Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities
Dhanyashree Narayanan, Manish Bhat, Norottom Paul, Narendra Khatri, Anil Saroliya
Abstract
Globally, wastewater management is a major issue. Using AI has improved treatment facility design and efficacy. AI techniques for wastewater treatment, such as pollutant identification, process optimization, and equipment maintenance, are often studied. In a standardized experimental setup, AI frameworks are not comprehensively evaluated. This study compares wastewater treatment AI paradigms to fill this gap. The evaluation includes accuracy, robustness, processing efficiency, and usability. Researchers hope to find the best AI methods for wastewater treatment tasks. Known methods like SVM, decision tree, ANN, Random Forest, and Deep Learning are examined. Each method is detailed to illuminate its principles. The comparison uses empirical data from a wastewater treatment plant (WWTP). ANN, LSTM, and SVM are more accurate and outperform with R values of 0.9958, 0.9939, and 0.9957. This study emphasizes the importance of tailoring AI methodologies to the needs and challenges of wastewater treatment. Researchers and practitioners can use the findings to choose AI strategies to optimize and manage wastewater treatment plants. This supports Sustainable Development Goal (SDG) 6: Clean Water and Sanitation and global sustainability efforts. • AI methodologies enhance wastewater treatment efficacy globally, addressing pressing concerns. • Current research emphasizes individual AI techniques for specific treatment aspects. • Lack of comprehensive evaluation across AI frameworks identified. • Study evaluates and compares AI paradigms' performance in wastewater treatment. • ANN, LSTM, and SVM show superior performance; Random Forest and Decision Tree excel in interpretability.