Please wait a minute...
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Front. Eng    2020, Vol. 7 Issue (3) : 373-383    https://doi.org/10.1007/s42524-020-0123-3
REVIEW ARTICLE
Development and challenges of planning and scheduling for petroleum and petrochemical production
Fupei LI1, Minglei YANG2, Wenli DU2(), Xin DAI1
1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
 Download: PDF(444 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Production planning and scheduling are becoming the core of production management, which support the decision of a petrochemical company. The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production. The optimization problem considered in industry and academic research is of different levels of realism and complexity, thus increasing the gap. Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem. Additionally, modeling the processes, objectives, and constraints and developing the optimization algorithms are significant for industry and research. This paper introduces the perspective of production planning and scheduling from the development viewpoint.

Keywords planning and scheduling      optimization      modeling     
Corresponding Author(s): Wenli DU   
Just Accepted Date: 28 June 2020   Online First Date: 20 July 2020    Issue Date: 06 August 2020
 Cite this article:   
Fupei LI,Minglei YANG,Wenli DU, et al. Development and challenges of planning and scheduling for petroleum and petrochemical production[J]. Front. Eng, 2020, 7(3): 373-383.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-020-0123-3
https://academic.hep.com.cn/fem/EN/Y2020/V7/I3/373
Fig.1  Scope of planning and scheduling in a refinery supply chain.
Fig.2  Architecture of the virtual manufacturing system.
1 K Al-Qahtani, A Elkamel (2010). Robust planning of multisite refinery networks: Optimization under uncertainty. Computers & Chemical Engineering, 34(6): 985–995
https://doi.org/10.1016/j.compchemeng.2010.02.032
2 A M Alattas, I E Grossmann, I Palou-Rivera (2011). Integration of nonlinear crude distillation unit models in refinery planning optimization. Industrial & Engineering Chemistry Research, 50(11): 6860–6870
https://doi.org/10.1021/ie200151e
3 A M Alattas, I E Grossmann, I Palou-Rivera (2012). Refinery production planning: Multiperiod MINLP with nonlinear CDU model. Industrial & Engineering Chemistry Research, 51(39): 12852–12861
https://doi.org/10.1021/ie3002638
4 I Alhajri, A Elkamel, T Albahri, P L Douglas (2008). A nonlinear programming model for refinery planning and optimisation with rigorous process models and product quality specifications. International Journal of Oil, Gas and Coal Technology, 1(3): 283–307
https://doi.org/10.1504/IJOGCT.2008.019846
5 A Barbaro, M J Bagajewicz (2004). Managing financial risk in planning under uncertainty. AIChE Journal, 50(5): 963–989
https://doi.org/10.1002/aic.10094
6 M C Carneiro, G P Ribas, S Hamacher (2010). Risk management in the oil supply chain: A CVaR approach. Industrial & Engineering Chemistry Research, 49(7): 3286–3294
https://doi.org/10.1021/ie901265n
7 P A C Castillo, P M Castro, V Mahalec (2017a). Global optimization of nonlinear blend-scheduling problems. Engineering, 3(2): 188–201
https://doi.org/10.1016/J.ENG.2017.02.005
8 P C Castillo, P M Castro, V Mahalec (2017b). Global optimization algorithm for large-scale refinery planning models with bilinear terms. Industrial & Engineering Chemistry Research, 56(2): 530–548
https://doi.org/10.1021/acs.iecr.6b01350
9 Y Chu, F You, J M Wassick, A Agarwal (2015). Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method. Computers & Chemical Engineering, 72: 255–272
https://doi.org/10.1016/j.compchemeng.2014.02.023
10 A S Drud (1994). CONOPT—A large-scale GRG code. ORSA Journal on Computing, 6(2): 207–216
https://doi.org/10.1287/ijoc.6.2.207
11 A Elkamel, M Ba-Shammakh, P Douglas, E Croiset (2008). An optimization approach for integrating planning and CO2 emission reduction in the petroleum refining industry. Industrial & Engineering Chemistry Research, 47(3): 760–776
https://doi.org/10.1021/ie070426n
12 G D Eppen, R K Martin, L Schrage (1989). A scenario approach to capacity planning. Operations Research, 37(4): 517–527
https://doi.org/10.1287/opre.37.4.517
13 G Fu, P A C Castillo, V Mahalec (2018). Impact of crude distillation unit model accuracy on refinery production planning. Frontiers of Engineering Management, 5(2): 195–201
https://doi.org/10.15302/J-FEM-2017052
14 G Fu, V Mahalec (2015). Comparison of methods for computing crude distillation product properties in production planning and scheduling. Industrial & Engineering Chemistry Research, 54(45): 11371–11382
https://doi.org/10.1021/acs.iecr.5b02919
15 G Fu, Y Sanchez, V Mahalec (2016). Hybrid model for optimization of crude oil distillation units. AIChE Journal, 62(4): 1065–1078
https://doi.org/10.1002/aic.15086
16 X Gao, Y Jiang, T Chen, D Huang (2015). Optimizing scheduling of refinery operations based on piecewise linear models. Computers & Chemical Engineering, 75: 105–119
https://doi.org/10.1016/j.compchemeng.2015.01.022
17 K Glismann, G Gruhn (2001). Short-term scheduling and recipe optimization of blending processes. Computers & Chemical Engineering, 25(4–6): 627–634
https://doi.org/10.1016/S0098-1354(01)00643-3
18 I E Grossmann (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7): 1846–1857
https://doi.org/10.1002/aic.10617
19 I E Grossmann (2012). Advances in mathematical programming models for enterprise-wide optimization. Computers & Chemical Engineering, 47: 2–18
https://doi.org/10.1016/j.compchemeng.2012.06.038
20 I E Grossmann, R Raman (2020). DICOPT. Available at: gams.com/latest/docs
21 T Gueddar, V Dua (2011). Disaggregation-aggregation based model reduction for refinery-wide optimization. Computers & Chemical Engineering, 35(9): 1838–1856
https://doi.org/10.1016/j.compchemeng.2011.04.016
22 O J Guerra, G A C Le Roux (2011a). Improvements in petroleum refinery planning: 1. Formulation of process models. Industrial & Engineering Chemistry Research, 50(23): 13403–13418
https://doi.org/10.1021/ie200303m
23 O J Guerra, G A C Le Roux (2011b). Improvements in petroleum refinery planning: 2. Case studies. Industrial & Engineering Chemistry Research, 50(23): 13419–13426
https://doi.org/10.1021/ie200304v
24 Y Hou, N Wu, M Zhou, Z Li (2017). Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(3): 517–530
https://doi.org/10.1109/TSMC.2015.2507161
25 S Hu, G Towler, F X X Zhu (2002). Combine molecular modeling with optimization to stretch refinery operation. Industrial & Engineering Chemistry Research, 41(4): 825–841
https://doi.org/10.1021/ie0010215
26 R R Iyer, I E Grossmann (1998). A bilevel decomposition algorithm for long-range planning of process networks. Industrial & Engineering Chemistry Research, 37(2): 474–481
https://doi.org/10.1021/ie970383i
27 M Jalanko, V Mahalec (2018). Supply-demand pinch based methodology for multi-period planning under uncertainty in components qualities with application to gasoline blend planning. Computers & Chemical Engineering, 119: 425–438
https://doi.org/10.1016/j.compchemeng.2018.09.016
28 X Ji, S Huang, I E Grossmann (2015). Integrated operational and financial hedging for risk management in crude oil procurement. Industrial & Engineering Chemistry Research, 54(37): 9191–9201
https://doi.org/10.1021/acs.iecr.5b00903
29 Z Jia, M Ierapetritou (2003). Mixed-integer linear programming model for gasoline blending and distribution scheduling. Industrial & Engineering Chemistry Research, 42(4): 825–835
https://doi.org/10.1021/ie0204843
30 Z Jia, M Ierapetritou (2004). Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computers & Chemical Engineering, 28(6–7): 1001–1019
https://doi.org/10.1016/j.compchemeng.2003.09.007
31 Z Jia, M Ierapetritou, J D Kelly (2003). Refinery short-term scheduling using continuous time formulation: Crude-oil operations. Industrial & Engineering Chemistry Research, 42(13): 3085–3097
https://doi.org/10.1021/ie020124f
32 Y Jiao, H Su, W Hou, Z Liao (2012a). A multiperiod optimization model for hydrogen system scheduling in refinery. Industrial & Engineering Chemistry Research, 51(17): 6085–6098
https://doi.org/10.1021/ie2019239
33 Y Jiao, H Su, W Hou, Z Liao (2012b). Optimization of refinery hydrogen network based on chance constrained programming. Chemical Engineering Research & Design, 90(10): 1553–1567
https://doi.org/10.1016/j.cherd.2012.02.016
34 M Joly, L F L Moro, J M Pinto (2002). Planning and scheduling for petroleum refineries using mathematical programming. Brazilian Journal of Chemical Engineering, 19(2): 207–228
https://doi.org/10.1590/S0104-66322002000200008
35 N Julka, I Karimi, R Srinivasan (2002a). Agent-based supply chain management 2: A refinery application. Computers & Chemical Engineering, 26(12): 1771–1781
https://doi.org/10.1016/S0098-1354(02)00151-5
36 N Julka, R Srinivasan, I Karimi (2002b). Agent-based supply chain management 1: Framework. Computers & Chemical Engineering, 26(12): 1755–1769
https://doi.org/10.1016/S0098-1354(02)00150-3
37 R Karuppiah, K C Furman, I E Grossmann (2008). Global optimization for scheduling refinery crude oil operations. Computers & Chemical Engineering, 32(11): 2745–2766
https://doi.org/10.1016/j.compchemeng.2007.11.008
38 J Kim, K Tak, I Moon (2012). Optimization of procurement and production planning model in refinery processes considering corrosion effect. Industrial & Engineering Chemistry Research, 51(30): 10191–10200
https://doi.org/10.1021/ie300270s
39 H Lee, J M Pinto, I E Grossmann, S Park (1996). Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial & Engineering Chemistry Research, 35(5): 1630–1641
https://doi.org/10.1021/ie950519h
40 J Li, I A Karimi, R Srinivasan (2009). Recipe determination and scheduling of gasoline blending operations. AIChE Journal, 56(2): 441–465
https://doi.org/10.1002/aic.11970
41 J Li, X Xiao, F Boukouvala, C A Floudas, B Zhao, G Du, X Su, H Liu (2016). Data-driven mathematical modeling and global optimization framework for entire petrochemical planning operations. AIChE Journal, 62(9): 3020–3040
https://doi.org/10.1002/aic.15220
42 W Li, C W Hui, A Li (2005). Integrating CDU, FCC and product blending models into refinery planning. Computers & Chemical Engineering, 29(9): 2010–2028
https://doi.org/10.1016/j.compchemeng.2005.05.010
43 W Li, C W Hui, P Li, A X Li (2004). Refinery planning under uncertainty. Industrial & Engineering Chemistry Research, 43(21): 6742–6755
https://doi.org/10.1021/ie049737d
44 X Li (2013). Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty. Computers & Chemical Engineering, 55: 97–108
https://doi.org/10.1016/j.compchemeng.2013.04.006
45 Z Li, M G Ierapetritou (2010). Production planning and scheduling integration through augmented Lagrangian optimization. Computers & Chemical Engineering, 34(6): 996–1006
https://doi.org/10.1016/j.compchemeng.2009.11.016
46 C A Méndez, I E Grossmann, I Harjunkoski, P Kaboré (2006). A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers & Chemical Engineering, 30(4): 614–634
https://doi.org/10.1016/j.compchemeng.2005.11.004
47 B C Menezes, J D Kelly, I E Grossmann (2013). Improved swing-cut modeling for planning and scheduling of oil-refinery distillation units. Industrial & Engineering Chemistry Research, 52(51): 18324–18333
https://doi.org/10.1021/ie4025775
48 B C Menezes, J D Kelly, I E Grossmann, A Vazacopoulos (2015). Generalized capital investment planning of oil-refineries using MILP and sequence-dependent setups. Computers & Chemical Engineering, 80: 140–154
https://doi.org/10.1016/j.compchemeng.2015.05.013
49 R Misener, C A Floudas (2014). ANTIGONE: Algorithms for continuous/integer global optimization of nonlinear equations. Journal of Global Optimization, 59(2–3): 503–526
https://doi.org/10.1007/s10898-014-0166-2
50 S Mitra, J M Pinto, I E Grossmann (2014). Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition. Computers & Chemical Engineering, 65: 102–111
https://doi.org/10.1016/j.compchemeng.2014.02.012
51 L F L Moro, A C Zanin, J M Pinto (1998). A planning model for refinery diesel production. Computers & Chemical Engineering, 22: S1039–S1042
https://doi.org/10.1016/S0098-1354(98)00209-9
52 S Mouret, I E Grossmann, P Pestiaux (2009). A novel priority-slot based continuous-time formulation for crude-oil scheduling problems. Industrial & Engineering Chemistry Research, 48(18): 8515–8528
https://doi.org/10.1021/ie8019592
53 S Mouret, I E Grossmann, P Pestiaux (2011). A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling. Computers & Chemical Engineering, 35(12): 2750–2766
https://doi.org/10.1016/j.compchemeng.2011.03.026
54 S M S Neiro, J M Pinto (2004). A general modeling framework for the operational planning of petroleum supply chains. Computers & Chemical Engineering, 28(6–7): 871–896
https://doi.org/10.1016/j.compchemeng.2003.09.018
55 S M S Neiro, J M Pinto (2005). Multiperiod optimization for production planning of petroleum refineries. Chemical Engineering Communications, 192(1): 62–88
https://doi.org/10.1080/00986440590473155
56 J Park, S Park, C Yun, Y Kim (2010). Integrated model for financial risk management in refinery planning. Industrial & Engineering Chemistry Research, 49(1): 374–380
https://doi.org/10.1021/ie9000713
57 J M Pinto, M Joly, L F L Moro (2000). Planning and scheduling models for refinery operations. Computers & Chemical Engineering, 24(9–10): 2259–2276
https://doi.org/10.1016/S0098-1354(00)00571-8
58 A Pongsakdi, P Rangsunvigit, K Siemanond, M J Bagajewicz (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1): 64–86
https://doi.org/10.1016/j.ijpe.2005.04.007
59 Jr R Rejowski, J M Pinto (2003). Scheduling of a multiproduct pipeline system. Computers & Chemical Engineering, 27(8–9): 1229–1246
https://doi.org/10.1016/S0098-1354(03)00049-8
60 Jr R Rejowski, J M Pinto (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers & Chemical Engineering, 28(8): 1511–1528
https://doi.org/10.1016/j.compchemeng.2003.12.001
61 Jr R Rejowski, J M Pinto (2008). A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints. Computers & Chemical Engineering, 32(4–5): 1042–1066
https://doi.org/10.1016/j.compchemeng.2007.06.021
62 R T Rockafellar, S Uryasev (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3): 21–41
https://doi.org/10.21314/JOR.2000.038
63 N V Sahinidis (1996). BARON: A general purpose global optimization software package. Journal of Global Optimization, 8(2): 201–205
https://doi.org/10.1007/BF00138693
64 O Santander, C L Betts, E E Archer, M Baldea (2020). On the interaction and integration of production planning and (advanced) process control. Computers & Chemical Engineering, 133: 106627
https://doi.org/10.1016/j.compchemeng.2019.106627
65 N Shah, G K D Saharidis, Z Jia, M G Ierapetritou (2009). Centralized-decentralized optimization for refinery scheduling. Computers & Chemical Engineering, 33(12): 2091–2105
https://doi.org/10.1016/j.compchemeng.2009.06.010
66 N K Shah, M G Ierapetritou (2011). Short-term scheduling of a large-scale oil-refinery operations: Incorporating logistics details. AIChE Journal, 57(6): 1570–1584
https://doi.org/10.1002/aic.12359
67 N K Shah, Z Li, M G Ierapetritou (2011). Petroleum refining operations: Key issues, advances, and opportunities. Industrial & Engineering Chemistry Research, 50(3): 1161–1170
https://doi.org/10.1021/ie1010004
68 N K Shah, N Sahay, M G Ierapetritou (2015). Efficient decomposition approach for large-scale refinery scheduling. Industrial & Engineering Chemistry Research, 54(41): 9964–9991
https://doi.org/10.1021/ie504835b
69 M R Siamizade (2019). Global optimization of refinery-wide production planning with highly nonlinear unit models. Industrial & Engineering Chemistry Research, 58(24): 10437–10454
https://doi.org/10.1021/acs.iecr.9b00887
70 L M Simao, D M Dias, M Pacheco A C (2007). Refinery scheduling optimization using genetic algorithms and cooperative coevolution. In: IEEE Symposium on Computational Intelligence in Scheduling. Honolulu, HI,151–158
71 D D Slaback, J B Riggs (2007). The inside-out approach to refinery-wide optimization. Industrial & Engineering Chemistry Research, 46(13): 4645–4653
https://doi.org/10.1021/ie0608814
72 S A van den Heever, I E Grossmann (2003). A strategy for the integration of production planning and reactive scheduling in the optimization of a hydrogen supply network. Computers & Chemical Engineering, 27(12): 1813–1839
https://doi.org/10.1016/S0098-1354(03)00158-3
73 N Wu, Z Li, T Qu (2017). Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming. Journal of Cleaner Production, 166: 49–57
https://doi.org/10.1016/j.jclepro.2017.07.222
74 J Yang, H Gu, G Rong (2010). Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations. Industrial & Engineering Chemistry Research, 49(1): 276–287
https://doi.org/10.1021/ie900968x
75 Y Yang, P I Barton (2016). Integrated crude selection and refinery optimization under uncertainty. AIChE Journal, 62(4): 1038–1053
https://doi.org/10.1002/aic.15075
76 Y Yang, P Vayanos, P I Barton (2017). Chance-constrained optimization for refinery blend planning under uncertainty. Industrial & Engineering Chemistry Research, 56(42): 12139–12150
https://doi.org/10.1021/acs.iecr.7b02434
77 F You, I E Grossmann, J M Wassick (2011). Multisite capacity, production, and distribution planning with reactor modifications: MILP model, bilevel decomposition algorithm versus Lagrangean decomposition scheme. Industrial & Engineering Chemistry Research, 50(9): 4831–4849
https://doi.org/10.1021/ie100559y
78 B J Zhang, B Hua (2007). Effective MILP model for oil refinery-wide production planning and better energy utilization. Journal of Cleaner Production, 15(5): 439–448
https://doi.org/10.1016/j.jclepro.2005.08.004
79 J Zhang, X X Zhu, G P Towler (2001). A level-by-level debottlenecking approach in refinery operation. Industrial & Engineering Chemistry Research, 40(6): 1528–1540
https://doi.org/10.1021/ie990854w
80 J D Zhang, G Rong (2008). An MILP model for multi-period optimization of fuel gas system scheduling in refinery and its marginal value analysis. Chemical Engineering Research & Design, 86(2): 141–151
https://doi.org/10.1016/j.cherd.2007.11.002
81 H Zhao, M G Ierapetritou, N K Shah, G Rong (2017). Integrated model of refining and petrochemical plant for enterprise-wide optimization. Computers & Chemical Engineering, 97: 194–207
https://doi.org/10.1016/j.compchemeng.2016.11.020
82 H Zhao, G Rong, Y Feng (2014). Multiperiod planning model for integrated optimization of a refinery production and utility system. Industrial & Engineering Chemistry Research, 53(41): 16107–16122
https://doi.org/10.1021/ie502717e
83 H Zhao, G Rong, Y Feng (2015). Effective solution approach for integrated optimization models of refinery production and utility system. Industrial & Engineering Chemistry Research, 54(37): 9238–9250
https://doi.org/10.1021/acs.iecr.5b00713
[1] Zhe SUN, Cheng ZHANG, Pingbo TANG. Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure shutdowns[J]. Front. Eng, 2021, 8(1): 109-121.
[2] Mingyue LI, Zhuoling MA, Xi TANG. Owner-dominated building information modeling and lean construction in a megaproject[J]. Front. Eng, 2021, 8(1): 60-71.
[3] Shuai LI, Da HU, Jiannan CAI, Hubo CAI. Real option-based optimization for financial incentive allocation in infrastructure projects under public–private partnerships[J]. Front. Eng, 2020, 7(3): 413-425.
[4] Shubin SI, Jiangbin ZHAO, Zhiqiang CAI, Hongyan DUI. Recent advances in system reliability optimization driven by importance measures[J]. Front. Eng, 2020, 7(3): 335-358.
[5] Sameh Al-SHIHABI, Mohammad AlDURGAM. Multi-objective optimization for the multi-mode finance-based project scheduling problem[J]. Front. Eng, 2020, 7(2): 223-237.
[6] Mahdi JEMMALI, Bassem SALHI. Corporate governance impact on banking risk[J]. Front. Eng, 2020, 7(2): 182-195.
[7] Hans VOORDIJK. Building information modeling and its impact on users in the lifeworld: a mediation perspective[J]. Front. Eng, 2019, 6(2): 193-206.
[8] Ziyou GAO, Lixing YANG. Energy-saving operation approaches for urban rail transit systems[J]. Front. Eng, 2019, 6(2): 139-151.
[9] Fred GLOVER, Saïd HANAFI, Oualid GUEMRI, Igor CREVITS. A simple multi-wave algorithm for the uncapacitated facility location problem[J]. Front. Eng, 2018, 5(4): 451-465.
[10] Jorge Ignacio CISNEROS-SALDANA, Seyedmohammadhossein HOSSEINIAN, Sergiy BUTENKO. Network-based optimization techniques for wind farm location decisions[J]. Front. Eng, 2018, 5(4): 533-540.
[11] Panos M. PARDALOS, Mahdi FATHI. A discussion of objective function representation methods in global optimization[J]. Front. Eng, 2018, 5(4): 515-523.
[12] Albert P. C. CHAN, Xiaozhi MA, Wen YI, Xin ZHOU, Feng XIONG. Critical review of studies on building information modeling (BIM) in project management[J]. Front. Eng, 2018, 5(3): 394-406.
[13] Stefan JANAQI, Mériam CHÈBRE, Guillaume PITOLLAT. Online gasoline blending with EPA Complex Model for predicting emissions[J]. Front. Eng, 2018, 5(2): 214-226.
[14] Marcel JOLY, Darci ODLOAK, Mario Y. MIYAKE, Brenno C. MENEZES, Jeffrey D. KELLY. Refinery production scheduling toward Industry 4.0[J]. Front. Eng, 2018, 5(2): 202-213.
[15] Gang FU, Pedro A. Castillo CASTILLO, Vladimir MAHALEC. Impact of crude distillation unit model accuracy on refinery production planning[J]. Front. Eng, 2018, 5(2): 195-201.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed