A data-driven approach for predictive modelling of pressure requirements and permeate salinity for multi-stage reverse osmosis plant operation employing neural networks

Abstract In alignment with the global issue of increasing clean water demand while aiming at achieving sustainability and enhancing energy efficiency. The implementation of Artificial Neural Networks (ANN) in Reverse Osmosis (RO) desalination plants has exhibited a substantial success. This study introduces a new approach that implements Deep Neural Network (DNN) with multi-output, employing Adam optimizer, to predict two critical parameters for desalination plants: output water quality in terms of Total Dissolved Solids (TDS) and the required membrane pressure during the plant’s operation. As these two predicted targets are the main reference parameters when evaluating the production quality and energy consumption for RO desalination plants. The consideration of multiple membrane configurations and feed flow rates, which are relatively unexplored features in ANN research on desalination, is the key innovation of this study. The multi-output DNN achieved an R 2 score of 0.99, signifying a high level of prediction accuracy of the model, which was additionally validated by using experimental data to endorse the proposed model. The integration of the DNN model in RO desalination plants will enhance output water quality and operational efficiency, which will be reflected in reduced energy consumption. The model’s capability to adapt to multiple plants’ parameters and membrane configurations will lead to resource optimization, reduce environmental impacts, and boost profitability, while consistently producing high-quality water. This will benefit the industry through providing advanced water treatment solutions. This innovation underscores the potential of AI-driven solutions in revolutionizing desalination technologies and fostering sustainable water management practice.