In this paper we propose a novel procedure for obtaining reduced dimensional models of large scale multi-phase, non-linear, reactive uid ow systems with geometric parameter uncertainty (corrosion). Our approach is based on the combinations of methods of Proper Orthogonal Decomposition (POD), black box System Identi??cation (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 e??cient reduced dimension models for processes with parameter uncertainty. The e??ciency 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.
|Title of host publication||Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, July 6-8, 2009, Saint Malo, France|
|Place of Publication||Oxford|
|Publication status||Published - 2009|
Wattamwar, S. K., Weiland, S., & Backx, A. C. P. M. (2009). Identification of low order parameter varying models for large scale systems. In Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, July 6-8, 2009, Saint Malo, France (pp. 1-6). Pergamon.