A Theoretical Digital Twin Framework for Multi-Stage Point-of-Use Water Purification Systems: Sensor Fusion and Stochastic Remaining Useful Life Prediction

Decentralized Point-of-Use (POU) water purification systems face extreme influent variability, yet current maintenance relies on reactive schedules, risking membrane failure. This study proposes a theoretical Digital Twin framework to overcome these vulnerabilities. We developed a 15-sensor topology across seven analytical nodes, using mass-balance equations to bypass physical sensors on the reverse osmosis (RO) reject stream. Component-specific degradation models—Hermia’s physical clogging, Yoon–Nelson adsorptive exhaustion, and solution–diffusion—were integrated into a Bayesian Particle Filter coupled with a Gamma process. This algorithm dynamically processes simulated noisy IoT sensor data to estimate the system’s Remaining Useful Life (RUL) and State of Health (SoH). Computational simulations under extreme operational scenarios demonstrated the framework’s ability to decouple physical fouling from chemical exhaustion. To address the lack of a physical prototype, internal consistency and algorithmic robustness were verified through Monte Carlos simulations under varied noise profiles, yielding high empirical interval coverage. The stochastic model adaptively updated RUL predictions, accurately tracking the sigmoidal chlorine breakthrough curve to protect the RO membrane. This framework provides a scalable, mathematical architecture for transitioning to Condition-Based Maintenance, establishing a foundation for future physical validation and deployment.