Robust planning of multisite refinery networks: Optimization under uncertainty

被引:38
|
作者
Al-Qahtani, K. [1 ]
Elkamel, A. [1 ]
机构
[1] Univ Waterloo, Dept Chem Engn, Waterloo, ON N2L 3G1, Canada
关键词
Planning under uncertainty; Robust optimization; Multisite coordination; AVERAGE APPROXIMATION METHOD; SIMULATION-BASED APPROACH; ALGORITHM; DECOMPOSITION; DESIGN; MODEL;
D O I
10.1016/j.compchemeng.2010.02.032
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper considers the problem of multisite integration and coordination strategies within a network of petroleum refineries under uncertainty and using robust optimization techniques. The framework of simultaneous analysis of process network integration, originally proposed by Al-Qahtani & Elkamel [Al-Qahtani, K., & Elkamel, A. (2008). Multisite facility network integration design and coordination: An application to the refining industry. Computers & Chemical Engineering. 32, 2198], is extended to account for uncertainty in model parameters. Robustness is analyzed based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and integration network clue to variations. Parameters uncertainty considered include coefficients of the objective function and right-hand-side parameters in the inequality constraints. The proposed method makes use of the sample average approximation (SAA) method with statistical bounding techniques. The proposed model was tested on two industrial-scale studies of a single refinery and a network of complex refineries. Modeling uncertainty in the process parameters provided a practical perspective of this type of problems in the chemical industry where benefits not only appear in terms of economic considerations, but also in terms of process flexibility. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:985 / 995
页数:11
相关论文
共 50 条
  • [1] Data-driven Wasserstein distributionally robust optimization for refinery planning under uncertainty
    Zhao, Jinmin
    Zhao, Liang
    He, Wangli
    IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2021,
  • [2] A deep learning-based robust optimization approach for refinery planning under uncertainty
    Wang, Cong
    Peng, Xin
    Shang, Chao
    Fan, Chen
    Zhao, Liang
    Zhong, Weimin
    COMPUTERS & CHEMICAL ENGINEERING, 2021, 155
  • [3] Optimization of a multiperiod refinery planning problem under uncertainty
    Boucheikhchoukh, Ariel A.
    Swartz, Christopher L. E.
    Bouveresse, Eric
    Lutran, Pierre
    Robert, Anna
    AICHE JOURNAL, 2022, 68 (09)
  • [4] Refinery-wide planning operations under uncertainty via robust optimization approach coupled with global optimization
    Zhang, Lifeng
    Yuan, Zhihong
    Chen, Bingzhen
    COMPUTERS & CHEMICAL ENGINEERING, 2021, 146
  • [5] Refinery planning under uncertainty
    Li, WK
    Hui, CW
    Li, P
    Li, AX
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2004, 43 (21) : 6742 - 6755
  • [6] Robust optimization for fleet planning under uncertainty
    List, GF
    Wood, B
    Nozick, LK
    Turnquist, MA
    Jones, DA
    Kjeldgaard, EA
    Lawton, CR
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2003, 39 (03) : 209 - 227
  • [7] Data-driven two-stage distributionally robust optimization for refinery planning under uncertainty
    He, Wangli
    Zhao, Jinmin
    Zhao, Liang
    Li, Zhi
    Yang, Minglei
    Liu, Tianbo
    CHEMICAL ENGINEERING SCIENCE, 2023, 269
  • [8] Multisite Planning under Demand and Transportation Time Uncertainty: Robust Optimization and Conditional Value-at-Risk Frameworks
    Verderame, Peter M.
    Floudas, Christodoulos A.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2011, 50 (09) : 4959 - 4982
  • [9] Chance-Constrained Optimization for Refinery Blend Planning under Uncertainty
    Yang, Yu
    Vayanos, Phebe
    Barton, Paul I.
    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2017, 56 (42) : 12139 - 12150
  • [10] Refinery production planning optimization under crude oil quality uncertainty
    Li, Fupei
    Qian, Feng
    Du, Wenli
    Yang, Minglei
    Long, Jian
    Mahalec, Vladimir
    COMPUTERS & CHEMICAL ENGINEERING, 2021, 151