Modeling and Optimization for Reverse Osmosis Water Treatment Using Artificial Neural Network and Genetic Algorithm Approach: Economic and Operational Perspectives

This study contributes to the modeling and optimization model for reverse osmosis water treatment (ROWT) due to a lack of economic and operational aspects. This study proposes a hybrid modeling and optimization framework using a hybrid artificial neural network (ANN) and genetic algorithm (GA) to enhance the accuracy of economic and operational predictions for ROWT. The ANN model is developed using seventeen key process parameters extracted from various ROWT plants, including flow rate, pH, conductivity, and turbidity. The GA is employed to optimize the network architecture and learning parameters based on the mean absolute percentage error (MAPE) as the fitness function. The findings of this study indicate that the GA-optimized model significantly outperforms the baseline model, reducing MAPE for the economic aspect (84.9% improvement) and the operational aspect (32.2% improvement). The findings from this study indicate that the hybrid ANN–GA approach is a management decision-making tool for reducing expenses without compromising water quality in ROWT management. The practical implications of this study are that predictions not only meet operational parameters but also predict expenses incurred, allowing managers to plan future budgets by optimizing ROWT resources and maintenance activities.