Multi-agent distributed control of integrated process networks using an adaptive community detection approach

Bibliographic Details
Title: Multi-agent distributed control of integrated process networks using an adaptive community detection approach
Authors: AmirMohammad Ebrahimi, Davood B. Pourkargar
Source: Digital Chemical Engineering, Vol 13, Iss , Pp 100196- (2024)
Publisher Information: Elsevier, 2024.
Publication Year: 2024
Collection: LCC:Chemical engineering
LCC:Information technology
Subject Terms: Multi-agent systems, Distributed control, Integrated process systems, Model predictive control, System decomposition, Spectral community detection, Chemical engineering, TP155-156, Information technology, T58.5-58.64
More Details: 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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2772-5081
Relation: http://www.sciencedirect.com/science/article/pii/S2772508124000589; https://doaj.org/toc/2772-5081
DOI: 10.1016/j.dche.2024.100196
Access URL: https://doaj.org/article/9039f07b28db499aa36c8e2b23658f3f
Accession Number: edsdoj.9039f07b28db499aa36c8e2b23658f3f
Database: Directory of Open Access Journals
More Details
ISSN:27725081
DOI:10.1016/j.dche.2024.100196
Published in:Digital Chemical Engineering
Language:English