An Artificial Neural Network Mechanism for Optimizing the Water Treatment Process and Desalination Process

Abstract

An artificial neural network (ANN) was created to predict the operation of a reverse osmosis desalination process, and then it was utilized to supply water temperature modeling. The techniques used for producing the new water by utilizing the water treatment process and desalination of seawater process are growing rapidly. Desalination produces water for residential use, the processing industry, water supply, and other reasons. Membrane procedures (Reverse Osmosis RO) or thermal desalination are always the most common desalination techniques. The optimization process consists of some units such as coagulation, sedimentation, disinfection, settling, and filtration. The process of desalination has various parameters such as vacuum pressure, feed salt concentration, temperature feed into the inlet, and rate of feed flow. For the desalination process, reverse osmosis is the most helpful process for desalination that can be coupled with more variable renewable energy sources are wind and solar. Using a couple of renewable energy to produce freshwater from saltwater can able to reduce the impact on the environment that desalination can create due to the power consumption of energy. The optimization of water treatment and desalination experimental results was developed by the model of an artificial neural network (ANN).