摘要
Over time, the performance of processes may deviate from the initial design due to process variations anduncertainties, making it necessary to develop systematic methods for online optimality assessment basedon routine operating process data. Some processes have multiple operating modes caused by the set pointchange of the critical process variables to achieve different product specifications. On the other hand, theoperating region in each operating mode can alter, due to uncertainties. In this paper, we will establish anoptimality assessment framework for processes that typically have multi-mode, multi-region operations,as well as transitions between different modes. The kernel density approach for mode detection is adopt-ed and improved for operating mode detection. For online mode detection, the model-based clusteringdiscriminant analysis （MclustDA） approach is incorporated with some a priori knowledge of the system. Inaddition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principalcomponent regression （MPPCR） method, and dynamic principal component regression （DPCR） is used toinvestigate transitions between different modes. Moreover, a probabilistic causality detection method basedon the sequential forward floating search （SFFS） method is introduced for diagnosing poor or non-optimumbehavior. Finally, the proposed method is tested on the Tennessee Eastman （TE） benchmark simulation pro-cess in order to evaluate its performance.
Over time, the performance of processes may deviate from the initial design due to process variations and uncertainties, making it necessary to develop systematic methods for online optimality assessment based on routine operating process data. Some processes have multiple operating modes caused by the set point change of the critical process variables to achieve different product specifications. On the other hand, the operating region in each operating mode can alter, due to uncertainties. In this paper, we will establish an optimality assessment framework for processes that typically have multi-mode, multi-region operations, as well as transitions between different modes. The kernel density approach for mode detection is adopted and improved for operating mode detection. For online mode detection, the model-based clustering discriminant analysis(Mclust DA) approach is incorporated with some a priori knowledge of the system. In addition, multi-modal behavior of steady-state modes is tackled utilizing the mixture probabilistic principal component regression(MPPCR) method, and dynamic principal component regression(DPCR) is used to investigate transitions between different modes. Moreover, a probabilistic causality detection method based on the sequential forward floating search(SFFS) method is introduced for diagnosing poor or non-optimum behavior. Finally, the proposed method is tested on the Tennessee Eastman(TE) benchmark simulation process in order to evaluate its performance.
基金
supported in part by the Natural Science Engineering Research Council of Canada
by Alberta Innovates Technology Futures
