Identification of low-order parameter-varying models for large-scale systems

In this paper we propose a novel procedure for obtaining low-dimensional models of large-scale multi-phase, non-linear, reactive fluid flow systems. Our approach is based on the combination of methods of proper orthogonal decompositions, black-box system identification techniques and non-linear spline based blending of local linear black-box models to create a reduced order linear parameter-varying model. The proposed method, which is of empirical nature, gives computationally very efficient low-order process models for large-scale processes. The proposed method does not need Galerkin type of projections on equation residuals to obtain the reduced order models and the proposed method is of generic nature. The efficiency of the proposed approach is illustrated on a benchmark problem of an industrial glass manufacturing process where the process non-linearity and non-linearity arising due to the corrosion of refractory materials is approximated using a linear parameter varying model. The results show good performance of the proposed framework.