Identification of low dimensional models for slow geometric parameter variation in an Industrial Glass Manufacturing Process

In this paper we apply the method of Proper Orthogonal Decomposition (POD) to identify a lower dimensional model of a benchmark problem representing an Industrial Glass Manufacturing Process (IGMP). In particular, we identify a reduced model by identifying the mapping from process inputs to POD modal coefficients by a subspace identification method. Reduced models obtained from POD are not well equipped to capture the process behavior under time varying uncertain process parameters. For this reason we propose a novel hybrid detection scheme which approximates the process (benchmark CFD model) exhibiting non-smooth geometric parameter dependence (corrosion and wear) by using lower dimensional models. Given state or output information this detection mechanism detects the process parameter operation regime and suggests a computationally faster lower dimensional model as an approximate for real process.