Iterative Error-based Nonlinear PLS Method for Nonlinear Chemical Process Modeling

A novel nonlinear Partial Least Square (PLS) method is proposed to enhance modeling capability for nonlinear chemical processes. The proposed method incorporates a modified back-propagation algorithm for artificial neural network within Nonlinear Iterative Partial Least Square (NIPALS) algorithm for PLS methods, without deteriorating the robustness of PLS methods. The modified back-propagation algorithm iteratively updates the weights within the networks. The proposed method circumvents the pseudo-inverse calculation of the error-based neural network PLS proposed by Baffi et al. (1999b), and thus makes the weight updating procedure more stable and the solutions more accurate. The modeling capability of the proposed method was investigated through three case studies: 1) a pH neutralization process, 2) cosmetic data, and 3) an industrial crude column process. Simulation results showed that the proposed method represented more stable convergence and enhanced prediction power than those of the linear PLS, the neural network PLS, and the error-based neural network PLS.

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