Robust Nonlinear PLS Based on Neural Networks and Application to Composition Estimator for High-Purity Distillation Columns

−The accurate and reliable on-line estimation of product quality is an essential task for successful process operation and control. This paper proposes a new estimation method that extends the conventional linear PLS (Partial Least Squares) regression method to a nonlinear framework in a more robust manner. To handle the nonlinearities, nonlinear PLS based on linear PLS and neural network has been employed. To improve the robustness of the nonlinear PLS, the autoassociative neural network has been integrated with nonlinear PLS. The integration allows us to handle the nonlinear correlation as well as nonlinear functional relationship with fewer components in a more robust manner. The application results have shown that the proposed Robust Nonlinear PLS (RNPLS) performs better than previous linear and nonlinear regression methods such as PLS, NNPLS, even for the nonlinearities due to operating condition changes, limited observations, and measurement noise.