[1]侯 艳,任丙飞,滕少华,等.带罐约束的多目标短期炼油调度优化研究[J].江西师范大学学报(自然科学版),2023,(03):307-316.[doi:10.16357/j.cnki.issn1000-5862.2023.03.11]
　HOU Yan,REN Bingfei,TENG Shaohua,et al.The Multi-Objective Short-Term Scheduling Optimization with Charging-Tank-Switch-OverlapConstraint in Refinery[J].Journal of Jiangxi Normal University:Natural Science Edition,2023,(03):307-316.[doi:10.16357/j.cnki.issn1000-5862.2023.03.11]
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带罐约束的多目标短期炼油调度优化研究()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年03期
页码:
307-316
栏目:
出版日期:
2023-05-25

文章信息/Info

Title:
The Multi-Objective Short-Term Scheduling Optimization with Charging-Tank-Switch-OverlapConstraint in Refinery
文章编号:
1000-5862(2023)03-0307-10
作者:
侯 艳; 任丙飞; 滕少华; 朱清华
(广东工业大学计算机学院, 广东 广州 510006)
Author(s):
HOU Yan; REN Bingfei; TENG Shaohua; ZHU Qinghua
(School of Computers, Guangdong University of Technology, Guangzhou Guangdong 510006,China)
关键词:
炼油;  生产计划和调度;  带罐约束;  NSGA-Ⅲ-CPS算法
Keywords:
refining;  production planning and scheduling;  charging-tank-switch-overlap constraint;  NSGA-Ⅲ-CPS
分类号:
TP 301
DOI:
10.16357/j.cnki.issn1000-5862.2023.03.11
文献标志码:
A
摘要:
短期炼油调度问题不仅涉及多个优化目标,而且存在复杂的约束条件.由于带罐约束加剧了蒸馏塔对供油罐资源的争夺,所以这将导致调度解空间呈指数级膨胀,供油速度联调下协同优化效应急剧恶化.基于此,为保证任意供油速率下调度决策的可靠性和效率,该文提出了一种带罐约束的短期炼油多目标调度优化算法NSGA-Ⅲ-CPS.首先,任给一组炼油任务,在带罐约束下通过随机设置不同供油速度,使得算法能够获取在每种速度下可行调度方案、参数以及策略.其次,通过炼油成本计算与多目标评估,选择获取优化调度方案及策略.此外,该算法改进了交叉算子,通过优化交叉操作的父代选择策略,对父代种群进行快速非支配排序,支配等级高的父代赋予更高的被选中概率,使父代优良基因能更好地遗传到子代.因此,该算法综合考虑了供油罐使用成本与切换成本、原油在管道中的混合成本与在罐底的混合成本等多维炼油调度目标,在带罐约束下获取了可变供油速度下的炼油任务优化调度策略.最后,通过与4种经典多目标进化算法进行比较,对比结果表明:应用新算法以及策略在炼油成本上降低了10.0%～64.3%.
Abstract:
There are not only multiple optimization objectives, but also complex constraints in short-term refining scheduling problem. Because of the introduction of Charging-Tank-Switch-Overlap constraint, it will intensify the competition of distillation for charging tanks and lead to the exponential expansion of the scheduling solution space and the sharp deterioration of the collaborative optimization effect. Based on this, in order to ensure the reliability and efficiency of scheduling decisions at an arbitrary oil supply rate, the short-term refining multi-target scheduling optimization algorithm is proposed under Charging-Tank-Switch-Overlap constraints, called NSGA-Ⅲ-CPS. Firstly, giving a group of refining tasks, the evolution algorithm in this paper can obtain feasible scheduling schemes, parameters, and strategies for each speed. Secondly, the optimized scheduling schemes and strategies through refining cost calculation and multi-objective evaluation can be selected. Moreover, the evolutionary algorithm in this paper improves the cross operator by optimizing the parent selection strategy, conducts rapid non-dominant ranking of the parent population, and the parents with a high dominance rank have a higher probability of being selected, so that the parent’s good genes can be better inherited to the offspring. The algorithm considers the multi-dimensional refining scheduling objectives, such as the cost of the charging tanks and the charging tank switching, the mixing cost of crude oil in the pipeline and at the bottom of the tank, and obtains the refining task optimization scheduling strategy at the variable speed with consideration of Charging-Tank-Switch-Overlap constraint. Finally, by comparing with four classical multi-objective evolution algorithms, the experimental results show that it have a 10.0% to 64.3% improvement in the refining cost by applying the proposed algorithm and strategy.

参考文献/References:

[1] 亓仁东,王勇.原油加工成本控制分析 [J].广东化工,2013,40(16):118-119.
[2] LI Fupei,YANG Minglei,DU Wenli,et al.Development and challenges of planning and scheduling for petroleum and petrochemical production [J]. Frontiers of Engineering Management,2020,7(3):373-383.
[3] DE ASSIS L S,CAMPONOGARA E.A MILP model for planning the trips of dynamic positioned tankers with variable travel time [J]. Transportation Research Part E:Logistics and Transportation Review, 2016,93:372-388.
[4] SCHNEIDER G A,NEVES F,MAGATAO L,et al.A mathematical programming approach to optimize the scheduling of tanks in oil refineries [J]. IEEE Latin America Transactions,2016,14(2):818-830.
[5] BAYU F,SHAIK M A,RAMTEKE M.Scheduling of crude oil refinery operation with desalting as a separate task [J].Asia-Pacific Journal of Chemical Engineering,2020,15(6):2539.
[6] 伍青青,程辉.连续汽油调和调度问题的建模与优化[J].信息与控制,2020,49(5):615-624,632.
[7] DA NEIRO S M,MURATA V V,JAHN B,et al.Dealing with multiple tank outflows and in-line blending in conti-nuous-time crude oil scheduling problems [J].Industrial & Engineering Chemistry Research,2019,58(11): 4495-4510.
[8] DE ASSIS L S,CAMPONOGARA E,MENEZES B C,et al. An MINLP formulation for integrating the operational management of crude oil supply [J].Computers & Chemical Engineering,2019,123:110-125.
[9] WU Naiqi,ZHOU Mengchu,LI Zhiwu.Short-term scheduling of crude-oil operations:enhancement of crude-oil operations scheduling using a Petri net-based control-theoretic approach [J].IEEE Robotics & Automation Magazine,2015,22(2):64-76.
[10] WU Naiqi,CHU Chengbin,CHU Feng,et al.Schedulability analysis of short-term scheduling for crude oil operations in refinery with oil residency time and charging-tank-switch-overlap constraints [J].IEEE Transactions on Automation Science and Engineering,2011,8(1):190-204.
[11] 于国龙,崔忠伟,左羽.基于离散粒子群优化的MPSoC节能调度算法 [J].江西师范大学学报(自然科学版),2016,40(3):307-311.
[12] 庄景明,彭昕昀.基于改进遗传算法的新鲜农产品配送路线优化研究 [J].江西师范大学学报(自然科学版),2012,36(4):399-402.
[13] HOU Yan,WU Naiqi,ZHOU Mengchu,et al.Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm [J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(3):517-530.
[14] HOU Yan,WU Naiqi,LI Zhiwu,et al.Many-objective optimization for scheduling of crude oil operations based on NSGA-Ⅲ with consideration of energy efficiency [J].Swarm and Evolutionary Computation,2020,57:100714.
[15] 侯艳,黄康焕,张亿仙,等.原油一次加工过程的多目标调度优化 [J].工业工程,2020,23(4):131-139
[16] 侯艳,伍乃骐,白丽平.原油处理过程短期生产计划优化 [J].工业工程,2014,17(3):79-85.
[17] DEB K,JAIN H.An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach,part I:solving problems with box constraints [J].IEEE Transactions on Evolutionary Computation,2014,18(4):577-601.
[18] ZHANG Qingfu,LI Hui.MOEA/D:A multi-objective evolutionary algorithm based on decomposition [J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[19] KIM I Y,DE WECK O L.Adaptive weighted-sum method for bi-objective optimization: Pareto front generation [J]. Structural and Multidisciplinary Optimization,2005,29(2):149-158.
[20] CHENG Ran,JIN Yaochu,OLHOFER M,et al.A reference vector guided evolutionary algorithm for many-objective optimization [J].IEEE Transactions on Evolutionary Computation,2016,20(5):773-791.
[21] EMMERICH M,BEUME N,NAUJOKS B.An EMO algorithm using the hypervolume measure as selection criterion [EB/OL].[2022-03-19].https://doi.org/10.1007/978-3-540-31880-4_5.
[22] ZITZLER E,DEB K,THIELE L.Comparison of multi-objective evolutionary algorithms:empirical results [J].Evolutionary Computation,2000,8(2):173-195.

备注/Memo

备注/Memo:
收稿日期:2022-10-19
基金项目:国家自然科学基金(61603100,61972102)和广东省重点领域研发计划课题(2020B010166006)资助项目.
作者简介:侯 艳(1977—),女,湖北公安人,副教授,博士,主要从事离散事件系统、生产计划与调度优化研究.E-mail:houyan@gdut.edu.cn
更新日期/Last Update: 2023-05-25