Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21

Contents lists available at ScienceDirect

Process Safety and Environmental Protection

jou rn al hom epage: www.elsev ier .com/ locate /psep

The role of a commercial process simulator incomputer aided HAZOP approach

Ján Janosovsky, Matej Danko, Juraj Labovsky, L’udovít Jelemensky ∗

Institute of Chemical and Environmental Engineering, Slovak University of Technology, Bratislava, Slovakia

a r t i c l e i n f o

Article history:

Received 9 September 2016

Received in revised form 16 January

2017

Accepted 20 January 2017

Available online 31 January 2017

Keywords:

Computer aided hazard

identification

HAZOP study

Aspen HYSYS

Process safety

Chemical reactors

Mathematical modeling

a b s t r a c t

Process safety is one of the key pillars of sustainable industrial development. In combination

with the increasing use of computer aided process engineering, the demand for an appro-

priate model-based safety analysis tool capable to identify all hazardous situations leading

to a major accident has increased. Commercial process simulators are equipped with exten-

sive property databases and they employ high accuracy mathematical models providing the

capability to simulate real behavior of a process operated within the area of the mathemati-

cal model validity. The main focus of this work is to improve standard hazard identification

methods by the combination of hazard and operability (HAZOP) study and process simula-

tion in commercial process simulator Aspen HYSYS. Software tool consisting of modules for

computer simulation and complex analysis of simulation data will be proposed. The devel-

oped tool was applied to modern chemical productions exhibiting strong nonlinear behavior,

where proper prediction of consequences can be very difficult. In the first case study, haz-

ard identification in continuous glycerol nitration employing user-dependent analysis is

presented. Mathematical methods of simulation data analysis independent of the user is

demonstrated in the second case study of ammonia synthesis. Possibilities and limitations

of the proposed tool are revealed and discussed in this work.

© 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

1. Introduction

Several serious industrial accidents (e.g. Flixborough, Seveso, Bhopal

and Tianjin disasters) in the past have underlined the importance

of loss prevention approach in chemical industry. Dynamic develop-

ment of industry has not only resulted in more efficient and profitable

chemical productions, but also in the increase of plants complexity

as well as the variety of chemicals and processes used in the plant.

In addition, the majority of modern processes exhibit strong nonlinear

behavior. Therefore, the task of identifying potential sources of hazards

has become more complex (De Rademaeker et al., 2014). In combina-

tion with the ever growing use of computer aided process engineering,

the demand for an appropriately detailed safety analysis tool capable

to identify all hazardous situations leading to a major accident has

increased. The safety point of view should be implemented not only

in the design stage of any chemical plant, but also during the entire

plant life cycle. Identification of all possible fault propagation paths

is thus, for example, the key feature of proper design of control sys-

∗ Corresponding author.E-mail address: ludovit.jelemensky@stuba.sk (L’. Jelemensky).

http://dx.doi.org/10.1016/j.psep.2017.01.0180957-5820/© 2017 Institution of Chemical Engineers. Published by Elsev

tems (Leveson and Stephanopoulos, 2014; Parmar and Lees, 1987; Seider

et al., 2014).

Actual trend in computer aided loss prevention is to improve

standard hazard identification methods by employing mathematical

modeling and process simulation in commercially available simulators.

Commercial process simulators are equipped with extensive property

databases and utilize high accuracy mathematical models thus provid-

ing the capability to simulate real behavior of a process operated within

the area of the mathematical model validity. Model-based hazard iden-

tification also benefits from the fact that mathematical modeling of the

analyzed process is usually employed as a part of process design and

optimization activities, e.g. optimization of biorefinery downstream

processes employing SimSci PRO/II (Corbetta et al., 2016), design of

hydrocarbons separation unit using Aspen Plus (de Riva et al., 2016)

and Aspen HYSYS supported design of syngas production proposed by

Sunny et al. (2016). If the use of process simulators is well implemented

in the company policy, successful adoption of safety extensions for

these simulators is more likely.

Model-based approach was applied not only in hazard identifica-

tion, but also in reliability engineering (Favarò and Saleh, 2016) and

quantitative risk assessment (Labovsky and Jelemensky, 2011; Qi et al.,

2014). While mathematical modeling in these areas is accepted by

ier B.V. All rights reserved.

Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21 13

Nomenclature

a Reaction order of glycerolA Pre-exponential factorb Reaction order of nitric acidc Molar concentration, l mol−1

Ea Activation energy, J mol−1

p Partial pressure, barr Reaction rateR Universal gas constant, J K−1 mol−1

Greek symbols Enhancement factor�c Catalyst bulk density, kg m−3

t

i

p

P

c

o

s

g

A

c

r

o

s

b

t

s

d

i

2

c

u

a

i

c

g

c

s

d

s

o

a

he safety engineering community, model-based hazard identification

s still subject of discussion because of model validity and its input

arameters uncertainty (Labovská et al., 2014; Svandová et al., 2009).

ublished model-based tools vary in the complexity of mathemati-

al models and simulation data evaluation methods. The complexity

f mathematical models depends on whether they were constructed

pecifically for the analyzed system or the developed tool employed a

roup of mathematical models, e.g. a commercial process simulator.

lthough the computing time increases with the increase of model

omplexity, several efforts were made towards shortening the time

equired for the solution of large nonlinear systems, e.g. utilization

f parallel computing (Danko et al., 2015; Labovsky et al., 2015). The

imulation data can be evaluated manually, automatically or by a com-

ination of both ways. The majority of published works benefited from

he robustness and complexity of the hazard and operability (HAZOP)

tudy that belongs to the most used process hazard analysis proce-

ures worldwide and is recognized as an effective and accurate hazard

dentification method in chemical industry (Dunjó et al., 2010; Kletz,

001).

Eizenberg et al. (2006) combined a standard HAZOP study and pro-

ess simulation in MATLAB in order to develop a software tool for better

nderstanding of hazards for the safety education process. Similar

pproach was adopted in the work of Li et al. (2010). The exam-

ned system was a three-phase hydrogenation in an intensified stirred

ontinuous reactor and the simulation results of hazardous cases

enerated based on the HAZOP principles were presented. HAZOP prin-

iples were also applied in the safety assessment based on parametric

ensitivity analysis of the key operating parameters in a hydrogen pro-

uction unit (Ghasemzadeh et al., 2013).

Previously mentioned works focused on safety analysis based on a

pecific mathematical model of the unit under review. A disadvantage

f such an approach is its limited application. If the safety analysis of

nother unit was required, it was necessary to decompose the current

Fig. 1 – Methodology of the p

mathematical model and to form and validate a new set of equations

describing the behavior of the new unit. Therefore, this approach is not

suitable for the development of a universal model-based hazard iden-

tification tool. This limitation can be eliminated by involving the use of

a commercial process simulation software with predefined and preval-

idated sets of unit operations commonly used in industry. In this case,

the safety analysis of different units in a plant requires only switching

between the generally prepared mathematical models.

A successful combination of the HAZOP study and simulation

in Aspen Plus in the case study of biodiesel production was pre-

sented by Jeerawongsuntorn et al. (2011). Alternatives including

standard and reactive distillation were analyzed for the purpose of

the decision-making process improvement and safety instrumented

system implementation. The K-Spice software was used for process

simulation followed by the HAZOP analysis in the work of Enemark-

Rasmussen et al. (2012). Results of the simulated deviations were

recorded, evaluated and ranked according to the severity of deviations

determined by the sensitivity measure. Tian et al. (2015) introduced

the dynamic simulation-based HAZOP (DynSim-HAZOP) methodology

employing dynamic simulation in process simulators such as Aspen

Dynamics, Aspen Plus and Aspen HYSYS to perform model-based

safety analysis of an extractive distillation column and an ammonia

synthesis plant. Both Jeerawongsuntorn et al. (2011) and Tian et al.

(2015) used monitoring of user defined threshold values (e.g. auto-

ignition temperature or maximum allowed liquid level in the separator)

in the simulation data evaluation. Enemark-Rasmussen et al. (2012)

partially automated the process of data evaluation by quantifying the

deviation effects and their ranking according to the sensitivity mea-

sure, i.e. comparing the change of the selected parameter (temperature,

pressure . . .) to the change of the deviated parameter. The proposed

ranking system allowed the elimination of deviations with negligible

impact on the process. Systematic approach combining advantages of

previously mentioned works applied in process simulation in Aspen

HYSYS was proposed by Janosovsky et al. (2016a) and it was further

analyzed (Janosovsky et al., 2016b).

Principal objective of this paper is to summarize issues with the

developing computer aided hazard identification tool based on process

simulation in the Aspen HYSYS environment. Two case studies focused

on modern continuous productions exhibiting strong nonlinear behav-

ior with various levels of complexity are presented. In the first case

study, hazards of glycerol nitration in a continuously stirred tank reac-

tor are identified and evaluated. The presence of the multiple steady

states phenomenon in an ammonia synthesis reactor with a preheat-

ing system and the related numerical complications are discussed in

the second case study.

2. Model-based HAZOP tool

Aspen HYSYS v8.4 simulation environment was selected as

the commercial simulation tool. Aspen HYSYS is a power-ful engineering software tool for steady state and dynamic

roposed software tool.

14 Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21

odu

Fig. 2 – Operations carried out by the simulation m

modeling designed for continuous processes consisting ofmultiple process units especially in gas and oil industry(AspenTech, 2015). All information necessary for the descrip-tion of physico-chemical properties of individual componentsand their mixtures are contained in fluid packages stored inthe Aspen HYSYS library. Its extensive size allows more effec-tive search for the solution of complex mathematical modelsby providing an accurate estimation of all necessary modelparameters and an appropriate numerical solver. Componentsnot available in the Aspen HYSYS library can be specifiedas “Hypo Components” by entering the required properties(density, boiling point etc.) in the program. Although variousstudies found negligible differences between the process sim-ulations in Aspen Plus and Aspen HYSYS (Øi, 2012; Smejkaland Soós, 2002), the use of Aspen HYSYS in model-based haz-ard identification is scarce.

Deviations observed by a conventional HAZOP study aregenerated by a simple logic combination of guide words(more, less, none etc.) with process parameters (temperature,pressure, flow etc.). Such information is insufficient for math-

ematical modeling. The model-based HAZOP study requiresnot only the existence of the deviation but also its value and,

le with insight into the data storage methodology.

in case of dynamic simulation, also its duration. Therefore,determination of the deviation range is added to the processof standard HAZOP deviation generation. In the final devia-tion list, deviations characterized as “higher temperature” or“lower flow” are not satisfactory; but instead, deviations like“temperature higher by 10%” or “flow lower by 10%” (in caseof dynamic simulation, “flow lower by 10% lasting for onehour”) are to be given. This fundamental demand for devia-tion quantification in the model-based HAZOP study providesa promising way of at least partial elimination of the disadvan-tages of the conventional HAZOP study such as the possibilityof overlooking hazardous events especially when they havenever been observed before.

The amount of data necessary to be handled during ahazard identification process geometrically increases whenthe dimension of size is assigned to conventional HAZOPdeviation. This task requires a robust software solution withwell-arranged data structure to enable the expert HAZOP teamto smoothly process and visualize complex relations betweenvarious process parameters. The increase in the amount

of data being processed and the complexity of relations iseven more significant when time dimension of a deviation is

Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21 15

cptnbspH

mtacFacaIfituteiitiHsGfc<dsstisof

tsodsiHaflof

3

3s

Npi

Table 1 – Kinetic parameters of glycerol nitration.

Variable Value Unit

A1 9.78 × 1022 l−1.052 mol1.052 min−1

Ea,1R 14674.04 K

a 0.935 Dimensionlessb 1.117 Dimensionless

Fig. 3 – Glycerol nitration model in Aspen HYSYS

onsidered. As summarized in Section 1, although dynamicrocess simulation provides more detailed insight into poten-ial hazard and operability problems resulting from processonlinearity, the majority of possible industrial accidents cane, at least partially, revealed employing just the steady stateimulation approach. For the simplicity and explanatory pur-ose, only possibilities of steady state simulation in the AspenYSYS environment are further discussed in this paper.

Methodology of the proposed software tool consists of aodule utilizing Aspen HYSYS and a module for the applica-

ion of HAZOP principles (Fig. 1). A unique software dictionarynd infrastructure have been developed to ensure a reliableonnection between the simulator and the HAZOP module.irst, the access to Aspen HYSYS and the availability of anctive simulation case and its flowsheet are tested. After theonnection is established, information about individual unitsnd streams is transferred and stored in the internal database.n the next step, parameters of individual streams availableor the HAZOP study are highlighted for the user. The users then able to select the desired parameters and to createheir deviations using the default value range or creating aser specified value range. Only the application of quantita-ive guide words is currently considered. From the softwarengineering point of view, the use of qualitative guide wordsn computer aided approach is limited because of practicallynfinite possibilities of their interpretation. When the devia-ion list is complete, it is saved. When the process simulations launched, deviations from the list are sent to the AspenYSYS environment and simulated one by one. This process is

chematically illustrated for steady state simulation in Fig. 2.enerated deviations are stored in form of data containing

our levels: <type><ID><parameter><deviation value>. Pro-ess footprints contain information also in four-level structuretype><ID><parameter><value after deviation>. In case ofynamic simulation, additional levels quantifying the dimen-ion of time have to be introduced. After each simulation,teady state found by the Aspen HYSYS solver is stored inhe internal database in form of a process footprint contain-ng all necessary information about the individual units andtreams. List of process footprints presents de facto the listf HAZOP consequences in form of pairs <deviation><processootprint>.

After the last deviation from the list is simulated, evalua-ion of the simulation data takes place. Consequences of eachimulated deviation are investigated and hazardous events orperability problems are recognized. The investigation proce-ure presents predefined methods of analysis, e.g. parametricensitivity analysis or user-defined critical values monitor-ng. Detected significant consequences are formulated in anAZOP-like report which serves as a supporting process haz-rd analysis for the human expert HAZOP team. The scope andexibility of the proposed methodology were demonstratedn two case studies focused on modern nonlinear processesrequently used in chemical industry.

. Results and discussion

.1. Case study 1—continuous glycerol nitration intirred tank reactor

itroglycerin belongs to chemical compounds widely used in

harmaceutic industry (Boden et al., 2012) and as propellant

ngredients (Pichtel, 2012). One of the most common indus-

environment.

trial ways of nitroglycerin production is glycerol nitration. Thenitration of glycerol is usually expressed as an esterificationreaction of glycerol and nitric acid shown in Eq. (1) with theappropriate reaction kinetic model (Eq. (2)). Nitric acid is inthe industrial manufacture of nitroglycerin present in form ofmixed acid, a solution of water, nitric acid and sulfuric acid,which acts as a dehydrating agent. Kinetic data (Table 1) anddesign conditions (Table 2) have been adopted from the workof Lu et al. (2008).

C3H5(OH)3 + 3HNO3 → C3H5(ONO2)3 + 3H2O (1)

rnitration = A1e− Ea,1

RT CaGcbN (2)

To calculate the physico-chemical properties of pure com-ponents and their mixtures, the Wilson equation of state withparameters taken from the Aspen HYSYS library was selected.In this case study, mixed acid and pure glycerol were fed ina CSTR with internal cooling coils containing brine as thecooling medium. The output stream formed a heterogeneousliquid reaction mixture. Mathematical model of CSTR in theAspen HYSYS environment consists of Aspen HYSYS modelsof “Continuously Stirred Tank Reactor” and “Cooler” (Fig. 3).Additional vapor stream as the second output stream fromCSTR was required to authorize the simulation of CSTR inAspen HYSYS. Models of “Continuously Stirred Tank Reactor”and “Cooler” were connected by heat flow Q and therefore,the heat transfer rate was not taken into account. In order tosimulate real behavior of the examined system, the follow-ing issues had to be considered: coefficients of pre-definedpolynomial expansion in the Aspen HYSYS library to calcu-late nitroglycerin heat capacity as a function of temperaturewere mistaken and their modification in order to correct thepolynomial correlation according to the observed behavior ofnitroglycerin (Lu et al., 2008; Suceska et al., 2010) had to bedone. After this correction, the calculated heat removal inCSTR was not in an agreement with the plant operating datadue to the underestimation of the heat of mixing in the prede-fined calculation method. Parameters of calculation were thus

corrected to satisfy the relationship between the heat of dilu-

16 Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21

Table 2 – Design parameters of glycerol nitration.

Stream Mass flow [kg/h] Temperature [◦C] Mass fraction

C3H5(OH)3 HNO3 H2SO4 C3H5(ONO2)3 H2O

Glycerol 63.6 19 1.00 0.00 0.00 0.00 0.00Mixed acid 311.6 19 0.00 0.51 0.49 0.00 0.00Product 375.2 15 0.00 0.08 0.41 0.41 0.10

0.00 0.08 0.41 0.41 0.10

Fig. 4 – Effect of heat removal deviation in CSTR100 on thetemperature in CSTR100 (bold red line—runawayconditions, X mark—design point, empty circle—lastnumerical solution in Aspen HYSYS environment, wherereaction rate was calculated). (For interpretation of thereferences to color in this figure legend, the reader isreferred to the web version of this article.)

Table 3 – HAZOP-like report on glycerol nitration.

Deviation Consequence

Heat removal in CSTR100 lower by 11% Possible runaway“Glycerol” mass flow higher by 12% Possible runaway

Vapor output 0.0 15

tion and the composition of the reaction mixture correlatedby Lu et al. (2008).

Several hazardous events occurred during the nitrationreaction and product storage due to the thermal instabilityof nitroglycerin (Lu and Lin, 2009; Lu et al., 2008; Pichtel, 2012).Recommended temperature of the mixture in the reactor is20 ◦C and thus the probability of thermal decomposition ofnitroglycerin resulting in the runaway effect at above 30 ◦C isvery high (Astuti et al., 2014). Therefore, appropriate safetyassessment is needed to recognize potentially dangerous devi-ations leading to runaway situations. A HAZOP study utilizingthe proposed software tool was performed. Deviated parame-ters for this case study were: flow of the stream “brine in”, i.e.heat removal in unit “CSTR100”, and temperature, flow andcomposition of input streams “glycerol” and “mixed acid”. Theabsolute and relative deviations were defined as:

absolute deviation = parameter value at failure state −design value of the parameter (3)

relative deviation = absolute deviation

design value of the parameter× 100 (4)

The relative deviation range for this case study was setfrom − 30% to + 30% with the step of 1%. The evaluationof simulation data was focused on monitoring of the reac-tor temperature, i.e. user specified critical value of a selectedparameter. The reactor temperature was assumed to be equalto the temperature of stream “product”. If the simulated devia-tion caused exceeding of the safety limit (temperature of 30 ◦Cin the reactor), the deviation was highlighted for user andclassified as dangerous.

Visualized effect of the analyzed deviations is depicted inFigs. 4–6. As it can be seen, safety constraints were exceededin case of cooling system and glycerol flow control failure.Fig. 4 shows the effect of the heat removal deviation onthe reactor temperature. When the cooling system failurecaused heat removal decrease of more than 11%, the “prod-uct” temperature and temperature in the CSTR100 exceededthe critical value of 30 ◦C and runaway would have occurred.A similar effect was observed and is plotted in Fig. 5, wherethe effect of “glycerol” parameters deviation is shown. Whenmass flow of stream “glycerol” was increased, temperaturein CSTR100 increased. Above the mass flow of approximately71 kg/h (absolute deviation of 7.6 kg/h and relative deviation of12%), the safety constraint was exceeded. On the other hand,the “glycerol” temperature deviation had negligible effect onprocess safety as well as the effect of “mixed acid” parametersdeviation (Fig. 6).

It is necessary to point out numerical problems regard-ing the solution in case of process variables deviation—heatremoval in CSTR100, “glycerol” temperature and “mixed acid”

composition and mass flow (empty circles in Figs. 4–6). Theseproblems cause significant decrease of the temperature in

CSTR100 (below—100 ◦C) because the reaction was switchedoff by the Aspen HYSYS solver. Inability to find a satisfactorysteady state was attributed to the presence of the heteroge-neous liquid phase and values of reaction temperature nearthe freezing point. Aspen HYSYS was not capable of modelingthe effect of solidification. Another important observation isthe existence of steady states found above the critical temper-ature. Aspen HYSYS was unable to detect runaway effect in theCSTR100 because of the selected mathematical reactor modelthat takes into account only user defined chemical reactions.Thus, the reaction kinetics of nitroglycerin decomposition wasnot taken into account. Steady states found above 30 ◦C bythe Aspen HYSYS solver were only hypothetical steady statescontrary to the real behavior of the reaction mixture above30 ◦C.

Although previously stated problems limit the extent ofsafety analysis, the developed software tool successfully iden-tified hazardous events and generated a HAZOP-like report(Table 3) based on simulation data analysis using a user-specified critical value of the selected parameter. Results of theconducted HAZOP study were in good agreement with safetyanalysis done by Lu et al. (2008).

Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21 17

Fig. 5 – Effect of mass flow (a) and temperature (b) of stream “glycerol” on the temperature in CSTR100 (bold redline—runaway conditions, X mark—design point, empty circle—last numerical solution in Aspen HYSYS environment,where reaction rate was calculated). (For interpretation of the references to color in this figure legend, the reader is referredto the web version of this article.)

Fig. 6 – Effect of mass flow (a), temperature (b) and composition (c) of stream “mixed acid” on the temperature in CSTR100(

3

Atingois

N

r

Table 4 – Kinetic parameters of ammonia synthesis.

Variable Value Unit

A2 1.79 × 104 kmol1 m−3 bar−1.5 h−1

Ea,2R 10475.10 K

A−2 2.57 × 1016 kmol1 m−3 bar0.5 h−1

Ea,−2R 23871.06 K

ˇ 4.75 Dimensionless

X mark—design point).

.2. Case study 2—ammonia synthesis

mmonia has wide application in chemical industry, e.g. inhe production of fertilizers, cleaning agents or explosives. Its produced by heterogeneously catalyzed hydrogenation ofitrogen in the gaseous phase (Eq. (5)). The reaction rate wasiven by Froment et al. (2010) and modified to include the effectf higher activity of modern industrial catalysts as presented

n Eq. (6). Kinetic parameters (Morud and Skogestad, 1998) areummarized in Table 4.

2 + 3H2 ↔ 2NH3 (5)

( 1.5 )

hydrogenation = ˇ

�cA2e

− Ea,2RT pN2

pH2

pNH3

− A−2e− Ea,−2

RTpNH3

p1.5H2

(6)

One of the most used industrial ways of ammonia synthesisis its continuous production in an adiabatic fixed-bed catalyticreactor. Fixed-bed reactors usually consist of several beds con-nected in series with feed quenching between the beds. Thepurpose of feed quenching is to achieve optimal temperature

profile throughout the whole reactor system. The goal of this

18 Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21

Fig. 7 – Ammonia plant in Asp

Table 5 – Design parameters of ammonia synthesis.

Stream Mass flow[103 kg/h]

Temperature[◦C]

Mole fraction

N2 H2 NH3

Fresh feed 252 250 0.239 0.719 0.0424 127 250 0.239 0.719 0.042Quench1 58 250 0.239 0.719 0.042Quench2 35 250 0.239 0.719 0.0421a 127 424 0.239 0.719 0.042R101out 185 520 0.215 0.645 0.140R102out 220 530 0.210 0.630 0.160R103out 252 525 0.209 0.625 0.1666 252 436 0.209 0.625 0.166Liquid ammonia 50 8 0.003 0.015 0.982

case study was to reproduce the strongly nonlinear processbehavior that occurred in German ammonia plant in 1989 andwas well documented by Mancusi et al. (2000). The cause ofthe unusual behavior of the reaction mixture was the pres-ence of the steady state multiplicity phenomenon in ammoniasynthesis (Lassák et al., 2010; Mancusi et al., 2000; Morudand Skogestad, 1998; Pedernera et al., 1997). Although iden-tification of paths between stable and unstable steady statesgenerally requires complex mathematical methods such asbifurcation and continuation analyses (Labovsky et al., 2006;Labovsky et al., 2007), AspenTech software was successfullyused to obtain approximate locations of stable steady statesof an industrial acetic acid dehydration system (Li and Huang,2011). The ammonia plant under review consisted of one reac-tor separated to three segments and a feed preheater. Thereactor system was extended by a separation unit consistingof a refrigeration system and a flash separator (Fig. 7). Designvalues of the key operating process variables (Table 5) werespecified by Janosovsky et al. (2015). Operating pressure was20 MPa and the total reactor volume was 31.52 m3 with thediameter of 0.9 m.

The Peng–Robinson equation of state with parameters fromthe internal Aspen HYSYS library was used to calculate theproperties of gaseous mixtures. Reaction types pre-defined inAspen HYSYS did not satisfactorily correlate reaction kineticsin Eq. (6). Therefore, HYSYS extension containing the proposedkinetics was registered through the customization procedure.According to previous studies (Honkala et al., 2005; Lísal et al.,2005), the Aspen HYSYS model of “Plug Flow Reactor” wasselected as the appropriate mathematical model for ammoniasynthesis reactor performance simulation. Model of the feedpreheating system was built from the Aspen HYSYS modelof “Heat Exchanger”. The overall heat transfer coefficient wascalculated to approach the observed behavior of the preheater(Morud and Skogestad, 1998). Although no material recyclewas present, the “Recycle” unit had to be applied because of

the energy recycle (heat generated by the reaction was par-tially transferred from stream “R103out” to stream “4” in the

en HYSYS environment.

preheater). In this form, mathematical model of ammoniasynthesis in the Aspen HYSYS environment was verified andready for HAZOP analysis.

For this case study, results of the HAZOP study applied forthe operating pressure and the temperature of “fresh feed” arediscussed. Operating pressure relative deviation was set from−50% to +100% with the step of 5% (equal to the operating pres-sure absolute deviation of 1 MPa). Parameters of “fresh feed”were deviated in the range of relative deviations from −30% to+30% with the step of 2% (equal to the temperature absolutedeviation of 5 ◦C). The effect of the “fresh feed” temperatureand the operating pressure on the temperature profile of thereaction mixture is plotted in Fig. 8. Step change of the tem-perature of streams “R103out”, “R102out” and “R101out” wasclearly caused by steady state multiplicity. If the temperatureof “fresh feed” was decreased by 18% or the operating pres-sure was lowered by more than 25 %, the reactive system wasshifted towards lower solution branch, where the reaction ratewas practically equal to zero. Even after the deviated param-eter was set back to the design value, the reactive systemremained in the steady state with low reaction conversion.To restore the original design point, new reactor start-up wasrequired. Solution branches composed of stable steady stateswere plotted. However, the Aspen HYSYS solver was incapableof finding the position of unstable steady states during thesimulation.

Unlike the first case study, definition of critical values bythe user was not employed. On the contrary, mathematicalmethods of analysis independent from the user have beendeveloped. Fig. 9 shows possible graphical output of suchan analysis when applied on the data set plotted in Fig. 8b.The curves in Fig. 9 were constructed as follows: the startingposition of the analysis was the design point (on the highersolution branch). Operating pressure was first increased andthen decreased; i.e. only the shift from the higher to the lowersolution branch is described by these curves. Parameters usedin the analysis are defined by Eqs. (3), (4) and (7).

sensitivity of consequence to deviation = d (consequence)d (deviation)

(7)

As depicted, a small change of one parameter caused asignificant change of another one, e.g. operating pressureabsolute deviation of −6 MPa (−30%) resulted in a suddendecrease of the “R103out” temperature by more than 250 ◦C(50%) (Fig. 9a, b). This phenomenon was more significantlyexposed in the parametric sensitivity analysis (Fig. 9c), wherethe peak of the “R103out” temperature sensitivity to the oper-ating pressure was clearly identified in the region of theoperating pressure absolute deviation of −6 MPa. It was pos-sible to automatically detect nonstandard behavior of the

analyzed reactive system by monitoring these step changes.This approach is particularly applicable to strongly nonlinear

Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21 19

Fig. 8 – Effect of “fresh feed” temperature (a) and operating pressure (b) on the temperature of streams ‘R103out’ (blacksquare), “R102out” (red circle) and “R101out” (blue triangle) (design point—thick square). (For interpretation of thereferences to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 9 – Effect of operating pressure devia

Table 6 – HAZOP-like report on ammonia synthesis.

Deviation Consequence

Operating pressure lower by70%

Operability problem—reactionconversion too low

“Fresh feed” temperaturelower by 18%

Operability problem—reactionconversion too low

ptm

rpotiLlp(im

4

Ps

rocesses characterized by a rapid or a step change of cer-ain process parameters, e.g. systems exhibiting steady state

ultiplicity.Despite the inability to simulate real behavior of the

eactive system in the region of unstable steady states, the pro-osed software tool satisfactorily simulated sudden changesf operating conditions between the higher and lower solu-ion branch. Location of the found stable steady states wasn strong agreement with the solution diagrams obtained byassák et al. (2010) and Mancusi et al. (2000). Operability prob-ems corresponding to those observed in industrial ammonialants were detected and summarized in a HAZOP-like report

Table 6). In addition, the HAZOP study was accelerated bymplementation of mathematical methods for partially auto-

ated identification of hazards and operability problems.

. Conclusions

rocess hazard analysis supported by a commercial processimulator can be a very powerful tool for the analysis of unex-

tion on the “R103out” temperature.

pected operating conditions resulting from nonlinear processbehavior. In this paper, construction of a mathematical modelin the Aspen HYSYS environment and steady state simula-tions applied to two case studies were discussed. Hazardsand operability problems identified were in good agreementwith industrial practice. Process simulations in Aspen HYSYSbenefited from the pre-defined property fluid packages andmathematical models of frequently used unit operations.However, several modifications of the pre-defined modelswere necessary in order to simulate real process behavior. Inthe first case study, alterations in the built-in model param-eters and interaction with the user were crucial to recognizevalid results of computer simulations. The Aspen HYSYS built-in solver was unable to find steady state solutions in thewhole range of deviations. In the second case study, a set ofrobust mathematical methods for proper HAZOP analysis ofthe reactive system exhibiting steady state multiplicity weredeveloped, but the region of unstable steady states remainedunidentified.

Resulting from the general simulation environment ofAspen HYSYS, the developed tool can be adapted to otherchemical processes while maintaining its reliability and accu-racy. Universal application of the proposed tool presents apromising way of more effective and less time consuming pro-cedure of hazard identification based on HAZOP principles.The presented output reports can serve as a guide for human

HAZOP expert team or in the design and operation phase ofmodern chemical plants.

20 Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21

Robustness and applicability of numerical methods cur-rently employed in the proposed software tool are strictlylimited by the Aspen HYSYS built-in solver capability. Futureresearch will be focused on the elimination of revealeddisadvantages of Aspen HYSYS modeling and on the expan-sion of the range of software application towards dynamicsimulations. The use of Aspen HYSYS will be extended bydeveloped modules for the simulation of modern indus-trial production and separation units and by utilization ofnumerical procedures optimized for process safety engineer-ing purposes. With such modules, more effective analysis ofcomplex fault propagation paths and in-depth investigationof deviation–consequence interactions will be achievable.

Acknowledgments

This work was supported by the Slovak Scientific Agency,Grant No. VEGA 1/0749/15 and the Slovak Research and Devel-opment Agency APP-14-0317.

References

AspenTech, 2015. Aspen Technology EngineeringProducts—Aspen HYSYS (Accessed 22 July 2016)http://www.aspentech.com/products/aspen-hysys/.

Astuti, E., Supranto Rochmadi Prasetya, A., Ström, K., Andresson,B., 2014. Kinetic modeling of nitration of glycerol. Mod. Appl.Sci. 8 (2), 78–86.

Boden, W.E., Finn, A.V., Patel, D., Frank Peacock, W., Thadani, U.,Zimmerman, F.H., 2012. Nitrates as an integral part of optimalmedical therapy and cardiac rehabilitation for stable angina:review of current concepts and therapeutics. Clin. Cardiol. 35(5), 263–271.

Corbetta, M., Grossmann, I.E., Manenti, F., 2016. Processsimulator-based optimization of biorefinery downstreamprocesses under the generalized disjunctive programmingframework. Comput. Chem. Eng. 88, 73–85.

Danko, M., Labovsky, J., Janosovsky, J., Labovská, Z., Jelemensky,L’., 2015. Utilization of parallel computing in chemicalengineering. Acta Chim. Slovaca 8 (2), 146–151.

De Rademaeker, E., Suter, G., Pasman, H.J., Fabiano, B., 2014. Areview of the past, present and future of the European lossprevention and safety promotion in the process industries.Process Saf. Environ. Prot. 92 (4), 280–291.

de Riva, J., Ferro, V.R., Moreno, D., Diaz, I., Palomar, J., 2016. AspenPlus supported conceptual design of the aromatic–aliphaticseparation from low aromatic content naphtha using4-methyl-N-butylpyridinium tetrafluoroborate ionic liquid.Fuel Process. Technol. 146, 29–38.

Dunjó, J., Fthenakis, V., Vílchez, J.A., Arnaldos, J., 2010. Hazardand operability (HAZOP) analysis. A literature review. J.Hazard. Mater. 173 (1–3), 19–32.

Eizenberg, S., Shacham, M., Brauner, N., 2006. Combining HAZOPwith dynamic simulation—applications for safety education.J. Loss Prev. Process Ind. 19 (6), 754–761.

Enemark-Rasmussen, R., Cameron, D., Angelo, P.B., Sin, G., 2012.A simulation based engineering method to support HAZOPstudies. In: Iftekhar, A.K., Rajagopalan, S. (Eds.), ComputerAided Chemical Engineering. Elsevier, pp. 1271–1275.

Favarò, F.M., Saleh, J.H., 2016. Toward risk assessment 2.0: safetysupervisory control and model-based hazard monitoring forrisk-informed safety interventions. Reliab. Eng. Syst. Saf. 152,316–330.

Froment, G.F., Bischoff, K.B., De Wilde, J., 2010. Chemical ReactorAnalysis and Design, 3rd ed. John Wiley & Sons, Inc, NewJersey, USA.

Ghasemzadeh, K., Morrone, P., Iulianelli, A., Liguori, S., Babaluo,A.A., Basile, A., 2013. H2 production in silica membrane

reactor via methanol steam reforming: modeling and HAZOPanalysis. Int. J. Hydrogen Energy 38 (25), 10315–10326.

Honkala, K., Hellman, A., Remediakis, I.N., Logadottir, A.,Carlsson, A., Dahl, S., Christensen, C.H., Nørskov, J.K., 2005.Ammonia synthesis from first-principles calculations. Science307 (5709), 555–558.

Janosovsky, J., Danko, M., Labovsky, J., Jelemensky, L’., 2016a.Investigation of nonlinear behaviour of chemical reactorsusing Aspen HYSYS as a useful tool for model-based hazardidentification. In: Proceedings of 4th International Conferenceon Chemical Technology, ICCT 2016.

Janosovsky, J., Labovsky, J., Jelemensky, L’., 2015. Ammoniasynthesis fundamentals for a model-based HAZOP study. ActaChim. Slovaca 8 (1), 5–10.

Janosovsky, J., Labovsky, J., Jelemensky, L’., 2016b. AutomatedModel-based HAZOP study in process hazard analysis. Chem.Eng. Trans. 48, 505–510.

Jeerawongsuntorn, C., Sainyamsatit, N., Srinophakun, T., 2011.Integration of safety instrumented system with automatedHAZOP analysis: an application for continuous biodieselproduction. J. Loss Prev. Process Ind. 24 (4), 412–419.

Kletz, T.A., 2001. Hazop and Hazan. IChemE, Rugby, UK.Labovská, Z., Labovsky, J., Jelemensky, L’., Dudás, J., Markos, J.,

2014. Model-based hazard identification in multiphasechemical reactors. J. Loss Prev. Process Ind. 29, 155–162.

Labovsky, J., Danko, M., Janosovsky, J., Labovská, Z., Jelemensky,L’., 2015. Chemical engineering simulation on parallelcomputers. In: Proceedings of 3rd International Conference onChemical Technology, ICCT 2015.

Labovsky, J., Jelemensky, L., 2011. Verification of CFD pollutiondispersion modelling based on experimental data. J. Loss Prev.Process Ind. 24 (2), 166–177.

Labovsky, J., Jelemensky, L’., Markos, J., 2006. Safety analysis andrisk identification for a tubular reactor using the HAZOPmethodology. Chem. Paper 60 (6), 454–459.

Labovsky, J., Svandová, Z., Markos, J., Jelemensky, L’., 2007.Model-based HAZOP study of a real MTBE plant. J. Loss Prev.Process Ind. 20 (3), 230–237.

Lassák, P., Labovsky, J., Jelemensky, L., 2010. Influence ofparameter uncertainty on modeling of industrial ammoniareactor for safety and operability analysis. J. Loss Prev. ProcessInd. 23 (2), 280–288.

Leveson, N.G., Stephanopoulos, G., 2014. A system-theoretic,control-inspired view and approach to process safety. AlChE J.60 (1), 2–14.

Li, S., Bahroun, S., Valentin, C., Jallut, C., De Panthou, F., 2010.Dynamic model based safety analysis of a three-phasecatalytic slurry intensified continuous reactor. J. Loss Prev.Process Ind. 23 (3), 437–445.

Li, S., Huang, D., 2011. Simulation and analysis on multiplesteady states of an industrial acetic acid dehydration system.Chin. J. Chem. Eng. 19 (6), 983–989.

Lísal, M., Bendová, M., Smith, W.R., 2005. Monte Carlo adiabaticsimulation of equilibrium reacting systems: the ammoniasynthesis reaction. Fluid Phase Equilib. 235 (1), 50–57.

Lu, K.-T., Lin, P.-C., 2009. Study on the stability of nitroglycerinespent acid. Process Saf. Environ. Prot. 87 (2), 87–93.

Lu, K.-T., Luo, K.-M., Yeh, T.-F., Lin, P.-C., 2008. The kineticparameters and safe operating conditions of nitroglycerinemanufacture in the CSTR of Biazzi process. Process Saf.Environ. Prot. 86 (1), 37–47.

Mancusi, E., Merola, G., Crescitelli, S., Maffettone, P.L., 2000.Multistability and hysteresis in an industrial ammoniareactor. AlChE J. 46 (4), 824–828.

Morud, J., Skogestad, S., 1998. Analysis of instability in anindustrial ammonia reactor. AlChE J. 44 (4), 888–895.

Øi, L.E., 2012. Comparison of Aspen HYSYS and Aspen Plussimulation of CO2 absorption into MEA from atmospheric gas.Energy Procedia 23, 360–369.

Parmar, J.C., Lees, F.P., 1987. The propagation of faults in processplants: hazard identification for a water separator system.

Reliab. Eng. 17 (4), 303–314.

Process Safety and Environmental Protection 1 0 7 ( 2 0 1 7 ) 12–21 21

P

P

Q

S

S

S

simulation and its application in chemical processes.Asia-Pac. J. Chem. Eng. 10 (6), 923–935.

edernera, M., Borio, D.O., Porras, J.A., 1997. Steady-statemultiplicity in cocurrently cooled autothermal reactors.AlChE J. 43 (1), 127–134.

ichtel, J., 2012. Distribution and fate of military explosives andpropellants in soil: a review. Appl. Environ. Soil Sci. 2012, 33.

i, G., Wang, D., Chen, Y., Xin, H., Qi, X., Zhong, X., 2014. Theapplication of kinetics based simulation method in thermalrisk prediction of coal. J. Loss Prev. Process Ind. 29, 22–29.

eider, W.D., Soroush, M., Arbogast, J.E., Oktem, U.G., 2014. Designfor process safety—a perspective. In: Mario, R., Eden, J.D.S.,Gavin, P.T. (Eds.), Computer Aided Chemical Engineering.Elsevier, pp. 795–800.

mejkal, Q., Soós, M., 2002. Comparison of computer simulationof reactive distillation using aspen plus and hysys software.Chem. Eng. Process. 41 (5), 413–418.

uceska, M., Musanic, S.M., Houra, I.F., 2010. Kinetics andenthalpy of nitroglycerin evaporation from double base

propellants by isothermal thermogravimetry. Thermochim.Acta 510 (1–2), 9–16.

Sunny, A., Solomon, P.A., Aparna, K., 2016. Syngas productionfrom regasified liquefied natural gas and its simulation usingAspen HYSYS. J. Nat. Gas Sci. Eng. 30, 176–181.

Svandová, Z., Labovsky, J., Markos, J., Jelemensky, L’., 2009. Impactof mathematical model selection on prediction of steady stateand dynamic behaviour of a reactive distillation column.Comput. Chem. Eng. 33 (3), 788–793.

Tian, W., Du, T., Mu, S., 2015. HAZOP analysis-based dynamic