Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives
學年 113
學期 1
出版（發表）日期 2024-10-03
作品名稱 Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives
作品名稱（其他語言）
著者 Stefano Cairone; Shadi W. Hasan; Kwang-Ho Choo; Chi-Wang Li; Antonis A. Zorpas; Mohamed Ksibi; Tiziano Zarra; Vincenzo Belgiorno & Vincenzo Naddeo
單位
出版者
著錄名稱、卷期、頁數 Euro-Mediterranean Journal for Environmental Integration (2024)
摘要 Membrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. Membrane technologies play a primary role in combating water scarcity and pollution challenges. However, the need for more effective strategies to mitigate membrane fouling remains a critical concern. Artificial intelligence (AI) modeling offers a promising solution by enabling accurate predictions of membrane fouling, thus supporting advanced fouling mitigation strategies. This review examines recent progress in the application of AI models, with a particular focus on artificial neural networks (ANNs), for simulating membrane fouling in wastewater treatment processes. It highlights the substantial potential of ANNs, particularly the widely studied multi-layer perceptron (MLP) and other emerging configurations, to accurately predict membrane fouling, thereby enhancing process optimization and fouling mitigation efforts. The review discusses both the potential benefits and current limitations of AI-based strategies, analyzing recent studies to offer valuable insights for designing ANNs capable of providing accurate fouling predictions. Specifically, it provides guidance on selecting appropriate model architectures, input/output variables, activation functions, and training algorithms. Finally, this review highlights the critical need to connect research findings with practical applications in full-scale wastewater treatment plants. Key steps crucial to address this challenge have been identified, emphasizing the potential of AI modeling to revolutionize process control and drive a paradigm shift toward more efficient and sustainable membrane-based wastewater treatment.
關鍵字 Digital water;Sustainable wastewater treatment;Smart wastewater management;Advanced fouling control;Data-driven modeling;Machine Learning
語言 en_US
ISSN 2365-7448
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
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