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Laboratory Study and Optimization of Coal-blending Coking Without Fat Coal

Author: SunXiWei
Tutor: ZhangDeXiang
School: East China University of Science and Technology
Course: Chemical processes
Keywords: Coking coal blending Minerals catalytic Index Coke microstructure BP neural network
CLC: TQ520.62
Type: Master's thesis
Year: 2011
Downloads: 207
Quote: 0
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Abstract


In this paper, the conditions of lack of fat coal -based coal , coking coal blending program and optimization of laboratory . By adding lean agent and binder and deployment of coking coal, other kinds of measures to improve the coke cold strength ( crushing strength of M13 and abrasion resistance M3 ) and thermal properties ( CRI and the reaction of C02 reactive strength CSR ) . Studied by means of the macroscopic properties of the coke coal analysis , analysis of the characteristics of coke ; using X-ray diffraction to study the microstructure of coke . The results showed that the amount of coke powder for thin agents, to improve the crush strength of the coke ; temperature asphalt as a binder modified with the coal , but the ratio should not exceed 5% ; in other conditions fixed ratio , gas coal content exists an optimum value of the coke quality to achieve the best ; catalytic mineral matter in coal coke CO2 reactivity , which is the most obvious coal M02YL at . Microcrystalline structure is the most essential factors that affect the macroscopic properties of coke , coke crystalline structure by the degree of coalification , live idler ratio and significant influence of mineral matter in coal . The laboratory crucible naphthalene experimental results and small coke oven comparison of the experimental results showed that the laboratory crucible feasibility of coking coal blending method supported by a small coke oven experimental verification can be quickly and efficiently determine the appropriate blending program . Using BP neural network, the mapping relationship established with the coal coke from single coal properties to nature . The choice of the three - tier network to predict the quality of coke , results show that the deviation of the indicators are less than 1% , the prediction accuracy of the neural network , adaptable .

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CLC: > Industrial Technology > Chemical Industry > Coke chemical industry > General issues > Coking process > Blending
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