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Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks
Author(s) -
Schäfer Pascal,
Caspari Adrian,
Kleinhans Kerstin,
Mhamdi Adel,
Mitsos Alexander
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16568
Subject(s) - compartmentalization (fire protection) , artificial neural network , nonlinear system , reduction (mathematics) , rectification , computer science , orthogonal collocation , optimal control , theory of computation , control theory (sociology) , differential equation , biological system , mathematical optimization , algorithm , ordinary differential equation , collocation method , mathematics , control (management) , chemistry , engineering , artificial intelligence , voltage , enzyme , mathematical analysis , biology , biochemistry , geometry , quantum mechanics , physics , electrical engineering
The availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.