Multi-agent distributed control of integrated process networks using an adaptive community detection approach
This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control f...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508124000589 |
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| author | AmirMohammad Ebrahimi Davood B. Pourkargar |
| author_facet | AmirMohammad Ebrahimi Davood B. Pourkargar |
| author_sort | AmirMohammad Ebrahimi |
| collection | DOAJ |
| description | This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency. |
| format | Article |
| id | doaj-art-9039f07b28db499aa36c8e2b23658f3f |
| institution | Kabale University |
| issn | 2772-5081 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-9039f07b28db499aa36c8e2b23658f3f2024-12-12T05:24:22ZengElsevierDigital Chemical Engineering2772-50812024-12-0113100196Multi-agent distributed control of integrated process networks using an adaptive community detection approachAmirMohammad Ebrahimi0Davood B. Pourkargar1Tim Taylor Department of Chemical Engineering, Kansas State University, Manhattan, KS 66503, USACorresponding author.; Tim Taylor Department of Chemical Engineering, Kansas State University, Manhattan, KS 66503, USAThis paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.http://www.sciencedirect.com/science/article/pii/S2772508124000589Multi-agent systemsDistributed controlIntegrated process systemsModel predictive controlSystem decompositionSpectral community detection |
| spellingShingle | AmirMohammad Ebrahimi Davood B. Pourkargar Multi-agent distributed control of integrated process networks using an adaptive community detection approach Digital Chemical Engineering Multi-agent systems Distributed control Integrated process systems Model predictive control System decomposition Spectral community detection |
| title | Multi-agent distributed control of integrated process networks using an adaptive community detection approach |
| title_full | Multi-agent distributed control of integrated process networks using an adaptive community detection approach |
| title_fullStr | Multi-agent distributed control of integrated process networks using an adaptive community detection approach |
| title_full_unstemmed | Multi-agent distributed control of integrated process networks using an adaptive community detection approach |
| title_short | Multi-agent distributed control of integrated process networks using an adaptive community detection approach |
| title_sort | multi agent distributed control of integrated process networks using an adaptive community detection approach |
| topic | Multi-agent systems Distributed control Integrated process systems Model predictive control System decomposition Spectral community detection |
| url | http://www.sciencedirect.com/science/article/pii/S2772508124000589 |
| work_keys_str_mv | AT amirmohammadebrahimi multiagentdistributedcontrolofintegratedprocessnetworksusinganadaptivecommunitydetectionapproach AT davoodbpourkargar multiagentdistributedcontrolofintegratedprocessnetworksusinganadaptivecommunitydetectionapproach |