Neural Network Simulation Method for Desulfurization Efficiency of Seawater Desulfurization Plant

Abstract

The invention discloses a method for simulating desulfurization efficiency of a seawater desulfurizer through a neural network. The method is characterized by comprising the following steps: taking parameters such as a plurality of groups of smoke volumes, inlet smoke SO2 concentrations, seawater yields, seawater temperatures, first booster pump currents A and second booster pump currents B acquired in the running process of a seawater desulfurization system as input of a BP neural network mode according to the practical running condition of a seawater desulfurizer in a thermal power plant, and taking seawater desulfurization efficiency as output of the BP neural network; determining the number of input layer nodes of a BP neural network, the number of hidden layer nodes of the BP neural network and the weighted and threshold parameters of the BP network so as to train the model, and establishing a nonlinear function relationship between six desulfurization parameters and the desulfurization efficiency; importing parameters which are monitored in real time into the established model to carry out simulation output, and predicting the seawater desulfurization efficiency. According to the method, the complicated and changeable working conditions can be better predicted.

Description

technical field [0001] The invention relates to a neural network simulation method for the desulfurization efficiency of a seawater desulfurization device. Background technique [0002] Seawater desulfurization uses natural seawater as an absorbent to remove SO from flue gas 2 The advanced wet desulfurization technology has the advantages of high desulfurization efficiency, no secondary pollution, and low investment and operation costs. At present, more than a dozen coal-fired power plant seawater desulfurization projects have been put into operation in China, and seawater desulfurization has a good application prospect in the future. [0003] Most of the existing seawater desulfurization model research is on the numerical simulation of the mass transfer process of the desulfurization process and the research on the laboratory model device, studying the liquid-gas ratio, flue gas temperature, SO 2 The influence of factors such as concentration, seawater salinity and seawate...

AI Technical Summary

Problems solved by technology

[0003] Most of the existing seawater desulfurization model research is on the numerical simulation of the mass transfer process of the desulfurization process and the research on the laboratory model device, studying the liquid-gas ratio, flue gas temperature, SO 2 The influence of factors such as concentration, seawater salinity and seawater temperature on the desulfurization efficiency has a good effect on the experimental simulation under laboratory conditions. In the process of working condition prediction and operation optimization and adjustment applied to actual desulfurization devices, there are often deviations in the prediction of process parameters. Larger, poor prediction accuracy for changing working conditions and other issues

At the same time, the algorithm of the model is complex, and it is also difficult for the mathematical modeling of complex nonlinear systems in practical engineering applications.

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