A Multi-Model Ensemble for Advanced Prediction of Reverse Osmosis Performance in Full-Scale Zero-Liquid Discharge Systems

= 0.960) predictions, capturing spatial and temporal dynamics. For long-term (30 d) forecasting, LSTM and ConvLSTM models achieved comparable performance, confirming suitability for extended prediction horizons. External validation across multiple industrial scenarios demonstrated the adaptability of the framework, enabling selection of optimal models for reliable predictions under diverse operational conditions. These findings demonstrated the capability of the framework to support proactive operational adjustments in response to fouling trends and enhance RO system stability. This study highlights the value of data-driven strategies in supporting operational decisions for industrial wastewater reuse and sustainable ZLD applications.