Underflow concentration prediction model of deep-cone thickener based on data-driven

作     者：Wang Huan Liu Ting Cao Yuning Wu Aixiang 

作者机构：School of Civil and Environmental EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing 100083China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2019年第26卷第6期

页      面：63-72页

核心收录：

学科分类：081901[工学-采矿工程] 0819[工学-矿业工程] 081903[工学-安全技术及工程] 08[工学] 

基　　金：supported by the National Key Research and Development Program of China(2016YFB0700500) the National Science Foundation of China(61572075,61702036) Fundamental Research Funds for the Central Universities(FRF-TP-17-012A1) China Postdoctoral Science Foundation(2017M620619) 

主　　题：paste filling underflow concentration machine learning XGBOOST prediction model 

摘      要：The underflow concentration prediction of deep-cone thickener is a difficult problem in paste filling. The existing prediction model only determines the influence of some parameters on the underflow concentration, but lacks a prediction model that comprehensively considers the thickening process and various factors. This paper proposed a model which analyzed the variation of the underflow concentration from a number of influencing factors in the concentrating process. It can accurately predict the underflow concentration. After preprocessing and feature selection of the history data set of the deep-cone thickener, this model uses the eXtreme gradient boosting(XGBOOST) in machine learning to deal with the relationship between the influencing factors and the underflow concentration, so as to achieve a more comprehensive prediction of the underflow concentration of the deep-cone thickener. The experimental results show that the underflow concentration prediction model based on XGBOOST shows a mean absolute error(MAE) of 0.31% and a running time of 1.6 s on the test set constructed in this paper, which fully meet the demand. By comparing the following three classical algorithms: back propagation(BP) neural network, support vector regression(SVR) and linear regression, we further verified the superiority of XGBOOST under the conditions of this study.