期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 121, 期 -, 页码 67-74出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2018.10.007
关键词
Surrogate-based global optimization; McCormick relaxations; Reduced-space formulation; Organic Rankine cycle; Thermodynamic properties; MAiNGO
资金
- Kopernikus project SynErgie by the Bundesministerium fur Bildung und Forschung
- Federal Ministry for Economic Affairs and Energy (BMWi) [19U14010C]
Global deterministic process optimization problems have recently been solved efficiently in a reduced-space by automatic propagation of McCormick relaxations (Bongartz and Mitsos, J. Global Optim, 2017). However, the previous optimizations have been limited to simplified thermodynamic property models. Herein, we propose a method that learns accurate thermodynamic properties via artificial neural networks (ANNs) and integrates those in deterministic global process optimization. The resulting hybrid process model is solved using the recently developed method for deterministic global optimization problems with ANNs embedded (Schweidtmann and Mitsos, J. Optim. Theory Appl., 2018). The optimal operation of a validated steady state model of an organic Rankine cycle is solved as a case study. It is especially challenging as the thermodynamic properties are given by the implicit Helmholtz equation of state. The results show that modeling of thermodynamic properties via ANNs performs favorable in deterministic optimization. This method can rapidly be extended to include properties from existing thermodynamic libraries, based on models or data. (C) 2018 Elsevier Ltd. All rights reserved.
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