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| Nonlinear prediction model for caking index based on coal petrology |
| WANG Chengyong1,MEN Dongpo2,CHEN Peng1,LI Ziwen1 |
(1.School of Chemistry and Materials Engineering, Liupanshui Normal University, Liupanshui
553004, Guizhou, China;2. Science and Technology Department, CHN Energy, Beijing 100010, China) |
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Abstract In order to obtain an accurate and reliable prediction model for caking index of blended coal, two different coal mines in Hebei province were used as research objects. Prediction models of the caking index based on the coal petrographic blending utilizing nonlinear regression analysis were established. The macerals were effectively separated by the gravity separation method. The coal petrographic blending was carried out in low density products. The contents of different macerals were measured by a polarizing microscope. The results show that the molecular structure of the active component is moderate and contains a large number of active structures, which determines the caking index. The inert component forms the core and influences the caking index. With the increasing of the active component content and the decreasing of the inert component content, the caking index gradually increases. The S model, inverse model and progressive regression model based on the ratio between the reactive and the inert component had a higher fitting effect and accuracy. The regression model had the highest fitting degree, but the structure was complex. The inverse model had a simple structure and a good reliability, which could be used to predict the caking index of blended coal.
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