Soft-sensor modeling of papermaking wastewater treatment process based on Gaussian process [造纸废水处理过程的高斯过程回归软测量建模]

作者：Song L.; Yang C.; Zhang H.; Liu H.-B.^*

来源：China Environmental Science, 2018, 38(7): 2564-2571.

• Covariance function
• Gaussian process regression
• Machine learning
• Soft sensor
• Wastewater treatment processes

Considering the time-varying, nonlinear, and complex characteristics of papermaking wastewater treatment processes, an advanced soft-sensor model was proposed based on Gaussian process regression (GPR). Seven GPR models, consisting of the combinations of squared exponential covariance, linear covariance, and periodic covariance function were built and compared for the prediction of the effluent chemical oxygen demand (COD) and effluent suspended solids (SS). The GPR-based prediction results are also compared with those of multiple linear regression, principle component regression, partial least square, and artificial neural network. The results showed that the prediction accuracy of GPR models were better than other models. Furthermore, with regard to the prediction of the effluent COD, the GPR model with the combination of linear covariance function and periodic covariance function achieved the best performance. In terms of the prediction of the effluent SS, the best GPR model is the one with the combination of squared exponential covariance function and lin

• 出版日期2018
• 单位南京林业大学; 华南理工大学