Neural network based sewage disposal process optimal control method

A technology for sewage treatment and process optimization. It is applied in electrical program control, comprehensive factory control, and comprehensive factory control. It can solve the problems that the model accuracy has a great influence on the control performance, the calculation amount is large, and the global convergence speed is slow.

Active Publication Date: 2014-05-21
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Although the optimal control method based on model predictive control (MPC) can reduce energy consumption to a certain extent, MPC takes the system mechanism model as the core and calculates the optimal control quantity through certain mathematical means. Due to the complexity of the sewage treatment process, It is difficult to establish an accurate mathematical model, and the accuracy of the model has a great influence on the control performance, and the inability to measure some key water quality parameters online will also reduce the control performance
The optimization control method based on the genetic algorithm GA has a large amount of calculation and a slow global convergence speed, and the accuracy of the optimization result is controlled by the length of the code

Method used

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  • Neural network based sewage disposal process optimal control method
  • Neural network based sewage disposal process optimal control method
  • Neural network based sewage disposal process optimal control method

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Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] The experiment in this paper is based on the data of the BSM1 model under sunny weather. The specific steps are as follows:

[0067] 1. Establish performance index prediction model

[0068] Among the established performance indicators, α 1 、α 2 Take them as 0.8 and 0.2 respectively. The input of the prediction model is the set value of dissolved oxygen concentration and nitrate nitrogen concentration, and the output is the performance index value. The number of internal neurons is 45, that is, the structure of the prediction model is 2-45- 1. Initialize the weight of the network and input the weight W of the internal state P in The dimension of is 45×2, and the connection weight W between internal states P The dimension of is 45×45, and the weight W from the internal state to the output P out The dimension of is 1×45, and the weight W output to the internal state P back The dimension of is 45×1, the sparsity SD is 5%, and the weight spectrum radius is 0.48.

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Abstract

The invention provides a neural network based sewage disposal process optimal control method and aims to solve the problem of excessive energy consumption during a sewage disposal process. The sewage disposal process is a highly non-linear, time-varying and complicated process, and on the premise that effluent qualities meet the standard, reduction of operation energy consumption is much challenging. The method mainly includes two neural networks, wherein one neural network is used for establishing a sewage disposal process prediction model so as to achieve prediction of performance indexes, and the other neural network is used for real-timely optimizing control variable set values according to system states and predicted performance indexes. Finally, optimal control of dissolved oxygen concentration and nitrate nitrogen concentration is achieved, so that requirements of effluent qualities can met, and meanwhile, operation costs of the sewage disposal process can be effectively reduced.

Description

technical field [0001] Aiming at the problem of high energy consumption in the sewage treatment process, the invention utilizes the neural network to optimize the control of the dissolved oxygen concentration and the nitrate nitrogen concentration in the sewage treatment process in the BSM1. Neural network is one of the main branches of intelligent control technology. The optimal control of sewage treatment process based on neural network belongs not only to the field of water treatment, but also to the field of intelligent optimal control. Background technique [0002] With the continuous acceleration of urbanization and industrialization, my country's water environment has been severely damaged and has a tendency to continue to deteriorate. Sewage discharge not only seriously affects the daily life of residents, but also destroys the ecological balance of nature. In order to reduce the discharge of sewage, sewage treatment plants have been established all over the country...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCY02P90/02
Inventor 乔俊飞王莉莉韩红桂赵慢
Owner BEIJING UNIV OF TECH
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