Soft Sensor Modeling of Papermaking Waste Water Treatment Process Using PCA Dimensional Reduction Models [基于PCA降维模型的造纸废水处理过程软测量建模]

作者：Liu H.-B.^*; Li X.-Y.; Yang C.

来源：Transactions of China Pulp and Paper, 2018, 33(4): 50-57.

DOI：10.11981/j.issn.1000-6842.2018.04.50

• Artificial neural network
• Papermaking waste water treatment processes
• Partial least squares
• Principal component analysis
• Soft sensor
• Support vector regression

To deal with the complicated characteristics of papermaking waste water treatment process, two new soft sensors based on dimensional reduction models, where principal component analysis (PCA) was combined with support vector regression (SVR) and artificial neural network (ANN), respectively, were developed for the prediction of the effluent chemical oxygen demand (COD) concentration and the effluent suspended solids (SS) concentration in a papermaking waste water treatment process. Conventional modeling methods including partial least squares (PLS), SVR, and ANN were used for comparison purpose. The results showed that the prediction performance of the PCA-based dimensional reduction models was better than that of the conventional models. Furthermore, PCA-based ANN (PCA-ANN) showed the most accurate prediction results. In terms of the prediction of the effluent COD concentration, the coefficient of determination (R2) and mean square error (MSE) of PCA-ANN were 0.984 and 1.892, respectively, which were optimized by 9.7% and 71.5% in comparison with ANN respectively. Compared with the prediction results of the effluent SS concentration based on ANN, the R2 (0.762) of PCA-ANN was increased by 31.2% and the MSE (0.228) of PCA-ANN was decreased by 58.7%.

• 出版日期2018
• 单位华南理工大学; 南京林业大学