Multiple Model Identification for a High Purity Distillation Column Process Based on EM Algorithm

Abstract

Due to the strong nonlinearity and transition dynamics between different operating points of the high purity distillation column process, it is difficult to use a single model for modeling such a process. Therefore, the multiple model based approach is introduced for modeling the high purity distillation column plant under the framework of the expectation maximization (EM) algorithm. In this paper, autoregressive exogenous (ARX) models are adopted to construct the local models of this chemical process at different operating points, and the EM algorithm is used for identification of local models as well as the probability that each local model takes effect. The global model is obtained by aggregating the local models using an exponential weighting function. Finally, the simulation performed on the high purity distillation column demonstrates the effectiveness of the proposed method.