Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty
نویسندگان
چکیده
• Data-driven ARO is firstly applied in industrial multi-type energy systems optimization under uncertainty. The data-driven uncertainty set formed by RKDE based on the big data. Deterministic and are formulated as MINLP problems. effect of parameters solution explored. operational great significance for improving overall efficiency processes. Facing new challenges brought widespread uncertainties, a adaptive robust framework was proposed bridging machine learning methods this paper. data were used to capture demand actual process. Hybrid models units first developed considering characteristics, system model then mixed-integer nonlinear programming problem. uncertain parameter process power demands using historical whole operating period. Afterward, constructed applying kernel density estimation method, which can reduce conservatism distributional information. By integrating derived set, two-stage aiming at minimizing weighted total consumption developed. multi-level reformulated tractable single-level employing affine decision rule. A case study plant-wide ethylene plant performed, minimum optimal 25,350 kg/h, whose price robustness only 2.18%. results guide operators plant.
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B.Eng, Software Systems Engineering, Royal Melbourne Institute of Technology (2002) S.M., High Performance Computations for Engineered Systems, Singapore-MIT Alliance, National University of Singapore (2004) Submitted to the Sloan School of Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Operations Research at the MASSACHUSETTS INSTITUTE OF TECHNO...
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ژورنال
عنوان ژورنال: Applied Energy
سال: 2022
ISSN: ['0306-2619', '1872-9118']
DOI: https://doi.org/10.1016/j.apenergy.2021.118148