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

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...

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Veröffentlicht in:Processes Jg. 7; H. 10; S. 744
Hauptverfasser: Zhao, Liang, Zhong, Weimin, Du, Wenli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 2019
Schlagworte:
Boilers
Case studies
Compressors
Dirichlet problem
Efficiency
Ethylene
Heat exchangers
Hypothesis testing
Integer programming
Linear programming
Mathematical models
Mathematical programming
Mixed integer
Operating costs
Optimization
Optimization techniques
Parameter uncertainty
Plant extracts
Power
Robustness (mathematics)
Steam electric power generation
Steam pressure
Steam turbines
Turbines
Valves
ISSN:2227-9717, 2227-9717
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Zusammenfassung: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.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr7100744