Soft Measurement Modeling Based on Improved Simulated Annealing Neural Network for Sewage Treatment

摘要

Considering the issues that the sewage treatment process is a complicated and nonlinear system, and the key parameters of sewage treatment quality can not be detected on-line, a soft measurement modeling method based on improved simulated annealing neural network (ISANN) is presented in this paper. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is organic combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm, instead of gradient falling algorithm of BP network to train network weight. It can get higher accuracy and faster convergence speed. We construct the network structure. With the ability of strong self-learning and faster convergence of ISANN, the soft measurement 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 experimental results show that this method is feasible and effective.