As a rapidly developing and potent instrument for resolving practical issues, artificial intelligence (AI) has garnered considerable interest and has been widely used in many different domains. Diverse AI models have also been used in wastewater treatment (WWT) to optimize processes, forecast efficiency, and assess performance in order to explore high-efficiency and cost-effective solutions because of their remarkable learning and predictive capabilities. This review gathers the latest developments and applications of AI technologies in wastewater treatment plants and carefully examines the application and outcomes of various AI models, including artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and deep learning (DL), in domains such as water quality monitoring, process optimization, fault diagnosis, membrane fouling prediction and control, and resource recovery. This study examines the benefits of these models in real-world engineering applications through a comparison with traditional approaches, as well as current issues like data collection and model generalization. Additionally, it looks to the future, where AI will be used in conjunction with emerging technologies like cloud computing, big data, and the Internet of Things (IoT) to drive the automated and intelligent advancement of wastewater treatment.
Sun et al. (Sun,) studied this question.
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