中南大学学报（自然科学版）2018，Vol.49Issue(1)：124-130,7.DOI:10.11817/j.issn.1672-7207.2018.01.017
充填膏体流变参数优化预测模型
Optimal prediction model of backfill paste rheological parameters
摘要
Abstract
In order to predict the backfill paste rheological parameters more accurately, the prediction model was established based on the principal component analysis and the improved BP neural network. By doing the backfill paste mix proportion experimental results in a metal mine, factors as backfill paste mass fraction, sand-cement ratio in mass, slurry weight, collapsed slump, and etc influencing backfill paste rheological parameters were firstly processed by the method of principal component analysis. The main ingredients were obtained. Rheological parameters were then predicted accurately by the improved BP neural network model. The results show that model of backfill paste rheological parameter prediction relative errors of predicting outcomes are all controlled within 5％,and compared with the prediction errors by BP neural network without principal components analysis, the relative errors of yield stress are reduced by 0.48％-7.29％, and relative errors of viscosity are reduced by 1.67％-6.20％, which shows that the model of backfill paste rheological parameter prediction is reasonable and effective, the prediction precision of yield stress and viscosity are significantly improved. It provides a new method to the prediction of backfill paste rheological parameters.关键词
充填膏体/流变参数/预测模型/影响因素Key words
backfill paste/rheological parameters/prediction model/influence factors分类
矿业与冶金引用本文复制引用
张钦礼,刘伟军,王新民,陈秋松..充填膏体流变参数优化预测模型[J].中南大学学报（自然科学版）,2018,49(1):124-130,7.基金项目
国家科技支撑计划项目(2013BAB02B05) (Project(2013BAB02B05) supported by the National Science and Technology Support Program) （2013BAB02B05）
