Nonlinear reduced order models for separation processes via augmentation of linear subspace models

This paper presents a general methodology for developing nonlinear low-order model (NLLOM) from data collected from large detailed nonlinear models. This methodology is divided into two tasks: development of an average linear low-order model (ALLOM) and augmentation of the ALLOM with selected nonlinear terms to form the NLLOM. The tools required for the augmentation step that is the focus of this paper include stepwise regression and nonlinear optimization. Results will be presented for the application of these techniques in the development of an NLLOM from a detailed high purity air separation distillation column model supplied by Praxair, Inc