Identification of low order parameter varying models for large scale systems

Abstract In this paper we propose a novel procedure for obtaining reduced dimensional models of large scale multi-phase, non-linear, reactive fluid flow systems with geometric parameter uncertainty (corrosion). Our approach is based on the combinations of methods of Proper Orthogonal Decomposition ( POD ), black box System Identification ( SID ) techniques and nonlinear spline based blending of local black box models to create Reduced Order Linear Parameter Varying ( RO-LPV ) model. The proposed method gives computationally very efficient reduced dimension models for processes with parameter uncertainty. The efficiency of proposed approach is illustrated on a benchmark problem depicting industrial Glass Manufacturing Process ( GMP ) with corrosion of refractory materials as a process parameter uncertainty. The results show good performance of the proposed method.