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| Soft-sensor modeling of papermaking wastewater treatment process based on Gaussian process |
| SONG Liu1, YANG Chong1, ZHANG Hui1, LIU Hong-bin1,2 |
1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China;
2. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, China |
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Abstract 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 linear covariance function.
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Received: 20 November 2017
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