Abstract
Abstract This paper presents a modified Partial Least-Squares (PLS) method that integrates an bias update scheme and an advanced cross-validation method that takes into account the correlation coefficient (CC) between the observed quality values and the predicted ones as well as into account prediction error sum of squares (PRESS) to determine the optimal number of latent variables. The modified PLS method has a great advantage that it improves the robustness of an inferential model without updating model parameters at short intervals for a very changeful chemical process with frequent changes of the operational condition or disturbance. It has shown that the proposed PLS method has a better performance when it was applied to an industrial chemical process.
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