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

A technology based on neural network and neural network, applied in the field of control and water treatment, can solve problems such as limited information processing capacity, nonlinearity, and inability to modify the structure

Inactive Publication Date: 2009-11-11
BEIJING UNIV OF TECH
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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 probl...

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

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Embodiment Construction

[0065] Below in conjunction with specific embodiment, the present invention will be further described;

[0066] see figure 1 Shown, be the RBF neural network topological structure of the present invention; figure 2 It is a structural diagram of the controller of the present invention.

[0067] The invention obtains a controller of dissolved oxygen (DO) concentration in the sewage treatment process based on a dynamic radial basis (RBF) neural network; the controller analyzes the sewage treatment process and controls the aeration amount in the sewage treatment process So as to achieve the purpose of controlling DO concentration;

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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...

Claims

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Application Information

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IPC IPC(8): G05B13/00C02F3/12
CPCY02W10/10
Inventor 乔俊飞韩红桂郭迎春
Owner BEIJING UNIV OF TECH
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