Dissolved oxygen control method based on dynamic radial basis function neural network

Abstract

The invention discloses a dissolved oxygen control method based on a dynamic radial basis function neural network, which adopts the following steps of: determining a control object; designing a dynamic RBF neural network topology structure used for a dissolved oxygen DO controller during sewage treatment; correcting sample data; training a neural network by using part of the corrected data, controlling dissolved oxygen (DO) by using the trained RBF neural network, and taking an error between anticipant DO concentration and actually output DO concentration and an error change rate as the inputof the RBF neural network, wherein the output of the RBF neural network is the input of a frequency transformator, and the frequency transformator achieves the purpose of controlling a blower by adjusting the rotating speed of an electromotor so as to finally control aeration rate. The output of the whole control system is the actual DO concentration; the control effect of the controller is improved; the dissolved oxygen meets the anticipant requirements quickly and accurately; and the problem of poor self-adaptive capability based on switch control and PID control is solved.

Description

technical field [0001] The present invention utilizes a controller based on dynamic radial basis RBF neural network to realize the control method of dissolved oxygen DO in the sewage treatment process, and the control of dissolved oxygen DO in the sewage treatment process is an important part of sewage treatment and an important branch of the advanced manufacturing technology field , both in the field of water treatment and in the field of control. Background technique [0002] With the growth of the national economy and the enhancement of public awareness of environmental protection, sewage treatment automation technology has ushered in unprecedented opportunities for development. The national medium and long-term scientific and technological development plan proposes to research and promote new technologies for sewage treatment with high efficiency and low energy consumption. Therefore, the research results of the present invention have broad application prospects. [00...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to construct a dynamic radial basis RBF neural network model by providing a method for controlling dissolved oxygen based on a dynamic radial basis neural network, analyze the sewage treatment process, change the neural network structure in real time, and improve the control ability of the controller ; Solve the problem of automatic adjustment of the controller according to the environment, and achieve the effect of controlling DO concentration by controlling the aeration amount in the sewage treatment process; improve the accuracy of DO control in the sewage treatment process, and ensure the normal operation of the sewage treatment process;

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