Sewage-disposal soft measurement method on basis of integrated neural network

Abstract

The invention discloses a sewage-disposal soft measurement method on the basis of an integrated neural network, and belongs to the field of sewage disposal. A sewage disposal process is high in nonlinearity, time-varying characteristics and complexity, and measurement for key water quality indexes is crucially significant in control of water pollution. In order to improve precision of simultaneous soft measurement for various key water quality parameters in a sewage-disposal soft measurement process by the sewage-disposal soft measurement method, an integrated neural network model is provided for measuring COD (chemical oxygen demand) of outlet water, BOD (biochemical oxygen demand) of the outlet water and TN (total nitrogen) of the outlet water, coupling relation between the three key water quality parameters is sufficiently utilized in the model, the integrated neural network model contains three feedforward neural sub-networks, and the various neural sub-networks are trained by particle swarm optimization, so that the optimal structure of each neural sub-network can be obtained. The COD of the outlet water, the BOD of the outlet water and the TN of the outlet water are predicted by the trained neural network finally, and prediction results are accurate.

Description

technical field [0001] Soft sensing is one of the main development trends of detection technology and instrumentation research, and is an important branch of the advanced manufacturing technology field. The invention relates to a soft sensing method for effluent water quality indicators in the process of sewage treatment, which belongs to the field of sewage treatment. Background technique [0002] With the rapid development of our country's national economy, the scale of cities continues to expand, and the number of people grows rapidly, followed by the continuous increase in the amount of urban sewage water, and the water quality is becoming more and more complicated, and there is a tendency to continue to deteriorate. 95% of the sewage in most cities in my country is discharged into the water body without treatment. Only relying on dilution and self-purification of the water body can no longer meet the requirements of discharge standards, and will cause great pollution and...

AI Technical Summary

Problems solved by technology

[0006] In recent years, a variety of neural network models have been applied to soft sensors for sewage treatment. In terms of the number of outputs of neural networks, they can be divided into multi-input and single-output neural networks and multi-input and multi-output neural networks. Measuring multiple key water quality parameters, multi-input and multi-output neural network is the current research trend, but this kind of network also has big problems, such as the selection of neural network learning algorithms, the problem of output accuracy, etc. For these problems, The present invention proposes a soft-sensing method for key water quality parameters of sewage treatment based on an integrated neural network, which is a multi-input multi-output neural network model with higher precision

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