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基于多时间尺度协同的大规模原油调度进化算法

张莞婷,杜文莉,堵威   

  1. 能源化工过程智能制造教育部重点实验室（华东理工大学）
  • 收稿日期:2024-03-12 发布日期:2024-04-26 出版日期:2024-04-26
  • 通讯作者: 杜文莉
  • 作者简介:张莞婷（1998—），女，陕西汉中人，博士研究生，主要研究方向：大规模调度、进化计算； 杜文莉（1974—），女，山东淄博人，教授， 博士，主要研究方向：工业过程建模、控制与优化； 堵威（1987—），男，江苏南通人，副教授，博士，主要研究方向：进化计算、大规模优化、复杂工 业过程决策。
  • 基金资助:
    国家重点研发计划项目(2022YFB3305900); 国家自然科学基金面上项目(62173144); 上海市青年科技启明星计划项目(22QA1402400); 上海人工智能实验室资助项目。

Multi-timescale cooperative evolutionary algorithm for large-scale crude oil scheduling

ZHANG Wanting，DU Wenli*，DU Wei   

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process， Ministry of Education （East China University of Science and Technology）， Shanghai 200237， China
  • Received:2024-03-12 Online:2024-04-26 Published:2024-04-26
  • About author:ZHANG Wanting，born in 1998，Ph. D. candidate. Her research interests include large-scale scheduling，evolutionary computation. DU Wenli，born in 1974，Ph. D.，professor. Her research interests include industrial process modeling，control and optimization. DU Wei，born in 1987，Ph. D.，associate professor. His research interests include evolutionary computation， large-scale optimization， decision-making of complex industrial process.
  • Supported by:
    This work is partially surpport by National Key Research and Development Program of China (2022YFB3305900)，National Natural Science Foundation of China (62173144)， Shanghai Rising-Star Program (22QA1402400)， Shanghai Artificial Intelligence Laboratory.

摘要： 针对原油调度过程存在的资源规模庞大、约束条件复杂、多时间尺度决策衔接困难等问题，提出一种基于多时间尺度协同的进化算法。首先，根据炼油企业的规模结构和实际需求，建立了一种大规模多时间尺度原油调度优化模型，该模型由面向资源的中长期调度模型和面向操作的短期调度模型构成，通过引入原油资源动态分组策略，实现原油资源的合理配置，以满足不同的调度规模、多时间尺度的特征和精细化的生产要求；其次，为促进不同时间尺度调度决策的融合衔接，设计基于多时间尺度协同的进化算法，并针对不同时间尺度调度模型中的连续决策变量构造子问题进行求解，以实现不同时间尺度调度决策之间的协同优化；最后，在3个实际工业案例进行了算法性能验证，并与3种具有代表性的大规模优化方法和3种高性能混合整数非线性数学求解器进行对比分析。结果表明，相较于对比方法，所提算法的求解最优性和稳定性分别提高了30%和25%以上。这些显著的性能提升证明了所提方法在大规模多时间尺度原油调度决策中的实际应用价值和优势。

关键词: 进化算法, 大规模优化, 协同优化, 原油调度, 多时间尺度

Abstract: Abstract: Aiming to solve the problems of large-scale resources, complex constraints, and difficult cooperation of multi-timescale decision-making in the crude oil scheduling process, we proposed an evolutionary algorithm based on multi-timescale cooperation. Firstly, a large-scale multi-timescale crude oil scheduling optimization model was established according to the scale structure and actual demand of oil refining enterprises, which consists of a resource-oriented medium- and long-term scheduling model and an operation-oriented short-term scheduling model and achieves a reasonable allocation of crude oil resources through employing a dynamic grouping strategy of crude oil resources to satisfy the requirements of different scheduling scales, multi-timescale characteristics, and fine-tuning of the production requirements. Secondly, to promote the integration of scheduling decisions at different time scales, an evolutionary algorithm based on multi-timescale cooperation was designed and solved by constructing subproblems for the continuous decision variables in scheduling models at different time scales to achieve cooperation optimization between scheduling decisions at different time scales. Finally, the proposed algorithm was verified in three real industrial cases and compared with three representative large-scale optimization methods and three high-performance mixed-integer nonlinear mathematical solvers. The results show that the solution optimality and stability of the proposed algorithm are improved by more than 30% and 25%, respectively, compared with the comparison methods. These significant performance improvements demonstrate the practical application value and advantages of the proposed method in large-scale multi-timescale crude oil scheduling decisions.

Key words: Keywords: evolutionary algorithm, large-scale optimization, cooperative optimization, crude oil scheduling, multi-timescale

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