Safety Improvement and Operational Enhancement via Dynamic Process Simulator - A Review

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Volume 5,Issue 1 2010 Article 25

Chemical Product and Process

Modeling

Safety Improvement and Operational

Enhancement via Dynamic Process Simulator:

A Review

A.L. Ahmad, Universiti Sains Malaysia

E.M. Low, Universiti Sains Malaysia

S.R. Abd Shukor, Universiti Sains Malaysia

Recommended Citation:

Ahmad, A.L.; Low, E.M.; and Abd Shukor, S.R. (2010) "Safety Improvement and Operational

Enhancement via Dynamic Process Simulator: A Review," Chemical Product and Process

Modeling: Vol. 5 : Iss. 1, Article 25.

Available at: http://www.bepress.com/cppm/vol5/iss1/25

DOI: 10.2202/1934-2659.1502

2010 Berkeley Electronic Press. All rights reserved.


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Safety Improvement and Operational

Enhancement via Dynamic Process Simulator:

A Review

A.L. Ahmad, E.M. Low, and S.R. Abd Shukor

Abstract

This paper aims to fast track the historical development in simulation technology as a

powerful tool of computer aided process engineering and discusses versatility of the dynamic

process simulators. The focus of this paper is on the application as a dynamic operator training

simulator, appreciating the benefits it brings especially in the process safety and operability

improvements. Motivations behind the utilization of this enabling tool are thoroughly explored.

KEYWORDS: dynamic process simulator, operator training simulator, operational enhancement,

safety improvement

Author Notes: E.M. Low gratefully acknowledges Universiti Sains Malaysia for the financial

support received for her graduate program, via Vice Chancellors Award and USM-RU-PRGS.

Please send correspondence to AL Ahmad at [email protected], Tel: +6-04-5941012, Fax:

+6-04-5941013.


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1.0 Introduction

Process simulation is hardly a new concept but has been adopted in process

engineering for decades. In fact, in this age, computer simulation covers practically all the activities existed in process engineering. Traditionally,simulation could be classified into two types; steady state simulation and dynamicsimulation, with the latter deemed as the more powerful and versatile engineeringtool in computer aided process engineering or process system engineering. Thecapability of dynamics simulation as a flexible and powerful tool for variousengineering applications has increasingly been recognized, as demonstrated in thefollowing sections.

Evolutions in simulation technology are fast tracked in Section 2.0;meanwhile Section 3.0 captures the essentiality of modeling and simulationtechnology throughout the life cycle of a process plant. The discussion in Section

4.0 focuses on the application as a dynamic operator training simulator,appreciating the benefits it brings especially in process safety improvement andoperability enhancements. Motivations behind the utilization of this enabling toolare thoroughly explored from both intangible and tangible aspects in Section 5.0.

2.0 Brief Description of the Historical Developments in Simulation

Technology

Modeling and simulation garner lots of attention since its beginning in the 1920s, predominantly focusing on analogue techniques initially. Simulation entered itsnew era in the 1950s when digital computers emerged (Astrom et al., 1998).

Transcendence of digital computers for simulation is inevitable (Brennan andLinebarger, 1964). Advances in digital computers and software techniques werecontinuously being explored and exploited in the simulation techniquesdevelopment. The state of the art, development and advances in modelling andsimulation have been intensively discussed throughout the years. A large numbersof publication is available covering these topics, hence wont be covered in this paper. For the convenience of the readers to trace evolution of the simulationtechnology, some selected publications would be listed, and tabulated in Table 1and Table 2.

Early development of simulation technology could be found in reports byBrennan and Linebarger (1964), Tiechroew et al. (1967), Nilsen and Karplus

(1974), Korn (1974) and Rosen (1980), which mostly focused on steady statesimulation. The subsequent developments of the technology could be found inmonographs by Breitenecker (1983), Motard (1983), Takamatsu (1983),Stephanopoulos (1987), Biegler (1988), Lirov et al. (1988), Pantelides (1988) andPritchard (1989). Development of mini-computers in the 1970s (Hangos and

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Cameron, 2001a), followed by major developments in hardware and software thattook place in the mid 1980s rendered the development of modelling andsimulation field. Work stations and personal computer with sophisticated graphics

were made available.

Table 1: List of selected monographs published prior the 21st century that arereporting the progress in simulation

Year Ranges Focuses References / Publications

1960s Steady-statesimulation

Brennan and Linebarger (1964)Tiechroew et al.(1967)

1970s Development ofmini-computers

Nilsen and Karplus (1974)Korn (1974)Rosen (1980)

1980s Majordevelopments inhardware andsoftware

Breitenecker (1983)Motard (1983)Takamatsu (1983)Stephanopoulos (1987)Biegler (1988)Lirov et al. (1988)Pantelides (1988)Pritchard (1989)

1990s Development incomputing hardwaresystems

Burton and Malinowski (1990)Ming Rao et al. (1990)Shaw (1990)Pantelides and Bartont (1993)

Nilsson (1993)Merkuryeva and Merkuryev (1994)Bogusch and Marquardt (1995)Gaubert et al. (1995)Jensen and Gani (1995)Kevrekidis (1995)Maguire et al. (1995)Ponton (1995)Longwell (1994)Lien and Perris (1996)Lee et al. (1996)

Marquardt (1996)Riksheim and Hertzberg (1998)Torvi and Hertzberg (1998)Astrom et al. (1998)Pham (1998)

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Advances in the computing technology continue to excite the developmentof modelling and simulation techniques and its technologies in the 1990s, asreported in a number of monographs (Table 1). Stephanopoulos and Han (1995)

provided a comprehensive review on the status of the intelligent system in processengineering, which essentially discussing about the state-of-art of modelling andsimulation. Technology advancement in the computing hardware systems furtherfuelled up the development of dynamic simulations as disclosed in monographsby Longwell (1994), Lien and Perris (1996), Lee et al. (1996), Marquardt (1996),Riksheim and Hertzberg (1998), Torvi and Hertzberg (1998), Astrom et al. (1998)and Pham (1998).

Table 2: List of selected monographs published in the 21st century that arereporting the progress in simulation

Focuses References / Publications

Development in computingsoftware systems

Software interoperability

Open system architectures

Braunschweig et al. (2000a)Braunschweig et al. (2000b)Shacham et al. (2000)Barak(2001)Hangos and Cameron (2001b)Pantelides et al. (2001)Pingen (2001)Belaud et al. (2002)Benqlilou et al. (2002)Banks et al. (2005)Braunschweig (2005)

Testard et al. (2005)Pigeon et al. (2006)M. Barrett et al. (2007)Balasko et al. (2007)Charpentier et al. (2007)Gani et al. (2007)Klatt et al. (2007)Morales-Rodrguez et al. (2007)Cameron and Ingram (2008)Gani et al. (2009)Klatt and Marquardt (2009)

O'Connell et al. (2009)Stephanopoulos et al. (2009)

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The interest in modeling and simulation continues to spur research in 21stcentury; and discussions of its development are abundant (Table 2). Computersare not just available, but are ubiquitous, providing instantaneous access to the

sophisticated mathematical and informatics tools which have been and will bedeveloped. In contrast with dominance of hardware development previously,development in software started to dominate the modelling and simulation area(Ponton, 1995).

The emergence of abundant process engineering software is clearly a positive factor for the chemical process industries and has resulted in major benefits (Braunschweig et al., 2000b). Numerous publications could be foundcovering the progress of simulation, mainly focussing on software interoperabilityand open software architectures (Braunschweig et al., 2000a, Braunschweig et al.,2000b, Shacham et al., 2000, Barak, 2001, Hangos and Cameron, 2001b,Pantelides et al., 2001, Pingen, 2001, Belaud et al., 2002, Benqlilou et al., 2002,

Banks et al., 2005, Braunschweig, 2005, Testard et al., 2005, Pigeon et al., 2006,M. Barrett et al., 2007). The importance and prospect of process modelling andsimulation are continuously mooted by researchers (Sargent et al., 2004, Banks etal., 2003, Belaud et al., 2002, ren, 2002a, ren, 2002b, Perkins et al., 2003,Gani, 2004, Saraph, 2004, Sargent, 2004, Cameron et al., 2005, Sargent, 2005).More recent discussions of the progress of modelling and simulation could befound in publications by Balasko et al. (2007), Charpentier et al.(2007), Gani etal. (2007), Klatt et al. (2007), Morales-Rodrguez et al. (2007), Cameron andIngram(2008), Gani et al (2009), Klatt and Marquardt (2009), O'Connell et al.(2009) and Stephanopoulos et al.(2009).

Despite its long history, research interest in modeling and simulation area

is here to stay. Continuous efforts will be invested to achieve advances in theunderstanding, representation and manipulation of complex system (Ponton,1995). The focus is not limited to its science, methodology and technology; butalso its application areas (ren, 2002b). Models and their parameters are to beproperly and efficiently matched to lab- or pilot-scale experiments and to existingproduction plants, accordingly (Klatt and Marquardt, 2009).

3.0 Applications of Simulation Technology

The applications of simulation are almost limitless, but more importantly is, itneeds to be meaningful. Attempted modeling is driven largely by the availability

of high performance computing and the demands of an increasingly competitivemarketplace. In fact, a shift in paradigm a decade ago saw that simulation isinvolved through the complete life cycle of a process plant, from the cradle to thegrave (Cameron, 2008; Virkki-Hatakka, 2003; Merritt, 2006; Mexandre, 2003).Advances in simulation technology could be applied starting with the idea,

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through experiments in laboratory, during scale up at different levels, to processdesign and operation (Edgar, 2000).

Area of applications for simulation is vast. Process simulation is a well-

established tool in the process industry, applicable for studying individual unitoperations as well as multiple interconnected units or entire plants (Bezzo et al.,2004). A significant numbers of publications can be found reporting on theapplications of simulation (Table 3).

Table 3: Selected examples of applications of simulationNo. References Applications

1. Doig (1977) Mass balance steady state modelsSequential plant start-up and shutdown modelsPlant simulator

2. Greathead (1982) Operator training simulator3. Ben Clymer and

Ricci (1986)Design checkingRefresher training of operators

4. Womack (1986) Engineering design of capital projectsOperator trainingSolution of operational problemsEvaluation of process changesDe-bottlenecking studies

5. Jones (1992) Training simulatorOperational use such as operating proceduredevelopment and testing; incident analysis andWhat if analysis

Engineering purposes such as checking out thedesign of the plant; distributed control systemconfiguration validation

6. Cole and Yount(1994)

Control strategy development and demonstrationComparison of alternative control strategiesImproved process understandingOperator trainingSafety analysis

7. Laganier (1996) Retrofitting studies for a heat furnaceStart-up studies for a distillation columnSafety studies for a gas cleaning section

8. Mayer andSchoenmakers (1998)

Process developmentProject managementPlant operation

9. Cameron et al. (2001) Operability and safety studiesControl system checking and validation

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Operator training10. ren (2002b) Training

Evaluation of alternative courses of actionOperational supportEngineering designPrototypingFault diagnosisProof of concept

11. Horner et al. (2003) Scale-up studiesProcess development

13. Virkki-Hatakka et al.(2003)

Research and developmentConceptual designDetailed designOperation and further development

12. Bezzo et al. (2004) Plant operating condition studiesControl system performance assessment

13. Yln et al. (2005) Automation testing and control designGrade change optimisationSafety analysis

14. Bausa et al. (2006) Automation and control studies15. Merritt (2006) Control system development and startup

TroubleshootingOperator training

16. Charpentier et al.(2007)

Optimal process controlSafety analysis and environmental impact studies

17. Patel et al. (2007) Design, operation and troubleshooting18. Santos et al. (2008) Operator training19. Seccombe (2008) Operator training20. Okol'nishnikov and

Zenzin (2008)Development of control system; debugging,optimization and testingOperator training

21. Brambilla and Manca(2009)

Safety analysis

22. Klatt and Marquardt(2009)

Process synthesis and designProcess control and operations

23. Monroy et al. (2010) Fault diagnosis system

24. Liu et al. (2010) Fault detection and identification25. de la Mata and

Rodrguez (2010)Control system reconfiguration

26. A.L.Ahmad (2010) Operator Training

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However, the application of simulation is beyond those demonstrated inthe published works listed in Table 3. The capabilities and versatilities of bothsteady state and dynamic simulation are tremendous, mainly in industry outlook;

with the latter deemed as the more powerful tool.

3.1Application of a Simulator in Chemical Process Industry

Figure 1 provides a comprehensive overview of utilization of a simulator inchemical process industry, reflecting the versatilities of simulation tool (Yln etal., 2005). Three major application areas depicted are the engineering, researchand development, and operation and maintenance, which lead to the ultimateobjective of obtaining optimal operation of the plant. Simulation is the coreactivity in these applications, reflecting the involvement of process simulator atdifferent stages of a plant lifecycle (Mexandre, 2003, Yln et al., 2005). These

application areas are interconnected, with several activities that are overlappingwith each other.

Figure 1: Overview of utilization of a simulator in chemical process industries(Yln et al., 2005).

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The list is far from complete, and each category can be further extended.Most of the time, each application area may require different simulation modelsdepends on its objective (Mexandre, 2003), yet they share a large number of first

principle models, such as thermodynamics, chemical kinetics, transport phenomena and other fundamental theories (Hangos and Cameron, 2001a). It isdesirable that the same models are used, with minimum changes at all stages of aprocess life-cycle (Virkki-Hatakka et al., 2003). If a model is to be reused acrossthe lifecycle, it helps to minimize the engineering efforts through theimplementation of a same modelling and simulation method in an efficient andeconomical way. However, it still remains a further challenge to properly matchmodels and their parameters to lab- or pilot-scale experiments and to in-operationplants, accordingly (Klatt, 2009).

The engineering phase or the design stage sees that simulation providesmeans for systematic investigation at different alternatives that can be developed

for a given design problem. Combined steady state and dynamic simulation canhelp to understand the process dynamics, which forms the basis for processcontrollability studies and plantwide control strategy implementation (Mexandre,2003). Dynamic modelling provides possibility of process control evaluation bytesting and debugging the plant functions by simulation even before the facilityitself is being built. A good process simulator is also applicable for pre-engineering and commissioning planning to operator training and troubleshooting(Yln et al., 2005). The key benefit reaped from a dynamic model comes from theimproved process understanding that a user can get.

Among others, some examples of dynamic simulation applications in process design are operability of heat integrated processes, scale-up or surge

capacity sizing, process improvement and also design and analysis of batch andcyclic processes. In process control and operability, simulation is utilized indevelopment and analysis of control studies, development of advanced controlalgorithm and operability studies. It is also reported that dynamic simulation isused in safety studies for design and analysis of emergency and relief systems,investigation of previous accidents and to determine the consequences of possibleaccidents (Cai and Craddock, 2002, Brambilla and Manca, 2009). Fault diagnosissystem, HAZOP, FMEA and other safety studies are also commonly usingsimulation technology as demonstrated by Monroy et al. (2010) and Rossing et al.(2010).

As depicted in Figure 1, process simulation enables minimization of

research experimental effort. Innovation of novel sustainable processes is possibleto be simplified using modelling and simulation, where innovative solutions thatare difficult to be investigated experimentally can be explored (Mexandre, 2003).

For operation and maintenance, simulation is not limited to processtroubleshooting, but is also used for integrated preventive maintenance system.

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This can be further extended to integrate the manufacturing sector with the supplychain, which leads to enterprise-wide control system. Perhaps, the most prominentand possibly the most important application of dynamic simulation is as a training

simulator (Jones, 1992), the main focus in this paper, which will be exploredfurther in the following section.

4.0 Dynamic Operator Training Simulators

Figure 2 shows the analogy of a Dynamic Operator Training Simulator (DOTS) orprocess plant simulator system to a real plant. A process plant simulator system isa virtual replica of physical process plant. It is designed to represent thecharacteristic of the real plant productions, with the necessary functions of theoperation (Li et al., 2006) and control system. The plant dynamic model and theactual process plant were analogous. These dynamic models are mathematical

representations of actual plants that accurately mimic the process conditions onthe plant using chemical engineering theory (Jago, 2008).

Figure 2: Analogy of process plant simulator system to the real plant.

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A plant in the context of chemical engineering can be defined in terms ofmajor process equipment items, chemical unit operations, and process materialproperties. Some of the major equipment items include vessels, heat exchangers,

pumps, and control valves. This equipment provides the environment to performthe desired unit operations, such as reaction, separation, absorption and materialtransfer. The material properties include the desired thermodynamic state and physical properties of the product. The control content in the chemicalengineering viewpoint is the stream properties such as flow rate and temperaturecoupled with the adjustments to the streams to achieve the desired operatingconditions (Erickson and Hedrick, 1999).

A process model is a mathematical representation of an existing orproposed industrial (physical or/and chemical) process. Process models normallyinclude description of mass, energy and fluid flow, governed by known physicallaws and principles. A plant model is a complex of mathematical relationship

between dependent and independent variables of the process in a real unit. Plantmodels are basically obtained by assembling one or more process models. It willbe ideal to build the simulator using the same modelling technology that had beenused during the process design and offline engineering studies, for the effectiveutilization of the engineering effort. Dynamic process modelling is beingdeveloped to be used in macroscopic scale. A full complex plant models mayconsists of up to 5.0 x 104 variables, 2.0 x 105 equations and over 1.0 x 105optimization variables (Dobre and Marcano, 2007).

Control system in the physical plant will be imitated using an engineeringworkstation. A control system generally consists of data acquisition andrectification, database, and advanced applications. The data acquisition and

rectification and the database work together to realize data exchange among thebasic control systems and advanced applications. Advanced applications include process monitoring, fault diagnose, safety evaluation, online optimization andsteady state simulation of process, etc (Li et al., 2006).

The third element; the operator console is the human-machine interface(HMI) for process monitoring, and getaway to process controlling whenever theneed arises. In the simulator system, operator console can be emulated or directlyconnected to the original console. However, the latter option is normally notpreferred as it involves high cost. The former option is realized through utilizationof graphical imitation, closely resembling the actual display of the console.

In the context of chemical engineering, the focus and attention given are

on the process models and control systems emulation. However, it must bestressed that process plant simulator system is not merely modeling andsimulation. High fidelity models become the backbone of a process simulatorsystem where the rigorous dynamic process models are based on a physical, first

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principle equations that capture the hydraulic, thermo, phase and reactive behaviour of the process. Implementation of a process simulator requiresengineering insight and in-depth understanding of the process requirements and

the effect on controllability and dynamic operability of not only simple unitoperations, but also the interaction between existing unit operations in theprocess.

5.0 Safety mprovement and perational nhancement via ynamic

Application of dynamic simulation as training simulator is not a new concept.Adoption of training simulators has been widely practiced in industries wherecapital investment is high, processes with high complexities and enormoushazardous consequences in case of failure. Industries of this type are not limited

to chemical processes, but are also apparent in aviation, shipping, power andenergy industry, medical and nuclear system (Yang et al., 2001, Cameron et al.,2002, Merritt, 2006, Seccombe, 2008, Murugappan, 2009). DOTS is an exampleof high fidelity simulation models application. The use of such high fidelitymodel within operation has many important applications with significantimplications. The consistent improvement in the area of dynamic processsimulation and the steadily increasing computational power gave rise to theincreasing use of operator training simulators in the chemical and petrochemicalindustry in recent years (Klatt and Marquardt, 2009).

Generally, training simulator can be classified as operator trainer and process trainer. The latter is a rigorous dynamic process models used to train

operation engineers in a general process and control of the plant, which focuseson the process, with little or no effort to replicate the look or feel of the actualcontrol graphics in the plant itself. Operator trainer or virtual plant simulator orDOTS trains facility control room operators and engineers in both the process

behaviour of the plant as well as the use of distributed control system (DCS) tocontrol the plant. A DOTS either includes the actual hardware used within acontrol room or a very good emulation of its graphics and control outlook (Jones,1992). More examples of applications of simulation in training and educationcould be found in monograph by ren (2002b), whereas Cameron et al. (2002)

provides comprehensive descriptions on dynamics operator training simulators.Discussions in the previous sections spelt out the involvement of

simulation throughout the process lifecycle. This scenario similarly applies toDOTS application. DOTS can be used as a mean to transmit and retain knowledgethroughout the process lifecycle (Cameron, 2002). Experiences and historical datafrom previous operations can be transferred and translated into the design offuture plants and also the retrofit of existing facilities (Merritt, 2006). However,

I O E D

Process Simulator

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the focus of this monograph is to appreciate the beneficial values of DOTSapplication in safety improvement and operational enhancement, as detailed out inthe following sub-sections.

5.1 Safety Improvement

The tagline Safety first is commonly found in almost any working facilitiesthroughout the world. This reflected how highly important safety is beingstressed. In fact, process safety, health and environmental is at the heart of allresponsible process engineering (Preston et al., 1996). A number of safety policiesand standardizations are in place to govern the compliance of safety practice inworkplace. As an example, Occupational Health and Safety Assessment Series,OHSAS is an international management system and standardization that is widelyadopted in Malaysia to enhance the safety practice in workplace. It is well

recognized and accepted that safety issue is the most important aspect of industry process operations (Ming et al., 2003). Regardless of how comprehensive andextensive the safety guidelines provided, essentially it is the competency of theoperator that really matters to guarantee safety in a workplace. The need toinclude human factors in operability and safety assessment of chemical processoperations is unquestionable (Sebzalli et al., 2000).

In a survey conducted by a consortium led by Honeywell around the worldincluding UK, USA, Canada, Europe and Japan, about 40% of abnormaloperations were caused by human errors (Sebzalli et al., 2000). In anotherseparate monograph by Yang et al., it the results of industry studies on the causeof accident in the hydrocarbon processing industry over a span of 30 years was

reported. It was shown that 28% of the 170 largest property-damage losses in thestudy were due to operational error or process upsets (Yang et al., 2001). Thesestatistical figures imply that operators lack of skills and/or careless operations aremain causes of accidents in chemical industries (Goh et al., 1998, Brambilla andManca, 2009). Thus, properly trained operators are critical to ensure plant safetyand profitability. Employee continually seeks way to improve and increase thecompetency and efficiency of its workforce; and an operator training simulator isused to address this issue.

In fact, there are governmental legislations in some countries forcompulsory emergency operations training using realistic simulations (Podmore etal., 2008). Government scrutiny has increased and regulatory demands to certify

that plants can be safely operated (Dissinger, 2008). In addition to that, a morestringent and stricter environmental and hazard regulations is also another pushingfactor in promoting utilization of dynamic process simulator. DOTS is part of thestrategy to properly train and certify plant operators (Cheltout et al., 2007).Exposure to abnormal process operations, such as process upsets and emergency

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are addressed in achieving operational readiness. Study carried out by ARCAdvisory Group for Honeywell identified the key component as operationalpersonnel readiness that can be achieved through the use on an operator training

simulator, as tabulated in Table 4 (ARC, 2009). The design of the operatorprocess interface is also critical, ensuring best practices are incorporated into theautomation (Tomschi et al., 2007).

Table 4: Early Incorporation of Technologies Like Simulation and AdvancedProcess Control (APC) Can Significantly Reduce Project Costs (2009)

Operations readiness - source of savingsTypical startup

savings

Process technology training 5 days

Simulator based training 5 days

Procedural training for operators 2 daysLicensor prepared scenarios 2 days

Licensor specified process models 1 days

Operating procedure validation/optimization on

simulator5 days

Controls check/verification on simulator 5 days

Safety shutdown system verification on simulator 5 daysBetter initial controller tuning from simulator 1 days

Faster start-up from operations readiness 26 days US$26M

Operations effectiveness Startup SavingsStartup Availability

Procedural operations 1 day 1 day

ASM Graphics 1 day 1 day

Mishap avoidance from operation

effectiveness4 days US$4M

PRODUCTION OPTIMIZATION Improved Performance

APC delivered sooner through

simulation program

6 months early US$19M

TOTAL BENEFITS ~US$49M

Training, or people readiness, is perhaps the most important aspect of thesuccess of operational readiness. An untrained operator is not competent to runthe plant to the optimum degree of efficiency. Taking an analogy to autopilot,advanced process control (APC) typically removes the reactive actions required

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by a process operator to allow more time to be spent on optimizing production.However, from time to time, operators need to be able to take control of theprocess to manage an upset. This gap can be filled by a DOTS, analogous to a

flight simulator, it is proved to be extremely valuable, allowing the operator tocontinually develop skills, make mistakes and learn in a safe simulatedenvironment (Merritt, 2006, ARC, 2009, Murugappan, 2009). For ensuring theeffectiveness of a DOTS, its backbone, the dynamic models need to be rigorousand robust enough to cover all the operations. These high fidelity models need torepresent key operating scenarios such as start up, shutdown, normal operationsand also abnormal situations, such as equipment failure (Mohammed et al., 2005,Muravyev and Berutti, 2007). This feature is inarguably the most important andvital element for a good and reliable simulator.

Another beneficial application of dynamic simulators which oftenoverlooked on a project is during the delivery phase of the projects. Start up and

operating procedures can be developed and validated using DOTS along withhuman-man interface (HMI) design. Simulators can be used during factoryacceptance test (FAT) to test and effectively pre-commission automation systems.APC can also be step tested on simulators (Mexandre, 2003). These benefits canalso have a large impact on reducing start up time by uncovering design flaws thatwould have delayed start up. Control loops can be tuned prior to actual plant startup, leading to smoother and quicker plant start ups, bringing substantial economicbenefits (Dissinger, 2008). The use of dynamic simulation will increase due to theincreasing requirements on process design and process automation (Cox et al.,2006). The ever increasing requirements on operation also lead to the intensiveutilization of dynamic simulation for operational enhancement.

For the applications of these dynamic simulations, the advantages soughtare tangible measures such as more profitable operation, less off spec-products,improved process operability, improved process safety, and less human errors inoperation and due to better process understanding. An example of successapplication reported a tangible achievement was in the case of Freeport LNG.Honeywell was able to provide economic value proposition through reducedstartup time, running the end users through the business simulation scenario thatidentified potential problems before startup, and achieving operational readinessat startup without incident and running at full potential from day one. DOTSdrove a 25 percent reduction in the overall training cost, uncovered design flawsand reduced overall startup time from three days to one (ARC, 2009).

Human capital is another huge motivational behind the utilization ofDOTS. With the dwindling numbers of chemical engineering graduates andageing population, workforce training becomes vital (Jago, 2008, Dissinger, 2008,A.L. et al., 2010)). Recent years, the rapid surge in oil and gas sector has created ahuge demand for human capital. The construction of major new facilities and

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assets in regions of the world like oil platforms off the coast of Africa, newchemical plants and refineries in the Middle East and central and eastern Asia hascreated a surge in demand for technically trained personnel (Dissinger, 2008).

Some major companies opt to lure international labour force rather thandeveloping their available manpower. Malaysia is not spared of the adverse effectfrom this phenomenon. Many experienced, semi-skilled and skilled localworkforce are targeted by these foreign companies. Lucrative employmentpackages are being offered and have successfully attracted many local labours towork overseas. Spinning off from there, companies are looking into effectivetraining to address knowledge or skills gaps of new recruits of all levels, that is areliable DOTS.

6.0 Conclusion

Simulation technology holds tremendous promise for overall process safetyimprovement and operation enhancement, by functioning as an enabling toolthroughout the life cycle of a process plant. From process design to human capital building, the endless effort to achieve operational efficiencies improvement andplant safety will place modeling and simulation at an utmost important position inprocess engineering. It is no longer only considered an added benefit or value tobe able to model and thereby predict, modify and adapt proactively to changingconditions, but this competitive advantage is actually a hallmark attribute ofcompanies in pursuit of operational excellence of sustainability. Extensivedevelopment of computing hardware and intensive research in the modelling andsimulation software will continue to spur the interest for DOTS utilization. The

cost of computing hardware shall no longer hinder a company to adopt DOTS intoits operation. For the years to come, it is the authors hope that all technicaleducation institutes develop its own DOTS and incorporated it into the institutessyllabus. Early exposure to DOTS may enhance ones understanding of processoperation and application of chemical engineering theories in a greaterperspective.

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Safety Improvement and Operational Enhancement via Dynamic Process Simulator - A Review