A New Termination Criterion for Sampling for Surrogate Model Generation using Partial Least Squares Regression

Abstract

This paper proposes a new incremental sampling method for the generation of surrogate models based on the application of partial least squares regression (PLSR) as a termination criterion. Compared to existing incremental and adaptive methods, the proposed method allows the sampling algorithm to stop without needing to fit a surrogate model at each iteration step. The proposed procedure was applied to a motivating pipe model and two case studies; the reaction and the separation section of an ammonia synthesis loop. In all cases, the new sampling method allows a small number of sampling points, corresponding to a regular grid with less than two points in each independent variable. The two surrogate models of the ammonia loop are combined for overall optimization. The optimum for the combined surrogate models is close to the optimum obtained with the original model.