Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation
•Novel framework for representing CCU pathways as a superstructure with state-task network (STN).•Logic-based outer approximation (LOA) for solving the MINLP.•Optimization based identification of sustainable pathways in a large superstructure model.•A case study to elucidate the main features and fu...
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| Veröffentlicht in: | Computers & chemical engineering Jg. 178; S. 108408 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2023
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| ISSN: | 0098-1354, 1873-4375 |
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| Abstract | •Novel framework for representing CCU pathways as a superstructure with state-task network (STN).•Logic-based outer approximation (LOA) for solving the MINLP.•Optimization based identification of sustainable pathways in a large superstructure model.•A case study to elucidate the main features and functionalities of the framework.
Carbon capture and utilization (CCU) can be a pertinent solution to avoid millions of tons of carbon emission. The challenge is to identify, among numerous available options of carbon sources capture/utilization technologies, and products, the CCU pathways with best economic and/or CO2 reduction potential. In this work, we propose a novel framework for identifying sustainable CCU pathways, i.e., combinations of sources, processes, and products, using a superstructure based on state-task network (STN) representation. STN allows incorporation of nonlinear models including first-principles or surrogate models into the superstructure representation of potential CCU pathways. The proposed framework solves the superstructure optimization problem of mixed-integer nonlinear programming (MINLP) by introducing logic-based outer approximation (LOA), to reduce the computational time and improve the solvability greatly. A case study using a sizable CCU superstructure demonstrates that LOA can reduce the computational time from hours to minutes while identifying any sustainable pathway from a superstructure with highly nonlinear surrogate models. |
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| AbstractList | •Novel framework for representing CCU pathways as a superstructure with state-task network (STN).•Logic-based outer approximation (LOA) for solving the MINLP.•Optimization based identification of sustainable pathways in a large superstructure model.•A case study to elucidate the main features and functionalities of the framework.
Carbon capture and utilization (CCU) can be a pertinent solution to avoid millions of tons of carbon emission. The challenge is to identify, among numerous available options of carbon sources capture/utilization technologies, and products, the CCU pathways with best economic and/or CO2 reduction potential. In this work, we propose a novel framework for identifying sustainable CCU pathways, i.e., combinations of sources, processes, and products, using a superstructure based on state-task network (STN) representation. STN allows incorporation of nonlinear models including first-principles or surrogate models into the superstructure representation of potential CCU pathways. The proposed framework solves the superstructure optimization problem of mixed-integer nonlinear programming (MINLP) by introducing logic-based outer approximation (LOA), to reduce the computational time and improve the solvability greatly. A case study using a sizable CCU superstructure demonstrates that LOA can reduce the computational time from hours to minutes while identifying any sustainable pathway from a superstructure with highly nonlinear surrogate models. |
| ArticleNumber | 108408 |
| Author | Al-Hunaidy, Ali S. Kim, Sunwoo Lee, Jay H. Chung, Wonsuk Imran, Hasan Jamal, Aqil |
| Author_xml | – sequence: 1 givenname: Wonsuk surname: Chung fullname: Chung, Wonsuk organization: Clean Energy Research Center, Korea Institute of Science and Technology (KIST), 02792, Seoul, Republic of Korea – sequence: 2 givenname: Sunwoo orcidid: 0000-0002-9799-2274 surname: Kim fullname: Kim, Sunwoo organization: Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea – sequence: 3 givenname: Ali S. surname: Al-Hunaidy fullname: Al-Hunaidy, Ali S. organization: Carbon Management Division, Research & Development Centre, Saudi Aramco, Dhahran 31311, Kingdom of Saudi Arabia – sequence: 4 givenname: Hasan surname: Imran fullname: Imran, Hasan organization: Carbon Management Division, Research & Development Centre, Saudi Aramco, Dhahran 31311, Kingdom of Saudi Arabia – sequence: 5 givenname: Aqil surname: Jamal fullname: Jamal, Aqil organization: Carbon Management Division, Research & Development Centre, Saudi Aramco, Dhahran 31311, Kingdom of Saudi Arabia – sequence: 6 givenname: Jay H. orcidid: 0000-0001-6134-6118 surname: Lee fullname: Lee, Jay H. email: jlee4140@usc.edu organization: Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea |
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| Keywords | Mixed-inter nonlinear programming Carbon capture and utilization State-task network representation Superstructure Sustainable pathway |
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