AI-Based Predictive Maintenance for Sewage Treatment Plants

Small capacity STPs (0.5-5 MLD) in particular are prone to high maintenance expenses, frequent equipment failures, and unscheduled downtimes due to reactive or schedule-based maintenance approaches. In this study, the application of Artificial Intelligence (AI) for predictive maintenance of STPs is investigated, where machine learning models are utilized to forecast the failures of equipment and optimize maintenance schedules. AI models including Random Forest, LSTM neural networks, and Gradient Boosting algorithms were developed and trained with real-time IoT sensor data and previous maintenance records. The technology can reliably predict failures of important components such as pumps, blowers, diffusers, and valves. The implementation results indicated a substantial reduction in unscheduled downtime (68-78%), an increase in equipment lifespan (25-40%), and overall maintenance cost savings (42-58%).  The AI system achieved an average prediction accuracy of 92.5%, a precision of 89%, and a recall of 91% and enabled proactive interventions before failures occur. The suggested AI-based predictive maintenance framework may be simply integrated in existing hybrid treatment systems and IoT monitoring platforms, with a user-friendly dashboard for maintenance planning and real-time notifications. This approach reduces dependence on human inspections, improves plant reliability and ensures compliance of uniform effluent quality as per CPCB/NGT requirements for small and medium towns. The findings of this research suggest that AI-enabled predictive maintenance is a groundbreaking, economical and sustainable solution to upgrade operations in decentralized STPs. The wider implementation has the potential to greatly increase infrastructure longevity and minimize operational expenditure and meet national goals under Swachh Bharat Mission-Urban and AMRUT programs.