Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering 303130232X, 9783031302329

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Environmental Science and Engineering

Zuoyu Sun Prodip Das   Editors

Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering

Environmental Science and Engineering Series Editors Ulrich Förstner, Buchholz, Germany Wim H. Rulkens, Department of Environmental Technology, Wageningen, The Netherlands

The ultimate goal of this series is to contribute to the protection of our environment, which calls for both profound research and the ongoing development of solutions and measurements by experts in the field. Accordingly, the series promotes not only a deeper understanding of environmental processes and the evaluation of management strategies, but also design and technology aimed at improving environmental quality. Books focusing on the former are published in the subseries Environmental Science, those focusing on the latter in the subseries Environmental Engineering.

Zuoyu Sun · Prodip Das Editors

Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering

Editors Zuoyu Sun Department of Energy and Power Engineering Beijing Jiao Tong University Beijing, China

Prodip Das School of Engineering University of Edinburgh Edinburgh, UK

ISSN 1863-5520 ISSN 1863-5539 (electronic) Environmental Science and Engineering ISBN 978-3-031-30232-9 ISBN 978-3-031-30233-6 (eBook) https://doi.org/10.1007/978-3-031-30233-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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

Organizing Committee

Program Committee Chairs Prof. Kuo-Lin Huang, National Pingtung University of Science and Technology, Taiwan Assoc. Prof. Zuoyu Sun, Beijing Jiaotong University, China

Technical Program Committees Prof. Gordon Huang, University of Regina, Canada Prof. Teik-Thye LIM, Nanyang Technological University, Singapore Prof. Ay¸segül A¸skın, Eski¸sehir Osmangazi University, Turkey Assoc. Prof. Melih Onay, Van Yuzuncu Yil University, Turkey Dr. Parthiba Karthikeyan Obulisamy, Purdue University, IN, USA Prof. Zhe Chen, Aalborg Universitet, Denmark Prof. Huan Yu, Chengdu University of Technology, China Assoc. Prof. Yaolin Lin, University of Shanghai for Science and Technology, China Prof. Bachir Achour, University of Biskra, Algeria Prof. Ahmad Zuhairi Abdullah, Universiti Sains Malaysia, Malaysia Assoc. Prof. Maciej Dziku´c, University of Zielona Gora, Poland Asst. Prof. Svenja Hanson, University of Nottingham Ningbo, China Prof. Alam Md. Mahbub, Harbin Institute of Technology (Shenzhen), China Prof. Jose Francisco Armendariz-Lopez, Universidad Autónoma de Baja California, México Assoc. Prof. Andrés Honrubia-Escribano, Universidad de Castilla-La Mancha, Spain Prof. Siti N. B. Rahmat, Universiti Tun Hussein Onn Malaysia, Malaysia Assoc. Prof. Wong Ling Tim, Hong Kong Polytechnic University, Hong Kong, China Dr. Daniele Contini, Istituto di Scienze dell’Atmosfera e del Clima, Italy

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Organizing Committee

Asst. Prof. Xiaolei Zhang, Shenzhen Graduate School of Harbin Institute of Technology, China

Publication Chairs Assoc. Prof. Zuoyu Sun, Beijing Jiaotong University, China Assoc. Prof. Prodip Das, Newcastle University, UK

Preface

Dear Distinguished Authors and Guests, The 2022 9th International Conference on Energy Engineering and Environmental Engineering (ICEEEE2022) was held on December 9–10, 2022, successfully. Based on the COVID-19 in different provinces in China, we have to switch the conference to a virtual one. ICEEEE is dedicated to building an overarching technology platform for researchers in academia. The proceedings tend to collect the up-to-date, comprehensive and worldwide state-of-art knowledge and look far out enough in time, space and cross disciplines on energy engineering and environmental engineering and some other related topics. The papers have been selected because of the quality and relevance to the conference. We hope the book will not only provide the readers a broad overview of the latest research results but also provide the readers a valuable summary and reference in these fields. We are grateful to the Program Committee Chairs and Technical Program Committee Members for their dedicated efforts in ensuring the quality of the papers. Also, on behalf of the organizing committee, we would like to express our sincere gratitude to the distinguished keynote speakers and invited speakers, as well as to all the participants. Last but not least, we are thankful to the Springer book series: Environmental Science and Engineering for producing the proceedings. We are expecting more experts and scholars from all around the world to join the ICEEEE2023. With our warmest regards! The committee of ICEEEE2022 Beijing, China Edinburgh, UK

Zuoyu Sun Prodip Das

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Contents

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Production Performance Characteristics of Horizontal Well in Lacustrine Pure Shale Reservoirs of Gulong Shale Oil . . . . . . . . . . Lifeng Liu, Yuxiang Xiao, Zhongbao Wu, and Xin Wang

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Application of Air-Foam Flooding in Petroleum Field: Progress and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yicheng Zhang

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Dynamic Simulation of Hydrogen Fueling Performance Based on Look-Up Table and MC Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Luo, Jinsheng Xiao, Pierre Bénard, Richard Chahine, and Tianqi Yang Artificial Neural Network Prediction for Breakthrough Curves of H2 /CO2 Adsorption on CuBTC . . . . . . . . . . . . . . . . . . . . . . . Chenglong Li, Fangxin Du, Pierre Bénard, Richard Chahine, Tianqi Yang, and Jinsheng Xiao The Effect of Culture Conditions on Microbial Remediation of Contaminated Soil in Antimony Ore Area . . . . . . . . . . . . . . . . . . . . . Xinyue Shi, Peng Zheng, Xinglan Cui, Xiaokui Che, Ying Liu, Lei Wang, Hongxia Li, and Qi Zheng A Research Progress on Stabilization/Solidification of Electrolytic Manganese Residue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoying Ma, Xingyu Liu, Ying Lv, Xiao Yan, Xuezhe Zhu, and Mingjiang Zhang Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform . . . . . . . . . . . . . . . . . . . Yongfei Bai, Zhiqiang Wu, Jie Liu, Wei Jiang, Ke Zhong, Wenxing Han, and Zhi Chen

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Supercritical CO2 Enhanced Shale Gas Production Technology: Progress and Prospect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yicheng Zhang and Mingyao Song The Role of Digital Twins in Isolated Energy System Control . . . . . . Sergey A. Gordin, Viktor D. Berdonosov, and Igor E. Lyaskovskiy

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10 The Effect of Land Degradation on Changes in Water Availability in Watershed Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Zainuddin, Dinar Dwi Anugerah Putranto, and Febrian Hadinata 11 Optimization of Membrane Distillation Conditions for Waste Water Generated by Wet Treatment of Waste Incineration Slag . . . 115 Tongshan Zhu, Guang Yang, Hongbin Jiang, Wenchen Dai, Dechang Han, and Yan Li 12 On the Methodology for Ensuring Specified Availability Factors of NPP Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Alexandr Arzhaev, Alexey Arzhaev, Aleksandr Kalyutik, Valentin Makhanev, and Viktor Modestov 13 Economic Analysis on Reform of Flue Gas Waste Heat Utilization System in Thermal Power Plant . . . . . . . . . . . . . . . . . . . . . . 139 Liang Cheng, Yi-Peng Sun, and Tian-Liang Wen 14 Study on the Influence of Air Leakage Plugging in Gob-Side Entry Retaining on Air Flow Field and Spontaneous Combustion “Three Zones” in Goaf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Zhanyou Sa, Yongliang Yang, Lizhi Tian, Jingbo Wu, Xin Zhang, Jie Liu, and Shouqing Lu 15 Numerical Study of Smoke Distribution Characteristics in Roadway Fires Considering Different Cross-Sectional Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Qi Yao and Yongliang Zhang 16 Increasing Environmental Awareness in Facing Water Scarcity Problems Through Education Based on Local Wisdom Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 N. T. Martoredjo, Benny, and D. A. Kusumajati 17 Numerical Study on Acoustic Environment in Long Traffic Tunnels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Zhenxiong Jiang, Jun Wang, Mingqing Xiao, Huading Lou, and Hequn Min

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18 Noise Reduction and Sound Intelligibility Improvement in Acoustic Environment in Long Traffic Tunnels with Wall Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Yang Zhao, Junwei Chen, Shaofeng Wang, Huading Lou, and Hequn Min 19 Study of Recycled Plastic Panels for the Reduction of Pathologies in Low-Income Housing in Guayaquil, Ecuador . . . . 205 Pedro Napoleón Chara Moreira, Rosanna Elizabeth Rivera Castro, Juan Carlos Briones Macias, Alex Leonardo Mecias Tenorio, and Bryan Alfonso Colorado Pástor 20 Preparation and Performance Evaluation of Triazine Desulfurizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Lipeng Gou, Xiaoming Ren, Yibin Yang, Bing Li, Rensan Lv, Shiyong Li, and Kai Li 21 Adaptation for the Impacts of Climate Change in a Subtropical Archipelagic Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Omid Shir Mohammad, Jun-ichiro Giorgos Tsutsumi, and Ryo Nakamatsu 22 Research on Optimization of Nuclear Power Security DCS Gateway Encryption Communication Based on OPC UA Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Pengjun Zhou, Zhi Chen, Wei Jiang, Jie Liu, Yan Zhang, Ke Zhong, and Zhiqiang Wu 23 Size Effect on the Lithium Storage Properties of Si/rGO Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 M. Wang, C. X. Yang, P. F. Fang, Y. He, and X. Y. Leng 24 Engineering Performance Analysis of Palygorskite and Bentonite Mixture in High Radioactive Nuclear Waste Disposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Xiang Fan 25 Research on Investment Value Evaluation of Chinese Certified Emission Reduction Wind Power Projects Under Different Substitution Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Guobin Yuan, Ping Zhao, Tianyou Xie, Jian Fang, and Xinglei Jiang 26 Analysis of Runoff Generation Mechanism of the Xun River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Bin Yi, Lu Chen, Yizhuo Liu, Hexiang Guo, Siming Li, and Binlin Yang

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27 Ecological Architecture in the Steppe Zone of Northern Kazakhstan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Semenyuk Olga, Malibekylu Zhandarbek, Zhanabergen ov Temirkhan, Maslov Khalil, Dukombaiev Azamat, Arynov Kaldybay, and Kozybayeva Makhabbat 28 Study on Flocculation Treatment Effect of Dredged Mud with Extremely High Water Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Xingsai Dang, Jingyu Xie, Zhao Hong, Haijun Yan, Kui Yang, and Yu Liu 29 Effect of Vertically-Layered Vegetation on the Velocity of Open Channel Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Xiaonan Tang, Yutong Guan, Jiaze Cao, Hanyi Wang, Nanyu Xiao, and Suyang Zhang 30 Study on the Mechanism of Pore Structure Change After Flocculation of Very High Water Content Dredging Slurry . . . . . . . . 329 Chunyuan Zhang, Jingyu Xie, Xingsai Dang, Zhao Hong, Kui Yang, and Yu Liu 31 Study on Soil Pressure Distribution Law Based on Model Test of Revetment of Wave Sheet Pile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Yuedong Wu, Fan Yang, and Ziyu Zhao 32 Analysis of Rainfall Characteristics of East Kalimantan Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Zuhdi Yahya, Puji Astuti, Zikri Azham, Maya Preva Biantary, Lisa Astria Milasari, and Akas Pinaringan Sujalu 33 Study on Influence of Geological Conditions of Complex Layered Soil Layer on Seepage Characteristics of Embankment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Zongchun Li, Yongyao Wei, and Yiduo Wang 34 Construction and Performance of Foam Drainage Corrosion Inhibition Integral Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Chao Huang, Shiyong Li, Degang Wang, Biao Zhao, Xiangzhe Jin, and Yan Xue 35 Study of the Performance of Foam Drainage Corrosion Inhibition Integral Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Hai Lin, Jun Xu, Xiaoming Wang, Xiaolei Guo, Liang Guo, Huaibing Chen, and Jianqiang Zhang

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36 Preparation of Green Low-Carbon Baking-Free Bricks from Iron Tailings with Metallurgical Slag-Cementing Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Qingping Chai, Changqing Chu, Shiping Wang, Yunyun Li, Xinying Chen, Xinglan Cui, and Wen Ni 37 Study on the Preparation of Green Low-Carbon Baking-Free Bricks from Iron Tailings Co-granulated Blast Furnace Slag-Desulfurization Ash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Chun Yang, Dawei Pan, Wenduo Xue, Yunyun Li, Xinying Chen, Xinglan Cui, and Wen Ni

Chapter 1

Production Performance Characteristics of Horizontal Well in Lacustrine Pure Shale Reservoirs of Gulong Shale Oil Lifeng Liu, Yuxiang Xiao, Zhongbao Wu, and Xin Wang

Abstract Gulong shale oil is the largest continental pure shale oil reservoir discovered in China in recent years. Due to the complex geological characteristics, it is difficult to understand the law of oil well production performance. Taking the typical horizontal well in Gulong shale reservoir as an example, this paper preliminarily analyzes the production performance of the oil well by using the analysis methods of double logarithm curve, square root curve, blasingame curve, and sets up different seepage models to fit the production performance of the well through simulation calculation. The results show that although the physical properties of shale reservoir are poor, the SRV external reservoir of horizontal wells still contributes to the production of oil wells, and the selection of development mode has a great impact on the development effect of oil wells. Keywords Unconventional hydrocarbon resources · Shale oil · Lacustrine · Production performance · Reservoir engineering

1.1 Introduction Shale oil is an important substitute resource for conventional reservoirs. China has a large amount of shale oil resources and has great development potential. Gulong shale reservoir is a typical lacustrine pure shale reservoir in China. Unlike other shale reservoirs in the world, its lithology is mainly shale, and the thickness of shale accounts for more than 95%. Gulong shale oil reservoir has poor physical properties, the average effective porosity is 7%, and the matrix permeability is usually less than 0.1 md. The pore throat of shale is small, and the analysis of electron microscope, L. Liu (B) · Y. Xiao · Z. Wu PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China e-mail: [email protected] X. Wang Sinopec Research Institute of Petroleum Exploration & Production, Beijing 100083, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_1

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nano-CT, nuclear magnetic resonance and high-pressure mercury injection shows that the pore diameter range is 10–100 nm. However, it has a lot of page seams, with a density of 1000–3000 pieces/m (He et al. 2022; GAO et al. 2022). Because the geological characteristics of Gulong shale reservoir are unique and complex, and there is little understanding of the law of reservoir development performance that can be used for reference, it is difficult to evaluate the contribution of nano-pores of reservoir matrix to oil production under current formation conditions, and it is impossible to determine the reasonable development mode of oil wells. These problems have brought great challenges to its effective development. Based on the actual production data of oil wells, this paper reveals the development performance law of Gulong shale oil well through the rate transient analysis method and the fitting evaluation of typical models, which provides support for the effective development of oil reservoirs.

1.2 Basic Characteristics of Shale Oil Well Production Performance Due to the poor physical properties of shale reservoir, horizontal well + large-scale fracturing is currently adopted for development (Robert et al. 2012; Lucas et al. 2012; Liu et al. 2019). The exploration and development of Gulong shale reservoir has been carried out since 2019. The time is short and the number of wells is small. Taking G1 well with long production time as an example, the basic characteristics of reservoir production performance are explained. The horizontal section of well G1 is 1562 m long, with 36 sections of hydraulic fracturing. The fracturing scale is large, and the amount of construction fluid reaches more than 80,000 m3 . Casing is used for flowback after well pressure, and its production curve is shown in Fig. 1.1. When the fracturing fluid flowback rate reached 19.9%, oil began to appear. At the initial stage of production, the peak production of the oil well was 29.6 m3 /d, the stable production was 13.8 m3 /d, and the production and pressure decreased slowly. In order to increase crude oil production, the oil well was changed from flowing production to wide range electric pump production in July 2021, and the casing pressure was 4 MPa before the change of working system. At the initial stage of production, the output of the electric pump is high, with a peak oil production of 42 m3 /d, but the output and pressure decrease rapidly. In January 2022, the oil well was changed to flowing production again.

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Fig. 1.1 Production performance curve of well G1

1.3 Production Performance Analysis of Shale Oil Well Based on RTA 1.3.1 Flow Pattern Analysis Based on Log–Log Curve Log–log curve is an important tool for analyzing the coupled flow of oil in horizontal wells and reservoirs. The different slopes of the curve reflect the different flow patterns of the fluid. A slope of 0.25 indicates that it is in the bilinear flow stage, a slope of 0.5 indicates that it is in the linear flow stage, and a slope of 1 indicates that it is in the boundary control flow stage (Anh and Duong 2011; Heidari et al. 2011; Rajagopal et al. 2018). The double logarithmic curve drawn based on the production data of well G1 is shown in Fig. 1.2. It is obvious from the figure that the oil well has experienced two stages of bilinear flow and boundary control flow after fracturing fluid flowback in the early stage. There may be linear flow between the two flow states, but the curve trend is not obvious. Tight oil wells usually have obvious linear flow stage (Liu and Ran et al. 2014; Qanbari et al. 2017; Chu et al. 2019), and the curve characteristics of G1 well are quite different from those of common tight oil wells. This shows that the flow pattern of shale reservoirs is different from that of tight oil wells.

1.3.2 Time Square Root Curve Analysis The square root curve is usually used to evaluate the artificial fracture parameters in the linear flow stage of the reservoir (Ekaterina and Louis 2016; Ozkan et al. 2011; Mattar and Anderson 2006). Although there is no obvious linear flow stage in the log–log curve, and the square root curve cannot be used to accurately calculate the length and conductivity of artificial fractures, it can still be used as one of the important basis to reflect the characteristics of seepage state in the reservoir. The

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square root curve of well Q1 is shown in Fig. 1.3. From the figure, it can be found that after the square root material balance time 20, there are two curves with different slopes, which indicates that the reservoir has two different flow forms. The reasons for this situation will be discussed later through production dynamic fitting.

Fig. 1.2 Log–log curve of well G1

Fig. 1.3 Time square root curve of well G1

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1.3.3 Blasingame Curve Analysis Blasingame production decline analysis method is one of the powerful tools for evaluating well controlled reserves (Wei et al. 2017; Tom et al. 2022). After sorting out the production and pressure data of well Q1, draw them on the typical chart of Blasingame for curve fitting. The fitting results are shown in Figs. 1.4 and 1.5. Figure 1.4 is the fitting diagram of the curve on the normalized rate chart, and Fig. 1.5 is the fitting diagram of the curve on the integral normalized rate chart. It can be found from the two figures that the production data fit well. The fitting results show that the well controlled reserves of well Q1 are 14.2 × 104 m3 .

1.4 Analysis of Oil Well Production Performance Based on Productivity Model Simulation In order to further study the production performance of shale oil wells, we assume that different reservoir productivity models, based on the actual oil production data, fit the bottom hole flow pressure, and observe what kind of model calculation results are closer to reality. The capacity model is calculated by using the commercial software Harmony.

Fig. 1.4 Fitting diagram of the curve on the normalized rate chart

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Fig 1.5 Fitting diagram of the curve on the integral normalized rate chart

1.4.1 Seepage Mode in Which Only the Reservoir in SRV Participates in Production Due to the poor physical properties of shale reservoir matrix, assuming that there is no reservoir outside SRV to participate in the oil production contribution, the theoretical model 1 of the reservoir near well G1 is established, as shown in Fig. 1.6. Figure 1.7 is the underground micro-seismic monitoring results of the fracturing construction of well G1. The figure shows that the fracturing has formed relatively consistent parallel fractures, and the interpreted average fracture half-length is 145 m. The fitting results of production performance are shown in Fig. 1.8. From the figure, it can be found that the bottom hole flow pressure obtained by simulation calculation fits well in the stage of well flowing. Figure 1.9 shows the dynamic fitting results on the square root curve. It can be found that the calculation results are consistent with the data points in the lower half. The fitting result of model 1 for the effective half fracture length of artificial fracture is 280 m, which is much larger than the monitoring result of micro-seismic. Generally speaking, the crack length monitored by micro-seismic will exceed the actual effective length of the fracture (John and Mark 2011), so model 1 cannot reflect the real underground seepage.

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Fig. 1.6 Horizontal well model of SRV outer reservoir not participating in seepage

Fig. 1.7 Underground micro-seismic monitoring results of well G1

Fig 1.8 Production dynamic fitting results of model 1

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Fig. 1.9 Fitting result of time square root curve of model 1

1.4.2 Seepage Mode of SRV Outer Reservoir Participating in Production On the basis of model 1, we assume that the reservoir outside SRV also participates in the contribution of oil production, and establish seepage model 2 for simulation calculation. Model 2 is shown in Fig. 1.10. Under the condition of single reservoir and artificial fracture parameters, it is difficult to realize the accurate fitting of the whole process of flowing production stage and pumping production stage. Two different fitting results are shown in Figs. 1.11 and 1.12. The fitting results in Fig. 1.11 are good for the production performance in the

Fig. 1.10 Horizontal well model of SRV outer reservoir participating in seepage

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flowing production stage. The effective half length of the artificial fracture obtained by fitting is 100 m, the reservoir width of SRV external participating in seepage is 400 m, and the well controlled reserves are 15.6 × 104 m3 . The fitting result of the corresponding square root curve is shown in Fig. 1.13. The fitting results in Fig. 1.12 are good for the production performance in the pumping stage. The effective half length of the artificial fracture obtained by fitting is 65 m, the reservoir width of SRV external participating in seepage is 100 m, and the well controlled reserves are 8.2 × 104 m3 . The corresponding square root curve fitting results are shown in Fig. 1.14. The effective half-length of fractures in the two fitting results of model 2 are less than 145 m, which is consistent with the understanding of micro-seismic monitoring

Fig. 1.11 Fitting results of model 2 on production performance of G1 well in flowing stage

Fig. 1.12 Fitting results of model 2 on production performance of G1 well in pumping stage

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Fig. 1.13 Fitting result of time square root curve of production performance of model 2 in flowing stage

Fig. 1.14 Fitting result of time square root curve of production performance of model 2 in pumping stage

results. The bilinear flow characteristics of artificial fractures and SRV external reservoirs are consistent with the interpretation results of the log–log curve, and the well controlled reserves are also equivalent to the evaluation results of Blasingame curve. These results show that SRV external reservoir contributes to oil well production.

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Comparing the two fitting results of model 2, it can be found that when the production pressure difference is amplified by pumping, the half-length of the fracture and the effective drainage volume of the reservoir will be significantly reduced, and the well development effect will become worse. This shows that shale reservoir has strong stress sensitivity, and different development methods have a great impact on oil wells. The calculation results also explain why there are two trend lines with different slopes on the time square root curve.

1.5 Conclusion In this paper, the production performance of typical horizontal well in Gulong lacustrine pure shale reservoir is analyzed in detail through different reservoir engineering methods, and different seepage models are set to analyze the reasons for the change of production performance. The understanding is as follows: (1) During large-scale fracturing development of horizontal wells in shale reservoirs, there may be a long-term bilinear flow stage, and the characteristics of the linear flow stage are not obvious, indicating that the foliation fractures developed in the external reservoir of SRV have a great contribution to oil well production. (2) Shale reservoir is highly sensitive to stress. After the fluid pressure drops, the artificial fractures will close quickly and the physical properties of the reservoir will become worse. Therefore, the production pressure difference needs to be controlled in the development of shale oil wells. (3) During the production of oil wells in shale reservoirs, the oil with large reservoir area outside SRV can participate in the flow. Therefore, when developing and arranging wells, it is necessary to set a relatively large well spacing to ensure that a single horizontal well has large well controlled reserves to obtain better development results.

References Chu WC, Pandya ND, Flumerfelt RW (2019) Rate-transient analysis based on the power-law behavior for Permian wells. SPE Reserv Eval Eng 22(04):1360–1370 Duong AN (2011) Rate-decline analysis for fracture-dominated shale reservoirs. In: SPE reservoir evaluation & engineering, spe-137748-pa, pp 377–387 Ekaterina S, Louis M (2016) Analytical Methods for single-phase oil flow: accounting for changing liquid and rock properties. In: SPE Europec featured at 78th EAGE conference and exhibition held in Vienna, Austria, SPE-180139-MS Gao B, He WY, Feng ZH (2022) Lithology, physical property, oil-bearing property and their controlling factors of Gulong shale in Songliao Basin. Petrol Geol Oilfield Dev Daqing 41(3):68–79 He WY, Meng QA, Feng ZH (2022) In-situ accumulation theory and exploration& development practice of Gulong shale oil in Songliao Basin. Acta Petrolei Sinica 43(1):1–14

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John PV, Mark DZ (2011) Hydraulic fracturing, microseismic magnitudes, and stress evolution in the Barnett shale, Texas, USA. In: SPE hydraulic fracturing technology conference and exhibition held in The Woodlands, Texas, USA, SPE 140507 Liu LF, Ran QQ, Wang X (2014) A new way to determine the fracturing intervals on horizontal wells in tight oil reservoirs. Int J Earth Sci Eng 7(3):816–820 Liu LF, Li N, Ran QQ (2019) Multi-scale and multiphase modeling of flow behavior in hybrid continuum fracture networks for tight oil reservoirs. J Porous Media 22 Lucas W, Bazan, Michael G (2012) Hydraulic fracture design and well production results in the Eagle Ford Shale: one operator’s perspective. In: Americas unconventional resources conference, Pittsburgh, Pennsylvania, USA, SPE 155779 Mattar L, Anderson D (2006) Dynamic material balance (Oil or Gas-In-Place Without Shut-Ins). J Can Pet Technol 45(11):7–10 Ozkan E, Brown M, Raghavan R (2011) Comparison of fractured-horizontal-well performance in tight sand and shale reservoirs. SPE Reserv Eval Eng 248–259 Qanbari F, Clarkson CR, Shahama MS (2017) Incorporation of formation water into rate-transient analysis of tight oil wells with high water-oil ratio: a field example from North America. In: 2017 SPE western regional meeting held in Bakersfield, California, USA, SPE-185745-MS Rajagopal RR, Chih-Cheng C (2018) A conceptual structure to evaluate wells producing fractured rocks of the Permian Basin. In: 2018 SPE annual technical conference and exhibition, Dallas, Texas, SPE-191484-MS Robert LK, Rajdeep G, Sergey K (2012) Optimized shale resource development: proper placement of wells and hydraulic fracture stages. In: Abu Dhabi international petroleum exhibition & conference, Abu Dhabi, UAE, SPE 162534 Sureshjani MH, Gerami S (2011) A new model for modern production-decline analysis of gas/ condensate reservoirs. J Can Pet Technol 4:10–23 Tom B, Ozkan E, Mohan K (2022) History matching of petroleum engineering graduation rate. J Petrol Technol 74(03):28–31 Wei MQ, Duan YG, Chen W (2017) Blasingame production decline type curves for analyzing a multi-fractured horizontal well in tight gas reservoirs. J Cent South Univ 24:394–401

Chapter 2

Application of Air-Foam Flooding in Petroleum Field: Progress and Challenges Yicheng Zhang

Abstract With the increasing development of domestic oil and gas resources, people gradually turn their attention to the low permeability reservoirs which are difficult to exploit. Low permeability reservoirs are widely distributed and rich in reserves. Affected by the compact reservoir matrix and complex fracture system, the conventional water injection development method can achieve low recovery. In this regard, researchers have developed an air foam flooding method for low permeability reservoirs, which is mainly used to expand the swept volume of the subsequently injected fluid, reduce the water yield, and then improve the oil recovery. In this paper, the action mechanism, influencing factors, and research status of air foam flooding are summarized in detail, and the development of air foam flooding is prospected, to provide some reference and guidance for the follow-up research work. Keywords Air-foam flooding · Recovery factor · Low permeability reservoir

2.1 Introduction With the development of major oilfields in China gradually entering the middle and late stages, the situation of high water cut and low production occurs frequently. There is no oil displacement in the macro-pore of the reservoir, and the productivity increase of conventional measures is too low. Therefore, new development methods are urgently needed to improve oil recovery (Yuan and Wang 2018; Liu et al. 2018; Yuan 2014). Gas injection (such as CO2 , natural gas, N2 , etc.) is the same as water injection or polymer injection. To a certain extent, although it can effectively improve oil recovery, it is often limited by the shortage of gas sources and high-cost factors, so it still stays in the experimental research stage. Oilfield types in China are complex and diverse, and most of the reservoirs are continental deposits, which also makes Y. Zhang (B) The Erlian Filiale of PetroChina Huabei Oilfield Company, Xilinhot 026000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_2

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the reservoir heterogeneity serious (Wang et al. 2013; Zhang et al. 2014). Because the injected air viscosity is low, there will be some problems such as gas invasion and viscous fingering. It is difficult to achieve high displacement efficiency by gas injection alone, and the invasion of oxygen will bring some safety problems to production wells. As a new production system, foam is different from other fluids, and foam can selectively block high permeability pores. At the same time, it will not destroy the low permeability reservoir, to adjust the fluidity difference of the subsequent injection water in the high and low permeability layer, and reduce the channeling of the injected fluid in the high permeability layer, commonly known as “plugging big, not plugging small”. Through the combination of the air foam injection method, the two advantages of foam flooding and air flooding have not only the advantages of low-temperature oxidation (LTO) and flue gas drive of air flooding but also the role of foam flooding in plugging large channels or high permeable layers, such as gas channeling, water cut reduction and selective diversion (Sharma and Shah 1984; Zhang 2017). This method has a low cost and obvious stimulation effect and is especially suitable for high water cut and serious heterogeneous reservoirs. The mechanism, influencing factors, latest technological status, and progress of air foam flooding were reviewed and summarized, and the future development direction of air foam flooding has been prospected.

2.2 Principle of Air-Foam Flooding The air foam flooding combines the air drive and foam flooding organically. Based on the principle of “edge adjustment and side drive”, the air is used as an oil displacement agent and the foam is used as an oil displacement agent. It can not only increase the formation pressure but also effectively avoid water channeling and gas channeling, thus increasing the oil production, oil displacement efficiency, and recovery efficiency of a single well (Wang et al. 2015). Its related mechanism can be expounded from two aspects: air drive and foam flooding. In gas drive, there are two kinds of reactions when injected air comes into contact with crude oil: one is oxidation at high temperature (HTO) and the other is oxidation at low temperature (LTO), During the oxidation process at low temperature, a large number of hydrocarbons (such as ether, aldehyde, ketone, etc.) containing carbon dioxide, hydrogen peroxide, and oxygen will be produced (see Fig. 2.1). Gas injection drive has other functions after oxidation reaction between gas and crude oil. When air enters the reservoir, oxygen first reacts with crude oil. During this low-temperature oxidation reaction, carbon oxides are formed. With the increase of reservoir temperature, the light components in crude oil will volatilize continuously. Among them, the flue gas is composed of CO, CO2 , N2 , and light components produced by the reaction in the reservoir. To a certain extent, the effect of air injection is equivalent to that of flue gas injection, so it has played the role of a variety of substances to drive oil together and improve the ultimate oil displacement efficiency of reservoirs (Qi 2017; Jin and Pan 2017). In terms of foam flooding mechanism,

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Fig. 2.1 LTO reaction mechanism

foam flooding improves oil recovery in two ways: first, to increase the sweep efficiency by the Jamin effect generated by the foam displacement; two, to reduce the interfacial tension through the active components of the foam system and improve the displacement efficiency. Foam flooding mainly expanded the microcosmic sweep volume of the displacement medium and enhanced the oil displacement efficiency. The foam first enters the large pore throat with small resistance. Because the liquid flow resistance is smaller than the bubble resistance, the foam enters the small hole throat and smaller pore throat, thus enlarging the swept volume. When the small bubbles enter the throat of the small hole, they accumulate into bubbles in the roar channel and the blind end of the pore to expel the residual oil. At the same time, as a surfactant, the foaming agent can reduce the interfacial tension between oil and water. Utilizing oil emulsification and liquid film replacement, the immobile oil can be changed into mobile oil, thus improving the oil displacement efficiency.

2.3 Analysis of Influencing Factors on Air-Foam Flooding The advantages of foam flooding and the advantages of air flooding are reflected in the air foam flooding, but the plugging capacity of air foam is the main factor affecting the displacement effect (Li et al. 2010; Chen 2011). The main influencing factors are core permeability, oil Saturation, gas–liquid ratio, alternating slug, and injection pressure.

2.3.1 Influences of Core Permeability Core permeability has a great influence on the plugging ability of air foam flooding. Generally, the greater the permeability, the greater the plugging ability of foam in the reservoir.

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2.3.2 Effect of Oil Saturation Crude oil has a certain destructive effect on foam, and the degree of destruction of different foam is different from the formation of the original oil. The same foam behaved differently when it encountered crude oil.

2.3.3 Influence of the Gas–Liquid Ratio The gas–liquid ratio is an important parameter affecting its plugging ability, which not only affects its plugging ability but also has a greater impact on the oil displacement effect. On the one hand, when the ratio of gas to liquid is small, the foaming effect of the foaming agent will be affected, and the drag coefficient is low. On the other hand, the exploitation of reservoirs needs more starting pressure, and the pressure of air foam injection needs to be greater. Moreover, the liquid membrane can cause blockage of the migration channel of the reservoir, thus reducing the recovery efficiency of the reservoir. On the contrary, when the gas and liquid are large, the foam will become very thin and easy to break, and the drag coefficient is low. On the other hand, although the injection pressure can be reduced, the blockage of the fluid to the migration channel of the reservoir can also be reduced. However, due to a large amount of gas injection and high-pressure injection, it is difficult for the reservoir to migrate from the porous medium, which affects the migration of foam in the medium and is not conducive to improving reservoir recovery (Li 2009).

2.3.4 Effect of Alternating Slug In the field of air foam flooding, air and foaming agents are often injected alternately. Through laboratory study, it is found that the size of alternating slug has an important effect on foam plugging ability. The slug is too small, and it is difficult to form foam due to leakage and adsorption of the formation (Chen and Yu 2012). Therefore, the optimization of slug size is very important. When the gas–liquid ratio is 1:1, the recovery of the alternating section with blowing agent, air, and water as alternating injection mode is 2.54% higher than that with blowing agent and air as alternating injection mode. The alternate section of foam and air is 1.91% higher than that of blowing agent, air, and water. The recovery rate of foam and air is 4.45% higher than that of blowing agent and air alternately injection mode. It can be seen that the foam and air alternate injection method has the best harvest effect. When the gas–liquid ratio is 2:1, the recovery of foaming agent and air alternating injection is the largest, the recovery of foaming agent, air and water alternating injection is the second, and the recovery of foaming agent and air alternating injection is the worst.

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2.3.5 The Injection Pressure of Air Foam Injection pressure can affect air foam flooding from many aspects. For example, pressure rise and foam stability are better, and the recovery rate of the reservoir is improved. However, too high pressure will also cause a decline in the oil recovery rate. At the same time, the quality and density of foam can be improved by pressure, and the foam performance will be indirectly changed. If the pressure is small, the volume of gas in the foam fluid will change, which will seriously affect the quality and parameters of the foam. In the foam system, pressure occupies an important position. The increase of pressure will cause the bubble to be compressed and the average size will decrease. When the shear rate is constant, the viscosity of the foam fluid surface will also increase with the increase of pressure.

2.4 Recent Developments in Air-Foam Flooding As an advanced reserve technology for low permeability reservoirs, air foam flooding is still in the process of accelerating research. It is one of the most valuable research topics in the future. This paper selects the representative research results in the past five years. Wang et al. (2018) carried out experimental research on air foam flooding in the G271 long X reservoir. Through the selection of 5 injection wells in the northern infill area, an air foam flooding test is carried out. The results show that under the condition of infill well pattern, the air foam flooding technology can seal up cracks and high permeability sections through foam solution. When the gas enters a dense hole, it can reduce the risk of water flooding, and the production capacity of 5 lateral wells increases and the water cut of 2 main wells decreases. Zhao et al. (2017) through laboratory experiments, the effects of reservoir heterogeneity, gas/ liquid ratio, foam injection volume, foam injection slug combination, and injection timing on the efficiency of air foam flooding were studied. The results show that for the heterogeneous reservoirs, the residual oil in low permeability reservoirs can be effectively utilized by air foam flooding. The optimal injection gas/liquid ratio is 1:1, the optimal injection volume is 0.2 PV, the optimal slug size is 0.05 PV, and the optimal injection timing and moisture content are more than 80% when foam injection is applied. Xing et al. (2017) through air foam flooding in the 8 test wells in the “Tang 80” well area of the Gangu Yi oil production plant, it is found that the apparent absorbency index has dropped by 71%, and the injection capacity is lower than that of the water flooding well group. However, the water cut has dropped to 18.8%, which is 29.6% lower than that of the water flooding well group. The average monthly increase of oil in the single well is more than 11 T, which has an obvious effect on controlling water and increasing oil production. Chen et al. (2017) chose air foam flooding suitable for reservoir conditions in the F118 block as injection parameters. Indoor experiments of air foam flooding combined with different methods were carried out, and the fluid and reservoir properties of the F118 block

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were simulated. The abilities of different foaming agents and stabilizers were evaluated, and the plugging performance of the foam system with different gas/liquid ratios was evaluated. The results show that the reasonable gas–liquid ratio of air foam flooding in the F118 block is 3:1, the best foam concentration is 0.4%, and the best foam stabilizer concentration is 0.1%. Kang et al. (2016) evaluated the foam height, half-life, and foam comprehensive index of different frothers under 30% conditions without oil and oil, screened foaming agent with good foaming and foam stability and optimized low interfacial tension foam system (0.12%FC-2 + 0.08%TC-12 + 0.1%BS + 1500 mg/LHPAM). The simulation results show that the air foam flooding technology is safe and feasible in low temperature and ultra-low permeability reservoirs at 30 degrees centigrade. 8 wells in the field test increase oil production by 4898t, and the ratio of input to output is 1:2.57. Wu et al. (2018) established the permeability difference model to investigate the effect of permeability difference on air foam flooding in the Bohai heterogeneous reservoir. The results show that with the increase of permeability gradient, the recovery of the high and low permeability layer model increases first and then decreases. When the difference is less than 8.7, the air foam flooding has better promotion and a strong plugging effect. Zhang H et al. (2018) found that sedimentary facies control systems and physical property control systems have an important influence on air foam flooding efficiency. If the channel control system and flow unit system of injection wells and production wells correspond, they can be completely effective. If the injection wells and production wells belong to different channel control systems, they are mostly ineffective; if the injection wells and production wells belong to the same channel control system, but the flow unit system is partially connected or not, they may cause partial or completely ineffective. Jiang et al. (2013) carried out the low-temperature oxidation characteristics of air foam flooding and the selection and evaluation of blowing agents. It was found that the air foam injection method can effectively play the role of foam blocking, slow down gas breakthroughs and improve displacement efficiency, and verified the safety of air foam injection technology through field application. Yang et al. (2016) aimed at the instability of foam systems and a new estimation model of air foam flooding recovery rate is proposed. Once the breakthrough time of gas and surfactant solution is estimated, the dynamic performance of each stage of air foam flooding can be accurately predicted. Based on providing reservoir, fluid, and operation parameters, the relationship between recovery and production time or PV can be predicted. The results show that the maximum difference of recovery after ten years is less than 6.2% compared with the simulation results. Dong et al. (2012) used the physical simulation method to study the feasibility of air foam flooding in flooded reservoirs, and dynamic foam flooding experiments were also carried out.

2.5 Future Challenges The main challenges in the application of air foam flooding are as follows:

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2.5.1 Foam Formula Optimization Foam formulation produces poor foam stability and is easily broken in porous media. This may lead to gas breakthroughs. In addition to the serious heterogeneity of the reservoir, the poor stability of the foam formulation may also be the cause of the gas invasion. Under the reservoir condition, the foam capacity and foam stability are poor, and the profile control ability is weak. Therefore, the foaming formulation system should have the characteristics of strong foaming ability, high stability, low adsorption capacity, and strong oil resistance.

2.5.2 Monitoring of Oxygen Content In the process of air foam flooding, the target reservoir conditions should be carefully determined and the oxygen content in the reservoir should be monitored to ensure safety. Based on the mechanism of low-temperature air oxidation, the recommended temperature of the target area of air foam flooding should be higher than 70 °C. However, according to the experience of the air foam flooding pilot test, the actual production layer temperature is slightly lower than the recommended temperature. It takes more time to carry out an air oxidation reaction under the sample. If the reaction time is further prolonged, the oxygen in the produced gas will remain at a safe level. To better understand the underground gas flow, the production pressure and oxygen concentration of the produced gas should be regularly monitored and analyzed during the air foam flooding process.

2.5.3 Corrosion There are corrosion phenomena in the process of air foam flooding. Due to different corrosion factors, the location and state of corrosion are different. Therefore, in future research on air foam flooding, the corrosion factors should be taken into account.

2.6 Prospects A low permeability reservoir is the main body of oil reserves and production growth in the present and future periods. At present, water drive development is the main method in China. However, considering the present situation of water cut in major oilfields, the key to follow-up technological development is to further increase single well production, change development mode and reduce development cost. Although the majority of air foam flooding is still in the experimental simulation stage, with

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the deepening of technological progress and technological innovation, it is believed that it will occupy a dominant position in the future displacement mode.

References Chen H (2011) Experimental studies on air-foam flooding in a heterogeneous reservoir at late high water cut stage. J Shandong Univ (Eng Sci) 41(1):120–125 Chen Z, Yu H (2012) Reformation effect of hydrocarbon alkali fluid on carboniferousvolcanic reservoir of malang sag in santanghu basin. Sci Technol Eng 12(18):4363–4368 Chen Q (2017) Laboratory study of air foam flooding system in block F118 of Jilin Oilfield. Petrol Knowl 2017(1):53–54 Dong X, Liu H, Sun P et al (2012) Air-foam-injection process: an improved-oil-recovery technique for waterflooded light-oil reservoirs. SPE Reservoir Eval Eng 15(4):436–444 Jiang Y, Wang B (2013) A cost-effective method to enhance oil recovery of water-flooded reservoirs: air-foam flooding. society of petroleum engineers Jin Z, Pan T (2017) Numerical simulation of air bubble foam flooding mechanism in tight reservoir. Petrochem Ind Appl 36(1):64–66 Kang X, Wang W et al (2016) Application of air-foam flooding to low-temperature and ultralowpermeability reservoir–A case study of tang 80 well area in Ganguyi Oilfield. Unconventional Oil & Gas 3(3):68–74 Li N et al (2010) Mechanism of air foam flooding in medium high permeability reservoirs. Pioneer Sci Technol Monthly 23(2):185–186 Li N (2009) Study on enhancing oil recovery by air-foam flooding in gudong oilfield. China University of Petroleum (East China) Liu P, Zhang X, Wu Y et al (2018) Enhanced oil recovery by air-foam flooding system in tight oil reservoirs: study on the profile-controlling mechanisms. J Petrol Sci Eng 150(2):208–216 Qi Y (2017) Air-foam flooding mechanism of Chang 6 oil reservoir with low permeability sandstone in Ganguyi resgion. Northwest University, Ordos Basin Sharma M, Shah D (1984) Effect of oil viscosity on recovery processes in relation to foam flooding. J Am Oil Chemists’ Soc 61(3):585–590 Wang J, Wang T, Lei H et al (2013) Experimental study of improved oil recovery through air foam flooding in ultra-low permeability reservoir. J Southwest Petrol Univ Wang C, Liu H, Pang Z et al (2015) Enhance oil recovery for steam flooding: low-temperature oxidative decomposition of heavy oil with air injection. Energy Fuels 29(10):6242–6249 Wang X, Chen C, Chen C et al (2018) Experimental study on air foam flooding in Chang X reservoir of G271 area. Petrochem Ind Appl 37(2):105–108 Wu W (2018) Effect of permeability differential on oil flooding of air foam in homogeneous reservoir in Bohai Oilfield. Adv Fine Petrochem Xing Q, Xu T, Xue J et al (2017) Feasibility evaluation on air foam flooding in chang 6 ultra low permeability reservoir in the west area of Huaziping. Sino-Global Energy 22(8):58–62 Yang J, Wang X, Wang S et al (2016) A theoretical model for dynamic performance prediction of airfoam flooding in heterogeneous reservoirs. In: International petroleum technology conference Yuan S, Wang Q (2018) New progress and prospect of oilfields development technologies in China. Pet Explor Dev 45(8):657–668 Yuan P (2014) Reservoir simulation study on air-foam flooding to enhance oil recovery in waterflooding sandstone reservoir. Adv Mater Res 962(4):461–464 Zhang S, Jiang G, Wang L et al (2014) Foam flooding with ultra-low interfacial tension to enhance heavy oil recovery. J Dispersion Sci Technol 35(3):403–410 Zhang R (2017) Review of foam flooding to enhance oil recovery. Energy Chem Ind 38(4):73–76

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Zhao J, Li P, Du J (2017) Experimental study on displacement efficiency of air foam flooding in ulrra-low permeability reservoir. Chem Eng Oil Gas 46(1):63–66 Zhang H, Zhang X, Yang X et al (2018) Influence of geological factors on air foam flooding efficiency in a tight oil reservoir. J China Univ Petrol (edition of Natural Science) 42(5):105–113

Chapter 3

Dynamic Simulation of Hydrogen Fueling Performance Based on Look-Up Table and MC Methods Hao Luo, Jinsheng Xiao, Pierre Bénard, Richard Chahine, and Tianqi Yang

Abstract To ensure the safety and speed of the fueling for hydrogen fuel cell vehicles, the Society of Automotive Engineers (SAE) has promulgated the look-up table and the MC methods. Their fueling performance is worthy for further study and improvement. We analyzed and established a thermodynamic model of a hydrogen fueling system, and carried out the dynamic simulation of the look-up table and MC methods according to the SAE J2601 protocol. The simulation results agree well with the reference data. Based on the validated model, the fueling performance between the look-up table method and the MC method was compared under the same conditions. Finally, the key parameters MATexp and Ptrans in the MC method were studied. Our research shows that compared to the look-up table method, the MC method reduces the total fueling time by 10.5%. But the peak cooling power increases by 18.9%, meaning more investment in refrigeration equipment. The MATexp cannot be too large or too small and seems more appropriate to approximately equal the precooling temperature at the end of filling. The Ptrans can be appropriately increased under the upper-temperature limit in the onboard tank, not just the pressure midpoint of the fueling process described in SAE J2601, further to improve the fueling performance of the MC method. Keywords Look-up table method · MC method · Fueling performance · Dynamic simulation

H. Luo School of Mechanical and Electrical, Wuhan Business University, Hubei 430056, China H. Luo · J. Xiao · T. Yang (B) Hubei Research Center for New Energy & Intelligent Connected Vehicle and Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Hubei 430070, China e-mail: [email protected] H. Luo · J. Xiao · P. Bénard · R. Chahine Hydrogen Research Institute, Université du Québec à Trois-Rivières, Québec, QC G8Z 4M3, Canada © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_3

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3.1 Introduction When fueling a light-duty hydrogen fuel cell vehicle, the total fueling time must be 3–5 min, the temperature in the onboard tank must be −40 to 85 °C, and the maximum pressure in the onboard tank must be less than 125% of the nominal working pressure (SAE J2601 2022). To achieve these above goals, the Society of Automotive Engineers (SAE) has promulgated two fueling methods: the look-up table and the MC methods, which have undergone rigorous simulation and experimental verification (Schneider et al. 2014; Mathison et al. 2015). Some groups have conducted research in this field. Mathison et al. first introduced the theoretical derivation of the MC method in detail and determined the test procedure of the thermodynamic parameters for any storage tank system (Harty and Mathison 2010). Then, based on the actual fueling results of the MC method, Mathison et al. explained the factors that influence the implementation of this fueling method and demonstrated the method’s adaptability to station capabilities (SAE technical paper 2022). Reddi et al. (2017) compared the fueling time and state of charge (SOC) between the look-up table and the MC methods based on the H2SCOP model. They found that the MC method significantly reduced the fueling time under certain conditions. Based on the Dymola platform, Chochlidakis et al. (2020) established a dynamic simulation model of the look-up table and the MC methods, compared the performance of the two fueling methods under different conditions, and found that the MC method refuels faster but consumes more energy. Based on the MC method, Handa et al. (2021) proposed the MC Multi Map method, which can select from multiple fueling control maps in real-time according to the actual heat capacity of the piping system. Without increasing the fueling time, the precooling temperature level is reduced to −20 °C, significantly reducing the cooling energy consumption. Different from the traditional look-up table and MC methods, some groups creatively proposed the new fueling method. Wang et al. (2022) proposed using machine learning to predict the final SOC of the filling process and the final temperature and pressure inside the onboard tank. Chae et al. (2020) proposed a new communication hydrogen fueling protocol based on the real-time response method with good stability, safety and convenience. Literature research shows that while the look-up table and the MC methods have been proposed for many years, there is not much research on them. Therefore, indeep research can be conducted on these two fueling methods, especially the MC method, to improve the understanding of their advantages and disadvantages and further improve them. This paper established a dynamic simulation model of the hydrogen fueling system based on the Matlab/Simulink platform. The look-up table and MC methods were simulated according to SAE J2601, and the results were compared with the references. The fueling performance between the look-up table and the MC methods under the same conditions was compared based on the validated simulation model. Finally, the key parameters MATexp and Ptrans in the MC method were studied, and suggestions for improvement were put forward.

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Fig. 3.1 Structure of hydrogen fueling system

3.2 Model of the Hydrogen Fueling System 3.2.1 System Overview A hydrogen fueling system usually includes the station storage tank, onboard tank, heat exchanger, reduction valve, nozzle and control center, as shown in Fig. 3.1. The control center controls the reduction valve to obtain the required pressure ramp rate (PRR) and the heat exchanger to obtain the required precooling temperature. The control center also needs to monitor the pressure and temperature in the onboard tank during the communication fueling process.

3.2.2 Models of the Control Volume Inside the Tank and Tank Wall The conservation of mass and energy during filling and emptying of the hydrogen storage tank are written as (Xiao et al. 2016) dm = m˙ in − m˙ out dt

(3.1)

d(mu) = m˙ in h in − m˙ out h out + Q˙ dt

(3.2)

where m˙ in and m˙ out are the mass flow rates of hydrogen into and out of the tank, respectively. m˙ out = 0 for the filling and m˙ in = 0 for the emptying. u is the specific internal energy and h is the specific enthalpy of hydrogen. Q˙ is the heat transfer rate between the hydrogen gas and the tank wall.

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The heat transfer of the tank wall can be regarded as the heat conduction of the flat plate because the tank wall’s thickness is small relative to its inner diameter. The one-dimensional heat transfer model’s boundary conditions and heat transfer equation along the tank wall’s radial direction can be expressed as (Rothuizen et al. 2013) ∂ Tw |x=0 = ain (T − Tw |x=0 ) ∂x

(3.3)

∂ Tw |x=L = aout (Tw |x=L − Ta ) ∂x

(3.4)

−λw −λw

dTi Q˙ i = −(ρcΔV )i dt

(3.5)

where T is the hydrogen temperature, Tw is the wall temperature, and Ta is the ambient temperature. For tank wall material, λw is thermal conductivity, ρ is density, and c is specific heat capacity. αin is the heat transfer coefficient between hydrogen gas and the inner wall, and αout is the heat transfer coefficient between the outer wall and ambient. Q˙ i is the heat transfer rate for element volume i, which is equal to the sum of its two neighbor elements, that is Q˙ i = Q˙ i−1 + Q˙ i+1

(3.6)

Ti − Ti−1 Q˙ i−1 = Ri−1

(3.7)

Ti − Ti+1 Q˙ i+1 = Ri+1

(3.8)

/ where Ri is the thermal resistance, Ri =Δxi (Ai λw ). For element volume i, Ai is the area of the tank wall’s / cylindric layer and Δx/i is the thickness. / ain = 0.14λgas Re0.67 Din , Redin = (ρvdin ) μ = (4m) ˙ (π μdin ) (Bourgeois din and Ammouri 2015). Re is the Reynolds number. Din is the onboard tank’s internal diameter and din is the injector’s diameter. λgas is the thermal conductivity and μ is the dynamic viscosity of hydrogen. αout is assumed to be 5 W/m2 /K.

3.2.3 Model of the Heat Exchanger The hydrogen in the heat exchanger obeys the law of energy conservation (Talpacci et al. 2018): ) ( dU =m(h ˙ in − h out )=m˙ c pv Tv − c pc Tc dt

(3.9)

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where c pv and c pc are the specific heat capacity of hydrogen under constant pressure at the heat exchanger’s inlet and outlet, respectively. / The energy consumption for the heat exchanger to precool hydrogen is W = U COP. COP is the performance coefficient of the refrigeration equipment.

3.2.3.1

Pressure Drops in the System

The pressure drops occur when hydrogen flows through the valves, tubes, filters and flowmeter. All the pressure drops can be written as ΔP = k

m˙ 2 ρH2

(3.10)

where m˙ is the mass flow rate and k is the pressure drop coefficient.

3.3 Model Implementation and Validation 3.3.1 Model Implementation The models of the two fueling methods are implemented on the Matlab/Simulink platform and use the same initial and boundary conditions. Our study does not consider top-off fueling for communication filling when the ambient temperature exceeds 0 °C and the initial pressure is 0.5–5 MPa. The fueling process mainly includes fueling speed control and target pressure control. Look-up table method. The control of the fueling speed and target pressure for this method depends on a series of tables, which are classified according to the capacity of the onboard tank, the station’s precooling capacity, the station’s pressure level and whether it is refueled by communication. After selecting a specific table, the required fixed average PRR is determined according to the ambient temperature, and the target pressure is determined according to the onboard tank’s initial pressure. For non-communicating fueling, when the filling pressure (at the dispenser outlet) reaches the target pressure, the fueling process ends. For communication fueling, the control center monitors the pressure and temperature in the onboard tank in real time. When the SOC calculated using the onboard tank’s measured temperature and pressure reaches 100%, fueling ends. MC method (Reddi et al. 2017). The method actively measures the precooling temperature (at the dispenser outlet) in real-time to calculate the mass average temperature (MAT) and the mass average enthalpy of hydrogen, then further determines the PRR and target pressure. The fueling process is divided into three stages.

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When fueling time t( j) ≤ 30 s, MATC( j) = MATexp . MATexp is consistent with the station’s precooling capacity. When fueling time t( j) > 30 s and Pcontrol( j) ≤ Ptrans , MATC( j) = MAT30( j) . MAT30( j) means the calculation of the MAT starts from the 30th s. The equation for calculating the MAT can be expressed as ) ( ) ∑j ( 1 m ( j ) − m j−1 × 0.5 T( j ) + T( j−1) = ) ∑j ( 1 m ( j ) − m j−1

MAT( j)

(3.11)

When fueling time t( j) > 30 s and Pcontrol( j) > Ptrans , MATC( j) can be expressed as ( MATC( j) = MAT30( j )

Pfinal − Pcontrol( j) Pfinal − Ptrans

)

( ) Pfinal − Pcontrol( j) + MAT0( j) 1 − Pfinal − Ptrans (3.12)

At last, the equations for calculating the PRR can be expressed as tfinal( j ) = a × MATC3( j) + b × MATC2( j) + c × MATC( j ) + d PRR( j) =

Pfinal − Pramp( j ) ( ) −Pinitial − t( j) tfinal( j) PPfinal −P final min

(3.13) (3.14)

where: Ptrans Pcontrol Pinitial Pramp Pfinal Pmin

is the pressure midpoint between the Pinitial and Pfinal is the real-time control pressure outlet of the dispenser. is the initial pressure in the onboard tank at the beginning of filling. is the pressure upon which the PRR is based. Pramp is equal to Pv in our study. is the final pressure used to derive coefficients for t final , usually 83.5 MPa in 70 MPa filling. is the minimum pressure used to derive coefficients for t final , usually 5 MPa for Pinitial > 5 MPa and 0.5 MPa for Pinitial < 5 MPa.

There are two ways for the MC method to determine the target pressure: the Ending Pressure Tables method and the MC formula method. For the MC formula method, the MC can be calculated by Eq. (3.15). The hydrogen temperature at the end of fueling in the onboard tank can be calculated by Eq. (3.16). At last, the target pressure can be determined by combining the target SOC with T final for non-communication fueling and measured temperature for communication fueling. (

Uadiabatic MC = AC + BC ln Uinitial

)1/2

)JC ( + GC 1 − e−kΔt

(3.15)

3 Dynamic Simulation of Hydrogen Fueling Performance Based …

mCv Tadiabatic + MCTinitial MC + mCv ∮ m Uadiabatic = m 0 u 0 + h j dm

Tfinal =

29

(3.16) (3.17)

m0

where: Cv

is the specific heat capacity of hydrogen under constant volume. T initial is the initial hydrogen temperature in the onboard tank. m is the final total hydrogen mass, and m0 is the initial hydrogen mass. AC, BC, GC and JC are coefficients, which can be obtained by tables in SAE J2601. T adiabatic is the final hydrogen temperature in the onboard tank, assuming adiabatic. U initial is the initial internal energy and u 0 is the initial specific internal energy of hydrogen.

3.3.2 Model Validation We assume that the onboard tank’s initial pressure is 5 MPa, and the ambient temperature is 25 °C. There is no pre-soaking for the onboard tank, so the initial hydrogen temperature in the tank is 25 °C. Table 3.1 shows the parameters of the onboard tank used in model verification. Because only simple geometric parameters (129 L, Type IV and capacity of 5.2 kg) of the tank are given in Ref. (Reddi et al. 2017), our study uses the geometric parameters of a similar tank (Type IV, 4 kg and 70 MPa) in SAE J2601. The storage tank’s specific heat capacity and thermal conductivity are selected as minimum values within the ranges given in Reddi et al. (2017). Due to the difference in the selection of the above parameters, there is a maximum error of about 5 °C for the hydrogen temperature between the simulation results and reference data, as shown in Fig. 3.2. The simulation results of the pressure in the onboard tank, the filling pressure, the PRR and the mass flow rate agree well with the reference data, indicating that our model meets the requirements and can be used for further research.

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Table 3.1 Parameters of the onboard tank in this study Tank thermal properties (Reddi et al. 2017)

Tank geometry (SAE J2601. 2022) Volume of the tank, L

129

Nominal working pressure, MPa 70

Thermal conductivity for Liner, W/(m K)

0.3

Specific heat capacity for Liner, J/(kg K)

1000

Internal length, m

0.307

Density for Liner, kg/m3

975

Internal diameter, m

0.420

Thermal conductivity for CFRP, W/(m K) 0.3

Outer diameter, m

0.493

Specific heat capacity for CFRP, J/(kg K)

500

Diameter of the injector, m

0.003

Density for CFRP, kg/m3

1550

Fig. 3.2 Comparison of the simulation results of our model with the data of Reddi et al. (2017) using look-up table method (a) and MC method (b)

3.4 Comparative Study and Parametric Study 3.4.1 Comparison Between Look-Up Table and MC Methods We assume that both the fueling methods use non-communication fueling and the same initial and boundary conditions as the model validation. The target pressure of the MC method adopts the Ending Pressure Tables. The target pressure for the two fueling methods is all 75.1 MPa when the initial and boundary conditions for comparative study are met according to the SAE J2601 protocol. Table 3.2 shows the performance comparison of the two fueling methods. The final hydrogen pressure in the onboard tank, total cooling consumption and SOC are similar for both methods. The MC method reduces the total fueling time by 10.5% over the look-up table method, which is the most crucial advantage of the MC method. Table 3.2 also shows the disadvantages of the MC method. Due to the faster fueling speed of the MC method, the hydrogen does not have time to lose more heat to the tank wall, resulting in a higher maximum hydrogen temperature in the onboard tank than the look-up table method. Figure 3.2 shows that the maximum difference

3 Dynamic Simulation of Hydrogen Fueling Performance Based …

31

Table 3.2 Comparison of fueling performance between look-up table and MC methods Fueling method

Total t [s]

Final Pon [MPa]

Max T on [°C]

Final SOC

Total cooling consumption [MJ]

Peak cooling power [kW]

Look-up table

228

74.69

64.96

0.934

4.19

28.36

MC

204 −10.5%

74.80 0.1%

67.04 3.2%

0.931 −0.3%

4.12 −1.7%

33.71 18.9%

between the pressure in the onboard tank and the filling pressure is greater in the MC method than in the look-up table method. That is, the ΔP2 in Fig. 3.1 is larger in the MC method, leading to a greater peak mass flow rate. The mass flow rate is related to the pressure drop between the station and the onboard tank (SAE J2601. 2022). The MC method increased the peak cooling power by 18.9% more than the look-up table method, meaning more powerful refrigeration equipment and investment are needed. As shown in Fig. 3.2, the peak mass flow rate of the MC method is higher than that of the look-up table method, so a larger peak mass flow rate leads to a larger peak cooling power in Eq. (3.9).

3.4.2 Parametric Study for MC Method MATexp. For MATexp in the SAE J2601 protocol, only the acceptable value range, not a specific value, is given. The precooling temperature at the end of filling is −34 °C, as shown in Fig. 3.2. Therefore, four different MATexp around −34 °C are selected for parametric study. Table 3.3 shows that with the decrease of MATexp , the total fueling time increases, the maximum temperature in the onboard tank increases, the total cooling energy consumption decreases, and the peak cooling power first decreases and then increases. The law of variation for the maximum temperature in the onboard tank is interesting. As shown in Fig. 3.3b, the temperature in the onboard tank is lower when the MATexp is higher, contrary to our empirical thinking. The reason is shown in Table 3.3 Fueling performance with different MATexp MATexp [°C] Total Final Max Final t [s] Pon [MPa] T on [°C] SOC

Total cooling Peak cooling power consumption [MJ] [kW]

−30

201

74.62

66.03

0.932 4.16

32.62

−32

202

74.72

66.23

0.932 4.15

32.41

−34

203

74.76

66.54

0.932 4.14

32.58

−36

204

74.80

67.04

0.931 4.12

33.71

−38

205

74.84

67.76

0.930 4.10

37.03

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Fig. 3.3 Fueling performance of pressure ramp rate (a) and temperature in the onboard tank (b) with different MATexp at the Ptrans of 50 MPa

Fig. 3.3a, the MC method increases the PRR in the first 30 s through dynamic calculation when MATexp decreases. Unlike the fixed average PRR of the look-up table method, the MC method can dynamically increase the PRR and speed up the fueling speed when the precooling temperature decreases. The faster-fueling speed leads to stronger compression and Joule Thomson effects, resulting in a higher temperature rise. When MATexp is minimum, the PRR is maximum for the first 30 s, resulting in greater peak cooling power. When MATexp is maximum, the PRR after 30 s is maximum, resulting in greater peak cooling power. Based on the above analysis, it seems that the value of MATexp cannot be too large or too small. It seems more appropriate when it is close to the precooling temperature at the end of filling. P trans . In the SAE J2601 protocol, Ptrans is defined as the midpoint between the initial and final pressures. But Reddi et al. (2017) mentioned that Ptrans is usually 50 MPa in 70 MPa filling, which is not the midpoint. Therefore, four different Ptrans around 50 MPa are selected for the parametric study. Table 3.4 shows that with the increases of Ptrans , the total fueling time decreases by 8.1%, the maximum temperature in the onboard tank increases slightly, and the final SOC, total cooling energy consumption and peak cooling power are similar. Table 3.4 Fueling performance with different Ptrans Ptrans [MPa]

Total t [s]

Final Pon [MPa]

Max T on [°C]

Final SOC

Total cooling consumption [MJ]

Peak cooling power [kW]

30

210

74.74

66.85

0.931

4.126

33.714

40

208

74.94

66.95

0.933

4.130

33.714

50

204

74.80

67.04

0.931

4.124

33.714

60

200

75.00

67.15

0.933

4.128

33.714

70

193

74.82

67.28

0.931

4.121

33.714

3 Dynamic Simulation of Hydrogen Fueling Performance Based …

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Fig. 3.4 Fueling performance of pressure ramp rate (a) and temperature in the onboard tank (b) with different Ptrans at the MATexp of −36 °C

Figure 3.4a shows that with the increase of Ptrans , the time for PRR entering the third stage, where PRR starts to decrease, will be later, which means that the average PRR over the entire fueling phase is larger. The faster the fueling, the shorter the total fueling time. The Ptrans seems to be a critical factor in determining the total fueling time. For example, compared to the look-up table method, the total fueling time is reduced by 15.4% when Ptrans is 70 MPa. So a reasonable increase in Ptrans is beneficial to improve the fueling performance of the MC method. But Fig. 3.4b shows that the maximum temperature in the onboard tank increases when Ptrans increases. Therefore, it is suggested that Ptrans can be appropriately increased within the uppertemperature limit in the onboard tank, not just the pressure midpoint of the fueling process described in SAE J2601, further to improve the fueling performance of the MC method.

3.5 Conclusion We analyzed and built a thermodynamic model of a hydrogen fueling system. According to SAE J2601, the dynamic simulation of the look-up table and MC methods were carried out. The simulation results agree well with the reference data. Based on the verified model, the fueling performance of the look-up table and the MC methods under the same conditions were further compared. Finally, the key parameters MATexp and Ptrans in the MC method were studied. The specific conclusions are as follows: (1) Compared to the look-up table method, the MC method reduces the total fueling time by 10.5%, increases the maximum temperature in the onboard tank by 3.2%, and increases the peak cooling power of the heat exchanger by 18.9%, meaning more investment for the refrigeration unit. So, the MC method seems to have pros and cons.

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(2) The temperature in the onboard tank is higher when the MATexp is lower, contrary to our empirical thinking. The parameter MATexp cannot be too large or too small and seems more appropriate to approximately equal the precooling temperature at the end of filling. (3) The parameter Ptrans can be appropriately increased under the upper-temperature limit in the onboard tank, not just the pressure midpoint of the fueling process described in SAE J2601, further to improve the fueling performance of the MC method. This study does not consider top-off fueling, fallback procedure and pre-soaking. Therefore, in the follow-up research, we will further research the MC method, establish a more accurate model, and propose improvement suggestions. Acknowledgements The authors are grateful for the financial support from the National Natural Science Foundation of China (51476120, 52176191), the Industry-University-Research Project of Wuhan Education Bureau (CXY201804), the Scientific Research Project of Hubei Provincial Department of Education (B2019248) and the International Cooperation Training Project of China Scholarship Council (202106950012).

References Bourgeois T, Ammouri F et al (2015) Evaluating the temperature inside a tank during a filling with highly pressurized gas. Int J Hydrog Energy 40(35):11748–11755 Chae CK, Park BH, Huh YS, Kang SK et al (2020) Development of a new real time responding hydrogen fueling protocol. Int J Hydrog Energy 45(30):15390–15401 Chochlidakis CG et al (2020) Overall efficiency comparison between the fueling methods of SAEJ2601 using dynamic simulations. Int J Hydrog Energy 45(20):11842–11854 Handa K, Oshima S, Rembutsu T (2021) Precooling temperature relaxation technology in hydrogen refueling for fuel-cell vehicles. Int J Hydrog Energy 46(67):33511–33522 Harty R, Mathison S (2010) Improving hydrogen tank refueling performance through the use of an advanced fueling algorithm—the MC method. In: NHA hydrogen conference and expo 2010, vol 2, pp 1–38. Long Beach Mathison S, Handa K, McGuire T, Brown T et al (2015) Field validation of the MC default fill hydrogen fueling protocol. SAE Int J Altern Powertrains 4(1):130–144 Reddi K, Elgowainy A, Rustagi N, Gupta E (2017) Impact of hydrogen SAE J2601 fueling methods on fueling time of light-duty fuel cell electric vehicles. Int J Hydrog Energy 42(26):16675–16685 Rothuizen E, Mérida W, Rokni M, Wistoft-Ibsen M (2013) Optimization of hydrogen vehicle refueling via dynamic simulation. Int J Hydrogen Energy 38(11):4221–4231 SAE J2601 (2022) Fueling protocols for light duty gaseous hydrogen surface vehicles. https://www. sae.org/standards/content/j2601_202005/. Last accessed 24 August 2022 SAE technical paper (2022) Application of MC method-based H2 fueling. https://doi.org/10.4271/ 2012-01-1223. Last accessed 24 August 2022 Schneider J, Meadows G, Mathison SR, Veenstra MJ, Shim J, Immel R et al (2014) Validation and sensitivity studies for SAE J2601, the light duty vehicle hydrogen fueling standard. SAE Int J Altern Powertrains 3(2):257–309 Talpacci E, Reuβ M, Grube T, Cilibrizzi P, Gunnella R, Robinius M, Stolten D (2018) Effect of cascade storage system topology on the cooling energy consumption in fueling stations for hydrogen vehicles. Int J Hydrog Energy 43(12):6256–6265

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Wang Y, Wang S, Decès-Petit C (2022) Less is more: robust prediction for fueling processes on hydrogen refueling stations. Int J Hydrog Energy Xiao J, Bénard P, Chahine R (2016) Charge-discharge cycle thermodynamics for compression hydrogen storage system. Int J Hydrog Energy 41(12):5531–5539

Chapter 4

Artificial Neural Network Prediction for Breakthrough Curves of H2 /CO2 Adsorption on CuBTC Chenglong Li, Fangxin Du, Pierre Bénard, Richard Chahine, Tianqi Yang, and Jinsheng Xiao

Abstract Breakthrough curves can reflect the gas adsorption kinetics in the adsorption bed. In this paper, an artificial neural network (ANN) model is used to predict the breakthrough behavior of binary system (H2 /CO2 ) in the CuBTC adsorbent. The artificial neural network training data was derived from a heat and mass transfer model built from the Aspen Adsorption software using the Latin hypercube sampling (LHS) strategy. The established ANN model with 5 hidden layers can well reflect the gas breakthrough behavior. Finally, the parametric studies of the breakthrough curve based ANN were studied. The results show that the breakthrough time can be improved with lower feed flow rate, lower initial temperature and higher pressure. The temperature change has the greatest impact on the gas breakthrough behavior. It is indicted that reducing the temperature of the adsorption bed through effective thermal management will improve the purity of the product. Keywords Hydrogen purification · Pressure swing adsorption · Artificial neural network · Breakthrough curve · Parameter studies

C. Li · F. Du Automobile Technology and Service College, Wuhan City Polytechnic, Hubei 430064, China P. Bénard · R. Chahine · J. Xiao Hydrogen Research Institute, Université du Québec à Trois-Rivières, Quebec, QC G8Z 4M3, Canada C. Li · T. Yang (B) · J. Xiao Hubei Research Center for New Energy & Intelligent Connected Vehicle, School of Automotive Engineering, Wuhan University of Technology, Hubei 430070, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_4

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4.1 Introduction The pressure swing adsorption (PSA) technology has been widely used in various industrial gas separations due to its flexible operation and efficient separation characteristics. High-purity hydrogen with a purity of 99.99% can be obtained through the PSA (Silva et al. 2013; Regufe et al. 2015; Moon et al. 2018). At present, the commonly used adsorbents for hydrogen purification are activated carbon and zeolite (Moon et al. 2018; Golmakani et al. 2017; Ahn et al. 2012). With the emergence of new materials named metal organic frameworks (MOFs) such as CuBTC (Silva et al. 2013), MIL-125(Ti)_NH2 (Regufe et al. 2015), UTSA-16 (Brea et al. 2019) are also gradually used to improve the purification performance of hydrogen, but the research on MOFs materials for hydrogen purification is still relative less. This paper mainly studies the breakthrough curves of the binary system of H2 /CO2 in CuBTC. The whole adsorption experiment process to choose a suitable adsorbent is very time-consuming and expensive, and the most effective method is to carry out mathematical modeling. The mathematical model of adsorption has to solve a large number of partial differential equations (PDEs), including mass, energy and momentum balances, which can be very time-consuming (Poursaeidesfahani et al. 2019). Therefore, it is very necessary to design a fast and accurate surrogate model. Numerical simulations of PSA breakthrough curves are currently performed mainly through software such as Aspen Adsorption, gPROMS and PSASIM®. Brea et al. used PSASIM® to compare the purification performance of UTSA-16 and BPL AC (Brea et al. 2019). Xiao et al. used Aspen Adsorption software to study the effect of layered beds composed of activated carbon and zeolite adsorbents on the hydrogen purification performance under different operating conditions (Xiao et al. 2020). Park et al. used gPROMS software to explore the effect of the number of adsorbent beds on the hydrogen purification performance (Park et al. 2021). Artificial neural network (ANN) is also increasingly used in the optimization design of PSA due to its strong nonlinear characteristics. Ghalandari et al. used the artificial neural network-genetic (ANN-GA) algorithm model to predict the behavior of gas adsorption on activated carbon (Ghalandari et al. 2020). Pai et al. used a feedforward neural network to predict product purity, recovery, productivity and energy consumption (Pai et al. 2020). By comparing artificial neural networks and partial least squares (PLS) regression algorithms to optimize CO2 purity and recovery, Subraveti et al. showed that ANN-based optimization offers ~10× reduction in computational efforts while achieving the same performance as that of PLS models (Subraveti et al. 2019). It can be found that there are many studies on the prediction and optimization of PSA cycle performance based on ANN model, but there are few studies on the prediction of PSA breakthrough curve combined with ANN. At present, only Ye et al. used the ANN algorithm to predict and optimize the breakthrough time of hydrogen. The maximum breakthrough time of hydrogen was used as the optimization objective function (Ye et al. 2019). To the best of the authors’ knowledge, there are few predictions about the breakthrough curve based on ANN in

4 Artificial Neural Network Prediction for Breakthrough Curves …

39

PSA application. In this paper, an artificial neural network surrogate model was built to predict the hydrogen breakthrough curve. The artificial neural network surrogate model can accurately predict the influence of feed flow rates, pressures and temperatures on the breakthrough curves of hydrogen.

4.2 Breakthrough Curve Verification Mathematical models of breakthrough curves include mass, energy, and momentum balances. In this paper, a heat and mass transfer model was established using Aspen Adsorption software. For more details about the heat and transfer model, the reader can be referred to Xiao et al. (2018). The parameters of the adsorption bed and CuBTC are shown in Table 4.1, and the competitive adsorption of the binary system of H2 /CO2 is represented by the extended Langmuir model, as shown in Eq. (4.1). The Langmuir parameters are shown in Table 4.2. In Fig. 4.1, The Langmuir model can fit the static adsorption process of CO2 and H2 well. qi = qm,i

1+



(−Hi ) with bi = b∞,i exp Rg T j=1 bn pn

bi pi n

 (4.1)

where q represents the equilibrium adsorption amount, P represents the equilibrium adsorption pressure, qm and b are the saturation capacity and the affinity parameter. Table 4.1 Characteristics of adsorbents and adsorption bed (Silva et al. 2013) Parameter

Value

Parameter

Value

823

Wall density, ρw

Solid density, ρs (kg/m3 )

1379

Wall specific heat, C pw (J/(kg K))

500

Particle specific Heat, C ps (J/kg/K)

1457

Gas phase heat conductivity, K Lg (W/ m/K)

0.4

Particle radius, Rp (m)

0.0015 Solid phase heat conductivity, K Ls (W/ 0.7 m/K)

Particle density, ρb

(kg/m3 )

(kg/m3 )

8238

Internal bed diameter, Db (m)

0.021

Wall heat conductivity, K w (W/m/K)

16

Bed length, L (m)

0.31

Particle porosity, ε p

0.78

0.52

Heat transfer coefficient, hin (W/m2 /K) 60

92

Heat transfer coefficient, hout (W/m2 / K)

Bed porosity, εb Heat transfer coefficient, hgs

Table 4.2 Langmuir parameters of Cu-BTC (Silva et al. 2013)

(W/m2 /K)

Component

qm (mol/kg)

CO2

15.0

H2

1.46

b∞ (*10−4 bar−1 ) 0.372 12.3

25

H i (J/mol) −23,004 −10,620

40

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CO2

308K 343K 373K

10

0.5

(a)

H2

303K 343K

(b)

0.4

q (mol/kg)

q (mol/kg)

8 6 4

0.3 0.2 0.1

2 0

0.0 0

2

4

P (bar)

6

8

0

2

4

6

8

P (bar)

Fig. 4.1 Adsorption isotherms of CO2 (a) and H2 (b) on CuBTC (Line: Simulation; Dot: Experiment from Ref. Silva et al. 2013)

The initial adsorption pressure of the breakthrough experiment was set to 2 bar, the initial temperature was 303 K, the concentration ratio of CO2 and H2 was 0.472:0.528, and the feed rate was 9.48 * 10−6 m3 /s. As shown in Fig. 4.2, the simulated value of the breakthrough curve is in good agreement with the experimental value (Silva et al. 2013), indicating that the heat and mass transfer model established by Aspen Adsorption can well describe the kinetics of the adsorption process. The bed was initially filled with He, so H2 initially could not be detected at the outlet. When there is no He in the adsorption bed, CuBTC adsorbs CO2 , and the concentration of hydrogen at the outlet is close to 1. The breakthrough time of CO2 is about 500 s. When CO2 breakthroughs, the concentration of hydrogen decreases and the concentration of CO2 increases. When the adsorbent is saturated, H2 and CO2 reach dynamic equilibrium, and the concentration at the outlet is equal to the concentration at the inlet.

4.3 Data Set Selection of ANN Model Based on Latin Hypercube Sampling The data for training artificial neural networks (ANN) is a first step for establishing a reliable model and should be selected reasonably. The training data of ANN is generated through the validated Aspen Adsorption model. The chosen method of training data generation is via Latin hypercube sampling (LHS). LHS is a stratified sampling technique proposed by Mckay et al., which can effectively stratify the values range of each variable. The significant advantage of Latin hypercube samples is that a small number of samples can represent the entire sample space, which ensures that each sample is represented in a fully hierarchical manner (McKay et al. 1979). Through LHS, 80 sample points are selected, the upper and lower boundaries of the LHS are shown in Table 4.3. Figure 4.3 shows the scatter plot matrix of the

4 Artificial Neural Network Prediction for Breakthrough Curves …

41

1.0

Fig. 4.2 Simulation results of CO2 /H2 breakthrough curves (Line: Simulation; Dot: Experiment from Ref. Silva et al. 2013)

H2 CO2 Simulation

Molar fraction

0.8

0.6

0.4

0.2

0.0 0

200

400

600

800 1000 1200 1400 1600 1800 2000

Time (s)

Table 4.3 Lower (lb) and upper (up) bounds of LHS Condition

Flow rate (m3 /s)

Pressure (bar)

Temperature (T)

lb

9 * 10–6

2

300

ub

13 * 10–6

6

350

input variable samples. The sample point data is inputted into the verified Aspen Adsorption model to generate a training data set of ANN. The total simulation time is set to 2000s, an interval time point is set 5 s. For the breakthrough curves of a binary system, if the breakthrough curve of one gas is accurately predicted, then the concentration change of another gas can be easily obtained. Therefore, we only need to obtain the breakthrough curve data of hydrogen. The breakthrough curve data of CO2 is 1 minus the hydrogen concentration. Therefore, the total number of input sample data is 80 * 5 * 401 = 160,400.

4.4 Artificial Neural Network Model There are more and more applications of artificial neural networks (ANN) in the simulation and optimization design of PSA. It mainly includes an input layer, an output layer and one or more hidden layers. The more the number of hidden layers, the better the nonlinear fitting characteristics of the neural network, and the better it is to deal with complex problems, such as CNN, RNN, LSTM and other deep learning networks (Oliveira et al. 2020). Since the amount of data in this paper is too large, one hidden layer can no longer handle this complex amount of data. In this paper, the number of hidden layers is set to 5. The structure of the neural network is shown in Fig. 4.4.

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Fig. 4.3 Scatter plot matrix of input variables

The algorithm of ANN is completed by Matlab software. The whole data set is divided into training set, test set and validation set, with a ratio of 70% to 15% to 15%. As shown in Fig. 4.5, the correlation coefficients of the training set, validation set and test set of the neural network are all close to 1, indicating that the ANN model fits the data well.

4.5 Breakthrough Curves Parameter Studies Based on Artificial Neural Network In order to further verify the robustness of the ANN model, we need to perform a robustness test, that is, to test the prediction effect for the input data that was not “seen” by the ANN model. Parameter studies based on ANN were carried out to observe the adsorption dynamics of the breakthrough curves. We focus on the effects

4 Artificial Neural Network Prediction for Breakthrough Curves …

43

Fig. 4.4 ANN structure for predicting breakthrough curves

of temperature, feed flow rate and adsorption pressure on breakthrough curves. The initial gas of the adsorption bed was filled with H2 , and the ANN prediction results were compared with the Aspen Adsorption model.

4.5.1 Effect of Feed Flow Rate As shown in Fig. 4.6, the breakthrough curve data predicted by the ANN model were in good agreement with the data simulated by the Aspen model, which further verifies the correctness of the ANN model. By reducing the feed flow rate, the hydrogen breakthrough time increases correspondingly, and the concentration wavefront becomes slightly steeper, which is consistent with the results of a previous related study (Jee and Kim 2001). The contact time of gas in the adsorption bed increases with the decrease of feed flow rate. So the adsorbent can more fully absorb CO2 and the breakthrough time of CO2 increased.

4.5.2 Effect of Adsorption Pressure As shown in Fig. 4.7, the breakthrough time increases because the adsorption capacity of the adsorbent for impurity gas increases as the pressure increases. However, the breakthrough time and adsorption pressure do not increase linearly. There was not as much of an elongation effect on the breakthrough time at 4 bar as compared to the effect at 3 bar. According to the adsorption isotherm model of CO2 , the adsorption capacity of CuBTC to CO2 at the initial pressure of 2, 3 and 4 bar is about 3.68, 4.91

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Fig. 4.5 Correlation coefficient between Aspen Adsorption model target and ANN prediction output based on the training set, validation set, test set, and overall dataset

and 5.9 mol/kg, respectively. The rate of increase in adsorption capacity decreases with increasing pressure, so the rate of increase in breakthrough time of CO2 gas also decreases. It is indicated that the design of PSA cannot blindly increase the pressure to improve the purification performance. On the one hand, the adsorbent will have a saturated adsorption capacity. Excessively increasing the pressure cannot improve the adsorption capacity of the adsorbent for impurity gases and also increase the energy consumption of the whole system.

4 Artificial Neural Network Prediction for Breakthrough Curves … 1.0 9.48*10-6 m3/s 10.48 *10-6 m3/s 11.48*10-6 m3/s ANN ANN ANN

H2

Molar fraction

Fig. 4.6 Effect of feed flow rate on the breakthrough curve for the H2 /CO2 system at 2 atm and 303 K (Line: Aspen model simulation; Dot: ANN model simulation)

45

0.8

0.6

0

500

1000

1500

2000

Time (s)

1.0 2 bar 3 bar 4 bar ANN ANN ANN

H2

Molar fraction

Fig. 4.7 Effect of pressure on the breakthrough curve for the H2 /CO2 system at 9.48 * 10–6 m3 /s and 303 K (Line: Aspen model simulation; Dot: ANN model simulation)

0.8

0.6

0

500

1000

1500

2000

Time (s)

4.5.3 Effect of Temperature There have been relevant studies on the effects of operating variables such as feed flow rate and pressure (Xiao et al. 2018; Jee and Kim 2001; Moon et al. 2016) on gas breakthrough behavior, but there are few studies on the effect of temperature on the breakthrough curve. The feed flow rate and pressure hardly affect the slope of the breakthrough curve, but only affect the gas breakthrough time. However, as shown in Fig. 4.8a, the change of temperature will not only affect the gas breakthrough time but also significantly change the slope of the breakthrough curve. The reason for this phenomenon is that the adsorption isotherms of the gas will be different at different temperatures, and the temperature change will affect the adsorption affinity of the

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(a)

303K 323K 343 K ANN ANN ANN

H2

0.8

303K 323K 343K

6

q (mol/kg)

Molar fraction

1.0

CO2

(b)

4 3.68

2.34

2

1.51

0.6

0 0

500

1000

Time (s)

1500

2000

0.0

0.5

0.944 1.0

1.5

2.0

P (bar)

Fig. 4.8 a Effect of temperature on the breakthrough curve for the H2 /CO2 system at 9.48 * 10–6 m3 / s and 2 bar (Line: Aspen model simulation; Dot: ANN model simulation); b Adsorption isotherms of CO2 at 303 K, 323 K and 343 K

adsorbent to CO2 . However, even if the temperature greatly changes the gas breakthrough behavior, the ANN model can still predict this fluctuation well, showing the excellent robustness of the ANN model. It is worth noting that, like the effect of pressure on the behavior of the breakthrough curve, changes in temperature to breakthrough time are also not linear. Figure 4.8b shows the adsorption isotherms of CO2 at different temperatures calculated from the adsorption isotherm parameters. The adsorption amounts of CO2 calculated by Langmuir model at 303 K, 323 K and 343 K are 3.68 mol/kg, 2.34 mol/kg and 1.51 mol/kg on CuBTC, respectively. Therefore, the decrease of temperature will significantly increase the adsorption capacity of the adsorbent to impurity gas. And this gap of adsorption capacity will become larger with the increase of pressure, as shown in the shaded area of Fig. 4.8b. This suggests that effective thermal management should be carried out in PSA design, reducing the temperature of the adsorption bed to improve the purification performance of the product, such as the design of heat exchangers (Ali Abd et al. 2021; Chen et al. 2021) or adding phase change materials (PCM) in the adsorption bed (Horstmeier et al. 2016).

4.6 Conclusion At present, there is almost no application of ANN in the prediction of the breakthrough curve of PSA, and this paper has attempted in this field. The heat and mass transfer model of CO2 /H2 binary system established by Aspen Adsorption was well verified with experiments, and 80 sets of simulated data were generated based on the LHS method to train the ANN model. Parameter studies show that the breakthrough curve data simulated by the trained ANN is consistent with the data simulated by the Aspen Adsorption model, which verifies the robustness of the ANN model. The

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parametric study based on the ANN model shows that the breakthrough time can be improved with lower feed flow rate, lower initial temperature and higher pressure. By comparison, it is found that the temperature change has the greatest impact on the gas breakthrough behavior, which is mainly related to the adsorption isotherm of the gas. So reducing the temperature of the adsorption bed through effective thermal management will improve the purity of the product. Acknowledgements Supported by the National Natural Science Foundation of China (52176191, 51476120) and the Shuguang Project of Knowledge Innovation Program from the Wuhan Science and Technology Bureau (2022010801020432).

References Ahn S, You Y-W, Lee D-G, Kim K-H, Oh M, Lee C-H (2012) Layered two- and four-bed PSA processes for H2 recovery from coal gas. Chem Eng Sci 68:413–423 Ali Abd A, Roslee Othman M, Helwani Z (2021) Evaluation of thermal effects on carbon dioxide breakthrough curve for biogas upgrading using pressure swing adsorption. Energy Convers Manage 247 Brea P, Delgado JA, Águeda VI, Uguina MA (2019) Comparison between MOF UTSA-16 and BPL activated carbon in hydrogen purification by PSA. Chem Eng J 355:279–289 Chen L, Deng S, Zhao R, Zhu Y, Zhao L, Li S (2021) Temperature swing adsorption for CO2 capture: Thermal design and management on adsorption bed with single-tube/three-tube internal heat exchanger. Appl Therm Eng 199 Ghalandari V, Hashemipour H, Bagheri H (2020) Experimental and modeling investigation of adsorption equilibrium of CH4 , CO2 , and N2 on activated carbon and prediction of multicomponent adsorption equilibrium. Fluid Phase Equilib 508 Golmakani A, Fatemi S, Tamnanloo J (2017) Investigating PSA, VSA, and TSA methods in SMR unit of refineries for hydrogen production with fuel cell specification. Sep Purif Technol 176:73– 91 Horstmeier JF, Gomez Lopez A, Agar DW (2016) Performance improvement of vacuum swing adsorption processes for CO2 removal with integrated phase change material. Int J Greenhouse Gas Control 47:364–375 Jee J-G, Kim M-B, Lee C-H (2001) Adsorption characteristics of hydrogen mixtures in a layered bed: binary, ternary, and five-component mixtures 40:868–878 McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21:239 Moon D-K, Lee D-G, Lee C-H (2016) H2 pressure swing adsorption for high pressure syngas from an integrated gasification combined cycle with a carbon capture process. Appl Energy 183:760–774 Moon D-K, Park Y, Oh H-T, Kim S-H, Oh M, Lee C-H (2018) Performance analysis of an eightlayered bed PSA process for H2 recovery from IGCC with pre-combustion carbon capture. Energy Convers Manage 156:202–214 Oliveira LMC, Koivisto H, Iwakiri IGI, Loureiro JM, Ribeiro AM, Nogueira IBR (2020) Modelling of a pressure swing adsorption unit by deep learning and artificial Intelligence tools. Chem Eng Sci 224 Pai KN, Prasad V, Rajendran A (2020) Generalized, adsorbent-agnostic, artificial neural network framework for rapid simulation, optimization, and adsorbent screening of adsorption processes. Ind Eng Chem Res 59:16730–16740

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Park Y, Kang J-H, Moon D-K, Jo YS, Lee C-H (2021) Parallel and series multi-bed pressure swing adsorption processes for H2 recovery from a lean hydrogen mixture. Chem Eng J 408 Poursaeidesfahani A, Andres-Garcia E, de Lange M, Torres-Knoop A, Rigutto M, Nair N et al (2019) Prediction of adsorption isotherms from breakthrough curves. Microporous Mesoporous Mater 277:237–244 Regufe MJ, Tamajon J, Ribeiro AM, Ferreira A, Lee UH, Hwang YK et al (2015) Syngas purification by porous amino-functionalized titanium terephthalate MIL-125. Energy Fuels 29:4654–4664 Silva B, Solomon I, Ribeiro AM, Lee UH, Hwang YK, Chang J-S et al (2013) H2 purification by pressure swing adsorption using CuBTC. Sep Purif Technol 118:744–756 Subraveti SG, Li Z, Prasad V, Rajendran A (2019) Machine learning-based multiobjective optimization of pressure swing adsorption. Ind Eng Chem Res 58:20412–20422 Xiao J, Fang L, Bénard P, Chahine R (2018) Parametric study of pressure swing adsorption cycle for hydrogen purification using Cu-BTC. Int J Hydrog Energy 43:13962–13974 Xiao J, Li C, Fang L, Böwer P, Wark M, Bénard P et al (2020) Machine learning–based optimization for hydrogen purification performance of layered bed pressure swing adsorption. Int J Energy Res 44:4475–4492 Ye F, Ma S, Tong L, Xiao J, Bénard P, Chahine R (2019) Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption. Int J Hydrog Energy 44:5334–5344

Chapter 5

The Effect of Culture Conditions on Microbial Remediation of Contaminated Soil in Antimony Ore Area Xinyue Shi, Peng Zheng, Xinglan Cui, Xiaokui Che, Ying Liu, Lei Wang, Hongxia Li, and Qi Zheng

Abstract With the extensive development of mining and smelting activities, the pollution of antimony (Sb) and arsenic (As) in soil becomes more and more serious, which is highly toxic to public health. In order to deeply understand the characteristics of Sb and As contaminated soil, we analyzed the mineral composition, pollutant content and the occurrence state of pollutants in the collected contaminated soil. The results showed that the main components in the contaminated soil were quartz, mica, calcite and dolomite, and the contents of antimony and arsenic were up to 24,666 and 240 mg/kg respectively, the Sb and As in the soil mainly existed in the residue state. Under different carbon source conditions, SRB can effectively cure antimony in soil, among which ethanol and glycerol are the best. More acidic conditions are more favorable for SRB bacteria to cure antimony and arsenic in soil. Keywords Antimony · Arsenic · Contaminated soil · SRB bacteria

5.1 Introduction Heavy metal pollution of soil caused by mining and smelting activities has become a major threat to land use (Karczewska et al. 2004). Contamination of antimony (Sb) and arsenic (As) in soil is an important environmental problem worldwide (Filella et al. 2002; Wilson et al. 2010). Sb is a suspected carcinogen with potential damage to the immune and nervous systems (Cavallo et al. 2002; Gebel 1997) and is restricted by the Basel Convention (Kim et al. 1998). As, which is a group I carcinogen (Hughes 2004), is released into the soil environment by natural processes X. Shi · P. Zheng · X. Cui (B) · X. Che · Y. Liu · L. Wang · H. Li · Q. Zheng GRINM Resources and Environmental Technology Corporation Limited, Beijing 101407, China e-mail: [email protected] GRIMAT Engineering Institute Corporation Limited, Beijing 100088, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_5

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from sediment into groundwater or released through anthropogenic activities, such as mining (Matschullat 2000; Rizoulis et al. 2014). Sb and As belong to group 15 of the periodic table and exist mainly in the III and V states. The geochemical characteristics of Sb and As are similar, but there are still differences in some aspects. Simultaneous contamination of As and Sb is often observed in the environment, especially in environments affected by mining activities (Anawar et al. 2011). China is the main Sb reserves and Sb production country, the status of heavy metal pollution is not optimistic. The exploitation of metal mines will pollute groundwater and surrounding soil, and the traditional lime precipitation method (Tsukamoto and Miller 1999) will produce a large amount of metal polluted sludge, and the cost is high (Wakao et al. 1979). A promising alternative is the biologically induced precipitation of metal sulfides, which is based on hydrogen sulfide production by sulfate-reducing bacteria (SRB) (Kaksonen et al. 2003). In the process of organic matter metabolism, SRB uses sulfate as the terminal electron acceptor to produce sulfide, which can remove metal by forming insoluble metal sulfide precipitation (Dvorak et al. 1992; Jong and Parry 2003; Kieu et al. 2011). Although some previous studies have reported the biological removal of Sb or As as a single contaminant, there are few studies on the treatment of combined As and Sb contamination by SRB (Altun et al. 2014; Sahinkaya et al. 2015; Zhang et al. 2016). In this work, the biological treatment effect of mixed culture with SRB on soil containing Sb and As was studied by batch experiment. In particular, the mineral composition and the occurrence state of pollutants in Sb and As contaminated soil were studied, and the effects of different carbon sources and pH on the treatment of Sb and As contaminated soil by SRB were explored, so as to screen out the best conditions and provide a basis for the remediation of Sb and As contaminated soil by SRB.

5.2 Experimental Materials and Methods 5.2.1 Materials The test soil was obtained from an antimony smelting area in Xihe County, Longnan City, Gansu Province. The smelter was established in 1986 and closed in 2013, mainly engaged in antimony smelting. The alkaline slag produced by smelting, containing high antimony, is stacked in the waste alkali slag stacking area, where the content of Sb and As is high. The SRB bacteria used in the experiment were from the National Engineering Research Center for Environment-friendly Metallurgy in Producing Premium Non-ferrous Metals. Bacterial acclimation was carried out using eutrophication basic salt medium containing yeast extract 1.2 g/L, Ca(NO3 )2 0.01 g/

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L, K2 HPO4 0.5 g/L, MgSO4 ·7H2 O 0.5 g/L and KCl 0.1 g/L. Sodium lactate, glucose, ethanol and glycerol were selected to explore the most suitable carbon sources. The pH of the medium was adjusted with 0.1 mol/L H2 SO4 and 0.1 mol/L NaOH, respectively.

5.2.2 Methods Mineral composition analysis of Sb and As contaminated soil. The mineral composition of Sb and As contaminated soil was analyzed by X-ray powder diffraction (XRD) (Rigaku-Smart Lab, Japan) in the national standard (Beijing) Inspection and Certification. The radiation source was CuKα (λ = 0.15418 nm) with the step width of 0.02, the scanning range of 10°–90°, the scanning speed of 8°/min, the voltage of 45 kV and the current of 200 mA. The test was carried out at room temperature. Analysis of pollutant content in Sb and As contaminated soil. The soil was digested by HNO3 -HF-HClO4 digestion method, and the contents of Sb and As in the soil were determined by inductively coupled plasma atomic emission spectrometry (Leman-profile, US). Morphological analysis of soil pollutants contaminated with Sb and As. The contents of metal exchangeable state (exchangeable state), carbonate bound state (carbonate state), iron (manganese) oxide bound state (iron/manganese state), organic and sulfide bound state (organic state) and residue lattice bound state (residue state) of Sb and As in soil were determined by Tessier five-step continuous extraction method. Effects of different carbon sources and pH on Sb and As in soil solidified by SRB. Using sodium lactate, glucose, ethanol and glycerol as carbon sources, the treatment effects of SRB bacteria on curing Sb and As in soil were compared under different carbon sources. The pH was adjusted to 3, 5, 7, 9, and 11 to explore the optimal pH for curing Sb and As in soil by SRB bacteria.

5.3 Results and Discussion 5.3.1 Elemental Composition and Morphological Analysis of Contaminated Soil The XRD analysis results are shown in Fig. 5.1. The main mineral phases in the contaminated soil are quartz, mica, calcite, dolomite, and a small amount of stibnite and pyrite. On the whole, the soil in this area has more carbonate minerals and higher content of calcium and magnesium. The chemical composition of contaminated soil is shown in Table 5.1. It is a typical sandy soil, mainly containing SiO2 , but also containing a small amount of

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Fig. 5.1 XRD pattern of contaminated soil

Table 5.1 Chemical composition of contaminated soil SiO2 (%)

Al2 O3 (%)

Fe2 O3 (%)

MgO(%)

CaO(%)

S(%)

Sb(ppm)

As(ppm)

41

10.3

4.7

2.6

11.2

0.1

24,666

240

CaO, Al2 O3 , Fe2 O3 and MgO. The content of antimony in soil is 24666 mg/kg, and the content of As is 240 mg/kg. The pollution of Sb and As in this area is serious, so the remediation of pollution is imminent. The occurrence forms of Sb and As in contaminated soil are shown in Fig. 5.2. Sb and As mainly exist in the residue state, while the contents of organic, Fe–Mn, carbonate and exchangeable antimony and arsenic are relatively low.

5.3.2 Effects of Different Carbon Sources and pH on Sb and as in SRB Cured Soil The addition of carbon source can provide nutrients for the growth of SRB bacteria, increase the pH of the system, and maintain the reduction potential. As shown in Fig. 5.3, the leaching concentration of Sb decreased rapidly after adding different carbon sources, and then increased slowly. The addition of ethanol and glycerolhad

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Fig. 5.2 The morphology of Sb and As in contaminated soil

better curing effect on Sb in soil. However, the addition of carbon sources promoted the dissolution of As from the soil. Microorganims have strong adaptability and buffering ability to acid/alkaline environment. As shown in Fig. 5.4, SRB has good inhibition effect on Sb under different pH conditions, among which the inhibition rate of Sb under acidic conditions is

Fig. 5.3 Effect of carbon source on Sb and As in soil solidified by SRB

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Fig. 5.4 Effect of pH on Sb and As in soil solidified by SRB

higher, alkaline environment promotes the leaching of As, and acidic environment is more conducive to the inhibition of Sb and As by SRB.

5.4 Conclusions The contamination of Sb and As in the soil in Xihe County of Longnan City, Gansu Province is serious. The soil type of this area is sandy soil. Sb and As in soil mainly exist in the form of residue. Under different carbon source conditions, SRB can effectively cure antimony in soil, among which ethanol and glycerol are the best. More acidic conditions are more favorable for SRB bacteria to cure Sb and As in soil. Acknowledgements The project was funded by the Youth Fund Project of GRINM (No. 12120), the National Key Research and Development Project (No. 2020YFC1807700), the Scientific Research and Technology Development Program of Guangxi (grant nos. GuikeAB22080078), the Open Foundation of State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization (No. 2021P4FZG13A) and the National Key Research and Development Project (No. 2019YFC1805900).

References Altun M, Sahinkaya E, Durukan I, Bektas S, Komnitsaset K (2014) Arsenic removal in a sulfidogenic fixed-bed column bioreactor. J Hazard Mater 269:31–37 Anawar HM, Freitas MC, Canha N, Regina IS (2011) Arsenic, antimony, and other trace element contamination in a mine tailings affected area and uptake by tolerant plant species. Environ Geochem Health 33:353–362

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Cavallo D, Iavicoli I, Setini A, Marinaccio A, Perniconi B, Carelli G, Iavicoli S (2002) Genotoxic risk and oxidative DNA damage in workers exposed to antimony trioxide. Environ Mol Mutagen 40:184–189 Dvorak DH, Hedin RS, Edenborn HM, McIntire PE (1992) Treatment of metal contaminated water using bacterial sulfate reduction: results from pilot-scale reactors. Biotechnol Bioeng 40:609– 616 Filella M, Belzi N, Chen YW (2002) Antimony in the environment: a review focused on natural waters I. Occurrence. Earth-Sci Rev 57:125–176 Gebel T (1997) Arsenic and antimony: comparative approach on mechanistic toxicology. Chem Biol Interact 107:131–144 Hughes MF (2004) IARC monographs on the evaluation of carcinogenic risks to humans. Int Agency Res Cancer Jong T, Parry DL (2003) Removal of sulfate and heavy metals by sulfate reducing bacteria in short-term bench scale upflow anaerobic packed bed reactor runs. Water Res 37:3379–3389 Karczewska A, Bogda A, Szulc A, Czwarkiel D, Li ZS (2004) Soil pollution with arsenic in the areas of former arsenic mining and processing in Lower Silesia, SW Poland. Proceedings of EGU 2004. Nice Kaksonen AH, Franzmann PD, Puhakka JA (2003) Performance and ethanol oxidation kinetics of a sulfate-reducing fluidized-bed reactor treating acidic metal-con-taining wastewater. Biodegradation 14:207–217 Kieu HTQ, Müller E, Horn H (2011) Heavy metal removal in anaerobic semi-continuous stirred tank reactors by a consortium of sulfate-reducing bacteria. Water Res 45:3863–3870 Kim SJ, Arai M, Tamura M, Suzuki Y (1998) A study on antimony-bearing ferrite. J Hazard Mater 57:1–12 Matschullat J (2000) Arsenic in the geosphere-a review. Sci Total Environ 249:297–312 Rizoulis A, Lawati AWM, Pancost BRD (2014) Microbially mediated reduction of FeIII and AsV in Cambodian sediments amended with 13C-labelled hexadecane and kerogen. Environ Chem 11:538–546 Sahinkaya E, Yurtsever A, Toker Y, Elcik H, Cakmaci M, Kaksonen AH (2015) Biotreatment of As-containing simulated acid mine drainage using laboratory scale sulfate reducing upflow anaerobic sludge blanket reactor. Min Eng 75:133–139 Tsukamoto TK, Miller GC (1999) Methanol as a carbon source for microbiological treatment of acid mine drainage. Water Res 33:1365–1370 Wilson SC, Lockwood PV, Ashley PM, Tighe M (2010) The chemistry and behaviour of antimony in the soil environment with comparisons to arsenic: a critical review. Environ Pollut 158:1169– 1181 Wakao N, Takahashi T, Sakurai Y, Shiota H (1979) A treatment of acid mine water using sulfatereducing bacteria. J Ferment Technol 57:445–452 Zhang G, Ouyang X, Li H, Fu Z, Chen J (2016) Bioremoval of antimony from contaminated waters by a mixed batch culture of sulfate-reducing bacteria. Int Biodeter Biodegr 115:148–155

Chapter 6

A Research Progress on Stabilization/ Solidification of Electrolytic Manganese Residue Guoying Ma, Xingyu Liu, Ying Lv, Xiao Yan, Xuezhe Zhu, and Mingjiang Zhang

Abstract Electrolytic manganese residue (EMR) is a waste residue produced during the production of electrolytic manganese. EMR contains a large amount of NH+ 4 -N, Mn, and other heavy metal elements such as Zn, Ni, Cu, and Cr. Although many EMR treatment methods have been proposed in recent years, there is no mature and economical industrial process to treat EMR. The long-term stockpiling of large quantities of EMR poses a severe hazard to the environment and the human body. Stabilization/solidification is a method to treat large amounts of manganese slag by immobilizing the contaminants through chemicals, industrial solid waste, etc. without causing them to leach out. Therefore, this paper reviews the physicochemical properties and hazards of EMR, summarizes and evaluates the curing methods of EMR, and sorts out the curing mechanism to provide a reference for the safe treatment of EMR. At the end of this review, the existing problems and the development prospects of the stabilization/solidification technology are also discussed. Keywords Electrolytic manganese residue · Stabilization/solidification · Chemical solidification · Waste control by waste

G. Ma · Y. Lv · X. Yan · X. Zhu · M. Zhang (B) National Engineering Research Center for Environment-Friendly Metallurgy in Producing Premium Non-ferrous Metals, China GRINM Group Corp., Ltd., Beijing 101407, China e-mail: [email protected] GRINM Resources and Environment Tech. Co., Ltd., Beijing 101407, China General Research Institute for Nonferrous Metals, Beijing 100088, China GRIMAT Engineering Institute Co., Ltd., Beijing 101407, China X. Liu Institute of Earth Science, China University of Geosciences, Beijing 100083, China Shenzhen Green-Tech Institute of Applied Environmental Technology Co., Ltd., Shenzhen 518001, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_6

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6.1 Introduction Electrolytic manganese residue (EMR) is produced in the process of manganese electrolytic, containing high concentrations of Mn2+ and NH+ 4 (Du et al. 2015; Lan et al. 2021a). China is the world’s largest electrolytic manganese producer and exporter (Chen et al. 2020a). Producing 1 ton of electrolytic manganese could create 10–12 tons of EMR (Du et al. 2014). China produces about 20 million EMR annually, accumulating over 160 million tons (Lan and Sun 2021; Shu et al. 2019a; Lan et al. 2019). The rapid development of the electrolytic manganese industry brings benefits and severe environmental pollution. So far, the comprehensive utilization rate of EMR in China is less than 7% (Chen et al. 2020b). The unpretreated EMR is directly stacked in the waste slag field, seriously damaging the local ecological environment. Pollutants in EMR seep into nearby soils and migrate to groundwater. The electrolytic manganese industry faces huge environmental pressure, and the disposal of EMR with massive production has become an increasingly prominent problem. EMR contains high concentrations of ammonium nitrogen and soluble manganese (Mallick et al. 2006). Recovery of manganese and ammonia nitrogen is a harmless treatment and resource utilization method of EMR, but its application range is limited (Lan et al. 2019; Tian et al. 2019; Shu et al. 2016a; Zheng et al. 2020). Zhou applied Solidification/stabilization (S/S) technology to EMR for the first time (Zhou et al. 2013). The S/S is more suitable for the consumption of large volume EMR. S/S is a cost-effective and environment-friendly method for industrial solid wastes. S/ S technology is widely used in the harmless treatment of solid waste due to its simplicity, low cost, and high efficiency (Bednarik et al. 2005).

6.2 Characteristics and Hazard of EMR EMR is acidic or weakly acidic black mud solid with small particles, low fineness, high water content, poor mechanical properties, poor air permeability, high toxicity, and other characteristics. The density of EMR is 2–3 g/cm3 , pH is 5.0–6.5, the moisture content is 25–35 wt% and particle size is 20–500 µm (Chen 2016). Manganese in EMR has three primary forms: water-soluble manganese, manganese carbonate, and manganese dioxide. Water-soluble manganese mainly exists in the form of manganese sulfate (Long et al. 2011). Through the material balance analysis of raw materials, products, and waste in the production process of electrolytic manganese, the residue contains 1.5–2.0% raw material manganese, and soluble manganese accounts for 60% of total manganese in EMR (Ning et al. 2010). The chemical composition of EMR varies with raw materials and production processes. EMR mainly contains elements such as Mn, Fe, Ca, Si, S, and Al, and trace elements such as Zn, Ni, Cu, Cr, Pb, and Co. The direction of elements in electrolytic manganese production is shown in Table 6.1. In the production process of electrolytic manganese, 28.1% of manganese remains in residues, anode mud and

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Table 6.1 Directions of main elements in electrolytic manganese industry (Ning et al. 2010) Unit: % Element

Product

Anode mud

Residues

Effluent

Evaporation

Mn

71.9

12.6

13.7

1.8

–

Cr

2.4

–

–

97.6

–

Se

60.7

22.3

17.0

–

–

NH+ 4 -N SO2− 4

52.4

1.2

44.1

–

2.3

–

0.2

55.3

44.5

–

wastewater, which causes waste of resources and adverse effects on the later safe stacking (Ning et al. 2010). Mn2+ is soluble manganese, Mn4+ is insoluble manganese, and Mn3+ is in an unstable oxidation state and can be easily oxidized to form Mn4+ (Du et al. 2015). The oxidation–reduction potential and pH significantly influence the stability of manganese. When the Eh–pH conditions change, manganese is converted to the corresponding stable phase (Martin 2005). Manganese in EMR can also be treated harmlessly by converting it into stable insoluble compounds, converting soluble Mn2+ to Mn4+ . The ammonia nitrogen forms in the EMR include free ammonia nitrogen and ionic ammonium nitrogen, which are converted to ammonia at different Eh–pH values for both ionic and free ammonia nitrogen. With the development of the electrolytic manganese industry, a large number of EMR are stored for a long time without treatment. A large number of deposited EMR consume a lot of land resources, aggravating the tension between land resources. The pollution of manganese is the most serious in EMR, far exceeding the Integrated Wastewater Discharge Standard of China (GB 8978-1996) (Mn2+ < 2 mg/L). The pollutants in the EMR leak downwards to cause pollution of water sources, and the contaminated drinking water is harmful to human health (Hoyland et al. 2014). Figure 6.1 shows the infiltration of contaminants in the EMR. Excessive manganese absorption by the human body can cause various diseases, such as hepatobiliary function dysfunction, renal dysfunction, and intellectual degradation (Crossgrove and Zheng 2004; Andersen et al. 1999; Aschner et al. 2005). High concentrations of NH+ 4 -N in EMR can also be harmful to organisms. High ammonia concentrations in the body can lead to gastric cancer, blue baby syndrome, and liver damage (Gupta et al. 2000).

6.3 S/S of EMR S/S has developed rapidly recently, and its application scope has gradually expanded. S/S treatment of EMR is to convert soluble substances into insoluble precipitate compounds or to be absorbed by other precipitate compounds to form physical encapsulation.

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Fig. 6.1 Infiltration of contaminants in the EMR

6.3.1 Chemical Solidification Chemical solidification is the main technology for solid waste remediation at present, with high remediation efficiency, which can effectively prevent the release of harmful chemicals in hazardous waste. In the harmless treatment of EMR, the combined immobilization method of manganese and NH+ 4 -N is adopted chiefly (Chen et al. 2016). Chemical reagents for S/S of EMR mainly include alkaline materials, phosphate, sulfide, carbonate, and other chemical reagents (He et al. 2022a, 2021; Mocellin et al. 2017; Chen et al. 2020c). Alkaline additives When alkaline materials are used, Mn2+ in EMR solidified to insoluble precipitate Mn(OH)2 , MnO2 , Mn3 O4 , and MnO(OH), and NH+ 4 escapes to NH3 . The mechanism is as follows: Mn2+ + OH− → Mn(OH)2 (s) ↓

(6.1)

2Mn(OH)2 + O2 → 2MnO2 (s) ↓ +H2 O

(6.2)

− NH+ 4 + OH → NH3 (g) + H2 O

(6.3)

The mechanism of Mn2+ solidification includes immobilization and stabilization. Immobilization is the encapsulation of Mn2+ ; stabilization is the oxidation of Mn2+ to a stable insoluble substance. MnO2 is stable under natural conditions and can be used as the last step of Mn2+ curing (Yza et al. 2020). Ammonium salt can be converted into free ammonia and released into the environment to remove NH+ 4 from EMR.

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Quicklime is an effective additive for solidifying heavy metals. For the treatment of heavy metals in EMR, CaO is commonly used in China (Du et al. 2016; Chen et al. 2021a). Previous research has shown that lime additive is often used to pretreat EMR to stabilize manganese and remove ammonia (Zhou et al. 2014). Luo used quicklime curing EMR with 99.8% Mn2+ curing efficiency and 97% NH+ 4 removal (Luo et al. 2017a). Solidifying EMR by CaO is to solidify soluble Mn2+ into insoluble MnO2 and MnO(OH) effectively, NH+ 4 -N into gaseous NH3 by adsorption, embedding and chemical reaction of CaO so that the concentration of pollutants in leaching solution can meet the relevant standards (Luo et al. 2017b). CaO and NaOH additives can effectively solidify Mn2+ and remove ammonia nitrogen, but CaO is safer and easier to operate than NaOH (Zhou et al. 2013). In addition to lime, sodium hydroxide, and other commonly used alkaline curing agents, cement is also an alkaline material, but due to the complex composition of composite cement, curing products are not the same as the curing products of traditional alkaline materials. In solidification technology, Portland cement (PC) and lime are the most commonly used adhesives. EMR is cured/stabilized with composite Portland cement (PC32.5R) as a curing agent (Mu et al. 2020a). MnSO4 ·2H2 O, (NH4 )2 Mn(SO4 )2 ·6H2 O, and soluble Mn in EMR are solidified by PC32.5R and then transformed into more stable insoluble minerals such as CaMnSi2 O6 and MnSiO3 . At the same time, the hydration gel can cement the components of EMR together, thus effectively inhibiting the dissolution of Mn in EMR solidified body. Basic burning raw material is an excellent S/S agent for EMR, but the stabilization mechanism and long-term stability are still unclear (Shu et al. 2020a). He et al. then explored and solved the problem of unclear S/S mechanism and long-term stability of heavy metals (He et al. 2022b). The main component of basic burning raw material is CaO, and it also contains Al2 O3 , SiO2 , and Fe2 O3 . Mechanism studies showed that the hydration of basic burning raw material forms OH− , calcium silicate hydrate gels, and ettringite, thus stabilizing/solidifying heavy metal ions, such as Mn2+ , Pb2+ , 2+ is solidified/ Cu2+ , Ni2+ , and Zn2+ . NH+ 4 is removed in the form of NH3 , and Mn 2+ 2+ stabilized in tephroite, johannsenite, and davreuxite. Pb , Cu , Ni2+ , and Zn2+ are solidified/stabilized by Calcium silicate hydrate and ettringite via substitution and encapsulation. The properties of the Calcium silicate hydrate gels and ettringite ensure the long-term effectiveness of S/S EMR because the phase and morphology of S/S EMR leaching residue have little change in long-term leaching experiments. The solidification mechanism of alkaline additives on EMR is shown in Fig. 6.2. Although alkaline additives can achieve the fixation of manganese in EMR, the curing process is accompanied by the release of ammonia gas, which causes secondary pollution. Phosphate In the phosphate system, Mn2+ and NH+ are stabilized to Mn(OH)2 , 4 Mn5 (PO4 )2 (OH)4 , Mn3 (PO4 )2 ·3H2 O, and NH4 MgPO4 ·6H2 O (see Fig. 6.3) (Feng et al. 2021). Phosphate can remove ammonia and manganese simultaneously from EMR. There is a large amount of ammonia nitrogen in the EMR. Phosphate and magnesium salt is added to the ammonia nitrogen solution in a certain proportion to

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Fig. 6.2 Solidification mechanism of EMR by alkaline additives (Chen et al. 2016; Luo et al. 2017b; Mu et al. 2020a; He et al. 2022b)

form struvite (MgNH4 PO4 ·6H2 O) precipitation to remove ammonia nitrogen (Shu et al. 2018). Shu reported the simultaneous removal of ammonia and manganese in EMR using only phosphate sources. When N:P ratio is 1:1.15, pH is 9.5, and manganese is removed in the form of Mn3 (PO4 )2 ·7H2 O, and then ammonia is removed in the form of MgNH4 PO4 ·6H2 O. The removal rates of ammonia and manganese are 95.0 and 99.9%, respectively (Shu et al. 2016b). Sulfide Sulfide precipitation is a common method in the industry. When Na2 S is used as a precipitant, pH is 10.0, and the molar ratio of S:Mn is 0.9:1, the removal rate of manganese could reach 99.99% (Shu et al. 2019b). In addition, Li et al. found that the leaching concentration of Mn and other heavy metals in EMR is below GB 89781996 after adding 15% calcium sulfide into EMR and curing for 3 h (Li et al. 2014; GB 1996). The reaction equation of sulfide precipitation is as follows:   S2− + Mg2+ , Fe2+ , Ca2+ → (Mg, Fe, Ca)S ↓

(6.4)

Fig. 6.3 Solidification mechanism of EMR by phosphate (Shu et al. 2018, 2016b, c, 2020b)

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  (Mg, Fe, Ca)S + Mn2+ + H2 O → MnS · H2 O + Mg2+ , Fe2+ , Ca2+

(6.5)

  (Mg, Fe, Ca)S + Mn2+ + H2 O → MnOH · SH + Mg2+ , Fe2+ , Ca2+

(6.6)

However, the sulfide precipitation method has certain corrosion and toxicity, and the amount of sulfide is challenging to control, so its application has been limited (Lewis 2010). Carbonate Carbonate precipitation is an effective and practical method for manganese removal. Manganese is removed in the form of MnCO3 . Carbonate precipitation is superior to hydroxide and sulfide precipitation in removing manganese and ammonia nitrogen (Shu et al. 2019b). The reaction equation of carbonate precipitation is as follows: 2+ CO2− → MnCO3 ↓ 3 + Mn

(6.7)

(Mg2+ , Fe2+ , Ca2+ ) + CO2− 3 → (Mg, Fe, Ca)CO3 ↓

(6.8)

  (Mg, Fe, Ca)CO3 + Mn2+ → MnCO3 + Mg2+ , Fe2+ , Ca2+

(6.9)

Other chemical agents There are also studies using ozone curing manganese slag, and strong oxidant ozone can convert bivalent manganese into a high valence state and then into insoluble manganese oxide. The results show that the solidification efficiency of Mn2+ is more than 99.9%, and the concentration in the solution is lower than 0.10 mg/L, which is in line with GB 8978-1996 (Yang et al. 2014). It is also a new direction in recent years to reduce heavy metals by using CO2 produced by microorganisms and reaction products of chemical agents. This method combines reactive MgO solidification technology and microbial technology, bridging particles and filling pores to improve EMR strength, to solidify EMR (Chen et al. 2021b). Reactive MgO can also be added to Portland cement as a partial substitute, and r-MgO and cement hydrate are independent and do not affect each other (Liska and Al-Tabbaa 2009). Multi-chemical agents Using only one curing agent is usually difficult to achieve a variety of pollutants curing effects, and several chemical agents are generally used together. EMR backfilling is an economical and environmentally-friendly backfilling method. Fractional removal can achieve phased removal of pollutants by using multiple chemical agents. Remove Mn2+ from EMR by Na2 CO3 and then NH+ 4 from EMR by phosphate (Shu et al. 2019b). The research mechanism is shown in Fig. 6.4. In addition, alkaline additives and carbon dioxide (CO2 ) are carbonated to immobilize Mn. Mg and phosphate are precipitated by struvite to immobilize NH+ 4 -N.

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Fig. 6.4 Fractional removal (Shu et al. 2019b)

The efficiency of CO2 + lime (CaO) immobilizing Mn is higher than that of CO2 + sodium hydroxide (NaOH) immobilizing Mn (Chen et al. 2016). Due to the unstable physicochemical properties of the solidified body after curing with traditional alkaline materials, the structure is easily destroyed by carbonization, while chemically bonded phosphate ceramic materials can effectively achieve stable control of Mn and ammonia nitrogen. Traditional chemical bonded phosphate ceramic materials have high processing costs, so industrial waste slags such as ferronickel slag or copper slag rich in metal oxides (MgO, FeOx) are used to replace pure MgO (Mu et al. 2020b). The solidification of EMR is realized through the synergistic effect of chemical bonding, physical encapsulation, and adsorption. This method is different from the traditional method of waste treatment and waste treatment. Different phosphates have different effects on the S/S of EMR. Shu uses MgO and various phosphate resources to solidify/stabilize Mn2+ and NH+ 4 -N in EMR (Shu et al. 2018). Compared with HPM (MgO and Na2 HPO4 ·12H2 O) and HOM(MgO and NaH2 PO4 ·2H2 O) processes, HPM process has lower cost and higher efficiency in extracting Mn2+ and NH+ 4 -N from EMR at the same stabilizer dose. The S/S efficiencies of Mn2+ and NH+ 4 -N are 91.58 and 99.98%, respectively. Due to the high cost of phosphate and MgO stabilizing EMR, it is essential to find an economical and effective method to solidify EMR. P-LGMgO (low-grade MgO and NaH2 PO4 ·2H2 O), P-CaO (CaO and NaH2 PO4 ·2H2 O), P-MgCa (low-grade MgO, CaO, and NaH2 PO4 ·2H2 O) are used for the S/S of EMR (Shu et al. 2016c). Mn is stabilized by bermanite (Mn3 (PO4 )2 (OH)2 ·4H2 O) and pyrochroite (Mn(OH)2 ). Ammonia nitrogen is transformed into struvite (NH4 MgPO4 ·6H2 O) or turned into ammonia and escapes from EMR. A low-cost phosphate-based binder can simultaneously stabilize Mn2+ and NH+ 4 -N in EMR (Shu et al. 2020b). EMR particle size is small, with 83.3% of the particles below 30 µm, and EMR contains a large amount of NH+ 4 in addition to heavy metals (Tian et al. 2019). Based on these characteristics, a

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small amount of MgO and CaHPO4 ·2H2 O are added into the mixed solution of EMR, and the harmful components are transformed into manganese (MnOOH), gypsum (CaSO4 ), MnNH4 PO4 H2 O and struvite (MgNH4 PO4 ·6H2 O) (Lan et al. 2021b).

6.3.2 Waste Control by Waste Treating waste with waste and treating slag with slag is the new direction of curing EMR in recent years. According to the chemical composition and acidity of EMR, other solid wastes are used instead of commercial chemicals to neutralize the acidity of EMR and solidify the harmful substances in EMR. The method of waste control by waste greatly reduces the restoration cost. Treating industrial solid wastes with one or more other industrial solid wastes to achieve the S/S of harmful substances. At present, red mud, blast furnace slag, phosphogypsum, and other harmful elements are used to cure EMR to achieve the purpose of safe stacking. Carbide slag Carbide slag is an alkaline solid waste produced by acetylene gas prepared by the calcium carbide hydrolysis method, with calcium hydroxide as the main component (Zhang et al. 2020a). Carbide slag is an ideal S/S agent compared with traditional CaO or Ca(OH)2 . EMR and Carbide slag synergistic S/S are discussed in science. Carbide slag could stabilize or solidify EMR and reduce its corrosivity (He et al. 2022a). EMR and Carbide slag synergistic S/S mechanism is shown in Fig. 6.5. 2+ NH+ is stabilized as MnFe2 O4 , 4 in EMR escaped in the form of NH3 and Mn Mn2 SiO4 , CaMnSi2 O6 . EMR synergistic S/S with carbide slag is low in cost and environmentally friendly.

Fig. 6.5 Solidification mechanism of EMR by carbide slag (He et al. 2022a; Zhang et al. 2020a)

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Phosphogypsum Phosphogypsum is an industrial waste residue in the wet process of phosphoric acid from phosphate rock. Synergistic S/S of EMR and Phosphogypsum is studied (Shu et al. 2019a). Huang et al. investigated the feasibility of synergistic S/S of heavy metal ions in EMR and PG and determined the most appropriate S/S conditions. The results showed that the output of some heavy metal ions after EMR and phosphogypsum treatment was far lower than the allowable level of GB8978-1996 (Huang et al. 2021). Phosphogypsum-cured EMR has disadvantages, the residual manganese content in cured EMR leaching solution is too high, which cannot meet the discharge standard. Combining phosphate and alkaline agents is studied for the best curing effect. Chen et al. used phosphogypsum leachate as a low-cost phosphate source and MgO/ 2+ in EMR (Chen et al. 2020c). Under the CaO process to stabilize NH+ 4 -N and Mn 2+ optimal curing conditions, the stability efficiencies of NH+ 4 -N and Mn are 93.65 and + 99.99%, respectively. NH4 -N is stable to form NH4 MgPO4 ·6H2 O, and Mn2+ is stable to form Mn5 (PO4 )2 (OH)4 , Mn3 (PO4 )2 ·3H2 O, and Mn(OH)2 . That study contributes to the harmless treatment of EMR and removing pollutants from phosphogypsum leachate. Red mud Red mud can stabilize and solidify soluble manganese ions. The soluble alkali in red mud exists in the form of OH− in an aqueous solution, which can precipitate with manganese ions, and the bound soda in red mud can react with hydrogen ions to improve the pH of EMR leachate. Liu et al. studied the solidification of soluble manganese ions in EMR with red mud. Due to the loss of some alkaline substances in old red mud during storage, the solidification effect of fresh red mud is better than that of old red mud but worse than that of quick lime. The use of red mud instead of part of lime can also achieve a good curing effect and reduce the curing cost. It is expected to consume a large amount of red mud and reduce its impact on the ecological environment (Liu et al. 2021). In short, red mud has the potential to be used as S/S agent for EMR. However, considering that EMR and red mud still have other hazardous substances, it is necessary to comprehensively evaluate whether it will cause secondary risks in large-scale utilization. Synergistic treatment of multi-solid waste EMR and solid waste co-solidification can often be used as road fill material. For instance, EMR, red mud, and carbide slag are used as the main raw materials to prepare road base materials (Zhang et al. 2019b). Wang et al. reported combining ground granulated blast furnace slag with magnesium oxide, cement, and pulverized fuel ash to stabilize heavy metals (Wang et al. 2015). Xue studied ground granulated blast furnace slag, EMR, clinker, and lime (60:18:16:6) to synthesize a new cementbased composite material (Xue et al. 2020). The mobility of heavy metals in all cured samples is less than 10%, and the cured product has mechanical properties and environmental effects.

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Zhang et al. treated EMR with red mud, mixture (alkaline solid waste and neutral solid waste), and cement, and prepared samples with certain strength. The hydration products are mainly aluminum-substituted calcium-silicate-hydrates gel, ettringite, and CaAl2 Si2 O8 ·4H2 O, which promote strength improvement. The leaching test results show that heavy metals can be effectively solidified, which meets the Chinese groundwater standard GB 14848-2017 (Zhang et al. 2019a). In a subsequent experiment, red mud, carbide slag, and blast furnace slag are used as S/S agents. The content of active Al(IV) and Al(VI) increased after S/S treatment. The charge balance effect produces Mn2 SiO4 and Ca4 Mn4 Si8 O24 . Simultaneously, Mn2+ is oxidized to more stable MnO2 to realize the S/S of Mn2+ (Zhang et al. 2020b). Fly ash is one of the largest industrial solid wastes in the world. Its main components are SiO2 , Al2 O3 , Fe2 O3, and CaO (Phoo-ngernkham et al. 2013; Zhu and Yan 2017). EMR, fly ash, and clinker are blended to make fly ash-based cementitious material. Pollutants (Mn, Cd, Cr, As, and Pb) in EMR are adsorbed by hydration products, and NH3 -N in EMR can be effectively removed (Wang et al. 2019, 2021). In addition, barium slag is a solid waste produced in the process of industrial production. One study used the synergistic reaction of EMR, barium slag, limestone, and bauxite to prepare beryllium-calcium sulphoaluminate cement (He et al. 2022c).

6.3.3 Geopolymer System EMR can be used as a source of aluminosilicate to participate in geopolymer reactions (Yang et al. 2022). Compared with traditional silicate cement, geopolymer has many advantages, such as high strength, low energy consumption, good durability, and no pollution (Guo et al. 2014; Ji and Pei 2019; Duxson et al. 2006). Zhao et al. prepared geopolymers from EMR, fly ash, magnesium slag (containing boric acid), sodium silicate, sand, and calcined kaolin as raw materials (Zhao and Han 2013). Zhan et al. realized the co-disposal of municipal solid waste incineration fly ash and EMR by using a geopolymer system (Zhan et al. 2018). Using EMR combined with other industrial wastes to produce geopolymer can effectively solidify heavy metal ions in EMR, which is an effective way to solve environmental pollution problems. Due to the special structure of geopolymer, it has been widely studied and applied in the S/ S of toxic heavy metal ions. The curing mechanism of heavy metal ions by geopolymer mainly includes physical adsorption and chemical binding (Baldermann et al. 2019). Zeolite-like phase, the final product of geopolymer, has a three-dimensional network and a cage structure, which has good physical and chemical sealing effects on toxic heavy metal ions. Some toxic heavy metal ions may be involved in the hydration process of geopolymer. The special hydration products and aluminosilicate network gel structure affect the effective adsorption capacity of geopolymer for toxic heavy metal ions (Zheng et al. 2010).

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6.3.4 Other Materials Humic acid has good adsorption, complexation, chelating, and ion exchange capacity. The surface of chitosan-modified humic acid composites became rough and the pores increased, which increased the adsorption capacity and improved the curing efficiency of Mn. Lignite and sodium humate is less efficient in curing manganese from manganese slag, but the modification could enhance the curing of manganese by humic acid materials (Feng 2020).

6.4 Comparison and Prospects S/S of EMR can significantly reduce environmental hazards of harmful components, facilitate integrated management, and be used as a pretreatment technology for resource utilization. The advantages and disadvantages of the solidification method of EMR are shown in Table 6.2. The synergistic treatment of EMR and other solid wastes is a feasible method to reduce the treatment cost and make full use of other solid wastes. But many synergistic treatments cannot be applied on a large scale and remain in the laboratory stage. The long-term stability of the solidification product also remains to be evaluated. S/ S technology is helpful for direct storage of EMR, but ecological restoration of the surface of the slag yard also needs to be considered. In addition, some solidification methods produce ammonia contamination during solidification. Therefore, the treatment of multiple pollutants and surface ecological restoration should be considered in the follow-up study. Table 6.2 Comparison of solidification methods of EMR S/S of EMR

Advantages

Disadvantages

Chemical agents

An excellent short-term solidification effect

The release of ammonia gas, Secondary pollution, cost high, The amount of slag increased, Still needed to be hazard-free landfill

Waste control by waste

Low cost, synergistic treatment of multiple pollutants, large quantities of consumption of EMR

A lack of long-term stability evaluation of solidified products, there are still pollution risks, risk of introducing new pollutants

Geopolymer system

Synergistic treatment of multiple Secondary pollution, lack of mature pollutants, resource utilization of processes, lack of market demand for EMR products

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6.5 Conclusions Large-scale storage of EMR has become a complicated problem that restricts the development of the electrolytic manganese industry. Improper disposal of EMR will cause severe environmental pollution and affect human health. The harmless treatment of EMR can significantly reduce the environmental hazards of its harmful components, which is conducive to the comprehensive management and resource utilization of EMR. S/S technology of EMR mainly includes chemical curing, industrial solid waste curing, and the generation of geopolymer systems. Chemical reagents for S/S of EMR mainly include alkaline additives, phosphate, sulfite, carbonate, etc. The solid wastes for S/S of EMR mainly include calcium carbide slag, phosphate, red mud, fly ash, and several solid wastes co-curing. The synergistic treatment between industrial waste costs is low, can consume several solid wastes simultaneously, and has good prospects for environmentally sound treatment, but to waste solid waste also has the risk of introducing new pollutants. The treatment and disposal of solid waste after synergistic solidification is also the direction of future research. Acknowledgements This research was funded by the National Key Research and Development Program of China [Grant Numbers 2018YFC18027 and 2018YFC18018], the Guangxi Scientific Research and Technology Development Plan [GuikeAB17129025], the National Natural Science Foundation of China [Grant Numbers 51974279].

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Shu J, Cai L, Zhao J, Feng H, Chen M, Zhang X, Wu H, Yang Y, Liu R (2020b) A low cost of phosphate-based binder for Mn2+ and NH4 + -N simultaneous stabilization in electrolytic manganese residue. Ecotoxicol Environ Saf 205:111317 Tian Y, Shu JC, Chen MJ, Wang JY, Wang Y, Luo ZG, Wang R, Yang FH, Xiu FR, Sun Z (2019) Manganese and ammonia nitrogen recovery from electrolytic manganese residue by electric field enhanced leaching. J Clean Prod 236:8 Wang F, Wang H, Jin F, Al-Tabbaa A (2015) The performance of blended conventional and novel binders in the in-situ stabilisation/solidification of a contaminated site soil. J Hazard Mater 285:46–52 Wang Y, Gao S, Liu X, Tang B, Mukiza E, Zhang N (2019) Preparation of non-sintered permeable bricks using electrolytic manganese residue: environmental and NH3 -N recovery benefits. J Hazard Mater 378:120768 Wang YG, Zhang N, Ren YY, Xu YT, Liu XM (2021) Effect of electrolytic manganese residue in fly ash-based cementitious material: hydration behavior and microstructure. Materials 14(22):7074 Xue F, Wang T, Zhou M, Hou HB (2020) Self-solidification/stabilisation of electrolytic manganese residue: mechanistic insights. Constr Build Mater 255:118971 Yang WQ, An J, Yuan XL, Xia WT (2014) Manganese removal from electrolytic manganese residue using ozone. Adv Mater Res 997:754–757 Yang TY, Xue Y, Liu XM, Zhang ZQ (2022) Solidification/stabilization and separation/extraction treatments of environmental hazardous components in electrolytic manganese residue: a review. Process Saf Environ Prot 157:509–526 Yza B, Xl A, Yx A, Bt A, Yw A (2020) Preparation of road base material by utilizing electrolytic manganese residue based on Si-Al structure: mechanical properties and Mn2+ stabilization/ solidification characterization. J Hazard Mater 390:122188 Zhan X, Wang L, Hu C, Gong J, Xu T, Li J, Yang L, Bai J, Zhong S (2018) Co-disposal of MSWI fly ash and electrolytic manganese residue based on geopolymeric system. Waste Manag 82:62–70 Zhang Y, Liu X, Xu Y, Tang B, Wang Y, Mukiza E (2019a) Preparation and characterization of cement treated road base material utilizing electrolytic manganese residue. J Clean Prod 232:980–992 Zhang Y, Liu X, Xu Y, Tang B, Wang Y, Mukiza E (2019b) Synergic effects of electrolytic manganese residue-red mud-carbide slag on the road base strength and durability properties. Constr Build Mater 220:364–374 Zhang J, Tan H, He X, Yang W, Deng X (2020a) Utilization of carbide slag-granulated blast furnace slag system by wet grinding as low carbon cementitious materials. Constr Build Mater 249:118763 Zhang YL, Liu XM, Xu YT, Tang BW, Wang YG (2020b) Preparation of road base material by utilizing electrolytic manganese residue based on Si-Al structure: mechanical properties and Mn2+ stabilization/solidification characterization. J Hazard Mater 390:122188 Zhao R, Han FL (2013) Preparation of geopolymer using electrolytic manganese residue. Key Eng Mater 591:130–133 Zheng L, Wang W, Shi Y (2010) The effects of alkaline dosage and Si/Al ratio on the immobilization of heavy metals in municipal solid waste incineration fly ash-based geopolymer. Chemosphere 79(6):665–671 Zheng F, Zhu H, Luo T, Wang H, Hou H (2020) Pure water leaching soluble manganese from electrolytic manganese residue: leaching kinetics model analysis and characterization. J Environ Chem Eng 8(4):103916 Zhou C, Wang J, Wang N (2013) Treating electrolytic manganese residue with alkaline additives for stabilizing manganese and removing ammonia. Korean J Chem Eng 30(11):2037–2042 Zhou C, Du B, Wang N, Chen Z (2014) Preparation and strength property of autoclaved bricks from electrolytic manganese residue. J Clean Prod 84:707–714 Zhu J, Yan H (2017) Microstructure and properties of mullite-based porous ceramics produced from coal fly ash with added Al2 O3 . Int J Miner Metall Mater 24(3):309–315

Chapter 7

Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform Yongfei Bai, Zhiqiang Wu, Jie Liu, Wei Jiang, Ke Zhong, Wenxing Han, and Zhi Chen

Abstract At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. Keywords March X · Memory · PBIST · Nuclear power · Safety level DCS

Y. Bai · J. Liu School of Computer Science, University of South China, Hengyang 421200, China e-mail: [email protected] Z. Wu (B) · W. Jiang · K. Zhong · W. Han · Z. Chen Nuclear Power Institute of China, Chengdu 610000, China e-mail: [email protected] Y. Bai · J. Liu Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan, Hengyang, China CNNC Key Laboratory on High Trusted Computing, Hengyang, Hunan, China © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_7

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7.1 Introduction With the rapid development of the nuclear power industry, more and more people in the industry pay attention to the safety of the memory in the nuclear power safety level DCS system. In the nuclear safety level DCS system, PBIST (Programmable Built In Self Test) (Roberto 2009; An et al. 2019) module is used for memory fault test. PBIST can test the memory when the nuclear safety level DCS system is started and during operation. This paper tests the memory during the operation of the nuclear safety level DCS system. The PBIST module is a destructive test. After the test, the contents of the memory will be overwritten. It is necessary to save the contents before the test. After the test, the saved data will be written to the memory. The nuclear safety level DCS system has three requirements for memory testing: (1) The test algorithm must be controlled within a time slice (500 ms); (2) When caching the data in the memory, ensure the safety of the additional memory; (3) 90% coverage of common failures in nuclear safety level DCS system; The main module in PBIST is memory test algorithm. Different test algorithms have different fault coverage and test time for memory (Lv 2020). Memory test algorithm is to test the memory function, and to judge whether the memory fails by comparing the input and output of the memory. A good test algorithm is to cover as many fault types as possible by using as little test time and test data as possible. Common test algorithms include March algorithm (Chen and Ma 2020), ping pong algorithm, chessboard algorithm and MACSN algorithm. The common algorithm in the industry is March algorithm. Its algorithm idea is to initialize each storage element in ascending or descending order of address, read and compare, then write the opposite logical value to the unit, and then read and compare. In the third process, it needs to cooperate with the increasing or decreasing address. The traditional March X (Liu and Özsu 2018) algorithm test is aimed at the bit of memory. There are only two kinds of test data, ‘0’ and ‘1’, which are proved in the literature (Ge et al. 2022; Wang 2019; Rajesh Kumar and Babulu 2018). However, the test of memory in the nuclear power safety level DCS system is aimed at byte test. Therefore, the traditional March X algorithm cannot meet the current demand for byte test. It is necessary to optimize the March X algorithm bit by bit test to byte test to expand the background of test data, the data ‘0’ and ‘1’ in bytes are combined to get more test data. Among the common faults of nuclear power safety level DCS system, the March X algorithm cannot cover the interference coupling fault and write break coupling fault in the open circuit fault and coupling fault. This is explained in literature (Acharya et al. 2022). On the basis of March X algorithm, wait for N clock cycles to detect data storage faults, add two consecutive reads and two consecutive writes to detect open circuit faults and interference coupling faults, and add read operations to detect write damage coupling faults at the beginning and end of each test step, reaching 90% of nuclear safety level DCS system faults. When testing the memory in operation, the data in the memory is cached in the additional memory

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in advance. This method cannot guarantee the security of the additional memory, which is described in literature (Mengfei and Zengqi 2005). This paper innovatively proposes to cache data in registers, which can not only ensure that the data is cached, but also make the cache address more secure.

7.2 Analysis of PBIST Module and Memory Structure Analyze the organization structure and working principle of PBIST module in nuclear safety level DCS system, analyze the fault type of test target memory and the test method for each fault, and analyze the indicators of nuclear safety level DCS system for memory test algorithm.

7.2.1 PBIST Module System Analysis PBIST structure in nuclear safety level DCS system consists of three parts, including built-in self-test ROM, built-in self-test controller, and data path. Its structure diagram is shown in Fig. 7.1. The PBIST ROM module stores the memory test algorithm and memory configuration information. Since the nuclear safety level DCS system mainly tests SRAM memory, memory testing algorithms only include SRAM testing algorithms. Memory configuration information includes memory RAM group and selection of returned data. PBIST controller will generate comparator or compressor according to memory type. DCS system only tests RAM, so only comparator is generated. The data path module connects the target memory SRAM. The test algorithm for memory in the nuclear power safety level DCS system is added to the task linked list as a task for CPU scheduling. After the CPU sends the instruction, the CPU control interface in Fig. 7.1 transmits the signal to the PBIST

Fig. 7.1 PBIST structure

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Fig. 7.2 Memory structure

controller, which selects the test algorithm and RAM group in ROM to test the selected memory.

7.2.2 Memory Analysis of Nuclear Safety DCS System The memory in the nuclear safety level DCS system is composed of storage unit, address decoding circuit and read–write logic circuit (Jidin et al. 2022). The memory adopts row and column array mode, and its structure is shown in Fig. 7.2. The address resolver parses the address of the address bus, and the row column decoder selects the data in the row column intersection. The data stored in the memory are the input and output signals and important configuration information in the nuclear power safety level system.

7.2.3 Memory Failure Analysis 84% of memory faults are fixed faults, conversion faults, open circuit faults, data retention faults and coupling faults. The coupling faults are divided into 16% in

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address decoder faults, sensitive faults, etc. (Wang 2019). The following describes the fault types and how to solve each fault. Fixed fault is that the value in the storage unit is always 1 or 0, which cannot be modified by the outside world. The causes of this type of fault include open circuit of data line, wrong connection of read/write line, wrong connection of chip selection line, and error of the storage unit itself. To test the fault, write 0 or 1 to the storage unit, and then read to check whether it is correct. Conversion fault refers to that the value in the storage unit cannot be converted from 0 to 1 or from 1 to 0. To test this fault, it needs to initialize it to 0 (1), write 1 (0) to the memory, and read 1 (0). The open circuit fault (Yang et al. 2021) is caused by the failure of MOS tube, which makes the memory cell unable to read and write. The detection method is to read the data of the memory cell for two consecutive times. Data retention failure means that the value written to the storage unit cannot be permanently saved. The test method is to read the value after a certain time interval after the value is written to the memory, and compare whether the written value and the read value are the same. Coupling fault (Yang et al. 2021) refers to that the writing of memory unit will affect the value of other units and change the value of other units. This type of fault includes state coupling, read coupling and write coupling, which are the most common, complex and most influential. This type of fault is caused by short circuit or crosstalk between data lines. The address decoding failure is the failure of the address parser, which has four manifestations: no address can access the storage unit; An address value has no corresponding storage unit; A storage unit is accessed by multiple address values; One address value can access multiple memory cells.

7.2.4 Test Algorithm Indicators In the nuclear power safety level DCS system, the CPU scheduling adopts the time slice rotation method, and its time slice is 500 ms, so the memory test algorithm time must be controlled within 500 ms. GALPAT algorithm is used to test the memory. The time complexity of the test algorithm is O (N2 ). This algorithm cannot meet the requirements of existing modules for the test algorithm. In addition to certain time requirements, the fault coverage of memory testing algorithm is required to reach more than 90% coverage of common faults of memory in nuclear safety level DCS system.

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Table 7.1 March X algorithm steps March X

M0

M1

M2

M3

↑(w0)

↑(r0, w1)

↓(r1, w0)

↓(r0)

7.3 March X Algorithm Analysis and Optimization 7.3.1 Optimization of March X Algorithm Although many scholars have proposed a variety of test algorithms, no matter which test algorithm is used, it can not cover all faults (Qin and Wang 2019). The March X algorithm is the most widely used standard memory test algorithm in the industry, which is composed of a series of March elements. The test algorithm is shown in Table 7.1. In Table 7.1, ‘↑’ means to access the memory in ascending order according to the memory address, ‘↓’ means to access the memory in descending order according to the memory address, read (r) and write (w) means to read and write the memory unit, where ‘r 0’ and ‘r1’ mean to read the value of the address selected by the memory and judge whether the read value is ‘0’ or ‘1’, ‘w0’ and ‘w1’ mean to write ‘0’ and ‘1’ respectively to the address selected by the memory, M i (i = 0, 1, 2, 3) indicates the ‘i’ test stage for the storage unit of the memory. March X algorithm cannot detect open circuit fault, write destroy coupling fault and interference coupling fault (Yang et al. 2021). The open circuit fault detection method is to read the storage unit twice, add r1 operation to each M1, and add r0 operation to M2. Write interference coupling fault requires adding w0 and r0 in M2, and adding a phase r0, w1, w1 and r1 between M2 and M3. Interference coupling fault requires continuous read and write operations in ascending and descending order. Add r0 and w0 stages between M0 and M1, and add w1 operation in M1 to obtain a new test algorithm March X+: ↑ w0 ↑ (r0, w0) ↑ (r0, w1, w1, r1) ↓ (r1, w0, w0, r0) ↓ (r0, w1, w1, r1) ↓ r0

7.3.2 Optimization of Test Data The operation unit of the standard March X algorithm is a bit, and each bit has only two states: “0” and “1”. If there are only two states of “00” and “FF” for bytes, each address lacks “10” and “01” states, so it is necessary to optimize on the basis of the March X algorithm, and give the formula for calculating the number of data: x = log2B + 1

(7.1)

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Express

Forward data

Express

Reverse data

WD0

00000000

W D0

11111111

WD1

01010101

W D1

10101010

WD2

00110011

W D2

11001100

WD3

00001111

W D3

11110000

where, x is the number of test data, and B is the memory length. In this paper, it is a byte, equal to 8. X is calculated to be 4, and the expression formula of the data background is as follows:

Di =

⎧ {0, 0, 0, . . . , 0} ⎪ ⎪ ⎪ ⎪ {0, 1, 0, . . . , 0} ⎪ ⎪ ⎪ ⎨ C S P (D1 ) ⎪ ⎪ ⎪ ⎪ ⎪ Dq(1) ⊕ Dq(2) ⊕ · · · ⊕ Dq(r ) ⎪ ⎪ ⎩

if i = 0 if i = 1   if i = 2 P 1 ≤ P ≤ log2B r  if i = 2 P , i = q( j ), r < b

(7.2)

j=1   q( j ) = 2w , 0 ≤ w ≤ log2B

The “0” in the March algorithm can be extended to “00000000”, “01010101”, “00110011”, “0000111”, positive data, “11111111”, “10101010”, “11001100”, “11110000”, reverse data, and byte test data from bit data, as shown in Table 7.2.

7.3.3 March X+ Algorithm Steps Suppose the memory system stores in bytes, the memory size is N, the memory address is represented by A0,… AN-1, corresponding to the cell content [A i] (i = 0, 1, 2, n-1), and the available registers for testing are AX; Algorithm parameters: memory address and test block size. The specific test steps are as follows: Step 1: A0 → AX; Step 2: A0 ← 0; Step 3: judge whether A0 is equal to 0, and continue correctly, otherwise memory error report; Step 4: Repeat steps 2–3 according to the steps of the March X+ algorithm; Step 5: AX → A0; Step 6: Repeat steps 1–6 according to the tested memory size 0–N;

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7.4 Experiment In order to verify the feasibility of the algorithm designed in this paper, the algorithm test time and functional verification are carried out in this chapter under a nuclear power safety level DCS system platform.

7.4.1 Experimental Environment The experimental environment is a computer with ARM as the core, the processor is 1.5 GHz, the memory is 2G, the hard disk is 500G, the operating system is WindowXPSP3, the development tool is Code Composer Studio IDE V6.0.100040, and the compiler version is TIARM Compiler V6. ARM software runs on Hercules RM48L952ZWT.

7.4.2 Experimental Analysis When testing the memory unit, first save a byte of test unit data to the register, then test the test algorithm for the test unit, and finally write the fingers saved in the register to the original memory location. This paper compares the memory test time effectiveness of SRAM memory using the March C+ algorithm, March X algorithm, March 13n algorithm and March X+ algorithm respectively. The memory sizes are tested using 64, 128, 256 K and 1 M respectively. The test results are shown in Fig. 7.3. Perform functional verification on the algorithms of March X+, March X, and March C+ to verify the effectiveness of the three memory test algorithms. Perform fault injection for SRAM through Verilog language, and then detect SRAM through the three algorithms. When injecting faults into the memory, four fixed faults, four conversion faults, four address decoding faults, four open circuit faults, eight interference coupling faults, eight pseudo read destruction faults, four write destruction coupling faults and two data retention faults are injected. The test results are shown in Table 7.3.

7.4.3 Result Analysis This experiment aims at the SRAM used in the nuclear power safety level DCS system as the test target. When testing the memory, the original data in the memory is saved to the register, without using additional memory space, which ensures the safety of the data in the test unit, and also avoids the additional memory overhead.

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Fig. 7.3 Time comparison of different memory test algorithms

Table 7.3 Fault coverage test results

Fault type

Number of fault injections

Fault detection algorithm March X

March X+

Fixed fault

4

4

3

Conversion failure

4

1

4

Address decoding failure

4

2

3

Open circuit fault

4

4

4

Interference coupling fault

8

0

6

Pseudo read corruption fault

8

0

7

Data hold failure

2

1

2

By comparing the test performance of March X algorithm and March X+ algorithm, March X+ can cover 94% of the common faults in the security level DCS system, and can cover all common faults. For the test time of memory, within 500 ms of DCS security level DCS system requirements, the use of March X+ algorithm can meet the requirements for events, and 256 K memory is detected in each event slice, which is an algorithm that can detect the maximum memory space in a time slice.

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7.5 Conclusion Based on the analysis of the March X algorithm and the use of SRAM in the security level DCS system, this paper proposes a storage area testing algorithm for March X+. This method first saves the data of the storage unit to the register when detecting the memory, which speeds up the data storage time and also ensures that no additional memory is needed to reduce the memory overhead. In the SRAM test experiment, compared with the March X algorithm, the coupling interference fault and pseudo read destruction fault are added, and the fault coverage is increased by some percent. The subsequent work improves the complexity of the memory testing algorithm to use less time to detect a higher test rate. Subject Group: Open Fund of State Key Laboratory (KFKT-24-2021006).

References Acharya GP, Rani MA, Kumar GG et al (2022) Indones J Electr Eng Comput Sci 6(1):96–104 An B, Qiao S, Liu Q (2019) Application of BIST circuit in embedded nonvolatile memory reliability test. Electron Des Eng 27(11):33–37+42 Chen J, Ma Y, Li S, Liu F (2020) Data compression design for dynamic fault diagnosis of embedded memory. J Electron Meas Instrum 34(07):203–209 Ge Y, Wu Q, Zhao Y (eds) (2022) An optimized FPGA embedded BRAM self-test March C+ algorithm. Foreign Electron Meas Technol 41(04):1–7 Jidin AZ, Hussin R, Fook LW et al (2022) Automatic generation of user-defined test algorithm description file for memory BIST implementation. Int J Reconfigurable Embed Syst (IJRES) 11(2):103–114 Liu L, Özsu T (2018) Encyclopedia of database systems. Springer, New York Kai Lv (2020) Research and implementation of low voltage memory BIST test technology. Nanjing Univ Posts Telecommun Mengfei Y, Zengqi S, Jian G (2005) A new method for memory fault testing in real-time systems. China Space Sci Technol (06):26–29 Qin P, Wang J, Zhu F, Jiao G (2019) SOC embedded memory built in self repair method. Comput Eng Sci 41(10):1749–1754 Rajesh Kumar G, Babulu K (2018) Low power March memory test algorithm for static random access memories. Int J Eng Trans B 31(2):292–298 Roberto B (2009) PBIST and its projects: with focus on port security. In: NATO science for peace and security series C: environmental security, pp 17–21 Wang C (2019) Algorithm research and circuit implementation of low voltage SRAM built in self test. Nanjing Univ Posts Telecommun Wu Y, Wang Y, Yu X et al (2021) On orbit self checking algorithm of static random access memory. J Beijing Univ Aeronaut Astronaut 47(06):1233–1240

Chapter 8

Supercritical CO2 Enhanced Shale Gas Production Technology: Progress and Prospect Yicheng Zhang and Mingyao Song

Abstract In order to achieve the strategic goal of carbon peaking and carbon neutralization in China, the technology of enhanced exploitation of shale gas by supercritical CO2 (scCO2 ) provides a new way for the green and efficient development of unconventional oil and gas resources in China. Because of its diffusivity, low viscosity and solubility, supercritical CO2 can rapidly penetrate into shale micropores. Supercritical CO2 not only competes with hydrocarbon components for adsorption and displacement, but also can be used for fracturing construction, thereby improving the recovery rate of shale resources and demonstrating enormous application potential. This paper systematically summarizes the mechanism, development mode, advantages and disadvantages of scCO2 exploitation of shale gas, aiming to provide some reference for the current development and utilization of shale gas resources. Keywords Supercritical CO2 · Shale · Recovery ratio · Development mode

8.1 Introduction In the world, the resource reserves of shale reservoirs are more abundant than those of deep salt water reservoirs, gas reservoirs, depleted oil and gas reservoirs, and they are of great exploitation value. Shale gas reservoirs are mainly distributed in the southwest of China, with rich reserves. However, due to poor physical conditions of shale gas, porosity and permeability are very low (Tan et al. 2019; Chen et al. 2019a). Because its storage mode is usually adsorption and dissociation, and the adsorption capacity of shale reservoir for CO2 is much higher than that for methane. Therefore, CO2 can be used to desorb the shale gas in the adsorption state, and then the piston method can be used to displace the free shale gas, so as to improve the recovery rate. In addition, due to the low porosity and permeability of shale Y. Zhang (B) · M. Song The Erlian Filiale of PetroChina Huabei Oilfield Company, Xilinhot 026000, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_8

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reservoir, fracturing treatment is usually required in the actual development process (Mojid et al. 2021; Lyu et al. 2021; Zhang and Ranjith 2018). Now, the vast majority of shale gas reservoirs need to be fractured before they can be exploited. Conventional hydraulic fracturing is the simplest and more mature, which is usually used as an early stimulation measure. However, due to its large water demand, it will cause waste of water resources. The subsequent development of LPG fracturing technology has effectively solved the problem of water use, and the fracturing method has good compatibility with the reservoir, but the cost is high and the safety is low. The use of CO2 can not only avoid the waste of water resources, but also store a large amount of CO2 in the stratum, reducing the carbon emission intensity from the source, so as to achieve the purpose of energy conservation, emission reduction and environmental protection (Li et al. 2018). This paper systematically summarizes the mechanism, development mode, advantages and disadvantages of scCO2 exploitation of shale gas, aiming to provide some reference for the current development and utilization of shale gas resources.

8.2 Mechanism of Exploitation of Shale Gas by scCO2 8.2.1 Competitive Adsorption Mechanism of CO2 /CH4 in Shale In the process of using scCO2 to develop shale reservoir resources, the injected CO2 will compete with CH4 stored in shale for adsorption. The adsorption of CO2 and CH4 is mainly affected by the physical properties, microscopic characteristics and action environment of the reservoir, and the competing adsorption will also be affected by temperature and pressure. In the supercritical state, the adsorption capacity of CO2 is more than 4 times that of CH4 (Liu et al. 2020; Duan et al. 2016).

8.2.2 Mechanism of Displacement and Displacement of Shale Gas by scCO2 In gas drive, there are two kinds of reactions when injected air comes into contact wi As the transfer rate and flow capacity of CO2 are far less than CH4 , the interfacial tension is close to zero in the supercritical state, and the diffusivity is very strong, which makes CO2 show greater seepage resistance and displacement efficiency in the macro large pores in the shale reservoir. In addition, high-density scCO2 has a certain resistance to the dissolution of CH4 , which has obvious piston displacement characteristics for CH4 dominated shale gas under the high pressure reservoir environment (Huang et al. 2018a).

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8.2.3 scCO2 Storage Mechanism CO2 can be captured in shale reservoir by physical absorption and chemical absorption. The physical absorption method uses organic solvent with high solubility, good selectivity and stable performance to capture CO2 by dissolving CO2 under pressure. The principle of chemical absorption is to use alkaline solution to dissolve and separate CO2 , and then separate it by desorption and decomposition. The captured CO2 is finally stored in the deep shale gas formation in the form of adsorption, supercritical, dissolved and mineral. The burial mechanism of CO2 shale reservoir mainly includes: (1) CO2 in the formation is competitively adsorbed with CH4 , and is captured on the surface of organic matter in an adsorbed state. (2) After replacement and displacement of CH4 , the residual CO2 is stored in the deep reservoir in a supercritical state. (3) Under a certain temperature, pressure and salinity, part of carbon dioxide will be captured in dissolved form and dissolved in formation water. (4) The supercritical carbon dioxide in the sealed structural trap reacts with the formation water to generate CO2− 3 , which forms stable carbonate minerals through interaction with Ca2+ , Fe3+ and Mg2+ in the shale reservoir, and is buried as a mineral.

8.3 Key Technology 8.3.1 scCO2 Jet Technology scCO2 jet rock breaking is to transport liquid CO2 to the depth of the formation to reach a supercritical state, and use CO2 to break rock with low threshold pressure. Compared with N2 jet, CO2 jet has stronger impact shear force and denudation force, lower nozzle pressure energy loss, greater thermal fracturing effect, wider rock breaking range and higher efficiency. Huang and Hu (2018) studied the destruction of high-pressure scCO2 jet on shale, and found that the maximum fracture depth of shale core plug depends on the combined effect of jet pressure, jet temperature and target distance. He et al. (2016) found that the rock material broken by supercritical CO2 jet is mainly brittle tensile failure mechanism, and there is shear failure at a specific location of perforation. Du et al. (2012) studied the influence of nozzle diameter, distance, jet pressure, rock compressive strength and jet temperature change on the rock breaking performance of scCO2 jet. It is found that the rock breaking performance of scCO2 jet is obviously better than that of high-pressure water jet and subcritical liquid CO2 jet. The rock breaking performance of scCO2 jet increases with the increase of nozzle diameter or distance, monotonously improves with the increase of jet pressure, and monotonically deteriorates with the increase of rock compressive strength. Cai et al. (2020)

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studied the influence of shale bedding on the fracture morphology and perforation damage of scCO2 jet fracturing. It was found that when the perforation direction was perpendicular to the bedding plane, a small strain could be obtained. When the jet pressure increased from 25 to 50 MPa, the mass loss and CO2 absorption of shale samples increased by 350 and 300%, respectively. Yang et al. (2020) found that the rotary round jet has the advantages of both rotary jet and round jet, and the use of the comprehensive technology of rotary round scCO2 (SR-sc-CO2 ) jet is expected to greatly improve the rock crushing efficiency. Through simulation, it is found that SR-sc-CO2 jet can maintain relatively large axial and tangential velocities compared with the single method of rotary jet or circular jet. Huang et al. (2017) studied the influence of nozzle structure on rock erosion characteristics. It is found that Laval-1 nozzle can enhance the erosion capacity of rock. Compared with convergent nozzle, Laval-1 nozzle can maximize the erosion amount by 10, 21.2 and 30.3% at the inlet pressure of 30, 40 and 50 MPa; Laval-2 nozzle increased the erosion by 32.5, 49.2 and 60% to the maximum. In addition, the jet of Laval-2 nozzle with smooth internal contour has greater erosion capacity than that of Laval-1 nozzle. Huang et al. (2018b) studied the impact characteristics of self-excited oscillating pulse scCO2 jet (SOPSJ) generated by jet driven Helmholtz oscillating nozzle. Through multiple impact pressure tests, it was found that SOPSJ had a larger peak impact pressure than continuous jet. It is precisely because of the strong impact and pressurization of pulse scCO2 jet fracturing that the initiation position and fracture network become more complex. Cai et al. (2019) proposed and comprehensively studied a new method of multiple pulse scCO2 jet fracturing. In this method, the CO2 absorption and fracture volume are increased by 77.72 and 2283% respectively compared with single pulse scCO2 jet fracturing. Compared with water jet, scCO2 produces more complex fracture morphology, larger fracture volume and CO2 absorption.

8.3.2 scCO2 Fracturing Technology scCO2 has strong permeability, low friction and remarkable pressurization effect. When shale gas reservoir is artificially modified with liquid CO2 , due to its large diffusion coefficient, scCO2 can quickly enter into the reservoir microfractures. At the same time, CO2 is easy to interact with the salt water in the formation, making the formation water slightly acidic, which can inhibit the expansion of clay minerals and ease the blockage of pores, and can effectively prevent the occurrence of water lock. Under high pressure, scCO2 will reduce the formation fracture pressure, promote the reservoir fractures to extend around, and greatly improve the reservoir permeability. He et al. (2019) used fresh water and scCO2 to conduct hydraulic fracturing experiments on shale cores under uniaxial stress. They found that compared with hydraulic fracturing, scCO2 fracturing caused small slip on the surface of microcracks due to the effect of deviating stress, which increased the propagation width of microcracks, resulting in more complex fractures and microcracks. He et al. (2020) conducted an experimental study on the mechanical response and fracture propagation of shale

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fractures under different stress states and injection rates using water and scCO2 . It is found that the fracture pressure of scCO2 fracturing is smaller and more timeconsuming than that of hydraulic fracturing. Its fracture pressure is affected not only by its own anisotropy, but also by external factors such as injection rate and deviatoric stress. He et al. (2018) found that the fracturing pressure of shale core is affected not only by the bedding direction but also by the fracturing fluid in the study of scCO2 crack propagation mode, because the anisotropic structure and fracturing fluid will affect the path of hydraulic fracturing. In order to study shale scCO2 fracturing considering anisotropic effect, Zhang et al. (2019a) conducted hydraulic fracturing experiments under different injection rates and stress states. It is found that the fracture pressure generally decreases with the increase of bedding angle. Higher injection rate can lead to higher fracture pressure, while higher deviatoric stress can lead to lower fracture pressure. Chen et al. (2019b) studied the effect of perforation direction on fracture initiation and propagation caused by scCO2 fracturing, and compared it with hydraulic fracturing. It is found that the acoustic emission energy release rate of scCO2 fracturing is higher than that of hydraulic fracturing, and the induced fractures are more complex. Hydraulic fracturing mainly leads to double wing fractures, while scCO2 fracturing leads to double wing and triple wing fractures. Zou et al. (2018) found that calcite, dolomite and other minerals will be dissolved to varying degrees after being soaked in CO2 saturated brine during supercritical CO2 fracturing of high-temperature and high-pressure shale reservoirs. Mineral composition, reaction time, temperature, pressure, porosity and permeability have obvious effects on rock reactivity. Strong reaction may occur in carbonate rich shale with relatively high porosity and permeability, thus affecting the growth process of supercritical CO2 induced fractures.

8.3.3 Technology of Replacing Shale Gas with CO2 The competitive adsorption of CO2 and CH4 is the main principle of replacement of CH4 by CO2 . Shale gas reservoirs are rich in a variety of organic matter. These porous and disordered organic matter have complex microstructure, extremely uneven pore surface and strong adsorption capacity. After fracturing the formation with scCO2 as the base fluid, due to the greater selectivity of organic matter to CO2 , CH4 adsorbed on the pore surface can be replaced. At the same time, more complex fracture network structure will be formed after rock breaking and fracturing of shale reservoir, leading to more shale gas being replaced. Under the same pressure gradient, the mobility of CO2 is much lower than that of CH4 which is easy to drive CH4 out of shale gas organic matter. Xie et al. (2019) simulated the competitive adsorption behavior of CO2 and CH4 under in-situ conditions using high-pressure multi-component adsorption experiments, and discussed the effects of binary gas components, shale properties and pore structure on CO2 adsorption affinity. The results show that the selectivity coefficient of CO2 to CH4 decreases with the increase of CO2 mole fraction, but increases

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with the increase of total organic carbon (TOC) and clay content. Huo et al. (2017) found in discussing the adsorption behavior of shale and the displacement behavior of CO2 injection on shale adsorbing CH4 . The amount of recovered CH4 and stored CO2 increases with the increase of CO2 injection pressure. For shale with small micropore parameters or low adsorption performance, CO2 injection can effectively improve the recovery of CH4 . It is pointed out that the CH4 recovery rate and CO2 storage rate of the target shale reservoir can be effectively improved by injecting CO2 after the decomposition and absorption of CH4 . Sun et al. (2021) used GCGM (Grant Canonical Monte Carlo) and MD (molecular dynamics) to simulate and study the molecular mechanism of recovering shale gas from CO2 in kerogen slit nanostructures, and compared it with kerogen matrix. It was found that pressure had a positive effect on the adsorption capacity of CH4 and CO2 , while temperature had an opposite effect. In kerogen slit nanopore, the adsorption state of CH4 is not as stable as that in kerogen matrix, which leads to higher diffusion of CH4 . Hu et al. (2019) studied the competitive adsorption of CO2 /CH4 binary mixture in various clay minerals (montmorillonite, illite and kaolinite) using giant canonical Monte Carlo (GCMC) simulation. It is found that the adsorption capacity of clay minerals for CO2 is in the order of montmorillonite > illite > kaolinite. CO2 molecules are more easily adsorbed on the surface of montmorillonite and illite nanopores with cation exchange than on the surface of kaolinite without cation exchange. Zhang et al. (2019b) discussed the replacement of CH4 by CO2 in micropores, studied the recovery of CH4 after replacement and the adsorption of residual CH4 and carbon dioxide. The results show that the contribution of micropores to CH4 recovery depends on pore size, CO2 ratio, temperature and pressure. Pores below 0.61 nm have no contribution to CH4 recovery, but 0.55–0.60 nm pore diameter is conducive to CO2 storage. The 0.65–0.70 nm pore shows the highest CH4 storage capacity and high selectivity for CO2 . Liu et al. (2019) characterized the competitive adsorption of CO2 and CH4 in shale with low field nuclear magnetic resonance. When CO2 and CH4 have the same initial partial pressure in shale, shale CO2 adsorption has a significant competitive advantage over CH4 . By increasing the pressure ratio of CO2 /CH4 to over 1:1, the adsorption capacity of shale for CO2 can be effectively increased. The maximum solid storage capacity of CO2 adsorbed during the competition period of CO2 –CH4 is about 3.87 cm3 /g (YDN-1) to 5.13 cm3 /g. Zhou et al. (2020) studied the adsorption behavior and limiting effect of CO2 /CH4 in real kerogen nanopores using grand canonical Monte Carlo method. The effects of temperature, pressure and pore size on the competitive adsorption behavior and adsorption mechanism of CO2 /CH4 were discussed. The results showed that the adsorption capacity of CH4 in kerogen matrix without kerogen nanopores was lower than that of CO2 . Sun et al. (2020) analyzed the effects of pressure, temperature, moisture and gas type on the isothermal adsorption and desorption of shale gas, and compared the adsorption and desorption capacities of CO2 and CH4 in shale. It was found that, within a certain range, the adsorption capacity of CH4 increased with the increase of pressure, the decrease of temperature and humidity. The order of adsorption capacity is CO2 > CH4 > N2 , and the order of desorption capacity is CH4 > CO2 > N2 . In a certain range, the CH4 recovery rate

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increases with the increase of CO2 injection pressure, injection rate and injection volume.

8.4 Technical Advantages and Limitations The integration technology of scCO2 enhanced shale gas exploitation and underground storage has become a new direction of shale gas exploitation and development in the future due to its characteristics of energy conservation, emission reduction and efficient shale gas exploitation. At the same time, this technology also has certain advantages and disadvantages.

8.4.1 Technical Advantages Due to the high displacement and displacement capacity of scCO2 , when used as a displacement phase, it can effectively improve the single well production and recovery factor of gas wells. At the same time, scCO2 has high density similar to liquid, strong permeability, low friction, and remarkable pressurization effect. When used in fracturing operations, it can achieve efficient fracturing of shale reservoirs and improve the conductivity of multiple fractures. In addition, scCO2 fluid has low reservoir damage and pollution, avoiding waste of water resources, and has played a certain role in reducing costs and increasing efficiency and environmental protection.

8.4.2 Limitations In terms of limitations, due to the corrosivity of CO2 in the reservoir environment, long-term underground storage will corrode metal pipes, affecting normal production. When pressurized to scCO2 state, on the one hand, it will increase the input cost, on the other hand, it will increase the risk of CO2 leakage and casualties. In addition, when it is used for fracturing operations, due to the weak sand carrying capacity of CO2 fracturing fluid, the fractures at low proppant concentration are easy to be closed, so further improvement is required.

8.5 Conclusions The integration technology of scCO2 enhanced shale gas exploitation and underground storage has become a new direction of shale gas exploitation and development in the future due to its characteristics of energy conservation, emission reduction

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and efficient shale gas exploitation. scCO2 can not only effectively displace shale gas through competitive adsorption mechanism, but also replace water jet for rock breaking and fracturing of the reservoir. At the same time, it can also store residual CO2 in place. However, due to the limitations of imperfect technical facilities, vague reservoir characterization and many engineering risks, the feasibility and process mechanism of scCO2 technology still have many shortcomings. How to seek the technology of large-scale, efficient, scientific and integrated exploitation of shale gas is still in the stage of indoor experimental research and exploration. It is imperative to open up a new way of green, environment-friendly and efficient shale gas development.

References Cai C, Kang Y, Wang X et al (2019) Experimental study on shale fracturing enhancement by using multi-times pulse supercritical carbon dioxide (SC-CO2 ) jet. J Pet Sci Eng 178(7):948–963 Cai C, Kang Y, Yang Y et al (2020) The effect of shale bedding on supercritical CO2 jet fracturing: a experimental study. J Pet Sci Eng 195(12):1–13 Chen T, Feng XT, Cui G et al (2019a) Experimental study of permeability change of organic-rich gas shales under high effective stress. J Nat Gas Sci Eng 64(4):1–14 Chen H, Hu Y, Kang Y et al (2019b) Fracture initiation and propagation under different perforation orientation angles in supercritical CO2 fracturing. J Pet Sci Eng 183(12):1–14 Du Y, Wang R, Ni H et al (2012) Determination of rock-breaking performance of high-pressure supercritical carbon dioxide jet. J Hydrodyn Ser B 24(4):554–560 Duan S, Gu M, Du X et al (2016) Adsorption equilibrium of CO2 and CH4 and their mixture on Sichuan Basin shale. Energy Fuels 30(3):2248–2256 He Z, Li G, Tian S et al (2016) SEM analysis on rock failure mechanism by supercritical CO2 jet impingement. J Pet Sci Eng 146(10):111–120 He J, Afolagboye LO, Lin C et al (2018) An experimental investigation of hydraulic fracturing in shale considering anisotropy and using freshwater and supercritical CO2 . Energies 11(3):557– 571 He J, Zhang Y, Li X et al (2019) Experimental investigation on the fractures induced by hydraulic fracturing using freshwater and supercritical CO2 in shale under uniaxial stress. Rock Mech Rock Eng 52(10):3585–3596 He J, Zhang Y, Yin C et al (2020) Hydraulic fracturing behavior in shale with water and supercritical CO2 under triaxial compression. Geofluids 4:1–12 Hu X, Deng H, Lu C et al (2019) Characterization of CO2 /CH4 competitive adsorption in various clay minerals in relation to shale gas recovery from molecular simulation. Energy Fuels 33(9):8202– 8214 Huang F, Hu B (2018) Macro/microbehavior of shale rock under the dynamic impingement of a high-pressure supercritical carbon dioxide jet. RSC Adv 8(66):38065–38074 Huang M, Kang Y, Wang X et al (2017) Effects of nozzle configuration on rock erosion under a supercritical carbon dioxide jet at various pressures and temperatures. Appl Sci 7(6):606–620 Huang L, Ning Z, Wang Q et al (2018a) Effect of organic type and moisture on CO2 /CH4 competitive adsorption in kerogen with implications for CO2 sequestration and enhanced CH4 recovery. Appl Energy 210(1):28–43 Huang M, Kang Y, Wang X et al (2018b) Experimental investigation on the impingement characteristics of a self-excited oscillation pulsed supercritical carbon dioxide jet. Exp Therm Fluid Sci 94(6):304–315

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Huo P, Zhang D, Yang Z et al (2017) CO2 geological sequestration: displacement behavior of shale gas methane by carbon dioxide injection. Int J Greenhouse Gas Control 66(11):48–59 Li YF, Sun W, Liu XW et al (2018) Study of the relationship between fractures and highly productive shale gas zones, Longmaxi Formation, Jiaoshiba area in eastern Sichuan. Pet Sci 15(3):498–509 Liu J, Xie L, Elsworth D et al (2019) CO2 /CH4 competitive adsorption in shale: implications for enhancement in gas production and reduction in carbon emissions. Environ Sci Technol 53(15):9328–9336 Liu P, Wang X, Li X et al (2020) Competitive adsorption characteristics of CH4 /C2 H6 gas mixtures on model substances, coal and shale. Fuel 279(11):1–12 Lyu Q, Tan J, Li L et al (2021) The role of supercritical carbon dioxide for recovery of shale gas and sequestration in gas shale reservoirs. Energy Environ Sci 14(8):4203–4227 Mojid MR, Negash BM, Abdulelah H et al (2021) A state–of–art review on waterless gas shale fracturing technologies. J Pet Sci Eng 196(1):1–17 Sun Y, Li S, Sun R et al (2020) Study of CO2 enhancing shale gas recovery based on competitive adsorption theory. ACS Omega 5(36):23429–23436 Sun Q, Liu W, Zhang N (2021) Molecular insights into recovery of shale gas by CO2 injection in kerogen slit nanopores. J Nat Gas Sci Eng 90(6):1–14 Tan Y, Pan Z, Feng XT et al (2019) Laboratory characterisation of fracture compressibility for coal and shale gas reservoir rocks: a review. Int J Coal Geol 204(3):1–17 Xie W, Wang M, Chen S et al (2019) Effects of gas components, reservoir property and pore structure of shale gas reservoir on the competitive adsorption behavior of CO2 and CH4. Energy 254(9):1–12 Yang Y, Liu H, Mao W et al (2020) Study on the impact pressure of swirling-round supercritical CO2 jet flow and its influencing factors. Energies 14(1):106–109 Zhang C, Ranjith PG (2018) Experimental study of matrix permeability of gas shale: an application to CO2 -based shale fracturing. Energies 11(4):702 Zhang Y, He J, Li X et al (2019a) Experimental study on the supercritical CO2 fracturing of shale considering anisotropic effects. J Pet Sci Eng 173(2):932–940 Zhang H, Diao R, Mostofi M et al (2019b) Monte Carlo simulation of the adsorption and displacement of CH4 by CO2 Injection in shale organic carbon slit micropores for CO2 enhanced shale gas recovery. Energy Fuels 34(1):150–163 Zhou W, Wang H, Yang X et al (2020) Confinement effects and CO2 /CH4 competitive adsorption in realistic shale kerogen nanopores. Ind Eng Chem Res 59(14):6696–6706 Zou Y, Li S, Ma X et al (2018) Effects of CO2 –brine–rock interaction on porosity/permeability and mechanical properties during supercritical-CO2 fracturing in shale reservoirs. J Nat Gas Sci Eng 49(1):157–168

Chapter 9

The Role of Digital Twins in Isolated Energy System Control Sergey A. Gordin , Viktor D. Berdonosov , and Igor E. Lyaskovskiy

Abstract Despite the abundance of energy grids, isolated (autonomous) energy systems continue to play a significant role in energy supply. This is especially true for heating systems. The issue of optimal control of such systems is directly tied to their size: the cost of implementing standard control systems is comparable to the savings from their implementation. The smaller the system, the more it depends on outside factors which means that more sophisticated adaptive control systems are required. It is no longer enough to use tracking systems (PID controllers) or algorithm-based systems to account for inertial processes and predict changes in external systems (e.g. changes in consumer loads). Digital twin technology is the most promising option for adaptive control systems. This technology allows for adaptive control with a limited range of sensors and measurement systems as well as reduces the cost of a control system and makes it adaptive. It also allows for the unification of control systems for similar energy systems with different criteria, thereby reducing their development costs. Keywords Isolated energy systems · Digital twins · Optimal control system

9.1 Introduction 9.1.1 A Subsection Sample Despite the advances in creating interconnected networks, there is still a large number of isolated energy systems around the world. For the purposes of energy supply, systems are considered to be isolated when they are located in the territories without S. A. Gordin (B) · V. D. Berdonosov Komsomolsk-na-Amure State University, 27, Lenina Street, Komsomolsk-on-Amur, Russia e-mail: [email protected] S. A. Gordin · I. E. Lyaskovskiy Far Eastern State Transport University, 47, Seryshev Street, Khabarovsk 680021, Russia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_9

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technological connection to the unified energy system of the country in question. For the purposes of heat supply, systems are considered to be isolated when they supply heat by boiler houses with a heating capacity of up to 50 Gcal/h. In Russia, the area of territories with isolated energy systems is around 10% of the total area of the country. This is due to the low population density in the northern and eastern regions. The situation is different in China. China has no district heating; therefore, most heating systems can be considered isolated. For example, in 2020 the established heat output in China constituted 1.24 billion kW (56.58% of the total established heat output in China). The main source of heat in isolated heating systems is a hot water or steam boiler which converts heat generated by fuel combustion into heat transfer fluid energy. Coal boilers have a simpler design and are therefore cheaper to produce in comparison to gas and liquid fuel ones. This is why they are more common despite their many disadvantages: significant environmental pollution, lower efficiency and high volume of harmful emissions. There are ways to minimise harmful emissions from coal CHP plants. For example, a German energy group Vattenfall has just launched an ultra-low-CO2 coal CHP plant in Germany. However, all non-standard solutions complicate control systems. One of the ways to increase the efficiency of isolated energy systems is to use digital twin technology. Digital twins differ from a simple set of models in that they have an operational link to real objects. This link allows for significantly increased accuracy in modelling and process control, including cases when sensors and measurement systems provide insufficient or inaccurate data.

9.2 Digital Twins in the Energy Industry 9.2.1 The Concept of Digital Twins in Energy Systems Isolated energy systems are often implemented as hybrid energy systems (HES). In addition to traditional energy sources (fuel oil, coal, gas), HES also use renewable energy sources (wind, solar. water, geothermal) and so on. The particular feature of using renewable sources is the need for storage (Berdonosov et al. 2019). Although a considerable variety of energy storage systems exist (electric batteries, hydrogen storage, hydro-storage, etc.), their use complicates control systems. Combining conventional and renewable energy sources requires a special approach to control systems. This is due to the fundamental intermittency of renewable energy sources: the periodicity of solar activity during the day, periods of strong and weak winds including zero wind, seasonal variations in water flow, etc. Thus, it can be concluded that such systems have virtually no stationary processes. Implementation of transition control, on the other hand, requires a new approach. One such approach is using the digital twin paradigm. This paradigm has been actively developed over the past decade. Some estimates rate it second in modern technology.

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The basic digital twin concept is presented as physical space (physical objects), digital space (models of physical objects) and a two-way link between physical and digital spaces (see Fig. 9.1) (Rassõlkina et al. 2021). A more comprehensive definition of digital twin is as follows: a system comprising a digital model of a physical object and two-way information links to the physical object and (or) its constituents. A digital model means a system of mathematical and computer models as well as electronic documents for a physical object describing its structure, functionality and behaviour at different stages of the life cycle. Reference digital twin structure was given by Steindl et al. (2020) (see Fig. 9.2). Figure 9.2 shows four main structural digital twin blocks: “physical entity platform”, “digital entity platform”, “data management platform” and “services platform”. Physical entity platform, in turn, represents “physical objects”, “physical nodes” and “people”. Digital entity platform comprises many digital entity models, including information for a mirror image of a particular physical entity aspect. It is responsible for creating and maintaining “semantic models” (geometric models, physical models, behavioural models, rule models, process models) of the physical entity to create its digital representation. Data management platform ensures collection, management (collection, transmission, storage, integration, processing,

Fig. 9.1 Basic digital twin concept

Fig. 9.2 Reference digital twin structure

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cleansing, analysis, extraction of data and information) and storage of data. Services platform consists of service models and a service management layer to organise services for specific applications. Initially, most digital twins (both in Russia and worldwide) were virtual models of individual elements of production: products, equipment, systems, lines and processes. Quickly enough, these disparate models began to merge, creating complete “twins” of the entire value chain. Such twin operates as follows: a “smart” system compares the digital representation of the original against data from the real world, taking into account, for example, outside temperature, humidity and other parameters. The system then notifies personnel, learns from the decisions taken and predicts changes in certain processes of the real object over time. In energy generation systems, this digital “mirror” monitors and optimises equipment operation. Intelligent doubles help to select the most efficient mode of CHP operation by optimising fuel consumption, taking into account the objective of improving marginal profits. With a digital twin, energy generation specialists can find out online how much heat a customer needs and can adjust temperature to avoid insufficient or excessive heating. Finally, a digital twin at a power plant makes it easier to plan repairs and manage equipment. Currently, digital twin technology is evolving in the direction of improving modelling mechanisms. On the one hand, models based on physical principles are the most accurate. However, such models are usually described by systems of differential equations in partial derivatives. Analytical solutions for such systems are practically non-existent. Numerical solutions, on the other hand, have significant computational costs. The alternative solution would be data models which use neural networks. Such models are less computationally intensive but also less precise. In addition to that, they require collecting a large amount of data.

9.2.2 Adaptive Energy System Control Automated control systems for isolated energy systems implement standard algorithms related to both stabilising a set parameter (automated stabilisation systems) and to changing a parameter according to a given rule (programmable systems). The first case widely uses various PID controllers with a fixed set point or a set point that could be changed by operating personnel. The second case uses various microprocessor controllers that operate according to a set programme. Such solutions have proved to be highly reliable at low cost and manage basic control tasks well at stable loads. In case of variable loads, the limitations of automation equipment mean that many decisions related to energy system control are made by operating personnel, taking into account the instructions in place, their experience and qualification. With sharply varying loads, the decisions made are more in line with the averages, so this “manual” control is less efficient, although it does meet reliability and safety requirements.

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Development and implementation of adaptive automatic control systems for energy systems allows to increase control efficiency at all modes of operation, however, it makes control systems more complicated and less universal as such systems are difficult to transfer even to similar energy systems. Considering that adaptive control systems are based on microprocessors and use multiple sensors with a given level of accuracy to collect data on system status, economic feasibility of introducing such a system depends on the amount of savings and cost of energy resources as well as the economic effect of improving the reliability and continuity of the energy supply system. Our experience in conducting commissioning tests at various energy facilities shows that the increase in efficiency can reach up to 2.5…3% when optimal PID controller values and other settings are selected for systems with non-adaptive control. Adaptive automated control systems increase energy system efficiency up to 5.5…6%. The cost of coal 350 USD/T makes it economically feasible to implement a standard adaptive control system at boiler houses with useful output of more than 1 Gcal/h. Unfortunately, isolated energy systems almost always use customised solutions due to their size, and it is impossible to make a standard adaptive control system and reduce its development and implementation cost with mass implementation. This greatly increases the minimum capacity of an isolated power system, for which it is economically feasible to design a customised adaptive control system.

9.2.3 Generalised Mathematical Model of an Isolated Energy Supply System The implementation of digital twin technology in a control system for an isolated energy supply system requires a generalised mathematical model of the physical and technological processes in the system. If such a model is universal enough, then, in addition to increased control efficiency, digital twin technology will reduce design and development costs of an adaptive control system as it will be able to adjust itself and adapt to existing sensors and control elements, and the cost of implementation and operation could be comparable to, and in some cases lower than, the cost of adaptive control systems due to mass implementation. The main issue in creating a control system for isolated energy systems using digital twin technology is the lack of generalised mathematical models of the physical processes of heat and mass transfer and energy transformation from one form to another. Even though separate tasks (e.g. fuel combustion (Alobaid et al. 2017), heat and mass transfer of heat transfer fluid (Fang et al. 2018, etc.) have good mathematical models, their integration and joint use are not trivial. Considering that all physical and technological processes of an isolated energy system have been pretty well studied and described by now, the issue of creating a generalised mathematical model can be reduced to the problem of linking individual

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knowledge and identifying the relationships between the different elements of the system. Our team has worked on creating a generalised mathematical model of a heating system and applying analytical methods to describe the state of the system at each point in time. The studies showed that analytical representation of such a model can be achieved only with substantial assumptions on the parameters of the media and processes, as well as the stationarity of all processes (Gordin et al. 2021). As we move towards non-stationary processes and the requirements for the accuracy of media and process parameters increase, the analytical solution becomes unattainable and the use of numerical methods, including iterative methods, is required. The generalised mathematical model of the heating system was tested in simulation environment AnyLogic. This allowed not only to study the model’s adequacy and stability but to study the possibility of moving from static to dynamic modelling of district heating processes (Gordin et al. 2022). Figure 9.3 shows a general simulation model scheme. When considering dynamic processes, a generalised mathematical model can only be solved by numerical methods which significantly complicates practical application of the achieved results: even though modern industrial controllers have significant computing resources, they still cannot solve problems numerically at the speed of real-world systems.

9.2.4 The Prospective Role of Digital Twins in Isolated Energy System Control Currently, digital twin technology mostly evolves in the direction of improving the models that are used. As mentioned above, they can be physical or data models, i.e. models built on neural networks. Combined models that implement both approaches are the natural next step. Additionally, new models have appeared: projection models, parametric models, tracking dynamic models, autoregressive models with prediction of physical object response (Chatzi et al. 2010). Predictive digital twins seem the most promising (Kapteyn et al. 2020). In predictive digital twins, three elements are distinguished in physical space and three in digital. Physical space elements are the following: St is the physical state of the system; Ot is observation data (data from sensors); Ut is control actions. Physical space elements are the following: Dt is the state of digital twin; Qt is predicted parameters; Rt is quantified gain from using a digital twin (see Fig. 9.4). A probabilistic graphical model representing digital and physical twins is made using these six elements. This model is a dynamic Bayesian network representing the physical state of a system as it develops with time. The arrows (edges) represent conditional transition probabilities. In this way, the physical states of the system are modelled as random variables. In digital space, the evolution of the digital state occurs in a similar way. However, digital states are corrected with observations coming from

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Fig. 9.3 Heating system simulation model

the physical states. Then a control action is formulated based on the digital space and observations. The dynamic Bayesian network becomes a dynamic decision-making network. This approach allows making predictions n steps ahead.

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Fig. 9.4 Diagram of predictive digital twin operation

9.3 Conclusion Digital twin technology is a promising direction for creating adaptive universal control systems for isolated energy systems. The complexity of development and implementation of this technology is compensated by its universality and possibility of application without significant changes at very different energy supply systems. The main approaches considered herein provide a framework for the development of digital twin technology and its application in both heating systems and hybrid power plants. Acknowledgements The study was conducted using a grant from the Russian Science Foundation. No. 22-29-01232, https://rscf.ru/project/22-29-01232/.

References Alobaid F, Mertens N, Starkloff R et al (2017) Progress in dynamic simulation of thermal power plants. Prog Energy Combust Sci 59:79–162. https://doi.org/10.1016/j.pecs.2016.11.001 Berdonosov V, Shamak V, Zhivotova A et al (2019) Hybrid power systems optimization for regions of the far north B cbopnike: 2019 international multi-conference on industrial engineering and modern technologies, FarEastCon 2019. C. 8934884 Chatzi E, Smyth A, Masri S (2010) Experimental application of on-line parametric identification for nonlinear hysteretic systems with model uncertainty. Struct Saf 32(5):326–337. https://doi. org/10.1016/j.strusafe.2010.03.008 Fang C, Xu Q, Wang S, Ruan Y (2018) Operation optimization of heat pump in compound heating system. Energy Procedia 152:45–50. https://doi.org/10.1016/j.egypro.2018.09.057

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Gordin SA, Sosnin AA, Shamak VA, Khryapenko KD (2022) Simulation modeling of dynamic processes in district heating systems. Inf Sci Control Syst 3(73):73–83. https://doi.org/10.22250/ 18142400_2022_73_3_73 Gordin SA, Kozlova OV, Zaychenko IV (2021) Modeling the combustion process of solid fuel boilers. In: Lecture notes in networks and systems, vol 200, pp 136–146 Kapteyn M, Pretorius J, Willcox K (2020) A probabilistic graphical model foundation for enabling predictive digital twins at scale. Preprint, pp 1–11 Rassõlkina A, Orosz T, Demidova G et al (2021) Implementation of Digital Twins for electrical energy conversion systems in selected case studies. Proc Est Acad Sci 70:19–39. https://doi.org/ 10.3176/proc.2021.1.03 Steindl G, Stagl M, Kasper L et al (2020) Generic digital twin architecture for industrial energy systems. Appl Sci 10:8903. https://doi.org/10.3390/app10248903

Chapter 10

The Effect of Land Degradation on Changes in Water Availability in Watershed Areas Zainuddin, Dinar Dwi Anugerah Putranto, and Febrian Hadinata

Abstract Land degradation is defined as a condition in which the quantity and quality of land resources needed to support ecosystem functions and services and increase food security continues to decline on a temporal and spatial scale under certain ecosystem conditions (Sarino et al. in IOP Conf Ser Earth Environ Sci, pp 1– 10, 2019). These changes, combined with population growth, have led to the conversion of agricultural land to developed land, high exploitation of natural and mineral resources, and urbanization, which usually leads to more significant land degradation (Putranto et al. in pp 100016-1–100016-10, 2017). Sub-watershed scale analysis related to the distribution of degraded land by utilizing multi-temporal geospatial layers (Putranto et al. in Int J Geomate 14(45):28–34, 2018) helps provide a regionally consistent data set of required land distribution. Therefore, it can support agendas aligned with sustainable development goals (SDGs) (Guo et al. in 11:1–20, 2019). By utilizing scientific big earth data, which is periodically integrated with terrestrial, aquatic, and hydrological data, it has the potential to assess degraded land as outlined in SDG’s target point 15.3.1 (Yuono et al. in J Ecol Eng 21:126–130, 2020). The Rawas sub-watershed is a river drainage area that originates at one of the peaks of Mount Kerinci. Geologically, the Rawas sub-watershed includes lowlands which are quarterly alluvial deposits from the Pleistocene to the present age. The availability of water in the Rawas watershed until 2020 is sufficient to serve the villages in the watershed. With the most significant water resource potential in November, December, and January to March, approximately 52 mm/month. Meanwhile, in other months, water availability in the Rawas watershed is below 3 mm/month and even close to 0 mm/month. Keywords Land degradation · Geospatial · Water balance Zainuddin Doctoral Program in Engineering, Postgraduate Program, Faculty of Engineering, Universitas Sriwijaya, Palembang, Indonesia D. D. A. Putranto (B) · F. Hadinata Department of Civil Engineering and Planning, Faculty of Engineering, Universitas Sriwijaya, Palembang, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Z. Sun and P. Das (eds.), Proceedings of the 9th International Conference on Energy Engineering and Environmental Engineering, Environmental Science and Engineering, https://doi.org/10.1007/978-3-031-30233-6_10

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10.1 Introduction The existence of good land cover is found to be ensured with the availability of water resources and vice versa, as observed in a management theory. This indicates that the loss of forest cover leads to a more significant increase in total runoff. Based on the occurrence of land degradation, other previous evidence showed unsustainable forest use in the watershed area (Putranto et al. 2017, 2018). This is often accompanied by an increase in the volume of surface water runoff, which occurs during the rainy season (Guo et al. 2019; Yuono et al. 2020). Meanwhile, a shortage of water is often observed during the long dry period (Kurniawan et al. 2020; Yuono et al. 2019). This indicates a reduction in the ability of the soil to absorb water, due to the inability to infiltrate surface fluid (Pelacani et al. 2008; Borrelli et al. 2017). High population growth, especially in several new autonomous districts, has shown increasing land degradation. Since it was designated as a new autonomous region (Ministry of Home Affairs of the Republic of Indonesia, 2015), North Musi Rawas Regency has granted permits to open oil palm plantations, an increase of almost 24% (Statistics of North Musi Rawas Regency, 2021). This has led to increased land degradation, reduced availability of groundwater during the dry season (April– August) (Sarino et al. 2019), increased surface water runoff and river sedimentation (Ivits et al. 1999) and has caused flooding in Muara Rupit city every time it rains 2 years birthday (PBB 2018). Therefore, it is necessary to consider immediate and timely steps to avoid, reduce, and restore land degradation, increase food and water security, substantially contribute to climate change adaptation and mitigation, as well as decrease biodiversity loss to avert conflict. These considerations are expected to fulfill the sustainable development goals agreed in the 2030 Agenda (SDG’s 15), which was ratified by Indonesia in 2017 (Giuliani et al. 2020). To stop and restore the present land degradation trend, increasing national capacity is also very necessary, to immediately carry out quantitative assessments. As required in the sustainable development goals (SDGs), particularly SDG indicator 15.3.1, appropriate mapping of degraded lands should not be more than the total site area (Sarino et al. 2019). On this basis, it is necessary to the sub-watershed scale analysis helps in providing a regionally consistent dataset of required land distribution, which are used to support the agendas that are aligned with sustainable development goals (SDGs) (Krajewski et al. 2019). This is related to the distribution of degraded land, through the multitemporal geospatial layers (Feinan et al. 2018). In this research, using the GIS techniques, the analysis of topographic conditions, land cover types, rain duration and season, as well as erosivity magnitude and distribution (based on soil type and slope), are found to support data collection and predictive information on the spread of critical sites, as well as potential environmental damage in the watershed area (Putranto and Pratami 2017). Also, it is used to predict fluid availability in the watershed area.

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10.2 Materials and Methods 10.2.1 Study Area Rawas sub-watershed is one of the 36 water facilities in the South Sumatra Province Administrative Region and is also part of the Musi river basin. This facility has a catchment area (DAS) of approximately 5,891 km2 , with a geographical location at variational coordinates of 102° 4' 4.8'' –103° 27' 36'' east longitude and 2° 19' 15'' –3° 06' 36'' south latitude. The upstream area of this facility is at an altitude of 2,100 m above sea level, which is one of the highest peaks in the Kerinci Seblat National Park zone, partly included in the administrative region of north Musi Rawas regency (1,884.61 km2 or 32%). Moreover, Kerinci Seblat National Park (TNKS) is part of the global lungs, which has been designated by UNESCO as a world heritage area, with more than 4,000 species of primary forest flora and 115 species of ethnobotanical plants. Two major rivers or sub-river systems flow in Rawas watershed, namely Rupit river, which fulfills and empties into Rawas water body. At the confluence of the river, this has become the centre of growth for the Muara Rupit urban area in north Musi Rawas regency. The water resources in the sub-systems of Rawas and Rupit rivers have been very beneficial to the residents in the watersheds. However, the licensing development for several lands utilized for plantation activities is increasingly massive in this area. This indicates that there is an imbalance in water availability during the rainy and dry seasons, as observed in other river systems within the Musi river basin (Krajewski et al. 2019). The provincial and district governments subsequently issued several permits for oil palm and rubber plantations, mining, infrastructure, other economic developments, as well as many illegal operations in the hills and along Rawas River, based on the extraction of coal, gold, sand, and Iron ore. Therefore, this problem is found to disrupt the raw water supply system. The mining activities in the hills and along the river basin of Rawas sub-watershed also affects the erosion magnitude and flooding volume, in Tugu Mulyo district, Musi Rawas regency.

10.2.2 Methods The basic concept used in each model was the hydrological cycle, to analyze the availability of water resources in the watershed area. In preparing the hydrological model, the analysis focused on the conversion of rain into discharge through the watershed system, as all components influencing the process were carefully observed and studied. These components included the topographical, meteorological (radiation, wind, rain, humidity, and temperature), and geological (soil types and properties) elements, which significantly affected the watershed characteristics. One of the commonly used designs was the Mock model developed by Mock (1978), based on the hydrological cycle (Feinan et al. 2018). The analysis of this study focused on

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the discharge through the watershed system, which included the components of rain, evapotranspiration, interception, groundwater, and water balance. This indicated that the calculation of the water balance modified the Thornthwaite method (Putranto and Pratami 2017; Komariah and Matsumoto 2019), by including limited evapotranspiration. Also, the persistence of the greenery and maximum utilization of 200 mm soil moisture was assumed in this study. For the analysis of water availability, the R80 mainstay discharge was used.

10.3 Results and Discussion A thorough analysis of watershed morphometry was beneficial in understanding the impact of environmental flow morphometry. This is the measurement and mathematical analysis of the earth surface configuration, as well as the shape and dimensions of its landscape, concerning the observation area. Using the data from DEM NAS (National Digital Elevation Model) Rawas Sub-watershed Research Area, South Sumatra, Indonesia, this study was performed at a contour interval and pixel size of 15 and 7.5 m, respectively. This analysis was subsequently carried out using the Digital Elevation Model (DEM) technique, to extract the river sub-system boundaries. Furthermore, the watershed was used as a basic unit in morphometric analysis, river length, drainage slope, flow pattern and density, as well as infrastructural shape, area, and perimeter, to understand the hydrogeological behaviour of drainage basins. It was used to indicate the occurrence of climatic, geological, geomorphological, and structural conditions in the watershed.

10.3.1 Analysis of Rawas Sub-watershed Drainage Network In the watershed system, the drainage analysis determined the good and bad flow patterns, inundation areas, and water availability within the study area. Flow length. Flow length is one of the essential morphometric characteristics in a watershed area, due to providing information on runoff characteristics. This indicates that rivers with relatively small or short channel lengths have steep slopes and better soil texture. However, straight and elongated morphometric rivers without many branches generally show a gentler slope (Portela et al. 2019). In this study, the total length of the river section in Rawas and Rupit sub-watersheds was of the 0ne and fifthorder, respectively (see Fig. 10.1). This indicated that the total Stream Orders (km) of Rupit and Rawas (Upstream and Downstream) sub-watersheds were 525,095, as well as 707,638 and 81,966 km, respectively. In these sub-systems, all the drainage channels in the upstream area were dendritic patterns. This indicated that the study area was hilly in the upstream area, with a slope of approximately 3–8%. Also, it was

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a basin between two hills, with fluvial deposits of former sediment or river overflow (see Fig. 10.1). Average slope drainage (%). This is the flow slope from upstream to downstream, which is expressed as a percentage describing the characteristics of the drainage fluidity in the watershed system. These parameters were obtained by subtracting the highest difference at the beginning of the drainage to the lowest point of the flow. For example, the downstream of Rawas River sub-catchment had the most downward drainage slope at 0.13% (see Table 10.1). Therefore, is a basin, which is slow to drain water. Meanwhile, upstream Rawas River and Rupit sub-catchments had sufficient drainage slopes of 3.38% and 1.51%, respectively.

Fig. 10.1 Morphometry and soil texture of Rawas watershed

Table 10.1 Morphometric of Rawas river sub-watershed No

Name of watershed

Watershed area (km2 )

Stream length (km)

Perimeter (P) km

Drainage density (km/ km2 )

Slope drain (%)

1

Upstream of Rawas river

1,782.94

707.638

339.8

0.397

3.38

2

Downstream of Rawas river

2,280.92

819.66

272.1

0.359

0.13

3

Rupit river

1,824.17

525.095

260.8

0.288

1.51

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Basin Area (A). The watershed area is another critical parameter, in determining the amount of run-off. The relationship between the total area of the watershed and the amount of run-off will determine the amount of run-off to the watershed outlet. The area of the basin can be seen in Table 10.1. Based on Table 10.1, the upstream and downstream sub-watersheds of Rawas River had areas of 1,782.94 and 2,280.92 km2 ; respectively, with Rupit sub-system having 1,824.17 km2 . Watershed perimeter (P). This is the outermost boundary of the watershed, often known as the circumference of the sub-system. It is measured along the border between adjacent watersheds and is also used as a size and shape indicator. In this study, the fenced basin perimeter for Rupit sub-watershed was 260.8 km, with upstream and downstream Rawas sub-system observed at 339.8 and 272.1 km, respectively. Drainage pattern. Based on this study, the drainage pattern analysis reflected the influence of slope, lithology, and structure, where the erosion cycle stages were identified. This showed several watershed characteristics through the drainage pattern and texture. Also, the inferences on the basin’s geology, rock strike and deposition, presence of faults, and other information about this parameter was highly considered. By relating drainage patterns to geological data, the texture method reflected climate, soil permeability, vegetation, and relief ratio. Based on upstream Rawas sub-watershed, an elongated and trellis pattern was observed, indicating that the planning area had steep slopes and easily eroded soil structure, as well as one extensive river system (floodplain basin). Drainage density. Drainage density is the flow length per unit area of a basin or watershed. This is a better quantitative expression for landform discretion and study. However, climate functions, lithology and structure, as well as the relief history of the area were used as indirect indicators, to explain these variables and landform morphogenesis. In this study (see Table 10.1), the drainage density of upstream and downstream Rawas sub-watersheds was 0.397 and 0.359 km/km2 , respectively, with Rupit sub-system being 0.288 km/km2 .

10.3.2 Textures of Soil This is an essential factor in observing the fertility, porosity, and adhesive levels of the soil to the foundation of a building. In this study, variations were observed in the conditions and distributions of permeability and soil textures. The most considerable dominance of this parameter is observed in Fig. 10.1 and Table 10.2, respectively. This indicated that the Clay soil and dusty clay had the largest size and distribution in Rawas sub-watershed (5,295.21 and 1,073.46 km2 ). For the distribution of the minor soil types in this sub-system, Silence of dusty clay was observed, at an area of 4.03 km2 . This indicated that the site was less and quite fertile for agriculture and

10 The Effect of Land Degradation on Changes in Water Availability … Table 10.2 Soil textures at Rawas watershed

Textures Soak clay

Area (km2 )

109

Permeability

73.19

Currently

Silence of dusty clay

4.03

Currently

Soft clay

1.07346

Medium-slow

Clay

5.29521

Slow

Dust clay

353.97

Slow

Loamy loam

543.40

Currently

Sand

129.02

Fast

Dusty sand

6.55

Medium-fast

plantations, respectively. Also, only perennial crops were cultivated in the area, with water observed not to be slowly absorbed. In addition, these infiltration wells were not suitable for clean water sources in urban areas.

10.3.3 Land Use Land use significantly determined the magnitudes of the soil loss index (F) and overland flow (Q). This indicated the difference in each land-use type, due to being influenced by a different infiltration coefficient (C) and the rainfall percentage entering the soil (A). Based on Fig. 10.2, the distribution of infiltration coefficient values is shown as follows.

Fig. 10.2 Land use classification of Rawas watersheds

110 Table 10.3 Land use

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Land use Primary dry land forest

Area (km2 )

Coefficient infiltration (C)

0.002

0.030

Secondary dry land forest

133,976

0.500

Open land

124,510

0.350

Community plantations

52,364

0.100

Settlement

22,321

0.200

Dry land farming mixed with bush

65,630

0.630

Swamps

806,337

0.010

Rice field

63,741

0.020

Shrubs

6,648

0.700

Swampy scrub

570,715

0.500

Body of water

5,308

0.010

Land-use type is one of the factors for degradation and the formation of critical land. Based on the Landsat imagery data in 2015, 2017, 2019, and 2021, a land area of 59.90% or 352,691 km2 in Rawas watershed management region was non-forest (see Table 10.3). This was dominated by community plantations, covering an area of 52,364 km2 . Moreover, North Musi Rawas Regency was famous for its plantations of oil palm, sugar cane, coffee, coconut, pepper, and others. Four commodities were also found to be very dominant, namely palm oil, coffee, rubber, and coconut.

10.3.4 Hydrology Rainfall data were obtained from 3 observation stations around Rawas watershed, namely (1) The North Musi Rawas Sorolangun rain station, (2) The Srikaton station of Musi Rawas regency, and (3) The Sekayu station of Musi Banyuasin regency. Based on Table 10.4, the Surolangun rain station had a dominant influence in describing the distribution of rainfall in Rawas watershed area, at 554,676.404 Ha with a rain intensity of 30 min for a 5 year return period of 69.5 mm. Meanwhile, the Srikaton rain station had the least effect rainfall at 15,865,141 Ha, with a rain intensity of 30 min for a 5 year return period of 90.81 mm.

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Table 10.4 Location and distribution of rainfall in Rawas sub-watershed Station

Area (Ha)

H (m)

(I30)-5

R (mm)

n

Ro

1

554.67,640

191.2

69.56

2,248.58

137.7

195.2

2

15.865,141

745

3

18.503,476

777

90.81 187.5

2,615.39

144.9

212.5

2,520.88

138.0

213.7

10.3.5 Water Balance The potential availability of water was estimated from rainfall, which is the primary source of Rawas watershed, which greatly affects water availability. However, the water availability in this sub-system was not evenly distributed. Besides the climatic factors, natural conditions also affected water availability, such as topography, geology, soil, and vegetation. Damages to natural conditions such as the upstream area (TNKS; a water catchment area), also affected the water availability variations in Rawas watershed. This indicated that Rawas water yield was strongly influenced by the nature of the rain input and the physical parameters (characteristics) of the watershed which is the constituent element of the watershed. In addition, the features of each system produced different water production for the same amount of rain input and vice versa. Daily rainfall in Rawas watershed was also dominated by 24.00 to