Energy Technology 2021: Carbon Dioxide Management and Other Technologies (The Minerals, Metals & Materials Series) 3030652564, 9783030652562

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Energy Technology 2021 Carbon Dioxide Management and Other Technologies

EDITED BY

Alafara Abdullahi Baba Lei Zhang Donna P. Guillen Neale R. Neelameggham Hong Peng Yulin Zhong

The Minerals, Metals & Materials Series

Alafara Abdullahi Baba · Lei Zhang · Donna P. Guillen · Neale R. Neelameggham · Hong Peng · Yulin Zhong Editors

Energy Technology 2021 Carbon Dioxide Management and Other Technologies

Editors Alafara Abdullahi Baba University of Ilorin Ilorin, Nigeria

Lei Zhang University of Alaska Fairbanks Fairbanks, AK, USA

Donna P. Guillen Idaho National Laboratory Idaho Falls, ID, USA

Neale R. Neelameggham IND LLC South Jordan, UT, USA

Hong Peng University of Queensland Brisbane, QLD, Australia

Yulin Zhong Griffith University Gold Coast, QLD, Australia

ISSN 2367-1181 ISSN 2367-1696 (electronic) The Minerals, Metals & Materials Series ISBN 978-3-030-65256-2 ISBN 978-3-030-65257-9 (eBook) https://doi.org/10.1007/978-3-030-65257-9 © The Minerals, Metals & Materials Society 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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

This volume contains selected papers presented at the Energy Technologies and CO2 Management Symposium (sponsored by the TMS Extraction and Processing Division, Light Metals Division and Energy Committee), organized in conjunction with the TMS 2021 Virtual Annual Meeting & Exhibition. The papers in this volume intend to address the issues, intricacies, challenges, and development of new strategies as the reliance on fossil fuels for energy is unsustainable and has released an unprecedented amount of carbon dioxide into our atmosphere. The continual research and development effort into clean and sustainable energy technologies and efficient carbon dioxide management are of paramount importance to ensure the responsible progress of human civilization and innovations. The Energy Technologies and CO2 Management Symposium was open to participants from both industry and academia with a focus on energy-efficient technologies including innovative ore beneficiation, smelting technologies, recycling and waste heat recovery, as well as emerging novel energy technologies. The topics cover various technological aspects of sustainable energy ecosystems, processes that improve energy efficiency, reduce thermal emissions, and reduce carbon dioxide and other greenhouse emissions. Contributions from all areas of non-nuclear and non-traditional energy sources are discussed. Topics include, but are not limited to, renewable energy resources to reduce the consumption of traditional fossil fuels; emerging technologies for renewable energy harvesting, conversion, and storage; new concepts or devices for energy generation, conversion, and distribution; waste heat recovery and other industrial energyefficient technologies; energy education and energy regulation; scale-up, stability, and life cycle analysis of energy technologies, and improvement of existing energyintensive processes; theory and simulation in energy harvesting, conversion, and storage; design, operation, and optimization of processes for energy generation (e.g., carbon capture) and conversion of energy carriers; energy efficiency improvement in process engineering (e.g., for biomass conversion and improved combustion) and electrical engineering (e.g., for power conversion and developing smart grids); thermoelectric/electrolysis/photoelectrolysis/fundamentals of PV; emission control; CO2 capture and conversion, carbon sequestration techniques, CO2 and other greenhouse gas reduction metallurgy in ferrous (iron and steel making and forming), v

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Preface

non-ferrous and reactive metals including critical rare-earth metals; sustainability and life cycle assessment of energy systems; thermodynamics and modelling for sustainable metallurgical processes. We hope this volume will serve as a reference for materials scientists and engineers as well as metallurgists for exploring innovative energy technologies and novel energy materials processing. We would like to acknowledge the contributions from the authors of the papers in this volume, the effort of the reviewers involved with the manuscript review process, and the help received from the publisher.

Alafara Abdullahi Baba, FCSN, FMSN Lead Organizer

Energy Technology 2021 Editors Alafara Abdullahi Baba, University of Ilorin Lei Zhang, University of Alaska Fairbanks Donna P. Guillen, Idaho National Laboratory Neale R. Neelameggham, IND LLC Hong Peng, University of Queensland Yulin Zhong, Griffith University

Contents

Part I Application of Carbon-Based Oxygen Evolution Reaction Electrocatalyst in Zinc Electrowinning System . . . . . . . . . . . . . . . . . . . . . . . Jing Zhao, Yanfang Huang, Bingbing Liu, Guihong Han, and Shengpeng Su

3

Characteristic Studies of Critical Rare Earths Scandium and Yttrium from Circulating Fluidized Bed Coal Fly Ashes . . . . . . . . . . . Quang Tuan Lai, Thenepalli Thriveni, and Ji Whan Ahn

13

COVID-19 Impacts on Climate Change—Sustainable Technologies for Carbon Capture Storage and Utilization (CCUS) . . . . . . . . . . . . . . . . . . Quang Tuan Lai, Lulit Habte, Thenepalli Thriveni, Lee Seongho, and Ji Whan Ahn

23

CO2 Emission Calculation Model of Integrated Steel Works Based on Process Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li, Xinchuang Li, Weijian Tian, Zhe Chen, and Hao Bai

29

Experimental Study on Dust Removal Performance of Dynamic Wave Scrubber for Smelting Flue Gas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fang Dong, Yan Liu, Xiao-long Li, Gui-li Liu, and Ting-an Zhang

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Homogenization of the Dense Composite Membranes for Carbon Dioxide Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dragutin Nedeljkovic

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Hydrodynamics of Gas–Liquid Two-Phase Flow in the Reverse Spray Washing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao-long Li, Ting-an Zhang, Yan Liu, Gui-li Liu, and Fang Dong

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Influence of Coal Reactivity on Carbon Composite Briquette Reaction in Blast Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zi Yu, Tao Rong, and Huiqing Tang

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Contents

Part II Low Energy Mesoporous Silica Recovery from a Nigerian Kaolinite Ore for Industrial Value Additions . . . . . . . . . . . . . . . . . . . . . . . . . Alafara A. Baba, Abdullah S. Ibrahim, Dele P. Fapojuwo, Kuranga I. Ayinla, Daud T. Olaoluwa, Sadisu Girigisu, Mustapha A. Raji, Fausat T. Akanji, and Abdul G. F. Alabi Prediction Model of Converter Oxygen Consumption Based on Recursive Classification and Feature Selection . . . . . . . . . . . . . . . . . . . . . Zhang Liu, Zheng Zhong, Zhang Kaitian, Shen Xinyue, and Wang Yongzhou

87

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Reduction Behaviors of Hematite to Metallic Iron by Hydrogen at Low Temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Kun He, Zhong Zheng, Hongsheng Chen, and Weiping Hao Simulation and Optimization of Defluorination and Desulfurization Processes of Aluminum Electrolysis Flue Gas . . . . . . . . . . . . . . . . . . . . . . . . 123 Xueke Li, Yan Liu, Xiaolong Li, and Tingan Zhang Physical Simulation of Bubble Behaviors and Optimization of Converting Phosphogypsum into Ammonium Sulfate . . . . . . . . . . . . . . . 133 Bing-Wei Liu, Yan Liu, Shuai-Dong Mao, and Ting-an Zhang The Influence of Hydrogen Injection on the Reduction Process in the Lower Part of the Blast Furnace: A Thermodynamic Study . . . . . . 149 Zeji Tang, Zhong Zheng, Hongsheng Chen, and Kun He Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

About the Editors

Alafara Abdullahi Baba is a Professor of Analytical/Industrial and Materials Chemistry in the Faculty of Physical Sciences, University of Ilorin, Nigeria. He holds a Ph.D. in Chemistry from the University of Ilorin in 2008. His dissertation “Recovery of Zinc and Lead from Sphalerite, Galena and Waste Materials by Hydrometallurgical Treatments” was judged the best in the area of Physical Sciences at the University of Ilorin in 2010. Until his current appointment as Head of the Department of Industrial Chemistry in 2017, he was a Deputy Director––Central Research Laboratories, University of Ilorin (2014–2017). He is a fellow of the Chemical Society of Nigeria (CSN) and the Materials Science & Technology Society of Nigeria (MSN); is currently the Secretary of the Hydrometallurgy and Electrometallurgy Committee of the Extraction and Processing Division (EPD) of The Minerals, Metals & Materials Society (TMS); is a co-organizer of the Rare Metal Extraction & Processing Symposium and Energy Technologies & Carbon Dioxide Management Symposium at the TMS Annual Meeting and Exhibition; and is on the TMS Materials Characterization, Education, and EPD Awards committees. Prof. Baba has a keen interest in teaching, community services, and research covering solid minerals and materials processing through hydrometallurgical routes; reactions in solution and dissolution kinetic studies; and preparation of phyllosilicates, porous, and bioceramic materials for industrial value additions. He has more than 120 publications in nationally and internationally acclaimed journals of high impact, and has attended many national and international workshops, ix

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About the Editors

conferences, and research exhibitions to present his research breakthroughs. He is the recipient of several awards and honors including the 2015 MISRA AWARD of the Indian Institute of Mineral Engineers (IIME) for the best paper on Electro-/Hydro-Bio-Processing at the IIME International Seminar on Mineral Processing Technology––2014 held at Andhra University, Visakhapatnam, India; 2015 MTN Season of Surprise Prize as Best Lecturer in the University of Ilorin––Nigeria category; Award of Meritorious Service in recognition of immense contributions to the Development of the Central Research Laboratories, University of Ilorin, Nigeria (2014–2017); and 2018 Presidential Merit Award in Recognition of Passion, Outstanding and Selfless Service to the Materials Science and Technology Society of Nigeria. Lei Zhang is an Associate Professor in the Department of Mechanical Engineering at the University of Alaska Fairbanks (UAF). Prior to joining UAF, she worked as a postdoctoral associate in the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania. Dr. Zhang obtained her Ph.D. in Materials Science and Engineering from Michigan Technological University in 2011, and her M.S. and B.E. in Materials Science and Engineering from China University of Mining and Technology, Beijing, China, in 2008 and 2005, respectively. Her current research mainly focuses on the synthesis of metal-organic frameworks (MOFs) and MOF-based nanocomposites, and the manipulation of their properties and applications in gas storage, separation, and water treatment. She is also working on the development and characterization of anti-corrosion coatings on metallic alloys for aerospace and biomedical applications. Dr. Zhang has served on the TMS Energy Committee since 2014, including the Vice-Chair role in 2018–2019, and served on a Best Paper Award Subcommittee of the committee. She has served as a frequent organizer and session chair of TMS Annual Meeting symposia (2015– present). She was the recipient of the 2015 TMS Young Leaders Professional Development Award.

About the Editors

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Donna P. Guillen has over 35 years of research and engineering experience and has served as principal investigator/technical lead for numerous multidisciplinary projects encompassing waste heat recovery, combustion, heat exchangers, power conversion systems, nuclear reactor fuels and materials experiments, waste vitrification, and advanced manufacturing. Her core area of expertise is computational modeling of energy systems, materials, and thermal fluid systems. She is experienced with X-ray and neutron beamline experiments, computational methods, tools and software for data analysis, visualization, application development, machine learning and informatics, numerical simulation, and design optimization. As Principal Investigator/Technical Lead for the DOE Nuclear Science User Facility Program, she has engaged in irradiation testing of new materials and performed thermal analysis for nuclear reactor experiments. She actively mentors students, serves in a leadership capacity as well as routinely chairs and organizes technical meetings for professional societies, provides subject matter reviews for proposals and technical manuscripts, has published over 100 papers and received three Best Paper awards, authored numerous technical reports and journal articles, and has written/edited several books. Neale R. Neelameggham is “The Guru” at IND LLC, involved in international technology and management consulting in the field of metals and associated chemicals, thiometallurgy, energy technologies, soil biochemical reactor design, lithium-ion battery design, and agricultural uses of coal. He has more than 38 years of expertise in magnesium production and was involved in the process development of its startup company NL Magnesium to the present US Magnesium LLC, UT until 2011, during which he was instrumental in process development from the solar ponds to magnesium metal foundry. His expertise includes competitive magnesium processes worldwide and related trade cases. In 2016, Dr. Neelameggham and Brian Davis authored the ICE-JNME award-winning paper “TwentyFirst Century Global Anthropogenic Warming Convective Model.” He is presently developing Agricoal® to greening arid soils. He authored the ebook The Return of Manmade CO2 to Earth: Ecochemistry, published through Smashwords in November 2018.

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About the Editors

Dr. Neelameggham holds 16 patents and patent applications and has published several technical papers. He has served in the Magnesium Committee of the TMS Light Metals Division (LMD) since its inception in 2000, chaired in 2005, and since 2007 has been a permanent co-organizer for the Magnesium Technology Symposium. He has been a member of the Reactive Metals Committee, Recycling Committee, Titanium Committee, and Program Committee for LMD and LMD council. Dr. Neelameggham was the Inaugural Chair, when in 2008, LMD and the TMS Extraction and Processing Division (EPD) created the Energy Committee, and has been a Co-Editor of the Energy Technology Symposium through the present. He received the LMD Distinguished Service Award in 2010. As Chair of the Hydrometallurgy and Electrometallurgy Committee, he initiated the Rare Metal Technology Symposium in 2014 and was a co-organizer for it through 2021. He organized the 2018 TMS Symposium on Stored Renewable Energy in Coal. Hong Peng is currently the Advance Qld Industry Research Fellow (mid-career) after being an Amplify Fellow (2019–2020) and Advance Queensland Research Fellow (2016–2019) at the School of Chemical Engineering at the University of Queensland (UQ). He obtained a bachelor’s degree in Minerals Engineering and a master’s degree in Microbiology at Central South University, China followed by a Ph.D. in Chemical Engineering at UQ. Before joining UQ, Dr. Peng had experience as a chemical engineer in the industry working for the technology center and Olympic Dam at BHP Billiton between 2006 and 2009. He was the recipient of the 2020 TMS Young Leaders Professional Development Award. Dr. Peng’s research focuses on the fundamental aspects of mineral processing, interfacial colloid science, leaching kinetics, and precipitation reactions as well as molecular dynamics simulation. These projects are of interest to the nanobubbles, mine tailings, zeolite, clay minerals, base metals, and alumina refining industries that have attracted more than two million in funds from government and industry partners via research fellowship and patent license within 2 years.

About the Editors

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Yulin Zhong completed his B.Appl.Sc. (Hons) and Ph.D. in Chemistry at the National University of Singapore (NUS). He did his postdoctoral training at Princeton University (2009) and Massachusetts Institute of Technology (2011). After spending 3 years in the United States, he worked as a research scientist at the Institute of Bioengineering and Nanotechnology, A*STAR Singapore (2012) and as an ARC DECRA Fellow at Monash University (2013). In 2016, he accepted a Senior Lecturer position at Griffith University, was promoted to Associate Professor in 2020, and was awarded the ARC Future Fellowship in the same year. His research group interests include electrochemical production of 2D nanomaterials, 3D printing, smart windows, and wearable devices.

Part I

Application of Carbon-Based Oxygen Evolution Reaction Electrocatalyst in Zinc Electrowinning System Jing Zhao, Yanfang Huang, Bingbing Liu, Guihong Han, and Shengpeng Su

Abstract Electrode polarization potential is an important factor of cell voltage during electrowinning, which can affect the total energy consumption of the zinc hydrometallurgy process. In this work, we creatively use a metal-free oxygen evolution catalyst of water electrolysis to the hydrometallurgy system. An amino-rich hierarchical-network carbon(Amino-HNC) electrocatalyst was prepared by a simple two-step method of amino-assisted polymerization and carbonization process. The surface morphology, phase composition, and electrochemical properties were characterized. The results show that Amino-HNC is an electrocatalyst for oxygen evolution reaction with an amino-rich network structure, and the oxygen evolution overpotential in 0.5 mol L−1 H2 SO4 electrolyte is 389 mV (@ 10 mA cm−2 ). Meanwhile, the prefabricated catalyst is directly used as an anode in the zinc electrowinning system (50 g L−1 Zn2+ + 150 g L−1 H2 SO4 ) to measure the cell voltage. Compare with the traditional pure lead anode, the cell voltage decreased in a short time during the zinc electrowinning process. Keywords Oxygen evolution reaction · Electrocatalyst · Anode materials · Zinc electrowinning · Carbon-based material

Introduction Electrowinning is one of the most important unit operations in the zinc hydrometallurgy industry. The main flow of zinc electrowinning is through the direct current into the pre-treated electrolyte which contains zinc ions so that the zinc can be deposited on the cathode plate to obtain pure zinc [1]. This method consumes a large amount of electric energy [2]. Considering the background of the current energy shortage and the control of the total cost of actual production process, it is necessary to find a solution that can effectively reduce the energy consumption of zinc electrowinning process. J. Zhao · Y. Huang · B. Liu · G. Han (B) · S. Su School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou 450001, People’s Republic of China e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_1

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The essence of zinc electrowinning process is the reduction deposition of Zn2+ at the cathode and the evolution of oxygen at the anode in the electrolytic solution. The reaction formula of zinc electrowinning is shown below. The cathodic reaction is shown in Eq. (1), Zn2+ + 2e− → Zn↓

(1)

the anodic reaction is shown in Eqs. (2) and (3), 1 2OH− − 2e− → H2 O + O2 ↑ 2

(2)

or, H2 O − 2e− →

1 O2 ↑ +2H+ 2

(3)

the total reaction is shown in Eq. (4). 1 ZnSO4 + H2 O → Zn ↓ +H2 SO4 + O2 ↑ 2

(4)

Traditional industrial zinc electrowinning anodes are lead and lead-based alloy anodes [3]. When a current flow passes through the electrode surface, the lead on the anode surface is oxidized to form a dense oxide layer of β-PbO2 , and the subsequent reaction occurs on this active layer [4]. The overpotential of the lead anode in zinc electrowinning is about 1 V, accounting for ~1/3 of the cell voltage. It is urgent to develop new active anode materials to reduce the reaction potential of the anode. Since the anodic reaction is similar to the anodic reaction of water splitting, and there is many research of water splitting oxygen evolution reaction (OER) catalyst nowadays [5–11]. For this reason, we can introduce the OER catalyst to the metal electrowinning process to reduce the OER potential. In order to reduce the OER overpotential, some researchers have studied oxide electrocatalysts, Nobel-metal-based oxides such as IrO2 , RuO2 have been proved that have superior OER performance in both alkaline and acid conditions [5, 8, 9]. Due to the high cost of precious metal oxides, researchers turned eyesight to non-precious metal oxides to reduce the cost. Hu and Ahmed found out that transmission metal elements such as Fe, Co, Ni, Cu, and their oxide or phosphide show similar or better performance during OER process than precious metals [6, 7]. Many studies have shown that compared with expensive precious metal catalysts, non-metal carbon-based functional materials have the advantages of strong earth abundance and acid resistance, which can be considered as one of the potential alternatives for OER electrocatalysts [10, 11]. Amino-rich hierarchical-network carbon (Amino-HNC) is a reported oxygen evolution reaction catalyst with excellent OER catalytic performance in acidic media, which has a low OER overpotential about 281 mV (@ 10 mA cm−2 ) in 0.5 M H2 SO4 [12].

Application of Carbon-Based Oxygen Evolution Reaction …

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Consider its superior OER performance in acid solution, we introduce the catalyst to zinc electrowinning system. The Amino-HNC catalyst was prepared by a twostep method of electrodeposition and carbonization, use the catalyst as the anode of the zinc electrowinning system directly. The voltage change of the Amino-HNC anode during the zinc electrowinning process was measured, and compared with the traditional industrial pure lead anode.

Experimental Materials Conductive carbon paper (Ningbo Weitai Energy Technology Co., LTD.), aniline (AR, ≥99.5%, Shanghai Macklin Biochemical Technology Co., LTD.), concentrated nitric acid (AR, Luoyang Haohua Chemical Reagent Co., LTD.), high purity lead (99.999%), sulfuric acid (AR, Luoyang Haohua Chemical Reagent Co., LTD.), zinc sulfate (AR, Tianjin Yongda Chemical Reagent Co., LTD.). High purity nitrogen (99.999%). All reagents were used without further purification. Deionized water was homemade and used for all steps.

Preparation of Amino-HNC Referring to the method of Xu et al. [12], Amino-HNC was prepared directly on conductive carbon paper through two steps of room temperature electrodeposition and carbonization at high temperature. Firstly, carbon paper (CP) was treated at 500 °C under the air atmosphere about 2 h in a muffle furnace and washed with nitric acid at room temperature with ultrasonic treatment for 30 min. After that, using three-electrode system for polyaniline (PAni) electrodeposition on CP. The working, counter, and reference electrodes were the acid-treated CP, carbon rod, and saturated calomel electrode (SCE), respectively. The electrolyte mixture with 7 ml concentrated HNO3 , 5 ml aniline, and 88 ml deionized water was used for electrodeposition. The electrodeposition of PAni on CP was under 0.7 V (vs. SCE) constant voltage for 600 s. Finally, the Amino-HNC catalyst was obtained by calcining 900 °C for 3 h in the N2 atmosphere at a heating rate of 3 K min−1 . The comparative sample was prepared through a one-step method. The carbon paper was cut into 2 * 4 cm2 , treated at 500 °C under air atmosphere about 2 h in a muffle furnace and washed with nitric acid at room temperature with ultrasonic treatment for 30 min. Next, calcining the acid-treated sample under 900 °C for 3 h in the N2 atmosphere at a heating rate of 3 K min−1 , the obtained CP acted as the comparative sample.

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Characterization of Material The morphology and surface element distribution of the materials were characterized by using a scanning electron microscope from the ZEISS Auriga SEM/FIB Crossbeam System, German. The crystal phase and structure of the catalyst were measured by the Empyrean X-ray diffractometer from PANalytical, Netherlands. The test tube current and voltage are 40 mA and 45 kV, respectively. The Cu Kα target ray has a 2θ angle ranging from 5° to 90°.

Electrochemical Measurement of Material The electrochemical performance was measured with a three-electrode system by using an electrochemical workstation (Autolab PGSTAT302N, Netherlands). The working electrode was 1 * 1 cm2 Amino-HNC, the counter and reference electrodes were carbon rod and saturated calomel electrode (SCE), and the electrolyte was 0.5 mol L−1 H2 SO4 . Nitrogen flow was fed into the electrolytic cell during the electrochemical test process to eliminate the interference of dissolved oxygen. Use a thermostatic water bath to adjust the system temperature. Magnetic agitation (~300 rpm) is used to eliminate bubble effects during testing.

Zinc Electrowinning Experiment The zinc electrowinning simulation experiment used a two-electrode system in a selfmade electrolytic cell. The zinc electrowinning device diagram was shown in Fig. 1. The anode and cathode are as-prepared Amino-HNC with a working area of 2 * 2 cm2 and high purity aluminum plate with a working area of 2 * 4 cm2 , respectively. The electrolyte of the electrowinning experiment contained 50 g/L Zn2+ and 150 g/L H2 SO4 . The working current density was 50 mA cm−2 , and the constant current density was used for electrowinning for 1 h. The chronopotentiometry method was used to record the voltage change during electrowinning, weigh the mass of the aluminum plate before and after electrowinning, and calculate the current efficiency. The current efficiency is calculated by Eq. (5) η=

G × 100% qINt

(5)

where η is current efficiency, G is the mass difference of the aluminum cathode after electrowinning, q is the electrochemical equivalent of zinc (1.22 g (A h)−1 ), I is current intensity, t is deposition time, N equals 1 for the number of electrowinning cell in this study.

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Fig. 1 The zinc electrowinning device diagram. (Color figure online)

Results and Discussion Characterization of Amino-HNC The SEM and SEM-EDS determination results of materials are shown in Fig. 2. It can be seen from Fig. 1a that the prepared catalyst is in a network structure. Compared with the comparative sample of CP (Fig. 1c), the Amino-HNC (Fig. 1b) surface shows a porous structure, which can provide more active sites for the oxygen evolution reaction proceeding. The SEM-EDS mapping results (Fig. 1d) show that the carbon surface is mixed with N element, which improves the activity of the catalyst. XRD is mainly used to analyze the composition of prepared materials and determine the crystal structure. As shown in Fig. 3, the diffraction pattern shows a sharp, strong peak at 26.4°, corresponding to (002) of graphite (JCPDS#08-0415). Meanwhile, the peaks at 44.5°, 54.5°, and 77.4°, corresponding to (101), (004), (110), respectively, although fixed well with graphite. XRD results indicated that the as-prepared Amino-HNC catalyst has similar properties to graphite.

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Fig. 2 a High power SEM figure of Amino-HNC structure. b, c Low power scan of Amino-HNC and comparative CP. d SEM-EDS mapping images for Amino-HNC, and the C, N element distribution figure. (Color figure online)

Fig. 3 XRD patterns of a Amino-HNC and CP. b partial enlargements for Amino-HNC and CP. (Color figure online)

Electrochemical Test  ν  a O According to Nernst equation E = E θ + nRTF ln aOν R , when reference electricity is R SCE, the conversion relation of standard electric potential at different temperature follows by the equation: EθSCE = (0.2415 − 0.000761(T − 298.15)) V. Therefore, the potential of the electrode reaction relative to the reversible hydrogen electrode

Application of Carbon-Based Oxygen Evolution Reaction …

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  (RHE) at different temperatures is E = ESCE + 0.0591pH + EθSCE V. The overpotential (η) of OER is matched with this formula: η = (ERHE − 1.23) V, where 1.23 V is the standard potential for oxygen evolution reaction. Figure 4a and b show linear scanning polarization curves of Amino-HNC and CP in 0.5 M H2 SO4 solution at different temperatures, respectively. As can be seen in the figure, Amino-HNC shows a lower OER overpotential trend compared with CP at different temperatures. In the range of 278–318 K, oxygen evolution reaction starts around 1.50 V on the Amino-HNC anode, and occurs around 1.71 V on the CP anode, indicated that Amino-HNC has a better OER catalytic performance compared with CP. Further, consider the zinc electrowinning condition of 308 K, we focus on the performance at that temperature of Amino-HNC. As shown in Fig. 3c, the OER potential of Amino-HNC is 1.618 V on 308 K, which led to a low OER overpotential of 389 mV (@ 10 mA cm−2 ), and has a low Tafel slope for 38 mV dec−1 (Fig. 3d).

Fig. 4 Linear scanning polarization curves of a Amino-HNC, b CP in 0.5 M H2 SO4 solution at different temperature. Amino-HNC in 0.5 M H2 SO4 at 308 K c Linear scanning polarization curve. d Tafel fitting curve. (Color figure online)

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Fig. 5 Zinc electrowinning cell voltage. a Amino-HNC anode. b Pure Pb anode. (Color figure online)

Zinc Electrowinning Experiment Zinc electrowinning cell voltage change can be seen in Fig. 5. The cell voltage of Amino-HNC anode increases with the increase of electrolysis time, but here are two voltage platforms. The first one appeared near the 600 s of 2.86 V, and the second one appeared near 1500 s for 3.15 V. The cell voltage of control group Pb anode was stable after a downward trend, with the increase of electrolytic time, cell voltage stability in 3.07 V. The current efficiency of the two anodes in zinc electrowinning is shown in Table 1. From the results we have obtained, the current efficiency of Amino-HNC anode differs little from that of Pb anode, but the tank voltage of Amino-HNC is lower. Ruiz et al. pointed that during long-term cycling, the carbon-based supercapacitors performed a decreasing performance and durability in acid aqueous, which can be associated with strong oxidation under working conditions [13]. During the electrochemical reaction process, the electrolyte is easy to enter into the gap of graphite electrode, under the influence of electro-oxidation, which loosens the graphite structure and leads to the increase of electrode resistance, thus increasing the working voltage of the entire electrochemical reaction process [14]. It is obvious that the Amino-HNC electrode has certain OER catalytic performance in zinc electrowinning reaction, which can reduce the cell voltage in a short time. Table 1 The current efficiency of zinc electrowinning

Anode

Cell voltage/V

Current efficiency

Amino-HNC

2.86

74.78

Pb

3.07

79.50

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Conclusion To solve the problem of high energy consumption of zinc electrodeposition, especially the high overpotential of anodic oxygen evolution reaction, an electrocatalyst with a low OER overpotential of 389 mV was prepared in this paper, which provided a new idea for the electrocatalytic active layer in the electrode materials for zinc electrodeposition. Compared with the traditional industrial anode, the active catalyst shows superior performance in electrowinning. Indicated that Amino-HNC has the potential to be used as an electric catalyst in the zinc electrodeposition system. However, the use of the Amino-HNC catalyst should be further explored and optimized. In the subsequent promotion and application, it can be combined with metal materials to prepare composite electrode materials to improve their mechanical properties and working life.

References 1. Lu J, Dreisinger D, Glück T (2014) Manganese electrodeposition—a literature review. Hydrometallurgy 141:105–116 2. Li Y, Jiang LX, Lv XJ et al (2011) Oxygen evolution and corrosion behaviors of co-deposited Pb/Pb-MnO2 composite anode for electrowinning of nonferrous metals. Hydrometallurgy 109(3–4):252–257 3. Clancy M, Bettles CJ, Stuart A et al (2013) The influence of alloying elements on the electrochemistry of lead anodes for electrowinning of metals: a review. Hydrometallurgy 131–132:144–157 4. Zhong X, Yu X, Jiang L et al (2015) Electrochemical behavior of Pb–Ag–Nd alloy during pulse current polarization in H2 SO4 solution. T Nonferr Metal Soc 25(5):1692–1698 5. Zhao M, Li H, Li W et al (2020) Ru-doping enhanced electrocatalysis of metal-organic framework nanosheets toward overall water splitting. Chem-Eur J. https://doi.org/10.1002/chem.202 002072 6. Hu F, Zhu S, Chen S et al (2017) Amorphous metallic NiFeP: a conductive bulk material achieving high activity for oxygen evolution reaction in both alkaline and acidic media. Adv Mater 29(32):1606570 7. Ahmed ATA, Hou B, Chavan HS et al (2018) Self-assembled nanostructured CuCo2 O4 for electrochemical energy storage and the oxygen evolution reaction via morphology engineering. Small 14:1800742 8. Reier T, Oezaslan M, Strasser P (2012) Electrocatalytic oxygen evolution reaction (OER) on Ru, Ir, and Pt catalysts: a comparative study of nanoparticles and bulk materials. ACS Catal 2(8):1765–1772 9. Lee SW, Baik C, Kim TY et al (2019) Three-dimensional mesoporous Ir–Ru binary oxides with improved activity and stability for water electrolysis. Catal Today 352:39–46 10. Liu ZC, Zhang G, Zhang K et al (2018) Facile dispersion of nanosized NiFeP for highly effective catalysis of oxygen evolution reaction. ACS Sustain Chem Eng 6(6):7206–7211 11. Cheng N, Liu Q, Tian J et al (2015) Acidically oxidized carbon cloth: a novel metal-free oxygen evolution electrode with high catalytic activity. Chem Commun 51(9):1616–1619 12. Zhao X, Su H, Cheng WR et al (2019) Operando insight into the oxygen evolution kinetics on the metal-free carbon-based electrocatalyst in an acidic solution. ACS Appl Mater Interfaces 11(38):34854–34861

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13. Ruiz V, Santamaría R, Granda M et al (2009) Long-term cycling of carbon-based supercapacitors in aqueous media. Electrochim Acta 54(19):4481–4486 14. Fan Z, Yan J, Zhi L et al (2010) A three-dimensional carbon nanotube/graphene sandwich and its application as electrode in supercapacitors. Adv Mater 22:3723–3728

Characteristic Studies of Critical Rare Earths Scandium and Yttrium from Circulating Fluidized Bed Coal Fly Ashes Quang Tuan Lai, Thenepalli Thriveni, and Ji Whan Ahn

Abstract Globally, coal is the largest primary source of electricity. As per the global demand for coal production, coal ash has also subsequently increased. The coal by products such as fly ash and bottom ashes are the main sources for rare earth and other metals. The tremendous benefits of recycling these ashes have wide energy applications. In this paper, we reported characteristic studies of critical rare earth such as scandium and yttrium from the circulating fluidized bed combustion fly ashes. The preliminary study investigated the performance of three reagents, HCl, H2 SO4 , and HNO3 on rare earth leaching from a Korean circulating fluidized bed combustion fly ash. Hydrochloric acid was selected as the proper reagent used for subsequent experiments. The variables including reagent concentration, leaching time, and temperature in the range of 1.5–4 mol/L, 5–120 min, and 25–80 °C were optimized. The leaching efficiency of scandium and yttrium were at 48 and 45% under the optimized conditions. Keywords Characteristics · Critical rare earths · Circulating fluidized bed combustion · Fly ash · Leaching

Q. T. Lai Department of Resources Recycling, University of Science & Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea Tectonic and Geomorphology Department, Vietnam Institute of Geosciences and Mineral Resources (VIGMR), 67 Chienthang Street, Hadong District, Hanoi 151170, Vietnam Q. T. Lai · J. W. Ahn (B) Mineral Resources Division, Center for Carbon Mineralization, Korea Institute of Geosciences and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Republic of Korea e-mail: [email protected] T. Thriveni Department of Chemistry, Sri Venkateswara University, Tirupati, Chittoor (Dt) 517502, Andhra Pradesh, India © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_2

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Introduction Fossil fuels are playing a primary role in the production of heat and electricity globally. Therein, coal has been the largest single source for electric generation. Approximately 38.8% of the global generated electricity has been supplied by coal-derived power [1]. In 2016, the world consumed 7.50 billion tons of coal, of which 4.92 billion tons of coal was used to produce electricity and heat [2]. Coal ash including fly ash and bottom ash are the remnants after burning coal at power plants. Coal ash (CA) was the second-largest anthropological powdery substance after the mine wastes. Driven by surging electricity needs, the demand for coal keeps increasing. Consequently, massive coal ash (CA) is produced [3, 4]. Stockpiled CA has been creating numerous problems that are requiring proper management practices and appropriate recycling technologies. CA is commonly disposed at open landfills or surface impoundments. Whereas, CA typically includes toxicities, arsenic, lead, chromium, mercury, cadmium, etc., [5–7] which are known carcinogens and can damage organs, among other health effects [8]. The improper management of coal ash or casual lined of the ash ponds enable transmitting the toxicities to the nearby waterways and soils [9]. Additionally, the massive unused CA has occupied the vast area of land and cost large sums of money for disposal and management. Coal ash disposals, especially in the flood zone, also involve inherent threats to the environment, nearby communities, and human health [10]. The encapsulated uses of CA are concrete and cement production, mining applications, and structural fills [11, 12]. Hydrometallurgy process has been globally accepted for REE extraction from coal ash [13]. Acid leaching is a critical method to mobile the low concentration of REE from the flash ash (FA) sample and acts as a precursor to the following recovery process [14]. However, there exist numerous investigations on the extraction of REE from pulverized coal combustion (PC) fly ash but from circulating fluidized bed combustion (CFBC) fly ash. This study investigates the leaching behavior of critical rare earths scandium and yttrium from CFBC fly ash in Korea. The dependence of leaching efficiency on reagent strength, leaching time, and temperature has also been uncovered. The optimization of leaching conditions provided a higher yield of scandium and yttrium dissolution.

Materials and Methods Materials Chemical and Mineralogical Composition of the FA The FA sample was collected from a Korean coal-fired power plant coupled with the circulating fluidized bed combustion (CFBC) boiler. The CFBC FA was dried

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Fig. 1 Mineral phases of the dried FA sample analyzed by XRD. (Color figure online)

at 120 °C for 12 h before use in subsequent experiments. The container is 250 mL Duran flask. An IST-4075 Incubated shaker (Jeiotech) was used to adjust the mixing speed and the temperature. The leaching reagents including hydrochloric acid (36%), nitric acid (70%), and sulfuric acid (95%) are provided by Junsei chemical company (Japan). The leach liquor was stored in a 50 mL conical tube. The major elemental oxide was identified via an X-ray fluorescence analyzer (Shimadzu, Japan). The major chemical composition of the fly ash sample in the oxide forms obtained from the X-ray fluorescence analysis are SiO2 (28%), CaO (22.8%), Fe2 O3 (13.3%), MgO (11.9%), and Al2 O3 (10.8%). The minor oxides include K2 O, Na2 O, TiO2 , MnO, P2 O5 with the content lower than one percent each. The mineralogical composition of the FA sample was characterized via X-ray diffraction (XRD) analysis. The dominant mineral phases were quartz (SiO2 ), anhydrite (CaSO4 ), calcite (CaCO3 ), magnetite (Fe3 O4 ), and hematite (Fe2 O3 ) (Fig. 1). This result was coincided with the elemental composition of FA sample.

Rare Earths Concentration in the FA The FA sample was undergone a complicated digested process [15] to obtain the accurate result of REE content in the FA sample. Content of REE in the digested liquor was determined via inductively coupled plasma-mass spectroscopy (ICP-MS, Perkin Elmer). ICP-MS was proved to have sufficient reproducibility and precise for detecting REE. The obtained result for the total concentration of REE was 208.5 ppm, which was higher than that in the CFBC bottom ash sample of 78.4 ppm [16] but relatively lower than the mean REE content in the world’s FA of 445 ppm [17].

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However, the public desire for REE and the considerable content of noteworthy elements such as scandium and yttrium. In this sample, the concentration of scandium and yttrium was 17.5 ppm and 29.9 ppm, respectively. This can emerge this FA sample to be a noticeable source for REE. Therefore, the present study investigated the extraction efficiency of scandium and yttrium concerning the varied experimental condition.

Leaching Experiments The notable amount of Ca-based compounds such as anhydrite, lime, and calcite was due to the addition of limestone in the desulfurization stage at the CFBC power plants. However, the significant amount of calcium and iron in the FA sample favored more REE concentrated in Ca–Fe aluminosilicate minerals [18]. Therefore, acid leaching is expected to yield the desired REE extraction efficiency. The previous studies reported that the pulp density (w/v) of FA leaching tests targeted REE extraction at 100 g/L was the optimum condition providing desired leaching efficiency [15]. Thereafter, the pulp density (w/v) in the subsequent experiments was remained unaltered at 100 g/L. The mixing speed was unchanged at 200 rpm. The leaching performance of reagents including HCl, H2 SO4 , HNO3 was investigated in the preliminary tests. The better acid was selected for subsequent experiments. Subsequently, the effects of leaching time, acid strength, and temperature on REE leaching were investigated. The leaching time, acid strength, and temperature were varied between 5–120 min, 1.5–4 mol/L, and 25–80 °C, respectively. When the reaction time finished, the leaching solution was separated from the residue by vacuum filtration equipped with Advantec filter paper (ø110 mm). The concentration of targeted REE in the diluted leachate was identified by inductively coupled plasma-optical emission spectrometry (ICP-OES). Eventually, the leaching efficiency (E, %) was calculated by the Eq. (1). E=

Caq .v ∗ 100. Cs .m

(1)

where Caq is the concentration of REE in leach liquor, mg/L; v is the volume of leaching solution, L; Cs is the content of REE in the FA, mg/kg; and m is the weight of FA used in experiment, kg.

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Fig. 2 Effect of different agents on leaching of scandium and yttrium. (Color figure online)

Results and Discussion Performance of Leaching Reagent on Rare Earths Extraction Three typical acids, nitric acid, sulfuric acid, and hydrochloric acid, were investigated regarding their effectiveness toward REE dissolution. As aforementioned, 100 g/L of pulp density and 200 rpm of mixing speed were fixed for all experiments. The conditions for time, acid strength, and temperature were 3 h, 0.5–1.5 mol/L, and 25 °C, respectively. The leaching results were shown in Fig. 2. The leaching of scandium and yttrium raised with the elevation of agent’s strength. The contrasting results were observed in the H2 SO4 medium. Sulfuric acid dissolved a considerable amount of REE even at very low acid concentration and the leaching efficiency did not effectively increase with the increase in acid strength. Generally, HCl and HNO3 exhibited the same performance on REE leaching from the FA sample. However, the use of HNO3 could generate environmental issues rather than using HCl due to its higher corrosiveness and the formation of NOx and insoluble nitrate salts [19]. Besides, HCl was also reported as the effective leaching agent targeted REE dissolution from coal ash [15]. Therefore, HCl has constituted the proper reagent for subsequent experiments toward REE leaching optimization.

Effect of Leaching Time It was reported that beyond 120 min of reaction time, REE dissolution had negligible growth [20]. Thus, 120 min was chosen as the maximum reaction time in the investigation. The condition of other factors was 2 mol/L HCl and temperature at 25

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Fig. 3 Effect of time on the leaching efficiency (2 mol/L HCl, 25 °C). (Color figure online)

°C, respectively. The results for REE dissolution with respect to leaching time were depicted in Fig. 3. Five minutes leaching provided notable REE leaching efficiency, for example, scandium (31%) and yttrium (41%). The leaching efficiency was slightly increased when the reaction time increasing up to 60 min. The leaching efficiency of scandium and yttrium after 60 min leaching was 33% and 42%, respectively. Beyond 60 min, the dissolution of scandium and yttrium was negligible. Hence, the proper period targeted REE leaching from the FA sample was selected as 60 min.

Effect of HCl Concentration The effects of HCl concentration on scandium and yttrium leaching were investigated. The concentration of HCl was varied between 1.5–4 mol/L. The condition for time and temperature was at 60 min and 25 °C, respectively. Leaching efficiency of scandium and yttrium slightly increased, 30–33% for scandium and 40–41% for yttrium, when the HCl concentration increased from 1.5 to 4 mol/L (Fig. 4). However, the higher concentration of HCl used lead to the higher possibility of precipitation and gel formation. Therefore, the concentration of HCl at 3 mol/L was selected for subsequent experiments.

Effect of Temperature Experiments were operated to observe the influence of temperature on REE leaching in 3 mol/L HCl for 60 min. The leaching yield was studied between the temperature

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Fig. 4 Leaching of scandium and yttrium as the function of varying acid strength. (Color figure online)

range 25–80 °C in adjustable of the shaker. Figure 5 exhibited the percentage of scandium and yttrium leaching corresponding to varied temperatures. The leaching efficiency of scandium and yttrium was increased from 36 and 41% to 48, and 45% when the reaction temperature increased from 25 to 70 °C. Leaching efficiency was found unchanged with a further increase in temperature, i.e., 80 °C. Therefore, 70 °C was selected as the optimum temperature.

Fig. 5 REE leaching yielded as the function of temperature within 1 h leaching using 3 mol/L HCl. (Color figure online)

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Among the three investigated factors, modifying temperature prompted the most significant change in REE leaching efficiency due to the effective assistance in the diffusion of FA particle’s layers. The condition for leaching time, HCl concentration, and temperature at 60 min, 3 mol/L, and 70 °C was selected as the optimal condition targeting REE dissolution. The leaching of scandium and yttrium was respectively increased from 31 and 41% up to 48 and 45% after the leaching condition optimized. REE leaching efficiency from the FA sample in the HCl medium was relatively high. The reasons were the low combustion temperature in the CFBC boiler (800– 900 °C) and the addition of limestone for desulfurization. It resulted in the lower amount of REE trapped in the acid-insoluble amorphous aluminosilicate matrix and the higher REE combined to the acid-soluble form [18]. However, the optimization process of rare earths leaching reveals that about 55% REE was entrapped in the acid-insoluble aluminosilicate lattice. The remaining 45% of REE was adsorbed on the surface of FA particle or bound to acid-soluble compounds.

Conclusion REE is the critical and non-renewable material for the development of clean innovation technologies. The value of REE in the Korean FA has created the perspective application of its FA with respect to REE recycling. This will not only mitigate the burden on CA managing practices but also help to reduce the intensive need of REE. The focal point in this study was the leaching of scandium and yttrium from the CFBC Korean FA. HCl was chosen prior subsequent optimization process after testing the performance of HCl, HNO3 , and H2 SO4 on REE leaching. The REE leaching efficiency as the function of HCl concentration, leaching time, and temperature was investigated. Leaching efficiency was increased by about 17% for scandium and 4% for yttrium after the leaching condition optimized. Yield of scandium and yttrium leaching were 48%, and 45% under the optimum condition, which was reaction time, concentration of HCl, and leaching temperature at 60 min, 3 mol/L, and 70 °C, respectively. Acknowledgements This research was supported by the National Strategic Project-Carbon Mineralization Flagship Center of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Trade, Industry, and Energy (MOTIE) (2017M3D8A2084752).

References 1. Sahoo PK, Kim K, Powell MA, Equeenuddin SM (2016) Recovery of metals and other beneficial products from coal fly ash: a sustainable approach for fly ash management. Int J Coal Sci Technol 3:267–283

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2. IEA (2018) Coal information 2018. Int Energy Agency. https://doi.org/10.1787/coal-2018-en 3. He Y, Luo Q, Hu H (2012) Situation analysis and countermeasures of China’s fly ash pollution prevention and control. Procedia Environ Sci 16:690–696 4. Kaufman R (2011) Seeking a safer future for electricity’s coal ash waste [WWW Document]. Natl Geogr NEWS. https://www.nationalgeographic.com/news/energy/2011/08/110815-saferways-to-reycle-fly-ash-from-coal/. Accessed 12 November 2019 5. Li R, Wu H, DIng J, Fu W, Gan L, Li Y (2017) Mercury pollution in vegetables, grains and soils from areas surrounding coal-fired power plants. Sci Rep 7:1–9 6. Pandey VC, Singh JS, Singh RP, Singh N, Yunus M (2011) Arsenic hazards in coal fly ash and its fate in Indian scenario. Resour Conserv Recycl 55:819–835 7. Zhang G, Zhang L, Fan H, Hu E (2017) Concentration, enrichment, and partitioning behavior of heavy metals in ash from a down-fired furnace burning anthracite coal. Energy Fuels 31:9381– 9392 8. Sierra Club (2019) Coal waste in America [WWW Document]. Beyond coal. https://content. sierraclub.org/coal/disposal-ash-waste. Accessed 11 May 2019 9. Turrentine J (2019) Coal ash is hazardous. Coal ash is waste. But according to the EPA, coal ash is not “hazardous waste.”| NRDC [WWW Document]. https://www.nrdc.org/onearth/coal-ashhazardous-coal-ash-waste-according-epa-coal-ash-not-hazardous-waste. Accessed 11 May 19 10. Weissman G, Rumpler J (2018) Accidents waiting to happen: coal ash ponds put our waterways at risk 11. Singh N, Mithulraj M, Arya S (2018) Influence of coal bottom ash as fine aggregates replacement on various properties of concretes: a review. Resour Conserv Recycl 138:257–271 12. Surabhi (2017) Fly ash in India: generation vis-à-vis utilization and global perspective. Int J Appl Chem 13. Wang Z, Dai S, Zou J, French D, Graham IT (2019) Rare earth elements and yttrium in coal ash from the Luzhou power plant in Sichuan, Southwest China: concentration, characterization and optimized extraction. Int J Coal Geol 203:1–14 14. Peterson R, Heinrichs M, Taha R, Winecki S, Argumedo D (2017) Recovery of rare earth elements from coal ash with a recycling acid leach process. In: 34th annual international Pittsburgh coal conference: coal-energy, environment and sustainable development. PCC 2017 (2017) 15. Cao S, Zhou C, Pan J, Liu C, Tang M, Ji W, Hu T, Zhang N (2018) Study on influence factors of leaching of rare earth elements from coal fly ash. Energy Fuels 32:8000–8005 16. Tuan LQ, Thenepalli T, Chilakala R, Vu H, Ahn J, Kim J (2019) Leaching characteristics of low concentration rare earth elements in Korean (Samcheok) CFBC bottom ash samples. Sustainability 11:2562 17. Seredin VV, Dai S (2012) Coal deposits as potential alternative sources for lanthanides and yttrium. Int J Coal Geol 94(1):67–93 18. Kolker A, Scott C, Hower JC, Vazquez JA, Lopano CL, Dai S (2017) Distribution of rare earth elements in coal combustion fly ash, determined by SHRIMP-RG ion microprobe. Int J Coal Geol 184:1–10 19. Reid S, Tam J, Yang M, Azimi G (2017) Technospheric mining of rare earth elements from bauxite residue (red mud): process optimization, kinetic investigation, and microwave pretreatment. Sci Rep 7:1–9 20. Kumari A, Parween R, Chakravarty S, Parmar K, Pathak DD, Lee JC, Jha MK (2019) Novel approach to recover rare earth metals (REMs) from Indian coal bottom ash. Hydrometallurgy 187:1–7

COVID-19 Impacts on Climate Change—Sustainable Technologies for Carbon Capture Storage and Utilization (CCUS) Quang Tuan Lai, Lulit Habte, Thenepalli Thriveni, Lee Seongho, and Ji Whan Ahn Abstract Coronavirus disease (COVID-19) has spread around the world like wildfire, impacting health, industry, the global economy, and the environment. This paper focuses on climate change, discussing the global trend in CO2 emissions and how COVID-19 is impacting climate change. Global warming is the greatest environmental challenge our planet has ever faced. According to the International Energy Agency (IEA), CO2 emissions declined by 8% during 2020. Of the wide range of sustainable technologies available for carbon capture, mineralization technology is the first to produce carbonate minerals by directly reacting minerals with low concentration CO2 . This long-term technology affords extended capacity for CO2 storage. We consider the extensive guidelines required for climate change during the battle against COVID-19. Keywords Sustainable technologies · CCUS · COVID-19 · Climate change

Q. T. Lai · L. Habte Department of Resources Recycling, University of Science & Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea Q. T. Lai Tectonic and Geomorphology Department, Vietnam Institute of Geosciences and Mineral Resources (VIGMR), 67 Chienthang Street, Hadong District, Hanoi 151170, Vietnam Q. T. Lai · L. Habte · L. Seongho · J. W. Ahn (B) Center for Carbon Mineralization, Mineral Resources Division, Korea Institute of Geosciences and Mineral Resources (KIGAM), 124 Gwahagno, Yuseong-gu, Daejeon 34132, Republic of Korea e-mail: [email protected] T. Thriveni Department of Chemistry, Sri Venkateswara University, Chittoor (Dt), Tirupati 517502, Andhra Pradesh, India © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_3

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Introduction Currently, global warming is the biggest environmental challenge and is caused by the large quantity of greenhouse gas (GHG) emitted by anthropogenic activities. There are a variety of industries emitting CO2 at peak levels, such as iron and steel, cement, chemicals, paper and pulp, nonferrous, food processing, textiles, leather, transport, and mining industries. The major greenhouse gas, CO2 , emitted by various global industries reached 33 gigatonnes (Gt), as shown in Fig. 1 [1]. According to the Paris Agreement, CO2 emissions need to be reduced as a matter of urgency to meet the world’s long-term temperature goal of keeping the increase in global average temperature to well below 2 °C (3.6 °F) above pre-industrial levels. The regional trends in CO2 emissions vary according to each country’s economic growth and energy-related development. In the United States, CO2 emissions dropped to almost 1Gt in 2000 due to reduced electricity demand, a mild summer, and a mild winter. The figure for the European Union was 12% (due to the utilization of gas instead of coal); Germany, 8% (utilization of renewable energy sources); UK, 2% (supplied solar and wind power); Japan, 4.3% (generating nuclear power rather than using power from coal fueled plants); China, 10Gt; and India slightly reduced its CO2 emissions [1].

Fig. 1 Global CO2 emissions 1990–2019 (adapted from the International Energy Agency [1]). (Color figure online)

COVID-19 Impacts on Climate Change—Sustainable Technologies … Table 1 CO2 emissions trend in different countries

Country name

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CO2 emissions (Mt) 2019/2020

China

3,563/3248

United States

1753/1615

Europe/UK

1,153/1008

India

896/831

Russia

560/536

Climate Change before and during the COVID-19 Pandemic In response to the global health crisis brought about by the rapid onset of the COVID19 pandemic, most countries announced national lockdowns, as a result of which CO2 emission levels have dropped [2]. This research study revealed that the daily CO2 emissions decreased by −17% due to the lockdown of transport. In general, CO2 emissions are reported annually; as a result of the pandemic, researchers have been monitoring CO2 emissions before and during the COVID-19 pandemic. Restricted movement has led to a decrease of nearly −26% in CO2 emissions from industries such as surface transport, power plants, industry, aviation, residential construction, and manufacturing industries. According to Nature [4], the largest climate change impact due to COVID-19 has occurred in China, where the CO2 emissions have decreased by around 315 million tonnes, which is equal to France’s annual emissions of CO2 . Globally, during the first four months of the lockdowns, CO2 emissions decreased by around 8.3%, as can be observed in Table 1. The focus of recent articles published in reputable scientific journals relates to the impact on climate change before COVID-19 and during COVID-19. The quantity of CO2 emissions during the lockdowns has temporarily decreased [5–11]. Forster et al. [12] investigated the unexpected reduction of pollutants and GHG emissions during the pandemic period. The major pollutants NOx and SOx decreased by 30% and 20%, respectively.

Sustainable Technologies for Carbon Capture Storage and Utilization (CCUS) The above climate change impact strategy promotes the concept of CO2 capture storage and utilization after the COVID-19 pandemic is under control. The captured and stored CO2 can be utilized extensively in the cement industry and for enhanced oil recovery (EOR) and algae production. Due to the COVID-19 pandemic, the demand for petroleum and oil is low, as a result of which oil production is temporarily shut down and seeking permanent CO2 storage options [13, 14].

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Enhanced Oil Recovery (EOR)

For Fertilizers

Food Processing

Carbonation of Beverages

Algae Production

Red Mud Processing

Curing of Concrete

Carbon Mineralization

Fire Suppression

Fig. 2 CCUS utilization concept. (Color figure online)

Carbon mineralization is one of the sustainable technologies for CO2 utilization and will soon play an important role in reducing GHG emissions by approximately 250–500 million tons per year in 2030 (CO2 value Europe). Mineral carbonation is one of the best examples of CCUS where CO2 is stored in the form of inert carbonate rock. The CCUS utilization concept is shown in Fig. 2. The main principle of carbon mineralization is shown in Fig. 3, which presents the development of CO2 utilization and mitigation technology using low-grade minerals. The structure of the resource industry is adapting as climate change emerges as an important global agenda. Carbon mineralization technology is the first method used to produce carbonate minerals by directly reacting to natural minerals that are alkaline compounds, inorganic by-products, and solid waste with a low concentration of CO2 (8–13%). The resulting carbonate minerals are used in the manufacture of

Fig. 3 Schematic diagram of carbon mineralization. (Color figure online)

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paper and plastics, and for the backfilling of mines. This highly beneficial carbon mineralization technology has the longest-term CO2 storage capacity.

Conclusions Not only has the COVID-19 pandemic brought new challenges to public health safety, it has also forced us to face up to climate change. Short-term reductions in CO2 emissions are not sufficient for long-term sustainable climate change goals. Due to the COVID-19 pandemic, almost all nations are facing economic crisis. This situation dictates that we need international cooperation to create new climate change policies, planning to provide public health safety, and plants with suitable advanced sustainable technologies to reduce GHG. Carbon mineralization is the most beneficial technology for utilizing CO2 in a manner that leads to the net reduction of CO2 emissions into the atmosphere. Acknowledgements This research was supported by the National Strategic Project-Carbon Mineralization Flagship Center of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Trade, Industry, and Energy (MOTIE) (2019M3D8A2112963).

References 1. International Energy Agency (IEA) (2019) Global CO2 emissions in 2019, Article 11, Feb, 2020 https://www.iea.org/articles/global-co2-emissions-in-2019 2. Le Quéré C, Jackson RB, Jones MW, Smith AJP, Abernethy S, Andrew RM, De-Gol AJ, Willis DR, Shan Y, Canadell JG, Friedlingstein P, Creutzig F, Peters P (2020) Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nat Clim Chang 10:647–653 3. Fischedick M, Roy J, Abdel-Aziz A, Acquaye A, Allwood JM, Ceron J-P, Geng Y, Kheshgi H, Lanza A, Perczyk D, Price L, Santalla E, Sheinbaum C, Tanaka K (2014) Industry. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA 4. Tollefson J, Jackman P (2020) Carbon in the time of Covid-19. Nature 582(11):158–159 5. Damani A (2020) COVID-19 and climate change. Sushruta 13(2): pre-print v1, 1–5 6. United Nations Economic Commission for Africa (UNECA), Climate change and development in Africa post COVID-19: some critical reflections, ACPC. Discussion paper, 2020, 1–17 7. Klenert D, Funke F, Mattauch L, O’Callaghan B (2020) Five Lessons from COVID-19 for advancing climate change mitigation. Environ Resource Econ 76:751–778 8. Zakaria F (2020) The pandemic is too important to be left to the scientists. Washington Post. https://www.washingtonpost.com/opinions/itll-take-more-than-just-scientists-to-stem-thispandemic/2020/04/30/9ee1daf6-8b1d-11ea-9dfd-990f9dcc71fc_story.html. Accessed 5 May 2020

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9. Whitmarsh L, Corner A (2017) Tools for a new climate conversation: a mixed-methods study of language for public engagement across the political spectrum. Glob Environ Change 42:122– 135 10. Wang CJ, Ng CY, Brook RH (2020) Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 323(14):1341–1342 11. Rosenbloom D, Markard J (2020) A COVID-19 recovery for climate. Science 368(6490):447 12. Forster PM, Forster HI, Evans MJ, Gidden MJ, Jones CD, Keller CA, Lamboll RD, Le Quéré C, Rogelj J, Rosen D, Schleussner C-F, Richardson TB, Smith CJ, Turnock ST (2020) Current and future global climate impacts resulting from COVID-19. Nat Clim Chang https://doi.org/ 10.1038.s41558-020-0883-0 13. Kurth HA, Eames FR (2020) CCUS after the pandemic. National Law Rev X(142):1 14. Leonzio G, Foscolo PU, Zondervan E, Bogle IDL (2020) Scenario analysis of carbon capture, utilization (particularly producing methane and methanol), and storage (CCUS) systems. Ind Eng Chem Res 59(15):6961–6976 15. CO2 Europe Value (CEV), CO2 Mineralisation a way to transform CO2 emissions into useful construction materials, 2018, 1–2

CO2 Emission Calculation Model of Integrated Steel Works Based on Process Analysis Hui Li, Xinchuang Li, Weijian Tian, Zhe Chen, and Hao Bai

Abstract Iron and steelmaking consumes large amounts of fuel and CO2 emissions are also huge. In this paper, a new calculation model was proposed to calculate CO2 emissions based on process analysis, and the technical emission and combustion emissions were distinguished to reflect the CO2 emission characteristics in iron and steel production. As for electric arc furnace (EAF) steelmaking, the results of the CO2 emissions calculation show that the CO2 emission intensity of the enterprise’s EAF process was 53.84 kg/t-cs, including electric emissions and non-electric emissions. The electric emissions were calculated as 41.09 kg/t, accounting for 76% of the total emissions. Considering the complexity of power consumption in iron and steel enterprises, a calculation method based on electric power structure was applied to determine the power emission factor. Finally, the carbon flow of the entire process was analyzed, and the possible ways for carbon reduction in steel complexes was discussed. Keywords Steel metallurgy · CO2 emissions · Carbon flow · Emission factors

Introduction CO2 emissions reduction strategy of iron and steelmaking is of significance to the sustainable development of the world [1]. China has the world’s largest steel output. With the steady growth of steel annual production in the recent years, China’s steel production accounts for approximately half of global steel production [2]. As a resource-intensive and energy-intensive industry, iron and steelmaking is a complex process based on iron-carbon chemical reactions at high temperature and a large amount of energy consumption leads to massive CO2 emmisions. H. Li · W. Tian · Z. Chen · H. Bai (B) School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Beijing 100083, China e-mail: [email protected] H. Li · X. Li China Metallurgical Industry Planning and Research Institute, 36 North 3rd Ring East Road, Beijing 100013, China © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_4

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Industrial metabolism is a method to describe and analyze the material and energy flow of an industry, and substance flow analysis (SFA) is a popular method of industrial metabolism. SFA mainly quantifies flows of a certain substance or a certain group of substances related to specific environmental impacts in a well-defined system, so that to obtain the methods for improving substance utilization efficiency and reducing industrial waste discharge and therefore to give guidance on industrial policies and economic activities [3]. In this paper, a carbon metabolism model for the iron and steel production was established. This model is for calculating and discussing carbon emissions at enterprise level in order to lay the foundation for enterprises’ comprehensive emissions reduction plan, as well as for the subsequent implementation of carbon tax and establishment of carbon trading system [4]. In the integrated process steel production (BF-BOF), carbon enters the steel production system in the form of fuel and reducing agent, etc. After a series of transformations, some of the carbon becomes a part of the products or by-products and the other carbon is discharged into the environment in the form of CO2 [5]. The CO2 from the combustion of fossil fuels or by-product gases is defined as combustion emission, while the CO2 from chemical reactions is defined as technical emission. The sum of the two kinds of emissions is direct emission caused by steel enterprises. Short process steelmaking refers to the process smelting scrap in an electric arc furnace (EAF) to get rid of high carbon emission processes such as coking, sintering, and blast furnace ironmaking, etc. [6]. EAF steelmaking only accounts for about 10% of China’s total steel production [7]. Therefore, EAF steelmaking is the development direction of reducing carbon emissions in China. In order to understand and analyze the carbon emission of EAF steelmaking, the carbon metabolism model of EAF steelmaking process is established in this paper.

Methods The System Boundary Figure 1 shows the typical integrated process system boundary in an iron and steel enterprise, which is composed of raw material input, in-plant material flow and circulation and product output. For the in-plant part, carbon flows and circulates in these processes. In this paper, the carbon emissions of nine processes are calculated, among which the direct emission of CO2 by carbon metabolism was considered in other processes except EAF process. As for the EAF process, the carbon emissions caused by the power consumption which are the main energy consumption should be considered. The electric power supply in iron and steel enterprises include purchased electricity and self-powered electricity, and the latter is from captive power plant and waste energy power generation. In general, waste heat and energy power generation

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Fig. 1 Long and short process integrated steel enterprise system boundary

is regarded as zero CO2 emission. Therefore, the CO2 emissions caused by captive power plant will be calculated as the emission of self-powered electricity.

Carbon Metabolism Models in Main Iron and Steel Production Processes Carbon Flows of By-Product Gases The by-product gases like COG (coke oven gas), BFG (blast furnace gas), LDG (linz-donawita process gas) play a vital role in the iron and steelmaking. CO, hydrocarbons, and CO2 are the main components of the gases. When calculating the carbon emissions of the whole process of iron and steelmaking, it is not necessary to consider the carbon emission of by-product gases, and only the carbon-containing materials entering and leaving the enterprise boundary should be considered. However, the carbon emissions of by-product gases should be considered when evaluating the carbon emissions of different processes of iron and steelmaking. For example, the CO2 in the by-product gases which are the products of the chemical reactions in coke oven, blast furnace, or converter, is called technical emission. While CO2 produced from the combustion of CO and hydrocarbons should be called combustion emission of the gases. Therefore, for the by-product gases, there should be two emission factors which are the factor of combustion emission and the factor of process emission. If the gases are regarded as the process products, the factor of process emission is used. If the gases are regarded as fuels, the factor of combustion emission is used. For the gas bleeding, the emission factor herein is actually the sum of the factor of the combustion emission and that of process emission. The composition of by-product gas seriously affects the determination of emission factors [8–10].

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Fig. 2 Flow chart of carbon metabolism of BF process

Carbon Metabolism of Blast Furnace Ironmaking Process The flow chart of the carbon metabolism of blast furnace ironmaking is shown in Fig. 2. Most BFG generated are collected into the gas tank and some of it is wasted, which is called gas bleeding and this part of carbon emissions should be technical emission. However, the situation of the BFG stored in the gas tank is complicated. Only the CO2 in BFG of the gas tank is regarded as technical emission. The CO in the BFG used as fuel for hot stoves will convert into CO2 after combustion and this should be combustion emission.

Converter Steelmaking Process The flow chart of the carbon metabolism of converter steelmaking is shown in Fig. 3. For the converter steelmaking process, since only the furnace gas in blowing period is recovered into the gas tank as LDG, a quite amount of gas from the converter is wasted. Similar to the ironmaking process, for the converter steelmaking, the carbon in the bled gas is regarded as technical emission of CO2 . The CO2 carried by the LDG is also treated as technical emission. The steelmaking process can also cause combustion emission as a result of burning fuels like LDG.

Electric Arc Furnace Steelmaking Process As for the input end, carbon enters EAF steelmaking system mainly in the form of hot metal and scrap, etc. Meanwhile carbon can be brought in by blowing auxiliary

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Fig. 3 Flow chart of carbon metabolism of converter process

fuels into the EAF. As for the output end, carbon is discharged from the system in the form of liquid steel and furnace gas. If ignoring the carbon entrained in the slag, all the carbon in the furnace gas which is not collected at all, is regarded as technical emission of CO2 and the CO2 emission from fuel combustion is regarded as combustion emission. In addition, the EAF consumes enormous electricity during the smelting operation. The electricity does not cause CO2 emission directly in the EAF process. However, it is necessary to estimate the indirect emissions from the consumed electricity used in EAF process to study the effect of electricity consumption on CO2 emissions. Therefore, considering the unique energy structure of EAF, the carbon emissions from the EAF contain not only technical emission, combustion emission but electricity emissions. Electricity emissions are further classified into the CO2 emission from consuming purchased electricity by enterprises and the CO2 emission from the selfpowered electricity of the enterprise.

Total Direct CO2 Emissions and Emission Intensity As mentioned above, for most processes, the CO2 emissions in iron and steelmaking are classified into combustion emission and technical emission. The combustion emission refers to the CO2 emission resulting from the combustion of purchased fossil fuels or by-product gases, and the technical emission refers to the CO2 emission from non-combustion processes, such as the coking and the thermal decomposition of limestone and dolomite, etc. The sum of the two types of emissions is called total direct CO2 emissions, which is shown in formula 1.

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E sum,i = Csum,i + psum,i

(1)

In formula 1, Esum, i is the total CO2 emissions from the process i; Csum, i is the combustion emission of the process i; Psum, i is the technical emission of the process i. In addition, CO2 emission intensity is also an important indicator for analyzing and evaluating CO2 emissions in iron and steel enterprises. Emission intensity can be calculated with formula 2 shown as follows: eC O 2 ,i =

E sum,i P

(2)

In formula 2, the eC O 2 ,i is the CO2 emission intensity (kgCO2 /t steel) of process i; P is the steel output.

Determination of Emission Factors Most of the emission factors are selected from the “Greenhouse gas emission accounting and reporting requirements [11].” The carbon emission factor of purchased electricity is affected by the provincial power grid in which the purchased electricity is located [12], which can be selected according to the location of the target enterprise. The emission factor of self-powered electricity can be calculated based on the CO2 emission in the self-owned power generation system. All the CO2 emission factors are shown in Table 2. Table 2 CO2 emission factors Type

Carrier

Emission Factor

Unit

Process emission factor

Limestone

0.440

tCO2 /t

Dolomite

0.471

tCO2 /t

Hot metala

E F hotmetal

tCO2 /t

Liquid

Combustion emission factor

steela

E F liquidsteel

tCO2 /t

BFG/COG/LDGa

E F T gas

tCO2 /10,000 Nm3

Purchased electricity

E F D pe

tCO2 /10,000 kWh

Self-powered electricity

E F D sg

tCO2 /10,000 kWh

Bitumite

1.747

tCO2 /t

Anthracite

1.924

tCO2 /t

Cleaned coal

2.208

tCO2 /t

Coke/coke powder

2.862

tCO2 /t

Natural gas

5.897

tCO2 /t

BFG/COG/LDGa

E FC gas

tCO2 /10,000 Nm3

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The calculation of an emission factor is based on the actual situation of iron and steel enterprises; the other emission factors are available in “Guideline”.

Case Analysis of Carbon Emissions of Integrated Iron and Steel Enterprises Overall Analysis In this paper, an integrated iron and steel enterprise including both BF-BOF and EAF processes was taken as a case to calculate and analyze the carbon emissions. The scale of production is of 10 million tons of steel annually. The enterprise covers the processes such as coking, pelletizing, sintering, BF ironmaking, converter steelmaking, EAF steelmaking, rolling, boiler power generation, and lime, etc. The analysis is based on the statistical data of energy consumption of the enterprise in 2018. The total direct CO2 emissions of this enterprise reached 1885.60 million tons. The emission intensity ratios of each process are shown in Fig. 4. Figure 4 shows that sintering, ironmaking, and boiler power generation are the major processes causing CO2 emissions. Among them, the emission ratio of ironmaking process is as much as 53.64%, which is the process that emits the most CO2 . Figure 5 shows the combustion emission and technical emission of each process, in

Fig. 4 Emission intensity ratio of each process

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Fig. 5 Combustion emission and technical emission of each process

which the combustion emission of sintering, ironmaking, and boiler power generation are the most, and this is the reason for the relatively large amount of CO2 emissions in these three processes. Apparently, ironmaking process has the most technical emission, which are 3.5 times as much as the combustion emission of this process. This is the reason why ironmaking process has the most CO2 emissions. According to the date from Fig. 5, the combustion emission ratio is 54.12%, which indicates that the enterprise should pay primary attention to the optimization of the energy consumption system to reduce the CO2 emissions. The technical emission ratio is 45.88%, and most of which are from ironmaking process.

Analysis of Carbon Emissions from EAF Process EAF steelmaking is the trend of development of iron and steel enterprises. The CO2 emission intensity of the electric furnace steelmaking process of the enterprise is 12.75 kg/t, which is far lower than the 60.84 kg/t of its converter steelmaking process. The electricity used in the case enterprise is purchased electricity, self-powered electricity and waste heat and energy power generation. Waste heat and energy power

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Table 3 Electric structure of EAF process Type of electricity consumption

Boiler power generation

Waste heat power generation

Purchased electricity

Electricity consumption (10,000kWh)

11,594.93

1849.48

16,699.90

38.46

6.14

55.40

Proportion (%)

generation is considered as zero carbon emission. Table 3 shows the electricity structure of EAF process of the enterprise, in which the purchased electricity and selfpowered electricity account for nearly 94% of the total direct electricity, which was used to calculate the electricity emission of CO2 from EAF process as 41.09 kg/t steel. Figure 6 shows the shares of the three emissions (combustion emission, technical emission, and electricity emissions), where the electricity emission accounts for as much as 76.33% of the total emissions. It is obvious that power saving is the key to carbon emission reduction in the EAF process. Additionally, improving the power generation efficiency of the self-provided power plant can also significantly reduce carbon emissions [13]. Finally, making full use of waste heat and energy resources and increasing the proportion of power generation by waste heat and energy is an effective means of reducing carbon emissions.

Fig. 6 Combustion, technical and electricity emission ratio of electric furnace process

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Conclusions (1) A CO2 emissions model for integrated iron and steel enterprise with both BFBOF and EAF process was established based on carbon metabolism analysis. For each process, CO2 emissions are classified into technical emission and combustion emission. As for EAF process, electricity structure should be considered to analyze the indirect carbon emissions. (2) The carbon emission intensity of the case enterprise reaches 1876.82 kg/t steel, and the carbon emissions from BF process account for 53.64%. Thus, to reduce the carbon emissions, more attention should be taken to the BF process. (3) The case analysis indicates that for the EAF process, the electricity emission accounts for 76.33%. Power saving is the key to carbon emission reduction in the EAF process. Furthermore, improving the efficiency of self-provided generation and making full use of waste energy for power generation are very important for carbon reduction in EAF process.

References 1. Hu JL, Chen CP, Chen YH (2016) Energy consumption and CO2 emission in Taiwan’s iron-steel industries. Energy Sour Part B-Econ Plann Policy 11(1):87–95 2. Dong HJ, Dai HC, Geng Y (2017) Exploring impact of carbon tax on China’s CO2 reductions and provincial disparities. Renew Sustain Energy Rev 77:596–603 3. Brunner (2017) Substance flow analysis. J Ind Ecol 16(3):293–295 4. Duan Y, Li N, Mu HL (2017) Research on CO2 emission reduction mechanism of China’s iron and steel industry under various emission reduction policies. Energies 10(12):2026 5. Xu B, Lin BQ (2016) Assessing CO2 emissions in China’s iron and steel industry: a dynamic vector autoregression model. Appl Energy 161:375–386 6. Zhang Q, Li Y, Xu J (2018) Carbon element flow analysis and CO2 emission reduction in iron and steel works. J Clean Prod 172:709–723 7. Xu RJ, Xu L, Xu B (2017) Assessing CO2 emissions in China’s iron and steel industry: evidence from quantile regression approach. J Clean Prod 152:259–270 8. Rhee CH, Kim JY, Han K (2011) Process analysis for ammonia-based CO2 capture in ironmaking industry. Energy Procedia 04:1486–1493 9. Liu ZG, Chu MS, Wang ZC (2009) Utilization and CO2 treatment of by product gas in iron and steel enterprises. In: Proceedings of the 7th (2009) China Iron and steel annual meeting, pp 243–250 10. Yang ZB, Zhang YW, Zhang YY (2010) Thermodynamic analysis and experimental study on hydrogen production from methane reforming of coke oven gas. J Phys Chem 26(02):350–358 11. China National Standardization Administration (2015) Greenhouse gas emission accounting and reporting requirements part 5: steel production enterprises: GB/T 32151.5–2015, China Standards Press, Beijing 12. Ma CM (2014) Study on greenhouse gas emission factors of provincial power grid. Resour Sci 36(05):1005–1012 13. Na HM (2017) MFA-based analysis of CO2 emissions from typical industry in urban-As a case of steel industry. Ecol Model 365:45–54

Experimental Study on Dust Removal Performance of Dynamic Wave Scrubber for Smelting Flue Gas Fang Dong, Yan Liu, Xiao-long Li, Gui-li Liu, and Ting-an Zhang

Abstract In order to efficiently remove the fine particles in the smelting flue gas, especially PM2.5, this paper designed and built a dynamic wave scrubber dust removal device. Through the optimization of equipment, nozzle structure, and operating conditions (liquid–gas flow rate ratio, dust mass concentration), the final dust removal efficiency has been achieved. The effects of liquid–gas flow rate ratio, gas– liquid flow pattern, dust mass concentration on the dust removal efficiency were studied. The results show that the dust removal efficiency increases with the increase of liquid–gas flow rate ratio and initial dust mass concentration. When the foam flow pattern is formed in the washing zone, the performance of the dynamic wave scrubber is better than its performance when forming other flow patterns. The total dust removal efficiency can be more than 99%, the classification efficiency of PM2.5, the particles above 5 µm, and the particles above 10 µm are more than 98.35%, 99%, and almost 100%, respectively, when the liquid–gas volume ratio is more than 0.004. Keywords Dynamic wave scrubber · Foam flow pattern · Efficiency · PM2.5

F. Dong · Y. Liu (B) · X. Li · G. Liu · T. Zhang Key Laboratory for Ecological Metallurgy of Multimetallic Mineral, Ministry of Education, School of Metallurgy, Northeastern University, Shenyang 110819, Liaoning, China e-mail: [email protected] F. Dong e-mail: [email protected] X. Li e-mail: [email protected] G. Liu e-mail: [email protected] T. Zhang e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_5

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Introduction Smelting flue gas contains a lot of dust, which is mixed with many harmful impurities, such as heavy metals, arsenic, fluorine, chlorine, and carbon monoxide. The particle size distribution of dust is mostly between 1 and 100 µm. Those larger than 10 µm can settle naturally under the action of gravity. Particles with a diameter of fewer than 10 µm, such as PM2.5, can float in the air for a long time, and they will move unevenly and irregularly [1, 2]. The emission of fine particles is a cause for concern because they can penetrate deep into the lungs, thereby exacerbating conditions such as bronchitis and asthma, which can lead to the premature death of already vulnerable people. Industrial dust will not only cause environmental pollution, but also threaten the health of workers, and even bring safety hazards such as explosions [3, 4]. Most of the general wet dust scrubber only uses the kinetic energy of the gas or liquid phase, so the dust removal efficiency is not ideal. The dust removal efficiency of particles below 5 µm, especially PM2.5, is low, which seriously affects the gas purification and dust removal performance of the scrubber [5]. The dynamic wave scrubber utilizes the reverse collision of gas and liquid in the pipeline to form a highspeed turbulent foam zone, thereby making full use of the gas–liquid two-phase energy. It has the advantages of high-efficiency removal of particles and medium pressure drop [6]. Dynamic wave scrubbing technology was first developed and patented by DuPont in the 1970s. In 1987, Monsanto Environmental Chemical Company and DuPont signed a license agreement, which will be used in the flue gas purification of sulfuric acid plants and a wider range of air pollution control [7]. There have been many related studies on the hydrodynamics of the dynamic wave scrubber. Zhou and Wang [8] studied the dynamic wave scrubber and its hydrodynamic characteristics. They found that the ideal performance can be achieved in the foam area. The load performance graph was drawn, and the quasi-number correlation was obtained, which was used to predict the pressure drop of the dynamic wave device. Li et al. [9] combined experimental research and numerical simulation methods to study the two-phase flow field of the dynamic wave scrubber. The results show that the turbulence intensity in the foam zone formed by gas–liquid collision is high. Chen et al. [10] studied the hydrodynamic characteristics and gas–liquid two-phase mass transfer performance of a reverse jet scrubber with a new three-liquid-inlet nozzle through model experiments and compared it with the currently recognized dynamic wave nozzle [11] with good performance. The results show that the new three-liquid-inlet nozzle has a good mass transfer effect and better operational flexibility. Wang et al. [12] studied the influence of liquid–gas flow rate ratio, liquid jet velocity, and gas velocity on the pressure drop and dust removal efficiency of the dynamic wave scrubber. Compared with the venturi scrubber, the pressure drop and the fog entrainment concentration of the dynamic wave scrubber are lower, so it is more practical. There are some other studies on the dust removal efficiency of scrubbers, but most of them use common

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nozzles, such as spiral nozzles and powerful nozzles. There are few studies on the classification efficiency of PM2.5 in scrubber. In this paper, a small model experimental device with a three-liquid-inlet nozzle was established, investigating the effects of operating parameters (e.g., liquid–gas flow rate ratio, dust mass concentration) and the gas–liquid flow pattern on the dust removal efficiency, providing basic data for industrial application.

Experimental Experimental Procedure The schematic diagram of the experimental device is shown in Fig. 1. The scrubber consists of a scrubbing tube with a diameter of 0.12 m, a liquid storage tank, and a three-liquid-inlet nozzle with an outlet diameter of 6.5 mm. The nozzle structure is shown in Fig. 2. The fly ash acts as the fine particles in the air to simulate the flue gas. The flue gas enters the scrubber tube at a high speed from top to bottom through the air inlet, and the liquid is sprayed into the flue gas from bottom to top through the three-liquid-inlet nozzle. With the high-speed collision of the gas and liquid phases, when the momentum of the gas and liquid phases reaches equilibrium, a foam region is formed. With the continuous renewal of the contact surface, the mass transfer, heat

Fig. 1 Schematic diagram of experiment equipment. (Color figure online)

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Fig. 2 Schematic diagram of three-liquid-inlet nozzle. (Color figure online)

transfer, and collision in the foam area are strengthened to achieve the purpose of efficient dust removal.

Experimental Method The experiment uses an isokinetic sampling method [13], using a dust sampler to simultaneously collect the quantitative gas at the inlet and outlet into a collection device equipped with a microporous membrane. The formula of dust mass concentration as follows: C=

3.6 × 106 m t ·q

(1)

where C: dust mass concentration, g/m3 ; m: dust mass, g; t: sampling time, s; q: sampling rate, L/min. The formula of dust removal efficiency as follows: η =1−

C1 C0

(2)

where η: dust removal efficiency, %; C 0 : inlet dust concentration, g/m3 ; C 1 : outlet dust concentration, g/m3 .

Experimental Study on Dust Removal Performance …

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The classification efficiency experiment uses a wet sampling method. Also under the condition of isokinetic sampling, a certain amount of gas is sucked into the gas scrubber, and the particles are left in the water. Using a laser particle size analyzer, the particle size distribution can be obtained. The formula of classification efficiency as follows [14]: ηi = 1 − (1 − η)

g1i g0i

(3)

where ηi : classification efficiency, %; η: dust removal efficiency, %; g0i : the proportion of particle size i in the inlet dust, %; g1i : the proportion of particle size i in the outlet dust, %.

Results and Discussion Flow Patterns and Load Performance As shown in Fig. 3, adjusting the axial and tangential liquid flow ratio (A/T ) of the nozzle and flow rate of gas and liquid will result in four different flow patterns. When a laminar flow pattern is formed in the washing zone, as shown in Fig. 3a, the thickness of the liquid film is small and the spray height of the liquid is low, so the pressure drop of the gas phase through the liquid layer is not large. Since both the liquid flow rate (L) and the gas flow rate (G) are low, it is difficult to provide momentum for forming a stable foam zone in either the gas phase or the liquid phase. Therefore, the gas–liquid two-phase flow is separated at this time, and the mass transfer effect between the two phases is not ideal. It can be seen from Fig. 3b that increasing the L and G begins to form a foam flow pattern . At this moment, the liquid surface is turbulent at high speed, and a stable foam layer can be seen on the wall of the washing pipe. At this stage, the gas–liquid two-phase momentum in the foam zone reaches an equilibrium state. After the gas– liquid two-phase violently collide, a strong turbulent foam zone is generated. This area increases the contact area between the gas and the liquid, and at the same time strengthens the gas–liquid phase. Due to the good mass transfer effect, the foam area is an ideal operating area. It can be seen from Fig. 3c that when the gas flow rate is large, the liquid–gas flow rate ratio (L/G) is significantly reduced. At this stage, an atomized flow pattern is formed. The end surface of the liquid flow is directly washed away by the high-speed airflow, and the liquid in the entire washing pipe is in the form of finely atomized droplets, and jet height is significantly reduced. The main reason is that the gas flow energy is obviously greater than the liquid flow energy, and the liquid flow is directly blown away by the gas quickly, so the gas stays in the liquid layer for a short time and the mass transfer effect between gas and liquid is not good.

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Fig. 3 The gas–liquid flow patterns of foam curve. a laminar flow pattern; b foam flow pattern ; c atomized flow pattern; d bubbling flow pattern

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390

A

360 330

B

G/ m⋅h

-1

300 270

C

240 210

E

180

D

150 120

0.0024

0.0036

0.0048

0.0060

0.0072

L/G Fig. 4 Load performance chart of three-liquid-inlet nozzle

When the L is large and the G is small, the gas–liquid two-phase flow enters the bubbling area, as shown in Fig. 3d, it can be seen that the upper end of the liquid fluctuates more and the jet occurs fracture to form a large number of droplets. At the same time, due to the increase of liquid velocity, the renewal speed of the jet surface is accelerated, and the thickness of the liquid layer increases, thereby enhancing the gas barrier effect. When the pressure increases to a certain extent, the gas flows through the scrubbing pipe in the form of bubbling, reducing the contact area between gas and liquid. The foam flow pattern is an ideal operating flow pattern. The three-liquid-inlet nozzle used in this experiment, when the axial and tangential liquid flow ratio (A/T ) is 0.5, the foam flow pattern has the largest operating range, which is much higher than other nozzles. The area enclosed by ABCDE in Fig. 4 is the formation interval of the foam flow pattern when the A/T is 0.5.

Influence of Liquid–Gas Flow Rate Ratio on Dust Removal Performance In the experiment, A/T of the three-liquid-inlet nozzle is 0.5. It can be seen from Figs. 5 and 6 that when the gas or the liquid velocity remains constant, the dust removal efficiency increases with the increase of the L/G. When the L/G is relatively small, especially 0.003–0.004, the dust removal efficiency increases faster. When the L/G exceeds about 0.004, the improvement of dust removal efficiency becomes slow. This is because, on the one hand, it can be seen from Fig. 3 that when the G

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F. Dong et al. 100 460

3

Dust mass concentration=12 g/m 99

3

440

G= 250 m /h

420

98

380 360

96

η

340

ΔP

95 94

ΔP/ Pa

η/ %

400

97

320

0.0025

0.0030

0.0035

0.0040

300

0.0045

0.0050

L/G Fig. 5 Effect of liquid–gas flow rate ratio on dust removal efficiency with different liquid velocity 700 99.5 99.0

3

Dust mass concentration=12 g/m

600

3

L=1.0887 m /h 500

η 98.0

ΔP/ Pa

η/ %

98.5

ΔP

400

97.5 300

97.0 96.5

0.003

0.004

0.005

0.006

0.007

200 0.008

L/G

Fig. 6 Effect of liquid–gas volume ratio on dust removal efficiency with different gas velocity

is fixed at 250 m3 /h and the L/G is around 0.003, the flow pattern changes to foam. The mass transfer effect of the type is higher than that of other flow types, so the increase in dust removal efficiency is relatively large, on the other hand, with a fixed gas velocity, an increase in the L/G is equivalent to an increase in the L. The relative velocity of the gas and the liquid droplet increases, and the gas–liquid contact surface area also increases. The probability of the dust-containing gas flow colliding with the liquid droplet increases, so the dust removal efficiency is also increased. However, the G and dust content is constant, most of the dust particles have been captured by

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47

the liquid, the remaining dust particles are greatly reduced. Therefore, the effective gas–liquid collision decreases and dust removal efficiency will gradually decrease. When the liquid velocity is fixed, increasing the L/G is equivalent to reducing the G. The G decreases, so the gas–liquid collision degree and gas–liquid contact surface area are reduced, and the gas phase mass transfer resistance is relatively reduced, the absorption rate is reduced. However, as the amount of scrubbing gas decreases, the absolute amount of dust in the scrubbing gas at the same time decreases. Therefore, when the G decreases, the dust removal efficiency will also increase. It can also be seen from Figs. 5 and 6 that in the same range of L, the influence of liquid velocity on dust removal efficiency is greater than that of gas velocity. It can be seen from the trend of pressure drop (ΔP) that in the process of dust removal, the mass transfer resistance is mainly on the gas phase side. Therefore, considering the factors of dust removal efficiency and investment and operating costs, the optimal L/G of the dynamic wave scrubber should be about 0.004.

Influence of Dust Mass Concentration on Dust Removal Performance It can be seen from Fig. 7 that with the experimental fixed L/G, the dust removal efficiency first increases with the increase of dust mass concentration, then stabilizes and slightly decreases. Because the G and L are constant, when the dust mass concentration is not large, the amount of dust per unit of gas increases with the increase of mass concentration. As the dust particles that can be captured by the droplets 99.5

99.0

98.5

η/ %

G=250 m3/h L/G=0.004 98.0

97.5

97.0

4

6

8

10

12

14 -3

Dust mass concentration/ (g⋅m )

Fig. 7 Effect of dust mass concentration on dust removal efficiency

16

48

F. Dong et al.

increase, the gas–liquid contact can be used more effectively. So the dust removal efficiency is improved. When the dust mass concentration reaches a certain value, the gas–liquid contact area has been fully utilized and tends to be saturated, so the dust removal efficiency begins to no longer increase but has a slight downward trend. It can be seen from the figure that the maximum dust removal efficiency occurs when the dust mass concentration is about 12 g/m3 . When the dust mass concentration increases from 4 to 16 g/m3 , the dust removal efficiency is unaffected and can be maintained above 98.5%.

Classification Efficiency Table 1 is the experimental result of classification efficiency. The operating parameters used in the experiment are the gas flow rate of 250 m3 /h, the L/G of 0.004, and the inlet dust mass concentration of 12 g/m3 . Under this working condition, the gas–liquid flow pattern is a foam flow pattern , and the total dust removal efficiency is 99.2%, which has a good purification performance. It can be seen from the table, the classification efficiency of PM2.5, the particles above 5 µm, the particles above 10 µm are more than 98.35%, 99%, and almost 100%, respectively.

Conclusions According to the experimental results, the following conclusions can be drawn: 1. The dust removal efficiency of the dynamic wave scrubber increases with the increase of the L/G and the inlet dust mass concentration. When the gas–liquid flow pattern in the scrubber shows a foam type, the dust removal efficiency increases greatly. The L/G for better scrubber performance should be around 0.004. The maximum dust concentration that can be handled by the dynamic wave scrubber is about 12 g/m3 , and the dust removal efficiency is slightly reduced when this concentration is exceeded. 2. When the dynamic wave scrubber performance is relatively best, the classification efficiency can reach 98.35% for the PM2.5, the classification efficiency Table 1 The result of classification efficiency of dynamic wave scrubber Particle size/µm 20

g0i /%

24.78

18.65

15.83

18.61

22.13

g1i /%

50.99

29.32

15.37

4.32

ηi /%

98.35

98.74

99.22

99.81

0 100

Experimental Study on Dust Removal Performance …

49

for the particles above 5 µm can reach 99%, and the classification efficiency for the particles above 10 µm is almost 100%. For fine particles, this dynamic wave scrubber still has high classification efficiency. 3. The dynamic wave scrubber combined with a three-liquid-inlet nozzle has better operating flexibility, high purification efficiency, and low-pressure drop. It is a washing technology with great application prospects. Variables A G L T A/T L/G

the axial liquid flow rate. the gas flow rate. the liquid flow rate. the tangential liquid flow rate. the axial and tangential liquid flow ratio. the ratio of liquid flow rate to the gas flow rate.

Acknowledgements The authors would like to thank the National Key R&D Program of China (2017YFC0210403) for financial support.

References 1. Yang FM, Ma YL, He KB (2000) A brief introduction to PM2.5 and related research. World Environ 4:32–34 2. Tucker W (2000) An overview of PM2.5 sources and control strategies. Fuel Process Technol 65(1):379–392 3. Sloss LL, Smith IM (2000) PM10 and PM2.5: an international perspective. Fuel Process Technol 65:127–141 4. Zheng N, Liu J, Wang Q, Liang ZZ (2009) Health risk assessment of heavy metal exposure to street dust in the zinc smelting district, northeast of China. Sci Total Environ 408(4):726–733 5. Xu JS, Yang XG (2007) Application of dynamic wave scruber technology to metallurgical off-gas cleaning. Sulphuric Acid Ind 4:34–37 6. Meyer SF, Wibbenmeyer LK, Xue K (2005) A new technology for FCC tail gas desulfurization and dust removal—dyna-wave reverse spraying washing tower. 35(2):25–29 7. Zhan WB, Li QP, Shao GX, Cheng JW (2010) Progress of research and application of dynamic wave scrubber. Chem Ind Eng Prog 29(S2):12–16 8. Zhou DJ, Wang DS (2004) Hydrodynamics characteristics of the dynamic wave equipment. Chem Eng 100(1):17–19 9. Li M, Li Z, Qin D, Fan YP, Lu CX (2017) Experimental research and numerical simulation of flow field in a gas-liquid countercurrent scrubber. Chin J Process Eng 17(4):689–696 10. Chen S, Fan YP, Lu CX (2013) The gas-liquid two-phase mass transfer characteristics of a new reverse jet washing nozzle. J Chem Eng Chin Univ 27(6):37–94 11. Dong HW, Ma HM, Li DJ, Sun GG (2006)Comparative study on scrubbing desulfurization performances of spiral nozzle and dynamic wave nozzle. 36(12):32–35

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12. Wang Y, Du LH, Xu JA, Li QP (2015) Experimental study on performance of dynamic wave scrubber. Chem Eng 43(09):58–62 13. Xie CG, Atkinson I, Hammond P, Oddie G, Bolchover P (2010) Isokinetic sampling. US. Patent 7717000, 18 May 2010 14. Yuan CM (2003) Analysis and measurement of the classification efficiency. Ind Saf Dust Control 29(10):31–33

Homogenization of the Dense Composite Membranes for Carbon Dioxide Separation Dragutin Nedeljkovic

Abstract A possible approach to the carbon dioxide removal from flue gases is application of the dense composite membrane (matrix: polymer material; dispersed phase: zeolite powder). This type of membrane is based on a solution-diffusion mechanism. Carbon dioxide is dissolved in the membrane bulk, and then diffuses to the permeate side. A successful membrane should have high permeability of the carbon dioxide and low permeability for all other gasses commonly present in the combustion process (oxygen, nitrogen, hydrogen…). The main challenge is to provide good contact between long and usually hydrophobic polymer chains and relatively small, but electrically charged, zeolite particles. Two different polymers and four different zeolites were tested for this purpose. As the polymer bulk material, different co-polymers of ethylene-oxide and phthalimide were used. Five different zeolite powders in combination with two different potential additives were tested. Keywords Polymers · Zeolite powder · Carbon dioxide separation · Composite membranes

Introduction Increased demand for energy, and the combustion of fossil fuels as the main source of it, has significantly increased the amount of the flue gases that are emitted into the atmosphere in recent decades. Waste gases are formed not only as a consequence of industrial combustion processes, but also as products of communal processes important for day-to-day life of the population. Typical examples of huge emitters of carbon dioxide include (but are not limited to) power plants, heating plants, process industry, and automobile engines. As a consequence, the emission of carbon dioxide has rapidly increased, which has led to global warming and increased greenhouse effect [1, 2]. As currently renewable energy sources cannot provide a feasible alternative to the fossil fuels and combustion processes on the global scale, removal of carbon D. Nedeljkovic (B) College of Engineering and Technology, American University of the Middle East, Kuwait City, Kuwait e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_6

51

52

D. Nedeljkovic

dioxide became one of the main research topics in modern chemical and environmental engineering [3, 4]. Current conventional processes are based on various techniques for the carbon dioxide separation that have significant disadvantages, either due to the high energy demand (cryo-processes) or because of demand for costly equipment and chemicals (chemical adsorption). Possible approach to the carbon dioxide separation may be the separation membrane. The applicable membrane should exhibit high permeability for the carbon dioxide and as low permeability as possible for all other gasses commonly present in the flue gases (nitrogen, oxygen, unburnt fuel, hydrogen). As this exclusion must be obtained on the molecular size, standard porous membrane (the one that acts as a sieve) would not be suitable for this application [5]. With the current technology, it is not possible to obtain industrially feasible porous membrane that would separate the gas mixtures based on the size of their molecules [6]. Additional problem is in the size of the molecules of interest as the carbon dioxide molecule is larger and bulkier in comparison with hydrogen or nitrogen molecule [7]. A good alternative is so-called non-porous, dense membranes whose separation mechanism is based on the different solubility and diffusivity of the components of the mixture in the bulk of the membrane. Mechanism of the separation is adsorption— solution—diffusion. One component of the mixture is adsorbed on the surface of the membrane; it is then dissolved in the bulk of the membrane and diffuses through the membrane to the permeate side [8–10]. It was reported that the presence of the ethylene-oxide as the repeating units of the polymer material improves the solubility of the carbon dioxide in comparison to nitrogen and oxygen [11]. On the other hand, polymer that contains only poly(ethylene oxide) (PEO) as the repeating unit, has a strong affinity to crystallization, and therefore decrease permeation properties of the membrane [12]. Therefore co-polymers of PEO with other polymers is a good choice for this application. One of the possible choices may be PEBAX, commercially available co-polymer (supplier: Arkema) which is poly(amide-b-ether) by composition [13]. PEBAX belongs to the group of the thermoplastic elastomers where polyamide block can be composed of nylon-6 or nylon-12 (this block gives the stiffness to the membrane and serves as the mechanical carrier) and polyether is soft, rubbery poly(tetramethylene oxide), or standard poly(ethylene oxide) (this block is responsible for the diffusion [14, 15]. Variation in composition and fraction between two blocks can tailor the required properties of the membrane (both chemical and physical) [16]. Based on the given facts, PEBAX appeared as a good candidate for the construction of the membrane for the carbon dioxide separation [17–20]. As a second possibility, the co-polymer of poly(ethylene glycol) and poly(butylene terephthalate) was taken. This polymer is produced and supplied by IsoTis OrthoBiologics under the commercial name of Polyactive. The molar mass of the copolymer was 1500 g/mol, and it contained 77 mass% of PEG. As the size and the mass of the carbon dioxide molecule is significantly higher than the mass and size of the hydrogen molecule, and that hydrogen molecule is non-polar, it is expected that the latter will have higher diffusion coefficient if temperature and pressure of both the gases are same (as it is the case in the gas mixture). Therefore,

Homogenization of the Dense Composite Membranes …

53

the good selectivity of the carbon dioxide versus hydrogen may be obtained only if the solubility of the carbon dioxide is increased as much as possible. Although the masses and dimensions of the molecules of both oxygen and nitrogen are smaller in comparison with carbon dioxide, the difference is not so high as in the case of the hydrogen, so the difference in diffusion coefficients will also be smaller. Anyway, in order to provide good separation, the solubility of both oxygen and nitrogen should remain at the lowest possible level [21–25]. As an additive for better solubility of the carbon dioxide, various zeolite powders may be added. Zeolites are various minerals based on the aluminosilicates with a framework that can accommodate dissolved molecules of the carbon dioxide. If the powders are applied as additives to the polymers for the carbon dioxide separation, zeolite powders should be dispersed in the same solvent as polymer. This step is important in order to obtain thin membrane with a smooth surface. The main challenge in this process is to provide good contact between highly polar, hydrophilic inorganic zeolite particles and hydrophobic, hydrocarbon chains of the polymer. Possible approach is to add different additives whose task would be to provide good contact between polymer chains and zeolite particles, resulting in homogenous, smooth, pinholes’ free membranes.

Materials and Methods In this study, mixing properties and possibility of application of two different polymers were tested. The structures of the polymers used in this work are presented in Fig. 1 (PEBAX) and Fig. 2 (Polyactive).

Fig. 1 Structure of the PEBAX 1657 used as a matrix for group of the membranes

Fig. 2 Structure of the polyactive used as a matrix for group of the membranes

54 Table 1 Properties of the zeolites used in experiment

D. Nedeljkovic Zeolite type

Channel system

Pore size, pm

Max. diff. diam., pm

ITR

3d

64

57

IWS

3d

82

67

OWE

2d

58

38

CFI

1d

75

73

Zeolite powder (inorganic aluminosilicates) was dispersed in order to improve the solubility properties of the carbon dioxide. The properties of the inorganic powder can be described on two different levels. On the frame (micro) level, the diffusion properties of the zeolite are determined by the direction of the pores (one, two, or three dimensions) and the maximum diameter of the sphere that can diffuse through the available frame. On the bulk (macro) level, the main property of the powder is its specific surface which represents surface available for the adsorption per unit mass. Zeolite powders used in this work have specific surface in the range between 500 and 900 m2 /g. Four different types of powder were used for this experiment, with different sizes and orientation of the pores. All zeolite powders tested in this paper are selected based on their relatively high maximum diameters of the sphere that can diffuse through it. Zeolites designed as ITR and IWS are both with three dimensional pores with maximal diameter of the sphere that can diffuse through it of 57 and 67 pm respectively. Frame of the OWE zeolite contain two-dimensional pores and maximal diffusion sphere diameter of 38 pm. CFI is composed of one-dimensional pores with maximum sphere diffusive diameter of 73 pm. Structural properties of all powders are presented in Table 1. High values for the spheres that can diffuse through the pores should provide good diffusive properties of the relatively bulky carbon dioxide molecule. Characterization of the zeolites was provided by the supplying company (NanoScape). Two different additives to the polymer—zeolite systems were tested. One possibility was n-tetradecane trimethyl ammonium bromide (n-C14-TMABr). It was supposed that polar nitrogen—bromine bond would behave as an “anchor” to the highly charged surface of the zeolite particle while long, normal, hydrocarbon “tail” would be dispersed in the polymer matrix. The attraction forces between the additive and both polymer matrix and zeolite particle should be sufficiently strong to provide the homogenous mixture and to prevent forming the voids between the zeolite and polymer. The second additive tested was dimethylaminopyridine (DMAP). Beside the mechanism similar to TMAB, it was supposed that weak alkali properties of DMAP would increase the solubility of carbon dioxide. Membranes were prepared by the following procedure: Polymers were dissolved in appropriate solvents. The solvent for PEBAX was a mixture of water and ethanol (70 mass% of water) and the solution was stirred at 80 °C under reflux. For Polyactive, water was used as the solvent, and the solution process was performed at room temperature. The zeolite powder was dissolved in a small amount of the solvent (same as polymer), and (if applicable) the appropriate amount of the additive was

Homogenization of the Dense Composite Membranes …

55

added. The amount of additive was calculated versus the mass of the zeolite powder. Additive solution was mixed using ultrasound mixer (power source 90 W, frequency 40 kHz). The solution of the zeolite and (eventually) additive was added to the polymer solutions and mixing was continued overnight at the same conditions as the pure polymers. Obtained viscous solution was casted on the Teflon surface bordered with the Teflon ring to prevent the membrane from stitching to the surface during the drying process. The drying process was done overnight at room temperature with the casted membranes covered with non-woven textile. If the higher temperatures or lower pressures were applied, there was a high probability of bubbles being formed inside the membrane which would rapidly decrease the permeation properties. The crucial parameter in this step was the optimal viscosity of the polymer solution. If the viscosity is too high, the surface tension dominates the casting process and the thickness of the membrane would be uneven. If the viscosity is too low, the sedimentation of the zeolite powder would occur before the membrane is dried, so the particles would be unevenly distributed through the thickness of the membrane, and the membrane would self-roll. Prior to measurement, membranes were positioned in the apparatus at the vacuum line (pressure below 250 Pa) in order to remove potential traces of the residual solvent. Permeability properties were determined by applying the time lag method applying the solution-diffusion model which takes into account both solubility and diffusivity. The parameters were determined by the following equations [26–28]: α A/B =

PA D A SA = PB D B SB

l2 6θ   V p l p p2 − p p1   P = D·S= p +p A RT t p f − ( p2 2 p1 ) D=

In those equations, S is solubility, D is diffusivity, P is permeability, αA/B is selectivity of the component A versus component B (defined as the ratio of permeability for gas A versus permeability for gas B), Vp is the permeate volume, l is the thickness of the membrane, R is the universal gas constant, t is for the time required for permeate pressure to increase from value pp1 to value pp2 , pf is feed pressure, θ is time lag. The permeability measurement was done by the application of the gas on the feed side of the membrane and the vacuum on the permeate side, so the driving force for the solution and the diffusion was the pressure difference. Gases were measured sequentially with high vacuum applied between the measurements of different gases in order to remove residual dissolved gases. The sequence of measurement of different gases was as follows: helium, hydrogen, nitrogen, oxygen, carbon dioxide, methane. This sequence was chosen in order to minimize the possibility of formation of flammable

56

D. Nedeljkovic

or explosive mixtures. Helium as a small, ideally round, non-polar, non-flammable gas was measured in order to detect potential presence of the pinholes on membranes. Selectivity of each gas was recalculated versus carbon dioxide.

Results and Discussion The properties of the synthesized membranes (composition and the appearance) are presented in Table 2. All presented percentages are mass percentages versus the overall mass of the membrane. In the ideal case, the membrane should appear transparent or slightly opaque, and to be smooth on the touch. Transparent membrane indicates that the zeolite particles are in good contact with polymer matrix, which indicates that practically all particles are in contact with additive. Absence of white spots or zones also indicates that the zeolite particles are evenly distributed through the volume of the membrane. The presence of the white spots on the membranes indicates that zeolite particles aggregated at the particular position within the membrane, and therefore the distribution of the zeolite particles is not uniform. As a consequence, the permeation properties are significantly lower in comparison with smooth membranes with uniformous distribution. Secondary consequence of the agglomeration is reasonable assumption that zeolite powder particles are not properly surface treated. In other words, most of the particles remained uncovered by the additive, so the attraction electrostatic forces prevailed, and agglomeration zones of particles were formed. White color of the membrane indicates that the contact between the zeolite particles and polymer chains is bad. If there are voids formed between zeolite particles and polymer chains, the light refraction will occur, so the membrane is not translucent or opaque. Opacity of membranes might indicate, at least partially bad contact. However, as they were not completely non-transparent, it can be assumed that most of the zeolite particles were covered with appropriate additive, and that the contact between the matrix and dispersed phase is mainly obtained and only limited amount of the particles were surrounded by the air gaps. White membranes were not used for the permeability measurements, but opaque membranes were measured. For each polymer, one sample was made with pure polymer and one with the polymer and additive (without zeolite powder) as a control and testing sample. As the gained results for thickness and appearance were good, membranes with zeolite additives were synthesized. Analyzing the data from Table 2, it is obvious that application of the n-C14TMABr as an additive significantly improved the appearance of the membrane. There was no system with better appearance without the additive in comparison with the system with additive. This improvement was observed for both PEBAX and Polyactive based membranes. In the case of DMAP, it did not show the acceptable results. Agglomeration was still present in most of the cases, and in some cases membrane even could not be synthesized. Transparent and opaque membranes were used for the permeability measurements, and the obtained results are presented in Table 3.

Homogenization of the Dense Composite Membranes …

57

Table 2 The composition and optical appearances of the constructed membranes Membrane number

Polymer

Zeolite

Filler, %

Additive

Additive %

Appearance

1

PEBAX

–

–

–

–

Transparent

2

PEBAX

–

–

n-C14-TMABr

8.1

Transparent

3

PEBAX

–

–

DMAP

8.3

Transparent

4

PEBAX

CFI

22.3

–

–

White

5

PEBAX

CFI

22.0

n-C14-TMABr

8.6

Opaque

6

PEBAX

CFI

22.1

DMAP

8.5

White spots

7

PEBAX

OWE

22.3

8

PEBAX

OWE

21.9

n-C14-TMABr

8.5

Opaque

9

PEBAX

OWE

22.1

DMAP

8.7

White

10

PEBAX

IWS

22.0

–

–

White

11

PEBAX

IWS

21.8

n-C14-TMABr

8.5

Transparent

12

PEBAX

IWS

22.1

DMAP

8.4

White areas

13

PEBAX

ITR

22.0

–

-

Opaque

14

PEBAX

ITR

22.5

n-C14-TMABr

8.3

Transparent

15

PEBAX

ITR

22.6

DMAP

8.8

White spots

16

Polyactive

–

–

–

–

Transparent

17

Polyactive

–

–

n-C14-TMABr

8.0

Transparent

18

Polyactive

–

–

DMAP

8.5

Transparent

19

Polyactive

CFI

22.0

–

–

White

20

Polyactive

CFI

22.1

n-C14-TMABr

8.4

Opaque

21

Polyactive

CFI

22.7

DMAP

8.2

White

22

Polyactive

OWE

22.4

23

Polyactive

OWE

22.5

n-C14-TMABr

8.7

Transparent

24

Polyactive

OWE

22.0

DMAP

8.8

White spots

25

Polyactive

IWS

22.0

–

–

White

26

Polyactive

IWS

21.6

n-C14-TMABr

8.3

Opaque

27

Polyactive

IWS

22.4

DMAP

8.4

White

28

Polyactive

ITR

22.2

–

–

Opaque

29

Polyactive

ITR

22.3

n-C14-TMABr

8.4

Transparent

30

Polyactive

ITR

22.5

DMAP

8.2

White spots

White spots

White spots

As it is common in the membrane community, permeability of each membrane was expressed in unit Barrer. By the definition, permeability of one Barrer is the permeation of 1 cm3 of oxygen through the membrane with surface area of 1 cm2 and thickness of 1 mmHg during the time of 1 s with the pressure difference of 1 mmHg, multiplied with factor 10–10 . Formally speaking, the relation to the appropriate SI unit is that one Barrer is equivalent to 3.35 × 10–16 m3 /m2 Pasm.

58

D. Nedeljkovic

Table 3 Permeability and selectivity of the constructed membranes Membrane number Thickness, μm P CO2 , Barrer α (CO2 /H2 ) α (CO2 /O2 ) α (CO2 /N2 ) 5

192

110

8.5

20.9

62.2

8

241

122

7.9

21.0

54.9

11

185

125

8.9

20.8

62.8

13

192

121

9.1

20.7

60.5

14

221

125

8.8

20.6

60.0

20

237

108

8.5

21.0

59.3

23

217

106

8.3

20.5

62.1

26

205

109

8.4

21.4

61.1

28

247

112

9

22.0

61.0

29

199

115

8.5

20.9

60.2

As it can be observed from Table 3, all membranes that were measured (transparent and opaque) have shown good and acceptable result for the permeability and selectivity. Carbon dioxide permeability of the PEBAX based membranes was around 120 Barrer, and the value for Polyactive based membranes was around 110 Barrer. For both of polymers, the values were slightly lower, but still comparable to similar systems. The differences did not exceed 10%. The selectivity of the carbon dioxide versus hydrogen, which is the second crucial parameter in membrane evaluation, was in the acceptable range of 8–9 for all systems. Selectivity of carbon dioxide versus nitrogen was around 60, and versus oxygen around 20. If the values for permeability are analyzed based on the pore systems of the zeolite powder, it can be concluded that three-dimensional systems (samples 11, 13, 14, 16, 28, and 29) have shown the best results. Possible explanation of the slight advantage of three-dimensional over one- or two-dimensional system is the orientation of the pores. The pores are oriented completely randomly in the bulk of the polymer, so in the three-dimensional pore systems, there will always be the channel in the frame that would provide the diffusion of the carbon dioxide molecule through the membrane. On the other hand, if one-dimensional pore is perpendicular to the direction of the diffusion, the potential diffusion path is significantly higher and diffusion is slower. This effect might be avoided if the orientation of the zeolite particles and their pores is controlled during the membrane synthesis process. However, the consequence would be that the procedure is too tedious and demanding. The slight decrease in the selectivity between the membranes that contain additive may be attributed to the fact that additive increases the permeability of both carbon dioxide and hydrogen. As a future task, few directions may be identified. The measurements of similar systems should be conducted with the presence of more than one gas in the mixture. This should provide more accurate results concerning the selectivity as the diffusion of two gases will occur simultaneously which should give more accurate results. Another set of measurements may be done for wet gases, by saturation the gases and/or membranes with water vapor, which should give the condition similar to the

Homogenization of the Dense Composite Membranes …

59

conditions in the real industry application. Decrease in the membrane thickness with maintaining mechanical stability is also one possible direction for research.

Conclusion The mixing properties of the different polymers, zeolite powders and additives suitable for the construction of the mixed matrix membranes were studied in this paper. Materials suitable for the construction of the dense mixed matrix membranes were tested. Two different polymers (PEBAX and Polyactive); five different zeolite powders (ITR, IWS, OWE, and CFI), and two different additives (n-C14-TMABr and DMAP) were tested. By the observation of the membranes that were synthesized, it was observed that the addition of the n-C14-TMABr improves the contact between the hydrophobic polymer chains and highly charged zeolite particles. The membranes synthesized with this additive did not contain any pinholes, zeolite particles agglomerates, or voids on the contact area between zeolite and polymer. On the other hand, the presence of DMAP did not improve the properties of the membranes. The main focus was to obtain the membrane with the good carbon dioxide permeability and selectivity of carbon dioxide versus hydrogen. Obtained values for the carbon dioxide permeability was 120 Barrer for PEBAX based membranes and 110 Barrer for Polyactive based membranes, while the selectivity for carbon dioxide versus hydrogen was between eight and nine for all membranes. The permeability obtained for the membranes that are synthesized with three-dimensional pores are slightly higher in comparison with systems with one- and two-dimensional pores membranes. The possible reason for this behavior might be the orientation of the pores in the bulk of the membrane that can influence the diffusion path of the carbon dioxide molecule.

References 1. Desideri U, Corbelli R (1998) CO2 capture in small size cogeneration plants: technical and economic considerations. Energy Convers Manag 39:857–867 2. Rao AB, Rubin ES (2002) A technical, economic, and environmental assessment of aminebased CO2 capture technology for power plant greenhouse gas control. Environ Sci Technol 36:4467–4475 3. The United Nations Framework Convention on Climate Change (1997) Kyoto 4. Meisen A, Xiaoshan S (1997) Research and development issues in CO2 capture. Energy Convers Manag 38:37–42 5. Koros WJ, Fleming GK (1993) Membrane-based gas separation. J Membr Sci 83:1–80 6. Nunes SP, Peinemann KV (2006) Membrane technology in the chemical industry, 2nd edn. Wiley-VCH Verlag GmbH, pp 53–150 7. Ghosal K, Freeman BD (1994) Gas separation using polymer membranes: an overview. Polym Adv Technol 5:673–697 8. Baker RW (2002) Future directions of membrane gas separation technology. Ind Eng Chem Res 41:1393–1411

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9. Yampolskii YP, Pinnau I, Freeman BD (2006) Materials science of membranes. Wiley, England, pp 1–47 10. Baker RW (2000) Membrane technology and applications. McGraw-Hill, New York, pp 301– 392 11. Lin H, Freeman BD (2005) Materials select ion guidelines for membranes that remove CO2 from gas mixtures. J Mol Struct 739:57–74 12. Lin H, Freeman BD (2004) Gas solubility diffusivity permeability in poly(ethylene oxide). J Membr Sci 239:105–117 13. Utracki LA (1995) History of commercial polymer alloys and blends. Polym Eng Sci 35:2–17 14. Bondar VI, Freeman BD, Pinnau I (2000) Gas transport properties of poly(ether-b-amide) segment block copolymers. J Polym Sci Part B Polym Phys 38:2051–2062 15. Deleens G, Legge NR, Holder G, Schroeder HE (1987) Thermoplastic elastomers: a comprehensive review. Hanser Publishers, New York, pp 215–230 16. Yoshino M, Ito K, Kita H, Okamoto KI (2000) Effect of hard-segment polymers on CO2 /N2 gas separation properties of poly(ethylene oxide)-segmented copolymers. J Polym Sci Part B Polym Phys 38:1707–1715 17. Blume I, Pinnau I (1990) Composite membrane, method of preparation and use. U.S. Patent 4,963,165 18. Bondar V, Freeman BD, Pinnau I (1999) Gas sorption, characterization of poly(ether-b-amide) segmented block copolymers. J Polym Sci Part B Polym Phys 37:2463–2475 19. Chen JC, Feng X, Penlidis A (2005) Gas permeation through poly(ether-b-amide) (Pebax 2533) block copolymer membranes. Separation Sci Tech 3:149–164 20. Kim JH, Ha SY, Lee YM (2001) Gas permeation of poly(amide-6-b-ethylene oxide) copolymer. J. Membr Sci 190:179–193 21. Car A, Stropnik C, Yave W, Peinemann KV (2008) Tailor-made polymeric membranes based on segmented block copolymers for CO2 separation. Adv Funct Mater 18(18):2815–2823 22. Kulprathipanja S, Neuzil RW, Li NN (1988) Separation of fluids by means of mixed matrix membranes. US patent 4740219 23. Kulprathipanja S Neuzil RW Li NN (1992) Separation of gases by means of mixed matrix membranes. US patent 5127925 24. Car A, Stropnik C, Yave W, Peinemann KV (2008) PEG modified poly(amide-b-ethylene oxide) membranes for CO2 separation. J Membr Sci 307(1):88–95 25. Paul DR, Kemp DR (1973) The diffusion time lag in polymer membranes containing adsorptive fillers. J Polym Sci Polym Phys 41:79–93 26. Qiu J, Zheng JM, Peinemann KV (2007) Gas transport properties of poly(trimethylsilylpropyne) and ethylcellulose filled with different molecular weight trimethylsilylsaccharides: impact on fractional free volume and chain mobility. Macromolecules 40:3213–3222 27. Wijmans G, Baker RW (1995) The solution–diffusion model: a review. J Membr Sci 107:1–21 28. Shishatskii AM, Yampolski YP, Peinemann KV (1996) Effects of film thickness on density and gas permeation parameters of glassy polymers. J Membr Sci 112:275–285

Hydrodynamics of Gas–Liquid Two-Phase Flow in the Reverse Spray Washing Process Xiao-long Li, Ting-an Zhang, Yan Liu, Gui-li Liu, and Fang Dong

Abstract The efficient separation of fine particles from industrial flue gas is still a challenging task. Reverse spray washing technology is gradually used in dust removal due to its advantages of simple structure and large interphase contacting area. In this paper, the lab-scale reverse spray washing device was established. The changes of gas–liquid two-phase flow pattern, pressure drop, and structure characteristics under different operating conditions were studied. It was found that four types of flow regimes (hollow tapered, foaming, annular, and column types) will form. The foaming type has fast surface renewal speed and large contacting area, which is more conducive to the removal of fine particles in the flue gas. The operation and performance diagram of the nozzle was drawn. The formation of different flow patterns can be controlled by controlling the ratio of liquid to gas and the axial and tangential liquid flow ratio of the nozzle. Keywords Hydrodynamics · Flow regimes · Reverse spray · Turbulent flow · Optimization

X. Li · T. Zhang (B) · Y. Liu · G. Liu · F. Dong Key Laboratory for Ecological Metallurgy of Multimetallic Mineral, Ministry of Education, School of Metallurgy, Northeastern University, Shenyang, Liaoning 110819, China e-mail: [email protected] X. Li e-mail: [email protected] Y. Liu e-mail: [email protected] G. Liu e-mail: [email protected] F. Dong e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_7

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Introduction With the rapid development of chemical engineering, metallurgy, energy, and other industrial processes, the emissions of smelting gas increase year by year. Industrial waste gases usually contain a certain amount of sulfide, nitride, carbon dioxide and fine particles. The fine particulate matter (aerodynamic diameter below 2.5 µm, PM2.5) in the flue gas has a large specific surface area and is easy to absorb toxic substances, such as various heavy metals and polycyclic aromatic hydrocarbons [1, 2]. Once fine particulate matter accumulates in human lungs, it may cause serious respiratory diseases. With the strengthening of people’s environmental awareness, the problems of industrial flue gas purification and dust removal have been paid more and more attention. Wet dedusting is a method of capturing dust by liquid nets, films and droplets in the process of contacting and mixing between dusty airflow and liquid. The reasons why the liquid can catch dust flying in the air may be the inertial collision, diffusion, gravity, interception, etc. [3–6]. Wet dedusting technology can not only remove more than 0.1 µm of fine particles, especially hydrophilic dust, but also can absorb a variety of harmful gases, such as SO2 , so it is widely used in industrial dust removal processes [7–10]. Although wet dedusting technology has been widely used, its complete and accurate dedusting mechanism has not been reported. As a typical wet dedusting technology, the reverse spray scrubber utilizes the intense collision between the high-speed flue gas and liquid jet to form the steady foaming layer. The fine particles in the air are intercepted by the foaming layer and are wrapped by turbulent liquid film and flow into the collecting tank, to achieve the purpose of dust removal. The dust removal mechanism is the collision and mixing between liquid droplets or films and fine particles, and the trapping process involves a quite complex gas–liquid-solid multiphase flow problem [11, 12]. There have been some reports on hydrodynamics and parameter optimization for dynawave washing and reverse spray washing technologies. Li et al. [13] obtained the empirical correlations of liquid trajectory height and resistance through theoretical analysis and many experimental investigations, which provides an important theoretical basis for the application of dynawave scrubber. Zhou et al. [14] studied the hydrodynamic characteristics of the dynawave device, and the gas-liquid flow regimes were divided into foaming, laminar, bubbly and atomization flows. The correlation that can predict the pressure drop was also obtained. Huang et al. [15] found the foaming flow is beneficial to dust removal and the dedusting efficiency can be up to 99%. The scrubber with the diameter of 55 mm achieved the desulfurization efficiency at 89% under the optimum operation conditions. Wang et al. [16] investigated the relationship between the L/G (the ratio of liquid flow rate to gas flow rate), liquid velocity, gas velocity and the dust collection rate. The pressure loss of the dynawave scrubber was less than 1/3 of that of venturi with the same dust removal efficiency. Chen et al. [11] developed a new washing nozzle with three liquid inlets and investigated its hydromechanical properties and gas–liquid two-phase mass transfer performance in the reverse jet scrubber.

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In this paper, combined with the characteristics of three-liquid-inlet nozzle, a hydrodynamic experimental system with a diameter of 120 mm for reverse spray section was established. The effects of axial and tangential liquid flow ratio (A/T), gas and liquid flow rate on the gas–liquid two-phase flow regimes, fluid structure and pressure drop of gas phase were investigated by high-speed photographic technique and pressure sensors, providing theoretical guidance and data support for the optimization and scale-up of reverse spray dust removal technology.

Experimental Work Experimental System Figure 1 shows the physical model of the reverse spray washing dust removal technology. The structural parameters of the physical model and the physical properties of the solution as well as the relevant information of the industrial prototype are clearly shown in Table 1. The whole fluid dynamic experimental system is composed of gas system, liquid reverse spray and circulation system, data and image acquisition system. During the experimental operations, the air enters the reverse nozzle from the air inlet under the suction of the centrifugal fan (9–19Type, 0–700m3 /h), then goes across the foaming layer to the tank, and finally discharges from the system through

Fig. 1 Schematic diagram of whole experimental system. (1-Tank, 2-Flue gas inlet, 3-Nozzle, 4-Liquid tangential inlet, 5-Liquid axial inlet, 6-Centrifugal pump, 7-Loop line, 8-Draught fan, 9-Flue gas outlet, 10-Fine particles, 11-Conical liquid column, 12-Circulating liquid, 13-Pressure measuring points). (Color figure online)

64 Table 1 Geometric parameters and physical properties of fluids in industrial prototype and physical model

X. Li et al. Geometric parameters

Industrial prototype

Physical model

Diameter of tank/m

8.5

0.34

Height of tank/m

18

0.72

Diameter of reverse spray section/m

3

0.12

Height of reverse spray section /m

10

0.4

Physical properties

Industrial prototype

Physical model

Gas

Flue gas

Air

Flow rate/Nm3 ·h−1

280,000

251

Temperature/°C

280

25

Washing liquid

Diluted acid

Water

Density/kg·m−3

1.01 × 103

1.0 × 103

Circulating flow rate/m3 ·h−1

500

0.448

the outlet pipeline on the top of the tank. The nozzles in the experimental system have three liquid inlets [11]. Water, as the liquid phase, in the washing tank is fed to the nozzles from two tangential inlets and one axial inlet after passing through the centrifugal pump, and finally the liquid flows back to the circulation tank, forming a complete liquid circulation system. The advantage of the three-liquid-inlet nozzle is that it can control the ratio of axial and tangential flow rate to transform the fluid regimes and change the dust removal performances.

Pressure Measurement Intensive collision between gas and liquid is a characteristic of reverse spray washing process. Pressure drop of gas phase is caused by gas–liquid momentum exchange and the resistance of liquid phase with a certain thickness. Theoretically, the violent collision generates the excellent contact, and increases the probability of fine particles being captured by liquid phase, thus improving the dust removal rate. The pressure drop can be used to quantitatively analyze the collision strength between the gas and liquid phases under different operating conditions. In the experiment, the U-shaped differential pressure device was employed to measure the gas phase pressure at the measuring points shown in Fig. 1.

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Measurement of Flow Patterns The change of operating conditions affects the collision intensity and mixing performances of gas and liquid phases, thus forming different types of flow regimes, and determining the capture efficiency of fine particles in the air. Therefore, it is essential to reveal the flow states of gas–liquid two-phase under different conditions. Highspeed photographic technique (Olympus I-Speed 3, resolution 1280 × 1024, CMOS sensor size 21.504 × 26.88 mm) was utilized to record the gas–liquid two-phase flow regimes and fluid structures under different operating parameters. In the experiment, the image acquisition frequency was set at 400 FPS to capture transient motion images of gas–liquid two-phase flow.

Results and Discussion Flow Regimes of Gas–Liquid Two-Phase Flow In reverse spray washing tube, the gas phase (from top to bottom) collides with the liquid jet (from bottom to top) at high speed, accompanied by the momentum and mass transfer of soluble gas. With the changes of axial and tangential liquid flow ratio (A/T), four flow regimes, namely hollow tapered, foaming, annular and column types shown in Fig. 2, form in the reverse spray section.

Fig. 2 Flow regimes of gas–liquid two-phase flow, a Hollow tapered flow; b Foaming flow; c Annular flow; d Column flow. (Color figure online)

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When the tangential liquid flow rate is much larger than the axial figure, small A/T, the flow pattern of gas–liquid two-phase flow is hollow tapered type, as shown in Fig. 2a with the A/T at 0.1. Meanwhile, the gas flow rate is controlled at 150m3 /h and the total liquid flow rate is set as 0.62m3 /h. Due to the large spray angle, the liquid spins counterclockwise and hits the wall of tube, creating a small foaming zone. After reaching the top, one part of the liquid flows down along the wall and the other part flows down along the center of the tube for the secondary downstream washing of the gas. Therefore, the pressure loss of gas for hollow tapered flow is large. Foaming flow formed, as shown in Fig. 2b, when the axial flow rate was increased. The top of the foaming layer is turbulent strongly under the action of inertia force and intensive collision of high-speed airflow, and is broken into many tiny drops. The mass transfer between gas and liquid is further enhanced due to the continuous renewal of gas- liquid interface. The foaming zone covers a large area and liquid film thickness increases, which improves the removal efficiency of fine particles in flue gas. The gas–liquid two-phase flow will alter to the annular flow when the axial and tangential liquid flow ratio reaches 2.5 (shown in Fig. 2c). The trajectory height of the liquid phase is significantly higher than that of the foaming type, and the top liquid phase is broken into large droplets. However, due to the reduction of tangential flow, the coverage area of the liquid is significantly reduced. In addition, there is a gap between the center of the annular flow and the side wall of the washing tube, which results in that most airflow takes a shortcut and greatly reduces the dust removal efficiency. When the axial flow continues to increase, the liquid column is reached when the A/T is 4.0, as shown in Fig. 2d. The liquid flow is straight up in a column and flows through the entire reverse spray section. After reaching the top, the liquid flow spreads out in all directions and then flows down along the axis. The spray angle of the liquid column type is very small, and the covering area of the liquid column flow is also small, so the contact condition between the gas–liquid phases is poor.

Hydrodynamics of Gas–Liquid Two-Phase Flow with A/T Pressure Drop of Gas Phase Changing the axial and tangential liquid flow ratio of the nozzle can alter the flow patterns of gas–liquid two-phase flow. As the liquid spray angle increases, the covering area of liquid phase increases, so the contact surface and the collision strength between gas and liquid increase, leading to an increment in the pressure drop for gas phase. A significant downward trend for pressure drop with the increase of the A/T can be found in Fig. 3. Moreover, when the A/T is less than 2.0, the downward trend is more obvious, mainly because the gas–liquid flow patterns are foaming type or hollow tapered flow. With the increase of the A/T, the liquid covering

Hydrodynamics of Gas–Liquid Two-Phase Flow … Fig. 3 Pressure drop characteristics of gas phase with different liquid inlet modes. (Color figure online)

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600

L=0.73m3/h

550 500

G=150m3/h G=250m3/h G=350m3/h

ΔP/Pa

450 400 350 300 250 200 150 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

A/T

area decreases and the tangential force also decreases, so the foaming area and thickness decrease sharply. Therefore, the existence of the foaming flow is the result of the intensive collision of gas and liquid phases, which is the ideal flow regime for dedusting process. When the A/T is greater than 2.0, the downward trend for pressure drop tends to be flat, mainly because the liquid flow pattern presents annular pattern and liquid column pattern. As a large amount of airflow passes through the thin liquid film or the gap between the liquid columns, causing the little pressure loss. Meanwhile, with the increase of the axial flow rate, the thickness of the liquid film decreases dramatically, which reduces the resistance for gas phase. To sum up, the pressure drops of the hollow tapered type and the foaming type are higher than that of the annular and the liquid column flow. The main reason is that the spray angle of gas–liquid two-phase flow is large, so the contacting surface is increased, resulting in the increase of the collision energy loss between gas and liquid. Moreover, the thickness of the liquid phase for hollow tapered and the foaming flow is large, and the flow resistance as the gas passing through the liquid layer is improved.

Trajectory Height of Liquid Phase Figure 4 shows that the trajectory height of the liquid increases with the increment in the axial and tangential liquid flow ratio. When the A/T is less than 1.0, the trajectory height tended to increase gently. The main reason is that when the A/T is small, the spray angle is large, generating weak axial momentum of the liquid. The liquid is basically in the form of liquid film, and the impact of the airflow on the liquid spray is strong, so the trajectory height increases slightly. When the A/T is greater than 1.0, the trajectory height increases significantly, because the axial momentum of the liquid increases, and the gas–liquid contacting area decreases dramatically. The

68 55 50

Trajectory height/cm

Fig. 4 Trajectory height of liquid phase with different liquid inlet modes. (Color figure online)

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45 40 35 30 25 20

G=150m3/h G=250m3/h G=350m3/h

15 10 5 0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

A/T

thickness of the liquid film becomes smaller, and most of the airflow passes through the thin liquid film, which weakens the momentum loss of the airflow. It can also be seen from Fig. 4 that when the air flow rate reaches 350 m3 /h and the A/T is greater than 1.0, the trajectory height is basically similar to that of 250 m3 /h, mainly because the coverage area decreases when the A/T is greater than 1.0. When the air flow rate reaches 250 m3 /h, the momentum of the gas phase is significantly higher than that of the liquid phase. At this point, a large amount of high-speed airflow can directly break the top liquid into droplets, and the rest flow through the thin liquid film.

Load Performance The axial and tangential liquid flow ratio is an important factor affecting the gas– liquid flow pattern. It can be seen from Fig. 5 that the area of the foaming zone (area ABCDE) gradually decreases with the increase of the A/T. Figure 5a is the load performance diagram when the A/T is 0.5. At this time, the ratio of liquid flow rate to the gas flow rate (L/G) ranges from 2.62 × 10–3 to 6.05 × 10–3 , and the gas flow is controlled between 180 and 350 m3 /h, which generates the steady foaming flow. Figure 5b illustrates that the operating area for foaming flow shrinks slightly after increasing the A/T from 0.5 to 1.0. The curve CD moves upward, while the curve BC moves to the right, causing the area ABCDE to decrease. The main reason is that when the A/T increases, the spray angle of liquid decreases and the covering area of the liquid also decreases. Therefore, only by increasing the liquid flowrate can keep the same surface area as the A/T of 0.5, decreasing the operating elasticity. As can be seen from Fig. 5c, when the A/T increases to 1.5, the operating area for foaming flow is obviously smaller than that of 0.5. The curve AB moves down, and the curve BC moves to the right with the curve CD moving up. The main reason is that the axial liquid flow rate increases obviously and the tangential one decreases.

Hydrodynamics of Gas–Liquid Two-Phase Flow … 390

Gas flow rate /m3 .h-1

330

(a)

A

360 B

300 270

C

240 210

E

180

D

150 120

0.0024

0.0036

0.0048 L/G

0.0060

0.0072

390

(b)

Gas flow rate /m3 .h-1

360 330

A

B

300 270

C

240 210

E

180 D

150 0.0024

0.0036

0.0048 L/G

0.0060

390

0.0072

(c)

360

Gas flow rate /m3 .h-1

Fig. 5 Load performances of reverse spray washing process, a A/T = 0.5; b A/T = 1.0; c A/T = 1.5

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330 300

A B

270 240

C

210 E

180

D

150 120

0.0024

0.0036

0.0048 L/G

0.0060

0.0072

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A small part of the airflow breaks the liquid flow into small droplets, while most of the airflow passes through the thin liquid film, thus weakening the blocking effect of the liquid on the airflow and reducing the pressure loss. Therefore, only in the case of high liquid flow rate, the axial liquid column can block the flow and strengthen the collision between gas and liquid to achieve the performance of the foaming flow.

Hydrodynamics of Gas–Liquid Two-Phase Flow with Gas Flow Rate Gas flow rate is an important factor affecting gas–liquid flow pattern and the dust removal performance of scrubber. Figure 6 shows that the pressure drop increases with the increase of gas flow rate. When the gas flow rate is less than 200 m3 /h, and the pressure loss is mainly due to the blocking effect of the liquid on the airflow. However, when the gas flow rate is 200–250 m3 /h, the pressure drop curve shows obvious changes, indicating that the gas–liquid two-phase flow has changed, that is, alter to the foaming flow. Pressure loss increases significantly after the gas flow rate is greater than 250 m3 /h, which is due to the decrease of trajectory height and the increase of travel resistance of liquid. In addition, the airflow also needs to pass through the foaming layer, so the pressure drop increases significantly. When the gas flow rate continues to increase to 300 m3 /h, the pressure drop is mainly caused by the travel resistance. Trajectory height has an important influence on the mass transfer effect between gas and liquid. Generally, the higher the trajectory height is, the longer the contact time between gas and liquid is, the better the mass transfer effect will be. However, the trajectory height also determines the resistance loss and momentum loss. The greater the trajectory height is, the greater the energy consumption loss will be. Therefore, it 800

Fig. 6 Effect of gas flow rate on pressure drop. (Color figure online)

700

P/Pa

600 500

∇

400 300

L=0.616m3/h L=0.852m3/h L=1.088m3/h

200 100

150

200 250 300 Gas flow rate / m3·h-1

350

400

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18

Fig. 7 Effect of gas flow rate on trajectory height. (Color figure online)

L=0.616m3/h L=0.852m3/h L=1.088m3/h

Trajectory height /cm

17 16 15 14 13 12 11 10 9

150

200 250 300 Gas flow rate /m3·h-1

350

400

is of great significance to select the appropriate trajectory height to guide the design and industrial operation of scrubber. As can be seen from Fig. 7, the trajectory height of the liquid decreases with the increase of gas flow rate, and the curve of trajectory height also has a sudden change point. When the liquid flow rate is fixed, the larger the gas flow rate is, the stronger the impact force on the liquid will be, so the trajectory height will gradually decrease. However, at the sudden change point, the trajectory height curve will change due to the change of the flow pattern of the gas–liquid two-phase flow.

Hydrodynamics of Gas–Liquid Two-Phase Flow with Liquid Flow Rate The liquid flow rate influences the flow pattern and the collision between the liquid and the dusty airflow, which decides the dedusting performance of the washing scrubber. As can be seen from Fig. 8, the pressure drop increases with the increase of liquid flow rate. The pressure drop also increases with the increase of gas flow rate under the same liquid flow rate. Figure 9 shows that the trajectory height increases with the increase of liquid flow rate, and decreases with the air flow rate under the same liquid flow rate. This is mainly because at a certain A/T, the thickness of the liquid film increases when the liquid flow rate increases, and the resistance of the liquid film to the airflow increases. In addition, the trajectory height of liquid increases, the time for gas to pass through the liquid layer increases, and the flow resistance between gas and liquid increases. However, with the increase of air flow rate, the covering area of liquid film increases, and the collision between the gas and liquid phases is more intensive, so the energy loss is large.

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Fig. 8 Effect of liquid flow rate on pressure drop. (Color figure online)

800

G=150m3/h G=250m3/h G=350m3/h

700

P/Pa

600 500

∇

400 300 200 100

0.6

18

Fig. 9 Effect of liquid flow rate on trajectory height. (Color figure online)

1.1

G=150m3/h G=250m3/h G=350m3/h

17

Trajectory height /cm

0.7 0.8 0.9 1.0 Liquid flow rate /m3 .h-1

16 15 14 13 12 11 10 9

0.6

0.7 0.8 0.9 1.0 Liquid flow rate /m3 .h-1

1.1

Conclusions In this paper, a lab-scale reverse spray washing hydrodynamic system was established, and the effects of liquid inlet mode, liquid flow rate and gas flow rate on gas–liquid two-phase flow pattern, fluid structure and gas phase pressure characteristics were investigated, and load performance was drawn under different conditions. The experimental results show that: (1) By changing the axial and tangential liquid flow ratio of the nozzle and the liquid/gas ratio of the scrubber, the gas–liquid two-phase flow in the washing tube can be divided into four flow types: hollow tapered, foaming, annular and column types. Foaming flow is more suitable for separating the fine particles in flues gas.

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(2) With the increase of A/T, the pressure drop decreases and the trajectory height increases. The pressure drops of hollow tapered and foaming types are higher than that of the annular and column types. (3) The operating area of the foaming zone gradually decreases with the increase of the A/T. The suitable A/T is 0.5 and the ratio of liquid flow rate to the gas flow rate (L/G) ranges from 2.62 × 10–3 to 6.05 × 10–3 , the gas flow is controlled between 180 to 350 m3 /h, which generates the steady foaming flow. (4) When the liquid flow rate is constant, the pressure drop gradually increases with the increase of gas flow rate, and the trajectory height decreases with the increase of gas flow rate. When the gas flow rate is constant, the pressure drop and trajectory height gradually increase with the increase of liquid flow rate. Variables A G L T A/T L/G

the axial liquid flow rate the gas flow rate the liquid flow rate the tangential liquid flow rate the axial and tangential liquid flow ratio the ratio of liquid flow rate to the gas flow rate.

Acknowledgements This work was supported by the National Key Research and Development Project, China (2017YFC0210403).

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Influence of Coal Reactivity on Carbon Composite Briquette Reaction in Blast Furnace Zi Yu, Tao Rong, and Huiqing Tang

Abstract In this research, using the carbon composite briquette (CCB) containing carbon: 20.30 wt%, Fe3 O4 : 29.70 wt%, FeO: 39.70 wt%, metallic iron: 1.57 wt%, and gangue: 8.73 wt%. the reaction behavior of the CCB in BF and the influence of coal reactivity was examined by the numerical investigation. Results showed that the development of the CCB reaction in BF was divided into six stages. The initial temperature of the CCB self-reduction was 850 K, the dominant temperature range in CCB reaction being effective for BF energy-saving was from 1000 K to 1150 K, its final reduction fraction, and final carbon conversion was 1.0 and 0.9, respectively. By decreasing the activation energy of coal gasification, the initial temperature of CCB self-reduction became lower, the effective temperature range of CCB reaction for BF energy-saving was wider, and the final carbon conversion increased, indicating to improve the coal reactivity in CCB could intensify the effect of its reaction on BF energy-saving. By increasing the activation energy of coal gasification, the initial temperature became higher, the effective temperature range of CCB reaction for BF energy-saving was narrower, and the final carbon conversion decreased, reflecting that to reduce coal reactivity in CCB could weaken the effect of its reaction on BF energy-saving. Keywords Carbon composite briquette · Coal reactivity · Reaction behavior · Blast furnace

Introduction In the foreseeable future, the manufacturing route (blast furnace (BF) ironmakingbasic oxygen furnace (BOF) steelmaking) would continue to be the dominant route for producing iron and steel all over the world. Although this process is wellestablished and highly efficient, it is facing challenges to reduce energy consumption and CO2 emission for more sustainable development. As the main sector in steel Z. Yu · T. Rong · H. Tang (B) State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, 30 Xueyuan Rd., Beijing 100083, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_8

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production, the BF accounts for approximately 50% of the total energy consumption and generating most CO2 . Consequently, decreasing energy consumption and reducing CO2 emission in BF is important. Carbon composite briquette (CCB) represents a class of materials produced by agglomerating fine powders of carbonaceous materials with iron ore fines. Charging CCB in BF is considered to be a state-of-the-art technology to achieve these targets [1]. Much work has been performed on the preparation method of CCB [2–4] and some types of CCB have been adopted in commercial plants [4, 5]. Presently, to understand CCB reaction behavior in BF becomes important as the CCB reaction directly related to the change of sinter reduction and coke gasification in the BF upper part, and thus to the improvement of BF energy efficiency. CCB reaction behavior in BF could be influenced by many factors. Generally, the reactivity of the employed carbon material plays significant roles. Presently, all of these studies were carried out under simulated BF conditions. However, in actual practice, the BF is with high pressure. Therefore, these researches are still insufficient to understand how the coal reactivity influences CCB reaction in BF. This research aimed to reveal correctly the CCB reaction in BF by modeling. Thereafter, the influence of coal reactivity on CCB reaction behavior in BF was investigated.

Method of Numerical Investigation CCB Reaction Model The CCB model developed by the present authors [6] was applied. The CCB model is applied to describe CCB reaction behavior above the cohesive zone (CZ) in BF (the temperature range of CZ is from 1473 to 1673 K). The gangue in CCB usually includes SiO2 , CaO, Al2 O3 , and so on. In the BF upper part, CCB softening or melting could not occur and reactions involving gangue components are not active because the solid temperature is less than 1473 K, so they are not considered in the model. The following is the outline. The model is established for the reaction of a single spherical CCB in BF and thus is one dimensional in the radial direction. The model includes reactions that occurred in CCB, the internal gas diffusion, and the mass transfer between CCB and BF gas. The concept of the model is shown in Fig. 1. Both the gas phase and the solid phase are considered in the model. The gas phase is an ideal gas and includes CO, CO2 , and N2 . The solid phase includes Fe2 O3 , Fe3 O4 , FeO, Fe, and C. Assumptions in the model are (1) CCB volume is constant in the reaction process, (2) mass transfer by convection is not considered, (3) the involved reactions are the reactions given in Table 1. Governing equations of the gas phase are built based on the mass conservation of CO and CO2 in the CCB. They are Eqs. (1, 2).

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Fig. 1 Concept of CCB reaction model in BF. (Color figure online)

Table 1 Reactions involved in model No

Reaction

Reaction rate/(mol·m−3 s−1 )

1

3 Fe2 O3 (s) + CO(g) = 2 Fe3 O4 (s) + CO2 (g)

Ri = 1500

2

Fe3 O4 (s) + CO(g) = 3 FeO(s) + CO2 (g)

3

FeO(s) + CO(g) = Fe(s) + CO2 (g)

4

C(s) + CO2 (g) = 2 CO(g)

(PCO −PCO2 /K i )/(8.314T ) (1 − f i )2/3 ) (K i /(ki (1+K i ))

(i = 1,

2, 3) k1 = exp(−1.445 − 6038/T ) K 1 = exp(7.255+3720/T ) k2 = 1.70 exp(2.515 − 4811/T ) K 2 = exp(5.289−4711/T ) k3 = exp(0.805 − 7385/T ) K 3 = exp(−2.946 + 2744.63/T ) R4 = ρC,0 k4 (1− f 4 )2/3 (PCO2 /1.01 × 105 )/MC k4 = k0 exp(−E C /RT ), k0 = 1500

1 ∂ ∂(α Pco2 ) ∂ Pco2 = 2 (r 2 Deff, CO2 -N2 ) + RT (R1 + R2 + R3 − R4 ) ∂t r ∂r ∂r

(1)

1 ∂ ∂ Pco ∂(α Pco ) = 2 (r 2 Deff, CO - N2 ) + RT (2R4 − R1 − R2 − R3 ) (2) ∂t r ∂r ∂r √ √ where, Deff,CO-N2 =DCO-N2 α 2 / 3 and Deff,CO2 -N2 =DCO2 -N2 α 2 / 3. For Eqs. (1, 2), the boundary conditions are Eqs. (3–5) and the initial conditions are Eq. (6). r =0: r = d/2 : Deff, CO-N2

∂ PCO2 ∂ PCO = 0, = 0. ∂r ∂r

(3)

∂ PCO 1/3 = (DCO-N2 (2.0 + 0.6Re1/2 ScCO-N2 )/d)(PCO − PCO,BF ) ∂r (4)

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∂ PCO2 ∂r 1/3 = (DCO-N2 (2.0 + 0.6Re1/2 ScCO2 -N2 )/d)(PCO2 − PCO2, BF )

r = d/2 : Deff, CO2 - N2

t= 0,r ∈ (0, d/2) : PCO = PCO,BF ,PCO2 = PCO2 ,BF

(5) (6)

where, Re = u g,BF ρg,BF d/μg , ScCO-N2 = μg /(ρg,BF DCO-N2 ) , and ScCO2 -N2 = μg /(ρg, BF DCO2 -N2 ) . The governing equations of the solid phase are constructed based on the mass conservation of the solid phase species. They are Eq. (7). ∂ρ j /∂t = S j

(7)

where j = Fe2 O3 , Fe3 O4 , FeO, Fe, and C; SFe2 O3 = 3MFe2 O3 (−R1 ), SFe3 O4 = MFe3 O4 (2R1 − R2 ), SFeO = MFeO (3R2 − R3 ), SFe = MFe R3 , and SC = −MC R4 . The initial conditions for Eq. (7) are Eq. (8). t = 0, r ∈ (0, d/2); ρ j = ρ j,0

(8)

Equations (1, 2) are spatially and temporally discretized using an explicit scheme. Eq. (7) is solved using an explicit time integration method. Eqs. (1, 2) and (7) are solved simultaneously.

Modeling CCB Reaction in BF In the present research, the CCB reaction behavior was examined along a burden flowing path near the BF mid-radial zone (Fig. 2a). The BF variables required in the CCB model were obtained from the simulation results of a BF of 2500 m3 under normal operation conditions [7]. In case that the CCB mixing ratio in the solid burden is small (e.g., less than 10%), the BF in-furnace phenomena are not significantly changed. The scope of the path for investigation was from the burden surface to the upper surface of the CZ. Along the path, variations of gas pressure, gas composition, solid temperature, gas physical velocity with the solid flowing time were plotted in Fig. 2b. In Fig. 2b, the CCB  s descending time was counted from the burden surface and was calculated by 0 (1/VS )ds, where, s is the distance on the path from the burden surface, (m); and V s is the solid physical velocity, (m/s). In the simulation, these variables formed the boundary conditions of the CCB reaction model. At a given time t, the CCB mass-loss degree was calculated by 1.0 −  t  d/2 (4π 0 0 (MO (R1 +R2 +R3 )+MC R4 )r 2 dr dt)/(m C,0 +m O,0 ), the CCB reduction  2 fractionby 1.0−(4π d/2 0 (3.0ρFe2 O3 /MFe2 O3 +4.0ρFe3 O4 /MFe3 O4 +1.0ρFeO /MFeO )r dr )/

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Fig. 2 Solid flowing path for simulation: a illustration of the path in BF, and b change of BF variables along the path. (Color figure online)

 d/2 (1.5m 0 TFe /MFe ), the CCB carbon conversion by 1.0−(4π 0 ρC r 2 dr )/m C,0 , the  d/2 CO generation rate of CCB by 4π 0 (2R4 − R1 − R2 − R3 )r 2 dr , and the CO2  d/2 generation rate of CCB by 4π 0 (R1 + R2 + R3 − R4 )r 2 dr .

Results and Discussion Development CCB Reaction in BF The CCB prepared in the previous research [6] was employed as a base sample (sample A) in the present research. Sample A had a diameter of d = 0.015 m and a mass of m = 4.8 g. Its chemical composition is given in Table 2. Sample A was prepared using cold briquetting followed by heat treatment, therefore, volatile in sample A was negligible. For sample A, αgs = 1500 m2 · m-3 , and E c kJ/mol. The values of αgs and E c here were validated by experiments [6]. By modeling, the development of the reaction of sample A in BF was elucidated, and the results are shown in Fig. 3. Figure 3a is the curves of CCB reduction fraction and CCB carbon conversion along the path, Fig. 3b is the curves of CO and CO2 generating rates in CCB along the path, and Fig. 3c is the curve of the CCB mass-loss degree along the path. From Fig. 3, the reaction development of sample A along the path is assumed to Table 2 Chemical composition of CCB (wt%) Carbon 20.30

Fe3 O4 29.70

FeO 39.70

Metallic iron 1.57

Gangue (SiO2 , CaO, Al2 O3 , …) 8.73

Total iron 54.00

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Fig. 3 Reaction behavior of sample A along the path in BF: a changes in reduction fraction and carbon conversion of CCB along the path, b changes in CO and CO2 gas generating rates of CCB along the path, and c change in the mass-loss degree of CCB along the path. (Color figure online)

be the following. When the temperature reached 500 K, the reduction of iron ore in CCB started. The reduction was attributed to the BF gas as it consumed CO and contributed CO2 to the BF gas. When the temperature reached 850 K, the carbon gasification was triggered. Owing to some CO2 in CCB was converted to CO by reaction (4), the reduction of iron ore became faster. When the temperature was in the range from 950 to 1150 K, both the iron ore and the coal exhibited high reaction rates. The CCB then contributed both CO and CO2 to BF gas, indicating that the CCB underwent its full self-reduction. After the temperature increased to more than 1150 K, the iron ore reduction was finished and the carbon gasification still existed, however, the carbon gasification diminished quickly as the temperature further increased. The CCB then consumed CO2 and contributed CO to BF gas. Between 950 and 1100 K, a temperature of 1000 K could be identified. Before the temperature reached 1000 K, the CCB produced more CO2 than CO, and after the temperature reached 1000 K, the CCB produced more CO than CO2 . Therefore, the development of CCB reaction along the path could be sequentially divided into six stages: from 500 to 850 K, the CCB iron ore was reduced by BF gas (stage 1); from 850 to 950 K, the CCB underwent partial self-reduction and reduction by BF gas (stage 2); from 950 to 1000 K, the CCB underwent CO2 -rich self-reduction (stage 3); from 1000 to 1100 K, the CCB underwent CO-rich self-reduction (stage 4); from 1100 to 1150 K, the CCB underwent its self-reduction and gasification by BF gas

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(stage 5); and when the temperature was above 1150 K, the CCB carbon was gasified by BF gas (stage 6). The above analysis indicates that the initial temperature of self-reduction in sample A was 850 K. In the stages with CCB self-reduction involved, sample A presented a high reaction rate. When the temperature was higher than 1000 K, the reaction of sample A can increase the local CO partial pressure and decrease the local CO2 partial pressure in BF gas (at 1000 K, PCO /PCO2 = 1.0 in BF gas), indicating that the reaction in sample A has the effects of BF energy-saving (suppressing coke gasification and prompting sinter reduction). The dominant temperature range for BF energy-saving was from 1000 to 1150 K. From Fig. 3, it could be observed that the final reduction fraction of sample A was 1.0 and the final carbon conversion of sample B was 0.90.

High Coal Reactivity Higher coal reactivity could be represented by decreasing the activation energy of coal gasification. The reaction development of the CCB with E C = 118 kJ/mol (sample B) was analyzed, and the results are shown in Fig. 4. From Fig. 4, it is known that temperature ranges of all individual stages in sample B are stage 1: 500–750 K, stage 2: 750–800 K, stage 3: 800–820 K, stage 4: 820–880 K, stage 5: 880–1140 K, and stage 6: >1140 K. The initial temperature of self-reduction in sample B was 750 K, and the dominant temperature range in the reaction of sample B for BF energy-saving was from 880 to 1140 K. Form Fig. 4, it could be observed that both its final reduction fraction and its final carbon conversion were close to 1.0. Therefore, compared to sample A, the initial temperature of self-reduction in sample B became lower, and the temperature range in the reaction of sample B for BF energy-saving became wider, and the final carbon conversion in sample B was higher. From the above analysis, it is known that to improve the coal reactivity in CCB could intensify the effect of CCB reaction on BF energy-saving.

Low Coal Reactivity Lower coal reactivity could be represented by increasing the activation energy of coal gasification. The reaction development of CCB sample with E C = 158 kJ/mol (sample C) was analyzed and the results are shown in Fig. 5. Figure 5 shows that temperature ranges of each stage in sample C are: stage 1: 550–980 K, stage 2: 980– 1150 K, stage 3:1150–1155 K, stage 4: 1155–1170 K, stage 5:1170–1180 K, and stage 6: >1180 K. Therefore, the initial temperature of self-reduction in sample C was 980 K, and the dominant temperature range in the reaction of sample C for BF energy-saving was from 1155 to 1180 K. From Fig. 5, it could be seen that its final reduction fraction was 1.0, while its final carbon conversion was 0.4.

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Fig. 4 Reaction of sample B along the path in BF: a changes in reduction fraction and carbon conversion, and b changes in CO and CO2 gas generating rates of CCB along the path. (Color figure online)

Compared to sample A, the initial temperature of self-reduction in sample C was higher, and the temperature range for BF energy-saving in sample C was far narrower. Moreover, compared to sample A, the final carbon conversion in sample C was significantly decreased. As the ungasified carbon particles may lead to clogging in the lower part of BF [8], to charge sample C in BF is not desired. From the above analysis, it is known that to reduce the coal reactivity in CCB would weaken the effect of CCB reaction on BF energy-saving, and may increase the risk of clogging in the lower part of BF.

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Fig. 5 Reaction of sample C along the path in BF: a changes in reduction fraction and carbon conversion, and b changes in CO and CO2 gas generating rates. (Color figure online)

Conclusions In this study, using the CCB containing carbon: 20.30 wt%, Fe3O4: 29.70 wt%, FeO: 39.70 wt%, metallic iron: 1.57 wt%, and gangue: 8.73 wt% as the base sample. CCB reaction behavior in BF was numerically investigated; and the influences of coal reactivity on its reaction behavior were examined as well. Some conclusions could be drawn and are the following. (1) The development of the CCB reaction in BF included six stages. They were reaction with BF gas, reduction with BF gas and partial self-reduction, CO2 -rich self-reduction, CO-rich self-reduction, partial self-reduction, gasification by BF

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gas, and carbon gasification. The initial temperature of CCB self-reduction was 850 K, The main effective stage for BF energy-saving was stage 4. (2) By decreasing the activation energy of coal gasification, the initial temperature of CCB self-reduction became lower, the temperature range in its reaction for BF energy-saving became wider, and its final carbon conversion was increased, indicating to improve the coal reactivity could intensify the effect of CCB reaction for energy-saving. (3) By increasing the activation energy of coal gasification, the initial temperature of CCB self-reduction became higher, the temperature range in its reaction for BF energy-saving became narrower, and its final carbon conversion was decreased, indicating to reduce the coal reactivity could weaken the effect of CCB reaction for BF energy-saving and increase the risk of clogging in the BF lower part. Acknowledgements The authors thank the National Natural Science Foundation of China (No. U1960205), and the State Key Laboratory of Advanced Metallurgy USTB for the financial support of this work. Nomenclature Meanings of the symbols in this paper are the same as those in Ref. [6].

References 1. Kasai A, Toyota H, Nozawa K et al (2011) Reduction of reducing agent rate in blast furnace operation by carbon composite iron ore hot briquette. ISIJ Int 51(8):1333–1335 2. Narita CYB, Mourao M, Takano C (2015) Development of composite briquettes of iron ore and coal hardened by heat treatment. Ironmak Steelmak 42(7):548–552 3. Singh M, Björkman B (2007) Testing of cement bonded briquettes under laboratory and blast furnace conditions Part 1—effect of processing parameters. Ironmak Steelmak 34(1):41–53 4. Kawanari M, Matsumoto A, Ashida R et al (2011) Enhancement of reduction rate of iron ore by utilizing iron ore/carbon composite consisting of fine iron ore particles and highly thermoplastic carbon material. ISIJ Int 51(8):1227–1233 5. Yokoyama H, Higuchi K, Ito T et al (2012) Decrease in carbon consumption of a commercial blast furnace by using carbon composite iron ore. ISIJ Int 52(11):2000–2006 6. Tang HQ, Sun YJ, Rong T (2019) Experimental and numerical investigation of of carbon composite briquette in blast furnace. Metals 10(1):49 7. Tang HQ, Rong T, Fan K (2019) Numerical investigation of applying high-carbon metallic briquette in blast furnace. ISIJ Int 59(5):810–819 8. Gavel DJ (2017) A review on nut coke utilization in the blast furnaces. Mater Sci Tech 33(4):381– 387

Part II

Low Energy Mesoporous Silica Recovery from a Nigerian Kaolinite Ore for Industrial Value Additions Alafara A. Baba, Abdullah S. Ibrahim, Dele P. Fapojuwo, Kuranga I. Ayinla, Daud T. Olaoluwa, Sadisu Girigisu, Mustapha A. Raji, Fausat T. Akanji, and Abdul G. F. Alabi

Abstract The increasing industrial demands for mesoporous silica warrant the continuous development of less energy intensive, cheaper, and eco-friendly technologies. Mesoporous silica from an indigenous kaolinite ore was synthesized by hydrochloric acid dissolution. Experimental reaction parameters were optimized to maximize silica recovery and the solid product was characterized using Xray diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Brunauer–Emmett–Teller (BET) Nitrogen adsorption– desorption measurements. The dissolution data show the rate of reaction increase with increasing concentration temperature but with decreasing solid-to-liquid ratio. The Avrami model proved most suitable model for describing the leaching process with Avrami parameter n value of 0.8 and calculated apparent activation energy of 7.22 kJ/mol suggested a diffusion controlling rate mechanism. The mesoporous product as characterized gave a pore size of 3.67 nm at the maximum probability and possessed suitable morphological applications as additive in polymers and catalysis. Keywords Kaolinite · Mesoporous silica · Avrami · Industrial application A. A. Baba (B) · A. S. Ibrahim · K. I. Ayinla · D. T. Olaoluwa · S. Girigisu · M. A. Raji · F. T. Akanji Department of Industrial Chemistry, University of Ilorin, P.M.B. 1515, Ilorin 240003, Nigeria e-mail: [email protected] D. P. Fapojuwo Centre for Synthesis and Catalysis, University of Johannesburg, Johannesburg, South Africa D. T. Olaoluwa Science Laboratory Technology Department, Federal Polytechnic Ede, Ede, Nigeria S. Girigisu Science Laboratory Technology Department, Federal Polytechnic Offa, P.M.B. 420, Offa, Nigeria F. T. Akanji Chemistry Advance Research Centre, Sheda Science and Technology Complex, FCT, P.M.B 186, Garki, Abuja, Nigeria A. G. F. Alabi Department of Material Science and Engineering, Kwara State University, P.M.B 1530, Malete, Nigeria © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_9

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Introduction Today, various clay minerals such as talc, halloysite, and kaolin have been utilized in the development of various nanomaterials [1]. Among these nanomaterials, mesoporous materials preparation, characterization with possible industrial applications have been the most popular. This among others, is majorly attributed to its wide and diverse applications in catalysis, adsorption, drug delivery, reinforcement of elastomer products and so on [2, 3]. Thus, efforts in meeting the high demands for mesostructured materials has led to the development of varieties of starting materials as well as low energy consuming preparation techniques. Consequently, kaolinite with a layered tetrahedral structure of silica and octahedral alumina, high surface area, non-toxic and small particle size was proposed for the preparation of mesoporous silica and alumina [4]. In obtaining the aforementioned industrial raw materials, various processing techniques have been utilized to fabricate porous materials including sol–gel technique or micro-emulsion and pillaring. But the techniques to prepare this material need expensive organic raw materials, a large amount of organic liquid, expensive precursors and rigorous reaction conditions; so, it is very difficult to be widely applied. The selective leaching method involving different lixiviants (acids/bases and their salts) are often used to extract silica in the form of either silicic acid or alkali metal silicate solutions. This causes exchangeable cations to be replaced by H+ ions, while the SiO4 groups are largely intact in the acid environment, as Al3+ escape out of octahedral sites [3, 4]. Consequently, this study was carried out using hydrochloric acid to produce mesostructured silica with improved specific surface area for defined industrial uses at optimized conditions. Also, the effects of hydrochloric acid concentration, reaction temperature and solid–liquid ratio on the rate of pore formation were evaluated. The aforementioned reaction parameters will help to broaden our understanding of dissolution kinetics in the scale-up process for low energy mesoporous silica production from the local kaolinite ore.

Materials and Methods Materials Kaolinite sample sourced from Share area of Kwara State, Nigeria was thermally activated at 850 °C to produce metakaolin (MK) which was used as the starting material for the preparation of the porous materials. The starting material and synthesized products were accordingly characterized by using the MINI PAL 4 EDXRF spectrometer, EMPYREAN X-ray diffractometer, the FEI Nova NanoSEM 230 TEM and Nitrogen adsorption/desorption isotherms at 77 K using a Micromeritics BET, Tristar II 3020. The measurements were performed after degassing overnight at 90 °C. Concentrated hydrochloric acid (HCl) of analytical BDH grade was used in this study. Deionized water was used for the preparation of all aqueous solutions in this study.

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Preparation of Mesoporous Silica The activated metakaolin (MK) was magnetically stirred, then mixed with 100 ml aqueous acids solution. The leaching temperature was controlled at 60, 80 or 100 °C, with varying acid concentration and solid-to-liquid ratio. Finally, the resultant solid was filtered off, washed with deionized water and dried at 60 °C overnight [4–6].

Results and Discussion Characterization of the Calcined Kaolinite and Porous Product The calcined kaolinite maintains similar chemical composition with raw Share kaolinite (Al2 Si2 O5 [OH]4 ) (Nigeria) as examined by XRF majorly contains 48.83 wt.% SiO2, 41.5 wt.% Al2 O3 ; 1.59 wt.% Fe2 O3 and 1.56 wt.% K2 O while other components were in trace quantities. The XRD confirmed the composition to be made up of quartz (96-900-5018) and traces of accessory minerals such as magnetite (Fe24 O32 : 96-900-9770, usually it is Fe3 O4 ). The micrograph studies revealed the calcined sample shared similar flaky particles as the raw kaolinite. The chemical composition of the solid products at optimized showed that the products are silica dominated. The metal oxides content was reduced after acid activation, except for SiO2 as well as silica module (SiO2 /Al2 O3 ). The results also affirm the possible occurrence of cationexchange reactions during acid leaching. The XRD of the product was detected to be amorphous and is composed mainly of silica (Fig. 1), well characterized with broad halo peak. The nitrogen adsorption–desorption isotherms are detailed in Table 1. The results showed that leached product was nanoporous with specific surface area (SBET ) of up to 45.23 m2 /g for hydrochloric acid leaching. Also, leached products are characterized

Fig. 1 XRD patterns of the HCl-product. (Color figure online)

SBET m2 /g

45.23

Sample

HCl-4M-100 °C

2.76

Smicro m2 /g 42.47

Sexternal m2 /g

Table 1 Porosity parameters of the prepared mesoporous silica 0.402

Vtotal cm3 /g 0.001

Vmicro cm3 /g

0.401

Vmeso cm3 /g

3.665

DBJH Nm

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Fig. 2 SEM (left) and TEM (right) images of HCl-4M-100 °C. (Color figure online)

with type IV adsorption isotherm which occurs via multilayer adsorption followed by capillary condensation [7]. Isotherms with type H3 hysteresis are characterized with non-rigid aggregates of plate-like particles or assemblages of slit-shaped pores noticed in the electron microscopy studies (Fig. 2) [8].

Kinetics of Dissolution Studies The variation of the leaching rate with leaching time for the different leaching parameters such as concentration, temperature and solid-to-liquid ratio. It is evident that acid concentration and reaction temperature plays significant role in the dissolution activities [9–11]. The phase ratio of the leachants to the calcined kaolinite showed that at a lower solid-to-liquid ratio, the rate of dissolution is higher [12]. Hence, the dissolution rates were studied by subjecting the leaching results to the various reaction kinetic models. All tested models fit the data and most data point lies outside the 95% prediction interval. The ability of so many different reaction models to give good fits is due to the possible appearance of X in various forms in the model functions [12] (Fig. 3). The correlation coefficient (R2 ) of Avrami model was always >0.98 as compared with the other models, the Avrami model is more suitable for describing the dissolution subsequent pore formation with average Avrami model parameter (n) of 0.80. Consequently, the plots of lnk versus 1000/T in Fig. 4 were used to calculate apparent activation energy of the leaching reaction. The calculated apparent activation energy of 7.22 kJ/mol suggests the leaching process is controlled by the diffusion process [13].

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R2

1

RCCS

0.99

PDCS

0.98

RCSS

0.97

FDSS

0.96

PDSS PHZ

0.95

PHF

0.94

PHS

0.93

AVM

0.92 2

3

4 Acid ConcentraƟon (mol/L)

5

Fig. 3 Relationship of R2 and acid concentration: RCCS-cylindrical particles reaction control, PDCS-Cylindrical particles product diffusion, RCSS-chemical reaction control of shrinking core model, FDSS-film diffusion control of shrinking core model, PDSS-product diffusion control of shrinking core model, PHZ-zero order pseudo homogeneous model, PHF-first order pseudo homogeneous model, PHS second order pseudo homogeneous model, AVM-Avrami model for Share kaolinite in hydrochloric acid solutions. (Color figure online) -5.45 2.65

2.7

2.75

2.8

2.85

2.9

2.95

3

3.05

-5.5

In k

-5.55 y = 0.868x - 8.086 R² = 0.999

-5.6 -5.65 -5.7

Ea = 8.314 × 0.868 = 7.22kJ/mol

-5.75 -5.8

1000/T (K-1)

Fig. 4 Arrhenius plots for hydrochloric acid leaching process

Conclusion In summary, mesoporous silica was prepared by the thermal treatment of kaolinite and subsequent selective acid leaching. The prepared mesoporous silica has specific surface area of 45.2 m2 /g with total pore volume of 0.402 cm3 /g and uniform pore size at 3.67 nm maximum probability. The prepared porous silica as characterized could find possible applications as important raw materials in adsorption and industrial catalytic processes. Also, kinetics investigation suggests the leaching can best be

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described by the Avrami model with average Avrami model parameter (n) of 0.80 and low energy value energy of 7.22 kJ/mol, supports a diffusion controlled leaching process for industrial mesoporous silica production.

References 1. Ibrahim AS, Baba AA, Bale RB, Olaoluwa DT, Adekola FA (2018) Preparation of mesoporous silica from a Nigerian talc ore by acetic acid treatment. Commun Fac Sci Univ Ank Ser B 60:1–16. https://doi.org/10.1501/commub_0000000556 2. Moritz M, Geszke-Moritz M (2015) Mesoporous materials as multifunctional tools in biosciences: principles and applications. Mater Sci Eng C 49:114–151. https://doi.org/10.1016/ j.msec.2014.12.079 3. Shu Z, Li T, Zhou J, Chen Y, Yu D, Wang Y (2014) Template-free preparation of mesoporous silica and alumina from natural kaolinite and their application in methylene blue adsorption. Appl Clay Sci 102:33–40. https://doi.org/10.1016/j.clay.2014.10.006 4. Shu Z, Li T, Zhou J, Chena Y, Sheng Z, Wang Y, Yuan X (2016) Mesoporous silica derived from kaolin: specific surface area enlargement via a new zeolite-involved template-free strategy. Appl Clay Sci 123:76–82. https://doi.org/10.1016/j.clay.2016.01.009 5. Li T, Shua Z, Zhou J, Chena Y, Yu D, Yuana X, Wang Y (2015) Template-free synthesis of kaolin-based mesoporous silica with improved specific surface area by a novel approach. Appl Clay Sci 107:182–187. https://doi.org/10.1016/j.clay.2015.01.022 6. Temuujin J, Okada K, Jadambaa T, Mackenzie KJD, Amarsanaa J (2002) Effect of grinding on the preparation of porous material from talc by selective leaching. J Mater Sci Lett 21:1607– 1609. https://doi.org/10.1023/A:1020373617167 7. Ibrahim AS (2020) Preparation of amorphous silica from Nigeria kaolinite minerals for organic dye remediation. MPhil/PhD dissertation, Department of Industrial Chemistry, Unpublished 8. Thommes M (2010) Physical adsorption characterization of nanoporous materials. Chem Ing Tech 82:1059–1073. https://doi.org/10.1002/cite.201000064 9. Pinna EG, Suarez DS, Rosales GD, Rodriguez MH (2017) Hydrometallurgical extraction of Al and Si from kaolinitic clays. Metall Mater 70:451–457. https://doi.org/10.1590/0370-446 72017700006 10. Crundwell FK (2014) The mechanism of dissolution of minerals in acidic and alkaline solutions: part II–application of a new theory to silicates, aluminosilicates. Hydrometallurgy 149:265– 275. https://doi.org/10.1016/j.hydromet.2014.07.003 11. Battsengel A, Batnasan A, Narankhuu A, Haga K, Watanabe Y, Shibayama A (2018) Recovery of light and heavy rare earth elements from apatite ore using sulphuric acid leaching, solvent extraction and precipitation. Hydrometallurgy 179:100–109. https://doi.org/10.1016/j. hydromet.2018.05.024 12. Liddell KC (2005) Shrinking core models in hydrometallurgy: what students are not being told about the pseudo-steady approximation. Hydrometallurgy 79:62–68. https://doi.org/10.1016/ j.hydromet.2003.07.011 13. Zhang C, Wang S, Zhan-Fang C, Zhong H (2018) Kinetics and mechanism of one-step reductive leaching of manganese oxide ores by EDTA/EDTA-2Na. Physicochem Probl Miner Process 54:858–867. https://doi.org/10.5277/ppmp1887

Prediction Model of Converter Oxygen Consumption Based on Recursive Classification and Feature Selection Zhang Liu, Zheng Zhong, Zhang Kaitian, Shen Xinyue, and Wang Yongzhou

Abstract Oxygen consumption prediction for steelmaking converter is essential for optimal scheduling and energy saving of oxygen systems. To improve the prediction accuracy of oxygen consumption, an integrated prediction method based on feature space recursive division and feature selection is proposed. The feature space containing the whole converter production data is recursively divided into several feature subspaces containing the training subset. And the complexity of the data distribution will be reduced in each subspace. The simple data distribution will be more easily fitted by the prediction model. Based on recursive feature elimination, the appropriate feature variable combination and the corresponding oxygen consumption prediction models of the converter will be selected for each subset. For the test sample, it will be matched to a corresponding feature space by recursive division conditions. Then oxygen consumption is predicted by the corresponding prediction model based on the optimal combination of feature variables. A converter production data of a steel enterprise are used for testing. SVR and MLP will be used, respectively, for comparison in two groups of comparative experiments. The results show that the prediction performance of the integrated model is better than that of a single prediction model in multiple indicators. Keywords Steelmaking converter · Oxygen consumption · Integrated prediction · Feature space · Recursive partition · Feature selection

Introduction At present, oxygen blowing steelmaking is the main steelmaking process in the world [1], so oxygen is an important energy substance in converter steelmaking. The oxygen consumption of the converter is about half of the oxygen consumption of steelmaking. And the power consumption in the production of oxygen accounts for about 1/5 of the total power consumption of steel enterprises. The prediction of Z. Liu · Z. Zhong (B) · Z. Kaitian · S. Xinyue · W. Yongzhou College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_10

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oxygen consumption in converter steelmaking provides an important basis for the optimal scheduling of the oxygen system and is of great significance to further save energy and improve comprehensive economic benefits. As the statistical learning method has been widely used in industrial intelligence, more and more scholars have studied the oxygen consumption prediction in converter steelmaking based on a statistical learning method. Qin et al. [2] solve the Support Vector Regression model (SVR) by particle swarm optimization algorithm, which improves the prediction accuracy and generalization ability of the model; Wang et al. [3, 4] combine the grey model with the BP network in parallel. They weighted the sum of the results of individual models and improve the performance of a single model; Zhao et al. [5, 6] optimize the extreme learning machine model by improved genetic algorithm. And it improved the prediction performance of the extreme learning machine; also, they combine the non-equidistant gray model with the generalized regression neural network in series, and it showed some improvement effect; Li et al. [7, 8], based on oxygen decarbonization efficiency, used SVR to predict oxygen consumption, respectively, in static and dynamic control stages. And the oxygen consumption prediction performance of the model in two stages was improved. Zhang et al. [9] embedded the grey model into the Elman neural network to solve the problems of local optimization and over-fitting of ordinary neural networks. To sum up, the statistical learning method has achieved some success in the oxygen consumption prediction of the converter, and the hybrid model has become the development trend of energy prediction [10–12]. In the process of converter steelmaking, the production data has a high dimension and strong correlation, which leads to complex data distribution, so it is difficult for a single model to accurately fit the whole data distribution. At present, in the research of converter oxygen consumption prediction, the researchers mainly select the feature variables based on mechanism. And it is difficult to select the optimal combination of feature variables for prediction. For this problem, the complexity of data distribution is reduced by a recursive division of feature space, and the optimal combination of feature variables will be selected based on Recursive Features Eliminate (RFE). Firstly, based on the Decision Tree Regression (DTR) idea, the feature space containing the whole data set is recursively divided to feature subspace including data subset. Based on each feature subspace, the corresponding optimal feature combination and prediction model based on the statistical learning method are selected by the RFE algorithm. For the sample to be predicted, according to the feature space division condition, it will be matched to one of the feature subspaces. Then the corresponding model is used for prediction.

Converter Oxygen Consumption Analysis In the process of converter steelmaking, oxygen is blown into the converter and redox reaction occurs with the chemical elements (Fe, C, Si, P, Mn, and S, etc.) in the raw materials of steelmaking (steel scrap and molten iron, etc.). This emits huge heat and increases the temperature in the furnace. At the same time, it is necessary to

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add some other materials to help remove the impurity elements in the furnace and make them meet the requirements of molten steel composition and stability. In this process, there are many random and uncertain factors, such as the operation of the staff, the spatter of molten iron in the furnace, the specific time of adding oxygen, and so on. They all may affect the consumption of oxygen in varying degrees. There are many factors affecting converter oxygen consumption, among which there are different degrees of correlation. Based on the converter steelmaking mechanism, 12 main influencing factors are selected as feature variables. As shown in Tables 1 and 2, for the correlation analysis of the historical data of dephosphorization and decarbonization converter in a steel enterprise, the numbers in the table are Pearson correlation coefficients between the two variables. The range of its variation is [–1, 1], and the absolute value represents the correlation degree, and the positive and negative, respectively, represent the positive and negative correlation. There are the correlation coefficients among feature variables in the first 12 rows in the table, and the underlined number marks the biggest four correlation coefficients. Both tables indicate that C and Si contents in molten iron, P and Mn contents in molten iron, endpoint P and Mn contents, molten steel quality and steel scrap quality are four pairs of highly correlated variables, but it is difficult to be explained from the mechanism. The last row contains the correlation coefficients between feature variables and oxygen consumption , in which the bold numbers are the biggest two correlation coefficients. They are corresponding to molten steel quality and steel scrap quality, respectively, in Table 1, and corresponding to P and Mn contents in molten iron, respectively, in Table 2. Therefore, the main influencing factors of different converters are different, and the related mechanism is not clear. To sum up, the oxygen consumption process of the converter has features with large data dimensions, strong correlation, unclear mechanism, and influence of converter function, which makes the data distribution complex. Therefore, it is difficult to select a suitable combination of feature variables and a single model for accurate prediction through simple mechanism analysis. Therefore, we hope to reduce the complexity of data distribution by recursively dividing the feature space, and then the fitting pressure of the model will be reduced. Then in the divided subspaces, the suitable combination of feature variables is selected by the RFE method, and then further modeling and prediction are carried out.

Modeling Methods Recursive Division of Feature Space In this paper, the feature variables are the influence factors of BOF oxygen consumption selected above. The feature space is composed of feature variables, and the number of feature variables is the dimension of feature space. The purpose of the recursive division of feature space is to divide the feature space with a complex

0.526

0.331

0.062

0.129

0.219

0.202

0.108

0.12

−0.105

0.226

0.262

−0.091

0.057

0.054

−0.007

0.053

0.168

[% Si]iron

[% P]iron

[% Mn]iron

steel scrap

Tiron

Tsteel

[% C]steel

[% P]steel

[% Mn]steel

Molten steel 0.35

O2

0.087

1

0.017

0.306

0.266

0.092

0.092

[% C]iron

0.224

0.079

−0.005

0.19

0.316

0.142

0.071 0.122

−0.026

0.225

0.114

0.395

0.029

0.063

0.058

0.137

0.074

−0.051

−0.07

1

0.715

0.345

0.331

0.262

−0.045

0.715

1

0.253

0.266

0.226

0.257

0.096

0.22

0.054

0.345

0.253

1

0.526

−0.105

0.137

−0.051

0.36

0.091

−0.085

−0.042 −0.132 −0.266 0.036

0.104

0.343

0.225

−0.372 0.495

−0.088

0.18

−0.088 1

1

0.082

−0.045 0.425

0.836

0.063

0.071

0.257

0.219

−0.007

−0.357

−0.45

0.63

1

0.091

0.425

−0.045

−0.538

0.029

0.122

−0.026

0.017

0.053

−0.297

−0.225

1

0.63

0.36

0.343

0.225

−0.372

0.395

0.316

0.142

0.202

0.168

0.486

1

−0.225

−0.45

−0.132

−0.042

0.104

0.836

0.114

0.079

−0.005

0.108

0.35

[% C]steel [% P]steel [% Mn]steel Molten steel

−0.102 −0.135

0.058

0.074

0.096

0.129

0.054

Tsteel

−0.538

0.18

0.082

−0.102 −0.135

1

0.039

0.039

−0.07

1

0.22

−0.045

0.306

0.057

0.054

0.062

−0.091

Molten iron [% C]iron [% Si]iron [% P]iron [% Mn]iron Steel scrap Tiron

Molten iron 1

Pearson

Table 1 Correlation coefficient of dephosphorization converter production data

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0.541

0.325

0.158

0.187

0.151

0.109

−0.091

0.302

0.246

−0.227

0.061

0.031

0.125

0.137

0.109

[% Si]iron

[% P]iron

[% Mn]iron

Steel scrap

Tiron

Tsteel

[% C]steel

[% P]steel

[% Mn]steel

Molten steel 0.195

O2

0.142

1

0.107

0.077

0.205

0.04

−0.085

−0.075

0.104

0.283

0.184

0.297

0.215

1

0.541

−0.091

0.068

0.099

0.321

0.252

0.001

0.001

[% C]iron

0.216

0.356

0.102

0.276

0.178

0.364

0.193

0.414

0.152

0.221

0.039

0.199

0.071

−0.039

−0.002

1

0.711

0.297

0.325

0.246

−0.09

0.711

1

0.215

0.252

0.302

0.231

0.667

0.221

0.216

0.04

0.068

0.125

−0.065

0.135

0.054

0.244

0.358

−0.098

−0.271

−0.055 0.161

0.625

1

0.268

0.358

−0.03

−0.429

0.319

0.268

−0.065 1

1

0.085

0.152

0.178

−0.172

−0.076

1

0.625

0.319

0.244

0.271

−0.209

0.414

0.276

0.205

0.187

−0.075 −0.085

0.109

0.137

0.32

1

−0.076

−0.271

−0.055

0.054

0.133

0.667

0.193

0.102

0.077

0.151

0.195

[% C]steel [% P]steel [% Mn]steel Molten steel

−0.035 −0.2

−0.214 0.061

0.133

0.271

−0.209

0.135

−0.2 −0.03

0.085

−0.035 −0.429

1

0.083

0.083

0.199

−0.039 1

−0.002 0.071 0.039

0.283

0.104

0.099

0.031

−0.09

0.321

0.061

Tsteel

0.184

0.158

−0.227

Molten iron [% C]iron [% Si]iron [% P]iron [% Mn]iron Steel scrap Tiron

Molten iron 1

Pearson

Table 2 Correlation coefficient of production data of decarburization converter

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distribution of oxygen consumption variables into several feature spaces with a simpler distribution of oxygen consumption variables, to facilitate the model to fit the data distribution. Here, the idea of Classification And Regression Tree (CART) [13] generation algorithm is used to divide the feature space. Given the training data set based on the feature variable X= [x (1) , . . . , x (12) ] and the oxygen consumption variable Y:D = {(x1 , y1 ), (x2 , y2 ), . . . , (x N , y N )}, and feature variable X constitutes feature space S. The i-th feature variable x (i) and its value si can be selected as division variable and critical point, and define two subspaces: S1 (i, si ) = {x|x (i) < si }, S2 (i, si ) = {x|x (i) ≥ si }

(1)

Then find the optimal division variable and optimal critical point based on the Minimum Square Error Principle (MSEP), namely solve: ⎡ i, si = arg min⎣ i,si

 x j ∈S1 (i,si )

(y j − c1 )2 +



⎤ (y j − c2 )2 ⎦

(2)

x j ∈S2 (i,si )

where, cm is the mean value of the output variable y in S m , namely cm = ave(yi |xi ∈ Sm ), m = 1, 2

(3)

where, ave() is the mean calculation function. Repeat the above process for each subspace S m , and the recursion depth keeps increasing, and the number of samples contained in each subspace keeps decreasing. Finally, the feature space S is divided into M subspaces, and the Mean Square Errors (MSE) of the output variable in each subspace are e1,. . . , eM . em = ave((yi − cm )2 |xi ∈ Rm ), m = 1, . . . , M

(4)

The whole feature space division algorithm is shown in the Table 3. The division process of feature space is shown in Fig. 1. First, the feature space S is divided into subspaces S 1 and S 2 , and then S 1 and S 2 are divided into subspaces S 1,1 , S 1,2 and S 2,1 , S 2,2, respectively, and then recursively perform division until the stop condition is satisfied. To intuitively display the divided feature space, take 3D Table 3 Recursive division of feature space algorithm based on CART Input: The feature space S corresponding to the training set {X, Y} Output: M subspaces: S 1 ,…,S M Step 1: Choose the optimal division variable x (i) and critical point si , namely solve formula (2); Step 2: Based on the selected (x (i) , si ), the feature space S is divided to subspaces S 1 and S 2 as Step 3: formula (1); Let S = S 1 and S = S 2 , respectively, and repeat steps 1 and 2 for the two subspaces, respectively, until the stop condition is satisfied

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Fig. 1 The recursive division process of feature space. (Color figure online)

feature space as an example, as shown in Fig. 2. The first division is based on x (3) = s3 , and the second division is based on x (2) = s2,1 and x (2) = s2,2 , respectively. Finally, four feature subspaces S 1,1 , S 1,2 and S 2,1 , S 2,2 are obtained. In summary, the MSEP can make the MSEs of the corresponding output variables on the divided feature subspaces as small as possible, thereby simplifying the distribution of oxygen consumption variables in the entire feature space. Usually, the maximum recursive division depth and the minimum subset sample size are selected as the stop division condition. The deeper the recursion depth or the smaller the minimum subset sample size, the more refined the division of the feature space, the smaller the MSE of the output variable in the subspace, and the simpler the oxygen consumption distribution. However, the too refined division can easily lead to too few samples, which in turn leads to over-fitting of the prediction model. Fig. 2 Visual display of recursive division of 3D feature space. (Color figure online)

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Feature Selection In the converter data from steel enterprises, the high dimension and high correlation of feature variables make the useful information carried highly redundant and carry more data noise [14, 15]. For the 12 feature variables analyzed above, it is necessary to select the appropriate combination of feature variables in each feature subspace containing data subset, to carry more useful information and less data noise as far as possible. And it is conducive to the performance prediction of the model. Here, the appropriate combination of feature variables is selected based on RFE. The feature variable selection algorithm based on RFE is shown in Table 4. The input is a training set with 12-dimensional feature variables and the outputs are the optimal feature variable combination and the corresponding prediction model. Based on the current feature variables, an independent prediction model is trained, and its prediction performance will also be evaluated. And the importances of the current feature variables are calculated and sorted, and the feature variable with the lowest importance will be eliminated. For the remaining feature variables, repeat the above process until the number of feature variables is zero. In the above process, the number of feature variables gradually decreases from 12 to 1, so 12 different combinations of feature variables and 12 corresponding prediction models will be produced. The model with the best prediction performance on the training set is selected as the final prediction model of the subspace, and the corresponding feature variable combination is the selected feature variable. The Mean Absolute Error (MAE) is selected as the evaluation indicator in this article. In the RFE algorithm, to select the optimal combination of feature variables, the importance calculation of feature variables not only needs to consider the correlations between feature variables and oxygen consumption, but also the correlation between feature variables. Here, Baptiste Gregorutti’s method [16] is used to calculate the importance of feature variables. The feature variables and oxygen consumption can be regarded as following a Gaussian distribution, namely    C τ (X, y) ∼ Nn+1 0, τT σ y2

(5)

Table 4 Feature variable selection algorithm based on RFE Input: Training set {X, Y}with n feature variables, n = 12 Output: Optimal feature variable combinations and corresponding prediction model Step 1: Step 2: Step 3: Step 4: Step 5:

Train an independent prediction model with n feature variables; Calculate the importance of each feature variable; Sort the importances of feature variables and eliminate the least important feature, then n = n – 1; Repeat Step 1–Step 3 for the remaining features until the feature amount is 0; Select the best in n combinations with different number of feature variables and n corresponding prediction models

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where, τ = (τ1 , . . . , τn )T , τi = C(x (i) , y), and C = [C(x (i) , x ( j) )] represents the covariance matrix of X, C() represents the covariance function. The importance of the i-th feature variable is as follow: I (xi ) = 2αi2 V(x (i) )

(6)

where, αi = [C−1 τ]i , V() represents the variance function.

Integrated Prediction Model Framework As shown in Fig. 3, it is a framework of the model training and prediction process. The solid arrow indicates the generation process of the integrated prediction model. According to the MSEP mentioned above, the feature space including the entire training set is recursively divided into some feature subspaces including training subsets. And the recursive division feature variables and critical points in the recursive division process are recorded and will be as the matching condition between the predicted sample and the feature subspace. On the training subset contained in each feature subspace, the best performance prediction model and corresponding feature combination are selected through the REF algorithm. The dotted arrow indicates the prediction process. For the test sample, it is matched to the corresponding feature subspace, and then the prediction will be made through the corresponding optimal model and feature combination. As shown in Fig. 4, it is the prediction flow chart for each sample. First, according to the feature variables of the sample and the calculated dividing conditions, namely, recursive division feature variables and critical points, the sample will be assigned to the corresponding feature subspace. The corresponding feature variable combination is inputted to the corresponding prediction model, and finally the prediction result will be obtained.

Fig. 3 Model training and prediction framework. (Color figure online)

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Fig. 4 Sample prediction flow chart

Experiment Data Preparation To verify the validity of the model, the production data of a P-removal converter in a steel enterprise within 3 months is sampled for the experiment. The sample data is selected with 12 feature variables and the total converter oxygen consumption mentioned above. After incomplete samples and outliers were removed, and 1,273 converter samples were obtained. 90% of the samples are randomly divided and are as the training set, then the remaining 10% are as the test set. To avoid the influence of the variable dimension, all variables are normalized to the interval [0 1]. The calculation formula is as follows. x (i) =

x (i) − ave(x (i) ) , i = 1, 2, . . . , 13 max(x (i) ) − min(x (i) )

(7)

where, x (i) is the original value of the i-th variable in the feature variables and the oxygen consumption variable, and x (i) is its corresponding normalized value; max(), min(), and ave() are the maximum, minimum, and mean calculation functions separately.

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Model Parameter Setting and Evaluation Indicators The experiment environment is mainly based on Windows7, python3, and sci-kitlearn(v-0.22.2). The recursive division depth is set to 2, and the minimum divided subset capacity is 100. Multi-layer Perceptron (MLP) and Support Vector Regression (SVR) are as the subspace prediction models for experiments. SVR’s hyperparameters contain: regularization parameter C ∈ {1, 0.3, 0.1}, relaxation factor ς ∈ {0.3, 0.1, 0.03}, RBF kernel function; MLP has three layers, with hyperparameters: the number of hidden layer neurons N ∈ {10, 30, 90}, regularization coefficient α ∈ {0.03, 0.1, 0.3}, the initial learning rate is 0.01, the optimizer is Adam, and the maximum number of iterations is 300. C, ς in SVR and N , α in MLP are optimized through grid search and K-fold cross-validation on the corresponding training set. As for converter oxygen consumption prediction, in the current research, the main evaluation indicators mainly include the Accuracy (ACC) with prediction error in a certain range, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), the calculation

formula is shown below. m m |yi − yˆi |, R M S E = m1 (yi − yˆi )2 , M A P E = ACC = mn , M AE = m1 i=1 i=1

 m  1  yi − yˆi   m yi . i=1

Where, m is the number of test samples; n is the number of samples whose prediction absolute error is within 800 m3 ; yi is the true value of oxygen consumption of the i-th sample, and yi is its corresponding model estimate.

Experimental Results and Analysis Feature Space Division and Analysis The feature space is divided after normalizing all samples. As shown in Fig. 5 with three level charts, it is the feature space division process on the training set. In the first level chart, the training set contains 1,073 samples with the feature space S. yO2 is the oxygen consumption variable, and its MSE is 0.025, and its distribution is scattered. In the second level chart, firstly, according to the principle of feature space division, namely, the principle of minimum MSE, the divided feature variable x (5) is obtained, namely, the scrap steel quality, and the critical point is x (5) = 0.254, the feature space S is divided into S 1 and S 2 . The S 1 contains 295 samples, yO2 ’s MSE is 0.014, therefore yO2 ’s distribution is much more concentrated than that in S. S 2 contains 778 samples, yO2 ’s MSE is 0.02. Compared with S, its distribution is more concentrated.

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Fig. 5 Feature space division process. (Color figure online)

In the third level chart, according to the same division principle, S 1 is divided into S 1,1 , S 1,2 by the feature variable x (6) , namely, the molten iron temperature, with critical point x (6) = 0.522. S 1,1 , S 1,2 contain 102 and 193 samples, respectively. The corresponding yO2 s’ MSEs are 0.012 and 0.011, respectively, and their distribution concentration was slightly improved. Similarly, S 2 is divided into S 2,1 , S 2,2 by the feature variable x (6) , with critical point x (6) = 0.527. S 2,1 , S 2,2 contain 263 and 515 samples, respectively. The corresponding yO2 s’ MSEs are 0.021 and 0.018, respectively, and their distributions are a little more concentrated.

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At this point, the recursion depth is 2, which has reached the division stop condition. The yO2 ’s distribution concentration corresponding to each feature space is also difficult to be greatly improved. It indicates that the stopping condition is appropriate. With the increase of the division depth, the number of samples contained in the feature space gradually decreases, and the corresponding yO2 s’ MSEs gradually decrease and their distribution becomes more and more concentrated to facilitate the fitting of the prediction model.

Feature Variable Selection and Analysis Feature variable selection needs to be performed in each feature subspace, namely, S 1,1 , S 1,2 , S 2,1 , S 2,2 . Since the feature selection method on each subspace is the same, here is only the case of feature selection on subspace S 2,1 for illustration. Here, MLP is selected as the prediction model, and 12 feature variable combinations and the corresponding trained MLPs are obtained based on the RFE algorithm. The evaluation indicators of each MLP are shown in Fig. 6. The first, second, third, and fourth level charts, respectively, show that the changes of ACC, MAE, RMSE, and MAPE in the implementation process of the RFE algorithm with the feature variables being eliminated one by one. MAE, RMSE, and MAPE are optimal when the number of feature variables is 7. ACC is optimal when the number of feature variables is 8, 9, and 11, and it is suboptimal when the number of feature variables is 7. Therefore, when the number of feature variables is 7, it is the best combination of feature variables. It can be seen from the four-level chart that as the number of feature variables continues to decrease, the evaluation indicators generally show a trend of first increasing and then decreasing. When there are too many feature variables, the

Fig. 6 Feature selection process based on RFE. (Color figure online)

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useful information carried is highly redundant, while the data noise is large. This will result in a low signal-to-noise ratio and poor prediction performance. When the number of feature variables is too small, which results in too little useful information carried and it causes poor prediction performance. Therefore, too many and too few feature variables are not conducive to the prediction performance of the model. Only when the appropriate number of feature variables are combined, the MLP can perform best.

Comparison of Prediction Performance Two groups of comparative experiments are designed, the first group is a comparison between SVR and CART-RFE-SVR, and the second group is a comparison between MLP and CART-RFE-MLP. CART- and CART-RFE-MLP use SVR and MLP as the feature subspace prediction models mentioned in this article, respectively. As shown in Fig. 7, the predicted value and prediction error of 30 selected test samples are compared. The predicted value and prediction absolute error of SVR and CART-RFESVR are compared, respectively, in the upper and lower layers of (a), and CART-RFESVR predicted value is closer to the true value and its errors are generally smaller. The predicted value and prediction absolute error of MLP and CART-RFE-MLP are also compared, respectively, in the upper and lower layers of (b), and CARTRFE- MLP predicted value is closer to the true value and its errors are generally smaller. Therefore, (a) and (b) intuitively show that the prediction performance of CART-RFE-SVR and CART-RFE-MLP is better than SVR and MLP, respectively. As shown in Table 5, there are two groups of comparisons of the model’s prediction performance on the test set. The bold fonts in the table indicate better indicators in every group of comparison. Compared with SVR, CART-RFE-SVR is better than SVR in four indicators. Compared with MLP, CART-RFE-MLP is superior to MLP in three indicators, and only slightly inferior to MLP in ACC. Two groups of comparative experiments show that the integrated learning method in this paper has improved prediction performance compared with a single model.

Fig. 7 Comparison of predicted value and prediction error. (Color figure online)

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Table 5 Comparison of the prediction performance of the models on the test set ACC

MAE/m3

RMSE/m3

MAPE

SVR

0.850

420

532

0.0284

CART-RFE-SVR

0.866

405

526

0.0274

MLP

0.858

441

548

0.0302

CART-RFE-MLP

0.850

402

520

0.0273

Conclusions This paper studies an integrated prediction method based on feature space recursive division and feature selection for converter oxygen consumption. This paper draws on the idea of CART and divides the feature space into multiple subspaces recursively. In the subspace, the MSE of the oxygen consumption becomes smaller, which simplifies the data distribution and facilitates the fitting of the model. In each subspace, the appropriate feature variable combination and the corresponding prediction model based on statistical learning method are selected through RFE. For the sample to be predicted, it is matched to a corresponding feature space according to the division conditions to select corresponding model and complete the prediction. Based on the historical production data of a certain converter in a steel enterprise, SVR and MLP are selected as the prediction models of each subspace in this paper to compare with a single SVR and MLP model, respectively, namely the two group of comparative experiments. The experiment results show that the integrated prediction method in this paper improves the prediction performance of a single model on multiple evaluation indicators. Acknowledgements The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (No. 51734004) and National Key R&D Program of China (No. 2017YFB0304005).

References 1. Wang Z, Liu Q, Xie FM (2013) Model for prediction of oxygen required in BOF steelmaking. Ironmak Steelmak 39(3):228–233 2. Qin B, Wu QZ, Zhang JJ (2014) Blowing oxygen volume prediction of BOF steelmaking based on PSO-SVM. Meas Control Technol 33(12):121–124 3. Wang HJ, Jiang WJ, Zhao H (2017) The converter oxygen consumption forecast based on optimization combination model. J Henan Polytech Univ (Nat Sci) 36(2):94–98 4. Wang HJ, Jiang WJ, Zhao H (2017) The research of converter steelmaking oxygen consumption forecast model in steel enterprises. Comput Simul 34(4):410–414 5. Zhao H, Yi XM, Wang HJ (2017) Prediction model research of oxygen consumption in BOF. Comput Simul 34(1):380–383 6. Zhao H, Zhou YY, Wang HJ (2013) Application of converter steelmaking based on combination model of non-equidistant GM_GRNN. Control Instrum Chem Ind 40(4):505–507

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7. Li Y, Han M, Jiang LW (2012) Blowing oxygen volume calculation model of BOF steelmaking based on oxygen decarburization efficiency prediction. J Dalian Univ Technol. 52(5):725–729 8. Li Y, Han M, Jiang LW (2012) Prediction model of oxygen decarburization efficiency based on mutual Information case-based reasoning. Inf Control 41(2):261–266 9. Zhang ZY, Sun YG (2018) Prediction of oxygen amount in converter based on grey Elman neural network. Comput Appl Softw 35(11):109–113 10. Mosavi A, Salimi M, Faizollahzadeh Ardabili S (2019) State of the art of machine learning models in energy systems, a systematic review. Energies 12(7):1301–1342 11. Deb C, Zhang F, Yang J (2017) A review on time series forecasting techniques for building energy consumption. Renew Sust Energy Rev 74:902–924 12. Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renew Sust Energy Rev 81:1192–1205 13. Yeh CH (1991) Classification and regression trees (CART). Chemometr Intell Lab 12(3):95–96 14. Bühlmann P, Rütimann P, van de Geer S (2013) Correlated variables in regression: Clustering and sparse estimation. J Stat Plan Infer 143(11):1835–1858 15. Genuer R, Poggi J-M, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225–2236 16. Gregorutti B, Michel B, Saint-Pierre P (2016) Correlation and variable importance in random forests. Stat Comput 27(3):659–678

Reduction Behaviors of Hematite to Metallic Iron by Hydrogen at Low Temperatures Kun He, Zhong Zheng, Hongsheng Chen, and Weiping Hao

Abstract Low-temperature reduction of hematite to metallic iron by hydrogen is an essential process for ironmaking based on the blast furnace and non-blast furnace technologies. In this work, the reduction behaviors of Brazilian hematite in 20%H2 – 80%Ar at 400–570 °C were investigated in a micro-fluidized bed. Results indicate that the effect of the gaseous external diffusion can be eliminated as the gas flow rate reaches 400 mL/min at 500 °C. According to the conversion X, the reaction from hematite to metallic iron can be divided into two stages, which include the first stage that corresponds to the process of Fe2 O3 → Fe3 O4 with X < 1/9 and the second stage that corresponds to the reaction of Fe3 O4 → Fe. During the reduction process, magnetite is formed gradually and a large number of pores and fissures are observed on the surface of the ore and peripheral part of the unreacted core of hematite. The rate constants of all individual reactions tend to increase with increasing temperature, and the reaction rate of the entire reduction process is suggested to be determined by the phase boundary reaction. Keywords Reduction behaviors · Hematite · Hydrogen · Low temperature

Introduction Carbon emissions from the blast furnace (BF) are the main force of the iron and steel manufacturing that can be reduced by replacing the carbon with H2 as a reducing agent and energy source. Schenk et al. [1] suggested that the only possibility to reduce the carbon emissions in the ironmaking process is increasing the use of H2 . Recently, H2 was utilized in the ironmaking process in two ways. The gas-injection BF, which is a new technology with injecting gas instead of the pulverized coal injection [2], is the first way. For instance, Germany’s first hydrogen-based steel production plant that uses the H2 as a reducing agent in the BF has begun operation in Dillingen on August 24, 2020 [3]. The other way is non-blast furnace technology such as direct K. He · Z. Zheng (B) · H. Chen · W. Hao College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_11

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reduction and smelting reduction [4]. Several smelting reduction processes such as COREX, FINEX, and ITmk3 have been commercially proven. However, some of the limitations of these processes are still existence for their commercialization [5]. For these processes, Schenk et al. [6, 7] thought that fluidized bed direct reduction or prereduction processes have the possibility of using only H2 as a reducing agent so that an ironmaking process without carbon emissions is possible [7]. Consequently, the reduction behaviors of hematite by H2 needs to be understood and mastered, which is very important in further promotion and application of corresponding technologies. As a reducing agent, H2 has been studied for many years and has been utilized in a wide range of applications [8–13]. For example, D˙Ilmaç et al. [9] investigated the reduction kinetics of Attepe iron ore by H2 during the temperature range from 600 to 800 °C in a batch fluidized bed and reported that the most suitable model for predicting the transforming process of iron ore is the “ad-hoc” model. Spreitzer et al. [7, 14] studied the reduction of fine ore by H2 –N2 mixtures under 600–800 °C in a laboratory scale fluidized bed and analyzed the difference in the reduction of the ore with different composition [7]. These studies were practiced on the three-step reaction process, i.e., Fe2 O3 → Fe3 O4 → FeO → Fe, with the reaction temperature higher than 570 °C. But for FINEX® process, in general, the operating temperature is in the range from 400 to 800 °C. Hence, the reduction behaviors of hematite in the temperature range 400–570 °C also need to be further understood. Jozwiak et al. [15] studied the reduction of various iron oxides in H2 and CO by temperatureprogrammed reduction systematically and reported that the lowest border limit of the thermodynamic stability of FeO is the temperature 570 °C. Some other researchers, i.e., Hou et al. [16], Gavira et al. [17], Pineau et al. [18], and so on, had investigated the kinetics of reduction of iron oxides by H2 at low temperatures (220–680 °C). Nevertheless, the morphological and internal structure during the reduction of ore fines, which is very important for a complete understanding of the reaction mechanism, were ignored in these researches. For this reason, investigation of the reduction behaviors, i.e., morphological, internal structure, and reaction kinetics, is necessary. The objective of this study is to investigate the reduction behaviors of hematite to metallic iron by H2 (20%H2 + 80%Ar) under low temperature (400–570 °C) in a micro-fluidized bed reaction analyzer (MFBRA). A gas flow rate that attenuated the effect of gas external diffusion on the reduction process is determined in the microfluidized bed (MFB). The crystalline phases, morphological, and internal structures of products during the reduction process are analyzed under 500 °C. Moreover, the kinetics of two-step reactions, i.e., Fe2 O3 → Fe3 O4 and Fe3 O4 → Fe, are also discussed.

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Materials and Methods Experimental Conditions The reduction of hematite is performed in the MFBRA, which has been widely used to explore the reaction mechanism of gas–solid reaction in recent years [19–23]. Figure 1 shows the schematic diagram of the MFBRA, and details of the MFBRA can be found in He et al. [11]. The detected equipment of the MFBRA is the online repaid mass spectrometer (MS, AMETEK, LC-D300M), which was calibrated with pure H2 (purity: 99.999%) at NTP using the external standard method [24]. Before the experimental test, Brazilian hematite and the fluidization media (Silica sand) were dried at 110 °C for 10 h before they were used. The mean size of Brazilian hematite and silica sand are 260.4 μm and 314.0 μm, respectively, and the chemical composition of Brazilian hematite is listed in Table 1. Before starting the reaction test, 4 g of silica sand pre-loaded in the MFB, and then it was heated up to the reaction temperature (400–570 °C) in Ar (purity: 99.999%). During the reaction test, 20 mg of Brazilian hematite was injected into the MFB by Ar, the reducing gas is the 20%H2 –80%Ar mixture and the inlet gas flow was stable at 400 mL/min (at NTP).

Fig. 1 Schematic diagram of the MFBRA. (Color figure online)

Table 1 Chemical composition of Brazilian hematite (wt.%) TFe

Fe2 O3

SiO2

Al2 O3

MnO

P2 O5

MgO

TiO2

CaO

58.453

81.678

11.277

6.052

0.282

0.192

0.124

0.120

0.099

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In this study, the crystalline phases, morphological, and internal structure of reduction products were examined by X-ray diffraction (XRD, D/Max2500pc, Rigaku), SEM/EDS (TESCAN VEGA 3 LMH SEM), and optical microscope (Axioskop 40, Carl Zeiss).

Analysis Method Generally, the data obtained from MS need to be further addressed. Wang et al. [19] suggested that the dimensionless conversion X of solid reactant in MFBRA under the isothermal conditions can be defined as the ratio of oxygen loss at time t (W t ) to the total oxygen content of solid reactant (W ∞ ) [25], which can be expressed as t Wt t (I − It0 )dt X= =  t∞0 W∞ t (I − It0 )dt

(1)

0

where I and I 0 are the intensities of water vapor at t and t 0 in the MS in Torr, as shown in Fig. 2. The gas–solid reaction rate can be described by the differential equation, which is shown in Eq. (2) [26]: dX = kr f (X ) dt

Fig. 2 Curve of MS response of Brazilian hematite. (Color figure online)

(2)

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where f (X) is the mechanism model [27], k r is the rate constant. Logarithm treatment on both sides of Eq. (2) yields 

dX ln dt

 = ln(A) + ln[ f (X )] −

E RT

(3)

Results and Discussion According to Huang [28], the intermediate phase, FeO phase, within the process of Fe2 O3 reduction cannot occur at an arbitrary temperature such as reaction temperature is lower than 570 °C. Consequently, when the reaction temperature T < 570 °C, the reduction of hematite to iron is described into two reaction processes as [15] Fe2 O3 + 1/3 H2 = 2/3 Fe3 O4 + 1/3 H2 O

(4)

Fe3 O4 + 4H2 = 3Fe + 4H2 O

(5)

Effect of Inlet Gas Flow As well known, the gas flow rate U g is the main factor affecting external diffusion. Xu et al. [22, 29] reported that the effect of the external diffusion could be weakened by increasing the gas flow rate in MFBRA. To explore the effect of U g on the reduction of hematite, five gas flow rates were selected as 300, 350, 400, 500, and 600 mL/min. Figure 3 shows the reduction process at different gas flow rates under 500 °C, it illustrates that the reaction rate increase with increasing U g until U g is higher than 400 mL/min. Thus, the external diffusion is considered to be eliminated significantly when U g is 400 mL/min, which is finally selected as the gas flow rate in this study.

Effect of Temperature Figure shows that, with 20% H2-80% Ar, the reduction of Brazilian hematite to iron at the temperature range from 400 to 570 °C. As Fig. 4b illustrates, for 400 and 500 °C, the reaction rate increased with increasing reaction time at the initial reaction stage (about X < 0.05), decreased above X = 0.05 until it reached minimum value (around X = 0.11), and then increased again at the middle stage, and finally slowly decreased (about X > 0.35); and for 540 and 570 °C, the reaction rate also

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Fig. 3 Conversion versus time at different gas flow rates. (Color figure online)

Fig. 4 The reduction process of hematite at different temperatures. (Color figure online)

increased with increasing reaction time at X < 0.05, then there is a slow variation at the conversion range from 0.05 to 0.35, and finally slowly decreased. It demonstrates that a transaction mechanism of the reaction exit at X is approximately 0.11. To understand the transaction process, a series of the detected experiment were explored using an XRD, SEM/EDS, and optical microscope. Table 2 illustrates the different cases for detection. Table 2 Detailed information on different cases Case no

0

1

2

3

4

5

6

Reaction time (s)

0

10

20

50

100

200

800

Conversion X (−)

0.000

0.034

0.061

0.132

0.270

0.558

0.977

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Figure 5a shows the XRD analyses results of hematite and reduction products. All of the iron oxides in the raw material are Fe2 O3 . The Fe3 O4 phase form gradually after the initiation of the reaction. And then the Fe phase starts to be detected until ~200 s of reduction and increases gradually. Meanwhile, the intermediate phase, FeO, does not appear during the whole reduction process. It indicates that the Fe phase is obtained by reducing Fe3 O4 . The morphological structures of raw material and its products are observed by SEM as presented in Fig. 5c–l, and the element contents (O and Fe) are analyzed by EDS analyses as shown in Fig. 5b. From Fig. 5c, the raw material has a smooth surface and a close-grained structure. Some light micropores and bumps begin to appear on the surface of the hematite from the start of the reduction as shown in Fig. 5d–f. In this case, the element contents of bumps (point #7 in Fig. 5f and point 13 in Fig. 5j) are examined, it is found that the iron content is significantly higher than

Fig. 5 XRD and SEM/EDS analysis of the raw materials and the reduction products. a Present XRD analyses, b is EDS analysis of the points in SEM(c–l). (Color figure online)

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Fig. 6 Optical microscopy of the reduction products in 20% H2 at 500 °C. (Color figure online)

other points at the same moment. The bump size and the number of pores increase gradually with further reduction. After the reduction, an uneven and porous surface like a sponge can be observed (Fig. 5k, l). From Fig. 5c, it can be found that Fe content increases progressively with reaction time, and O content decreases progressively with reaction time. On the other hand, it is also necessary to analyze the internal structure of hematite since XRD and SEM can only detect the morphology structure and the composition of elements and phases on the hematite surface. Figure 6 shows the internal structures of reduced particles. Fe3 O4 phase forms on the peripheral part of unreacted Fe2 O3 that is gradually decreased, as shown in Fig. 6b, c. From Fig. 6d, e, it is clearly observed that the Fe phase randomly forms intraparticle of the Fe3 O4 phase and then its size gradually increase with increasing reaction time, it is also related to the XRD analyses for #Case 4 in Fig. 5a, which is not characterized the 2θ value of Fe phase. Afterward, the Fe3 O4 phase is further reduced until it disappears.

Reaction Kinetics of Fe2 O3 → Fe3 O4 Corbari et al. [30] suggested that the reduction of Fe2 O3 to Fe3 O4 is much faster than the other reduction steps, i.e., Fe3 O4 → FeO and FeO → Fe. Chen et al. [10] found that the reduction of Fe2 O3 → Fe3 O4 and Fe3 O4 → Fe occurred before and after the oxygen stoichiometric conversion of 1/9, respectively. And based on the XRD analyses and optical microscopy analyses in this study, the Fe phase cannot be characterized and observed at the initial stage, respectively. Therefore, we assume that the reduction form Fe2 O3 to Fe3 O4 occurs at X < 1/9 lonely and the reduction process can be described by the first-order reaction model according to Piotrowski et al. [31] − ln(1 − X ) = kr t

(6)

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Fig. 7 Arrhenius plots of Fe2 O3 → Fe3 O4 . (Color figure online)

The linear regression of the experimental data of −ln(1-X) against t determined k r . According to the Arrhenius equation, a plot of lnk versus 1/T for the reduction of Fe2 O3 to Fe3 O4 is shown in Fig. 7, indicating a good fit to Eq. (6). The activation energy is estimated to be 17.39 kJ/mol form the slope of the regression line in Fig. 7.

Reaction Kinetics of Fe3 O4 → Fe For the reduction of Fe3 O4 → Fe, it is described as a single-step reaction in this study. Accordingly, the gas flow rate is maintained at 400 mL/min (at NTP) that external diffusion is considered to be eliminated. Therefore, the reaction rate of the Fe3 O4 → Fe process is controlled by a phase boundary or internal diffusion mechanism. To obtain the specific mechanism, a generalized Johnson-Mehl-Avrami (JMA) model, which was developed by Hancock et al. [32], is used, and it has the integral form of ln[− ln(1 − X )] = n ln k + n ln t

(7)

where n is the kinetic exponent, which depends upon the controlled mechanism of reduction [33]. The plot of ln[1–ln(1–X)) against lnt is illustrated in Fig. 8. Data in Fig. 8 show that the slopes of the resulting straight lines increased with increasing temperature. The value of n range from 1.017 to 1.211 with R2 for all fits being greater than 0.99, indicating a phase boundary-controlled mechanism [32]. If this is the case, the reaction process may be described by a 3D geometrical contraction models, which can be expressed as 1 − (1 − X )1/ 3 = kt

(8)

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Fig. 8 The plot of ln [–ln(1−X)) against lnt for extraction the kinetic exponent, n, in Eq. (7). (Color figure online)

Table 3 Parameters obtained from Eqs. (6) and (8) at different temperatures T (°C)

Fe2 O3 → Fe3 O4 k1

Fe3 O4 → Fe R2

k2

R2

400

2.12 ×

10–3

0.990

7.46 ×

10–4

500

3.03 ×

10–3

0.996

1.00 ×

10–3

0.994

540

3.44 × 10–3

0.990

1.08 × 10–3

0.996

570

4.10 × 10–3

0.999

1.12 × 10–3

0.990

0.999

By plotting 1–(1–X)1/3 versus t according to Eq. (8), the rate constant k 2 , listed in Table 3, with R2 above 0.99, were calculated from the slope of a regression line. And a plot of lnk 2 versus 1/T is shown in Fig. 7. The pre-exponential factor, A, and the activation energy, E, were calculated to be 0.00595 s−1 and 11.577 kJ/mol, respectively. Pineau et al. [34] reported activation energy of 26.8 kJ/mol for the reduction of Fe3 O4 → Fe for temperature higher than 450 ± 10 °C, which is comparable to this study.

Conclusion In this work, the reduction behaviors of Brazilian hematite by hydrogen at low temperatures (400–570 °C) are investigated in a MFBRA. The gas flow rate is maintained at 400 mL/min such that external diffusion is considered to be eliminated. We found that porous magnetite covers on the unreacted core of hematite, and metallic iron are randomly formed on the inner porous magnetite as the hematite is completely reduced. The reaction processes of Fe2 O3 → Fe3 O4 and Fe3 O4 → Fe can be analyzed by the first-order reaction model and 3D geometrical contraction model, respectively,

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and the rate-determining step is found to be the reaction at phase boundaries. The apparent activation energies, E, of Fe2 O3 → Fe3 O4 and Fe3 O4 → Fe are calculated to be 17.39 kJ/mol and 11.58 kJ/mol, respectively. Acknowledgements The authors wish to acknowledge the Fundamental Research Funds for the Central Universities (No: 2018CDYJSY0055) and the National Natural Science Foundation of China (No. 51874056).

References 1. Spreitzer D, Schenk J (2019) Reduction of iron oxides with hydrogen—a review. Steel Res Int 90:1900108 2. Lyu Q, Qie Y, Liu X et al (2017) Effect of hydrogen addition on reduction behavior of iron oxides in gas-injection blast furnace, Thermochim. Acta 648:79–90 3. FuelCellsWorks (2020) First plant in Germany in hydrogen-based steel production goes into operation 4. Agency IE (2010) The energy technology systems analysis program (ETSAP)—technology brief: iron and steel. Paris 5. Hasanbeigi A, Arens M, Price L (2014) Alternative emerging ironmaking technologies for energy-efficiency and carbon dioxide emissions reduction: a technical review. Renew Sustain Energy Rev 33:645–658 6. Schenk JL (2011) Recent status of fluidized bed technologies for producing iron input materials for steelmaking. Particuology 9:14–23 7. Spreitzer D, Schenk J (2020) Fluidization behavior and reducibility of iron ore fines during hydrogen-induced fluidized bed reduction. Particuology 52:36–46 8. Beheshti R, Moosberg-Bustnes J, Kennedy MW et al (2016) Reduction of commercial hematite pellet in isothermal fixed bed—experiments and numerical modelling. Ironmaking Steelmaking 43:31–38 9. D˙Ilmaç N, Yörük S, Gülabo˘glu SM ¸ (2015) Investigation of direct reduction mechanism of attepe iron ore by hydrogen in a fluidized bed. Metall Mater Trans B 46:2278–2287 10. Chen H, Zheng Z, Chen Z et al (2017) Reduction of hematite (Fe2 O3 ) to metallic iron (Fe) by CO in a micro fluidized bed reaction analyzer: a multistep kinetics study. Powder Technol 316:410–420 11. He K, Zheng Z, Chen Z (2020) Multistep reduction kinetics of Fe3 O4 to Fe with CO in a micro fluidized bed reaction analyzer. Powder Technol 360:1227–1236 12. Su M, Ma J, Tian X et al (2017) Reduction kinetics of hematite as oxygen carrier in chemical looping combustion. Fuel Process Technol 155:160–167 13. Su M, Zhao H, Tian X, et al (2017) Intrinsic reduction kinetics investigation on a hematite oxygen carrier by CO in chemical looping combustion. Energy Fuels 31 14. Spreitzer D, Schenk J (2019) Iron ore reduction by hydrogen using a laboratory scale fluidized bed reactor: kinetic investigation—experimental setup and method for determination. Metall Mater Trans B 50:2471–2484 15. Jozwiak WK, Kaczmarek E, Maniecki TP, et al (2007) Reduction behavior of iron oxides in hydrogen and carbon monoxide atmospheres. Appl Catal A 326:17–27 16. Hou B, Zhang H, Li H et al (2012) Study on kinetics of iron oxide reduction by hydrogen. Chin J Chem Eng 20:10–17 17. Gaviría JP, Bohé A, Pasquevich A et al (2007) Hematite to magnetite reduction monitored by Mössbauer spectroscopy and X-ray diffraction. Phys B 389:198–201 18. Pineau A, Kanari N, Gaballah I (2006) Kinetics of reduction of iron oxides by H2 : Part I: low temperature reduction of hematite, Thermochim. Acta 447:89–100

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19. Wang F, Zeng X, Shao R et al (2015) Isothermal gasification of in situ/ex situ coal char with CO2 in a micro fluidized bed reaction analyzer. Energy Fuels 29:4795–4802 20. Liu Y, Wang Y, Guo F et al (2017) Characterization of the gas releasing behaviors of catalytic pyrolysis of rice husk using potassium over a micro-fluidized bed reactor. Energy Convers Manage 136:395–403 21. Guo F, Dong Y, Lv Z et al (2016) Kinetic behavior of biomass under oxidative atmosphere using a micro-fluidized bed reactor. Energy Convers Manage 108:210–218 22. Yu J, Yao C, Zeng X et al (2011) Biomass pyrolysis in a micro-fluidized bed reactor: characterization and kinetics. Chem Eng J 168:839–847 23. Zhang Y, Yao M, Gao S et al (2015) Reactivity and kinetics for steam gasification of petroleum coke blended with black liquor in a micro fluidized bed. Appl Energy 160:820–828 24. Pigini D, Cialdella AM, Faranda P et al (2006) Comparison between external and internal standard calibration in the validation of an analytical method for 1-hydroxypyrene in human urine by high-performance liquid chromatography/tandem mass spectrometry. Rapid Commun Mass Spectrom 20:1013–1018 25. Chen H, Zheng Z, Chen Z et al (2017) Multistep reduction kinetics of fine iron ore with carbon monoxide in a micro fluidized bed reaction analyzer. Metall Mater Trans B 48:841–852 26. And SV, Wight CA (1997) Kinetics in solids. Annu Rev Phys Chem 48:125–149 27. Sandmann JCW (1986) Fundamental studies on gas-solid reactions: pore structure and reactivity of coal chars Rice Univ. USA 28. Huang X (2013) Iron and steel metallurgy principle. Metallurgical Industry Press, Beijing 29. Yu J, Zeng X, Zhang J et al (2013) Isothermal differential characteristics of gas–solid reaction in micro-fluidized bed reactor. Fuel 103:29–36 30. Corbari R, Fruehan RJ (2010) Reduction of iron oxide fines to wustite with CO/CO2 gas of low reducing potential. Metall Mater Trans B 41:318–329 31. Piotrowski K, Mondal K, Wiltowski T et al (2007) Topochemical approach of kinetics of the reduction of hematite to wüstite. Chem Eng J 131:73–82 32. Hancock JD, Sharp JH (1972) Method of comparing solid-state kinetic data and its application to the decomposition of Kaolinite, Brucite, and BaCO3 . J Am Ceram Soc 55:74–77 33. Monazam ER, Breault RW, Siriwardane R (2014) Kinetics of magnetite (Fe3 O4 ) oxidation to hematite (Fe2 O3 ) in air for chemical looping combustion. Ind Eng Chem Res 53:140807120832006 34. Pineau A, Kanari N, Gaballah I (2007) Kinetics of reduction of iron oxides by H2: Part II. Low temperature reduction of magnetite. Thermochim Acta 456:75–88

Simulation and Optimization of Defluorination and Desulfurization Processes of Aluminum Electrolysis Flue Gas Xueke Li, Yan Liu, Xiaolong Li, and Tingan Zhang

Abstract Aiming at the harmful gas pollutants of low concentration sulfur dioxide and fluoride in electrolytic aluminum flue gas, the process of fluorine and sulfur removal was simulated by NaOH-CO2 gas–liquid absorption system, and the influence of operation and structure parameters such as gas flow rate, liquid flow rate, size, and type of filler on gas-liquid mass transfer process was explored. The results show that the gas flow rate is 1.5–2.0 m3 /h, the liquid flow rate is 10–12 m3 /h, and the filler is a Bauer ring with the nominal diameter (DN) of 16 mm, the gas–liquid mass transfer effect is the best and the maximum CO2 utilization rate is about 46%. According to the simulation results, when ammonia system is used to absorb sulfur dioxide and hydrogen fluoride, the concentration of sulfur dioxide and hydrogen fluoride can be reduced to 42 mg/m3 and 0.8 mg/ m3 , respectively. Keywords Mass transfer process simulation · Defluorination; desulfurization · Absorption rate · Aluminum electrolysis flue gas

Introduction Chinese aluminum production is developing rapidly in the world’s aluminum production, especially in the production capacity of electrolytic aluminum. Aluminum electrolysis flue gas contains harmful substances such as sulfur dioxide and fluoride, which has serious harm to the human body and environment. Therefore, the deep X. Li · Y. Liu (B) · X. Li · T. Zhang Key Laboratory of Ecological Metallurgy of Multi-metal Intergrown Ores of Education Ministry, School of Metallurgy, Northeastern University, Shenyang 110819, China e-mail: [email protected] X. Li e-mail: [email protected] X. Li e-mail: [email protected] T. Zhang e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_12

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purification and removal of fluorine and sulfur in electrolytic aluminum flue gas can effectively solve the technical problems of aluminum electrolysis flue gas purification, and provide technical support for the realization of ultra-low emission in the Chinese aluminum electrolysis industry [1]. At present, there are mainly limestone/lime gypsum washing method, sodium alkali method, and ammonia method. In the limestone/lime gypsum washing method, the pipeline is easy to scale and block, the wastewater is difficult to be treated, and the gypsum quality is low. The sodium alkali washing method has high removal costs and is difficult to be widely used. While the ammonia method has a wide application range, high desulfurization and defluorination rate, no secondary pollution, and certain removal capacity for nitrogen oxides, which can realize the integrated purification of fluorine and sulfur [2–4]. Xiang Gao [5] et al. studied the absorption of SO2 by ammonium sulfite in the ammonia-based process in power plants, and studied the absorption reaction between sulfur dioxide and ammonium sulfite solution in a stirred tank reactor. Wang [6] et al. used ANSYS CFX software to conduct a comprehensive gas–liquid two-phase flow field simulation study on a sintering flue gas desulfurization tower, and established a detailed model to simulate the gas– solid two-phase flow field in a large-scale ammonia WFGD tower. Dong Chengyong [7] described the treatment of aluminum electrolysis flue gas by a company used the limestone-gypsum wet method. The limestone slurry absorbed and reacted with acid gases such as fluoride and sulfur dioxide to obtain a calcium salt mixture. At present, there are still few studies on the synergistic removal of fluorine and sulfur in the aluminum electrolysis flue gas . This article focuses on the problems of short gas–liquid contact time, small mass transfer driving force, and low gas–liquid mass transfer efficiency in the aluminum electrolysis flue gas. The physical simulation was used to study the influence law of mass transfer coefficient in the chemical reaction process of defluorination and sulfur removal from ammonia water. The operation parameters and equipment parameters of different axial position, gas flow rate, liquid flow rate, filler types, and sizes in the filled tower were studied, so as to strengthen gas–liquid mass transfer and increase the gas–liquid reaction rate.

Experimental Equipment and Experimental Methods Experimental Equipment The physical simulation [8–10] was based on the similarity criterion and the establishment of the physical model based on the criterion that the modified Froude number was equal. In this experiment, in the process of studying ammonia defluorination and desulfurization, the physical model used a filled tower made of plexiglass, and the system of NaOH-CO2 was used to simulate the ammonia desulfurization system. This experiment was based on the industrial data [11] of Yunnan Asia Pacific Environmental Design and economy Company with an annual flue gas treatment capacity

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of 150,000–200000Nm3 /h as the prototype, the ratio of the prototype to model was determined to be 50:1. The inner diameter of the filler tower is 100 mm and the height of the packed section is 720 mm. The experimental flow chart is shown in Fig. 1. The flow of CO2 gas was regulated by the rotor flowmeter and entered the reaction zone of the absorption tower from the bottom of the filled tower, and the gas passed upward through the filler absorption section. The NaOH solution was transported to the top of the filled tower by the absorption liquid pump and sprayed downward from the top of the filled tower, and the spray volume was adjusted by an electromagnetic flowmeter. The CO2 and NaOH solutions were in countercurrent contact in the filled tower, and the mass transfer absorption reaction was carried out in the filler layer to simulate the removal of SO2 and HF in the flue gas. The pH during the absorption process was measured by a pH meter and recorded by the data acquisition system. When CO2 was used for research, the unreacted tail gas could be directly discharged. When ammonia was used to absorb sulfur dioxide and hydrogen fluoride, a liquid absorption device was added at the gas outlet. The initial concentrations of SO2 and HF were 200 mg/m3 and 3 mg/m3 , respectively. The concentration changes of sulfite and fluorine ions before and after the absorption liquid were measured by

1-Data acquisition system; 2-pH electrode; 3-gas outlet; 4-filler layer; 5-water pump; 6-storage tank; 7-CO2 gas cylinder

Fig. 1 Experimental device diagram of absorption rate. (Color figure online)

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an ultraviolet spectrophotometer to determine the defluorination and desulfurization rates.

Experimental Method In this paper, the relationship between pH value and CO2 concentration can be obtained by Eq. 1 [12, 13].   K H2 O K 1 K 2 + K 1 (H + ) + (H + )2 + Y = (H ) + X − (H + ) 2K 1 K 2 + K 1 (H + )

(1)

where: Y is the CO2 concentration at any time, X is the initial NaOH solution concentration, KH2O , K1, and K2 are equilibrium constants (at 25 °C, KH2O = 10–14 , K1 = 10–6.352 , K2 = 10–10.329 ). According to the double-membrane theory and chemical reaction kinetics, the total mass transfer rate equation can be expressed by Eq. 2, and the volumetric mass transfer coefficient AK can be used to express the absorption rate. ln[(Ce − Ct)/(Ce − C0 )] = −(AK /V )t

(2)

where: A—reaction surface area, cm2 ; V —the volume of NaOH solution, cm3 ; t— reaction time, s; K—mass transfer coefficient of CO2 , cm/s; Ce, Ct, Co—respectively, represent the equilibrium concentration of CO2 , the concentration of CO2 absorbed by the NaOH solution after t seconds and the initial concentration of CO2 , mol/L. The utilization rate of CO2 gas in the solution can be expressed as Eq. 3. η = [V (CC O2 I I − CC O2 I )/t]/[ρC O2 Q/M]

(3)

where: C CO2II —CO2 concentration in solution at tII , mol/L; C CO2I —CO2 concentration in solution at tI , mol / L; t—Difference between tII and tI , s;— CO2 gas density, 1.997 g/L; Q—CO2 gas flow rate, L/s; M—CO2 molecular weight, 44 g/mol. The initial concentration of the NaOH solution used in this experiment was 0.025 mol/L. The main purpose of this experiment was to investigate the utilization of CO2 η in the process of changing the pH value from 12 to 9. The formula used in this experiment can be simplified as Eq. 4. η = 6.45/Qt

(4)

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Results and Discussion Distribution of Absorption Rate in Axial Position The distribution laws of the absorption rate in the axial position of the filled tower were studied when the gas flow rate and liquid flow rate were constant by using the DN 16 mm Bauer ring. The volume mass transfer coefficient and CO2 utilization rate in the axial position were obtained by measuring the changes of solution pH values at the quartering position of the filler section. It can be seen from Fig. 2 that most of the CO2 at position 1 is close to the top of the tower, and most of the CO2 has been reacted at the bottom, so the solution at position 1 absorbs less CO2 . The volumetric mass transfer coefficient AK and CO2 utilization ratio are relatively low, and the mass transfer rate of CO2 absorbed by NaOH is relatively slow. Position 2 and position 3 belong to the middle part of the filler layer, and the volumetric mass transfer coefficient and CO2 utilization rate of the two places are similar, and the reaction rate of the gas staying in the filler section is similar at these two places. Position 4 is close to the air inlet, where a large amount of CO2 reacts with the solution, the volumetric mass transfer coefficient and CO2 utilization rate are relatively high, and the mass transfer rate of CO2 absorption by NaOH is relatively fast.

Fig. 2 Volumetric mass transfer coefficient AK and CO2 utilization factor at axial positions. (Color figure online)

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Fig. 3 Effect of the gas flow rate change on volumetric mass transfer coefficient and CO2 utilization ratio. (Color figure online)

Effect of Gas Flow Rate on Absorption Rate When the liquid flow rate was constant, the volumetric mass transfer coefficient and CO2 utilization rate were obtained by changing the gas flow rate with a constant liquid flow rate. It can be seen from Fig. 3 that with the increase of gas flow rate, the volumetric mass transfer coefficient first increases and then decreases, and the utilization rate of CO2 gradually decreases. It shows that under the condition of constant liquid flow rate, the increase of gas flow rate can accelerate the mass transfer rate between gas and liquid. However, when the absorption of CO2 by NaOH reaches saturation, the mass transfer rate becomes slow, and the utilization rate of CO2 decreases with the increase of gas flow rate. In the experiment, the gas flow rate can be 1.5 ~ 2.0 m3 /h.

Effect of Liquid Flow Rate on Absorption Rate When the gas flow rate was constant, the influence of the liquid flow rate on the absorption rate was studied by using the DN 16 mm Bauer ring. With the increase of liquid flow rate, the volumetric mass transfer coefficient and CO2 utilization efficiency η also increase, which indicates that the increase of liquid flow improves the spray density in filled tower and the wettability between fillers, increases the contact area between gas and liquid, and increases the mass transfer rate. Moreover, when the concentration of CO2 is unchanged, the more NaOH absorption liquid enters the

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Fig. 4 Effect of liquid flow rate on volumetric mass transfer coefficient AK and CO2 utilization. (Color figure online)

filled tower, the more absorption liquid. When the liquid falls in the filler layer, the liquid flow rate can improve the wettability of the filler, increase the mass transfer area between gas and liquid in the filler layer, and effectively increase the absorption rate . Considering the economic benefits of increasing the liquid flow rate, the liquid flow rate of 10–12m3 /h can be selected (Fig. 4).

Effect of Filler Type on Absorption Rate It can be seen from Fig. 5 that the volumetric mass transfer coefficient and CO2 utilization efficiency of the Bauer ring are higher than those of the step ring under the same experimental conditions. This is because the filling density of the Bauer ring is higher than that of the Stepped ring, which indicates that the Bauer ring has better hydrodynamic performance as the filler. It can make the gas–liquid distribution in the packing section more uniform, more residence time, greater mass transfer driving force, better mass transfer effect, more thorough absorption reaction, and faster gas– liquid mass transfer and absorption rate. Therefore, Bauer ring can be used as filler in the experiment.

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Fig. 5 Effect of filler type on volumetric mass transfer coefficient AK and CO2 utilization. (Color figure online)

Effect of Filler Size on Absorption Rate In Fig. 6, the comparison between the Bauer ring (large) with DN 25 mm and the Bauer ring (small) with DN 16 mm shows that under the same experimental conditions, the volumetric mass transfer coefficient and CO2 utilization rate of the small

Fig. 6 Effect of filler size on volumetric mass transfer coefficient AK and CO2 utilization. (Color figure online)

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Bauer ring are relatively large. This is because the small Bauer ring has greater filling density, porosity, and better hydrodynamic performance under the same humidity so that CO2 gas and NaOH liquid can be separated in the small Bauer ring. It has the advantages of uniform distribution effect, longer residence time, and greater mass transfer driving force, which is favorable for gas–liquid two-phase mass transfer absorption and higher gas–liquid mass transfer efficiency. The experiment can be carried out by using the Bauer ring with the diameter of 16 mm.

Defluorination and Desulfurization Rate By optimizing the structure and operating parameters of the reactor, experiments were carried out on the absorption of SO2 and HF by ammonia under the conditions of using the Bauer ring filler with a diameter of 16 mm, the gas flow rate of 1.0 m3 /h, and the liquid flow rate of 12 m3 /h. Figure 7 shows the concentrations of SO2 and HF decrease with the increase of liquid flow. It was determined by UV spectrophotometer, the sulfur dioxide concentration decreased from 200 mg/m3 to 42 mg/m3 , and the fluoride concentration decreased from 3.0 to 0.8 mg/m3 . Based on the experimental and theoretical research, the concentration of fluorine and sulfur flue gas after removal is far lower than the emission limit concentration issued by the State Environmental Protection Administration, and will also meet the more stringent emission standards in the future.

Fig. 7 The concentration of SO2 and HF changes with the liquid flow rate after absorption

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Conclusions In summary, in the experiment of ammonia defluorination and desulfurization , the gas flow rate could be 1.5–2.0 m3 /h and the liquid flow rate was 10–12 m3 /h to improve the contact density of gas–liquid two-phase mass transfer. The mass transfer coefficient of the Bauer ring was higher than that of the stepped ring, and the mass transfer coefficient of the DN 16 mm Bauer ring was higher than that of the DN 25 mm Bauer ring. In the experiment of defluorination and desulfurization with ammonia water, the concentration of sulfur dioxide and hydrogen fluoride can be reduced to 42 mg/m3 and 0.8 mg/m3 , respectively. Acknowledgements This work was supported by the National Key Research and Development Project, China (2017YFC0210404).

References 1. Srivastava RK, Jozewicz W, Singer C (2010) SO2 scrubbing technologies: a review. Environ Prog Sustain Energy 20(4):219–228 2. Dou B, Pan W, Jin Q (2009) Prediction of SO2 removal efficiency for wet flue gas desulfurization. Energy Convers Manage 50(10):2547–2553 3. Wang H, Song Q, Yao Q (2008) Experimental study on removal effect of wet flue gas desulfurization system on fine particles from a coal-fired power plant. Proc Csee 28(5):1–7 4. Tang Z, Zhou CC, Chen C (2004) Studies on flue gas desulfurization by chemical absorption using an ethylenediamine−phosphoric acid solution. Ind Eng Chem Res 43(21):6714–6722 5. Gao X, Ding H, Du Z, Wu ZL, Fang MX, Luo ZY, Cen KF (2010) Gas–liquid absorption reaction between (NH4 )2 SO3 solution and SO2 for ammonia-based wet flue gas desulfurization. Appl Energy 87(8):2647–2651 6. Wang SJ, Zhu P, Zhang G, Zhang Q, Wang ZY, Zhao L (2015) Numerical simulation research of flow field in ammonia-based wet flue gas desulfurization tower. J Energy Inst 88:284–291 7. Dong CY (2018) Research and discussion on advanced treatment of pot fume in aluminum reduction process. Light Metals 2018(4):29–32 8. Pritchard PJ (2011) Fox and McDonald’s introduction to fluid mechanics. Times Roman, A Macmillan Company, pp 290–327 9. Wang DX, Zhang TA, Liu Y, Zhu XF (2013) Water model experiment of bubble behavior in oxygen bottom blowing process. J Northeast Univ 34(12):1755–1758 10. Liu Y, Zhang TA, Sano M, Wang Q, He JC (2011) Study on absorption rate by eccentric mechanical stirring in gas injection refining for iron and steel making. J Iron Steel Res Int 18(S2):166–171 11. Zeng ZP, et al (2011) Yunnan asia-pacific environmental engineering design and research corporation. Integrated desulfurization, defluorination and dust removal of electrolytic aluminum flue gas (2011). China,Patent CN 102228775 B. 02 Nov 2011 12. Liu Y, Zhang TA, Zhao HL, Wang SC, Dou ZH, Jiang XL, He JC (2009) Study on absorption of CO2 bubble disintegration in NaOH solution. Chin J Process Eng 2009:9 13. Inada S, Watanabe A (1976) A study of the effects of CO2 absorption in the NaOH solution-CO2 gas jet model. Iron Steel 62(7):807–816

Physical Simulation of Bubble Behaviors and Optimization of Converting Phosphogypsum into Ammonium Sulfate Bing-Wei Liu, Yan Liu, Shuai-Dong Mao, and Ting-an Zhang

Abstract With the rapid development of the phosphorus chemical industry, the accumulation of by-product phosphogypsum has increased year by year. The comprehensive utilization of phosphogypsum has received extensive attention. In this paper, physical simulation is used to investigate the effects of the shape of agitator, stirring speed, inlet gas flow, eccentric stirring (the ratio of the distance from the center of the agitator to the radius of the cylinder), and height of agitator (the distance between the agitator and the bottom of the reactor) on the bubble size distribution. The optimized conditions are: the agitator is SSB-D, and the stirring speed is 600 r/min, inlet gas flow is 0.12 m3 /h, eccentric stirring is 0.4, and the height of agitator is 3 cm, the size of the bubbles is distributed between 0.5 and 3 mm. Under this condition, the conversion rate of phosphogypsum can reach 91.95%. The experiment has certain guiding significance for the resource utilization of phosphogypsum. Keywords Phosphogypsum · Physical simulation · Bubble size · CO2

Introduction Phosphogypsum is the main waste residue in the wet production of phosphoric acid and phosphate fertilizer. It not only has a huge output, but also has renewable resources. At present, there are three main sources [1–3]: in the production of phosphate fertilizer from wet-process phosphoric acid; in the production of phosphate B.-W. Liu · Y. Liu (B) · S.-D. Mao · T. Zhang Key Laboratory for Ecological Metallurgy of Multimetallic Mineral, Ministry of Education, School of Metallurgy, Northeastern University, Shenyang 110819, Liaoning, China e-mail: [email protected] B.-W. Liu e-mail: [email protected] S.-D. Mao e-mail: [email protected] T. Zhang e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_13

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from wet-process phosphoric acid; in the production of wet-process phosphoric acid production. At present, the main source of phosphogypsum is wet-process phosphoric acid. The main chemical reaction is as follows [4]: Ca5 F(PO4 )3 + 5H2 SO4 + 10H2 O = 3H3 PO4 + 5CaSO4 · 2H2 O + HF

(1)

On average, each ton of wet-process phosphoric acid is produced, and about 4.5– 5.0 tons of phosphogypsum is discharged. At present, the total annual output of world wet-process phosphoric acid is about 260 million tons, and the total annual output of by-product phosphogypsum is about 150 million tons. With the rapid development of China’s phosphogypsum industry, the cumulative stock of phosphogypsum byproducts ranks first in the world. Data shows that China’s annual emissions of phosphogypsum have reached 75 million tons. After years of accumulation, China’s phosphogypsum stacks The stock has exceeded 600 million tons. However, the utilization rate is still less than 40% [5]. The treatment of phosphogypsum is generally natural accumulation [6]. The accumulation of a large amount of phosphogypsum not only takes up a lot of land resources, but also seriously damages the environment. With the development of the phosphorus compound fertilizer production industry, the emissions of phosphogypsum continue to increase. How to solve the comprehensive utilization of phosphogypsum has become an urgent problem. At present, the conversion of phosphogypsum into ammonium sulfate is divided into double decomposition method and carbonization method. Cordell [7] first discovered the use of phosphogypsum and (NH4 )2 CO3 to produce (NH2 )SO4 fertilizer and CaCO3 as raw materials for building materials, which is a very promising comprehensive utilization approach for phosphogypsum. Yang [8] and others used NH4 HCO3 as a carbon source to react with phosphogypsum to produce (NH2 )SO4 and CaCO3 , which is also a way of comprehensive utilization of phosphogypsum. Because the use of (NH4 )2 CO3 and NH4 HCO3 to produce (NH2 )SO4 is not economical, the metathesis method cannot be used for large-scale industrial production. In order to save costs, He [9] and others explored the use of CO2 as a carbon source to react with phosphogypsum under alkaline conditions, which has certain guiding significance for the industrial production of phosphogypsum (NH2 )SO4 and CaCO3 . The micro-bubble is widely used in chemical, metallurgical, biopharmaceutical, and other fields [10–12]. During the stirring process, the bubbles continue to rupture and fusion, resulting in size distribution of bubbles in the reactor. For a long time, scholars in various fields have devoted themselves to studying the size and distribution of bubbles in gas–liquid two-phase stirred reactors. The mass transfer and reaction processes of the gas–liquid two-phase in the reactor are carried out on the surface of the bubble, so the size of the bubble is a very important factor in the chemical reaction with the participation of gas [13]. The mass transfer between gas and liquid phases has a direct impact on the reaction time and the size of the reactor [14]. The better the effect of micro-bubble, the more conducive to the full progress of the reaction. This experiment uses the method of physical simulation to study the effect of the micro-bubble. By improving the effect of miniaturization of bubbles, it promotes

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Table 1 Chemical components of phosphogypsum Composition

CaO

SO3

SiO2

Al2 O3

Fe2 O3

Na2 O

P2 O5

Others

Mass fraction/%

38.8

49.0

6.51

0.685

0.335

0.222

0.938

3.15

Fig. 1 XRD pattern of phosphogypsum

the chemical reaction of phosphogypsum and CO2 in ammonia solution, thereby increasing the conversion rate of phosphogypsum [15].

Experimental Materials and Principles Raw Materials The raw material phosphogypsum sample was taken from Hubei Chuxing Chemical Co., Ltd. The content of CaSO4 ·2H2 O (mass fraction) was 91.93%. Its chemical composition is shown in Table 1. The X-ray diffractometer detects the phosphogypsum raw material sample (Fig. 1) and found that the main phases in the sample are CaSO4 ·2H2 O and SiO2 . The reagents used in the experiment are mainly CO2 gas (purity > 99.5%), NH3 ·H2O (25–28% mass fractions), and air.

Experimental Principle and Process The reaction of ammonium sulfate with phosphogypsum, ammonia water, and CO2 is as follows:

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CaSO4 · 2H2 O(S) + 2NH3 OH(aq) + CO2 (g) = CaCO3 (s) + (NH4 )2 SO4 (aq) + 2H2 O(l)

(2)

This is the gas–liquid-solid three-phase reaction system. Including the following four reaction steps: + CO2 + H2 O → CO−3 2 + 2H

(3)

− NH3 + H2 O → NH+ 4 + OH

(4)

Ca2+ + CO2− 3 → CaCO3 ↓

(5)

2− 2NH+ 4 + SO4 → (NH4 )2 SO4

(6)

The solubility product constant of CaCO3 at normal temperature is Ksp = 2.8 × 10–9 , and the solubility product constant of CaSO4 ·2H2 O is Ksp = 9.1 × 10–6 . The solubility product of CaCO3 is much smaller than that of CaSO4·2H2 O. The conversion of the reaction is relatively complete. The flowchart is shown in Fig. 2.

Conversion Rate Weigh 0.6 g of the reaction product sample which was dried to a constant mass at 105 °C ± 2 °C in advance, placed in a 250 mL beaker, and wetted with a little water. Cover with a watch glass and add HCl solution dropwise along the mouth of the cup until the product is completely dissolved. Then filter with filter paper, collect the filtrate and washing liquid in a 250 mL volumetric flask, dilute to the mark with water, shake well, this solution is the test solution. Then use a pipette to transfer 25 mL of the test solution, place it in a 250 mL Erlenmeyer flask, add 5 mL of triethanolamine solution, 25 mL of water and a small amount of C21 H14 N2 O7 S indicator, use NaOH solution to make wine red, and excess 0.5 mL, use The EDTA-2Na standard titration solution is titrated to pure blue as the end point. The actual mass M1 of CaCO3 in the reaction product is calculated. According to formula (1), the theoretical output M of CaCO3 can be calculated, and finally the mass conversion rate X of phosphogypsum can be obtained. X=

M1 × 100% M

(7)

where, M1 and M represent the mass of CaCO3 in the actual and the theoretical output, respectively.

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Fig. 2 Flow chart of conversion of phosphogypsum to ammonium sulfate

Experiment Apparatus The water simulation device is a cylindrical vessel reactor made of plexiglass. The size of the cylinder is 200 mm, the height is 235 mm. The side length of the square water tank is 266 mm and the height is 300 mm. Figure 3 shows the I-SPEED3 high-speed camera and reactor for taking pictures. Figure 4 shows three different shape of agitators, where a is a CBY agitator, b is an SSB agitator, and c is a SSB-D agitator (a circular plastic plate is added on the basis of the SSB agitator). The size of the three agitators is 70 mm.

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Fig. 3 I-SPEED3 high-speed camera and water simulation reactor

(a) CBY

(b) SSB

(c) SSB-D

Fig. 4 Three different shape of agitators

Experimental Program This experiment mainly investigates the different shape of agitators, stirring speed, aeration flow, eccentric stirring, and the height of agitator the effect of micro-bubble below. During the experiment, I-SPEED3 high-speed camera was used, and the photo was taken with Adobe Photoshop to select the 10.00 mm × 10.00 mm area above the static image shape of agitator for bubble processing. The number of bubble sizes was analyzed by image-pro software, and the data was expressed by origin software (Table 2).

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Table 2 Experimental design Variable

Shape of agitator

Shape of agitator CBY

Stirring speed (r/min)

Eccentric stirring

Inlet gas flow (m3 /h)

Height of agitator

600

0.4

0.12

3

400

0.4

0.12

3

0

0.12

3

0.06

3

SSB SSB-D Stirring speed (r/min)

SSB-D

500 600 700

Eccentric stirring

SSB-D

Inlet gas flow (m3 /h)

SSB-D

600

0.2 0.4 600

0.4

0.09 0.12 0.15

Height of agitator

SSB-D

600

0.4

0.12

2 3 4

Results and Discussion Effects of Agitators When the stirring speed is 600 r/min, the inlet gas flow is 0.12 m3 /h, eccentric stirring is 0.4, and the height of agitator is 3 cm, inquiry into effects of three agitators on the micro-bubble. It can be seen from Fig. 5 that when the CBY agitator is used, even though the eccentric stirring is 0.4, a strong eddy current is still generated and a large vortex is

(a) CBY

(b) SSB

Fig. 5 Static images of bubbles with different agitators

(c) SSB-D

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CBY SSB SSB-D

140 130 120

Number of bubbles

110 100 90 80 70 60 50 40 30 20 10 0

0

1

2

3

4

5

6

7

Diameter(mean)/mm

Fig. 6 Bubble size distribution under different agitators. (Color figure online)

formed, resulting in poor effect of micro-bubble. Under the stirring of SSB agitator, the effect of bubble dispersion is better than that of CBY agitator, but a slight vortex is formed at the upper left of the reactor. There are few bubbles in the center of the agitator, and a dead zone is formed due to uneven distribution of bubbles. When using the SSB-D agitator, the existence of the disc hinders the rising speed of the bubbles, which makes the contact time of the bubbles and the agitator longer, which is beneficial to the micro-bubble. It can be seen from Fig. 6 that under CBY and SSB agitators, the number of bubbles with an average size of 0.5–3 mm is small, and bubbles with an average size greater than 4 mm begin to appear, which is due to the fact that small bubbles converge into large bubbles under the action of vortex. When using the SSB-D agitator, the average bubble size is distributed between 0.5–3 mm, and the effect of the micro-bubble is the best.

Effects of Stirring Speeds When the inlet gas flow rate is 0.12 m3 /h, eccentric stirring is 0.4, and the height from bottom is 3 cm, and the agitator is SSB-D, inquiry into effects of four stirring speeds on the micro-bubble. It can be seen from Fig. 7 that as the stirring speed increases, the bubbles are more evenly distributed in the reactor and the dead zone gradually decreases, which is extremely beneficial to the reaction process. As the stirring speed increases, the displacement of the agitator increases, which expands the distribution area of the bubbles in the cross section of the agitator and reduces the dead zone area. At the

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(a) 400r/min

(b) 500r/min

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(c) 600r/min

(d) 700r/min

Fig. 7 Static images of bubbles with different stirring speeds

same time, the shear rate of the agitator increases sharply with the increase of the stirring speed, causing the large bubbles to continuously deform and eventually be sheared and broken. It can be seen from Fig. 8 that when the stirring speed is increased to 700 r/min, the number of bubbles between 1 and 3 mm decreases, and the number of large bubbles with an average bubble diameter greater than 5 mm slowly increases. This is because the stirring speed is too high and small bubbles converge when they float up. Causes poor effect of micro-bubble. 400r/min 500r/min 600r/min 700r/min

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Number of bubbles

120 100 80 60 40 20 0

0

1

2

3

4

5

6

7

8

9

Diameter(mean)/mm

Fig. 8 Bubble size distribution under different stirring speeds. (Color figure online)

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(a) e=0.2

(b) e=0.2

(c) e=0.4

Fig. 9 Static images of bubbles with different eccentric stirring

Effects of Eccentric Stirring When the stirring speed is 600 r/min, the inlet gas flow is 0.12 m3 /h, and the height of agitator is 3 cm, and the agitator is SSB-D, inquiry into effects of three eccentric stirring on the micro-bubble. It can be seen from Fig. 9 that as the eccentric stirring increases, the bubbles are more evenly distributed in the reactor. When the eccentric stirring is small, due to the generation of stable tangential flow, a vortex will be formed in the center of the agitator, and the bubbles will be drawn into the vortex, resulting in a small number of bubbles in the liquid outside the vortex, and the effect of micro-bubble is poor. With the greater eccentric stirring, the farther the agitators is from the center of the reactor, the formation of stable tangential flow in the horizontal direction is effectively inhibited, thereby preventing the appearance of vortex and the merger of bubbles, which is beneficial to the micro-bubble. According to Fig. 10, when eccentric stirring is 0.4, the average bubble size is distributed between 0.5 and 3 mm, and the effect of micro-bubble is the best.

Effects of Inlet Gas Flow When the stirring speed is 600 r/min, eccentric stirring is 0.4, and the height of agitator is 3 cm, and the agitator is SSB-D agitator, inquiry into effects of four inlet gas flow on the micro-bubble. It can be seen from Fig. 11 that with the increase of the inlet flow rate, the bubble distribution becomes more uniform, and the more small bubbles are formed. According to Fig. 12, it can be seen that the effect of micro-bubble is the best when the inlet flow rate is 0.12 m3 /h, and the average bubble size is mainly distributed between 0.5 and 3 mm. When the inlet flow rate reaches 0.15 m3 /h, the number of large bubbles in the reactor increases. According to the law of conservation of mass, the gas flow Q is proportional to the bubble diameter D3 . When the stirring speed is constant, the larger the inlet flow rate, the less the number of bubbles. This is due

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150

e=0 e=0.2 e=0.4

140 130 120

Number of bubbles

110 100 90 80 70 60 50 40 30 20 10 0

0

1

2

3

4

5

6

7

8

9

10

Diameter(mean)/mm

Fig. 10 Bubble size distribution under different eccentric stirring. (Color figure online)

(a) 0.06m3/h

(b) 0.09m 3/h

(c)0.12m3/h

(d) 0.15m 3/h

Fig. 11 Static images of bubbles with different inlet gas flow

to the increase of gas flow rate, which increases the speed of bubbles rushing to the liquid surface, and makes small bubbles merge to large bubbles after splitting.

Effects of the Height of Agitator When the stirring speed is 600 r/min, the inlet gas flow is 0.12 m3 /h, and eccentric stirring is 0.4, and the agitator is SSB-D, inquiry into effects of three height of agitator on the micro-bubble. It can be seen from Fig. 13 that the height of the agitator from the bottom of the reactor has little effect of micro-bubble. When the height of agitator is 2 and 3 cm, the dead zone in the reactor is smaller. When the height of agitator is 4 cm, the bottom left of the reactor the end dead zone is larger. According to Fig. 14, it can be seen that the average bubble size is mainly distributed between 0.5 and 3 cm. When

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0.06m /h 3 0.09m /h 3 0.12m /h 3 0.15m /h

140

Number of bubbles

120 100 80 60 40 20 0

0

1

2

3

4

5

6

7

8

9

10

Diameter(mean)/mm

Fig. 12 Bubble size distribution under different inlet gas flow. (Color figure online)

(a) h=2cm

(b) h=3cm

(c) h=4cm

Fig. 13 Static images of bubbles with different height of agitator

the height of agitator is 3 cm, the number of bubbles is the largest, and the effect of micro-bubble is better.

Application of Water Simulation According to the exploration of the previous water simulation, it is known that the optimal bubble refinement conditions are: the shape of agitator is SSB-D, the stirring speed is 600 r/min, eccentric stirring is 0.4, the inlet gas flow is 0.12 m3 /h, and the height of agitator is 3 cm. Under this condition, the average bubble size is distributed between 0.5 and 3 mm, and the effect of micro-bubble is the best. It also shows that the gas–liquid contact area increases and the degree of gas–liquid mixing is higher. It is more conducive to the chemical reaction between gas and liquid.

Physical Simulation of Bubble Behaviors and Optimization …

145 h=2cm h=3cm h=4cm

140

Number of bubbles

120 100 80 60 40 20 0

0

1

2

3

4

5

6

Diameter(mean)/mm

Fig. 14 Bubble size distribution under different height of agitator. (Color figure online)

Since the conversion of phosphogypsum to ammonium sulfate also involves a gas– liquid two-phase reaction, the optimal conditions for gas–liquid mixing obtained by water simulation exploration are suitable for the experiment of converting phosphogypsum to ammonium sulfate. Other fixed factors in the experiment are: liquid–solid ratio of 1:10, nitrogen-sulfur ratio of 2.5:1, temperature of 55 °C, reaction time of 60 min, eccentric stirring is 0.4, the inlet gas flow is 0.12 m3 /h, and the height of agitator is 3 cm. The effect of three different agitators on the conversion rate of phosphogypsum was studied under different stirring speeds. It can be seen from Fig. 15 that no matter which agitator is selected, the conversion rate of phosphogypsum increases with the increase of the stirring speed. This is because the stirring speed can increase the gas–liquid-solid three-phase mixing during the reaction. Which improve the gas–liquid contact area and promote the reaction. When the stirring speed is 600 r/min, the conversion rates of CBY, SSB, and SSB-D agitator are 87.2%, 88.9%, and 91.95%, respectively. This is because the circular plastic plate on the SSB-D agitator hinders the rise of gas, it improves the uniform mixing effect of gas–liquid-solid three-phase, and accelerates the formation of CaCO3 and (NH4 )2 SO4 .

Conclusion (1) The difference in the height of agitator has little effect of micro-bubble in the reactor, which can be ignored in the experiment of converting phosphogypsum to ammonium sulfate.

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Conversion rate/%

88 86 84 82 80

CBY SSB SSB-D

78 76 74 72 70

400

450

500

550

600

650

700

Stirring speed/(r/min)

Fig. 15 Effect of agitators on conversion rate. (Color figure online)

(2) The optimal experimental conditions in the reactor are: the shape of agitator is SSB-D agitator, the stirring speed is 600 r/min, eccentric stirring is 0.4, the inlet gas flow is 0.12 m3 /h, and the height of agitator is 3 cm. Under this condition, the average bubble size is distributed between 0.5 and 3 mm, and the effect of micro-bubble is the best. This optimal condition can be used in the experiment of phosphogypsum conversion to ammonium sulfate. (3) Under the optimal conditions explored by the water simulation, the conversion rate of phosphogypsum can reach up to 91.95%. Variables (1) Eccentric stirring is the ratio of the distance from the center of the agitator to the radius of the cylinder. (2) Height of agitator is the distance between the agitator and the bottom of the reactor. (3) M1 and M represent the mass of CaCO3 in the actual and the theoretical output, respectively.

References 1. Pan W, Wu S, Zeng QY (2019) Progress in resource utilization of phosphogypsum. Yunnan Chem Ind 46(04):16–19+22

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2. Chen HY (2019) The status quo of resource utilization of phosphate phosphogypsum in Guizhou. Sulfuric Acid Ind (07):5–6+10 3. Zhang W, Zhang FZ, Ma LP (2020) CO2 capture and process reinforcement by hydrolysate of phosphogypsum decomposition products. 36:253–262 4. Huang YH (2020) Study on the production of ammonium sulfate from phosphogypsum. J Chengdu Teach Coll 4(04):66–74 5. Jiang YQ, Wang Y (2020) The strategic planning, implementation and guarantee of phosphogypsum consumption in Guizhou Province. Heilongjiang Sci 11(08):128–131+135 6. Zhang XF, Liu SQ, Chen M (2002) Study on the preparation of ammonium sulfate and calcium chloride from phosphogypsum. J Hefei Univ Technol (Nat Sci Ed) 01:67–70 7. Cordell GB (1968) Reaction kinetics of production of ammonium sulfate from anhydrite. Ind Eng Chem Process Des Dev 7(2):278–285 8. Yang BJ, Chen X, Wang BN (2011) Precipitation conditions for the preparation of light calcium carbonate by one-step phase transfer-precipitation method. J Hefei Univ Technol (Nat Sci Ed) 10:1551–1554 9. He SQ, Sun HJ, Peng TJ (2013) Experimental study on carbonation and fixation of carbon dioxide by phosphogypsum. J Rock Mineral 32(6):899–904 10. Hou DB, Wang KB, Song CL, Qin QK, Guo WQ, Cui SRD, LW, (2019) Experimental study on the effect of vibration on fine bubbles. J Jilin Instit Chem Technol 36(03):30–33 11. Liu A, Han F, Li ZH, Liu AR, Fan MQ (2018) Research progress on the application of nanobubbles in the flotation of fine-grained minerals. Miner Resour Conserv Util 215(03):81–86 12. Wang J, Hu H (2020) Microbubble-assisted pressure carbonation for preparation of high purity lithium carbonate. J Mater Res Technol 9(5):9498–9505 13. An YH, Peng ZL, Du XQ (2017) Application of microbubble air flotation system in coking wastewater pretreatment. Chem Eng Des Commun 43(09):224–225 14. Guchi M, Kaji M, Morita Z (1998) Effects of pore size, bath surface pressure, and nozzle size on the bubble formation from a porous nozzle. Metall Mater Trans B J 29(11):1209–1218 15. Gadallah AH, Siddiquib K (2015) Bubble breakup in co-current upward flowing liquid using honeycomb monolith breaker. Chem Eng Sci 131:22–40

The Influence of Hydrogen Injection on the Reduction Process in the Lower Part of the Blast Furnace: A Thermodynamic Study Zeji Tang, Zhong Zheng, Hongsheng Chen, and Kun He

Abstract To reduce the CO2 emission in ironmaking, H2 is suggested to be the best alternative to the fossil fuels used in the blast furnace, and it attracts increasing attention in recent decades. It is well known the reduction behavior of iron oxides with hydrogen is significantly different from that with carbon. Thus, the operation of the blast furnace will change greatly if hydrogen is injected into the blast furnace. However, the influence of hydrogen injection on the reduction process in the blast furnace is seldom addressed in the literature. In this work, the principle of minimum Gibbs free energy is applied to analyze the thermodynamics under working conditions in the lower part of the blast furnace taking into account hydrogen injection. Results indicate that the addition of H2 plays a crucial role in the thermal balance of the system and the reduction process of wustite. When the amount of heat supplied by hydrogen injection is less than 25%, the gas utilization ratio increases by injecting hydrogen. In this circumstance, wustite can be completely reduced by carbon, and no water is formed because H2 only acts as a transmission medium. The situation becomes totally different when it is over 25%, and the coexistence of carbon and wustite can be observed. Keywords Ironmaking · Hydrogen · Blast furnace · Thermodynamics

Introduction The blast furnace is of great advantage in the ironmaking process due to its higher thermal efficiency and huge production scale. However, ironmaking using blast furnace suffers from coke resource shortage and severe CO2 emission considering the National Sustainable Development Strategy. To solve these problems, hydrogen injection into the blast furnace is a promising ironmaking technology. Compared to

Z. Tang · Z. Zheng (B) · H. Chen · K. He College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China e-mail: [email protected] © The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9_14

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carbon reduction, hydrogen is a renewable energy source and has better thermodynamic and kinetic advantages [1], so it is considered to be one of the most potential and promising reducing agents [2, 3]. So far, metallurgists have done a lot of research on the blast furnace hydrogen injection, and have achieved remarkable results. From the perspective of research methods, the research on the blast furnace hydrogen injection is mainly divided into three aspects: theoretical analysis research [4–7], physical experiment research [8, 9], and numerical simulation research [10–12]. In terms of theoretical analysis and research, Kim [4] found that hydrogen injection will increase the slope of the operating line which includes C and H2 , but reduce the carbon consumption, based on the improved RIST operating line; Wang [5] analyzed and proved the feasibility of hydrogen injection in an oxygen blast furnace by The Conservation of Mass and Energy; Bernasowski [6] studied the effect of gas mixtures of CO and H2 in different proportions on the reduction of iron oxides under equilibrium conditions and the presence of carbon in the system; Li Bin [7] established the thermodynamic model of the gas–solid reduction reaction of iron oxides based on the principle of minimum Gibbs free energy, studied the thermodynamics of the gas–solid reduction of iron oxides, and a three-dimensional equilibrium diagram of the reduction of iron oxides with CO and H2 mixed gas was made. However, these studies either only consider high temperature or only consider gas composition, and most of them are carried out under a standard atmospheric pressure and do not fully consider the working environment of the blast furnace. Therefore, the promotion of conclusions is somewhat limited. As we all know, the blast furnace is a high temperature and high-pressure gas–solid countercurrent reactor, so it is necessary to consider the effects of high temperature, high pressure, and gas composition on the reduction of iron oxides in the blast furnace. Therefore, the scope of present work is that the reduction of the Wustite in the lower part of the blast furnace was taken as the research object and the working environment of the blast furnace as the thermodynamic condition, a thermodynamic model was established based on the principle of simultaneous equilibrium and the principle of minimum Gibbs free energy, to study the effect of hydrogen injection at the lower part of the blast furnace on the reduction process. First, a thermodynamic model that can reflect the carbon reduction of the lower part of the blast furnace was established; then, based on this thermodynamic model, the influence of hydrogen injection on the reduction process was considered.

Establishment of Thermodynamic Model and Conditions Establishment of Thermodynamic Model The lower part of the blast furnace is defined as the space below the cohesive zone, the active coke zone, and the upper tuyere zone. As shown in Fig. 1, the reduction of iron

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Fig. 1 Definition of the lower part of the blast furnace and two thermodynamic models. (Color figure online)

oxide has the characteristics of gradual transformation, plus a high temperature (more than 1000 °C) and a high-pressure environment. Therefore, the reduction reaction of iron oxide in this area is mainly the reduction of liquid Wustite.

The Principle of Simultaneous Equilibrium Simultaneous equilibrium [13] means that two or more reactions that influence each other proceed at the same time and reach equilibrium in the actual reaction system, and the equilibrium composition can be determined by the number of independent reactions. Based on this principle, a thermodynamic model of carbon reduction Wustite and a thermodynamic model of hydrogenation reduction Wustite are established, as illustrated in Fig. 1.

The Principle of Minimum Gibbs Free Energy The principle of minimum free energy [7, 14, 15] was first proposed by White in 1958. Its basic principle is that the total free energy is minimum when the system reaches thermodynamic equilibrium for a multi-phase system, the minimum Gibbs free energy thermodynamic model is as follows: min G =

M  i=1

ni G i =

M 

  n i G i + RT ln ai

i=1

Based on the above two principles, a thermodynamic model is established for subsequent balance calculations and thermodynamic analysis.

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Determination of Thermodynamic Conditions Temperature The temperature has a great influence on the standard equilibrium constant of the chemical reaction. Although the temperature of the lower part of the blast furnace varies with the blast furnace object, most of the temperature is between 1373.15 and 1673.15 K. The temperature for this calculation is 1523.15 K.

System Pressure The blast furnace is a high-pressure countercurrent gas–solid reactor, and the influence of system pressure on the balance of chemical reactions cannot be ignored either. The high-pressure operation of blast furnaces at domestic and foreign [16– 19] is shown in Table 1. The pressure drop is mainly consumed to overcome the resistance of the charge to the gas movement, and the cohesive zone at the lower part of the blast furnace is the area with the worst permeability. Since the pressure loss from the tuyere to the cohesive zone is not large, and the following assumptions are made: the furnace top pressure is 200 kPa, the blast pressure is 400 kPa, and the system pressure in the calculated area is 350~400 kPa.

Oxygen Enrichment Rate In the process of conventional blast furnace blasting, it is not pure oxygen (full oxygen blast furnace) that is blown in, but hot air with an oxygen enrichment rate of 2%–5% [20, 21] (under normal circumstances, 21% O2 , 79% of N2 of the air), so the actual partial pressure of O2 or the influence of N2 on the balance needs to be considered. This calculation assumes that the oxygen enrichment rate is 4%, that is, the partial pressure of oxygen in the blast is 25% and the partial pressure of nitrogen is 75%. At the same time, when the temperature is determined, the thermodynamic data is also determined. The thermodynamic data for this calculation comes from the thermodynamic software HSC Chemistry 6.0, and the calculation process is realized through the Equilibrium Compositions module. In summary, the final thermodynamic conditions are listed in Table 2. Table 1 High-pressure operation of blast furnaces at domestic and abroad

Pmin /kPa

Pmax /kPa

Top pressure

200

300

Blast pressure

350

580

Pressure drop

150

200

The Influence of Hydrogen Injection on the Reduction … Table 2 Thermodynamic conditions for equilibrium calculation

153

Thermodynamic Conditions

Value

Temperature

1523.15 K

Thermodynamic data

From HSC Chemistry 6.0

System pressure

350~400 kPa

Gas partial pressure

25%O2 + 75%N2

Result and Discussion Thermodynamics of the Reduction of Wustite by Carbon The balance calculation is based on the production of one ton of iron, and the initial input amount of each substance is determined by the stoichiometric number. Assuming that the reaction heat required for carbon reduction Wustite is all provided by the combustion of pulverized coal which includes complete combustion and incomplete combustion. The degree of progress of the two combustion reactions is directly related to the initial input of pulverized coal, so determining the distribution ratio of the combustion reaction is a very critical issue. To determine the combustion ratio distribution, the ratio assumption listed in Table 3 was carried out. The reaction ratio calculated according to the equilibrium composition is compared with the assumed ratio, and the appropriate combustion ratio is finally determined. Figure 2 shows the calculation of the relative deviation of different combustion ratios under current thermodynamic conditions. It can be seen from Fig. 2 that the minimum value of the relative deviation is almost at the lowest point K that the ratio is 0.35:0.65, and the relative deviation is about 3.4% when the pressure is between 3.5 and 4.0 bar. The r Gm −T relationship curve of complete combustion and incomplete combustion of carbon intersects at 965.50 K. The reason why the balance calculation result is inconsistent with the thermodynamic calculation result that incomplete combustion of carbon at high temperature should occupy the main part is that this is a comprehensive result of being affected by three other reactions at the same time. Table 3 The distribution ratio assumption of the carbon combustion reaction Num

Incomplete combustion Complete combustion /%

Num

Incomplete combustion Complete combustion /%

Num

Incomplete combustion Complete combustion /%

A

85:15

G

55:45

M

25:75

B

80:20

H

50:50

N

20:80

C

75:25

I

45:55

O

15:85

D

70:30

J

40:60

P

10:90

E

65:35

K

35:65

Q

5:95

F

60:40

L

30:70

–

–

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Fig. 2 Relative deviation under different pressure and different combustion ratio. (Color figure online)

0.09

=

assumed value-calculated value assumed value

0.08

100%

3.5bar 3.6bar 3.7bar 3.8bar 3.9bar 4.0bar

Relative deviation

0.07 0.06 0.05 0.04 0.03 0.02

A B C D E F G H

I

J K L M N O P Q --

Combustion ratio

It can be seen from Fig. 3 that the partial pressure of gas after equilibrium under different pressures is almost the same, and the partial pressure of N2 is about 44%, CO is about 48%, and CO2 is about 8%, which illustrates that the influence of pressure is not obvious. The lower part of the blast furnace is dominated by the direct reduction reaction, and the carbon gasification reaction is strongly developed, which keeps CO2 at a low level. Besides, the gas utilization ratio is about 14% at this time by calculation [22], which is consistent with the utilization ratio of top gas of the blast furnace at about 50% [21], because there is also the influence of CO produced by the reduction of high-valent iron oxide and the gasification of carbon from the bottom to the top of the blast furnace. In summary, the carbon combustion ratio of 0.35:0.65 is reasonable under the current thermodynamic conditions. 100

Fig. 3 Gas partial pressure at equilibrium at point K under different pressures. (Color figure online) Gas partial pressure/%

80

N2 CO2 CO

44.352%

44.352%

44.343%

44.343%

44.343%

44.343%

7.8848%

7.8848%

7.9022%

7.9022%

7.9022%

7.9022%

47.763%

47.763%

47.754%

47.754%

47.754%

47.754%

3.5

3.6

3.7

3.8

3.9

4.0

60

40

20

0

Pressure/bar

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Thermodynamics of the Reduction of Wustite by Hydrogenation On the basis of the above conditions, the same method was used to study the influence of different amounts of hydrogen injection on carbon reduction of Wustite. The reaction heat required for carbon reduction Wustite is provided by the combustion of pulverized coal and hydrogen. The heating ratio of the two reactions is shown in Table 4, the carbon combustion ratio is always kept at 0.35:0.65. It can be observed from Fig. 4 that the reduction degree is slightly affected by the pressure, and the change curves under different pressures are almost overlapped, which is consistent with the results in Fig. 3, but the reduction degree is significantly affected by the amount of hydrogen injection, and its change trend is divided into two stages: before point E, reduction degree does not change with the amount of hydrogen injection, and the reduction degree is always maintained at 100%; from point E to point S, the reduction degree decreases with the increase of the amount Table 4 The distribution of heating ratio of the hydrogen combustion and carbon combustion Num

H2 :C/%

Num

H2 :C/%

Num

H2 :C/%

A

5: 95

H

40: 60

O

75: 25

B

10: 90

I

45: 55

P

80: 20

C

15: 85

J

50: 50

Q

85: 15

D

20: 80

K

55: 45

R

90: 10

E

25: 75

L

60: 40

S

95: 5

F

30: 70

M

65: 35

–

–

G

35: 65

N

70: 30

–

–

Fig. 4 Reduction degree versus combustion ratio under different pressure. (Color figure online)

3.5bar 3.6bar 3.7bar 3.8bar 3.9bar 4.0bar

1.00

Reduction degree

0.95

0.90

0.85

e

R

nFeO 0

nFeO

100%

0.80

0.75

A B C D E F G H I

J K L M N O P Q R S --

Heating ratio

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of hydrogen injection, indicating that FeO is not completely reduced, the injected hydrogen affects the reduction process of FeO by carbon, and the more hydrogen sprayed, the more significant the effect is. When the amount of heat supplied by hydrogen injection is 95%, even more than 20% of FeO is not reduced. According to Fig. 5, regardless of whether hydrogenation is added, the remaining carbon percentage at system equilibrium increases with the increase of pressure. This is because the high pressure is favorable for the reaction to proceed in the direction of volume reduction, mainly the reverse reaction of the carbon gasification reaction. When the system is in equilibrium, the remaining carbon percentage decreases first and then increases with the increase in the amount of hydrogen injection, and is the minimum at point E. Analyzing the situation before point E, the partial pressure of the gas at equilibrium at the same pressure (take 3.7 bar as an example) under different hydrogen injection amounts is obtained as in Fig. 6. As the amount of hydrogen injection increases,

3.5bar 3.6bar 3.7bar 3.8bar 3.9bar 4.0bar

0.0045

Remaining carbon percentage

Fig. 5 Remaining carbon percentage versus combustion ratio under different pressure. (Color figure online)

0.0040 No hydrogenation

0.0035 e

nC 0

nC

0.0030

100%

0.0025

0.0020 A B C D E F G H I J K L M N O P Q R S -- --

Heating ratio

Fig. 6 Gas partial pressure versus combustion ratio under 3.7 bar before point E. (Color figure online)

100 8.5824%

9.3043%

2.9762%

4.0495%

10.058%

10.844%

41.480%

40.436%

39.348%

45.487%

44.671%

43.818%

C

D

E

5.1680% 11.665%

Gas partial pressure/%

80 43.450%

42.483%

60

40

20

0

47.014%

46.268%

A

B

Heating ratio

H2 O H2 CO2 N2 CO

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the CO partial pressure gradually decreases, and the CO2 partial pressure is just the opposite, which shows that adding a little hydrogen is beneficial to the improvement of the gas utilization ratio. However, the partial pressure of H2 O always remains at zero. From the perspective of the equilibrium results, the added hydrogen does not participate in the reduction of FeO which is completely completed by carbon. What needs to be emphasized is that it is not ruled out the possibility that hydrogen reduces FeO to produce H2 O, and H2 O reacts with carbon to produce H2 . H2 only acts as a transmission medium due to the presence of carbon [6, 23]. The hydrogen will then enter the upper part of the blast furnace through the cohesive zone along with the gas flow, and participates in the stepwise reduction reaction of high-valent iron oxides [8], the generated water and the remaining unreacted hydrogen pass through the layer of iron ore and coke to the top of the blast furnace and are discharged with the blast furnace gas [5, 22]. Figure 5 shows that the remaining carbon percentage is less than the situation without hydrogenation, indicating that adding a little hydrogen can promote the production of CO and CO2 in the system, and CO further converted to CO2 so that the gas utilization ratio is improved. Analyzing the situation between point E and point S, Fig. 7 shows the partial pressure of the gas at equilibrium with the amount of hydrogen at the same pressure (take 3.7 bar as an example) for different injections. It can be known from Fig. 7 that the H2 O partial pressure at equilibrium gradually increases with the increase in the amount of hydrogen injection, and the CO partial pressure gradually increases, while the CO2 partial pressure gradually decreases, that is, the gas utilization ratio decreases with the increase in the amount of hydrogen injection. From point E to point S, the remaining carbon percentage increases with the increase in the amount of hydrogen injection. The previous analysis shows that there is unreduced FeO at this stage, and it also increases with the increase in the amount of hydrogen injection. That is to say, carbon and FeO coexist when the system is in equilibrium, which shows that the addition of hydrogen greatly affects the equilibrium of the system. 100

Fig. 7 Gas partial pressure versus combustion ratio under 3.7 bar between E and S. (Color figure online)

2.4323% 6.2032%

11.759%

7.1808%

11.504%

8.1576%

11.257%

9.1375%

11.017%

Gas partial pressure/%

80

10.123%

10.782%

11.116%

10.550%

12.121%

10.323%

3.0282%

13.139%

10.097%

3.6933%

14.171%

9.8730%

4.4326%

15.220%

9.6498%

5.2515%

16.289%

6.1567%

17.378%

7.1557%

8.2585%

18.489%

17.421%

8.9773%

8.7506%

19.579%

19.624%

9.4264% 9.2026%

60

38.219%

37.042%

35.813%

34.525%

33.178%

31.766%

30.283%

28.726%

27.088%

25.361%

23.540%

21.616%

40

20

0

43.687%

43.900%

44.103%

44.298%

44.485%

44.666%

44.841%

45.011%

45.175%

45.336%

45.494%

45.647%

45.799%

45.945%

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

Heating ratio

H 2O H2 CO2 N2 CO

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Figures 8, 9 shows the change trend of CO2 partial pressure and CO partial pressure with the amount of hydrogen injection. It can be seen that the change trends of the two graphs are just the opposite and there is a turning point: E point which represents the amount of hydrogen injection when hydrogen heating is 25%. This turning point corresponds to Figs. 4 and 5. In summary, the impact of hydrogen injection at the lower part of the blast furnace on the process of carbon reduction Wustite can be divided into two stages: when the amount of hydrogen injection for heating is less than 25%, as the amount of hydrogen injection increases, the gas utilization ratio gradually increases. Moreover, FeO can be completely reduced; when greater than 25% (less than 95%), the gas utilization ratio gradually decreases, FeO cannot be completely reduced, and the remaining FeO increases with the increase of the amount of hydrogen injection. Fig. 8 CO2 partial pressure versus combustion ratio under 3.7 bar. (Color figure online)

0.120

CO2 partial pressure

0.115 0.110 0.105 0.100 0.095 0.090 0.085 A B C D E F G H I

J K L M N O P Q R S --

Heating ratio

0.475

Fig. 9 CO partial pressure versus combustion ratio under 3.7 bar. (Color figure online)

0.470

CO partial pressure

0.465 0.460 0.455 0.450 0.445 0.440 0.435 A B C D E F G H I

J K L M N O P Q R S --

Heating ratio

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Conclusion The model was established based on the principle of simultaneous equilibrium and the principle of minimum Gibbs free energy, and under the working conditions of the conventional blast furnace, the influence of hydrogen injection at the lower part of the blast furnace on the Wustite reduction process was analyzed based on the thermodynamic equilibrium calculation through the thermodynamic software HSC Chemistry 6.0. The results show that when the amount of hydrogen injection for heating is less than 25%, the gas utilization ratio gradually increases with the increase of the amount of hydrogen injection, FeO can be completely reduced, and no water is generated because H2 only acts as a transmission medium. The hydrogen then enters the upper part of the blast furnace, part of which participates in the reduction reaction of high-valent iron oxides, and part of it is discharged with the blast furnace gas. When more than 25% (less than 95%), the gas utilization ratio will gradually decrease with the increase of the amount of hydrogen injection, FeO cannot be completely reduced, the remaining FeO will increase with the increase by injecting hydrogen, and the coexistence of solid carbon and FeO appears. Acknowledgements The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (No. 51874056).

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Author Index

A Ahn, Ji Whan, 13, 23 Akanji, Fausat T., 87 Alabi, Abdul G. F., 87 Ayinla, Kuranga I., 87

B Baba, Alafara A., 87 Bai, Hao, 29

C Chen, Hongsheng, 111, 149 Chen, Zhe, 29

D Dong, Fang, 39, 61

F Fapojuwo, Dele P., 87

G Girigisu, Sadisu, 87

H Habte, Lulit, 23 Han, Guihong, 3 Hao, Weiping, 111 He, Kun, 111, 149 Huang, Yanfang, 3

I Ibrahim, Abdullah S., 87

K Kaitian, Zhang, 95

L Lai, Quang Tuan, 13, 23 Li, Hui, 29 Liu, Bingbing, 3 Liu, Bing-Wei, 133 Liu, Gui-li, 39, 61 Liu, Yan, 39, 61, 123, 133 Liu, Zhang, 95 Li, Xiaolong, 39, 61, 123 Li, Xinchuang, 29 Li, Xueke, 123

M Mao, Shuai-Dong, 133

N Nedeljkovic, Dragutin, 51

O Olaoluwa, Daud T., 87

R Raji, Mustapha A., 87 Rong, Tao, 75

© The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9

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162 S Seongho, Lee, 23 Su, Shengpeng, 3 T Tang, Huiqing, 75 Tang, Zeji, 149 Thriveni, Thenepalli, 13, 23 Tian, Weijian, 29 X Xinyue, Shen, 95

Author Index Y Yongzhou, Wang, 95 Yu, Zi, 75

Z Zhang, Tingan, 39, 61, 123, 133 Zhao, Jing, 3 Zheng, Zhong, 111, 149 Zhong, Zheng, 95

Subject Index

A Absorption rate, 47, 125–130 Aluminum electrolysis flue gas, 123, 124 Analysis of carbon emissions from EAF process, 36 Anode materials, 4 Application of water simulation, 144 Avrami, 87, 91–93, 119

B Blast furnace, 30–32, 35, 38, 75–84, 111, 149–152, 154, 157–159 Bubble size, 133, 138, 140–146

C Carbon-based material, 4 Carbon Capture Storage and Utilization (CCUS), 26 Carbon composite briquette, 75–84 Carbon-dioxide separation, 52, 53 Carbon flow, 29–31 Carbon flows of by-product gases, 31 Carbon metabolism models in main iron and steel production processes, 31 Carbon metabolism of blast furnace ironmaking process, 32 Case analysis of carbon emissions of integrated iron and steel enterprises, 35 CCB reaction model, 76 Characteristics, 13, 29, 40, 61–64, 67, 72, 151 Characterization of Amino-HNC, 7 Characterization of material, 6 Characterization of the calcined Kaolinite and porous product, 89

Chemical and mineralogical composition of the FA, 14 Circulating fluidized bed combustion, 13– 16, 20 Classification efficiency, 48 Climate change, 25, 26 Climate changes before and after COVID-19 pandemic, 25 CO2 , 24–26, 30–35, 37, 58, 76, 79–83, 123–131, 135, 154, 157, 158 CO2 emissions, 24, 25, 29, 31, 33–36, 38, 75, 76, 149 Coal reactivity, 75, 76, 81–84 Comparison of prediction performance, 108 Composite membranes, 51 Conversion rate, 136 Converter oxygen consumption analysis, 96 Converter steelmaking process, 32 COVID-19, 25 Critical rare earths, 13, 14

D Data preparation, 104 Defluorination, 124, 126, 131, 132 Defluorination and desulfurization rate, 131 Desulfurization, 16, 20, 62, 124, 126, 131, 132 Determination of emission factors, 34 Determination of thermodynamic conditions, 152 Development CCB reaction in BF, 79 Distribution of absorption rate in axial position, 127 Dynamic wave scrubber, 39, 40, 47–49

© The Minerals, Metals & Materials Society 2021 A. A. Baba et al. (eds.), Energy Technology 2021, The Minerals, Metals & Materials Series, https://doi.org/10.1007/978-3-030-65257-9

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164 E Effect of filler size on absorption rate, 130 Effect of filler type on absorption rate, 129 Effect of gas flow rate on absorption rate, 128 Effect of HCl concentration, 18 Effect of inlet gas flow, 115 Effect of leaching time, 17 Effect of liquid flow rate on absorption rate, 128 Effect of temperature, 18, 115 Effects of agitators, 139 Effects of eccentric stirring, 142 Effects of inlet gas flow, 142 Effects of stirring speeds, 140 Effects of the height of agitator, 143 Efficiency, 6, 10, 13, 14, 16–20, 30, 37–43, 45–49, 62, 65, 66, 76, 96, 124, 128, 129, 131, 149 Electrocatalyst, 3, 4, 11 Electrochemical measurement of material, 6 Electrochemical test, 8 Emission factors, 29, 31, 34, 35 Establishment of thermodynamic model, 150 Establishment of thermodynamic model and conditions, 150 Experimental program, 138 Experimental system, 63

F Feature selection, 95, 102, 107, 109 Feature space, 95–97, 100, 101, 103, 105– 107, 109 Feature space division and analysis, 105 Feature variable selection and analysis, 107 Flow patterns and load performance, 43 Flow regimes, 61–63, 65, 67 Flow regimes of gas-liquid two-phase flow, 65 Fly ash, 13–15, 41 Foam flow pattern, 39, 43–45, 48

H Hematite, 15, 111–118, 120 High coal reactivity, 81 Hydrodynamics, 40, 62, 63, 66, 70–72, 129, 131 Hydrodynamics of gas-liquid two-phase flow with A/T, 66 Hydrodynamics of gas-liquid two-phase flow with gas flow rate, 70

Subject Index Hydrodynamics of gas-liquid two-phase flow with liquid flow rate, 71 Hydrogen, 8, 51–53, 55, 58, 59, 111, 120, 123, 125, 132, 149, 150, 155–159

I Industrial application, 41, 88 Influence of dust mass concentration on dust removal performance, 47 Influence of liquid-gas flow rate ratio on dust removal performance, 45 Integrated prediction, 95, 103, 109 Integrated prediction model framework, 103 Ironmaking, 30, 32, 35, 36, 75, 111, 112, 149

K Kaolinite, 87–89, 91, 92 Kinetics of dissolution studies, 91

L Leaching, 13–20, 87–89, 91–93 Leaching experiments, 16 Load performance, 68 Low coal reactivity, 81 Low temperature, 112, 120

M Mass transfer process simulation, 123 Measurement of flow patterns, 65 Mesoporous silica, 87–90, 92, 93 Method of numerical investigation, 76 Modeling CCB reaction in BF, 78 Model parameter setting and evaluation indicators, 105

O Optimization, 14, 17, 20, 36, 39, 62, 63, 96 Overall analysis, 35 Oxygen consumption, 95–97, 100–102, 104, 105, 109 Oxygen enrichment rate, 152 Oxygen evolution reaction, 3–5, 7, 9–11

P Performance of leaching reagent on rare earths extraction, 17 Phosphogypsum, 133–137, 145, 146 Physical simulation, 124, 133, 134 PM2.5, 39–41, 48, 62

Subject Index Polymers, 51–59, 87 Preparation of Amino-HNC, 5 Preparation of mesoporous silica, 89 Pressure drop of gas phase, 66 Pressure measurement, 64 Principle of minimum Gibbs free energy, The, 151 Principle of simultaneous equilibrium, The, 151 R Rare earths concentration in the FA, 15 Raw materials, 135 Reaction behavior, 75, 76, 78, 80, 83 Reaction kinetics of Fe2 O3 →Fe3 O4 , 118 Reaction kinetics of Fe3 O4 → Fe, 119 Recursive division of feature space, 97 Reduction behaviors, 111, 112, 120, 149 Reverse spray, 61–66, 69, 72 S Steelmaking converter, 95 Sustainable technologies, 26

165 Sustainable Technologies for Carbon Capture Storage and Utilization (CCUS), 25 System boundary, The, 30 System pressure, 152

T Temperature, 152 Thermodynamics, 112, 149–155, 159 Thermodynamics of the reduction of Wustite by carbon, 153 Thermodynamics of the reduction of Wustite by hydrogenation, 155 Total direct CO2 emissions and emission intensity, 33 Trajectory height of liquid phase, 67 Turbulent flow, 62

Z Zeolite powder, 51, 53–56, 58, 59 Zinc electrowinning, 3–7, 9, 10 Zinc electrowinning experiment, 6, 10