The study of soft sensor modeling method based on wavelet neural network for sewage treatment

Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameter of sewage treatment quality can not be detected on-line, a soft sensor modeling method based on wavelet neural network is presented. The wavelet network structure for soft sensor of sewage treatment quality is established. We adopt a method of reduce the number of the wavelet basic function by analysis the sparse property of sample data, the learning algorithm based on the gradient descent was used to train network. With the ability of strong function approach and fast convergence of wavelet network, the soft sensor modeling method can truly detect and assess the quality of sewage treatment in real time by learning the sewage treatment parameter information of sensors acquired. The detection results show that this method is feasible and effective.