Development and challenges of planning and scheduling for petroleum and petrochemical production
Fupei LI, Minglei YANG, Wenli DU, Xin DAI
Development and challenges of planning and scheduling for petroleum and petrochemical production
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.
planning and scheduling / optimization / modeling
[1] |
Al-Qahtani K, Elkamel A (2010). Robust planning of multisite refinery networks: Optimization under uncertainty. Computers & Chemical Engineering, 34(6): 985–995
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[5] |
Barbaro A, Bagajewicz M J (2004). Managing financial risk in planning under uncertainty. AIChE Journal, 50(5): 963–989
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[7] |
Castillo P A C, Castro P M, Mahalec V (2017a). Global optimization of nonlinear blend-scheduling problems. Engineering, 3(2): 188–201
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[10] |
Drud A S (1994). CONOPT—A large-scale GRG code. ORSA Journal on Computing, 6(2): 207–216
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[12] |
Eppen G D, Martin R K, Schrage L (1989). A scenario approach to capacity planning. Operations Research, 37(4): 517–527
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[15] |
Fu G, Sanchez Y, Mahalec V (2016). Hybrid model for optimization of crude oil distillation units. AIChE Journal, 62(4): 1065–1078
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[17] |
Glismann K, Gruhn G (2001). Short-term scheduling and recipe optimization of blending processes. Computers & Chemical Engineering, 25(4–6): 627–634
CrossRef
Google scholar
|
[18] |
Grossmann I E (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7): 1846–1857
CrossRef
Google scholar
|
[19] |
Grossmann I E (2012). Advances in mathematical programming models for enterprise-wide optimization. Computers & Chemical Engineering, 47: 2–18
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[35] |
Julka N, Karimi I, Srinivasan R (2002a). Agent-based supply chain management 2: A refinery application. Computers & Chemical Engineering, 26(12): 1771–1781
CrossRef
Google scholar
|
[36] |
Julka N, Srinivasan R, Karimi I (2002b). Agent-based supply chain management 1: Framework. Computers & Chemical Engineering, 26(12): 1755–1769
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[40] |
Li J, Karimi I A, Srinivasan R (2009). Recipe determination and scheduling of gasoline blending operations. AIChE Journal, 56(2): 441–465
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[43] |
Li W, Hui C W, Li P, Li A X (2004). Refinery planning under uncertainty. Industrial & Engineering Chemistry Research, 43(21): 6742–6755
CrossRef
Google scholar
|
[44] |
Li X (2013). Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty. Computers & Chemical Engineering, 55: 97–108
CrossRef
Google scholar
|
[45] |
Li Z, Ierapetritou M G (2010). Production planning and scheduling integration through augmented Lagrangian optimization. Computers & Chemical Engineering, 34(6): 996–1006
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[55] |
Neiro S M S, Pinto J M (2005). Multiperiod optimization for production planning of petroleum refineries. Chemical Engineering Communications, 192(1): 62–88
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[59] |
Rejowski Jr R, Pinto J M (2003). Scheduling of a multiproduct pipeline system. Computers & Chemical Engineering, 27(8–9): 1229–1246
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[62] |
Rockafellar R T, Uryasev S (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3): 21–41
CrossRef
Google scholar
|
[63] |
Sahinidis N V (1996). BARON: A general purpose global optimization software package. Journal of Global Optimization, 8(2): 201–205
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[75] |
Yang Y, Barton P I (2016). Integrated crude selection and refinery optimization under uncertainty. AIChE Journal, 62(4): 1038–1053
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
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