Coal blending cost optimization method and device, and storage device

Abstract

The present invention provides a coal blending cost optimization method and device, and a storage device. A gray relative analysis method and an SVM are employed to establish a coke quality predictionmodel, a dynamic timing sequence matching method is employed to register routine ERP data, experiment data and lithofacies data, establishing a coal blending cost optimization model, and employing anelite learning chaotic particle swarm optimization method to solve a method for optimization of coal blending cost and an optimal coal blending scheme so as to achieve the purpose of reduction of thecoal blending cost and full utilization of the coal blending materials. The coal blending cost optimization device, and the storage device are used to achieve a coal blending cost optimization method. The coal blending cost optimization method and device, and the storage device can improve the accuracy, the stability and the calculation precision of the coal blending cost optimization method, andmaximally reduce the coal blending cost while ensuring that coke quality meets the enterprise requirements.

Description

technical field [0001] The invention relates to the field of coal blending and coking, in particular to a coal blending cost optimization method, equipment and storage equipment. Background technique [0002] With the large-scale blast furnace and the development of oxygen-enriched pulverized coal injection technology, the coke quality requirements for blast furnace production are getting higher and higher, and it is difficult for most single types of coal to produce high-quality metallurgical coke with high mechanical strength in the coke oven. To make reasonable use of coal resources, save high-quality coking coal, and produce coke that meets the quality requirements, coal blending coking technology must be adopted. [0003] The theory of coal petrology reveals the coking mechanism of coal from the perspective of microscopic components, and then reflects the influence of coal materials with different components and degrees of coalification on coking, which can effectively ...

AI Technical Summary

Problems solved by technology

Therefore, how to effectively use the lithofacies data in coal petrology theory and traditional ERP data, on the basis of small coke oven experimental data, accurately determine the proportion of each single coal, and optimize the cost of coal blending is the key issue; There are many constraints in the coal cost optimization process, it is easy to fall into local optimum, and it is difficult to obtain the optimal cost and coal blending plan

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