Machine Learning-Based Model Prediction of an Adsorption Desalination System and Investigation of the Impact of Parameters on the System’s Outputs

Adsorption desalination (AD) has emerged as a novel technique for desalination, which works cyclically and via switching, and various variables have an effect on its performance. This study uses machine learning procedures to present a model predictive approach for adsorption desalination systems. The adsorption desalination system will be modeled through the utilization of multilayer perceptron (MLP) and radial-based function (RBF) neural network approach hes. The purpose of this research is to provide valuable insights into optimizing system efficiency and expanding the applicability of adsorption desalination technologies by investigating the strengths and limitations of each model. Hence, the Specific Daily Water Production (SDWP), coefficient of performance (COP), and specific cooling power (SCP) are determined. There are 55 instances in the dataset, each with five input variables: temperatures of the evaporator and condenser, adsorption beds, and inlet hot saltwater. Additionally, three output variables are recorded: COP, SCP, and SDWP. The results of this investigation show that the MLP is more effective for simulating the AD system, and the Roots of Mean Square Error of COP, SCP, and SDWP are 0.002, 0.5921, and 0.0465, respectively. Then, the impact of input factors on output parameters was examined. The results show that the inlet hot saltwater temperature parameter affected the output parameters the most. Subsequently, the COP parameter is mainly affected by the adsorption beds, evaporator, and condenser temperature. The SCP parameter is primarily influenced by the inlet hot saltwater temperature, condenser temperature, temperatures of the two adsorption beds, and evaporator temperature.