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 input of 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, which is an advanced An important branch of the field of manufacturing technology, it belongs to both the field of water treatment and 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 appl...

AI Technical Summary

Problems solved by technology

[0004] Although the traditional switch control or PID control is a widely used control method at present, because the dissolution process of oxygen is affected by the water quality, temperature and pH value of the incoming water, it is highly nonlinear, strongly coupled, time-varying, and large characteristics such as hysteresis and uncertainty

The traditional switch control or PID control method has poor self-adaption ability and often cannot achieve ideal control effect

In recent years, there have been researches on intelligent control methods based on fuzzy and neural networks at home and abroad, which have solved the problem of poor adaptive ability of traditional switch control or PID control methods.

But there are still some deficiencies. The above fuzzy control and neural network control must determine its own rule number or neural network structure before application. During the application process, only its parameters can be modified, but its structure cannot be modified; The network structure has better learning accuracy, but often requires a large storage space and computing time; while the fuzzy rule or neural network structure with too small scale has a relatively simple network structure, but its information processing capacity is limited.

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