AI-Powered Railway Sanitation: A Unified System for CDTS, ZDTS, and Bio-Toilet Optimization

The sanitation challenges in Railways remain persistent despite advances in Controlled Discharge Toilet Systems (CDTS) and Zero Discharge Toilet Systems (ZDTS). This paper identifies the gaps in these existing systems, including the inability to handle non-biodegradable waste, lack of real-time monitoring, and energy inefficiencies. To address these challenges, a comprehensive AI-driven solution is proposed, integrating waste segregation, IoT-enabled monitoring, and advanced geofencing. Central to this system is an AI-enhanced Constraint Solver, which dynamically optimizes waste discharge operations by leveraging predictive analytics and real-time environmental data. By combining Machine Learning (ML) for waste prediction, Reinforcement Learning (RL) for geofence adaptation, and Integer Linear Programming (ILP) for operational optimization, the system achieves adaptive discharge control, enhanced resource efficiency, and reduced environmental impact. The proposed solution is expected to reduce maintenance downtime, minimize sensitive zone violations by over 90%. This approach offers a scalable and sustainable solution for modernizing sanitation in and beyond Indian Railways, including Bio-toilets as well.