Artikel ; Online: A Distributed Proximal Consensus Algorithm for Energy Saving in Ethylene Production.
IEEE transactions on neural networks and learning systems
2024 Band 35, Heft 3, Seite(n) 3052–3061
Abstract: This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus ...
Abstract | This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus. |
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Sprache | Englisch |
Erscheinungsdatum | 2024-02-29 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article |
ISSN | 2162-2388 |
ISSN (online) | 2162-2388 |
DOI | 10.1109/TNNLS.2023.3320691 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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