Lessons learned from developing and implementing refinery production scheduling technologies

doi:10.15302/J-FEM-2017033

Front. Eng

2017, Vol. 4

Issue (3) : 325-337 https://doi.org/10.15302/J-FEM-2017033

RESEARCH ARTICLE

Lessons learned from developing and implementing refinery production scheduling technologies

Marcel JOLY¹(

), Mario Y. MIYAKE²

¹. Institute for Technological Research of the São Paulo State, São Paulo SP 05508-901, Brazil; Centre of Excellence for Industrial Automation, Petrobras SA, São Paulo SP 05508-900, Brazil
². Institute for Technological Research of the São Paulo State, São Paulo SP 05508-901, Brazil

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Abstract

An increasing number of novel and highly specialized computer-aided decision-making technologies for short-term production scheduling in oil refineries has emerged and evolved over the past two decades, thereby encouraging refiners to permanently rethink the way the refining business is operated and managed. In this report, we discuss the key lessons learned from one of the pioneering, yet daring, enterprise-wide programs entirely implemented in an energy company devoted to developing and implementing an advanced refinery production scheduling (RPS) technology, i.e., the RPS system of Petrobras. Apart from mathematical and information technology issues, the long-term sustainability of a successful RPS project is, we argue, the outcome of a virtuous cycle grounded on permanent actions devoted to improving technical education inside the organization, reinspecting organizational cultures and operational paradigms, and developing working processes.

Keywords automation decision making oil refinery optimization production scheduling

Corresponding Author(s): Marcel JOLY

Just Accepted Date: 08 September 2017 Online First Date: 28 September 2017 Issue Date: 30 October 2017

Cite this article:

Marcel JOLY,Mario Y. MIYAKE. Lessons learned from developing and implementing refinery production scheduling technologies[J]. Front. Eng, 2017, 4(3): 325-337.

URL:

https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2017033
https://academic.hep.com.cn/fem/EN/Y2017/V4/I3/325

Tab.1 Business layers and the corresponding opportunities, popular solution approaches, key hallmarks, and current trends in refinery optimization^(a)

Fig.1 A real-world hierarchical decision-making framework for refinery automation (Joly, 2012)

Tab.2 Commercial decision-making technologies applicable to RPS

Fig.2 A quick look at the problem: (a) may lead to an understatement of the actual complexity of the refinery production scheduling (RPS) problem (b)

Fig.3 (a) Operation cycle of an RPS application based on event-based simulation technology; (b) enlarged details of the Gantt chart depicting scheduling information (e.g., crude oil composition, batch size and flow rates, operation modes of crude distillation units, and expected crude yields)

Fig.4 Ishikawa diagram showing the key relationships among potential factors that may cause overall project failure. Abbreviations: BA, business area; CPU, central processing unit; DMQ, decision-making quality; HR, human resources; IT, information technology; KPI, key performance indicator; OR, operations research; R&D, research and development; TE, technical education

Threat	Potential factors
A. Deficient technology	(1) Prohibitive investments in software (e.g., acquisition or licensing costs);
	(2) Operational problems related to system failure (e.g., bugs) or availability;
	(3) Poor computational performance due to (4) costly computation; (5) System inadequacy in meeting the needs of schedulers (e.g., poor usability and navigability), which may arise from (6) deficient know-how about RPS and related disciplines, such as operations research (OR);
	(7) Deficient integration of the RPS solution into corporate systems and databases, thereby possibly causing prohibitive manual workload, which renders RPS impractical;
B. Deficient working processes	(8) Failure to meet certain prerequisites for achieving sustainability; (9) Mature bounding conditions related to database infrastructure building; (10) Local priority at the refinery in the early phases of the RPS project (right people at the right time); (11) Availability of a corporate scheduling support team that can respond quickly to schedulers with regard to technological questions concerning the RPS application (from simple usage doubts up to requests for technology sophistication);
	(12) Unavailability of a consulting team with a clear understanding of the RPS project purpose, capabilities, limitations, and (13) complexity as an interdisciplinary activity in which several actors must work together to accomplish desired goals;
	(14) Improper implementation strategy, which fails to speed up the production of tangible benefits (e.g., good practices recommend the adoption of increasing scope over time);
	(15) The lack of a consistent and automatized (as much as possible) key performance indicator (KPI) model for assessing the decision-making quality in RPS (Fig. 5); if tangible benefits associated with the RPS project are not measured (16) and evidenced (17), then continued investments on RPS may not occur, thereby compromising project sustainability (8);
	(18) Ineffective working processes combined with (19) Undisciplined practices for model maintenance, (20) Poor corporate foresight concerning the supply chain operation, (21) Immediatism (high priority for short-term results at the expense of long-term benefits normally associated with good decision making, which may indicate high rates of rescheduling (22) and poor RPS performance (23), thereby compromising the sustainability of the RPS project (8), and (24) “Irreplaceable” people playing key roles in the RPS project;
	(25) Improper software development model, which neglects the fact that refinery automation systems require approaches different from those applied to conventional (e.g., transactional) IT systems; (26). In this study, agile (i.e., not bureaucratized [27]) software development methods should be considered to strengthen the collaboration between the organization and third parties (e.g., technology companies and universities) in the appropriate working structure (e.g., center of excellence) (28).
C. HR-related deficiencies at operational levels	(29) Low commitment, which may be unavoidable if the refinery is understaffed (30) and/or highly demanded at the time of project implementation (31);
	(32) Deficient technical education (i.e., lack of managerial understanding of the strategic significance of OR for the modern refining business), which underscores unfruitful bounding conditions (33) for developing costly actions devoted to improving the decision-making quality (34);
	(35) Deficient project leadership by a corporate consulting team, which may also introduce serious obstacles for achieving and sustaining success in RPS projects; (36) Deficient planning for implementing this kind of project, which emerges from the lack of timely and integrated actions involving many actors (37) or the poor expertise or experience of the leading staff (38), which must be appropriately sized and multidisciplinary (e.g., backup personnel should be available) (39);
	(40) Reduced celerity from these technological areas to satisfy the short- and long-term needs of RPS system users, which may be due to insufficient human resources (41) or deficient HR qualifications (42);
	(43) Loss of RPS expertise in the IT and R&D areas, which can be solved by adopting efficient knowledge management policies (44) that consider the importance of continuous learning (e.g., advanced educational programs and participation in technological consortia) (45) for achieving excellence and increasing staff motivation;
	(46) Volatility of valuable human resource; (47) Costly learning curve normally associated with the RPS activity, which is particularly problematic if the staff does not have preexisting interdisciplinary expertise (48), and is an additional factor that negatively contributes to the sustainability of excellence in human resource qualifications (42);
	(49) Deficient work processes and educational policies (50), which may produce “irreplaceable” people in the IT and R&D departments (51), thereby causing project failure;
D. HR-related deficiencies at strategic levels	(52) Immediatism, which is a well-known threat to RPS projects, from conception to operation, the hallmark of deficient strategies for knowledge management at the highest levels of the corporation (53), and is among the major nontechnical reasons for poor plant-wide optimization (54); (55) Deficient technical education at the managerial level, which damages business performance given that principals are responsible for strategic definitions;
	(56) Principals who do not know about OR possibilities and cause the strategic role of OR in refining the business to not be acknowledged in the highest levels of the company (57);
	(58) Cultures and myths that are still present in the minds of some individuals (e.g., “maximal load indicates maximal profit”), which may result in the design of improper KPI models (59) that hinder the accurate measurement of RPS benefits (60, 61), thereby causing poor appreciation of OR-based technology within operational environments (62) and leading to a global result that is of low priority to enterprise-wide efforts in developing the RPS activity in business areas (63), as well as in technological areas (64), where RPS projects compete for available resources with other corporate projects (65).

Tab.3 Potential factors that may threaten an RPS project

Fig.5 Educational actions in operations research (OR) are strategic for the successful implementation of supply chain optimization projects

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