Dissolved oxygen control method based on fuzzy neural network

Abstract

The invention discloses a dissolved oxygen control method based on a fuzzy neural network. The method comprises the following steps: S1, modeling is carried out; S2, fuzzy identification is carried out, and a fuzzy identification method is adopted to identify the structure and parameters of an object model; S3, a clustering method is adopted to determine the number of rules; S4, a neural network is used for learning reasoning data of an expert, a fuzzy reasoning rule is acquired, and each connection weight is adjusted at the same time; S5, after a least square method is used for order identification, a transitive closure method is used for clustering analysis according to a fuzzy similarity relation; S6, a Smith predictor is introduced; and S7, two independent control circuits of blower pressure and oxygen dissolution are adopted. The wastewater treatment effects are good.

Description

technical field [0001] The invention belongs to the field of environment, and in particular relates to a method for controlling dissolved oxygen based on a fuzzy neural network. Background technique [0002] In recent years, the Chinese government has begun to pay attention to environmental pollution. Especially after the sustainable development strategy was proposed, the government has also paid attention to environmental protection and governance while the economy is developing rapidly. Environmental protection has become one of the basic national policies of our country. From the perspective of environmental governance in recent years, there is not much difference between my country's environmental protection theory, process research and application and foreign countries. The gap is relatively large in environmental protection unit equipment and automatic control systems. The status quo of the automatic control system of my country's sewage treatment plants is: both manu...

AI Technical Summary

Problems solved by technology

Most of the detection instruments used in some sewage treatment are off-line instruments with poor accuracy and reliability. The monitoring method is also to measure after sampling, and then adjust the operating status of the equipment according to the measurement results.

Therefore, it is difficult to carry out fast and effective online real-time control, which often leads to unstable water quality

The sewage biochemical treatment process is a typical nonlinear, time-varying, uncertain, and large-delay complex large-scale system. Its modeling and control pose severe challenges to the control community.

This paper summarizes the status quo of sewage treatment process control as follows: 1) Due to the simple control, traditional control methods (conventional control, optimal control, adaptive control) are still widely used, but because of the large number of variables in the sewage treatment process, the process is nonlinear, Strong coupling and other characteristics, and various parameters are also time-varying, so traditional control methods based on precise mathematical models are difficult to achieve ideal control effects; 2) Although intelligent control has become the frontier and hot spot in the research and application of sewage treatment, but It is still in the initial stage of wide application at home and abroad

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