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• W2966884989 abstract "A dynamic Gaussian process regression based partial least-squares (D-GPR-PLS) model is proposed to improve estimation ability and compared to the conventional nonlinear PLS. Considering the strong ability of GPR in nonlinear process modeling, this method is used to build a nonlinear regression between each pair of latent variables in the partial least-squares. In addition, augmented matrices are embedded into the D-GPR-PLS model to obtain better prediction accuracy in nonlinear dynamic processes. To evaluate the modeling performance of the proposed method, two simulated cases and a real industrial process based on wastewater treatment processes (WWTPs) are considered. The simulated cases use data from two high fidelity simulators: benchmark simulation model no. 1 and its long-term version. The second study uses data from a real biological wastewater treatment process. The results show the superiority of D-GPR-PLS in modeling performance for both data sets. More specifically, in terms of the prediction for effluent chemical oxygen demand of the real WWTP data, the value of the root-mean-square error is decreased by 31%, 16%, and 52%, respectively, in comparison with that for linear PLS, quadratic PLS, and least-squares support vector machine based PLS." @default.
• W2966884989 created "2019-08-22" @default.
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• W2966884989 date "2019-08-07" @default.
• W2966884989 modified "2023-10-18" @default.
• W2966884989 title "Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression" @default.
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• W2966884989 doi "https://doi.org/10.1021/acs.iecr.9b00701" @default.
• W2966884989 hasPublicationYear "2019" @default.
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