Refinery production scheduling toward Industry 4.0

doi:10.15302/J-FEM-2017024

Front. Eng

2018, Vol. 5

Issue (2) : 202-213 https://doi.org/10.15302/J-FEM-2017024

RESEARCH ARTICLE

Refinery production scheduling toward Industry 4.0

Marcel JOLY¹(

), Darci ODLOAK³, Mario Y. MIYAKE², Brenno C. MENEZES⁴, Jeffrey D. KELLY⁵

¹. Technological Research Institute IPT, São Paulo 05508-901, Brazil; Federal University of Technology UTFPR, Curitiba 85601-970, Brazil
². Technological Research Institute IPT, São Paulo 05508-901, Brazil
³. University of São Paulo, São Paulo 05508-010, Brazil
⁴. Carnegie Mellon University, Pittsburgh 15213, United States
⁵. Industrial Algorithms Limited, Toronto ON, MIP 4C3, Canada

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Abstract

Understanding the holistic relationship between refinery production scheduling (RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling can impact operations in the high-performing chemical process industry. We conclude that, more than any traditional off-the-shelf RPS solution available today, flexible and integrative specialized modeling platforms will be increasingly necessary to perform decentralized and collaborative optimizations, since they are the technological alternatives closer to the advanced manufacturing philosophy.

Keywords cyber-physical systems optimization petrochemical industry scheduling smart manufacturing

Corresponding Author(s): Marcel JOLY

Just Accepted Date: 22 November 2017 Online First Date: 26 December 2017 Issue Date: 28 June 2018

Cite this article:

Marcel JOLY,Darci ODLOAK,Mario Y. MIYAKE, et al. Refinery production scheduling toward Industry 4.0[J]. Front. Eng, 2018, 5(2): 202-213.

URL:

https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2017024
https://academic.hep.com.cn/fem/EN/Y2018/V5/I2/202

Aspect	Industry 3.0	Industry 4.0
Approach	Reductionist. Emergent properties of the true complex system are, to a greater or a lesser extent, unavoidably ignored by the current production management philosophy and technology.	Holistic. The CPS exploits the organic synergy among numerous components – be they of physical, technological or human nature – underlying the RPS problem. The emergent behavior of the whole is captured.
Scope	Pseudocomponent-based analysis. Bulk properties. Focus on refinery subsystems.	Compositional-based analysis. RPS 4.0 must ensure the right molecule, at the right place, at the right time and at the right price (^{Resasco and Crossley, 2009}).
Technology	Strongly based on (customized) electronic spreadsheets. Less than half of refineries worldwide use commercial applications typically running standalone in off-line mode. These are usually based on event-based simulation technology, are normally composed of a black-box mathematical core integrated to a preformatted graphical user interface (GUI). Emergent properties of the real-world system are neglected, or even remain unknown. Only (good) feasible solutions are typically obtained.	Lessons learned taught us that, rather than a finished software, a specialized modeling platform for the process industry is welcome (see ^{Joly and Miyake, 2017}). It offers extended possibilities in-silico, such as flexibility to model specificities of the real-world problem, to build the solution technique and to perform systems integration. Indeed, modeling platforms should be considered state-of-the-art in RPS technology since they are technological alternatives closer to the advanced manufacturing philosophy. Optimal solutions are obtained (^{Kelly et al., 2017}; ^{Menezes et al., 2017}).
Human Factor	Over the decades, the operationalization of RPS remained highly dependent on the human factor, which has long played a crucial role on the business performance. Preformatted RPS solutions have rendered bright process engineers merely specialized users of electronic spreadsheets or, at best, of a particular black-box technology.	The human factor will play a key role on the in-silico design of self-conscious technologies devoted to solving the RPS problem, no longer in the operationalization of the activity supported by them. Even more technically skilled and experienced people will be required. This corroborates with the view of many authors, such as Yuan and coworkers, who preach the urgent need for industry-university coalitions.

Tab.1 Contextual distinctions of RPS in both Industry 3.0 and Industry 4.0

Fig.1 Conceptual model for an oil refinery running under a self-consciousness technological background

Fig.2 Production scheduling theorized for distinct technological backgrounds (Industry 3.0 vs. Industry 4.0). In this figure, the red circles denote the need for a setup time (tank preparation) before charging the CDU; the green circle denotes no constraint for tank usage before the indicated time

Fig.3 (Left panel) Process flow diagram for CDU charging operation. (Right) Conceptual model illustrating the ramp time (R1) associated with the light crude-oil tank switchover operation (see Fig. 2)

Tab.2 Summary of RPS concerns related to Examples I and II

Academia	Industry
How to improve the agile, robustness, computation speed of scheduling problems? By developing MILP technologies, predictive (hybrid) modeling and solution algorithms for consensus seeking, cooperative learning and distributed detection.	How to connect the scheduling module with the CPS module? By developing new (e.g., geometric) data frameworks to fuse the digital twins (cloud) with modeling platforms (e.g., IMPL(a)) primarily devoted to optimizing discrete (logic) and nonlinear decisions.
How to develop petroleomics and molecular management technologies? By building tight, long-term technological partnerships with industry (e.g., ^{Marshall and Rodgers, 2004}) to develop, for instance, in-line laboratories and novel process models based on compositional information.	How to integrate real-time scheduling optimization with feedback and hybrid model predictive control? By extending identification and prediction methods to apply under mild assumptions on a dynamic system; by providing process model standardization and synchronization among applications.
How to capture the emergent behavior of open complex systems in silico? By developing nonlinear dynamics and chaos, network theory and agent-based models (^{Ottino, 2003}).	How to efficiently incorporate real-world feedback of ongoing changes into the virtual environment? How to reduce timeliness? By developing soft sensors and digital models of manufacturing processes (the ‘cyber’ or ‘digital twins’) in the cloud.
How to support the transformation of industrial RPS systems which handle sensitive information and cannot be patched continuously or taken out of service without a planned outage? By providing consulting expertise to extend the control system security to new platforms with new requirements.	How to overcome cultural resistances to the arrival of a new production philosophy? By implementing continued technical and technological education actions inside the industrial environment (e.g., ^{Joly et al., 2015}).

Tab.3 Summary of top four challenges and promising routes (in italics) of RPS in an I4 reality

1	Biegler L T, Grossmann I E ( 2004). Retrospective on optimization. Computers & Chemical Engineering, 28(8): 1169–1192 https://doi.org/10.1016/j.compchemeng.2003.11.003
2	Castro P, Grossmann I E ( 2014). Global optimal scheduling of crude oil blending operations with RTN continuous-time and multiparametric disaggregation. Industrial & Engineering Chemistry Research, 53(39): 15127–15145 doi:10.1021/ie503002k
3	Davis J, Edgar T, Porter J, Bernaden J, Sarli M ( 2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47: 145–156 https://doi.org/10.1016/j.compchemeng.2012.06.037
4	Feital T, Lima P, Pinto J C, Souza M B Jr, Xavier G, Lima M J, Joly M ( 2013). Rethinking petroleum products certification. Journal of Petroleum Engineering, 7: 594368 doi:10.1155/2013/594368
5	General Electric (2016). The digital twin – compressing time to value for digital industrial companies.
6	Grossmann I E ( 2005). Enterprise-wide optimization: a new frontier in process systems engineering. AIChE Journal. American Institute of Chemical Engineers, 51(7): 1846–1857 doi:10.1002/aic.10617
7	Grossmann I E, Lee S ( 2003). Generalized disjunctive programming: Nonlinear convex hull relaxation and algorithms. Computational Optimization and Applications, 26(1): 83–100 https://doi.org/10.1023/A:1025154322278
8	Gupta D, Maravelias C T, Wassick J M ( 2016). From rescheduling to online scheduling. Chemical Engineering Research & Design, 116: 83–97 https://doi.org/10.1016/j.cherd.2016.10.035
9	Horgan J ( 1995). From complexity to perplexity. Scientific American, 272(6): 104–109 https://doi.org/10.1038/scientificamerican0695-104
10	Jazdi N (2014). Cyber physical systems in the context of Industry 4.0. In: 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. Cluj-Napoca (Romania): IEEE, 14452379 doi: 10.1109/AQTR.2014.6857843
11	Joly M ( 2012). Refinery production planning and scheduling: The refining core business. Brazilian Journal of Chemical Engineering, 29(2): 371–384 https://doi.org/10.1590/S0104-66322012000200017
12	Joly M, Miyake M Y ( 2017). Lessons learned from developing and implementing refinery production scheduling technologies. Frontiers of Engineering Management, 4(3): 325–337 https://doi.org/10.15302/J-FEM-2017033
13	Joly M, Pinto J M ( 2003). Mixed-integer programming techniques for the scheduling of fuel oil and asphalt production. Chemical Engineering Research & Design, 81(4): 427–447 https://doi.org/10.1205/026387603765173691
14	Joly M, Rocha R, Sousa L C F, Takahashi M T, Mendonça P N, Moraes L A M, Quelhas A D ( 2015). The strategic importance of teaching operations research for achieving high performance in the petroleum refining business. Education for Chemical Engineers, 10: 1–19 https://doi.org/10.1016/j.ece.2014.11.001
15	Kelly J D (2003). Next generation refinery scheduling technology. In: NPRA Plant Automation and Decision Support 2003 Conference; San Antonio
16	Kelly J D, Mann J L ( 2003). Crude oil blend scheduling optimization: An application with multimillion dollar benefits. Hydrocarbon Processing, 6: 47–53
17	Kelly J D, Zyngier D ( 2007). An improved MILP modeling of sequence-dependent switchovers for discrete-time scheduling problems. Industrial & Engineering Chemistry Research, 46(14): 4964–4973 https://doi.org/10.1021/ie061572g
18	Kelly J D, Zyngier D ( 2008a). A new and improved MILP formulation to optimize observability, redundancy and precision for sensor network problems. AIChE Journal. American Institute of Chemical Engineers, 54(5): 1282–1291 https://doi.org/10.1002/aic.11475
19	Kelly J D, Zyngier D ( 2008b). Hierarchical decomposition heuristic for scheduling: Coordinated reasoning for decentralized and distributed decision-making problems. Computers & Chemical Engineering, 32(11): 2684–2705 https://doi.org/10.1016/j.compchemeng.2007.08.007
20	Kelly J D, Zyngier D (2008c). Continuously improve planning and scheduling models with parameter feedback. In: FOCAPO, Boston
21	Kelly J D, Zyngier D ( 2017). Unit-operation nonlinear modeling for planning and scheduling applications. Optimization and Engineering, 18(1): 133–154 https://doi.org/10.1007/s11081-016-9312-7
22	Kelly J D, Menezes B C, Engineer F, Grossmann I E ( 2017). Crude-oil blend scheduling optimization of an industrial-sized refinery: A discrete-time benchmark. In: Foundations of Computer Aided Process Operations/Chemical Process Control, At Tucson
23	Lasi H, Fettke P, Feld T, Hoffmann M ( 2014). Industry 4.0. Business & Information Systems Engineering, 4(4): 239–242 https://doi.org/10.1007/s12599-014-0334-4
24	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 https://doi.org/10.1021/ie950519h
25	Lee J, Bagheri B, Kao H ( 2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3: 18–23 https://doi.org/10.1016/j.mfglet.2014.12.001
26	Li X, Li D, Wan J, Vasilakos A V, Lai C, Wang S ( 2015). A review of industrial wireless networks in the context of Industry 4.0. Wireless Networks, 23(1): 23–41 doi:10.1007/s11276-015-1133-7
27	Li D ( 2016). Perspective for smart factory in petrochemical industry. Computers & Chemical Engineering, 91: 136–148 https://doi.org/10.1016/j.compchemeng.2016.03.006
28	Li J, Xiao X, Boukouvala F, Floudas C A, Zhao B, Du G, Su X, Liu H ( 2016). Data-driven mathematical modelling and global optimization framework for entire petrochemical planning operations. AIChE Journal. American Institute of Chemical Engineers, 62(9): 3020–3040 https://doi.org/10.1002/aic.15220
29	Loshin D (2011). The practitioner's guide to data quality improvement.Burlington (MA), USA: Morgan Kaufmann OMG Press
30	Magalhães M V O, Moro L F L, Smania P, Hassimoto M K, Pinto J M, Abadia G J (1998). SIPP- A solution for refinery scheduling. In: NPRA Computer Conference, CC-98–145, San Antonio, TX
31	Marshall A G, Rodgers R P ( 2004). Petroleomics: the next grand challenge for chemical analysis. Accounts of Chemical Research, 37(1): 53–59 https://doi.org/10.1021/ar020177t pmid: 14730994
32	Menezes B C, Grossmann I E, Kelly J D ( 2017). Enterprise-wide optimization for operations of crude-oil refineries: Closing the procurement and scheduling gap. Computer-Aided Chemical Engineering, 40: 1249–1254 https://doi.org/10.1016/B978-0-444-63965-3.50210-5
33	Monostori L (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Proceedings of the 47th CIRP Conference on Manufacturing Systems, 17: 9–13
34	Moro L F L (2009). Optimization in the petroleum refining industry- the virtual refinery. In: Alves, R.M.B., Nascimento, C.A.O, Biscaia Jr., E.C. (Eds.), 10th International Symposium on Process Systems Engineering (PSE 2009), 41–46
35	Moro L F L, Zanin A C (2014). The role of industrial automation in the operational excellence of petroleum refining. In: Annals of the 20th Brazilian Congress of Automation, Belo Horizonte MG (Brazil), 3790–3797 (in Portuguese)
36	Ottino J M ( 2003). Complex systems. AIChE Journal. American Institute of Chemical Engineers, 49(2): 292–299 https://doi.org/10.1002/aic.690490202
37	Ottino J M ( 2011). Chemical engineering in a complex world: grand challenges, vast opportunities. AIChE Journal. American Institute of Chemical Engineers, 57(7): 1654–1668 https://doi.org/10.1002/aic.12686
38	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 https://doi.org/10.1016/S0098-1354(00)00571-8
39	Qin S J ( 2014). Process data analytics in the era if big data. AIChE Journal. American Institute of Chemical Engineers, 60(9): 3092–3100 https://doi.org/10.1002/aic.14523
40	Resasco D E, Crossley S ( 2009). Molecular engineering approach in the selection of catalytic strategies for upgrading of biofuels. AIChE Journal. American Institute of Chemical Engineers, 55(5): 1082–1089 https://doi.org/10.1002/aic.11893
41	Steinschorn D, Hofferl F(1997). Refinery scheduling using mixed integer LP and dynamic recursion. In: NPRA Computer Conference, Louisiana
42	Symonds G H (1955). Linear programming: the solution of refinery problems. Technical Report. New York: Esso Standard Oil Company
43	Wu Y, Zhang N ( 2010). Molecular characterization of gasoline and diesel streams. Industrial & Engineering Chemistry Research, 49(24): 12773–12782 https://doi.org/10.1021/ie101647d
44	Yuan Z, Qin W, Zhao J ( 2017). Smart manufacturing for the oil refining and petrochemical industry. Engineering (Beijing), 3(2): 179–182 https://doi.org/10.1016/J.ENG.2017.02.012
45	Zhang S, Wang S, Xu Q ( 2015). A new reactive scheduling approach for short-term crude oil operations under tank malfunction. Industrial & Engineering Chemistry Research, 54(49): 12438–12454 https://doi.org/10.1021/acs.iecr.5b03278

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