基于人工神经网络的水力旋流器分离性能预测  

Prediction of artificial neural network-based hydrocyclones classification performance

作　　者：韦鲁滨[1] 杜长江[1] 王月丽[1] 徐欢[1] 

机构地区：[1]中国矿业大学化学与环境工程学院,北京100083

出　　处：《黑龙江科技学院学报》2012年第2期116-118,139,共4页Journal of Heilongjiang Institute of Science and Technology

基　　金：国家自然科学基金项目(51174214);国家重点基础研究发展计划(973计划)项目(2012CB214900);中央高校基本科研业务费专项资金项目(2010YH06);高等学校博士学科点专项科研基金项目(20060290004)

摘　　要：水力旋流器分离过程复杂,其性能指标与影响因素之间属于典型的多维非线性关系。以往旋流器分离过程的理论和经验模型大多在特定的简化条件下得到,且预测单一。为了全面预测分离器性能指标,建立了三层BP神经网络模型,通过输入结构参数和操作参数,模拟输出分离粒径、生产能力、底流质量分数等多个分离性能指标。以生产能力为例,分析了神经网络与理论和经验模型计算值的预测精度。结果表明:在各传统预测公式中,庞学诗法的预测精度最高,误差为20.88%,与其相比,BP神经网络的预测误差仅为16.64%,优于其他各模型的预测精度,且能够实现性能指标的全面预测。人工神经网络是预测水力旋流器分离性能的可靠方法。Aimed at addressing complex separating process of hydrocyclone which suffers from a typical multidimensional nonlinear relationship between the influencing factors and the performance indexes,compounded by the previous theoretical and empirical models available often under simplifying some conditions and limited in prediction capability,this paper features a three-layers BP neural network model capable of predicting separated particle size,production capacity,the underflow concentration and so on,with the structure and operating parameters,for comprehensive prediction of the separator performance index.Comparison between the results derived from the BP network and the previous model associated with the production capacity shows that BP neural network boasts the prediction precision of 16.64%,comparing favourably with 20.88% for Pang Xueshi law,the best of all traditional prediction formula.The BP neural theoretical model proves a reliable way for predicting classification performance of hydrocyclones.

关 键 词：水力旋流器 BP神经网络 磁铁矿粉 分级性能 

分 类 号：TD455[矿业工程—矿山机电]