@article {61569,
	title = {Modelling of Fire-Atmosphere interaction by the finite volume method: Case of NOx life cycle},
	year = {Submitted},
	author = {HAMZI, Rachida and Noureddine Bourmada and HADDAD, Djamel and LONDICHE, Henry}
}
@article {103123,
	title = {Quantitative Assessment of Thermal Runaway Risk in a Chemical Reactor: HybridApproach},
	journal = {International Journal of Safety and Security Engineering},
	volume = {15},
	number = {9},
	year = {2025},
	pages = {1949-1959},
	abstract = {
	Thermal runaway of a chemical process is a dangerous phenomenon that threatens human life, equipment, installations, and the environment. The aim of our work is to propose a methodology for analyzing and quantitatively assessing the risk of thermal runaway in a polymerization reactor. Firstly, HAZard and OPerability analysis(HAZOP)is used to determine the most critical deviations likely to occur in the polymerization reactor under study and leading to the thermal runaway phenomenon. The various accident sequences are determined and evaluated using event tree analysis (ETA). The causes of the failure of safety barriers implemented in the reactor to prevent the runaway phenomenon are determined using fault tree analysis (FTA). Finally, an economic analysis is carried out to show the economic impact of safety failure barriers on the company. Application resultsof the proposed methodology show its relevance as a decision-making tool for good industrial risk management. The novelty of this hybrid approach lies in its systematic workflow. Qualitative identification (HAZOP) directly informs quantitative frequency estimation (ETA), which in turn relies on detailed reliability analysis (FTA) to assess safety barrier performance. This integrated methodology not only provides a quantitative risk frequency but also identifies the most critical safety barriers and offers an economic rationale for investment decisions through cost-benefit analysis (CBA), thereby bridging the gap between technical risk assessment and managerial decision-making

},
	url = {https://www.iieta.org/pdf-viewer/21984},
	author = {Benamrane, Badrtamam and Ouazraoui, Nouara and Lakehal, Brahim and Noureddine Bourmada}
}
@article {7797,
	title = {Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study},
	journal = {International Journal of Quality \& Reliability Management},
	volume = {38},
	number = {6},
	year = {2021},
	pages = {1409-1424},
	abstract = {Purpose The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs. Design/methodology/approach The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system. Findings The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system. Originality/value The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.},
	url = {https://www.emerald.com/insight/content/doi/10.1108/IJQRM-07-2019-0249/full/html},
	author = {Samia Chebira and Noureddine Bourmada and Abdelali Boughaba and M{\'e}barek Djebabra}
}
@proceedings {6809,
	title = {Artificial Neural Networks for Fault Diagnosis of Milk Pasteurization Process - A Comparative Study},
	journal = {International Conference on Industrial Engineering and Operations Management , March 10-12},
	year = {2020},
	address = {Dubai, UAE},
	abstract = {The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.},
	url = {http://www.ieomsociety.org/ieom2020/papers/603.pdf},
	author = {Samia Chebira and Noureddine Bourmada and Abdelali Boughaba}
}
@article {63072,
	title = {Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study},
	journal = {International Journal of Quality \& Reliability ManagementInternational Journal of Quality \& Reliability Management},
	year = {2020},
	month = {2020},
	isbn = {0265-671X},
	author = {Samia Chebira and Noureddine Bourmada and Abdelali Boughaba and Djebabra, MEBAREK}
}
@proceedings {66662,
	title = {Fleet management: Assessment of the best practices},
	journal = {QUALITA2013},
	year = {2013},
	month = {2013},
	author = {HAMZI, Rachida and Noureddine Bourmada and Bouda, Mohamed}
}
@article {67348,
	title = {Thermal runaway analysis for the safety of a chemical reactor},
	journal = {Journal of Information, Intelligence and KnowledgeJournal of Information, Intelligence and Knowledge},
	volume = {3},
	year = {2011},
	month = {2011},
	pages = {275},
	isbn = {1937-7983},
	author = {Benamrane, Badrtamam and Noureddine Bourmada and Chetouani, Yahya}
}
@article {67664,
	title = {An Environmental Analysis of the Impact of an Accidental Fire in Process Industries},
	journal = {International Journal of Chemical Reactor EngineeringInternational Journal of Chemical Reactor Engineering},
	volume = {7},
	year = {2009},
	month = {2009},
	isbn = {1542-6580},
	author = {HAMZI, Rachida and Innal, Fares and Noureddine Bourmada and LONDICHE, Henry}
}
@article {67670,
	title = {One dimensional transient numerical study of the mass heat and charge transfer in a proton exchange membrane for PEMFC},
	journal = {International journal of hydrogen energyInternational Journal of Hydrogen Energy},
	volume = {34},
	year = {2009},
	month = {2009},
	pages = {5010-5014},
	isbn = {0360-3199},
	author = {HADDAD, Djamel and Hocine Ben Moussa and Noureddine Bourmada and Oulmi, Kafia and Mahmah, Bouziane and Belhamel, Ma{\"\i}ouf}
}
@article {62298,
	title = {Fire-LCA model for environmental decision-making},
	journal = {Chemical Engineering Research and DesignChemical Engineering Research and Design},
	volume = {86},
	year = {2008},
	month = {2008},
	pages = {1161-1166},
	isbn = {0263-8762},
	author = {HAMZI, Rachida and LONDICHE, Henry and Noureddine Bourmada}
}
@article {61714,
	title = {Constantes de vitesse des r{\'e}actions du radical OH avec C2H6, C6H12 et C6H6 par fluorescence de r{\'e}sonance en tube {\`a} {\'e}coulement rapide},
	year = {1989},
	month = {1989},
	author = {Noureddine Bourmada}
}
@article {61715,
	title = {Constantes de vitesse des reactions du radical OH avec C$_{2}$ H$_{6}$, C$_{6}$ H$_{1}$$_{2}$ et C$_{6}$ H$_{6}$ par fluorescence de resonance en tube a ecoulement rapide},
	year = {1989},
	month = {1989},
	author = {Noureddine Bourmada}
}
