New Developments in Adsorption/Separation of Small Molecules by Zeolites (Structure and Bonding, 184) 3030638529, 9783030638528

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Structure and Bonding  184 Series Editor: D. Michael P. Mingos

Susana Valencia Fernando Rey   Editors

New Developments in Adsorption/ Separation of Small Molecules by Zeolites

184 Structure and Bonding Series Editor: D. Michael P. Mingos, Oxford, UK

Editorial Board Members: Christine Cardin, Reading, UK Xue Duan, Beijing, China Lutz H. Gade, Heidelberg, Germany Luis Gómez-Hortigüela Sainz, Madrid, Spain Yi Lu, Urbana, IL, USA Stuart A. Macgregor, Edinburgh, UK Joaquin Perez Pariente, Madrid, Spain Sven Schneider, Göttingen, Germany Dietmar Stalke, Göttingen, Germany

Aims and Scope Structure and Bonding is a publication which uniquely bridges the journal and book format. Organized into topical volumes, the series publishes in depth and critical reviews on all topics concerning structure and bonding. With over 50 years of history, the series has developed from covering theoretical methods for simple molecules to more complex systems. Topics addressed in the series now include the design and engineering of molecular solids such as molecular machines, surfaces, two dimensional materials, metal clusters and supramolecular species based either on complementary hydrogen bonding networks or metal coordination centers in metal-organic framework materials (MOFs). Also of interest is the study of reaction coordinates of organometallic transformations and catalytic processes, and the electronic properties of metal ions involved in important biochemical enzymatic reactions. Volumes on physical and spectroscopic techniques used to provide insights into structural and bonding problems, as well as experimental studies associated with the development of bonding models, reactivity pathways and rates of chemical processes are also relevant for the series. Structure and Bonding is able to contribute to the challenges of communicating the enormous amount of data now produced in contemporary research by producing volumes which summarize important developments in selected areas of current interest and provide the conceptual framework necessary to use and interpret mega-databases. We welcome proposals for volumes in the series within the scope mentioned above. Structure and Bonding offers our authors and readers: • OnlineFirst publication. Each chapter is published online as it is finished, ahead of the print volume • Wide dissemination. The chapters and the volume will be available on our platform SpringerLink, one of the largest collections of scholarly content in the world. SpringerLink attracts more than 50 million users at 15.000 institutions worldwide. • Easy manuscript preparation. Authors do not have to spend their valuable time on the layout of their contribution. Springer will take care of all the layout related issues and will provide support throughout the complete process. More information about this series at http://www.springer.com/series/430

Susana Valencia • Fernando Rey Editors

New Developments in Adsorption/Separation of Small Molecules by Zeolites

With contributions by J. J. Arroyo-Gómez
D. Barrera
S. Brandani
S. Calero
H. J. Choi
B. Claessens
J. Cousin-Saint-Remi
J. F. M. Denayer
A. F. P. Ferreira
J. J. Gutiérrez-Sevillano
S. B. Hong
K. C. Kemp
E. Mangano
V. F. D. Martins
J. G. Min
J. Pérez-Pellitero
G. D. Pirngruber
A. M. Ribeiro
A. E. Rodrigues
K. Sapag
J. Villarroel-Rocha

Editors Susana Valencia Instituto de Tecnología Química (Consejo Superior de Investigaciones Científicas – Universitat Politècnica de València) Valencia, Spain

Fernando Rey Instituto de Tecnología Química (Consejo Superior de Investigaciones Científicas – Universitat Politècnica de València) Valencia, Spain

ISSN 0081-5993 ISSN 1616-8550 (electronic) Structure and Bonding ISBN 978-3-030-63852-8 ISBN 978-3-030-63853-5 (eBook) https://doi.org/10.1007/978-3-030-63853-5 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The application of zeolites as selective adsorbents for separation processes is one of the most recognizable uses of this family of porous materials. Indeed, the earliest contributions of pioneers in zeolite research, like R.M. Barrer, D.W. Breck, and E.D. Flanigen, among many others pointed out the very special features of zeolites for separating molecules of different sizes and/or polarities. Thus, the whole family of zeolitic materials was named as “molecular sieves.” The most common industrial applications of molecular sieves involve drying of gases or liquids, being commercialized as molecular sieves 3A, 4A, 5A, and 13X, among others. Other industrial separations based on the use of zeolites as adsorbents are linear from branched hydrocarbon separation and O2 recovery from air. However, the field of zeolite-based selective adsorbents is in constant evolution and new relevant separation processes are under development, such as CO2 trapping from exhaust gases, olefins/paraffins separation, methane upgrading, etc. This blooming research area is based on the discovery of new zeolitic materials with varied structural porosity and in the possibility of tuning the polarity of the zeolite adsorbent by controlling its chemical composition and/or presence of structural defects. Thus, there is a growing interest to increase the understanding of such selective adsorbents and this volume aims to compile and discuss the fundamental and multidisciplinary knowledge on adsorption and separation processes using zeolites as adsorbents. The first chapter shows how the control of zeolite properties, such as pore aperture, extra-framework cations, and chemical composition, may enhance the selectivity during small molecules separation processes as well as possibly reducing the energy consumption of currently used technologies. This has been nicely exemplified for CO2 adsorption, small hydrocarbon separation, and other small molecules (noble gases, water, etc.). For achieving this degree of adsorbent engineering, it is mandatory to carefully characterize the porosity of the zeolitic adsorbents. The second chapter is a comprehensive overview of the best practices for textural characterization following the

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IUPAC recommendations, as well as providing tricks for overcoming common problems during gas adsorption experiments. The third chapter provides a brief description of the state of the art of molecular simulations applied to zeolites, as well as a discussion of current challenges in the field. Today, experimental and computational studies must be closely linked to gain a deep understanding of adsorption and diffusion of molecules through the zeolite channel systems needed for the optimization of the adsorption and separation processes. The fourth chapter is a compendium of the above approaches. There, it is shown how the rational selection of adsorbents, by taking into account their chemical and structural properties is possible to achieve high separation performances in industrially and environmentally relevant processes for the recovery of bio-alcohols coming from renewable resources. Chapter “Measurement of Diffusion in Small Pore Zeolites to Improve Selectivity in Separation Processes” focuses on the experimental determination of adsorption kinetics in zeolites. The determination of diffusional parameters is a keystone in the design of the separation processes. In this chapter, the zero-length column (ZLC) technique has been extensively discussed for understanding the kinetics of CO2 adsorption in zeolite Rho. The basis for bridging the gap between academia and industrial application is discussed in the sixth chapter of this volume. The authors show the main methods adopted in scaling-up adsorptive gas-phase separations using zeolites, including the above strategies and process simulation and optimization. Finally, the last chapter describes the main industrial applications based on zeolitic adsorbents. Also, this chapter provides insights into how zeolitic adsorbents should evolve for adapting to new adsorption technologies to result in more efficient processes. We hope that this volume on “New Developments in Adsorption/Separation of Small Molecules by Zeolites” will be of interest for researchers arriving to this field as well as those that are skilled in the area but could find “inspiration” in what is described here. Valencia, Spain Valencia, Spain

Susana Valencia Fernando Rey

Contents

Small Gas Adsorption and Separation in Small-Pore Zeolites . . . . . . . . . Kingsley Christian Kemp, Jung Gi Min, Hyun June Choi, and Suk Bong Hong Critical Overview of Textural Characterization of Zeolites by Gas Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jhonny Villarroel-Rocha, Deicy Barrera, José J. Arroyo-Gómez, and Karim Sapag Computational Approaches to Zeolite-Based Adsorption Processes . . . . Juan José Gutiérrez-Sevillano and Sofía Calero Efficient Downstream Processing of Renewable Alcohols Using Zeolite Adsorbents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benjamin Claessens, Julien Cousin-Saint-Remi, and Joeri F. M. Denayer

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Measurement of Diffusion in Small Pore Zeolites to Improve Selectivity in Separation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Enzo Mangano and Stefano Brandani Perspectives of Scaling Up the Use of Zeolites for Selective Separations from Lab to Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Vanessa F. D. Martins, Ana M. Ribeiro, Alexandre F. P. Ferreira, and Alírio E. Rodrigues Industrial Zeolite Applications for Gas Adsorption and Separation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Javier Pérez-Pellitero and Gerhard D. Pirngruber Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

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Struct Bond (2020) 184: 1–30 https://doi.org/10.1007/430_2020_67 # Springer Nature Switzerland AG 2020 Published online: 6 August 2020

Small Gas Adsorption and Separation in Small-Pore Zeolites Kingsley Christian Kemp, Jung Gi Min, Hyun June Choi, and Suk Bong Hong

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Comparison Between Zeolites and Other Microporous Materials as Adsorbents . . . . . . . . . . 3 Small-Pore Zeolites for CO2 Adsorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 LTA-Type Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 CHA-Type Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 KFI-Type Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 The RHO Family of Embedded Isoreticular Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Other Small-Pore Zeolites and Zeotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Small-Pore Zeolites for Small Hydrocarbon Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Ethylene and Ethane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Propylene and Propane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Butenes and Butane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Small-Pore Zeolites as Adsorbents for Other Small Molecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Methane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Nitrogen Oxides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 4 5 6 7 8 9 12 15 16 17 19 20 20 20 22 23 24

Abstract The capture, storage, and utilization of small gases by economically viable recyclable microporous adsorbents is an ever-expanding field of research toward industrially relevant separations, as well as toward pollution remediation and catalysis. The use of thermochemically stable zeolitic adsorbents has the potential to fill these markets, as they are already widely used industrially, compared to other crystalline adsorbents such as metal organic frameworks and activated carbons. In this chapter, we discuss the use of small-pore zeolites in the adsorption and separation of small gases (e.g., CO2, C2H4, C3H6, NOx, etc.), based on how their K. C. Kemp, J. G. Min, H. J. Choi, and S. B. Hong (*) Center for Ordered Nanoporous Materials Synthesis, Division of Environmental Science and Engineering, POSTECH, Pohang, South Korea e-mail: [email protected]

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pore structure, composition, and type of extraframework cations influence the maximum uptake and adsorption kinetics of the target adsorbate. A careful analysis of knowledge accumulated in this field thus far allows us to conclude that small-pore zeolites will likely be further employed in the adsorption/separation of economically useful or noxious small gas molecules. Therefore, there is still a strong need for the synthesis of zeolites with new pore structures and/or compositions, which will in turn lead to improved adsorbents for selective small gas capture. Keywords Adsorption/separation · Chemical composition · Pore topology · Small gases · Small-pore zeolites

1 Introduction The name zeolite was first coined in 1756 when Axel F. Cronstedt, the Swedish mineralogist, discovered that a mineral, likely stilbite (framework type STI), desorbed water when heated [1]. Therefore, it is not surprising that zeolites and related microporous materials are widely used in the current gas adsorption and separation processes. However, the chemical industry still employs, for example, energy-intensive distillation processes as a major hydrocarbon separation technology. This has considered the adsorptive separation using porous materials as an alternative technology, due to the high energy efficiency and low material cost. Consequently, a multitude of such solids, i.e., zeolites, metal organic frameworks (MOFs), activated carbons (ACs), etc., have been explored as adsorbents for various separations [2–4]. Among the solid adsorbents known to date, zeolites have a clearcut advantage over other porous materials for industrial applications, especially in terms of physicochemical stability. In general, zeolitic materials are hydrothermally synthesized under autogenous pressure at 373–473 K, using synthesis mixtures containing inorganic (alkali and/or alkaline earth cations, hydroxide or fluoride anions, and heteroatoms other than Si) and/or organic (amines or alkylammonium ions) structure-directing agents (SDAs) [5]. Thus, when the zeolite synthesis conditions are appropriately controlled by varying the crystallization temperature and time; type of heteroatoms; synthesis mixture composition; charge, shape, and size of the organic SDA; type of mineralizing agent; etc., it is possible to synthesize novel structures or compositions. The resulting zeolites may then be applied as adsorbents for a target molecule if they meet specific criteria like pore size and polarity, which are determined by the desired target molecule (adsorbate). Zeolites possess a three-dimensional (3D), four-connected framework structure consisting of corner-sharing TO4 tetrahedra, where T is Si or Al. While the wellknown physicochemical stability of this important class of microporous solids can be rationalized by considering their structural and compositional features, an almost

Small Gas Adsorption and Separation in Small-Pore Zeolites

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infinite number of hypothetical structures have been proposed so far [6]. However, only a limited number (ca. 250) of these structures have been discovered or synthesized. Furthermore, an even more limited number ( 3.0 have an obvious adsorption of N2, suggesting the absence of complete occupation of pore windows by trapdoor cations. It is worth noting that the optimized Si/Al ¼ 3.0 is only a theoretical critical threshold, as K-chabazite with a Si/Al close to 3.0 (e.g., 3.2) still shows a very small yet

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Fig. 3 The molecular trapdoor mechanism in Cs-chabazite with high CO2/CH4 selectivity [15]

measurable N2 adsorption isotherm, probably due to the random framework Al distribution. Pham et al. have investigated the effect of Si/Al ratio (6 and 12) and cation type (H+, Li+, Na+, and K+) on the CO2 and N2 adsorption properties of SSZ-13 (CHA) by comparing the adsorption isotherms and heats of adsorption [32]. They found that SSZ-13 with Si/Al ¼ 6, which translates into a higher cation content, has higher CO2 and N2 uptakes, whereas the adsorption uptake follows a trend based on the inherent properties of the cations. Consequently, Li-SSZ-13 with Li+ ions of the smallest size and thus largest polarizability gave the highest CO2 and N2 uptakes. In another work, Hudson et al. examined the CO2 uptakes (3.98 vs 3.75 mmol g1) and CO2/N2 selectivities (74 vs 72) on H- and Cu-SSZ-13 zeolites with Si/Al ¼ 6 at 298 K and 1.0 bar [33]. Although the results obtained were not different from each other, they found that in Cu-SSZ-13 with Cu loadings of 0.5 to 1.5, CO2 preferentially adsorbs at 8-ring windows and do not coordinate to the Cu2+ ion, unlike the previously reported cases. For example, in a series of alkali ion-exchanged Y zeolites (FAU), Na-X (FAU), and H- or Na-ZSM-5 (MFI), CO2 interacts end-on with the cations [34–36]. Therefore, varying the Cu2+ content in Cu-SSZ-13 could lead to an improved CO2 adsorption capacity and/or selectivity, because water would preferentially bind to Cu2+, but not interfere with CO2 binding in 8-ring windows. Such an adsorption mechanism, in contrast to other cation-exchanged zeolites, would eliminate the competitive adsorption between the extraframework cations and H2O/CO2 in Cu-SSZ-13, leading to an increase in adsorption capacity and selectivity under wet conditions.

3.3

KFI-Type Zeolites

Lobo and co-workers have studied the effect of cation type (H+, Li+, Na+, K+, Mg2+, Ca2+) and Si/Al ratio (1.7–4.7) on the CO2 and N2 adsorption properties of ZK-5 with KFI topology [37, 38]. They showed that Li-ZK-5 with Si/Al ¼ 4.7 has the highest CO2 uptake (5.0 mmol g1 at 303 K and 1.0 bar), not only because the Li+ ion, with the smallest ionic radius among the cations studied, has a stronger

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polarizing power and can produce a higher local electrostatic field and field gradient but also because this zeolite has a slightly higher effective micropore volume than its other cation forms [37]. The Lobo group also evaluated the CO2 capture and separation ability of ZK-5 in simulated PSA/VSA processes. Mg-ZK-5 was shown to be the most promising adsorbent for PSA applications with the highest working capacity (△CO2 ¼ 2.1 mmol g1) and excellent selectivity (121) over N2. While for the VSA process, Li-, Na-, and K-ZK-5 zeolites were found to have impressive working capacities (△CO2 ¼ 1.6–2.2 mmol g1) and excellent selectivities (103–128) [37]. In another study the same group reported that Li-ZK-5 with Si/Al ¼ 1.7 shows an improved adsorption capacity of 4.8 mmol g1 at 303 K and 40 bar. This indicates that a decrease in adsorption capacity with increasing Si/Al ratio was caused by an increase in extraframework content, which in turn leads to a smaller accessible pore volume [38].

3.4

The RHO Family of Embedded Isoreticular Zeolites

In 2015, Zou, Wright, and our group discovered a novel zeolite family with increasing structural complexity and embedded isoreticular structures, denoted the RHO family (Fig. 4) [39–42]. This zeolite family starts from rho (RHO) consisting of 10-hedral ([4882]) double 8-rings (d8rs) and 26-hedral ([4126886]) lta cages and expands as follows: (1) the scaffolds are extended by inserting an extra pair of d8r and 18-hedral ([41286]) pau cages between the lta cages along each unit-cell edge, which increases the isoreticular dimension by approximately 10 Å per generation and (2) the space between the scaffolds is completely filled up with the four types of embedded cages, i.e., 14-hedral([466286]) t-plg, 8-hedral ([4583]) t-oto, 10-hedral ([4684]) t-gsm, and 12-hedral ([4785]) t-phi cages, to form fully tetrahedrally connected frameworks. As described above, rho is the first (RHO-G1) generation of this RHO family, and the natural zeolite paulingite (PAU) and the synthetic zeolite ZSM-25 (MWF) are the third (RHO-G3) and fourth (RHO-G4) generations, respectively. It is remarkable that ECR-18, the synthetic version of paulingite, has crystallized using Na+ and K+, Rb+, or Ba2+, together with tetraethylammonium (TEA+) ions, the same organic SDA used in ZSM-25 synthesis [43]. A series of more complex members of the RHO family of embedded isoreticular zeolites were predicted by extending the approach given above and synthesized via the so-called multiple inorganic cation approach in the presence of TEA+ as an organic SDA [5]. The intentional use of a small amount of alkaline earth cations, specifically Ca2+ and Sr2+, allowed us to crystallize four more complex higher generations: PST-20 (RHO-G5), PST-25 (RHO-G6), PST-26 (RHO-G7), and PST-28 (RHO-G8) [39–41]. One member of this family, which has remained synthetically elusive, is the RHO-G2 structure, first proposed by Gordon et al. in 1966, with one pau and two d8r cages per unit-cell edge [44]. Very recently, we have also been able to synthesize this long-term missing generation in the Na+-K+-N,

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Fig. 4 Tile representations of the structures of RHO-G1 to RHO-G6 in the RHO family of embedded isoreticular zeolites. Structure expansion is achieved by the isoreticular expansion of the scaffold by inserting a pair of pau and d8r cages along each unit cell edge (top) followed by the embedding of other cages (middle) in the inter-scaffold space (bottom) [39].

N0 -dimethyl-1,4-diazabicyclo[2.2.2]octane mixed-SDA system and to denote it as PST-29 (PWN) [45]. All known members of this RHO family are accessible to the molecules that can pass through their 8-ring windows, and so they are potentially useful as small molecule adsorbents. As such, we and other groups have examined the CO2 adsorption properties of various alkali cation forms of the first five members of the RHO family at 298–373 K and 0–1.0 bar [30, 39, 45–47]. Among them, the firstgeneration Na-rho gave the highest CO2 uptake (4.6 mmol g1) at 298 K and 1.0 bar. This uptake is lower than the value (5.8 mmol g1) of the commercial large-pore zeolite Na-X with Si/Al ¼ 1.3, but is rather higher than the capacities (3.6 and 3.7 mmol g1, respectively) of two widely studied small-pore zeolites Na-A with Si/Al ¼ 1.0 and K-chabazite with Si/Al ¼ 3.0 [32, 48]. One serious drawback of Na-rho as an industrial CO2 adsorbent is its very slow kinetics: this zeolite achieves equilibrium only after about 2 h, mainly due to the restricted diffusion of CO2 molecules into its internal void space via d8r units. As shown in Fig. 5, however, higher generations of the RHO family of embedded isoreticular zeolites (i.e., Na-PST-29, NaTEA-ECR-18, NaTEA-ZSM-25, and NaTEA-PST-20) achieve equilibrium within 5 min. In these samples, CO2 can diffuse not only via d8r units but also via single 8-ring (s8r) windows that are located between the t-oto cages and the pau or t-plg cages and as such equilibration

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Fig. 5 CO2 adsorption kinetics at 298 K and 1.2 bar of Na-rho (pink), Na-PST-29 (navy), NaTEAECR-18 (green), NaTEAZSM-25 (orange), NaTEAPST-20 (violet). Inset: CO2 adsorption kinetics for the first 5 min [45]

is reached faster. Most likely, the Na+ ions are more weakly held in the “thinner” s8r windows than in d8r cages so that CO2 molecules can displace the cations in the former windows more readily than those in the latter ones: these interactions would then permit other CO2 molecules to pass more easily. More interestingly, the selectivity for CO2 over CH4 for all members of the RHO family is high, and much greater than that observed for Na-X, Na-A, or K-chabazite [45], due to the trap door effect [15, 30]. More recently, we have reported that among the RHO-family zeolites containing multiple cations, NaTEA-ZSM-25 and NaTEA-PST-20 show unexpected two-step CO2 adsorption isotherms with large CO2 uptakes of 3.5 and 3.2 mmol g1 at 298 K and 1.0 bar, respectively [47]. We also found that CsTEA-ZSM-25 and CsTEAPST-20 exhibit one-step isotherms, where their one step shifts to higher pressures and disappears with increasing temperature (Fig. 6). Intriguingly, these two Cs+containing zeolites showed impressive CO2 working capacities of 1.3–1.4 mmol g1 under a TSA process at 298–343 K and 0.15 bar, probably owing to their unique one-step adsorption isotherm, which are even higher than the working capacities (0.8 and 1.1 mmol g1, respectively) of the commercial Na-A and Na-X adsorbents. It is worth mentioning that CsTEA-ZSM-25 and CsTEA-PST-20 are characterized by very favorable CO2 selectivities (ca. 100 and 50, respectively) over N2 and CH4, as well as by good long-term stability. Our group also tested the high-pressure (up to 25 bar) CO2 separation ability of RHO-family zeolites and found that NaTEA-ZSM25 and NaTEA-PST-20 are quite promising candidates for high-pressure CO2 separation from CH4 over a wide temperature and pressure range (298–373 K and 0–25 bar) [46]. More interestingly, both zeolites exhibited impressive CO2/N2 selectivities (>20) at 298 K and 25 bar and relatively high CO2 uptakes (4.0 and 3.6 mmol g1, respectively) even at 373 K and 25 bar, together with long-term durability.

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Fig. 6 (Left) Adsorption-desorption CO2 isotherms on (a) CsTEA-ZSM-25 and (b) CsTEA-PST20 at 0–1.0 bar and 298 (navy), 323 (green), 348 (pink), and 373 K (orange). Adsorption, closed symbols; desorption, open symbols. (Middle) TSA conditions at 298–343 K and 0.15 bar. (Right) Repeated CO2 adsorption-desorption curves at 1.0 bar and different temperatures [47]

3.5

Other Small-Pore Zeolites and Zeotypes

Zukal et al. have investigated the effects of pore size on the CO2 adsorption properties of the family of zeolites IM-12 (UTL), IPC-2, IPC-4 (PCR), IPC-6 (*PCS), and IPC-7 at 273–333 K and 0–1.0 bar [49]. This series of isoreticular zeolites, which were prepared from the extra-large-pore germanosilicate material IM-12 using the assembly-disassembly-organization-reassembly strategy (Fig. 7), possess the same structure of individual layers but gradually reduced average channel size: 9.5  7.1 (14-ring; IM-12 and IPC-7), 8.5  5.5 (12-ring; IM-12 and IPC-7), 6.6  6.2 (12-ring; IPC-7, IPC-2, and IPC-6), 5.4  5.3 (10-ring; IPC-7, IPC-2, and IPC-6), 5.8  3.8 (10 ring; IPC-6 and IPC-4), and 4.5  3.6 (8-ring; IPC-6 and IPC-4) Å [50]. As expected from their average channel size, Zukal et al. found that the CO2 capacity at low pressures increases in the order IM-12 < IPC7 < IPC-2 < IPC-6 < IPC-4), although a different capacity order was observed at higher pressures (IPC-4 < IPC-2 < IPC-6 ~ IM-12 < IPC-7). This is interesting in that the isosteric heat of adsorption increases with decreasing average channel size: IM-12 (23 kJ mol1) < IPC-7 (25 kJ mol1) < IPC-2 (27 kJ mol1) ~ IPC-6 (27 kJ mol1) < IPC-4 (29 kJ mol1). Given that the zeolites under study are puresilica in framework composition, except IM-12, it is most likely that CO2 adsorption is mainly influenced by dispersion interactions [51]. SAPO-34 with CHA topology is the most widely studied silicoaluminophosphate (SAPO) molecular sieve in gas adsorption known to date. Bellatreche et al. examined the CO2 adsorption properties of various alkali metal ion-exchanged forms of SAPO-34 at 273 K and 1.0 bar and reported that the adsorption capacity increases in the order K-SAPO-34 ~ Cs-SAPO-34 < Li-SAPO-34 ~ Na-SAPO-34, suggesting a

Small Gas Adsorption and Separation in Small-Pore Zeolites

13

Fig. 7 Schematic of the assembly-disassembly-organization-reassembly process. Steps A, D, O, and R represent the assembly (or synthesis) of a zeolite from the starting material, the controlled disassembly of the zeolite, the organization where a new linking unit or an organic SDA is incorporated, and the reassembly to form the final zeolite product, respectively [50]

strong influence of the cation size [52]. Also, they also found that divalent cation exchange is more suitable for CO2 adsorption applications, which can be rationalized by considering an increase in isosteric heat of adsorption and hence in capacity [53, 54]. It should be noted that Sr-SAPO-34 shows very sharp isotherm slopes over the low pressure range 0.0–1.0 bar at 273–348 K, indicative of strong adsorbateadsorbent interactions [53]. Synchrotron powder XRD and Rietveld analyses demonstrated that Sr2+ is preferentially located in 8-ring windows, where the interactions with CO2 molecules are quite strong, because of the high electric field-quadrupole moment of this divalent cation. However, exchange of trivalent cations like Ce3+ and Ti3+ into SAPO-34 resulted in very low adsorption capacities [53], mainly due to pore blockage by the exchanged cations [52]. Hedin and co-workers compared the CO2 adsorption properties of several other cage-based small-pore SAPO molecular sieves, including SAPO-17 (ERI), SAPO35 (LEV), SAPO-56 (AFX), and SAPO-RHO (RHO) [55]. Among them, SAPO-56 was less water sensitive and exhibited a comparable CO2 adsorption capacity (5.4 vs 5.2 mmol g1) to Na-X at 273 K and 1.0 bar. Interestingly, SAPO-RHO was shown to exhibit a CO2/N2 selectivity of 26, which is considerably higher than the values (9–11) of the other SAPO materials, as well as the value (7) of Na-X. The Liu group subsequently reported that M-DNL-6, the SAPO-RHO version with a high framework Si content (Si/(Si + Al + P) ¼ 0.l82) and thus an increased number of Brønsted acid sites, has a CO2 uptake of 4.7 mmol g1 and CO2/CH4 and CO2/N2 selectivities of 12 and 20 at 298 K and 1.0 bar, respectively [56]. Recently, our group has synthesized the SAPO version of merlinoite without using any organic SDA [57]. When the number of extraframework K+ ions per unit cell of this SAPOMER was carefully minimized by controlling Si content in the synthesis gel, the resulting K-SAPO-MER with the smallest number (9.1) of K+ ions per 32 T-atoms,

14

K. C. Kemp et al.

Fig. 8 (Top) Breathing response of K-merlionite with Si/Al ¼ 3.8 upon introduction of CO2 and (bottom) its breakthrough curves showing the rapid response and CO2/CH4 selectivity [61]

namely, the largest Si fraction, showed not only a non-negligible CO2 uptake of 3.0 mmol g1 at 1.0 bar and 298 K but also a fairly high CO2/CH4 selectivity of 20, mainly due to the trapdoor effect. Small-pore aluminophosphate (AlPO4) molecular sieves with different framework topologies have also been tested as CO2 adsorbents [58–60]. Liu et al. examined the CO2 adsorption capacities and CO2/N2 selectivities of AlPO4-17 (ERI), AlPO4-18 (AEI), AlPO4-25 (ATV), and AlPO4-53 (AEN) at 273 K and 1.0 bar and observed that AlPO4-17 has the highest adsorption capacity (2.3 mmol g1) among these AlPO4 materials, whereas the CO2/N2 selectivities (46 and 76, respectively) of AlPO4-25 and AlPO4-53 are considerably higher than the selectivity (27) of the former material. It is interesting to note that the monoliths of AlPO4-17 and AlPO453 outperform commercial Na-X granules under PSA conditions, with working capacities of 1.4, 0.8, and 0.7 mmol g1 at 298 K and 0.9 to 0.15 bar, respectively [60]. Very recently, the Wright group has reported that the Na+, K+, and Cs+ forms of merlionite (MER) with Si/Al ¼ 3.8 exhibit a CO2 initiated “breathing” effect, similar to that found in MOFs (Fig. 8) [61]. This breathing effect has led the framework to be inaccessible to CH4 until CO2 is introduced. However, although CH4 is then

Small Gas Adsorption and Separation in Small-Pore Zeolites

15

adsorbed upon “breathing” transition, it gets displaced by CO2 with the introduction of further CO2. According to the same group, a mixed gas stream of 40% CH4/10% CO2/50% He can be purified using various cation forms of merlionite, with K-merlionite exhibiting the most rapid and efficient response to the introduction of CO2. While such a “breathing” mechanism is considered present in the RHO family of embedded isoreticular zeolites as well [61], the complicated structures of the higher RHO family members, i.e., ZSM-25 and PST-20, make this impossible to discern.

4 Small-Pore Zeolites for Small Hydrocarbon Separation Olefin/paraffin separation is one of the most important, yet most energy consuming, processes in the petrochemical industry [62], because olefin/paraffin mixtures with same carbon number, especially ethylene/ethane and propylene/propane, are quite similar in many of their physicochemical properties, e.g., boiling point, molecular weight, polarizability, kinetic diameter, etc. [63, 64]. Among the olefins, ethylene and propylene play a significant role because they are not only used for manufacturing of polymers, i.e., polyethylene and polypropylene, but are also major feedstocks for the chemical industry. Olefins are produced by several processes such as steam cracking, catalytic cracking, and catalytic dehydrogenation of paraffins. Of these, the most widely utilized process for the production of olefins is the cracking of C4-hydrocarbons fractions followed by dehydrogenation, where the corresponding paraffins are also produced [65]. Thus, to fully utilize these light hydrocarbons, it is essential to separate olefins (ethylene and propylene) from the corresponding paraffins (ethane and propane). The energy-intensive cryogenic distillation or condensation processes are currently being used for olefin/paraffin separation for the production of polymer-grade ethylene and propylene [66]. To afford some perspective of this energy consumption, therefore, olefin/paraffin separation processes usually need two huge splitter columns to separate the raw vapor mixtures. For example, C2 hydrocarbon separation is carried out on 160 tray columns at 243 K and 22 bar, whereas C3 hydrocarbon separation requires 220 tray columns at 243 K and 2 bar [67]. Considering the high capital and operation cost associated with such refrigerated distillations, however, there is enough motivation for researchers to explore cost- and energy-effective alternative technologies. Among the energy-efficient technologies proposed for olefin/paraffin separation so far, on the other hand, adsorptive separation using porous materials has been considered the most promising alternative. The two major categories of this separation technology are the kinetic and π-complexationdriven separations. In the former case, the difference in the diffusion rates between olefin and paraffin with the same number of carbon atoms into zeolite pores separates the mixture: the olefin normally has a faster diffusion rate because of its slightly smaller kinetic diameter. In the latter case, the olefin is selectively adsorbed from the

16

K. C. Kemp et al.

gas mixture through the interactions between the unsaturated outer most s orbitals of transition metal ions like Cu+ and Ag+ and the C¼C double bond of the olefin [68]. However, both technologies described above have drawbacks. In the case of kinetic separation, the paraffin remains as an impurity in the adsorbent employed. Thus, additional energy consumption steps, including high-pressure feeds, highpressure purge, and co-current blow down, are required to purify the olefin [69]. In the case of π-complexation-driven separation, in addition, the working capacity can gradually decrease with repeated regeneration because olefin oligomerization usually occurs due to the polarity of the adsorbents [70]. Up to date, as a result, only a very limited number of zeolites and MOFs have shown potential for kinetically separating olefins and paraffin. Given their high selective nature, however, the use of these two classes of crystalline porous materials is expected to be more dominant in future practical applications [71].

4.1

Ethylene and Ethane

There are many examples where zeolites and molecular sieves have been used for ethylene/ethane (C2¼/C2) separation, and the zeolites known to be potentially useful for this separation include Na-A, Ca-A, and Na-X [72–79]. However, even these zeolites show relatively low C2¼/C2 selectivities (5) [77–79]. Thus, to further enhance the uptake and selectivity of C2¼, Cu+ or Ag+ ions have been introduced to induce the π-complexation between the metal cation and C2¼. Miltenburg et al. dispersed CuCl into Na-X and then measured the C2¼ and C2 adsorption isotherms on the resulting CuCl/Na-X physical mixture [80]. They found an enhanced C2¼ selectivity compared to Na-X. However, there was also a decrease in adsorption capacity due to a decrease in surface area of the CuCl/Na-X mixture [72, 80]. In another work Aguado and co-workers reported that the introduction of Ag+ as a charge balancing cation into Ca-A leads to infinite C2¼ selectivity at 1.0 bar and 303 K, i.e., total C2 exclusion, which may be a result of a combination of π-complexation and size exclusion effects (Fig. 9) [81]. One way to prevent olefin polymerization during olefin/paraffin separation is to use pure-silica zeolites with no acid sites or transition metals in order to ensure that only size selection takes place. Researchers at ITQ and ExxonMobil have recently reported that the small-pore pure-silica zeolite ITQ-55 is very selective for C2¼/C2 separation [82]. ITQ-55 was synthesized using the N2,N2,N2,N5,N5,N5,3a6aoctamethyloctahydropentalene-2,5-diammonium dication as an organic SDA in either OH or F media. This sophisticated SDA was supposed to be responsible for the formation of 48 T heart-shaped cages interconnected through an 8-ring window (Fig. 10). While ITQ-55 showed an exceedingly high selectivity of 100 due to its unique pore topology and framework flexibility, the intracrystalline C2¼ diffusion rate was relatively slow compared to other known small-pore zeolites because of its narrow pores.

Small Gas Adsorption and Separation in Small-Pore Zeolites

17

Fig. 9 (a) Adsorption isotherms of ethylene (closed symbols) and ethane (open symbols) on Ag-A (black squares) and Ag-X (red circles) at 303 K. (b) Breakthrough curves of an ethane and ethylene mixture in nitrogen (10:10:80 v/v/v), 25 cm3 min1 at 303 K on 0.9 g of Ag-A (black lines) and Ag-X (red lines) [81]

Fig. 10 (a) The organic SDA used in ITQ-55 synthesis. (b) The 48 T-atom heart-shaped cage (left) in ITQ-55 and its dimeric 48 T cages (right). (c) C2¼/C2 breakthrough curves on ITQ-55 using an C2¼/C2 (50:50 v/v) mixture at 8.5 bar with a total gas flow rate of 10 cm3 min1 [82]

4.2

Propylene and Propane

The effective 8-ring pore diameters of many small-pore zeolites are not much different from the size of propylene (C3¼) or propane (C3). So small-pore zeolites have received much attention for C3¼/C3 separation since the fine control of their pore size can result in excellent separation performance. Indeed, many studies have shown that C3¼/C3 separation is very sensitive to even small changes in the 8-ring window size that are caused by variations in framework flexibility and composition and in type of extraframework cations introduced. For example, the Rodrigues group systematically investigated C3¼/C3 separation on Na-A, Ca-A, and Na-X and found that at 303 K and 1.0 bar, only Na-A can preferentially adsorb C3¼ (1.9 mmol g1) and mostly exclude C3 (20)

18

K. C. Kemp et al.

[83–86]. This has been explained by size exclusion, owing to the slight difference between the effective pore size of Na-A and the critical molecular diameter of C3¼ and C3. Ca-A was reported to show a higher C3¼ uptake (2.5 mmol g1 at 323 K and 1.0 bar) but a much lower C3¼/C3 selectivity (ca. 1) compared to Na-A zeolite because of its larger pore size [87]. The same conclusion can be drawn from the large-pore zeolite Na-X with higher C3¼ and C3 uptakes (2.7 and 2.1 mmol g1 at 303 K and 1.0 bar, respectively) [79, 88–90]. The C3¼ and C3 uptake and selectivity on commercial zeolites described above can be further modified via ion exchange. For example, in Na-X, although the uptake of C3¼ and C3 decreased after Li+ ion exchange, the selectivity increased since there is a larger decrease in C3 uptake [91]. Similarly, Padin et al. reported that exchange of most of the Na+ ions in Na-A with smaller Li+ (5% Na+ - 95% Li+) could optimize the C3¼ uptake (2.3 mmol g1 at 393 K and 1.0 bar) and C3¼/C3 selectivity (>15) [92]. Such an increase in selectivity has been attributed to an increased C3¼ diffusion rate, whereas C3 adsorption is still hindered. To confirm this speculation, Hedin et al. measured the self-diffusion coefficient rates of C3¼ and C3, using pulsed field gradient (PFG) NMR spectroscopy, in LTA zeolites with different Si/Al ratios and extraframework compositions. They found that a series of NaCa-A with different Ca/Na ratios have higher diffusion rates than cation-free ITQ-29 with Si/Al ¼ 1. While a slower olefin diffusion rate should be observed for cation-containing zeolites due to interactions between the target molecule and acidic sites, it has been postulated that the lower diffusion rate in ITQ-29 is mainly caused by its smaller pore size. Olson et al. have determined the diffusion rates of C1-C4 hydrocarbons on pure-silica DDR, CHA, and LTA zeolites using PFG NMR spectroscopy and found that the rate increases with larger 8-ring pore size and with smaller kinetic diameter of adsorbates [94]. This means that to kinetically separate olefin/paraffin mixtures, one needs to not only control the pore size but also consider the diffusion rate. Another way to tune hydrocarbon separation is to change the zeolite framework composition. In fact, significant differences in the diffusion rate of C3¼ and C3 were observed for pure-silica chabazite and AlPO4-34 with the same framework topology (CHA) but different compositions [94, 95]. Pure-silica chabazite showed a high C3¼ selectivity due to size exclusion, while AlPO4-34 exhibited similar C3¼ and C3 adsorption capacities (2.5 and 2.8 mmol g1, respectively) at 298 K and 1.0 bar. However, the latter material showed faster C3¼ diffusion rates than the former material because of its larger 8-ring window size [93]. As already noted in C2¼/C2 separation, in general, adsorbent regeneration is one of the main issues in the separation of olefin/paraffin with same number of carbon atoms. In general, C3¼ uptake gradually decreases in aluminosilicate zeolites since oligomerization takes place due to the inherent acidity and/or framework polarity. Therefore, to avoid this difficulty, many studies have investigated C3¼/C3 separation on pure-silica or AlPO4 molecular sieves. However, in the case of AlPO4 materials, a polarity still exists, owing to the relatively large electronegativity difference between Al (1.61) and P (2.19). In fact, Padin et al. reported that although AlPO4-14 (AFN) has a large propylene working capacity of ca. 0.6 mmol g1 between 0.1 and 1.0 bar at 393 K

Small Gas Adsorption and Separation in Small-Pore Zeolites

19

under simulated PSA conditions, it show irreversible desorption isotherms at low pressures [92], which is not the case of pure-silica, small-pore zeolites ITQ-12 (ITW), ITQ-29, and ITQ-32 (IHW) [96–100]. The ITQ-12 structure consists of interconnected double 4-rings, where the interconnectedness occurs around the organic SDA employed, creating a cage. Intriguingly, although this zeolite has a 2D channel system, both C3¼ and C3 molecules cannot pass through its slit-shaped 8-ring windows (2.4  5.3 Å) [96]. Therefore, ITQ-12 can be considered a 1D channel system for thermodynamic C3¼/C3 separation. However, it shows a high diffusion ratio (2.8  106) and fast diffusion rate (5.6  102 s1) for C3¼ at 353 K and 0.8 bar, together with a moderate uptake (1.3 mmol g1) [96, 97]. On the other hand, ITQ-29 has a low selectivity (1.1), but it shows a high C3¼ uptake of 2.5 mmol g1 at 298 K and 1.0 bar. Then again, ITQ-32 was reported to show a faster C3¼ diffusion rate (3.86  105 s1 at 298 K and 0.04 bar) than even ITQ-12, probably due to its 2D structure [100]. Other small-pore zeolites such as pure-silica chabazite, ITQ-3 (ITE), and high-silica ZSM-58 (DDR) have also been studied for C3¼/C3 separations. Among them, pure-silica chabazite presents the highest sorption capacity (2.7 mmol g1 at 303 K and 0.8 bar) and the highest ratio of C3¼ to C3 diffusion constants (>40,000). Its outstanding separation behavior can be attributed to the finely tuned small pore dimension, allowing C3¼ to flow freely while impeding C3¼ diffusion [101]. Recently, our group has examined the C3¼/C3 separation properties of ferroaluminosilicate levyne (FeAl-LEV) zeolites with different framework Si/Al and Fe/Al ratios [102]. Among the LEV-type zeolites tested, the mixed Ca2+NH4+ form of an FeAl-LEV with Si/Al ¼ 15.5 and Fe/Al ¼ 0.27 showed the highest C3¼/ C3 selectivity (11 at 298 K and 1.0 bar). More interestingly, this zeolite was characterized by a higher C3¼ uptake (ca. 1.0 vs 0.7 mmol g1) under VSA mode at 298 K and reservoir pressure 1.2 bar than its aluminosilicate version, together with better regenerability due to the relatively weaker acidity [102]. This suggests that in contrast to the general belief, the zeolite adsorbent selective for C3¼/C3 separation is not necessarily a pure-silica composition.

4.3

Butenes and Butane

Pure-silica zeolites have also been studied for C4 paraffin/olefin separation. Bart et al. reported that RUB-41 (RRO) with two intersecting 8- and 10-ring channels, which was obtained by calcination of the layered silicate RUB-39, can separate trans-2-butene and cis-2-butene from 1-butene in the liquid phase [103]. Another example is ITQ-32 which has been applied in C3¼/C3 separations. This small-pore zeolite is an excellent adsorbent for the separation of linear C4 olefins, with diffusion rates of 4.73  105 s1 at 298 K and 0.04 bar and 6.26  108 at 333 K and 0.04 bar for trans-2-butene and 1-butene, respectively. Therefore, ITQ-32 appears to be applicable to the separation of linear C4 hydrocarbons through a kinetic separation process. DDR also exhibits inherently good properties for the separation of C4

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K. C. Kemp et al.

hydrocarbon such as buta-1,3-diene and but-2-ene isomers. The cages in this zeolite are only accessible to trans-but-2-ene and buta-1,3-diene, whereas they can exclude but-1-ene and cis-but-2-ene due to their larger kinetic diameters [104]. Besides these pure-silica zeolites, ERI-type AlPO4-based materials (i.e., AlPO4-17 and SAPO-17) were shown to selectively separate trans-2-butene from 1-butene and cis-2-butene under near-catalytic conditions and in liquid phase since the trans isomer can easily access their 8-ring windows [105]. Finally, for convenience of the reader, the diffusion rates and uptakes of C2 to C4 olefins and paraffins on selected zeolites are listed in Table 1.

5 Small-Pore Zeolites as Adsorbents for Other Small Molecules 5.1

Methane

Apart from the olefin/paraffin separations mentioned above, there are many separation studies related to methane (CH4). Due to ever-increasing demands for cleaner fuels and to reduce the strong reliance on crude oil, huge efforts have been devoted to developing alternative energy sources. Among several candidates, CH4 is considered promising as an alternative fuel since it is naturally abundant and relatively environmentally friendly compared to other liquefied hydrocarbons. Unfortunately, most zeolites exhibit a relatively small methane uptake (1.1  102

C2H6

>1.4  102

C3H6

1.5  103

C3H8 C2H4

2.2  106 –

303/0.8 0.68  103 –

C3H6

5.6  102

C3H8 C2H4 C2H6 C3H6 C3H8 C3H6

2.0  108 21.4  108 20.9  108 0.5  1011 – 1.1  104

353/ 0.87 353/0.8 2.8  106 301/1.0 301/1.0 1.02 301/1.0 – – 333/0.3

IZA code Gas LTA C2H4 C2H6 C3H6 C3H8 Ag-A LTA C2H4 C2H6 Ca-A LTA C2H4 C2H6 C3H6 C3H8 Na-X FAU C2H4 C2H6 C3H6 C3H8 Si-ZSM-5 MFI C2H4 C2H6 Si-CHA CHA C2H4 C2H6 C3H6 C3H8 ZSM-58 DDR C3H6

AlPO4–14 AFN ITQ-3

ITQ-12

ITE

ITW

ITQ-29

LTA

ITQ-32

IHW

Da – – 5.9  1012 2.7  1014 – – – – 7.5  107 4.4  106 – – – – – – 3.6  102 0.48  108 4.6  104 1.0 x 108 1.2  104

αc

T/Pb – – – – – – – – 323/ 323/ – – – – – – 303/0.8 301/ 303/0.8 303/ 303/ 0.87 303/ 0.87 393/ 393/ 353/ 0.87 353/ 0.87 303/0.8

Material Na-A

– 220

0.17

7.5  106 4.6  104

Nd 1.5 0.4 1.9 ~0 2.2 ~0 2.8 2.2 3.3 3.1 3.6 2.8 3.7 3.1 2.1 2.3 1.8 2.1 2.8 – –

T/Pb 293/1.0 298/1.0 303/ 303/ 303/1.0 303/1.0 303/1.0 303/1.0 323/1.0 323/1.0 323/1.0 323/1.0 323/1.0 323/1.0 305/2.0 305/2.0 303/0.8 301/1.0 303/0.8 – –

αc 3.75 – – 1.27 1.06 1.28 1.19 0.91 0.86 –

Ref(s). [77] [77] [83, 85] [83, 85] [84] [81] [77] [77] [87] [87] [79] [79] [79] [79] [75] [75] [101] [94] [101] [101] [101]

0.13  105 –

–

[101]

3.1

– – –

– – –

[92] [92] [101]

–

–

–

[101]

1.4 313/ [101] 0.87 – – [101] 0.7 303/ [97] 0.87 0.9 353/ 0.79 [97] 0.87 – – [97] 1.6 301/1.0 [94] 1.6 301/1.0 1 [94] 2.5 301/ [94, 98] 2.2 298/ 1.14 [98] 1.2 333/1.0 [100] (continued)

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K. C. Kemp et al.

Table 1 (continued) Material

ITQ-55

IZA code Gas C3H8 C4H8e C4H8f C4H8g C4H10 C2H4 C2H6 C4H8

Da 6.6  108 2.5  104 6.3  108 1.1  106 1.8  108 6.0  1017 7.0  1019 1.1  106

T/Pb 333/0.3 333/0.3 333/0.3 333/0.3 333/0.3 303/ 0.65 303/0.7 333/0.3

αc Nd 4 0.17  10 – 1.2 – – – – 1.5

T/Pb αc – 333/1.0 – – – 303/1.0

Ref(s). [100] [100] [100] [100] [100] [25]

0.86  102 – –

– –

[25] [22]

Diffusion rate (s1) Temperature/pressure (K/bar) c Selectivity d Uptake (mmol g1) e trans-2-Butene f cis-2-Butene g 1-Butene a

b

cation [111]. For example, bigger cations like Cs+ tend to make zeolites more hydrophobic as they decrease pore volume and display weaker interactions with H2O. Given that the hydrophilicity and hydrophobicity of zeolites can be tuned by controlling their Si/Al ratio, high- or pure-silica zeolites can be excellent candidates for the selective adsorption of a hydrophobic adsorbate in the presence of H2O, when their pore size allows access for the target adsorbate [112]. Manabu et al. reported that pure-silica chabazite exhibits a somewhat impressive CO2 adsorption capacity (5.0 mmol g1) at 313 K and 30 bar compared to the hydrophilic commercial zeolite Na-X (6.2 mmol g1) [113]. Unlike the case of Na-X, however, its adsorption capacity is not influenced by the presence of H2O. Another method to change the hydrophilic nature of zeolites is to functionalize them with amine groups, as has been explored for CO2 capture in wet flue gas. However, the functionalized zeolites suffer from significant amine degradation, due to acidic impurities such as SO2, as well as urea formed upon CO2 adsorption [114]. The more hydrophilic nature of AlPO4 molecular sieves compared to pure-silica molecular sieves can also be utilized for CO2 adsorption in the presence of H2O, because many of them (e.g., AlPO4-18, AlPO4-21 (AWO), AlPO4-25, AlPO4-53, etc.) exhibit negligible water uptake at low partial pressures [58].

5.3

Nitrogen Oxides

Nitrogen oxides (NOx) emission is one of the main contributors to stratospheric ozone depletion and global warming. To adsorb NOx from dry and wet flue gases,

Small Gas Adsorption and Separation in Small-Pore Zeolites

23

especially from internal combustion engine exhausts at low temperatures (< 420 K) during cold-start, much attention has been directed to the use of zeolites containing various transition metal ions as adsorbents [115–119]. Similar to NH3-SCR catalysis, the adsorption/desorption properties of NOx on such zeolites are severely influenced by temperature, aging, types of the metal cation, zeolite support structure employed, etc. Chen et al. have compared the passive NOx adsorption (PNA) capacities of Pd-SSZ-13, Pd-ZSM-5, and Pd-beta (*BEA), respectively [120]. When 1 wt% Pd was loaded and then hydrothermally treated at 1023 K, the NO storage capacity at 373 K was measured to be 48, 58, and 64 NOx μmol (g sorbent)1 or 0.52, 0.62, and 0.68 NOx mol (mole Pd)1 for Pd-SSZ-13, Pd-ZSM-5, and Pd-beta, respectively. These authors also showed that the maximum NOx desorption peak temperature increases with decreasing zeolite pore size: Pd-beta (530 K) < Pd-ZSM-5 (550 K) < Pd-SSZ-13 (630 K). Szanyi and co-workers studied the low-temperature NOx adsorption behavior of Pd-SSZ-13, Pd-ZSM-5, and Pd-beta in order to understand how the metal oxidation state, composition, and structure in these small-pore zeolites effect performance [121]. They reported the coexistence of multiple Pd species, such as atomically dispersed Pd in the cationic sites of zeolite supports, as well as PdO2 and PdO particles on the external surface of zeolite crystals. More recently, these researchers have developed a simple and scalable route to prepare atomically dispersed highly loaded (>0.3 wt%) Pd- and Pt-SSZ-13 materials, using the NH4+ form of the zeolite support and the modified incipient wetness impregnation method [122]. The 1.9 wt% Pd-SSZ-13 (Si/Al ¼ 6) material prepared in this way was found to abate 180 μmol NOx g1 during cold-start, an unprecedented NOx storage capacity, while keeping atomic dispersion.

6 Conclusions and Prospects It is now clear that the small gas adsorption capacity and selectivity of small-pore zeolites are strongly influenced by their pore structure and extra- and intraframework compositions. A high degree of tunability in the surface selectivity could motivate researchers in this field to consider novel zeolite structures and compositions that can also be used for the selective adsorption of inert gases, e.g., He, Ne, Ar, etc. Of course, the negative effect of water in their adsorption/separation needs to be considered, as hydrophobic pure-silica zeolites can exhibit very low adsorption capacities for the target adsorbate. There are also some limitations and concerns that should be taken into account when designing zeolite adsorbents for a specific application. For example, the use of highly sophisticated and thus expensive organic SDAs and environmentally unfriendly fluoride anions in zeolite synthesis may be a major hurdle to be overcome for the commercial applications of zeolites with superior separation and adsorption properties. With respect to energy consumption and alternative fuels, on the other hand, the most urgent challenge is the discovery of new zeolitic adsorbents that are kinetically

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fast but unprecedentedly selective for the olefin/paraffin separation [62, 123, 124]. Although the prominent adsorbents in terms of olefin capacity and selectivity are MOFs, they need open metal sites on which to adsorb olefin molecules, increasing the cost. In this regard, heteroatom-substituted or exchanged zeolites hold the same, if not greater, potential to separate hydrocarbon mixtures, as demonstrated using FeAl-LEV and Ag-LTA [81, 102]. We have also noted that zeolites and related crystalline microporous materials are desirable over other materials like MOFs or ACs, in terms of the cost, industrial applicability, and stability [4]. Furthermore, zeolites can outperform some MOFs with respect to selectivity as well as showing relatable adsorption kinetics [107]. Nevertheless, however, the preparation of zeolite-MOF composite materials can in our view lead to an enhancement of the adsorption capacity, selectivity, or both. This can be supported by a very recent study on the CH4/N2 separation using ZIF/Zn-Y and ZIF/Zn-ZSM-5 hybrid adsorbents, where an increase in selectivity relative to its constituents is observed [125]. Apart from the current research motivations in the small gas adsorption field, such as climate change mitigation, environmentally friendly catalysis, and capture/separation of poisonous and noble gases, among others, it would be wise if zeolite researchers start to consider how current and future framework types may be adapted or utilized in the future hydrogen economy. For example, current zeolite materials are not suited for hydrogen storage, yet they seem highly functional for separation of hydrogen from gas mixtures, as well as its deuterium isotope [126]. With this is mind, we expect that many small-pore zeolitic materials with new framework structures and/or compositions and their composite materials will certainly be discovered in the near future and then applied in existing gas adsorption and separation equipment without the need for refurbishing. We also anticipate that both new and existing zeolites will be synthesized in a more environmentally benign manner. Acknowledgments We acknowledge financial support from the National Creative Research Initiative Program (2012R1A3A2048833) through the National Research Foundation of Korea.

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Struct Bond (2020) 184: 31–56 https://doi.org/10.1007/430_2020_69 # Springer Nature Switzerland AG 2020 Published online: 24 October 2020

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption Jhonny Villarroel-Rocha, Deicy Barrera, José J. Arroyo-Gómez, and Karim Sapag

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Gas Adsorption: Basic Principles and Experimental Considerations . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Analysis Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sample Pretreatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Choice of Adsorptive Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Textural Properties Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Specific Surface Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Pore Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Pore Size Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions and Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Porous materials with pores within molecular size are essential to solve several technological problems taking advantage of their textural properties related to their exposed surface and porosity. Among these materials, zeolites are in the podium, with technological and industrial applications, which are directly related to pore properties (e.g., size, surface chemistry, among others). To obtain their textural properties, there are several techniques, but gas adsorption plays an important role, and it is among the most widely used for this purpose. Despite being a popular technique, with nitrogen at 77 K as the reference probe molecule to obtain adsorption isotherms, the estimation of the textural properties is not a trivial procedure. On the other hand, it is possible to perform adsorption measurements with different gases at different temperatures and pressures, whereby is crucial the choice of adequate

J. Villarroel-Rocha, D. Barrera, J. J. Arroyo-Gómez, and K. Sapag (*) Laboratorio de Sólidos Porosos (LabSoP), Departamento de Física, INFAP-CONICET, Universidad Nacional de San Luis, San Luis, Argentina e-mail: [email protected]; [email protected]; [email protected]; [email protected]

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analysis conditions because the adsorption-desorption isotherm will be the unique information obtained from the experiment. Once the data are obtained, a careful selection of methods and models to analyze them is mandatory to evaluate textural properties of the samples in a reliable and reproducible manner. Particularly for zeolites, due to their pore sizes and the presence of surface functional groups, the application of this characterization technique is not straightforward, thus needing to pay attention to the previous knowledge that exists about this type of materials, to carry out the experiment as well as to choose the appropriate methodology for data treatment. In this chapter, we introduce an overview of the experimental procedure and data treatment to obtain the more reliable textural properties for zeolites. Keywords Gas adsorption · Isotherms analysis · Porous materials · Textural characterization of zeolites

1 Introduction Zeolites are aluminosilicate compounds forming a framework consisting of tetrahedrons of silicon cations (Si4+) and aluminum cations (Al3+) that are surrounded by oxygen anions (O2
). Oxygen form bonds between Si-O and Al-O tetrahedrons, resulting in a three-dimensional framework of SiO2 and AlO2 building blocks, i.e., [SiO4]4
and [AlO4]5
, denominated primary building units (PBUs) [1]. The different special arrangements that take place when PBUs share oxygen resulting in simple geometric forms are called secondary building units (SBUs), and these SBUs come in a variety of forms (i.e., rings and polyhedral) linked together to produce a unique system of channels and cages. In addition to PBUs and SBUs, zeolites may contain other components such as double rings, cages, and cavities; these are called composite building units (CBUs). The different combinations of SBUs, CBUs, and the presence of counterions (Na+, K+, Ca2+, etc.) result in a vast number of zeolite structures (more information regarding zeolite structure and their denomination can be found in specialized bibliography [1–3] and in the database of the International Zeolite Association [4]). An interesting result of the different zeolite structures is the presence of cavities and channels with different sizes (i.e., porosity), making the zeolites a versatile material for numerous applications. Porosity is defined as the ratio of the volume of pores (cavities or channels which are deeper than wide) and voids to the volume occupied by the solid [5], and it is crucial because its porous structure is relevant in several applications of these materials. This property can be measured by several techniques (e.g., direct methods, optical methods, computed tomography, water evaporation, among others). However, for porous materials, the most accepted technique to assess their porosity is gas adsorption. After a careful analysis of the adsorption data, considering adsorption mechanisms, pore shape and surface chemistry of the solid, probe gas, and other

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criteria [5–7], one can obtain information about the called textural properties of the solid, related with their surface and pore structure. Pores can be classified according to their sizes as micropores (12-MR, 12-MR, 10-MR, and 8-MR, respectively. In order to understand the various types of openings that are considered components that form the pores in zeolites, the International Union for Pure and Applied Chemistry (IUPAC) established some definitions [13]: • Windows: rings formed of tetrahedral units that define the faces of polyhedral pores. • Cages: polyhedral pores with narrow windows that do not allow the passage of molecules larger than water. • Cavities: polyhedral pores with at least one face defined by a ring large enough to allow the penetration of a guest species but is not infinitely extended. • Channels: pores extended infinitely in one dimension and wide enough to allow the diffusion of guest species along its length. The number of dimensions in which the pore has infinite extension defined by the pore dimensionality. The parameters of the pore system are defined by means of the pore descriptor, which includes dimensionality, the shape of the pore, direction of the channel, and the effective pore width [13, 14]. Another meaningful feature of zeolites, besides their narrow pore sizes, is the presence of surface functional groups, displaying diffusional problems for molecules to access inside them. This fact is a critical impediment to obtain an adequate textural characterization of this class of materials by gas adsorption. To avoid this inconvenient, and to assess the presence of narrow micropores as well, the IUPAC recommends the use of different gases (e.g., N2, Ar, CO2, Kr, O2) to guarantee an accurate evaluation of materials porosity, regardless of the texture and surface chemistry of the material. The characterization of the textural properties of the porous solids is relevant to establish a further correlation between them and the performance in specific applications. In this chapter, we present an overview of the textural characterization by gas adsorption applied to zeolites, pointing out the critical aspects of this procedure. We will discuss the basic principles of gas adsorption, highlighting the main concepts of this phenomenon, experimental requirements to carry out an accurate procedure and data analysis.

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2 Gas Adsorption: Basic Principles and Experimental Considerations Adsorption phenomenon occurs when a solid surface is exposed to a fluid, and it is defined as the increase in the density of that fluid close to the interface [5]. The inverse process is called desorption. Depending on the forces involved, the adsorption can be either physical (physisorption) or chemical (chemisorption). The former involves Van der Waals forces, whereas the latter leads to the formation of chemical bonds. Physisorption process, which is reversible and non-localized, is used in the textural characterization of solids, allowing to study the exposed surface to the fluid. It occurs when a gas, adsorptive (in the fluid phase), is put into contact with a solid, adsorbent, whereby a spontaneous process, the gas condensates on the surface reaching the final state known as adsorbate [5, 6]. It is important to note that this is a phenomenon of attraction between the solid and the gas molecules, which is spontaneous and exothermic. In Fig. 1 a general scheme of the gas adsorption process is shown. The relationship between the amount adsorbed and the equilibrium pressure at constant temperature is known as the adsorption isotherm. When the amounts adsorbed and desorbed are not the same at a given pressure, the so-called adsorption hysteresis appears [15, 16]. As a characterization technique, gas adsorption allows assessing the textural properties of the porous materials, i.e., specific surface area, pore volume, and pore size distribution. To perform a thorough and accurate textural analysis of porous materials, some experimental considerations must be taken into account and will be discussed in the following sections.

Fig. 1 General scheme of the adsorption phenomenon

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption

2.1

35

Analysis Conditions

A relevant factor for obtaining reliable adsorption data is the establishment of the appropriate operating conditions. Once the gas adsorptive is selected, it is important to know its thermodynamic characteristics and to choose an adequate adsorption temperature, which is usually below the critical point. Defining the temperature of the analysis for the chosen gas, it is necessary to select the relative pressure ( p/po) range, where p is the measured equilibrium pressure, and po is the saturation vapor pressure of the adsorptive at the adsorption temperature, which is advisable to measure during the experiment. To obtain a high-resolution isotherm, the p/po range must be between 10
7 and 1, being advisable to use pressure transducers of 100 Pa, 1 kPa, and 0.1 MPa; to achieve this, low pressure is necessary the use of a turbomolecular pump. In manometric equipments, once the analysis conditions were established, the determination of the dead space (void volume) under the operational conditions is usually carried out with helium [17]. However, it has been confirmed that materials with narrow micropores, as in zeolites, may adsorb helium at 77 K, changing the adsorbed amount of the adsorptive at ultralow relative pressures [18]. Thus, for materials with ultramicropores, this determination should be carried out once the adsorption isotherm has been obtained. Finally, if the analysis starts from that low relative pressure, the equilibrium time should be long enough to guarantee suitable isotherm data.

2.2

Sample Pretreatment

In order to remove all physisorbed molecules from the adsorbent surface, a pre-treatment of outgassing is necessary to ensure a clean initial surface of the adsorbents before the measurement. This procedure must be carried out by vacuum pumping below 0.5 Pa and at an advisable temperature, depending on the nature and synthesis conditions of the sample. The selected temperature should not cause irreversible changes on the adsorbent surface during the outgassing process; this temperature can be determined through thermogravimetric analysis. Thommes [19] recommends, for zeolites, temperatures below 200 C to remove physisorbed water from the adsorbent surfaces and high temperatures up to 400 C for water adsorbed in pores smaller than 0.7 nm and not less than 8 h [20]. An example of the importance of the outgassing treatment is performed for a Zeolite 5A, which has a window size of 0.49 nm is shown in the following figure. The thermogravimetric analysis revealed that the advisable temperature to use must be above 320 C (curve not shown). In Fig. 2, the CO2 adsorption isotherms at 273 K for Zeolite 5A are displayed with an outgassing process at different temperatures for 12 h. As it can be seen for outgassing temperature equal to or higher than 350 C, isotherms are coincident.

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Fig. 2 CO2 adsorption isotherms at 273 K of Zeolite 5A outgassed at different temperatures

2.3

Choice of Adsorptive Gas

The nitrogen at its boiling temperature (77 K) has been generally accepted as the most common and standard probe gas to perform the adsorption-desorption isotherms [7]. However, it is well-known that its quadrupole moment leads to specific interactions with different surface functional groups. The more known are the silanol groups on silica surfaces or ionic charges in zeolites channels or windows of the micropore region. To avoid this issue, it is possible to use another probe gas, such as argon, which lacks a quadrupole moment; therefore, it does not interact with surface functional groups. Argon isotherms can be measured at 77 K (6.5 K below its triple point) where its experimental saturation pressure is close to 205 Torr. At this conditions, argon fills narrow micropores at similar pressures to nitrogen at the same temperature. Also, the pore size analysis with argon at 77 K only can be measured up to 15 nm [21], and the micropore analysis is obtained in a narrow range of relative pressures, where argon molecules have low mobility. The better and more complete analysis can be made using Ar at 87 K, where its saturation pressure is near the atmospheric. In this case, the narrow micropores are filled at a higher pressure, with faster molecular mobility than N2 and Ar at 77 K, and the porosity range of analysis is similar to the obtained with nitrogen at 77 K. Because commercial transducers are very accurate in the pressure range where the Ar isotherms at 87 K are carried out, the adsorption isotherms are obtained with high resolution, which are often considered as a fingerprint of the pore structure [22]. Figure 3 shows argon isotherms at 77 and 87 K and nitrogen isotherm at 77 K for the ITQ-29 zeolite. In this figure, we can see the behavior discussed above. Nakai et al. [24] investigated the advantage of using Ar isotherms at 87 K to obtain more realistic information about the pore structure of zeolites in the presence of surface functional groups. In this work, they present high-resolution adsorption

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Fig. 3 N2 and Ar at 77 K and Ar at 87 K adsorption isotherms on ITQ-29 zeolite. Ar adsorption isotherm at 87 K adapted with permission from Fraccarollo et al. [23]. Copyright (2017) American Chemical Society

Fig. 4 High-resolution adsorption isotherms for ZSM-5 zeolites: (a) Ar isotherms at 87 K, and (b) N2 isotherms at 77 K. Reproduced with permission from Elsevier

isotherms for zeolites with the same pore structure but with different charges in their channels, as is shown in Fig. 4. The sample studied was a ZSM-5 zeolite with two Si/Al molar ratios, 12.5 and 500. The former had more protons in its structure; therefore, a higher interaction with nitrogen is expected, as clearly observed in Fig. 4b. The argon isotherms (Fig. 4a) show identical isotherms for both samples according to the same pore structure, indicating an absence of interaction between the gas and the surface of the sample.

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Fig. 5 N2, Ar and CO2 adsorption isotherms of Zeolite 4A

Both nitrogen and argon have slow diffusion at ultralow pressure at cryogenic temperature and sometimes hinder a proper measurement in the ultramicropore region (< 0.7 nm) [22]. To solve this inconvenience, the use of CO2 at 273 K appears as an alternative to evaluate this narrow microporosity. Although the kinetic diameters of nitrogen, argon, and carbon dioxide are similar (i.e., 0.36, 0.34, and 0.33 nm, respectively) [25], the adsorption behavior of these three adsorptives is quite different. At 273 K the CO2 molecule has higher mobility, and its saturation pressure is around 3.5 MPa; therefore, up to atmospheric pressure (0.1 MPa), the narrow micropores (0.4–1 nm) can be filled, whereas, at 1 MPa, it reaches pores up to 1.5 nm. Although CO2 has advantages to assess micropores, this adsorptive has a higher quadrupole moment than nitrogen, affecting the adsorption analysis of materials with surface functional groups [26]. In spite of that, CO2 adsorption can still be useful for assessing the microporosity of materials with pores where neither nitrogen nor argon can access. In Fig. 5, an example for a Zeolite 4A is shown, where CO2 can access the narrow micropores, unlike nitrogen and argon, because the window size of this zeolite is ca. 0.38 nm. It is crucial to highlight that, with CO2 at 273 K, up to atmospheric pressure, corresponding to ca. 0.03 of p/po, not all the micropores are filled. To analyze the complete micropore range of this zeolite, it is necessary to perform the analysis up to higher pressure. In the figure, the CO2 analysis was performed up to 1 MPa (ca. 0.3 of p/po). Therefore, to obtain accurate and consistent results, it is crucial to choose the right adsorptive for each sample. The comparison between adsorption measurements with nitrogen, argon, carbon dioxide, and even water could provide complimentary textural information.

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3 Textural Properties Analysis Once the adsorption isotherm has been measured under the right conditions, the results must be presented and analyzed using the proper units and methods/models, respectively. It is important to point out that this technique can only analyze the micro and mesopores of the sample. The IUPAC in its technical report of 2015 [7] suggests the graphical representation of the adsorption isotherms in terms of the amount adsorbed in mol per adsorbent gram (usually presented as mmol g
1) versus the relative equilibrium pressure ( p/po); the latter may be changed to p when the analysis temperature is above the critical temperature of the adsorptive, or it can be changed to gas fugacity when the conditions deviate the gas behavior far from ideality (e.g., high-pressure measurements). The resulting isotherm (with or without a hysteresis loop) provides information regarding pore size and adsorption/desorption mechanisms that will be essential to make a complete elucidation of the pore structure. As above named, the textural properties obtained by the analysis data of the isotherms are specific surface area, pore volumes, and pore size distribution. The surface area assessment is based on the Brunauer-Emmett-Teller (BET) method [27], which is the most used procedure for evaluating porous materials. By taking into account specific criteria to ensure an objective analysis, even microporous materials can be studied with this method [5, 7, 28]. The evaluation of the micropore volume can be made with t-plot [29], αS-plot [30], and Dubinin-Radushkevich (DR) [31], where some of them give us other properties as the internal and external surface area of porous materials [5, 32]. The total pore volume (micro- and mesopores volumes) can be calculated using the Gurvich rule [5]. Mesopore volume can be calculated by subtracting the micropore volume to the total pore volume. Finally, the pore size distribution (PSD) allows determining the volume corresponding for each pore size. To obtain the PSD of a given porous material by gas adsorption, it is necessary to know the filling/emptying mechanism of each pore size. For the micropore region, this mechanism is not universally defined; nonetheless, there are available methods to obtain a reliable micropore size distribution; some of them are based on the Horváth-Kawazoe (HK) method [33]. However, for the mesoporous region, and even more in the case of nitrogen at 77 K, the capillary condensation/evaporation theory adequately describes its behavior. Based on this theory, the so-called macroscopic methods arise, which use the original and modified Kelvin equation [34]; these methods include Barrett-Joyner-Halenda (BJH) [35], Dollimore-Heal (DH) [36], and Villarroel-Barrera-Sapag (VBS) [37]; and they can be applied to obtain the mesoporous size distributions from the isotherms data. To obtain the PSD in size range corresponding to micro- and mesopores, computational or also called microscopic approaches were developed, where the most applied ones are those based on Monte Carlo simulations (MC) [38] and the density functional theory (DFT) [39]. It is noteworthy that despite the existence of several models, methods, and equations to evaluate the PSD, it is necessary to consider the following aspects: nature and pore geometry of the adsorbent, the adsorptive and its thermodynamic

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properties, physical fundaments of the models, choice of the suitable isotherm branch, and kernel availability, among others.

3.1

Specific Surface Area

The Brunauer-Emmett-Teller (BET) method is the most widely used procedure to calculate the specific surface area of porous materials, despite its limitations [7, 28]. The BET equation (Eq. 1) represents how is the increasing of the adsorbed thickness in function of p/po where the mono-multilayer is formed. From the adsorption isotherms data (amount adsorbed, nads, versus p/po), and using the Eq. 1, obtaining a plot of points within a pressure region defined by the BET model (BET-plot). By a linear fitting of these points, it is possible to obtain both the monolayer capacity (nm) and the C constant (exponentially related to the energy of monolayer adsorption, then C > 0). p=p

nads  1
p=p




¼


1 C
1 þ  p=p nm  C nm  C

ð1Þ

From the monolayer capacity per gram of adsorbent (nm), the area occupied by the adsorbate molecule in the complete monolayer, σ m (molecular cross-sectional area), and the Avogadro constant (N ), the specific surface area (SBET) is obtained (Eq. 2). SBET ¼ nm  N  σ m

ð2Þ

The linear behavior of this model corresponds to the region of relative pressures that starts when the micropores filling is finishing, continues with the monomultilayer formation, and ending before the capillary condensation begins. In the validity region of the BET equation, the adsorption isotherm presents a knee followed by a linear increasing, where the called Point B, corresponding to the completion of monolayer coverage, is within that zone (the location of Point B depends on the isotherm Type). The C value gives a useful indication of the isotherms shape in the BET range; if C ~ 80 the knee of the isotherm is sharp and Point B is fairly well-defined, for C < 50, the Point B cannot be identified as a single point and the estimation of the nm is not precise; a high C value (>150) is generally associated with either adsorption on high energy surface sites or the filling of narrow micropores [7], and when this value increases, the BET range moves to lower relative pressures. Hudec et al. [40] studying the sensitivity of the C value for micro- mesoporous materials concluded that extremely high values (C > 2,000) have no physical meanings to associate it with the energy of adsorbate-adsorbent interaction in the BET model. In the case of materials without or with a small amount of micropores, where the “knee” of the isotherm is well defined (C ~ 80), the SBET

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Fig. 6 N2 adsorptiondesorption isotherm on zeolite 13X, marking the different relative pressures ranges (original and recommended) to calculate specific surface area by BET method. Inset: isotherm in semi-logarithmic scale

can be considered as the effective area available for adsorption of the specified adsorptive. In the case of microporous materials as the zeolites, extreme caution is necessary because it could be impossible to separate the mono-multilayer adsorption and micropore filling processes. The original range of relative pressures proposed by the BET model is between 0.05 and 0.35, but it has been found that the linear region of BET-plot is shorter and even appears below 0.05. Then, with an adsorption isotherm data, different values of SBET can be achieved depending on the chosen relative pressures range. If the only criterion to choose this range is to find a linear behavior of the data in the BET-plot, then this selection could be subjective and even wrong because we can find several linear sections at different relative pressure ranges in the plot. Therefore, a linear fitting of the data is not enough, and other parameters must be considered. Rouquerol et al. [28] proposed a useful procedure to avoid any subjectivity in the evaluation of the specific surface area by the BET method, which was included in the IUPAC recommendations [7]. This proposal is based on the following criteria: (1) the C value should be positive, because negative value lacks physical meaning; (2) the application of the BET method should be restricted to the range where the term nads(1 – p/po) continuously increases with p/ po; (3) the p/po value corresponding to nm should be within the selected BET range. This procedure allows a standard and reproducible method to obtain the SBET, where, depending on the C value, different interpretations can be made. For example, for 0 < C < 150, SBET can be assumed as the real accessible surface area of the adsorbent for the probe molecule. Nevertheless, for most zeolites that have a high C value, the SBET value is considered as the apparent surface area, and it only may be regarded as a useful “fingerprint” of the adsorbent and not related with a real property. Examples of the SBET assessment for some zeolites are given as follows. Figure 6 shows the N2 adsorption-desorption isotherm of Zeolite 13X presenting a marked

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Fig. 7 Plot of the Rouquerol criterion from the isotherm data of Fig. 6

adsorption at low pressure, characteristics of microporous materials, followed by the formation of the mono-multilayer, and, finally, at higher relative pressures, the gas adsorption due to the presence of mesopores. The inset of Fig. 6 shows the semilogarithmic plot of the isotherm, where the original relative pressure range to apply the BET method is marked with black diamonds and the corresponding range using IUPAC criteria by red triangles. In this figure, we can see that these relative pressure ranges differ significantly; therefore the SBET value will be different. In Fig. 7 the plot suggested by Rouquerol is shown, where the term nads(1
p/po) should continuously increase with p/po up to reach a maximum (at 0.02 of p/po, in this case), and then it decreases as the relative pressure keep increasing. Thus, for this isotherm, the maximum relative pressure to apply the BET equation must be 0.02. From this maximum, it is necessary to select at least 5 points below to consider the new relative pressure range, in which the following conditions need to be taken into account: C > 0, the Point B must be within this range, and the linear regression coefficient must be higher than 0.999. This criterion can be considered as a suitable way to choose an appropriate relative pressure range, and several authors have applied it in other microporous materials [5, 41]. Figure 8 shows the BET-plot for the original relative pressure range proposed by this method (blue squares) and the obtained following the IUPAC recommendations (red circles). In this figure the ranges differ greatly, the one suggested by the IUPAC is located at lower relative pressures, and there are not points in common with the original BET range. The orange values in the figure, corresponding to each marked blue square, are the results obtained by applying the BET equation, selecting that point as the highest in the relative pressure range. In this number set, the first is the SBET, followed by the C value in parenthesis, and the final value is the linear regression coefficient R2, i.e., SBET (C) R2. Analyzing these values, despite a good R2, the C values are negative in all of these cases; it can be concluded the original

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption

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Fig. 8 BET-plot with the original relative pressure range proposed by BET method (blue squares), and those based on the IUPAC recommendations (empty red circles)

Table 1 Values for Zeolite 13X obtained by BET method using the relative pressure ranges proposed by the original method and the recommended by the IUPAC p/po range 0.05–0.35 0.0027–0.020

Zeolite 13X SBET (m2 g
1) 460 650

C
35 8,739

R2 0.990827 0.999999

relative pressure range to apply the BET method is not adequate to assess a reliable SBET value. Table 1 presents the SBET, the C value, and the linear regression coefficient (R2) obtained with the p/po range proposed by the original BET method and by the IUPAC suggestions. It must be highlighted that the SBET values obtained in each range differ by 30%, where the value of 650 m2/g is adequate to represent the apparent surface area of a Zeolite 13X by the BET method. Figure 9 shows the nitrogen isotherms for two zeolites with different porosity, also here, the relative pressure range obtained by IUPAC criteria is different from the original proposed by the BET method. It is noteworthy that in new ranges, the region where the knee is present is more representative. Table 2 compiles the information obtained from these relative pressure ranges for both zeolites. If the original BET range (0.05–0.35) is used, the C value is negative; therefore, it is necessary to apply the IUPAC recommendations, and these results are also shown in Table 2. It is worthy of highlighting that, for microporous materials, this new relative pressure range is narrower and at lower relative pressures than the original. Also, the new C values could be very high (typical of materials with narrow microporosity), which leads to considering the specific surface area as “apparent.” Despite this latter issue, by taking into consideration the IUPAC recommendations, it is possible to obtain a reproducible SBET, which can be considered as a “fingerprint” of the sample.

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Fig. 9 N2 adsorption-desorption isotherm at 77 K of (a) Zeolite 5A and (b) clinoptilolite modified with acid treatment. In both graphs, the relative pressure ranges to calculate specific surface area by BET method are marked, in black diamonds, the original BET range, and, in red triangles, the proposed by IUPAC recommendations. Inset: isotherms in semi-logarithmic scale Table 2 Values obtained for Zeolite 5A and clinoptilolite modified with acid treatment by the BET method using the relative pressure ranges proposed by the original method and by the IUPAC recommendations p/po range 0.05–0.35 0.003–0.020 0.010–0.075

Zeolite 5A SBET (m2 g
1) 555 785 –

C
35 10,894 –

R2 0.99102 0.99999 –

Clinoptilolite SBET (m2 g
1) 130 – 165

C
44 – 1,137

R2 0.99280 – 0.99998

To obtain the BET surface area, it is advisable to use nitrogen at 77 K because of its quadrupole moment, among other properties, facilitating the monolayer formation. In some cases, argon at 77 K can be used, but this adsorptive could not be adopted instead of nitrogen. Mainly because at this temperature, the physical state of argon is in doubt, although at low relative pressures, its state can be assumed as a fluid, at higher relative pressures could be solid. Therefore, at these conditions, Ar can be used to analyze micropores, but it not is advisable to analyze the complete mesoporous range. As an example, Fig. 10 shows nitrogen and argon isotherms at 77 K reported by Mańko et al. [42], where these authors synthesized a Zeolite Y with the surfactant CTAB to obtain a zeolite with micro- and mesopores (HY-CTAB). In this figure, it is possible to see that argon fills the narrowest micropores at very low relative pressures; this zone is harder to observe with nitrogen. Another feature is the presence of hysteresis in the argon isotherm (Fig. 10b), indicating the presence of mesopores. Besides, this figure shows the relative pressure ranges by the original BET method and the IUPAC recommendations. In both isotherms, it is clear that the original BET range is extend up to higher relative pressure values far from the zone where the monolayer is formed.

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Fig. 10 Adsorption-desorption isotherms at 77 K of HY-CTAB with different adsorptives: (a) N2 and (b) Ar; marking in both graphs, the relative pressure ranges to calculate specific surface area by BET method (black diamonds) and the proposed by IUPAC (red triangles). Inset: isotherms in a semi-logarithmic scale. Adapted from Mańko et al. [42] with permission from Elsevier Table 3 Values obtained by BET method using pressure ranges proposes by the original method and the IUPAC recommendations for HY-CTAB p/po range 0.05–0.35 0.04–0.15 0.05–0.225

N2 SBET (m2 g
1) 865 860 –

C 172 244 –

R2 0.996493 0.999902 –

Ar SBET (m2 g
1) 715 – 720

C 91 – 81

R2 0.999813 – 0.999978

Table 3 presents the relative pressure ranges and the results obtained by the application of the BET method for each case. For both isotherms, the original BET range gives positive C values and a good linear regression coefficient. Here also, the Rouquerol criterion establishes a maximum relative pressure value to apply the BET equation, which is less than 0.35. From these results, we can corroborate that the SBET obtained with Ar is below than that obtained with N2 at the same temperature, and the adsorbate-adsorbent interaction is also lower with argon. Even though the SBET is an apparent value for the two adsorptives, the value obtained by nitrogen at 77 K can be considered more suitable.

3.2

Pore Volume

As mentioned above, in addition to SBET, another essential textural property of porous solids is the pore volume (micro-, meso-, and macropores). Specifically, for zeolites, the micropore volume (VμP) is the most relevant because it is strongly related to the performance of the material in a given application. In this sense, the

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Table 4 Pore volume of the analyzed samples

Sample Zeolite 5A MOR HYCTAB

SBET (m2 g
1) 785

VμP (cm3 g
1) tαSDRd plote plotf 0.29 0.27 0.27

SEXT (m2 g
1) tαSplote plotf 70 75

VμP-DFTa (cm3 g
1) 0.30

Vmesob (cm3 g
1) 0.06

VTPc (cm3 g
1) 0.33

470 865

0.17 0.34

50 625

0.18 0.10

0.07 0.55

0.23 0.65

0.16 0.10

0.16 0.10

55 635

a

Non-localized density functional theory (NLDFT) kernel for silica based on the adsorption data ([46] and [47]) was used b Vmeso ¼ VTP
VμP-α c Obtained by Gurvich rule at 0.98 of p/po d DR method was calculated at relative pressures values less than 0.05 e Harkins–Jura (HJ) equation [43] was used, in a range of p/po between 0.05 and 0.25 f LiChrospher Si-1000 macroporous silica [45] was used as the reference material, in a range of p/po between 0.05 and 0.25

most used methods/models to calculate VμP include DR, t-plot, αS-plot, and DFT, each method presenting different assumptions, physical criteria, or application ranges. In the case of the DR method, it must be applied within p/po range where the micropores are being filled, whereas t-plot and αS-plot methods must be applied from the point where the micropores have been fully filled. Besides, to apply the last two methods correctly, it is necessary to use a non-porous reference material with similar chemical nature to that of the analyzed porous material. In general, for zeolites, which are mainly microporous, the micropore volume, VμP, can be calculated with the DR method due to its simplicity (only requiring the use of one equation). However, when zeolites present mesoporosity, DR method overestimates VμP which increases with the mesoporosity degree of the material [32]. In this case, it is necessary to use the other methods mentioned above, where, for zeolites, it is advisable to use the Harkins and Jura equation [43, 44] for t-plot method and LiChrospher Si-1000 macroporous silica [44, 45] as reference for αSplot method. It is worthy of highlighting that both last methods, t-plot and αS-plot, also provide information about the external surface area, SEXT, which is related to the area where the mono-multilayer formation takes place (outside the micropores). Although the DFT method is widely used to evaluate the PSD, the pore volume (micro and meso) can also be estimated. On the other hand, the total pore volume (VTP) is estimated by applying the Gurvich rule [5] from the amount adsorbed at a relative pressure close to saturation. Finally, the mesopore volume (Vmeso) is obtained by subtracting the micropore volume from the total pore volume, as shown in Table 4. Figure 11 presents the N2 adsorption-desorption isotherms at 77 K of mordenite (MOR) and 5A microporous zeolites and HY-CTAB micro-mesoporous zeolite. It can be seen that both MOR and 5A display an isotherm characteristic of microporous

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption 20

Amount adsorbed / mmol⋅g -1

Fig. 11 N2 adsorptiondesorption isotherms of MOR, 5A, and HY-CTAB zeolites at 77 K. Nitrogen adsorption isotherm at 77 K of HY-CTABC from Mańko et al. [42], adapted with permission from Elsevier

47

16

HY-CTAB 5A MOR

12

8

4

0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

p/p o materials (Type I(a) isotherm, [7]) and HY-CTAB zeolite has an isotherm typical of materials with micro- and mesopores (Type IV(a) isotherm, [7]). Table 4 displays the micropore volume of the studied zeolites obtained with DR, t-plot, αS-plot, and DFT methods. From these results, it is possible to observe that, for MOR and 5A, the VμP values obtained with all the methods are similar, thus concluding the reliability of data obtained with the DR method (VμP-DR). However, for HY-CTAB, when the different methods were used, the VμP values differ due to its high degree of mesoporosity. It can be seen that the micropore volume values obtained with t-plot, αS-plot, and DFT methods are in good agreement, whereas the DR method overestimates it, giving a VμP value three times higher than the obtained with the others methods. In some cases, there are differences between VμP-t and VμP-α because the t-plot method (based on the BET model) uses a semiempirical equation to estimate t values instead of using experimental standard isotherms of reference materials as the αS-plot method. For this reason, the αS-plot method is considered a more versatile and reliable macroscopic method to characterize porous materials. Finally, the SEXT values of the studied zeolites obtained with t-plot and αSplot methods are shown in Table 4, where HY-CTAB presents the highest value, thus indicating a lower contribution of the micropores to the surface area.

3.3

Pore Size Distribution

In addition to SBET and pore volumes, the third textural property suggested by the IUPAC to evaluate porous materials is the pore size distribution (PSD), which is also crucial in their performance in specific applications. Among the different available methodologies to assess the PSD are the microscopic methods, based in statistical thermodynamics such as DFT and MC. Nonetheless, the access to them is often

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limited due to the requirement of specialized software and an adequate database of simulated isotherms (i.e., kernel), which describes the adsorbate-adsorbent interaction and the adsorbent pore geometry. In addition to the methods mentioned above, there also exist the classical macroscopic (thermodynamic) methods, some of them using adsorbent-adsorbate interactions such as HK, and other based in classical thermodynamic using Kelvin equations and its modified forms as BJH, DH, and VBS, among others; these can be implemented by simply using a spreadsheet. In the case of zeolites, the micropore size distribution is often obtained with the HK method for different pore geometries, because it has been demonstrated that for microporous materials it works correctly, as long as the proper interaction parameters (of both adsorbate and adsorbent) and the adsorbent pore geometries are considered [48]. These methods are HK for slit pores [33], Saito and Foley (SF) for cylindrical pores [49], and Cheng and Yang (CY) for spherical pores [50]. All models also consider the type of adsorbent, adsorptive, and the temperature of the analysis. To apply all these methods, it is essential to have a high-resolution adsorption isotherm at low relative pressures from the beginning up to the completion of micropore filling. For the analysis of the microporous zone of zeolites, it is difficult to find a suitable microscopic model, whereas there exist macroscopic methods as HK, which works well. This problem is not present in mesoporous regions, because there are several available kernels, which provide reliable information. On the other hand, the use of the macroscopic method, using the Kelvin equation, is more direct because it is only needed the isotherms data and some other accessible information. In the following sections, examples for the analysis of the pore size distribution in the micro and mesoporous zone of some zeolites are given.

3.3.1

Micropore Size Distribution

Figure 12 shows the Ar adsorption isotherms (at 77 or 87 K) for three different zeolites: two of them with spherical cavities (Zeolite 5A and faujasite (FAU)) and the third with cylindrical-like channels (ZSM-5). The isotherms presented in the figure include the stage of micropore filling, and from these data the micropore size distribution is obtained. Figures 13 and 14 show the PSD of the zeolites obtained with the HK, CY, and SF methods and using the interaction parameters of aluminosilicate oxide ion for the adsorbent [48]. Figure 13 is the PSD for the ZSM-5 zeolite, an aluminosilicate presenting three-dimensional pore channels of 0.56  0.53 nm of width, assuming a cylindrical geometry. It can be observed that the PSD obtained with the SF method predicts a micropore sizes of 0.53 and 0.56 nm, which is in good agreement with the dimensions and the geometry assumed for the pore channels of this zeolite. In contrast, HK and CY methods underestimate these values. Figure 14 shows the PSD and the scheme of the respective structures for Zeolite 5A and FAU (theoretical window sizes of 0.49 and 0.74 nm, and spherical cavities of 1.14 and 1.37 nm, respectively). In this figure it can be seen that with CY method is

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption

49

Fig. 12 Argon adsorption isotherms of 5A and FAU at 87 K and ZSM-5 at 77 K. Argon isotherms of 5A and ZSM-5 adapted with permission from Maglara et al. [51]. Copyright (1994) American Chemical Society [54]

Fig. 13 Micropore size distributions with HK, SF, and CY methods for ZSM-5. The structures shown in each PSD correspond to each zeolite, indicating the sizes of channels (green)

possible to estimate the internal size of the spherical cavities of 1.13 and 1.36 nm for the Zeolite 5A and FAU, respectively. In contrast, HK and SF methods provide inferior cavity sizes. Another result worth to highlight is that the PSD obtained with the HK (slit pores) method predicts cavity or channel sizes of 28 and 48% less than the theoretical value for these samples. Thus, it is imperative to take into account the most representative pore geometry for each sample. In Fig. 15 is presented the comparison between the micropore size distribution obtained by CY and non-local DFT (NLDFT) methods, using the argon adsorption data at 87 K of Zeolite 5A. For NLDFT, the kernel used was “Ar-zeolite/silica adsorption branch kernel at 87 K based on a spherical pore model (pore diameter

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Fig. 14 Micropore size distributions with HK, SF, and CY methods for 5A and FAU zeolites. The structures shown in each PSD correspond to each zeolite, indicating the sizes of cavities (blue) and windows (red)

Fig. 15 Micropore size distributions of Zeolite 5A using CY and NLDFT methods

2 nm)” from Quantachrome data reduction software. From Fig. 15 can be seen that the NLDFT method predicts a micropore modal size of 0.87 nm (similar to the recently reported by Orsikowsky-Sanchez et al. [52]) and can be related with the windows of the cavity, while CY correctly estimates the theoretical size of the internal cavity (1.14 nm). Orsikowsky-Sanchez et al. [52] considered that the value obtained is an intermediate size between the windows and cages. This example is to address the care that must be taken into the application of the different methods to obtain the PSD.

Critical Overview of Textural Characterization of Zeolites by Gas Adsorption

3.3.2

51

Mesopore Size Distribution

The mesopore size distribution of zeolitic materials can be evaluated with the classical BJH method; however, it is known that BJH underestimates the mesopore size up to a 25% [53]. For this reason, methods such as VBS have been developed [54], and the results obtained with it coincides with the value predicted using DFT, with the advantage of being applicable without a specific kernel or software. As example, we will analyze N2 adsorption isotherm at 77 K shown previously for the HY-CTAB sample (Figs. 10a and 11) to obtain its PSD. From this isotherm, it can be seen that the capillary condensation in mesopores begins at relative pressures ca. 0.3 which could be associated with the primary mesoporosity (pores in the structure). Figure 16 presents the mesopore size distribution of HY-CTAB using BJH, VBS, and NLDFT methods. In all of them was considered a cylindrical pore geometry and the data from desorption branch was used. The kernel used for DFT model was “N2 at 77 K on silica, NLDFT equilibrium model” [55]. It can be observed a unimodal distribution, with a modal mesopore size of ca. 4.2 nm. On the other hand, it is clearly observed that BJH underestimates the mesopore size distribution in ca. 24%.

4 Conclusions and Final Remarks One of the most relevant characteristics of zeolites in technological applications is the textural properties, which are the specific surface area, pore volumes, and pore size distribution. The analysis of these properties is carried out by gas adsorption with different adsorptives at different temperatures, where most of the experiments

Fig. 16 Mesopore size distributions obtained by BJH, VBS and NLDFT methods for HY-CTAB

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are carried out up to atmospheric pressure, although in some cases when CO2 is used, it is necessary to reach higher pressures. In this chapter, we introduced an overview of the textural analysis by physisorption applied to zeolites, including a few suggestions to obtain in-depth and reliable information about the studied samples. The previous knowledge about the material to be studied is crucial to select the most suitable experimental conditions, such as sample pretreatment, adsorptive, pressure range, and analysis temperature to obtain the adsorption isotherm. It is worth to emphasize that a complete and reliable isotherm must be measured since from it; all the textural properties will be obtained. The adsorptive selection is related to both the characteristics of the zeolite and the required information. Nitrogen at 77 K is the most used adsorptive to obtain the specific surface area and to evaluate the mesoporosity. In the presence of functional surface groups, Ar at 87 K is the suggested adsorptive due to the lack of quadrupole moment. However, Ar at 77 K can also be used only to obtain information about the micropore region. For samples with narrow micropores, CO2 at 273 K is the alternative adsorptive due to its mobility to access in these pores. The BET method is still widely used for determining the specific surface area of micro–mesoporous materials. However, it should be clearly stated that, in the presence of micropores, as in zeolites, the application of this method only leads to an apparent surface area (i.e., SBET), which serves as a useful “fingerprint” of the adsorbent. To analyze the micropore volume is advisable to use the plots methods; in particular, αS-plot is the most reliable method (if we have the corresponding standard isotherms). Furthermore, additional information such as external surfaces (Sext) of the samples can be obtained, from which the contribution of the micropores to SBET can be calculated. To obtain the PSD of zeolites is advisable to evaluate the microporous and mesoporous region separately, considering that there is not a unique and suitable method that allows assessing the complete range of pores. To obtain a micropore size distribution, the HK methods and its modifications as SF and CY, work well, and their results agree with theoretical value for the zeolite structure. Regarding the mesoporous region, VBS and DFT methods give the best information. In summary, it is recommended to carry out good experiments, based on the previous knowledge of the samples, and take into account the adequate models or methods, to analyze the different textural properties of zeolites.

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Struct Bond (2020) 184: 57–84 https://doi.org/10.1007/430_2020_66 # Springer Nature Switzerland AG 2020 Published online: 2 October 2020

Computational Approaches to Zeolite-Based Adsorption Processes Juan José Gutiérrez-Sevillano and Sofía Calero

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Models for Adsorbents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Models for the Adsorbates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Models for Extra Framework Cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Force Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Classical Simulation Methods and Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Molecular Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Adsorption Solution Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58 58 59 62 63 64 66 66 69 71 73 74 75

Abstract Computational methods to calculate the properties of zeolites in gas adsorption and separation have proven to be a valuable complement to experimental work. Molecular simulation provides a molecular understanding of the mechanisms involved in the adsorption, desorption, and transport. The accuracy and reliability of the predictions depend on the models used for adsorbates and adsorbents, the force fields that describe the interaction, and the computational methods to calculate the properties. The selection of force fields and methods depends on the properties of the systems and on characteristics such as the flexibility of the framework, the J. J. Gutiérrez-Sevillano Department of Physical, Chemical and Natural Systems, University Pablo de Olavide, Sevilla, Spain S. Calero (*) Department of Physical, Chemical and Natural Systems, University Pablo de Olavide, Sevilla, Spain Department of Applied Physics, Eindhoven University of Technology, Eindhoven, The Netherlands e-mail: [email protected]

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hydrophobicity/hydrophilicity of the zeolite, the chirality, the silicon atom substitutions, the nature and concentration of extra framework cations, the composition of the guest gases, the measured property, etc. In this chapter, a brief description of the state of the art of molecular simulation applied to porous materials is provided, as well as a discussion of current challenges in the field. Keywords Crystalline porous materials · IAST · Models · Molecular dynamics · Molecular simulation · Monte Carlo

1 Introduction During the last decade, molecular simulation has proven to be a powerful tool for the study of adsorption, diffusion, and separation processes with nanoporous materials, especially with zeolites [1–28]. The use of computational methods for computing properties of zeolites or gases in zeolites is well established. It is possible to accurately obtain quantitative values related to the characterization of zeolites such as pore volume, surface area, helium void fraction, or pore size distribution along with information on interactions of different gas molecules with zeolites. The adsorption and diffusion properties that can be computationally obtained are the heat of adsorption, Henry coefficient, adsorption isotherm, diffusion coefficient, saturation capacity, adsorption selectivity, and permselectivity. Besides reproducing experimental results, simulations can also predict properties to gain insights into the processes occurring inside the zeolite cages. Molecular simulation is a complement to experimental work, providing a molecular-level understanding of the interaction mechanisms of adsorption and desorption of various gas molecules within a material. This chapter describes the basis of classical molecular simulation and expands on the current state of the-art and challenges in the field. Firstly, main modeling strategies for zeolites and guest gases are described followed by a brief description of force fields and simulation methodology. A small section on the available codes to perform molecular simulation in zeolite-gas systems is also included at the end of the chapter.

2 Models Roughly, one can consider molecular simulation as a set of methods that use force fields and models to generate how a system evolves microscopically. These simulations can be analyzed, either afterward or on the fly, to extract properties and behavior of the system simulated. The three main parts are (a) models, models for zeolites and gases; (b) force fields, force fields to describe the interaction between

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molecules; and (c) methods, computational methods to calculate the physical quantities. The accuracy and validity of the results rely equally on all three components. This section summarizes the most common models for adsorbates and adsorbents. Force fields are addressed in Sect. 3, and the methods used for the simulation of guest molecules in zeolites are described in Sect. 4. In systems involving host-guest interactions, like zeolite-gas interactions, one can use different strategies to model both the zeolite framework and the adsorbates. For the zeolite framework, the simplest model is to consider the zeolite as rigid, where each atom acts as a single interaction center placed at the crystallographic positions reported by experiments. The atoms are considered “frozen,” neglecting interatomic interactions between framework atoms. More complex models for the zeolites take into account the flexibility of the framework as well. In such cases, potential energy functions describing the bonding, bending, and torsion of the atoms are considered. Although one could think that the increasing complexity of the model should increase accuracy, several reports have shown that in most cases rigid and flexible models lead to similar results when computing adsorption isotherms or adsorption properties. However, when the focus is on understanding transport properties of gases, the use of flexible framework models can make a significant difference [29– 31].

2.1

Models for Adsorbents

Using flexible models increases the computational cost of simulations significantly. The most popular flexible models were developed in order to reproduce peaks of the infrared spectra [32–35] in ensembles with fixed volume. However, such flexible models usually failed to predict structural changes. To evaluate the degree of accuracy of these force fields [32, 33, 35] in reproducing infrared spectra, BuenoPérez et al. [36] calculated the IR spectra of a large variety of zeolite frameworks in the limit of pure silica composition and compared them with experimental reports. They found that the force field developed by Nicholas et al. [33] reproduces best the experimental IR spectra, but still it was not accurate enough to enable the identification of unknown frameworks based on the comparison of their experimental spectra with simulated ones. The length of the unit cell of some frameworks can vary upon adsorption, and therefore, flexible force fields for zeolites that change their volume were needed. In a similar work, Balestra et al. [23] studied structural changes in zeolite RHO and found that several of the framework models available in the literature were unable to reproduce qualitatively a structural phase transition and highlighted the role of the point charges in the model. The force field reported by Nicholas et al. [33] predicted two stable structures for the pure silica zeolite RHO, whereas the models proposed by Schröder et al. [34] and Sauer et al [35] retained structural stability and reproduced cell lengths similarly to the experiment by incorporating a core-shell structure for the first model and strong torsion energies for the second, respectively. Nevertheless, it was discovered that these models can

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only predict a single stable structure instead of the two phases reported experimentally. Subsequently, Balestra et al. [19] reported that the extra framework cations play an important role in phase transition in this type of zeolite, finding that the force exerted by the cations has a similar effect to applying an external pressure to the zeolite. Besides, they found a direct relation between the composition, concentration, and distribution of the cations and the distortion of the framework rings. For these systems, the polarizability of the oxygen atoms needs to be explicitly modeled to get a realistic description of the phenomena. Several such studies show that a careful selection of models for the elements of the system is required, and further research on force field development is needed to accurately describe structural changes in zeolites. Pure silica zeolites only have two types of atoms: oxygen and silicon. Usually, a given set of point charges (for the electrostatic interactions) and Lennard-Jones or Buckingham parameters (for the van der Waals interactions) is used to describe the interactions of atoms in a given system. However, one can use other approximations for modeling the zeolite, such as having gases and silicon atoms interact only via long-range forces (i.e., electrostatic), resulting in a model in which the silicon atoms are described only with point charges and the oxygen atoms with both point charges and Lennard-Jones parameters. These parameters are calculated to include the van der Waals interaction corresponding to the silicon atoms. Since the primary building units of zeolites are oxygen tetrahedra with a silicon atom in the middle, it is reasonable to assume that the van der Waals interaction between the silicon atom and the guest gases is screened by the surrounding oxygen atoms.

2.1.1

Zeolites with Aluminum and Germanium

Most zeolites have some silicon atoms replaced by aluminum atoms. Early models described for these zeolites assign the same partial charge to both silicon and aluminum atoms [37, 38]. However, this approach fails to place the non-cations in their proper positions. To model a zeolite specifically distinguishing the two types of atoms, Lowenstein’s rule is usually applied. It states that zeolites are more stable when no two atoms of alumina are connected to the same oxygen atom [39, 40], and therefore, there should be at least one Si-tetrahedra between any two Al-tetrahedra. The atoms of silicon can also be substituted by other types of atoms such as germanium, modifying the properties of the framework. Gutierrez-Sevillano et al. [13] combined an experimental and theoretical study to show that the presence of atoms of germanium in zeolites confers dynamic flexibility to the framework. This results in extensive, breathing-like pore behavior, and, in the zeolite ITQ-29 (pure silica LTA), the diffusion coefficients of alkanes increase at least by a factor of 3. The large pore window deformations in the framework shown in Fig. 1 are linked to the breathing dynamics and to the faster diffusion of the gas despite the fact that the molecules are attracted more strongly to the zeolite framework in the presence of Ge. Later, Rigo et al. [41] studied in detail the systematic substitution of Si-atoms by Ge-atoms in the zeolite STW. They generated more than 4,000 configurations

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Fig. 1 Pure silica LTA (ITQ-29) zeolite (left) and Ge-LTA zeolite (right). The atoms of germanium increase the flexibility of the structure deforming the window

ranging from one to four Si/Ge substitutions of the unit cell and showed that the isomorphous substitution of silicon by germanium leads to an expansion of the structure that is roughly linear. This work shows that both Ge concentration and the extension of Ge pairing are extremely important for the modeling. The presence of atoms of germanium can also result in chiral zeolites intrinsic to the topology and independent of the models used for the silicon and the germanium atoms [42]. Interestingly, chiral zeolites do not guarantee chiral selectivity. For example, the STW structure is enantioselective for both CHBrClF and 4-ethyl-4methyloctane isomers, while SOF and ITQ-37 are not. The pore geometry and the interaction of structure and enantiomer can be main factors preventing enantioselectivity in these zeolites. On the other hand, the adsorption selectivity in STW can be attributed to different packing efficiencies of the isomers when adsorbed as pure components or as a mixture. In addition to this, the separation factor is strongly related to the electrostatic interaction of the molecule with the zeolite, meaning that it is very sensitive to the point charges used in the model [42]. This also explains that chiral zeolites can show chiral selectivity depending on the nature of the guest molecules.

2.1.2

Aluminophosphates

On the boundaries of zeolites, aluminophosphate molecular sieves (AlPOs) have also been studied using molecular simulation. AlPOs have zeolite-like frameworks built of corner-sharing tetrahedra: negatively charged [AlO4]
and positively charged [PO4]+ positioned alternately. The net charge of the AlPO framework is zero, excluding the presence of extra framework cations. Unlike zeolites, the Al atoms of AlPOs may be either fourfold, fivefold, or sixfold coordinated [43]. In particular, the adsorption and diffusion of alkanes in the AlPO4-5 framework have been widely studied showing that the chemical nature of the atoms forming the

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tetrahedral units does not alter the interactions of the framework with paraffin molecules [44].

2.2

Models for the Adsorbates

Adsorbates can be modeled using full atom models or united atom models. The former are models in which all the atoms in a molecule are explicitly described by interaction centers, and the latter are models assigning a single interaction center to two or more atoms in a molecule (see Fig. 2). For instance, the methane molecule is frequently modeled as a single interaction center that describes the carbon atom plus the four hydrogen atoms at once [45]. United atom models are widely used to describe alkanes, alkenes, and alcohols [45–47]. For the molecules of carbon dioxide, oxygen, nitrogen, and argon, it is common to use full atom models [48– 51]. The case of the water molecule is, however, different. The adsorption properties of water in zeolites are difficult to describe both experimentally and by molecular simulation. The experiments are complicated by the fact that the water molecules adsorb at very low pressure at defect sites in the framework, and hence, the values of pressure where the sharp increase in adsorption occurs are sensitive to defects, as well as to the crystallographic positions (in addition to pore blockage/collapse, etc.) of these defects. There are many models for the water molecule [52, 53], but only a few of them are suitable for studying water adsorption in zeolites. The Tip5pEw model [54] is one of the most frequently used models. However, there is still much uncertainty about the appropriate value of the point charges, especially for the framework atoms. The dipole moment of water results in a behavior that is completely different from other molecules of similar size but without dipole moment, so the partial charges of the zeolite atoms are of critical importance and need to be assigned carefully. The adsorption of water is very sensitive to small

Fig. 2 Full atom model (left) and united atom model (right) for methane. In the latter, just one interaction center is needed to describe the interactions of the molecule with other molecules, while the full atom model has five interaction centers

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changes in the location of the zeolite atoms. This is related to the coupling of the dipole of the water molecules with the electric field induced by the zeolite. Therefore, one has to be cautious when computing the properties of water and highly polar molecules in zeolites [55].

2.3

Models for Extra Framework Cations

Non pure silica zeolites generally contain extra framework cations to compensate the net negative charge of the structure. From the point of view of modeling, it is necessary to model these cations as well. Calcium and sodium cations are modeled as point charges that can interact with the zeolite and gases; they can move freely in the available space inside the zeolite and can block the access of guest molecules to certain parts of the material. Available experimental works reporting the position of the cations can be used as initial position of the cations during simulation [56, 57] Otherwise, ab initio calculations or energy minimization can be performed to predict the most stable positions [58, 59]. The presence of cations in the zeolite usually modifies the adsorption of water in the material, turning hydrophobic pure silica zeolites into hydrophilic zeolites. At the same time, water adsorption has an influence on the cation distribution in the framework [60, 61], making the simulation of water in zeolites more complex. There is evidence that water confined in the zeolites is hydrogen bonded [62, 63]. While in pure silica zeolites water rapidly nucleates, in zeolites with cations, the degree of association gradually increases with coverage of cations, but the number of water
water hydrogen bonds decreases. The average number of hydrogen bonds in confined water with bulk-like densities was found to be about 2.6 and 2 in all silica and cationic zeolites, respectively, far from bulk liquid (3–3.5) [62]. Gómez-Álvarez et al. [63] have shown that cations play a key role in the adsorption and clustering of water molecules inside the pores, due to the very nature of cations rather than the concentration of cations itself. Besides changing the hydrophobicity/hydrophilicity of the zeolites, cations can produce other unexpected effects on the frameworks such as the induction of local chirality in non-chiral zeolites [64]. Although most aluminosilicates are non-chiral, some of them can discriminate chiral molecules, as long as the adsorbing gas consists of a scalemic mixture. Initially, one of the enantiomers is present in higher concentrations than the other in the mixture, and once adsorbed, this concentration can either increase or decrease. These two types of enantiospecific adsorption are called homoselective and heteroselective. According to the work of van Erp et al. [65], this adsorption behavior is enforced by a particular positioning of the cations in the framework. For example, the inclusion of mono or divalent cations in the aluminum-substituted MFI zeolites results in heteroselective or homoselective adsorption of the 4-ethyl-4- methyloctane. This enantioselectivity can be explained by introducing the concept of chiral cells: when one enantiomer is adsorbed, the cations are relocated. This relocation can originate a configuration where the same

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Fig. 3 Alternating pattern of calcium non-framework cations in an MFI structure with a Si/Al ratio of four proposed by van Erp et al. [65]

enantiomer can fit (homoselectivity) or where the specular enantiomer fits better (heteroselectivity). It is surprising to see that in the latter case, the arrangement of cations resembles the move of a knight on a chess board (see Fig. 3). The distribution of charges in frameworks with Ca2+ tends to create chiral cells of the same type, whereas the double number of Na+ favors neighboring cells to be of opposite chirality.

3 Force Fields In layman terms, a force field can be defined as a set of functions that describe the interactions between the elements of the system. It is common to split these functions into two bonded energies (bonding, bending, torsions, etc.) and nonbonded energies (electrostatic and van der Waals interactions). Electrostatic interactions are frequently described using the coulombic potential (Eq. 1), while van der Waals interactions can be described by more than one, such as the Buckingham [66], or the Lennard-Jones 6–12 (Eq. 2), the latter being one of the most used potential forms for zeolite-gas systems.
 1 qi q j U electrostatic r ij ¼ 4πε0 r ij "   6 #
 σ ij 12 σ ij VdW U r ij ¼ 4εij
r ij r ij

ð1Þ ð2Þ

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Equations (1) and (2) contain parameters (q, ε, σ) that need to be fixed for each system. These parameters can be derived from ab initio calculations, fitted to experimental results or developed by means of other methods. Based on this, a force field can be defined as a set of functions and parameters to describe the interactions between the elements of a system. There are a wide variety of force fields [67] that can be applied to zeolites. Some examples are the universal force field (UFF) [68], Discover (CFF) [69], MM2 [70], MM3 [71–73], MM4 [74], Dreiding [75], SHARP [76], VALBON [77], AMBER [78], CHARMM [79], OPLS [80], Tripos [81], ECEPP/2 [82], GROMOS [83], MMFF [84], BKS [85], and specific force fields for morphology predictions [86] or for computing adsorption [87]. Usually, the ε and σ parameters of these force fields are parameterized to define the interaction between atoms of the same type (atom A to atom A, atom B to atom B, etc.). To obtain the parameters for the interaction between different atoms (atom A to atom B), it is common to use mixing rules [88–94], the Lorentz-Berthelot rules [95] being the most popular ones. Although most of these force fields have been developed to be generic and transferable for any system (not only for zeolites), they do not perform well for zeolite-gas systems. For this reason, several ad hoc force fields have been developed that could be used with specific zeolites and gases [25, 47, 49, 96–106]. During simulations, it is important to set a cut-off for the potentials. For shortranged potentials like the Lennard-Jones potential, the cut-off is usually chosen as half of the shortest width of the unit cell. For long-range potentials (coulombic) this construction is inadequate because of the need to include a prohibitively huge number of interaction pairs. To overcome this problem, Ewald summations [107] are used to compute the electrostatic interactions [67]. The extra framework cations of zeolites can have a significant impact on the interactions of zeolite-guest molecules. For instance, Martín-Calvo et al. [97] explored the mechanisms governing the CO2 adsorption in zeolites with different aluminum content and concentration and nature of extra framework cations. They found that the accessible pore volume of the zeolite was affected by the amount and strength of carbonate-like complexes formed upon carbon dioxide adsorption. Due to the strong interaction of carbonate-like complexes in zeolites with a high number of cations, previous force fields were not able to reproduce CO2 adsorption within the structures. Therefore, they developed a new set of charges to accurately reproduce the experimental adsorption in structures containing carbon dioxide-cation complexes [97]. Their work shows the importance of having an accurate force field to describe the motion of the cations. In another example, Perez-Carbajo et al. [7] focused on the aluminosilicate zeolite MFI and established the link between the nature of the cation, the region in which the cation is free to move, and the diffusion of adsorbates. The differences observed between the molecules of CO2 and methane highlight the importance of using a proper force field to describe the interactions between the framework atom and the guest molecules and between the guest molecules and the extra framework cations.

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4 Classical Simulation Methods and Theory Molecular simulation encompasses a set of methods and techniques that, by using models and force fields, allows to calculate a variety of properties. There are two main methods that are commonly used in simulation: Molecular dynamics (MD) and Monte Carlo (MC). Molecular dynamics is based on the integration of Newton’s laws of motion, while Monte Carlo methods are based on statistical mechanics.

4.1

Monte Carlo

To compute the adsorption properties like heat of adsorption, Henry coefficient, or adsorption isotherms, Monte Carlo methods are most commonly used. Essentially, the particles of the system are randomly selected, and one Monte Carlo move (e.g., translation, rotation, insertion, deletion, etc.) is applied [108]. Moves are key feature of the MC methods, and they are accepted or rejected based on an energy criterium. Different ensembles are used depending on the desired calculation. For instance, Henry coefficients are directly related to the excess free energy (or excess chemical potential) of the adsorbed molecules. The heat of adsorption can be computed from the energy of the system. Then, both quantities can be obtained from the total energy of the system by means of the Widom test-particle method [109], having fixed the number of molecules, the volume, and the temperature of the system (NVT ensemble). This technique can also be used to compute the pore volume, surface area, pore size distribution, and helium void fraction of the structure. For this purpose, helium atoms are employed as probes. The test particles are randomly placed inside the zeolite; keeping track of the fraction that does not overlap with the structure, the geometric pore volume can be computed.

4.1.1

Blocking

It is important to block non-accessible pores of the zeolite when calculating equilibrium adsorption properties using Monte Carlo simulations. This is essential to avoid the spurious inclusion of inaccessible volumes and to prevent an overestimation of the adsorption. A comparison between the blocked and the non-blocked LTA and FAU zeolites is depicted in Fig. 4. As can be observed, there are small cavities (red spheres) that are not connected to the zeolite framework, and therefore they are not physically accessible to guest molecules. As these spots are reachable by Monte Carlo moves, they have to be blocked to prevent that guest molecules populate these additional sites. The first attempts of blocking non-accessible pores in a zeolite were reported in 2004 [101]. Since then, this methodology has been widely accepted and used. Gómez-Álvarez et al. [110] made a pseudo-flexible approach to account for the instantaneous variations of

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Fig. 4 Equipotential energy surfaces showing the pore space of LTA (top) and FAU (bottom) zeolite without blocking (left) and blocking the inaccessible pore space (right). Inaccessible pore spaces are colored in red and cyan

pore window sizes resulting in a better agreement with experimental results. This work highlights the use of appropriate pore blocking by taking into account the sizes of the guest molecules and including the role of the framework flexibility. The development of refined methods to account for the blocking of molecules with noncircular cross-sections was also required [110]. The importance of blocking is especially crucial for hydrogen [22] wherein the quantum nature of this molecule is relevant when studying systems at very low temperature (25 K). It has been demonstrated that merely combining Lennard-Jones and coulomb potentials is insufficient to describe the interactions between such a small molecule and the porous material that requires specific interactions [49, 111, 112]. At cryogenic temperature, the quantum behavior of hydrogen is non-negligible in the nanoscale confinement of zeolite pores, and the use of blockings affects the heat of adsorption calculated using molecular simulation.

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Adsorption

To calculate adsorption isotherms, simulations are performed in the grand canonical ensemble (the number of molecules can vary at a given volume, temperature, and chemical potential). During simulation, molecules are allowed to rotate, translate, and exchange with a reservoir. The chemical potential is directly related to fugacity, which is calculated from the pressure using an equation of state [23, 97]. The total number of molecules adsorbed can vary during the simulation, and the average is computed based on the absolute adsorption, which is directly related to excess adsorption [113] through Eq. (3): adsorptionexc ¼ adsorptionabs


PV zRT

ð3Þ

where P, V, and T are the pressure, volume, and temperature of the system, R is the gas constant, and z the gas compressibility. Using grand canonical Monte Carlo methods, it is possible to compute adsorption isotherms for pure gases and for multicomponent mixtures. These adsorption isotherms can be used to study the capacity of zeolites to selectively adsorb gases. Selectivity can be directly obtained by applying Eq. (4): Si=j ¼

θi X j θ j Xi

ð4Þ

where θi and θj are the adsorption loadings of molecules i and j, and Xi, Xj are the mole fractions in the feed. To improve the efficiency of the Monte Carlo simulation, new methods such as reactive canonical Monte Carlo (RCMC) [114, 115] or kinetic Monte Carlo (KMC) [116, 117], and many other algorithms and moves [118–120] are being developed. Among them, some are specifically designed for the study of fluids in nanoporous materials. The continuous fractional component Monte Carlo method (CFCMC) is an algorithm to improve the efficiency of ensembles where the number of molecules varies, as it allows increasing the number of successfully inserted molecules [121– 124]. The reactive Monte Carlo method (RxMC) allows computing equilibrium properties for chemically reacting fluids [114, 124, 125]. RxMC extends the GCMC ensuring that the chemical reaction equilibria between reactants and products is maintained. This is achieved by sampling forward and backward the reaction, using standard MC moves and “reaction” moves. Reactants are removed, and products are inserted in the system in such a way that an equilibrium distribution is obtained. Matito-Martos et al. [126] applied these methods to study the effect of confinement on the ammonia synthesis reaction in pure silica zeolites. They investigated several working conditions, with pressures up to 1,000 bar and temperatures ranging from 80 to 873 K and found that it is necessary to work at a high temperature (above 400 K), because otherwise a phase transition of ammonia to liquid ammonia occurs within the zeolite. Furthermore, the effect of confinement increases the

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ammonia production in all the zeolites studied. RxMC has been also applied to the study of nitrogen oxides in zeolites [127]. Another type of MC move developed in the last decade is the “chiral inversion move.” Van Erp et al. [128] added a replica exchange procedure to the configurational bias move, in such a way that in simulations describing one particular gas content swaps of molecules are possible. Then, for chiral mixtures, they introduced the chiral inversion, allowing to exchange a chiral molecule for its enantiomer. They also developed a mathematical model [129] to describe enantioselective adsorption in non-chiral nanoporous materials. One advantage of this method is that it allows the study of chiral mixtures in the macroscopic limit [21, 65, 130]. Because simulation methods are improving over time and the computational capacity of computers increases, molecular simulation is currently a powerful tool that enables the study of a given property over all (or a very large number of) reported zeolites [20, 131–138]. This screening has been applied, for example, to the adsorption of sulfur hexafluoride [15] and nitrogen oxides [127] or to perform gas separation of carbon dioxides and other gases [1, 20, 133]. Screening studies also explore the capacity of zeolites to store and release hydrogen [8] or to separate hydrogen isotopes [139, 140].

4.2

Molecular Dynamics

Molecular dynamics (MD) simulation is a method for computing the equilibrium and transport properties of a system. It is based on computing the trajectories of the particles in the system by solving Newton’s laws. The idea of MD is very simple: in a given system, the initial positions and velocities of the particles are known (the setting of initial conditions can be done using randomness and/or crystal positions and may be constrained by further structural constraints). The forces between particles are a function of the position, so it is possible to compute them from the initial configuration. The force acting on one particle is the sum of the forces due to all neighbors of the particle (this is the most time-consuming part of the method). Once that all the forces between particles have been calculated, the next step is to integrate the Newtonian equations of motion to obtain the new positions of the particles. To perform this integration, there are many algorithms to choose from. One of the simplest is the so-called Verlet algorithm. In this algorithm, the new positions of the particles are obtained from Eq. (5), which are derived from a truncated Taylor expansion of the coordinates of a particle: r ðt þ Δt Þ  2r ðt Þ
r ðt
Δt Þ þ

f ðt Þ 2 Δt m

ð5Þ

The velocities of the particles are not needed to generate their trajectorie; nevertheless, they are used to compute the kinetic energy and the temperature of the system and are calculated using Eq. (6):

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vð t Þ ¼


 r ðt þ Δt Þ
r ðt
Δt Þ þ O Δt 2 2Δt

ð6Þ

By repeating this cycle (old position, compute forces, integrate forces, new position), one can obtain the time-dependent evolution of the system and the trajectories of the particles. The error in the new position is of the order of Δt4, where Δt is the time step. Δt is fixed and has to be selected according to the characteristics of the system. A short time step increases the accuracy of the simulations but will require more cycles to simulate a given time span. On the other hand, a long-time step will allow to simulate a long period of time at a given computational cost but at lower accuracy. Usually, Δt is set up to one order of magnitude lower than the period of the fastest motion in the system, in such a way that there is a balance between accuracy and computational cost. As mentioned above, MD allows to compute equilibrium properties of the systems. Usually, at the beginning of the simulation, the system is not at equilibrium. For this reason, a number of cycles are needed to equilibrate the system, after which the production phase may start. To speed up the equilibration phase, a MC simulation can be performed to obtain a starting configuration closer to equilibrium. Once the system has reached equilibrium, one can start collecting data from the simulation. From a computational point of view, this is a challenging process, but it is possible to compute diffusion coefficients based on the motion of the particles in the system [67, 141–144]. Auxiliary information such as radial distribution functions, coordination numbers, or velocity autocorrelation functions can be derived from the trajectories obtained with the MD simulation. Most studies compute selfdiffusivities for pure gases and for mixtures [145–147]. Nevertheless, it is also possible to compute diffusivities [148–154] which are more relevant to technological applications. Transport diffusivities can be obtained from the Fick, the Onsager, and the Maxwell Stefan formulations [155–157]. These coefficients are important to study separation processes using zeolites and zeolite-based membranes. Permselectivity is used to quantify the separation capacity of a membrane. It can be calculated from Eq. (7) by knowing the ratio between the diffusion coefficients (Di/j) of the component of the mixture: Permselectivityi=j ¼ Si=j

Di θ X j Di ¼ i D j θ j Xi D j

ð7Þ

As explained above, MC methods can be used to obtain the adsorption selectivity of a zeolite. To obtain the permselectivity, a MD run is required to compute diffusion coefficients. Combining both methods enables to identify the mechanism that preferentially rules the separation in each system. There are systems or cases in which a standard MD run does not enable to determine diffusion coefficients with reasonable accuracy because diffusion processes occur outside of the time scale accessible (typically limited to diffusion rates of the order of 10
12 m2/s). However, other methods have been developed for overcoming this time scale limitation [158]. Systems characterized by a sequence of rare events can be described by

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Fig. 5 Free energy profiles of propylene in ITQ-12 zeolite at 300 K computed using two models. Differences between models lead to enormous differences between the free energy profiles resulting in a diffusion coefficient of 2.410
14 m2/s (solid line) and in negligible diffusion (dashed line). Reprinted with permission from J Phys. Chem. C 2010, 114, 35, 14,907–14,914. Copyright 2010 American Chemical Society

transition state theory (TST) methods like the Bennett-Chandler approach [159, 160], the Ruiz-Montero method [161], path sampling [162], transition interface sampling [163, 164], hyperdynamics [165], parallel replica dynamics [166], temperature-accelerated dynamics [167], and on-the-fly kinetic Monte Carlo [168]. In principle, all of these methods can be used to study events on a time scale which is several orders of magnitude lower than typical diffusion rates in zeolites while still retaining full atomistic detail [169–173]. One example is the diffusion of propane and propylene in the zeolite ITQ-12, but many others can be found in the literature [172, 174–176]. In the case of the ITQ-12 zeolite, the narrow channels of the framework make diffusion along them too slow. The study using dynamically corrected transition state theory (dcTST) revealed that diffusion of propane is of the order of 10
16 m2/s, while for propylene (see Fig. 5), the order of magnitude for diffusion varies between 10
14 m2/s and 10
17 m2/s or is even neglected, depending on the model chosen [47].

4.3

Adsorption Solution Theory

The ideal adsorbed solution theory (IAST) of Myers and Prausnitz [177, 178] was developed to predict the properties of adsorbed mixtures using single component

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isotherms as input and is widely used to predict adsorption isotherms of gas mixture [178]. Essentially, IAST is analogous to Raoult’s law for vapor-liquid equilibrium, i.e.,: Pi ¼ P0i ðπ i Þxi

ð8Þ

where Pi is the pressure of component i in the mixture, and P0i is its pure component hypothetical pressure, which amounts to the same spreading pressure as that of the mixture, and xi and π i are the molar fraction and spreading pressure of component i in the adsorbed phase, respectively. At the adsorption equilibrium, the reduced spreading pressures must be the same for each component and the mixture: ZPi

n

π π i ¼ i ¼ RT

n0i ðPÞ dP P

i ¼ 1, 2, 3, . . . , N

ð9Þ

0

π 1

¼ π 2 ¼ . . . ¼ π N ¼ π 

ð10Þ

The function n0i ðPÞ is the pure component loading as a function of pressure, and is the pure component hypothetical pressure which yields the same spreading pressure as that of the mixture. By assuming ideal mixing at constant π and T, the total amount adsorbed, nt, is: P0i

" # N 1 X xi
0 ¼ 0 nt i¼1 ni Pi

ð11Þ

P i ¼ P t yi

ð12Þ

where xi is the molar fraction of component i in the adsorbed phase. Taking into account that: Pt yi ¼ P0i ðπ i Þxi

ð13Þ

where Pt is the total pressure of the mixture and yi the molar fraction of component i in the bulk phase, Eq. (8) can be rewritten as: N X

xi ¼ 1

ð14Þ

i¼1

Additionally, molar fractions have to satisfy. Then, there is a system of nonlinear equations (Eqs. 9, 10, 11, 13, and 14) that can be solved to obtain the total amount of the mixture adsorbed and, therefore, the loading of each component in the mixture by using:

Computational Approaches to Zeolite-Based Adsorption Processes

ni ¼ nt x i

73

ð15Þ

Since the equations are nonlinear and the integrals of π i cannot be solved analytically for most of the pure component isotherm equations, the classical IAST needs iterative integration processes; however it does not require any mixture data, and it is independent of the actual model of physical adsorption. Some of the codes available in the literature that solve IAST equations are included in the next section. In those codes, the user only needs to choose the model [179–187] of isotherm to fit the experimental data. Despite the theory being in many cases successful in predicting the adsorption of mixtures [133, 140, 188–190], in some cases, predictions differ markedly from experimental results. In general, IAST provides accurate results for mixtures that are close to ideal, but if the system deviates from ideal, predictions can lead to the wrong results [191]. It is known that IAST does not describe correctly the behavior for mixtures of polar species, for mixtures in which there are strong interactions between gases, and for mixtures in which one of the components is strongly adsorbed, and the other component only weakly interacts with the zeolite. The presence of defects in the adsorbent as well as a lack of heterogeneity can also lead to non-reliable predictions of mixture adsorptions [178]. Other theories, like real adsorption solution theory (RAST) [192], try to overcome the limitations of IAST for predicting multicomponent adsorption of mixtures in zeolites [16, 193–195] and other systems; however the conceptual and practical simplicity of IAST make it the most widely used approach to study adsorption of multicomponent mixtures.

5 Codes There are many codes to perform molecular simulation in zeolites that are available for free. MUSIC [196, 197] is a code that performs MD and MC simulations in a number of different ensembles, minimizations, and free energy calculations for bulk and adsorbed phases, but not for treating flexible adsorbent frameworks. RASPA [124] is a general purpose classical simulation package that can be used for the simulation of molecules in gases, fluids, zeolites, aluminosilicates, metal-organic frameworks, and carbon nanotubes. This code integrates MC methods, MD, and energy minimization, as well as other utilities to study the systems. More generic codes such as LAMMPS [198], GROMACS [199, 200], or DLPOLY [201] are useful to perform MD simulations. GULP [202] is a code able to perform a high variety of simulations in periodic systems. For visualization purposes, the reader can find numerous alternatives [203], such as iRASPA [204], JMOL [205, 206], VMD [207], pyMOL [208], and VESTA [209]. Finally two examples of codes that solve IAST equations are gaIAST [210] and pyIAST [211].

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6 Conclusions Today, molecular simulation is considered a powerful tool of the research community in general and of the zeolite community in particular. Experimental works are complemented with atomistic information provided by these techniques. Furthermore, molecular simulation play a key role in the understanding of processes at a molecular level. For this purpose, it is essential to have good models, force fields, and methods able to reproduce experimental results and to make realistic predictions. Over the last few years, there have been substantial improvements in the three mentioned aspects; however, some challenges still remain. Regarding the modeling, the development of the definitive water model might be a chimera; nevertheless refining and testing current models are a valuable task which is still necessary to improve the predictions of molecular simulation. Another challenge is related to the role of the extra framework present in the zeolites. New approaches and better adjustment of cation models need to be developed in order to gain further understanding on cation mobility in the zeolites, as well as their influence on the adsorption, diffusion, and separation processes. At the same time, a better description of the interaction with the zeolite atoms and guest gases is required, which leads to the need of more accurate force fields to describe the interaction between the cation and the zeolite and between the cation and the host gases, with a particular interest for the cation-hydrogen interaction. Although there are many validated force fields (both universal and for specific systems), there is still a need for research. A key feature is flexibility, and clearly better force fields would be able to properly describe the flexibility of the frameworks and the effect of the flexibility on the adsorption and transport properties. Current methods have proven to be efficient in predicting adsorption isotherms, Henry coefficients, isosteric heats of adsorption, cation distributions, preferred adsorption sites, and diffusion coefficients. Lately, great efforts have been put in developing methods that are suitable to correctly describe and predict chemical reactions by using molecular simulation. Commonly, the huge amount of conditions (such as temperature, pressure, composition, defects, etc.) that are of interest in a molecular simulation or in the screening studies over all zeolites makes their analysis for specific processes unaffordable, not only experimentally but also via simulation techniques. In those cases, the use of machine learning in big data context is emerging as a very powerful tool, and it is reasonable to think that will have a big development in the next few years. Acknowledgments The authors thank Salvador R. G. Balestra for his help with Figure 1and Sneha R. Bajpe, Ana Martin-Calvo, and Patrick J. Merkling for a critical reading of the manuscript.

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Struct Bond (2020) 184: 85–120 https://doi.org/10.1007/430_2020_68 # Springer Nature Switzerland AG 2020 Published online: 26 September 2020

Efficient Downstream Processing of Renewable Alcohols Using Zeolite Adsorbents Benjamin Claessens, Julien Cousin-Saint-Remi, and Joeri F. M. Denayer

Contents 1 The Production of Renewable Alcohols in a Biorefinery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 MFI Zeolites: ZSM-5 and Silicalite-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Structure and Selectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Which Channel to Choose? The Adsorption Mechanism of Alcohols on MFI Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 The Adsorption of Water: Si/Al Ratio, Cations, Defects, and Co-adsorption . . . . . . . 2.4 Diffusion of Alcohols and Water in MFI Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Process Aspects and Mixture Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cage-and-Window-Type Zeolites: LTA and CHA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Framework Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Equilibrium: Chain-Length and Entropic Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Kinetics: Co-diffusion and Crystal Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Separation of ABE Mixtures on ITQ-29 and CHA Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Combining the Selectivity of CHA and LTA Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions and Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Increasing energy prices, global warming, and concerns for environmental pollution has been pushing the chemical industry to look for alternatives for traditional, fossil-based chemical feedstocks. Important platform molecules are alcohols, which can be produced from renewable feedstocks via fermentation. The implementation of these fermentation processes to produce chemicals leads to important challenges regarding the downstream purification. Adsorption-based purification technologies are alternatives for traditional energy-intensive distillation processes. The well-defined pore structure of zeolites makes them ideal candidates for the removal of alcohols from these complex fermentation mixtures, which contain cells and cell debris, acids, sugars, lipids, and proteins. The following B. Claessens, J. Cousin-Saint-Remi, and J. F. M. Denayer (*) Vrije Universiteit Brussel, Brussels, Belgium e-mail: [email protected]

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chapter covers important aspects in the adsorption mechanism of alcohols and water in (mainly) hydrophobic zeolite pores, such as cluster formation and hydrogen bonding. These effects inevitably also play a role when looking at the diffusion of alcohols inside the zeolite pores. Finally, this chapter will cover some examples of studies where hydrophobic zeolites have been used to recover bio-alcohols, such as biobutanol and bioethanol, from model solutions or fermentation broths via fixedbed separations. Keywords Adsorption · Biobutanol · Chabazite · LTA · Silicalite

1 The Production of Renewable Alcohols in a Biorefinery Important environmental challenges, such as global warming and pollution, lead to an increasing pressure on chemical industry to move away from fossil-based feedstocks. An alternative source of energy (fuels) and materials (platform chemicals) is waste biomass from the agricultural or food industry. The conversion of biomass to fuels and chemicals can be performed either via more traditional, catalytic processes [1–3] or via bioconversion or fermentation processes, based on the use of enzymes and microorganisms [1, 4–8]. In fermentation processes, a biomass-based substrate is very selectively converted into a chemical product via a microorganism. The desired chemical product can be either a side-product or a main product of the natural metabolism of the microbe. Microorganisms can be genetically engineered to increase the yield of the desired product [9–11]. Furthermore, during a fermentation, the usually high selectivity of the microbe metabolism can be exploited under mild process conditions. For instance, fermentation processes usually take place at relatively low temperatures (20–40 C) and mild pH [6, 11, 12]. These mild process conditions and high selectivity of microorganisms are significant advantages of fermentation processes compared to catalytic conversions [1–3]. Many different promising platform molecules can be produced via fermentation: organic acids (succinic acid, lactic acid, acetic acid, . . .) [13–16], hydrocarbons (isoprene, isobutene, . . .) [17, 18], diols (butanediol, propanediol) [19, 20], and short-chain alcohols [6, 9, 12, 21, 22]. Large-scale productions of alcohols via fermentation are performed worldwide, for example, in Brazil using sugarcane as feedstock [23, 24]. Besides being a biofuel replacement for gasoline, bioethanol can be dehydrated into ethylene, which is a key component in the material industry [25, 26]. 1-butanol or n-butanol is another example, which can serve as a feedstock for the production of butene [27]. The production of n-butanol via the acetonebutanol-ethanol (ABE) fermentation processes was carried out worldwide on a very large scale before World War II [22] but phased out with the discovery of cheap fossil-based resources. While it has remained important in Russia and China [28], it

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is currently again gaining interest in the western world. In contrast, isobutanol is a new platform molecule, being a precursor for isobutene and xylenes [29], that can be commercially produced using a genetically modified yeast [9]. A major challenge in any fermentation process for platform molecules is product inhibition. When the product concentrations become too high in the fermentation broth, the metabolism of the microorganism slows down, and the production of the desired product stops. This limits the final concentrations obtain during an alcohol fermentation process. For example, of the concentration of ethanol in a batch fermentation is limited to 11–12 wt% [30] and for n-butanol and isobutanol to 2 wt% [9]. Conventionally, these alcohols are recovered using distillation [21, 31]. However, due the low product concentrations, the energy demand for these distillation processes is very high. In the case of n-butanol, the recovery via distillation requires more than 50 MJ/kg [32], while the energy content of n-butanol itself is only 33 MJ/kg [33]. Therefore, a large amount of research effort is undertaken to increase the efficiency of the downstream processing of bio-alcohols. Many different techniques have been studied to increase the efficiency of the separation and purification of fermentatively produced alcohols. Ideally, these separation processes can be partially integrated with the fermentation itself, also called in situ product recovery, to relieve product inhibition during the fermentation and increase the yield of the fermentation process. Examples of techniques which have been studied for bio-alcohol recovery include liquid-liquid extraction [34–36], gas-stripping [37–40], the use of membranes (pervaporation, membrane extraction, membrane distillation) [39, 41–46], and recovery via adsorption [37, 47– 61]. Among these different techniques, adsorption-based recovery has been highlighted for its energy efficiency [53]. An example of the traditional downstream processing of biobutanol is shown in Fig. 1 [22, 62–64]. Biobutanol is produced in the so-called acetone-butanol-ethanol (ABE) fermentation process, typically by using bacteria of the genus Clostridium. These microorganisms also produce acetone and ethanol as side-products, hence the name of the process. In a first step, prior to the fermentation process, the biomass source, which can be lignocellulose or starch-based, is pre-treated to obtain fermentable sugars. The substrate is subsequently sterilized and fed to the fermenter. During the fermentation, acetone, n-butanol, and ethanol are produced. As consequence of its metabolism, the used Clostridium sp. also produces CO2 and H2. After batch fermentation, the broth contains about 0.5 wt% acetone, 1.3 wt% butanol, and 0.15 wt% ethanol [64] and is fed to a steam stripper [44, 64–66]. By adding steam, the ABE products are vaporized, concentrated, and separated from the solid components (e.g., cells) of the fermentation broth. The remaining solid mass is subsequently dried in multiple evaporators [64]. In a next step, the different products are purified in a sequence of distillation towers. The component with the lowest boiling point, acetone, is distilled first, followed by ethanol [44, 64–66]. n-Butanol forms an azeotrope with water at 57.5 wt% butanol; however, this azeotrope is heterogeneous [67], and phase separates in a water-rich (7 wt% butanol) and n-butanol-rich phase (80 wt% butanol) [67]. This phase separation is exploited in the purification of n-butanol: in a first distillation column, n-butanol/water mixture is

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Fig. 1 Overview of the biobutanol production process via batch fermentation. Black arrows indicate major product streams, while grey arrows indicate recycle streams. (1) Biomass pretreatment, (2) sterilization, (3) fermentation, (4) steam stripper, (5) acetone distillation column, (6) ethanol distillation column, (7) n-butanol/water decanter, (8) water distillation column, (9) butanol distillation column. Adsorptive separations can be employed to recover biobutanol either from the fermentation gas or from the liquid fermentation broth, as indicated by the dashed red lines. This summary is based on references [22, 62, 63]

distilled toward the azeotrope [44, 64–66]. Subsequently, in a decanter, the butanolrich and water-rich phases are separated. The water-rich phase is recycled toward the distillation column, while the n-butanol-rich phase is sent to a final tower, where it is purified [44, 64–66]. Adsorptive separations can be implemented as an alternative for these complex and energy-demanding distillation steps. In a first (conventional) strategy, n-butanol is recovered from the liquid fermentation broth, after separation of the solid components via filtration to avoid fouling of the downstream adsorbent bed (Fig. 1) [54]. As an alternative, the produced fermentation gasses can also be used to continuously strip the produced solvents form the fermentation broth and subsequently recovered via adsorption, in a combined gas-stripping adsorption process [59, 68]. This technique has the additional advantage that no filtration step is needed to separate the produced acetone, n-butanol, and ethanol from the fermentation broth. Since the produced alcohols are usually present in a low concentration (2–12 wt %), in water, mostly hydrophobic adsorbents are studied for the recovery of alcohols produced via fermentation, such as activated carbon [37, 55, 68–72], resins [49, 50, 53, 71–85], metal organic frameworks (MOFs) [56–58, 86–97], and high-silica zeolites [48, 52, 53, 57, 59, 61, 87, 97–129]. However, in the case of bioethanol production, hydrophilic adsorbents (such as zeolite 4A) can be used to dry the distilled ethanol and break the ethanol/water azeotrope [130]. Due to their narrow pore size distribution, fine molecular sieving properties, and a high structural

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Table 1 Structural properties of zeolite frameworks used in bio-alcohol recovery and discussed in this work Framework

MFI CHA LTA

Pore dimensions Straight channel 5.6  5.4 Å Window 3.8 Å  3.8 Å 4.2 Å  4.2 Å

Micropore volume determined via Ar isotherms (mL/g) Sinusoidal channel 5.5 Å  5.1 Å Cage 6.8 Å  10 Å 11.4 Å

0.15 [135] 0.27 [59] 0.29 [59]

stability, zeolites are among the most promising adsorbents for the recovery of bio-alcohols. The use of zeolites for alcohol recovery is not limited to adsorptive separations; many studies have been published considering zeolite membranes for the separation of alcohols and water. However, in this contribution, we will focus on adsorption-based separations. For the interested reader, we refer to the some recent reviews considering zeolite membranes for alcohol recovery [131–133]. ZSM-5 and its all-silica analogue silicalite-1 are by far the most studied zeolites for the recovery of bio-alcohols. They crystallize according to the MFI framework topology that possesses two types of interconnected channels: straight and sinusoidal with a different size and shape [135]. A lot of attention has also been given to smaller pore-size, cage-and-window-type zeolites having the LTA or CHA topology. A short summary of the pore dimensions of these materials is given in Table 1. More details on the structure of these materials are provided in the subsequent sections. As a brief introduction to the properties of the discussed zeolites, the vapor phase adsorption isotherms of ethanol, n-butanol, and water on an all-silica LTA (ITQ-29), all-silica chabazite, and silicalite-1 are shown in Fig. 2. Firstly, silicalite-1 and ITQ-29 both show a high adsorption capacity at saturation for ethanol and n-butanol. In contrast, on the all-silica chabazite zeolite, the saturation capacity of ethanol is higher compared to that of n-butanol. More specifically, the reported adsorbed amounts are not in complete equilibrium [59]. As will be shown throughout the chapter, the specific structure and pore geometry of zeolites lead to these specific equilibrium and kinetic effects, which can be exploited for bio-alcohol recovery. Secondly, although the isotherm of water has a similar shape for all three materials, the adsorbed amount of water is very different. The presence of silanol defects and cations can have a profound effect on the adsorption of water. The mechanism of alcohol and water adsorption, diffusion, and the application of these zeolites in separation processes is discussed throughout the chapter. Many of the discussed materials are also catalysts, but the focus will lie on the physisorption mechanism and not chemisorption.

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Fig. 2 Vapor adsorption isotherms of ethanol, n-butanol, and water on all-silica LTA (ITQ-29 – (a), all-silica chabazite (b) and silicalite-1 (c). ITQ-29 and CHA isotherms are at 40 C [59], while the vapor isotherms on silicalite-1 are at 27 C (ethanol/water) [117] and 35 C (n-butanol) [136]. Due to diffusional limitations, the n-butanol isotherm on the CHA zeolite is not in complete equilibrium

2 MFI Zeolites: ZSM-5 and Silicalite-1 2.1

Structure and Selectivity

Silicalite-1 was one of the first all-silica zeolites to have been synthesized [137] and almost immediately identified as a promising material for bioethanol and biobutanol recovery via adsorption [103, 138]. Silicalite-1 is a member of the MFI framework family and a structural analogue of ZSM-5. MFI zeolites have a channel-type pore structure, with ten-membered ring pore openings [135, 139]. Two different channels exist in the structure, which are perpendicular to each other (Fig. 3a). The straight channels have the biggest size (5.6 Å  5.4 Å) and run in the [010] crystallographic direction. The other channels present in the structure are sinusoidal and run along the

Fig. 3 (a) View along the straight channels of MFI zeolites. (b) View along the sinusoidal channels of MFI zeolites. Structure images were obtained using the drawing tool available on the website of the International Zeolite Structure Database [140]

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Fig. 4 Single solute alcohol adsorption isotherms of C1–C4 alcohols diluted in water on silicalite1 at 20 C as reported by Milestone and Bibby [104, 138]

[100] direction, having a size of 5.5 Å  5.1 Å (Fig. 3b). The typical micropore volume of MFI-type materials lies around 0.15 mL/g [134]. Liquid-phase (single solute) isotherms on silicalite-1 of different linear and branched alcohols (dissolved in water) are shown in Fig. 4 as reported by Milestone & Bibby [138]. For the linear alcohols, the amount adsorbed at low concentration (> 1, and therefore γ  0 can be taken as an excellent approximation, which numerically reduces the solution to the limiting case originally considered by Eic and Ruthven [4]. The key parameter in this case is L, which represents the ratio of the diffusional time constant and the time constant of the washout of the adsorbed phase. This parameter controls which regime the system is under. If L  1, the rate at which the concentration is being varied is much faster than the internal diffusion process; therefore the system is limited by diffusion, and we are under kinetic control. If L < 1, we are in the opposite regime, and diffusion is so fast that the internal

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Fig. 5 Typical ZLC desorption curves as c/c0 vs. t plot at increasing flowrate (L value) (adapted from [8])

concentration profile remains essentially flat and we are under equilibrium control. The first limit allows the measurement of diffusion coefficients, and from the definition of L, this requires small samples and high flowrates. For equilibrium measurements, it is better to increase the volume of the sample and reduce the flowrates. While it is not possible to predict a priori which regime the experiments are in, a plot of cc0 vs Ft allows to see if the system is under equilibrium or kinetic control. If the curves in this plot overlap, the system is under equilibrium control. Furthermore, this plot can provide a simple check for consistency since the mass balance can be integrated to obtain [8]: Z V S q0 þ V F c 0 ¼

1

Fcdt

ð4Þ

0

The terms on the LHS represent the accumulation terms in the solid and fluid phase, respectively, which are independent of flowrate, so the area under the curves should be constant. Figures 5 and 6 show the typical ZLC response curves at increasing flowrates, i.e. at increasing L values. Under kinetic control, ZLC curves at different flowrates are characterised by nearly parallel long-time asymptotes with intercepts proportional to the flowrate. In Fig. 6 the same curves are plotted as a

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Fig. 6 ZLC desorption curves as c/c0 vs. Ft plot at increasing flowrate (L value) (adapted from [8])

function of Ft showing how curves at different flowrate cross under kinetic control but have the same area, i.e. the overall capacity of the column is independent of flowrate and therefore independent of the controlling regime. When kinetic control conditions can be achieved, experiments at different flowrates should be fitted simultaneously to a single diffusional time constant. While this is often sufficient to obtain the diffusivity, there are cases where different values can be obtained depending on the length of time of the observation. This is particularly true for slow diffusion processes that are of interest here. In this case, it is therefore useful to introduce a different time constant in the ZLC experiment, and this is readily achieved by switching the valve before the system reaches equilibrium. This is the partial loading ZLC experiment introduced by Brandani and Ruthven in 1995 [9]. This experiment is based on the fact that for spherical particles, full equilibration requires a time approximately equal to 0.5R2/D. By switching the valve before 0.05R2/D, it is now possible to achieve two positive outcomes. The first is that the partial equilibration leads to cc0 vs t curves that shift down by an amount directly related to the time of the partial load. This can be used to confirm and fix without uncertainty the time constant being measured. The second advantage lies in the fact that this simple experiment allows to distinguish clearly between surface resistances and internal mass transfer kinetics. The integral of the mass balance can be used to calculate qq from cc0 since: 0

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Z V Sq ¼

1

Z

t

Fcdt 

0

Fcdt  V F c

ð5Þ

0

If the mass transfer is controlled by a surface resistance, the qq vs t plots will all 0 overlap, while if the system has an internal mass transport mechanism, there will be a shift in the long-time asymptote also in this plot. A final advantage of the qq vs t plot 0 is that the integration process filters out the random noise, simplifying the match of the model to the data. If the signal is normalised so that time zero is the time at which the desorption of the partial loading starts and c0 is the fluid phase concentration at this time, the solution to the diffusion model becomes [9, 10]: 1 P

cðt Þ n¼1 ¼ c0

h



i



β2 Dτ β2 Dt 2L 1  exp Rn2 exp Rn2 2 2 2 ðL1γβn Þ þL1þγβn  2  1 P βn Dτ 2L 1 exp 2 2 2 2 R2 n¼1 βn þðL1γβn Þ þL1þγβn

β2n þ

 ð6Þ

In Eq. (6) τ is the time the sample has been exposed to the sorbate which is fixed experimentally. At a fixed flowrate, it is therefore possible also to carry out several partial loading experiments with different switch times. The resulting desorption curve for the partially loaded sample will have the same shape as the fully saturated one but with a lower intercept for the long-time asymptote dependent on the parameter τ. The use of the partial loading experiment in combination with the full loading experiment provides a tool to assess unambiguously the time constant of the process. In fact, the correct combination of L and D/ R2 is the one that not only allows the prediction of the ZLC curves at different flowrates but also the prediction of the corresponding partial loading experiment at a given adsorption time τ. The most important assumption used in this section is that the system is linear. This implies that the concentrations are low and the adsorption isotherm is in the Henry law region. Experimentally, larger signals are obtained at higher concentrations, so isotherm non-linearity is one of the main issues to consider. For CO2 in most nanoporous materials, the deviation from linearity at low concentration is typically well described by a Langmuir isotherm, and theoretical considerations as well as experimental validation show that matching the desorption curves at different flowrates and low concentrations will yield the correct diffusional time constant [11, 12]. Clearly it is also possible to use a non-linear model and fit the parameters to the ZLC curves, and this was demonstrated by Friedrich et al. [13].

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4 Measurement of CO2 Diffusivities in Zeolite Rho In this section the use of the ZLC system is demonstrated on a series of cationexchanged Rho zeolites. These experiments allow to highlight also some of the challenges that can be faced when trying to determine the kinetics of adsorption of CO2 of novel and complex materials. Zeolite Rho has a structure characterised by a body-centred cubic arrangement of α-cages connected by double eight-rings, with an effective channel diameter of about 3.6 Å [14]. The available sites which can accommodate the extra-framework cations are the single eight-ring (S8R), the double eight-ring (D8R), and the single six-ring (S6R) sites [15]. It has been proved that, passing from the hydrated form to the ion-exchanged one, the structure of Rho zeolites passes from the centrosymmetric structure to the non-centrosymmetric one [15]. From this follows that once extra framework cations are introduced in the structure, the deformation induced, together with the size and the position occupied by the cations, may create a severe blocking action to the cages. Distribution and positioning of the cations inside the Rho structure have a key role in the hindering effect to gas transport. These effects highly depend from the size of the cation and if positioned in the D8R windows may even prevent the access to molecules such as N2 and CO2. Webley and co-workers were among the first ones to identify what was defined as the “molecular trapdoor” effect in samples of ion-exchanged Chabazite zeolites [16, 17]. Binary breakthrough experiments using a mixture of CO2 and CH4 on Cs and K Chabazites show complete exclusion of CH4 over CO2 establishing that only molecules having affinity with the cations can “open” the trapdoor and access the cage. An equivalent behaviour was identified for the cation exchanged Rho zeolites used for this study. Initial N2 adsorption measurements in cryogenic conditions showed no access for N2. On the other hand, CO2 adsorption tests at higher temperatures with both ZLC and volumetric methods showed a clear uptake, indicating the role of the cation mobility in allowing access to the cages to the gas molecules [18]. The mechanism is of clear scientific interest, and the study of these systems serves as a comprehensive example on the use of the ZLC technique. The kinetic study reported in this section refers to two samples of cation-exchanged Rho zeolites, a fully exchanged Na-Rho and Na,Cs-Rho. Figure 7 shows the cation distribution for these two samples. Understanding the transport mechanism in Na-Rho has led to the development of Li-Rho zeolites with improved performance. Breakthrough curves measured on three variant of Li-Rho samples are discussed at the end of the section. All samples were synthesised at the University of St. Andrews by Prof. Wright’s research group. For Na-Rho samples, ZLC columns were packed using close to 10 mg of crystals: 11.6 mg for Na,Cs-Rho and 8.4 mg for Na-Rho. All tests were carried out at 35 C and 0.1 bar of CO2 in He as carrier gas.

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Fig. 7 Cations distribution in Na,Cs-Rho (left), Na-Rho (right). Na+ cations in orange, Cs+ cations in purple [18]

Prior to the experiments, the samples were regenerated at 350 C overnight under He flow. Samples were heated to the regeneration temperature using a ramp of 1 C/ min to 110 C first, followed by a second ramp to 350 C. The first evidence of a slow diffusion process is the fact that the adsorbed amount is highly dependent on the adsorption time. In practice, the calculated adsorbed amount from the ZLC desorption curve would increase with the adsorption time indicating that the sample has not reached full equilibration. In this case the true equilibration time can be determined by running adsorption/desorption tests at increasing adsorption times. Full equilibration is achieved once the calculated adsorption capacity would not change any more with the adsorption time, and after the diffusion time constant was obtained, it was confirmed that the equilibration time exceeded 0.5R2/D. This is a key step in the initial assessment of slow samples, and it is always good practice to have a way to check that equilibrium has been achieved, especially when dealing with novel materials. The final CO2 adsorbed amount at 35 C and 0.1 bar is 2.13 mol/kg for Na,Cs-Rho and 3.22 mol/kg for Na-Rho. It should also be noted that the monitored variable is the concentration in the gas phase. This means that for very slow samples relying on the trend of the monitored signal may not be enough to ensure that the system is at equilibrium. A stable plateau in the recorded signal does not automatically indicate that equilibrium has been reached in the adsorbed phase. Therefore, the approach suggested above of calculating the adsorbed amount at different exposure times constitutes a more reliable check. Equilibration time for the Rho samples tested was between 5 and 7 h. The first step when trying to measure kinetics is to ensure that the system is under kinetic limitation. The long equilibration time is already an indication per se of the presence of a transport resistance, but, as mentioned above, the ZLC offers also a simple graphical check for equilibrium/kinetic control by plotting the desorption curve in terms of eluted volume, Ft [8]. Figure 8 shows both the t and the corresponding Ft plot for the Na,Cs-Rho sample. ZLC curves at different flowrates clearly cross in the Ft plot proving that the system is in kinetically controlled regime. When dealing with very slow kinetics, the interpretation of the ZLC experiments requires a careful analysis of the ZLC desorption curves as the identification of the long-time asymptote can lead to misleading kinetic constants. In Fig. 8 it is clear that,

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Fig. 8 ZLC desorption curves for Na,Cs-Rho at 2 ad 8 ml/min: (a) t plot; (b) Ft plot

Fig. 9 ZLC desorption curves at 35 C for Na,Cs-Rho analysed at two different observation times, 400 (a) and 4,000 s (b). In red the predicted curves using the ZLC model (parameter reported in the plots)

given the slow rate of diffusion, the desorption curves show a clear signal with a slowly varying exponential decay for several hours, making it difficult to assess where the long-time asymptote should be taken to extract the kinetic constant. An example is displayed in Fig. 9, where the desorption curves for Na,Cs-Rho are analysed using the full ZLC model (Eq. 2) using data over different observation times of 400 and 4,000 s. What is important to note is that the curves at two flowrates are matched well by the standard model in both cases. The match of the ZLC curves is carried out only at one flowrate by choosing the appropriate D/R2, L and γ parameters; the other flowrate is simply predicted by calculating the new L value based on the flowrate used (L is the only flowrate-dependent parameter). This indicates that using only the long-time asymptotes can lead to different values of diffusivities depending on the observation time. One possible explanation for such a response would be the presence of more than one time constant in the process, due, for example, to a distribution of crystal sizes

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Fig. 10 Experimental ZLC desorption curves for the fully and partially saturated Na,Cs-Rho sample; in green and red, the model predictions using the parameters from the analysis at 400 s and 4,000 s observation time, respectively

(i.e. same D, but different R) [19]. In this case, the short time would correspond to the CO2 molecules desorbing from the smallest crystals, while larger crystals would be characterised by slower time constants. The partial loading experiment is a very useful tool to resolve unequivocally what the actual time constant of this sample is. By imposing a time constant on the system and comparing not only the response at different flowrates but also at different equilibration times, the experiments contain significant additional information that constrains the fit to the point that one can reach a conclusion as to whether one has a distribution of time constants or a main time constant that matches correctly all the cases. Figure 10 shows the experimental curves at a single flowrate for both the fully and partially equilibrated case with the corresponding predicted curves. It should be noted that the exposure time in the partial loading experiment, τ, is a known parameter from the experiment; therefore the partial loading curve can be simply predicted using Eq. (6) and the parameters obtained from Fig. 9. The experimental curves are matched with both the predictions obtained from the parameters extracted from the observations up to 400 and 4,000 s. From the comparison, it can be seen that while using the parameters from the shorter observation time allows to predict the first part of the full loading curve, it fails to predict the long-time asymptote and, more importantly, the partial loading curve. The predicted time constant is too fast resulting in a partial loading curve closer to the fully equilibrated one compared to

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Fig. 11 ZLC model prediction for Na,Cs-Rho at different flowrates and partial loading times (parameters from Fig. 9 – 4,000 s case)

the experimental one. Using the parameters from the longer observation time, instead, provides an excellent prediction of both the partially and fully equilibrated experiment. This confirms that the process is governed by a single time constant, excluding a significant contribution from the effect of particle size distributions or other processes. It is important to emphasise that “goodness of fit” alone does not provide an assurance that the correct time constant has been obtained, unless both different flowrates and partial loading experiments are considered. The fact that the correct time constant has been extracted is further confirmed by the more extensive dataset shown in Fig. 11. Here with a single set of parameters, curves at different flowrates (including two partial loading experiments) are matched very accurately. A total of six different experimental curves are predicted using the same time constant. From Figs. 10 and 11, it can be noted that in the case of the fully equilibrated experiment, the theoretical model slightly underestimates the desorption rate at high CO2 concentration. This can be associated to the fact that at the initial concentration, c0, the system is operating slightly beyond the Henry’s law region, as confirmed by the CO2 isotherm independently measured at the University of St. Andrews [18]. It is

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Fig. 12 Calculated desorption curve of the adsorbed phase concentration, partial saturated experiment for Na,Cs-Rho

q q0 ,

for the fully and

worth to point out that the deviation from linearity does not affect the slope of the long-time asymptote, i.e. the estimation of the diffusional time constant, which falls in the Henry’s law region [11], and as a result, the shift of the long-time asymptote of the partial loading experiment should be predicted accurately by the standard model, as is the case in the examples considered. The fact that the partial loading experiments show a more accurate match to the standard model is consistent with the limited uptake during the short exposure time to the sorbate. In the partial loading experiments, the adsorbed phase concentration remains low at all times and therefore either in the linear region or close to it. The partial loading experiment allows also to confirm or exclude the presence of surface barriers. Figure 12 shows the qq vs. t plot corresponding to the data in Fig. 10. 0 While the curves are very smooth, these are in fact curves calculated directly from the experimental data applying the mass balance, Eq. (5). The partial loading curve shows a very distinct shift from the fully equilibrated case, allowing to exclude a dominant effect from the presence of surface barrier resistances. If adsorption were to be dominated by surface barrier resistances, the concentration profile inside the particle at the time of the switch of the valves will be flat, just as the case of the fully

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Fig. 13 Experimental ZLC desorption curves for the fully and partially saturated Na-Rho sample (fully exchanged); in red the predicted curves using the ZLC model

equilibrated case; therefore the curves would overlap in the qq plot [9]. The time 0 constant obtained is therefore a diffusivity inside the crystals. Fully exchanged Na-Rho has a similar cation distribution as the Na,Cs Rho (Fig. 7). Therefore, a similar behaviour in terms of gas transport is expected. Figure 13 shows the ZLC desorption curves and the model prediction for this sample for fully equilibrated and partial loading experiments. The time constant used in the model is very close to the one measured for the Na,Cs sample, but there is a very interesting behaviour, which is amplified and made clear by the comparison between the standard model and the data. The fully equilibrated experiment shows a large gap between the predicted curve and the experimental data at high CO2 concentrations. There is however a very good match in the long-time asymptote, and, more importantly, there is a perfect match of the partial loading experiment. This confirms that the time constant of the process is correct, but at the same time, the shape of the full saturation curve points to an additional mechanism. The shape of the curve indicates that the experimental response is considerably faster at high CO2 concentration if compared to the predicted curve: the model underestimates the desorption rate at high CO2 concentrations. Initially this may seem somewhat counterintuitive but can

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be explained considering that a rapid change in the fluid concentration corresponds to slow mass transport out of the particles. Differently from the case of the Na,Cs-Rho sample, this discrepancy cannot be linked exclusively to the non-linearity of the isotherm, since the shape of the isotherm does not differ considerably from the one of Na,Cs-Rho [18]. Moreover, a careful observation of the initial part of the experimental curve shows a difference in the shape of the curve between the full and partial loading case; this cannot be related simply to the non-linearity on the ZLC response. The trend observed indicates that a second contribution influences the response of this system. Independent in situ XRD measurements of CO2 adsorption proved that structural changes occur in Na-Rho with variable CO2 loading: the size of the 8MR windows increases from 2.26 Å in the dehydrated form to 2.75 Å at partial pressure of CO2 of 0.1 bar, resulting in a faster diffusion of CO2 at higher coverages [18]. The fact that this effect is visible in the ZLC experiments indicates that the time constant of the structural change is comparable to that of the diffusion of CO2 through the sample. As the CO2 desorbs from the sample, emptying the cages, the 8MR windows switch from the large to the restricted size. This is reflected in the time constant of the CO2. Should the structural change be a much slower process, a faster diffusivity (corresponding to the original structure) would have been observed. If the structural change was much faster, then the external layer of the crystals would rearrange rapidly to the final configuration, and the measured curve would have been very similar to a single diffusion process. To confirm this hypothesis, a low-concentration ZLC experiment was carried out. In this experiment the sample was fully equilibrated with a feed mixture with just 1% of CO2: the aim is to operate the system at a sorbate concentration low enough to exclude the contribution of the non-linearity. More importantly, in these conditions the low concentration of CO2 should induce only minor changes in the structure of the framework. The experiment is reported in Fig. 14, where the same time constant obtained from the experiment at 10% of CO2 was used to predict the experimental data at 1%. Clearly, the model allows to predict the experimental data in the entire range of concentrations, indicating that in these conditions, the desorption process is governed by a single time constant, i.e. a single structure. The good prediction of the partial loading experiment in Fig. 14 indicates that, due to the diffusional resistance, the average concentration inside the solid is below the one that would induce structural modifications: the operating conditions should not differ much from the ones of the experiment at 1% of CO2. For this reason, it becomes interesting to compare the predicted profile of the adsorbed phase concentration between the three cases of interest: full saturation at 10% and 1% of CO2 and partial saturation. The ZLC model can be solved with respect to the adsorbed phase concentration, and one can also predict, based on the diffusivity, the adsorbed concentration profile inside the crystal as function of the radius of the particle [9]. Figure 15 shows the predicted adsorbed concentration profiles inside the crystal for the partially and fully equilibrated cases at different desorption times: the curves were obtained using the

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Fig. 14 Low-loading ZLC experiment on Na-Rho at 1% of CO2 and 1 mL/min; in red the predicted curve using the ZLC model

same time constant extracted from the analysis of the ZLC desorption curves. At equilibrium a flat concentration profile is achieved across the crystal; as the desorption starts and progresses, the adsorbed phase concentration decreases first close to the surface of the particle, and then the profile progressively moves towards the centre of the crystal. In the partial loading case, the internal concentration profile at time zero results in a more complex transient response. Initially the adsorbate molecules move both towards the centre of the particle and the external surface; there are two distinct maxima in the concentration profile. Eventually the concentration at these maxima decreases below the concentration at the centre, and the dynamic response becomes similar to the standard experiment. This is the reason why the slopes of the long-time asymptotes of the fully equilibrated and partial loading experiments are the same. What is important to notice is that at time zero, the average concentration inside the particle for the partially loaded sample is not very far from the one corresponding to the sample saturated with a CO2 concentration of 1%. This also the average concentration seen by the fully equilibrated sample after 5,000 s desorption time, i.e. the region of the long-time asymptote used to extract the kinetic information. All this confirms that for the partially loaded sample, the concentration inside the solid is low enough that linear equilibrium and constant diffusivity can be considered as valid assumptions. With this time constant determined, if a suitable model that includes the kinetics of the structural transition was developed, the fully equilibrated experiments could be used to determine this additional time constant.

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Fig. 15 Adsorbed phase profile inside a crystal of Na-Rho for the full (left) and partial (right) saturation cases at different desorption times

One of the main advantages of using the ZLC as a tool to characterise the adsorption performance of novel adsorbents is that it allows a rapid feedback of the relevant information to the chemists working on the synthesis process. This allows to identify the strength and weaknesses of the material and modify the synthesis procedure to further improve and engineer the materials. Beyond the purely scientific and academic interest for these complex adsorbents, the Rho samples present some properties, namely, the flexible structure and the very high selectivity towards CO2, which makes them promising candidates for carbon capture applications. Nevertheless, the high selectivity comes with the price of an extremely slow CO2 kinetics which limits their applicability in real processes. The insights provided by the ZLC analysis allowed to identify the key factors responsible for the limitations and represented the foundations on which a new series of Rho zeolites with improved characteristics were synthesised. This is the case of the Li- form of Rho zeolites. Similar to Na-Rho, Li-Rho presents a distorted framework which makes the material selective to CO2 retaining a relatively high CO2 adsorption. In addition, Li+ cations seat preferentially in 6R sites leaving the majority of 8R and D8R sites free facilitating the access of CO2. For this reason, the research effort concentrated on the preparation of the variant of the Li- form Rho zeolites: a fully exchanged version and two samples with low occupancy of larger cations, Li,Na-Rho and Li,Cs-Rho. To compare the sample directly and at conditions

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Fig. 16 Breakthrough curves for Li-Rho, Li,Na-Rho and Li,Cs-Rho. Feed mixture 5% CO2, 40% CH4 in a balance of He; temperature 35 C; total pressure 1 atm

relevant for a real process separation, a breakthrough experiment was designed using an extended version of the ZLC column to test the performance of the samples to separate CO2 from CH4 in dry conditions [20]. The experimental apparatus is the same, but a longer bulkhead union is used in place of a normal union. This extended version of the ZLC was used with the main advantage of allowing to run a breakthrough experiment while still using a small amount of sample (~30 mg). This allows to visualise directly the separation efficiency of the different materials. Figure 16 shows the breakthrough curves obtained for the three samples using a mixture consisting of 5% CO2 and 40% CH4 in a balance of He at 35 C and total pressure of 1 atm. To facilitate the comparison, curves are normalised by the sample mass used and plotted as cc0 vs Ft M. From the curves above, one can immediately notice that all three samples show similar CO2 capacities with almost complete exclusion of CH4. The use of inert He in the mixture allows to confirm this, because as the more weakly adsorbed component if CH4 was initially adsorbed, it would have been displaced by the CO2 leading to a roll-up in the CH4 signal. Without an inert carrier, one would need to measure the outlet flowrate from the column to detect the displacement of the more weakly

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adsorbed component. The data show that both the pure Li and the Na,Li samples completely exclude CH4. The Cs,Li sample does adsorb a very small amount of CH4. The fully exchanged sample shows the slowest CO2 uptake rate, which leads to a very broad transition to the final CO2 concentration. Adding a small amount of cations clearly improves the CO2 kinetics. The improvement is just marginal for the Li,Na sample but very significant for the Li,Cs-Rho, as can be clearly seen from the sharp profile of its breakthrough curve. These observations are consistent with the fact that as the rate of CO2 uptake improves, a small amount of CH4 is also adsorbed. From the simple observation of the breakthrough profiles, one can then conclude that, compared to the pristine sample, the addition of cations improves the gas transport by enlarging the window opening. There is therefore a trade-off; a larger access facilitates the access of larger molecules lowering the selectivity. Complementing the kinetic studies with more in-depth characterisation allows a full understanding of the mechanisms involved. In situ XRD have in fact revealed that the presence of Li+ (even if not directly blocking the D8R windows) results in framework distortion that makes the 8R windows very elliptical resulting in the slow CO2 kinetics seen for the fully exchanged Li-Rho. The presence of small amounts of larger cations has the main advantage to control the extent of such distortion allowing a fine-tuning of the molecular sieving properties of these materials. This represents a new type of cation-controlled molecular sieving which acts on the extent of framework distortion rather than on the partial blocking of the pore access. The main advantage is the capability of creating materials that can be tuned at the molecular level for the size selection of specific molecules.

5 Conclusions The effort of researchers in developing novel materials with improved equilibrium and kinetic properties has produced a wide range of new prototype materials that require rapid and reliable methodologies to determine their process performance. This creates the engineering challenge to continuously tune and improve experimental techniques to cope with the increased complexity of the materials. In this, the key step is the capability to provide rapid feedback to the inventors of the novel materials using only very small samples. This is where the ZLC technique has a very important role at the early stages of development where samples are generated in small batches, given that it is essential to be able successfully progress towards the synthesis of competitive materials that can be used in real applications and be scaled up to tackle the upcoming separation challenges. The correct use of the technique requires an understanding of the theory of the experiment and points to the need to carry out at least a minimal set of experiments: two flowrates to confirm whether the system is in equilibrium or kinetic control; when clearly in kinetic control (L > 10) an additional partial loading experiment to identify unambiguously the time constant of the system and provide direct evidence that can confirm or exclude the presence of surface barriers.

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The need to combine different flowrates and partial loading experiments is particularly true for the small pore zeolites presented in this contribution where it was shown that different time constants could be extracted using only the results from experiments at different flowrates. This approach offers also insights in the case where structural transitions in the materials are triggered by the adsorbing molecule. When ZLC experiments are properly designed, either as the traditional experiment or as the extended ZLC version, they are an essential tool allowing rapid feedback to the material experts because both configurations need only very small sample quantities. The kinetic analysis and the comparison between the different Rho zeolite samples allowed to guide the research on new synthesis procedures to progressively improve the material properties in terms of the actual separation performance. In the case presented, the identification and understanding of the key role of the cation distribution and how their presence alters the structure were essential to prepare a second generation of materials which had the correct tradeoff between improved kinetics and size exclusion towards CO2 capture. This represents a clear example of systematic characterisation of materials followed by a direct assessment in reference process conditions, which is an essential step for the development of the next generation of nanoporous adsorbents.

References 1. Abanades JC, Arias B, Lyngfelt A, Mattisson T, Wiley DE, Li H, Ho MT, Mangano E, Brandani S (2015) Emerging CO2 capture systems. Int J Greenhouse Gas Control 40:126–166 2. Rajagopalan AK, Rajendran A (2018) The effect of nitrogen adsorption on vacuum swing adsorption based post-combustion CO2 capture. Int J Greenhouse Gas Control 78:437–447 3. Kärger J, Ruthven DM, Theodorou DN (2012) Diffusion in nanoporous materials. Wiley-VCH, Weinheim 4. Eic M, Ruthven DM (1988) A new experimental technique for measurement of intracrystalline diffusivity. Zeolites 8(1):40–45 5. Hu X, Brandani S, Benin AI, Willis RR (2015) Development of a Semiautomated zero length column technique for carbon capture applications: rapid capacity ranking of novel adsorbents. Ind Eng Chem Res 54(16):6772–6780 6. Gibson JAA, Mangano E, Shiko E, Greenaway AG, Gromov AV, Lozinska MM, Friedrich D, Campbell EEB, Wright PA, Brandani S (2016) Adsorption materials and processes for carbon capture from gas-fired power plants: AMPGas. Ind Eng Chem Res 55(13):3840–3851 7. Brandani S, Ruthven DM (2002) Analysis of ZLC desorption curves for liquid systems. Chem Eng Sci 50(13):2055–2059 8. Brandani S (2016) A simple graphical check of consistency for zero length column desorption curves. Chem Eng Techonol 39(6):1194–1198 9. Brandani S, Ruthven D (1996) Analysis of ZLC desorption curves for gaseous systems. Adsorption 2(2):133–143 10. Gunadi A, Brandani S (2006) Diffusion of linear paraffins in NaCaA studied by the ZLC method. Microporous Mesoporous Mater 90(1–3):278–283 11. Brandani S (1998) Effects of nonlinear equilibrium on zero length column experiments. Chem Eng Sci 53(15):279–2798 12. Brandani S, Jama MA, Ruthven DM (2000) ZLC measurements under non-linear conditions. Chem Eng Sci 55:1205–1212

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13. Friedrich D, Mangano E, Brandani S (2015) Automatic estimation of kinetic and isotherm parameters from ZLC experiments. Chem Eng Sci 126:616–624 14. Baerlocher Ch, McCusker LB, Olson DH (2007) Atlas of zeolite framework types, 6th edn. Elsevier, Amsterdam 15. Lee Y, Reisner BA, Hanson JC, Jones GA, Parise JB, Corbin DR, Toby BH, Freitag A, Larese JZ (2001) New insight into cation relocations within the pores of zeolite rho: in situ synchrotron X-ray and Neutron powder diffraction studies of Pb- and Cd-Exchanged Rho. J Phys Chem B 105:7188–7199 16. Shang J, Li G, Singh R, Gu Q, Nairn KM, Bastow TJ, Medhekar N, Doherty CM, Hill AJ, Liu JZ, Webley PA (2012) Discriminative separation of gases by a “molecular trapdoor” mechanism in Chabazite zeolites. J Am Chem Soc 134:19246–19253 17. Shang J, Li G, Singh R, Xiao P, Liu JZ, Webley PA (2013) Determination of composition range for “molecular trapdoor” effect in Chabazite zeolite. J Phys Chem C 117:12841–12847 18. Lozinska MM, Mangano E, Mowat JPS, Shepherd AM, Howe RF, Thompson SP, Parker JE, Brandani S, Wright PA (2012) Understanding carbon dioxide adsorption on univalent cation forms of the flexible zeolite rho at conditions relevant to carbon capture from flue gases. J Am Chem Soc 134(42):17628–17642 19. Duncan WL, Möller KP (2002) The effect of a crystal size distribution on ZLC experiments. Chem Eng Sci 57(14):2641–2652 20. Lozinska MM, Mangano E, Greenaway AG, Fletcher R, Thompson SP, Murray CA, Brandani S, Wright PA (2016) Cation control of molecular sieving by flexible Li-containing zeolite rho. J Phys Chem C 120(35):19652–19662

Struct Bond (2020) 184: 145–194 https://doi.org/10.1007/430_2020_71 # Springer Nature Switzerland AG 2020 Published online: 11 October 2020

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from Lab to Industry Vanessa F. D. Martins, Ana M. Ribeiro, Alexandre F. P. Ferreira, and Alírio E. Rodrigues

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Adsorptive Gas-Phase Separation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 From Laboratory to Industrial Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Pure Component Adsorption Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Dynamic Adsorption Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Mathematical Modeling and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 VPSA and SMB at Pilot Scale Using Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Cryo-PTSA Scale-up Using Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract Different synthetic zeolites can be obtained by varying the composition, porosity, and active centers, making them of great interest in industry, especially as adsorbents in gas separation and purification processes. On the other hand, adsorption separation processes are increasingly common in industrial applications due to the technical and economic advantages of this technology. In this context, zeolites have emerged as promising candidates for these processes due to their high temperature stability, resistance to harsh environments combined with unique molecular sieve characteristics, ion exchange, and selective adsorption. In this chapter, we will focus on two cases, paraffin/olefin separation (ethane/ethylene and propane/propylene) and carbon dioxide/methane separation. Some innovative alternatives to replace conventional distillation have emerged for paraffin/olefin separation, with emphasis on simulated moving bed (SMB)

V. F. D. Martins, A. M. Ribeiro, A. F. P. Ferreira, and A. E. Rodrigues (*) Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal e-mail: [email protected]

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technology. A wide variety of zeolites has been studied for this process, such as zeolites 13X, 4A, and 5A. The second case study is the removal of carbon dioxide (CO2) from natural gas stream. Adsorption processes are considered a competitive solution, once the adsorbent can be regenerated either by TSA or PSA. Concerning the use of zeolites for CO2 removal, natural chabazite, zeolite 4A, H-mordenite, and zeolite 13X are the ones with more available information in literature. In this review, we will focus on the strategy and importance of the lab/pilot scale with perspectives of scaling up adsorptive gas-phase separations using zeolites. The main methods adopted in lab/pilot scale studies include adsorbent characterization, adsorption equilibrium, adsorption dynamic studies, and process simulation and optimization. Keywords 13X · Adsorption · Cryo-PTSA · Gas phase · Industrial · Laboratorial · PSA · SMB · Zeolites

1 Introduction With the gradual increase of energy costs in the 1960s and 1970s, the petrochemical industry was forced to find new alternatives for which distillation has become less favorable in terms of energy input and CO2 emissions. The adsorption-based separation processes were implemented in the petroleum and petrochemical industry as they offer fluid separation based on the shape, size, and chemical nature of the molecules. The large pore surface area and the high adsorption affinity ensure the densification of gases at moderate temperature and moderate pressure. The success of the adsorptive technologies is intrinsically associated with the adsorbent selection. So, the characteristics of the adsorbent, such as selectivity, cost, and regeneration method – pressure swing, the temperature swing, purge gas stripping, or displacement desorption – must be taken into consideration, since those characteristics determine the size and then the cost of the adsorbent bed [1]. The adsorbent materials can be classified into three main categories: steric (molecular sieving by size exclusion), kinetic (differences in diffusion rates), or equilibrium (differences in adsorption capacity). The physicochemical properties of the adsorbent, as well as the adsorption characteristics of the components on that adsorbent, are the main aspects that determine the dominant mechanism for a given separation [2]. Zeolites are porous crystalline aluminosilicate extensively used in a wide range of industrial applications. The synthetic zeolites have become of great importance in the industry, especially as adsorbents, molecular sieves, and catalysts. Faujasite zeolites (zeolites X and Y) are among the most widely used zeolites in gas separation and purification processes. The difference between zeolite X and Y is based on the Si/Al ratio of the structure: for zeolite X, Si/Al ratio is between 1.0 and 1.5; for zeolite Y it is above 1.5 [3]. Zeolite X has a three-dimensional open framework

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consisting of AlO4 and SiO4 tetrahedra linked to each other by sharing oxygens. The framework is composed of linking sodalite cages through double six-rings (D6R), which create a large cavity called the supercage accessible by a three-dimensional 12-membered ring with a free diameter of approximately 7.4 Å. Small gases can also enter the sodalite cage via the six-membered ring window (S6R). Each unit cell contains 192 (Si/Al)O4 tetrahedra. The number of aluminum atoms per unit cell varies from 96 to 77, corresponding to a Si/Al ratio between 1 and 1.5. Exchangeable extra framework cations balance the negative charges on AlO4 tetrahedron. In commercial zeolite NaX (13X), the charge balancing cations are sodium ions. These cations are located in different sites in the structure. Adsorption and diffusion properties of the adsorbate molecules in the zeolite NaX depend on the interaction of the zeolite framework and the extra framework (Na+) cations with these molecules [2]. The most challenging industrial problems in separation in gas phase are light gases (e.g., H2, N2, O2, CO2, and CH4), light olefins and paraffins (e.g., ethane/ ethylene and propane/propylene), harmful gases (e.g., H2S, SO2, and CO), noble gases (e.g., Ar, He, and Xe), and vapors (e.g., solvents, xylene isomers, and hexane isomers) [4]. To explore the adsorption-based technologies and its possible application in the abovementioned challenging industrial separations, cost-effective options using zeolites were proposed by different studies. However, the first studies concerning this topic were focused on the adsorption mechanism and properties, such as adsorption equilibrium, adsorption kinetics, and the adsorption selectivity for the mixtures. Regarding the propane/propylene separation, there are many published studies focused on the selectivity of commercial zeolites such as zeolite 13X [5–10], zeolite 4A [5, 11–17], and zeolite 5A [11, 18]. Other materials such as zeolite 13X containing Li+, Na+, K+, Rb+, and Cs+ cations [19, 20]; CsY, RbY, and KY [21]; natural zeolites ERI including its K+ - or Ag+ - exchanged forms [22]; and binderless zeolite 13X [23] were also investigated. It is not the goal of this chapter to extensively present all the materials and separations studied; for that, the readers should refer to the existing reviews [24, 25]. In recent work, Narin and co-workers tested a binderless zeolite 13X that presented a higher adsorption capacity for propylene than for its respective homologous paraffin. Propane and propylene presented an adsorption capacity of 3.5 and 3.9 mol
kg1, respectively. The authors suggested that the higher adsorption capacity for propylene could be related to the π-bond interactions and dipole and quadrupole moments [23]. Also, many zeolites have been proposed in the literature for natural gas upgrading by carbon dioxide (CO2) removal, showing the stronger surface interactions with CO2, adsorbing more significant amounts of this component when compared to methane (CH4). These zeolites are equilibrium-based materials and were investigated by several authors [26–31]. Among the zeolites, natural chabazite, zeolite 13X, zeolite 4A, HMordenite, and a binderless zeolite 13X are the ones with more available information on the adsorption of CH4 and CO2 [28, 29, 32–38]. Palomino et al. have demonstrated that the polarity of a zeolite can be changed via its Si/Al ratio and has shown the effect on the adsorption of CO2/CH4 mixtures [39]. Reducing

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Table 1 Examples of pilot tests found in the literature Separation Ethane/ethylene Ethane/ethylene Propane/propylene Propane/propylene Propane/propylene Propane/propylene Propane/propylene Propane/propylene Propane/propylene CO2/CH4 CO2/CH4 CO2/CH4

Zeolite Binderless zeolite 13X Binderless zeolite 13X Binderless zeolite 13X Zeolite 13X Zeolite 13X Zeolite 13X (11% binder) Zeolite 4A (Rhône-Poulenc) Zeolite 4A Zeolite 13X Zeolite 13X Zeolite 13X (11% binder) Binderless zeolite 13X

Process SMB VPSA VPSA PSA VSA VPSA VSA PSA SMB VSA VSA Cryo-PTSA

Ref. [42] [23] [23] [43] [44] [10] [45] [46] [47] [48] [49] [29]

the Si/Al ratio, the polarity increases, improving the selectivity toward CO2. However, the regeneration step becomes more demanding because of the strong interaction between CO2 and zeolite [40]. The ideal CO2 adsorbent would be the one with an intermediate CO2 affinity combining a relatively high adsorption capacity with high selectivity and easy regeneration step [41]. An extensive review of CO2/CH4 separation is presented by Yu et al. [35]. Some of these zeolites have been combined with adsorption-based technologies, and highly promising results were achieved in the context of olefin/paraffin and CH4/ CO2 separations. In recent years, to seek more sustainable options, there has been a significant increase in the research activity on olefin/paraffin and CH4/CO2 separation using zeolites by pressure swing adsorption (PSA) [23, 43–45, 48, 50] and simulated moving bed in gas phase (gas-SMB) [42, 47, 51] technologies. In some cases, the process design and optimization to the industrial scale were also proposed [29, 52]. A few examples can be found in the literature of tests at the pilot scale and process design. In Table 1 are presented some of the examples available in the literature, the separation of ethane/ethylene using binderless zeolite 13X by VPSA [23], and SMB [42]; the separation of propane/propylene mixture with zeolite 13X by SMB [47]; the propane/propylene separation on different samples of zeolite 13X by PSA [43], VSA [10, 44], and VPSA [23]; the propylene production using different samples of zeolite 4A by PSA [46] and by VSA [45]; and CO2 removal for CH4 upgrading on different zeolite 13X samples [29, 48, 49].

2 Adsorptive Gas-Phase Separation Processes The backbone of the petrochemical industry is ethylene, as it is the most essential raw material in terms of volume, number of derivatives, and sales value. Propylene is the second product in importance as a petrochemical feedstock. Butadiene is a basic

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petrochemical obtained as a co-product in ethylene plants and has more than 80% of its demand associated with the production of elastomers. Benzene is almost entirely used as a raw material in the production of other petrochemical products and is rarely used as a solvent because of its potential toxicity. One of the leaders of the basic petrochemicals, toluene, is fourth in the ranking in importance after ethylene, propylene, and benzene [53]. Recently, the appearance of new shale/natural gas (light feedstocks) resources has been changing the availability of the key chemical building blocks, increasing the tendency to use light feedstock (shale/natural gas) sources to the detriment of the heavier (crude oil) ones. Light feedstocks are mostly used to make ethylene, while heavy feedstocks produce propylene, butadiene, and benzene [54]. It is also expected that the production of methane will increase due to the exploitation of the new nonconventional natural gas (including shale gas) reserves. Indeed, the use of natural gas has been growing as an energy option and chemical raw material due to the exceptional combination of properties, price, and the guarantee of available reserves. Natural gas is a mixture of light hydrocarbons, especially methane that can reach concentrations above 80%, and various impurities in smaller quantities, usually N2 and CO2 [20]. Several factors can affect the composition of natural gas once the composition is determined by the field in which the gas is produced, the production process, conditioning, processing, and transportation [48]. To meet the specifications of commercial natural gas, the so-called pipeline quality methane, it is necessary to remove ethane, hydrocarbons of higher molecular weight, carbon dioxide, helium, and nitrogen. Typically the amount of N2 may not surpass 2–4%, while the amount of CO2 cannot exceed 2% [55]. Among the several separations required to achieve “pipeline quality methane,” CH4/CO2 is the most expensive one, once CO2 content in natural gas is usually higher than 25% [50]. CO2 is an inert gas that decreases the combustion power of natural gas, and also in the presence of moisture, CO2 forms carbonic acid that causes the pipeline and equipment damage. The main technologies employed to remove CO2 from natural gas are cryogenic distillation, solvent extraction by alkylamines, and adsorbent-based processes. Despite the high operating costs, cryogenic distillation and solvent extraction by alkylamines are appropriate technologies when H2S and other impurities need to be removed at the same time; when the required final product is liquefied natural gas (LNG); and when it is necessary to treat large quantities of gas. For intermediate quantities of gas, adsorptive-based technologies become economically more attractive [40]. The high operating and capital costs of the current commercially practiced separation processes have been the driving force to seek more sustainable options to reach the products with the purity desired. The high-energy demand has been the driving force to develop new technologies and materials which must be cheaper, yet environmentally friendly. In this field, the separation based on adsorption appears as a reliable substitute, to the currently available distillation processes in use. Pressure swing adsorption (PSA) [10, 23, 56] and simulated moving bed (SMB) [9, 42, 47, 57, 58] arose as potential adsorptive processes for the target separations. The concept of adsorption-based separation in the gas phase is relatively simple. To understand the basic idea, we first can consider a binary mixture, composed by

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components A and B, which component A is the more adsorbed species on a porous material (adsorbent). So, component B has less affinity with the adsorbent and consequently is the less adsorbed. We can also consider adsorption and regeneration as the main stages of this process. During the adsorption stage, the binary mixture (A + B) is fed, and a stream enriched in the less adsorbed component is produced by the contact with the solid in the packed column. In the stage of the adsorbent regeneration, the retained components are desorbed, so that the adsorbent can be reused. In this stage, the desorbed gases are enriched in the more strongly adsorbed components of the feed mixture [29, 30]. PSA technology was initially created to produce pure light products at high pressures. In Germany, at the beginning of the 1940s was patented the first concept of PSA, appearing later with the name “Sorbogen” for water and CO2 removal from the air [59, 60]. During the 1960s, more PSA patents were conceived with new approaches and apparatus to separate mixtures in the gas phase, and the first versions of the classical Skarstrom stripping-type cycle appeared [61, 62]. This classical process uses a simple four-step cycle, which begins with bed pressurization with feed, adsorption and production of the light product at high pressure, followed by the blowdown/depressurization, and finally the purge step at low pressure. Commercially, the Skarstrom-type cycles are usually used to produce light products with high purity and a moderate to low recovery. Recovery will be affected by the amount of light component required to purge the heavy component at the final stage of the cycle, as this heavy stream is normally treated as a waste stream. At the end of the 1960s, PSA technology was gaining industrial importance in hydrogen purification, air drying, and air enrichment with oxygen. Only in the last two decades, the PSA technology began to be exploited to the bulk separation of olefin/paraffin mixtures [59, 63]. Considerable work has been performed combining some of the zeolites reported above with the PSA technology for light olefin/paraffin separation [5, 10, 23, 43–46, 52, 64, 65]. In the literature, it was reported a five-step VPSA cycle scheme to obtain propylene with a purity of 98% on zeolite 13X pellets from 0.50propane/0.50propylene mixture. However, the proposed cycle showed a low recovery of about 19% [43, 44]. Campo and co-workers tested an enhanced zeolite 13X with an 11% binder content to produce propylene with high purity. A five-step VSA cycle was designed and experimentally performed obtaining a purity above 99.5%, a recovery of 85% and a productivity of 1.5 molC3H6
h1
kg1 [10]. Narin et al., in a recent study, suggested three different VPSA cycle schemes with five steps and using binderless zeolite 13X, to produce polymer-grade ethylene. In the cycle that showed better performance, high recovery of ethylene, over 96%, was obtained with an ethylene purity of 99.5% and productivity of 2.2 molC2H4
h1
kg1, after 13 cycles. Additionally, in the same work, two five-step VPSA cycles were also implemented to produce propylene from 0.25propane/0.75propylene, and for the best cycle, after 25 cycles, a propylene purity of 99.5% was reached with a productivity of 1.0 molC3H6
h1
kg1, with a moderate propylene recovery of 74.6% [23]. In another study, the zeolite 4A was used to obtain high-purity propylene (97% purity and 26% recovery) also from 0.50propane/0.50propylene but diluted in 50% of N2 by a VPSA cycle with five steps [45]. Grande and

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co-workers used zeolite 4A extrudates to reach propylene with high purity (99%) with a high recovery of 85% from a C3’s mixture by VPSA; the proposed cycle comprised five steps [46]. More recently, they proposed another five-step VPSA cycle on a commercial zeolite 4A to obtain also polymer-grade propylene with a recovery of 67% [52]. To enhance the recovery, they proposed a dual VPSA concept, although the high energy consumption remains an unsolved problem. Indeed, few studies were performed, targeting the reduction of energy consumption. Furthermore, propylene with 92% purity and 29% recovery was produced on another study with a four-bed/eight-step VPSA cycle on zeolite 5A [64]. A low energy consumption seven-step PVSA cycle was developed, based on zeolite 4A as adsorbent, to produce high-purity (>99.5 wt %), high-recovery (>99%) propylene, yet the initial feed mixture was already rich in propylene, i.e., 84.4/15.6 propylene/ propane (wt %) feed mixture [66]. Cavenati and co-workers tested a Skarstrom-type cycle to separate 0.60CH4/ 0.20CO2/0.20N2 using zeolite 13X in a single-column VSA-PSA unit, and the CO2 was removed to levels lower than 2% as required by fuel-grade methane, with a methane recovery of 80.3%. A five-step cycle was simulated, suggesting that 90% of methane can be recovered, improving the process [48]. Campo et al. proposed a VSA cycle for CH4 upgrading from contaminated natural gas. A fourstep VSA cycle was implemented using an improved zeolite 13X to separate a stream with a composition of 0.60CH4/0.20CO2/0.20N2. Methane was obtained with a high purity of 74%, a high recovery of 96%, and a productivity of 2.5 molCH4
h1
kg1 [49]. More recently, Moreira et al. designed and simulated a new industrial-scale pressure and temperature swing adsorption process at low temperature (Cryo-PTSA) to obtain almost pure methane and maximizing the methane recovery, using binderless zeolite 13X as adsorbent. A CH4 recovery of 90.7% and a product stream with 41.8 ppm of CO2 in methane were attained, with CH4 productivity of 100.1 mol kgads1 h1 [29]. It should be noted that, for CO2 removal in gas natural upgrading, some PSA-based technologies are already being implemented by major companies, as presented in Table 2. More recently, SMB appeared as a valuable choice within the cyclic adsorptionbased processes. SMB came from older technology, the true moving bed (TMB), where both fluid and solid phases contact continuously in countercurrent. The major constraint in the TMB technology is the displacement of the solid phase, which was solved in the SMB technology by simulating the motion of the solid. In this type of process, it is necessary to ensure the continuous countercurrent interaction between the stationary and the mobile phases. The permanent contact between the fluid and Table 2 PSA processes for CO2/CH4 separation, operating or/in patent claim Technology Xebec M3100 PSA CO2 sponge® Molecular gate™

Licensor Xebec IACX energy BASF

Adsorbent Metal-based Activated carbon Titanosilicate

Flow rate (Sm3/h) 50 nm). This occurrence is due to the overlapping of adsorption potentials of the micropore walls. Micropores play a more significant role in physisorption processes, while meso- and macropores act as transport systems [85]. Nitrogen (N2) adsorption at 77 K and argon adsorption at 87 K are standard characterization techniques, while carbon dioxide (CO2) adsorption at 273 K appears as a complementary technique to evaluate the narrow microporosity. So, usually, the microporosity of each adsorbent is assessed by physical sorption of N2 at 77 K and by physical sorption of CO2 at 273 K.

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Additionally, to assess the adsorbents’ macroporosity, the porosimetry by mercury (Hg) intrusion appears as an indispensable technique, once it provides the quantitative description of the porous structure of the solid [86]. An important criterion is whether the adsorbent will tolerate the operating conditions in the adsorber after it has been packed. The physical limitation of the adsorbent pellets results in dust formation, causing the increase of the pressure drop and, consequently, the substitution of the adsorbent. The possibility of this occurrence is readily assessed by crushing strength determination. The crushing strength is a function of the density, moisture content, and temperature [87]. In summary and as explained, before the application of the adsorption-based technology itself, the process begins with the selection of an adsorbent for a given separation. Usually, this choice is made through the literature review to select possible materials that best suit our requirements. After the first screening of existing materials, the final selection is made, taking into account the feasibility of shaping the material and producing it on a large scale. The characterization of the shaped material is then carried out, as well as the characterization of the whole system by measuring the adsorption equilibrium and dynamic data. Only at this stage, it is possible to choose the operating conditions and combine the material with the most favorable technology for a given separation. However, throughout this process, from the characterization to the measurements of dynamic and equilibrium data, it is necessary to understand the obtained experimental results and make appropriate conclusions. For this, it is essential to find the theoretical models that best fit the system under study, adjust the data, and interpret. These theoretical models are also crucial for the mathematical modeling of the process, to represent the system behavior as best as possible. For this reason, this choice is of the utmost importance, taking into account that it is from the mathematical modeling that it is possible to design, analyze, optimize, and upscale the process. Mathematical modeling for the design, analysis, and optimization of processes is becoming increasingly important in the industry since it offers several advantages over traditional design methodologies. In this field, the gPROMS (general PROcess Modelling System) software is a tool as an equation-based modeling language to describe and simulate complex systems of chemical engineering [88]. In this chapter, we will present the several steps necessary to prove the concept at pilot scale, since the equilibrium to the dynamic studies and modeling to the final optimization and industry-scale process design itself, through some examples available in the literature.

3.1

Pure Component Adsorption Equilibrium

The usual way to obtain adsorption equilibrium parameters, required for system modeling and characterization, involves first measuring adsorption equilibrium

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isotherms for the pure components and then the selection of a model that can best fit the data. When an adsorbate is contacted with the porous adsorbent, the molecules tend to flow from the gaseous phase to the pore surface of the adsorbent until the pressure (P) remains constant. At this stage, the system reached the equilibrium, and the adsorption capacity of the adsorbent (q) is determined for that same pressure. Graphs involving the q versus P can be obtained from experimental data. Then the relation q versus P can be expressed in a mathematical form. Several thermodynamic models have arisen to describe the adsorbent-adsorbate equilibrium. The simplest case is Henry’s law, whose equation describes linear behavior and should apply to any isotherm at low pressures (when the pressure tends to zero) [89]. The choice of the most suitable isotherm model to represent the adsorption equilibrium appears as a prerequisite for the adsorption process modeling since it affects the multicomponent behavior prediction of the overall process. From a wide range of existing models able to describe the equilibrium data, the single-site Langmuir and dual-site Langmuir models are often chosen to represent the behavior of the single-component adsorption equilibrium on the several materials. These models provide a simplified quantitative description of the surface framework and can be easily implemented for non-isothermal system modeling. These choices, in some cases, are supported on previous works for similar systems and whose modeling was successful considering these models. The single-site Langmuir (SSL) model is one of the most used for the representation of adsorption equilibrium. Therefore, the SSL model equation is used in many studies to describe the equilibrium adsorption data [89]: ΔH

q ¼ qsat

b0 e RT P ΔH 1 þ b0 e RT P

ð1Þ

where qsat is adsorption saturation capacity, b0 is the adsorbate/adsorbent interaction constant, ΔH represents the heat of adsorption, R is the ideal gas constant, and T is the temperature. This equation presupposes the existence of a well-defined and localized number of adsorption sites, all energetically equivalent, where only one molecule is adsorbed per site, without any interaction with molecules adsorbed at neighboring sites [89]. In some cases, the dual-site Langmuir (DSL) is more suitable to fit the singlecomponent adsorption equilibrium data, as this model takes into account the adsorbent surface heterogeneity with two different adsorption sites. For example, the DSL model has been used in previous works to describe the adsorption equilibrium isotherms of ethane, ethylene, propane, propylene, CH4, and CO2 over different zeolite 13X samples [9, 23, 47, 90]. In this model, two adsorption sites on the adsorbent surface are considered (A and B), wherein both sites follow a Langmuir behavior, as reflected by Eq. 2 [89]:

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q ¼ qA þ qB ¼ qA,sat

bA P bB P þ qB,sat 1 þ bA P 1 þ bB P

ð2Þ

where q is the adsorbed amount in equilibrium with the bulk gas at total pressure P and qA,sat and qB,sat are the saturation capacities of sites A and B, respectively. The affinity constants of the two sites are defined as bA and bB, respectively. Both affinity constants, bi, depend on temperature as defined in the van’t Hoff equation [89]: ΔH i

bi ¼ b0,i e RT

ð3Þ

where the affinity constant at infinite temperature is b0,i and –ΔHi is the heat of adsorption for each site i. The optimal fitting parameters that describe the adsorption equilibrium data are usually obtained by the minimization of the sum of squared residuals (objective function Fobj): F obj ¼

N  X

qexp  qmodel

2

ð4Þ

i¼1

where qmodel is the amount in the adsorbed phase predicted by the model and qexp is the adsorbed amount obtained experimentally. N is the total number of measurements performed. Another model extensively used to describe the equilibrium adsorption, of CO2, CH4, and C2/C3 components on zeolites, is the Toth model. This model is an empirical model that was developed to yield an enhanced fit when compared with the previous models. This empirical model has the correct Henry law-type behavior for low concentration and limits the saturation capacity for high concentration. The Toth equation was developed in 1971 and allowed a good description of many systems with sub-monolayer coverage [89]. The Toth isotherm equation is given by:  n1 ΔH b0 e RT P i q ¼ qsat 1  ΔH  1 þ b0 e RT P ni

ð5Þ

where ni are a temperature-dependent constant for component i. The parameter ni can be determined by: ni ¼ Ai þ Bi T

ð6Þ

and Ai and Bi are parameters relating to the thermal variation of the heterogeneity coefficient.

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Campo et al. measured the adsorption equilibrium isotherms on an improved zeolite 13X with 11% binder, provided by CECA, in the spheres form by a gravimetric method. Single-component adsorption equilibrium isotherms were determined experimentally at 308, 323, and 348 K, up to 500 kPa for methane and carbon dioxide over the zeolite 13X with 11% binder [49]. The experimental data were fitted by the SSL model for CH4, as this model described well the experimental data. The authors fitted the experimental data for CO2 using the Toth model due to the higher CO2 affinity on this zeolite and to describe adequately the equilibrium data taking into consideration the adsorbent surface heterogeneity [49]. Cavenati et al. obtained the adsorption equilibrium data over a classical zeolite 13X pellets, containing 20% binder for CO2 and CH4 at 298, 308, and 323 K up to 5,000 kPa [48, 91]. The equilibrium data were measured for pure gases also by a gravimetric method using a magnetic suspension microbalance (Rubotherm, Germany) operated as a closed system. The authors used two different models to fit the experimental results, and they conclude that the Toth equation can predict better this system when compared with the fitting provided with the DSL model. However, only small differences between the two models were observed for both components. The DSL model is a straightforward model, easy to implement, and describes several systems under study quite well, being therefore so attractive for the use in mathematical modeling [91]. Moreira and co-workers selected a binderless zeolite 13X beads to perform measurements over a wide range of temperatures and pressure, thus obtaining the adsorption equilibrium data for methane and carbon dioxide at 180, 220, 269, 323, 373, and 473 K, up to 8,000 kPa. The experimental data were regressed using the DSL model, and the experimental data were accurately described by this model [29]. Comparing the adsorption equilibrium isotherms determined in the three studies, it is seen that the binderless zeolite 13X presents a similar adsorption capacity with the improved zeolite 13X with an 11% binder content, as can be observed in Fig. 1. The two enhanced materials show for CO2 much higher adsorption capacity at

Fig. 1 Adsorption isotherm comparison at 323 K, on different zeolite 13X samples for (a) methane, and (b) carbon dioxide [29, 49, 91]

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Table 3 Equilibrium parameters for the adsorption of methane and carbon dioxide on different zeolite 13X materials Adsorbate Methane

Carbon dioxide

qA,sat/qB,sat (mol/kg) 9.842 5.30 3.47/23.6 9.842 7.06 5.78/1.16

bA,1/bB,1 (105 kPa1) 0.250 0.141 0.29/1.4 0.686 0.048 0.41/1.3

ΔHA/ΔHB (kJ/mol) 14.234 17.2 15.8/1.9 30.731 35.4 23.0/19.0

nA/nB 0.637 1 / 0.658 0.48 /

Ref. [91]a [49]b [29]c [91]a [49]a [29]c

a

Toth model Langmuir model c Dual-site Langmuir model b

500 kPa and 323 K, than the zeolite 13 X used by Cavenati et al. For methane, it can be seen that the adsorbed amounts obtained for the three samples are quite similar; however, both enhanced materials present slightly higher adsorption capacity, as can be seen in Fig. 1. In Table 3 are given the adsorption equilibrium parameter values for the adsorption of methane and carbon dioxide on zeolite 13X found in the literature. In order to study the propane/propylene separation, several authors performed the adsorption equilibrium isotherms in zeolite 13X samples with 20% binder, 11% binder, and binderless [5, 10, 23, 47]. Additionally, zeolite 4A was also extensively studied for this separation [5, 13, 14]. Exist on the literature several studies regarding the adsorption equilibrium isotherm measurements on the classical zeolite 13X, usually with 20% binder, with distinct shapings. In industry, the use of a material in the powder form is limited by the constant need of its regeneration, which is hampered by the size and packing of the bed [92] and by the high-pressure drop across the packed bed. The pressure drop is directly related to the particle size, determining the energy consumption of the supplying pumps and compressors [93–96]. Therefore, it is necessary to convert the adsorbent into a shaped form that allows a lower pressure drop [92]. The downside of shaping is the inevitable damage of the crystalline structure and other severe structure changes during the shaping process. Additionally, the crystalline material is diluted with a binder resulting in a decrease in the adsorption capacity and formation of a secondary (meso- and macroporous) pore system [97]. Da Silva et al. measured the single adsorption equilibrium isotherms for propane and propylene at 303, 323, 343, 373, 423, and 473 K up to 100 kPa on a zeolite 13X in the pellet form [5]. The authors selected the Toth model to fit the experimental data. The suggested model suited well the experimental isotherms at higher temperatures between 373 and 473 K. However, at 303 K more significant deviations can be observed, being more critical for propane isotherms at lower loadings. Propylene presents a higher adsorption capacity than propane, and the selectivity calculated gave an average value of around 10 in the temperature range studied by the authors.

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Table 4 Equilibrium parameters for the adsorption of propane and propylene on different zeolite 13X materials Adsorbate Propane

Propylene

a

qA,sat/qB,sat (mol/kg) 2.68 2.79/0.69 3.08 2.10/0.70 2.68 2.71/1.14 3.47 2.27/0.87

bA,1/bB,1 (105 kPa1) 0.035 7.77/0.133 0.102 2.41/1.35 0.035 15.2/0.0125 0.0158 0.788/1.86

ΔHA/ΔHB (kJ/mol) 43.00 35.32/38.00 33.6 39.12/33.14 51.00 39.82/50.20 49.4 48.59/33.16

nA/nB 0.580 / 0.778 / 0.608 / 0.497 /

Ref. [5]a [23]b [10]a [47]b [5]a [23]b [10]a [47]b

Toth model Dual-site Langmuir model

b

This material was posteriorly used to separate propane/propylene mixtures by PSA and VSA [43, 44]. Martins et al. investigated another zeolite 13X from CECA, but with a different shaping, in the extrudate form. The authors determined gravimetrically the adsorption equilibrium isotherms for propane and propylene. The experimental adsorption equilibrium isotherms were well represented by the DSL model. The comparison between these two materials is presented in Table 4 and Fig. 3. Campo and co-workers measured the single adsorption equilibrium isotherms on an enhanced zeolite 13X with an 11% binder content. The isotherms were obtained at 323, 373, and 423 K up to 500 kPa, and the Toth model was suggested to represent the experimental data [10]. This enhanced zeolite 13X presented a higher adsorption capacity for propylene of almost 30% when compared to the values reported by Da Silva et al. [5]. Narin et al. tested binderless zeolite 13X beads (Köstrolith® 13XBFK, Chemiewerk Bad Köstritz GmbH, Si/Al ¼ 1.18), as a potential adsorbent for separation of propane/propylene. The higher adsorption capacity was obtained for this zeolite when compared with other zeolite 13X containing any binder content. The main objective of the work developed by Narin was the production of polymergrade propylene (99.5% purity) from 0.28/0.72 propane/propylene mixture [23]. Since adsorption equilibrium data is essential for the process design, the adsorption isotherms of propane and propylene were measured at 323, 373, and 423 K up to 500 kPa by a gravimetric method. The DSL model was fitted to the adsorption equilibrium data, and the Clausius-Clapeyron equation was applied to calculate the isosteric heat of adsorption as a function of adsorbate loading. The DSL model makes physical sense for systems such as the adsorption of polar (or quadrupolar) molecules on a cationic zeolite, where the most favorable sites are those associated with the exchangeable cations (sodium ions in the case of 13X) and the less favorable sites correspond to adsorption elsewhere on the framework. Consistency of the model assumption with the structural information on the adsorbent was validated by the molecular simulations [98, 99]. This model has been previously applied to describe olefin and paraffin adsorption isotherms on NaX crystals

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Fig. 2 Adsorption equilibrium isotherms measured on binderless zeolite 13X beads for (a) propane and (b) propylene at 323, 373, and 423 K. Symbols represent the experimental points, and the solid lines represent DSL model fit to experimental points [23]

[100]. The adsorption equilibrium isotherms of propane and propylene on binderless zeolite 13X beads obtained at 323, 373, and 423 K are displayed in Fig. 2. Propylene in the four zeolite 13X samples has higher adsorption capacity when compared with propane and becomes weaker with the temperature increase. The higher adsorption capacity of the binderless zeolite 13X for propylene than for propane can be attributed to the higher dipole moment of propylene, although propylene has a larger diameter (4.6 Å) than that of propane (4.3 Å). Indeed, propylene has a significantly larger dipole moment than propane so that dipole-dipole interaction between propylene molecules could lead to higher adsorption uptake [101, 102]. For easy comparison, Table 4 and Fig. 3 present the adsorption equilibrium capacities of propane and propylene, on four different samples of zeolite 13X. In Fig. 3 are also illustrated the isotherms for the two C3 components, at 373 K for the four samples listed in Table 4, with the total saturation capacity corrected for the binder content. As expected, the binderless zeolite 13X shows the higher adsorption capacity for the two components in the study. The zeolite 13X shows a higher adsorption capacity when compared with the other sample in the pellet form, evaluated by Da Silva et al. in previous work [5]. As can be seen in Fig. 3, the zeolite 13X pellets evaluated by Da Silva et al. present lower capacity even when the correction for the binder content was performed, while for the other three samples, the isotherms practically overlap. All the isotherms become less abrupt at higher temperatures. The linear adsorption isotherm is desirable in PSA applications because it imparts the adsorbent with high working capacity. The statistical models are fundamental in the description of the phenomenon of adsorption, not only for the clarity of the formulation and potential for improvement but also for its particular predictive capacity of the multicomponent adsorption adsorbed amount.

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Fig. 3 Adsorption isotherm comparison at 373 K, on different zeolite 13X samples for (a) propane, (b) propane with total saturation capacity corrected for the binder content, (c) propylene, and (d) propylene with total saturation capacity corrected for the binder content

The design of industrial adsorption equipment requires the availability of reliable equilibrium data and theoretical models for accurate prediction of these data. Multicomponent adsorption equilibrium data is of the utmost importance in the modeling of adsorption separation processes, once these data reflect the competition between components for the different adsorption sites. The multicomponent adsorption equilibrium for the olefin/paraffin mixtures was estimated for in some studies by the extended SSL (ExSSL) model. The assumptions of the SSL model apply to the multicomponent mixtures of N components. The adsorbed amount of component i in the multicomponent mixture is given by [54]: qi ¼ qi,sat

bi Pi , i ¼ 1, 2 . . . N N P 1 þ ð bi P i Þ

ð7Þ

i¼1

where qi,sat is the adsorption saturation capacity for component i (i ¼ 1, 2 . . . N ), the parameter bi is the affinity constant of component i, and Pi is the partial pressure of component i.

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This model is a straightforward model, easy to implement, and describes several systems under study quite accurately, being therefore so interesting for the use in modeling. The extended DSL (ExDSL) model was used on other studies, to predict the multicomponent behavior. So, the ExDSL equation was chosen to model the multicomponent data, providing an explicit expression for the adsorbed amount of component i in the multicomponent mixture [89]: qi ¼

qi,A,sat bi,A Pi q bi,B Pi þ i,B,sat N N P P 1 þ ðbi,A Pi Þ 1 þ ðbi,B Pi Þ i¼1

ð8Þ

i¼1

where qi,A,sat and qi,B,sat are the adsorption saturation capacity of component i in sites A and B, respectively. The parameter bi,A and bi,B are the affinity constants of component i for each adsorption site. Pi is the partial pressure of component i. Its simplicity in the implementation for mathematical modeling makes this model very attractive and justifies its choice, despite the empirical nature of the model. The assumptions adopted for the ExDSL model, for mixtures of N components, are precisely the same that were applied to the DSL model.

3.2

Dynamic Adsorption Experiments

Given the importance of the data obtained in dynamic studies through fixed bed experiments, it is essential at this stage to establish the conditions to conduct these experiments at the pilot scale. The adsorption equilibrium isotherms provide information to help on the definition of the operating temperature and pressure to perform the dynamic adsorption experiments by breakthrough curve measurements. Based on the column dimension and adsorbent mass, a total feed flow rate can be established to perform the breakthrough curves and gather the maximum information for fixed bed model validation. Besides single-component breakthrough curves, carried out on the chosen adsorbent, multicomponent breakthrough curves, involving two or more components, were also performed. The objective is the analysis of the adsorption behavior of a mixture in a bed filled with a given component, which may be an inert gas or another component of the case study. Thus, it is essential to observe how the affinity of each component toward the solid is affected by the presence of the others, during the adsorption and desorption steps. Indeed, in the case of SMB experiments, they involve the use of a different component as desorbent, so it is essential to analyze the behavior of each component of the mixture against the desorbent and vice versa. The adsorption and desorption steps of the binary breakthrough curves were obtained by feeding each mixture against a regenerated bed with an inert gas. On the other hand, for the pseudo-ternary breakthrough curves, the adsorption and

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from. . . Table 5 Adsorbent physical properties and column details used in the fixed bed and posterior VSA-PSA experiments for CO2 removal from natural gas using zeolite 13X from CECA

Properties Column radius, m Column length, m Column porosity Column density, kg/m3 Density of column wall, kg/m3 Specific heat of the column wall, J/kg.K Extrudate radius (infinite cylinder), m Extrudate density, kg/m3 Extrudate porosity Extrudate tortuosity (estimated) Adsorbent specific heat, J/kg.K

163 Value 0.0105 0.83 0.33 756.46 8,238 500 0.8  103 1,130 0.54 2.2 920

desorption steps were determined to feed each mixture against a bed filled with a third component, which can be the desorbent. After collecting all the information, we proceed to the mathematical modeling for the design, analysis, and optimization of processes. Nowadays, mathematical modeling is becoming essential in the industry since it offers many advantages over traditional trial-and-error design methodologies. For the fixed bed, the way to determine the breakthrough curves is a crucial issue, because it provides the basic, yet essential information for the design of a column adsorption system. Cavenati and co-workers developed a study for CH4/CO2 separation by VSA-PSA using zeolite 13X as the selective adsorbent for CO2 and followed this methodology. Before the cycle design, they performed a set of fixed bed experiments to obtain the information for modeling. The physical properties of the adsorbent, together with the fixed bed column details used, are shown in Table 5. Citing the authors, they used the fixed bed runs for single- and multicomponent as “mathematical model verification” experiments, where it was possible to calculate the values of the mass and energy parameters to insert in the program for future use in PSA simulations. The authors measured single breakthrough curves for carbon dioxide against helium to check the CO2 capacity and diffusivity in the zeolite 13X extrudates at 320 kPa, 299 K, and using a flow rate of 2.07 SLPM (liters per minute under the standard conditions of 298 K and 1 atm). The authors also performed two different ternary breakthrough experiments at 299 K and 323 K by feeding a stream containing 0.60CH4/0.20CO2/0.20N2 against helium. In both tests, the CO2 mass front at the exit of the column was dispersed by the temperature changes inside the bed. In the run at 299 K, the ratio of the amounts adsorbed was qCH4/qCO2 ¼ 0.075 confirming the very high selectivity of the zeolite 13X for CO2 removal from a natural gas stream. In a very similar way, Campo et al. obtained single breakthrough curves for methane and carbon dioxide diluted in helium against a bed previously also regenerated with helium at 320 kPa and 323 K and feed flow rate of 1 SLPM on the zeolite 13X containing only 11% of binder. For multicomponent prediction, they also measured for the same temperature and pressure a ternary breakthrough curve

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Table 6 Bed dimensions and asdorbent properties

Properties Bed length, m Bed diameter, m Bed porosity Adsorbent Adsorbent shape Particle radius, m Particle density, kg/m3 Particle porosity

Table 7 Adsorbent characteristics

Properties Shape Particle diameter (dp) Apparent particle density (ρp) Solid density (ρs) Particle porosity (εp) Solid heat capacity (Cps) Average crystal diameter (dc)

Values 0.83 0.0215 0.5 Zeolite 13 Spheres 3.5  104 1,300 0.39

Values Beads 1.2–2.0 mm 1,117 kg∙m3 1,502 kg m3 0.26 920 J∙kg1∙K1 2.5 μm

by feeding a mixture comprising 0.60CH4/0.20CO2/0.20N2 on the same sample. The authors only proceeded to the design of a pilot-scale separation process, after the mathematical modeling based on the data obtained up to this point and adequately validated against the experimental data. The physical properties of the adsorbent, together with the fixed bed column details used, are shown in Table 6. The separation of light olefin from its homologue paraffin is particularly complex due to their similar physical properties, and as already mentioned, several authors explored the zeolite 13X to produce polymer-grade propylene grade by VPSA and SMB in gas phase. Focusing on the work performed by Narin et al., they used a binderless zeolite 13X sample in the bead form to conduct a several fixed bed experiments at 1.0 SLPM, 373 K, and 150 kPa feed conditions on a VPSA unit to obtain fundamental information on the adsorption dynamics for the process modelling. The bead diameter, the average crystal diameter, as well as other general characteristics of the adsorbent used are summarized in Table 7. With the column initially filled with helium, at 373 K, and pressure set at 150 kPa, the single-component breakthrough curves were performed by feeding the column with a stream of each adsorbate. Propane shows a retention time of 735 s; then the mass front exits the column, and propane is detected. As expected, propylene is the most adsorbed component, once it presents a breakthrough time of 965 s. The respective temperature histories reveal the adsorption exothermic; however, the heat released during the adsorption of propane is quite inferior when compared with the heat released during the adsorption of propylene. These results are in agreement with the heats of adsorption that authors calculated using the DSL model. The binary breakthrough curves were also measured by feeding a representative mixture of propane/propylene over a bed filled with helium at 373 K and

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150 kPa. For the multicomponent breakthrough curves, the bed was initially saturated with one of the components, and the feed was the other component (pseudobinary). It is essential to get this data in dynamic, not only to corroborate the equilibrium data but also collect bed information for further experiments by PSA/SMB. After all these analyses, it is possible to decide whether the material is competitive to be applied for the target separation by PSA or SMB technology.

3.3

Mathematical Modeling and Validation

Based on the mode of operation, the processes under study are classified as dynamic adsorption processes, since these are open processes where the adsorbate continuously passes through a column packed with an adsorbent. For the fixed bed, the way to determine the breakthrough curves is a crucial issue, because it provides essential information for the design of a column adsorption system. To predict the adsorption and desorption behavior of the single- and multicomponent breakthrough curves on selected material, proper software is used to implement the model assuming the necessary conditions [103]. For the cases discussed above using zeolite as an adsorbent for olefin/paraffin and CO2/CH4 separation, the model considered describes an adsorption bed, and it was developed from the material, momentum, and energy balances, by making appropriate assumptions regarding the specific characteristics of the system. Equation 9 to Eq. 33 represent the material, momentum, and energy balance equations, as well as the equations of the boundary and initial conditions employed in simulations. More details about the model and equations can be found in detail in the literature [103].

3.3.1

Material Balance

Gas Phase    ∂C g,i ∂y ∂ ∂  εDax Cg,T i  u0 C g,i  ε ∂z ∂t ∂z ∂z





ð9Þ

ð1  εÞapM kfi C g,i  C s,i ¼ 0 Cg,T ¼

n X

C g,i

ð10Þ

i¼1

C g,i ¼ yi Cg,T

ð11Þ

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Fluxe Equality at the Particle Surface    εp ΩDp,i  C s,i  C p,i ¼ apM k f Cg,i  Cs,i 2 Rp

ð12Þ

Solid Phase (Macropore)  ρp ∂qi ∂C p,i ΩDp,i  ¼ Cs,i  Cp,i  2 εp ∂t ∂t Rp Cp,T ¼

n X

ð13Þ ð14Þ

C p,i

i¼1

Solid Phase (Micropore)  ∂qi ΩDc,i   ¼ 2 qi  qi rc ∂t

ð15Þ

Boundary Conditions

z¼0

u0,inlet C g,T,inlet yi,inlet ¼ u0 C g,i  εDax ∂C g,i ¼0 ∂z

z¼L

3.3.2

∂C g,i ∂z

ð16Þ ð17Þ

Momentum Balance

2



1:75ð1  εÞρ ∂P 150μð1  εÞ u0 þ ju0 ju0 ¼ 2 3 ε3 d p ∂z ε dp

ð18Þ

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from. . .

P ¼ Cg,T RT g

167

ð19Þ

Boundary Conditions z¼0

u0,inlet Cg,T,inlet ¼ u0 Cg,T z¼L

3.3.3

P ¼ Pexit

ð20Þ ð21Þ

Energy Balance

Gas Phase   ∂T g ∂T g ∂Cg,T ∂ λ  εRT g   u0 C g,T C p ∂z ∂z ∂z ∂t   4h   ∂T g ¼0 ð1  εÞapM h f T g  T p  w T g  T w  εCg,T Cv Dw ∂t

ð22Þ

Solid Phase " ð1  εÞ εp ¼ ρb

n X i¼1

n X i¼1

Cp,i Cv,i þ ρp

n X i¼1

# ∂T p b ¼ qi Cv,ads,i þ ρp C ps ∂t

  ∂q ðΔH ads Þi i þ ð1  εÞapM h f T g  T p ∂t

ð23Þ

Column Wall

b p,w ρw C

  ∂T w ¼ αw hw T g  T w  αwl U ðT w  T 1 Þ ∂t

ð24Þ

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αw ¼ αwl ¼

Dw eð D w þ e Þ 2

ðDw þ eÞ ln



ð25Þ

Dw þ2e Dw



ð26Þ

Boundary Conditions

z¼0

u0,inlet Cg,T,inlet C p T inlet ¼ u0 C g,T C p T g  λ ∂T g ¼0 ∂z

z¼L

3.3.4

∂T g ∂z

ð27Þ ð28Þ

Initial Conditions

yi ¼ C p,i ¼ qi ¼ 0

for

i 6¼ inert

ð29Þ

yinert ¼ 1

ð30Þ

C p,inert ¼ Cg,T

ð31Þ

T g ¼ T p ¼ T w ¼ T inlet

ð32Þ

P ¼ Cg,T RT g

ð33Þ

Usually, the adsorbent particles are considered to be spherical and uniform in size, and the bed is packed homogeneously. For this type of system, only a mass gradient in the axial direction of the bed is considered (radial direction is negligible). So, an axially dispersed plug flow model type is usually assumed to describe the gas flow in the software selected; gPROMS and Aspen Adsorption packages are the most known ones. The equilibrium state of the adsorbed phase with the fluid phase is of paramount importance. However, the composition of the fluid phase may not be uniform throughout the adsorbent particle, due to the diffusion phenomena, which prevail in the pores of the solid material. Considering these phenomena, it was necessary to accurately describe the mass transfer within the particle, at the different levels: macroporous and microporous. The film model represents the external mass and heat transfer resistances, and to account for the bimodal pore size distribution of the

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shaped adsorbent (intracrystalline micropores and intercrystalline meso/ macropores), the internal mass transfer resistances were described by a double Linear Driving Force model. The choice of the isotherm model to represent the adsorption equilibrium appears as a prerequisite for the modeling of adsorption processes since it affects the multicomponent behavior of the overall process and, consequently, inadequate representation of the system. As already explained above, SSL and DSL were the leading models considered to fit the adsorption equilibrium data. Consequently, the ExSSL and ExDSL models were, respectively, introduced to describe the multicomponent adsorption equilibrium. In the momentum balance, only the axial velocity gradient was considered, while the radial gradient was neglected. The Ergun equation described well the relation between gas velocity and pressure drop along the length of the packed bed. This equation also represents the pressure drop dependency with the packing size, length of the bed, fluid viscosity, and fluid density. In all the cases mentioned, the authors assumed that the temperature of the gas and the solid varies, and so the energy balance to both was considered. That is the energy balance to the fluid phase and the energy balance to the adsorbent particle. Both equations allow the determination of the respective gas and solid temperatures. Finally, a third energy balance associated with the wall is usually taken into consideration, aiming at accounting for possible heat transfers between the wall and the environment. The temperature was considered uniform throughout the solid, and the heat transfer is usually assumed to be linear between the different phases. This energy balance has been established by conducting various approximations such as not taking into account the thermal conduction through the solid and neglecting the heat transfer between the solid phase and the wall. The column wall only interchanges energy with the gas phase and the surroundings. Additionally, no radial heat gradient was considered. The procedure used to calculate the transport parameters and general properties of the gases and their mixtures is well described by Da Silva et al. [103]. Briefly, the transport parameters such as molecular diffusivity and macropore diffusivity can be estimated by the Chapman-Enskog and Bosanquet equations, respectively [104]. The Wakao and Funazkri correlation can be used to determine the axial mass and heat dispersion coefficients [104]. The film heat transfer coefficient between the gas and the column wall can be estimated with the Wasch and Froment correlation [104]. The viscosity of pure components can be obtained by the method of Chung et al., and the viscosity of the gas mixtures can be calculated from the method of Wilke [105]. The thermal conductivity of the pure gases and gas mixture can be determined by the use of Eucken and Wassiljewa equations, respectively [104, 105]. Information extracted from single- and multicomponent dynamic studies is used to validate the adopted mathematical model and allows the evaluation of many different process schemes (VPSA or SMB) and different operating conditions for the separation by adsorption-based technologies. As an example, we will show some single- and multicomponent breakthrough experiments performed by Narin et al.

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Fig. 4 (a) Adsorption of propane over a bed initially full of helium at 373 K and 150 kPa; (b) desorption of previously adsorbed propane in flowing helium at 373 K and 150 kPa; gas temperature history along the (c) adsorption and (d) desorption at 0.20 m, 0.45 m, and 0.70 m from the bottom end of the column. Symbols represent experimental results and solid lines simulation results

with the respective simulation for the system propane/propylene using binderless zeolite 13X as an adsorbent in bead form [23]. In Fig. 4 and Fig. 5 are displayed the column outlet molar flow rates for the breakthrough curves of propane and propylene, as well as the gas and wall temperature histories at three different positions of the column recorded during the reported experiments. The model results, presented as lines in the same graphics, are in good agreement with those obtained experimentally for both C3 components. As can be seen, the simulation represents quite well the experimental molar flow rate at the column exit. The simulations also reproduce pretty well the experimental results for gas and wall temperature histories, which means that the heats of adsorption calculated from the equilibrium model also represent adequately the system. The analysis of the binary breakthrough curve regarding the 0.27propane/ 0.73propylene representative mixture, displayed in Fig. 6, concludes that the stronger adsorbed propylene displaces the propane. The results are in good agreement with the data collected from the adsorption equilibrium isotherms. In this experiment, the propane molar flow rate at the column exit exceeded the feed molar flow rate, which is due to its displacement by the stronger adsorbed species (propylene), resulting in a further increase of the molar flow rate at the column exit. The displacement of the propane by the propylene, and vice versa, is well predicted by

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Fig. 5 (a) Adsorption of propylene over a bed initially full of helium at 373 K and 150 kPa; (b) desorption of previously adsorbed propylene in flowing helium at 373 K and 150 kPa; gas temperature history along the (c) adsorption and (d) desorption at 0.20 m, 0.45 m, and 0.70 m from the bottom end of the column. Symbols represent experimental results and solid lines simulation results

the model (Fig. 7). This is rather pertinent for VPSA cycle design since it allows the accurate simulation of rinse and purge steps, which are counter-adsorption steps.

3.4

VPSA and SMB at Pilot Scale Using Zeolites

The steam cracking of naphtha produces light olefins, such as propylene and ethylene. Alternatively, they can be obtained by the steam cracking of ethane or as a by-product of fluid catalytic cracking of gas oils in refineries [106]. The two main processes achieve different propane/propylene mixtures; a mixture containing 50–60% of propylene is usually obtained by steam cracking, while the alternative process performed in the refineries produces a mixture containing 80–87% of propylene. None of these processes produces propylene pure enough that can be used directly in the polymer industry since it requires propylene with a purity grade above 99.5% [47]. This grade is needed for polymer production as the impurities lead to the formation of side products, which affect the properties of the final polymer [107]. To obtain the polymer-grade propylene, the separation of the propylene from the uncracked propane is required [108]. Nowadays, the propane/ propylene separation after the steam cracking process is commonly performed at

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Fig. 6 (a) Adsorption of a mixture 0.27/0.73 propane/propylene over a bed initially full of helium at 373 K and 150 kPa; (b) desorption of previously adsorbed mixture in flowing helium at 373 K and 150 kPa; gas temperature history along the (c) adsorption and (d) desorption at 0.20 m, 0.45 m, and 0.70 m from the bottom end of the column. Symbols represent experimental results and solid lines simulation results

cryogenic temperatures and high pressures in the C3-splitter distillation towers containing over 100 trays. A particularly efficient distillation column is required due to the close relative volatilities and molecular sizes of the two compounds presented in the mixture. These extreme operating conditions make this separation one of the most cost- and energy-intensive process in the petrochemical industry [109].

3.4.1

Propane/Propylene Separation by PSA Using Binderless Zeolite 13X

In a recent study, Narin et al. show an example with high potential to transform the propane/propylene separation to produce polymer-grade propylene less demanding. In that study, they suggested the combination of the VPSA technology with the enhanced binderless zeolite 13X [23]. In the PSA technology, the adsorption step is typically performed at a pressure above the atmospheric (PH). In the adsorbent regeneration, the retained components are desorbed by lowering the partial pressures (PL) inside the column. This process usually is performed without providing any external heat [1]. In the literature, numerous distinct nomenclatures are found to define the most varied concepts

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Fig. 7 (a) Adsorption of propylene over a bed initially full of propane at 373 K and 150 kPa; (b) desorption of previously adsorbed propylene in flowing propane at 373 K and 150 kPa; gas temperature history along the (c) adsorption and (d) desorption at 0.20 m, 0.45 m, and 0.70 m from the bottom end of the column. Symbols represent experimental results and solid lines simulation results

[63, 110, 111]. As mentioned above, the adsorption step in the PSA is carried out at a pressure above the atmospheric, and the regeneration is achieved at pressures near the atmospheric. In a vacuum swing adsorption (VSA) process, the adsorption step is achieved at pressures near the atmospheric, and the regeneration is achieved under vacuum. In the case of a VPSA process, both concepts – PSA and VSA – are combined. These processes are complex due to the multi-column design, where the adsorbent beds operate under a cyclic steady state, in a sequence of non-isothermal, nonisobaric, and non-steady-state steps. A series of steps are designed to follow a specific configuration that allows the improvement of product purity and recovery and the overall separation performance optimization. The modeling of cyclic adsorption processes such as VPSA includes the combination of the necessary steps in a sequence that forms the desired cycle. Each step corresponds to a dynamic process model with specific boundary and initial conditions, according to the characteristics of that step. It is important to note that the final state of a given step is set as the initial condition of the following step. The model equations were described previously, and the boundary conditions employed for the different steps of the VPSA cycle simulation are given by the equations detailed in the literature [112].

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The performance of the VPSA cycles can be evaluated according to product purity, product recovery, and adsorbent productivity which are defined as follows [112]: Heavy Compound Purity ð%Þ ¼   Rt R tblowdown C heavy u0 z¼0 dt þ 0purge C heavy u0 z¼0 dt 0   100 n R   R P t blowdown  dt þ tpurge C i u0  dt C u i 0 0 0 z¼0 z¼0

ð34Þ

i¼1

Light Compound Purity ð%Þ ¼    R tCocD Rt R tads  C light u0 z¼L dt þ 0rinse C light u0 z¼L dt 0 C light u0 z¼L dt þ 0  100  n R  P t ads  C u dt i 0 z¼L 0

ð35Þ

Heavy Compound Recovery ð%Þ ¼    Rt Rt R tblowdown C heavy u0 z¼0 dt þ 0purge C heavy u0 z¼0 dt  0rinse Cheavy u0 z¼0 dt 0  R tads  100  0 C heavy u0 z¼0 dt

ð36Þ

i¼1

Light Compound Recovery ð%Þ ¼    R tCocD Rt R tads  Clight u0 z¼L dt þ 0rinse C light u0 z¼L dt 0 C light u0 z¼L dt þ 0  þ R tads  ð37Þ 0 C light u0 z¼0 dt   R tpurge R tpress    C light u0 z¼L dt  0 Clight u0 z¼L dt  þ 0  100 R tads  0 C light u0 z¼0 dt Heavy Compound Productivity ðmol=kgadsorbent =hÞ ¼    Rt Rt R tblowdown C heavy u0 z¼0 dt þ 0purge C heavy u0 z¼0 dt  0rinse Cheavy u0 z¼0 dt 0 madsorbent  t cycle

ð38Þ

Light Compound Productivity ðmol=kgadsorbent =hÞ ¼    R tCocD Rt R tads  C light u0 z¼L dt þ 0rinse C light u0 z¼L dt 0 C light u0 z¼L dt þ 0 þ madsorbent  t cycle   R tpurge Rt  0 C light u0 z¼L dt  0press C light u0 z¼L dt þ madsorbent  t cycle ð39Þ The best VPSA cycle suggested by Narin et al. to obtain polymer-grade propylene from 0.28/0.72 propane/propylene mixture is illustrated in Fig. 8. The cycle was designed to start with pressurization of the bed with a pure propane stream countercurrently at a pressure above the atmospheric (PH) that, in this case, was 150 kPa. In the adsorption step, performed at 150 kPa (PH), a stream rich in propane was obtained, while the mixture containing 0.28/0.72 propane/propylene

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Fig. 8 Five-step VPSA cycle configuration proposed by Narin et al. for polymer-grade olefin from paraffin/olefin mixtures [23]

was being fed. Since propylene, the adsorbate with more affinity with the solid, was the desired component, a rinse step was implemented after the adsorption step. This step serves to increase the purity of the propylene by feeding co-currently pure propylene continuously to saturate the adsorber, while much of the propane still adsorbed to the bed is recovered at the end. At the end of the rinse step, the adsorber was mainly filled with propylene, and a blowdown step countercurrently was applied. The components retained in the solid were desorbed by lowering gas-phase partial pressures (PL ¼ 10 kPa) inside the column. At the end of this stage, a stream rich in propylene should be produced. The last step implemented was a purge, with a flow of pure propane operating in countercurrent. This step allowed the further desorption of the remaining propylene from the bed, preparing the adsorbent for the next cycle. Nevertheless, the cycle was performed in order to have a process operation under specific conditions and further validate the mathematical model. Due to experimental limitations, a pure propylene stream was used in the rinse step, and a pure propane stream was used in the purge and pressurization steps. The operating conditions used in the VPSA cycles are summarized in Table 8. The experimental and simulated temperature, pressure, and molar flow rate histories recorded during the VPSA experiment are shown in Fig. 9. The molar flow rates of propane and propylene at the column outlet at cyclic steady state are also shown in the same figure. It can be concluded that the model predicts well the system behavior observed experimentally using the parameters given in Table 9.

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Table 8 Experimental conditions of VPSA cycles Cycle scheme Pressurization Step countercurrent Adsorption Time, s 200 220 Pressure, 150 150 kPa Feed, 1.0 (C3H8) 1.0(0.28C3H8/ SLPM 0.72C3H6) Initial Filled with C3H8 at 150 kPa. state

Rinse 220 150

Blowdown countercurrent 500 10

1.0 (C3H6)

Purge 320 10 0.2 (C3H8)

For propane/propylene VPSA cycle, the simulation shows that the steady state will be reached only after 25 cycles. In this cycle proposed by Narin and co-workers, a propylene purity of 99.2% was achieved with a productivity of 1.1 molC3H6
h1
kg1, with a moderate propylene recovery of 85.2% (see Table 10). Both the experimental and simulation molar flow rate histories showed that the molar flow rate of propylene and propane was zero during the pressurization step since the exit of the column was kept closed during this step. When the column pressure reached the adsorption pressure, the backpressure regulator opens, and paraffin starts to elute from the column. At this point, the molar flow rate of the propylene at the column outlet was very low and increased slowly during this step since the propylene concentration front had not reached the column outlet. The propylene and propane molar flow rates increased at the beginning of the blowdown and declined gradually afterward. This abrupt increase in the molar flow rates results from the increase in velocity at the column outlet due to the desorption of propylene and propane. The purge step allows further desorption of the adsorbed propylene; thus the propylene molar flow rate increased at the beginning of this step again and decreased gradually afterward. No polymer-grade propylene was obtained with the cycle scheme proposed, but the collected experimental data was necessary for the VPSA complete model validation. On a VPSA industrial unit, part of the obtained products will be recycled to be used in the rinse, pressurization, and purge steps. Indeed, none of the cycles performed does not represent the real operating conditions that will be used in the industrial process; however, with the information gathered in this study, it is possible to design an industrial cycle with improved performance parameters.

3.4.2

Propane/Propylene Separation by SMB Using Zeolite 13X

Martins et al. proved the concept of the gas-phase SMB for propane/propylene separation to produce polymer-grade propylene. In that study, they suggested the combination of the SMB technology with a classical zeolite 13X [47]. The SMB is an efficient continuous process of separation by adsorption, and several new applications have been studied. The main objective is to obtain the desired products in the

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Fig. 9 VPSA cycle on binderless zeolite 13X beads: (a) pressure history during the experiment, gas temperature history at (b) 0.20 m and at (c) 0.45 m from the bottom end of the column; column wall temperature at (d) 0.70 m from the bottom end of the column; propane and propylene molar flow rates at the column exit: (e) during the whole VPSA experiment and (f) zoom to the last and 13th cycle performed. Symbols represent experimental results and lines the simulation results

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Table 9 Transport parameters

Parameter U, W
m2
K1 hw, W
m2
K1 hf, W
m2
K1 λ, W
m1
K1 kf, m
s-1 Dax, m2
s1 Cpw, J
kg1
K1 Cps, J
kg1
K1 DcA, m2
s1

Values 20 40 100 0.3 4  102 8  105 500 920 C2H6: 1.1  1010 C2H4: 5.4  1011 C3H8: 1.7  1011 C2H6: 4.3  1011 C2H4: 1.8  1011 C3H8: 1.9  1011

DcB, m2
s1

Table 10 Performance parameters of the VPSA cycles proposed for propylene production PuR, % C3H8 93.3

CSS 25 a

molC3 h

1

RecR, %

ProdRa

94.8

0.5

PuX,% C3H6 99.2

RecX, %

ProdXa

85.2

1.1

kg1 adsorbent

extract and raffinate streams, with the purities higher than the process specifications, making effective use of the total adsorptive capacity of the adsorbent and minimizing the consumption of desorbent. The SMB technology is also intended as a chromatographic separation with origins in the TMB approach. So, the underlying chromatographic assumptions are also applied to the SMB technology, namely, the existence of two mass separating agents – the adsorbent or stationary phase and the desorbent or the mobile phase. In both TMB and SMB, the mixture is eluted in the mobile phase, which transports the mixture through the stationary phase, in a countercurrent movement. The various components of the mixture travel at different velocities, due to the different affinities with the adsorbent, promoting their separation. In TMB, there is a real movement of the solid and fluid phases countercurrently, while in SMB, this movement is simulated using valves operating synchronously. In consequence, SMB is a continuous chromatographic process in which separation of the components takes place by countercurrent contacting of the two phases. SMB technology constitutes a rather complex separation process, which requires a deep understanding to make its use efficient. The significant advantages of countercurrent contact between the stationary (solid) and mobile (fluid) phases of the SMB technology are higher productivity, lower desorbent consumption, and an increase in separation performance. This allows obtaining high-purity products with high recovery, even if the stationary phase efficiency is low (selectivity close to unity). The SMB technology can be adapted for a specific separation, leading to a

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significant number of possible applications. As already mentioned, the countercurrent movement is promoted by sequentially switching the inlet and outlet valves of interconnected columns in the direction of the fluid flow, keeping the solid fixed. The inlets and outlets are switched one column downstream after each switching period, ts. Considering the closed-loop SMB approach and according to the position of the columns relative to the nodes, the process can be organized into four sections. The flow rates are different in each section, and each section plays a different role in the operation. A binary mixture consisting of a less retained and a more retained species, A and B, respectively, is considered. If one considers a 4-2-1-1 configuration, section I, between the desorbent inlet and the extract ports, consists then of four columns. Section II, between the extract outlet and the feed inlet, consists of two columns; and section III, between the feed inlet and the raffinate outlet, is comprised of one column. At last, section IV, between the raffinate outlet and the recycle port, is also constituted by one column. Section II and section III form the separation zone. Section I and section IV work as regeneration zones: the desorbent is used to regenerate the adsorbent by desorption of component B in section I, and component A must be adsorbed in section IV to regenerate the desorbent. Component B is recovered in the extract outlet stream, while component A is recovered in the raffinate outlet stream. The SMB is designed to operate under a cyclic steady state (CSS). The cyclic steady state is generally reached after a few cycles. There are two main approaches to model the SMB operations: the approach of the true moving bed (TMB) as an SMB equivalent and the direct approach of the SMB. The second strategy was considered to model and simulate the SMB process in all studies reported in the literature for the separation of light olefins/paraffins separation by SMB. This approach implies the inclusion of the switching of the input/output streams at the boundary conditions of each adsorption column. For the case study considered, the authors modeled the complex SMB unit considering the interconnected eight adsorption column models that correspond to the eight single SMB columns. To complete the SMB model, a manifold interconnection model was implemented between columns, also considering the cyclic switching operation. A third “model” was built to link all the individual parts, to represent the physical SMB unit, with the eight single adsorption columns and the eight manifolds. The desorbent node is referred to the desorbent inlet point, between zones IV and I; the node of the extract is the point of withdrawal of the extract stream between zones I and II; the feed node is the feed supply point of the unit between zones II and III; and the node of the raffinate is the node located between zones III and IV, through which the raffinate stream is withdrawn. The balances to the nodes used in the SMB simulation can be found in the literature [9]. The cyclic port switching is performed, to represent the switching of the eight single columns simultaneously. So, the feed, desorbent, raffinate, and extract streams are switched one column downstream after each switching time. It is important to mention that each column has a specific function according to the section in which it is positioned and the initial conditions for each column change after each switching time. From a mathematical point of view, the switching time is precisely the shifting of the initial conditions of every single column. Each simulation started with the

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same initial state as the experiments, that is, all the beds saturated with the desorbent at a given initial pressure [113]. The SMB process performance was evaluated according to Equations 40 until 47, where Q represents the flow rates in standard liters per minute (SLPM) and P and T are the standard pressure and temperature. R is the ideal gas constant and Mi is the molar mass in kg
mol1. yi is the average molar fraction of species i during one step, yi is the molar fraction of species i, and Vs is the adsorbent volume in m3. The subscripts X, R, F, and D correspond to extract, raffinate, feed, and desorbent, respectively. A and B superscripts correspond to the less retained species and the more retained species, respectively.

Desorbent Free Raffinate Purity (%)

yAR  100 þ yBR

ð40Þ

yBX  100 yAX þ yBX

ð41Þ

RecR ¼

yAR QR  100 yAF QF

ð42Þ

Re cX ¼

yBX QX  100 yBF QF

ð43Þ

PuR ¼

yAR

Desorbent Free Extract Purity (%)

PuX ¼

Raffinate Recovery (%)

Extract Recovery (%)

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Desorbent Consumption (m3STP,desorbent/kgA or B):

QD

DC R ¼  A

=



y QR P RT R

QD

DC X  B

=



y QX P RT X

ð44Þ

MA

ð45Þ

MB

Productivity (kgA or B/(h
m3adsorbent)  ProdR ¼  ProdX ¼

=

yA QR P RT R

VS =

yB QX P RT X



MA



ð46Þ

MB

ð47Þ

VS

Martins et al. validated the SMB model with experimental data obtained in several experiments; however, the best cycle was performed using zeolite 13X/iC4H10 as adsorbent/desorbent pair, a 4-2-2 configuration in open loop at 423 K, 150 kPa, and switching time of 80 s. The experimental conditions are presented in Table 11. The performance parameters obtained for the experiment are presented in Table 12. This experiment presents the best performance results in terms of propylene purity at the extract stream (PuX ¼ 99.9%), recovery, (RecX ¼ 99.5%), productivity Table 11 Experimental conditions of the SMB cycles performed using iC4H10 as desorbent, with a 4-2-2 column configuration at 423 K and 150 kPa Switching time, s 80

QF, SLPM 0.03 C3H8 (yC3H8 ¼ 0.19) 0.14 C3H6 (yC3H6 ¼ 0.81)

QD, SLPM 1.31

QX, SLPM 1.01

QR, SLPM 0.45

Table 12 Performance parameters of the SMB experiment Run Exp Sim

C3H6 PuX,% 99.9 100.0

a m3STP,iC4H10 kg1 C3 b molC3 h1 kg1 adsorbent

RecX,% 99.5 100.0

DCX,a 0.8 –

ProdX,b 2.9 2.5

C3H8 PuR,% 98.1 100.0

RecR,% 99.7 100.0

DCR,a 1.3 –

ProdR,b 0.7 0.5

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Fig. 10 Schematic representation of an SMB cycle with a 4-2-2 column configuration

(ProdX ¼ 2.9 molC3H6
h1
kg1), and desorbent consumption (DCX ¼ 0.8 m3STP, 1 iC4H10
kg C3H6) and also in terms of propane purity at the raffinate stream (PuR ¼ 98.1%), recovery, (RecR ¼ 99.7%), productivity (ProdR ¼ 0.7 molC3H8
h1
kg1), and desorbent consumption (DCR ¼ 1.3 m3STP, 1 iC4H10
kg C3H8). The desorbent consumption in these cases was always high since there is no desorbent recycle, i.e., during the experiments it is only used as fresh desorbent, as mentioned above. This is not a critical problem since the desorbent can be recovered in two downstream separation units, e.g., PSA units using MIL-100(Fe) as adsorbent [114] or two depropanizer columns. The high desorbent flow rate and the four columns allow almost complete adsorbent regeneration in section I, and, consequently, the extract purity and propylene recovery were improved. In section II the molar fraction of propane decreases, and a pure propylene stream (~99.9%) is obtained in the extract port. The feed stream is introduced in the node placed between section II and section III (see Fig. 10). It can be observed that the propylene concentration decreases in section III and that only propane moves forward to the raffinate stream at the end of section III. As explained previously, a mathematical model for the simulation of the SMB process was developed and validated against the experimental results obtained for zeolite 13X and in an open-loop 4-2-2 configuration. The experimental internal profile, as well as the extract and raffinate compositions, is illustrated in Fig. 11. It can be concluded that the model predicts well the system behavior observed experimentally using the parameters given in Table 13. The zone I of the internal profile at the middle of the switching half time (see Fig. 11a) shows that the adsorbent was almost clean. Figure 11b shows the extract composition as a function of the switching time, and it is possible to observe the presence of almost pure propylene. The raffinate stream (see Fig. 11c) shows a high propane purity without compromising the purity of the extract and other performance parameters. With this work, a significant breakthrough was attained in the propane/propylene separation field since, for the first time, this separation was performed experimentally, in the gas phase by SMB obtaining polymer-grade propylene with a high recovery. The next step is the optimization and the scale-up of this unit based on the model validated.

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183

Fig. 11 Experimental molar fraction profiles obtained by SMB, at cyclic steady state: (a) internal profile as a function of column number and sample collected at the middle of the switching time (26th cycle), (b) extract (18th cycle), and (c) raffinate (21st cycle) composition as a function of a switching time. Points correspond to experimental data and lines represent the simulation results

Table 13 Transport parameters

Parameter U, W
m2
K1 hw, W
m2
K1 hf, W
m2
K1 λ, W
m1
K1 kf, m
s1 Dax, m2
s1 Cpw, J
kg1
K1 Cps, J
kg1
K1 DcA, m2
s1

DcB, m2
s1

Values 20 40 128 0.3763 2  1002 7.95  1005 500 920 C3H8: 2.9  1011 C3H6: 9.6  1012 i-C4H10: 3.7  1012 C3H8: 5.9  1011 C3H6: 6.0  1011 i-C4H10: 1.5  1011

184

3.5 3.5.1

V. F. D. Martins et al.

Cryo-PTSA Scale-up Using Zeolites CH4/CO2 Separation by Cryo-PTSA Using Binderless Zeolite 13X

Pilot scale can be used to investigate new processes or improve the existing ones and to provide valuable data for large-scale production. At the pilot scale, it is possible to perform the first studies on the influence of several factors such as temperature, pressure, pH, and composition, among others. It is important also to gather detailed process data and to optimize the process to the fullest. As an example of the design and optimization of an industrial adsorptive-based process, we will describe the scaling up strategy, followed by Moreira et al. [29]. They started with choosing the adsorption column size and considered a diameter of 3.5 m based on the transport limitations. The length of the column was determined to take into consideration the limitations in the mass transfer and temperature, effective use of the adsorbent of 65%, and a bed porosity of 0.4. The authors obtained a column length of 6 m, after assuming an adsorption time of 8,500 s, a CO2 feed molar flow rate of 74.52 kmol/h, working capacity of 8.41 mol/kg, and an apparent adsorbent density of 1,200 kg m3. The mathematical model used to simulate the cycles is the same described above for PSA, where the equation of state used was the GERG-2008, instead of ideal gas law, to cover the range of temperature and pressure used in the study and to obtain the most realistic results. To obtain a purified CH4 stream with low CO2 content, a cycle with four steps was conceived. The cycle started with pressurization in co-current feeding the target mixture containing 99.1% CH4 and 0.9% CO2, followed by the blowdown in countercurrent and at last a purge step with a heated stream. During the pressurization and adsorption steps, an enriched CH4 stream with a CO2 content lower than 50 ppm was produced. In the blowdown step, where the system pressure decreased from 4,000 to 500 kPa countercurrently, part of the CO2 desorbed and exit the column. During the purge step, part of the product stream was fed countercurrently to the bed at 500 kPa and 473 K, allowing the additional desorption of CO2 cleaning the bed. The pressurization step had a duration time of 8,450 s, followed by the blowdown step with 300 s and purge/heating step of 8,150 s. To perform the Cryo-PTSA simulations, it was assumed that the column was initially filled with a gas mixture containing 50 ppm of CO2 in CH4 at 473 K and 500 kPa. Transport parameter values, as well as stream conditions necessary for modeling, are presented in Table 14. As can be possibly observed in the molar fraction history during the adsorption step, all CO2 fed to the column was adsorbed, resulting in a product stream with a CO2 amount below 50 ppm. During the blowdown and heating step, the column was regenerated, and it is possible to obtain a maximum CO2 amount of 14% during the heating step. The average CO2 amount produced in a CO2-enriched stream is around 8.8%. The simulation showed high recovery of CH4, approximately 90.7%, is possible to obtain a product stream with 41.8 ppm of CO2 in methane. Additionally, methane productivity of 100.1 mol kgads1 h1 was obtained.

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from. . . Table 14 Parameter values used in the Cryo-PTSA simulations

Values Feed conditions 4,000 Pin Tin 190 Fin 2,300 yin CH4 0.991 yin CO2 0.009 Purge conditions Pheat 500 Theat 473 Fheat 200 Blowdown conditions Pbld 500 Transport parameters at feed conditions Dax 5.94  103 Λ 2.04 kf 1.16  103 hf 340.1 hw 996.3

185 Units kPa K Mol/s kPa K Mol/s kPa m2/s J/s m K m/s J/s m2 K J/s m2 K

The methane recovery value of 90.7% achieved by Cryo-PTSA simulation is higher when compared with those obtained by the cryogenic distillation technology. Cryo-PTSA proved to be a superior technology when comparing the power consumption (2.2 MW) obtained with that of cryogenic distillation (22.3 MW). The authors concluded that it is advantageous to replace the third column of the cryogenic distillation process by a Cryo-PTSA, achieving higher methane recovery and lower energy consumption at the industrial scale.

4 Summary Two of the leading industrial separation processes are the light olefin/paraffin separation and pre-combustion CO2 capture. Therefore, this chapter focuses on the perspectives of scaling up the use of zeolites for these two separations, from laboratory-scale to industrial-scale process. In the literature, many studies suggested that PSA and SMB can be alternative options to the ethane/ethylene, propane/ propylene, and CO2/CH4 separations since promising results were achieved. Such proliferation of studies focused on these separations suggest that they have high potential to be the next to be scaled up from lab-scale tests to a real industrial process. In PSA and SMB, as in other adsorption processes, the adsorbent has an impact on the process performance. In this chapter, some studies using zeolite 13X with

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V. F. D. Martins et al.

different binder content and shapes were described to show the potential of these materials for the future implementation of adsorptive-based technology at an industrial scale for the separations mentioned. To achieve high performance, it is necessary to take into account the selectivity, working capacity, adsorption affinity, and regeneration capacity of the adsorbent. Since adsorption equilibrium data are essential for the process design, the majority of the studies start with its assessment. Toth, SSL, and DSL adsorption isotherm models and the most used for the two systems have reasonably described the equilibrium data. Therefore, ExSSL and ExDSL models are selected for the multicomponent equilibrium prediction, due to their easy implementation and reasonable performance on the prediction of the multicomponent equilibrium on the different adsorbents. Dynamic studies are also frequently presented on the selected adsorbents by measuring single- and multicomponent breakthrough curves and used to obtain important information, not only for the PSA and SMB cyclic pilot-scale tests but also for the future process modeling. In all the cases, the dynamic results corroborate the collected adsorption equilibrium data. A robust mathematical model describing the dynamic behavior of multicomponent adsorption in a fixed bed is essential for the laboratory-scale results, as well as to the posterior upscale to larger scales. The model must be composed of material, momentum, and energy balances. Finally, after the model validation, the developed model can be applied to the design PSA and SMB cycles for target separations. The model validation with experimental results, of the pilot-scale VPSA and SMB experiments, is of utmost importance since it allows the industrial design of a process, as well as its further optimization. In summary, both technologies proved to have a high potential for the three target separations. However, they still can be improved using different configurations and adsorbents, and in the SMB case, also different desorbents can be used in order to optimize the process. The desorbent is a third component used in the separation by SMB that should be easily separated from the components of the binary mixture, for the sake of the downstream separation effectiveness, necessary to recover the desorbent. However, the main issue remains to be answered: can any of these technologies be truly competitive to be implemented on an industrial scale? To answer this question, it is necessary to evaluate each process from the balance between energy consumption and process performance point of view and how all this is reflected in the process economy.

Nomenclature qi qi C p,i C p,T b ps C

Adsorbed concentration of component i in equilibrium with C p,i , mol kg1 Adsorbed concentration of component i in the micropores, mol kg1 Average concentration of component i in the macropores, mol mfluid3 Average total concentration in the macropores, mol mfluid3 Particle specific heat at constant pressure (per mass unit), J kgsolid1 K1

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from. . . b pw C yi ap M b0 bA bB bi bi,A bi,B C Cg,i Cg,T Cheavy Clight Cp Cs,i Cv Cv,ads,i Cv,i D Dax DC Dc,i Di Dj dp Dp,i Dw e Fobj hf hw kf i L Mi ms N P Pi Prod Pu q Q qA,sat qB,sat qexp qi,A,sat qi,B,sat qi,sat qmodel qsat Qst

187

Wall specific heat at constant pressure (per mass unit), J kgwall1 K1 Average molar fraction of species i, - - “Macro particle” specific area, mpart1 Adsorbate/adsorbent interaction constant, kPa1 Affinity constant of the site A, kPa1 Affinity constant of the site B, kPa1 Affinity constant of component i, kPa1 Affinity constants of component i for site A, kPa1 Affinity constants of component i for site B, kPa1 Column,  - Bulk phase concentration of component i, mol mfluid3 Total bulk phase concentration, mol mfluid3 Concentration of the component with more affinity, mol mfluid3 Concentration of component with less affinity, mol mfluid3 Gas mixture molar specific heat at constant pressure, J mol1 K1 Concentration of component i at the particle surface, mol mfluid3 Gas mixture molar specific heat at constant volume, J mol1 K1 Molar specific heat of component i in the adsorbed phase at constant volume, J mol1 K1 Molar specific heat of component i at constant volume, J mol1 K1 Diffusivity, m2
s1 Axial dispersion coefficient, mbed2 s1 Desorbent consumption, m3STP,desorbent/kg Crystal diffusivity of component i, mpart2 s1 Diffusivity of the component i, m2
s1 Diffusivity of the component j, m2
s1 Particle diameter, mpart Macropore diffusivity of component i, mpart2 s1 Bed diameter, mbed Wall thickness, mbed Objective function,  - Film heat transfer coefficient between the gas and particle, J s1 K1 mpart2 Film heat transfer coefficient between the gas and wall, J s1 K1 mwall2 Film mass transfer coefficient of component i, mfluid3 mbed2 s1 Bed length, mbed Molar mass of component i, kg
mol1 Activated adsorbent mass, kg Total number of measurements performed,  - Pressure, Pa Partial pressure of component i, Pa Productivity, kgA/B/(h
m3ads) Purity, % Adsorbed amount in equilibrium with the bulk gas, mol kg1 Flow rate, SLPM Saturation capacity of site A, mol kg1 Saturation capacity of site B, mol kg1 The adsorbed amount obtained experimentally, mol kg1 Adsorption saturation capacity of component i in site A, mol kg1 Adsorption saturation capacity of component i in site B, mol kg1 Adsorption saturation capacity for component i, mol kg1 Amount in the adsorbed phase predicted by the model, mol kg1 Adsorption saturation capacity, mol kg1 Isosteric heat of adsorption, J mol1

188 R rc Rec Rp t T1 Tb Tc tcycle Tg Tp Tw U u0 Vs xi xj yi yj z Z Τ

V. F. D. Martins et al. Ideal gas constant, J mol1 K1 Crystal radius, mpart Recovery, % Particle radius, mpart Time, s Ambient temperature, K Boiling temperature, K Critical temperature, K Duration of one cycle, s Bulk phase temperature, K Solid temperature, K Wall temperature, K Overall heat transfer coefficient, J s1 K1 mwall2 Superficial velocity, mfluid3 mbed2 s1 Adsorbent volume, m3 Mole fraction of component i in the adsorbed phase at equilibrium, - - Mole fraction of component j in the adsorbed phase at equilibrium, - - Mole fraction of component i in the fluid phase at equilibrium, - - Mole fraction of component j in the fluid phase at equilibrium, - - Axial position, mbed Zone, - - Temperature, K

Symbol αw αw‘ ΔH ΔH0 Δqi Δqj ε εp λ μ ρ ρads ρg ρp ρw Ω

Ratio of the internal surface area to the volume of the column wall, mwall1 Ratio of the log mean surface to the volume of column wall, mwall1 Heat of adsorption, J mol1 Heat of adsorption at zero coverage, J mol1 Working capacity for components i, mol kgs1 Working capacity for components j, mol kgs1 Bed porosity, mfluid3 mbed3 Particle porosity, mpores3 mpart3 Heat axial dispersion coefficient, J s1 mbed1 K1 Bulk gas mixture viscosity, kgfluid mfluid1 s1 Bulk gas mixture density, kgfluid mfluid3 Density of the adsorbed phase, kggas m3 Adsorbate gas density, kggas m3 Particle density, kgsolid mpart3 Wall density, kgwall mwall3 LDF factor, - - -

Abbreviation CAGR CSS DSL

Compound annual growth rate Cyclic steady state Dual-site Langmuir

Perspectives of Scaling Up the Use of Zeolites for Selective Separations from. . . EDS ExDSL ExSSL FCC/DCC FCCU gPROMS IUPAC PSA SEM SLPM SMB SSL TMB UOP VPSA VSA XRD

Energy dispersive spectroscopy Extended dual-site Langmuir Extended single-site Langmuir Fluid/deep catalytic cracking Fluid catalytic cracking units General PROcess Modelling System International Union of Pure and Applied Chemistry Pressure swing adsorption Scanning electron microscope Standard liter per minute Simulated moving bed Single-site Langmuir True moving bed Universal Oil Products Inc. Vacuum pressure swing adsorption Vacuum swing adsorption X-ray powder pattern diffraction

Subscript 1 A ads ax B c c CocD D exp F g H i I II III IV j L model obj p p press R s sat STP T

Infinity Site A Adsorbed Axial Site B Critical Crystal Co-current depressurization Desorbent Experimental Feed Gas High Site i Zone I Zone II Zone III Zone IV Site j Low Model Objective Particle Pore Pressurization Raffinate Solid Saturation Standard temperature and pressure Total

189

190 w X

V. F. D. Martins et al. Wall Extract

Superscript A B i

More retained species Less retained species Component i

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Struct Bond (2020) 184: 195–226 https://doi.org/10.1007/430_2020_75 # Springer Nature Switzerland AG 2020 Published online: 11 October 2020

Industrial Zeolite Applications for Gas Adsorption and Separation Processes Javier Pérez-Pellitero and Gerhard D. Pirngruber

Contents 1 2 3 4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Is Expected from an Ideal Industrial Adsorbent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of Commercial Zeolites and Their Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main Industrial Separative Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Natural Gas and Air Drying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Oxygen Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Hydrogen Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 CO2 Separations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 n-Paraffin/i-Paraffin Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Xylene Isomers Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Other Industrial Separations Involving Zeolites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Industrial Zeolite Development and Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Tuning Selectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Balancing Capacity, Selectivity, and Regenerability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Balancing Capacity and Mass Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Ensuring the Continuous Regenerability and Thermochemical and Mechanical Properties of the Adsorbent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Perspectives for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

196 197 198 201 203 203 205 207 209 210 212 212 213 217 219 221 222 223

Abstract The industrial use of zeolites for adsorptive and separative applications at a mature level has been generalized in the last decades. Thanks to the advantages associated with the usual higher reversibility of the adsorption process, improved energy efficiencies can be obtained leading to a consolidated alternative to the traditional separation techniques. In a first part, the main industrial applications and the associated zeolite adsorbents are covered. Thus, the most relevant and critical properties of zeolites for a selected application are highlighted. Afterward, based on the mentioned properties, the development and improvement of adsorbents for industrial separations is addressed. A collection of selected examples is presented J. Pérez-Pellitero (*) and G. D. Pirngruber Catalysis, Biocatalysis and Separation Division, IFP Energies Nouvelles, Solaize, France e-mail: [email protected]

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to illustrate different theoretical and experimental approaches linking (in qualitatively or quantitative ways) the main properties of a given adsorbent and the industrial performances. The proposed methodologies are expected to help in the diversification of industrially available adsorbents by integrating the process perspective from the initial stages of the adsorbent development. Keywords Adsorption · Hydrogen · Industrial · Natural gas · Oxygen · PSA · Separation · SMB · VSA · Xylenes · Zeolites

1 Introduction Separation agents and technologies have focused the attention of the research community during the last decades. Although the progress realized in particular in the last decades is huge, only a limited amount of separation agents and techniques is nowadays industrially available. This fact reflects the need for well-targeted adsorbent selection criteria allowing to identify the candidates that maximize the probability of living up to the challenges of industrial operation. Indeed, besides a good separation factor, certainly crucial to implement a given separation, several other conditions must be fulfilled in order to meet the industrial operation criteria. These other criteria can extend from economical ones through thermal stability or those associated with the mechanical resistance of the material. Thus, the selection of a given adsorbent cannot be dissociated from the foreseen associated separation technique and vice versa. As a consequence, the need of covering the different selection criteria rapidly reduces the amount of available candidates and positions the family of zeolites as one of the most important group of materials. Zeolites present several advantages like high hydrothermal stability, limited disposal problems, and a low or moderated manufacturing cost. The mentioned advantages explain the leadership position of zeolites in the adsorbents consumption market. Among others, the advantages of zeolites make them well adapted to oil and gas refining, petrochemical, or air separation industries. While the mentioned longestablished industries continue growing at a maintained rate in Europe and the USA, the Asia Pacific region is expected to experience the highest growth rate. In parallel new applications associated with the irruption in the market of biomassbased processes or water treatment are potentially demanding of new zeolitic adsorbents. The total consumption of synthetic zeolites was estimated to be 1.817 million ton per year in 2013 [1], while three million of additional tons of natural zeolites are produced each year, 2/3 of which is mined in China and mostly used as a cement additive. The most important synthetic zeolites, considering the produced volume, are Linde Type A (LTA) and gismondine (Zeolite P, MAP), used in detergent applications. In monetary value, they are beaten by zeolite Y presenting the faujasite (FAU) topology, used in oil refinery catalysis.

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The demand for zeolitic materials is primarily covered by several companies from the USA, Europe, and Japan. Some of the major actors in the field of synthetic zeolites are among others UOP (Honeywell), ARKEMA, BASF, TOSOH, CLARIANT, or ZEOLYST. In the general context of adsorbent materials, some others like AXENS, CALGON, or CABOT must be added.

2 What Is Expected from an Ideal Industrial Adsorbent Independently of the chosen separation technology, when implementing a zeolite in an industrial application, a balance between several criteria must be found (Fig. 1). Although the selectivity of the chosen adsorbent for the targeted compound remains a mandatory property, this single feature alone is not enough to ensure an optimum separation process. In other words, as long as the selectivity is driven by enthalpic interactions between the adsorbent and the targeted molecule, the regeneration step becomes more difficult when the selectivity is increased. In a similar way, when exploiting the mass transfer improvements associated with the use of new synthetic routes such as the introduction of hierarchical porosity in the materials, the impact on the mechanical resistance of the material should not be ignored. Indeed, most of the applications of zeolites for gas adsorption and separation processes require the use of a huge number of adsorption/regeneration cycles. As a consequence, when screening or testing potential new candidates for the mentioned applications, the use of appropriate descriptors becomes essential. Specific parameters like the working capacity or the separation factor are directly linked to the performance of the adsorption processes. The working capacity is the amount that can be adsorbed in each adsorption-desorption cycle, i.e., the difference between the adsorbed amount after adsorption and desorption. The separation factor is the ratio of the working capacity of the target adsorbate and its competitor. The use of such descriptors

Fig. 1 Schematic representation of the main relevant properties of an adsorbent

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instead of the selectivity of the zeolite at a single condition makes much more efficient the development process of a new zeolitic adsorbent. In the following sections, first a description and classification of the commercially available zeolitic adsorbents and associated separation technologies will be provided. In a second part, the attention will be focused on a collection of selected examples devoted to illustrating different approaches available for implementation when tailoring a new zeolitic adsorbent. Based on the main properties of a given adsorbent, the proposed methods are intended to establish a link with the performances of a given industrial application.

3 Overview of Commercial Zeolites and Their Evolution Although the synthesis of 237 ordered as well as 11 additional partially disordered zeolite structure types have nowadays been registered by the International Zeolite Association [2], only a handful of zeolites are commercially available. A non-exhaustive list of them is provided in Table 1. When focusing on those employed for gas adsorption or separation applications, the different forms of faujasite (FAU) and Linde Type A (LTA) are the most employed. As can be seen, most of the commercially available and industrially found zeolites are generated by the modification of a very limited number of topologies.

Table 1 Non-exhaustive list of commercially available low silica zeolites Zeolite Faujasite (Na)X

Structural code FAU

Faujasite (Na)Y

FAU

Zeolite A

LTA

Zeolite A

LTA

Zeolite A

LTA

Commercial denomination 13X, Zeolum F-9, Sylosiv A10 LZY-54, HSZ-320NAA, CBV100 Siliporite NK30, 3A, Zeolum A-3, Sylosiv A3 Siliporite NK10, 4A, Zeolum A-4, Valfor G-100, Sylosiv A4 Siliporite NK20, 5A, Zeolum A-5

Cation Na

Window size [Å] 7.4

Pore sizea [Å] 11.2

Manufacturers/ suppliers Zeolyst, Arkema, Tosoh, GRACE

Na

7.4

11.2

Zeolyst, TOSOH, UOP

K

3

11.0

Na

4

11.0

Arkema, GRACE, TOSOH Arkema, GRACE, TOSOH

Ca

5

11.0

Arkema, GRACE, TOSOH

a Determined by Delaunay triangulation [3] making ideal assumptions (in absence of water and extraframework cations)

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Fig. 2 Schematic representation of the different molecules accessing a given zeolitic material

Three main axes of development can be identified: (1) modification of the Si/Al ratio, (2) cationic exchange [4], and (3) post-synthetic structure modification. The zeolite choice is evidently related to the nature of the industrial application. The use of the different forms of LTA zeolite is widespread in the domain of the separation of small molecules (Fig. 2) where very often a molecular sieving separation mechanism is contemplated. Besides, the different and diverse forms of the FAU topology find their application in a very wide range of industrial applications. Indeed, the combination of a medium-high capacity system of 3D cages connected by 12-R windows makes the series of faujasite materials very versatile. Although the two previously mentioned topologies are not the only ones industrially implemented, their very abundant applications make evident the vast materials phase space covered by the modification strategies previously mentioned. It is worthwhile mentioning that this fact is also a result of the industrial zeolite manufacturing context. As previously introduced, different features must converge in order to produce a satisfactory zeolitic adsorbent for a given application. Many times, at the end of the industrial chain, different specifications concerning the crystallite size, porous size distribution, mechanical resistance, or external surface area must be monitored and warranted. As a consequence, a full control of the mutual impact of the effects associated with the different operation units involved in the manufacture of zeolites is necessary. Contrary to the case of the huge number of adsorption studies, the number of studies referring either to the synthesis of zeolites or more in particular to the shaping associated operation units is lower (Fig. 3). In addition, the existing publications in the synthesis/shaping field are often found in

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Fig. 3 Comparison of the ratios of publication numbers associated with different fields for zeolites and MOFs. Adapted from [5]

the forms of patents. To summarize, the fact that the know-how of the full process of zeolite production is often in possession of the principal manufacturers intrinsically limits the industrially available zeolites to those whose synthetic process is sufficiently robust. In spite of the mentioned context, the large-scale production of new zeolitic materials is far from being considered as an unrealistic possibility. Although the existing literature is rich and diverse, there is still a need for new studies exploiting the different materials available by the existent modification strategies or highlighting the benefits of a given new topology for an associated application. As will be discussed later and among other improvement axes, understanding the impact of the cationic exchange over the zeolite properties, exploring new exchange ratios to identify the presence of potential cooperative effects, or identifying modified synthesis routes allowing to access nonexistent Si/Al ratios can have a strong impact in order to overcome the existing manufacturing barriers. By means of such kind of improvements, low-moderate cost new zeolitic materials can be produced taking advantage of the cumulated years of experience in industrial production and synthesis. It is worthwhile mentioning here that apart from the acquired know-how, the intrinsic properties of zeolites make them ideal candidates for their use in large-scale industrial production. Zeolites are robust and thermostable due to the inorganicbased structures and are therefore useful for the usually industrial harsh conditions. At the same time, the simple hydrothermal synthesis routes employed for the most common industrial zeolites do not require costly templating agents. Furthermore, some zeolitic frameworks are natural existing materials. To advance in the development of more efficient adsorption processes, it seems crucial to integrate from the very beginning of the screening phase of the new materials, descriptors, and concepts associated with the industrial operation. In other words, bridging the gap between the academic work and the final application seems to be indispensable. As shown in Fig. 3 [5], when comparing the nature of the research associated with zeolites and their more recent organic counterparts metal organic frameworks (MOFs), the proportion between synthesis and applicative works is clearly unbalanced. Without prejudice to the large and high-quality existing literature about

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zeolite synthesis, most of the recent research is devoted to applications of zeolites, while less than 10% is dedicated to the investigation or improving of the zeolitic synthesis routes. Contrary, almost 40% of MOF’s research works deal with their associated synthesis, while the works dedicated to their applications are significantly lower than in the case of zeolites. In the last case, it is noteworthy to mention that very often, the studies dealing with the potential applications of MOFs in separation technology are focused on benchmarking their performances versus the actually implemented adsorbents (mainly zeolites but not only). The fact that MOFs present some intrinsic limitations in terms of cost and hydrothermal stability diminishes the success probability. Contrary to the case of the major zeolites (the more commercialized), the reactants involved in the synthesis of MOFs are significantly more expensive than the silica-alumina sources employed in zeolite manufacturing. Finally, only a limited number of MOFs offer the hydrothermal stability necessary to face industrial operation conditions. In order to correct for this bias, the selection of applications presenting associated high-added values seems crucial. The other way around, the experience accumulated in the tailoring of the new MOFs structures could be partially transposed to zeolites potentially leading to new breakthroughs for zeolites. Designing virtual framework structures based on a set of desired physical properties can be a future challenge for the family of zeolites.

4 Main Industrial Separative Applications After detergents (
70%) and catalysis (
15%), the use of synthetic zeolites for adsorption constitutes the third main application (
15%) in terms of production. Since the energetic cost of the unit operations associated with separation processes represents the main part of a given industrial application [6], the correct choice of separation techniques becomes central. Molecular sieving or adsorption-based separation techniques are placed among those presenting the lowest costs. This fact is mainly explained by the process reversibility leading to higher efficiencies than those associated with other conventional separation processes. Indeed, highly engineered distillation towers present overwhelming energy consumptions when compared to the thermodynamic limit [7]. Adsorption processes are then receiving increasingly more attention, and high surface area materials with high specificity for target molecules are continuously being developed. As a result, adsorption is applied in several industrial domains from refining and petrochemicals to fine chemistry. In spite of the mentioned advantages associated with adsorptive processes, the choice between conventional distillation and other more disruptive technologies such as liquid-liquid extraction, absorption, membrane separation, or adsorption is far from being evident [8]. Indeed, the debate is often driven by the assumptions established about the ratio between the costs of energy and capital. Similarly, the origin of the energy employed in alternative techniques (such as the pumping energy

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associated with membrane separations) can strongly impact the thermodynamic efficiency of the whole process. Such a situation can be illustrated by means of two consolidated applications of zeolites in adsorption processes. The choice of the first one (air separation) is directly driven by economic reasons (balance between capital and operational expenditures). In the case of the second one (xylene separation), the intrinsic difficulty of the separation reduces the problematic making evident the choice for adsorption. In the case of the market of air separation, the adsorption-based technologies are in competition with the more widespread cryogenic distillation technology. Since the operational expenditures are directly linked to the final product purity, when intermediate values of purity are required, adsorption becomes well suited. Nevertheless, when higher capacities are expected, the capital expenditures of adsorption-based technologies rise linearly when increasing the oxygen production implying an important penalty for their applications. As an extreme case, the separation of xylenes is entirely driven by the difficulty associated with separate compounds with very close boiling points ( VSA (265 kWh/ton) > VPSA (245 kWh/ton) [12]. In a PSA system, the inlet air is compressed to a given level by a compressor. The adsorption phase operates at the pressure imposed by the compressor, while desorption phase takes place at atmospheric pressure. The implemented zeolites present higher affinities for nitrogen thanks to the polarity of the system. During the adsorption phase, while nitrogen is adsorbed due to the presence of a moderate quadrupole moment, oxygen is recovered at the outlet of the adsorption vessel. For oxygen enrichment, the PSA adsorption phase takes generally place at a minimum pressure of 1.5 atm. Once the zeolite reaches nitrogen saturation, the vessel needs to be regenerated. This is done by decreasing the pressure of the tank back to atmospheric pressure, thus liberating the adsorbed nitrogen. Compared to cryogenic distillation, PSA systems are best suited for processes that do not require purities of oxygen higher than 95%. Although purities as high as 99.9% can be reached, considering purities beyond 99.5% leads to a very fast increase in the cost of the PSA device. When considering the production rates, adsorption-based devices (in their different forms) are best adapted to relatively low volumes of oxygen production (typically 20–100 tons/day). Due to the fact that the oxygen productivity is mainly governed by the bed size in the adsorption-based systems, the capital investments rapidly increase when high productivities are considered. A drawback of the oxygen adsorption concerns the global productivity decrease associated with the low usability of nitrogen as a by-product. Due to the intrinsic trade-off between purity and recovery, the nitrogen contains significant levels of oxygen.

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Vacuum swing adsorption (VSA) constitutes a variant of PSA technology where desorption phase takes place at subatmospheric pressure. Vacuum is obtained by means of a blower, and specific zeolite adsorbents are employed. The main advantages of VSA systems (compared to PSA) are lower specific separation energy consumptions and the simplification of the system obtained by the reduction of some system components. This fact results in compact design layouts and more automated systems that can be delivered as fully packaged pre-tested skids. Nevertheless, the capital investment costs are higher than those associated with PSA, and the choice between both methodologies is usually driven by the projected production amounts. As a general rule, VSA systems are preferred to PSA systems for oxygen productions beyond 20 tons/day. Starting from this value, the higher energetic efficiencies of VSA systems (competitive with those offered by cryogenic distillation) compensate the associated increase in capital investment. Hybrid systems called VPSA (vacuum pressure swing adsorption) are also available in the market.

4.3

Hydrogen Production

Pure hydrogen is primarily obtained by steam reforming of natural gas. In this process, high temperatures (>700 C) are used combined with medium pressures (20–30 bar) in order to catalytically obtain a gas mixture containing H2 (70–80%), CO2 (15–25%), CO (3–6%), and CH4 (1–3%) [13]. PSA is then used to separate hydrogen at molar purities higher than 99.99% from the impurities (CO2, CO, and CH4, among others, at traces level). In order to optimize H2 purity and recovery, the PSA schemes (Fig. 4) have been considerably sophisticated over the years [14– 16]. At least four to ten columns are used in parallel. Figure 5 shows an example illustrating the different steps of a typical H2 PSA cycle [17]. The key steps are I, II and V, VI. Steps I and II are the adsorption steps. The impurities are captured in the column and pure H2 is produced. After depressurization, the blowdown steps V and VI desorb the impurities CO2, CH4, and CO. The intermediate steps are pressure equalization steps, aimed at minimizing the energy consumption and maximizing H2 recovery, as well as keeping the top of column clean to assure an extremely high H2 purity. In contrast to what was said about oxygen production, where PSA processes were more adapted for moderate purity O2, PSA is the only purification process that can produce H2 of 99.999% purity. In order to adapt to the multicomponent nature of the feed, different adsorbent layers are used in series. In the first bed layer, desiccants (alumina or silica gel) are used in order to remove H2O (the synthesis gas is saturated with water). In a second layer, low polar adsorbents such as activated carbons are used to remove the second most polar constituent of the synthesis gas, i.e., CO2. Once the polar compounds are largely removed, the remaining impurities, i.e., CH4 and CO, are treated by means of zeolitic adsorbents (generally 5A or NaX). The stacking of adsorbents allows achieving the best compromise between high retention of the impurities (i.e., high H2 purity) and good regenerability for all the components

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Fig. 4 Typical configuration of a H2 PSA process

Fig. 5 Scheme illustrating the different steps found in a typical H2 PSA production cycle. AD stands for adsorption, PP for product pressurization, EqD for equalization down, PPG for provide purge gas, BD for blowdown, PG for purge, and EqU for equalization up

of the gas mixture. Using zeolites (A or X) throughout the adsorbent bed would, of course, allow adsorbing very selectively all the impurities, but the strong interaction of zeolites with H2O and CO2 would render regeneration via depressurization difficult. A very strong and selective adsorption is only needed for the components that would break through first at the column exit and pollute the H2 raffinate, i.e., CH4 and CO. Therefore, zeolites are only employed in the last adsorbent layer. This is again a good example of how the adsorbent properties have to be adapted to the requirements of the process.

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4.4

207

CO2 Separations

In the previous section we mentioned that zeolite adsorbents were not used for adsorption of CO2 in the context of H2 purification because the CO2 selectivity was not critical for the purity of H2. However, the context changes when we want to combine H2 production with CO2 capture from the synthesis gas. For the production of two pure products from the synthesis gas mixture, a two-stage PSA process is needed. Such two-stage PSA processes, which could desorb pure CO2 for utilization or storage, as well as produce pure H2, were already proposed in the 1980s [18– 20]. The first stage of such a PSA process must selectively separate CO2 from the other components of the synthesis gas mixture. For this purpose, zeolite adsorbents are more appropriate than the fairly unselective activated carbon materials [21]. The problem of selective CO2 adsorption at medium pressure also arises in the purification of natural gas or biogas. The objective in this application is to produce pure CH4, with CO2 contents below the legislative norms. Producing high purity CH4 is not difficult as such, but the challenge is to minimize the loss of CH4 in the waste CO2 stream, which is caused by co-adsorption of CH4 on the adsorbent material. As mentioned in the introduction, the descriptor that best describes the minimization of CH4 losses is the separation factor, i.e., the ratio of the working capacities of CO2 and CH4. On the other hand, the working capacity of CO2 alone will be strongly related to the bed size factor. We have demonstrated how the working capacity and the separation factor in CO2/CH4 separations can be nicely tuned via the content of extraframework cations in FAU or LTA zeolites (which in turn is related to the framework Si/Al ratio) [22]. CO2 being a quadrupolar molecule, which strongly interacts with the extraframework cations, its adsorption constant is exponentially related to the Na+ content of the zeolite framework, while that of CH4 is quite insensitive to extraframework cation content (Fig. 6). Theory allows us to calculate the value of the CO2 adsorption constant that will optimize working capacity and separation factor1 (Fig. 7), and we can, thus, deduce the optimal extraframework cation content. The predictions from theory were fully validated by experimental measurements of working capacity and selectivity. A method for experimentally screening working capacity and selectivity (the separation factor is the product of both) is described in [23]. We note, however, that PSA processes for selective CO2 separations from natural gas, biogas, or synthesis gas mixtures are in competition with other separation processes, i.e., CO2 absorption by solvents or membrane separations. Whether PSA or solvent absorption processes are preferred depends on scale and on the availability of thermal energy. Since most solvent processes rely on high temperature steam for regeneration, the availability of such a heat source on site may give absorption processes a cutting edge. 1 We will treat the reasons for the different optima of working capacity and separation factor in more detail in a separate section of this chapter.

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K*103 [molec/barÅ3]

100 CO2 CH4

10

1

0.1 0

5

10

15

Na wt%

Fig. 6 Adsorption constant of CO2 as a function of the Na content of FAU zeolites. Adapted from [22]

Relative working capacity

0.15

4.0

3.0 0.10

2.0

0.05 1.0

0.00

0.0

0.1

1

10

100

1000

KCO2X103 [molec/barÅ3] Fig. 7 Working capacity and separation factor for the CO2/CH4 separation, calculated from the Ruthven Statistical Model as a function of the adsorption constant of CO2. Adapted from [22]

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4.5

209

n-Paraffin/i-Paraffin Separation

The separation of the normal (linear) paraffins from the cuts containing their branched and cyclic counterparts found its application in different fields such as the upgrading of gasoline (normal paraffins contribute to the decrease of the octane number), solvent production, or still the production of LAB (linear alkylbenzenes)based detergents. Different process variations are available to provide n-paraffin/i-paraffin separation. In the case of solvent production, PSA technology allows separating [24] cuts containing 20 to 40% n-paraffins either by means of vacuum countercurrent desorption or more frequently by means of a purge gas. The choice of the latter is done depending on the adsorption strength of the feed mixture. Thus, inert gases or short linear paraffins can be employed as desorbing agents. These are subsequently easily separated by distillation. When referring to the gasoline upgrading field, usually an integrated scheme between the isomerization and the n-paraffin/i-paraffin separation units is proposed. As an example the IPSORB (Fig. 8) and HEXORB processes allow removing the unconverted normal paraffins from the raw isomerate in the vapor phase via cyclic adsorption. In the IPSORB process, isopentane is used as desorbing agent and is then separated again by the deisopentanizer distillation column. n-Paraffin/i-paraffin separation processes are also available in liquid phase. Process such the MOLEX of UOP or ELUPAR commercialized by AXENS operate at significantly lower temperatures than their gas phase counterparts. This fact constitutes an advantage since it prevents the chemical degradation of the stream. The technology implemented in both cases is the simulated moving bed (SMB), which will be discussed in detail in the section devoted to xylene separation, as this is the main and original application of the mentioned technology.

Fig. 8 Schematic representation of the IPSORB isomerization/adsorption integrated process

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The separation of paraffins is operated with 5A zeolite, because it exhibits good normal/isoparaffins selectivity. The separation mechanism relies in the steric hindrance of isoparaffins and cyclic hydrocarbons to enter the zeolite porosity. Then normal paraffins are preferentially adsorbed. The operating temperatures are selected in order to find a good compromise between the prevention of condensation, chemical degradation, and mass transfer phenomena.

4.6

Xylene Isomers Separation

Paraxylene is the one of three isomers of xylene presenting the highest commercial interest due to its use as raw material in the polymerization of terephthalic acid. One of the polyesters obtained is polyethylene terephthalate (PET) which is widely used in the fabrication of plastic bottles or synthetic fibers for clothing, among others. The paraxylene market has experienced high growth rates over the years, and it is expected that this trend will continue in the following years driven by the Asian and Middle East regions. Mixed xylenes (a mixture from the different isomers) are generally obtained from catalytic reforming of naphtha and, to a lesser extent, from pyrolysis gasoline and toluene disproportionation. Associated with the specifications of the polymerization reaction, paraxylene must be obtained at high purity. In parallel, the market demand requires associated high yields and capacities. In this way, the separation of paraxylene from the rest of the isomers becomes the key operation of the aromatic complex. Typical values are 99.7% for the purity, 96% for the recovery, and 800,000 ton/year for the capacity (even more) [25]. Due to the difficulty of the xylene separation associated with the very close boiling temperatures of the different isomers, adsorption and crystallization technologies have been historically implemented. Although paraxylene exhibits a melting temperature significantly different from those of the other isomers, the presence of a eutectic mixture [26] avoids high recoveries of paraxylene at temperatures higher than 53 C. Consequently a considerable energy penalty must be paid in order to overcome the thermodynamic barrier. As a consequence, in the last years, adsorption has progressively been consolidated as the main technology in paraxylene separation. The use of cationic exchanged X or Y zeolites associated with high-level technology allows overcoming the limitation associated with the crystallization technique. In order to meet the market demands, liquid-phase countercurrent separation is implemented. The advantage of countercurrent configuration arises from the maximization of the mass transfer driving forces. In this way, the adsorbent potential is better exploited than in a cyclic process. The main difficulties of the technology lie in appropriately handling the circulation of both adsorbent and fluid phases. Regarding the first, considering a real circulation is almost unfeasible because of attrition problems (among others). Thus, as will be later introduced, a simulated circulation implemented by means of a valve manifold arrangement is systematically adopted. Concerning the more evident fluid circulation, the main challenge is minimizing the fluid dispersion in order to keep the theoretical plate number of the column intact.

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Fig. 9 Schematic representation of a typical SMB configuration

The configuration of a typical adsorption column operated by SMB is shown in Fig. 9. The adsorber is constituted by a series of chromatographic beds. The different beds can be grouped in four different zones which are delimitated by the presence of two different withdrawals (extract and raffinate) and injections (feed and desorbent). By means of this configuration, the continuous injection and collection of a feed containing A (the most strongly adsorbed compound) and B (the least strongly adsorbed) is achievable. In this way A and B are, respectively, recovered in the extract and the raffinate withdrawals diluted in the desorbent. Obtaining pure compounds is then straightforward by conventional distillation. As previously mentioned, the continuous circulation of the liquid is ensured by means of a recycle stream. The different zones delimitated according to their relative position with respect to the inlet and the outlet ports are: 1. Zone I between desorbent injection and extract withdrawal: desorption of the more strongly adsorbed component 2. Zone II between extract withdrawal and feed injection: desorption of the less adsorbed component

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3. Zone III between feed injection and raffinate withdrawal: adsorption of the more strongly adsorbed component 4. Zone IV between raffinate withdrawal and desorbent injection: adsorption of the less adsorbed component The countercurrent motion of the solid phase with respect to the liquid phase is achieved by synchronously advancing the injections and withdrawals of the system in the direction of the liquid flow. The two main technical solutions commercially implemented are the UOP rotary valve and the more recent system of multiple on-off valves commercialized by AXENS. The latter offers a high level of operation flexibility since it allows the modification of the chromatographic beds distribution associated with the different zones. As illustrated, in spite of its very high efficiency, the complexity of the SMB operation is often restrained to difficult separations involving highly valuable products. In addition to the complexity of this process, the choice of an adsorbent selective for paraxylene is the critical point.

4.7

Other Industrial Separations Involving Zeolites

Besides the previously introduced applications, other less central ones can be found. When referring to countercurrent liquid-phase separations, the SMB concept has been extended to a variety of similar isomeric separations comprising the separation of ethylbenzene, cymene, or cresol. The concept is also applied to other systems such sugars separation where the technology has evolved from the use of zeolitic adsorbents to ion-exchange resins [27]. In the past commercial processes involving X, Y, and L zeolites were available [28]. These processes initially applied in the field of the food industry are actually inspiring the development of separation applications processes in the context of the biorefinery. Finally, a variety of applications can be found for the purpose of purification. Among them, one can mention natural gas drying, sulfur compounds capture, or ethylene purification.

5 Industrial Zeolite Development and Improvement The following sections are devoted to the development and improvement of adsorbents for industrial separations. A collection of selected examples is devoted to illustrate different theoretical and experimental approaches linking (in qualitatively or quantitative ways) the main properties of a given adsorbent and the industrial performances.

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5.1

213

Tuning Selectivity

Although the industrial performance of a given zeolitic adsorbent is impacted by many different parameters (related to the nature of the zeolite but also to the operating conditions), selectivity is the one impacting in a strongest way the final performance. Therefore, the research for selective adsorbents has traditionally attracted the researchers’ attention. Since the choice of commercially available zeolite topologies is relatively small, the main degree of freedom is the modification of the extraframework cation distribution. As an example, in the field of air separation, different studies dealing with the cationic exchange of zeolites A and X are available. MgA zeolites [29] exchanged up to 75% presented significantly higher separation factors than that of the reference CaA. In the same manner, LiLSX an LiX zeolites show increased N2 adsorption capacities as the Si/Al ratio becomes closer to one [30]. Furthermore, as shown in Fig. 10, a significant threshold is observed when high site III occupancies are reached (beyond 80% Li+ exchange for X zeolite or 70% in the case of LiLSX). This fact is explained by the appearance of the less energetically favorable cationic site III after the complete filling of site II. The stronger energetic interaction between the nitrogen quadrupole and Li+ cations occupying site III results in a marked increase of the nitrogen loading. This situation is associated with the higher accessibility of this cationic site which contrasts with that observed for the site II cations. In this case, Li+ are less accessible since they almost integrated in the center of the hexagonal window. Rationalizing and improving the xylene separation process is a promising way to design innovative adsorbents for this application. For this purpose, we present herein a combinatorial approach allowing a rational mapping of exchanged faujasites for

Fig. 10 Dependence of N2 adsorption at 23 C and 1 atm on fractional Na+ exchange by Li+ in LSX. Data extracted from [31]

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Fig. 11 (a) Schematic representation of the library construction, (b) score plot showing the classification result

xylene separation. In total, a large, diverse library of 68 faujasites exchanged with mixtures of alkali cations (Na+, K+, Cs+) and alkali earth cations (Ca2+, Ba2+) was prepared and tested in the presence of three distinct mixture conditions of relevance to the separation process (Fig. 11a). From the measurements of the 68  3 corresponding breakthrough curves, the separation performances between the xylene isomers and the industrial desorbent were calculated. The set of performance properties generated for each zeolitic adsorbent was analyzed by statistical methods enabling sorting into different classes of selective adsorbents (Fig. 11b).

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The identified classes are described hereafter: • Class 1: adsorbents with a high adsorbed quantity of orthoxylene (oX) and then good oX selectivities, such as BaCaY. This class also includes the adsorbents that are best for separating paraxylene (pX) from ethylbenzene (EB) such as NaBaX (27–73) or NaBaX [49–51]. • Class 2: adsorbents with a high adsorbed quantity of EB and low para-selectivity toward EB. They are also characterized by low total adsorbed volumes. Most of these adsorbents are exchanged with Cs+. • Class 3: adsorbents with strong para-selectivity compared to all isomers. The classification of the affinity of the xylene isomers for these adsorbents is as follows: pX > EB > oX > mX. Regarding PDEB (p-diethylbenzene) which is the usual regeneration solvent for the process, these adsorbents are characterized by low pX/PDEB selectivities. This is a class of high practical value, because these materials adsorb the para-xylene preferentially compared to the other isomers in the feed, which can easily be desorbed by the PDEB in the regeneration step. They are mainly X and Y zeolites exchanged with mixtures of Ba2+ and K+ or mono-cationic forms of BaX or KY. • Class 4: adsorbents characterized by poor para-selectivity, good meta-selectivity, and a high adsorbed quantity of metaxylene (mX). These adsorbents are primarily exchanged with small cations such as Na+ and Ca2+. On the basis of the established experimental library, quantitative structureproperty relationship (QSPR) approaches can be implemented [32]. By means of a multi-linear predictive model, the separation properties are correlated with a set of structural descriptors of the zeolitic adsorbents. The selected descriptors (di) essentially characterize the nature of the confinement in the faujasite supercage, i.e., the size of the cations localized in adsorption sites II (d1), as well as the occupancy ratio of both adsorption sites II (d2) and III (d3). The implementation of such an approach makes it necessary to (1) set an appropriate design of experiments, (2) prepare an adsorbent database, and (3) test the adsorbent database for xylene separation. Based on the experimental measurements, a multiple linear regression model enables (among others) the prediction of para-/meta-xylene selectivity. A parity plot between the experimental and the predicted data is shown in Fig. 12. Based on the identified descriptors, an adsorption mechanism (illustrated in Fig. 13) can be proposed. At the high fillings corresponding to a liquid-phase separation, para-selectivity is generated by the occurrence of the 12-ring window site. This phenomenon takes place when all sites II in the supercage are occupied with an optimal average cation radius of 1.33 Å at the same time that the occupancy of sites III is minimized. We note that this mechanism very closely mimics the behavior of the BaLSX faujasite, which shows a para-selectivity value of about 4. In BaLSX faujasite, the average radius of SII cations is 1.35 Å, which is comparable to the optimal value. All sites II are occupied, and the occupancy of sites III is equal to zero [33].

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Fig. 12 Parity plot showing predicted px/mx selectivities against the respective experimental values

Fig. 13 Schematic representation of the pX adsorption mechanism

Although the provided examples are mainly related to the modification of the extraframework cationic distribution, it is worthwhile mentioning that similar effects could be obtained by employing other axes of modification, like the change of topology.

Industrial Zeolite Applications for Gas Adsorption and Separation Processes

5.2

217

Balancing Capacity, Selectivity, and Regenerability

Many academic papers dealing with the development of new adsorbents focus only on maximizing the adsorbate loading under adsorption conditions and then fully regenerate the adsorbent under unrealistic conditions, i.e., very high purge gas flows and/or heating times. However, what really counts in an adsorption process is not the absolute adsorption capacity (the amount that can be adsorbed on a virgin solid), but the so-called working capacity (or cyclic capacity or still delta loading), i.e., the amount that can be adsorbed repeatedly in hundreds or thousands of adsorptiondesorption cycles. In cyclic steady, the cyclic capacity, i.e., the amount, which can be adsorbed in each cycle, is equal to the amount that is desorbed in each cycle, knowing that desorption is subject to constraints: minimal use of purge gas to avoid dilution of the extract, limited temperature, and/or pressure change to minimize energy consumption. The regenerability of the adsorbent is, therefore, a key issue. If we want to discuss the adsorbent properties offering the best compromise between adsorption and desorption, it is advisable to distinguish pressure and temperature swing adsorption processes. Temperature swing adsorption is usually characterized by long cycle times, because heat transfer limitations generally do not allow a rapid heating or cooling of the adsorbent. Temperature swing adsorption is often used for the adsorption of trace components, i.e., for purification purposes. If the adsorption capacity is high and the concentration of the adsorbate in the feed very low, the adsorption time (i.e., the time before breakthrough) is also long. The long times needed for heating the adsorbent for desorption are, therefore, usually not an issue. In purification processes, the key objective is to achieve a high purity of the raffinate, i.e., a complete retention of the trace component even at very low concentrations. In that case, a very high affinity of the adsorbate with the adsorbent is favorable. This renders desorption difficult, but if the energy consumption for regeneration is not an issue, then maximizing the adsorbent-adsorbate interaction and the adsorption capacity is indeed the right strategy. This is the case, for instance, of siloxanes removal from biogas. As an extreme case, the purification technologies employing non-regenerable guard beds can be mentioned. Note that if we speak about adsorption capacity here, we refer to maximum adsorption capacity that can be reached when the solid is saturated (we will use the term saturation capacity in the following). Whether this capacity is reached depends on the process conditions (partial pressure, temperature). The critical parameters for other applications are not necessarily the same. In temperature swing processes for CO2 capture from flue gas, for example, the complete removal of CO2 is not at all critical, but minimizing the energy consumption necessary for regeneration is the key objective. This has a big impact on the adsorbent selection criteria. A high saturation capacity associated with conditions that allow approaching saturation is still important. If too little CO2 is adsorbed, heating up the solid for regeneration is not efficient. Concerning the interaction of CO2 with the sorbent, i.e., the heat of adsorption, a compromise between two factors

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Heat of adsorpon (kJ/mol) Fig. 14 Heat of regeneration and working capacity (relative to the maximal adsorption capacity) in a TSA process for CO2 capture from flue gas, as a function of the heat of adsorption of CO2. Adsorption temperature, 40 C; desorption temperature, 125 C

must be made [34]: a high heat of adsorption guarantees that saturation is reached under adsorption conditions and will also assure a very selective adsorption, but makes regeneration very difficult. There is a double penalty. First, it will be difficult to desorb a large amount, i.e., the working capacity is small, and second, the energy consumption needed for desorption is high. On the other extreme, if the heat of adsorption is weak, it becomes more difficult to approach saturation under adsorption conditions, i.e., the solid is not well exploited. Desorption is easy, but since little CO2 was adsorbed in the first place, energy is wasted for heating and cooling the solid while only desorbing a small amount of CO2. The best compromise is found for intermediate heats of adsorption. This is illustrated in Fig. 14. A similar trade-off between a strong adsorption and a high regenerability is found in pressure swing adsorption. As for a TSA, one generally aims for approaching the saturation capacity of the solid under adsorption conditions. For this purpose, one may play with the operating conditions, i.e., the pressure and temperature. However, in many cases, the range of operating conditions for adsorption and desorption is imposed by external factors (economic considerations, technical limitations, integration with upstream and downstream processes). That means that the adsorption properties of the solid have to be adapted to the process conditions. In PSA processes, we usually deal with physisorption, i.e., van der Waals forces and electrostatic interactions. Van der Waals forces depend on the pore size (pore curvature) of the adsorbent [35, 36]. Electrostatic forces depend on the electrostatic field (gradient), which is created by a non-centrosymmetric charge distribution in the solid [37]. An ideal solid would have a very high saturation capacity, be selective for adsorption of the desired component, but also easily regenerable. However, we cannot have it all at the same time. In physisorption the saturation capacity is determined by the pore volume. Large pore volumes mean high saturation capacities. But large pore volumes are linked to large pore sizes, i.e., weaker van der Waals interactions. For solids with large pore size, we have a high potential adsorption

Industrial Zeolite Applications for Gas Adsorption and Separation Processes Pore radius [Å] 0

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capacity, but interactions may be too weak to fill the available pore volume. This effect is less pronounced if electrostatic interactions are involved in adsorption. This is explained by the fact that the stronger and long-range electrostatic interactions can potentially cover even large pore sizes (Fig. 15). For the adsorption of CO2, for example, we showed that the best solution would be to use to use very large pore zeolites with high cation charges [38]. On top of the objective of efficiently exploiting the available pore volume, we have to find the best compromise between a too strong interaction, which renders desorption difficult, and a too weak interaction, which renders adsorption too weak. For a given set of adsorption and desorption conditions, it is possible to calculate the value of the adsorption constant that will offer the best trade-off, i.e., maximize the working capacity. However, working capacity is not the only criterion. We also need to adsorb selectively. This criterion is best expressed by the separation factor, which is the ratio of the working capacity of the desired adsorbate and its competitor(s). Interestingly, the adsorption constant which maximizes the separation factor is always higher than the adsorption constant that maximizes the working capacity (Fig. 7). That is because the maximization of the working capacity puts much emphasis on regenerability, while maximizing the separation factor puts the emphasis on selective adsorption, which is assured by a stronger interaction with the desired adsorbate. This also makes clear that high regenerability and high selectivity are opposing criteria, and it is impossible to optimize both of them at the same time.

5.3

Balancing Capacity and Mass Transfer

On the basis of a well-balanced zeolitic adsorbent from the point of view of the adsorption/regeneration couple, additional axes of improvement can be considered. The more evident one concerns the compromise between the adsorption capacity and the mass transfer. The productivity of a given separation process is directly

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proportional to both selectivity and working capacity and inversely to the access time (or the characteristic time of diffusion): P / α  W  τ1

ð1Þ

As it is widely known, the industrial implementation of a zeolitic adsorbent requires the use of adsorbent particles in the millimeter order of magnitude. Otherwise, the pressure drop generated in the bed limits the flow rates, particularly in the case of liquid-phase separations. Consequently, the adsorbent particles exhibit a bimodal porosity constituted by the microporous and the meso-/macroporous network. The system can then be regarded as a system of the respective diffusional resistances added to that generated by the external surface: R2part R2cryst Rpart 1 ¼ þ þ kK 3k f 15 εpart Dmacro 15KDμ

ð2Þ

being 1/kK the overall characteristic diffusion time, kf the mass transfer coefficient associated with the film in the external surface of the particle, εpart the particle void fraction, Rpart and Rcryst the respective particle an crystallite radii, Dmacro and Dμ the diffusion coefficients associated with both porous networks, and K the adsorption equilibrium constant. Since the mechanisms associated with the selective behavior of zeolites usually take place within the microporous network, a trade-off exists between the overall mass transfer and the amount of selective volume. According to the previous expression, optimizing the adsorbent in terms of mass transfer and capacity is a problem related to the identification of the appropriate characteristic diffusion lengths. Such an optimization is usually constrained by technical limitations associated both to the industrial manufacturing and operating conditions of the separation process. As a consequence, the know-how is usually in possession of process licensers and zeolite manufacturers. In the last years, the irruption of new synthesis routes allowing the introduction of additional porous networks in the adsorbent opens room for further optimization degrees [39–44]. The beneficial introduction of the new network is often presented from the point of view of a given application either in terms of activity increase (in catalytic applications) or diffusion speedup (in separative applications). Nevertheless, the involved phenomena are very often more complicated than the mentioned explanations and comprise the notions of connectivity or accessibility among others. In the particular case of hierarchical zeolites applied to separation processes, a better rationalization is necessary [45]. Contrary to many catalytic applications, where the amount and nature of the porous network have a lower influence, in the case of separation, the introduction of additional porosity often translates into an associated capacity decrease due to the reduction of selective volume. Besides, the impact of the operating conditions on the system behavior should not be minimized and accounted for in the adsorbent optimization. As it is well-known, relevant

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impacts of temperature and loading over both the diffusive and affinity properties of a zeolitic adsorbent can be observed. As a consequence, works rationalizing the impact of the use of the new available synthetic routes [46, 47] can significantly help improving the development of adsorbents and their associated industrial performances.

5.4

Ensuring the Continuous Regenerability and Thermochemical and Mechanical Properties of the Adsorbent

During the operating life, the adsorbent is exposed to tens of thousands of production/regeneration cycles. Either in the case of cyclic or countercurrent adsorption processes, the employed zeolitic materials are exposed to severe mechanical and/or thermal evolutions. In the case of PSA, both pressure and temperature (at a lesser extent) continuously evolve during the different cycle phases (pressurization, adsorption, blowdown, or purge). For TSA processes, the associated temperature operating range is larger than in the case of PSA. Finally, SMB operation is characterized by the use of significantly different linear velocities between the different adsorption zones. This fact usually results in important pressure shocks at some critical steps during the cycle. Although the selected employed technologies are chosen to minimize such effects, the adsorbent must present excellent mechanical and thermochemical properties. From the point of view of the mechanical resistance, the use of a binder during the agglomeration is the generalized technical solution. Nevertheless, the introduction of the mentioned binder results in a reduction of the volumetric adsorption capacity of the adsorbent. To palliate to this reduction, strategies like zeolitization allow an almost complete recovery of the original capacity [48, 49]. By means of the mentioned strategies, the mechanical resistance is considerably improved and values higher than 4 MPa can be obtained in the crushing strength test [50]. As a consequence, the production of fine particles by mechanical attrition is minimized and potential dramatic impacts during the operation avoided. Notwithstanding, the adoption of the before-mentioned or similar strategies is not always fully understood. Most of the related available information is found in the form of patents, and a lack of detailed studies exists [51, 52]. Indeed, the introduction of the binder as well as its ultimate transformation implies the modification of both the porous network and the surface nature. These impacts are not innocuous in the case of separations where the mass transfer in the meso-/ macroporous network governs the performance of the adsorbent. In an analogous way, separation driven by kinetic effects can be impacted if the pore mouth is modified at the moment of binder crystallization. Comparable effects are associated with the adsorbent shaping operations where the surface barrier phenomena can be altered.

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When referring to thermochemical properties, similar issues are addressed. Hence, any form of reactivity between the adsorbates and the zeolite may induce a fatal impact in the process performances. A good example to illustrate the problematic is the evolution in the last decades of the adsorbents employed in sugar separation [53]. In a similar manner, the use of MgA zeolite in oxygen production [54] constitutes an example of a stability issue not linked to the adsorbed phase. Due to the formation of MgO [55], high exchanged MgA zeolites are not commercially available owing to framework structure collapse upon calcination. Therefore, the beneficial impact of Mg2+ exchange for oxygen production cannot be exploited beyond 75% exchange. It is worthwhile mentioning that as long as the feed to separate becomes complex the stability problems may be accentuated. Then, in the framework of the separation units associated with the biorefinery, controlling the mentioned factors will be determinant.

6 Perspectives for Future Research Most of the optimization/improvement strategies illustrated in the previous sections rely on the modification of the extraframework cation distribution in order to modify the adsorption and separation behavior of zeolites for a given application. Indeed, by modifying the concentration and nature of the extraframework cations, both selectivity and capacities can be strongly modified. Thus, zeolites presenting good predisposition for such modifications are expected to present higher probabilities of finding final industrial applications. The analysis of the zeolite separation market reveals that among the “big five” zeolites [56, 57] (the more commercialized ones), those more extended in separation applications are FAU and LTA topologies. When asking ourselves what makes these topologies so suited for most separation processes, some of the answers confirm the impact of the extraframework cation distribution. Key factors are (1) an easy synthesis; (2) a high pore volume, i.e., a high potential adsorption capacity; (3) a good accessibility via their large pore apertures in the case of FAU or the possibility to tune accessibility via the cations located in the pore apertures in the case of LTA; and (4) a high extraframework cation content. Extraframework cations interact strongly with all polarizable molecules and are, therefore, often the key to achieve high selectivities. As a consequence, the vast structural diversity of zeolite is, therefore, not really exploited. This is certainly improvable since some exploratory work on MOFs has shown that very peculiar pore geometries can lead to unique separation properties [58, 59]. Among the 18 commercially available zeolites [60], few other zeolite topologies combine all of these assets. In particular, there is a lack of large pore volume, low Si/Al structures. Thus, concentrating the efforts of the synthesis research in topologies meeting the mentioned criteria seems a good strategy to maximize the success possibilities. Among others, we can mention a second potential axe of improvement related to the advancements in the integration of the separation process in the tailoring of the

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adsorbent and vice versa. Thus, adapting the adsorbents to the evolution of the adsorption technology seems essential to improve the efficiency of the processes. For example, the development of rapid PSA cycles [61] or the development of new adsorber designs can be mentioned [62]. In the same direction, the development of hybrid methodologies coupling adsorption with reaction or membrane separation opens room for the development of new zeolitic adsorbents allowing to serve new markets. We have also mentioned that the development of hierarchical zeolites is a booming research field. However, in many cases a more rigorous analysis of the relation between the hierarchization and the improvement (or not) of transport properties is still needed [63]. Moreover, hierarchization necessarily creates more non-micropore surface area. The chemical nature of this surface area [64] and its behavior with respect to adsorption is still poorly understood and merits further investigation. Last but not least, one can also observe that non-negligible efforts are necessary to transpose hierarchization protocols that were developed in academia to industrially viable production processes [65, 66].

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Index

A Adsorbate, 22, 34, 48, 59, 62, 164, 165, 197, 219, 222 Adsorbent, 2, 3, 16, 19–24, 34, 40, 48, 86, 97, 98, 108, 146, 221–223 Adsorption, 3, 7, 8, 12, 20, 34, 58, 62, 63, 68–69, 88, 91, 146, 147, 217, 219, 221 Adsorption solution theory, 71–73 Adsorptive gas-phase separation processes, 148–153 Aluminophosphates, 61–62 Aluminum, 60–61 Analysis conditions, 35

B Biobutanol, 87, 88, 90, 98, 102, 106, 107 Brunauer-Emmett-Teller (BET), 39, 40 Butane, 19–20

C Cages, 33 Carbon dioxide (CO2) adsorption, 5–6 Cavities, 33 Chabazite (CHA), 7, 89, 99, 100–102, 105, 106 Channels, 33 Clostridium sp., 87 Codes, 73 Co-diffusion, 103–105 Cryo-PTSA, 184–185 Crystal diversity, 103–105

D Desorbent free extract purity, 180 Desorption, 34 Downstream processes, 218 Dynamic adsorption experiments, 162–165

E Economic considerations, 218 Embedded isoreticular zeolites, 9–12 Energy dispersive spectroscopy (EDS), 153 Entropic effects, 100–103 Ethane, 16–17 Extended SSL (ExSSL) model, 161 Extract recovery, 180

F Ferroaluminosilicate levyne, 19 Force fields, 64–65 Framework structure, 99–100

G Gas adsorption analysis conditions, 35 choice, adsorptive gas, 36–39 sample pretreatment, 35–36 Gas-phase SMB chromatography (GC-SMB), 152 Germanium, 60–61

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H Heteroselective, 63 HEXORB, 209 Homoselective, 63 Hydrogen production, 205–206

adsorbates, 62–63 adsorbents, 59–62 extra framework cations, 63–64 Molecular dynamics (MD), 69–71 Monte Carlo methods, 66–69

I Ideal adsorbed solution theory (IAST), 71 Industrial zeolite applications adsorbent, 197–198 air drying, 203 CO2 separations, 207–208 commercial, 198–201 development balancing capacity, 217–219 mass transfer, 219–221 tuning selectivity, 213–216 hydrogen production, 205–206 mechanical properties, 221–222 natural gas, 203 n-paraffin/i-paraffin separation, 209–210 oxygen production, 203–205 thermochemical, 221–222 xylene isomers separation, 210–212 Integration, 218 Interference microscopy (IFM), 104 International Union for Pure and Applied Chemistry (IUPAC), 33 IPSORB, 209

N Nitrogen oxides (NOx), 22–23

K KFI-type zeolites, 8–9

O Oxygen production, 203–205

P Polyethylene terephthalate (PET), 210 Pore topology, 16 Pore volume, 45–47 Porous materials, 32, 34 Pressure swing adsorption (PSA), 148 Process aspects, 98 Propane, 17–19 Propane/propylene, binderless zeolite 13X, 172–176 Pulse field gradient (PFG), 97 Pure component adsorption equilibrium, 154–162

Q Quantitative structure property relationship (QSPR), 215

L Linde type A (LTA), 6–7, 196

R Raffinate recovery, 180 Regenerability, 217–219

M Mass transfer, 219–221 Mathematical modeling energy balance, 167–168 initial conditions, 168 material balance, 165–166 momentum balance, 166–167 Mesopore size distribution, 51 Metal organic frameworks (MOFs), 2, 4–6 Methane, 20 Micropore size distribution, 48–50 Mixture separation, 98 Models

S Sample pretreatment, 35–36 Scanning electron microscopy (SEM), 153 Simulated moving bed (SMB), 148, 149, 151–153, 162, 165, 171–172, 176–183, 203, 211, 212 Simulation methods molecular dynamics, 69–71 Monte Carlo, 66–69 Single-site Langmuir (SSL) model, 155 Small gases, 4 Small-pore zeolites adsorbents, 4–5

Index CO2 adsorption, 5–15 four-connected framework, 2 hydrocarbon separation butenes, 19–20 ethylene, 16–17 propylene, 17–19 methane, 20 nitrogen oxides, 22–23 three-dimensional, 2 water, 20–22 SMB zeolite 13X, propane/propylene, 176–183 Standard liters per minute (SLPM), 180

T Technical limitations, 218 Temperature swing adsorption (TSA), 3 Tetrapropylammonium (TPA), 95 Textural properties analysis pore size distribution, 47–51 pore volume, 45–47 specific surface area, 40–45 Transition state theory (TST), 71 True moving bed (TMB), 151, 178, 179 Tuning selectivity, 213–216

V Vacuum pressure swing adsorption (VPSA), 148, 150, 151, 164, 169, 171, 173–178, 186, 203, 204 Vacuum swing adsorption (VSA), 3, 4, 9, 19, 148, 151, 159, 163, 173, 203–205 Validation, 165–171

229 W Water, 20–21 Windows, 33

X X-ray diffraction (XRD), 153

Z Zeolite 13X, 41, 43, 147, 148, 150–152, 155, 157–161, 163, 172, 176, 177, 181, 185, 198 Zeolite adsorbents CHA, 105–107 equilibrium, 100–103 framework structure, 99–100 ITQ-29, 105–106 kinetics, 103–105 LTA, 106 MFI, 92–94 mixture separation, 98 process aspects, 98 pulse field gradient, 97 renewable alcohols, 86–90 silicalite-1, 90–92 water, 94–96 Zeolite Rho, 131–142 Zeotypes, 12–15 Zero length column (ZLC) technique CO2 diffusivities, 131–142 experiment, 123–126 theory, 127–130