Wavelet Neural Network Process Control Technology in the Application of Aluminum Electolysis

摘要

Aluminum electrolysis is a non-linear, time-varying and large time delay process, which interfered by the interaction of strong electric field, strong magnetic field and strong heat field. So, it is a high energy consumption process and the process control is very difficult. Therefore, the hot issue for the control system is how to save energy, improve the current efficiency, increase the yield and the quality of aluminum electrolysis. A wavelet neural network predictive control method is proposed in this paper which based on the analysis of characteristics and problems for the aluminum electrolysis process. The proposed method combines the neural network control technology and forecasting techniques. By tracking the parameter of the cell resistance which reflects the alumina concentration, the controller regulates the control strategy real timely to make the alumina concentration in an ideal range through controlling the alumina feeding quantity of the feeding device, and the system's hardware and software are also designed .The experiment results show that the method not only has a good effective control performance and an energy-saving effect, but also has an important significance of increasing the yield and quality of aluminum.