Neural Networks for Predictive Modeling of Large-Scale Commercial Water Desalination Plants

摘要

Artificial intelligence techniques could be introduced into water desalination plants in many ways. They could be involved in the design, operation, control, and optimization of desalination plants. The purpose is achieving better design, improving efficiency, and increasing operational safety. A neural network approach based on the backpropagation algorithm has been applied for the prediction and optimization of process performance parameters of large-scale desalination plants. In contrast to several previous studies, this work utilizes actual operating data (not simulated data) from a Multistage Flash (MSF) Distillation Plant (57.6 million imperial gallons per day, MGPD) and a Reverse Osmosis (RO) Plant (15 MGPD) located in Kuwait, and Saudi Arabia, respectively. The application of neural computing into the MSF and RO plants is necessary because of computational complexity, nonlinear behavior with many degrees of freedom, and the presence of uncertainty in the control environment. This study demonstrates the use of neural network predictor in conjunction with statistical techniques to determine the optimal operating conditions of the processes. A comparison