Optimal source-sink matching in carbon capture and storage systems under uncertainty
This study addresses the robust optimal source-sink matching in carbon capture and storage (CCS) supply chains under uncertainty. A continuous-time uncertain mixed-integer linear programming (MILP) model with physical and temporal constraints is developed, where uncertainties are described as interv...
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oai:animorepository.dlsu.edu.ph:faculty_research-46462021-09-21T02:16:14Z Optimal source-sink matching in carbon capture and storage systems under uncertainty He, Yi Jun Zhang, Yan Ma, Zi Feng Sahinidis, Nikolaos V. Tan, Raymond Girard R. Foo, Dominic C. Y. This study addresses the robust optimal source-sink matching in carbon capture and storage (CCS) supply chains under uncertainty. A continuous-time uncertain mixed-integer linear programming (MILP) model with physical and temporal constraints is developed, where uncertainties are described as interval and uniform distributed stochastic parameters. A worst-case MILP formulation and a robust stochastic two-stage MILP formation are proposed to handle interval and stochastic uncertainties, respectively. Then, two illustrative case studies are solved to demonstrate the effectiveness of the proposed models for planning CCS deployment under uncertainty. © 2013 American Chemical Society. 2014-01-15T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3644 info:doi/10.1021/ie402866d https://animorepository.dlsu.edu.ph/context/faculty_research/article/4646/type/native/viewcontent/ie402866d.html Faculty Research Work Animo Repository Carbon sequestration Chemical Engineering |
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Carbon sequestration Chemical Engineering He, Yi Jun Zhang, Yan Ma, Zi Feng Sahinidis, Nikolaos V. Tan, Raymond Girard R. Foo, Dominic C. Y. Optimal source-sink matching in carbon capture and storage systems under uncertainty |
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This study addresses the robust optimal source-sink matching in carbon capture and storage (CCS) supply chains under uncertainty. A continuous-time uncertain mixed-integer linear programming (MILP) model with physical and temporal constraints is developed, where uncertainties are described as interval and uniform distributed stochastic parameters. A worst-case MILP formulation and a robust stochastic two-stage MILP formation are proposed to handle interval and stochastic uncertainties, respectively. Then, two illustrative case studies are solved to demonstrate the effectiveness of the proposed models for planning CCS deployment under uncertainty. © 2013 American Chemical Society. |
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text |
author |
He, Yi Jun Zhang, Yan Ma, Zi Feng Sahinidis, Nikolaos V. Tan, Raymond Girard R. Foo, Dominic C. Y. |
author_facet |
He, Yi Jun Zhang, Yan Ma, Zi Feng Sahinidis, Nikolaos V. Tan, Raymond Girard R. Foo, Dominic C. Y. |
author_sort |
He, Yi Jun |
title |
Optimal source-sink matching in carbon capture and storage systems under uncertainty |
title_short |
Optimal source-sink matching in carbon capture and storage systems under uncertainty |
title_full |
Optimal source-sink matching in carbon capture and storage systems under uncertainty |
title_fullStr |
Optimal source-sink matching in carbon capture and storage systems under uncertainty |
title_full_unstemmed |
Optimal source-sink matching in carbon capture and storage systems under uncertainty |
title_sort |
optimal source-sink matching in carbon capture and storage systems under uncertainty |
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Animo Repository |
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2014 |
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https://animorepository.dlsu.edu.ph/faculty_research/3644 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4646/type/native/viewcontent/ie402866d.html |
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