文献类型：期刊文献

英文题名：Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty

作者：Shen, Feifei[1];Zhao, Liang[1,2];Wang, Meihong[3];Du, Wenli[1,2];Qian, Feng[1,2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 200092, Peoples R China;[3]Univ Sheffield, Dept Chem & Biol Engn, Sheffield S1 3JD, S Yorkshire, England

年份：2022

卷号：307

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20214711188894);WOS:【SCI-EXPANDED(收录号:WOS:000772833200007)】；

基金：Acknowledgements This work was supported by National Key Research and Develop-ment Program of China under Grant 2018AAA0101602, International (Regional) Cooperation and Exchange Project (61720106008) , National Natural Science Fund for Distinguished Young Scholars (61725301) and National Natural Science Foundation of China (61873092) .

语种：英文

外文关键词：Energy systems; Industrial big data; Machine learning; Uncertainty; Adaptive robust optimization

摘要：The operational optimization of energy systems is of great significance for improving the overall efficiency of industrial processes. Facing new challenges brought by widespread uncertainties, a data-driven adaptive robust industrial multi-type energy systems optimization framework was proposed by bridging robust optimization and machine learning methods in this paper. The industrial data were used to capture the demand uncertainty of the actual process. Hybrid models of units were first developed considering the operational characteristics, and the energy system optimization model was then formed as a mixed-integer nonlinear programming problem. The uncertain parameter set of process power demands was formed by the process models using historical data of a whole operating period. Afterward, the uncertainty set was constructed by applying the robust kernel density estimation method, which can reduce conservatism by considering the distributional information. By integrating the derived data-driven uncertainty set, a two-stage adaptive robust optimization model aiming at minimizing the weighted total energy consumption was developed. The multi-level robust optimization model was reformulated as a tractable single-level model by employing the affine decision rule. A case study on a plant-wide industrial energy system in the ethylene plant was performed, and the minimum optimal energy consumption was 25,350 kg/h, whose price of robustness was only 2.18%. The robust optimization results can guide the operational optimization of energy systems under uncertainty for the operators of the ethylene plant.