IoT-Integrated Quality Monitoring of Cartridges in Sediment Filtration Systems for Water Purification

Access to clean drinking water depends significantly on the effectiveness of water purification systems, with sediment filters being essential for removing suspended solids and particulate matter. To maintain optimal performance, these filters require consistent monitoring to detect functional decline or blockage. This research presents a comprehensive approach for real-time assessment of sediment filter performance through the integration of smart sensing technologies and predictive analytics. The system captures and evaluates dynamic water quality indicators—including turbidity, particle load, and pressure differentials—allowing for early identification of inefficiencies. By applying machine learning models to the sensor data, the method facilitates accurate forecasting of filter clogging events and degradation trends. This predictive maintenance framework enhances operational efficiency, reduces downtime, and supports long-term water quality assurance.