Dissolved oxygen model prediction control method based on self-organization radial basis function neural network

Abstract

The invention discloses a dissolved oxygen model prediction control method based on a self-organization radial basis function neural network, not only belongs to the field of control, but also belongs to the field of water treatment. Aiming to the characteristics of high nonlinearity, strong coupling, time varying, large lag, serious uncertainty and the like in a sewage disposal process, the control method improves the disposal capability of the neural network by automatically adjusting a neural network structure, builds a prediction model of the sewage disposal process, carries out control through a prediction model control method, and therefore improves a control effect, and enables dissolved oxygen to achieve expected requirements fast and accurately. The method solves the problem that current methods based on a switch control and a proportion integration differentiation (PID) control are poor in adaptive ability. Experimental results show that the method can control dissolved oxygen concentration fast and accurately, has strong adaptive ability, improves the quality and the efficiency of sewage disposal process, reduces sewage disposal cost, and promotes a sewage treatment plant to run efficiently and stably.

Description

technical field [0001] The present invention uses a model predictive control method based on self-organizing radial basis neural network (SORBFNN) to realize the control of dissolved oxygen (DO) in the process of sewage treatment. have a significant impact on energy consumption. As an important part of sewage treatment, the control of dissolved oxygen (DO) in the sewage treatment process is an important branch of the advanced manufacturing technology field, which belongs to both the control field and the water treatment field. 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 r...

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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

At the same time, due to the hysteresis characteristics of the sewage treatment process, it is very difficult to accurately express the relationship between the control variables and the control objectives

At present, the structure of the sewage treatment process model is complex, there are too many parameters to be tuned, and the identifiability is poor. It cannot dynamically reflect the implicit relationship between the operating variable and the control target, and cannot be used for online control.

In addition, the number and content of pollutants in sewage are large and varied, which is a big challenge for detection

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