Adaptive LSTM Forecasting for RO Membrane Fault Detection

The proposed research focuses on adaptive forecasting to predict faults in Reverse Osmosis (RO) membranes, crucial for maintaining water treatment efficiency and system longevity. By leveraging Long Short-Term Memory (LSTM) networks integrated with Adaptive Windowing (ADWIN) drift detection, the system dynamically adjusts to changes in membrane performance over time. This approach enables real-time monitoring and forecasting of membrane health through sensors that provide continuous data on the RO water quality. Early detection of potential faults is ensured before they compromise system operation which also affects the human health. Additionally, the system’s adaptability allows it to be effectively utilized in various locations with different water qualities, making it versatile for diverse environmental conditions. This method represents a significant advancement in proactive maintenance strategies for RO systems, contributing to more reliable and efficient water purification processes.