A review on state-of-the-art applications of data-driven methods in desalination systems

Abstract

The substitution of conventional mathematical models with fast and accurate modeling tools can result in the further development of desalination technologies and tackling the need for freshwater. Due to the great capability of data-driven methods in analyzing complex systems, several attempts have been made to study various desalination systems using data-driven approaches. In this state-of-the-art review, the application of various artificial intelligence and design of experiment data-driven methods for analyzing different desalination technologies have been thoroughly investigated. According to the applications of data-driven methods in the field of desalination, the reviewed investigations are classified into five categories namely performance prediction using operational parameters, performance prediction using design parameters, optimization and correlation development, maintenance, and control of desalination systems. For each category, valuable information about the data-driven methods such as inputs, outputs, hyper-parameter tuning methods, and size of datasets have been provided and the main remarks are reported. The findings showed that data-driven methods can play a vital role in each aforementioned application for both thermal and membrane-based desalination technologies. Eventually, the research gaps are highlighted and a roadmap is also provided for future data-driven analysis of various desalination systems and their further advancement.