TY - JOUR
T1 - Deterministic global process optimization
T2 - Accurate (single-species) properties via artificial neural networks
AU - Schweidtmann, Artur M.
AU - Huster, Wolfgang R.
AU - Lüthje, Jannik T.
AU - Mitsos, Alexander
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - MAiNGO
KW - McCormick relaxations
KW - Organic Rankine cycle
KW - Reduced-space formulation
KW - Surrogate-based global optimization
KW - Thermodynamic properties
UR - http://www.scopus.com/inward/record.url?scp=85055914731&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2018.10.007
DO - 10.1016/j.compchemeng.2018.10.007
M3 - Article
AN - SCOPUS:85055914731
SN - 0098-1354
VL - 121
SP - 67
EP - 74
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
ER -