污水处理
生化工程
废水
海水淡化
自动化
水处理
工艺工程
计算机科学
过滤(数学)
结垢
实施
化学需氧量
絮凝作用
环境科学
环境工程
工程类
化学
膜
数学
统计
机械工程
生物化学
程序设计语言
作者
Soma Safeer,Ravi P. Pandey,Bushra Rehman,Tuba Safdar,Iftikhar Ahmad,Shadi W. Hasan,Asmat Ullah
标识
DOI:10.1016/j.jwpe.2022.102974
摘要
Artificial intelligence (AI) is an emerging powerful novel technology that can model real-time problems involving numerous intricacies. The modeling capabilities of AI techniques are quite advantageous in water purification and wastewater treatment processes because the automation of such facilities resulted in easy and low cost operations; in addition to the significant reduction in the occurrence of human errors. AI technologies involve multi-linear or non-linear relationships and process dynamics that are usually impractical to model by conventional methodologies. This review presents a compendious synopsis of recent advancements and discoveries in various AI technologies applied to source water quality determination, coagulation/flocculation, disinfection, membrane filtration, desalination, modeling wastewater treatment plants, prediction of membrane fouling, removal of heavy metals, and monitoring of biological oxygen demand (BOD) and chemical oxygen demand (COD) levels. The analysis of the performance of various AI technologies in this review proves the successful implementation of these technologies in water treatment related applications. It also highlights the limitations that hinder their implementations in real-world water treatment systems.
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