Volume 48 Issue 7
Aug.  2022
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ZHAO Xianzhi, CHEN Junlin. Prediction method of coal calorific value based on quantile regression[J]. Journal of Mine Automation，2022，48(7)：130-134. DOI: 10.13272/j.issn.1671-251x.2022060023
Citation: ZHAO Xianzhi, CHEN Junlin. Prediction method of coal calorific value based on quantile regression[J]. Journal of Mine Automation，2022，48(7)：130-134. DOI: 10.13272/j.issn.1671-251x.2022060023
shu

Prediction method of coal calorific value based on quantile regression

  • 1.

    Inner Mongolia Coal Geology Exploration (Group) 153 Co., Ltd., Hohhot 010010, China

  • 2.

    School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China

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  • Received Date: June 07, 2022
  • Revised Date: July 18, 2022
  • Available Online: July 05, 2022
    Graphical Abstract
    • Abstract
  • At present, the traditional linear regression model is mainly used to predict the calorific value of coal. But it is difficult to express the complex relationship between independent variables and dependent variables. The model needs data to obey specific distribution assumptions. And the model is sensitive to abnormal values. In view of the above problems, a prediction method of coal calorific value based on quantile regression is proposed. The method selects the coal industry analysis indicators that are easy to measure, such as total moisture, ash and volatile matter. The method uses two quantile regression methods, linear quantile regression and quantile regression forest, to predict the calorific value of coal. The results are compared with that of the traditional linear regression method. The results show that the predicted value of calorific value of coal given by linear regression is only a conditional mean value. But the range of predicted value of calorific value of coal can be given by quantile regression. The prediction effect of quantile regression is better than linear regression and linear quantile regression. The importance of total moisture for the prediction of calorific value of coal is much greater than that of ash and volatile matter. Total moisture has great influence on the prediction of calorific value of low calorific value coal. But total moisture has little influence on the prediction of calorific value of high calorific value coal. Volatile matter and ash have little influence on the prediction of calorific value of low calorific value coal. But volatile matter and ash have a great influence on the prediction of calorific value of high calorific value coal.
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    • References (15)
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