Fly ash carbon content prediction method based on coal quality prediction and PSO-SVM

Abstract

The invention relates to a fly ash carbon content prediction method based on coal quality prediction and PSO-SVM, and the method comprises the steps: 1, selecting and constructing proper coal qualityinfluence parameters and coal quality feature parameters, constructing a coal quality prediction model based on a BP neural network, and carrying out the soft measurement of the coal quality of coal as fired through coal type matching; 2, constructing a support vector machine (SVM)-based fly ash carbon content prediction model. The method has the beneficial effects that the method can be used forsoft measurement of the carbon content of the fly ash, the model is optimized, and the prediction performance of the soft measurement model of the carbon content of the fly ash is further improved. Firstly, a BP neural network model is constructed for coal quality prediction by utilizing coal quality influence parameters so as to obtain relatively accurate coal quality test data; the fly ash carbon content prediction model provided by the invention integrates two technologies of coal quality prediction and fly ash carbon content prediction, realizes accurate measurement of the fly ash carbon content, and has important significance for improving the combustion economy of a boiler, reducing the coal consumption and improving the environment.

Description

technical field [0001] The invention relates to the field of optimization control of the power generation process of a coal-fired unit, and aims at the problem of online application of fly ash carbon content soft measurement, and particularly includes a method for predicting the carbon content of fly ash based on coal quality prediction and PSO-SVM. Background technique [0002] The carbon content of fly ash is one of the most important and basic parameters in boiler operation. It characterizes the important operating characteristics of the boiler and is an important indicator for calculating the heat loss of incomplete combustion of boiler machinery. The unburned combustibles in the fly ash can represent whether the air-to-coal ratio in the boiler combustion control is reasonable, and it is an important loss item that affects the thermal efficiency of the boiler. Accurate and effective measurement of carbon content in fly ash is conducive to adjusting the working conditions...

AI Technical Summary

Problems solved by technology

However, the coal quality test data of most power plants still rely on manual test methods, and it is difficult to obtain real-time coal test data

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