Gray-Box Modeling for the Optimization of Chemical Processes

被引：41

作者：

Asprion, Norbert ^{[1
]}

Boettcher, Roger ^{[1
]}

Pack, Robert ^{[1
]}

Stavrou, Marina-Eleni ^{[1
]}

Hoeller, Johannes ^{[2
]}

Schwientek, Jan ^{[2
]}

Bortz, Michael ^{[2
]}

机构：

[1] BASF SE, Digitalizat Proc Res & Chem Engn, Carl Bosch Str 38, D-67056 Ludwigshafen, Germany

[2] Fraunhofer Inst Ind Math ITWM, Fraunhofer Pl 1, D-67663 Kaiserslautern, Germany

来源：

CHEMIE INGENIEUR TECHNIK | 2019年 / 91卷 / 03期

关键词：

Data selection; Decision support; Flowsheet simulation; Gray-box models; Hybrid models; Optimization; Soft sensing; INCREMENTAL IDENTIFICATION; MULTICRITERIA OPTIMIZATION; DECISION-SUPPORT; HYBRID MODELS; SIMULATION; DESIGN;

D O I：

10.1002/cite.201800086

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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.

引用

页码：305 / 313

页数：9

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