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A Distributed Proximal Consensus Algorithm for Energy Saving in Ethylene Production
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DOI：10.1109/TNNLS.2023.3320691.png)
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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.
Keyword:
Production
Optimization
Energy consumption
Consensus algorithm
Upper bound
Petrochemicals
Linear programming
Distributed optimization
energy saving
ethylene plant-wide optimization
proximal consensus algorithm
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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