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Li C, Xie Y, Wang J, Guo C, Han L, Xia Z, Zhang Z, Wang J, Li M, Han W, Huang L, Yan J, Zhang H. Advances in sulfate-reducing bacteria-driven bioelectrolysis: mechanisms and applications in microbial electrolysis cell technology. ENVIRONMENTAL RESEARCH 2025; 279:121857. [PMID: 40379000 DOI: 10.1016/j.envres.2025.121857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2025] [Revised: 04/29/2025] [Accepted: 05/13/2025] [Indexed: 05/19/2025]
Abstract
The discharge of sulfate-rich wastewater from chemical and pharmaceutical and food processing industries results in serious environmental problems that impact both the natural environment and human health. The conventional sulfate removal process using chemical precipitation consumes much energy and results in the production of additional pollutants that decrease its scalability and treatment performance. Microbial electrolysis cells (MECs) using sulfate-reducing bacteria (SRB) is a promising sustainable technology for treating wastewater and recovering resources because the metabolic process of SRB in MECs can convert sulfate to sulfide while the cells also produce bioenergy through electrochemical processes. This review focuses on the processes of sulfate reduction in MECs that have demonstrated potential for sulfate removal and hydrogen production and heavy metal elimination and organic pollutant degradation. This review also systematically discussed machine learning systems that optimize MECs performance and result prediction and efficiency enhancement. The SRB-MECs systems have two advantages by producing clean energy while treating wastewater that makes them suitable for application in industrial processes. The two main challenges for the implementation of these systems are the scalability of the system and its long-term operational reliability. This review highlights the need for more research to enhance system performance and microbial efficiency and accelerate the practical implementation of SRB-MECs technology as a sustainable and energy-efficient solution for treating industrial effluents.
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Affiliation(s)
- Chenxi Li
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Yuchen Xie
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Jingyi Wang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Chengjun Guo
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Luoyi Han
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Ziyin Xia
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Zijian Zhang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Junhe Wang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Meng Li
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China.
| | - Wei Han
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region of China
| | - Lei Huang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China
| | - Jia Yan
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China; Guangzhou University-Linköping University Research Center on Urban Sustainable Development, Guangzhou University, Guangzhou, 510006, PR China
| | - Hongguo Zhang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou, 510006, PR China; Guangzhou University-Linköping University Research Center on Urban Sustainable Development, Guangzhou University, Guangzhou, 510006, PR China.
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Jin J, Wu Y, Cao P, Zheng X, Zhang Q, Chen Y. Potential and challenge in accelerating high-value conversion of CO 2 in microbial electrosynthesis system via data-driven approach. BIORESOURCE TECHNOLOGY 2024; 412:131380. [PMID: 39214179 DOI: 10.1016/j.biortech.2024.131380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Revised: 08/26/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
Microbial electrosynthesis for CO2 utilization (MESCU) producing valuable chemicals with high energy density has garnered attention due to its long-term stability and high coulombic efficiency. The data-driven approaches offer a promising avenue by leveraging existing data to uncover the underlying patterns. This comprehensive review firstly uncovered the potentials of utilizing data-driven approaches to enhance high-value conversion of CO2 via MESCU. Firstly, critical challenges of MESCU advancing have been identified, including reactor configuration, cathode design, and microbial analysis. Subsequently, the potential of data-driven approaches to tackle the corresponding challenges, encompassing the identification of pivotal parameters governing reactor setup and cathode design, alongside the decipheration of omics data derived from microbial communities, have been discussed. Correspondingly, the future direction of data-driven approaches in assisting the application of MESCU has been addressed. This review offers guidance and theoretical support for future data-driven applications to accelerate MESCU research and potential industrialization.
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Affiliation(s)
- Jiasheng Jin
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Yang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
| | - Peiyu Cao
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Xiong Zheng
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Key Laboratory of Yangtze River Water Environment, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
| | - Qingran Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
| | - Yinguang Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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Deng Z, Choi SJ, Li G, Wang X. Advancing H 2O 2 electrosynthesis: enhancing electrochemical systems, unveiling emerging applications, and seizing opportunities. Chem Soc Rev 2024; 53:8137-8181. [PMID: 39021095 DOI: 10.1039/d4cs00412d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Hydrogen peroxide (H2O2) is a highly desired chemical with a wide range of applications. Recent advancements in H2O2 synthesis center on the electrochemical reduction of oxygen, an environmentally friendly approach that facilitates on-site production. To successfully implement practical-scale, highly efficient electrosynthesis of H2O2, it is critical to meticulously explore both the design of catalytic materials and the engineering of other components of the electrochemical system, as they hold equal importance in this process. Development of promising electrocatalysts with outstanding selectivity and activity is a prerequisite for efficient H2O2 electrosynthesis, while well-configured electrolyzers determine the practical implementation of large-scale H2O2 production. In this review, we systematically summarize fundamental mechanisms and recent achievements in H2O2 electrosynthesis, including electrocatalyst design, electrode optimization, electrolyte engineering, reactor exploration, potential applications, and integrated systems, with an emphasis on active site identification and microenvironment regulation. This review also proposes new insights into the existing challenges and opportunities within this rapidly evolving field, together with perspectives on future development of H2O2 electrosynthesis and its industrial-scale applications.
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Affiliation(s)
- Zhiping Deng
- Department of Chemical and Materials Engineering, University of Alberta, 9211-116 Street NW, Edmonton, Alberta T6G 1H9, Canada.
| | - Seung Joon Choi
- Department of Mechanical Engineering, University of Alberta, 9211-116 Street NW, Edmonton, Alberta T6G 1H9, Canada.
| | - Ge Li
- Department of Mechanical Engineering, University of Alberta, 9211-116 Street NW, Edmonton, Alberta T6G 1H9, Canada.
| | - Xiaolei Wang
- Department of Chemical and Materials Engineering, University of Alberta, 9211-116 Street NW, Edmonton, Alberta T6G 1H9, Canada.
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Sun J, Xu Y, Yang H, Liu J, He Z. Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant. CHEMOSPHERE 2024; 351:141154. [PMID: 38211785 DOI: 10.1016/j.chemosphere.2024.141154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/19/2023] [Accepted: 01/06/2024] [Indexed: 01/13/2024]
Abstract
Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.
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Affiliation(s)
- Jiasi Sun
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Yanran Xu
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Haoran Yang
- School of Civil, Environmental and Infrastructure Engineering, Southern Illinois University, Carbondale, IL, 62901, USA
| | - Jia Liu
- School of Civil, Environmental and Infrastructure Engineering, Southern Illinois University, Carbondale, IL, 62901, USA
| | - Zhen He
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA.
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