Frontiers of Engineering Management >
Development and challenges of planning and scheduling for petroleum and petrochemical production
Received date: 26 Mar 2020
Accepted date: 06 Jun 2020
Published date: 15 Sep 2020
Copyright
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.
Key words: planning and scheduling; optimization; modeling
Fupei LI , Minglei YANG , Wenli DU , Xin DAI . Development and challenges of planning and scheduling for petroleum and petrochemical production[J]. Frontiers of Engineering Management, 2020 , 7(3) : 373 -383 . DOI: 10.1007/s42524-020-0123-3
1 |
Al-Qahtani K, Elkamel A (2010). Robust planning of multisite refinery networks: Optimization under uncertainty. Computers & Chemical Engineering, 34(6): 985–995
|
2 |
Alattas A M, Grossmann I E, Palou-Rivera I (2011). Integration of nonlinear crude distillation unit models in refinery planning optimization. Industrial & Engineering Chemistry Research, 50(11): 6860–6870
|
3 |
Alattas A M, Grossmann I E, Palou-Rivera I (2012). Refinery production planning: Multiperiod MINLP with nonlinear CDU model. Industrial & Engineering Chemistry Research, 51(39): 12852–12861
|
4 |
Alhajri I, Elkamel A, Albahri T, Douglas P L (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
|
5 |
Barbaro A, Bagajewicz M J (2004). Managing financial risk in planning under uncertainty. AIChE Journal, 50(5): 963–989
|
6 |
Carneiro M C, Ribas G P, Hamacher S (2010). Risk management in the oil supply chain: A CVaR approach. Industrial & Engineering Chemistry Research, 49(7): 3286–3294
|
7 |
Castillo P A C, Castro P M, Mahalec V (2017a). Global optimization of nonlinear blend-scheduling problems. Engineering, 3(2): 188–201
|
8 |
Castillo P C, Castro P M, Mahalec V (2017b). Global optimization algorithm for large-scale refinery planning models with bilinear terms. Industrial & Engineering Chemistry Research, 56(2): 530–548
|
9 |
Chu Y, You F, Wassick J M, Agarwal A (2015). Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method. Computers & Chemical Engineering, 72: 255–272
|
10 |
Drud A S (1994). CONOPT—A large-scale GRG code. ORSA Journal on Computing, 6(2): 207–216
|
11 |
Elkamel A, Ba-Shammakh M, Douglas P, Croiset E (2008). An optimization approach for integrating planning and CO2 emission reduction in the petroleum refining industry. Industrial & Engineering Chemistry Research, 47(3): 760–776
|
12 |
Eppen G D, Martin R K, Schrage L (1989). A scenario approach to capacity planning. Operations Research, 37(4): 517–527
|
13 |
Fu G, Castillo P A C, Mahalec V (2018). Impact of crude distillation unit model accuracy on refinery production planning. Frontiers of Engineering Management, 5(2): 195–201
|
14 |
Fu G, Mahalec V (2015). Comparison of methods for computing crude distillation product properties in production planning and scheduling. Industrial & Engineering Chemistry Research, 54(45): 11371–11382
|
15 |
Fu G, Sanchez Y, Mahalec V (2016). Hybrid model for optimization of crude oil distillation units. AIChE Journal, 62(4): 1065–1078
|
16 |
Gao X, Jiang Y, Chen T, Huang D (2015). Optimizing scheduling of refinery operations based on piecewise linear models. Computers & Chemical Engineering, 75: 105–119
|
17 |
Glismann K, Gruhn G (2001). Short-term scheduling and recipe optimization of blending processes. Computers & Chemical Engineering, 25(4–6): 627–634
|
18 |
Grossmann I E (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7): 1846–1857
|
19 |
Grossmann I E (2012). Advances in mathematical programming models for enterprise-wide optimization. Computers & Chemical Engineering, 47: 2–18
|
20 |
Grossmann I E, Raman R (2020). DICOPT. Available at: gams.com/latest/docs
|
21 |
Gueddar T, Dua V (2011). Disaggregation-aggregation based model reduction for refinery-wide optimization. Computers & Chemical Engineering, 35(9): 1838–1856
|
22 |
Guerra O J, Le Roux G A C (2011a). Improvements in petroleum refinery planning: 1. Formulation of process models. Industrial & Engineering Chemistry Research, 50(23): 13403–13418
|
23 |
Guerra O J, Le Roux G A C (2011b). Improvements in petroleum refinery planning: 2. Case studies. Industrial & Engineering Chemistry Research, 50(23): 13419–13426
|
24 |
Hou Y, Wu N, Zhou M, Li Z (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
|
25 |
Hu S, Towler G, Zhu F X X (2002). Combine molecular modeling with optimization to stretch refinery operation. Industrial & Engineering Chemistry Research, 41(4): 825–841
|
26 |
Iyer R R, Grossmann I E (1998). A bilevel decomposition algorithm for long-range planning of process networks. Industrial & Engineering Chemistry Research, 37(2): 474–481
|
27 |
Jalanko M, Mahalec V (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
|
28 |
Ji X, Huang S, Grossmann I E (2015). Integrated operational and financial hedging for risk management in crude oil procurement. Industrial & Engineering Chemistry Research, 54(37): 9191–9201
|
29 |
Jia Z, Ierapetritou M (2003). Mixed-integer linear programming model for gasoline blending and distribution scheduling. Industrial & Engineering Chemistry Research, 42(4): 825–835
|
30 |
Jia Z, Ierapetritou M (2004). Efficient short-term scheduling of refinery operations based on a continuous time formulation. Computers & Chemical Engineering, 28(6–7): 1001–1019
|
31 |
Jia Z, Ierapetritou M, Kelly J D (2003). Refinery short-term scheduling using continuous time formulation: Crude-oil operations. Industrial & Engineering Chemistry Research, 42(13): 3085–3097
|
32 |
Jiao Y, Su H, Hou W, Liao Z (2012a). A multiperiod optimization model for hydrogen system scheduling in refinery. Industrial & Engineering Chemistry Research, 51(17): 6085–6098
|
33 |
Jiao Y, Su H, Hou W, Liao Z (2012b). Optimization of refinery hydrogen network based on chance constrained programming. Chemical Engineering Research & Design, 90(10): 1553–1567
|
34 |
Joly M, Moro L F L, Pinto J M (2002). Planning and scheduling for petroleum refineries using mathematical programming. Brazilian Journal of Chemical Engineering, 19(2): 207–228
|
35 |
Julka N, Karimi I, Srinivasan R (2002a). Agent-based supply chain management 2: A refinery application. Computers & Chemical Engineering, 26(12): 1771–1781
|
36 |
Julka N, Srinivasan R, Karimi I (2002b). Agent-based supply chain management 1: Framework. Computers & Chemical Engineering, 26(12): 1755–1769
|
37 |
Karuppiah R, Furman K C, Grossmann I E (2008). Global optimization for scheduling refinery crude oil operations. Computers & Chemical Engineering, 32(11): 2745–2766
|
38 |
Kim J, Tak K, Moon I (2012). Optimization of procurement and production planning model in refinery processes considering corrosion effect. Industrial & Engineering Chemistry Research, 51(30): 10191–10200
|
39 |
Lee H, Pinto J M, Grossmann I E, Park S (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
|
40 |
Li J, Karimi I A, Srinivasan R (2009). Recipe determination and scheduling of gasoline blending operations. AIChE Journal, 56(2): 441–465
|
41 |
Li J, Xiao X, Boukouvala F, Floudas C A, Zhao B, Du G, Su X, Liu H (2016). Data-driven mathematical modeling and global optimization framework for entire petrochemical planning operations. AIChE Journal, 62(9): 3020–3040
|
42 |
Li W, Hui C W, Li A (2005). Integrating CDU, FCC and product blending models into refinery planning. Computers & Chemical Engineering, 29(9): 2010–2028
|
43 |
Li W, Hui C W, Li P, Li A X (2004). Refinery planning under uncertainty. Industrial & Engineering Chemistry Research, 43(21): 6742–6755
|
44 |
Li X (2013). Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty. Computers & Chemical Engineering, 55: 97–108
|
45 |
Li Z, Ierapetritou M G (2010). Production planning and scheduling integration through augmented Lagrangian optimization. Computers & Chemical Engineering, 34(6): 996–1006
|
46 |
Méndez C A, Grossmann I E, Harjunkoski I, Kaboré P (2006). A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers & Chemical Engineering, 30(4): 614–634
|
47 |
Menezes B C, Kelly J D, Grossmann I E (2013). Improved swing-cut modeling for planning and scheduling of oil-refinery distillation units. Industrial & Engineering Chemistry Research, 52(51): 18324–18333
|
48 |
Menezes B C, Kelly J D, Grossmann I E, Vazacopoulos A (2015). Generalized capital investment planning of oil-refineries using MILP and sequence-dependent setups. Computers & Chemical Engineering, 80: 140–154
|
49 |
Misener R, Floudas C A (2014). ANTIGONE: Algorithms for continuous/integer global optimization of nonlinear equations. Journal of Global Optimization, 59(2–3): 503–526
|
50 |
Mitra S, Pinto J M, Grossmann I E (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
|
51 |
Moro L F L, Zanin A C, Pinto J M (1998). A planning model for refinery diesel production. Computers & Chemical Engineering, 22: S1039–S1042
|
52 |
Mouret S, Grossmann I E, Pestiaux P (2009). A novel priority-slot based continuous-time formulation for crude-oil scheduling problems. Industrial & Engineering Chemistry Research, 48(18): 8515–8528
|
53 |
Mouret S, Grossmann I E, Pestiaux P (2011). A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling. Computers & Chemical Engineering, 35(12): 2750–2766
|
54 |
Neiro S M S, Pinto J M (2004). A general modeling framework for the operational planning of petroleum supply chains. Computers & Chemical Engineering, 28(6–7): 871–896
|
55 |
Neiro S M S, Pinto J M (2005). Multiperiod optimization for production planning of petroleum refineries. Chemical Engineering Communications, 192(1): 62–88
|
56 |
Park J, Park S, Yun C, Kim Y (2010). Integrated model for financial risk management in refinery planning. Industrial & Engineering Chemistry Research, 49(1): 374–380
|
57 |
Pinto J M, Joly M, Moro L F L (2000). Planning and scheduling models for refinery operations. Computers & Chemical Engineering, 24(9–10): 2259–2276
|
58 |
Pongsakdi A, Rangsunvigit P, Siemanond K, Bagajewicz M J (2006). Financial risk management in the planning of refinery operations. International Journal of Production Economics, 103(1): 64–86
|
59 |
Rejowski Jr R, Pinto J M (2003). Scheduling of a multiproduct pipeline system. Computers & Chemical Engineering, 27(8–9): 1229–1246
|
60 |
Rejowski Jr R, Pinto J M (2004). Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Computers & Chemical Engineering, 28(8): 1511–1528
|
61 |
Rejowski Jr R, Pinto J M (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
|
62 |
Rockafellar R T, Uryasev S (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3): 21–41
|
63 |
Sahinidis N V (1996). BARON: A general purpose global optimization software package. Journal of Global Optimization, 8(2): 201–205
|
64 |
Santander O, Betts C L, Archer E E, Baldea M (2020). On the interaction and integration of production planning and (advanced) process control. Computers & Chemical Engineering, 133: 106627
|
65 |
Shah N, Saharidis G K D, Jia Z, Ierapetritou M G (2009). Centralized-decentralized optimization for refinery scheduling. Computers & Chemical Engineering, 33(12): 2091–2105
|
66 |
Shah N K, Ierapetritou M G (2011). Short-term scheduling of a large-scale oil-refinery operations: Incorporating logistics details. AIChE Journal, 57(6): 1570–1584
|
67 |
Shah N K, Li Z, Ierapetritou M G (2011). Petroleum refining operations: Key issues, advances, and opportunities. Industrial & Engineering Chemistry Research, 50(3): 1161–1170
|
68 |
Shah N K, Sahay N, Ierapetritou M G (2015). Efficient decomposition approach for large-scale refinery scheduling. Industrial & Engineering Chemistry Research, 54(41): 9964–9991
|
69 |
Siamizade M R (2019). Global optimization of refinery-wide production planning with highly nonlinear unit models. Industrial & Engineering Chemistry Research, 58(24): 10437–10454
|
70 |
Simao L M, Dias D M, Pacheco M 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 |
Slaback D D, Riggs J B (2007). The inside-out approach to refinery-wide optimization. Industrial & Engineering Chemistry Research, 46(13): 4645–4653
|
72 |
van den Heever S A, Grossmann I E (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
|
73 |
Wu N, Li Z, Qu T (2017). Energy efficiency optimization in scheduling crude oil operations of refinery based on linear programming. Journal of Cleaner Production, 166: 49–57
|
74 |
Yang J, Gu H, Rong G (2010). Supply chain optimization for refinery with considerations of operation mode changeover and yield fluctuations. Industrial & Engineering Chemistry Research, 49(1): 276–287
|
75 |
Yang Y, Barton P I (2016). Integrated crude selection and refinery optimization under uncertainty. AIChE Journal, 62(4): 1038–1053
|
76 |
Yang Y, Vayanos P, Barton P I (2017). Chance-constrained optimization for refinery blend planning under uncertainty. Industrial & Engineering Chemistry Research, 56(42): 12139–12150
|
77 |
You F, Grossmann I E, Wassick J M (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
|
78 |
Zhang B J, Hua B (2007). Effective MILP model for oil refinery-wide production planning and better energy utilization. Journal of Cleaner Production, 15(5): 439–448
|
79 |
Zhang J, Zhu X X, Towler G P (2001). A level-by-level debottlenecking approach in refinery operation. Industrial & Engineering Chemistry Research, 40(6): 1528–1540
|
80 |
Zhang J D, Rong G (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
|
81 |
Zhao H, Ierapetritou M G, Shah N K, Rong G (2017). Integrated model of refining and petrochemical plant for enterprise-wide optimization. Computers & Chemical Engineering, 97: 194–207
|
82 |
Zhao H, Rong G, Feng Y (2014). Multiperiod planning model for integrated optimization of a refinery production and utility system. Industrial & Engineering Chemistry Research, 53(41): 16107–16122
|
83 |
Zhao H, Rong G, Feng Y (2015). Effective solution approach for integrated optimization models of refinery production and utility system. Industrial & Engineering Chemistry Research, 54(37): 9238–9250
|
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