Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit

[Image: see text] A framework to obtain optimal operating conditions is proposed for a cryogenic air separation unit case study. The optimization problem is formulated considering three objective functions, 11 decision variables, and two constraint setups. Different optimization algorithms simultane...

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Autores principales: Piguave, Bryan V., Salas, Santiago D., De Cecchis, Dany, Romagnoli, José A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017109/
https://www.ncbi.nlm.nih.gov/pubmed/35449930
http://dx.doi.org/10.1021/acsomega.1c06669
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author Piguave, Bryan V.
Salas, Santiago D.
De Cecchis, Dany
Romagnoli, José A.
author_facet Piguave, Bryan V.
Salas, Santiago D.
De Cecchis, Dany
Romagnoli, José A.
author_sort Piguave, Bryan V.
collection PubMed
description [Image: see text] A framework to obtain optimal operating conditions is proposed for a cryogenic air separation unit case study. The optimization problem is formulated considering three objective functions, 11 decision variables, and two constraint setups. Different optimization algorithms simultaneously evaluate the conflicting objective functions: the annualized cash flow, the efficiency at the compression stage, and capital expenditures. The framework follows a modular approach, in which the process simulator PRO/II and a Python environment are combined. The results permit us to assess the applicability of the tested algorithms and to determine optimal operational windows based on the resultant 3-D Pareto fronts.
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spelling pubmed-90171092022-04-20 Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit Piguave, Bryan V. Salas, Santiago D. De Cecchis, Dany Romagnoli, José A. ACS Omega [Image: see text] A framework to obtain optimal operating conditions is proposed for a cryogenic air separation unit case study. The optimization problem is formulated considering three objective functions, 11 decision variables, and two constraint setups. Different optimization algorithms simultaneously evaluate the conflicting objective functions: the annualized cash flow, the efficiency at the compression stage, and capital expenditures. The framework follows a modular approach, in which the process simulator PRO/II and a Python environment are combined. The results permit us to assess the applicability of the tested algorithms and to determine optimal operational windows based on the resultant 3-D Pareto fronts. American Chemical Society 2022-04-02 /pmc/articles/PMC9017109/ /pubmed/35449930 http://dx.doi.org/10.1021/acsomega.1c06669 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Piguave, Bryan V.
Salas, Santiago D.
De Cecchis, Dany
Romagnoli, José A.
Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title_full Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title_fullStr Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title_full_unstemmed Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title_short Modular Framework for Simulation-Based Multi-objective Optimization of a Cryogenic Air Separation Unit
title_sort modular framework for simulation-based multi-objective optimization of a cryogenic air separation unit
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9017109/
https://www.ncbi.nlm.nih.gov/pubmed/35449930
http://dx.doi.org/10.1021/acsomega.1c06669
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