%0 Journal Article
%A Cao Yuning
%A Liu Ting
%A Wang Huan
%A Wu Aixiang
%T Underflow concentration prediction model of deep-cone thickener based on data-driven
%D 2019
%R 10.19682/j.cnki.1005-8885.2019.1027
%J Journal of China Universities of Posts and Telecommunications
%P 63-72
%V 26
%N 6
%X <div style="line-height: 150%">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<br>
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.</div>
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2019.1027