Soft-Sensing Research on Ethylene Polymerization Based on PCA-SVR Algorithm - Shi, X.R.; Chi, Q.H.; Fei, Z.F.; Liang, J. (Asian Journal of Chemistry, 2013)
The important indices indicating product specifications and grades, melt index and density, can not be measured easily in gas phase ethylene polymerization reaction. Thus, a soft-sensing algorithm is proposed which mainly adopts the technique of support vector regression (SVR) to build prediction model. In this model, two important parameters, C and g are estimated through particle swarm optimization (PSO). Meanwhile, principal component analysis (PCA) is employed to extract features and by this preprocessing step the prediction accuracy can be improved to varying degree. The experiment results demonstrate that, compared with traditional neural network (NN) and single SVR methods, the proposed PCA-PSO-SVR model can achieve best results and is, hence, suitable for soft-sensing in similar processes.