Prediction and Modeling of Desalination Performance of Nanofiltration Membranes Based on Machine Learning

In the process of desalination, the optimization of nanofiltration membrane is very important to improve the removal rate of sodium chloride. The traditional method is often unstable when the operating conditions change, and it is difficult to adapt to different water quality, which limits its application in desalination processes. In this paper, particle swarm optimization algorithm is used to optimize the backpropagation artificial neural network, and the desalting performance of nanofiltration membrane is predicted and modeled successfully. The accuracy of prediction in the test stage reached 99.38%. This model is conducive to allocating the energy, resources and time of the experiment more rationally. It provides a valuable reference for improving the desalination efficiency of nanofiltration membrane in industry. In addition, this study provides a new perspective on water treatment technology in resource-limited areas, which helps to improve the sustainable use of water resources and the quality of life of residents.