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Optimized Random Forest Model for Predicting Salt Rejection Rate and Energy Consumption in Reverse Osmosis Desalination.

Lima Hongou
Volume 5

Abstract

Reverse Osmosis desalination is widely used for freshwater production because of its high salt removal efficiency and comparatively lower energy demand than many thermal desalination processes. However, maintaining optimal performance remains challenging due to the complex interactions among feedwater salinity, operating pressure, membrane properties, recovery ratio, temperature, fouling behavior, pretreatment efficiency, and flow conditions. Two of the most important performance indicators in Reverse Osmosis systems are salt rejection rate and energy consumption, as they directly influence permeate water quality, operational cost, membrane lifespan, and overall plant sustainability. Conventional empirical models and rule-based operational strategies often have limited ability to capture nonlinear process behavior under variable feedwater and operating conditions. To address these limitations, this paper proposes an optimized Random Forest model for predicting salt rejection rate and energy consumption in Reverse Osmosis desalination systems.

The proposed framework utilizes historical and real-time operational data collected from Reverse Osmosis plants, including feedwater conductivity, total dissolved solids, temperature, pH, turbidity, feed pressure, permeate pressure, concentrate pressure, feed flow rate, permeate flow rate, recovery ratio, membrane age, cleaning history, and specific energy consumption records. A comprehensive preprocessing pipeline is applied to remove abnormal readings, handle missing sensor values, normalize process variables, and construct relevant performance indicators. Feature selection and correlation analysis are incorporated to identify the most influential variables affecting salt rejection and energy demand. The Random Forest model is then optimized using systematic hyperparameter tuning to determine the optimal number of trees, maximum tree depth, minimum samples per split, feature selection strategy, and bootstrap configuration, thereby improving prediction accuracy and generalization.

The proposed model is designed to predict both salt rejection rate and energy consumption under diverse operating scenarios, including changes in feed salinity, pressure adjustment, temperature variation, membrane fouling progression, and recovery ratio modification. Model performance is evaluated using regression metrics such as Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, coefficient of determination, and prediction stability across different operational periods. Comparative analysis is conducted against conventional regression models, support vector regression, artificial neural networks, gradient boosting models, and non-optimized Random Forest baselines. Experimental evaluation is expected to demonstrate that the optimized Random Forest model achieves accurate and robust prediction of desalination performance while maintaining strong interpretability and computational efficiency.

The proposed framework can support desalination plant operators by providing reliable performance forecasting, identifying energy-intensive operating conditions, and assisting in operational decision-making. Furthermore, feature importance analysis is incorporated to reveal the dominant factors influencing salt rejection and energy consumption, such as feed pressure, feed salinity, recovery ratio, membrane condition, temperature, and permeate flow rate. By enabling early identification of inefficient operating regimes and supporting data-driven process optimization, the optimized Random Forest model can contribute to improved water quality, reduced energy consumption, lower operational cost, and enhanced sustainability of Reverse Osmosis desalination plants.

 

Keywords

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