Revolutionizing water and wastewater treatment: Data-driven approaches for advanced solutions
Quratulain Maqsood, Rida Fatima, Fatima Rafaqat, Tahir Mehmood, Shinawar Waseem Ali, Manzoor Hussain
Abstract
This paper explores the growing use of advanced computational models in water and wastewater treatment. A variety of methods such as decision trees, artificial neural networks (ANN), long short-term memory (LSTM), fuzzy logic, internet of things (IoT), and recurrent neural networks (RNN) have been studied for their ability to improve treatment efficiency and monitoring. These approaches have been used to evaluate and optimize key indicators, including biochemical oxygen demand (BOD), chemical oxygen demand (COD), nitrogen and sulfur removal, turbidity, hardness, and overall contaminant removal. The study reviewed numerous articles discussing the strengths and limitations of each method in practical applications. While these models show promise in reducing operational costs, improving treatment outcomes, and enhancing real-time monitoring, certain challenges remain. Limited access to reliable data, difficulties in model reproducibility, and constraints in large-scale implementation present ongoing obstacles. The paper also discusses hybrid models, which combine two or more approaches to capitalize on the unique strengths of each, resulting in better predictive accuracy and system performance. Overall, the review highlights the need to address current barriers and encourages further development in this field to make computational techniques more accessible and effective for real-world water and wastewater treatment systems. • Explores the use of AI and ML in the treatment of water and wastewater. • Summarizes important models, including hybrid methods, ANN, FL, and DT. • Identifies issues with repeatability, interpretability, and data availability. • Suggests using edge computing and IoT to monitor processes in real time.