Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

doi:10.1016/j.scitotenv.2024.173999

淡江大學機構典藏 > 工學院 > 水資源及環境工程學系暨研究所 > 期刊論文 > Item 987654321/125682

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125682

Title:	Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives
Authors:	Li, Chi-wang
Keywords:	Digital water;Fouling mitigation strategy;Machine learning;Smart technologies;Wastewater data analysis;Wastewater treatment automation
Date:	2024-06-13
Issue Date:	2024-07-31 12:08:00 (UTC+8)
Abstract:	Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.
Relation:	Science of The Total Environment 944,　173999
DOI:	10.1016/j.scitotenv.2024.173999
Appears in Collections:	[水資源及環境工程學系暨研究所] 期刊論文

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