期刊
INTERNATIONAL JOURNAL OF MINERAL PROCESSING
卷 155, 期 -, 页码 140-146出版社
ELSEVIER
DOI: 10.1016/j.minpro.2016.08.015
关键词
Hardgrove Grindability Index; Random Forest; Variable importance; Proximate analysis; Ultimate analysis; Petrography
Application of Random Forest (RF) via variable, importance measurements (VIMs) and prediction is a new data mining model, not yet wide spread in the applied science and engineering fields. In this study, the VIMs (proximate and ultimate analysis, petrography) processed by RF models were used for the prediction of Hardgrove Grindability Index (HGI) based on a wide range of Kentucky coal samples. VIMs, coupled with Pearson correlation, through various analyses indicated that total sulfur, liptinite, and vitrinite maximum reflectance (R-max) are the most importance variables for the prediction of HGI. These effective predictors have been used as inputs for the prediction of HGI by a RF model. Results indicated that the RF model can model HGI quite satisfactorily when the R-2 = 0.90 and 99% of predicted HGls had less than 4 HGI unit error in the testing stage. According to the result, by providing nonlinear VIMs as well as an accurate prediction model, RF can be further employed as a reliable and accurate technique for the evaluation of complex relationships in coal processing investigations. (C) 2016 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据