Theory and Applications of Intelligent Coal Blending in Coal Petrology, Edition 1

Dong, Li and Xuemei, ZHANG (2024) Theory and Applications of Intelligent Coal Blending in Coal Petrology, Edition 1. BP International. ISBN 978-93-48119-58-2

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Abstract

China is the world's largest coal consumer, mainly concentrated in the four major industries of electric power, building materials, metallurgy, and chemical industry. The three major directions of the coal chemical industry are coking, gasification and liquefaction. Coal coking refers to the process in which coal is isolated from air in a coke oven and heated to about 1000°C, so that coke, chemical products and coal gas can be obtained. The quality control of coke involves the quality control of coal blending and the actual operation of coke ovens. The technologies involved in coal blending include coke quality prediction, coal yard management and scientific coal blending. Coal petrology blending is the use of coal petrology parameters for coking coal blending, which is a major achievement of coal petrology application in the coking industry. The Ammosov-Schapiro method is a mathematical function that represents the vitrinite reflectance distribution, reflecting the mass details of all vitrinite and all microscopic components, vitrinite, inertinite, chitin, and minerals. These two quality details, vitrinite quality and microscopic component quality determine the strength and composition balance of the coke made from the coal blending and thus determine the coke performance. Based on the concept of fuzzy sets, data processing and calculation methods, the theory of intelligent coal blending in coal petrology creatively defines two sets, the pseudo-strength index set and pseudo-composition balance index set, and their elements to solve the problem of accurately predicting coke quality, effectively managing coking coal yards, and scientifically calculating coal blending ratios.

Item Type: Book
Subjects: GO for STM > Chemical Science
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 26 Oct 2024 06:25
Last Modified: 26 Oct 2024 06:25
URI: http://archive.article4submit.com/id/eprint/3018

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