Gray‐Box Modeling for the Optimization of Chemical Processes. Issue 3 (12th November 2018)

• Record Type: Journal Article
• Authors: Asprion, Norbert; Böttcher, Roger; Pack, Robert; Stavrou, Marina‐Eleni; Höller, Johannes; Schwientek, Jan; Bortz, Michael
• Other Names: Asprion Norbert guestEditor.; Eck Bernd guestEditor.
• Is Part Of: Chemie Ingenieur Technik. Volume 91:Issue 3(2019)
• Page Start: 305
• Page End: 313
• Publication Date: 2018-11-12
• Subjects: Data selection -- Decision support -- Flowsheet simulation -- Gray‐box models -- Hybrid models -- Optimization -- Soft sensing; Chemical engineering -- Patents -- Periodicals; Chemical engineering -- Periodicals; Chemical industry -- Periodicals; Chemistry, Technical -- Periodicals; 660.05
• DOI: 10.1002/cite.201800086
• Languages: English
• ISSNs: 0009-286X

Abstract

Abstract: The availability of predictive models for chemical processes is the basic prerequisite for offline process optimization. In cases where a predictive model is missing for a process unit within a larger process flowsheet, measured operating data of the process can be used to set up such models combining physical knowledge and process data. In this contribution, the creation and integration of such gray‐box models within the framework of a flowsheet simulator is presented. Results of optimization using different gray‐box models are shown for a virtual cumene process. Abstract : This work describes the implementation of a framework to support the setup of gray‐box models for flowsheets of chemical production plants. This framework is integrated into a flowsheet simulator enabling optimization of combined rigorous and data‐driven models.