# Agent-Based Modeling And Simulation Of Dehydration Process

Noureddine Seddari, Sohaib Hamioud, Abdelghani Bouras, Sara Kerraoui, Fouzia Krim

► To cite this version:

Noureddine Seddari, Sohaib Hamioud, Abdelghani Bouras, Sara Kerraoui, Fouzia Krim. Agent-Based Modeling And Simulation Of Dehydration Process. 37th ECMS International Conference on Modelling and Simulation, Jun 2023, Florence, Italy. pp.313-319, 10.7148/2023-0313. hal-04201554v2

HAL Id: hal-04201554

https://hal.science/hal-04201554v2

Submitted on 29 Nov 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
AGENT-BASED MODELING AND SIMULATION OF DEHYDRATION PROCESS

Noureddine Seddari¹,²,* , Sohaib Hamioud³, Abdelghani Bouras⁴,Sara Kerraoui², Fouzia Krim²

¹ LIRE Laboratory, Abdelhamid Mehri-Constantine 2 University
Constantine 25000, Algeria

Email: noureddine.seddari@univ-constantine2.dz

² LICUS Laboratory, Department of Computer Science, Université 20 Août 1955-Skikda
Skikda 21000, Algeria

Email: {n.seddari | s.kerraoui,f.krim}@univ-skikda.dz

³ LISCO Laboratory, Computer Science Department, Badji-Mokhtar University
Annaba 23000, Algeria

Email: sohaib.hamioud@gmail.com

⁴ Department of Industrial Engineering, College of Engineering, Alfaisal University
Riyadh 11533, Saudi Arabia

Email: abouras@alfaisal.com

KEYWORDS

Agent-based modeling and simulation (ABMS) ,
Industrial system, Multi-Agent Systems (MAS), Multi-
agent platform JADE.

ABSTRACT

Agent-based modeling and simulation (ABMS) is a new
approach to modeling autonomous systems and
interacting agents. This method is becoming more and
more popular for its efficiency and simplicity. It
constitutes an approach in the field of modeling complex
systems. Indeed, ABMS offers, contrary to other types
of simulations, the possibility of directly representing
the simulated entities, their behaviors, and their
interactions without having to recourse to mathematical
equations. This work is a contribution in this sense, the
goal is to propose an agent-based model to simulate an
industrial system. The latter presents the problem of
complexity, which can be mastered with the Multi-Agent
Systems (MAS) approach. Our model is validated by a
case study of the natural gas dehydration process. The
latter is consolidated by a simulation made in the multi-
agent platform JADE (Java Agent DEvelopment
Framework

INTRODUCTION

Agent-based modeling and simulation (ABMS) is an
approach to the field of complex system modeling. In
this framework, the model is composed of autonomous
interacting agents. The agents have behaviors described
by simple rules, and often interact with other agents,
which in turn can influence the individual and general
behavior of the system (Macal and North, 2005).

Complex systems can be viewed as a set of interacting
agents or entities such that the evolution of the system

cannot be predicted by the pure integration of the laws
governing these entities. Agents can represent
organisms, humans, firms, institutions, and any other
entity with the intent to achieve a goal (Abar et al.,
2017; Roy et al., 2004). Agent-based models are
particularly applied in the case of complex phenomena
in which many agents or active entities interact to
establish relationships and solve problems that may arise
(Simon, 1969; Bertalanffy, 1968)

The modeling and simulation (M&S) approach is an
activity that allows the study of a system using objects
called models. Several authors agree to define the
notions of modeling and simulation as the process of
designing a model of the real world, and then
conducting a set of experiments to understand its
behavior or to evaluate different strategies within the
limits of a set of fixed criteria for the evolution of the
system (Fishwick, 1995; Sonnessa, 2004; Shannon,
1976; Ingalls, 2001; Shannon, 1998; Fishwick, 1997;
Vangheluwe, 2008).

ABMS offers a way to model complex industrial
systems. This paradigm is increasingly known in
scientific disciplines such as agriculture, economics,
ecology, biology, sociology, and many other disciplines
(science, technology, engineering, and mathematics)
(Roy et al., 2004). It is useful for simulating dynamic
complex systems and for the observation of their
emergent behaviors (Allan, 2010; Macal, 2016;
Marvuglia et al., 2017).

Our work falls within this framework; we have opted for
an approach that allows the modeling and simulation of
complex industrial systems. The approach is based on
the ABMS paradigm, depending on their importance,
they may contain thousands of elements and even more.
This is the case, for example, with oil complexes. So,
what are the steps to follow, and what are the tools to
use?
The idea is to model the natural gas dehydration system as a distributed multi-agent system and use a model based on representing all the elements of this system by agents, so that they can be simulated and implemented within multi-agent platforms and more particularly, the JADE platform.

This article is organized as follows: Section 2 presents a background on agent-based modeling and simulation (ABMS). Section 3 introduces a use case of industrial system to be modeled and simulated. Section 4 is dedicated to the proposed approach. Section 5 describes the implementation of the proposed system. Finally, we conclude this paper in section 6.

## AGENT-BASED MODELING AND SIMULATION (ABMS)

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems composed of autonomous agents. ABMS is one of the most prominent and practical methods of modeling since the invention of relational databases. ABMS is used in various fields including: complexity science, systems science, systems dynamics, computer science, management science, several branches of the social sciences, and traditional modeling and simulation. The theoretical foundations of ABMS and its applicable modeling techniques, make it a very capable and practical choice to simulate and model complex systems more effectively and easily.

In an ABMS, an agent is an atomic entity (Chaib-Draa, 1999). The agent can be reactive, in the sense that it has limited action capabilities, cognitive. To be precise, it can develop action plans, make decisions, and other 'intelligent' tasks, or hybrid in the case where the agent is both reactive and cognitive (Chaib-draa, 1996; Moulin and Chaib-draa, 1996; Ferber, 1997; Michel et al., 2009; Krishnamurthy and Murthy, 2006; Bond and Gasse, 1988; Franklin and Graesser, 1997; Iglesias et al., 1997; Fabrice et al., 2015).

There is no common agreement on the definition of an agent. In general, the term agent refers to an entity (hardware or software), which is different from an ordinary program in a way that it is endowed with autonomy for functioning in environments shared by other agents (Ferber, 1995;1997). An agent is autonomous, social, and self-directed. It can function independently in its environment and during its interactions with other agents for a specific range of situations of interest. Agents can recognize and distinguish the traits of other agents with which they can communicate and interact via protocols.

ABMS offers us the possibility to simulate real systems in which very complex behaviors emerge from relatively simple and local interactions between many different individuals. Therefore, ABMS seems to us to be the most adequate approach for the simulation of complex systems such as industrial processes. Indeed, ABMS will be particularly well adapted to the description of a system from the point of view of the activity of its constituents, i.e. when the behavior of individuals is complex (and therefore difficult to describe with equations).

## USE CASE

Our work is realized in close collaboration with the Algerian oil and gas giant SONATRACH and in particular the Liquefied Natural Gas (LNG) units located in Skikda, in the east of Algeria. The goal is to create operational simulators of industrial systems used in the LNG. Our research team has conducted many similar projects by using different approaches (MAS, DEVS, etc.) (Seddari et al., 2013a; 2013b; 2014; 2015; 2017; 2021).

The operating principle of the dehydration system is based on the use of three molecular sieve bed dryers: the first one is in adsorption mode, the second is on stand-by (waiting) and the third is in regeneration.

The dehydration system can be divided into three subsystems: Pre-cooling and separation, Dehydration, and Filtration & regeneration. Figure 1 shows the technical diagram of this system.

<figure><img src="image_1.png" /><figcaption>Figures 1: Dehydration System</figcaption></figure>
## Pre-cooling and Separation

The main function of this system is to reduce the load of 13-MD02-A/B/C molecular sieve dryers.

Cooling the NG reduces the load on the dryer by lowering the water content in the NG entering the separator drum 13-MD01 before feeding the dryer to separate and recover any water and amine coming from the decarbonization unit (unit 12). The NG at the top of the separator goes to the molecular sieve dryer arranged in adsorption mode at that time (13-MD02 A or B or C).

From the bottom of the 13-MD01, the liquids are sent to the water flash drum of the 13-MD09 dryer for further separation.

## Dehydration

The dewatering system has three 13-MD02-A/B/C molecular sieve dryers. Under normal operating conditions, one dryer operates at 100% capacity in adsorption mode, the second is in regeneration mode and the third is in stand-by mode.

Gas from the 13-MD01 Dryer Feed Separator enters the top of the adsorption dryer. The wastewater in the NG is adsorbed by the dryer beds.

Each dryer is provided with type 4A molecular sieves which will each operate on a twelve (12) hour adsorption cycle. The dryer isolation valves are controlled by a sequence on the DCS. The dryer sequences are automatically programmed by a PLC from the distributed Control Systems (DCS).

## Regeneration

Each 13-MD02-A/B/C dryer operates on an automatic thirty-six (36) hour cycle, controlled by the DCS. Each dryer is regenerated once its bed is saturated. This is achieved by circulating hot gas from bottom to top in the dryer.

## Typical Operating Parameters

Table 1 contains typical operating variables for the dehydration system.

<table><caption>Table 1: Operating Variables for the Dehydration System</caption><thead><tr><th>Tag No.</th><th>Description</th><th>Typical Value</th><th>Units</th></tr></thead><tbody><tr><td>13-FFIC-1027</td><td>Nitrogen to Dryer Water Flash Drum, 13-MD09, Ratio Flow Controller</td><td>0.01</td><td></td></tr><tr><td>13-FIC-1021</td><td>Dryer Water Flash Drum, 13-MD09, Water to 12-MD39 Flow Controller</td><td>1,394</td><td>kg/hr</td></tr><tr><td>13-FIC-1023</td><td>Dryer Water Flash Drum, 13-MD09, Water to the Oily Water Sewer Flow Controller</td><td>4,250</td><td>kg/hr</td></tr><tr><td>13-FIC-1048</td><td>Regen Gas Heater, 13-MC02, Regen Gas Flow Controller</td><td>Fuel gas: 90,404<br>Dry Feed gas: 76,073</td><td>kg/hr<br>kg/hr</td></tr><tr><td>13-FIC-1244</td><td>Regen Gas Heater, 13-MC02, Hot Oil Flow Controller</td><td>104,347</td><td>kg/hr</td></tr><tr><td>13-LIC-1003</td><td>Dryer Precooler, 13-MC01, Level Controller</td><td>70</td><td>%</td></tr><tr><td>13-LIC-1004</td><td>Dryer Inlet Separator, 13-MD01, Level Controller</td><td>50</td><td>%</td></tr><tr><td>13-LIC-1022</td><td>Dryer Water Flash Drum, 13-MD09, Level Controller</td><td>36</td><td>%</td></tr><tr><td>13-LIC-1055</td><td>Regen Gas KO Drum, 13-MD03, Level Controller</td><td>50</td><td>%</td></tr><tr><td>13-PDI-1063</td><td>Regeneration Water Filter, 13-MD05, Pressure Differential Indicator</td><td>0.1</td><td>bar</td></tr><tr><td>13-PDI-1162</td><td>Dryed Gas Filter, 13-MD04, Pressure Differential Indicator</td><td>0.15</td><td>bar</td></tr><tr><td>13-PIC-1007</td><td>Dryer Precooler, 13-MC01, Proppane Refrigerant Pressure Controller</td><td>6.9</td><td>barg</td></tr><tr><td>13-PIC-1025</td><td>Dryer Water Flash Drum, 13-MD09, Pressure Controller</td><td>4.0</td><td>barg</td></tr><tr><td>13-PIC-1062A</td><td>Regeneration Gas KO Drum, 13-MD03, Vapor to Fuel Gas Pressure Controller</td><td>30.3</td><td>barg</td></tr><tr><td>13-PIC-1062B</td><td>Regeneration Gas KO Drum, 13-MD03, Vapor to Warm Flare Header Pressure Controller</td><td>35</td><td>barg</td></tr></tbody></table>

## THE PROPOSED APPROACH

The method proposed in this work is based on the use of an agent-based architecture to model the natural gas dehydration system.

The architecture defined in this work is based on Multi-agent systems (MAS), designed to simulate the operation of different dryers that compose the dehydration process.
The architecture expresses the fundamental structure of
the dehydration system to be modeled and simulated. It
defines the set of functional components described in
terms of their behaviors and interfaces, as well as the
way these components interact to properly accomplish
the simulation objective of a natural gas dehydration
system. Therefore, an architectural description is
mainly required for the specification of the system
structure.

The idea is to model the natural gas dehydration system
as a distributed multi-agent system.

In the following, we will present the specifications of
the different components, as well as the concepts
related to their operation.

Structure of agents in the dehydration system

We will describe in what follows the structure and the
functionalities of the different agents and which are:

**Agent 13-FFIC-1027:** The role of this agent is to
regulate the flow of nitrogen to the balloon 13-MD09.

**Agent 13-FIC-1021:** Its role is to regulate the flow of water from tank 13-MD09 to 12-MD39.

**Agent 13-FIC-1023:** The role of this agent is to
regulate the water flow from the 13-MD09 tank to the
sump.

**Agent13-FIC-1048:** this agent takes care of regulating
the gas flow of the regeneration gas heater 13-MC02.

**Agent 13-FIC-1244:** it is the agent that takes care of
the regulation of the hot oil flow of the regeneration gas
heater 13-MC02.

**Agent 13-LIC-1003:** it is the agent that deals with the
regulation of the level of the pre-cooler of the dryer 13-
MC01.

**Agent 13-LIC-1004:** it is the agent that deals with the
regulation of the level of the inlet separator of the dryer
13-MD01.

**Agent 13-LIC-1022:** it is the agent that takes care of
the regulation of the level of the water flash tank of the
dryer 13-MD09.

**Agent 13-LIC-1055:** it is the agent that deals with the
regulation of the level of the regeneration gas
separation of the tank 13-MD03.

**Agent 13-PDI-1063:** it is the agent that deals with the
regulation of the pressure of the propane refrigerant of
the dryer 13-MC01.

**Agent 13-PIC-1025:** it is the agent that takes care of
the pressure regulation of the water flash tank of the
dryer 13-MD09.

**Agent 13-PIC-1062A:** it is the agent that deals with
the regulation of the vapor pressure of 13-MD03.

**Agent 13-PIC-1062B:** this is the agent responsible for
regulating the pressure of the steam from 13-MD03 to
the hot torch manifold.

**Agent 13-TIC-1010:** it is the agent that deals with the
regulation of the supply temperature of the inlet
separator of the dryer 13-MD01.

**Agent 13-TIC-1045:** it is the agent that deals with the
regulation of the temperature of the regeneration gas.
Valve agent: this is the agent that controls the flow of
nitrogen, water, and gas to the various components of
the dehydration system.

**Cooling Agent (13MC-01):** This agent is responsible
for the cooling of the feed gas, it can communicate with
the liquid propane agent (16MC-10) to regulate the
temperature and can also communicate with the graphic
interface to give a visual representation of the cooling
agent.

**Propane Agent (16MC-10):** This agent controls the
flow of the propane condenser valve gas (16-MC10), it
can communicate with the agent (13MC-01), and can
also communicate with the graphical interface to give a
visual representation of the agent propane.

**Separation Agent (13MD-01):** This agent is
responsible for the separation of liquids and reducing
the water content of the gas in the dryers, it can
communicate with the agent (13XV-1011) and with the
graphical interface to give a visual representation of the
separation agent.

**Agent Reg-Valve (13XV-1011):** This agent controls
the flow of water winnowing unit 12, it can
communicate with the separation agent (13MD-01) and
can also communicate with the graphic interface.

**Drying Agent (13MD-02 A):** This agent is responsible
for heating the gas, it can communicate with the
separation agent (13MD-01) and the filtration agent
(13-MD04)

**Filtering Agent (13-MD04):** This agent filters the gas
from the dryer (13MD-02 A), it can communicate with
the drying agent (13MD-02 A) and the graphic
interface to give a visual representation of the filtration
agent.

**Security Agent:** This agent is responsible for the
emergency shutdown of the dehydration system, it can
communicate with all the agents of our system.

In the following figure, we present the proposed multi-agent-based modeling of our industrial process.
<figure><img src="image_2.png" /><figcaption>Figures 2: Dehydration System</figcaption></figure>

## THE SYSTEM IMPLEMENTATION

The simulator was developed by using the JADE platform (Bellifemine et al. 2008), to benefit from the power of the advanced tools offered by this platform.

The main interface of our simulator is presented in figure 3. It depicts the simulation of the dehydration system and allows the user to drive the system operation and supply it by loading different data inputs.

The simulator's graphical interfaces provide many tools permitting the operator to regulate and monitor changes in the dehydration system.

Thanks to displayed knowledge interface, the user will observe the steps of the system and may act on the input parts of the process by entering set point values and operating thresholds values and observe the behavior of the simulation consequently.

As it is intended, the simulator offers a particular range of functionalities permitting the user to follow the evolution of the dehydration system and to be ready, thus, to know its operation. Likewise, such a machine makes it potential to predict many cases: those, that square measure acceptable, and those, that square measure prohibited. further options will be further reckoning on operator needs.

<figure><img src="image_3.png" /><figcaption>Figure 3: Graphical simulation of the dehydration system</figcaption></figure>
## CONCLUSION

The goal of our work is to use a rigorous and efficient method to represent and simulate a complex industrial system (natural gas dehydration process). Our approach is based on the proposal of an agent-based model, validated by the JADE platform. The major benefit of using multi-agent systems is the distribution of tasks and managing complexity. Our simulation covers the different tasks of the dehydration process such as regulation of pressures, levels, temperatures, and the control of water, Nitrogen and gas flows, etc.

The advantage of our approach is to benefit from the power of the advanced tools offered by ABMS platforms for the implementation, development, and operation of complex industrial systems. We plan to apply our approach on a larger scale by simulating more complex systems and industrial processes, starting with the accessible sources and the great opportunities SONATRACH is offering to researchers in this field.

## REFERENCES

Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O'Hare, G. M. (2017). Agent Based Modelling and Simulation tools: A review of the state-of-art software. *Computer Science Review*, 24, 13-33.

Allan, R. J. (2010). *Survey of agent based modelling and simulation tools* (pp. 1362-0207). New York: Science & Technology Facilities Council.

Bellifemine, F., Caire, G., Poggi, A., & Rimassa, G. (2008). JADE: A software framework for developing multi-agent applications. Lessons learned. *Information and Software technology*, 50(1-2), 10-21.

Bertalanffy, L. V. (1968). General Systems Theory, Foundation, Development, Applications, New York: G. Braziller, trad. française:(1973), *Théorie générale des systèmes*, Paris: Dunod.

Bond, A. H., & Gasser, L. (Eds.). (2014). *Readings in distributed artificial intelligence*. Morgan Kaufmann.Bouquet, F. Sheeren, D. Becu, N., and Gaudou, B. (2015). Formalisms for the description of agent models.

Chaib-Draa, B. (1999). Agent and Multi-Agent System. Laval University, Quebec (Canada).

Chaib-draa, B. (1996). Interaction between agents in routine, familiar and unfamiliar situations. *International Journal of Intelligent and Cooperative Information Systems*, 20.

Cox, D. R. (1972). Regression models and life tables (with Discussion). *J. R. Statist. Soc. B*, 34:187-220.

Fan, J. and Peng, H. (2004). Nonconcave penalized likelihood with a diverging number of parameters. *Ann. Statist.*, 32:928-61.

Ferber, J. (1995). Les systèmes multi-agents: vers une intelligence collective. *Informatique, intelligence Artificielle*, Intereditions Paris.

Ferber, J. (1997). Multi-Agent Systems: A General Overview, Revue Technique et Science Informatiques, Hermes-Lavoisier.

Ferber, J. (1999). Multi-Agent Systems. Addison-Wesley, An Introduction to Distributed Artificial Intelligence.

Fishwick, P. A. (1995). Simulation Model Design and Execution. *Building Digital Worlds*, Prentice-Hall.

Fishwick, P. A. (1997). Computer simulation: growth through extension. *Transactions of the Society for Computer Simulation*, 14(1), 13-24.

Franklin, S., & Graesser, A. (1996, August). Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents. In *International workshop on agent theories, architectures, and languages* (pp. 21-35). Springer, Berlin, Heidelberg.

Heard, N. A., Holmes, C. C., & Stephens, D. A. (2006). A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: An application of Bayesian hierarchical clustering of curves. *Journal of the American Statistical Association*, 101(473), 18-29.

Iglesias, C. A., Garijo, M., González, J. C., & Velasco, J. R. (1997, July). Analysis and design of multiagent systems using MAS-CommonKADS. In *International Workshop on Agent Theories, Architectures, and Languages* (pp. 313-327). Springer, Berlin, Heidelberg.

Ingalls, R. G. (2011, December). Introduction to simulation. In *Proceedings of the 2011 Winter Simulation Conference (WSC)* (pp. 1374-1388). IEEE.

Krishnamurthy, E. V., & Murthy, V. K. (2005). Distributed agent paradigm for soft and hard computation. *Journal of network and computer applications*, 29(2-3), 124-146.

Macal, C. M., & North, M. J. (2005, December). Tutorial on agent-based modeling and simulation. In *Proceedings of the Winter Simulation Conference, 2005*. (pp. 14-pp). IEEE.

Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. *Journal of Simulation*, 10(2), 144-156.

Marvuglia, A., Rege, S., Gutiérrez, T. N., Vanni, L., Stilmant, D., & Benetto, E. (2017). A return on experience from the application of agent-based simulations coupled with life cycle assessment to model agricultural processes. *Journal of cleaner production*, 142, 1539-1551.

Michel, F., Ferber, J., & Drogoul, A. (2009). Multi-agent systems and simulation: a survey from the agents community's perspective. *Multi-Agent Systems: Simulation and Applications*, 5, 3-52.

Moulin, B., & Chaib-Draa, B. (1996). An overview of distributed artificial intelligence.

Roy, D., Anciaux, D., Monteiro, T., & Ouzizi, L. (2004). Multi-agent architecture for supply chain management. *Journal of Manufacturing Technology Management*.

Shannon, R. E. (1977). Simulation modeling and methodology. *ACM SIGSIM Simulation Digest*, 8(3), 33-38.

Shannon, R. E. (1998, December). Introduction to the art and science of simulation. In *1998 winter simulation conference. proceedings (cat. no. 98ch36274)* (Vol. 1, pp. 7-14). IEEE.

Seddari, N., Redjimi, M., & Benoudina, L. (2013a).
Operational approach for modeling and simulation of an
industrial process. In 2013 *International Conference on
Computer Applications Technology (ICCAT)* (pp. 1-6).
IEEE.

Seddari, N., Redjimi, M. (2013b). Multi-agent modeling of a complex system. In 2013 3rd International Conference on Information Technology and e-Services (ICITeS), pp. 1-6. https://doi.org/10.1109/ICITeS.2013.6624072

Seddari, N., Redjimi, M., & Boukelkoul, S. (2014). Using of DEVS and MAS tools for modeling and simulation of an industrial steam generator. *Journal of computing and information technology, 22*(3), 171-189.
https://doi.org/10.2498/cit.1002348

Seddari, N. (2015). *Outils formels et opérationnels pour la modélisation et la simulation des systèmes complexes.* (Formal and operational tools for modeling and simulation of complex systems) (Doctoral dissertation, University of Skikda, ALgeria).

Seddari, N., Belaoued, M., & Bougueroua, S. (2017). Agent/group/role organizational model to simulate an industrial control system. *World Academy of Science, Engineering and Technology, International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11*(10), 2376-2385.

Seddari, N., Boukelkoul, S., Bouras, A., Belaoued, M., & Redjimi, M. (2021). A new transformation approach for complex systems modelling and simulation: application to industrial control system. *International Journal of Simulation and Process Modelling, 16*(1), 34-48.
https://doi.org/10.1504/IJSPM.2021.113073

Simon, H. A. (2019). *The Sciences of the Artificial, reissue of the third edition with a new introduction by John Laird*. MIT press.

Sonnessa, M. (2004). Modelling and simulation of complex systems. Unpublished doctoral dissertation, PhD Thesis in Cultura e impresa, University of Torino, Italy.

Vangheluwe, H. (2008). Foundations of modelling and simulation of complex systems. Electronic Communications of the EASST, 10.

## AUTHOR BIOGRAPHIES

**NOUREDDINE SEDDARI** holds MS and PhD degrees in computer science from the University of Skikda, Algeria, in 2011 and 2015, respectively. He is currently an Associate Professor of computer science at the same University. He is also currently a researcher at the LIRE Laboratory - SIBC Team at the University of Constantine 2 - Abdelhamid Mehri, Algeria. He was an Associate Teacher at the same University from 2015 to 2017. His current research interests include agent oriented software engineering, artificial intelligence, fake news and malware detection, DEVS formalism, multiformalism modelling and computer simulations.

**SOHAIB HAMIOUD** received his Master degree in 2010 and holds PhD degrees in 2016 in computer science from the University of Annaba, Algeria. He is currently a lecturer at the University of Ourghela- Algeria and a member of SAFA Team in LISCO Laboratory at the University of Annaba. His research interests include model-driven engineering, domain specific languages, software architecture and software engineering.

**ABDELGHANI BOURAS** holds a Ph.D. in operations research, from Joseph Fourier University, Grenoble. He is an engineer and master of operations research. He is currently a Professor of Industrial Engineering at Al Faisal University, Riyadh. He was formerly Professor of Industrial Engineering at Ecole Centrale Casablanca and King Saud University. He worked as an associate professor of operations management and quantitative methods at the School of Business and Administration at Al Akhawayn University in Ifrane (AUI), and as an assistant professor of production management at the School of Business and Management at Liège University. He worked in industry as an operations research analyst at Usinor-Arcelor Group (steel industry), as a modeler-Analyst for Electrabel-Suez group (Belgian electricity producer), and finally as consultant for Pechiney (Aluminium industry). He is involved in many researches in mathematical modelling, operations research, operations supply chain management, and intrusion detection systems.

**SARA KERRAOUI** holds MS and PhD degrees in computer science from the University of Skikda-Algeria in 2011 and 2019 respectively. She is currently works at the department of computer science at the same University as a teacher and researcher. She is a member of the LICUS laboratory (Computing and Communication laboratory of the Skikda University). Her research interests include: multi agent system, modeling and simulation, agent based modeling, agent oriented software engineering, computational intelligence and software engineering.

**FOUZIA KRIM** holds a magister in literature and criticism from Yarmouk University, Jordan. She is currently an Associate Teacher of English at the University of Skikda, Algeria. She is also a researcher in domain of computer science engineering with LICUS Laboratory in the same university. Her research interests include contemporary criticism, artificial intelligence, fake news detection, and computer modeling.