerted action of understanding the interaction between reactor design parameters and physiological response of the cells. An architect designing a house for a family will ask firstly the family about their personal needs – in biotechnology it means measuring kinetics, then the architect considers technical / financial constraints – mainly engineering work in case of reactor design. After some time the architect will ask the family again, whether they feel happy in the house. In biotechnology that would mean measuring on-line signals and cell composition for different reactors and specific growth conditions. Unfortunately, results are practically never published, which would allow for a decision whether productivity was influenced, e.g. by light saturation or mechanical stress from bubbles, so here is room for more rational engineering in reactor design. For the time being, different

8

1 Introduction – Discovering Microalgae as Source for Sustainable Biomass

concepts will be alive, leaving conversion to a commonly accepted standard for photobioreactors to the future. To distinuish between open ponds and closed photobioreactors is not so natural as sometimes suggested in so far as covered ponds even with light diluting structures have been proposed. The next step, namely solid/liquid separation for harvesting, is unique for microalgae although the topic as such has been treated for decades in biotechnology. Reasons for this special role are low cell densities and low product values in the case of biofuels. All these advantages even though not perfectly developed for the time being makes it worth dealing with this exciting topic. The push by strain and reactor development and the pull by increasing market needs for chemicals, food, and fuel will finally drive microalgal biotechnology to a state where biomass from microalgae will remarkably contribute to the future supply of valuable biomass in huge amounts. The present book shows the state of the art in science and technology of these developments and highlights the frontiers of recent research. As much work is done actually in industry, accordingly industrial case studies are provided beside the detailed chapters from academia. Even if not all questions and developments could be considered under the practical constraints of this book and the diversity of research, going through the respective contents will open the door to further reading in original publications and to following threads in internet. So please enjoy reading the book and maybe you will find time to give feedback to the authors for a further fruitful development of the ongoing discussions.

The biological potential of microalgae

Thomas Friedl, Nataliya Rybalka and Anastasiia Kryvenda

2 Phylogeny and systematics of microalgae: An overview 2.1 Introduction Algae is a term of convenience that refers to a collection of highly diverse organisms that carry out photosynthesis and/or possess plastids (Keeling 2004). Many authors even include the prokaryotic cyanobacteria with algae because they exhibit a lifestyle rather similar to their eukaryotic counterparts and often share the same habitat with them. Microalgae suitable for biotechnological exploitation should be easy to grow in large quantities and robust in all stages of processing. Such algae may be found in almost all lineages of cyanobacteria and eukaryotes. Examples of microalgae (including cyanobacteria) currently used in biotechnological applications or with high potential for biotechnological exploitation are shown in Figures 2.1 and 2.2. Most suited are unicellular algae with cell walls, i.e. of coccoid organization, without attachment structures to prevent biofouling while processing, and they should not form resting stages (e.g. through sexual reproduction) under adverse culture conditions. Despite their apparent morphological simplicity, such algae often belong to different unrelated phylogenetic lineages and, therefore, may also exhibit different physiological and biochemical properties. For example, coccoid eukaryotic microalgae, which are from very distantly related evolutionary lineages, may look superficially rather similar at low magnification (see Fig. 2.2c,d). In light of the extreme diversity and exciting evolutionary history of microalgae (Fig. 2.3), it is surprising that so far only a very minor fraction of the algal diversity has been explored for biotechnological applications (Tab. 2.1). Currently, there are probably no more than about 30 species and genera from no more than 11 taxonomic classes of autotrophic microalgae in use. Although very promising results and applications have already been well established with such a small number of microalgae, a much broader variety and larger numbers of culture strains of microalgae are available from public culture collections (e.g. the SAG culture collection at Göttingen University, Germany (Friedl and Lorenz 2012)) which, in addition, may be ever expanded by new isolates. Therefore, the vast diversity of microalgae and many additional microalgal lineages still remain almost untapped even though they provide very promising resources to be further exploited. Many obstacles still apparent in microalgal biotechnology, e.g. those that delay the development of inexpensive methods for biomass production and the optimization of compound accumulation for using microalgae as solar-driven cellular factories or a source of biofuels may be solved by the extended and systematic exploitation of

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2 Phylogeny and systematics of microalgae: An overview

Fig. 2.1: Examples of genera and species of microalgae currently used in or suitable for biotechnological applications. (a–d) Filamentous cyanobacteria. (a, b) Arthrospira platensis (“Spirulina”) strain SAG 85.79. (a) Coiled filaments and cells with gas vesicles. (b) Overview of coiled filaments. (c) Anabaena catenula strain SAG 1403-1. Filaments with rounded vegetative cells and heterocyte (arrow head) at the intercalary position. (d) Oscillatoria sancta strain SAG 74.79. Straight filaments without heterocytes. Note the cross-walls within the filaments. (e) Porphyridium purpureum strain SAG 1380-1a, a unicellular red alga (Rhodophyta) from terrestrial habitats. Note the reddish chloroplast with a star-like appearance. In several cells, the centrally located pyrenoid is visible. (f) Resting stages of the green algal flagellate Haematococcus pluvialis strain 192.80 (Chlorophyta, Chlorophyceae) that accumulated the red pigment astaxanthin. (g) Resting stages of the green algal flagellate Tetraselmis tetrathele strain SAG161–2c (Chlorophyta, Chlorodendrophyceae). Note the accumulation of lipid droplets. Scale bars: a, c, e, f, 10 μm; b, 200 μm; d, 100 μm.

2.1 Introduction

13

Fig. 2.2: Examples of genera and species of microalgae currently used in or suitable for biotechnological applications. (a) Vegetative cells of the coccoid green alga Lobosphaera incisa strain SAG 2007 (Chlorophyta, Trebouxiophyceae) with smooth chloroplasts appressed to the cell wall. The species (formerly “Myrmecia” incisa or “Parietochloris” incisa) is known for its high lipid content. (b) Vegetative cells of Eustigmatos magnus strain SAG 36.89, a coccoid freshwater member of Eustigmatophyceae (Stramenopiles), with small oil droplets. (c) Nannochloropsis salina strain SAG 40.85, a marine coccoid member of Eustigmatophyceae (Stramenopiles). Some cells have oil droplets. (d) Model green alga Chlorella vulgaris strain SAG 211–11b (Chlorophyta, Trebouxiophyceae), type species of the genus Chlorella. Note the sphaeroid cells with almost central pyrenoid covered by starch and empty sporangial cell walls. (e) Model diatom Phaeodactylum tricornutum strain SAG 1090–1b (Bacillariophyceae, Stramenopiles). Note the dimorphism of vegetative cells, i.e. fusiform and oval morphotypes (in the upper and lower parts of the image). (f) Model diatom Thalassiosira weissflogii strain SAG 2135 (Bacillariophyceae, Stramenopiles); vegetative cells in the girdle (upper right) and valve view (centre), below the formation of male gametes (lower left). (g) Vegetative cells of Pavlova lutheri SAG 926-1 (Haptophyta, Chromalveolates). Note the cells with flagella (arrowhead). Scale bar: 10 μm.

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2 Phylogeny and systematics of microalgae: An overview

Genus, species, taxonomic position

Occurrence, growth

Compounds and application

Freshwater High salinity and high pH

Toxins Pigments, high protein content

Freshwater Marine Freshwater Freshwater

Recombinant proteins Carotenoids (β-carotene), glycerol Fatty acids, carotenoids (Astaxanthin) Carotenoids (lutein), aquaculture

Freshwater Freshwater

Oil, carbohydrates Fatty acids, carbohydrates

Marine, brackish and freshwater

PUFA (EPA), α-tocopherol, sterols: 24-methylenecholesterol; cosmetics: anti-wrinkles; aquaculture: rotifer feed, shrimp hatchery, oyster and clam feed; waste water treatment, cadmium removal,

Marine, brackish and freshwater

PUFA (EPA), pigments (phycoerythrin); antiviral activitiy, sulfated polysaccharides, antioxidants; poultry feed PUFA (EPA), lipids; β-
-glucans

Cyclotella cryptica

Marine, even at high salinities, thermophilic Freshwater

Haslea ostrearia

Freshwater

Nitzschia alba

Freshwater, heterotrophic growth possible Marine

Cyanobacteria Aphanizomenon flos-aquae Arthrospira (Spirulina) platensis Chlorophyta, Chlorophyceae Chlamydomonas reinhardtii Dunaliella (two species) Haematococcus pluvialis Scenedesmus/Desmodesmus Chlorophyta, Trebouxiophyceae Botryococcus braunii Chlorella vulgaris Chlorophyta, Chlorodendrophyceae Tetraselmis (three species)

Rhodophyta Porphyridium cruentum

Bacillariophyta (Stramenopiles) Chaetoceros (four species)

Odontella aurita

Phaeodactylum tricornutum

Freshwater, heterotrophic growth possible

Lipids, β-1,3-glucan, bioaccumulation of metals; prophylaxis of myocardial infarct; PUFA (EPA), linoleic acid; marennine (colouring agent); isoprenoids, tetra-unsaturated sesterterpenoids (haslenes) PUFA (EPA), lipids

PUFA (DHA, EPA), lipids; β-carotene, fucoxanthin;trace elements and vitamins for human diet; cosmetics PUFA (DHA, EPA), lipids; aquaculture: rotifer feed

Tab. 2.1: Overview of several species of microalgae currently used in biotechnology: their taxonomic position, growth characteristics, compound content and application.

2.1 Introduction

Genus, species, taxonomic position

15

Occurrence, growth

Compounds and application

Bacillariophyta (Stramenopiles) Skeletonema costatum

Marine

Thalassiosira (four species)

Marine

PUFA (EPA); antibacterial activity, β-1,3-glucan; cosmetics: anti-ageing and anti-cellulite effects, aquaculture: molluscs and oyster feed; waste water treatment: detoxification of cadmium and copper, biodegradation of phenolic compounds PUFA (DHA, EPA), lipids; carotenoids, fucoxanthin; aquaculture: feed for larvae, bivalves, oysters; waste water treatment, cadmium removal, biodegradation of phenolic compounds

Eustigmatophyceae (Stramenopiles) Monodopsis subterranea Freshwater Nannochloropsis Marine, brackish (three species) and freshwater

Rhaphidophyceae (Stramenopiles) Olisthodiscus sp. Marine Haptophyta (Chromalveolates) Isochrysis galbana

Pavlova lutheri

Dinophyta (Chromalveolates) Crypthecodinium cohnii

Euglenoids (Excavata) Euglena gracilis

Marine

Marine

Most promising algal EPA producer PUFA (EPA, DHA), lipids; pigments, carotenoids; β-1,3-glucan; poultry feed; aquaculture: rotifer and bivalve feed Lipids; β-1,3-glucan PUFA (DHA, EPA); sterols, alkenone; aquaculture: clam and oyster feed; waste water treatment PUFA (DHA, EPA); α-tocopherol, sterols, alkenone; cosmetics: anti-wrinkles; aquaculture: rotifer, oyster and clam feed; shrimp hatchery; cadmium removal

Freshwater, heterotrophic species

High content of PUFAs (DHA); pigments as colouring agents; extracts used in promoting lactic acid and bifidus, bacteria growth; pharmaceuticals; pet foods; aquaculture feed

Freshwater, heterotrophic growth possible

PUFA, lipids; β-1,3-glucan (Paramylon), α-tocopherol

Tab. 2.1: (continued)

the biodiversity of microalgae which must also include an expanded experimental study of growth conditions to obtain optimal yields of the desired algal products. In parallel, a new approach has occurred with the successful demonstration of

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2 Phylogeny and systematics of microalgae: An overview

genetically transformed microalgae, which may be optimized for the production of secondary metabolites by genetic engineering. This approach is currently further explored in the diatom Phaeodactylum (Fig. 2.2e), but also the green alga Chlamydomonas may constitute a promising alternative system of genetically transformed microalgae. Genetic manipulation of microalgae in order to use them most efficiently and with new capabilities in biotechnological applications is still a vision and associated with many problems (for a review, see León (2007)). The mass production of genetically modified microalgae may carry many risks; for example, the acceptability of products from genetically modified organisms is often perceived as problematic.

2.2 Diversity and evolution of microalgae 2.2.1 Algal diversity The biodiversity of microalgae is remarkable. The number of eukaryotic groups of microalgae is large, including at least 30 taxonomic classes assigned to a variety of evolutionary lineages that are interspersed among the protozoa, fungi, plants and animals. Eukaryotic life is seen to be distributed on at least five phylogenetically distinct “supergroups” as based on genome sequencing evidence (Keeling 2004; Keeling et al. 2005); the algae are distributed on at least four of the five recognized supergroups of eukaryotes (Keeling 2004; Keeling and Palmer 2008; Fig. 2.3). It is estimated that about 30,000–40,000 algal species have been described to date, but there is consensus that the number of still undiscovered species may exceed known species by a factor of four to eight (Norton et al. 1996). Other authors estimate the number of actually existing algal species at 350,000, with diatoms the most species-rich group (Brodie and Zuccarello 2006). For the diatoms, the highest estimate is that there may be thousands more species than are currently described (11,000; John (1994)). Microalgae are extremely abundant and diverse in nature. For example, microscopic marine phytoplankton are the primary photosynthetic organisms in the oceans, which cover over 70 % of the Earth’s surface; here, their abundance almost always exceeds 106 cells per litre (Worden et al. 2004). Productivity of microalgae is very high. Whereas the biomass of vascular plants on land accumulates over time, single-cell phytoplankton in the ocean are quickly eaten or killed by viruses, and so the standing crop remains more or less constant (Andersen 2008). Phytoplankton primary production – the amount of carbon fixed by photosynthesis – is estimated to be at least 40 % of global primary production (Bolin et al. 1977; Martin et al. 1987). In other words, every second breath we take comes from microalgae. Plastids are the organelles of plants and eukaryotic algae that harbour photosynthesis, which is the essential process by which CO2 is fixed to build up carbohydrates and other cellular substances using light energy. Plastids synthesize many chemical compounds also important for other bio-

2.2 Diversity and evolution of microalgae

17

chemical path ways (e.g. aromatic amino acids, heme, isoprenoids and fatty acids). Therefore, microalgae may be regarded as solar-driven cell factories.

2.2.2 Algal evolution An extensive reticulation occurred in algal evolution, i.e. mainly two major events of endosymbiosis have shaped the algal tree of life (Bhattacharya et al. 2007; Tirichine and Bowler 2011; Fig. 2.3). A so-called “primary” endosymbiosis with an ancient cyanobacteria lineage, i.e. the engulfment and retention of a cyanobacterium by a heterotrophic eukaryotic cell, was the start of eukaryotic algal life (Keeling and Palmer 2008; Keeling 2010; Tirichine and Bowler 2011). Subsequently, a massive gene transfer from the endosymbiont to the host nucleus occurred, so that only a minor fraction of the plastid’s proteins was still encoded by the organelle, whereas the majority of plastid genes were transferred to the nucleus (Keeling and Palmer 2008; Fig. 2.3). Once the endosymbiont was established and integrated with its host, the three lineages of Archaeplastida (or Plantae supergroup; (Adl et al. 2005; Tirichine and Bowler 2011)) diverged, i.e. the green algae incl. land plants (embryophytes), the red algae and the glaucophytes (Fig. 2.3). Although the Archaeplastida already represent a great diversity and collectively are ecologically significant, they represent only a fraction of eukaryotic phototrophs. Most algal lineages acquired their plastids through secondary endosymbiosis, which is the uptake and retention of a primary algal cell by another eukaryotic lineage (Keeling and Palmer 2008). In most cases, the primary algal cell involved in secondary endosymbiosis was a red algal cell (Fig. 2.3). However, recently, phylogenomic evidence emerged for a third partner that has been involved in the secondary endosymbiosis of diatoms and presumably other heterokont algae as well. An endosymbiosis with a green alga most likely preceded the red algal endosymbiont (Moustafa et al. 2009; Tirichine and Bowler 2011). Only two algal lineages, the Euglenoids and chlorarachniophytes, are derived from a green algal secondary endosymbiosis (Fig. 2.3). The secondary spread of plastids had a major impact on eukaryotic diversity. In secondary endosymbiosis, the retained primary algal cell, the endosymbiont, has progressively been degenerated including a massive endosymbiotic gene transfer until all that remained in most cases was the plastid (Keeling and Palmer 2008). Because the endosymbiont was inserted into the endomembrane system of the host, secondary plastids have one or two additional membranes around it forming so-called complex plastids with three or four membranes and are sometimes intimately connected to the nucleus through a shared endomembrane system, the plastid endoplasmatic recticulum. In most cases, the nucleus of the algal endosymbiont is completely absent, but a kind of relict nucleus (termed nucleomorph), representing the smallest known eukaryotic genomes, persisted in two algal groups, the Cryptomonads (with the plastid originated from a red algal cell) and

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2 Phylogeny and systematics of microalgae: An overview

Fig. 2.3: Schematic diagram summarizing endosymbiotic events and the evolution of eukaryotic algal groups (adapted from Tirichine and Bowler 2011). Eukaryotic supergroups are in grey and bold letters; algal classes and phyla discussed in the text are in bold letters; names of algal classes and phyla for which members are currently used in biotechnological applications are in italics. The two levels of endosymbiotic events, primary and secondary endosymbiosis, are in boxes.

the chlorarachniophytes (with plastids arose from a green algal cell) (Keeling and Palmer 2008; Keeling 2010). The majority of secondary algal lineages belong to the Chromalveolates supergroup, which includes those algae with plastids that exhibit

2.3 Cyanobacteria: The prokaryotic algae

19

chlorophylls c such as the photoautotrophic Stramenopiles (the heterokont algae, e.g. the diatoms and brown algae (Yang et al. 2012)), the Cryptophyta, the Haptophyta, as well as two algal lineages of the Alveolates, i.e. the dinophytes with complex plastids and the Apicomplexa, a parasitic group with non-phototrophic relict plastids (Fig. 2.3). To make the reticulate evolution of microalgae even more complex, some dinophyte lineages even abandoned their previous plastids that originated from a red algal cell and instead recruited cells of either cryptomonads, haptophytes or diatoms as their plastids (so-called tertiary endosymbiosis ); even a third green algal endosymbiosis may be found in the Dinophyta (Gould et al. 2008; Keeling 2010). Secondary endosymbiosis is without doubt of several, i.e. at least three, origins: at least once derived from a red algal endosymbiont, in the Chromalveolates, and twice from unrelated green algae in chlorarachniophytes in the Rhizaria and Euglenoids in the Excavata supergroups (Keeling 2004, 2010; Keeling et al. 2005). However, whether the Chromalveolates actually represent a single monophyletic supergroup of eukaryotes is still being debated by some authors (e.g. Baurain et al. 2010), while others consider this supergroup of eukaryotes to be even larger, that is the Rhizaria may fall within Chromalveolates (Tirichine and Bowler 2011, and references therein). For all the endosymbiotic events related to plastid evolution, a robust and significant wealth of genome or multigene sequence evidence has already accumulated over the past 10 years, even though several aspects and details still have remained a matter of debate; for a review, see Keeling (2010). This clearly demonstrates that the eukaryotic algae are of chimeric nature, i.e. at least two genomes, one from the endosymbiont and the other from the host, have been intermixed through endosymbiosis. This may be the major reason why microalgae have been so successful in evolution of life and are so diverse in their biochemical features, i.e. exhibit such an extremely broad variety of secondary metabolites to be exploited by biotechnology.

2.3 Cyanobacteria: The prokaryotic algae Cyanobacteria form a large and morphological diverse group of photoautotrophic Gram-negative bacteria. Many cyanobacteria have large cells, like the eukaryotic algae, with which they share oxygenic photosynthesis. Cyanobacteria occur in a broad variety of forms, from solitary unicells over colony forming and undifferentiated filamentous types to more complex forms that even display the hallmarks of multicellularity (Kauff and Büdel 2011). There is strong evidence that cyanobacteria are the oldest micro-organisms to perform oxygenic photosynthesis, dating back to about 2.45 billion years. Cyanobacteria exhibit a broad environmental tolerance range, which makes them well adapted to living under extreme and fluctuating conditions. From an evolutionary point of view, it appears that cyanobacteria may have been forced by the “modern” eukaryotes to withdraw themselves into such

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2 Phylogeny and systematics of microalgae: An overview

habitats. Many cyanobacteria can fix atmospheric molecular nitrogen gas (N2) into ammonia, which is then further assimilated into amino acids and proteins. The process of nitrogen fixation is most often confined to specialized cells, heterocytes, which are capable to protect nitrogenase, the oxygen-sensitive key enzyme of nitrogen fixation. However, nitrogen fixation can also occur in cyanobacteria in the absence of heterocytes if other mechanisms of nitrogenase protection are available. Nitrogen-fixing cyanobacteria increase soil and water fertility and are particularly attractive as symbionts, e.g. to form lichens with fungi or being endosymbionts in plants, some metazoa, dinoflagellates and diatoms (Sharma et al. 2011). Cyanobacteria contain the chlorophylls a and d; in some genera also chlorophyll b is present, as well as carotenoids and phycobilins as accessory pigments to capture light energy and convey it to the photosynthetic reaction centres located inside the thylakoid membranes. Phycobilins are blue or red water-soluble pigments and are present in large amounts. They are bound to proteins, which form hemispherical phycobilisomes, which occur on the outer thylakoid surfaces. Several particles in the cytoplasm of cyanobacteria may represent storage products, i.e. cyanophycin (a polymer of the nitrogen-rich amino acids asparagine and arginine), cyanophycean starch (a polyglucan) and polyphosphate. In addition, lipid droplets, carboxysomes (polygonal aggregations of Rubisco, the key enzyme in CO2 fixation) and gas vesicles may be present in the cytoplasm. Gas vesicles (assemblies of hollow, pointed cylinders, but not delimited by membranes) occur frequently in aquatic cyanobacteria and enable them to have buoyancy and to confer vertical mobility in the water column. The cell wall of cyanobacteria is basically the same as that for Gram-negative bacteria, i.e. a thin peptidoglycan layer (a polymer composed of sugar derivatives and amino acids) is outside the cell membrane (cytoplasm membrane). Outside the peptidoglycan layer is a space surrounded by another membrane, termed the outer membrane. A characteristic feature of cyanobacteria is that they are usually surrounded by a mucilaginous sheath (extracellular polymeric substances), mostly composed of polysaccharides, which protects the cells from drying out and enables them to attach to substrates. Cyanobacteria are recognized for their ability to occupy diverse aquatic and terrestrial habitats, cyanobacteria produce a large variety of organic compounds which in nature are used by other organisms, and they stabilize sediments and soils. The cyanobacteria metabolic activities make quantitatively important contributions to the carbon, nitrogen, sulphur and other biogeochemical cycles. Bloomforming cyanobacteria often release significant toxic substances, termed cyanotoxins (Sharma et al. 2011). Cyanobacteria are also well known from hot acidic springs throughout the world (e.g. Ward and Castenholz 2002). Many physiological properties that make the cyanobacteria able to adapt to a large variety of extreme and environmental conditions may also indicate their long evolutionary history. Many cyanobacteria are able to tolerate low oxygen conditions and even free sulphide at levels much higher than those tolerated by most eukaryotic algae. Many terrestrial

2.3 Cyanobacteria: The prokaryotic algae

21

cyanobacteria can tolerate high UVB and -C radiations, which may be also reminiscent of an adaption to early Earth’s conditions. Some cyanobacteria can even confer anoxygenic photosynthesis where H2S is utilized as an electron donor. Cyanobacteria are also very successful primary colonizers in terrestrial habitats owing to their tolerance of desiccation and to water stress. Probably owing to their high capacity to adapt to almost all kinds of habitats, the metabolic acitivities of cyanobacteria also provide a broad range of potential applications such as nutrition (food supplements and fine chemicals), in agriculture as biofertilizer and in wastewater treatment (production of exopolysaccharides and flocculants). In addition, they also produce a wide variety of secondary metabolites with biological activities, e.g. strong antiviral, antibacterial, antifungal, antitumoral and anti-inflammatory activities, useful for therapeutic purposes. In recent years, cyanobacteria have gained interest for the production of biofuels, owing to their biomass, ethanol and H2 production. Because of their simple growth requirements, it is also potentially cost-effective to exploit cyanobacteria for the production of recombinant compounds. An excellent review on the potential of cyanobacteria for various uses has been presented by Sharma et al. (2011). Classification of cyanobacteria above the level of species and genera has traditionally been based on cell organization, ability and strategies of nitrogen fixation, and modes of propagation (Kauff and Büdel 2011). In current schemes, the cyanobacteria are classified into five subsections (Subsections I–V) according to the bacteriological code of nomenclature, and five orders, Chroococcales, Pleurocapsales, Oscillatoriales, Nostocales and Stigonematales, which have been erected under the botanical code of nomenclature. Several of the groups described by current classification schemes are known to be polyphyletic in molecular phylogenies (for reviews, see, e.g. Sharma et al. (2011)), and phylogenies based on only a single gene may not be sufficient to infer a cyanobacteria classification that reflects phylogenetic relationships (Kauff and Büdel 2011). The cyanobacterial genus Arthrospira Sitzenberger ex Gomont (formerly Spirulina Turpin ex Gomont; without heterocytes; Fig. 2.1a,b) is used as human food supplement; the Spirulina industry is of increasing economic importance. Large filamentous colonies of Nostoc Vaucher ex Bornet & Flahault (with heterocytes) are used in China as a “hair vegetable” (Facai). Another filamentous freshwater cyanobacterium without heterocytes even being used as a food supplement despite the fact it has not been licenced as such is Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault (as “AFA” alga). Although it provides a high content of essential amino acids, fatty acids, minerals and vitamins, its use as food supplement needs to be viewed with caution because the species is known for forming algal blooms that produce strong hepatotoxins and inhibitors of nerve functions. In other biotechnological applications, additional filamentous cyanobacteria are used, e.g. those of the Anabaena Bory ex Bornet & Flahault (with heterocytes; Fig. 2.1c), the Lyngbya C. Agardh ex Gomont and Oscillatoria morphotypes without heterocytes (Fig. 2.1d). Surprisingly almost no unicellular cyanobacteria have been used for biotechnological exploitation, even though they also can be easily grown.

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2 Phylogeny and systematics of microalgae: An overview

2.4 Plantae or Archaeplastida supergroup: Green algae, red algae and glaucophytes The green algae and the embryophytes (multicellular land plants) constitute the Viridiplantae, which, together with the red algae (Rhodophyta) and the glaucophytes (Glaucophyta), form the “Plantae” or “Archaeplastida” supergroup of eukaryotes (Adl et al. 2005; Keeling 2010). They are the only photosynthetic eukaryotes with double membrane-bound plastids; they are derived from primary endosymbiosis. The phylum Rhodophyta is often regarded as a sister group to Viridiplantae, and the Glaucophyta is considered as the most basal lineage. The monophyletic origin of Archaeplastida has been supported by nuclear and chloroplast multigene analyses (Rodríguez-Ezpeleta et al. 2005; Archibald 2009).

2.4.1 Viridiplantae: The green algae distributed over two phyla Features that set apart the green algae (as well as embryophytes) from other Archaeplastida are that their double membrane-bound chloroplasts exhibit stacked thylakoids, and that they contain the accessory pigment chlorophyll b. The other accessory pigments are the carotenoids beta carotene and xanthophylls. The main reserve polysaccharide is starch, which is deposited inside the plastid (Lewis and McCourt 2004; Friedl and Rybalka 2012). A few genera of green algae contain even non-photosynthetic plastids without chlorophylls, e.g. Prototheca W. Krüger and the parasitic Helicosporidium Keilin and, therefore, grow heterotrophically in the dark. Most of these characters are shared with embryophytes (land plants), and therefore, apart from being mostly not of true multicellular organization (Leliaert et al. 2012) and often confined to aqueous habitats, it is difficult to delimit the green algae from embryophytes without considering their very diverse gross morphologies (Friedl and Rybalka 2012). Green algae vary morphologically from the smallest known eukaryote (the prasinophyte Ostreococcus Courties & Chret.-Dinet) and tiny flagellates to giant unicells with multiple nuclei or multicellular forms reminiscent of bryophyte gametophytes (Coleochaete Brébisson). The majority of green algae thrive in freshwater or terrestrial habitats, but some microscopic forms (prasinophytes) are abundant in marine phytoplankton. It is typical for green algae to live in various terrestrial habitats (Holzinger 2009). Among green algae, there are many “land plants,” i.e. transitions to the land happened many times in the evolution of the green algae (Lewis and Lewis 2005; Lewis 2007). Examples of terrestrial green algal life include the biofilms of building facades, biological soil crusts (Büdel et al. 2009) or algal crusts on trees. Excellent reviews on the peculiarities of terrestrial green algae have been presented by López-Bautista et al. (2007) and Rindi (2011). Green algae, especially a variety of members of Trebouxiophyceae, are frequently involved in symbioses with ciliates and metazoans.

2.4 Plantae or Archaeplastida supergroup

23

The green algae have evolved in two major lineages (Lewis and McCourt 2004; Wodniok et al. 2011; Friedl and Rybalka 2012; Leliaert et al. 2012). One lineage, the phylum Chlorophyta, comprises the majority of green algal diversity, i.e. the green algae in a more narrow sense or the majority of what have been traditionally called green algae, with a bewildering array of morphologies attributable to at least five different morphological organizations (Pröschold and Leliaert 2007). The other lineage, the phylum Streptophyta, comprises no more than four or five morphologically rather simple lineages and two advanced lineages with true multicellular organization (Becker and Marin 2009). A lineage uniting the flagellate Mesostigma Lauterborn and the sarcinoid Chlorokybus Geitler may represent the earliest diverging lineage of the Streptophyta. Which streptophyte algal group may be the closest living relatives of embryophytes has been heavily debated in recent years. Most recent analyses of nuclear genes as well as chloroplast phylogenomic analyses, however, revealed that either the Zygnematophyceae (synonym Conjugatophyceae) or a group uniting the Zygnematophyceae with Coleochaetophyceae is the sister group to embryophytes (Wodniok et al. 2011). The Charophyceae is now seen as an earlier divergence and not as the closest relatives of land plants. No single member of streptophyte green algae is currently used in biotechnological applications, most likely owing to the fact that no study has been carried out yet to screen for valuable compounds in these algae. However, because the streptophyte green algae are so closely related to land plants, they may be particularly interesting for precursor or compounds otherwise similar to those so far only known from multicellular land plants. The polyunsaturated fatty acid (PUFA) γ-linolenic acid (GLA) has been found to be more frequent in streptophyte green algae than in any other algal group, with relatively high percentages in some strains of the zygnematophycean genus Closterium (Lang et al. 2011). Other rather small unicellular zygnematophyceans, members of the order Desmidiales, are known to grow fast and to produce extracellular polysaccharides that may be interesting to explore further. The Chlorophyta comprises the vast majority of green algae, with four major lineages, i.e. the classes Chlorophyceae, Trebouxiophyceae, Ulvophyceae, and Chlorodendrophyceae, forming the “crown group” of Chlorophyta and, as their early offspring, an assemblage of various monophyletic lineages of unicellular prasinophyte algae (Friedl and Rybalka 2012). Members of Chlorophyceae thrive mostly in freshwater or terrestrial habitats, and there are only very few symbiotic genera. The Chlorophyceae includes a variety of unicellular or colonial flagellates (e.g. Chlamydomonas Ehrenberg, Volvox Linnaeus) and many members with firm unbranched (e.g. Oedogonium Link ex Hirn) or branched (e.g. Stigeoclonium Kützing) filaments. The Chlorophyceae may be divided into five clades that are robustly resolved in most phylogenetic analyses. Two of these clades, Sphaeropleales and Volvocales (sometimes also called Chlamydomonadales), are close relatives to each other; they contain green algae most widely used for biotechnological exploitation: the coccoid colony-forming genera

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Scenedesmus Meyen and Desmodesmus (Chodat) (R. Chodat) S. S. An, T. Friedl & E. Hegewald. (Sphaeropleales), the green flagellated genera Chlamydomonas, Haematococcus Flotow (Fig. 2.1f) and Dunaliella Teodoresco. (Volvocales). Both genera, Scenedesmus and Desmodesmus, belong to the Sphaeropleales and are clearly separated from each other by their cell wall features. In Desmodesmus, certain submicroscopic structures (ornamentations) are present on the outermost cell wall layer and one or several spines are on each cell. Those structures are absent in Scenedesmus, and only rib-like structures may be formed. Species of Dunaliella form a lineage of their own within the Volvocales (Nakada et al. 2008). The astaxanthin-producing Haematococcus (Fig. 2.1f) is within a distinct lineage of Volvocales, which also includes a variety of algae morphologically different from Haematococcus, e.g. the coccoid green Chlorococcum Meneghini (with a high relative DHA content (Lang et al. 2011)) and Ettlia Komárek (Nakada et al. 2008). The latter genus also comprises “Neochloris” oleoabundans, which was found to exhibit an extraordinarily high oil content. Close relatives of Haematococcus are species of the flagellate Chlorogonium Ehrenberg, which were found to accumulate a red pigment in lag phase cultures as well, probably astaxanthin (J. Fredersdorf and M. Lorenz, pers. comm.). Species of Chlamydomonas are distributed on several lineages of the Volvocales, which have to be attributed to different genera (Nakada et al. 2008). The type species C. reinhardtii P. A. Dangeard is, together with morphologically different flagellated green algae, in a lineage of its own (Nakada et al. 2008). Other green flagellates, species of Tetraselmis F. Stein (formerly Platymonas G. S. West; Fig. 2.1g), which are used in waste water treatment, aquaculture and cosmetics production, are not members of the Chlorophyceae but belong to the class Chlorodendrophyceae, which is the most basal of the four major Chlorophytan lineages (Friedl and Rybalka 2012). For the class Trebouxiophyceae, the majority of presently known members are coccoid unicells; in some lineages also colonial coccoids occur, and a few lineages may also form filaments. Flagellated vegetative forms are not known for the class. Members of Trebouxiophyceae are mostly found in drier habitats, e.g. in soil, or are aerophytic algae. Many lineages include minute freshwater phytoplankton (e.g. Chlorellales). There are numerous examples for symbioses in ciliates, metazoan (Pröschold et al. 2011) and lichens (Friedl and Büdel 2008). Very few members of Trebouxiophyceae are currently used for biotechnological exploitation; the most prominent may be Chlorella vulgaris Beijerinck (Fig. 2.2d) and a few more Chlorellalike green algae. The genus Chlorella in its traditional circumscription is of multiple origins and distributed over at least two classes of green algae. An overview of the “true” Chlorella species, i.e. close relatives of the type species, C. vulgaris, (e.g. C. sorokiniana Shihira & R. W. Krauss) and other members of the Chlorella-clade of the order Chlorellales, has been presented by Luo et al. (2010). Within C. vulgaris, a remarkable genetic diversity has been found among various isolates of the same species (Müller et al. 2005). An overview of the confusing taxonomic history of

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Chlorella species has been presented by Huss et al. (1999) and Krienitz et al. (2004). Several former “Chlorella” species have been transferred to other genera, e.g. Chloroidium Nadson, Parachlorella L. Krieniz, E. H. Hegewald, D. Hepperle, V. A. R. Huss, T. Rohr & M. Wolf, and Watanabea N. Hanagata, I. Karube, Chihara & P. C. Silva. C. pyrenoidosa H. Chick is regarded as a synonym with the type species, C. vulgaris. C. zofingiensis Dönz and C. fusca var. vacuolata Shihira & Krauss have been found to be members of Chlorophyceae and transferred to the genera Chromochloris and Graesiella (Guiry and Guiry 2012). The heterotrophic Prototheca zopfii W. Krüger, which exhibits a high relative DHA content (Lang et al. 2011), is a close relative of the “true” Chlorella species. The order Chlorellales comprises many more minute and fast-growing coccoid green algae (e.g. Nannochloris Naumann) from freshwater phytoplankton, for which it would be interesting to test their biotechnological potential. Two more coccoid green algae well known for their high biotechnological potential are members of two distinct lineages of Trebouxiophyceae: Botryococcus braunii Kützing which is regarded as a promising source of renewable hydrocarbon fuels (Weiss et al. 2010; Tanoi et al. 2011) and Lobosphaera incisa (Reisigl) Karsten, Friedl, Schumannn, Hoyer & Lembcke (synonyms Myrmecia incisa Reisigl and Parietochloris incisa (H. Reisigl) S. Watanabe; Fig. 2.2a) known as the richest “plant” source of the PUFA arachidonic acid (Bigogno et al. 2002a, 2002b). The Ulvophyceae is probably morphologically the most diverse group of the Chlorophyta (Leliaert et al. 2012). The class may be regarded as predominantly marine; it includes all macroscopic marine representatives of the Chlorophyta, e.g. those with multicellular bodies composed of uninucleate cells, forming blades or tubular forms as Ulva L., which is known to form “green tides”. The Trentepohliales is another unique lineage of Ulvophyceae with exclusively terrestrial members forming filaments of uninucleate cells. They are known for their capacity to accumulate β-carotene when exposed to air.

2.4.2 Rhodophyta: Red algae Red algae are a very large and diverse group of microscopic algae and macroalgae (Brodie & Zuccarello 2006). They are best known for their economic and ecological importance. Though they are present in freshwater with several genera, the majority of red algal genera occur on tropical and temperate marine shores, where they play important ecological roles (Graham et al. 2009). Of well-known economic importance are species of Porphyra C.Agardh and other red algal species, which are grown in mariculture operations for use as human food. Several other marine genera are cultivated or harvested for the extraction of gelling polysaccharides such as agarose and carrageen (Graham et al. 2009). The plastids of red algae lack chlorophyll b and c, but contain phycobilins (allophycocyanin, phycocyanin, and

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the red pigment phycoerythrin) as accessory pigments, which are located in phycobilisomes on the outer surface of the thylakoids. Red algae produce floridean starch, which is deposited in the cytoplasm. The rhodophytes lack flagella and centrioles in all stages of the life history (Graham et al. 2009). The Rhodophyta comprises only few microscopic forms. The unicellular species of Porphyridium Nägeli (Fig. 2.1e) are well known for their high content of polysaccharides as well as rich sources of pigments and some PUFAs, e.g. EPA. High relative EPA contents have also been detected in the multicellular, but more simply organized Compsopogonopsis leptocladus (Montagne) V. Krishnam. and Acrochaetium virgatulum (Harvey) Batters.

2.4.3 Glaucophytes Glaucophytes (or glaucocystophytes) form a small group of microscopic algae exclusively found in freshwater environments; only about 13 species of glaucophytes have been described so far. Glaucophyte plastids are unique among plastids in that they are retaining the prokaryotic peptidoglycan layer between their two membranes. Molecular phylogenetic analyses show the glaucophytes to be the earliest divergence within the plant supergroup (Keeling 2010). Glaucophyte plastids share the accessory photosynthetic pigments, phycobilins, which are organized in phycobilisomes (small particles on the outer surface of thylakoid membranes) with cyanobacteria and rhodophytes. So far, glaucophytes have not been used for biotechnological exploitation. However, a high frequency of EPA-containing strains has been found among Glaucophytes (Lang et al. 2011), and some species, e.g. Cyanophora paradoxa Korshikov, may also be easily grown in mass culture.

2.5 Chromalveolate algae: The photosynthetic Stramenopiles (heterokont algae) Stramenopiles are one major group of the Chromalveolates supergroup of eukaryotes (Keeling et al. 2005; Tirichine & Bowler 2011). They form a monophyletic group of photoautotrophic as well as heterotrophic organisms, which are characterized by swimming cells possessing at least one flagellum with distinct tripartite tubular hairs (Andersen 2004; Graham et al. 2009). Most Stramenopiles have two distinctly different flagella and, therefore, are also known as heterokonts (heterokont algae; Heterokontophyta). They have a long flagellum with tripartite hairs (tinsel or immature flagellum) to pull the cells through the water and a shorter smooth one (whiplash or mature flagellum) that lacks tripartite hairs, often with a light-sensing flagellar swelling at its base (Andersen 2004; Graham et al. 2009). Photosynthetic Stramenopiles usually appear brown or golden brown (and are thus sometimes referred to as chromophytic algae owing to the presence of characteristic accessory

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pigments, fucoxanthin or vaucheriaxanthin, and at least one form of chlorophyll c (except in eustigmatophytes). In most photosynthetic Stramenopiles, a chloroplast endoplasmatic reticulum is present, i.e. the plastid and nucleus are structurally connected. The photosynthetic Stramenopiles comprise a tremendous morphological diversity: a large variety of unicellular or colonial algae, including the diatoms with silica frustules as walls, multicellular simple filaments (e.g. in the xanthophytes) as well as the macroscopic complex brown algae forming a brown canopy at sea shores. Based on detailed analyses of pigment composition, ultrastructure and molecular phylogenies, more than a dozen different classes of photosynthetic Stramenopiles have been described (Andersen 2004; Graham et al. 2009). For a recent review of phylogenetic relationships of lineages of photosynthetic Stramenopiles (heterokont algae) see Yang et al. (2012). Classes of photosynthetic Stramenopiles that are currently used in or explored for biotechnological applications represent the diatoms, the Eustigmatophyceae and Raphidophyceae.

2.5.1 Diatoms (Bacillariophyta; photosynthetic Stramenopiles) Diatoms are unicellular or colonial eukaryotic algae and are one of the most easily recognizable algal groups because of their unique architecturally complex siliceous cell walls, termed frustules. They are composed of amorphous silica with regular patterns of ornamentation, extensively used as taxonomic “fingerprints”, and consist of two parts. Diatoms contain brown plastids owing to the presence of the brown carotenoid fucoxanthin and are derived from red algal secondary endosymbiosis. Other characteristic accessory pigments are the chlorophylls c1 and c2. There are two major types, centric and pennate diatoms. Centric diatoms have a radial symmetry in valve view, contain several small plastids and are oogamous, i.e. their gametes are an egg and motile spermatozoids with one flagellum. Pennate diatoms have elongated cell walls with bilateral symmetry. Those pennate diatoms with a raphe exhibit gliding motility. They contain one or few plastids and are isogamous, i.e. their gametes are of equal shape and without flagella. While the diatoms are often regarded as a class of photosynthetic stramenopiles (Bacillariophyceae; e.g. Yang et al. 2012), a more recent taxonomic arrangement even assigns the diatoms the rank of a phylum, Bacillariophyta. The latter is further divided into three classes: the Mediophyceae and Coscinodiscophyceae comprise the centric diatoms, whereas the Bacillariophyceae encompass all pennate forms (Medlin and Kaczmarska 2004). While pennate diatoms are represented in roughly equal numbers in the freshwater and marine habitats, the centric diatoms are present predominantly in the marine environment (Lee 2008). Diatoms are probably the most species-rich group of eukaryotic algae. Approximately 40 % of the global annual marine biomass production is due to diatoms, and these are responsible for about 25 % of total terrestrial primary production,

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making them the most dominant group of organisms sequestering carbon from the atmosphere (Bozarth et al. 2009). They may be the most important group of eukaryotic phytoplankton in marine and freshwater environments but are also abundant as benthic forms, growing on sediments or attached to submersed substrates, and can also be found in soils. Diatoms form important symbioses with nitrogen-fixing cyanobacteria in tropical seas. Diatom frustules predominate in the sediments of the ocean floor, and the analysis of diatom frustule containing sediments can provide information on past environmental conditions. Diatoms produced significant fossil records, and their fossil deposits are even mined and used commercially. Diatoms are considered to provide enormous economic potential (Kroth 2007; Bozarth et al. 2009). They are a valuable source for the acquisition of food supplements (e.g. because of their high content of vitamins and amino acids) as well as substitutes for synthetic substances (e.g. cosmetic chemicals) and produce antibiotics and pharmaceutically active substances. Diatoms are a rich source of polyunsaturated fatty acids (PUFA), especially eicosapentaenoic acid (EPA), arachidonic acid and docosahexaenoic acid. Other industrial applications considered and initiated for commercial use of diatoms include renewable energy, e.g. ethanol production via fermentation, and methane production as well as fluid-fuel production. Their unusual cell wall offers prospects for applications in nanotechnology and computer chips. Therefore, a number of diatom species have already been exploited for biotechnological applications, also due to their easy growth. There are even species that can grow heterotrophically (Perez-Garcia et al. 2011) and even without the addition of silica to the growth medium. A centric diatom (Coscinodiscophyceae) used for the production of PUFAs is the freshwater species Odontella aurita (Lyngbye) C. Agardh (Pulz and Gross 2004). Other Coscinodiscophyceae are used in aquaculture, e.g. as a food source for shrimp larvae and bivalve mollusk postlarvae, i.e. the marine Skeletonema costatum (Greville) Cleve, marine species of Chaetoceros and Thalassiosira (Fig. 2.2f). Cyclotella cryptica Reimann, Lewin & Guillard (Coscinodiscophyceae) has been used for silica production (Csögör et al. 1999) and as a source of PUFA for aquaculture; the species can also be grown heterotrophically (Pahl et al. 2010). Further examples of diatoms capable of heterotrophic growth are the pennate Phaeodactylum tricornutum Bohlin (Bacillariophyceae; Fig. 2.2e), Nitzschia alba J. C. Lewin & R. A. Lewin and Nitzschia laevis Hustedt (Wen and Chen 2003; Pahl et al. 2010; Perez-Garcia et al. 2011) When grown autotrophically, P. tricornutum can grow even independent of silica. The pennate diatom Haslea ostrearia (Gaillon) Simonsen contains the hydrosoluble pigment marininne and, therefore, is used as a food source in aquaculture for oyster greening (Lebeau and Robert 2003a, 2003b). Genome projects are regarded as promising milestones for the effective genetic engineering of diatoms. Complete genomes are already available for the pennate species P. tricornutum (Fig. 2.2e) and Thalassiosira pseudonana. Transformation protocols have already been developed which lead to the stable integration of for-

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eign DNA into diatom chromosomes. Future goals are to target different compartments of the diatom cell to introduce new capacities and also transform the plastid genome which would allow the expression of foreign genes of prokaryotic origin (Bozarth et al. 2009). The great potential of diatoms to serve as solar-powered expression factors has recently been demonstrated. The pathway of a polyester naturally produced by certain bacteria, poly-3-hydroxybutyrate (PHB), has been successfully introduced in P. cornutum, which has produced bioplastic as small granules in the cytosol (Hempel et al. 2011a). Furthermore, P. cornutum has been used successfully to produce a recombinant protein (human antibody against Hepatitis B surface protein) through photosynthesis with the advantage of being CO2-neutral compared with expression systems used so far, such as bacteria, yeasts or mammalian cells (Hempel et al. 2011b).

2.5.2 Eustigmatophyceae and Xanthophyceae (photosynthetic Stramenopiles) Despite being only distantly related within the Stramenopiles, the two classes of heterokont algae, Xanthophyceae and Eustigmatophyceae, may be phenotypically similar, and they are also linked by their taxonomic histories. Members of both classes lack fucoxanthin, and because chlorophyll b is also absent, they have a yellow-greenish coloration. Xanthophyceae are rather similar in their growth forms to certain green algae, i.e. coccoid, filamentous and siphonous organizational levels occur within the class as in Chlorophyceae. In fact, some green algae have previously been described as members of Xanthophyceae and vice versa. For Eustigmatophyceae, only coccoid members are known so far (Přibyl et al. 2012). Eustigmatophyceae even lack chlorophyll c (the only known exception within the heterokont algae), and their chloroplasts do not contain a girdle lamella, the presence of which is a characteristic feature of most stramenopile algae. Two genera of unicellular coccoid Eustigmatophyceae are currently used in biotechnological applications. Species of Nannochloropsis D.J. Hibberd occur in both marine (N. salina D. J. Hibberd; Fig. 2.2c; N. oculata (Droop) D. J. Hibberd) and freshwater (N. limnetica Krienitz, D. Hepperle, H.-B. Stich & W. Weiler) habitats. Monodus subterraneus J. B. Petersen, regarded as the most promising algal producer of the PUFA EPA (Cohen 1999), has been described as a member of Xanthophyceae, but later was found to be a member of Eustigmatophyceae and, therefore, has been transferred to a new genus as Monodopsis subterranea (J. B. Petersen) Hibberd. The position of M. subterranea as well as another species of Monodus in the Eustigmatophyceae has been proven by molecular phylogeny (Přibyl et al. 2012). M. subterranea is closely related to species of Nannochloropsis (N. salina; Fig. 2.2c; N. oculata, N. limnetica) which are among the most popular microalgae for biotechnological exploitation. Other unicellular coccoid yellow-greenish algae, currently described as members of Xanthophyceae, may actually be found as members of

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Eustigmatophyceae after a closer pigment analysis and molecular phylogenetic investigation (Přibyl et al. 2012). Their biochemical properties may even be similar to M. subterranea or species of Nannochloropsis, if phylogenetic relatedness may also indicate phenotypic similarity. Thus, it would be interesting to test additional unicellular members of both classes, Eustigmatophyceae (e.g. Vischeria Pascher, Pseudostaurastrum Chodat, Eustigmatos D. J. Hibberd; Fig. 2.2b, Trachydiscus Ettl) and Xanthophyceae (e.g. Chlorellidium Vischer, Mischococcus Nägeli), for their biotechnological potential. Even in recent literature, there is some confusion, and certain coccoid yellow-greenish algal species have sometimes been wrongly assigned to one of the two classes (e.g. Lang et al. 2011).

2.5.3 Other photosynthetic Stramenopiles 2.5.3.1 Raphidophyceae Members of this class of stramenopiles, also called “chloromonads” are unicellular flagellate algae with about 50 known species that occur in marine, brackish or freshwater habitats. The freshwater species of the Raphidophyceae are green, whereas the marine forms are yellowish and contain the carotenoid fucoxanthin. The chloroplasts of Raphidophyceae are usually small and numerous per cell. What makes Raphidophyceae interesting for biotechnological applications is that their photosynthetic assimilation products are oils that are stored as droplets in the cytosol. Some marine Raphidophyceae, e.g. Chatonella antiqua and Heterosigma carterae, produce strong neurotoxins and cause toxic red tides. A species of Olisthodiscus N. Carter has been used in biotechnological applications because of its rather high content of the fatty acid EPA, and it also produces β-1,3-glucan (Marshall et al. 2002).

2.5.3.2 Synurophyceae and Chrysophyceae Members of both classes of Stramenopiles are close relatives in molecular phylogenies (Yang et al. 2012); synurophytes and chrysophytes are able to switch between autotrophy, heterotrophy and even phagotrophy, which puts them at a distinct advantage in aquatic ecosystems that are low in nutrient concentrations and have reduced light penetration. This may be advantageous in certain biotechnological applications as well where the provision of sufficient light and nutrients can be an expensive cost factor. However, neither group has been used in any biotechnological applications until now. Cell surfaces of synurophytes are covered by overlapping silica scales, sometimes with spiny bristles, and are perforated (Graham et al. 2009). The scales’ perforation patterns are used as taxonomic features to delimitate species of synurophytes. A number of adhesive polysaccharides are involved in the scale attach-

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ment. In contrast to most diatoms, synurophytes can continue to divide and function also in the absence of an external silica covering. In cultures of naked cells, most cells will recover a complete cell covering when silicate is resupplied to silicadepleted cells. Synurophytes are abundant in neutral to slightly acidic freshwater. Some species are regarded as indicators of low levels of pollution, but others are characteristic of eutrophic lakes (Graham et al. 2009). Chrysophytes are golden-brown micro-algae that are mostly unicellular or colonial and occur as flagellates or non-motile cells; they include many unique and interesting morphologies (Graham et al. 2009). In contrast to synurophytes, scales covering the cell surface are absent in chrysophytes. Chrysophytes typically favour slightly acid freshwaters of moderate to low productivity; their abundance and species richness increase with lake eutrophic status. Chrysophytes may have strong ecological impacts because many are mixotrophs that are able to take up and metabolize dissolved organic compounds and particulate food as well as photosynthesize (Graham et al. 2009). Several chrysophytes are associated with the formation of undesirable blooms because living cells of certain species can produce toxic fatty acids that affect fish or excrete aldehydes and ketones into the water, which can give it an unpleasant taste and odour.

2.5.3.3 Phaeophyceae This class comprises the brown algae, an almost entirely marine group which includes the most conspicuous seaweeds of colder waters, particularly in the Northern Hemisphere. Most brown algae form large thalli, which can be up to several metres long (e.g. the Laminariales or “kelps”). They mostly grow in the intertidal belt and the upper littoral region of sea coasts. These macroalgae are harvested and are of important industrial use, e.g. for the production of alginates, and are important edible seaweeds. Because this review focuses on microscopic algae, they are not treated further here. For an introduction, see Graham et al. (2009).

2.6 Chromalveolate algae: coccolithophorids and haptophyte algae Haptophyte algae are a monophyletic group that includes all photosynthetic organisms with a haptonema, which is a microtubule-supported appendage that lies between two approximately equal flagella. Most haptophytes are marine, occurring in great abundance, even at greater water depths (up to 200 m). Haptophyte algae share many features, e.g. pigment composition, presence of a chloroplast endoplasmatic reticulum and plastids derived from red algal secondary endosymbiosis with the photosynthetic Stramenopiles (Andersen 2004). Based on structural and molecular evidence, the haptophytes form a monophyletic lineage, treated as the phylum

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Haptophyta, which is divided into two classes (Andersen 2004). The larger group, class Coccolithophyceae, comprises the coccolithophorids, which are haptophytes with a cell coat of mineralized scales (coccoliths) and two equal flagella or lack flagella altogether. Coccoliths are largely composed of calcium carbonate crystals in the form of calcite. Coccolithophorids remove large quantities of atmospheric CO2 through their photosynthesis and calcification and, therefore, are an important component of the global carbon cycle (McConnaughey et al. 1994), accounting for a substantial part of the ocean-floor limestone sediments. Coccolithophorids contribute at least 25 % of the total annual vertical transport of inorganic carbon to the deep ocean (Rost and Riebesell 2004). Coccolithophorids have an excellent fossil record, e.g. extensive chalk deposits that were laid down. Coccoliths are widely used as bioindicators in the oil industry and as indexes of past climate and ocean chemistry conditions (Young 1994). Haptophytes are ecologically significant in terms of both biotic interactions and biogeochemistry. Haptophyte algae are considered as high-quality foods for zooplankton; several species contain nutritionally important PUFAs, which makes them also commercially valuable for the production of fish in aquaculture systems. Other coccolithophorids may produce toxins that destroy cell membranes or produce copious amounts of organic slime and foam (Graham et al. 2009). Many coccolithophorids form blooms in ocean waters and produce large amounts of a volatile sulphur-containing molecule, dimethyl sulfide, that enhances cloud formation and increases acid rain. Another cooling effect on the climate comes from the coccoliths, which readily reflect light, thereby increasing the reflectance of the ocean’s surface. So far, only two species of Haptophyta have been used in biotechnological applications. Isochrysis galbana Parke and Pavlova lutheri (Droop) J. C. Green (Fig. 2g) are both unicellular flagellates from marine environments. While I. galbana is naked, the cell surface of P. lutheri is covered with cellulose scales. Both species are rich sources of PUFAs (EPA, DHA) as well as some sterols. They are often used in aquaculture, e.g. to feed clams and oysters.

2.7 Chromalveolate algae: Dinoflagellates (Dinophyta) The dinoflagellates are an important group of phytoplankton in marine and freshwaters. Their adaptation to a wide variety of environments is reflected by a tremendous diversity in form and nutrition and an extensive fossil record dating back several hundred million years (Graham et al. 2009). Dinoflagellate cells have two structurally distinct flagella whose motion causes the cells to rotate as they swim, but also non-flagellate unicells and filamentous forms of dinophytes are known. Two different cell types can be distinguished on the basis of the cell-wall covering or theca. The “naked” or unarmored forms have an outer plasmalemma surrounding a single layer of flattened vesicles (membrane sacs or alveoli). Armoured dinoflagellates have cellulose or other polysaccharides within each vesicle, giving the

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cells a more rigid, inflexible wall. These cellulose plates are arranged in distinct patterns, which are extensively used as taxonomic “fingerprints” (Hackett et al. 2004). The success of dinoflagellates as phytoplankton may be due in part to unique behaviour patterns, including diel vertical migration and their ability to live phagotrophically (feeding on particles such as the cells of other organism) in addition to photoautotrophically (mixotrophic life style). Some dinoflagellates produce toxins that are dangerous to man, marine mammals, fish, seabirds, and other components of the marine food chain. Others are bioluminescent and emit light; some function as parasites or symbionts that rely on host organisms for part of their nutrition. Many photosynthetic dinoflagellates live as endosymbionts with reef-building corals. In other symbiotic dinoflagellate associations, the hosts include foraminifera, radiolarians, flatworms, anemones, jellyfish and even bivalve mollusks (Hackett et al. 2004). While most dinoflagellates are photosynthetic, a considerable number live heterotrophically. So far, only a single species of Dinoflagellates is used in commercial applications, the heterotrophic Crypthecodinium cohnii (Seligo) Javornicky. It is known for its high content of PUFAs, used in various food applications and aquaculture, and to produce pharmaceuticals.

2.8 Euglenoids (Excavata supergroup) Euglenoids are a diverse group of common marine and freshwater flagellates, about half of which contain a plastid derived from green algal secondary endosymbiosis that is bounded by three membranes and contains chlorophyll b as accessory pigment. The remainder of the group are osmotrophs or heterotrophs that feed on bacteria or other eukaryotes. Molecular phylogenies show that photosynthetic euglenoids may have acquired their plastids from a green alga relatively late in evolution, even though the euglenoids may be a rather old group. Euglenoids are closely related to the parasitic trypanosomes (Kinetoplasids), together with diplonemids, making up the monophyletic Euglenozoa. The distinguishing features of euglenoids are that they produce a storage carbohydrate, a β-1,3-linked glucan (paramylon) in their cytoplasm, and that they display a unique surface structure composed of parallel ribbon-like proteinaceous stripes. For reviews of euglenoids, see Leedale and Vickerman (2000) or Milanowski et al. (2006). Euglena gracilis Klebs attracts interest for biotechnological exploitation because it provides a high content of paramylon when grown heterotrophically. When grown photoautotrophically, it is a rich source of the antioxidant α-tocopherol, which then occurs in the highest concentration known for any algae (Tani and Tsumura 1989).

Acknowledgements We would like to thank Clemens Posten and Carola Griehl, for their helpful discussions and suggestions. We acknowledge the support available through ebooks dis-

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tributed by “Alginet”, which was a thematic network project funded by the European Commission (project no. QLK3-CT-2002-02132). Parts of this review were financially supported by the State of Lower Saxony, Hannover, Germany (contract no. ZN2727).

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Friedl, T. and M. Lorenz. 2012. The Culture Collection of Algae at Göttingen University (SAG): a biological resource for biotechnological and biodiversity research. Procedia Environmental Sciences 15, 110–117. Friedl, T. and N. Rybalka. 2012. Systematics of the Green Algae: A Brief Introduction to the Current Status. In: Progress in Botany 73. (Ulrich Lüttge, Wolfram Beyschlag, Burkhard Büdel and Dennis Francis, eds). Springer Berlin Heidelberg. pp. 259–280. Gould, S.B., R. F. Waller and G. I. McFadden. 2008. Plastid Evolution. Annu. Rev. Plant Biol. 59, 491–517. Graham, J. E., L. W. Wilcox and L. E. Graham. 2009. Algae (2nd Edition). 01 Dec 2008 Edition. Benjamin Cummings, 720. Guiry, M. D. and G. M. Guiry. 2012. AlgaeBase. World-wide electronic publication, National University of Ireland, Galway. Hackett, J. D., D. M. Anderson, D. L. Erdner and D. Bhattacharya. 2004. Dinoflagellates: A remarkable evolutionary experiment. Am. J. Bot. 91, 1523–1534. Hempel, F., A. Bozarth, N. Lindenkamp, A. Klingl, S. Zauner, U. Linne, A. Steinbuchel and U. Maier. 2011a. Microalgae as bioreactors for bioplastic production. Microbial Cell Factories 10, 81. Hempel, F., J. Lau, A. Klingl and U. G. Maier. 2011b. Algae as Protein Factories: Expression of a Human Antibody and the Respective Antigen in the Diatom Phaeodactylum tricornutum. Plos One 6: e28424. Holzinger, A. 2009. Desiccation tolerant green algae: implications of physiological adaptation and structural requirements. In: (Kristian N. Hagen, ed.) Algae: nutrition, pollution control and engergy sources. Nova Science, New York. pp. 41–56. Huss, V. A. R., C. Frank, E. C. Hartmann, M. Hirmer, A. Kloboucek, B. M. Seidel, P. Wenzeler and E. Kessler. 1999. Biochemical taxonomy and molecular phylogeny of the genus Chlorella sensu lato (Chlorophyta). J. Phycol. 35, 587–598. John, D. M. 1994. Biodiversity and Conservation: An Algal Perspective. The Phycologist, 38, 3–15. Kauff, F. and B. Büdel. 2011. Phylogeny of Cyanobacteria: An Overview Progress in Botany 72. In: (Ulrich E. Lüttge, Wolfram Beyschlag, Burkhard Büdel and Dennis Francis, eds). Springer Berlin Heidelberg. pp. 209–224. Keeling, P. J. 2004. Diversity and evolutionary history of plastids and their hosts. Am. J. Bot. 91, 1481–1493. Keeling, P. J. 2010. The endosymbiotic origin, diversification and fate of plastids. Philosophical Transactions of the Royal Society B: Biological Sciences 365, 729–748. Keeling, P. J., G. Burger, D. G. Durnford, B. F. Lang, R. W. Lee, R. E. Pearlman, A. J. Roger and M. W. Gray. 2005. The tree of eukaryotes. Trends Ecol. Evol. 20, 670–676. Keeling, P. J. and J. D. Palmer. 2008. Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9, 605–618. Krienitz, L., E. Hegewald, D. Hepperle, V. A. H. Huss, T. Rohr and M. Wolf. 2004. Phylogenetic relationship of Chlorella and Parachlorella gen. nov. (Chlorophyta, Trebouxiophyceae). Phycologia 43, 529–542. Kroth, P. 2007. Molecular Biology and the Biotechnological Potential of Diatoms. In: Transgenic Microalgae as Green Cell Factories. (Rosa León, Aurora Galván and Emilio Fernández, eds). Springer New York. pp. 23–33. Lang, I., L. Hodac, T. Friedl and I. Feussner. 2011. Fatty acid profiles and their distribution patterns in microalgae: a comprehensive analysis of more than 2000 strains from the SAG culture collection. BMC Plant Biol. 11, 124. Lebeau, T. and J.-M. Robert. 2003a. Diatom cultivation and biotechnologically relevant products. Part I: Cultivation at various scales. Appl. Microbiol. Biotechnol. 60, 612–623. Lebeau, T. L. and J. M. R. Robert. 2003b. Diatom cultivation and biotechnologically relevant products. Part II: Current and putative products. Appl. Microbiol. Biotechnol. 60, 624–632.

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Pröschold, T. and F. Leliaert. 2007. Systematics of the green algae: conflict of classic and modern approaches. In: (J Brodie and J Lewis, eds) Unravelling the algae: the past, present, and future of algal systematics. CRC Press, Taylor & Francis, Boca Raton. pp. 123–153. Pulz, O. and W. Gross. 2004. Valuable products from biotechnology of microalgae. Appl. Microbiol. Biotechnol. 65, 635–648. Rindi, F. 2011. Terrestrial green algae: systematics, biogeography and expected responses to climate change. In Climate change, ecology and systematics, eds. Trevor R. Hodkinson, Michael B. Jones, Stephen Waldren and John A. N. Parnell. Cambridge: Cambridge University Press. Rodríguez-Ezpeleta, N., H. Brinkmann, S. C. Burey, B. Roure, G. Burger, W. Löffelhardt, H. J. Bohnert, H. Philippe and B. F. Lang. 2005. Monophyly of Primary Photosynthetic Eukaryotes: Green Plants, Red Algae, and Glaucophytes. Curr. Biol. 15, 1325–1330. Rost, B. and U. Riebesell. 2004. Coccolithophore calcification and the biological pump: Response to environmental changes. In: (H.R. Thierstein and J. R. Young, eds) Coccolithophores – From Molecular Processess to Global Impact. Springer Verlag, New York. pp. 99–126. Sharma, N., S. Tiwari, K. Tripathi and A. Rai. 2011. Sustainability and cyanobacteria (blue-green algae): facts and challenges. J. Appl. Phycol. 23, 1059–1081. Tani, Y. and H. Tsumura. 1989. Screening for tocopherol-producing microorganisms and alphatocopherol production by Euglena gracilis. Z. Agricultural and Biol. Chem. 53, 305–312. Tanoi, T., M. Kawachi and M. Watanabe. 2011. Effects of carbon source on growth and morphology of Botryococcus braunii. J. Appl. Phycol. 23, 25–33. Tirichine, L. and C. Bowler. 2011. Decoding algal genomes: tracing back the history of photosynthetic life on Earth. The Plant J. 66: 45–57. Ward, D. and R. Castenholz. 2002. Cyanobacteria in Geothermal Habitats. The Ecology of Cyanobacteria. In: (Brian Whitton and Malcolm Potts, eds). Springer Netherlands. pp. 37–59. Weiss, T.L., J. Spencer Johnston, K. Fujisawa, K. Sumimoto, S. Okada, J. Chappell and T. P. Devarenne. 2010. Phylogenetic placement, genome size, and GC content of the liquidhydrocarbon-producing green microalga Botryococcus braunii strain Berkeley (Showa) (Chlorophyta). J. Phycol. 46, 534–540. Wen, Z.-Y. and F. Chen. 2003. Heterotrophic production of eicosapentaenoic acid by microalgae. Biotechnol. Adv. 21, 273–294. Wodniok, S., H. Brinkmann, G. Glockner, A. Heidel, H. Philippe, M. Melkonian and B. Becker. 2011. Origin of land plants: Do conjugating green algae hold the key? Bmc Evol Biol 11, 104. Worden, A.Z., J. K. Nolan and B. Palenik. 2004. Assessing the Dynamics and Ecology of Marine Picophytoplankton: The Importance of the Eukaryotic Component. Limnol. Oceanogr. 49, 168–179. Yang, E. C., G. H. Boo, H. J. Kim, S. M. Cho, S. M. Boo, R. A. Andersen and H. S. Yoon. 2012. Supermatrix Data Highlight the Phylogenetic Relationships of Photosynthetic Stramenopiles. Protist 163, 217–231. Young, J. R. 1994. The functions of coccoliths. In: (A. Winter and W. G. Siesser, eds) Coccolithophores. Cambridge University Press, Cambridge pp. 63–82.

Christian Wilhelm and Torsten Jakob

3 Balancing the conversion efficiency from photon to biomass 3.1 Introduction Algal biomass production has been proposed to have an important potential to contribute significantly to the generation of biofuels in the post-oil era (Schenk et al. 2008; Stephens et al. 2010). This goal can be reached only if the whole process from sunlight-driven biomass production up to the final product as biodiesel, methane or hydrogen results in a positive net energy gain. Furthermore, in terms of sustainability, the whole process chain will be climate-friendly only if the CO2 emissions produced to generate a given amount of usable energy in the tank are substantially lower than the usage of fossil fuels. However, lifecycle studies show that at the present state of the art, a positive energy gain is not achieved and substantial improvements are needed to reduce the CO2 emissions during algal production and product refinement (Jorquera et al. 2010, Stephenson et al. 2010). The results of lifecycle studies are somewhat contradictory because the input parameters are based on different assumptions or measurements of non-optimized technologies. An important input parameter is the biological biomass productivity given in tons of product per area and year. Therefore, several studies have attempted to assess the biological limits of algal biomass production, and contradictory results of annual production rates are produced, ranging from 300,000 L oil per hectare down to only a couple of thousand liters of biodiesel per hectare (Weyer et al. 2010). The reasons for these discrepancies is mainly due to nonvalidated assumptions on algal productivity. This chapter aims to shed more light on this discussion by showing in detail how the photosynthetic and metabolic machinery works to convert light energy into energy of chemical bonds of compounds that form the cell. The biological limits for biomass production have been discussed by Wilhelm and Selmar (2011) and can be based on facts of measurements at four different biochemical conversion steps: (a) absorption, (b) photochemistry, (c) carboxylation and (d) metabolic conversion of primary photosynthates into the macromolecules of new cell biomass. Each step is now understood in sufficient detail so that the regulation and the biological efficiency can be measured. This detailed view of the energetics of biomass formation allows us in the final conclusion to predict the potential of future improvements and is a solid, experimentally based platform on which to decide between a real perspective and fantasy.

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3.2 Definition of important terms Photosynthesis is the driving force for biomass formation. The following sections explain how the present state of knowledge in photosynthesis research is helpful to identify and overcome the limits of algal biomass production. The biological regulations to balance light absorption, carboxylation and cell growth are rather complex. If the reader is interested in more detailed information about the present state of the art in photosynthesis research, it is recommended either to use a textbook for further reading (e.g. Taiz and Zeiger 2010) or to visit the website at the University of Illinois, where excellent teaching material is available (http:// www.life.illinois.edu/govindjee/teaching.html).

3.2.1 Photosynthetic efficiency The term “photosynthetic efficiency” (PE) is widely used in the literature, but with different meanings leading to misunderstandings. The first definition reflects the view of photosynthesis research where PE is traditionally understood as the number of absorbed photons needed to produce one oxygen molecule according to the photosynthesis equation: 6 CO2 + 12 H2O = C6 H12O6 + 6 H2O + 6 O2

(3.1)

The evolution of one oxygen molecule requires the extraction of four electrons from two water molecules. Since two photons are needed to transfer one electron through the photosynthetic electron-transport chain, the minimum quantum requirement is 8 photons per oxygen. According to Equation (3.1), the evolution of one molecule oxygen is equivalent to the assimilation of one molecule of CO2, and the minimum P/C value is theoretically 8. However, Bolton and Hall (1991) measured a P/C value of about 10 under low light conditions during a short experiment lasting a few minutes. A second understanding of PE reflects the view of photophysiology. PE is estimated on the basis of the so-called α-slope in the photosynthesis–irradiance curve (P–I curve) (e.g. Melis et al. 1999). This curve is used traditionally in algal photophysiology to characterize the light-acclimation status of algal cells (Wilhelm, 1993). As shown in Figure 3.1, this curve describes the relationship between incident light intensity and oxygen evolution rate. Formally, the reciprocal of the αslope represents the quotient of μmol photon (per area and time) per μmol oxygen (per mg Chl and time). This quotient is identical to the first definition of PE only, if the absorptivity of the cells were uniform. Absorptivity means the ratio of absorbed quanta per unit chlorophyll. This is obviously not the case because the chlorophyll content in a culture vessel can be adjusted to any technical demands. In addition, the P–I curve is normally plotted on the basis of incident, and not

3.2 Definition of important terms

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Fig. 3.1: Light-saturation curve (P–I curve) as a function of incident light intensity. The slope represents the apparent photosynthetic efficiency but can be compared between different species or conditions not in absolute terms, because the absorption per unit chlorophyll is not constant. Inset: the photo-use efficiency declines inversely with incident light. Therefore, maximum photosynthetic efficiency cannot be achieved at maximum growth rates.

absorbed, quanta per time. Since the ratio of incident to absorbed quanta depends on many parameters (chlorophyll content, light spectrum, absorptions spectrum of the cells, etc.), the α-slope cannot be used as a direct measure of PE and is an adequate description of photosynthetic efficiency only for comparative reasons, if the optical conditions in the measuring cuvette are well defined (see Blache et al. 2011, and later on in this chapter). The light-saturating curve reflects the actual light-acclimation status of the cells. The slope as well as Pmax can change over a few hours due to photoinhibition and/or unfavorable temperatures and CO2 limitation. Therefore, the photosynthesis rate cannot be used as the basis to plot light intensity against growth rate. Moreover, the latter includes the metabolic costs (see Section 3.2.2). The PE should not be confused with the term “photo-conversion-efficiency” (PCE), which is simply the energy of absorbed photons per amount of energy stored in the biomass. These empirical values are much lower than the theoretical PE values (Zijffers et al. 2010), and attempts have been made to optimize photobioreactors (Schenk 2008) according to this measure. There are four reasons why, in a photobioreactor, it is impossible to achieve PCE values close to the theoretical maximum.

3.2.2 Growth efficiency (photon to biomass efficiency) First, Equation (3.1) gives only the electron demand to produce sugar from CO2 and water. However, cellular growth requires biomass containing not only sugars but

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Fig. 3.2: Metabolic pathway of carbon from the primary photosynthates in the CALVIN cycle to the major macromolecular pools in a typical green alga, e.g. Chlorella. In some algae, the cell wall does not contain cellulose, so the carbon needed for the formation of cell walls is reduced or is replaced by other macromolecules, e.g. proteins in the case of Chlamydomonas.

also lipids and proteins, macromolecules that are more reduced than sugars. As shown in Figure 3.2, the synthesis of proteins or lipids depends on metabolic pathways that require decarboxylation steps. For instance, the biosynthesis of fatty acids starts from activated acetate, which is generated from glyceraldehyde with one decarboxylation per molecule acetate formed. Protein biosynthesis in the cytosol starts from amino acids whose carbon skeletons are taken from the Krebs cycle, which operates in the mitochondrion. The carbon in the Krebs cycle is obtained from glycolysis, which again performs a decarboxylation step to convert pyruvate to acetate. Therefore, the biosynthesis of lipids or proteins has an inevitably higher photon demand per carbon because the refixation of the CO2 from decarboxylation needs additional reductants and ATP. If the cell metabolism is changed in a way that the assimilated carbon is drained to lipids, they have to be used as the carbon store for cell maintenance. In this case, the lipids will underlie a permanent turnover, which leads to tremendous carbon losses per se. Any turnover costs, especially with proteins, will increase the metabolic costs because each enzymatic step is either energy-dissipative or ATP-consuming. Thus, any increase in the lipid or protein content in a cell is inevitably linked with higher P/C values.

3.2 Definition of important terms

43

Second, the minimum P/C value can be reached only if all electrons liberated from water are transferred to carbon. This is evidently not the case, because electrons are needed for the assimilation of nitrogen (especially if nitrate is used as the N-source) and also for sulfur. These “alternative electron acceptors” mean that about 20–60 % of the photosynthetic electrons are not transferred to carbon to reduce CO2 but are transferred to other “alternative acceptors” (Laws 1991, Suggett et al. 2009). Another important sink for these alternative electrons is oxygen. In the so-called “Mehler Reaction”, electrons are transferred from PSI to molecular oxygen producing a superoxide anion. This is very toxic and will be detoxified by the action of the enzyme superoxide dismutase, which forms hydrogen peroxide. The latter is decomposed to water and oxygen by the ascorbate peroxidase, which has a very high activity in the chloroplast in particular under stress conditions. In summary, some of the electrons released by the water-splitting apparatus of photosystem II are transferred through the photosynthetic electron transport chain back to oxygen. This water–water cycle (Asada, 1999) is not completely dissipative, because it generates additional ATP by its contribution to the formation of a transmembrane pH gradient, which drives photophosphorylation. Eisenstadt et al. (2010) have shown by oxygen isotope discrimination that in phytoplankton species, oxygen uptake in the light contributes to photoprotection. Finally, it can be assumed that in algae, as in higher plants, a mitochondrial alternative oxidase (AOX) allows efficient consumption of the energy of reductants with minimal contribution to ATP biosynthesis. Third, the minimum P/C value can be achieved only under low light. According to Figure 3.1, the photon-use efficiency drops down to low values under saturating light intensities. The excessive energy is dissipated by photoprotective mechanisms mainly as heat. At high light intensities when the amount of absorbed energy exceeds the capacity of the dark reaction, the cells activate the so-called xanthophyll cycle, which converts light-harvesting xanthophylls into light-protecting pigments (Goss and Jakob 2010). The conversion of violaxanthin/diadinoxanthin into zeaxanthin/diatoxanthin, respectively, is correlated with a change in conformation of the xanthophyll binding antenna proteins, which activates the relaxation of excited states of chlorophylls to heat. This heat emission can be measured by fluorescence techniques as so-called non-photochemical quenching (NPQ). Figure 3.3 shows a typical fluorescence trace during a transition from dark to light. This measurement can be carried out using a “Pulse Amplitude Modulated” (PAM) fluorometer online in a photobioreactor; for details of the method see Büchel and Wilhelm (1993). In the dark, all reaction centers are open, which means “photochemically competent”. A pulsed light beam that is too weak to induce photochemical activity delivers the minimum fluorescence level Fo. A saturating flash “closes” all reaction centers leading to the maximum fluorescence level Fm. During the following illumination period, a given fraction of reaction centers are closed, which induces an increase in fluorescence. The application of saturating light

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Fig. 3.3: Trace of a measurement using the pulse amplitude modulated fluorometer. The shadowed area represents the amount of absorbed energy that is dissipated via heat from the algal light-harvesting antenna systems. For details and explanations, see Schreiber et al. (1986).

flashes leads to a lower level of Fm in the light designated as F′m. The difference between Fm and F′m (non-photochemical quenching) gives the proportion of light that is converted into heat. This can be calculated according to Equation 3.2: NPQ = ( Fm − F ′m ) / F ′m

(3.2)

Fourth, the minimum P/E value can be obtained only during the light phase. If the carbon losses by respiration during the dark phase are taken into account, the P/C value will rise again. In Table 3.1, the influence of respiration is shown for three different scenarios.

1. HL acclimated cells with a respiration rate of 40 μmol O2/mg Chl h 2. LL acclimated cells with a respiration rate of 20 μmol O2/mg Chl h 3. HL acclimated cells with a respiration rate of 40 μmol O2/mg Chl h in low light

Illumination

Photosynthesis rate

Respiratory loss (%) in 14 h L:10 h D

P/C value compared to 10 in the light

Ik 0.5 Ik 5 Ik Ik 0.5 Ik 5 Ik Ik 0.5 Ik 5 Ik

250 90 350 90 45 150 90 45 150

11.4 31 8.2 15.8 31 9.5 31 63 19

11.3 14.5 54.4 11.9 14.5 55.2 14.5 27 55.2

Tab. 3.1: Respiratory losses under different conditions of illumination.

3.3 Physiological dynamics of processes

45

High light (HL)-acclimated cells perform higher photosynthesis rates at saturating light intensities but possess twofold-higher respiration rates than low light (LL)-acclimated cells. From these data, it is evident that the relative influence of respiration is the same for HL and LL-acclimated cells at an illumination close to the light-saturating light intensity (inclination point, Ik). An interesting scenario is the last one where HL acclimated cells are transferred to LL. These “mis-acclimated” cells will show drastically increased P/C values. Such a situation is likely to occur in a photobioreactor that runs under outdoor conditions. During sunny periods, the light uptake will be optimal at levels slightly higher than Ik, which results in maximum productivity. However, if the cells at the same mixing rate and cell density receive low light levels, the P/C value will increase drastically. This shows that the light-acclimation state of the cells is crucial for the productivity under outdoor conditions when the light uptake of the cells can be controlled only by the biomass concentration and the mixing velocity. Therefore, we will show the acclimation dynamics of the biological processes that define the efficiency of photon to biomass balance.

3.3 Physiological dynamics of processes which control biological energy conversion efficiency 3.3.1 Absorption In a first approximation, the absorption is a function of the chlorophyll content in the bioreactor. To achieve full absorption and to prevent gradients for CO2 and O2, the suspension will be mixed with a given velocity. This leads to a “flashing light climate”, which has been shown to be favorable for the P/C values (Terry 1986; Matthijs et al. 1996; Sato et al. 2010). Nevertheless, the quantum absorption per time and cell should be close to the Ik value for a given cell in order not to exceed the biochemical capacity. To achieve optimal light absorption, three different variables can be technically controlled in outdoor bioreactors – mixing of the cell suspension, chlorophyll concentration and light dilution (path length of incident irradiance) – whereas light intensity and absorptivity of the cells cannot. The absorptivity is characterized by the so-called chlorophyll a-in-vivo absorption coefficient a and describes physically the area per chlorophyll molecule. This means that in cells with densely packed chlorophyll molecules, the number of photons absorbed per chlorophyll is lower at a given photon flux compared to cells with less pigment packing. This so-called “package-effect” is nonlinearly correlated to the absorption efficiency: the higher the value, the higher the absorption efficiency. This means in practical terms that in a suspension with equal chlorophyll content cells that have a small a absorb fewer photons than cells with a high a . As shown in Figure 3.4, a is a nonlinear function of the chlorophyll content per cell. This ratio can be modified either by the cultivation conditions, e.g. the N-availability

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3 Balancing the conversion efficiency from photon to biomass

Fig. 3.4: Relationship between cell size and the absorptivity of the cells (a ). For details, see Blache et al. (2011).

(Jakob et al. 2007) or by the species specific cell size. Large cells are densely packed with chlorophyll and do not absorb efficiently. Consequently, the biodiversity of cells that can be used for biomass production is restricted to cells with a diameter below 10 μm. In addition to the selection of algal cells by their cell size, it is possible to manipulate a by antenna size engineering; for a review, see Melis (2009). The optimum strategy to reduce the absorptivity of the cells is to downregulate the light-harvesting chlorophyll complexes, which bind in green algae about 50 % of the total amount of chlorophyll a and about 90 % of chlorophyll b. Beckmann et al. (2009) have reported a successful molecular strategy by the expression of an LHC-specific translation repressor protein. Mussgnug et al. (2007) have given evidence that engineering the light-harvesting capacity will have a very favorable effect on algal mass cultivation because the suspension can be loaded with a higher biomass per volume, and the mixing energy can be reduced. From these data, it is evident that a should be part of any strategy for bioreactor optimization.

3.3.2 Regulation and efficiency of photochemistry As indicated by the light saturation curve, the efficiency of photochemistry becomes downregulated when the CO2 assimilation capacity is limiting. This downregulation becomes obvious at best by plotting PSII efficiency against the incident light intensity (Fig. 3.5). This curve shows that at very low light intensities, the efficiency of photochemistry is high but drops drastically to very low levels at high light. The heat emission that is represented by NPQ is strictly antiparallel. Thus, high photosynthetic efficiency can be found only under light-limiting conditions. However, under those conditions, when the photosynthetic quantum yield is at a maximum, the growth rate is light-limited because the amount of energy stored per

3.3 Physiological dynamics of processes

47

Fig. 3.5: Relationship between photochemical efficiency and heat emission via non-photochemical quenching (NPQ) measured by PAM fluorometry (see Fig. 3.3).

unit time is only marginal. The optimum condition to reconcile high photochemical efficiency with growth rate is to drive photosynthesis at a light level of Ik. Under outdoor conditions, photoinhibition can be expected as a second important mechanism that can decrease the efficiency of photochemistry; for a review, see Raven (2011). Photoinhibition occurs if excessive light reaches the cell over several hours. The light intensitity needed to overexcite the photosynthetic apparatus is different from species to species and depends on the light acclimation status of the cell. Any condition that slows down the enzymatic dark reactions will mean that the light-generated reductants cannot be consumed. This can happen under low temperatures and nutrients, especially under limited CO2 levels. The physiological consequences of photoinhibition are a reduction in the carbon assimilation capacity (Pmax) and of the efficiency of photochemistry. The latter is due to the damage to PSII reaction centers, which can be repaired only over several hours (Nixon et al. 2010, Campbell and Tyystjarvi, 2012).

3.3.3 Regulation of electron flow In typical photosynthesis-production models (Gilbert et al. 2000), it is assumed that all electrons from the splitting of water are used for carboxylation. As described in Section 3.2.1, alternative electron cycling is important for nitrogen and sulfur assimilation. In addition, similar to higher plants (Foyer and Noctor, 2000), alternative electron cycling around PSI is an important regulatory mechanism to prevent the cells from being photodamaged by reactive oxygen species. Therefore, it has been assumed that alternative electron cycling is a minor pathway for energy dissipation if the cells are kept under non-stressing conditions. Since, even in an

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Fig. 3.6: Electron transport rates during a dynamic daily light climate measured by oxygen evolution (blue line) and by fluorometry (red line). The differences between fluorescence and oxygen-based electron-transport rates reflect the activity of alternative electron cycling indicating the loss of reductants that cannot be used for carbon assimilation but might contribute to ATP biosynthesis (Mehler reaction). See text for more details.

optimal bioreactor design, the excitation pressure on the photosystems cannot be kept constant, it is important to know how alternative electron cycling can influence the metabolic losses. For many years, it was difficult to measure the alternative electron flow under real reactor conditions. Wagner et al. (2005) showed for the first time a photon-to-biomass balance where the alternative electron transport was quantified during a daily production cycle. From these results, two important conclusions can be drawn. First, alternative electron cycling is low at low light intensities and rises with the degree of light saturation (see Figure 3.6). Second, alternative electron cycling is highly variable between different taxa. It seems that green algae show higher losses by alternative electron cycling than diatoms, for example. The differences might be due to the fact that the NPQ mechanism to convert excessively absorbed energy into heat is much more efficient in diatoms than in green algae. On the other hand, it could be speculated that alternative electron cycling adjusts the ATP/NADPH ratio, such as when additional ATP is needed to compensate nutrient gradients, e.g. for CO2 . This hypothesis was raised from the observation that alternative electron transport increases the P/C value in Chlamydomonas by about 30 %, even at optimal illumination at Ik (Langner et al. 2009).

3.3.4 Regulation of carbon allocation The three main pools of cellular macromolecules, proteins, carbohydrates and lipids make up to 90 % of the cellular carbon. The relative amounts of these three different pools are variable between species and depend on culture conditions. Re-

3.3 Physiological dynamics of processes

49

Fig. 3.7: Photon demand for the production of new biomass with different macromolecular compositions. Note that these P/C values are measured under optimal light conditions at the inclination point of the P–I curve (see Fig. 3.1).

cently, Wagner (2010) developed an FTIR-based method, which allows the quantification of the relative amounts of protein, carbohydrate and lipids with minimal amounts of cell material. This technology makes it possible to measure changes in the three pools during illumination with a time resolution of 30 min. Together with the data on the absorbed quanta during that period and the number of oxygen released, the P/C ratio can be measured for each pool component. Figure 3.7 shows the changes in carbohydrates to the protein ratio of Chlamydomonas cells illuminated at Ik from the beginning to the end of the daily light phase. It is evident that during the first 6 h, the ratio carbohydrate to protein rises but then remains constant. This means that in the first part of the light phase, C is preferentially transferred to the carbohydrate pool, but in the second part equally to carbohydrates and lipids. Taking into account that the fixation of one C

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3 Balancing the conversion efficiency from photon to biomass

Composition of the biomass

Carbohydrate/lipid/protein = 3 : 2 : 5 Carbohydrate/lipid/protein = 6 : 1 : 3 Carbohydrate/lipid/protein = 1 : 6 : 3

Photons per carbon Theoretical

Real

12 10 12

28 12 29

Tab. 3.2: Required photons per carbon for different biomass compositions.

in carbohydrates needs four electrons, but six electrons per C are needed in proteins, it is clear that the P/C ratio is lower at the beginning of the light phase than at the end. These results can be confirmed by measurements with a respirometer quantifying the ratio of O2/CO2 in the light and in the dark, which have never been found to be constant during a day or a night. Using this method, it is possible to compare the theoretically expected electrons per carbon compared to the measured values that are integrated over a full day including the night. These values are shown in Table 3.2. From these data, it is evident that the P/C ratio measured and calculated is in very good agreement as long as the biomass is mainly composed of carbohydrates, whereas in protein- or lipid-rich biomass, the experimental values exceed the calculated up to a factor of two. The explanation is a physiological one. All three carbon pools underlie a permanent turnover. The turnover of carbohydrates is not linked with carbon losses, whereas the lipid and protein turnover is. The calculated values do not take into account the turnover costs and the carbon lost by these processes. These losses become obvious in a complete energy balance as increased respiratory losses during the light and in the dark.

3.4 Conclusions for microalgal biotechnology Figure 3.8 shows a complete energy balance from photon to biomass for Chlamydomonas under optimum conditions. The energy bound in the cells accounts for 19 %. This is an extremely high value and seems to be the upper biological limit. However, it must be emphasized that the three macromolecular pools cannot be freely exchanged, for of the following reasons. In each pool one has to differentiate between “investive” and “non-investive” carbon. For instance, lipids that are incorporated into membranes are part of the metabolic energy conversion machinery, which is typically investive. If the lipid is stored as neutral lipid droplets to produce biodiesel, this carbon is not investive and reduces the growth rate. The same holds true for proteins. If the protein content in a cell is decreased in favor of starch accumulation, the number of carbon atoms that contribute to growth become reduced because starch is physiologically inactive. Therefore, the cell has to decide: either to invest in productive structures to enhance growth performance or to funnel the carbon into storage pools, which inevitably decreases the carboxylation

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Fig. 3.8: Complete energy balance of Chlamydomonas reinhardtii cultivated under optimal fully replete chemostat conditions at a light intensity when the number of photons absorbed by the cells allows maximum photosynthesis without any overexcitation (Ik condition). Note the extremely high concentration of protein. This is because C. reinhardtii has a protein cell wall but also because under these conditions, carbon is drained completely into the investive pathway. For an explanation, see text and Wilhelm and Jakob (2011).

capacity and increases the P/C value drastically. Metabolic engineering can change the carbon allocation pattern, but there is little chance to overcome the conflict between “investive” and “non-investive” carbon pools (Wilhelm and Jakob, 2011).

References Asada, K. 1999. The water–water cycle in chloroplasts: scavenging of active oxygens and dissipation of excess photons. Ann. Rev. Plant Physiol. Plant Mol. Biol. 50: 601–639. Beckmann, J., F. Lehr, G. Finazzi, B. Hankamer, C. Posten, L. Wobbe, and O. Kruse. 2009. Improvement of light to biomass conversion by de-regulation of light-harvesting protein translation in Chlamydomonas reinhardtii. J. Biotechn. 142: 70–77. Blache, U., T. Jakob, W. Su, and C. Wilhelm. 2011. The impact of cell-specific absorption properties on the correlation of electron transport rates measured by chlorophyll fluorescence and photosynthetic oxygen production in planktonic algae. Plant Physiol. Biochem. 49: 801–808. Bolton, R. and D. O. Hall, 1991. The maximum efficiency of photosynthesis. Photochem. Photobiol. 53: 545–548. Büchel, C. and C. Wilhelm. 1993. In-vivo analysis of slow fluorescence induction kinetics in algae: progress, problems and perspectives. J. Photochem. Photobiol. 58: 137–148. Campbell, D. and E. Tyystjarvi. 2012. Parameterization of photosystem II photoinactivation and repair. Biochim. Biophys Acta (Bioenergetics) 1817: 258–265.

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Eisenstadt, D., E. Barkan, B. Luz, and A. Kaplan. 2010. Enrichment of oxygen heavy isotopes during photosynthesis in phytoplankton. Photosynth. Res. 103: 97–103. Foyer, C. and G. Noctor. 2000. Oxygen processing in photosynthesis: regulation and signaling. New Phytol. 146: 359–388. Gilbert, M., C. Wilhelm, and M. Richter. 2000. Bio-optical modelling of oxygen evolution using invivo fluorescence: comparison of measured and calculated photosynthesis/irradiance (P–I) curves in four representative phytoplankton species. J. Plant Physiol. 157: 307–314. Goss, R., Jakob, T. 2010. Regulation and function of xanthophyll cycle-dependent photoprotection in algae. Photosynth. Res. 106: 103–122. Jakob, T., H. Wagner, K. Stehfest, and C. Wilhelm. 2007. A complete energy balance from photons to new biomass reveals a light- and nutrient-dependent variability in the metabolic costs of carbon assimilation. J. Exp Bot. 58: 2101–2113. Jorquera, O., Kiperstok, A., Sales E. A., Embirucu, M. and M. L. Ghirardi. 2010. Comparative energy life-cycle analysis of microalgal biomass production in open ponds and photobioreactors. Bioresour. Technol. 101: 1406–1413. Langner, U., Jakob, T., Stehfest, K. and Wilhelm, C., 2009. A complete energy balance for Chlamydomonas reinhardtii and Chlamydomonas acidophila under neutral and extremely acidic growth conditions. Plant Cell Environ. 32: 250–258 Laws, E. A. 1991. Photosynthetic quotients, new production and net community production in the open ocean. Deep Sea Research 38: 143–167. Matthijs, H. C. P., Balke, H., Hes, U. M., Kroon, B. M. A., Mur, L. R. and R. A. Binot. 1996. Application of light-emitting diodes in bioreactors: flashing light effects and energy economy in algal culture (Chlorella pyrenoidosa). Biotechno. Bioeng. 50: 98–107. Melis, A. 2009. Solar energy conversion efficiencies in photosynthesis: Minimizing the chlorophyll antennae to maximize efficiency. Plant Sci 177: 272–280. Melis, A., Neidhardt, J. and J. Benemann. 1999. Dunaliella salina (Chlorophyta) with small chlorophyll antenna sizes exhibit higher photosynthetic productivities and photon use efficiencies than normally pigmented Cells. J. Appl. Phycol. 10: 515–525. Mussgnug, J. H., Thomas-Hall, S., Rupprecht, J., Foo, A., Klassen, V., McDowall, A., Schenk, P. M., Kruse, O. and B. Hankamer. 2007. Engineering photosynthetic light capture: impacts on improved solar energy to biomass conversion. Plant Biotech J. 5, 802–814. Nixon, P., Michoux, F. and Y. Jianfeng. 2010. Recent advances in understanding the assembly and repair of photosystem II. Ann. Bot. 106: 1–16. Raven, J. 2011. The cost of photoinhibition. Physiol. Plant. 142: 87–104. Sato, T., Yamada, D. and S. Hirabayashi. 2010. Development of virtual photobioreactor for microalgae culture considering turbulent flow and flashing light effect. Energ. Convers. Manage. 51: 1196–1201. Schenk, P. M., Thomas-Hall, S. R., Stephens, E., Marx, U. C., Mussgnug, J. H., Posten, C., Kruse, O. and B. Hankamer. 2008. Second Generation Biofuels: High Efficiency Microalgae for Biodiesel Production. Bioenerg Res 1: 20–43. Schreiber, U., Schliwa, U. and W. Bilger. 1986. Continuous recording of photochemical and nonphotochemical chlorophyll fluorescence quenching with a new type of modulation fluorometer. Photosynth. Res. 10: 51–62. Stephens, E., Ross, I. L., Mussgnug, J. H., Wagner, L. D., Borowitzka, M. A., Posten, C., Kruse, O. and B. Hankamer. 2010. Future prospects of microalgal biofuel production systems. Trends Plant 15: 554–564. Stephenson, A. L., Kazamia, E., Dennis, J. S., Howe, C. J., Scott, S. A. and A. G. Smith. 2010. Lifecycle Assessment of Potential Algal Biodiesel Production in the United Kingdom: A Comparison of Raceways and Air-Lift Tubular Bioreactors. Energy Fuels 24: 4062–4077.

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Suggett, D., H. MacIntyre, T. M. Kana and R. Geider. 2009. Comparing electron transport with gas exchange: parameterising exchange rates between alternative photosynthetic currencies for eukaroytic phytoplankton. Aquat. Microb. Ecol. 56: 147–162. Taiz, L. and E. Zeiger. 2010. Plant Physiology. 5th edition, Sinauer Associates, Sunderland, MA. Terry, K. L. 1986. Photosynthesis in modulated light: Quantitative dependence of photosynthetic enhancement on flashing rate. Biotechnol. Bioeng. 28: 988–995. Wagner, H., T. Jakob, C. Wilhelm. 2005. Balancing the energy flow from captured light to biomass under fluctuating light conditions. New Phytol., 169: 95–108. Wagner, H., Z. Liu, U. Langner, K. Stehfest and C. Wilhelm. 2010. The use of FTIR spectroscopy to assess quantitative changes in the biochemical composition of microalgae. J. Biophotonics 3: 557–566. Weyer, K. M., Bush, D. R., Darzins, A. and B. D. Willson. 2010. Theoretical Maximum of Algal Oil Production. Bioenerg. Res. 3: 204–213. Wilhelm, C. 1993. Some critical remarks on the suitability of the concept of the photosynthetic unit in photosynthesis research and phytoplankton ecology. Botanica Acta. 106: 287–293. Wilhelm, C. and D. Selmar. 2011. Energy dissipation is an essential mechanism to sustain the viability of plants: the physiological limits of improved photosynthesis. J. Plant Physiol. 168: 79–87. Wilhelm, C. and T. Jakob. 2011. From photons to biomass and biofuels: evaluation of possible light-dependent biotechnological processes based on comparative energy balances. Appl. Micro. Biotechnol. 92: 909–919. Zijffers, J. W., Schippers, K. J., Zheng, K., Janssen, M., Tramper, J. and R. H. Wijffels. 2010. Maximum photosynthetic yield of green microalgae in photobioreactors. Mar. Biotech. 12: 708–718.

Thomas Brück and Daniel Garbe

4 Algae symbiosis with eukaryotic partners 4.1 Introduction to algae-specific symbiosis 4.1.1 Importance of algae symbiotic relationships Symbiotic relations between photosynthetic and non-photosynthetic organisms are common in nature and the basis for a range of ecological and biogeochemical processes (Brownlee 2009), including atmospheric CO2 fixation, production of primary biomass, coral-reef formation and nitrogen recycling in water-treatment processes. The term symbiosis was first defined in 1879 and referred to any association between different organisms, provided that these are in persistent contact (Hoffmeister and Martin 2003). Symbiosis is an evolutionary strategy that can confer biological advantages. These may include: access to novel metabolic pathways, protection from predators, toxin degradation, increased mobility and even agriculture. When symbiotic partnerships involve exchange of metabolic products, the resulting benefits may be functionally distinct for each partner, ranging from growth advantages to protection against environmental stressors (Frank 1997). The eukaryotic cell per se represents a symbiotic relationship because all cells bear mitochondria, which have evolved from symbiotic bacteria. Algae form symbiotic relationships with various hosts. These symbioses are distinct in their different anatomical interactions. Symbiotic algae enter chemical and morphological environments that are significantly different from their free-living state (Muscatine et al. 1975). Therefore, significant alterations in cellular morphology and metabolism are often required to prevail in such a constrained environment (Srere and Mosbach 1974). The cellular environment of the host is different in nutrient composition, concentration and pH, from the surrounding aquatic environment, and photosynthetic acquisition of inorganic carbon by symbiotic algae is mediated through the host tissue. Therefore, it is invariably more complex for symbiotic algae to carry out efficient photosynthesis compared with their free-living counterparts. To alleviate this situation, hosts provide carbon-concentrating mechanisms to ensure the CO2 concentration is adequate for photosynthesis (Furla et al. 2005; Wooldridge 2010). In most symbiotic partnerships, the photosynthetic capability is the major contribution of algae symbionts. The resulting photosynthetic products (photosynthates) provide much of the carbohydrate required for the host’s aerobic respiration. The host influences the transport of algae derived photosynthate via specific chemical cues, which is particularly evident in symbioses between dinoflagellates

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and aquatic invertebrates. However, the chemistry of distinct host-derived factors remains to be established. Multicellular organisms enter symbiotic relationships with algae and cyanobacteria to gain access to photosynthetically fixed carbon (Venn et al. 2008). These symbiotic associations are commonly observed in the phyla Porifera (sponges) and Cnidaria (corals, sea anemones, hydroids and jellyfish). One significant factor contributing to the distribution of animal symbioses is the morphologically simple body plan of these animal classes and their large surface area:volume ratio, which is well suited to light capture by intracellular algae symbionts placed in their tissues. The resulting photosynthates are released from living symbiont cells to the animal host at substantial rates. The dominant photosynthetic symbionts in freshwater environments are chlorophytes of the genus Chlorella, which for example associate with the Spongilla sponges and hydras, while in benthic marine environments, dinoflagellates of the genus Symbiodinium sp. are predominant symbionts of virtually all reef-building corals. By contrast, cyanobacteria preferentially associate with marine sponges (Taylor 1973; Taylor et al. 2007). Although most symbioses are mutualistic, this does not mean it is a cooperative of equals. Conflicts among symbiotic organisms occur if a partner fails to provide a service or consumes common resources excessively. Therefore, the choice of suitable symbiotic partners is detrimental to lasting organismic interactions. Productive symbiotic relationships therefore depend on integration of gene expression and metabolism in both the symbiont and the host (Leggat et al. 2011). As symbiosis can lead rapidly to acquisition of novel traits that confer a biological advantage, it is an important addition to evolutionary mechanisms of isolated organisms, which are predominantly driven by genetic mutations that only result in small incremental phenotypic changes.

4.1.2 Modes of algae symbiosis with eukaryotes Specific symbiotic interactions are inherently linked to organismic compartmentation. Symbiotic interactions between different organisms are organized and constrained by spatial separation, which is defined by membrane-mediated compartmentation. Based on this model, two modes of symbiotic interactions can be defined: 1. Exo-symbiosis: In this symbiotic mode, the partnering organisms are each separated by their cellular membranes. The interaction may involve a loose contact between cell membranes or an engulfment of one symbiotic partner. The prototypic exo-symbiotic interaction is the formation of lichens. Lichens are a unique interaction between microalgae and filamentous fungi, in which both organisms form a completely new functional entity (Srere and Mosbach 1974). The two symbionts can be considered as two separate cellular compart-

Fig. 4.1: Submodes of endo-symbiosis: (1) primary, (2) secondary, (3) tertiary endosymbiosis. Specific gene transfer from the endosymbiont to the host nucleus is indicated by arrows. The remaining algal nucleus (nucleomorph) in secondary and tertiary endosymbiosis is depicted. The genome has been lost in all algae, with the exception of cryptophytes and chlorarachnio-phytes (Bhattacharya et al. 2004).

4.1 Introduction to algae-specific symbiosis

57

58

2.

4 Algae symbiosis with eukaryotic partners

ments. When entering a symbiotic interaction, the fungal partner will envelop the algal cell. This process may be associated with penetration of the algae cell wall utilizing specialized fungal membrane structures. In the symbiotic mode, the fungal cell membrane is thinner than under isolated living conditions, which facilitates metabolite exchange between the symbiotic partners. Endo-symbiosis: This interaction requires the uptake and integration of a symbiotic partner into a receiving host cell. The symbiont will be assimilated intracellularly by the host, which induces morphological and metabolic changes in both partners. The assimilated symbiont can be placed in a hostspecific cellular compartment (i.e. vacuoles). Alternatively, in a multicellular environment, symbionts are integrated between host cells. In either case, the symbiont cell is separated by cellular or organellar membrane structures of the host (McFadden 2001). The archetypical endo-symbiotic relationship is the assimilation of microalgae (i.e. Chlorella sp.) by aquatic organisms, such as protists (i.e. Paramecium bursaria) or cnidarians (i.e. Hydra viridis). Protists store algal symbionts in dedicated intracellular vacuoles. Cnidarians place algae cells in the endoderm, predominantly of the digestive tract. Each cell harbours multiple algal cells stored in specific vacuoles, which are termed the symbiosome. Recently, it has been recognized that plant plastids (much like mitochondria) have evolved through ancient symbiotic interactions between eukaryotes and photosynthetic microbes (i.e. cyanobacteria). To explain the rearrangements in host:symbiont membrane compartmentation leading to plastid formation and evolution of higher photosynthetic eukaryotes, three subtypes of endo-symbiosis have now been defined (Bhattacharya et al. 2004). The primary, secondary and tertiary symbiotic mechanisms depicted in Figure 4.1 were central to the evolution of modern microand macroalgae species. The evolution of plastid assimilation explains the different membrane structures observed in recent eukaryotic microalgae symbionts. While green algae (Genus: Chlorophyta, i.e. Chlorella sp.) have two cell membranes, dinoflagellate algae (Genus: Alveolata, i.e. Symbiodinium sp.) have three cell membranes.

The final organismic assembly of host and symbiont is termed the holobiont, which exhibits new functional mechanisms that confer survival advantages.

4.2 Aquatic systems 4.2.1 Algae symbiosis with Cnidaria Symbiosis with unicellular micro-organisms is characteristic of marine invertebrate, such as hard- and soft coral, and anemones living in nutrient poor environ-

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Fig. 4.2: Coral symbiotic assembly. The holobiont represents the assembly of coral polyps with eukaryotic, microalgae symbionts and associated fungal and bacterial endo- and exosymbionts. The endosymbiotic microalgae of marine shallow-water softcorals (i.e. Plexaurella sp. found in the Carribean Atlanic) are also referred to as zooxanthellae. They predominantly belong to the genus Symbiodinium sp.. These microalgae harbour specific exo-symbiotic fungi and bacteria.

ments such as tropical coral reefs (Rosenberg et al. 2007; Brownlee 2009). Indeed, corals have long been considered only as a symbiotic interaction between the coral and algae of the genus Symbiodinium, commonly referred to as zooxanthellae. However, recent data demonstrate that coral symbiosis is far more complex, including diverse and specific populations of other micro-organisms that have apparently co-evolved with corals. In fact, the mucus layer, skeleton and tissues of healthy corals all contain large populations of eukaryotic algae, bacteria and archaea (Fig. 4.2). Together, these micro-organisms confer benefits to their host by various mechanisms, including photosynthesis, nitrogen fixation, the provision of nutrients and infection prevention. The particular significance of algae symbionts is documented though the observation that the presence of these photosynthetic organisms increases the rate of calcification in reef-building coral, a process termed “light

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enhanced-calcification”. Since the symbiosis between algae and coral is well documented in the literature, it will serve as a template for cnidarian photosynthetic symbiosis.

4.2.1.1 Symbiont uptake and management De novo uptake of dinoflagellate symbionts is by far (> 85 %) the most common route for coral hosts (Wooldridge 2010). This process is induced by chemosensory signals that drive fee-living zooxanthellae within proximity to the coral’s coelenteral mouth, where they are engulfed into the gastric cavity. Subsequently, zooxanthellae are ingested into the endodermal cells lining the gastric cavity. After ingestion, zooxanthellae are engulfed by a host-derived “symbiosome” membrane, which separates them from the cytoplasm of the host cell (Fig. 4.3). While freeliving zooxanthellae can exist as a motile zoospore, symbiotic counterparts are forced in a cell-dividing (vegetative) non-motile state. The process is driven by host-derived compounds that induce morphological changes including loss of epicone and flagella (Muscatine et al. 1975). The capture and uptake process is initially non-selective in terms of the different types of zooxanthellae. However, with time a progressive selection process occurs whereby only a few genetically distinct zooxanthellae (clades) establish long-term symbioses. Current evidence suggests that this selection process is driven by the symbiotic performance of the zooxanthellae, in particular the capacity to maintain photosynthate transfer across a hierarchy of constraints, including: (1) the abiotic and biotic conspecifics of the intracellular habitat, (2) the microenvironment created by the skeletal morphology of the coral colony and (3) the variable envelope of external environmental conditions.

4.2.1.2 Flux of primary metabolites in host and symbiont The molecular interactions between algal cells and the invertebrate host are shown in Figure 4.3. The invertebrate host mainly provides a sheltered access to inorganic C, P and N as well as solar radiation (Shiroma et al. 2008). Metabolic upgrading of these compounds by the photo-symbiont results in the transfer of mobile organic molecules, such as glucose, glycerol, amino acids and dicarboxylic acids to the invertebrate hosts. The main intracellular metabolites produced by Symbiodinium microadreaticum are glucose, glycerol, alanine, glutamate, aspartate, succinate, fumarate, glycollate, lipids and organic phosphates. The storage carbohydrate of Symbiodinium microadreaticum is starch. The release of photosynthates from algae symbionts seems at least partially to be controlled by the extracellular medium pH, which can be controlled by host factors. One possible route to influence medium pH is uptake and solubilization of CO2. It has been observed in in vitro experiments that a low to medium pH actually stimulates the release of photosynthates to the extracellular space of the symbiotic algae (Taylor 1973). Approximately

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Fig. 4.3: Metabolite flux between invertebrate (coral) host and microalgae symbiont (i.e. Symbiodinium sp.). The host places the algae symbiont in dedicated cell vacuoles termed the symbiosome. MAAs: mycosporine-like amino acids, Krebs cycle intermediates: dicarboxylic acid, i.e. fumarate and succinate (Gordon and Leggat 2010).

50–95 % of the assimilated carbon is translocated to the host to meet carbon and energy requirements (Wooldridge 2010).

4.2.1.3 Optimizing photosynthesis for efficient metabolite exchange To maintain a high photosynthetic rate, the host utilizes various carbonic anhydrase isozymes that convert gaseous CO2 in water-soluble H2CO3, which is made available to the symbiont by specific molecular transporter systems (Venn et al. 2008; Gordon and Leggat 2010; Wooldridge 2010). Zooxanthellae have to conduct effective photosynthesis through light-filtering layers of the host tissue. To optimize photosynthesis even at low light intensities, specialized light-harvesting antenna pigments such as the carotinoid peridinin and the xanthophyll diadinoxanthin are utilized; these are more effective than equivalent mechanisms used by land-based plants (Taylor 1973; Hofmann et al. 1996).

4.2.1.4 Symbiont-derived secondary metabolites While primary metabolites synthesized by the dinoflagellate symbiont serve the metabolic requirements of the invertebrate host, the physiological role and bioac-

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tivities of symbiont derived secondary metabolites, such as polyketides (i.e. zooxanthellatoxin), betaines, ceramides (i.e. symbioramide), alkaloids (i.e. zooxanthellamide) and terpenes (i.e. pseudopterosin (Mydlarz et al. 2003)) remain largely unknown (Gordon and Leggat 2010). The biosynthesis of the algae symbiontderived diterpenglycoside, pseudopterosin has recently been elucidated (Bruck and Kerr 2006; Kerr et al. 2006). Pseudopterosins are of great pharmacological interest owing to their potent anti-inflammatory and anti-microbial activity (Correa et al. 2011).

4.2.1.5 Effects of environmental stress on symbiosis Perturbation of photosynthesis through deregulation of light collection and use results in the production of reactive oxygen species (ROS (Furla et al. 2005)) via Fenton-type reactions. The resulting oxidative stress can potentially impair protein and/or DNA function as well as membrane integrity, posing a serious threat to both the photosynthetic symbionts and their animal hosts. Similarly, thermal stress can trigger inactivation of a key component in the carbon-fixation process (e.g. carbon-concentrating mechanism, or photosystem damage (Venn et al. 2008)). The primary protection against light/oxidative stress is the concerted production and cellular distribution of UV protectants such as mycosporine-like amino-acids (MAA) by host and symbiont (Gordon and Leggat 2010) and ROS detoxifying enzyme systems, including superoxide dismutase, catalase and peroxidase (Furla et al. 2005). However, excessive stress can elicit a wide spectrum of cellular responses, including exocytosis of algae from host cells or lysis of both animal and algal cells by elements of necrotic and programmed cell-death pathways. All of these responses are consistent with the state of coral bleaching, which is symptomatically defined by loss of algae pigments, reduced coral growth and susceptibility to disease (Venn et al. 2008). More recently, specific bacteria (i.e. Vibrio sp.) and fungi (Aspergillus sp.) have been implicated in weakening symbiotic relations and induction of coral disease such as yellow blotch disease (Rosenberg et al. 2007; Rosenberg et al. 2009). In addition, anthropogenic climate change results in increasing ocean temperatures, which induce bleaching events that affect entire coral-reef biospheres and threaten long-term survival of coral communities.

4.2.2 Algae symbiosis with Porifera Sponges (phylum: Porifera) are among the oldest multicellular animals (Wang 2006). Their simple body layout shows little tissue differentiation and coordination. The three major sponge classes, the Hexactinellidas (glass), Calcera (calcerous) and Demospongiae (demosponges), encompass more than 600 different species. Sponges are the predominant sessile, filter feeders (pumping action: 24 m3

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Fig. 4.4: Sponge symbiotic assembly. The holobiont represents the assembly of sponge tissue with photosynthetic symbionts (prokaryotes or eukaryotes) and associated fungal and bacterial endo- and exosymbionts. Symbionts contribute up to 40 % w/w of the sponge biomass.

kg–1 sponge day–1) in freshwater and marine environments. Porifera enter diverse associations with eukaryotes (zooxanthellae, zoochlorellae) and prokaryotes (single-celled and multicellular cyanobacteria, bacteria, archaea; Fig. 4.4) as well as specialized macroalgae (Taylor et al. 2007). Indeed, microbial biomass can comprise up to 40 % of sponge tissue volume. While, in freshwater environments, sponges preferentially associate with eukaryotic microalgae of the genus Chlorella (zoochlorellae (Sandjensen and Pedersen 1994)), associations of marine species are more complex, ranging from cyanobacteria to macroalgae (Davy et al. 2002; Avila et al. 2007). There is clear evidence that photosynthetic symbionts stimulate growth and cellular maintenance of sponge hosts (Frost and Williamson 1980; Webster and Blackall 2009). The associations of marine sponges with cyanobacteria have been subject to intense studies in recent years mostly linked to detection of pharmacologically important metabolites.

4.2.2.1 Morphology of sponge–algae associations Generally photosynthetic micro-organism are located in the outer, light exposed layers of the sponge, while heterotrophic organisms populate the inner core (Wang

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2006). However, exceptions are common. In the siliceous sponge Tethya seychellensis, algae filaments of the chlorophyte Ostreobium sp. have been detected in the sponge core (Gaino and Sara 1994). Here, algae cells are tightly associated with siliceous spicules (i.e. matrix structure supporting needles), which may act as a light-conducting system for the inner sponge core. In association with macroalgae (i.e. Jania adherens), the sponge tissue (i.e. Haliclona caerulea) can fill the interstitial spaces between algae branchlets, supporting the erect microalgae structure. In contrast to coral symbiosis, sponges commonly do not assimilate algae cells into their intracellular space. Instead, sponges integrate algae into their organismic structure by depositing the structural protein spongin around the algae cell wall (Gaino and Sara 1994). The sponge–algae association may therefore be described as an exosymbiotic relationship.

4.2.2.2 Symbiont uptake, specificity and transmission As filter-feeders sponges largely thrive on assimilated micro-organisms, including cyanobacteria and eukaryotic microalgae. However, evidence suggests that sponges are selective in their feeding habit, excluding potential photosynthetic symbionts, such as the cyanobacterium Prochlorococcus sp. from digestion (Taylor et al. 2007). Recent data even demonstrate that sponges are very selective in their choice of photosynthetic partners. Phylogenetic analysis have detected abundant genetic signatures of the cyanobacterium Synechcoccus spongiarum in samples of various sponge species (i.e. Theonella conica, Aplysina aerophoba) that originated from different oceans. By contrast, signatures of the cyanobacterium were rare in seawater controls. Additionally, phylogenetic data have been employed to demonstrate that the cyanobacterium Oscillatoria spongeliae selectively associates with Dysidea sponges. Analysis of the mitochondrial marker cytochrome oxidase even indicates a co-evolution of cyanobacteria and the sponge hosts dating back ca. 500 million years (Wang 2006). Although, de novo uptake of algae is a common route to establish sponge-specific symbiosis, vertical transmission of symbionts during sexual and asexual repoduction cycles of the host has been documented (Taylor et al. 2007). In the sponge Chondrilla austaliensis, specialized cells ferry the cyanobacterial symbiont Synechcoccus spongiarum from the outer layers of the animal deeper into the sponge matrix, where they fuse with developing sponge oocytes in preparation for sexual reproduction. Similarly, sperm cells of the same species carry these cyanobacteria, indicating that both sexes are capable of transferring photosynthetic symbionts to their offspring. Developing sponge larvae containing vertically transferred photosynthetic symbionts seemed to fare better in response to environmental stressors, such as nutrient-poor environments.

4.2.2.3 Flux of primary metabolites in host and symbiont Heterotrophy, through digestion of microbes, is the most common form of carbon metabolism in sponges. However, in nutrient-poor environments, such as tropical

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reefs and alpine lakes with clear water and high light penetration, carbon metabolism is centred around photosynthetic symbionts, i.e. cyanobacteria and zoochlorellae. In adult sponge communities, symbiont management is associated with restriction of nutrients such as nitrogen and phosphorus, which attenuates symbiont protein synthesis and cell division. Conversely, an excess of carbon-rich photosynthate can be transferred by the symbiont to the host. At present, little is known about the host–symbiont signalling pathways, but the involvement of the chemically diverse secondary metabolites produced by the symbiotic partners is hypothesized. In marine sponge species, the main carbon-based photosynthate transferred from cyanobacterial symbionts is glycerol and other small organics (Venn et al. 2008). The sponge host may actually derive up to 50 % of its net energy requirement from cyanobacterial photosynthates (Taylor et al. 2007). In contrast to marine sponges, freshwater species such as Spongilla lacustris harbour eukaryotic zoochlorellae, which actually deliver glucose as the main photosynthate to the host (Frost and Williamson 1980). Current data suggest that catabolic end-products of sponge metabolism, such as ammonium and nitrate, are transferred to the photosynthetic symbiont, supporting its growth and cellular maintenance. Translocation of nitrogen sources back to the host via metabolites, such as amino acids, is minimal.

4.2.2.4 Symbiont-derived secondary metabolites Sponges are among the most prolific natural product producers, with more than 200 new metabolites documented each year (Taylor et al. 2007). With over 300 known compounds, current data demonstrate that cyanobacterial symbionts have a central role in the biosynthesis of these natural products (Tan 2007). They produce structurally diverse secondary metabolites including alkaloids, nonribosomal peptides, polyketides, terpenes and nucleosides. Isolated compounds have demonstrated various pharmacological action including antibacterial, cytostatic, immunosuppressive, anti-viral and anti-inflammatory activities. For some of these compounds, ecological functions, encompassing anti-fouling or predatorrepellent activities, could be assigned. A majority of natural products have been isolated from filamentous cyanobacteria of the order Nostocales (i.e. Lyngbya, Oscillatoria sp.). A prominent example of a novel anti-cancer drug is the Lyngbya sp.derived polyketide palauimide, which showed significant cytotoxic activities against the LoVo (IC50: 0.4 μM) cancer cell line (Lan et al. 2010). Probing an intact sponge–cyanobacterial assembly with state-of-the-art MALDI-MS-imaging technology has been demonstrated in situ that cells of Lyngbya sp. produce the potent neurotoxin jamaicamide (Esquenazi et al. 2008).

4.2.2.5 Effects of environmental stress on symbiosis The effects of light and environmental stress, such as temperature fluctuation, on sponge–algae symbiosis resembles that of coral. Zooxanthallae and cyanobacteria

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may produce UV-protective agents, such as mycosporine-like amino acids. These are subsequently distributed through the host tissue to shield from excess solar radiation. In the case of temperature stress, sponges can expel phototrophic symbionts and revert to heterotrophic metabolism. This situation is evident via bleaching of the sponge tissue. In contrast to corals, sponges are able to sustain growth and cellular activities, even for prolonged periods, in the absence of a photosynthetic partner (Venn et al. 2008).

4.2.3 Algae symbiosis with Mollusca In the order Mollusca, associations with photosynthetic organisms are found in the class of bivalves (i.e. shelled clams, mussels) and gastropoda (mostly nudibranch snails (Muscatin and Greene 1973)). While bivalves are sessile filter-feeder like corals and sponges, the gastropoda are herbivorous suctorial feeders (grazers). Freshwater and marine bivalves associate with zoochlorellae (i.e. Chlorophytae, Chlorella sp.) and zooxanthellae (Dinophytae, Symbiodinium sp.) respectively. While some marine gastropods associate with intact zooxanthellae, they predominantly form transient symbiotic associations with isolated chloroplasts, which they acquire during feeding. For both bivalves and gastropods harboring photosynthetic symbionts, deprivation of light results in weight loss, indicating at least some dependence on photopsynthates for metabolic maintenance. For phototrophic snails, the green coloration has also been postulated to be an active camouflage to evade predatory attacks (Wagele and Johnsen 2001).

4.2.3.1 Morphology of mollusc–algae associations Freshwater clams, such as Anodonta cygnea, aquire zoochlorellae de novo by filtration. The algae are deposited extracellularly between animal cells in regions of high illumination, including mantle, siphon and foot tissues. The association is transient, and algae are either digested or expelled with time. In contrast, marine bivalves, such as the family of Tridacnidae (giant clams; Fig. 4.5), acquire zooxanthellae through filtration and store them in specialized amebocyte cells, distributed throughout the mantle, gill and kidney tissue. When gastropods (i.e. Dermatobranchus semistriatus) associate with intact zooxanthellae, symbionts are first transported to the outer epithelium of the digestive gland, where they are assimilated intracellularly by specialized “carrier” cells (Wagele and Johnsen 2001). Since the highly branched digestive tract is placed just one layer beneath the epidermis, symbionts readily receive light for effective photosynthesis. Similarly, isolated chloroplasts acquired during the feeding process of gastropods (i.e. Elysia sp.) are stored in morphologically distinct epithelial cells that line the tubules of the digestive system. The assimilation of chloroplasts is dominant in the family of marine

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Fig. 4.5: Symbiotic assembly of giant clams (Tridacnidae sp.). The holobiont represents the assembly of clam tissue with photosynthetic zooxanthellae.

hermaphroditic snails belonging to the order Sacoglossa. A survey of 86 sacolossan species revealed that 82 % have a green colour, indicating the retention of chloroplasts. The experimental model species is the sea slug Elysia chlorotica (Green et al. 2000). It is remarkable that in contrast to other endosymbiotic relationships, the assimilated chloroplast is not encased in specific intracellular vacuoles but is in direct contact with the cytosol of host cells.

4.2.3.2 Symbiont uptake and maintenance Bivalves and gastropods acquire intact algae by water filtration for feeding or respiration. With a few exceptions, symbiotic associations are established de novo without any evidence of vertical transmission. The association with algal cells is dynamic, lasting from a few hours to months, largely dependent on the metabolic performance of the symbiont. Commonly, algae are digested by the host, when photosynthetic performance declines. By contrast, the uptake of isolated chloroplasts (plastids) by sacoglossan snails is more stable, lasting several months. These snails preferentially feed on siphonaceuous macroalgae, such as Vaucheria litorea. The algae filaments have few cross walls, and a centre occupied by large vacuoles with a multinucleate cytoplasm containing numerous chloroplasts. Therefore, sea slugs can extract multiple chloroplasts with minimal perturbation by puncturing the cell wall with their specialized radular tooth and sucking out the content. While cytosolic “sap”, nuclei and mitochondria are digested, chloroplasts are moved through digestive tubules by ciliary activity and are then phagocytosed by

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“storage” epithelial cells. During this process, the plastids remain largely structurally intact, with trilamellar, unstacked thylakoids encased by two membrane envelopes. However, phagocytosis results in the loss of the plastid’s endoplasmic reticulum, a structural characteristic of chromophytic plastids. Consequently, the resulting symbiotic plastid has an outer envelope that is in direct contact with the cytoplasm of the host cell. Interestingly, the captured plastid remains functional, i.e. capable of photosynthesis, and protein (transcription and translation) and carotenoid, but not chlorophyll, production. Interestingly, plastids remain active in excess of 5 months, even in the absence of algae-derived nucleomorph structures (Green et al. 2000). The sustained activity is unusual given the importance of nuclear-encoded proteins for plastid function. Recent evidence suggests that protein factors (enzymes, i.e. RUBISCO) essential for photosynthesis are synthesized de novo by the animal host. Therefore, it is conceivable that part of the algal nuclear genome is present in the animal host, supporting plastid function.

4.2.3.3 Flux of primary metabolites in host and symbiont Unkown host factors stimulate the zooxanthellae of Tridacnidae clams to selectively release glucose, glycerol and alanine within 2 h of symbiotic association (Masuda et al. 1994). These microalgae release up to 50 % of photosynthetically fixed carbon to the host. Photosynthates are converted to complex carbohydrates, and proteins are deposited in the hosts siphonal and glandular tissues (Muscatin and Greene 1973). In contrast to intact algae cells, symbiotic plastids associated with sacoglossan snails release glucose, galactose, alanine, glutamic and glycolic acid to the host. With reference to the total amount of photosynthetically fixed carbon, about 40 % is released to the host and transferred to mucus gland, brain and gut tissues. About 10 % of transferred sugars are utilized for biosynthesis of the slug’s protective mucus layer. While symbiotic plastids are not capable of chlorophyll synthesis, evidence suggests that the sacoglossan host transfers essential biosynthetic precursors, such as δ-amino-levulinic acid (ALA) to plastids, thereby actively sustaining its photosynthetic capacity (Muscatin and Greene 1973). At present, biochemical interactions between sacoglossan snails and their photosynthetic machineries are the focus of intense research activities, providing new insights into these unusual symbiotic relationships.

4.3 Terrestrial system 4.3.1 Lichens: Ecological pioneers At present, more than 18 000 lichen species are known (Molnar and Farkas 2010). As “extremists among photosynthetic organisms” (Lange 1992), lichen can grow on natural surfaces such as bare rocks, soil or tree trunks, and unnatural surfaces

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such as mortar, brick, rubber, plastic, glass or painted metal (Brightman and Seaward 1977; Lisická 2008; Seaward 2008; Büdel 2010). Their natural habitat covers the Antarctic or the Himalaya mountains (Hertel 1988) via arid deserts, e.g. the Sahara or the Atacama (Stoppato and Bini 2003), to humid, tropical regions (Müller 2001). Because lichens are primary colonizers of terrestrial areas, they have to adapt to extreme temperatures, drought, inundation, salinity, nutrient-poor environments and high concentrations of air pollutants (Nash III 2008). In addition to colonizing terrestrial environments, lichens can also be found in freshwater streams or in marine intertidal zones (Nash III 2008). Even the survival of lichens in space was reported (Sancho et al. 2007; de Vera et al. 2010). Nevertheless, many lichens are quite sensitive towards the nitrogen-, sulfur- and heavy-metal compounds which are common air pollutants. Owing to this sensitivity, they are widely used as bioindicators to determine the degree of air purity (Fernández-Salegui et al. 2007; Sheppard et al. 2007; Glavich and Geiser 2008).

4.3.2 Modes of lichen symbiosis In nature, lichen symbiosis comprises a relationship between a heterotrophic fungus (mycobiont) and a carbon-delivering and/or nitrogen-fixing photosynthesizing, autotrophic partner (photobiont) (Tehler 1996). Approximately, 21 % of all fungi can be lichenized (Honegger 1991), whereby 98 % are members of the class Ascomycota, and the remaining 2 % either belong to the class Basidiomycota or are mitosporic fungi (Tehler 1996). In contrast, only 100 genera can act as photobionts in lichen formation, namely ca. 85 algae (phycobiont) and ca. 15 cyanobacteria (cyanobiont) (Büdel 2010). The majority of lichens are characterized by a symbiotic relationship between fungi and green algae (Chlorophyta (Lewis and McCourt 2004)). By contrast, only 10 % of lichenized fungi prefer cyanobacteria as the photobiont. In special instances (about 3 %), the mycobiont enters a symbiotic relationship with both types of photobionts (Tschermak-Woess 1988; Honegger 1996). Trebouxia (ca. 20 % of all lichen species (DePriest 2004)), Trentepohlia, Chlorella and Coccomyxa are the main representatives of the green algae photobionts (Nash III 2008). Cyanobacteria, however, are dominated by the genera Nostoc and Chroococcidiopsis (Büdel 2010). In contrast to the single-celled Trebouxia (arctic and alpine regions), which is specialized in the role of a lichen symbiont and seldomly grows as free-living cells in nature, the filamentous green alga Trentepohlia (Mediterranean and tropical regions) can be found not only in lichens but also growing independently in nature.

4.3.3 Lichen taxonomy and evolution Since mycobiont species are far more taxonomically diverse than the photobionts, lichens are phylogenetically speaking a subgroup of the fungal kingdom (Tehler

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1996). Lichens are therefore named according to the mycobiont, while the photobiont pertains its taxonomic independence. Nevertheless, lichens can be distinguished from their isolated fungal and algal partners through their unique structure and metabolite spectrum (Crittenden et al. 1995; Nash III 1996). Therefore, scientists believe that lichens evolved in several independent events; the oldest lichen found is a 600-million-year-old fossil involving a cyanobacterium from the southern part of China (Yuan et al. 2005; Schoch et al. 2009; Büdel 2010). A recent publication by Schoch et al. presumed that ancient lichen species have lost their photobiont and evolved into independent fungi species of the genera Ascomycota (Schoch et al. 2009). In the case of Basidiomycetes, the evolution of lichenizing fungi seems to be a recent event in the terminal branches of the phylogenetic tree (Gargas et al. 1995).

4.3.4 Lichen morphology The symbiosis of fungi and algae results in the formation of a unique lichenized fungal fruit body, called a thallus. Two types of thallus can be distinguished. The first type consists of a homogeneous distribution of the photobiont and mycobiont parts throughout a thallus cross-section (also termed homoeomeric; Fig. 4.6A). The second type comprises a layered structure within the thallus cross-section (termed heteroeomeric; Fig. 4.6B) (Büdel 2010). The formation of a heteroeomeric thallus is the result of a morphogenetic cascade initiated by contact between a lichenizable fungus and an appropriate photobiont (Trembley et al. 2002). Consequently, the photobiont becomes an integral part of the built thallus. The lichen thallus is a compromise between a structure allowing different photobionts and allowing for the specialized needs of one particular photobiont partner (Grube and Hawksworth 2007). It was observed that the same fungal partner can form different morphological structures, dependent on the association with either a green alga or a cyanobacterium. This in turn allows lichens to explore a wide range of habitats (James and Henssen 1976; Armaleo and Clerc 1991; Goffinet and Bayer 1997; Stenroos et al. 2003). The second aspect of the structural compromise provides a survival benefit through tailored metabolite production and exchange. By optimizing the living conditions of the photobiont for maximal metabolite production, the choice of environmental habitats that are colonizable by lichens becomes limited. The heteroeomeric thallus consists of a well-defined structure with distinct zones for each lichen partner (Grube and Hawksworth 2007). The first layer, termed the cortex, is built by tightly packed fungal cells interspaced by a gelatinous matrix. Adjoined to this structure is the zone for the photobiont, which is stabilized by a medulla of loosely packed fungal hyphae that provide the interaction with the photosynthesizing partner. For CO2 supply and O2 removal, air pockets are incorporated into the medulla below the photosynthetic zone. The thallus is con-

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Fig. 4.6: Comparison of homo- with heteroeomeric lichen thallus structure. (a) In the case of Jenmannia goebelii, the the cyanobionts are distributed evenly throughout the thallus crosssection (homoeomeric). (b) Xanthoria parietina is shown on the right-hand side. The photobiont is located exclusively in the upper part of the medulla (heteroeomeric) (Büdel 2010).

cluded by a thin lower cortex layer, which can form rhizinae, root-like structures to anchor the lichen species into, and extract minerals from, surface crevices.

4.3.5 Symbiotic interactions An important aspect of lichen symbiosis is the organization of metabolite exchange between the photobiont and the mycobiont (Fig. 4.7). For this purpose, the hyphae in the medulla have established specialized cells that contact the photobiont (haustoria (Ahmadjian and Henriksson 1959)). The contact between the haustoria cells and the photobiont can differ significantly. The cellular interaction may include penetration of the photobiont, as in Trebouxia (Ahmadjian and Henriksson 1959; Plessel 1963) or may comprise only a superficial contact via thin cell walls (appressorium), as observed with cyanobacteria (Henriksson 1958; Plessel 1963). In either case, the photobiont is “forced” to release more than 60 % of its photosynthetic products to the fungus, provided the culture conditions are optimal (Richardson et al. 1967). In instances where the photobiont is a eukaryotic green alga, the transferred photosynthetic products are sugar alcohols, such as ribitol, which are subsequently converted to fungal storage products such as mannitol and arabitol by the mycobiont (Smith 1961). By contrast, cyanobacterial photobionts directly supply the fungus with neutral sugars such as glucose, which can be further metabolized or converted to storage compounds such as mannitol (Ahmadjian 1962). When lichens have a tripartite symbiotic relationship, comprising the mycobiont and a eukaryotic alga as well as a cyanobacterium, the eukaryotic photobiont will be retained in the medulla to conduct photosynthesis. The cyanobacterium is instead located either in an internal layer beneath the algal cells or in an external, fungal compartment at the thallus surface termed cephalopedia (Büdel and Scheidegger 1996). Here, it is responsible for nitrogen fixation from air, thereby produc-

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Fig. 4.7: Metabolic processes, and water and nutrient transport, respectively, in lichens. The symbiotic interaction between the mycobiont (yellow) and the photobiont (green) (Büdel 2010) is shown.

ing ammonia as a nitrogen source. Nitrogen fixation by the cyanobacterial symbiont therefore provides new avenues for the constant supply of nitrogen to support the growth of the remaining symbiotic partners (Bond and Scott 1955; Scott 1956). Interestingly, lichens solely containing a cyanobacterial photobiont often use photosynthesis and nitrogen fixation simultaneously to produce nutrients (Crittenden et al. 2007). The nitrogen-fixating ability of cyanobionts rises during lichen symbiosis, because the concentration of the nitrogen-fixing heterocysts increases by a factor of three through metabolitc intervention of the mycobiont (DePriest 2004). Furthermore, Paulsrud et al. showed that the same cyanobacterium can either occur in a bipartite or a tripartite symbiotic relationship. The symbiotic mode is influenced by the fungal partner or by environmental conditions (Paulsrud et al. 1998, 2000).

4.3.6 Lichen growth and propagation To retain the chosen organismic arrangements and specific lichen structure, both mycobiont and photobiont have to grow at the same rate. Consequently, a radial average increment of many thalli of less than 1 mm per year results (Karlen and Black 2002; Sancho and Pintado 2004). By contrast, some species can grow up to 36.5 mm/year (Richardson 1975). The low average growth rate reflects the low metabolic activity, which results from: 1. synchronized growth of all symbiotic partners; 2. the low net rate of carbon dioxide assimilation (Smith 1962);

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3. the growth on nutrient-poor substrates (Barkman 1958); 4. the challenging environmental conditions to which the lichens are exposed, which may include extreme temperature, light exposure and low air moisture content (Bliss and Hadley 1964); 5. the relatively low protein synthesis rate, which is linked to periods of minimal metabolism, which in turn is responsible for a reduction in essential biosynthetic enzymes (Smith 1960a, 1960b). The slow growth rate of lichens is directly linked to the extended lifetime of lichen, which may last up to 4500 years, as observed in arctic–alpine regions (Beschel 1961).

4.3.6.1 Lichen propagation The lichen symbiotic partners can disperse independently by vegetative propagation (Büdel and Scheidegger 1996). In this context, lichens apply several strategies. Either dry, brittle thallus fragments of lichens break off or stalk- (pillar-) like structures (termed isidia) are formed intentionally by lichens that eventually break off the main body (Barbosa et al. 2009). Alternatively, another dipsersal unit termed soredia can be formed (Ahmadjian 1966). These Soredia consist of a few photobiont cells enveloped in fungal hyphae to form a powdery mass near the centre of the lichen thallus or at the tips of some thallus lobes, which can be dispersed by the wind. All of the aforementioned dispersal strategies have the disadvantage that fragments with some mass can only travel short distances (Heinken 1999; Walser 2004). By contrast, spores, which can be formed by most mycobionts as the result of sexual reproduction in fruiting structures, i.e. apothecia, are easily dispersed over long distances, thus enabling the exploration of novel habitats (Murtagh et al. 2000). However, these spores have to find a new photobiont in order to form another lichen thallus when germinating (Bailey 1976; Walser 2004). In contrast to the mycobiont, sexual reproduction cycles of the phycobiont are effectively suppressed by the former (DePriest 2004).

4.3.7 Symbiotic benefits for algal photobionts Although lichen symbiosis is often called a “controlled parasitism” (Gargas et al. 1995) or explained by a domestication model (analogous to human agriculture) (Armaleo 1991), the photobiont also benefits from the interaction with the fungus. While primarily metabolite exchange supports the fungus, the mycobiont relies on a functioning relationship with the photobiont. Under environmental stressors, the photobionts tend to retain most of their carbohydrates to maintain an active metabolism without any fungal interference. Generally, the mycobiont aims to sup-

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port the life of the photobiont by providing structural support via the thallus medulla. The thallus structure particularly aids the photobiont as it stabilizes its cells and enables optimal gas transport (CO2/O2) as well as light penetration for effective photosynthesis owing to an increased surface area (Büdel 2010). Although light exposure is essential for the photosynthesis, algae have to be protected from excessive UV irradiation to avoid photobleaching effects. To prevent these photobleaching effects, the fungus produces pigments in the cortex cells that absorb light (Ertl 1951; Solhaug and Gauslaa 1996). Additionally, the fungus synthesizes aromatic secondary metabolites as UV(B) protectants, which convert the high energy level of UV irradiation into the lower energy level of visible light (Rao and LeBlanc 1965; Rancan et al. 2002; Nybakken et al. 2004; Torres et al. 2004; Sancho et al. 2007). Secondary metabolites produced by the fungal cells also serve a role in protection against herbivores (Lawrey 1986, 1989; Asplund and Gauslaa 2007, 2008). As allelopathic agents, they can influence the metabolism and growth of neighbouring lichens, mosses, micro-organisms and higher plants (e.g. fungi, jack pines, white spruces or crops, like cabbage, lettuce, tomato and pepper) as well, leading to benefits in the struggle for survival of the producing lichen (Huneck and Schreiber 1972; Marante et al. 2003; Armstrong and Welch 2007; Macias et al. 2007). Lichen survival is supported by secreted, secondary metabolites (e.g. oxalic acid) that can extract minerals out of the surfaces that support lichen growth (Adamo and Violante 2000; Kiurski et al. 2005). Lichens can actually differentiate between metal ions that are metabolically essential and those that will become toxic at high doses, which should consequently not be adsorbed by the thallus (Hauck et al. 2009). The secretion of secondary metabolites into the surface not only supplies lichen with minerals but also is important for the erosion of rocks to gain topsoil for the colonization of higher plants (Schatz 1962; Jones 1988). Undeniably, the most important function of the lichen thallus is to prolong the moisture supply for the photobiont (Lange et al. 2001). Since lichens are poikilohydric, they cannot actively store water or moisture. Therefore, they can only extract water, which is required for survival, passively from the environment (Büdel 2010). As water absorption and desorption cannot be regulated by lichens, they can only adapt restrictedly to environmental conditions, e.g. water sources (fog, dew formation, rain or timely being drowned), via the ratio of surface to thallus volume. When desiccated, lichens enter a dormant state. Under these conditions, no life is detectable (anabiosis). In this dormant state, lichens can survive for many years waiting for the next water supply. Some tropical lichens, however, are unable to cope effectively with desiccation and will sever away, when water is limited (Büdel 2010). Lichens with a phycobiont can recover within minutes to hours from anabiosis, if they are incubated in humid air (relative humidity > 75 %) (Lange and Kilian 1985; Lange 1988; Lange et al. 1988); in the case of a cyanobiont, however, liquid water is necessary (Lange et al. 1986, 1988; Lange 1988). Depending on the amount of water supplied to dry lichen, four distinct recovery phases of the photobiont can be observed (Fig. 4.8). Following a lag phase

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Fig. 4.8: Photosynthesis and respiration rate according to the lichen’s thallus water content (Büdel 2010).

after rehydration, the photobiont starts with respiration (phase 1). As the water content rises, lichens enter a positive net photosynthesis (phase 2) (with phycobiont: 15–30 % related to the thallus dry weight, with cyanobiont: 85–100 % (Lange et al. 1988)) until the optimal conditions for photosynthesis are reached (phase 3). In the final recovery phase, the water content achieves a level where a problem starts to arise from the slow gas transport in liquids, i.e. insufficient CO2 reaches the photobiont for optimal photosynthesis to occur. Some lichens have found solutions to minimize or even solve this problem, but the acting mechanisms are still uncertain. It has been suggested that hydrophobic secondary metabolites play a role in the CO2 exchange rate of lichens. However, data from Lange et al. in a case study with Diploschistes muscorum were unable to support this hypothesis (Lange et al. 1997). Nevertheless, phase 4 has to be subdivided into three different scenarios: – Scenario a: No decrease in CO2 supply is detectable, and the photosynthesis level remains at its optimum. – Scenario b: If the delivery of CO2 to the photobiont is limited, a reduced photosynthetic activity results. – Scenario c: Respiration stops, and no net photosynthesis is detectable anymore. Generally speaking, the choice of the best habitat is regulated by lichens via several mechanisms: (1) the interval of thallus water content for optimal net photosyn-

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thesis, (2) the activation after desiccation through humid air or liquid water, (3) light and (4) the temperature sensitivity of the lichen species (Büdel 2010).

4.3.8 Biotechnological aspects of lichen/mycobiont cultivation Nearly all secondary metabolites (aliphatic or aromatic substances of relatively low molecular weight), which accumulate in the cortex or in the medullary layer as small crystals on the exterior of the hyphae, are produced by the thallus cells of the mycobiont (Elix 1996; Huneck 1999). Interestingly, more than 5 % of the lichen thallus dry weight can be compromised by secondary metabolites (Stocker-Wörgötter 2008). Although these secondary metabolites possess several remarkable biological attributes for pharmaceutical and other applications, their exploitation has suffered from difficulties in lichen cultivation under laboratory conditions. In addition to the low growth rates, the interdependence of the photo- and mycobiont has caused problems in reproducibility during the early phases of lichen screening for bioactive substances (Zopf, 1895; Brunauer Stocker-Wörgötter 2005). However, in the last decade, improvements in the cultivation of lichenizable fungi have provided mechanisms to force lichens to produce secondary metabolites even in axenic cultures, i.e. in the absence of a photobiont (Stocker-Wörgötter 2008). Today, defined media containing specific amounts of sugars or polyols have allowed for the axenic cultivation of about 250 different mycobionts (Stocker-Wörgötter 2008). Furthermore, it was discovered that higher amounts of secondary metabolites are produced only when stress is applied to the fungus. Therefore, today, cultivation is accomplished in electronically adjusted culture chambers, which allow the simulation of day/night cycles, drought periods, the exposure to UV irradiation or high light intensities as well as cold and warm temperature treatments (Stocker-Wörgötter 1998, 2001a, 2001b, 2002a, 2002b). Additionally a morphological change, also a result of stressing the cultured mycelia, into a layered structure – in the absence of the photobiont – is necessary to induce secondary metabolite biosynthesis (Kinoshita et al. 1993a, 1993b; Opanowicz et al. 2006; Stocker-Wörgötter and Elix 2006). Axenic cultivation of mycobionts opens up a possibility to gain cDNA libraries encoding the biosynthetic enzymes responsible for production of lichen-derived secondary metabolites (Brunauer et al. 2009). These enzymes can subsequently be transformed into expression hosts (e.g. E. coli or yeasts), which enables recombinant production of lichen-specific metabolites using established technologies and cell systems (Kealey et al. 1998; Stocker-Wörgötter 2008).

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4.3.9 Potential of bioactive lichen-derived metabolites Improvements in cultivation of lichenizable fungi allowed accessibility to approximately 1050 known lichen-derived compounds (Stocker-Wörgötter 2008). Based on their chemical structure, lichen-specific secondary metabolites are predominantly formed via three biosynthetic mechanisms, including polyketide synthesis, the mevalonic acid (terpenes) or the shikimic acid pathway (Tab. 4.1). In the case of polyketide-based structures, recent cultivation studies indicate that a lack of secondary metabolite production may be caused by a biosynthetic switch from the polyketide towards fatty-acid synthesis (Molina et al. 2003). The vast diversity of secondary metabolites results in a huge activity spectrum ranging from allelopathy to pharmaceutical applications, encompassing cancer or HIV therapeutics to sustainable agrochemicals (Molnar and Farkas 2010). The potentials and risks of lichen-derived substances will be examined in key examples. Among the photobiont-produced secondary metabolites, carotenoids are by far the most dominant compound class (Friedl and Büdel 1996). Much like Secondary metabolite class pathway

Exemplary secondary metabolites of depicted pathway

Polyketide pathway

Anthraquinones Naphtaquinones Xanthones Chromones Usnic acid and related compounds Dibenzofurans Depsones Depsidones Depsides Monocyclic phenols Cycloaliphatic compounds Lactonecarboxylic acids Aliphatic acids Carotenoids Steroids Triterpenoids Sesterpenoids Diterpenes Sesquiterpenes Monoterpenes Amino acid aminoalcohol esters Cyclopeptides Picroroccellin Solorinin, sticticin Arthogalin Terphenylquinones Pulvinic acid derivatives

Mevalonic acid pathway

Shikimic acid pathway

Tab. 4.1: List of lichen metabolic pathways and secondary metabolite classes derived from them.

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carotenoids, the mycobiont-derived metabolites depsides and depsidones have antioxidant activities. These compound classes are by far the most preferentially synthesized structures of the mycobiont (Fahselt 1994; Elix 1996). The phenolic groups of these compounds can act as a highly effective antioxidant (Hidalgo et al. 1994; Marante et al. 2003). In nature, the antioxidant effects of these compounds protect lichens from negative effects of free radicals arising from high UV irradiation. Since, in polar regions, UV irradiation is higher than at the equator, these compounds can be found at significantly higher concentrations in arctic lichen species compared with their tropical equivalents (Luo et al. 2009). Interestingly, the depside sphaerophorin and the depsidone pannarin proved to be potent growth inhibitors for aggressive, therapy-resistant melanoma cells (Russo et al. 2008). To enable effective screening of these compounds for cytotoxic activities against cancer cells, Ingólfsdóttir and co-workers developed a method to increase the water solubility of depsides and depsidones by adding cyclodextrins (Kristmundsdóttir et al. 2005). At present, the best-defined lichen-based substance is the dibenzofuran usnic acid, which can be present in up to 6 % w/w in organic lichen extracts (Ingólfsdóttir 2002). Although usnic acid occurs in two enantiomeric forms, lichens selectively synthesize only one enantiomer, which results in a diverse activity spectrum of biosynthetic congeners. Ingólfsdóttir reviewed the antipyretic, anti-inflammatory and analgesic activities of usnic acid derivatives, some of which were already known in ancient times (Schindler 1988; Ingólfsdóttir 2002). Recent publications have demonstrated the application of usnic acid and its congeners as adjuvants. Usnic acid may also have a significant impact as an antibiotic, since its antibacterial activity is comparable to that of streptomycin, whose application is now limited due to bacterial resistance (Ranković et al. 2008). Usnic acid derivatives also have the ability to induce apoptosis in murine lymphotic leukaemia cells (Bezivin et al. 2004) and to decrease the proliferation of human breast and lung cancer cells, without damaging the DNA of surrounding normal cells (Mayer et al. 2005). Usnic acid also has a reported antiviral activity, which can be used to treat Argentine haemorrhagic fever in humans (Fazio et al. 2007). Francolini et al. described the change in morphology of bacterial biofilms using usnic acid, which can prevent the formation of biofilms on medical instruments or prostheses (Francolini et al. 2004). Furthermore, today, usnic acid is widely used as a component of cosmetics (e.g. perfumes, after-shave lotions or deodorants) and suntan preparations or antiseptic creams (Seifert and Bertram 1995; Fernandez et al. 1996; Ingólfsdóttir 2002). Next to their beneficial functions, lichen-derived substances can also have adverse effects in and on the human body (Molnar and Farkas 2010). These adverse effects are mostly allergic reactions (Dahlquist and Fregert 1980; Thune and Solberg 1980; Hausen et al. 1993). Occupational contact dermatitis caused by lichenderived compounds is a hazard for forestry workers (cf. “woodcutter’s eczema”),

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gardeners and florists; the non-occupational variant is associated with people performing outdoor activities such as cutting and handling firewood, picking berries or hunting (Ingólfsdóttir 2002; Aalto-Korte et al. 2005). Several lichen substances have the ability to cause photocontact dermatitis by photosensitizing the human skin, leading to an aggravation of symptoms upon exposure to sunlight (Thune and Solberg 1980; Elix and Stocker-Wörgötter 2008). Feeding experiments have shown the development of ataxia in sheep and cattle, leading to paralysis of the extremities in severe cases (Kingsbury 1964). The use of sodium usneate as a dietary supplement has resulted in severe chemical hepatitis (Favreau et al. 2002), and the consumption of weight loss products prepared with pure usnic acid has led to liver failure (Durazo et al. 2004; Neff et al. 2004).

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Anna Kirchmayr and Christoph Griesbeck

5 Genetic engineering, methods and targets 5.1 Introduction Inexpensive cultivation, biological safety, fast growth rates and a short gene-toprotein period are just some of the properties making microalgae attractive candidates for the expression of transgenes within a pharmaceutical or biotechnological context. The model organism Chlamydomonas reinhardtii is one of the best-studied microalgae concerning transformation techniques, markers and promoters. Several proteins with biotechnological and biopharmaceutical relevance have already been expressed in C. reinhardtii. Despite the fact that none of these proteins have reached the point of commercialization, certain fields, such as edible vaccines, have attracted great interest. To establish microalgae as an effective tool for biotechnological applications such as the production of pharmaceutical proteins, methods for genetic manipulation have to be available. In this chapter, we will give an overview of methods for genetic engineering in microalgae including transformation, promoters, reporters, markers, genetic structure and RNA interference. The methods described are limited to eukaryotic algae, since more comprehensive coverage would go beyond the intended scope of this chapter.

5.2 Methods in genetic engineering of eukaryotic microalgae 5.2.1 Transformation Microalgae species possess three genomes, namely the nuclear, the plastid and the mitochondrial genetic systems. In some microalgae, it has even been possible to genetically manipulate all of these systems in the same strain. Table 5.1 gives an overview of microalgae that have already been successfully transformed. Current methods for genetic manipulation are discussed further in this section.

5.2.1.1 Glass beads and silicon whiskers The glass beads method was first described by Kindle in 1990 and is nowadays one of the most established protocols for the introduction of foreign DNA into the nucleus. Algae cells are vortexed together with PEG6000 20 % and DNA for 15– 20 s. The agitation impacts the cell and makes it possible for DNA to enter. The major advantages of this method are that it is simple to establish in most laborato-

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ries and that the required equipment is relatively inexpensive. The fundamental shortcoming is that it necessitates the use of either cell-wall-deficient strains or wild type strains that have been treated enzymatically (Kindle 1990). It is also noteworthy that the copy number of integrated DNA is markedly lower in comparison with microparticle bombardment, a method that will be discussed later in this chapter (Kindle 1998). Silicon carbide whiskers have also been documented as viable alternatives to glass beads. The main limitation of this technique is its need for expensive, hazardous materials, and so this method is seldom used. In comparison with glass beads, the use of untreated wild type strains instead of mutants allows for an increased plating efficiency and lower cell lethality during vortexing (Dunahay 1993).

5.2.1.2 Particle bombardment Particle bombardment is a technique that is often used for plastid transformation: DNA is applied onto the surface of metal particles, usually of tungsten or gold (Boynton et al. 1988). These coated particles are then accelerated by a heliumdriven pistol, propelling DNA into the algae cells. When using this method for nuclear transformation, the central dilemma is the likelihood of multiple copy insertions (Sodeinde and Kindle 1993).

5.2.1.3 Electroporation The use of an electric field to depolarize the cell walls of microalgae is another possible transformation method. Initially, electroporation resulted in low frequencies of stable transformation (Brown et al. 1991), but in more recent times this method has become well established for C. reinhardtii and numerous other microalgae species (Tang et al. 1995; Shimogawara et al. 1998).

5.2.1.4 Agrobacterium tumefaciens-mediated transformation As in higher plants, the transformation of the chloroplast in microalgae may be carried out through the use of Agrobacterium tumefaciens, which possesses a T-DNA plasmid and causes a tumour in target cells. Up until the time of writing this article, this method has successfully been carried out only in Chlamydomonas reinhardtii and Haematococcus pluvialis (Kumar et al. 2004; Kathiresan et al. 2009; Kathiresan and Sarada 2009). Using this method of transformation for β-glucuronidase, green fluorescent protein and hygromycin phosphotransferase, the transformation frequency was 50 times higher than with the glass beads technique (Kumar et al. 2004; Kathiresan et al. 2009).

5.2 Methods in genetic engineering of eukaryotic microalgae

89

Organism

Transformation method

Reference

Chlamydomonas reinhardtii Chlamydomonas reinhardtii Chlamydomonas reinhardtii Chlorella ellipsoidea Chlamydomonas reinhardtii Volvox carteri Cyclotella cryptica Navicula saprophila Phaeodactylum tricornutum Chlorella sorokiana Chlorella ellipsoidea Amphidinium Symbiodinium microadriaticum Chlorella vulgaris Chlorella kessleri Cylindrotheca fusiformis Chaetoceros sp. Porphyridium spp. Chlamydomonas reinhardtii Dunaliella salina Dunaliella tertiolecta Haematococcus pulvialis

Particle bombardment Glass beads Electroporation Glass beads Silicon carbide whiskers Particle bombardment Particle bombardment Particle bombardment Particle bombardment Particle bombardment Particle bombardment Silicon carbide whiskers Silicon carbide whiskers Electroporation Particle bombardment Particle bombardment Particle bombardment Particle bombardment Agrobacterium tumefaciens Particle bombardment Electroporation Particle bombardment

Dunaliella viridis Dunaliella salina Closterium peracerosumstrigosum-littorale Lotharella amoebiformis Cyanidioschyzon merolae Nannochloropsis oculata Ostreococcus tauri Dunaliella salina Ulva pertusa Gonium pectorale Haematococcus pulvialis Chaetoceros sp.

Electroporation Electroporation Particle bombardment

(Boynton et al. 1988) (Kindle 1990) (Brown et al. 1991) (Jarvis and Brown 1991) (Dunahay 1993) (Schiedlmeier et al. 1994) (Dunahay et al. 1995) (Dunahay et al. 1995) (Apt et al. 1996) (Dawson et al. 1997) (Chen et al. 1998) (Ten Lohuis and Miller 1998) (Ten Lohuis and Miller 1998) (Chow and Tung 1999) (El-Sheekh 1999) (Fischer et al. 1999) (Falciatore et al. 1999) (Lapidot et al. 2002) (Kumar et al. 2004) (Tan et al. 2005) (Walker et al. 2005b) (Steinbrenner and Sandmann 2006) (Sun et al. 2006) (Wang et al. 2007) (Abe et al. 2008)

Particle bombardment Glass beads Electroporation Electroporation Glass beads Particle bombardment Particle bombardment Agrobacterium tumefaciens Particle bombardment

(Hirakawa et al. 2008) (Ohnuma et al. 2008) (Chen et al. 2008) (Corellou et al. 2009) (Feng et al. 2009) (Kakinuma et al. 2009) (Lerche and Hallmann 2009) (Kathiresan et al. 2009) (Miyagawa-Yamaguchi et al. 2011)

Tab. 5.1: Transformation of eukaryotic microalgae.

5.2.2 Promoters Foreign genes in microalgae can be expressed under promoters of other organisms or under strong endogenous promoters. Owing to the lack of suitable strong algal virus promoters to date, transgenes are mostly expressed under their own promoter elements. For some algae, the plant virus promoter cauliflower mosaic virus 35S has been successfully used to drive transgene expression (Ten Lohuis and Miller

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Promoter

Description

Genome

Reference

β-2-TUB cabII-1

Promoter of β-2-tubulin Chlorophyll-ab binding protein promoter Promoter of nopaline synthase from Agrobacterium tumefaciens Promoter of cauliflower mosaic virus 35S Promoter of a small subunit of ribulose bisphosphat carboxylase Promoter of nitrate reductase Promoter of Chlamyopsin Promoter of the heat shock protein 70A Promoter of the photosystem I complex protein Promoter of ribulose bisphosphate carboxylase large subunit Promoter of cytochrome c6 Promoter of ATPase alpha subunit Promoter of photosystem II protein D1

Nuclear Chloroplast

(Davies et al. 1992) (Blankenship and Kindle 1992)

Nuclear

(Hall et al. 1993)

Nuclear

(Tang et al. 1995)

Nuclear

(Stevens et al. 1996)

Nuclear Nuclear Nuclear

(Loppes et al. 1999) (Fuhrmann et al. 1999) (Schroda et al. 2000)

Nuclear

(Fischer and Rochaix 2001)

Chloroplast

(Franklin et al. 2002; Mayfield et al. 2003; Mayfield and Schultz 2004) (Quinn et al. 2003) (Mayfield et al. 2003; Sun et al. 2003; Mayfield and Schultz 2004) (Mayfield and Schultz 2004)

nos

CaMV RBCS2

NIA1 COP HSP70A psaD rbcL

cyc6 atpA psbA

Nuclear Chloroplast Chloroplast

Tab. 5.2: Promoters used in C. reinhardtii.

1998). In the case of endogenous promoters in C. reinhardtii, the strongest currently is that of a small subunit of ribulose bisphosphat carboxylase RBCS2 and is often used in combination with the heat shock promoter HSP70A (Stevens et al. 1996). Grounds for this success lie in the heat-inducible promoters, which have been shown to enhance expression by upstream fusion of other promoter elements (Stevens et al. 1996; Schroda et al. 2000). Other strong constitutive promoters such as ATPase alpha subunit (AtpA) or the β-tubulin promoter have also been the subject of research (Davies et al. 1992). Inducible promoters such as NIT1 or CYC6, which are regulated by media ingredients nitrate and copper, are used to avoid gene silencing and therefore enhance expression rates (Loppes et al. 1999; Quinn et al. 2003). Table 5.2 shows a detailed overview of constitutively and inducible promoters used in C. reinhardtii for the expression of trangenes.

5.2 Methods in genetic engineering of eukaryotic microalgae

91

5.2.3 Gene silencing Post-translational gene silencing plays a significant role in the gene regulation of many eukaryotes. In Chlamydomonas reinhardtii, this fact could also be observed, with short interfering RNAs (siRNAs) and microRNAs (miRNAs) being involved in the silencing machinery (Molnar et al. 2007). Low product profits when expressing foreign proteins and the variable expression rates of transgenes have been shown in C. reinhardtii to be major drawbacks of this silencing mechanism (Schroda 2005). Gene silencing can however also be used as a tool for targeted gene downregulation, an undertaking that has already been successfully carried out with C. reinhardtii. In 2001, a retinal protein was silenced through integration of a corresponding antisense construct (Fuhrmann et al. 2001). Furthermore, Koblenz and Lechtreck have recently published results on inducible RNAi with the NIT1 promoter (Koblenz and Lechtreck 2005). To study gene functions, artificial miRNAs have been developed that may find use in high-throughput examinations (Molnar et al. 2009).

5.2.4 Codon usage When expressing foreign genes in the nuclear genome of microalgae, expression rates are often low, barely detectable and far from being of commercial interest. A reason for this is the influence of codon usage on trangene expression; this effect has been described in C. reinhardtii and Phaeodactylum tricornuntum (Zaslavskaia et al. 2000, 2001). At 61 %, Chlamydomonas reinhardtii has a high GC content in the nuclear genome, and therefore expression of genes in the nucleus could only be performed with genes that have comparable GC contents, an effect first observed for gfp (Fuhrmann et al. 1999; Leon-Banares et al. 2004; Heitzer et al. 2007). Consideration of codon usage is, as a result, of importance in order to facilitate significant expression.

5.2.5 Improvement of expression rates and secretion of proteins Transgenic expression in the nucleus can alternatively be enhanced through the integration of intron sequences into expression cassettes. This process was first defined by Eichler-Stahlberg et al. in 2009 when the expression of the Renilla reniformis luciferase was investigated. A stimulatory effect was best achieved through the integration of all three RBCS2 introns arranged in physiological order, with expression driven by a fusion of HSP70A and RBCS2 promoters (Eichler-Stahlberg et al. 2009). The expression rate in C. reinhardtii has been similarly studied for optimization through the use of linearized DNA for transformations (Kindle

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Fig. 5.1: Principle of a modular vector system. A plasmid containing the expression cassette for the gene of interest and a plasmid containing a selection marker are constructed independently and subsequently fused utilizing the Cre/lox system. After digestion with a restriction endonuclease, a linear fragment for transformation is obtained, which contains only the relevant genetic elements for Chlamydomonas omitting bacterial selection markers (Heitzer and Zschoernig 2007).

1998). Figure 5.1 shows the principle of cre/lox recombination (Heitzer and Zschoernig 2007), which enables the fusion of two plasmids, allows for the fast generation of tandem vectors with varying selection markers and promoters. Linearization of the sequence can subsequently remove bacterial resistance genes that are no longer required for the transformation procedure in microalgae. In the work of Heitzer and Zschoernig in 2007, such tandem constructs provided noticeably higher coexpression rates of foreign genes and markers (Heitzer and Zschoernig 2007). Despite the fact that this process requires that numerous improvements be made in order to enhance nuclear gene expression, this means of expression allows for post-translational modification and secretion into the culture medium. Foreign proteins expressed in the nucleus are either transported into the cytoplasm or secreted via the endoplasmatic reticulum and Golgi apparatus into the culture medium, which is illustrated in Figure 5.2 (Griesbeck et al. 2006). In the chloroplast, expression in the plastid genome results in higher product yields. Foreign genes are integrated through homologous recombination, and no gene silencing occurs. The drawbacks of plastid genome expression are, however, that post-translational modifications may not be carried out and that the resulting

5.2 Methods in genetic engineering of eukaryotic microalgae

93

Fig. 5.2: Schematic drawing of possible expression pathways. (1) Expression in the nucleus and secretion of protein into the culture medium. (2) Expression in the nucleus and accumulation of protein in the cytoplasm. (3) Expression in the plastidic genome and accumulation of protein in the chloroplast without secretion and post-translational modification (Griesbeck et al. 2006).

product is accumulated in the chloroplast, not secreted into the medium (Griesbeck et al. 2006).

5.2.6 Selection markers For the selection of algae clones, several markers are available that contain resistance against antibiotics or herbicides. These include genes for aphVII, aphVIII and ble. To avoid such antibiotics and herbicides, algae may also be selected by the use of auxotrophic strains and corresponding media containments. For such forms of selection, mutant strains are required, the most commonly used markers for auxotrophic strains being ARG7 (Debuchy et al. 1989), NIA1 (Kindle et al. 1989), THI10 (Ferris 1995) and NIC7 (Ferris 1995). In Table 5.3, the selection markers currently used in microalgae are listed. Most of the markers are used for nuclear transformation; aadA (Goldschmidt-Clermont 1991; Cerutti et al. 1997) and aphA-6 (Bateman and Purton 2000) allow the selection of clones with integrations in the plastidic genome.

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Marker

Description

Organism

Reference

ARG7 NIT1 (NIA1) oee-1

Arginine prototrophy Nitrate prototrophy

Chlamydomonas reinhardtii Chlamydomonas reinhardtii

(Debuchy et al. 1989) (Kindle et al. 1989)

Oxygen-evolving enhancer protein Resistance to spectinomycin/streptomycin Neomycin phosphotransferase

Chlamydomonas reinhardtii

(Mayfield and Kindle 1990) (Goldschmidt-Clermont 1991; Cerutti et al. 1997) (Hall et al. 1993; Dunahay et al. 1995; Ten Lohuis and Miller 1998; Zaslavskaia et al. 2000)

aadA nptII

CRY1-1 NIC7 THI-10 cat ble

Resistance to cryptopleurine/emetine Nicotinamide prototrophy Thiamine prototrophy Resistance to chloramphenicol Resistance to zeocin

PPX1

Resistance to porphyric herbicides hpt HygromycinB phosphotransferase aphA-6 Resistance to kanamycin/ amikacin nat Nourseothricin resistance sat-1 Nourseothricin resistance act-2 Resistance to cycloheximide aphVIII Resistance to paromomycin/kanamycin ALS Resistance to sulfometuronmethyl aph7″ Resistance to hygromycin B PDS Phytoene desaturase

ARG9

Plastid N-acetyl ornithine aminotransferase

Chlamydomonas reinhardtii Chlamydomonas reinhardtii, Symbiodinium sp., Phaeodactylum tricornutum, Amphidinium sp., Cyclotella crytica, Navicula saprophila Chlamydomonas reinhardtii Chlamydomonas reinhardtii Chlamydomonas reinhardtii Chlamydomonas reinhardtii, Phaeodactylum tricornuntum Chlamydomonas reinhardtii, Phaeodactylum tricornutum

(Nelson et al. 1994)

Phaeodactylum tricornuntum Phaeodactylum tricornuntum Chlamydomonas reinhardtii Chlamydomonas reinhardtii

(Ferris 1995) (Ferris 1995) (Tang et al. 1995; Apt et al. 1996) (Apt et al. 1996; Stevens et al. 1996; Lumbreras et al. 1998) (Randolph-Anderson et al. 1998) (Ten Lohuis and Miller 1998) (Bateman and Purton 2000) (Zaslavskaia et al. 2000) (Zaslavskaia et al. 2000) (Stevens et al. 2001) (Sizova et al. 2001)

Chlamydomonas reinhardtii

(Kovar et al. 2002)

Chlamydomonas reinhardtii Chlorella zofingiensis, H. pluvialis

(Berthold et al. 2002) (Steinbrenner and Sandmann 2006; Huang et al. 2008) (Remacle et al. 2009)

Chlamydomonas reinhardtii Amphidinium, Symbiodinium Chlamydomonas reinhardtii

Chlamydomonas reinhardtii

Tab. 5.3: Selection markers for microalgae.

5.2.7 Reporter genes Similar to selection markers and promoters, most available reporter genes are compatible with Chlamydomonas reinhardtii. Table 5.4 provides an overview of the most

5.2 Methods in genetic engineering of eukaryotic microalgae

95

Reporter

Description

Organism

Reference

ARS

Arylsulfatase-colorimetric assay – not for sulfur starvation β-glucuronidase

Chlamydomonas reinhardtii

(Davies et al. 1992)

Phaeodactylum tricornutum, Amphidinium, Symbiodinium Chlamydomonas reinhardtii Chlamydomonas reinhardtii Phaeodactylum tricornutum Cylindrotheca fusiformis

(Ten Lohuis and Miller 1998; Zaslavskaia et al. 2000) (Fuhrmann et al. 1999)

Phaeodactylum tricornutum Chlamydomonas reinhardtii Chlamydomonas reinhardtii

(Zaslavskaia et al. 2001)

Chlamydomonas reinhardtii

(Mayfield and Schultz 2004)

Chlamydomonas reinhardtii Chlamydomonas reinhardtii

(Matsuo et al. 2006)

gus

crgfp rluc luc ε-frustulin eGfp gfpCt crluc

luxCt

lucCP cgluc

Nuclear codon-optimized GFP Chloroplast-luciferase from Renilla reniformis Luciferase from Horatia parvula Calcium-binding glycoprotein GFP adapted to human codon usage Chloroplast codon-optimized GFP Nuclear codon-optimized luciferase from Renilla reniformis Chloroplast codon-optimized Luciferase from Vibrio harveyi Chloroplast codon-optimized firefly luciferase Nuclear codon-optimized Gaussia princeps luciferase

(Minko et al. 1999) (Falciatore et al. 1999) (Fischer et al. 1999)

(Franklin et al. 2002) (Fuhrmann et al. 2004)

(Ruecker et al. 2008)

Tab. 5.4: Reporter genes for microalgae.

frequently used reporter genes in various microalgae. Luciferase of Renilla reniformis and Gaussia princeps were codon-optimized and are, aside from GFP, the most commonly used genes for expression studies (Minko et al. 1999; Fuhrmann et al. 2004; Mayfield and Schultz 2004; Matsuo et al. 2006; Ruecker et al. 2008). The GFP of jellyfish Aequorea victoria was also successfully used for monitoring expression after codon optimization for both nuclear and chloroplast genomes (Fuhrmann et al. 1999; Franklin et al. 2002). The gene of arylsulfatase is another possibility for measuring foreign gene expression, but its activity can be determined only when the endogenous enzyme is fully inhibited (Davies et al. 1992).

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5 Genetic engineering, methods and targets

5.3 Examples for biotechnological relevant proteins 5.3.1 Proteins expressed in Chlamydomonas reinhardtii To establish an expression system with any biotechnological relevance, it is necessary to prove that expression of relevant products are possible within the organism. Thus, it is important to point out that C. reinhardtii, which is to date the most researched of all eukaryotic microalga, has successfully expressed a variety of proteins. Table 5.5 provides an overview of the most significant of these. Despite the fact that a relatively high number of proteins have been expressed in Chlamydomonas reinhardtii, at the time of writing this chapter not one of them has achieved clinical relevance. The proteins include antibodies, enzymes, antigenic proteins and hormones. The rising number of expression attempts in the past few years indicates growing interest in microalgae as a viable alternative expression host. A publication in 2010 by Rasala et al. showed, for example, the successful expression of seven human therapeutic proteins in the chloroplast. These included a cytokine, antibody mimicking proteins and a vascular endothelial growth factor (Rasala et al. 2010). Furthermore, the functionality of such recombinant proteins was ascertained by Tran et al. in 2009, who showed that the binding affinity of an algal expressed antibody against anthrax protective antigen 83 was comparable with that of an antibody expressed in mammalian cells (Tran et al. 2009). Yet another significant moment was the expression of the heat stable vaccine D2 fibronectinbinding domain of Staphylococcus aureus fused with the cholera toxin B subunit. Transgenic algae were fed to mice and consequently protected against a Staphylococcus aureus infection (Dreesen et al. 2010). These studies give an initial example of the potential of the C. reinhardtii chloroplast as a protein production system. The efficacy of nuclear expression in microalgae is in need of further research before any conclusions may be drawn. Enhancement of productivity and examination of post-translational modifications in the nucleus are important topics in this field.

5.3 Examples for biotechnological relevant proteins

97

Recombinant protein

Location

Protein class

Yield/expression level

Reference

Avian metallothionein type II

Nucleus/ periplasm Nucleus

Metal binding

Not specified

Enzyme

Not specified

Chloroplast, nucleus/periplasm Chloroplast/ nucleus/ periplasm/ cytoplasm Chloroplast

Antigenic protein

Not specified

(Cai et al. 1999) (Siripornadulsil et al. 2002) (Sayre et al. 2003)

Antigenic protein

Not specified

Antigenic protein

Not specified

Chloroplast

Antibody

Not specified

(Mayfield et al. 2003)

Nucleus

Metal binding

Not specified

Nucleus/ medium Chloroplast

Antibody/ enzyme Ligand

Not specified

(Zhang et al. 2006) (Griesbeck et al. 2006) (Yang and Li 2006)

Chloroplast

Serum protein

~5 % TSP

Chloroplast

~2 % TSP

Nucleus

Antigenic protein Antigenic protein Hormone

Chloroplast

Antibody

Chloroplast

Antigenic protein

Chloroplast

Antibody mimic

14FN3: 3 % TSP

(Rasala et al. 2010)

Chloroplast

Hormone

Detectable

Chloroplast

Hormone

2 % TSP

(Rasala et al. 2010) (Rasala et al. 2010)

Chloroplast

Cytokine

2.5 % TSP

Mothbean Δ (1)-pyroline-5carboxylate synthetase Antigenic peptide P57 of the pathogenic Rennibacterium salmoninarium Antigenic proteins VP19, 24, 26, 28 of the white spot syndrome virus Foot-and-mouth disease virus VP1 protein fused with cholera toxin B subunit Anti-glycoprotein D of herpes simplex virus large singlechain antibody (human IgA) Human metallothionine-2 Anti-rabbit-IgG single-chain antibody fused with luciferase Human tumor necrosis factorrelated apoptosis-inducing ligand Bovine mammary-associated serum amyloid Classical swine fever virus E2 viral protein Human glutamic acid decarboxylase 65 Human erythropoietin

Anti-anthrax protective antigen 83 antibody D2 fibronectin-binding domain of Staphylococcus aureus fused with cholera toxin B subunit Humane fibronectin (domains 10 and 14) 10FN3: detectable Proinsulin Human vascular endothelial growth factor isoform 121 (VEGF) High mobility group protein B1 (HMGB1)

Chloroplast

Tab. 5.5: Recombinant proteins expressed in C. reinhardtii.

0.43–0.67 %

~0.3 % TSP 100 μg/l culture 0.01 % dry algal biomass 0.7 % TSP

(Sayre et al. 2003; Surzycki et al. 2009) (Sun et al. 2003)

(Manuell et al. 2007) (He et al. 2007) (Wang et al. 2008) (EichlerStahlberg et al. 2009) (Tran et al. 2009) (Dreesen et al. 2010)

(Rasala et al. 2010)

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5 Genetic engineering, methods and targets

5.3.2 Recombinant proteins in other microalgae Publications concerning recombinant protein production in microalgae other than C. reinhardtii are rare. The grounds for this phenomenon lie in the difficulty in transferring genetic engineering methods from one species to another as a result of large phylogenetic differences, and in addition most algae genomes are yet to be sequenced (Grossman 2007). Flounder growth hormone fGH was produced with 420 μg/l culture in the Chlorella ellipsoidea nuclear genome (Kim et al. 2002). The hepatitis B surface antigen was expressed in the nucleus of Dunaliella salina (Geng et al. 2003). The expression of human growth hormone (hGH) in the nuclear genome of Chlorella vulgaris with a yield of 200–600 ng/ml medium has also been described (Hawkins and Nakamura 1999). The production of a fish growth hormone in Nannochloropsis oculata with 0.42–0.27 μg/ml as well as its effect on fish larvae, which showed increased body length and weight when fed with this alga, was shown in 2008 by Chen et al. (2008) . In 2011, Hempel et al. published results on the production of the human IgG antibody against Hepatitis B surface protein in Phaeodactylum tricornutum with 21 mg antibody per gram algae dry weight (Hempel et al. 2011).

5.4 Future prospects/outlook 5.4.1 Methods for genetic engineering Despite microalgae being well suited for transgenic expression and possessing all the necessary approaches for genetic engineering, especially in the case of C. reinhardtii, some essential improvements in the methodology remain to be established. Expression in the nuclear genome of microalgae is central to the creation of certain biotechnological and biopharmaceutical products, as it enables the production of correctly folded and secreted glycosylated proteins. Regrettably, expression rates in the nucleus are low, and glycosylation patterns are yet to be adequately described. In order to raise the achievable expression levels, stronger promoters have to be employed in addition to optimized gene structures with inserted intron sequences as well as linearization of foreign DNA. Another important point in the genetic engineering methodology focuses on the targeted integration of foreign DNA into selected genomic regions by homologous recombination. This mode of integration is a rare event in C. reinhardtii compared with random integration by non-homologous end joining (Zorin et al. 2009). Studies on targeted double strand breaks through zinc finger nucleases to enhance homologous recombination have been documented in recent times for plant systems (Weinthal et al. 2010). Methods reported for plants could be promising starting points for experiments in microalgae. Furthermore, a great variety of microal-

5.4 Future prospects/outlook

99

gae aside from C. reinhardtii still await biotechnological exploitation as they become accessible for genetic engineering.

5.4.2 Products from genetically modified microalgae Microalgae are promising alternatives to conventional cell cultures or plants as production hosts. Specific areas where algae show promise are biopharmaceuticals, therapeutics and groups involving other substances with biotechnological relevance such as secondary metabolites. Reasons for this recent growth in interest are the cost-effective cultivation and a short time from gene to protein. Furthermore, many microalgae possess the GRAS (generally regarded as safe) status and as such do not contain any toxins or human pathogens e.g. viruses. One large advantage of microalgae in comparison with plant systems is the possibility of cultivation in closed bioreactors, which minimizes the risk of release and gene flow to other organisms (Griesbeck et al. 2006). As algae do not produce spores, and in most cases mutant strains are used which are incapable of growth under natural conditions, undesired dispersal into the environment is not an issue. Aside from the short generation time, the option to grow microalgae either under photosynthetic conditions or in the dark using a simple media containing only mineral salts and acetate is a notable advantage that can lead to biomass production even higher than that observed in plants. As depicted earlier in this chapter, a significant number of proteins have been expressed in microalgae, especially C. reinhardtii. Although algae will not replace mammalian or bacterial expression systems, edible vaccines could be one promising and possible application field of foreign protein expression in microalgae. Foreign proteins are, however, only a fraction of the full range of products that can be extracted from genetically modified microalgae. Algae are photosynthetically active organisms and therefore deal with light and oxygen; this leads to the development of diverse pigments and antioxidants. As a result, many other therapeutically and industrial relevant products such as biofuels, food additives or cosmetics may be produced in microalgae (Plaza et al. 2009). Examples of such products are carotenoids, which can be used either pharmaceutically to prevent and treat human diseases or as food additives such as food colourants. The medicinal properties of carotenoids may be traced to their anti-inflammatory, antioxidant and antitumoral properties (Guedes et al. 2011). Production of biofuels is yet another promising field. Many marine microalgae contain a high percentage of lipids, which may be directly converted into biodiesel (Wijffels and Barbosa 2010). Photosynthetic optimization as described by Stephenson et al. has the potential to lead to an increased product yield when producing hydrogen or bioethanol in microalgae (Stephenson et al. 2011). As shown with these examples, microalgae as production systems for biotechnologically and industrial applications are in many

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respects of high economical interest. Further improvement of the genetic engineering techniques as mentioned in Section 5.4.1 as well as more detailed metabolic engineering are however necessary to gain the full potential of this promising expression system.

Acknowledgements This work was supported by the MCI Doctoral Grant Program. We would like to thank Ian Wallace and Barbara Reitler for critically reviewing the manuscript.

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Randolph-Anderson, B. L., R. Sato, A. M. Johnson, E. H. Harris, C. R. Hauser, K. Oeda, F. Ishige, S. Nishio, N. W. Gillham and J. E. Boynton. 1998. Isolation and characterization of a mutant protoporphyrinogen oxidase gene from Chlamydomonas reinhardtii conferring resistance to porphyric herbicides. Plant Mol. Biol. 38, 839–859. Rasala B. A., M. Muto, P. A. Lee, M. Jager, R. M. Cardoso, C. A. Behnke, P. Kirk, C. A. Hokanson, R. Crea, M. Mendez and S. P. Mayfield. 2010. Production of therapeutic proteins in algae, analysis of expression of seven human proteins in the chloroplast of Chlamydomonas reinhardtii. Plant biotechnology journal 8, 719–733. Remacle C., S. Cline, L. Boutaffala, S. Gabilly, V. Larosa, M. R. Barbieri, N. Coosemans and P. P. Hamel. 2009. The ARG9 gene encodes the plastid-resident N-acetyl ornithine aminotransferase in the green alga Chlamydomonas reinhardtii. Eukaryot. Cell 8, 1460–1463. Ruecker, O., K. Zillner, R. Groebner-Ferreira and M. Heitzer. 2008. Gaussia-luciferase as a sensitive reporter gene for monitoring promoter activity in the nucleus of the green alga Chlamydomonas reinhardtii. Mol. Genet. Genomics 280, 153–162. Sayre, R., R. Wagner, S. Siripornadulsil and C. Farias. 2003. Transgenic algae for delivering antigens to an animal. US Patent 7,410,637. Schiedlmeier, B., R. Schmitt, W. Muller, M. M. Kirk, H. Gruber, W. Mages and D. L. Kirk. 1994. Nuclear transformation of Volvox carteri. Proc. Natl. Acad. Sci. USA 91, 5080–5084. Schroda, M. 2005. RNA silencing in Chlamydomonas: mechanisms and tools. Curr. Genet. 49, 69– 84. Schroda, M., D. Blocker and C. F. Beck. 2000. The HSP70A promoter as a tool for the improved expression of transgenes in Chlamydomonas. Plant J. 21, 121–131. Shimogawara, K., S. Fujiwara, A. Grossman and H. Usuda. 1998. High-efficiency transformation of Chlamydomonas reinhardtii by electroporation. Genetics 148, 1821–1828. Siripornadulsil, S., S. Traina, D. P. Verma and R. T. Sayre. 2002. Molecular mechanisms of prolinemediated tolerance to toxic heavy metals in transgenic microalgae. Plant Cell 14, 2837–2347. Sizova, I., M. Fuhrmann and P. Hegemann. 2001. A Streptomyces rimosus aphVIII gene coding for a new type phosphotransferase provides stable antibiotic resistance to Chlamydomonas reinhardtii. Gene 277, 221–229. Sodeinde O. A. and K. L. Kindle. 1993. Homologous recombination in the nuclear genome of Chlamydomonas reinhardtii. Proc. Natl. Acad. Sci. USA 90, 9199–9203. Steinbrenner, J. and G. Sandmann. 2006. Transformation of the green alga Haematococcus pluvialis with a phytoene desaturase for accelerated astaxanthin biosynthesis. Appl. Environ. Microbiol. 72, 7477–84. Stephenson, P. G., C. M. Moore, M. J.Terry, M. V.Zubkov, T. S. Bibby.2011. Improving photosynthesis for algal biofuels: toward a green revolution. Trends Biotechnol 29 (12), 615– 623. Stevens, D. R., A. Atteia, L. G. Franzen and S. Purton. 2001. Cycloheximide resistance conferred by novel mutations in ribosomal protein L41 of Chlamydomonas reinhardtii. Mol. Gen. Genet. 264, 790–795. Stevens, D. R., J. D. Rochaix and S. Purton. 1996. The bacterial phleomycin resistance gene ble as a dominant selectable marker in Chlamydomonas. Mol. Gen. Genet. 251, 23–30. Sun, M., K. Qian, N. Su, H. Chang, J. Liu and G. Chen. 2003. Foot-and-mouth disease virus VP1 protein fused with cholera toxin B subunit expressed in Chlamydomonas reinhardtii chloroplast. Biotechnol. Lett. 25, 1087–1092. Sun, Y., X. Gao, Q. Li, Q. Zhang and Z. Xu. 2006. Functional complementation of a nitrate reductase defective mutant of a green alga Dunaliella viridis by introducing the nitrate reductase gene. Gene 377, 140–149. Surzycki, R., K. Greenham, K. Kitayama, F. Dibal, R. Wagner, J. D. Rochaix, T. Ajam and S. Surzycki. 2009. Factors effecting expression of vaccines in microalgae. Biologicals 37, 133– 138.

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Tan, C., S. Qin, Q. Zhang, P. Jiang and F. Zhao. 2005. Establishment of a micro-particle bombardment transformation system for Dunaliella salina. J Microbiol 43, 361–5. Tang, D. K., S. Y. Qiao and M. Wu. 1995. Insertion mutagenesis of Chlamydomonas reinhardtii by electroporation and heterologous DNA. Biochem. Mol. Biol. 36, 1025–35. Ten Lohuis, M. R. and D. J. Miller. 1998. Genetic transformation of dinoflagellates (Amphidinium and Symbiodinium): expression of GUS in microalgae using heterologous promoter constructs. Plant J. 13, 427–435. Tran, M., B. Zhou, P. L. Pettersson and E. al. 2009. Synthesis and assembly of a full-length human monoclonal antibody in algal chloroplasts. Biotechnol. Bioeng. 104, 663–673. Walker, T. L., D. K. Becker, J. L. Dale and C. Collet. 2005b. Towards the development of a nuclear transformation system for Dunaliella tertiolecta. J. Appl. Phycol. 17, 363–368. Wang, T., L. Xue, W. Hou, B. Yang, Y. Chai, X. Ji and Y. Wang. 2007. Increased expression of transgene in stably transformed cells of Dunaliella salina by matrix attachment regions. Appl. Microbiol. Biotechnol. 76, 651–657. Wang, X., M. Brandsma, R. Tremblay, D. Maxwell, A. M. Jevnikar, N. Huner and S. Ma. 2008. A novel expression platform for the production of diabetes-associated autoantigen human glutamic acid decarboxylase (hGAD65). BMC Biotechnol. 8, 87–99. Weinthal, D., A. Tovkach, V. Zeevi, T. Tzfira, 2010. Genome editing in plant cells by zinc finger nucleases. Trends Plant Sci., 15(6), 308–321. Wijffels, R. H. and M. J. Barbosa. 2010. An outlook on microalgal biofuels. Science 329, 796–799. Yang, Z., Y. Li, 2006. Expression of human soluble TRAIL in Chlamydomonas reinhardtii chloroplast. Chin. Sci. Bull. 51, 1703–1709. Zaslavskaia, L. A., J. C. Lippmeier, C. Shih, A. R. Grossman and K. E. Apt. 2001. Trophic conversion of an obligate photoautotrophic organism through metabolic engineering. Science 292, 2073–2075. Zaslavskaia, L. A., J. C. Lippmeier, P. G. Kroth, A. R. Grossman and K. E. Apt. 2000. Transformation of the diatom Phaeodactylum tricornutum (Bacillariophyceae) with a variety of selectable marker and reporter genes. J. Appl. Phycol. 36, 379–386. Zhang, Y. -K., G. -F. Shen and B. -G. Ru. 2006. Survival of human metallothionein-2 transplastomic Chlamydomonas reinhardtii ultraviolet B exposure. Acta Biochim. Biophys. Sin. 38, 187–193. Zorin, B., Y. Lu, I. Sizova, P. Hegemann, 2009. Nuclear gene targeting in Chlamydomonas as exemplified by disruption of the PHOT gene. Gene, 432(1–2), 91–96.

Jean-Paul Cadoret, Alexandre Lejeune, Rémy Michel and Aude Carlier

6 Algenics: Providing microalgal technologies for biological drugs 6.1 Background and inception of the company Over the last 30 years, considerable progress has been made in medical treatment thanks to the development of biotechnology-derived pharmaceuticals (biopharmaceuticals). The vast majority of approved products consist of proteins and polypeptides with more than 50 % being produced from recombinant mammalian cellculture expression systems (Walsh 2010). This is due to the importance of posttranslational modifications (PTMs), particularly glycosylation, of biochemical and therapeutic properties. The workhorse for biomanufacturing of therapeutic glycoproteins is Chinese Hamster Ovary (CHO) cells. This host benefits from a proven safety profile validated by numerous approved products as well as the possibility to manufacture on a large scale in 10 000 litre bioreactors. Nevertheless, there are still several limitations for the expression of certain targets such as membrane proteins. Cost associated with mammalian cell-culture system will also be a major issue in the near future as biopharmaceuticals place an increasing burden on heath-care systems. Consequently, these challenges have driven the development of novel expression technologies. Among them, plant production systems have received considerable interest due to their low-cost potential. While being attractive, the specificity of the production process, i.e. cultivation, harvesting and primary processing, will make the adoption of such technologies by the pharmaceutical industry difficult. Algenics is developing a unique and cutting-edge technology for biomanufacturing recombinant therapeutics. Based on microalgae, this technology exploits microalgae’s natural capacity to grow as a high-density cell suspension in simple media. Culture can be adapted to scalable process performed under cGMP conditions in bioreactors. The safety profile of microalgae for pharmaceutical applications is also favorable, as the risk of human contamination by microalgal pathogens is unlikely. Another advantage of these unicellular eukaryotes pertains to the ability to produce post-translationally modified therapeutic proteins. Since its beginnings in 2008, Algenics has transitioned from a marine biotech to a biopharmaceutical company. This has involved successive milestones of development to set up and optimize innovative microalgal technologies, perform proofs of concept and finally initiate the development of proprietary drug candidates. In January 2008, Algenics was launched as a spin-off from the French Research Institute for Exploration of the Sea (Ifremer). The company benefited from the

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transfer of technologies developed for more than 5 years in the laboratory of Physiology and Biotechnology of Algae led by Dr Jean-Paul Cadoret, co-founder of Algenics and current Scientific Advisory Board President. During this time, Dr Aude Carlier who co-founded Algenics and serves as CEO and CSO of Algenics, has actively pioneered the work of recombinant protein expression in microalgae. The initial challenge was to identify the best species through microalgal biodiversity. Screening was realized based on requirements necessary for biomanufacturing of therapeutics such as a good safety profile (no prior history of toxicity in humans), availability of genetic information, PTMs of proteins suitable for human use (no immunogenicity) and advantageous productivity properties. This experiment led to the selection of a limited number of species with the diatom Phaeodactylum tricornutum offering very favorable characteristics for biomanufacturing. Remarkably, N-glycans of recombinant proteins expressed in this host are similar to certain glycoforms present in animal cells.

6.2 Development and optimization of proprietary technologies Since its creation, the company has focused on the production of therapeutic glycoproteins. With no prior industrial use of microalgae in this field of application, tools, technologies and processes have been developed extensively. Both gene and protein expressions have been largely optimized through the use of proprietary plasmid vectors and regulatory elements identified by reporter gene assays. The obtention of a large number of clones expressing the recombinant proteins is also a prerequisite to ensure the selection of high-yielding cell lines. Improvements in methods such as electroporation or particle bombardment used for the genetic transformation of Phaeodactylum tricornutum have successfully helped to achieve this objective with thousands of transformed clones obtained during each selection step. Cultivation of microalgae has also been extensively adapted to meet pharmaceutical standards. Axenic and clonal microalgal cell lines were isolated and further adapted to grow in fully chemically defined media based on pharmaceuticalgrade compounds. Flasks and disposable bioreactors similar to those used for

Fig. 6.1: Selection of high-yielding clones for production of recombinant proteins in disposable bioreactors illuminated by LEDs.

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109

mammalian cell culture have been retained and offer the advantage of certified materials (Fig. 6.1). Lastly, downstream processes for purification of both intracellular and extracellular recombinant proteins have also been developed. All methods and processes are integrated in a technological platform branded as Algebiosys™ using the optimized and proprietary cell line PTA of Phaeodactylum tricornutum. Pioneering work in the field of microalgae-based production of therapeutic proteins has enabled Algenics to establish a strong IP position.

6.3 From proofs of concept to therapeutic product candidates The capacity of the PTA cell line to express therapeutic proteins has been demonstrated on multiple targets including the cytokine interleukin-2, the human enzyme glucocerebrosidase and the hormone erythropoietin (EPO), the last two being glycosylated proteins. Our results have demonstrated that EPO was glycosylated and released by secretion (signal peptide is used) in the culture medium. Most importantly, EPO produced in PTA cell line was able to bind its receptor EPO-R and induced the proliferation of EPO-dependent animal cells in a similar way to that of the commercial EPO EPREX ® (Janssen-Cilag). The year 2011 was an important milestone for the company, as the development of two proprietary product candidates was initiated. Two glycoproteins, a monoclonal antibody for cancer treatment and a viral subunit, have a competitive advantage in the PTA cell line to deliver better and more efficient drugs to the market. The first clinical phase of a microalgae-manufactured biotherapeutic is expected in 2016. The microalgal technology of Algenics has already been commercialized for biopharmaceutical companies under licensing agreements.

Reference Walsh, G. 2010. Biopharmaceutical benchmarks 2010. Nature Biotech. 28, 917–924.

Technical Means for Algae Production

Yusuf Chisti

7 Raceways-based production of algal crude oil 7.1 Introduction Raceway ponds, or “high-rate algal ponds”, of various configurations have been used to treat wastewater since the 1950s. They are also known as Oswald ponds after their inventor W. J. Oswald. Large-scale outdoor culture of microalgae and cyanobacteria in raceways is well established (Terry and Raymond 1985; Oswald 1988; Borowitzka and Borowitzka 1989; Becker 1994; Lee 1997; Molina Grima 1999; Pulz 2001; Borowitzka 2005; Spolaore et al. 2006). Raceway culture is used commercially in the United States, Thailand, China, Israel and elsewhere, mostly to produce algae for relatively high-value applications. This chapter is focused on raceways typically used in the production of algal biomass and not in the treatment of wastewater. The engineering design, operation and performance characteristics of raceways are discussed. The biomass productivity of the raceways is assessed in relation to limits imposed by algal biology. The economics of algal oil production in raceways is discussed. Typical algae culture raceways are shown in Figure 7.1. A typical paddlewheel used for mixing a raceway culture is shown in Figure 7.2.

Fig. 7.1: Areal view of raceway ponds used by Cyanotech Corporation (www.cyanotech.com), Hawaii, USA.

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Fig. 7.2: A paddlewheel used in mixing a raceway pond.

7.2 Raceways 7.2.1 General configuration A raceway pond is made of a closed-loop recirculation channel that is typically about 0.25–0.30 m deep (Fig. 7.3) (Becker 1994; Chisti 2007a). The algal broth is continuously mixed and circulated in the raceway channel using a paddlewheel (Fig. 7.3). The surface area of the pond can vary greatly: individual ponds of up to 2.5 ha have been used in treating wastewater, but single ponds for producing algal biomass have not generally exceeded 0.5 ha. Although, pond configurations such as that shown in Figure 7.3a are quite satisfactory for producing algae, if the intention is to produce biomass for fuels, the energy expenditure for circulation must

Fig. 7.3: Areal views of raceway ponds: (a) a pond with multiple bends (Chisti 2007a) and (b) a raceway with a minimum number of bends.

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115

be kept to a minimum. Therefore, a pond must have a minimum number of bends, as in the configuration shown in Figure 7.3b. Raceways for microalgae culture generally have vertical walls and a flat bottom. For the raceway configuration shown in Figure 7.3b, the surface area A can be calculated as follows:

A =

π q2 + pq 4

(7.1)

The above equation assumes a negligible thickness for the central dividing wall. The p/q ratio (Fig. 7.3b) can be 10 or greater. If the value of p/q is too low, the flow disturbances caused by the bends at the ends of the raceway begin to affect the flow in the straight sections of the channel. The working volume VL depends on the surface area and the depth h of the broth, or: VL = Ah

(7.2)

The working depth ranges from 0.25 to 0.30 m in different ponds. Lower depths are preferred to improve light penetration, but the depth cannot be much less than 0.25 m in a large pond as discussed later (Section 7.2.12). The surface-to-volume ratio is always 1/h, or 3.3–4.0 m−1 for the typically used depths. The pond may be made of compacted earth lined with a 1–2 mm thick polymer membrane to prevent seepage. This configuration is relatively cheap, but uncommon for biomass production. More often, the ponds are made of concrete block walls and dividers lined with a plastic membrane. The plastic lining must be resistant to ultraviolet light. Membranes made of polyvinyl chloride (PVC), polypropylene and polyethylene are typically used and may have a useful life of up to 20 years. Liners made of PVC and certain other plastics may leach chemicals that inhibit growth or contaminate the culture (Borowitzka 2005) and therefore should be evaluated carefully before use. Ponds with raised walls of concrete blocks or bricks are actually easier to build than ponds dug into the ground. The walls are sometimes made from corrugated roofing sheets (Becker 1994), but this type of construction increases the energy requirement for generating flow. Raceway pond design needs to consider the requirements of culture mixing, feeding, harvesting, carbon dioxide supply, drainage, possible overflow and cleaning. Specific aspects of design and operation are discussed in the following sections.

7.2.2 Flow in a raceway The flow in a raceway channel is characterized by Reynolds number Re defined as follows:

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Fig. 7.4: Dimensions of a flow channel with a rectangular cross-section.

Re =

ρudh μ

(7.3)

where ρ is the density of the algal broth, u is the average velocity of flow, dh is the hydraulic diameter of the flow channel, and μ is the viscosity of the broth. Typically, for a dilute suspension of algae in water, the viscosity and density of water at the culture temperature are taken to closely approximate the properties of the broth. The hydraulic diameter dh for use in Equation (7.3) is calculated using the following equation: dh =

4wh w + 2h

(7.4)

where w is the width of the channel, and h is the average depth of the fluid in it (Fig. 7.4). For culturing algae, the flow in the raceway channel should be turbulent to keep the cells in suspension, prevent stratification and improve desorption of the oxygen produced by photosynthesis. Channel flow is laminar for Reynolds number values of less than 2,000. The flow is often considered to be turbulent once the Re value exceeds 4,000; however, the transition to turbulence is not as well defined in channels as in pipes, and therefore an Re value of about 8,000 is frequently considered to be the threshold of turbulence. Transition to turbulence occurs at a lower Re value in a rough channel compared to in a smooth channel. Water flowing at 25 °C in a 0.3 m deep smooth channel of 2 m width will have an Re value of 8,000 at an average flow velocity of 0.015 m ⋅ s−1. In practice, the flow velocity may be almost 20-fold greater than the calculated minimum. The recommended minimum flow velocity is discussed in Section 7.2.3. Raceway ponds with semicircular ends (Fig. 7.3b) are the most common configuration for producing algal biomass. In such ponds, curved baffles or flow deflectors are generally installed at both ends, as shown in Figure 7.5. The baffles ensure that the flow remains uniform throughout the curved bends and minimize the development of dead zones at the ends of the pond. Dead zones are not wanted, as they adversely affect mixing, allow solids to settle and cause unnecessary energy losses. If the baffles are not installed, dead zones would develop in the regions

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117

Fig. 7.5: Baffles for smoothening the turnaround of the fluid at the semicircular ends of a raceway pond: (a) the end near the paddlewheel; (b) the distal end. Such baffles reduce the development of dead zones, which are associated with energy loss, poor mixing and sedimentation of biomass.

Fig. 7.6: Dead zones in a raceway in the absence of the flow deflector baffles.

shown in Figure 7.6. An alternative method of preventing the development of the large circulating dead zones near the ends of the central channel divider wall (Fig. 7.6) is to completely fill in the potential dead zones (Chisti and Moo-Young 1987; Chisti 1989) to modify the raceway configuration to that shown in Figure 7.7. Ideally, at the design stage, the flow in a pond should be simulated using computational fluid dynamics software to identify potential dead zones and eliminate them by appropriate engineering design measures.

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Fig. 7.7: Modified configuration of the raceway dividing wall to eliminate the dead zones near its ends.

7.2.3 Power consumption for mixing The power requirement P (W) for a paddlewheel to generate a volume flow rate Q (m3 ⋅ s−1) of algal culture in a typical raceway is estimated using the following equation: P =

QρgH e

(7.5)

where ρ (kg ⋅ m−3) is the density of the culture broth, g (9.81 m ⋅ s−2) is the gravitational acceleration, H (m) is the hydraulic head generated by the paddlewheel, and e is the efficiency of the motor, drive and the paddlewheel. The e value is about 0.17 (Borowitzka 2005) for a paddlewheel located in a channel with a flat bottom (see Section 7.2.4). The volume flow rate Q depends on the velocity of flow (u, m ⋅ s−1), the channel width w (m) and the depth h (m) (Fig. 7.4), as follows: Q = uwh

(7.6)

The total hydraulic head loss H (m) for water flowing at a velocity u in a straight channel of hydraulic diameter dh and length Lr can be calculated using the following form of the Manning equation: 2

H = 6.35

( fMu ) L r 4

d3

h

(7.7)

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119

Surface

Manning factor fM (s ⋅ m−1/3)

Glass Smooth steel Compacted gravel lined with polymer membrane Concrete (unfinished) Asphalt Compacted gravel

0.010 0.012 0.012 0.015 0.016 0.025

Tab. 7.1: Manning roughness factor for various surfaces.

where fM is the Manning channel roughness factor (Tab. 7.1). The above equation does not account for head losses around bends but can be used for estimating an approximate head loss in a raceway of the total channel loop length L r . The hydraulic diameter dh is calculated using Equation (7.4). In view of Equations (7.4), (7.6) and (7.7), Equation (7.5) can be written in the following form:

P =

3 1.59 ρgu Lr (w + 2h)

ed h0.33

(7.8)

As the depth of the channel is typically much smaller than its width, i.e. h 39 °C. This can be estimated as follows: The total average daily insolation from the Sun is about 250 W ⋅ m−2. Of this, at least 15 % is reflected. If all of the remaining energy Ea is absorbed by the algal culture and converted to heat, and no heat is lost via evaporation, convection and other mechanisms, the total temperature rise ΔT in a 0.3 m deep raceway will be: 2

ΔT =

(1 − 0.15) ⋅ 250 ⋅ 24 ⋅ 60 Ea ⋅ 24 ⋅ 602 = 14.6 °C = 300 ⋅ 4200 mCp

(7.12)

where m is the mass of water per square meter of the raceway surface (≈ 1 × 0.3 × 103, or 300 kg) and Cp is the specific heat capacity of water (≈ 4200 J ⋅ kg−1 °C). If, therefore, the temperature of the algal broth in the raceway just before sunrise was 25 °C, the maximum temperature attained at sunset would be 25 + ΔT, or 39.6 °C. In practice, because of evaporation and other heat losses, the maximum ΔT would be typically less than 10 °C. An alga may not grow well at the extremes of temperatures that may occur, but generally will survive brief periods at up to 40 °C. The efficiency of photosynthesis is reduced with increased temperature as the rate of respiration increases more rapidly with temperature compared to the rate of photosynthesis (Davison 1991; Pulz 2001). Diurnal and seasonal variations in temperature in a raceway can be modeled reasonably well (James and Boriah 2010).

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7.2.8 Culture pH and carbon dioxide demand The pH in a typical raceway varies with the diurnal cycle (García et al. 2006; Moheimani and Borowitzka 2007). During midday, the broth tends to become quite alkaline unless the pH is controlled by injecting carbon dioxide. This is clear evidence of carbon dioxide limitation during peak sunlight hours. Thus, carbon dioxide absorption from the atmosphere through the surface of a raceway is entirely insufficient to support photosynthesis for a good part of the day. Attaining a high biomass productivity requires injection of carbon dioxide. The pH should be controlled to well below 8 by carbon dioxide supplementation; otherwise carbon limitation could reduce photosynthesis. A high pH also results in generation of toxic ammonia from dissolved ammonium salts, and this inhibits algal productivity. At a biomass productivity of 0.025 kg ⋅ m−2 d−1, a minimum of 0.046 kg ⋅ m−2 d−1 of carbon dioxide needs to be dissolved in an algal culture to prevent carbon limitation. The peak midday demand would be of course greater. In terms of volume, for the above-mentioned biomass productivity in a 0.3 m deep pond, the minimum required flow rate of carbon dioxide would be 5.9 × 10−5 vvm at 25 °C and atmospheric pressure, if no loss is incurred. If 70 % of the carbon dioxide were lost to the atmosphere, as discussed later, the minimum requirement of carbon dioxide would be 1 × 10−4 vvm. Gas diffusers similar to those used in aquariums are used in raceways to inject carbon dioxide in the form of microbubbles. Between 35 and 70 % of the pure carbon dioxide injected into a pond is lost to the atmosphere (Weissman et al. 1989). Commercial delivered carbon dioxide cost $ 50–100 per ton in 1987 (Weissman and Goebel 1987), or $ 96–192 per ton in 2010 dollars. Production of a ton of algal biomass requires a minimum of about 1.83 tons of carbon dioxide (Chisti 2007a). Thus, a carbon dioxide loss of 35–70 % translates to a minimum monetary loss of $ 61–123 per ton of dry algal biomass. For algae that can be grown in highly alkaline conditions, carbon dioxide may be supplied in the form of bicarbonate (Chi et al. 2011). This offers potentially useful opportunities in reducing the cost of supplying carbon dioxide (Chi et al. 2011). Culture under alkaline pH may not be feasible for marine algae, as sea salts precipitate at pH values of > 8. Carbon dioxide requirements (C, kg ⋅ ha−1 d−1) can be estimated from the expected biomass productivity of the raceway, using the following equation: 4

C = 1.83 × 10 fc Pa

(7.13)

where the areal productivity Pa is in kg ⋅ m−2 d−1, and the factor fc accounts for carbon dioxide loss to the atmosphere. The value of fc ranges from 1.35 to 1.70. The demand for carbon dioxide varies with irradiance. Therefore, the best strategy is to inject carbon dioxide in response to a signal from a pH controller. A controllerbased injection minimizes carbon dioxide loss while preventing carbon limitation.

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Periodic injection without control may be satisfactory, based on historical experience with a given alga and location (Becker 1994). In a process for producing algal fuels, biomass is most likely to be grown using cleaned flue gas from a coal-fired power plant, and not by using commercial carbon dioxide. Flue gas typically contains 12–14 % carbon dioxide by volume, the rest being mainly nitrogen and water vapor. Untreated flue gas contains oxides of sulfur (SOx) and nitrogen. Various flue-gas desulfurization (FGD) processes are used to clean the flue gas of sulfur oxides prior to discharge. Oxides of nitrogen, mainly in the form of nitric oxide (NO), are not removed but are only slightly soluble in water. Cooled desulfurized flue gas can be safely used for culturing algae, but the flow rates required would be substantially greater than if pure carbon dioxide were used. The flue gas must be free of heavy metals. If carbon dioxide is fed in the form of flue gas, the loss to atmosphere would be expected to be well above 80 %. Carbon dioxide diffusers should be placed at ∼ 30 m intervals along the flow path at the bottom of the raceway channel, if pure carbon dioxide is being injected. The placement interval should be shorter (e.g. 10 m) if flue gas is the source of carbon dioxide. Across the flow channel, diffusers should be spaced 0.4–0.5 m apart. The diffusers should be easily removable for cleaning and replacement. At 25 °C, the solubility of carbon dioxide in seawater is nearly half of its solubility in freshwater, and this adversely affects the mass transfer of carbon dioxide from the gas phase to water.

7.2.9 Oxygen removal Photosynthesis produces oxygen and is inhibited by a buildup of dissolved oxygen in the culture broth (Shelp and Canvin 1980; Suzuki and Ikawa 1984; Molina et al. 2001). Oxygen removal from raceway ponds is poor. The surface area is typically insufficient for oxygen removal during times of peak photosynthesis. The paddlewheel assists with oxygen removal to some extent but is not particularly effective. As a consequence, the culture experiences a diurnal variation in concentration of dissolved oxygen (García et al. 2006; Moheimani and Borowitzka 2007). During peak photosynthesis, the concentration of dissolved oxygen can rise to more than 300 % of the air saturation value (Richmond 1990; Moheimani and Borowitzka 2007). This level of dissolved oxygen can adversely affect the rate of photosynthesis (Molina et al. 2001) and biomass productivity, depending on the alga. In a culture medium saturated with pure oxygen at atmospheric pressure, the rate of photosynthesis is ∼ 35 % lower than in a medium in equilibrium with the normal atmosphere (Becker 1994). Concentration of dissolved oxygen may also affect the composition of the algal biomass (Richmond 1990).

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7.2.10 Potential for contamination Raceway cultures, being open to the atmosphere, are susceptible to rainfall and contamination by dust and other detritus. Contamination may be controlled by placing the ponds within greenhouses, but this is impractical for large production facilities as would be necessary for biofuels. Other problems include viral infections (Van Etten et al. 1991; Van Etten and Meints 1999; Wommack and Colwell 2000), infestations of algae-consuming zooplanktons and other predators (Turner and Tester 1997; Richmond 1990), and contamination by unwanted algae (Richmond 1990), bacteria and fungi. The low maximum possible alga concentration in a raceway may accentuate the effects of predators and other microbial contaminants. Filtration of water may help reduce the frequency of certain types of infestations. Viruses are not generally removed by conventional microfiltration of water (Chisti 2007b). Filtration methods capable of removing viruses exist (Chisti 2007b) but are too expensive for use in large-scale culture of microalgae. Management practices influence the frequency of culture failure due to contaminants. Development of methods for predator control in raceways is potentially possible (Lass and Spaak 2003; Borowitzka 2005; Van Donk et al. 2011), but nothing appears to have been done in this area.

7.2.11 Irradiance variation with depth Photosynthesis is driven by light of wavelengths in the range of 400–750 nm, the photosynthetically active radiation or PAR. The peak photosynthetically active irradiance level at solar noon on the surface of a raceway culture in a tropical zone is about 2,000 μE ⋅ m−2 s−1. Photosynthesis saturates at roughly 10–20 % of the peak PAR value. Therefore, an increase in PAR beyond about 100–200 μE ⋅ m−2 s−1 does not increase the rate of photosynthesis, and any excess energy is wasted as heat. At a PAR value of slightly greater than the saturation threshold, the culture becomes photoinhibited. That is, a further increase in irradiance actually reduces the rate of photosynthesis relative to the rate at the light saturation irradiance. Photosynthesis ceases if the irradiance level falls to the light compensation point. At and below the light compensation point, the biomass is consumed by respiration. The irradiance level in an algal culture declines rapidly with depth because of absorption of light by the cells. The local irradiance IL at any depth L from the surface depends on the concentration of the algal biomass and its light absorption coefficient roughly as follows: IL = Ioe

−KaCxL

(7.14)

In the above equation, Io is the irradiance incident on the surface of the raceway, Ka is the alga-dependent light absorption coefficient of the biomass, and Cx is the biomass concentration.

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Fig. 7.9: Computed light profile in a 0.3 m deep raceway at a dry biomass concentration of 0.5 kg ⋅ m−3. The profile was calculated for a suspension of the marine diatom Phaeodactylum tricornutum at an incident irradiance level of 2,000 μE ⋅ m−2 s−1 at the surface of the raceway. The zones of different metabolic activity are demarcated: photoinhibited zone (IL ≥ 800 μE ⋅ m−2 s−1); light-saturated zone (170 ≤ IL ≤ 800 μE ⋅ m−2 s−1); light-limited zone (4 ≤ IL ≤ 170 μE ⋅ m−2 s−1); and dark zone (IL ≤ 4 μE ⋅ m−2 s−1).

The irradiance–depth profile for a typical raceway with a peak biomass concentration of 0.5 kg ⋅ m−3 at solar noon is shown in Figure 7.9. From this profile, several zones of different metabolic activities can be demarcated in a raceway. These are: (1) a photoinhibited depth in which the irradiance level is greater than the photoinhibition threshold; (2) a light-saturated depth in which the rate of photosynthesis does not vary with changes in irradiance; (3) a light-limited depth in which the irradiance level ranges from the light compensation point to the light saturation threshold; and (4) a dark depth in which the light level is below the compensation irradiance. The light-saturated zone roughly spans the local irradiance values between twice the Monod light saturation constant and the inhibitory threshold. The lightlimited zone ranges from the compensation irradiance to an irradiance level that is twice the light-saturation constant. The depths corresponding to zones of different metabolic activity in Figure 7.9 are for a culture of the marine diatom Phaeodactylum tricornutum. For this diatom, the photoinhibition irradiance threshold is at ∼ 800 μE ⋅ m−2 s−1, the Monod light saturation constant is ∼ 85 μE ⋅ m−2 s−1, and the compensation irradiance is ∼ 4 μE ⋅ m−2 s−1. In view of Figure 7.9: more than 84 % of the culture volume is in the dark, i.e. below the light-compensation point; about 9 % of the culture volume is light-limited; < 4 % of the volume is photoinhibited; and < 3 % of the volume is light-saturated.

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The existence of a substantial dark volume explains the low biomass productivity of a raceway compared to what is biologically possible (Section 7.4.2). In a raceway, at peak concentration of the biomass and at the maximum level of incident irradiance, algal cells in > 80 % of the culture volume are actually consuming themselves instead of growing.

7.2.12 Local and average values of specific growth rate In a raceway, the specific growth rate of the alga varies with depth because of depth-related variation in irradiance (Section 7.2.11). In the absence of other limitations, the local value of the specific growth rate μL at any depth L can be estimated from the local value of irradiance IL (Eq. (7.14)) at that depth using the Haldane light-inhibited growth equation, as follows: μL =

μmax IL 2

I KL + IL + L Ki

(7.15)

where the constant maximum specific growth rate μmax, the light saturation constant KL , and the photoinhibition constant Ki depend on the alga and the growth temperature (Richmond 1990). The depth-averaged specific growth rate μav in the illuminated volume can be estimated from the local values of the specific growth rate, as follows: μav =

1 ∫L μL dL L 0

(7.16)

where L is the distance from the surface. This “growth integration” leads to lower values than the formal “light integration” (see Chapters 10 and 13). The above equation applies so long as L ≤ lc where lc is the depth at which the local irradiance level is at the light-compensation point. The actual average specific growth rate in the raceway will be less than the value calculated using Equation (7.16), because of self-consumption of the biomass at depths greater than the light compensation depth, lc . So long as sufficient carbon dioxide is provided, and the other dissolved nutrients are available in excess, the availability of sunlight limits the productivity of an algal culture. Light reaches the culture through the surface area exposed to light. For a given volume of broth, increasing the surface area available for light capture will increase productivity. In other words, a culture device with a high surface-to-volume ratio will attain a higher biomass productivity compared to a device with a lower surface-to-volume ratio (Grobbelaar 2007). For a raceway, the surface area A to volume (VL ) ratio always depends on the depth h of the culture broth, as follows:

7.2 Raceways

A 1 = VL h

129

(7.17)

In a typical operation with a depth of 0.30 m, the surface-to-volume ratio is 3.3 m–1. The only way to increase the surface-to-volume ratio is to reduce depth, but operation of a large raceway at depths of less than about 0.25 m is impractical. This is because a large raceway with an exact same depth everywhere is difficult to construct. In addition, the paddlewheel must generate a hydraulic pressure gradient, or a higher depth of fluid in front of the paddlewheel compared to the depth behind the paddlewheel, to drive the circulation of the broth.

7.2.13 Raceway capital cost Plastic-lined earthen raceway construction is apparently the least expensive for algae culture, as unlined earth ponds are not generally considered satisfactory for producing algal biomass. For a 100 ha plastic-lined pond of compacted earth, a 1987 construction cost estimate of $ 6.95 million has been published (Benemann et al. 1987). This includes the earth works, the lining, the carbon dioxide supply piping, inlets and outlets, baffles, paddlewheel and motor. This translates to a 1987 construction cost of $ 69,500 per ha and a 2010 construction cost of about $ 133,000 per ha. The 2010 cost has been estimated by multiplying the historical cost data by a correction factor φ calculated as follows: φ =

CPI 2010 CPI 1987

(7.18)

where CPIx is the Consumer Price Index (All Urban Consumers) for the year x. The CPI1987 and CPI2010 values (US Department of Labor, Bureau of Labor Statistics; www.bls.gov) were 113.6 and 218.056, respectively. The above cost estimate is for a very basic construction. Costs will be higher if, for example, the ends of the raceway and the dividing baffle are designed to minimize the occurrence of dead zones (Section 7.2.2). Plastic-lined concrete ponds would be significantly more expensive relative to the plastic-lined compacted earth ponds. Raceway ponds located within glass houses or plastic-covered structures allow a better control of the culture environment. Such ponds have been used commercially to produce algal and cyanobacterial biomass for food and neutraceutical purposes (Becker 1994; Lee 1997). Similar facilities are being installed in China and other places for producing algal oils.

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7.3 Algal crude oil as replacement petroleum Vegetable oils predominantly consist of triglycerides that can be readily converted to biodiesel. Algal lipids are generally more complex than vegetable oils and often contain a lower percentage of triglyceride oils compared to vegetable oils. In addition to triglycerides, algal oils may contain: pure terpenoid hydrocarbons (Banerjee et al. 2002) such as β-carotene and lycopene; polar lipids such as phospholipids and glycolipids; oxygen-containing nontriglyceride carotenoid oils such as astaxanthin, fucoxanthin and zeaxanthin; and chlorophyll a. All these components are energy-rich and carbon-rich. Therefore, a focus exclusively on triglycerides for converting to biodiesel ignores the substantial amount of energy present in the nontriglyceride fraction of the oil. Algal crude oil has an energy content of around 35,800 kJ ⋅ kg−1, or nearly 80 % of the average energy content of crude petroleum, and should be viewed as a petroleum substitute for producing all the same liquid transport fuels that are now derived from petroleum. These are mainly gasoline (petrol), diesel and jet fuel (kerosene). Triglycerides and some of the other lipids in the algal crude oil contain oxygen. Converting algal crude oil to hydrocarbon fuels therefore requires methods of deoxygenation of the various oils. Several potential methods have become available. For example, the NExBTL process patented by Neste Oil of Finland is a catalytic process for doxygenation of triglyceride oils to produce pure liquid alkanes. The alkanes are derived from the fatty acid portion of the triglyceride. The glycerol part of the triglyceride molecules is converted to propane. Another process capable of converting triglycerides to alkanes is the HBio process developed by Petrobras of Brazil. A third similar process is the Ecofining™ process developed by UOP/Eni. The alkanes produced by these processes can be cracked and isomerized to petroleum diesel, gasoline and jet fuel. Technology therefore exists for converting the algal crude oil to the various conventional transport fuels. The annual biomass requirements for producing 100 million US gallons of crude algal oil would be 8.84 × 105 tons (dry basis) per annum if the oil mass fraction in the biomass is 0.4, the recovery factor is 0.95 and the oils have the same density as palm oil. Producing this much biomass with a biomass recovery yield of 95 % from the broth and a raceway productivity of 0.025 kg ⋅ m−2 d−1 will require a raceway surface area of 11,326 ha, if the biomass production facility operates for 90 % of the calendar year. On an equal energy basis, a hundred million US gallons of algal crude oil can potentially displace 80 million US gallons of crude petroleum, or ∼ 0.027 % of US crude petroleum consumption in 2010. Of the total quantity of petroleum consumed in the United States in 2010, 73 % went to producing the three main transport fuels of gasoline (42 %), diesel (22 %) and jet fuel (9 %). If all these transport fuels were sourced from algal crude oil under the above-specified productivity scenario, a total raceway surface area of around 30.5 × 106 ha would be required. This is equivalent to ∼ 18 % of the arable area of the United States.

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7.4 Algae biomass production Algae may be grown in a batch operation or in continuous culture. In a batch operation, the raceway is filled with the nutrient medium and inoculated with a growing culture of the desired alga. The volume of the inoculum is generally 10 % of the volume of the medium in the raceway. The biomass concentration in the inoculum may be as low as 0.5 kg ⋅ m−3 if the inoculum is produced in a raceway. More commonly, the first raceway stage would be inoculated using a culture grown in a photobioreactor – the so-called “seeding reactor” – to a biomass concentration of 2–4 kg ⋅ m−3. Multiple inoculum production stages are therefore necessary for inoculating a large raceway. The alga grows to attain a biomass concentration that does not typically exceed 0.5 kg ⋅ m−3, or 1 kg ⋅ m−3 in exceptional circumstances (Borowitzka 2005). The biomass productivity is improved if the raceway culture is periodically diluted to keep the biomass concentration at less than the typical maximum value of 0.5 kg ⋅ m−3 to enhance light penetration in the raceway. In a tropical climate with a stable diurnal temperature of ∼ 25 °C, clear skies, an alga capable of rapid growth and a well-operated raceway, the average annual dry biomass productivity can be around 0.025 kg ⋅ m−2 d−1, but higher values have been recorded (Terry and Raymond 1985). A biomass productivity of > 0.03 kg ⋅ m−2 d−1 was observed for prolonged periods during the summer in a 0.16 m deep raceway culture of Pleurochrysis carterae (Moheimani and Borowitzka 2007), and for nearly 14 days, the productivity was well above 0.047 kg ⋅ m−2 d−1 in a 0.21 m deep raceway (Moheimani and Borowitzka 2007). Similarly, a productivity averaging 0.048 kg ⋅ m−2 d−1 over 3 months was reported for Scenedesmus obliquus in a 20 m2 raceway (Grobbelaar 2000). Biomass productivities exceeding 0.05 kg ⋅ m−2 d−1 have been observed (Weissman et al. 1989). The productivity will of course depend also on the alga being grown. Notwithstanding the high attainable productivities, vagaries of open culture can reduce the maximum annual average productivity to only 0.01 kg ⋅ m−2 d−1, or lower. Consequently, a freshly inoculated raceway can take 4–6 weeks to attain the peak biomass concentration of 0.5 kg ⋅ m−3 (Pulz 2001). Once a stable biomass concentration has been attained in a batch operation, all or most of the broth may be harvested to recover the biomass. Residual broth may become the inoculum for the next batch operation, or an entire new batch may be started after cleaning the raceway. A continuous culture is generally started as a batch operation. Once a high concentration of algal biomass has been attained, the operation is switched to a continuous mode. In continuous culture, a fresh medium is fed to the raceway at a constant flow rate. The feed point is generally just in front of the paddlewheel. Simultaneously, algal broth is withdrawn, or harvested, at the same constant flow rate as the feed flow rate. Continuous operation occurs only during daylight and must cease at night, or the cells in the raceway may wash out overnight. Prolonged continuous culture operation under stable weather and sunlight conditions allows

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a pseudo-steady-state biomass concentration to be maintained in the raceway. In continuous culture, the daytime feed flow rate Qf must be such that the dilution rate D does not exceed the maximum specific growth rate ( μmax) of the alga under the specific conditions of growth in the raceway. The culture will washout if the D value exceeds μmax. For stable operation, the dilution rate must remain well below the maximum specific growth rate under the operational conditions being used. Irrespective of the type of operation, as much as 25 % of the final biomass concentration attained at the end of the daylight period may be lost during the subsequent night because of respiration. The extent of this loss depends on the irradiance level under which the biomass was grown, the growth temperature and the temperature at night (Grobbelaar and Soeder 1985; Richmond 1990).

7.4.1 Productivity of biomass and oil The productivity of biomass may be expressed either in terms of the volume of the culture, or in terms of the surface area of the raceway. Productivity is the quantity of biomass produced per unit time per unit volume, or per unit surface area. In a batch operation, the volumetric productivity (Pv, kg ⋅ m−3 d−1) of the biomass is calculated as follows: Pv =

Xf – Xi Δt

(7.19)

where Xi (kg ⋅ m−3) is the initial concentration of the biomass, Xf (kg ⋅ m−3) is the peak concentration of the biomass, and Δt (d) is the time interval between inoculation and the attainment of the peak biomass concentration. The volumetric biomass productivity in a continuous flow operation is calculated as follows: Pv = Dxb

(7.20)

where xb is the pseudo-steady-state biomass concentration in the broth leaving the raceway, and D is the dilution rate. The dilution rate depends on the flow rate Qf of the feed and the volume of the broth (VL ) in the raceway, as follows: D =

Qf VL

(7.21)

In continuous culture, the productivity increases with increasing dilution rate until an optimal pseudo-steady-state biomass concentration is attained. Any further increase in dilution rate causes a rapid decline in productivity. The optimal

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pseudo-steady-state biomass concentration tends to be significantly lower than the maximum attainable biomass concentration in a batch raceway culture under otherwise identical conditions (Richmond 1990; Grobbelaar 2007). In the absence of other limitations, the maximum attainable productivity depends on the alga, irradiance level and temperature. The specific growth rate at optimal biomass concentration is about 50 % of the maximum because of light limitations (Richmond 1990), and therefore, the maximum biomass productivity in a continuous culture would likely occur at a dilution rate that is approximately half of the maximum possible specific growth rate for the alga at the operating temperature. The areal biomass productivity (Pa ) of a raceway can be converted to a volumetric productivity (Pv , kg ⋅ m−3 d−1) for comparison with other types of culture devices; thus: Pv =

Pa h

(7.22)

where h is the depth (m). Productivity of algal crude oil can be calculated on an area basis or volume basis by multiplying the corresponding biomass productivity by the oil content (% w/w) of the biomass. For example, if the mass fraction of oil in the biomass is xoil, and the areal productivity of the biomass is Pa, the areal productivity of the oil (Pa,oil, kg ⋅ m−2 d−1) is as follows: Pa,oil = xoil Pa

(7.23)

At an average daily biomass productivity of 0.025 kg ⋅ m−2 d−1 for an alga with an oil content of 40–50 % (w/w), a 95 % recovery of the biomass from the water, a 96 % recovery of the oils from the biomass, a 0.9 facility operational factor and no requirement for a separate nutrient starvation stage, the productivity of algal crude oil is expected to be of the order of 30–37 tons ha−1 year−1, or 33,926–42,155 L ha−1 year−1, assuming an oil density of 887 kg ⋅ m−3, as for palm oil. This oil productivity is six- to sevenfold greater than an average annual oil productivity of about 5,950 L ha−1 for oil palm, the most productive commercial oil crop (Chisti 2007a). Under the best conditions, the maximum specific growth rate of an alga in a raceway will generally not exceed 0.167 d−1, corresponding to a biomass doubling time of about 4 days, once the biomass concentration is close to the peak value of about 0.5 kg ⋅ m−3. The exact value of the growth rate will of course depend on the alga and the concentration of the biomass in the broth. Growth will be faster in a dilute broth compared to in a more concentrated broth because of the effects of biomass concentration on penetration of light. In algae cultures not limited by light and other nutrients, the maximum specific growth rate under controlled environmental conditions can be much higher than is attained in raceways. For example, the maximum specific growth rate values for algae reported by Goldman

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and Carpenter (1974) have ranged from 0.48 to 5.65 d−1. Thus, biomass productivity of a raceway is much lower than what is biologically possible.

7.4.2 Limits to algal biomass productivity Compared to microalgae, a lot more is known about biomass productivity of crop plants in the field. Like most terrestrial plants, green microalgae carry out a kind of photosynthesis known as C3 photosynthesis. Therefore, the productivity of crop plants is a useful guide to what may be achieved with microalgae. The maximum growth rate for C3 land crops is in the range of 0.034– 0.039 kg ⋅ m−2 d−1 (Monteith 1978), or a biomass productivity of up to 142 tons ha−1 year−1, if the maximum growth rate can be sustained for the entire year. This level of productivity is what the C3 photosynthesis is clearly capable of achieving. Plants of course do not grow at their maximum possible rate through the entire growing season. The maximum growth rate averaged over the growing season for C3 crops is about 0.013 kg ⋅ m−2 d−1 (Monteith 1978), or 47 tons ha−1 year−1 for a growing season of 365 days. The actual plant biomass productivity can be much less than this, as the growing season is often shorter than a calendar year. This contrasts with a growing season of up to 90 % of the calendar year for microalgae under suitable climatic conditions. A shorter growing season than a full calendar year is to allow for a scheduled shutdown of a production facility for maintenance. The aforementioned productivity threshold of 142 tons ha−1 year−1 for C3 vascular plants is not an upper limit on the capability of C3 photosynthesis. It is actually a limit imposed by the availability of carbon dioxide in the normal atmosphere. Biomass productivity of C3 crops increases by 35 % on average if the concentration of carbon dioxide in the atmosphere is doubled to 0.06 % by volume. This suggests that the biomass productivity threshold could be raised to 1.35 × 142, or ∼ 192 tons ha−1 year−1, on average, simply by increasing the availability of carbon dioxide. This level of average annual dry biomass productivity is clearly attainable by microalgae grown in the absence of carbon dioxide limitation. Microalgae are typically grown using a 5 % by volume level of carbon dioxide in the air. This is more than 100-fold higher than the concentration of carbon dioxide in the normal atmosphere. Under conditions of a nonlimiting supply of inorganic carbon, annual average biomass productivities of ≥ 1.535 g ⋅ m−3 d−1 have been attained in outdoor pilot-scale production operations in tubular photobioreactors (Acién Fernández et al. 1998; Sánchez Mirón et al. 1999; Chisti 2007a). This is at least 15-fold higher than the best-case volumetric biomass productivity of a raceway of about 0.1 kg ⋅ m−3 d−1. (A best-case areal biomass productivity of 0.025 kg ⋅ m−2 d−1 in a 0.25 m deep raceway is equivalent to a volumetric productivity of 0.1 kg ⋅ m−3 d−1, or 82 tons ha−1 year−1, assuming a facility operational factor of 0.9.) Clearly, therefore, the best-case productivity in a raceway is far lower than

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the capability of algal photosynthesis. A biomass productivity of 1.535 g ⋅ m−3 d−1 in tubular photobioreactors is equivalent to an estimated annual areal productivity of 158.4 tons ha−1 (Chisti 2007a). In practice, under suitable culture conditions, the observed biomass productivities of microalgae greatly exceed those of vascular plants (Chisti 2007a, 2008a). For example, the median value of the maximum specific growth rate of microalgal species is approximately 1 d−1 whereas for higher plants it is 0.1 d−1 or less (Nielsen et al. 1996).

7.4.2.1 Photosynthetic efficiency Under ideal conditions such as complete absorption of the photosynthetically active light, an absolute theoretical upper limit on the efficiency of photosynthesis has been calculated to be 13.0 % of the total incident solar irradiance (Bolton and Hall 1991; see also Chapter 3). As the absorption of light is never complete, and the actual quantum requirement is more than the minimum of eight photons, the practical upper limit on efficiency of photosynthesis is less than the value calculated from theory. A practical upper limit on photosynthetic efficiency has been estimated to be 9.3 % of the total incident sunlight, but under certain conditions this reduces to 8.3 % (Bolton and Hall 1991). In calculating this practical upper limit for green plants, Bolton and Hall (1991) assumed the fraction of the absorbed incident photosynthetically active radiation to be 0.84 and the quantum requirement of photosynthesis to be in excess of 9 photons. Both these assumptions are consistent with actual measurements. Therefore, in an algal culture, at most 8.3 % of the total incident sunlight can be converted to the biochemical energy of the algal biomass. This value takes into account a reflective loss of 16 % of irradiance incident on the surface of a culture. On the surface of a raceway, the reflective loss is similar at 10–20 % of the incident light, depending on the angle of incidence. Radiation reflected off the surface of a cell will most likely be reflected within the culture and should remain effective for photosynthesis. The day–night averaged annual total sunlight energy received at the Earth’s surface is about 250 W ⋅ m−2. This translates to 70,956 GJ ha−1 of annual energy received (Es) for a production facility with an operation factor of 0.9. If for such a facility the annual biomass productivity (B) is the 158.4 tons ha−1 reported earlier (Section 7.4.2; Chisti 2007a) the photosynthetic efficiency (Pe) can be calculated from the following energy balance: Pe Es = BEb

(7.24)

where Eb is the energy content of the algal biomass. Measurements of the energy content (i.e. the calorific value or enthalpy of combustion) of algal biomass have ranged from 16.9 to 21.0 MJ ⋅ kg−1 (Watanabe and Hall 1996; Hirano et al. 1998;

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Illman et al. 2000; Dismukes et al. 2008), but a value of 23.0 MJ ⋅ kg−1, or greater, is easily attained, for example, for the green alga Chlorella vulgaris, particularly if a nutrient-limited growth stage follows a period of nutrient-sufficient growth (Illman et al. 2000; Mazzuca Sobczuk and Chisti 2010). At a relatively high energy content of 23.0 MJ ⋅ kg−1 (or 23,000 MJ ⋅ ton−1) in the biomass and the aforementioned biomass productivity of 158.4 tons ha−1 attainable in certain types production systems in the field (Chisti 2007a), the average annual photosynthetic efficiency can be calculated, as follows: Pe =

158.4 × 23,000 BEb × 100 % = 5.1 % × 100 = Es 70,956 × 1,000

(7.25)

or well within the theoretical upper limit of 8.3 %. In a raceway, the best-case biomass productivity of about 0.025 kg ⋅ m−2 d−1 equates to a much lower photosynthetic efficiency of only 2.7 %.

7.4.2.2 Why are microalgae more efficient than terrestrial plants? In contrast to microalgae, the maximum observed photosynthetic efficiency (Pe ) for C3 plants is 2.4 % (Zhu et al. 2008). This is an average value for the entire growing season in the field (i.e. under a normal atmosphere). Although green algae share the same basic photosynthetic machinery as the C3 plants, they have a higher photosynthetic efficiency because of a combination of factors. Each algal cell is photosynthetically active, whereas only a fraction of the plant biomass photosynthesizes. For example, plant roots do not contribute to photosynthesis. Roots are respiring all the time. Between 8 % and 52 % of the carbon dioxide released by plants has been attributed to root respiration (Atkin et al. 2000). Each algal cell can absorb nutrients directly from its surroundings, and therefore, algae do not have to rely on energy-consuming, long-distance transport of nutrients via roots and stem (Chisti 2010). In contrast, in plants, ion uptake consumes 50–70 % of the energy produced by root respiration (Poorter et al. 1991). In addition to light, photosynthesis requires carbon dioxide. In plants, photosynthetic tissue can access carbon dioxide only through pores known as stomata. The fully open stomatal aperture area is generally 60–90 μm2, and the number of stomata per square centimeter of a leaf’s surface ranges from 5,000 to 30,000. Therefore, under the best circumstances, ≤ 2.7 % of a leaf’s surface area is available for absorption of carbon dioxide. Stomata are not always open, and carbon dioxide must move through them against a flow of water vapor (Chisti 2010). The carbon dioxide diffusion pathway from the surface of the photosynthetic tissue to a photosynthesizing cell is much longer in plants than in microalgae and increases with increasing thickness of the photosynthetic structure (Parkhurst 1986; Nielsen et al. 1996). Effective lateral CO2 diffusion coefficients inside leaves have been found to be only 20 %, or less, of the value in free air (Morison et al. 2005). These values

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137

are insufficient to support appreciable photosynthesis over distances of more than about 0.3 mm in leaves not limited by light (Morison et al. 2005). Algae, therefore, can access carbon dioxide more easily than vascular plants, and this contributes to the relatively rapid growth of algae. Owing to its high solubility in water, the equilibrium concentration of carbon dioxide in an algal suspension is greater than in the atmosphere above the suspension. Effectively, water enriches carbon dioxide that is essential for photosynthesis. This too improves algal productivity relative to plants. Furthermore, because of a short lifecycle, algal biomass can be harvested daily or hourly, whereas plant biomass typically remains in the field for much longer (Chisti 2010). In conclusion, the photosynthetic efficiency of microalgae cannot exceed the theoretically predicted value of 8.3 % based on total irradiance, but it can greatly exceed the maximum observed efficiency of the higher green plants.

7.5 Economics of algal crude oil To displace petroleum as a source of fuels, algal crude oil must be cost-competitive with petroleum oil. Therefore, the maximum price payable for an algal biomass with a certain content of oil will depend on the prevailing price of petroleum and the cost of extraction of the algal oil from the biomass. To establish the maximum acceptable price for the algal biomass, we need to consider first the energy content of algal oil relative to the energy content of petroleum. The energy content of crude algal oil is about 35,800 kJ ⋅ kg−1, or 5048 MJ per barrel if the oil is assumed to have the same density as palm oil, i.e. 887 kg ⋅ m−3. The energy content of crude petroleum depends on its source. On average, a barrel of crude petroleum has an energy content of 6278 MJ. Therefore, in terms of energy content, a barrel of petroleum is equivalent to 1.25 barrels of algal crude oil. Therefore, for algae-oil-based transport fuels to be economically competitive with petroleum-derived fuels, the cost of producing 1.25 barrels of algal crude oil should not exceed the market price of a barrel of petroleum. This assumes that the cost of processing algal crude oil to usable products such as diesel, gasoline and jet fuel would be identical to the cost of processing a barrel of petroleum to the same products. If a fraction f of the oil in the algal biomass can be extracted, and the biomass has an oil content of xoil (% w/w, dry basis), the quantity of biomass X (kg) needed to produce 1.25 barrels of algal crude oil can be calculated as follows: X =

0.159 ⋅ 1.25 ⋅ ρoil fxoil

=

0.2 ρoil fxoil

(7.26)

where ρoil (kg ⋅ m−3) is the density of algal crude oil. In Equation (7.26), the multiplier of 0.159 accounts for the fact that 1 US barrel is the equivalent of about 0.159 m3. The factor f is typically 0.95 but can be up to 1 (Goodall et al. 2011).

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7 Raceways-based production of algal crude oil

To proceed further, we need to consider the cost of extracting the oil from the biomass. A hexane-based process for extracting oil from algal biomass paste (∼ 90 % moisture content) has been developed by SRS Energy (Dexter, MI, USA; www.solutionrecovery.com) (Goodall et al. 2011). This proprietary process does not require prior drying of the biomass or disruption of the cells but does require a biomass “conditioning” step that makes the walls of algal cells permeable. As a contract service provider, SRS Energy has used this process to extract the oil from the biomass of several different algae for a number of companies. Using this technology at an extraction scale of 100 million US gallons of algal crude oil per year, the total cost of extraction from wet biomass (90 % water) has been estimated to be about $ 0.50 per gallon, or $ 21 per barrel, for 95 % recovery of oil from the biomass with an initial oil content of 40 % (w/w, dry weight basis) (Goodall et al. 2011). The operating cost of the oil recovery process does not seem to be sensitive to the oil content in the algal biomass so long as the biomass has ≥ 40 % oil by dry weight (Goodall et al. 2011). The SRS Energy’s extraction process has been effectively operated for more than two years at a scale of 100 kg of algal biomass (dry basis) per day (Goodall et al. 2011). Developments underway are expected to further reduce the cost of extraction (Goodall et al. 2011). Thus, for a biomass with an oil content of ≥ 40 % (w/w) on a dry basis, the following dollar balance can be set up to ensure that algal oil is cost-competitive with crude petroleum: Cb X

+

Total cost of biomass needed for 1.25 barrels of algal crude oil

1.25 Ce

= Cp

(7.27)

Total cost of extraction of 1.25 barrels of algal oil

where Cb is the acceptable cost of the biomass ($/kg, dry basis), Pp is the market price of petroleum ($/barrel), X (kg) is the quantity of the biomass needed to produce 1.25 barrels of algal oil (Eq. (7.26)), and Ce ($/barrel) is the cost of extraction of the oil. Equation (7.27) disregards any monetary value in the residual biomass, as will be discussed later. Substitution of Equation (7.26) in Equation (7.27) followed by rearrangement leads to the following equation:

Cb

(kg) = (0.2 ρ ) (C (barrel) − 1.25 C (barrel)) $

fx oil

$

p

$

e

(7.28)

oil

Assuming a Ce value of $ 21 per barrel as discussed above, an f value of 0.95, an algal biomass with an oil mass fraction xoil of 0.40, an algal oil density of 887 kg ⋅ m−3 and a nil monetary credit for the residual biomass, the affordable price for algal biomass can be calculated using Equation (7.28) for any chosen market price of petroleum.

7.5 Economics of algal crude oil

139

7.5.1 Residual biomass Next we need to consider the monetary value of the residual biomass. The only viable option for using the residual biomass is to digest it anaerobically to produce biogas and a fertilizer that is rich in nitrogen and phosphorus (Chisti 2008a, 2008b, 2010). There appears to be no alternative to this. Without anaerobic digestion, the N and P nutrients cannot be recovered for use in crop agriculture. Unfortunately, without nutrient recovery, production of algal biomass for fuels is not feasible on a meaningful scale because all existing supply of these nutrients is used in agriculture, and procuring fresh supplies is completely unrealistic (Chisti 2010). Anaerobic digestion is necessary for another reason: the biogas produced by anaerobic digestion is needed to produce electricity for the operation of the raceways (Chisti 2008a, 2008b). Unless this is done, the net energy recovery in the algal crude oil would be low. The biogas generated via anaerobic digestion will be consumed within the algal biomass production process and, therefore, will have no cash value. The fertilizer produced by anaerobic digestion will have a certain monetary value. To estimate this, we need to consider the composition of the algal biomass and the prices of commercial N and P fertilizers. Algal biomass grown without nutrient limitations typically contains 6.6 % w/w nitrogen and 1.3 % w/w phosphorus (Grobbelaar 2004; Chisti 2007a). The least expensive sources of N and P for growing algae are ammonium nitrate and phosphate rock, respectively. The US farm price of ammonium nitrate fertilizer in 2011 was about $ 479/ton. Ammonium nitrate contains 35 % N by weight. Therefore, the price in terms of mass of N is $ 1,368/ton. The price of phosphate rock in 2011 was about $ 180/ton. Phosphate rock typically contains 15–20 % phosphorus by weight. Assuming a low P content of 15 % in the rock, the price in terms of elemental P is about $ 1,200/ton. The monetary value M ($/ton) of the recoverable N and P in the biomass can be calculated as follows: M = y (xP PP + xN PN)

(7.29)

where y is the recoverable fraction of the N and P in the biomass, PP is the price of phosphate fertilizer ($/ton of P), PN is the price of nitrogen fertilizer ($/ton of N), xP is the mass fraction of P in the biomass, and xN is the mass fraction of N in the biomass. Thus, the credit associated with nutrient recovery from X kg of bioXy (xP PP + xN PN ) mass is $ . 3 10 In a raceway with an average biomass productivity of 0.025 kg ⋅ m−2 d−1, an operation factor of 0.9 and a biomass recovery factor of 0.95, the total annual recoverable biomass production will be 78 tons ha−1. The total N and P content of

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7 Raceways-based production of algal crude oil

78 tons of biomass will be 5.148 tons of N and 1.014 tons of P, assuming xN and xP values of 0.066 and 0.013, respectively. If a reasonable 60 % (i.e. y = 0.6) of both N and P present in the biomass are recovered in the effluent of the residual biomass digester, for use in crop production, the monetary value per ton of the recovered algal biomass would be $ 63.53. Of course the anaerobic digestion of the residual biomass will have an associated cost that needs to be accounted for. The cost of biogas production via anaerobic digestion in 2007 ranged from $ 2.99 to $ 28.98 per GJ (United States Department of Agriculture 2007). In terms of 2010 dollars, the cost range was $ 3.14 to $ 30.48 per GJ. The energy content of the biogas produced by anaerobic digestion ranges from 16,200 to 30,600 kJ ⋅ m−3, and the yield of biogas typically varies from 0.15 to 0.65 m3 per kg of dry biomass (Chisti 2008a). Assuming a conservative biogas yield of 0.15 m3 ⋅ kg−1 and an energy content of 16,200 kJ ⋅ m−3, the cost of biogas production can be estimated to be $ 7.63 per ton (dry basis) of the biomass digested. As previously noted, X kg of biomass is required for producing 1.25 barrels of algal oil and leaves behind X (1 − fxoil ) kg of residual biomass. The total cost of

(

)

−3 digesting this biomass is $ 7.63 × 10 X 1 − fxoil . A low production cost is used in

this estimation in view of the extremely large scale of biogas production operation that is likely to be associated with any production of algal crude oil. The dollar balance (Eq. (7.27)) can now be revised to include the cost of nutrient recovery by anaerobic digestion and the credit for the nutrients recovered; thus: Cb X + 1.25 Ce + 7.63 × 10−3 X(1 − fxoil) − Xy (xP PP + xN PN) × 10−3 = Cp Cost of anaerobic digestion of residual biomass

(7.30)

Credit for recovered nutrients

Substitution of Equation (7.26) in Equation (7.30) followed by rearrangement provides the following equation: Cb = (Cp − 1.25 Ce)

(

)

fxoil − 7.63 × 10−3(1 − fxoil) + y (xP PP + xNPN) × 10−3 0.2 ρoil

(7.31)

The affordable price of biomass (Cb, $/kg) for various market prices of petroleum (Cp, $/barrel) is shown in Figure 7.10 for the following values for the other variables: Ce = $ 21 per barrel, f = 0.95, xoil = 0.40, ρoil = 887 kg ⋅ m−3, y = 0.6, PP = $ 1,200/ton, PN = $ 1,368/ton, xN = 0.066 and xP = 0.013. Figure 7.10 shows the scenarios with and without a credit being allowed for the value in the residual biomass. In early 2012, the price of crude petroleum was around $ 100 per barrel. For algal crude oil to be competitive with petroleum at this price, the biomass with a

7.6 Concluding remarks

141

Fig. 7.10: Affordable price of algal biomass in relation to the market price of crude petroleum. The plots shown are for a biomass with an oil content of 40 % (w/w) and an oil recovery of 95 %. The cost of recovery of algal crude oil is taken to be $ 21 per barrel.

40 % oil content will need to be procured at $ 0.16 per kg if no credit is allowed for the residual biomass, or at $ 0.25 per kg if a credit is allowed for the nutrients in the residual biomass (Fig. 7.10). If the price of petroleum doubles, then algal biomass procured at up to about $ 0.50 per kg may be a viable basis for producing transport fuels. Compared to the above estimates, the cost of producing dry Dunaliella biomass in commercial raceway operations in Israel has been claimed to be about $ 18/kg (Ben-Amotz 2012). This price is based on a process that used purchased carbon dioxide, relatively high-g centrifuges for biomass recovery and a subsequent drying step. Clearly, at $ 18/kg the algal biomass is far too expensive for producing fuel oils competitively with petroleum oil. Whether raceway culture is the least expensive method of producing algal biomass, as commonly claimed, is debatable. Given other types of production systems and economies of scale, analyses suggest that the cost of producing algal biomass can indeed be reduced to around $ 0.5/kg. Therefore, the potential of microalgae to provide liquid transport fuels at prices comparable to the prices of similar fuels derived from petroleum cannot be overlooked.

7.6 Concluding remarks Raceway-type open ponds are well established for commercial production of algal biomass in many high-value applications. This chapter discussed the design and operation of raceway ponds for use in the production of microalgae for recovering oils for fuel. The limitations of the raceway technology were discussed. The limits to algal biomass productivity were explored in the context of the efficiency of

142

7 Raceways-based production of algal crude oil

photosynthesis relative to terrestrial plants. The affordable cost of algal biomass for producing liquid fuels competitively with petroleum-derived fuels was estimated. Microalgae were concluded to have a good potential for providing affordable biofuels. Raceways require a relatively low investment in capital but provide an extremely dilute algal broth. Under the best conditions, the biomass productivity of a raceway is much lower than what the algal biology is capable of achieving. This low productivity combined with the exceedingly low maximum attainable biomass concentration in a raceway culture raises important questions concerning the suitability of raceways for producing algal biomass for extraction of fuel oils.

7.7 Nomenclature A B C Cb Ce CPIx Cp Cx D dh Eb Es e FGD Fs f fc fM g H h IL Io Ka Ki KL L Lr

Surface area of raceway (m2) Annual biomass productivity (tons ha−1) Carbon dioxide requirement for biomass production (kg ⋅ ha−1 d−1) Acceptable cost of the biomass ($/kg, dry basis) Cost of extraction of algal oil ($/barrel) Consumer Price Index (All Urban Consumers) for the year x Specific heat capacity of water (≈ 4200 J ⋅ kg−1 °C) Biomass concentration (kg ⋅ m−3) Dilution rate (h−1) Hydraulic diameter of flow channel (m) Energy content of the algal biomass (MJ ⋅ kg−1) Average annual total sunlight energy received at the surface of the Earth (W ⋅ m−2) Efficiency of the motor, drive and paddlewheel Flue-gas desulfurization Salinity correction factor defined by Equation (7.10) Recoverable fraction of the oil in algal biomass Carbon dioxide loss factor Manning channel roughness factor (s ⋅ m−1/3) Gravitational acceleration (9.81 m ⋅ s−2) Hydraulic head loss (m) Culture depth in raceway (m) Local irradiance at depth L (μE ⋅ m−2 s−1) Irradiance incident on the surface of the raceway (μE ⋅ m−2 s−1) Light-absorption coefficient of the biomass (μE ⋅ m−2 s−1) Photoinhibition constant (μE ⋅ m−2 s−1) Light-saturation constant (μE ⋅ m−2 s−1) Depth (m) Total length of the flow loop (m)

7.7 Nomenclature

143

M m P PAR Pa Pa,oil Pe PN PP Pp PVC Pv p Q Qf q Re S SOx T ΔT Δt u VL w X Xf Xi xb xN xoil xP y

Depth at which the local irradiance level is at the light compensation point (m) Monetary value of the recoverable N and P in the biomass ($/ton of biomass) Mass of water per square meter of the raceway surface (kg) Power requirement for paddlewheel (W) Photosynthetically active radiation Areal productivity of biomass (kg ⋅ m−2 d−1) Areal productivity of oil (kg ⋅ m−2 d−1) Photosynthetic efficiency Price of nitrogen fertilizer ($/ton of N) Price of phosphate fertilizer ($/ton of P) Market price of petroleum ($/barrel) Polyvinyl chloride Volumetric biomass productivity (kg ⋅ m−3 d−1) Length as shown in Figure 7.3 (length of divider baffle) (m) Volume flow rate in the raceway channel (m3 s−1) Feed flow rate (m3 h−1) Length as shown in Figure 7.3 (width of pond) (m) Reynolds number defined by Equation (7.3) Salinity (ppt) Oxides of sulfur Absolute temperature (K) Total temperature rise (°C) Time interval (d) Flow velocity in channel (m ⋅ s−1) Working volume of the raceway (m3) Channel width (m) Quantity of biomass needed to produce 1.25 barrels of algal crude oil (kg) Peak concentration of biomass (kg ⋅ m−3) Initial concentration of the biomass (kg ⋅ m−3) Pseudo-steady-state biomass concentration in the raceway (kg ⋅ m−3) Mass fraction of N in the biomass Oil content of biomass (% w/w, dry basis) Mass fraction of P in the biomass Recoverable fraction of N and P in the biomass

Greek μ μav μL μmax ρ ρoil φ

symbols Viscosity of the algal broth (Pa ⋅ s) Depth-averaged specific growth rate in the illuminated volume (d−1) Local specific growth rate at depth L (d−1) Maximum specific growth rate (d−1) Density of algal broth (kg ⋅ m−3) Density of algal crude oil (kg⋅m−3) Cost correction factor defined by Equation (7.18)

lc

144

7 Raceways-based production of algal crude oil

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Jeff Obbard

8 Cellana LLC: Algae-based products for a sustainable future 8.1 Introduction Advanced biofuels, derived from marine microalgae biomass, offer major benefits over conventional biofuels by minimizing the use of land, fresh water and nutrients, and providing co-products for animal feed, nutraceuticals and specialty chemicals that can be utilized by the food, cosmetic, pharmaceutical and biotechnology industries. Toward this end, the development of a microalgae-based industry is a compelling value proposition for sustainable biofuels and co-products. In particular, the exploitation of marine microalgae allows for utilization of CO2 emitted from industrial processes.

8.2 Cellana technology and demonstration facility Cellana LLC is focused on the development of marine microalgae products to capitalize on the converging trends of renewable energy, sustainable feed and renewable chemicals to serve multi-billion-dollar, global markets. Cellana operates its ALDUO™ system (“AL” as in algae, “Duo” as in dual, or two) as a patented (Huntley and Redalje 2010) technology for the consistent and robust production of monocultures of microalgae in a hybrid system. An additional US patent on a proprietary hydrodynamic process for photobioreactor-based microalgae production is also applicable (Huntley et al. 1996). The ALDUO™ technology is designed for growing microalgae at a large scale on a modular basis using a combination of outdoor, horizontal photobioreactors (PBRs) and open raceway ponds. Cellana uses naturally occurring, non-genetically modified marine microalgae selected specifically for their biochemical and performance properties. From an initial screening over nearly 14,000 strains, Cellana has access to over 450 strains, representing most of the major genera (e.g. Chlorophyta, Rhodophyta, Haptophyta, Dinophyta, Heterokonophyta, etc.) isolated from marine habitats in Hawaii for use in the ALDUO™ system. Cellana’s production of microalgae takes place at its Kona Demonstration Facility (KDF) in Hawaii (see Figure 2.1). The KDF is one of the largest facilities of its type in the USA, and indeed the world. The facility is specifically designed to evaluate the cost and sustainability of producing algal biofuel feedstock and coproducts at demonstration pre-commercial scale. The 2.5 ha facility, with 1,500 m2 of laboratory and office space, and 1 ha of cultivation systems, was commissioned

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8 Cellana LLC: Algae-based products for a sustainable future

Fig. 8.1: Cellana’s Kona Demonstration Facility (KDF) (left and open raceway ponds at KDF (right).

in August 2009, with a full-time staff of up to 60 personnel. Strains grown at large scale in the ponds at KDF typically contain between 30 and 40 % lipid as a fraction of ash-free dry weight at approximately 25 g/m2/day, depending upon cultivation conditions, with residual defatted fractions comprising protein and carbohydrate. The ALDUO™ system at KDF comprises up to 12 × 25,000 liter PBRs and 6 × 90,000 liter open ponds (PBR and open-pond area shown on the right- and left-hand side of Figure 8.1, respectively). All fluid transfers related to cultivation and harvesting processes – including inoculations, nutrient additions and harvest volumes – are operated and monitored by a remote process-control system. The metrics of microalgae physiology, growth and lipid production are analyzed and monitored in on-site laboratories.

8.3 Biorefinery approach Figure 8.2 illustrates how the ALDUO™ technology is integrated into the microalgae production pathway, within the context of a biorefinery product stream. As such, KDF has the requisite facilities, equipment, staff and processes to enable the establishment of near-term, pre-commercial algal biomass production and supply services. Production trials at KDF have the potential to produce up to 1.5 metric ton dry weight of algal biomass per month using the combined PBR and open-pond ALDUO™ system, while Cellana systematically optimizes biomass cultivation, harvest and dewatering of biomass to produce algal paste, slurries and dry powder for fractionation. In its simplest application, this platform enables Cellana to produce whole algae biomass for animal feed and human supplements with enhanced nutritional benefits. With the separation of different components of the whole algae, this platform enables the creation of an integrated biorefinery capacity for multiple product streams from the same algae biomass, such as higher-value

Fig. 8.2: ALDUO™ technology in the biorefinery context.

8.3 Biorefinery approach

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8 Cellana LLC: Algae-based products for a sustainable future

nutraceuticals and specialty oil co-products, in addition to bulk oil for biofuels and protein for animal feed. In addition, by applying the ALDUO™ to industrial carbon dioxide emissions from power plants and other sources, the system will enable the recycling of carbon to produce these products, thereby representing a significant environmental benefit. Research work on the sustainability of microalgae biofuel feedstocks and protein feed at KDF is currently supported by grants from the US Departments of Energy and Agriculture.

8.4 Prospects Cellana’s facility has demonstrated the viability of the technology and various unit operations associated with water management, harvesting and dewatering at KDF, and is now pursuing development of a 100 ha biorefinery facilities at various locations around the world. To further this objective, Cellana has undertaken a comprehensive techno-economic assessment, and an associated life cycle assessment of an integrated modular design, based on ALDUO™ that encompasses both upstream and downstream elements of microalgae production.

References Huntley, M. E. and D. G. Redalje. 2010. Continuous-Batch Hybrid Process for Production of Oil and other useful Products from Photosynthetic Microbes. US Patent 2010/7770322 B2. Huntley, M. E., P. P. Niiler and D. Redalje. 1996. Method of Control of Microorganism Growth Process. US Patent 1996/5541056.

F. G. Acién Fernández, J. M. Fernández Sevilla and E. Molina Grima

9 Principles of photobioreactor design 9.1 Introduction Microalgae (with the exception of cyanobacteria) are eukaryotic micro-organisms that are able to grow in aquatic media to carry out photosynthesis. They use inorganic nutrients, CO2 and sunlight to produce O2 and microalgae biomass with about 40–50 % proteins, 10–30 lipids, 20–30 % carbohydrates and approximately 2–5 % ash. The culture system must provide enough light for cells and favor high growth rates, but at the same time the culture system must be operated at the optimal cell density (biomass concentration) to achieve high biomass productivity and take advantage of irradiance on the reactor surface. The culture system must allow the supply of CO2 and the removal of photosynthetic O2 generated, and provide sufficient mixing to avoid any gradients in the system. At the high cell densities required to achieve high productivities, the cultures are practically opaque to light due to self-shading between cells. In fact, in order to favor light/dark cycle frequencies, the fluid dynamics in the culture is of major importance. The objective of this chapter is (1) to review the major types of photobioreactors used for the culture of microalgae, (2) to show the design fundamentals that bring together the principles of fluid mechanics, gas–liquid mass transfer and irradiancecontrolled microalgal growth in a method for design, and finally (3) to select the best strategy for scaling up these culture systems.

9.2 Major factors governing the production of microalgae The biomass productivity in any culture system depends on how close the culture conditions match the requirements of the selected strain. Because mineral nutrient limitation is easily avoided in a microalgal mass culture, light availability inside the photobioreactor and temperature are found to be the main management factors in obtaining optimal system profitability. Thus, when the temperature culture is kept within appropriate intervals, light availability is the only factor determining growth. Figure 9.1 shows the major factors impacting on biomass productivity. The productivity is determined by the growth rate, which is a function of the light profile within the reactor and the light regime to which the cells are subjected. Once this function is known, it is possible to obtain a correlation between biomass productivity and the average irradiance within the reactor, Iav . On the other hand, Iav is a function of the irradiance impinging on the reactor surface, I0 , which is, at

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9 Principles of photobioreactor design

Fig. 9.1: Relationship between major factors influencing the biomass productivity of microalgal mass cultures (from E. Molina Grima 1999, Encyclopedia of Bioprocess Technology: Fermentation, Biocatalysis and Bioseparations. 1753–1769, John Wiley and Sons Inc., with permission).

the same time, dependent on geographic and environmental factors. The geographic location and day of the year determine the incident solar radiation and thus the temperature in the culture. While the temperature can be kept within a narrow interval by using suitable thermostatic systems, solar radiation cannot be controlled. The incident solar radiation, which is a function of climatic and geographic parameters of the facility location (Incropera and Thomas 1978), as well as the design and orientation of the photobioreactor (Lee and Low 1992; Qiang and Richmond 1996; Sierra et al. 2008; Posten 2009), determines the maximum energy available for growth. The incident solar radiation, along with the photobioreactor geometry and the biomass concentration, determines a heterogeneous light profile due to the mutual cell shading inside the culture (Molina Grima et al. 1994; Acién Fernández et al. 1997). The light availability or average irradiance inside the culture can be calculated by volumetric integration of the irradiance profile. The cell metabolism adapts to this light availability, as well as the biochemical composition and growth rate (Fernández et al. 1998). On the other hand, the photobioreactor fluid dynamics also determine the mass transfer and the light regime of the cells. The latter is affected by the time the cells spend in zones with a different irradiance level and the frequency of movement into and out of those zones (Terry 1986; Grobbelaar 1994). This light regime affects the behavior of the cells, determining

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the extent to which the cells are exposed to the photic and dark zones of the photobioreactor and, thereby, how efficiently the cultures use solar radiation (Posten, 2009). The biomass concentration is influenced by the growth rate, which is a function of the average irradiance, temperature, mass transfer and light regime. Both, growth rate and biomass concentration determine the final biomass productivity of the system (Fig. 9.1). Thus, in an optimum system with no limitations other than light, a direct interrelationship between light availability, rate of photosynthesis and productivity may be expected. In fact, it seems that other limitations not only limit growth by their direct effect but also impose limitations on the ability to utilize the absorbed solar energy. Therefore, ultimately, the most important design criterion is to enhance light availability per cell, since this consequently leads to a high efficiency in transforming sunlight reaching the cell within the culture into biomass.

9.3 Open systems Microalgae can be harvested directly from the environment where they grow naturally, such as lakes and large salt facilities now used to produce microalgae with minimal human intervention. These systems, called open ponds, are frequently excluded from the photobioreactors category because there are no modifications to the environmental conditions. On the other hand, these open ponds can be modified to improve the control of culture conditions to enhance productivity, turning them into open raceways.

9.3.1 Open raceways Open raceways, or shallow mixed ponds for microalgal production, were introduced in the 1950s and early 1960s by Oswald and co-workers. The principles for the design and construction of shallow paddle stirred raceways for large microalgal production were reviewed by Oswald (1988). The pond is set in a meandering configuration with channels, utilizing various designs of paddle wheel mixers that promote a low shear environment. The simplified diagram of an open raceway, showing the nomenclature of the major parts, is shown in Fig. 9.2a. Selection of a suitable bottom lining and wall construction are important factors for the success of a raceway pond. The lining may be made of concrete or sheets of plastic or rubber material. The size of commercial ponds varies from 1,000 to 5,000 m2, with stirring being accomplished using one or two paddlewheels per pond. A large-

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9 Principles of photobioreactor design

Fig. 9.2: Schematic diagram (a) (from W. J. Oswald 1988, with permission), and photograph (b) of an open 100 m2 raceway set-up at the author’s facilities, and scheme of the sump configurations tested in the latter (c). The channel length is the distance traveled by the culture from the discharge side of the paddle wheel to the entering side.

diameter wheel (ca. 2.0 m in diameter) revolving at a slow speed (e.g. 10 rpm) is preferable to smaller-diameter wheels rotating faster, because the latter produce excessive shear damage and foam. Under these conditions, biomass concentrations of up to 1.0 g · L–1 and yields of 0.1 g · L–1 · d–1 are possible.

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9.3.1.1 Technical issues Supply of carbon dioxide and removal of oxygen Techniques for the supply of CO2 represent an important element in the raceway, particularly for species that grow near neutral pH. Several systems aimed at supplying CO2 efficiently to shallow suspensions have been developed. In most cases, the gas is supplied in the form of fine bubbles. Due to the shallowness of the suspension, the residence time of the bubbles is not sufficient to allow all the CO2 to be dissolved, so most of CO2 supplied is lost to the atmosphere. The most effective method currently available to transfer CO2 to algal cultures is counter-current carbonation (Oswald 1988) in which the gas is injected as minute bubbles into a column of culture (sump). For this purpose, a sump divided by a baffle has been proposed (Fig. 9.2C). Using this sump structure, the culture velocity can be matched to the speed of the small bubbles of CO2 rising against the downward water current, and with this technique, Laws et al. (1986) reported a 70 % efficiency in CO2 transfer, a large improvement compared with the 13–20 % efficiency obtained when gas is injected in the shallow channel. However, we have carried out experiments with and without a baffle (Fig. 9.2C) with the raceway pond shown in Figure 9.2B, and the usefulness of introducing a baffle into the sump is questionable. There was a slight increase in mass transfer capacity in the sump in the experiment performed with a baffle at the expense of increased power consumption and a reduction in culture velocity and degree of mixing of the system (data not shown). In the authors’ opinion, a sump configuration with a baffle for the counter-current injection of CO2 should be considered a serious disadvantage. The CO2 transfer under both sump configurations was tested in the same experimental set-up, and similar CO2 uptake efficiencies in the sump were achieved (> 95 %). The mass transfer capacity of the system (i.e. Kla coefficient) and mixing are key parameters of the system (quantified respectively by the dispersion coefficient and the mixing time) to be taken into account for proper design of a raceway to obtain an optimal CO2 supply and sufficient removal of the photosynthetic oxygen generated. With an oxygen-generation rate of about 1.3–1.4 g O2 per gram of biomass and a CO2 consumption rate of roughly 2 g per gram of biomass produced, the minimum Kla values requested to yield a sufficient mass transfer rate for oxygen and carbon dioxide in a raceway are an order of magnitude greater than those currently obtainable in the existing industrial raceway reactors. Taking into account that it makes no sense to supply CO2 in the channel, because in this zone the mass transfer capacity is virtually zero, the only zone available for supplying CO2 is the sump. On the other hand, for O2 removal, the available zones are the sump and the paddlewheel zone. Nonetheless, the only practical way to increase O2 removal, for a given paddlewheel configuration rotating at a specific frequency, is by also increasing the relative volume of the sump with respect to the total volume of the raceway and by increasing the depth of the sump.

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9 Principles of photobioreactor design

Mixing The evolution of open culture technology is reflected in the evolution of the mixing systems that have been developed in parallel. Mixing is necessary to prevent cells from settling and sticking to the bottom, and to avoid thermal stratification of the culture. Mixing is of paramount importance, since it is directly linked to other key parameters (Fig. 9.1). Mixing also determines the light–dark cycle frequency, enhances the mass-transfer capability of the culture system, reduces the mutual shading between the cells and decreases the potential photoinhibition effect at the pond surface (Posten 2009). Properly designed paddle wheels are by far the most efficient and durable pond mixers. They discharge all of the culture entering them and are thus highly efficient. The engineering design of a raceway equipped with paddle wheels has been comprehensively described by Oswald (1988). With reference to Figure 9.2a, consider culture flowing at depth d in a channel with finite width, w, and unspecified length, L. The water depth (d) decreases after the paddle wheel. This depth reduction (Δd), termed head loss or depth change, determines the rate of energy that must be provided to maintain circulation at the chosen velocity. Head loss (energy dissipation) depends on: (1) flow around the two 180° curves (bend losses) and (2) the friction with the surface (side wall and bottom). The head loss as water flows in the bends is calculated by: Δdb =

k⋅v 2g

2

(9.1)

where v is the mean velocity (m s–1), g is the acceleration of gravity (9.8 m s–2), and k is the kinetic loss coefficient for each bend (k fluctuates between 10 and 40; for smooth plastic-lined channels, k is about 10). Similarly, the channel and wall friction loss can be calculated by: L Δdc = v 2n 2 4 R3 where n is the length Δdb + Δdc . for a given

(9.2)

the roughness factor, R is the channel hydraulic radius (m), and L is of the channel (m). The total head loss or change in depth is Δd = The channel length, L, that corresponds to the calculated head losses friction factor and a culture velocity (v) is given as (32): 4

L =

Δd(dw /(w + 2d )) 3 v2 ⋅ n2

(9.3)

where n is the Mannig friction factor (s · m–1/3), L is the channel length that corresponds to the head loss (Δd), and w is the channel width. The value of n varies according to the relative roughness of the channel. Experimentally determined n

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values in algae growth channels vary from 0.008 to 0.030, the former for smooth plastic-lined channels and the latter for relatively rough earth. The effect of channel velocity, v, on the paddle wheel’s power requirements is calculated as: P =

Q ⋅ ρ ⋅ Δd η

(9.4)

where P is the power (kW), Q is the culture flow rate in motion (m3s–1), ρ is the specific weight of culture (kg · m–3), Δd is the change in depth (m), and η is the efficiency of the paddle wheel. Since Δd is a function of v2, the power consumption, P, increases as the cube of velocity. It is therefore worthwhile minimizing velocity whenever energy is a major cost factor. Typical values of flow rates range between 15 and 30 cm · s–1, whereas the power supply is around 2 W · m–3. Velocities greater than 30 cm · s–1 will result in large values of Δd in long channels and may require high channel walls and higher divider walls. The mixing time decreases when liquid velocity through the system increases, and the channel length-to-width ratio (L/W) decreases (for the system shown in Fig. 9.2B, for example, this L/W ratio is about 100, and the mixing time is about 2 h when no baffle is inserted and about 5 h when the baffle is inserted in the sump). A more accurate quantification of mixing in the different zones of the raceway can be achieved by measuring the Bodenstein number (Bo), which is related to the dispersion coefficient Dz by (Verlaan et al. 1989): Dz =

vLsection Bo

(9.5)

where Lsection represents the length of each zone within the reactor (channel, sump, paddlewheel and bends). As a rule of thumb, when Bo is ≤ 20 (i.e. a high dispersion coefficient), the mixing pattern in that raceway zone signifies perfect mixing, and when Bo is ≥ 100, the pattern corresponds to a plug flow. Overall, in a raceway, the sump, paddle wheel and bends show a complete mix pattern, whereas in the channel, the pattern corresponds to a complete plug flow.

9.3.1.2 Scale-up Reactor scale-up is based on reactor surface area rather than on volume. A microalgae facility for biofuel purposes would require the production system running in continuous or semicontinuous mode (the biomass productivity in continuous mode is at least 2.3 times greater than in batch mode but generally is about 5 times on average). Let M be the productivity in tonnes per year of dry microalgae biomass of a specific strain to be produced in a raceway facility operating in continuous or semicontinuous mode for which the growth rate can be calculated by Equation

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9 Principles of photobioreactor design

(9.6). What should the land demand of this facility and the required number of raceway units be? n

μ =

μmax I av n I kn + I av

(9.6)

Equation (9.6) is one of the most commonly used equations to relate these variables (Molina Grima et al. 1994). In Equation (9.6), the maximum specific growth rate, μmax , the light saturation constant, Ik , and the shape parameter, n, are kinetic parameters that are species-specific and must be determined experimentally. In Equation (9.6), Iav represents the average irradiance within the raceway that can be estimated by: Iav =

Io (1 − exp (− Ka ⋅ p ⋅ Cb )) Ka ⋅ p ⋅ Cb

(9.7)

Equation (9.7) is a simplified model to calculate Iav . This model is suitable for any combination of disperse and direct light, as long as it is impinging uniformly on the reactor surface. According to this model, the average irradiance, Iav, is a function of the irradiance measured on the reactor surface, Io, the extinction coefficient of the biomass, Ka, the optical light path (depth of the culture), p, and the biomass concentration in the culture, Cb . The average volumetric biomass productivity (g L–1 d–1) all year long in a continuous, or semicontinuous, culture is determined by: Pbv = D ⋅ Cb

(9.8)

where D is the average dilution rate all through the year (d–1) and Cb the average biomass concentration during the year (g L–1). As a rule of thumb, D is about 40 % of the maximum specific growth rate of the strain and ranges between 0.2 d–1 and 0.5 d–1 for winter and summer time, respectively. From Equation (9.8), it is possible to calculate the areal productivity Pba , taking into account the volume-to-surface ratio of the culture system: Pba = Pbv

V S

(9.9)

The V/S ratio is a function strongly dependent on the culture depth, d, and ranges between 150 and 250 L m–2 for depths of culture in the raceway fluctuating between 15 and 25 cm, respectively. On the other hand, the land demand, S, i.e. the mixable area of raceway, for producing M tonnes of biomass per year is related to Pba by the following:

9.4 Enclosed photobioreactors

S = 2.74

M Pba

159

(9.10)

Note that, for a finite value of the channel width, W, the permissible mixing channel length, L, and thus the mixable area, S = L W, is a function strongly dependent on depth, d. From Equation (9.10), we can determine the number of raceway units taking into account that the optimal mixable surface of raceway should not overexceed 0.5 ha and that, as a rule, the typical permissible length (Eq. (9.3)) to raceway width ratio (L/W ) is about 30.

9.3.1.3 Drawbacks The control of contaminants in open pond systems is the most important problem raised by this technology. Water losses due to evaporation in these systems are about 0.2 kg m–2 h–1. Additionally, there are many other drawbacks related to temperature control and culture depth. There is no control of temperature (temperature is kept at midday within tolerable levels by the high evaporative loss of water). The culture depth for industrial units cannot be practically reduced below 12–15 cm; otherwise, a severe reduction in flow and turbulence would occur. This necessitates a large areal volume of 120–150 L m–2 (Richmond et al. 1990), with maintenance of rather low cell concentrations (around 500 mg/L). Those low cell concentrations in turn increase the cost of harvesting and of pond maintenance and greatly facilitate contamination with foreign species. These problems could, at least in principle, be minimized by photobioreactor designs that allow shallower cultures, maximize cell densities, are provided with better process control and minimize contamination. These objectives can be met with closed photobioreactors, which are dealt with in Section 9.4.

9.4 Enclosed photobioreactors Photobioreactors (PBRs) can be defined as algal culture systems that do not allow direct exchange of gases (e.g. CO2 , O2 , H2O) and gross contaminants between the culture and the atmosphere. This chapter considers only the flat plate and tubular reactors, which are the only ones that have been built to sizes exceeding 1,000 L. A summary of the state of the art in close PBRs has been provided (Borowitzka 1996; Lee et al. 1996), and some of these are in commercial operation.

9.4.1 Flat-panel photobioreactors Flat-panel PBRs consist of vertically translucent flat plates, illuminated on both sides and stirred by aeration. The construction of flat-plate reactors dates back to

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9 Principles of photobioreactor design

Fig. 9.3: Schematic diagram and photograph of a flat-panel photobioreactor. The reactor is equipped with an aeration system, heat exchanger, medium inlet and harvesting valve for the continuous operation of the photobioreactor (picture, courtesy of Prof. M. G. Guerrero, Univ. Sevilla, Spain).

the early 1950s (Little 1953). Samson and Leduy (1985) used vertically translucent flat plates, illuminated on both sides and stirred by aeration. This simple methodology for the construction of flat reactors provided the opportunity to easily build reactors with any desired light path. A new design of vertical flat-panel photobioreactor consisting of a plastic bag located between two iron frames has been pro-

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posed (Rodolfi et al. 2009); this brings substantial cost reductions to this type of reactor (Fig. 9.3). The bag can be replaced when needed (excessive fouling and contamination being the most common factors requiring a replacement). The method of aeration is via a PVC plastic tube drilled with minute holes (approximately 1 mm). The cooling system is a heat exchanger inserted into the reactor. The mass transfer, mixing and heat transport capacities in flat-panel reactors are usually very good. The main advantages of this reactor are the low power consumption (roughly 50 Wm–3 ) and the high mass transfer capacity (Kla = 0.007 s–1).

9.4.1.1 Technical issues Flat-panel orientation and light-path depth The productivity of any microalgal system is a direct function of the total solar radiation intercepted. As detailed in Section 9.2, for a specific location this depends on the orientation and type of photobioreactor employed. The solar radiation intercepted may vary significantly with orientation. For horizontal systems, this is not an important consideration, but it is a major issue for vertical systems. For the location studied here (Almería, Spain, 36° 48′ N; 2° 54′ W), the vertical photobioreactor’s east/west orientation maximizes the solar radiation intercepted over the year (Fig. 9.4). With this orientation, the photobioreactors intercept 5 % more radiation than horizontal systems on an annual basis; more importantly, the radiation intercepted in winter, when the cultures are more photo-limited, is increased, while in summer the proportion of intercepted radiation decreases, which is good at this

Fig. 9.4: Influence of position and orientation of photobioreactors in the capture of solar radiation over the year in Almería, Spain (36° 48′ N; 2° 54′ W). Points correspond to experimental measurements; lines correspond to simulated values of light irradiance using the methodology proposed in Sierra et al. (2008).

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9 Principles of photobioreactor design

Fig. 9.5: Influence of latitude and position of the photobioreactor in the annual mean daily solar radiation intercepted. Values obtained using proposed methodology in Sierra et al. (2008).

time of the year when the cultures tend to be more photo-inhibited (Fernández et al. 1998). On the other hand, a vertical photobioreactor oriented north/south duplicates the solar radiation intercepted by a horizontal photobioreactor in winter but obtains 65 % less in summer. The overall annual radiation intercepted vertical north/south increases only 1 % with respect to horizontal photobioreactors. However, this increase is a function of the location of the reactor (Fig. 9.5). For latitudes above 35° N, the east/west-faced orientation is favored over north/south, and the higher the latitude, the greater the increase in solar radiation intercepted. In contrast, for latitudes below 35° N, the north/south-orientated reactors intercept more radiation, and the difference is more pronounced closer to the equator. The position of the reactor also influences the type of radiation intercepted. In horizontal photobioreactors, direct radiation makes the most important contribution (Acién Fernández et al. 1997; Fernández et al. 1998), while in vertical photobioreactors, the proportion of disperse radiation is dominant (Qiang et al. 1998; García Camacho et al. 1999). Disperse radiation has been reported to be more efficient for microalgal cultures. Indeed, the photosynthetic efficiency of vertical photobioreactors has been reported to be higher than optimal tilt reactors, reaching values of 20 % (Qiang et al. 1998). This is due to the fact that low irradiance levels normally result in higher photosynthetic efficiencies, that is, when cells are growing under irradiance levels that are far from saturating. This can be accomplished by increasing the light-receiving surface of the photobioreactor per land square meter, a technique usually referred to as “dilution” of light. With respect to the panel depth, Fig. 9.6 features the variation of the average irradiance (estimated according to Eq. (9.6)) as a function of the reactor depth and the biomass concentration within the culture. As can be seen, to maintain average irradiances over 100 μE m–2 s–1, suitable for microalgae mass cultures, and to main-

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Fig. 9.6: Variation in daily average irradiance as a function of light path (thikness) and biomass concentration within a flat-panel reactor.

tain a cell density of about 1 g L–1 (to favor the photosynthetic efficiency of the culture system) the panel depth should be less than 7 cm.

Aeration rate and its impact on power supply, mass transfer and mixing The power input per volume unit due to aeration, PG /VL , in a flat-panel reactor is a function of aeration rate, the density of the liquid, ρL , and the gravitational acceleration, g, and can be calculated as: PG = ρL gUG VL

(9.11)

where UG is the superficial gas velocity in the aerated zone. UG is easily derived from the airflow rate, in v/v/m, by multiplying this by the total volume of the culture and dividing by the cross-sectional area of the aerated zone. The aeration rate also determines the gas holdup in the reactor, ε, which follows a potential relationship with the power supply: ε = 3.32 × 10−4

0.97

( ) PG VL

(9.12)

The power supply also impacts on the mass transfer capacity of the flat-panel reactor according to the following equation: −4

KLaL = 2.39 × 10

0.86

( ) PG VL

(9.13)

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9 Principles of photobioreactor design

Note that, in spite of the low power supply used, the volumetric mass transfer coefficient reached values of 0.0063 s–1. This mass transfer capacity in the flatpanel photobioreactor can be attained with a power supply of 50 W m–3 (Fig. 9.7). The low power supply required by the flat-panel photobioreactor and the relatively high mass transfer capacity are important advantages regarding the sensitivity of many microalgal cells to damage caused by intense turbulence. In addition to gasholdup and mass transfer capacity, the power supply also determines the mixing time inside the rector. In the range of aeration rates tested, 0.05–0.35 v/v/min (i.e. a power supply between 5 and 55 W m–3), the complete mixing in the flat-panel photobioreactor ranged between 150 and 100 s, much lower than that obtained in tubular systems and open raceways.

Temperature control Along with irradiance, temperature fluctuates daily and therefore is one of the most difficult (and expensive) factors to control. Culture temperature is an important parameter to control in outdoor photobioreactors. High temperatures impact on the rate of respiration, cause a reduction in CO2 solubility and therefore may hinder the unrestrained functioning of the Calvin cycle. Water-spray systems are often used to avoid overheating. However, according to the authors’ experience, the cooling capacity of spray systems is limited, and its application is possible only under certain environmental conditions (temperature, humidity, etc.) necessitating the use of a heat exchanger in most circumstances (Fig. 9.3). The use of heat exchangers for the temperature control of photobioreactors requires knowledge of the overall heat-transfer coefficients between the culture and the fluid circulating within the internal heat exchanger, as well as between the culture-broth photobioreactor and the surrounding air. These parameters have been determined in the flat-panel photobioreactor, considering the influence of air flow rate (i.e., the power supply) and water flow through the internal heat exchanger (Fig. 9.8). Experimental results show that the overall heat transfer coefficient for the internal stainless steel heat exchanger is much higher than the overall heat-transfer coefficient between the culture and the environment surrounding the photobioreactor, with maximum values of 500 (Fig. 9.8) and 37 W m–2 K–1 (data not shown), respectively. The internal heat transfer coefficient is mainly a function of the water flow through the heat exchanger, although enhancement of the air flow rate also improved the internal heat-transfer coefficient. The external heat-transfer coefficient (between the culture and surrounding air) ranged from 13 to 37 Wm–2 K–1, thus demonstrating the difficulty in controlling the temperature with water-spray systems only. A tubular heat exchanger can be designed based on the following heat balance: mwater ⋅ Cpwater ⋅ (Toutlet − Tinlet) = U ⋅ A

(Tculture − Tinlet) − (Tculture − Toutlet)

/

ln((Tculture − Tinlet) (Tculture − Toutlet)) (9.14)

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165

Fig. 9.7: Influence of power supply in the gas holdup (a) and volumetric mass transfer coefficient (b) of the flat-plate photobioreactor used. Lines correspond to values simulates using referenced Equations (9.12) and (9.13), whereas points correspond to experimental measurements.

where the left-hand side represents the heat flow gained by the cooling water passing within the heat exchanger, and the right-hand side represents the heat lost by the culture, which is transferred to the cooling water. U is the overall heat exchange coefficient (W m–2 °C–1), and A is the external surface of the heat exchanger.

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9 Principles of photobioreactor design

Fig. 9.8: Influence of the aeration rate and liquid velocity through the internal exchanger on the internal heat transfer coefficients of the flat-plate photobioreactor featured in Fig. 9.3.

9.4.1.2 Scale-up In a similar way to how we have estimated the open raceway surface needed to produce M tonnes of biomass per year of a specific microalgal strain, we can calculate the culture surface needed for flat panels, the number of modules and the land requirements. For this purpose, Equation (9.8) can be used to calculate the areal productivity, taking into account in this case that the V/S ratio ranges from 40 to 70 L · m–2 (i.e. panel depth 4–7 cm). The surface in Equation (9.8) represents the surface of flat panels, and therefore the number of modules needed is calculated taking into account the dimensions of the module (a typical module surface is 2.5 m in length and 1.5 m high). Finally, the land requirements can be estimated by using the distance between a row of modules and others; as a rule of thumb, this is approximately 1 m.

9.4.1.3 Drawbacks There are still many unresolved issues with this production technology. For the technology using plastic disposable bags located between two metal frames, perhaps the most serious issue is the frequent culture leakages, especially at the edges where the plastic end has been sealed (and by the glue connections in the case of a rigid plastic flat plate). Fouling is another serious problem. It is difficult to maintain the bags for more than three months without replacement. The scale-up of this technology requires many modules, and replacing the bags is a problem owing to the considerable amount of labor required as a result of the unavailability of

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167

Fig. 9.9: Top: Airlift-driven horizontally placed tubular photobioreactor, showing details of the airlift circulator (a) and solar collector (b). Bottom: pump-driven fence configuration tubular photobioreactors.

suitable machinery for automating the process. Overheating is another problem; although temperature control technology is straightforward using a heat exchanger (Fig. 9.3), its operation is costly.

9.4.2 Tubular photobioreactors Tubular reactors are the most widely used closed system for the culture of microalgae. Schematic diagrams of airlift-driven, horizontally placed, and pump-driven fence configurations of tubular photobioreactors are shown in Figure 9.9. The airlift column, or the pump, circulates the culture through the solar collector tubing where most of the photosynthesis occurs. The oxygen produced by photosynthesis accumulates in the broth until the fluid returns to the airlift zone, or the bubble column, where the accumulated oxygen is stripped by air. A gas–liquid separator in the upper part of the airlift, or in the upper part of the bubble column in the case of pump-driven systems, prevents the bubbles from being recycled into the solar collector. The major drawback of this technology is the excessive power consumption for the liquid impulsion through the tube, ranging from about 100 W m–3, for an airlift-driven configuration, to roughly 500 W m–3, for the pump-driven fence configuration. The solar loop is designed for efficient collection of solar radi-

168

9 Principles of photobioreactor design

ation, and to minimize the resistance to flow and occupy a minimal area so as to reduce the land requirements. The diameter of the solar tubing is selected so that the volume of the dark zone (i.e., the zone with light intensity below saturation) is kept to a minimum. Also, the interchange of fluid between the light and the dark zones in the solar collector should be fast enough so that the element of fluid does not remain continuously in the dark zone for too long. The tube length is constrained by the O2 buildup (as a rule, the maximum tube length is determined by the maximum dissolved oxygen that the specific strain can withstand with acceptable loss in biomass productivity). Therefore, reactors are usually modular. The relevant design aspects of these systems are discussed next.

9.4.2.1 Technical issues Biomass productivity and solar radiation As for previous technologies, production of biomass is governed mainly by the availability of light. This can be estimated by using the well-known principles of astronomy (to establish the position of the Sun relative to the photobioreactor), solar-power engineering (to determine the intensity of the incident radiation) and the Beer–Lambert relationship, as summarized elsewhere (Molina et al. 2001). For otherwise-fixed conditions, the geometric arrangement of the solar collector tubes also determines the irradiance on the surface of the tubes because the mutual shading by tubes is influenced by how they are arranged over a given surface area. From these principles, the average irradiance inside the tube, Iav , can be estimated as a function of tube diameter and arrangement of the tubes (Eq. (9.7)). Note that for tubular reactors, the optical path (p) in Equation (9.7) must be substituted by the ratio between the diameter of the tube, dt , and the cosine of the angle of declination (θ) of the Sun from the vertical (Acién et al., 1997). On the other hand, the growth rate, μ, is a function of average irradiance (Eq. (9.6)). Thus, from knowledge of the characteristic parameters of the algal strain (i.e. μmax , Ka, Ik , and n), the growth rate may be determined for any combination of external irradiance and the diameter of solar collector tubes. Once the specific growth rate is known, the volumetric and areal productivities of continuous cultures are calculated according to Equations (9.8) and (9.9).

Power supply and liquid velocity The design of a tubular photobioreactor must guarantee turbulent flow in the solar tube (i.e. the minimum Reynolds number should be over 3,000) so that the cells do not stagnate in the dark interior of the tube. At the same time, the dimensions of the fluid microeddies should always exceed those of the algal cells, so that turbulence-associated damage is prevented. The need to control eddy size places an upper limit on the flow rate through the solar tubing. The length scale of the

9.4 Enclosed photobioreactors

169

microeddies may be estimated by applying Kolmogorov’s theory of local isotropic turbulence (Kawase and Moo-Young 1990):

λ =

() μL ρ

3 4

ξ

−1 4

(9.15)

where λ is the microeddy length, ξ is the energy dissipation per unit mass, μL is the viscosity of the fluid, and ρ is the fluid density. The specific energy dissipation rate within the tube depends on the pressure drop and the liquid velocity, UL: 3

ξ =

2Cf UL dt

(9.16)

where Cf is the Fanning friction factor, which may be estimated using the Blausius equation (Eq. (9.17)). For any known strain, using its cell size as the limit for microeddy length places a maximum in the energy dissipation rate per unit mass (Eq. (9.15)), and from this the maximum liquid velocity that approaches microeddies length to cell size (Eq. (9.16)): −0.25 Cf = 0.0791Re

(9.17)

Another restriction on the design of the solar collector is imposed by the acceptable upper limit on the concentration of dissolved oxygen. Thus, the length of the solar collector must be short enough so as to prevent oxygen accumulation in the culture that would inhibit photosynthesis. This maximum length, L, is calculated as a function of the liquid velocity, dissolved oxygen concentration and photosynthesis rate as follows (Acién Fernández et al. 2001): L =

UL( [O2]out − [O2]in ) RO2

(9.18)

where UL is the liquid velocity (ratio between the liquid flow rate through the tube and the cross-sectional area of the tube). Note that UL is always lower than the maximum velocity imposed by microeddy length). [O2]in is the oxygen concentration at the entrance of the solar collector (i.e. the saturation value when the fluid is in equilibrium with the atmosphere), [O2]out is the oxygen concentration at the outlet of the solar collector (i.e. the maximum acceptable value that does not inhibit photosynthesis), and RO2 is the volumetric rate of oxygen generation by photosynthesis (function of the biomass productivity assumed in the design). If the circulation of the culture is achieved using pumps (a fence-type configuration), the type and power of the used pump determine the liquid velocity, whereas for airlift systems, the liquid velocity, UL , is a function of the height and aeration rate

170

9 Principles of photobioreactor design

in the airlift and can be estimated by extension of the well-known and widely tested model developed by Chisti and Moo-Young (1988):

UL =

[

ghdεr Ar L Ad 2Cf eq D

]

0.5

(9.19)

where Cf is calculated using Equation (9.16), Re is the Reynolds number, i.e. (ρL ULdt)/μ. Here, dt is the tube diameter, and Leq is the equivalent length of the loop. The latter is the straight tube length L plus all the bends and valves in the loop combined, and ρL and μ are the density and the viscosity of the liquid, respectively. As shown in Equation (9.19), the superficial liquid velocity in the tubular photobioreactor is a function of gas hold-up in the riser, εr, the height of the dispersion, hD, and the ratio between the cross-sectional area of both the riser and the downcomer. The gas holdup follows a potential relationship with the power supply (Eq. (9.12); the power supply due to aeration being a function of the density of the liquid, ρL , the gravitational acceleration, g, and the superficial gas velocity in the aerated zone, UG (Eq. (9.11)).

Combining flow and gas–liquid mass transfer As for the technologies previously described, the design of tubular photobioreactors must also take into account gas–liquid mass transfer and hydrodynamics. The carbon dioxide gas injected is transported from the gas phase to the aqueous medium to provide the inorganic carbon and to control the pH of the culture. Under constant conditions, the inorganic carbon supplied is incorporated into cells at a specific rate proportional to the intensity of photosynthesis. Similarly, oxygen is produced at a given rate and transferred from the culture to the gas phase. All these aspects have been treated in a tubular reactor equipped with an airlift system (Camacho Rubio et al. 1999). By applying mass balances to the different zones of the reactor for which the fluid-dynamic conditions are kept constant, the carbon dioxide and oxygen transfer between the liquid and gas phase can be modeled. For the liquid phase, the changes in concentrations of dissolved oxygen and dissolved inorganic carbon along the loop can be related to the gas–liquid mass transfer rates and the generation/consumption rates by mass balances as follows: QL d[O2] = KlaO2 ([O2]* − [O2])S dx + RO2 (1 − ε)S dx

(9.20)

QL d[CT] = KlaCO2 ([CO2]* − [CO2])S dx + RCO2 (1 − ε)S dx

(9.21)

In these equations, Kla O2 and Kla CO2 denote the volumetric gas–liquid mass transfer coefficient for oxygen and carbon dioxide, respectively; dx is the differential dis-

9.4 Enclosed photobioreactors

171

tance along the direction of flow in the solar tube; [O2], [CT] and [CO2] are the liquid-phase concentrations of oxygen, inorganic carbon and carbon dioxide, respectively; ε is the gas holdup; S is the cross-sectional area of the tube; RO2 and RCO2 are the volumetric generation and consumption rate of oxygen and carbon dioxide, respectively; and Q L is the volumetric flow rate of the liquid. Note that the concentration values marked with asterisks are equilibrium concentrations, i.e. the maximum possible liquid-phase concentration of the component in contact with the gas phase of a given composition. The mass balance considers the total inorganic carbon concentration [CT] and not just that of carbon dioxide. This is because CT takes into account the dissolved carbon dioxide and the carbonate, CO=3, and bicarbonate, HCO−3 , species generated by it. As for the liquid phase, a component mass balance can be established also for the gas; hence: dFO2 = − Kl aO2 ([O2]* − [O2])S dx

(9.22)

dFCO2 = − Kl aCO2 ([CO2]* − [CO2])S dx

(9.23)

Here, FO2 and FCO2 are the molar flow rates of the two components in the gas phase. Note that because of the changes in molar flow rates, the volumetric flow rate of the gas phase may change along the tube. The equilibrium concentrations of the two gases in the liquid can be calculated using Henry’s law relationships: [O2]* = HO2 PO2 = HO2 ( PT − Pv )

F O2 FO2 + FCO2

[CO2]* = HCO2 PCO2 = HCO2 ( PT − Pv )

FCO2 FO2 + FCO2

(9.24)

(9.25)

where HO2 and HCO2 are Henry’s constants for oxygen and carbon dioxide, and PO2 and PCO2 are the partial pressures (or mole fractions) of the components in the gas phase; the partial pressures can be calculated from the total pressure (PT), the water vapour pressure (Pv), and the molar flow rates FO2 and FCO2 at any location in the system. The numerical integration of Equations (9.20)–(9.23) along with the Equations (9.24) and (9.25) for equilibrium concentrations with the proper initial conditions allows the CO2 and O2 axial profiles in the liquid phase and the molar flow rates of the two components in the gas phase to be determined. The accuracy of the proposed equations was verified by Camacho Rubio et al. (1999), who used these equations to estimate the behavior of a tubular photobioreactor during the day. As shown in Figure 9.10, during any 24 h period, the simulated data – pH, carbon dioxide losses and amount of carbon dioxide injected – agreed closely with the measurements. Although the degree of correlation differs for different variables,

172

9 Principles of photobioreactor design

Fig. 9.10: Comparison between experimental (symbols) and simulated (lines) data of pH, total inorganic carbon, oxygen and carbon dioxide in the liquid and gas phase, on an outdoor tubular photobioreactor (0.22 m) (from Camacho Rubio et al., 1999, Biotechnology and Bioengineering, 62(1): 71–86, with permission).

the mean error for the entire set of variables is less than 15 %. The predictive capabilities of the proposed equations demonstrate its potential as a scale-up tool. The model is simple and can be adapted to any photobioreactor and photoautotrophic strains. Moreover, since the model simulated the system behavior as a function of the tube length and operational variables (gas flow rate to cross-sectional area of the riser ratio, i.e. superficial gas velocity in the riser, composition of the carbon dioxide injected in the solar receiver and its injected rate), it can be used for the analysis and rational design and scale-up of photobioreactors.

Oxygen removal and temperature control Once the solar collector is designed, it is necessary to design the unit for removal of oxygen and temperature control. This is performed in bubble columns or airlift

9.4 Enclosed photobioreactors

173

devices. The use of bubble columns (generally used in the fence configuration; Fig. 9.9B) is advantageous because these are standard systems widely used on an industrial scale. The mass transfer coefficient can be calculated as a function of aeration rate (Eqs (9.11) and (9.13)); thus, the volume of bubble column necessary to remove oxygen is: V =

QL([O2]in − [O2]out) KLaL([O*2] − [O2])lm(1 − ε)

(9.26)

where Q L is the liquid flow rate entering the bubble column, [O2]in is the oxygen concentration at the inlet of the bubble column, [O2]out is the oxygen concentration at the outlet of the bubble column, KLaL is the volumetric mass transfer coefficient, and ([O2*] – [O2]) is the driving force for the transport of oxygen from the liquid to the gas phase, calculated as the mean logarithmic value from the entrance to the outlet. To avoid recirculation of bubbles from the bubble column to the solar collector, the superficial liquid velocity down must be lower than the bubble rise velocity, Ub . Thus, the minimum diameter of the bubble column, dc, can be calculated using Equation (9.27) as well as the minimum necessary column height, hc (Eq. (9.28)): dc =

√πU

(9.27)

4V πdc 2

(9.28)

4 QL b

hc =

On the other hand, the use of an airlift circulator is the option generally used in horizontal tubular systems and always recommended when culturing shear-sensitive microalgal strains. The airlift device must have a small volume compared to that of the solar loop so that the fluid spends less time in this relatively darker region of the bioreactor. These demands are met by having the riser and downcomer tubes of the airlift device as vertical extensions of the ends of the solar loop. The volume in the gas–liquid separator is minimized if the spacing between the parallel walls of the separator (Fig. 9.9A) is the same as the diameter of the riser or the downcomer. This arrangement also improves light penetration in the degassing zone. Permanent settling of solids is prevented by slanting the floor of the separator at > 60° relative to the horizontal. To achieve effective separation of gas and liquid, the distance between the entrance and the exit of the degasser, LD , should be such that the smallest bubbles have sufficient time to disengage before the fluid enters the downcomer. Assuming a conservative value for bubble rise velocity of 0.1 m s–1 (usually bubble rise velocity fluctuates between 0.24 and

174

9 Principles of photobioreactor design

0.35 m s–1, for small and large bubbles, respectively) we can estimate a minimum value for LD (Acién Fernández et al. 2001) of: LD = 7.85 ⋅ dt ⋅ UL

(9.29)

where dt is the tube diameter, and UL is the liquid velocity. The liquid flow in the solar receiver is estimated using Equation (9.19). The heat-transfer equipment can be designed analogously. The objective of the heat-exchanging system is to remove the heat absorbed mainly as radiation. This is a function of the solar radiation received by the solar collector, Qrad , and the thermal absorptivity of the culture, arad . Finally, the area of heat exchange necessary, Aexchanger is calculated as a function of the overall heat-transfer coefficient, Uexchanger , and the temperature of the cooling water: Aexchanger =

Qrad arad Uexchanger (Tculture − Twater)

(9.30)

9.4.2.2 Scale-up For practical purposes, the scale-up of a tubular photobioreactor requires scaling up of both the solar receiver and the airlift device (or the bubble column in the case of a fence configuration). Scaling of the latter does not pose a limitation for any realistic size of photobioreactor. However, there are limitations to scaling up a continuously run solar loop. In principle, the volume of the loop may be increased by increasing the diameter and the length of the tube. In practice, only the tube diameter may be changed because the maximum length of the loop is constrained, and its diameter should not exceed 0.10 m (Brindley et al. 2004). Equations (9.7)–(9.8) and (9.15)–(9.30) were used to design industrial-size photobioreactors operated in a greenhouse in Almería, Spain. First, the light availability inside the greenhouse was calculated according to solar radiation knowledge. Then, simulations were performed to determine the optimal tube diameter. Selection was performed on the basis of the microalgae to be produced, Scenedesmus almeriensis, and its characteristic parameters (μmax , Ik, n), the objective being to maximize the size of the reactor that would allow a year-long biomass productivity of roughly 1.0 g · L–1 · day–1. From this analysis, a maximum tube diameter of 0.10 m was selected. According to the target biomass productivity, the photosynthesis rate was calculated, and the maximum length of the solar collector was established accordingly to 400 m. Finally, a bubble column was designed to connect it to the solar collector to remove oxygen generated by photosynthesis as well as to control the temperature of the culture. The designed tubular photobioreactors (Fig. 9.11) were built and have now been in operation for more than four years. The biomass productivity values were measured during the operation of the photobioreactors in an annual cycle. Figure 9.12 shows the variation in biomass productivity

9.4 Enclosed photobioreactors

175

Fig. 9.11: Top: photograph of and industrial size horizontally placed airlift driven tubular photobioreactor built according to design equations proposed (Eqs (9.7)–(9.8) and (9.21)–(9.35)). Bottom:photograph of an industrial size fence configuration tubular photobioreactor 30 m3 plant for the production of Scenedesmus almeriensis in Spain; property of Fundación CAJAMAR (with permission of Fundación Cajamar, Almería, Spain).

in the fence-configuration reactor. The performance of the 30 m3 industrial unit is quite similar to the predicted productivities using the model, Equations (9.7)–(9.8) and (9.15)–(9.30), with biomass productivities through the year higher and lower than the 1.0 g · L–1 · day–1 average.

176

9 Principles of photobioreactor design

Fig. 9.12: Variation in (a) solar radiation, (b) biomass concentration and (c) productivity over a year for the production of Scenedesmus almeriensis in a semi-industrial facility 30 m3 at Almería, Spain, built according to the proposed strategy design equation (Eqs (9.7)–(9.8) and (9.15)– (9.30)). Data correspond to experimental values, whereas the line corresponds to data predicted by the model used in the design.

9.5 Summary of major characteristics of large-scale algal cultures systems

177

9.5 Summary of major characteristics of large-scale algal cultures systems Table 9.1 compares the parameters of the major algal culture systems described in this chapter and their properties. From the table, it is clear that open systems are lacking in several aspects: fair mixing, poor mass transfer in the channel, poor temperature control of the culture, lack of sterility and high land demand. Despite this, open raceway systems are being used on a commercial scale for culturing a few species, and especially for waste-water treatment applications. The two principal advantages of open-culture systems are the small capital investment required for the production of the biomass and their low power consumption for liquid impulsion. They are the simplest methods, but the productivity obtained is far from the theoretical maximum. Enclosed photobioreactors allow better control of operational conditions and greater biomass productivities. It is clear that these systems are difficult to operate in several aspects. Tubular systems have a reasonable degree of mixing, high energy consumption for liquid impulsion and gradients of pH, O2 and CO2 along the tubes. On the other hand, disposable-bag flat-panel reactors suffer from culture leakage and operating difficulties at the industrial scale because they are laborintensive due to the lack of automated systems to replace the bags. Nonetheless, enclosed photobioreactors also have an important role in increasing the diversity of algae species that can be cultured, especially the sensitive and highly valuable strains used in the production of fine chemicals. Enclosed tubular photobioreactors have demonstrated the potential of this technology at the author’s facilities with a high biomass productivity of microalgal species such as Porphyridium, Isochrysis, Nannochloropsis, Scenedesmus and Phaeodactylum. Because of the better control of culture parameters in closed systems, which allows the biochemical composition to be modified by changing the system operating variables, they are especially

Volume, m3 Gas holdup Mass-transfer coefficient, s–1 Dispersion coefficient, m2 s–1 Mixing time, s Power consumption, W m–3 Dilution rate, day–1 Pbv, g L–1 day–1 Pba , g m–2 day–1 Cost, €/m3

Raceway

Flat panel

Tubular

40 0.01 0.010 0.0001 104 1 0.2 0.1 20 200

0.5 0.02 0.010 0.030 150 50 0.3 0.6 42 3,000

30 0.01 0.005 0.040 105 500 0.4 1.0 65 5,000

Tab. 9.1: Yearly average values for key operational parameters, biomass productivities and manufacturing costs for the photobioreactors described in the photographs of Figures 9.2, 9.3 and 9.11

178

9 Principles of photobioreactor design

useful for the production of valuable substances such as polyunsaturated fatty acids, polysaccharides, antioxidant, aquaculture feed, etc. This technology is more capital-intensive than the open-pond technology, but this additional cost can be justified. Temperature control and fouling (which is hard to clean) are major problems in such systems. Nonetheless, it should be noted that it is not possible to say that one culture system is better than another. The commercial target, geographic location and metabolite to be produced will determine the choice: axenic or nonaxenic, continuous or batch culture, intensive or extensive culture, opens ponds or closed photobioreactors.

Acknowledgments The authors wish to acknowledge the contribution of all the colleagues of the Marine Biotechnology Group of the University of Almería that have worked with us on this subject. Special acknowledgment to Cajamar Foundation, Acciona Energía and Endesa, and the financial support of the Plan Andaluz de Investigación (Junta de Andalucía), Secretaría de Estado de Investigación, Ministerio de Economía y Competitividad, and European Union for some of the work reported in this manuscript.

References Acién Fernández, F. G., J. M. Fernández Sevilla, J. A. Sánchez Pérez, E. Molina Grima and Y. Chisti. 2001. Airlift-driven external-loop tubular photobioreactors for outdoor production of microalgae: Assessment of design and performance. Chem. Eng. Sci. 56, 2721–2732. Acién Fernández, F. G., F. García Camacho, J. A. Sánchez Pérez, J. M. Fernández Sevilla and E. Molina Grima. 1997. A model for light distribution and average solar irradiance inside outdoor tubular photobioreactors for the microalgal mass culture. Biotechnol. Bioeng. 55, 701–714. Borowitzka, M. A. 1996. Closed algal photobioreactors: Design considerations for large-scale systems. J. Mar. Biotechnol. 4, 185–191. Brindley, C., M. C. Garcia-Malea, F. G. Acién, J. M. Fernández, J. L. García and E. Molina. 2004. Influence of power supply in the feasibility of Phaeodactylum tricornutum cultures. Biotechnol. Bioeng. 87, 723–733. Camacho Rubio, F., F. G. Acién Fernández, J. A. Sánchez Pérez, F. García Camacho and E. Molina Grima. 1999. Prediction of dissolved oxygen and carbon dioxide concentration profiles in tubular photobioreactors for microalgal culture. Biotechnol. Bioeng. 62, 71–86. Chisti, Y., M. Moo-Young. 1988. Prediction of liquid circulation velocity in airlift reactors with biological media. J. Chem. Technol. Biotechnol. 42, 211–219. Fernández, F. G. A., F. G. Camacho, J. A. S. Pérez, J. M. F. Sevilla and E. M. Grima. 1998. Modeling of biomass productivity in tubular photobioreactors for microalgal cultures: Effects of dilution rate, tube diameter, and solar irradiance. Biotechnol. Bioeng. 58, 605–616. García Camacho, F., A. Contreras Gómez, F. G. Acién Fernández, J. Fernández Sevilla and E. Molina Grima. 1999. Use of concentric-tube airlift photobioreactors for microalgal outdoor mass cultures. Enzyme Microb. Technol. 24, 164–172.

References

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Grobbelaar, J. U. 1994. Turbulence in mass algal cultures and the role of light/dark fluctuations. J. Appl. Phycol. 6, 331–335. Incropera, F. P. and J. F. Thomas. 1978. A model for solar radiation conversion to algae in a shallow pond. Solar Energy 20, 157–165. Kawase, Y. and M. Moo-Young. 1990. Mathematical models for design of bioreactors: Applications of Kolmogoroff’s theory of isotropic turbulence. Chem. Eng. J. 43, B19–B41. Laws, E. A., S. Taguchi, J. Hirata and L. Pang. 1986. High algal production rates achieved in a shallow outdoor flume. Biotechnol. Bioeng. 28, 191–197. Lee, Y. K., S. Y. Ding, C. H. Hoe and C. S. Low. 1996. Mixotrophic growth of Chlorella sorokiniana in outdoor enclosed photobioreactor. J. Appl. Phycol. 8, 163–169. Lee Y. K. and C. S. Low. 1992. Productivity of outdoor algal cultures in enclosed tubular photobioreactor. Biotechnol. Bioeng. 40, 1119–1122. Little, A. D. 1953. Pilot plant studies in the production of Chlorella. In: Algal Culture from Laboratory to Pilot Plant. J. S. Burlew, ed. Carnegie Institute, Washington, DC. pp. 253–273. Molina Grima, E., F. Garcia Camacho, J. A. Sanchez Perez, J. M. Fernandez Sevilla, F. G. Acien Fernandez and A. Contreras Gomez. 1994. A mathematical model of microalgal growth in light-limited chemostat culture. J. Chem. Technol. Biotechnol. 61, 167–173. Molina, E., J. Fernández, F. G. Acién and Y. Chisti. 2001. Tubular photobioreactor design for algal cultures. J. Biotechnol. 92, 113–131. Oswald, W. J. 1988. Large scale algal culture systems. In: Micro-Algal Biotechnology. M. A. Borowitzka and L. J. Borowitzka, eds, Cambridge University Press, Cambridge, pp. 357–394. Posten, C., 2009. Design principles of photo-bioreactors for cultivation of microalgae. Eng. Life Sci. 9, 165–177. Qiang, H. and A. Richmond. 1996. Productivity and photosynthetic efficiency of Spirulina platensis as affected by light intensity, algal density and rate of mixing in a flat plate photobioreactor. J. Appl. Phycol. 8, 139–145. Qiang, H. U., D. Faiman and A. Richmond. 1998. Optimal tilt angles of enclosed reactors for growing photoautotrophic microorganisms outdoors. J. Ferment. Bioeng. 85, 230–236. Richmond, A., E. Lichtenberg, B. Stahl and A. Vonshak. 1990. Quantitative assessment of the major limitations on productivity of Spirulina platensis in open raceways. J. Appl. Phycol. 2, 195–206. Rodolfi, L., G. C. Zittelli, N. Bassi, G. Padovani, N. Biondi, G. Bonini and M. R. Tredici. 2009. Microalgae for oil: Strain selection, induction of lipid synthesis and outdoor mass cultivation in a low-cost photobioreactor. Biotechnol. Bioeng. 102, 100–112. Samson, R. and A. Leduy. 1985. Multistage continuous cultivation of blue-green alga Spirulina maxima in the flat tank photobioreactors with recycle. Can. J. Chem. Eng. 63, 105–112. Sierra, E., F. G. Acién, J. M. Fernández, J. L. García, C. González and E. Molina. 2008. Characterization of a flat plate photobioreactor for the production of microalgae. Chem. Eng. J. 138, 136–147. Terry, K. L. 1986. Photosynthesis in modulated light: quantitative dependence of photosynthetic enhancement on flashing rate. Biotechnol. Bioeng. 28, 988–995. Verlaan, P., A. M. M. Van Eijs, J. Tramper, K. V. Riet and K. C. A. M. Luyben. 1989. Estimation of axial dispersion in individual sections of an airlift-loop reactor. Chem. Eng. Sci. 44, 1139– 1146.

Jérémy Pruvost and Jean-François Cornet

10 Knowledge models for the engineering and optimization of photobioreactors 10.1 Introduction Spherical coordinates: The modeling of microalgal photosynthetic biomass production draws some support from the abundant literature on bioprocess modeling, in particular when mineral or CO2 mass-transfer limitations on growth rates are considered in the same way as substrate and O2-transfer limitations in engineered bacterial cultivation. However, photosynthetic biomass growth exhibits highly specific features owing to its need for light energy: unlike dissolved nutrients, assumed to be homogeneous in well-mixed conditions, light energy is heterogeneously distributed in the culture due to absorption and scattering by cells, independently of the mixing conditions. As light is the principal energy source for photosynthesis, this heterogeneity alone sets microalgal cultivation systems apart from other classical bioprocesses, as they are generally limited by light transfer inside the culture media. Hence the design, optimization and control of photobioreactors (PBRs) require specific approaches. This chapter deals with developing useful knowledge models for engineering microalgal cultivation systems. Prerequisites and main concepts will be presented. Concrete illustrations will be given that use modeling to gain a deeper understanding of the complex influence of light transfer on the process, and to predict biomass productivities as a function of cultivation system engineering variables (especially depth of culture) and operating settings (residence time and incident light flux) in both artificial constant light and natural sunlight conditions.

10.2 Theoretical background for radiation measurement and handling 10.2.1 Main physical variables Given the crucial importance of radiative transfer description in photobioreactor modeling, it is essential to have a broad overview of the main physical quantities and definitions involved in radiation measurement and theory, together with a thorough knowledge of the conversion factors linking the two main practical systems of units (joules and micromoles of photons). There is often much confusion on these points in the microalgal growth modeling literature, conducive to misin-

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10 Knowledge models for the engineering and optimization of photobioreactors

terpretation of related physical and physiological processes. This section gives a brief summary of the definitions, units, roles and related sensors for the three main useful physical quantities in the field of PBR modeling. Strictly speaking, these three quantities are all spectral quantities (their definition holds for a small differential part of the electromagnetic spectrum d λ indicated by a subscript λ), but for simplicity, in what follows we will mainly consider mean averaged quantities for photosynthetically active radiation (PAR). Thus the absence of an λ subscript means that we are working with integral quantities such as X = ∫ Xλ dλ, in which Δ λ corresponds to the range of wavelengths between 400 and Δλ

700 nm (PAR). The first measurable variable (defined from an elemental oriented surface of reference d S) from which all the other useful and practical radiative quantities are deduced is the radiance, more generally named intensity I in many fields of physics (see Fig. 10.1). It is the ratio, in a given direction of the solid angle d Ω and in a point of the oriented surface d S of the radiant power d E to the projected area on the perpendicular plane to the outward normal n: I =

dE dS cos Θ

[W · sr

−1

]

· m−2 or μmolhυ · s-1 · sr−1 · m−2

(10.1)

This fundamental directional quantity may be integrated over the solid angle dω, giving the definition of the radiative flux density q such that the flux through an elemental surface dS of normal n is q ⋅ n dS. In a given direction x, the projection of q is then: 2ππ

qx = ∬ I cos Θ dω = ∫ ∫ I cos Θ sin Θ d Θ d Φ 4π

00

[W · m

−2

or μmolhυ · s−1 · m−2

]

(10.2)

where cos Θ is the angle between the outward normal n and the considered direction Ω (see Fig. 10.1), so defining the vectorial nature of this quantity. In the field of PBR modeling, this flux density is mainly used to define the boundary conditions relative to given illumination conditions. In this case, only the incoming radiation on one hemisphere that penetrates the culture medium is of interest, giving the definition of the incident hemispherical radiant flux density or photon flux density (PFD) perpendicular to a reference surface: 2ππ/2

q0 = ∫ ∫ I0 cos Θ sin Θ d Θ d Φ 0 0

(10.3)

We note that the angular nature of this PFD may be different, and in some cases (especially for solar illumination) it may be useful to use a subscript to characterize this flux, such as q// for a collimated incidence, q⊥ for the special case of normal

10.2 Theoretical background for radiation measurement and handling

183

Fig. 10.1: Definition of the solid angle dω and of the associated intensity (strictly, radiance) Iλ (x , y , z , Θ , Φ) from a fixed Cartesian r (x , y , z) or spherical r (r , θ , φ) frame of reference associated with a moving frame Ω (Θ , Φ) at a point P from which it is possible to derive all the radiative useful integral definitions for the description of radiative transfer in photobioreactor modeling.

collimation or q∩ for diffuse incidence. In all cases, this hemispherical radiant flux density component can be measured with a flat cosine sensor, once a surface of reference has been chosen (Pottier et al. 2005). The third and last integral quantity of interest, mainly to define the total radiant light energy available for photosynthesis and then formulate the kinetic and energy couplings, is the scalar spherical irradiance: 2ππ

G = ∬ I dω = ∫ ∫ I sin Θ d Θ d Φ 4π

00

(W · m−2 or μmolhυ · s−1 · m−2)

(10.4)

This quantity can be evaluated with a spherical quantum sensor, which strictly measures an energy fluence rate, assumed to be the irradiance if the sensor diameter is small compared with the characteristic extinction length for the radiation in the considered medium (Pottier et al. 2005). We note that for modeling purposes, the angular pattern of the light must necessarily be known, to calculate the integrals of Equations (10.3) and (10.4) but is extremely difficult to measure. It can be postulated a priori or calculated from models of radiative emission. In all cases, this angular distribution ranges between two extremes:

184 –

10 Knowledge models for the engineering and optimization of photobioreactors

A collimated radiation giving the following relations between I0, q0 and G0: 2ππ/2

col col q0 = q// = ∫ ∫ I0 δ (Θ − Θcol) δ (Φ − Φcol) cos Θ sin Θ d Θ d Φ = I0 cos Θcol 0 0

= G0 cos Θcol

–

A diffuse isotropic radiation for which:

2ππ/2 G 1 q0 = q∩ = I0dif ∫ ∫ cos Θ sin Θ d Θ d Φ = 2π I0dif = πI0dif = 0 2 2 0 0

10.2.2 Solar illumination As previously defined, the light energy received by a solar cultivation system is represented by the hemispherical incident light flux density q, or photon flux density (PFD), which has to be expressed within the range of photosynthetic active radiation (PAR), i.e. the 0.4–0.7 μm bandwidth. For example, the whole solar spectrum at ground level covers the range 0.26–3 μm. The PAR range thus corresponds to almost 43 % of the full solar energy spectrum (for AM = 1.5 normalization). As light is converted inside the culture volume, it is also necessary to add to the PFD determination a rigorous treatment of radiative transfer inside the culture (see later on in this chapter) strictly requiring knowledge of the angular distribution of the incoming light, together with the light-source positioning with respect to the optical transparent surface of the cultivation system, i.e. the incident polar angle θ (Fig. 10.2). Ideally, collimated (direct) q// and diffuse q∩ components of radiation should be considered separately. By definition, the direction of a beam of radiation, which represents direct radiation received from the light source, will define the incident polar angle θ with the illuminated surface and the direct light flux density q//. By contrast, diffuse radiation cannot be defined by a single incident angle but has an angular distribution over the illuminated surface (on a 2 π solid angle for a plane). Because this angular distribution is unknown, an isotropic angular distribution (Lambertian behavior) is generally assumed when using the value of q∩.

10.3 Modeling light-limited photosynthetic growth in photobioreactors 10.3.1 Overview of the modeling approach The photosynthetic activity (here represented by the specific oxygen production rate JO2) is directly related to the local light available inside the culture medium. This is usually represented by the light-response curve (Fig. 10.3). This curve is

10.3 Modeling light-limited photosynthetic growth in photobioreactors

185

Fig. 10.2: Solar radiation on a microalgal cultivation system: incident angle and diffuse-direct radiations (top), time course of solar sky path during the year (down).

characterized by progressive saturation of photosynthesis with irradiance G up to an irradiance of saturation Gs. For higher irradiances, photoinhibition processes can occur with a negative influence on growth (Vonshak and Torzillo 2004). We also note that a threshold value of irradiance is needed to obtain positive growth. This value is termed the irradiance of compensation GC (corresponding to the

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10 Knowledge models for the engineering and optimization of photobioreactors

Fig. 10.3: Relation between light attenuation and photosynthetic growth in microalgal cultivation systems.

“compensation point of photosynthesis”), which will prove relevant in the modeling and understanding of PBR operation (see later on in this chapter). In cultivation systems, this nonlinear, complex response of photosynthesis has to be considered in combination with the light-attenuation conditions. In extreme cases of high light illumination and high light attenuation (high biomass concentration), cells in different physiological states will co-occur: some, close to the light source, may be photoinhibited, while others deep in the culture will receive no light. Ideally, the control of the system would require taking all these processes into account, a far from trivial task. As described next, modeling the kinetic coupling of photosynthetic growth with the radiation field inside cultivation systems will enable us to represent the impact of such effects on process efficiency. The main features of such a model are presented in the following sections.

10.3.2 Mass balances The mass balance relates concentration in the cultivation system to kinetic rates of biological production (biomass, O2) or consumption (nutrients, CO2) and system

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187

input and output. For a continuous system, assuming perfectly mixed conditions (continuous stirred tank reactor or CSTR model), the concentration C of a given element is then given by (Cornet et al. 2003; Pruvost et al. 2008; Pruvost 2011): dC 1 = 〈r (t)〉 + τ (Ci − C) = 〈r (t)〉 + D (Ci − C) dt

(10.5)

with the mean volumetric production (biomass, metabolites) or consumption (nutrient) rate in the system, and τ the residence time resulting from the liquid flow rate of the feed of input concentration Ci (fresh medium) (with τ = 1 / D, where D is the dilution rate). Following the CSTR model, Equation (10.5) assumes homogeneous concentration in the cultivation system. Because of the slow growth rate of photosynthetic microorganisms compared with mixing time, this is usually the case, and so Equation (10.5) holds. In some cases of large tubular cultivation systems without recycling, it may be necessary to work with the steady-state plug flow tubular reactor (PFTR) model, assuming a constant concentration only on a cross-section of the tube: ux

d2C dC − DL 2 = 〈r (x)〉 dx dx

(10.6)

with ux the linear velocity obtained along the flowing x-axis (ux = Q / S with Q the liquid flow rate and S the tubular cross-section) and DL the axial dispersion coefficient. This equation represents the evolution of concentration as a function of the distance x (length of the tube).

10.3.3 Stoichiometry of photosynthetic growth 10.3.3.1 Simple stoichiometric equations Growth can be expressed in the form of a stoichiometric equation that can be deduced, for example, from a biomass elemental analysis (Roels 1983). As for bacteria, the stoichiometric equation for photosynthetic growth in optimal conditions is found to be largely independent of the cultivated species, and depends only weakly on illumination and radiation field conditions (nutrient starvation can, by contrast, strongly influence biomass composition (Pruvost et al. 2011a)). Below are two examples for Chlamydomonas reinhardtii (Eq. (10.7)) and Arthrospira platensis (Eq. (10.8)), emphasizing the difference in the photosynthetic quotient QP = υO2−CO2 due to the nitrogen source (ammonium for C. reinhardtii vs. nitrate for A. platensis), which is found to have the greatest impact on stoichiometric equation and redox balance, rather than the C-molar formula itself:

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CO2 + 0.593 H2O + 0.176 NH3 + 0.007 H2SO4 + 0.018 H3PO4 % CH1.781 O0.437 N0.176 S0.007 P0.018 + 1.128 O2

(10.7)

CO2 + 0.71 H2O + 0.14 HNO3 + 0.008 H2SO4 + 0.005 H3PO4 % CH1.59 O0.55 N0.14 S0.008 P0.005 + 1.32 O2

(10.8)

As for any biological production, a stoichiometric equation is useful for converting biomass growth rates into substrate or product rates, for example to determine nutrient requirements (especially in terms of nitrogen, phosphorus and sulfur sources for photosynthetic microorganisms). It is also practically useful for understanding the CO2 / O2 exchange and mass-transfer limitations or the CO2-related pH time course when the stoichiometric equation is expressed by the charge of ionic species (Cornet et al. 1998)

10.3.3.2 Structured stoichiometric equations More sophisticated structured equations can be developed if a deep understanding of the coupling between energetics, kinetics and stoichiometry in the photosynthesis process at the cell level is sought (Roels 1983). These equations involve the stoichiometric cofactor balances such as NADPH,H+ in photosynthesis and the associated ATP production rate defining the P / 2e− ratio in the Z-scheme of photosynthesis (Cornet et al. 1998). For example, in the case of A. platensis growth, the stoichiometric equation (Eq. (10.8)) may be rewritten, from a structured analysis of the P / 2e− ratio for each main cellular component (proteins, lipids, carbohydrates, etc.), and for a mean value of the incident PFD of 500 μmolhν · m–2 · s–1 as: + CO2 + 1.71 H2O + 0.14 HNO3 + 0.008 H2SO4 + 3.62 ATP + 2.62 (NADPH, H ) JX

+

#% CH1.59 O0.55 N0.14 S0.008 P0.005 + 3.61 Pi + 3.62 ADP + 2.62 NADP

(10.9)

This equation can be associated with the corresponding stoichiometric pair of structured equations for the Z-scheme of photosynthesis: JNADPH2

2.62 NADP+ + 2.62 H2O ###% 2.62 (NADPH, H+) + 1.32 O2 JATP

3.62 (ADP + Pi) ##% 3.62 ATP + 3.62 H2O

(10.10)

the sum of which corresponds to the previous non-structured stoichiometry (Eq. (10.8)). In this particular example with mean illumination conditions, the P/2e− ratio of the cells yields: P / 2e− =

JATP JNADPH2

= 1.38

(10.11)

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189

Knowing this ratio is most useful, as it enables us to calculate the stoichiometric molar quantum yield for the reaction φ′X (i.e. considering only the “conservative” photons linked to the electron transfers coming from water oxidation) from the definition (Cornet and Dussap 2009): φ′X =

1 2 υNADPH,H

+

− X (1

− + P /2e )

(10.12)

which emerges as an important parameter in the kinetic coupling model (see later on in this chapter). Most importantly, we note that this quantum yield has been shown to be weakly dependent on the radiation field because of antagonistic effects in the P / 2e– and υNADPH,H+−X deviations in Equation (10.12), and because its theoretical calculation (Cornet 2007) requires averaging fast kinetic rates over a period of time corresponding roughly to the circulation time in the PBR. Thus it may be considered as a mean constant value representative of the photon efficiency for a given N-source. In the present case for nitrate, this mean molar quantum yield is: ¯ φX′

−8

−1 ≅ 8 · 10 molX · μmolhυ

(10.13)

The same reasoning applied with ammonia as N-source gives: ¯ φX′

−1 ≅ 1 · 10−7 molX · μmolhυ

(10.14)

thus demonstrating that photosynthesis on ammonia is 25 % more efficient than on nitrate. If necessary, these molar quantum yields can be converted into mass quan−1 tum yields (kgX · μmolhυ), providing the C-molar mass of any given microorganism MX ≅ 0.024 kg/C-molX (to be determined exactly from the C-molar formula of bio−1 mass), or in molar and mass energy yields ((molX or kgX) · J ) from a conversion factor linking moles of photons to joules (e.g. 4.6 μmolhν / J in solar illumination).

10.3.4 Kinetic modeling of photosynthetic growth Solving the mass balance equation for a given compound (Eq. (10.5) or (10.6)) involves determining the mean volumetric production (or consumption) rate . In bioprocesses, this rate results from biological reactions and is linked to all the possible limitations that can occur in the cultivation system. Our discussion will focus here on light limitation. This is a specific feature of photosynthetic microorganism cultivation, and because of the high light demand, most cultivation systems are (at least) light-limited. As will be shown later, strictly light-limited condi-

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tions will also afford the best productivity. If needed, other limitations can obviously be considered (growth limitation by inorganic carbon or mineral nutrient concentration, temperature influence, etc.). This requires appropriate kinetic relations, and the interested reader can refer to Fouchard et al. (2009), where both light and nutrient limitations were modeled in the particular case of sulfur deprivation, which leads to hydrogen production by C. reinhardtii. Photosynthetic growth can be expressed first from the local specific rate of oxygen production or consumption JO2 , considered here at the scale of intracellular organelles, close to the primary photosynthetic and respiration events. A direct formulation on biomass concentration is another option (both oxygen and biomass productions being linked by the stoichiometric equation of growth). However, consideration of oxygen offers several advantages: it is well established that for dynamics shorter than several minutes, the resulting net oxygen evolution rate observed at the macroscopic reactor scale (considering a negative respiration volume; see later on in this chapter) cannot be related to an auto-consumption of the biomass itself (from intracellular reserves). This level of representation is also compatible with characteristic times such as mixing or circulation times in the PBR (a minute as an order of magnitude), which could interact with cofactor reduction and re-oxidation on the electron carrier chains, with a coupling at the primary stage of the intracellular metabolism, leading to the light/dark cycle effect (described below). These processes are thus all directly and stoichiometrically linked to oxygen evolution/consumption. When considering oxygen evolution/consumption, it is useful to introduce the compensation point of photosynthesis GC (Cornet et al. 1992; Cornet and Dussap 2009; Takache et al. 2010). By definition, irradiance values higher than GC are necessary for a net positive photosynthetic growth (strictly, a net oxygen evolution rate). Irradiances below the GC value have different effects depending on whether eukaryotic (microalgae) or prokaryotic (cyanobacteria) cells are considered. As cyanobacteria have their respiration inhibited by light for short residence time exposure to dark (Myers and Kratz 1955; Gonzalez de la Vara and Gomez-Lojero 1986), a nil oxygen evolution rate for irradiances below the GC value can be assumed. For eukaryotic microalgae, photosynthesis and respiration operate separately in chloroplasts and mitochondria. Hence microalgae, unlike cyanobacteria, present respiration both in the dark and in light. Oxygen-consumption rates will thus be obtained for values below GC. The kinetic response must be related to the heterogeneous light distribution in cultivation systems. Following the pioneering work of Irazoqui et al. (1976) and Spadoni et al. (1978) on photoreactors, and that of Aiba (1982) on photobioreactors, the authors have extensively developed this coupling formulation from the specific absorbed local radiant light power density A (μmolhν · s–1 · kg–1 or W · kgX–1) as deduced from the local value of irradiance G inside the PBR (A = ∫ EaλGλ dλ). PAR

This approach was improved recently by the authors (Cornet and Dussap 2009),

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10.3 Modeling light-limited photosynthetic growth in photobioreactors

who derived predictive values for the energetic and quantum yields involved in the case of cyanobacteria. As previously explained, regarding the inhibition of respiration by light, the following equation was obtained: φO′ 2 A H (G − GC) = ρM JO2 = ρ ¯

K ¯ φO′ A H (G − GC) K+G 2

(10.15)

where H (G − GC) is the Heaviside function (H (G − GC) = 0 if G < GC and K H (G − GC) = 1 if G > GC). ρ = ρM is the energetic yield for photon conK+G version of maximum value ρM (demonstrated to be roughly equal to 0.8), 1 ¯ φX′ = is the molar quantum yield for the Z-scheme of phoφO′ 2 = υO2−X ¯ 4 (1 + P/2e−) tosynthesis (deduced from the structured stoichiometric equations as presented above), and K is the half saturation constant for photosynthesis depending on the microorganism considered. This formulation was recently completed for the specific case of microalgae with an additional term (right-hand term in Eq. (10.16)) to consider respiration activity in light (Takache et al. 2012). This was found to be necessary, especially if a dark zone appears in the culture volume (a very common occurrence when cultivating algae) because of the significant contribution of respiration to the resulting growth in the whole PBR. In the case of microalgae, the following equation was thus proposed:

[

] [

]

JNADH2 JNADH2 K ¯ Kr Kr φO′ 2 A − υ × = ρM · JO2 = ρ ¯ φO′ A − υ NADH2 − O2 NADH2 − O2 Kr + G K+G 2 Kr + G

(10.16)

with JNADH2 the specific rate of cofactor regeneration on the respiratory chain, here linked to oxygen consumption by the stoichiometric coefficient υNADH2−O2 (the stoichiometric coefficient of cofactor regeneration on the respiratory chain). We note that the effect, well known to physiologists, of the radiation field on the respiratory activity term was taken into account as an adaptive process of the cell energetics (Peltier and Thibault 1985; Cournac et al. 2002; Cogne et al. 2011). The decrease in respiration activity with respect to light was modeled here by an irradiancedependent relation, by simply introducing in a preliminary approach an inhibition term with a constant Kr describing the decreased respiration in light. We emphasize that this parameter is entirely determined by the knowledge of the irradiance of compensation (JO2(GC) = 0) when the specific respiration rate JNADH2 is known. As a direct result of the light distribution inside the culture, the kinetic relation (Eq. (10.15) or Eq. (10.16) for cyanobacteria and microalgae respectively) is of the local type. This implies calculating the corresponding mean value by averaging over the total culture volume VR:

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< JO2 > =

10 Knowledge models for the engineering and optimization of photobioreactors

1∭ JO dV VRVR 2

(10.17)

For a cultivation system with Cartesian one-dimensional light attenuation (see later), this consists of a simple integration along the depth of culture z: < JO2 > =

1 ∫ z=L J dz L z=0 O2

(10.18)

in which L is the reactor depth. We note that in the particular case of cyanobacteria, for which growth can be neglected for values below Gc because of the inhibition of respiration by light (as explained by the Heaviside function in Equation (10.15)), the integrand can be restricted to the illuminated volume Vl of the cultivation system (values higher than GC corresponding to the illuminated fraction of the reactor γ – see Eq. (10.30)), reducing the integral to: < JO2 > =

1 ∭ J dV VR Vl O2

(10.19)

where Vl is obtained from the knowledge of the irradiance field in the PBR, enabling us to determine the proportion of the reactor volume in which the irradiance G is higher than the irradiance of compensation GC. Finally, once < JO2 > is known, the mean volumetric biomass growth rate < rX > can be deduced directly using the associated stoichiometry (considering the actual illumination conditions): < rX > =

< JO2 > CX MX υO2 − X

(10.20)

Hence the mass balance equation (Eq. (10.5) or (10.6)) can be solved for any operating conditions of light-limited growth.

10.3.5 Energetics of photobioreactors The energy analysis of photobioreactors, associated with their previous kinetic study, is also of prime importance, especially if the biomass growth is dedicated to producing an energy vector such as biofuel or hydrogen. This point has prompted intense controversy regarding, for example, the maximal surface productivities that could be achieved with intensive microalgal cultivation, and there is evidently much confusion on this issue in the literature. However, the rigorous equation giving the thermodynamic efficiency of any PBR from molar kinetic rates is established as follows (Cornet et al. 1994):

10.3 Modeling light-limited photosynthetic growth in photobioreactors

193

Fig. 10.4: Evolution of the thermodynamic efficiency ηth of a rectangular photobioreactor illuminated on one side with a collimated radiation versus the incident PFD q0. The negative effect of the incident angular dependence θ for the solar illumination (maximum¯ cos θ = 0.64) in comparison with the normal incidence (cos θ = 1) for artificial illumination is clearly established. The rapid decrease in the PBR efficiency with increasing the incident PFD is also emphasized.

r

n

∑ ∑ υp​j < r′j > ˜ μp

ηth =

j=1 p=1

−

r

m

(10.21)

∑ ∑ υs​j < r′j > ˜ μs j=1 s=1

in which the ˜ μp,s are respectively the chemical potentials for products and substrates involved in the jth reaction and the mean averaged volumetric radiant power density absorbed in the PBR (derived from the knowledge of the radiation field – see later on in this chapter). In a first approximation, the chemical potential may be substituted by standard Gibbs free enthalpies Δg′i0 for the products and substrates (Roels 1983) which enables us to use this equation, combined with the previous kinetic models for the assessment of the molar rates . On the other hand, we can envisage a direct calculation of the same thermodynamic efficiency ηth from knowledge models describing the energy conversion at each stage of the cell metabolism from primary photosynthetic events to total biomass synthesis (a dynamic analysis encompassing 15 orders of magnitude for characteristic time constants!). This work is currently being undertaken by the authors using the linear energy converters theory (Cornet et al. 1998), enabling us to derive general results for the efficiency of photosynthesis via microalgal cultivation in PBR (Cornet 2007). As an example, Figure 10.4 shows the results obtained for a microorganism cultivated on ammonia as N-source (such as C. reinhardtii) in a rectangular PBR

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artificially illuminated on one side with a quasi-collimated PFD, and the same PBR in ideal solar conditions (Pruvost et al. 2012) (only direct illumination with a mean maximum¯ cosθ = 0.64 – see later for explanations). The same calculations with nitrate as N-source (A. platensis, for example) would lead to the same evolution with 20 % lower efficiencies. These results have been shown to agree closely with experimental results obtained on different sizes of PBR (Cornet 2010). These results clearly demonstrate the marked decrease in PBR efficiency with increasing PFD because of different factors of dissipation mainly affecting the functioning of the Z-scheme in the photosynthesis. As the authors have clearly established that the surface productivity of a solar PBR is proportional to its thermodynamic efficiency, they recently proposed (Cornet 2010) the concept of dilution of the incident radiation to improve the performance of outdoor solar PBRs. It is possible (see Fig. 10.4), instead of working at an efficiency of 2–3 % with direct solar capture, to operate by capture/dilution at very low incident PFD with efficiencies of around 15 % (and with a very high specific illuminated area) as proposed in the DiCoFluV concept (Cornet 2010). Figure 10.4 also emphasizes the negative effect of the time-varying collimated incidence in solar illumination (see Fig. 10.2) in comparison with a continuous normal incidence on an artificially illuminated PBR. This effect has recently been analyzed (Pruvost et al. 2012) as a consequence of a different averaged field of radiation inside the reactor for the two situations, demonstrating once again the need for a proper description of the local radiation field inside the culture volume. Finally, these theoretical results, associated with Equation (10.21) and with ideal solar data for earth surface illumination, allow a rigorous calculation of the yearly maximum performances of solar PBR in optimal running conditions (hypothetical location at the Equator with maximum yearly ground illumination and ammonium as N-source) as a thermodynamic limit for photosynthesis engineering. The values obtained were respectively 50 tX · ha–1 · yr–1 for a fixed PBR with direct sunlight capture and 400 tX · ha–1 · yr–1 for a PBR with optimal light dilution and a tracking capture system (Cornet 2010). As explained above, these values are 20 % lower when nitrate is the N-source, giving 40 tX · ha–1 · yr–1 for a direct capture system and 320 tX · ha–1 · yr–1 for a dilution system.

10.3.6 Radiative transfer modeling The above energy and kinetic models emphasize the crucial importance of radiative transfer modeling as the only way to access local information in turbid cultivation media with confidence. This radiative transfer modeling may be performed from many different approaches depending on the final accuracy and robustness sought for the growth model (Yun and Park 2003). As regards empirical models for formu-

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lating the coupling between light and kinetics, there is no need to develop a rigorous description of the radiative transfer inside the culture bulk. In this case, although it holds only in a given direction (i.e. in intensity and not in flux density of irradiance, as often incorrectly assumed in the literature) and although it does not account for scattering by cells, the Lambert–Beer law (strictly, Bouguer’s law) can be applied to obtain a tendency and sometimes to calculate any mean averaged illumination quantity on the PBR. For the authors, who have spent a long time developing predictive knowledge models of PBRs, it is clear, by contrast, that a fine formulation of the kinetic coupling requires first knowledge of the local irradiance at any point of the culture bulk, and in this case the use of the rigorous radiative transfer equation (RTE) solutions is then necessary. The field of irradiance obtained in this way enables us to calculate the local volumetric radiative power density absorbed (see Section 10.3.4), which is the key variable needed to formulate both the energy coupling (Cornet et al. 1994, Cornet 2005) and the kinetic coupling, as is known from the pioneering work of Irazoqui et al. (1976) popularized by the team of Cassano and Alfano (Cassano et al. 1995) on photoreactors and used for the first time in PBR modeling by Aiba (1982).

10.3.6.1 Radiative transfer equation The radiative transfer equation or RTE (a linear Boltzmann-type integro-differential equation) was originally developed by Chandrasekhar (1960). It takes into account the scattering of light by the micro-organisms considered as scatterers, and enables us (if the incident PFD is known accurately enough) to calculate with accuracy and confidence the spectral field of irradiance inside the culture medium, once the angular integration over the intensities has been performed (see Part 2.1). From the notations adopted in this chapter, it takes the following form for direction Ω and wavelength λ: (Ω ⋅ ∇) Iλ (r , Ω , t) = − (aλ + sλ) Iλ (r , Ω , t) +

sλ ∬ I (r , Ω′ , t)pλ (Ω , Ω′) dΩ′ 4π 4π λ (10.22)

where aλ, sλ and pλ (Ω , Ω′) are the volumetric absorption and scattering coefficients with the phase function (the radiative properties – see later on in this chapter), requiring us to define a five-dimensional Euclidean frame of reference as presented in Figure 10.5. Finding a general three-dimensional solution to this equation (once the radiative properties of the micro-organisms are known – see later on in this chapter) using the appropriate form of the transport operator (see Tab. 10.1) is generally a difficult problem. There are deterministic numerical methods such as finite element methods (Cornet et al. 1994) and finite volume methods (Siegel and Howell

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Fig. 10.5: Definitions of fixed and moving frames of reference in different coordinate systems for the ETR.

2002), or stochastic numerical methods such as direct Monte Carlo methods (Aiba 1982; Csogör et al. 2001) and integral Monte Carlo methods (Dauchet et al. 2012a). Fortunately, for many practical situations, it is possible to reduce the above problem to a more simple treatment of the RTE involving a one-dimensional approximation. In this case, the RTE reduces to (for any coordinate axis u = z or r and defining systematically, in contrast to Figure 10.5 for curvilinear coordinates systems, the angle β between the axis u and the corresponding direction of Iλ): cos β

dIλ (u , β , t) du

π

= − (aλ + sλ) Iλ (u , β , t) +

sλ ∫ I (u , β′ , t) pλ ( β , β′) sin β′ d β′ (10.23) 20 λ

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197

This simpler integro-differential equation may be solved, for example, by the differential discrete ordinates method (DOM) as proposed for turbid water media by Houf and Incropera (1980) and improved by Kumar et al. (1990), but achieving the required accuracy then requires long calculation times if classical solvers of boundary value problems are used (Mattheij and Staarink 1984a, 1984b; Kumar et al. 1990). The authors have recently implemented a matrix method using Matlab® software, and this has proven to be very much faster than the other Fortran or C routines hitherto available. In this case, the one-dimensional ETR (Eq. (10.23)) is transformed into a differential system of N equations corresponding to the cosine directions (N × N diagonal matrix M for cos βi) and the weights (N × N diagonal matrix W) of a Lobatto quadrature. This leads to the following system in matrix notation, and in Cartesian coordinates: di M−1 ϖ = (N − 1) − D + λ PW i − (1 − ϖλ) i 2 N dτλ

[

(

)

]

(10.24)

in which D = δij is the Kronecker delta, i is the vector of intensities, P is an N × N matrix for the phase function calculation, ϖλ = sλ / (aλ + sλ) is the albedo of single scattering, and τλ = (aλ + sλ) u is the optical thickness. Lastly, a final simplification consists in retaining only two ordinates in Equation (10.24) and averaging the intensities over each positive and negative hemisphere, providing a hypothesis for its angular dependence in the medium considered. This is the well-known two-flux method originally developed in Cartesian coordinates by Schuster (1905) with the diffuse hypothesis and by Hottel and Sarofim (1967) for the collimated hypothesis: it was improved by the authors in the 2000s to allow work in any geometry and for any angular distribution of the intensities (Takache et al. 2010). The main advantage of this simple method is that it leads, in many practical cases of interest, to analytical solutions (if the lack of accuracy is accepted) for the calculation of the field of radiation. For the example of a slab irradiated from one side with a reflectivity ρλ at the rear (corresponding, for example, to a flat panel PBR with reflecting rear side as obtained with stainless steel), we obtain (Cornet et al. 1995; Pottier et al. 2005; Farges et al. 2009) for the spectral irradiance Gλ (and for the simpler special case of a nonreflecting back side, i.e. ρλ = 0):

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Gλ q0,λ = K

Gλ q0,λ +

[

[ ρλ(1 + αλ) exp(−δλL) − (1 − αλ) exp (−δλL)] exp [δλz] 2

2

2

(1 + αλ) exp (δλL) − (1 − αλ) exp (−δλL) + ρλ(1 − αλ) [exp(−δλL) − exp(δλL)]

[(1 + αλ) exp (δλL) − ρλ(1 − αλ) exp (δλL)] exp [−δλz] 2

2

2

]

(1 + αλ) exp (δλL) − (1 − αλ) exp (−δλL) + ρλ(1 − αλ) [exp(−δλL) − exp(δλL)]

[(1 + αλ) exp[−δλ(z − L)]] − [(1 − αλ) exp [δλ(z − L)]] Gλ = K if ρλ = 0 q0,λ 2 2 (1 + αλ) exp (δλL) − (1 − αλ) exp (−δλL)

(10.25)

in which n is the degree of collimation (n = 0 for isotropic intensities and n = ∞ for collimated intensity in direction βc), and: K = 2

αλ =

(nn ++ 21) secβ

c

√(a

λ

aλ + 2bλsλ)

δλ = secβc

(nn ++ 21) √a

λ

(aλ + 2 bλsλ)

and the backscattered fraction π

bλ =

1 ∫ p ( β , β′) sinβ dβ 2 π/2 λ

Likewise, the method can be used for a cylindrical PBR (Cornet 2010; Takache et al. 2010) leading, for example, in the case of a radial illumination with the same notations and considerations, to:

(

)

(

)

I0(δλr) Gλ n+2 qR, λ = 2 n + 1 (1 − ρλ) I0(δλR) + αλ(1 + ρλ) I1(δλR) I0(δλr) Gλ n+2 qR, λ = 2 n + 1 I0(δλR) + αλ I1(δλR) if ρλ = 0

(10.26)

where the In(x) are the n order modified Bessel functions of first species. The accuracy of the useful two-flux approximation is nevertheless not always satisfactory and depends mainly on the information required. The comparison between the rigorous differential discrete ordinates method (with N = 32, Eq. (10.24)) and the two-flux approximation (Eq. (10.25)) with the example of radiative properties of A. platensis at 540 nm is shown in Figure 10.6. As already discussed from a comparison with experimental data for other micro-organisms (Pottier et al. 2005),

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10.3 Modeling light-limited photosynthetic growth in photobioreactors

Cartesian coordinates:

(

(Ω ⋅ ∇) Ψ = 1 − μ2

∂Ψ ∂Ψ ) [cosφ ∂Ψ +μ + sinφ ] ∂x ∂z ∂y 1/2

= sinθ cosφ

∂Ψ ∂Ψ ∂Ψ + sinθ sinφ + cosθ ∂x ∂z ∂y

2ππ

2ππ

00

00

2ππ

2 2 qλ, x = ∫ ∫ Iλ cosφ sin θ dθ dφ , qλ,y = ∫ ∫ Iλ sinφ sin θ dθ dφ , qλ, z = ∫ ∫ Iλcosθ sinθ dθ dφ 00

2π 1

= ∫ ∫ Iλ μ dμ dφ 0 −1

Cylindrical coordinates: (Ω ⋅ ∇) Ψ = (1 − μ2)1/2cosφ

= sinθ cosφ

2 1/2 ∂Ψ (1 − μ ) ∂Ψ ∂Ψ ∂Ψ + sinφ − + μ r ∂r ∂z ∂φr ∂φ

[

[

]

]

∂Ψ ∂Ψ ∂Ψ sinθ sinφ ∂Ψ + − + cosθ r ∂r ∂z ∂φr ∂φ

2ππ

2ππ

00

00

2ππ

2 2 qλ,r = ∫ ∫ Iλ cosφ sin θ dθ dφ , qλ,φ = ∫ ∫ Iλ sinφ sin θ dθ dφ , qλ,z = ∫ ∫ Iλ cosθ sinθ dθ dφ 00

2π 1

= ∫ ∫ Iλ μ dμ dφ 0 −1

Spherical coordinates: 2 1/2

(Ω ⋅ ∇) Ψ = μ

∂Ψ (1 − μ ) + r ∂r

= cos θ

2 1/2

sin χ ∂Ψ (1 − μ ) + r sin θr ∂φr

2 1/2

cos χ

2 ∂Ψ 1 − μ ∂Ψ (1 − μ ) − + r ∂μ r ∂θr

sin χ ∂Ψ tan θr ∂χ

cos θ ∂Ψ sin θ ∂Ψ cosθ sin χ ∂Ψ ∂Ψ cos θ sin χ ∂Ψ + − + − cos χ r sin θr ∂φr r r ∂θ r tan θr ∂χ ∂r ∂θr

π

1

0

−1

qλ,r = 2π ∫ Iλ cosθ sinθ dθ = 2π ∫ Iλ μ dμ

Tab. 10.1: Definitions of the operator of transport (Ω ⋅ ∇) and of the radiant light flux density q in different systems of coordinates as defined in Figure 10.5

the two-flux approximation is rather good so long as G / q0 > 0.1 and in the special case of quasi-collimated incidence, a situation in close agreement with the forward scattering behavior of micro-organisms as scatterers. This assumption may thus be

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10 Knowledge models for the engineering and optimization of photobioreactors

Fig. 10.6: Comparison between a rigorous differential discrete ordinates method with N = 32 (DOM-32, Eq. (10.24)) and the two-flux approximation (Eq. (10.25)) for a rectangular PBR illuminated on one side with a quasi-collimated incident PFD and with the radiative properties of A. platensis at 540 nm. The effect of approximating the quasi-exact radiative properties by equivalent spheres using the Lorenz–Mie theory is also reported.

used in this case as a good approximation unless local information with low irradiance values is sought, such as irradiance of compensation GC. In this case, a more accurate method (e.g. DOM or Monte Carlo), as presented in this chapter, is needed. The two-flux approximation may also be useful for modeling light transfer in solar PBRs requiring us first to separate the collimated (direct) and diffuse (isotropic) contributions at any given time and second to solve the light transfer with dynamic variations in the angular pattern and intensity of the PFD, requiring longer computation time. This work was recently carried out by the authors to obtain full yearly simulations of rectangular PBRs installed at different terrestrial locations (Pruvost et al. 2012). The incident PFD q is thus divided into the direct q// (θ angle-dependent; see Fig. 10.2) and the isotropic diffuse q∩ parts (q = q// + q∩) respectively. The general analytical solution can be easily obtained from Equation (10.25), neglecting here the reflectivity at the rear surface ( ρ = 0) and using mean spectral averaged radiative properties for simplicity. Taking the degree of collimation n = 0 ( βc = θ = 0) for diffuse radiation and n = ∞ ( βc = θ a function of time) for direct radiation then gives the two analytical fields of irradiance: Gcol 2 (1 + α) exp[−δcol ( z − L)] − (1 − α) exp[δcol( z−L)] q// = cosθ 2 2 (1 + α) exp[δcolL] − (1 − α) exp[−δcolL]

(10.27)

10.3 Modeling light-limited photosynthetic growth in photobioreactors

(1 + α) exp[− δdif (z − L)] − (1 − α) exp[δdif (z − L)] Gdif q∩ = 4 2 2 (1 + α) exp[δdif L] − (1 − α) exp[−δdifL]

201

(10.28)

with:

δcol =

√a(a + 2 bs) cosθ

δdif = 2 √a (a + 2 bs)

The total irradiance (representing the amount of light impinging on algae) is finally given by simply summing the collimated and diffuse components: G (z) = Gcol (z) + Gdif (z)

(10.29)

Equations (10.27) and (10.28) show that penetrations of collimated and diffuse radiations inside the culture volume are markedly different (Pruvost et al. 2012). This will be especially important in solar conditions where the diffuse component of the radiation is non-negligible. We also note the influence of the incident angle θ on the collimated part, light penetration decreasing with increasing incident angle. Like the degree of collimation of the radiation, this will influence cultivation system efficiency (for a more detailed description, see Pruvost et al. 2012).

10.3.6.2 Optical and radiative properties for micro-organisms As explained above, a sound description of the radiant light transfer in the culture volume of the PBR is necessary if knowledge-based kinetic and energy coupling formulations are envisaged in the modeling approach. In this case, it is emphasized that the radiative properties that appear as parameters in the RTE have to be accurately determined beforehand. If this task is not performed with sufficient care, rigorously solving the RTE will be of little use, and an empirical kinetic model will be preferable. This point is clearly illustrated in Figure 10.6, which compares irradiance profiles calculated for A. platensis turbid media with quasi-exact radiative properties (A. platensis is then considered as a randomly oriented long circular cylinder) and approximated radiative properties by equivalent spheres, then evidencing a marked discrepancy. These radiative properties are the volumetric absorption aλ = Eaλ · CX and scattering sλ = Esλ · CX coefficients (Eaλ and Esλ being the mass absorption and scattering coefficients for the biomass concentration CX) and the phase function for scattering pλ ( Ω , Ω′ ), all appearing in the RTE (Eq. (10.22)). They physically represent the probability of a photon being absorbed by the cell or scattered in a given direction, and can be deduced theoretically from the absorption and scattering cross-sections of the micro-organisms. The assessment of these radiative properties for all the wavelengths in the PAR (we have demonstrated in fact that roughly 50 values over the PAR range afford sufficient accuracy

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10 Knowledge models for the engineering and optimization of photobioreactors

Fig. 10.7: Example of the calculation of radiative properties for the microalga Chlamydomonas reinhardtii calculated from optical properties as defined by Pottier et al. (2005) and using the equivalent sphere approximation (log-normal size distribution) with the Lorenz–Mie theory. Red solid line: mass absorption coefficient Ea; dashed line: mass scattering coefficient Es; blue solid line: backscattered fraction.

in most cases) is generally a difficult task. It can be tackled either experimentally or theoretically. The experimental determination of the absorption and scattering coefficients requires working with an integrating sphere to measure transmittance or reflectance of the samples, the single scattering condition simplifying the inversion procedure. The determination of the angular phase function for scattering is far more difficult and requires a nephelometer (the laser of which generally works only at a given set of wavelengths). This wide and important experimental field has been extensively explored and developed during the last 10 years by Pilon and Berberoglu (Berberoglu and Pilon 2007; Pilon et al. 2011). If the inversion is performed from transmittance or reflectance results obtained in multiple scattering conditions, it is necessary to guarantee an exact RTE solution using, for example, an integral Monte Carlo method minimizing emor bars (Dauchet et al. 2012a). One limitation of the experimental approach is its lack of predictability, as radiative properties vary with the cultivation conditions (CO2 or mineral limitations, PFD, etc.), which has marked effects on the pigment contents or the size distribution of the cells. It is then possible to calculate radiative properties using a purely theoretical approach by solving the Maxwell equations of electromagnetism around the particles in spherical coordinates (Mishchenko et al. 2000). Solving this problem using a model of equivalent sphere for the micro-organisms is referred to as the Lorenz–Mie theory, which today is quite easy to compute (Bohren and

10.4 Illustrations of the utility of modeling for cultivation systems

203

Huffman 1983; Pottier et al. 2005). As this approximation has been shown to be of low accuracy for the numerous different shapes encountered in the world of microalgae (see Fig. 10.6), often very different from spheres, the authors are currently developing a predictive method (Dauchet et al. 2012b) that allows radiative properties to be computed for any given shape of rotationally symmetric randomly oriented scatterers from the anomalous diffraction approximation (Van de Hulst 1981). The input parameters are merely the pigment contents and the size distributions of the cells, the former enabling us to calculate the imaginary part of the refractive index for the particles from “in vivo” databases (Bidigare et al. 1990), as previously explained elsewhere (Pottier et al. 2005). The real parts of the refractive indices are then computed according to the Kramers–Kronig relations (Lucarini et al. 2005). For microalgae with quasi-spherical shapes, the sphere-equivalent model may nevertheless be a good first approximation for the calculation of radiative properties. For example, Figure 10.7 illustrates results obtained by the proposed approach for C. reinhardtii, using the method of Pottier et al. (2005) for assessment of optical properties, and the Lorenz–Mie theory of equivalent spheres (Bohren and Huffman 1983) for the calculation of the radiative properties (here summarized as spectral absorption and scattering mass coefficients Eaλ, Esλ and spectral backscattered fraction bλ).

10.4 Illustrations of the utility of modeling for the understanding and optimization of cultivation systems 10.4.1 Understanding the role of light-attenuation conditions 10.4.1.1 Illuminated fraction γ Illumination conditions (as represented by the incident PFD) are a major operating parameter of any cultivation system. Their influence is, however, difficult to process. This is because of their relation to light-attenuation conditions in the culture volume, which in turn affect photosynthetic conversion and thereby the overall cultivation system. Modeling light-transfer conditions using adequate radiative transfer models as described above is in this regard of primary importance. A specific, easy-to-use parameter, named “illuminated volume fraction” and noted γ, has been found to be especially useful. This parameter is directly deduced by the irradiance distribution as obtained from the radiative transfer model (Cornet et al. 1992; Cornet and Dussap 2009; Degrenne et al. 2010; Takache et al. 2010). Schematically, the culture bulk can be delimited into two zones, an illuminated zone and a dark zone (Fig. 10.8). Partitioning is obtained by the compensation irradiance value GC corresponding to the minimum value of radiant energy required to obtain a positive photosynthetic growth rate. For example, compensation irradiances GC = 1.5 μmole · m–2 · s–1 (Cornet and Dussap 2009) and GC = 10 μmole · m–2 · s–1 (Takache et al. 2010) were found for A. platensis and C. reinhardtii respectively. The

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10 Knowledge models for the engineering and optimization of photobioreactors

illuminated fraction γ is then given by the depth of the culture zc where the irradiance of compensation G(zc) = GC is obtained (Fig. 10.3). In the case of cultivation systems with one-dimensional light attenuation, we have, for example: γ =

z V1 = c VR L

(10.30)

The γ parameter allows three typical cases of light-attenuation conditions to be represented for a given PFD (Fig. 10.8). If the biomass concentration is too low (Case c), part of the incident light is transmitted through the culture and lost for the photoreaction processes. Conversely, if the biomass is too high (Case a), a dark zone appears deep in the culture. The former case (c) with low light absorption is named the “kinetic” regime and is represented by the hypothetical condition “γ > l” (zc > L, in this case, the length zc appears rather as an extinction length, which would require a greater thickness L of the PBR to absorb all the incident radiation). The last case (a) with full light absorption is represented by the condition “γ < l” and corresponds to a light-limited culture. A third typical case can arise: full absorption of the light received, but with no dark zone in the culture volume. This meets the exact condition γ = l, also named the “luminostat” regime (Case b, a particular limit case of light-limited culture), and will be demonstrated later as the best condition for optimal productivity (i.e. growth rate) in the PBR.

10.4.1.2 Achieving maximal productivities with appropriate definition of lightattenuation conditions The growth of photosynthetic microorganisms depends on various parameters. If these can be kept optimal (appropriate regulation of temperature and pH, adequate medium composition), light-limited conditions where light alone limits growth will be achieved. This will, however, be insufficient to guarantee maximal performance of any given cultivation systems (in terms of biomass production of a given species). As shown in Figure 10.8, this requires appropriate light-attenuation conditions to be applied as represented by the illuminated fraction γ (Cornet and Dussap 2009; Takache et al. 2010). Because it does not allow full absorption of the light captured, the kinetic regime always leads to a loss of efficiency (γ > l). Full light absorption is thus to be preferred (γ ≤ l). A distinction must be made here between eukaryotic (microalgae) and prokaryotic (cyanobacteria) cells. In the case of cyanobacteria, which have no (or negligible) respiration during short time exposure in the dark (Gonzalez de la Vara and Gomez-Lojero 1986), a dark zone will have no (or little) influence. Meeting the condition γ ≤ l will thus be sufficient to guarantee maximal productivity. For eukaryotic cells presenting respiration in the light (microalgae), a dark zone in the culture volume where respiration is predominant will result in a loss of productivity due to reducing power consumption, thus lowering the kinetic rates. Maximal productivity will then require working in the “lumi-

10.4 Illustrations of the utility of modeling for cultivation systems

205

Fig. 10.8: Relation between the light absorption conditions (represented by the irradiance field G( z)) and corresponding mean biomass volumetric productivities (). The three typical cases of light-attenuation conditions are represented: full light absorption (Case a), luminostat (Case b) and kinetic regimes (Case c).

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10 Knowledge models for the engineering and optimization of photobioreactors

nostat” regime with the γ fraction meeting the exact condition γ = l. These theoretical conditions have been proved experimentally for both cyanobacteria (Cornet and Dussap 2009; Cornet 2010) and microalgae (Takache et al. 2010). Obviously, the determination of light-attenuation conditions by radiative transfer modeling was found in this regard to be most useful for finding the optimal biomass concentration to apply in the cultivation system; see Cornet and Dussap (2009), Cornet (2010) and Takache et al. (2010) for details.

10.4.1.3 Prediction of biomass concentration and productivity Solving the mass balance equation (Eq. (10.5)) gives biomass concentration Cx for the simulated operating conditions (PBR geometry, PFD, etc.) and for a given species (characterized by its radiative properties and kinetic growth parameters). This equation is linked here to an appropriate formulation of kinetic growth (Eq. (10.20), linked to Equation (10.15) or Equation (10.16) for cyanobacteria and microalgae, respectively, here in light-limited conditions) and to radiative transfer conditions in the culture bulk (Eq. (10.25), (10.26), (10.27) or (10.28), depending on the case). Once the biomass concentration is obtained, biomass productivity can be deduced in terms of volumetric (, kg · m–3 · h–1) or surface productivity (, kg · m–2 · h–1) with the illuminated surface as reference. Volumetric and surface productivities are linked by the following relation: < sX > =

< rX >VR

= a X light Slight

(10.31)

This equation introduces the specific illuminated surface alight, which represents the ratio of illuminated surface (Slight) to volume (VR) in the cultivation system. We also note that the performance of a cultivation system (in light-limited conditions) when expressed on a surface basis is independent of the cultivation system design (Cornet 2010; Pruvost et al. 2011b). Figure 10.9 presents results for batch conditions, given here as a first illustration. The illuminated fraction is also represented to emphasize the relation between light-attenuation conditions and resulting productivity. All results are given here for a constant PFD, assuming no limitation other than light. Thus, the time course of growth rate () is explained here only by the changes in light conditions in the culture volume due to biomass growth (no nutrient limitation). Because of their difference in photosynthetic response, microalgae and cyanobacteria present different growth curves. In both cases, the kinetic regime (γ > l), usually encountered at the beginning of a batch production run, leads to a loss of efficiency, as illustrated here by a growth rate below maximum (values in batch mode are given by the slope of Cx(t); see Eq. (10.5)). This is explained by light transmission, which prevents the full exploitation of the light energy received. Due to the increase in biomass, the γ value will decrease progressively to a value

10.4 Illustrations of the utility of modeling for cultivation systems

207

Fig. 10.9: Time course of biomass concentration during a batch cultivation of Arthrospira platensis (cyanobacteria, top) and Chlamydomonas reinhardtii (microalgae, down) (light-limited conditions). Light attenuation increases with biomass concentration, directly affecting growth kinetics (slope of the curve). This proves to depend entirely on the illuminated fraction γ. We note that due to their respiration activity, microalgae are affected negatively by the formation and expansion of the dark zone (γ < 1).

below 1. For prokaryotic cells (Fig. 10.9, top), as soon as full absorption is reached, the maximum value of the mean volumetric growth rate will be achieved and then remain constant (until a large dark zone is formed, inducing a shift in the cell metabolism, not represented here). For eukaryotic cells, the γ = 1 condition, giving the maximum value of the mean volumetric growth rate, will be only transiently satisfied. The increase in the dark volume will then progressively lower the mean volumetric growth rate (Fig. 10.9, down). The same model can be applied to simulate continuous cultivation (by applying only the appropriate formulation of the mass balance equation, i.e. τ ≠ 0 in Eq. (10.5)). In this case, a steady state is obtained for each set of operating conditions with constant biomass concentration and thus constant light-attenuation con-

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10 Knowledge models for the engineering and optimization of photobioreactors

Fig. 10.10: Prediction of PFD influence on resulting biomass productivity (continuous mode) calculated by the model presented. Comparison with experimental results for the green microalga Chlamydomonas reinhardtii cultivated in a torus-shaped PBR (Lz = 0.04 m). The negative influence of introducing a dark volume on microalgal growth is illustrated here, with lower productivities when working in full-light absorption (γ = 0.5) than in the case of a luminostat regime (γ = 1) giving maximal biomass productivity) (see Takache et al. 2012).

ditions. An example of the results is given in Figure 10.10 for C. reinhardtii growth. The model was found to be accurate over the wide range of PFD investigated (up to 1000 μmol · m–2 · s–1), and for any light-attenuation conditions as obtained by varying the residence time τ and thus biomass concentration. Results are given here for the luminostat regime with γ = 1 giving maximum biomass productivity, and for full-light absorption with γ = 0.5. Accurate predictions were also obtained in kinetic regime γ > 1; see Takache et al. (2012). Another example is given to illustrate the utility of modeling in PBR engineering. Figure 10.11 presents the results obtained here with the green microalga Neochloris oleoabundans cultivated in different PBRs (volume, culture depth) and operating conditions (PFD, residence time). The effects of all of these parameters were accurately predicted. Modeling thus emerges as a highly valuable tool in PBR engineering, enabling us to predict the influence of parameters that affect PBR performance profoundly but in a complex manner. Figure 10.11 illustrates the utility of increasing the specific illuminated surface (or decreasing the culture depth, i.e. alight = 1 / L for a flat panel) and PFD to increase volumetric productivity (or biomass concentration, the two being linked). This introduces the basic concepts of PBR intensification, detailed in Fig. 10.12. The utility of working in a thin film (alight > 100 m–1, L < 0.01 m) is clearly demonstrated here: compared with usual geometries (alight around 20 m–1 for PBR of depth 0.05 m, 0.3 m–1 for raceway of depth 0.3 m), two orders of magnitude on volumetric productivity can be gained allowing operators to work in high cell den-

10.4 Illustrations of the utility of modeling for cultivation systems

209

Fig. 10.11: Scaling up of biomass production from laboratory scale 1 L PBR to 130 L PBR with Neochloris oleoabundans. The light-limited growth model was used to predict effects of different parameters on productivities or biomass concentration (dashed line for PBR1 and continuous line for PBR2), such as the positive effect of increasing the PFD on biomass productivity, or the negative effect of increasing the depth of culture.

sity culture (Cx > 10 kg · m–3). We also note that increasing the PFD will lead to a further increase (but with a decrease in thermodynamic yield, as discussed above). As previously mentioned, this demonstrates the surface productivity as being independent of the specific illuminated surface, emphasizing a specific feature of PBR intensification with the possibility to increase drastically volumetric productivity while maintaining surface productivity (see also Eq. (10.31) combined with Eq. (10.32)). Generally, one direct utility of process intensification is that it reduces the system size needed to achieve a given production requirement. In the specific con-

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10 Knowledge models for the engineering and optimization of photobioreactors

Fig. 10.12: Influence of the illuminated surface-to-volume ratio (alight) on PBR productivities in the case of Chlamydomonas reinhardtii cultivation. A direct influence on volumetric productivity is shown (two orders of magnitude of variation). Surface productivity is found independent of this engineering parameter. PFD is found to have a positive effect on both volumetric and surface productivities (all values correspond to maximal performances as obtained in continuous cultivation, light-limited conditions, luminostat “γ = 1” regime).

text of microalgal cultivation, we also note that several processes have an energy consumption directly linked to the culture volume (pumping, mixing, temperature control, harvesting, etc.). Increasing volumetric productivity can thus drastically reduce energy needs for a given operation. This is of primary relevance, for example, in biofuel production, where both surface and volumetric productivities can be increased with appropriate engineering of photobioreactors (using, for example, models described in this chapter; the authors are developing optimized systems for solar production by these means).

10.4.1.4 Engineering formula for assessment of maximum kinetic performance in PBRs Among the many practical advantages of defining an illuminated volume fraction γ in the PBR, we note that it enables us to clearly define (at least from a didactic and theoretical point of view) optimal operating conditions for a given geometry of a PBR illuminated with a constant PFD. This means that from a sound control of the radiation field by acting on the biomass concentration guaranteeing the

10.4 Illustrations of the utility of modeling for cultivation systems

211

condition γ = 1 as rigorously as possible, it is possible to achieve the maximal kinetic performance of the PBR max. This also shows that for an existing reactor and fixed PFD, the radiation field may be controlled solely by the biomass concentration CX (varying the residence time τ as previously shown), demonstrating why batch cultivations for microalgae should be avoided if maximal performance is sought. The authors have recently shown (Cornet and Dussap 2009), on many different PBR geometries, that only in the special case γ = 1 ± 20 %, and accepting an accuracy of around 15 %, is it possible to use a simple engineering formula, already averaged over the total volume of the PBR, in which the complexity of the radiative transfer has vanished. This very useful relation, with for main parameters the design-specific illuminated area alight and the incident PFD q0 (here in μmolhν · m–2 · s–1) with its degree of collimation n, takes the form:

[ )

(

)

n+2 q n+1 0 2α K ¯ ln 1 + < rX >max = (1 − fd) ρM MX φ′X a 1 + α light n + 2 K n+1

(

]

(10.32)

in which all the variables have already been defined in this chapter except for fd, which represents the dark fraction of the reactor (any volume fraction of the PBR not lit by the incident PFD). In this equation, the only specific parameters of a given micro-organism are the linear scattering modulus α (default value 0.9), the molar mass MX (default value 0.024 kgX/molX) and the half saturation constant for photosynthesis K (default value 100 μmolhν · m–2 · s–1). This formula, originally validated for cyanobacteria, also proved very robust for microalgae (Takache et al. 2010).

10.4.2 Solar production 10.4.2.1 Prediction of PBR productivity as a function of radiation conditions Generally, in the current perspective of using mass scale production of algae as a new feedstock source for various applications, predicting productivity is obviously useful (productivity calculations, cultivation system engineering, advanced control settings, etc.). However, the broad variability of sunlight in time and space adds further complexity to the optimization and control of cultivation systems, compared with artificial illumination. Modeling can be very helpful in this regard, and the approach was recently extended by the authors to that end by considering specific features of solar use such as (1) direct/diffuse radiation proportions in sunlight, and (2) time variation of the incident light flux and corresponding incident angle on the surface of the cultivation system. All these variables can be

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obtained from a solar database giving time (day/night, season) and space variability of solar radiation. They can then be implemented in a PBR model, using the same approach as described above. Besides the specific nature of sunlight (nonnormal incident angle, non-negligible diffuse radiation), an important difference lies in the transient nature of sunlight. The transient form of the mass balance equation thus has to be solved (this can be achieved using the routine ode23tb in the Matlab® software), ultimately allowing the determination of the biomass concentration time course and calculation of the corresponding biomass productivity. Once the model has been set up, it enables us to link various interacting parameters (irradiation, PBR technology and implementation) and phenomena (light transfer in the culture bulk, growth kinetics). In solar conditions, where light is highly variable in quality and quantity, this is of critical importance. It allows a deeper understanding of solar PBR transient behavior, and various parameters can be easily investigated (PBR location, but also harvesting strategy, strains cultivated, effect of night, etc.). Production can also be determined for a whole-year period, giving data such as productivity, obviously difficult to obtain in real conditions (at least for reasons of time). An example of surface productivity is given in Figure 10.13 (a surface productivity is especially useful in the context of solar production to determine the required land area). Two locations were investigated by introducing adequate irradiation conditions, namely Dongola (Africa), here retained for its irradiation conditions close to the maximum available anywhere on Earth (around 2500 kWh · m–2 · year–1), and Nantes, with typical irradiation conditions of western Europe (around 1220 kWh · m–2 · year–1). The direct relation between irradiation conditions and biomass productivities is shown, with variations along the year, especially in Nantes, where a threefold increase is observed between winter and summer periods. Simulation obviously allows further analysis (influence of day/ night duration, influence of cloudy days, effect of high irradiation conditions as obtained in the summer, etc.). This, however, lies outside the scope of this chapter; the interested reader can refer to the authors’ work on the subject (Pruvost et al. 2011b for biomass production; Goetz et al. 2011 for thermal behavior prediction). Another advantage of modeling is the possibility it offers of calculating theoretical limits independently of practical constraints (which will inevitably lower productivities). This can be done, as recently illustrated by the authors, by introducing ideal conditions in the model. The concept of ideal reactor was introduced, as commonly done in chemical engineering (Aris 1999), by calculating maximal productivity for the light-limited regime and for an optimal running of the PBR with the ideal solar condition that could be achieved on Earth. This allows an estimate of the upper limit of biomass productivity for A. platensis cultivated on nitrate from a kinetic approach (calculation of and from the proposed coupling models), added to the previously described energetic approach (for a species growing on ammonia, 25 % higher productivity should be expected). For

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213

Fig. 10.13: Annual time course of surface productivity (month averaging) of a horizontal PBR located in Dongola (Soudan, 19.1° N, 30.3° W) and Nantes (France, 47° 12′ N, 01° 33′ W). Values are for a system cultivating Arthrospira platensis in chemostat mode (optimal residence time). Corresponding radiation time course is also given. Modeling allows the determination of annual averaged productivity as a function of location (in this case, average productivities of 9.5 g · m–2 · day–1 and 5.5 g · m–2 · day–1 are expected in Dongola and Nantes respectively due to the difference in radiation conditions).

a fix surface-lightened PBR, we found an excellent agreement with the previous thermodynamic approach, a maximal surface productivity of around 40 tX · ha–1. year–1 being obtained (Pruvost et al. 2012). Because of their difference in light-use principle (light dilution), volumetric-lightened systems with optimal internal light dilution enabled us to approach the thermodynamic limit of photosynthetic reactive systems (see Section 10.3.5), leading to an ideal productivity of 320 tX · ha–1 · year–1. By definition, ideal productivity represents an upper limit that cannot be exceeded, irrespective of the technology used. Any real system will have lower productivity due to: – non-ideal irradiation conditions such as induced by the location, meteorological conditions, partial shading by other units or surrounding buildings or trees, etc.; – the transient response of the PBR resulting from biological kinetics, daytime variation of irradiation and night periods; – poor control of the radiation field, leading, for example, to a kinetic regime;

214 –

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any engineering (PBR orientation and inclination, dark volumes in the system, arrangement of modules and self-shadowing), technical (optical transmittances, etc.) or operating constraints (non-ideal temperature or pH, nonoptimized harvesting strategies, contamination, etc.) resulting in non-ideal production conditions.

Practical constraints can be introduced in the model so that their respective contributions to process performance can be quantified. Again, modeling proves to be a highly valuable tool in the systematic engineering and optimization of complex processes such as solar microalgal production systems.

10.4.2.2 Engineering formula for maximal productivity determination As explained earlier in this chapter, the authors have demonstrated that it is possible to derive a simple engineering formula with constant artificial illumination conditions on a PBR of any given geometry in order to calculate its maximum kinetic performance (Cornet and Dussap 2009; Takache et al. 2010). This approach was recently extended to the case of a mean yearly solar illumination for which it was possible to calculate (for ideal case) or find in databases (for example Meteoq∩ PFD (from the norm anywhere in the world) the mean direct ¯ q// and diffuse ¯ knowledge of total PFD ¯ q and its diffuse fraction ¯ xd), together with the yearly averaged incidence angle¯ cosθ with the outward normal of the PBR for the direct illumination (maximal theoretical value of 2 / π = 0.64 at the Equator). The maximum surface productivity is then given, for any micro-organism, from the value of the quantum yield φ¯′X on nitrate or ammonium as N-source by the simple relation: < sX >max = (1 − fd) ρM MX ¯ φX′

[

[

]

[

]]

¯ q 2α ¯ xdK 2¯ q ln 1 + + (1 − ¯ xd)¯ cosθ K ln 1 + ¯ 1+α 2 K K cosθ

(10.33)

As it is related to maximum performance, this equation holds only in optimal running conditions (optimal operation of the cultivation system). However, it proved to give a good engineering estimation of maximum annual achievable productivity from the knowledge only of some kinetic parameters and yearly averaged incident radiation conditions. For example, the deviation, compared with the full simulated values, was found to be below 10 %, confirming the relevance of the proposed formula in the estimation of maximum performance of PBRs (Pruvost et al. 2012).

10.4.3 Modeling light/dark cycle effects Although many studies have shown the relevance of mixing conditions in microalgal cultivation systems, knowledge is still insufficient to provide engineering rules

10.4 Illustrations of the utility of modeling for cultivation systems

215

for their systematic optimization. Hydrodynamic conditions can have several outcomes: some are common to other bioprocesses (hydrodynamic shear stress, massand heat-transfer enhancement, cell sedimentation and biofilm formation), while others are specific to microalgal cultivation systems. This is especially so for the light–dark (L/D) cycle effects. In view of their specificity to microalgal cultivation, a brief overview of actual approaches to modeling L/D cycle effects is now presented. L/D cycles result from cell displacement in the heterogeneous radiation field, so that cells experience a specific history with respect to the light they absorb, composed of variations from high irradiance level (in the vicinity of the light source) to low or quasi-nil values (deep in the culture) if the biomass concentration is high. As widely described in the literature (Janssen et al. 2000; Richmond 2004; Perner-Nochta and Posten 2007; Rosello Sastre et al. 2007; Pruvost et al. 2008), this dynamic fluctuating regime can influence photosynthetic growth and thereby process efficiency. Some examples can be found in the literature on the characterization of light regimes in cultivation systems. Firstly, cell trajectories are determined using a schematic representation of the flow (Wu and Merchuk 2002; Janssen et al. 2003; Wu and Merchuk 2004), by experimental measurement with radiative particle tracking (Luo et al. 2003; Luo and Al-Dahhan 2004) or by a Lagrangian simulation (Pruvost et al. 2002a, 2002b). In this last case, trajectories are obtained from the PBR flowfield description. If microalgal cells are assumed to be passive tracers and are represented by elementary fluid particles (no mass effect, as their density is almost the same as that of the fluid, cell size smaller than the Kolmogorov scale), trajectories are then obtained step by step by calculating the successive positions, P, of a fluid element, using: P (t + Δ t) = P (t) + UP Δ t

(10.34)

where UP is the instantaneous velocity at a given position P and Δ t a time step to be specified. The velocity field can be determined using computational fluid dynamics (CFD). For a laminar regime, the velocity field then obtained is fully determined, and a direct calculation can be made. However, in most cases, a turbulent regime will be encountered. Due to the fluctuating nature of the velocity, a specific formulation will be required to consider turbulent effects on cell dispersion (using, for example, a stochastic model; see Pruvost et al. 2008). Once cell trajectories are known, the light regime is then obtained by introducing the radiative transfer model. However, as shown in Pruvost et al. (2008), attention must be paid to the formulation of the coupling. Mixing can influence the spatial distribution of particles participating in radiative transfer, resulting in a non-linear modification of the radiation field (Cassano et al. 1995). The calculation method for the radiative transfer has thus to be modified to take into account the

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Fig. 10.14: Example of cell displacement along the light gradient (top) and corresponding light/ dark cycles (down). Results were obtained here for a torus-shaped PBR with mechanical mixing (marine impeller). The flow field was determined using CFD. Cell trajectories from a Lagrangian approach were combined with radiative transfer modeling (corrected here to obtain an energetically consistent formulation) to calculate the resulting light regime. See Pruvost et al. (2008) for details.

effect of non-ideal mixing conditions. An oversimplified formulation (as usually proposed), where cell trajectories and radiative transfer are solved independently, results in a wrong formulation of the Lagrangian characterization of light regimes encountered by flowing cells in the PBR. This false representation of light availability in the reactor can lead to a significant overestimation of the L/D cycle effects

10.6 Nomenclature

217

on resulting growth (by increasing light received by algae, leading to an energy imbalance contravening the first law of thermodynamics). A correction of radiative transfer with respect to the time spent by flowing cells along the depth of culture is necessary here. Typical results of cell trajectories obtained using a Lagrangian approach are given in Figure 10.14. These results emphasize the rapid variations in light intensity when cells flow along the light gradient (the example is given here for a torusshaped PBR mixed with a marine impeller). If there is a dynamic kinetic coupling between biological response and fluctuating light regimes encountered by flowing cells (the L/D cycle effect), PBR efficiency will be modified due to the non-linearity thereby added to light conversion. As it allows cell history to be represented, the further coupling of the Lagrangian approach (with a rigorous treatment of the radiative transfer problem, as previously discussed) with kinetic models of photosynthesis is direct. This opens perspectives to adapt light regimes in PBRs with respect to biological response timescales (especially when using CFD, which allows various hydrodynamic conditions to be simulated, such as modification of flow rate, aeration or impeller rotation speed). Formulation of a kinetic model of photosynthetic growth able to represent L/D cycle effects is, however, far from trivial, L/D cycles being widely distributed in frequency and magnitude, with effects strongly dependent on the cultivated species. It is also not totally clear at what level L/D cycles interfere in the metabolism. Effects have been reported on the alteration of instantaneous photosynthetic conversion (Kok effect), but also photoacclimation with progressive pigment modifications (Janssen et al. 1999, 2000). Some attempts to devise dynamic models can be found in the literature (Pahl-Wostl 1992; Eilers and Peeters 1993; Wu and Merchuk 2001, 2002; Camacho et al. 2003; Luo and Al-Dahhan 2004; Wu and Merchuk 2004; Yoshimoto et al. 2005), but more work is still clearly needed to develop robust, generalizable dynamic models. Optimization and modeling of L/D cycle effects in microalgal cultivation systems thus await further research efforts.

10.5 Acknowledgments This book chapter is the result of many years of collaborative work. The authors thank all their colleagues and PhD students involved in this long-term ongoing research effort. This work was also supported by several French and European research programs (ANR BIOSOLIS, SHAMASH, ALGOH2, SOLARH2).

10.6 Nomenclature a Volumetric absorption coefficient (m–1) alight Specific illuminated area for any given photobioreactor (m–1)

218 A

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Specific local volumetric radiant power density absorbed (μmol · s–1 · kg–1 or W · kg–1) bλ Back-scattered fraction for radiation of wavelength λ (dimensionless) C(j) Mass concentration (for species j) (kg · m–3 or g · L–1) CX Biomass concentration (kg · m–3 or g · L–1) D Dilution rate of the photobioreactor (s–1 or h–1) DL Coefficient of axial dispersion (m2 · s–1) Ea Mass absorption coefficient (m2 · kg–1) Es Mass scattering coefficient (m2 · kg–1) fd Design dark volume fraction of any photobioreactor (dimensionless) G Local spherical irradiance (W · m–2 or μmol · s–1 · m–2) GC Local spherical irradiance for compensation point (W · m–2 or μmol · s–1 · m–2) I Specific radiant intensity (W · m–2 · sr–1 or μmol · s–1 · m–2 · sr–1) Ji Molar specific rate for species i (mol(i) · kg X–1 · s–1) K Half saturation constant for photosynthesis (W · m–2 or μmol · s–1 · m–2) Kr Saturation constant for respiration inhibition at light (W · m–2 or μmol · s–1 · m–2) L Total length of a rectangular photobioreactor (m) MX C-molar mass for the biomass (kg · mol–1) n Outward normal to a surface (dimensionless) n Degree of collimation for the radiation field (dimensionless) p (Ω , Ω′) or p( β , β′) Phase function for scattering (dimensionless) q Photon (or radiant) flux density (W · m–2 or μmol · s–1 · m–2) qx Projection of the hemispherical incident photon flux density on a surface perpendicular to the x-axis (W · m–2 or μmol · s–1 · m–2) q∩ Diffuse hemispherical incident photon flux density (PFD) in the PAR (W · m–2 or μmol · s–1 · m–2) q// Collimated hemispherical incident photon flux density (PFD) in the PAR (W · m–2 or μmol · s–1 · m–2) q⊥ Normally collimated hemispherical incident photon flux density (PFD) in the PAR (W · m–2 or μmol · s–1 · m–2) Q Volume liquid flow rate (m3 · s–1 or m3 · h–1) QP Photosynthetic quotient (dimensionless) ri Mass volumetric rate for species i (kg · m–3 · s–1 or kg · m–3 · h–1) ri′ Mole volumetric rate for species i (mol · m–3 · s–1 or mol · m–3 · h–1) rX Biomass volumetric growth rate (productivity) (kg · m–3 · s–1 or kg · m–3 · h–1) R Radius of any photobioreactor (m) s Volumetric scattering coefficient (m–1) S Surface (m2) Slight Illuminated surface of any photobioreactor (m2) t Time (s or h) u Liquid velocity (m-s–1) UP Particle velocity (m-s–1)

10.6 Nomenclature

V Vℓ xd x, z zC

219

Volume (m3 or L) Illuminated volume inside the photobioreactor (m3 or L) Fraction of diffuse radiation in the total incident solar flux density (PAR) (dimensionless) x- or z- direction, length (m) Extinction length corresponding to irradiance of compensation GC (m)

Greek letters α Linear scattering modulus for the two-flux model approximation (dimensionless) β, β′ Polar angles (rad) γ Fraction for working illuminated volume in the photobioreactor (dimensionless) δ Extinction coefficient for the two-flux model approximation (m–1) Δg′i0 Standard Gibbs free enthalpy for species i (J · mol–1) Thermodynamic efficiency of the photobioreactor (dimensionless) ηth Θ Polar angle (rad) θ Polar angle (rad) λ Wavelength (m) μ = cosθ (dimensionless) Chemical potential for species i (J · mol–1) μi ˜ ρ Energetic yield for photon conversion (dimensionless) ρM Maximum energetic yield for photon conversion (dimensionless) τ Hydraulic residence time (s or h or days) τλ Optical thickness (dimensionless) Stoichiometric coefficient (dimensionless) υi−j ′ Biomass mole quantum yield for the Z-scheme of photosynthesis φX (mol X · μmolhν–1) ′ Oxygen mole quantum yield for the Z-scheme of photosynthesis φO2 (molO2 · μmolhν–1) φ Azimuth angle (rad) Φ Azimuth angle (rad) ω Solid angle (rad) Albedo of single scattering for the wavelength λ (dimensionless) ϖλ Ω Unit directional vector (dimensionless)

Subscripts 0 Relative to the input surface of a rectangular photobioreactor (u = 0) c Relative to the compensation point for photosynthesis col Relative to collimated radiation

220 dif i R λ max

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Relative to diffuse radiation Relative to input quantity Relative to the reactor Relative to a spectral quantity for the wavelength λ Maximum value for the volumetric productivity rX

Superscripts col Relative to collimated radiation dif Relative to diffuse radiation

Other 1 ∫ X= X dt Time averaging Δt Δt 1 < X > = ∭ X dV Spatial averaging V V

¯

Abbreviations CSTR Completely stirred tank reactor DOM Differential discrete ordinates method PAR Photosynthetically active radiation PBR Photobioreactor PFD Photon flux density PFTR Plug flow tubular reactor RTE Radiative transfer equation

References Aiba, S. 1982. Growth kinetics of photosynthetic microorganisms. Adv. Biochem. Eng. Biotechnol. 23, 85–156. Aris, R. 1999. Elementary Chemical Reactor Analysis. Dover, New York. Berberoglu, H. and L. Pilon. 2007. Experimental measurements of the radiation characteristics of Anabaena variabilis ATCC 29413-U and Rhodobacter sphaeroides ATCC 49419. Int. J. Hydrogen Energy 32, 4772–4785. Bidigare, R. R., M. E. Ondrusek, J. H. Morrow and D. A. Kiefer. 1990. In vivo absorption properties of algal pigments. SPIE Ocean Optics X 1302, 290–302. Bohren, C. F., D. R. Huffman. 1983. Absorption and Scattering of Light by Small Particles. John Wiley and Sons, New York. Camacho, F. R., F. G. Camacho, F. J. M. Sevilla, Y. Chisti and E. Molina Grima. 2003. A mechanistic model of photosynthesis in microalgae. Biotechnol. Bioeng. 81 (4), 459–473. Cassano, A. E., C. A. Martin, R. J. Brandi and O. M. Alfano. 1995. Photoreactor analysis and design: fundamentals and applications. Ind. Eng. Chem. Res. 34, 2155–2201.

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Linda Oeschger and Clemens Posten

11 Construction and assessment parameters of photobioreactors 11.1 Introduction The current boom in microalgae biotechnology has led to a further strong increase in the expectation that the production of biofuels (methane, biodiesel, bioethanol) from microalgae will be sustainable both energetically and financially (Greenwell et al. 2010). Just the life cycle assessment (LCA) studies published in this and the previous year would fill an entire bibliography (e.g. Tredici 2010; Wijffels and Barbosa 2010; Amaro et al. 2011; Brentner et al. 2011; Norsker et al. 2011). Schlagermann et al. puplished a review about combustion issues in 2012. The core issues are that the data basis is very sparse and the criteria are not clearly formulated. The technical centerpiece of the production of microalgae biomass is naturally the photobioreactor. Several features for assessing a photobioreactor will be given in the following. The algae boom in the past few years has led to an almost incomprehensible number of new types of reactors, and the operation of some of them has been terminated almost as quickly. Even today there is no precise scientific consensus as to how a photobioreactor has to be constructed. Precise studies of defined aspects are, in contrast to simple trial and error, still infrequent (e.g. Rosello Sastre and Posten 2007). Consensus has, however, been reached on several issues, although these points are not always taken into account (Pulz 2001; Janssen et al. 2003; Richmond 2004; Zijffers et al. 2008; Kunjapur and Eldridge 2010; Morweiser et al. 2010). In this chapter, several types of reactors will be presented, the primary objective being to set a framework for assessing photobioreactors.

11.2 Technical design features In contrast to usual reactors, there must be a transparent surface in order to ensure the light influx reaches the contents. This results in two basic designs which are plate reactors and tubular reactors (Fig. 11.1). Plate reactors are perfused with gas from the bottom to ensure CO2 influx. The pneumatic energy brought in by the bubbles is converted into mechanical mixing energy. Circulation of the medium in tubular reactors is maintained by hydraulic pumps that provide the necessary mechanical energy. Gassing takes place at the beginning and end of a section of tube. Starting from these basic types, the important design features and parameters for process engineering will be discussed.

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Fig. 11.1: Schematic image of the two basic types of reactors: (a) plate reactor, (b) tube reactor (Posten 2009).

11.2.1 Material issues The possible transparent materials include glass, polyvinyl chloride (PVC), polyethylene (PE), polycarbonate (PC), acrylic glass (PMMA), and silicone, all of which have their specific pros and cons. PC exhibits good properties because of its high strength, the fact that it can be cleaned and its weatherability, yet PE is preferred for practical reasons such as availability. Glass is preferred for high-quality applications, but it is heavier and more complicated to process. Coatings are under discussion, for example, a nonstick coating for indoors or a coating for IR reflection outside. A certain amount of material must be employed to maintain mechanical stability and to withstand the hydrostatic pressure. Yet for cost reasons (the material itself, its manufacture and transportation) this should be as minimal as possible. Furthermore, the use of the material is part of the consideration of the amount of energy used, since, in terms of energy, only a limited recycling efficiency (transportation, reshaping) can be expected. From a harvest of biomass amounting to, for example, 20 kg/(m2*a), approximately 10 kg C is bound. This must naturally be clearly higher than the CO2 emitted during the manufacture of the reactor itself. There are hardly any actual values available from outdoor projects. Many reactors have to be supported by mechanical stands and must, furthermore, be under a roof or in a greenhouse to prevent weather-related damage as well as wind pressure. This results in costs and loss of light. Even a water basin can serve as a support for a floating plastic reactor (Fig. 11.4).

11.2.2 Geometric parameters The primary difference between customary bioreactors for heterotrophic processes and photobioreactors is naturally the light influx through the transparent external walls (Fig. 11.2). The relationship of the external surface area that is available to the volume that is supposed to be provided with light AR/VR is therefore an important factor. For a plate with a thickness d, for example, the relationship is 2/d.

11.2 Technical design features

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Fig. 11.2: Schematic representation of important parameters of a photobioreactor.

Common values are in the range of 50/m to 100/m. Modern plants could deliver even higher values for this factor. The photosynthetic activity of algae is dependent on the intensity of the light influx. At very low light intensities (< 20 μE/(m2*s)), photosynthesis hardly surpasses respiration. At small to medium intensities (< 200 μE/(m2*s)), growth increases linearly with light intensity. At high intensities, however, there is saturation or even photoinhibition. Real sunlight exceeds the critical value of beginning light saturation by a factor of 10–20 depending on the strain of algae, time of day, season and location. Any necessary dilution of light can be achieved by increasing the design factor AR/AF, surface area/ground surface area of the transparent surface, whether by adjusting the vertical height or by employing fiber-optic elements. Absorption and scattering attenuate the light (exponentially) passing through the algae suspension, so that only a residual amount reaches the opposite wall. The distance that the light travels is referred to as the free optical path length, dL. On the one hand, no light should be lost, while on the other no dark areas should result in which algae do not grow and even lose cell weight just as a result of respiration. The lower this value (thin layer), the higher the biomass concentration can be without creating a dark area that is too large. From a balanced viewpoint, the amount of algae produced depends on the irradiation, and this amount should be diluted in only a small amount of water. Naturally, a compromise has to be found for the given parameters, since the amount of material increases with increasing dilution of light and with shorter optical paths. While the given parameters refer to the reactor’s surface area, they also directly determine the volume of medium VR in the reactor and thus the amount of medium piled up on the ground area AF. The parameter fluid coverage VR/AF gives the amount of medium that is piled on the “footprint area”. A typical value is 100 L/m2. Higher values indicate a greater weight and a lower intensity for the process.

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In principle, the volumetric productivity measured in the laboratory cannot simply be multiplied by the amount of medium, since the amount of biomass produced per ground surface area is determined solely by the incident light. This mistake is made over and over again when the anticipated performance of reactors is predicted, leading to completely exaggerated expectations. The same false impression is caused by the statement that the volume of medium is necessarily the reaction volume. This tenet from classical reaction technology naturally cannot be applied in this case, since only the cell volume can count as the reaction volume. It is furthermore counterproductive that a high volume of medium leads to a lower concentration of biomass and to a loss of energy, as will be demonstrated in the next section.

11.2.3 Hydrodynamic parameters The pressure at the base of the reactor results from the hydrostatic pressure of the column of medium. It exerts a direct influence on the required strength of the transparent material and the energy needed to generate the bubbles. Neither of these aspects causes any particular problem in classical reactors for heterotrophic products. In photobioreactors, however, this can become a problem because at higher pressures, the qualities demanded of the material increase, as does, above all, the specific energy per volume or per area needed for gassing. As in any reactor, the contents must be mixed properly to prevent the formation of gradients. In the case of tubular reactors, the mixing time in the axial direction is the time the medium needs to be circulated once through the tubes or the passage time between two gassing points (Hall et al. 2003), and so this is denoted by tube length and pumping velocity. With increasing oxygen concentration and decreasing carbon dioxide concentration along the tube, the axial mixing time should not exceed 2 min, for example. In the case of plate reactors, mixing along the main axis, which here is the vertical axis, is mainly by the vortices, which are induced by the bubbles and the bubble rising time. Dispersion coefficient values in the range of 100 s are acceptable. The mixing time in the direction normal to the transparent surface (radial for tubes, thickness for plate) should not significantly exceed 1 s in order to utilize the fluctuating light effect, i.e. not to leave the cells in the particularly bright front areas or the dark rear areas too long (Grobbelaar et al. 1996). A nontrivial amount of mechanical energy is however necessary to this end. The flow through of a reactor in the axial direction is necessary in order to move the fluid from one gassing point to the next one and eventually remove sedimented cells, sometimes exceeding 0.3 m/s in tube reactors. This, furthermore, also improves radial mixing (Molina et al. 2001; Perner-Nochta and Posten 2007). In plate reactors, the bubbles induce circular flows that lead to axial fluid velocities rates of approximately 0.1 m/s, which can be significantly higher in certain specific designs.

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Fig. 11.3: Novel principle of a tubular reactor (“Christmas Tree”) from the firm Gicon (Cotta 2011). The design guarantees a good exposure to light during the course of the day. Low energy input and a high dedicated radial mixing rate are produced by a novel pulsed procedure during gas inflow. This also prevents fouling on the tubular walls. Short light paths are achieved using an internal tube, which also maintains the correct temperature.

This results in acceptable axial dispersion coefficients (Camacho Rubio et al. 2004). Turbulent flow facilitates mixing in the normal direction (which is particularly necessary for high concentrations of cells), while laminar flow saves energy and is gentler on the cells. A fundamental consideration of hydrodynamics in photobioreactors can be found in Pruvost et al. (2011). Both open ponds and, even more so, enclosed bioreactors thus need auxiliary power essentially for mixing, gassing and transport. In tube reactors, more (sometimes much more) than 400 W/m3 is customary (Babcock et al. 2002), while in plate reactors the lowest level is 50 W/m3 (Sierra et al. 2008). However, lower values for tubular values are possible (Norsker et al. 2012). This last value increases however because of the increase in hydrostatic pressure with height in plate reactors. Because of its enclosed design, the pump energy in tube reactors does not increase significantly with height. It is important to understand that high values for productivity are often purchased by using a high energy input to improve the light integration and mass transfer. For example, open ponds employ little auxiliary energy (e.g. 1 W/m3) but their productivity is also correspondingly low. Current directions of research attempt to reach a lowenergy mix by bundling the mechanical energy at certain frequencies that facilitate growth in the region of 10 Hz (see Chapter 12 and Fig. 11.3 for the Gicon reactor). Even the use of energy flows from the environment has been proposed, for example the use of wave energy at a NASA reactor (NASA 2011). It is hoped that a bubblefree gassing procedure can lead to another clear decline in pneumatic energy input (Fan et al. 2008; Posten 2009). Photobioreactors are gassed with a mixture of air and CO2, with the CO2 serving as carbon source for the algae. The stoichiometric requirement is approximately

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1.8–2.5 g of CO2 per gram of dry mass of algae created, depending on the lipid content. There must however be a sufficiently high partial pressure in the liquid phase e.g. 0.1 % so that the CO2 absorption by the algae is not limited (Yang and Gao 2003) and light energy is not lost. The air portion serves to prevent gradients and blending. Customary values are 0.2 vvm aeration rates or even higher if flue gasses (or other gasses from fermentation or the chemical industry e.g. 10 % CO2) are utilized. Reactors that are pumped directly can also be supplied with pure CO2. The parameters that can be used to assess this are the volumetric mass transfer coefficient (kLa value), the volumetric CO2 transfer rate (CPR), together with the corresponding CO2 production rate (CPR) and the degree of CO2 utilization. Open ponds have, for example, a relatively low need for auxiliary energy, yet their productivity is also low. There are hardly any systematically measured correlations between energy input and productivity.

11.3 Measured performance criteria A high value for volumetric productivity measured as “space–time yield” is a sign of a reactor’s intense operation. Yet it is not permissible to simply project laboratory values because an ideal supply of light is impossible on a large scale in outdoor operation. When sunlight is used, the energy input from light will not increase simply by piling up more medium on a certain ground surface area (see later on photoconversion energy, PCE). In the lab, only 1 g/(L*d) can be attained and only for mid-range concentrations of biomass. For photobioreactors operated only with sunlight, the decisive value is the solar irradiance per area, independent of the precise geometry. Here is a sample calculation. In central Europe, the entire energy falling on a square meter is, for example, 1,200 kWh/(m2*a) or 4,320 MJ/(m2*a). The energy in algae biomass is approximately 20 MJ/kg. If the maximal PCE is 5 %, then we can reckon with a maximal biomass harvest of 30 g/(m2*d) or 100 t/(ha*a). If the oil level is high, the energy in the algae climbs toward 27 MJ/kg. Given the same PCE, the productivity of dry biomass per area thus sinks because of the higher specific energy content of the biomass (Chisti 2008). Thus, the highest measured values for productivity and for oil concentration cannot simply be multiplied to calculate the anticipated productivity for oil. Even worse, the PCE values are lower for high oil contents; see Chapter 3. For sunnier regions, the insolation from higher solar irradiation can be maximally slightly more than twice as much. Reliable data on the actual productivity per area are available only for relatively few outdoor facilities, and then only for limited periods of a few weeks and areas far smaller than 1 ha (e.g. Chini Zittelli et al. 2006). The data for the 1 ha facility in Klötze, Germany, given as 100 t/(ha*a) (Roquette Klötze GmbH & Co. KG 2011), are frequently used as a reference value. Yet we must take into account that the culture is partially operated heterotrophically and therefore cannot serve as a standard for biofuel production. Furthermore, in terms of its investment costs and

11.4 Mode and stability of operation

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the energy required, the facility is designed for high-value products. The greatest caution is necessary when a life-cycle assessment (LCA) is prepared using data from the Internet or other literature without having further background information. To assess the effectiveness of a reactor, the productivity has to be related to the incident amount of light. The PCE (which should not be confused with photosynthetic efficiency or photon efficiency PE from biological studies) gives the relationship of the energy stored in the biomass to the radiant energy that reached the reactor’s ground surface area. Optimistic values are at 5 %, and values up to 10 % are considered theoretically possible (Chisti 2007; Schenk et al. 2008; Zhu et al. 2008). The higher the algae’s oil content, the lower (reduction of up to 30 %) the PCE (Wilhelm and Selmar 2011). This value takes into account losses of light in the reactor itself, such as reflections on the surface or light that falls to the ground after passing between the individual modules of the reactor. This value is the ultimate standard for the efficiency of algal growth, but in some circumstances it is paid for by costs at diverse points (mixing, light dilution; see above). This energy has to be deducted from the chemical product (biofuel) that is produced when the energy balance is determined. As described below, the energy flux that is produced can only amount to about 5 W/m2 in the form of the heat of combustion. Given, for example, a performance input of 50 W/m3 and medium amounting to 100 L/m2, that amount would already be exhausted. Only a reactor that employs the absolute minimum for mixing energy – pneumatically for creating bubbles in a plate reactor and mechanical energy for pumping in a tube reactor – can be used for the production of biofuels. One approach to such minimization is to adjust the actual need for CO2 and the mixing intensity to the momentary consumption, which is permanently changing in response, for example, to the current position of the Sun. It is extremely rare for genuine measurements of energy consumption for longer periods of cultivation to be available. At any rate, the amount of auxiliary energy is currently a reason obstructing the effective production of biofuels out of microalgae, but this is a topic that is the object of intense research. The concentration of biomass should in principle be as high as possible to make an intensive process possible. This also facilitates cell separation in the next step. Increasing the cell concentration for example from 1 g/L to 2 g/L reduces the fluid volume per amount of biomass to be fed through the centrifuge/filter by 50 % and thus contributes decisively to reducing the downstream costs. Typical values are 5 g⋅L–1. This has an immediate positive impact on water consumption and harvesting costs (Norsker et al. 2011). Temperature control in closed systems is often a major concern, as it comes at a high price in terms of investment and energy (e.g. sprinklers) and water use (evaporation). In the ProviAPT design, the temperature is buffered by the enclosed body of water supporting the structure. Water use is minimal. The equipment can also accommodate the use of excess low-grade heat to cope with negative effects of diurnal suboptimal temperatures at sunrise (such as in spring or in a desert environment) and/or to allow for year-round (or prolonged) production in temperate climates without the need for greenhouse construction. The setup can be easily interfaced with flue-gas sources as a means to feed the production process with CO2. This approach is generally recognized as essential, to reduce nutrient costs. Additional reductions in operational costs are obtained by thorough automatization of the cultivation process. At present, the entire production process is already remotely controlled. A reduction in labour demand to well below 0.18 FTE⋅ha–1 (see Lundquist et al. 2010) is envisaged. Such reduction in labour is vital for successful entry into the biofuels and commodity market (Acién 2012).

13.3 Prospects The ProviAPT concept is still in an (advanced) developmental phase, but at the time of publication, Proviron is running an 840 m2 operation close to Antwerp (Belgium) in which Nannochloropsis species and Isochrysis species are being cultivated. The ProviAPT system has, however, proven to be compatible with sensitive and recalcitrant microalgae species such as Chaetoceros and Pavlova. Proviron has adopted a dual marketing strategy: in the short term, it will market and sell algae in markets such as (but not limited to) aquaculture, cosmetics, pharmaceuticals, pet food and human food. In the long term, Proviron envisages licensing its algaeproduction technology to biofuel-producing companies or other large-scale biomass producers.

References Acién, F. G., Fernandez J. M., Magan J. J., Molina E. 2012. Production cost of a real microalgae plant and strategies to reduce it. Biotechnol. Adv. (in press) Lundquist, T. J., Woertz, I. C., Quinn, N. W. T., Benemann, J. R. 2010. A realistic technology and engineering assessment of algae biofuel production. Energy Biosciences Institute. Michiels, M. 2009. Bioreactor. EP 2009/2039753. Norsker, N. -H., Barbosa, M. J., Vermuë, M. H., Wijffels, R. H. 2011. Microalgal production – A close look at the economics. Biotechnol. Adv. 29: 24–27. Posten, C. 2009. Design principles of photo-bioreactors for cultivation of microalgae. Eng. Life Sci. 9: 165–177.

Alexander Piek

14 Case study: Biomass from open ponds 14.1 Introduction The subject of this case study is the production process of dried microalgae biomass from open ponds with main focus on the disc stack centrifuges in this process. Continuously operating disk-stack centrifuges (separators) have become established in the production of algae biomass on an industrial scale. Hemfort (1984) describes details concerning the function principle and selection criteria for separators.

14.2 Production process The production process for dried microalgae biomass from open ponds is shown in Figure 14.1. As soon as the microalgae in the open pond have attained an appropriate concentration, the suspension is pumped to a harvest tank which acts as a buffer. The harvest tank compensates for fluctuations in the feed and thus guarantees the stability of the process.

Fig. 14.1: Production process for dried microalgae biomass from open ponds.

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14.2.1 Removal of coarse solids Coarse solids such as leaves, birds’ feathers, insects and plastic components from the surroundings find their way into open ponds and have to be removed from the algae suspension. Continuously operating rotary brush strainers are used for this purpose. In moderate climate zones, sand can also be blown by the wind into the open ponds. Hydro-cyclones are used to remove the sand, which not only has a negative impact on the product quality but also causes damage by erosion to the separators and downstream equipment such as pumps, valves and also driers. The correct sequence must always be followed for using rotary brush strainers and hydro-cyclones. The rotary brush strainer is always installed upstream of the hydro-cyclone in order to prevent clogging as a result of coarse particles.

14.2.2 Concentrating the biomass After unwanted particles have been removed, the algae biomass is concentrated in fully continuous separators operating in parallel. Because the solids concentration is relatively low in this process stage (approx. 0.1 %/1 g/l), self-cleaning separators (clarifier) are used for this purpose. Separators with a self-cleaning bowl are able to periodically discharge the separated solids at full operating speed. For this purpose, several apertures are spaced regularly on the periphery of the bowl. These apertures are opened and closed by a moving piston valve, which is located in the bottom part of the bowl. The opening mechanism is operated by hydraulic means. This opening mechanism enables part of the bowl volume (partial discharges) and also the entire contents of the bowl (total discharges) to be discharged. Depending on the biomass concentration in the feed, the concentration factor in these arrangements is up to 500 (from 0.05 % to 25 %). The hydrostop system of GEA Westfalia Separator Group is a discharge system that, in terms of solids concentration, can be adjusted precisely and in a reproducible manner to specific requirements. This discharge system enables the discharge process to be optimized to the shortest period of time. The system reduces the actual discharge time to less than one tenth of a second and permits partial discharges at 30-second intervals. This ensures that even small volumes of 1.5–2 L can be discharged in a reproducible manner with an error of less than 10 %. With this technology, almost the entire free water can be separated from the algae biomass. Water that is bound or located in the algae cells cannot be separated by means of centrifugal separating technology. Depending on the type of algae, the attainable dry mass in the concentrate fluctuates between 14 and 25 % (30 % is also possible in exceptional cases). Most types of algae can be ejected at full speed without being damaged. A special feed system, which accelerates the algae cells to the bowl speed using

14.2 Production process

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Fig. 14.2: Chlorella biomass ejected by a hydrostop separator.

a particularly gentle process, is available for particularly shear-sensitive species. Furthermore, the bowl speed when discharging the algae biomass can be reduced, which considerably reduces the stress on the individual algae cells. Figure 14.2 shows Chlorella biomass with a dry mass content of approx. 30 % that has been discharged by a separator with a hydostop ejection system. As can be seen from the figure, this biomass is no longer fluid, which means that further processing with pumps and drying using spray or drum driers is not possible in this case. Most algae downstream processes – including that described in this case study – require a fluid algae concentrate with a dry mass of 14–18 %. The relevant dry mass of the algae concentrate can be adjusted for most separators. The centrate, in other words the clarified algae-free liquid, is discharged under pressure by a centripetal pump in the separator; it is disposed of in the form of effluent or recycled into the algae cultivation system.

14.2.3 Washing the biomass The algae biomass that is concentrated in the separators in the first stage is pumped by an excentric screw pump into a tank and mixed with fresh water (at a ratio of 1/3 to 2/3). The washing stage further improves the product quality and is also intended to remove bacteria and salts. The solids concentration in the washing tank is higher than the concentration in the first stage. Consequently, a nozzletype separator can then be used, with a continuous discharge for both phases, namely the solids and also the centrate. The solids concentrate is discharged into a buffer tank and is then spray-dried; the end product is a dry algae powder, which can be stored.

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14.2.4 Differences to closed photo-bioreactors In contrast to algae biomass from open ponds, production of algae biomass from closed photo-bioreactors does not require preprocessing using rotary brush strainers and hydro-cyclones, as, in principle, no foreign bodies enter the reactor. The algae biomass concentration in closed photo-bioreactors is also generally higher.

14.3 Energy consumption Separators in the latest energy-saving general administrative expenses with an integrated direct drive, as shown in Figure 14.3, require an electrical capacity of less than 0.8 kWh for each cubic meter of feed capacity. It should be borne in mind that the installed motor capacity of a separator does not provide any indication of the actual energy consumption during operation. The following formula is used for calculating the electrical capacity of a three-phase motor in W: P = √3 · U · I · cosφ · η where: P = power in Watts; U = motor terminal voltage in Volts; I = motor current in Amperes; cosφ = power factor; η = efficiency.

Fig. 14.3: Clarifying separator SSI 400-06-772.

14.4 Survey of process relevant data

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The energy consumption of a self-cleaning separator depends on several factors, such as feed capacity, frequency of emptying, feed pressure and clear-phase discharge pressure. Depending on the type of algae, 0.4–0.8 kWh is required to harvest 1 kg of algae dry mass.

14.4 Survey of process relevant data Table 14.1 shows the technical data for a clarifying separator from GEA Westfalia Separator, which is used for the industrial production of algae biomass. G-force at inner bowl diameter Equivalent clarification area ST Maximum feed capacity Installed motor power Energy consumption

8,800 400,000 m2 90 m3/h 55 kW 0.4–0.8 kWh/m3

Table 1.1: Technical data on an industrial algae separator from GEA Westfalia Separator Group.

Table 14.2 lists the process-relevant production parameters for the algae types that are produced on an industrial scale in open systems with separators. The economically important microalgae Spirulina is not listed here, as this is predominantly processed with belt filters. Algae species

Chlorella

Dunaliella

Nannochloropsis

Particle size (μm) Feed concentration (v/v) Separation temperature (°C) Cl– content (ppm) Viscosity of suspension (mPas) Specific gravity of cells (kg/L) Specific gravity of cell-free culture media (kg/L) pH

4–10 0.4–1.0 15–30 50–80 0.9–1.4 1.05–1.13 1.00–1.01 6.5–7.8

3–5 0.1–0.5 25–45 60,000–150,000 1.4–2.3 1.06–1.20 1.05–1.18 7.2–9.0

1–4 0.2–0.4 15–30 9,000–25,000 1.2–1.4 1.04–1.15 1.02–1.10 6.8–7.7

Table 1.2: Algae parameters for an open pond system.

With regard to separating efficiency, the feed capacity of a separator depends on the properties of the algae suspension. Therefore, if Chlorella is easier to separate than Dunaliella and Nannochloropsis, the feed capacity of a separator is accordingly higher in this case. Further information about the separation behaviour of suspensions can be found in Hemfort (1984). It should be noted that corrosion-resistant stainless steel must be used for processing salt-water algae. Since even algae of the same type can have a very different separation behaviour, trials should be carried out with a pilot separator if there are any doubts when designing an industrial system.

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Reference Hemfort, H. 1984. Separators – Centrifuges for Clarification, Separation and Extraction Processes. Westfalia Separator AG, Oelde.

Marco Brocken

15 Case study: Spiral plate technology for totally dewatering algae alive 15.1 Introduction In the dairy industry, the produce of most farmers is processed in one central plant. The interface between the farms and this processing plant is a standard cooled tank in which the fresh milk is stored and from which the product is pumped in the transportation truck. In this perspective, many algae growers will deliver their product to specialized processing plants. This method requires sufficient algae shelf-life and a standardized transportation method for the dewatered algae. Evodos has taken this widely used agricultural harvesting approach as the basis for developing its algae-harvesting technology. While harvesting using the Evodos machines, the algae are collected as a living organism free of extracellular water in the form of a paste. This algal paste is as compact as possible without losing organic material or changing the structure. Also, most bacteria do not appear in the algal paste. This will yield a pure product with the longest possible shelf-life and with minimal volume. Therefore, the algal paste that is harvested is the ideal interface between growing algae and further processing. To use the analogy, “Evodos is the milk cooling tank of the algae industry”, which is where Evodos is positioned in the algae value chain.

15.2 Separation technology 15.2.1 Evodos technology Evodos has decided to redesign the hydrodynamics of centrifugals fundamentally by introducing Spiral Plate Technology (SPT) (Figures 15.1 and 15.2). This technology is effective for suspended particles in the range of 1–100 μm, and the separation sharpness is especially high. The solids come out as a liquid-free cake. The free liquid content in the drum is removed before discharging the collected solids, resulting in a dry, solid, discharged cake that far exceeds industry standards. The machines (Fig. 15.3) are self-adjusting to changing process parameters. Self-Adjusting Interface Level technology allows real-time process adjustment, even for instantaneous changes in process parameters. There are no gravity rings and no electronics to be set. This allows many new centrifugal separation applications

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Fig. 15.1: Schematic top view on internal plates of the separator.

Fig. 15.2: Photograph of the internal plates.

such as handling liquids containing soft solids, fatty, sticky and greasy solids, and even abrasive solids. The use of chemicals is not required, so the costs are negligible.

15.2.2 Key design parameters The key design parameters are driven by the customer’s requirements and unlimited by pre-conceived notions. The solids are discharged as dry as possible; at only 800 G, the paste/solid discharge is gentle and non-pressurized (Fig. 15.4). The results are high separation efficiencies and cutoff rates combined with dry solid

15.2 Separation technology

Fig. 15.3: Photographs of (left) Evodos type 25 and (right) type 10.

Fig. 15.4: Discharge principle.

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Fig. 15.5: Biomass after the harvesting process.

discharge and low energy requirements. The minimal energy demand results in < 0.2 °C temperature difference between the feed and discharge effluent flow. With this technology, many types of solids can be separated. These can be nonpermeable, soft, greasy and/or abrasive solids. The coaxial laminar flow allows the solid–liquid separation to occur at the highest G-field. The machines work independently of the permeability of a mixture. The settling distance is minimal, and no chemicals are needed. With the free interface level, no gravity disks or pressure-controlled interfaces are required. The centrifuges are designed to have a free axis of rotation. Therefore, no balancing is needed during manufacturing or repair, and noise and vibration levels are minimized, which means minimal maintenance. For easy maintenance, the plate pack can be readily accessed without special tools.

15.3 Operational characteristics The harvesting process with Evodos results in a totally dewatered algal paste. This is demonstrated on multiple strains varying from saltwater microalgae such as Nannochloropis sp. to freshwater strains such as Chlorella. In all instances the result of the harvesting process is a totally dewatered algal paste that is over 98 % free of extracellular water. The dry solid content of this paste only differs because various algae strains have a different level of intercellular water. For example, with Nannochloropsis sp., the dry solid content of algae cake produced is ±31.5 % DW (Fig. 15.5). With the smooth separation and discharge process, all algae (100 %) are harvested alive. Tests on many different micro-algae strains such as Nannochloropsis sp., Chlorella sp., Haematococcus sp., Tetraselmis sp., Scenedesmus obliquus, S. dimorphus, S. quadricauda, Maesotaenium sp., Ankistrodesmus sp. and Tetraedron

15.3 Operational characteristics

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Input concentration ( g DW/L)

Energy consumption (kWh/kg DW)

Energy balance (%)

0.5 1 2.5 5

2.53 1.31 0.58 0.21

45.5 23.6 10.4 6.0

Tab. 15.1: Separation energy needed with Evodos type 25 as a function of Nannochloropsis feed concentration at 3.8 m³/h.

sp. show that there is no shearing or thermal damage to the harvested algae. Most bacteria are not captured in the harvested algal paste. Since the algae do not change in structure and/or temperature, the value of the harvested product is optimized. The algae remain intact, so there is no loss of biological material in the water fraction, and because all algae are harvested alive, this has the benefit of a long possible shelf-life of up to a week. The effluent water after total dewatering has excellent re-use potential. No biomass or lipids are lost during the harvesting process, since all algae are harvested alive. This reduces the risk of contamination when re-using the water for the next growth cycle. The rise in effluent water temperature is only 0.2 °C, which makes it convenient to re-use. Total dewatering with centrifugal machines uses the patented Evodos Spiral Platepack Technology (Boele 2009). The fluid and mechanical dynamics of this technology are completely different from conventional machinery. With an effectiveness of over 99 %, while running at high feed flow settings, the degree of separation is very high. For example, the Evodos type 25 machine has a feed flow capacity of 4.0 m³/h. Results with a fresh water Nannochloropsis specie 3 show that even this small strain is > 99 % separated at a feed flow setting of 3.8 m³/h. This technology is characterized by a high separation efficiency at high feed flow settings. Only a minimum G-force is required. The separation process runs under 3,000G, and the discharge process under 3,000G. One of the resulting benefits is the low energy requirement. The energy demand for each centrifugal varies between 0.85 and 0.95 kWh/m³ feed flow rate. When the dilution of the feed flow is low, a pre-concentration step can be applied to reduce energy requirements further. Table 15.1 shows the positive energy balance, even at low feed flow concentrations. There is an energy surplus when comparing the energy content of the algae (5.55 kWh/kg DW) with the energy requirement for harvesting the algae. The harvesting costs per ton of algae harvested (dry weight) are mainly dependent on the concentration of the algae in the feed flow. This also applies to the energy costs. In Table 15.2, the costs and energy are shown for the case of a 100 ha algae plant. At this plant, 6,150 ton of algae (DW) are harvested per year.

258

15 Case study: Spiral plate technology for totally dewatering algae alive

Input concentration (kg DW/m³)

Capex + Opex (E/t DW)

Energy consumption (kWh/t DW)

0.5 1 2.5 5

655 350 155 100

2.530 1.310 580 210

Tab. 15.2: Calculated costs and energy consumption of separation of a 100 ha plant as a function of algae feed concentration

The costs are listed in Table 15.2 for specific input concentrations. The following assumptions are made: – interest rate: 5 %; – depreciation on equipment: 10 years; – electricity costs: € 0.08/kWh. The field data are derived from MBD Energy in Australia, and the calculation method has been validated by the Chief Technological Officer of MBD Energy.

Reference Boele, H. A. 2009. WO 2009/005355A1.

Index absorption 39, 40, 41, 45 absorption and scattering coefficients 201 absorption efficiency 45, 46 absorptivity 40, 45, 46 acquisition of novel traits 56 Acrochaetium 26 activity is unusual 68 aeration, gassing 164, 225, 228, 229, 234, 238, 242 AF ground surface area 227, 228, 230, 231, 233 Agrobacterium tumefaciens 88 airlift 167, 173 alanine 60 algae 11, 16 algae biomass production 131 algae concentrate 249 algae symbiotic relationships 55 algae-specific symbiosis 55 algal crude oil 130 algal evolution 17 algal lipids 130 allelopathic agents 74 alternative electron cycling 47, 48 alveolates 19 δ-amino-levulinic acid 68 anabiosis 74 anaerobic digestion 139, 140 antibiotics 28 antibody 96 aphanizomenon flos-aquae 21 apicomplexa 19 apothecia 73 appressorium 71 aquaculture 28 AR surface area 227 arabitol 71 Archaeplastida 17, 22 areal biomass productivity 133 ARG7 93 arid deserts 69 Arthrospira 21 assimilation 40, 43, 46, 47 association 55 Astaxanthin 24 ATP 42, 43, 48 autotrophic photobiont 69

auxiliary energy demand 228, 229, 230, 231, 234, 235 average daily insolation 123 average irradiance 151, 168 axenic cultivation 76, 108 Bacillariophyceae 27 Bacillariophyta 27 baffles 116 batch operation 131, 132 bicarbonate 124 biodiversity of microalgae 15, 16 biofuels 225, 230, 231, 234 biogas 139, 140 biogas yield 140 bioindicator for air purity 69 biomanufacturing 107, 108 biomass 225, 226, 228, 230, 231, 232, 233, 237, 238, 241 biomass/cell concentration 227, 228, 229, 230, 231, 232 biomass dry weight DW 240 biomass productivity 113, 121, 122, 124, 125, 128, 131, 132, 133, 134, 135, 136, 139, 141, 142, 143, 144, 151 biopharmaceuticals 107 bioprocess modeling 181 bioreactor 41, 43, 45, 46, 48, 107, 108 biorefinery 148 bivalves 66 bleaching 62 Botryococcus braunii 25 brown algae 19 bubbles 225, 228, 231, 234 calorific value 135 capital cost 129 carbon allocation 48, 51 carbon dioxide demand 124 carbon dioxide diffusers 125 carbon dioxide loss 124 carbon dioxide requirements 124 carbon loss 42, 44, 50 carbon pool 48, 49, 50, 51 carbonation 155, 170 carboxylation, decarb. 39, 40, 42, 47, 50 carboxysomes 20 carotenoids 20, 22, 77

260

Index

cell energetics 191 cell suspension 107 cells trajectories 215 centric diatoms 27, 28 centrifugal 253, 257 centrifuges 256 cephalopedia 71 cGMP 107 Chaetoceros 28 Charophyceae 23 Chatonella 30 chemical hepatitis 79 chemicals 254, 256 Chlamydomonadales 23 Chlamydomonas 16, 23, 24, 48, 49, 50, 51 Chlamydomonas reinhardtii 87 chlorarachniophytes 17, 18, 19 Chlorella 24, 25, 136, 251 Chlorellidium 30 Chlorococcum 24 Chlorodendrophyceae 23, 24 Chlorogonium 24 Chloroidium 25 Chlorokybus 23 Chlorophyceae 23, 29 Chlorophyll a 45 Chlorophyta 23 chloroplasts 67 chromalveolates 18, 19, 26, 31, 32 chromophytic plastids 68 Chrysophyceae 30, 31 cleaning 226, 232, 233 clonal 108 clones 108 closed photobioreactors 250 Closterium 23 Cnidaria 58 coatings 226, 234 coccolithophorids 31, 32 coccolithophyceae 32 coccoliths 32 codon usage 91 Coleochaetophyceae 23 collimated incidence 182 combined heat and power unit CHP 241 composition of the algal biomass 125 Compsopogonopsis 26 computational fluid dynamics 117 conjugatophyceae 23 consumer price index 129

contact dermatitis 78 contamination 126 contamination, pathogens 226, 233 continuous culture 123, 131, 132 control 181 coral and algae of the genus Symbiodinium coral-reef formation 55 cortex 70 Coscinodiscophyceae 27, 28 cosmetic chemicals 28 costs 226, 230, 231, 232, 233 CO2 225, 226, 228, 229, 230, 231, 234, 237, 238, 240, 242 CO2 fixation 20, 55 Crypthecodinium 33 Cryptomonads 17 cultivation of lichenizable fungi 76 culture pH 124 Cyanobacteria 11, 19, 21, 63 Cyanophora paradoxa 26 cyanophycean starch 20 cyanophycin 20 Cyclotella 28 cylindrical PBR 198 C3 crops 134, 136 dark reaction 43, 47 dark volume 128 dead zones 116 degree of collimation 198 de-novo uptake 64 deoxygenation 130 depsides 78 depsidones 78 design 181 Desmodesmus 24 dewatering 253, 257 Diatoms 19, 27, 28, 29, 48, 108 different organisms 55 differential discrete ordinates method 197 diffuse incidence 182 diffuse radiation 184 dilution 194 dilution rate 132, 144 dinoflagellates 19, 32, 33 Dinophyta 32 direct radiation 184 disk-stack centrifuges 247 dispersion coefficient 157, 228, 229 dissipation 194

Index

dissolved oxygen 125 diterpenglycoside 62 diversity of microalgae 11 down-regulation 46 drought 69 Dunaliella 24, 141, 251 dynamic 67 economics of algal crude oil 137 efficiency of photosynthesis 123 efficiency of the motor 118 electron transport chain 40, 43 electronically adjusted culture chambers 76 electroporation 88 endosymbiont 17, 19, 33 endosymbiosis 17, 19, 58 energetics of photobioreactors 192 energy 256, 257, 258 energy and kinetic models 194 energy balance 135 energy consumption 250 energy content of crude algal oil 137 energy content of oil 130 energy dissipation 42, 43, 47 engineering formula 211 enthalpy of combustion see calorific value enzyme 43 Euglena 33 Euglenoids 17, 19, 33 Euglenozoa 33 eukaryotes 107 eukaryotic algae 87, 190 Eustigmatophyceae 27, 29, 30 Eustigmatos 30 evaporation 121 Excavata 19, 33 excentric screw 249 exo-symbiosis 56 expression rates 91 extracellular 109 extracting the oil 138 extreme temperatures 69 finite element methods 195 Flagellates 33 flashing light 45 flashing light effect 214, 238 flat panel 159, 177 flat panel PBR 197 flat-panel-airlift FPA 237, 238, 240, 241

– compartments 238 – static mixers 238 flow deflectors 116 flow in a raceway 115 flow velocity 119 flue gas 125, 237, 241, 242 flue gas desulfurization 125 fluorescence 43 food supplement 21, 28 footprint area 227, 233 four-phase system 237 frustule 27, 28 fucoxanthin 27, 29, 30 fungal hyphae 70 fungal pigments 74 galactose, 68 gas diffusers 124 gas vesicles 20 gastropoda 66 GC content 91 Gene Silencing 91 genetic transformation 108 geometry, design 225, 226, 227, 230, 231 Germany 237, 240, 241 GFP 95 girdle lamella 29 glass beads method 87 Glaucophyta 17, 22, 26 glucose 60 glycerol 60, 65 glycoproteins 107, 108, 109 glycosylation 107 glycosylation patterns 98 gram negative bacteria 19, 20 green algae 17, 19, 22, 23, 24, 25, 29, 33, 46, 48 greenhouses 126 growth, photon to biomass (efficiency) 41, 45, 48, 50 growth rate 40, 41, 46, 47, 50, 158, 168 Haematococcus 24 Haldane light-inhibited growth equation 128 haptonema 31 Haptophyta 19, 31, 32 harvest, separation 226, 230, 231, 232, 233 harvesting 253, 256, 257 Haslea 28 Haustoria 71

261

262

Index

head loss 156 heat emission 43, 44, 46, 48 Helicosporidium 22 heterocytes 20 heteroeomeric 70 heterokont algae 19, 26, 27, 29 Heterokontophyta 26 Heterosigma 30 Heterotrophic 226, 228, 230 heterotrophy 28, 33 high cell density culture 209 high-rate algal ponds see raceway ponds holobiont 58 homoeomeric 70 homologous recombination 98 horizontal reactor 234 humid, tropical regions 69 hydraulic diameter 116 hydraulic head 118 hydro-cyclones 248 hydrodynamic conditions 215 hydrostatic pressure 226, 228, 229, 234 ideal reactor 212 illuminated fraction 203 incident angle 201 incident hemispherical radiant flux density 182 incident polar angle 184 indoor cultivation 240 inhibition of respiration by light 191 intracellular 109 intracellular algae symbionts 56 investive, non-investive carbon 50, 51 IR reflection 226, 232, 234 irradiance 183 irradiance of compensation 185 irradiance of saturation 185 Irradiance variation with depth 126 isidia 73 Isochrysis 32 kinetic coupling 186 kLa, CTR, OPR knowledge models 181 Krebs cycle 42 Lagrangian approach 217 laminar, turbulent flow 229 large-scale 230, 232, 233, 235

layer thickness 238 Lichen 24, 68 lichen derived compounds 77 lichenized fungus 69 life cycle assessment LCA 225, 231, 235 life cycle study 39 light absorption coefficient 126 light acclimation 40, 41, 45 light compensation point 126, 127, 128 light/dark cycle effects see flashing light effect light dilution 227, 231, 237, 238 light influx 225, 226, 227, 228, 233 light intensity 40, 41, 43, 45, 46, 48 light intensity, availability 237, 238, 239, 240, 241 light intensity, irradiance 227, 230, 232 light limitation 189 light limited depth 127 light (photo-) limitation 237 light saturated depth 127 light saturation/curve 41, 43, 45, 46, 48 light saturation constant 128 light transfer 181 light-limited photosynthetic growth 184 liquid velocity 169 Lobosphaera incisa 25 local irradiance 126 location 121, 227, 232, 235 low average growth rate 72 low light intensities 61 lower cortex 71 Luciferase 95 luminostat 204 Lyngbya 21 macroalgae 25, 31 macromolecules 39, 42, 48 – Lipid 42, 48, 49, 50 – Protein 42, 48, 50 Manning channel roughness factor 119 Manning equation 118 mannitol 71 marininne 28 mass balance 186 mass transfer 155, 161, 163, 170, 173 material (transparent) 225, 226, 227, 228, 232, 234, 235 – Acrylic, Glass, PC, PE, PVC, Silicone 226 maximal productivities 204

Index

maximum performances of solar PBR 194 mechanical energy 225, 228, 229, 231, 234 mechanical stability 226, 234 Mediophyceae 27 medium 225, 227, 228, 230, 231, 234 medulla 70 membrane 234 Mesostigma 23 metabolic costs 41, 42 metabolites 60 mevalonic acid pathway 77 microalgae 147, 225, 231, 233, 234 microeddies 168 Mischococcus 30 mitochondrion 42, 43 mixing 45, 46, 156, 225, 228, 229, 231, 234, 237, 238 mixing time 228 mixotrophy 31 mode of operation, performance 228, 230, 231, 232, 233 modeling light transfer in solar PBR 200 molecular transporter systems 61 Mollusca 66 monoclonal antibody 109 Monod light saturation constant 127 Monodopsis 29, 30 Monodus 29 morphological change 76 morphology sponge-algae 63 mutualisitc symbioses 56 mycobiont 69 mycosporine-like amino acids 66 myrmecia incisa 25 Nannochloris 25 Nannochloropsis 29, 30, 251 Nannochloropsis s., Phaeodactylum t. 240, 241 nanotechnology 28 natural surfaces 68 Neochloris oleoabundans 24 net energy recovery 139 nighttime biomass loss 122 nitrogen fixation 20 nitrogen source 187, 242 nitrogenase 20 Nitzschia 28 Nostoc 21 nozzle-type separator 249

NPQ non-photochemical quenching 43, 44, 46, 48 nuclear expression 96 nuclear genome 68 nucleomorph 17 nutrient-poor environments 69 Odontella 28 Oedogonium 23 of posttranslational modifications 96 oil, lipid 230, 231 oil palm 133 olisthodiscus 30 one-dimensional approximation 196 open ponds 229, 230, 231, 247, 250 open raceway 153, 177 open raceway ponds 147 optical density 240 optical path dL 227, 234 optimal growth temperature 122 optimal running conditions 214 optimal temperature for photosynthesis 123 optimization 181 organismic assembly 58 organismic compartmentation 56 osmotrophy 33 Ostreococcus 22 Oswald ponds see raceway ponds outdoor conditions 45, 47 outdoor cultivation 238, 239, 240, 241 oxygen production rate 184 Oxygen removal 125 P/C photon per carbon ratio 40, 43, 44, 45, 48, 51 P/2e− ratio 188 paddlewheel 120, 153 PAM pulse amplitude modulation 43 Parachlorella 25 paramylon 33 Particle bombardment 88 paste 253, 254, 256, 257 Pavlova 32 PBR engineering 208 PBR intensification 208 PCE photo-conversion-efficiency 41 PE photosynthetic efficiency 40, 41, 46 penetration of the photobiont 71 pennate Diatoms 27 peptidoglycan 26

263

264

Index

performance of the zooxanthellae 60 peridinin 61 perturbation 62 Phaeodactylum 16, 28 Phaeodactylum tricornutum 108, 109, 127 Phaeophyceae 31 phagocytosed 67 phagotrophy 30, 33 pharmaceutically active substances 28 phase function 201 (photo-) bioreactor 237, 239 photo-acclimation 217 photobioreactor 147, 225, 244 photochemical efficiency 46, 47 photochemistry 39 photoconversion energy PCE 230, 231 photodamage 47 photoinhibited depth 127 photoinhibition 41, 47, 237 photoinhibition constant 128 photon flux density 182 photoprotection 43 photosynthates 56 photosynthesis 16, 19, 62, 227, 231 photosynthesis rate 41, 45 photosynthetic efficiency 135, 136, 137, 143 photosynthetic Stramenopiles 27, 29, 30 photosynthetically active radiation 182 photosynthetically active radiation, or PAR 126 photosynthetic-irridiance (P-I) curve, α-slope 40, 41 photosystem PSI+PSII 43, 46, 47, 48 phycobilins 20, 25, 26 phycobilisomes 20, 26 phycobiont 69 phytoplankton 16, 22, 24, 28, 32, 33 pigment 43, 45 – Chlorophyll 40, 41, 43, 45, 46 – Xanthophyll 43 pilot plant 239, 241 Plantae 17, 22 plasmid vectors 108 plastic lining 115 plastid 16, 17, 18, 19, 22, 25, 26, 27, 29, 31, 33 plate reactor 225, 228, 229, 231, 233 Platymonas 24 Pleurochrysis carterae 131 plug flow tubular reactor 187

pneumatic energy 225, 229, 231 poikilohydric 74 polyketide synthesis 77 polyketides 65 polypeptides 107 polyphosphate 20 Porifera 62 Porphyra 25 Porphyridium 26 post-translational modifications 107 power 157, 163, 167 power consumption 118, 120 power plant 237, 241 prasinophytes 22, 23 predators 126 predict 208 price of biomass 140 primary biomass 55 primary photosynthates 39 production of algae biomass 247 productivity 228, 229, 230, 231, 237, 238, 241 productivity of biomass 132 productivity of the oil 133 prokaryotic 190 prokaryotic algae 19 promoters 89 protection against herbivores 74 proteins 107, 108, 109 Prototheca 22, 25 pseudopterosin 62 Pseudostaurastrum 30 PTA cell line 109 PUFA 23, 25, 26, 28, 29, 32, 33 pumping 225, 228, 229, 230, 231, 234 purification 109 quantum requirement 135 quantum sensor 183 quantum yield 189 raceway pond – general configuration 114 raceway ponds 113 radiance 182 radiant light power density 190 radiation measurement and handling 181 radiative flux density 182 radiative properties 201 radiative transfer equation 195

Index

radiative transfer modeling 194 Raphidophyceae 27, 30 rate of photosynthesis 125, 126 RBCS2 promoter 91 reaction center 43, 47 reactor modules 231, 233 reactor volume, working volume 238, 241 recombinant 107, 108, 109 recombinant protein 29, 108 recombinant proteins 96 Red Algae 22, 25 reductant 42, 43, 47 reflection, scattering 231, 234 reflective loss 135 rehydration 75 reportergenes 94 residual biomass 138, 139, 140 respiration 44, 45, 50, 75 Reynolds number 115 Rhizaria 19 rhizinae 71 Rhodophyta 22, 25, 26 ribitol 71 rotary brush strainers 248 sacolossan 67 salinity 122 saturation, photoinhibition 227 scale-up 157, 166, 174 scattering 199 Scenedesmus 24 Scenedesmus obliquus 131 season (summer, winter) 227, 231, 232 seaweeds 31 secondary metabolites 61 secretion 109 selection markers 93 self-cleaning separators 248 separation 253, 256, 257, 258 separators 247 shear stress 215, 238 shearing 257 shikimic acid pathway 77 silicon whiskers 87 skeletonema 28 solar database 212 solar illumination 184 solar PBR transient behavior 212 solar radiation 161, 168 soredia 73

specific growth rate 122, 128, 132, 133, 135, 143 specific illuminated surface 206 Sphaeropleales 23, 24 Spirulina 21, 251 stable culture, process 231, 232 starch 50 sterilization 232, 233 Stigeoclonium 23 stoichiometric cofactor balances 188 stoichiometric equation 187 stomata 136 Stramenopiles 19, 26, 27, 29, 30, 31 Streptophyta 23 structured stoichiometric equations 188 sunlight, daylight 227, 230, 232 superficial contact 71 surface area 115 surface productivity 206, 212 surface-to-volume ratio 115, 128, 129 survival in low gravity 69 symbiosis 20, 24, 28, 33, 55 Synurophyceae 30, 31 temperature 231, 232 temperature control 164, 172 temperature rise 123 temperature variations 122 terrestrial green algae 22 terrestrial plants 134, 136 terrestrial system 68 Tetraselmis 24 Thalassiosira 28 thallus 70 thallus water content 75 therapeutic 107, 108, 109 therapeutic proteins 107 thermal stratification 119 thermodynamic efficiency 192 thermodynamic limit 194 thin film 208 tocopherol 33 Trachydiscus 30 tracking capture system 194 transformation 87 transformed microalgae 16, 28 transition to turbulence 116 Trebouxiophyceae 22, 23, 24 Trentepohliales 25 tripartite symbiotic relation 71

265

266

Index

tubular photobioreactor 167, 177 tubular reactor 225, 228, 229, 231 turbulence 237, 238 turn-over 42, 50 two-flux method 197 Ulva 25 Ulvophyceae 23, 25 unicellular 107 unnatural surfaces 68 usnic acid 78 V/S ratio 158 Vaucheria litorea 67 vegetative propagation 73 vertically transferred 64 viral infections 126 viral subunit 109 Viridiplantae 22

Vischeria 30 vitamins 28 volumetric biomass growth rate 192 volumetric productivity 132, 133, 134, 209 volumetric-lightened systems 213 Volvocales 23 VR medium volume in reactor 226, 227 washing stage 249 washout 131, 132 Watanabea 25 weatherability 226, 234 working volume 115 Xanthophyceae 27, 29, 30 Zooxanthellae 66 Z-scheme 188 Zygnematophyceae 23