Method for predicting coke quality through nonlinear optimization coal blending based on coal rock vitrinite total reflectance

Abstract

The invention discloses a method for predicting coke quality through nonlinear optimization coal blending based on coal rock vitrinite total reflectance, thereby providing important technical assurance for the improvement of the stability and quality of coke produced by a coke making enterprise. The method provided by the invention comprises the following steps of: establishing a coking coal resource information database, and inputting the caking property index of coking blended coal and the coal rock index of single coking coal into the coking coal resource information database; and establishing a coke quality prediction model through a support vector machine, and then predicting the quality index of the coke according to the coal-quality caking property index level of the coking blended coal, wherein the coal-quality caking property index level comprises two factors, namely the maximum thickness value Y of a gelatinous layer and a caking index value G, and comprises coal rock vitrinite total-component reflectance and the liver-inert ratio of macerals. The method provided by the invention is capable of characterizing the maximum thickness value Y of the gelatinous layer indicating the quantity of metaplast in the softening process of the coking coal and indicating the caking property quality of the metaplast, thereby realizing the prediction process with the goal of predicting the mechanical strength and thermal state performance of the coke.

Description

technical field [0001] The invention relates to a method for predicting coke quality in the coking production process of the coking industry in the technical field of coal chemical industry, and specifically relates to the cohesiveness index level of the mixed coal used in coking production, including two factors: the maximum thickness Y value of the colloidal layer and the G value of the cohesive index; Index level, including two factors of vitrinite total reflectance and microcomponent activity-inert ratio, constitutes a practical system with two levels and four factors to predict the mechanical strength and thermal performance of coke. In particular, it is a method for predicting coke quality by nonlinear optimization of coal blending for vitrinite total reflectance. Background technique [0002] The large-scale blast furnace and the improvement of oxygen-coal injection technology put forward higher requirements on the quality and stability of coke used in blast furnaces....

AI Technical Summary

Problems solved by technology

Chinese patent CN1749358A discloses a method for predicting coke quality indicators using multiple linear regression methods. The coal quality is complex, and the volatile content of coking coal under the same conditions V daf and G value, there will be a big difference in predicting the mechanical strength and thermal properties of coke, because after a single coal is mixed, different quantities and types of coal can be used to obtain the same volatile V daf and G value

At the same time, because coking coal and coke quality indicators are a complex relationship in the coking process, using a linear relationship to replace the nonlinear relationship in the coking process of coking coal will inevitably increase the error between the predicted results of the coke quality indicators and the actual ones.

Chinese patent CN101661026A uses BP neural network to predict coke quality. In fact, this method uses the principle of empirical risk minimization, but the actual result cannot minimize the expected risk. There are theoretical flaws, and it is easy to fall into local minimum points, and the generalization ability Not strong and other shortcomings

In addition, the number of hidden layers and the number of nodes in the hidden layer of the neural network are generally determined by experience, and there is no clear algorithm, which affects the prediction accuracy of the neural network to a certain extent, and is subject to certain restrictions in practical applications.

Images