Developing new products with kernel partial least squares model inversion

Abstract

In recent years, data-driven approaches (e.g., latent variable model) have excited the development of new products and the control of product quality. To derive an input space within which raw material properties or initial operating conditions can yield the required product quality, previous researchers developed a linear latent variable model and derived the solution of inputs through PLS model inversion. In this research, a novel method based on kernel partial least squares (KPLS) model inversion is proposed for product design of nonlinear processes and an input domain is derived. Constraints on the model inputs or outputs and on the KPLS model are discussed, and solutions are provided based on an optimization framework and the Monte Carlo sampling method. The effectiveness of the method is demonstrated by numerical simulation and a beer fermentation experiment, and the KPLS model inversion method outperforms the PLS model inversion method in terms of the precision.