基于PCA-PSO-SVR的PV型旋风分离器粒级效率建模  

Modeling the Grade Efficiency of PV Cyclone Separators Based on PCA-PSO-SVR

作　　者：张玮[1] 李立毅 张林林 ZHANG Wei;LI Liyi;ZHANG Linlin(College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

机构地区：[1]太原理工大学化学化工学院,太原030024

出　　处：《太原理工大学学报》2020年第5期663-668,共6页Journal of Taiyuan University of Technology

基　　金：山西省重点研发计划项目(201903D121027)。

摘　　要：为了准确地表达PV型旋风分离器的粒级效率与结构参数、操作参数之间复杂的非线性关系,采用PCA-PSO-SVR混合算法对PV型旋风分离器的粒级效率进行建模。采用主元分析法(PCA)对实验数据集进行降维处理,通过粒子群优化算法(PSO)对支持向量回归(SVR)模型中的超参数进行优化。将优化后的回归模型和其它机器学习模型在预测准确性、泛化性、鲁棒性以及运行速度方面进行了对比,结果表明,利用PCA-PSO-SVR算法对PV型旋风分离器的粒级效率建模是一种准确而有效的方法。A Hybrid PCA-PSO-SVR algorithm was used to model the grade efficiency of PV cyclone separators to describe the complex non-linear relationship between structural parameters and operation parameters.Principal component analysis(PCA)was used to reduce the dimension of the experimental data set,and particle swarm optimization(PSO)was used to optimize the parameters for the support vector regression(SVR).The support vector regression model of the cyclone separator grade efficiency optimized with PSO was compared with the multivariate regression model and other machine learning models in the prediction accuracy,generalization,robustness,and the operating speed.The results show that the PCA-PSO-SVR algorithm is an accurate and effective method to model the graded efficiency of PV cyclone separator.

关 键 词：PV型旋风分离器 支持向量回归算法 粒子群优化 粒级效率建模 主元分析法 

分 类 号：TQ028.1[化学工程] TP301[自动化与计算机技术—计算机系统结构]