P-graph and Monte Carlo simulation approach to planning carbon management networks

A P-graph and Monte Carlo simulation approach to planning carbon management networks is proposed. These networks are generalized systems for minimizing emissions of CO2. Application of the P-graph framework to such problems has the added advantage of being able to rigorously identify both optimal an...

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Main Authors: Tan, Raymond Girard R., Aviso, Kathleen B., Foo, Dominic C. Y.
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出版: Animo Repository 2017
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-34902021-09-02T00:48:47Z P-graph and Monte Carlo simulation approach to planning carbon management networks Tan, Raymond Girard R. Aviso, Kathleen B. Foo, Dominic C. Y. A P-graph and Monte Carlo simulation approach to planning carbon management networks is proposed. These networks are generalized systems for minimizing emissions of CO2. Application of the P-graph framework to such problems has the added advantage of being able to rigorously identify both optimal and near-optimal solutions, which is a feature that is useful for practical decision-making; Monte Carlo simulation can then be used to evaluate the robustness of a network to variations in system parameters. Two literature case studies are used to demonstrate this methodology. The first example is a carbon-constrained energy sector planning problem, while the second example is a CO2 capture and storage planning problem. In both cases, it is demonstrated that multiple solutions generated using P-graph methodology allow identification of robust, near-optimal carbon management networks. © 2017 Elsevier Ltd 2017-11-02T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/2491 https://animorepository.dlsu.edu.ph/context/faculty_research/article/3490/type/native/viewcontent Faculty Research Work Animo Repository Carbon dioxide mitigation Energy policy Chemical Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Carbon dioxide mitigation
Energy policy
Chemical Engineering
spellingShingle Carbon dioxide mitigation
Energy policy
Chemical Engineering
Tan, Raymond Girard R.
Aviso, Kathleen B.
Foo, Dominic C. Y.
P-graph and Monte Carlo simulation approach to planning carbon management networks
description A P-graph and Monte Carlo simulation approach to planning carbon management networks is proposed. These networks are generalized systems for minimizing emissions of CO2. Application of the P-graph framework to such problems has the added advantage of being able to rigorously identify both optimal and near-optimal solutions, which is a feature that is useful for practical decision-making; Monte Carlo simulation can then be used to evaluate the robustness of a network to variations in system parameters. Two literature case studies are used to demonstrate this methodology. The first example is a carbon-constrained energy sector planning problem, while the second example is a CO2 capture and storage planning problem. In both cases, it is demonstrated that multiple solutions generated using P-graph methodology allow identification of robust, near-optimal carbon management networks. © 2017 Elsevier Ltd
format text
author Tan, Raymond Girard R.
Aviso, Kathleen B.
Foo, Dominic C. Y.
author_facet Tan, Raymond Girard R.
Aviso, Kathleen B.
Foo, Dominic C. Y.
author_sort Tan, Raymond Girard R.
title P-graph and Monte Carlo simulation approach to planning carbon management networks
title_short P-graph and Monte Carlo simulation approach to planning carbon management networks
title_full P-graph and Monte Carlo simulation approach to planning carbon management networks
title_fullStr P-graph and Monte Carlo simulation approach to planning carbon management networks
title_full_unstemmed P-graph and Monte Carlo simulation approach to planning carbon management networks
title_sort p-graph and monte carlo simulation approach to planning carbon management networks
publisher Animo Repository
publishDate 2017
url https://animorepository.dlsu.edu.ph/faculty_research/2491
https://animorepository.dlsu.edu.ph/context/faculty_research/article/3490/type/native/viewcontent
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