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Open AccessFeature PaperArticle

Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty

by Liang Zhao 1,2, Weimin Zhong 1,2 and Wenli Du 1,2,*
1
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
2
Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(10), 744; https://doi.org/10.3390/pr7100744
Received: 18 August 2019 / Revised: 7 October 2019 / Accepted: 11 October 2019 / Published: 15 October 2019
(This article belongs to the Special Issue Process Optimization and Control)
In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method. View Full-Text
Keywords: ethylene plant; steam system; data-driven robust optimization; uncertainty ethylene plant; steam system; data-driven robust optimization; uncertainty
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Zhao, L.; Zhong, W.; Du, W. Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty. Processes 2019, 7, 744.

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