CIESC Journal ›› 2023, Vol. 74 ›› Issue (4): 1619-1629.DOI: 10.11949/0438-1157.20230012
• Process system engineering • Previous Articles Next Articles
Xiaoyong GAO1(), Fuyu HUANG1, Wanpeng ZHENG1, Diao PENG1, Yixu YANG1, Dexian HUANG1,2()
Received:
2022-01-04
Revised:
2022-02-23
Online:
2023-06-02
Published:
2023-04-05
Contact:
Dexian HUANG
高小永1(), 黄付宇1, 郑万鹏1, 彭雕1, 杨一旭1, 黄德先1,2()
通讯作者:
黄德先
作者简介:
高小永(1985—),男,博士,副教授,x.gao@cup.edu.cn
基金资助:
CLC Number:
Xiaoyong GAO, Fuyu HUANG, Wanpeng ZHENG, Diao PENG, Yixu YANG, Dexian HUANG. Scheduling optimization of refinery and chemical production process considering the safety and stability of scheduling operation[J]. CIESC Journal, 2023, 74(4): 1619-1629.
高小永, 黄付宇, 郑万鹏, 彭雕, 杨一旭, 黄德先. 考虑调度操作安全平稳性的炼油化工生产过程调度优化[J]. 化工学报, 2023, 74(4): 1619-1629.
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模式 | 收率/% | 操作成本/(元/吨) | ||||||
---|---|---|---|---|---|---|---|---|
直馏汽油 | 石脑油 | 煤油 | 轻柴油 | 常压瓦斯油 | 减压瓦斯油 | 减压渣油 | ||
1 | 3.10 | 9.80 | 9.20 | 14.1 | 22.3 | 24.1 | 12.1 | 20 |
2 | 4.50 | 12.8 | 12.1 | 12.4 | 21.8 | 23.4 | 10.7 | 25 |
3 | 5.10 | 18.4 | 15.8 | 10.3 | 20.1 | 21.9 | 7.90 | 30 |
4 | 6.40 | 20.2 | 18.1 | 8.60 | 19.4 | 21.4 | 5.70 | 35 |
Table 1 Yield and cost of crude distillation unit
模式 | 收率/% | 操作成本/(元/吨) | ||||||
---|---|---|---|---|---|---|---|---|
直馏汽油 | 石脑油 | 煤油 | 轻柴油 | 常压瓦斯油 | 减压瓦斯油 | 减压渣油 | ||
1 | 3.10 | 9.80 | 9.20 | 14.1 | 22.3 | 24.1 | 12.1 | 20 |
2 | 4.50 | 12.8 | 12.1 | 12.4 | 21.8 | 23.4 | 10.7 | 25 |
3 | 5.10 | 18.4 | 15.8 | 10.3 | 20.1 | 21.9 | 7.90 | 30 |
4 | 6.40 | 20.2 | 18.1 | 8.60 | 19.4 | 21.4 | 5.70 | 35 |
产品 | 需求量/t | |||||||
---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
90号汽油 | 0 | 0 | 200 | 0 | 0 | 0 | 0 | 700 |
93号汽油 | 0 | 0 | 300 | 0 | 0 | 0 | 0 | 400 |
97号汽油 | 0 | 0 | 800 | 0 | 0 | 0 | 0 | 0 |
0号柴油 | 100 | 0 | 0 | 0 | 0 | 200 | 0 | 0 |
-10号柴油 | 200 | 0 | 0 | 0 | 0 | 400 | 0 | 0 |
煤油 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 |
乙烯 | 0 | 0 | 200 | 0 | 0 | 0 | 0 | 800 |
丙烯 | 0 | 0 | 0 | 200 | 0 | 0 | 0 | 0 |
Table 3 Requirement information of product for case 1
产品 | 需求量/t | |||||||
---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
90号汽油 | 0 | 0 | 200 | 0 | 0 | 0 | 0 | 700 |
93号汽油 | 0 | 0 | 300 | 0 | 0 | 0 | 0 | 400 |
97号汽油 | 0 | 0 | 800 | 0 | 0 | 0 | 0 | 0 |
0号柴油 | 100 | 0 | 0 | 0 | 0 | 200 | 0 | 0 |
-10号柴油 | 200 | 0 | 0 | 0 | 0 | 400 | 0 | 0 |
煤油 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 0 |
乙烯 | 0 | 0 | 200 | 0 | 0 | 0 | 0 | 800 |
丙烯 | 0 | 0 | 0 | 200 | 0 | 0 | 0 | 0 |
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 10919 | 9655 | 1280 | 600.0 | 5.90 | 16689749.7 | 10 | 81.36 |
模型Ⅱ | 11101 | 9655 | 1280 | 49.0 | 0.01 | 18268224.6 | 12 | 93.24 |
模型Ⅲ | 10919 | 9655 | 1280 | 3.0 | 0 | 17470639.3 | 0 | 85.22 |
模型Ⅳ | 11101 | 9655 | 1280 | 22.3 | 0 | 112882537.2 | 0 | 96.24 |
Table 4 Statistics of solutions for case 1
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 10919 | 9655 | 1280 | 600.0 | 5.90 | 16689749.7 | 10 | 81.36 |
模型Ⅱ | 11101 | 9655 | 1280 | 49.0 | 0.01 | 18268224.6 | 12 | 93.24 |
模型Ⅲ | 10919 | 9655 | 1280 | 3.0 | 0 | 17470639.3 | 0 | 85.22 |
模型Ⅳ | 11101 | 9655 | 1280 | 22.3 | 0 | 112882537.2 | 0 | 96.24 |
产品 | 需求量/t | |||||||
---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
90号汽油 | 100 | 0 | 400 | 0 | 0 | 0 | 0 | 800 |
93号汽油 | 200 | 300 | 0 | 0 | 0 | 0 | 0 | 0 |
97号汽油 | 300 | 0 | 400 | 0 | 0 | 0 | 0 | 0 |
0号柴油 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 200 |
-10号柴油 | 0 | 0 | 0 | 0 | 400 | 100 | 400 | 500 |
煤油 | 300 | 0 | 0 | 0 | 300 | 0 | 0 | 700 |
乙烯 | 0 | 300 | 0 | 0 | 0 | 0 | 0 | 600 |
丙烯 | 0 | 0 | 0 | 0 | 300 | 0 | 0 | 400 |
Table 5 Requirement information of product for case 2
产品 | 需求量/t | |||||||
---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
90号汽油 | 100 | 0 | 400 | 0 | 0 | 0 | 0 | 800 |
93号汽油 | 200 | 300 | 0 | 0 | 0 | 0 | 0 | 0 |
97号汽油 | 300 | 0 | 400 | 0 | 0 | 0 | 0 | 0 |
0号柴油 | 0 | 0 | 0 | 0 | 0 | 500 | 0 | 200 |
-10号柴油 | 0 | 0 | 0 | 0 | 400 | 100 | 400 | 500 |
煤油 | 300 | 0 | 0 | 0 | 300 | 0 | 0 | 700 |
乙烯 | 0 | 300 | 0 | 0 | 0 | 0 | 0 | 600 |
丙烯 | 0 | 0 | 0 | 0 | 300 | 0 | 0 | 400 |
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 10919 | 9655 | 1280 | 600.0 | 0.07 | 35696277.6 | 10 | 81.21 |
模型Ⅱ | 11101 | 9655 | 1280 | 139.0 | 0.01 | 37803820.8 | 9 | 92.17 |
模型Ⅲ | 10919 | 9655 | 1280 | 5.3 | 0 | 38446318.6 | 0 | 86.74 |
模型Ⅳ | 11101 | 9655 | 1280 | 234.0 | 0 | 38545371.2 | 0 | 95.93 |
Table 6 Statistics of solutions for case 2
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 10919 | 9655 | 1280 | 600.0 | 0.07 | 35696277.6 | 10 | 81.21 |
模型Ⅱ | 11101 | 9655 | 1280 | 139.0 | 0.01 | 37803820.8 | 9 | 92.17 |
模型Ⅲ | 10919 | 9655 | 1280 | 5.3 | 0 | 38446318.6 | 0 | 86.74 |
模型Ⅳ | 11101 | 9655 | 1280 | 234.0 | 0 | 38545371.2 | 0 | 95.93 |
产品 | 需求量/t | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
90号汽油 | 0 | 200 | 500 | 400 | 0 | 500 | 0 | 100 | 0 | 100 | 0 | 500 |
93号汽油 | 200 | 0 | 0 | 0 | 0 | 400 | 0 | 400 | 0 | 0 | 0 | 600 |
97号汽油 | 300 | 0 | 0 | 100 | 0 | 700 | 0 | 0 | 0 | 300 | 0 | 700 |
0号柴油 | 0 | 0 | 100 | 0 | 0 | 100 | 200 | 300 | 0 | 400 | 0 | 500 |
-10号柴油 | 200 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 200 |
煤油 | 0 | 100 | 0 | 50 | 0 | 300 | 0 | 500 | 50 | 0 | 0 | 200 |
乙烯 | 100 | 0 | 0 | 200 | 0 | 0 | 100 | 0 | 400 | 0 | 0 | 800 |
丙烯 | 0 | 100 | 0 | 0 | 0 | 0 | 400 | 0 | 0 | 100 | 0 | 300 |
Table 7 Requirement information of product for case 3
产品 | 需求量/t | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
时间片段 | ||||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
90号汽油 | 0 | 200 | 500 | 400 | 0 | 500 | 0 | 100 | 0 | 100 | 0 | 500 |
93号汽油 | 200 | 0 | 0 | 0 | 0 | 400 | 0 | 400 | 0 | 0 | 0 | 600 |
97号汽油 | 300 | 0 | 0 | 100 | 0 | 700 | 0 | 0 | 0 | 300 | 0 | 700 |
0号柴油 | 0 | 0 | 100 | 0 | 0 | 100 | 200 | 300 | 0 | 400 | 0 | 500 |
-10号柴油 | 200 | 0 | 0 | 0 | 0 | 100 | 0 | 0 | 0 | 0 | 0 | 200 |
煤油 | 0 | 100 | 0 | 50 | 0 | 300 | 0 | 500 | 50 | 0 | 0 | 200 |
乙烯 | 100 | 0 | 0 | 200 | 0 | 0 | 100 | 0 | 400 | 0 | 0 | 800 |
丙烯 | 0 | 100 | 0 | 0 | 0 | 0 | 400 | 0 | 0 | 100 | 0 | 300 |
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 16523 | 14467 | 12547 | 600.0 | 3.42 | 36522116.2 | 12 | 75.06 |
模型Ⅱ | 16809 | 14467 | 12547 | 600.0 | 8.65 | 37694269.1 | 6 | 94.69 |
模型Ⅲ | 16523 | 14467 | 12547 | 164.0 | 0 | 37941169.1 | 0 | 82.46 |
模型Ⅳ | 16809 | 14467 | 12547 | 600.0 | 2.82 | 37969474.6 | 0 | 96.34 |
Table 8 Statistics of solutions for case 3
模型 | 约束总数 | 变量总数 | 二元变量数 | 求解耗时/s | 相对Gap值/% | 操作成本/元 | 模式切换次数 | 平稳度/% |
---|---|---|---|---|---|---|---|---|
模型Ⅰ | 16523 | 14467 | 12547 | 600.0 | 3.42 | 36522116.2 | 12 | 75.06 |
模型Ⅱ | 16809 | 14467 | 12547 | 600.0 | 8.65 | 37694269.1 | 6 | 94.69 |
模型Ⅲ | 16523 | 14467 | 12547 | 164.0 | 0 | 37941169.1 | 0 | 82.46 |
模型Ⅳ | 16809 | 14467 | 12547 | 600.0 | 2.82 | 37969474.6 | 0 | 96.34 |
1 | 郑万鹏, 高小永, 朱桂瑶, 等. 原油作业过程优化的研究进展[J]. 化工学报, 2021, 72(11): 5481-5501. |
Zheng W P, Gao X Y, Zhu G Y, et al. Research progress on crude oil operation optimization[J]. CIESC Journal, 2021, 72(11): 5481-5501. | |
2 | 周红军, 周颖, 徐春明. 中国碳达峰碳中和目标下炼化一体化新路径与实践[J]. 化工进展, 2022, 41(4): 2226-2230. |
Zhou H J, Zhou Y, Xu C M. Exploration of refining and chemical integration under China’s dualcarbon target[J]. Chemical Industry and Engineering Progress, 2022, 41(4): 2226-2230. | |
3 | 韩彪, 尚超, 江永亨, 等. 面向对象的炼油厂全厂调度优化模型及程序框架[J]. 化工学报, 2022, 73(4): 1623-1630. |
Han B, Shang C, Jiang Y H, et al. Object-oriented refinery plant-wide scheduling optimization model and program framework[J]. CIESC Journal, 2022, 73(4): 1623-1630. | |
4 | Alam M Z, Sakib M N, Islam M, et al. Various risks and safety analysis to reduce fire in oil refinery plant[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1078(1): 012028. |
5 | Lee H, Pinto J M, Grossmann I E, et al. Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management[J]. Industrial & Engineering Chemistry Research, 1996, 35(5): 1630-1641. |
6 | Joly M, Moro L F L, Pinto J M. Planning and scheduling for petroleum refineries using mathematical programming[J]. Brazilian Journal of Chemical Engineering, 2002, 19(2): 207-228. |
7 | 焦云强, 苏宏业, 侯卫锋. 炼油厂氢气系统优化调度及其应用[J]. 化工学报, 2011, 62(8): 2101-2107. |
Jiao Y Q, Su H Y, Hou W F. Optimal scheduling of hydrogen system in refinery and its application[J]. CIESC Journal, 2011, 62(8): 2101-2107. | |
8 | Gao X Y, Shang C, Jiang Y H, et al. Refinery scheduling with varying crude: a deep belief network classification and multimodel approach[J]. AIChE Journal, 2014, 60(7): 2525-2532. |
9 | Gao X Y, Jiang Y H, Chen T, et al. Optimizing scheduling of refinery operations based on piecewise linear models[J]. Computers & Chemical Engineering, 2015, 75: 105-119. |
10 | 高小永, 江永亨, 黄德先. 基于装置级优化控制与厂级调度优化集成的过程模型方法[J]. 化工学报, 2016, 67(12): 5105-5111. |
Gao X Y, Jiang Y H, Huang D X. Process modelling based on integration of unitwide optimal process control and plantwide scheduling[J]. CIESC Journal, 2016, 67(12): 5105-5111. | |
11 | Gao X Y, Huang D X, Jiang Y H, et al. A decision tree based decomposition method for oil refinery scheduling[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1605-1612. |
12 | 施磊, 江永亨, 王凌, 等. 一种求解炼油厂连续时间调度模型的Lagrange分解算法[J]. 清华大学学报(自然科学版), 2016, 56(4): 437-447. |
Shi L, Jiang Y H, Wang L, et al. Lagrangian decomposition approach for solving continuous-time scheduling models of refinery production problems[J]. Journal of Tsinghua University (Science and Technology), 2016, 56(4): 437-447. | |
13 | Wu N Q, Li Z W, Qu T. Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming[J]. Journal of Cleaner Production, 2017, 166: 49-57. |
14 | Hou Y, Wu N Q, Li Z W, 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. |
Hou Y, Huang K H, Zhang Y X, et al. A multi-objective scheduling optimization for crude oil operations[J]. Industrial Engineering Journal, 2020, 23(4): 131-139. | |
16 | 赵浩, 荣冈, 冯毅萍. 集成炼油企业生产与能量系统的生产计划优化[J]. 化工学报, 2015, 66(1): 228-236. |
Zhao H, Rong G, Feng Y P. Integrated optimization of production and utility system planning in refining industry[J]. CIESC Journal, 2015, 66(1): 228-236. | |
17 | 张鹏飞, 王子豪, 荣冈, 等. 面向石化企业间物流集成计划优化的模型及应用[J]. 化工学报, 2016, 67(11): 4678-4688. |
Zhang P F, Wang Z H, Rong G, et al. Optimized model and application of integrated logistics planning for petrochemical enterprises[J]. CIESC Journal, 2016, 67(11): 4678-4688. | |
18 | Zhao H, Ierapetritou M G, Shah N K, et al. Integrated model of refining and petrochemical plant for enterprise-wide optimization[J]. Computers & Chemical Engineering, 2017, 97: 194-207. |
19 | Xu J L, Qu H L, Wang S J, et al. A new proactive scheduling methodology for front-end crude oil and refinery operations under uncertainty of shipping delay[J]. Industrial & Engineering Chemistry Research, 2017, 56(28): 8041-8053. |
20 | Li M. Multi-periodic refinery scheduling based on generalized disjunctive programming[J]. Journal of Physics: Conference Series, 2020, 1575(1): 012195. |
21 | Duan Q Q. An MILP-NLP decomposition approach applied to a refinery scheduling problem[C]//2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2020: 411-417. |
22 | Chen Y D, Ding J L. Discrete-time scheduling model of entire refinery with multiscale operation time[C]//2021 3rd International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2021: 1-6. |
23 | Ossorio-Castillo J, Pena-Brage F. Optimization of a refinery scheduling process with column generation and a quantum annealer[J]. Optimization and Engineering, 2022, 23(3): 1471-1488. |
24 | Yu L, Wang S J, Xu Q. Optimal scheduling for simultaneous refinery manufacturing and multi oil-product pipeline distribution[J]. Computers & Chemical Engineering, 2022, 157: 107613. |
25 | Chen Y D, Ding J L, Chai T Y. A knowledge transfer based scheduling algorithm for large-scale refinery production[J]. IEEE Transactions on Industrial Informatics, 2022, 18(2): 869-879. |
26 | Al-Ahmari A, Kaid H, Li Z W, et al. A new MINLP continuous time formulation for scheduling optimization of oil refinery with unreliable CDUs[J]. Mathematical Problems in Engineering, 2022, 2022: 1-12. |
27 | Jiao Y Q, Su H Y, Liao Z W, et al. Modeling and multi-objective optimization of refinery hydrogen network[J]. Chinese Journal of Chemical Engineering, 2011, 19(6): 990-998. |
28 | Rabbani M, Farrokhi-Asl H, Ameli M. Solving a fuzzy multi-objective products and time planning using hybrid meta-heuristic algorithm: gas refinery case study[J]. Uncertain Supply Chain Management, 2016, 4(2): 93-106. |
29 | Liu X, Liu Y L, He X J, et al. Multi-objective nonlinear programming model for reducing octane number loss in gasoline refining process based on data mining technology[J]. Processes, 2021, 9(4): 721. |
30 | Su Y, Jin S M, Zhang X P, et al. Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm[J]. Computers & Chemical Engineering, 2020, 132: 106618. |
31 | Yang A, Wang W H, Sun S R, et al. Sustainable design and multi-objective optimization of eco-efficient extractive distillation with single and double entrainer(s) for separating the ternary azeotropic mixture tetrahydrofuran/ethanol/methanol[J]. Separation and Purification Technology, 2022, 285: 120413. |
32 | 郭庆强, 李歧强, 丁然, 等. 考虑一类过渡过程的连续过程生产调度建模[J]. 系统仿真学报, 2009, 21(22): 7009-7013. |
Guo Q Q, Li Q Q, Ding R, et al. Production scheduling modeling of continuous processes considering material switching[J]. Journal of System Simulation, 2009, 21(22): 7009-7013. | |
33 | 李明, 李歧强, 郭庆强, 等. 基于连续过程特性的炼油生产调度优化研究[J]. 计算机工程与应用, 2010, 46(21): 205-209, 234. |
Li M, Li Q Q, Guo Q Q, et al. Refinery scheduling optimization research based on continuous process characteristics[J]. Computer Engineering and Applications, 2010, 46(21): 205-209, 234. | |
34 | Shi L, Jiang Y H, Wang L, et al. Refinery production scheduling involving operational transitions of mode switching under predictive control system[J]. Industrial & Engineering Chemistry Research, 2014, 53(19): 8155-8170. |
35 | Parkash S. Treating processes[M]//Refining Processes Handbook. Amsterdam: Elsevier, 2003: 210-219. |
36 | 魏传江, 王浩, 谢新民, 等. GAMS用户指南[M]. 北京: 中国水利水电出版社, 2009. |
Wei C J, Wang H, Xie X M, et al. GAMS User Guide[M]. Beijing:China Water & Power Press, 2009. | |
37 | Dunning I, Huchette J, Lubin M. JuMP: a modeling language for mathematical optimization[J]. SIAM Review, 2017, 59(2): 295-320. |
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