Adaptive control method of dissolved oxygen (DO) based on recurrent neural network (RNN) model

Abstract

Aiming at the characteristics of high non-linearity, strong coupling property, time variation, large lag, serious uncertainty and the like in a sewage treatment process, the invention provides an adaptive control method based on a recurrent neural network (RNN) model, thereby realizing the control on the concentration of dissolved oxygen (DO) in the sewage treatment process. In the control method, an RNN is used for modeling the sewage treatment process so as to feed the concentration of the DO in the sewage treatment process back to a controller in real time, thus the adaptive ability of thecontroller is improved, and the DO can quickly and accurately reach the expected requirement. The method provided by the invention solves the problem that the current method based on switching control and PID (Proportion Integration Differentiation) control has poor adaptive ability. Experimental results show that the method can quickly and accurately control the concentration of DO, has strongeradaptive ability, improves the sewage treatment quality and efficiency, lowers the sewage treatment cost, and promotes the high-efficiency stable operation of a sewage treatment plant.

Description

technical field [0001] The present invention uses an adaptive controller based on a recursive neural network model to realize the control method of dissolved oxygen (DO) in the process of sewage treatment, and the control of dissolved oxygen (DO) in the process of sewage treatment is an important part of sewage treatment, which is an advanced manufacturing technology field. 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 application prospects. [0003...

AI Technical Summary

Problems solved by technology

By analyzing the sewage treatment process, construct a recursive neural network model, design a controller based on this model, improve the anti-interference ability of the controller, and solve the self-adaptive problem of the controller in a strong interference environment, so that the aeration rate can be changed very well The effect of controlling DO concentration is achieved; the present invention improves the precision of DO control in the sewage treatment process and ensures the normal operation of the sewage treatment process;

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