A Comprehensive Review of AI Algorithms for Performance Prediction, Optimization, and Process Control in Desalination Systems

Abstract

Water scarcity is a global issue which is predicted to worsen in the upcoming decades. To deal with this challenge, various desalination technologies have been deployed to produce freshwater from saline water. Artificial Intelligence (AI) algorithms have emerged recently as a tool for optimizing desalination processes and forecasting their performance. This comprehensive review examines the various AI algorithms employed in desalination literature. In addition, it reviews their various applications which include performance prediction models. These models make use of historical data to forecast system performance under differing circumstances. In order to improve efficiency, AI algorithms are also used in desalination modelling to optimize system parameters and aid in process control. The history of AI utilization in desalination in addition to its application in standalone desalination systems, hybrid desalination systems, renewable energy desalination, and poly/multi-generation systems was discussed thoroughly. In addition, a future outlook for the topic and gaps in the current literature have been identified and presented. In general, it was observed that AI algorithms have had a significant impact on desalination literature. This is especially true for process modelling where the impact is expected to expand. The reasons for this expansion are largely associated with the advantages offered by AI algorithms when compared with mathematical modelling such as their low computational requirements.

Keywords: Artificial intelligence, Artificial neural network, Genetic Algorithm, Hybrid desalination, Poly-generation, Renewable desalination