“A Machine Learning-Based Approach for Troubleshooting Sewage Treatment Plant Process”

Sewage Treatment Plants (STPs) often face operational challenges such as aeration failures, filtration inefficiencies, and fluctuating influent characteristics, leading to environmental non-compliance and increased maintenance costs. Traditional fault detection methods, that rely on manual inspections and predefined threshold-based systems, are slow, reactive, and prone to inaccuracies. This paper proposes an Artificial Neural Network (ANN)-based fault diagnosis system that utilizes historical and real-time sensor data to detect and classify operational issues in STPs. The model was trained on key wastewater parameters, including the influent flow rate, BOD, COD, TSS, pH, temperature, ammonia nitrogen levels, aeration rate, and sludge retention time. It predicts effluent quality indicators (Effluent BOD, Effluent COD, Effluent TSS) and identifies three operational states: No Issue, Aeration Issue, and Filtration Issue. A comparative analysis with conventional fault detection techniques demonstrates that the ANN model achieves higher accuracy, early fault detection, and proactive troubleshooting. The results highlight the potential of AI-driven diagnostics for optimizing wastewater treatment, reducing downtime, and improving process efficiency, thereby contributing to the development of smart and automated STPs.