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2020
MCHEBILA. Generalized markovian consideration of common cause failures in the performance assessment of safety instrumented systems. Process Safety and Environmental Protection [Internet]. 2020;2018 (141(9) : 28-36. . Publisher's VersionAbstract
Aiming to provide a generalized method for assessing the performance of safety instrumented systems with a flexible and accurate consideration of the common cause failures’ contribution. This paper is devoted to the development of a direct way to generate the transition rate matrix associated with the continuous-time Markov model of any typical KooN architecture using any parametric model. Such a choice is considered after a detailed comparison of the ability of several dependability methods (e.g., fault trees, reliability block diagrams, Markov models, Bayesian networks, etc) to provide simple representations and genuine results in this context. To validate the developed method, the unavailability and the unconditional failure intensity of a wide range of configurations are quantified using the Binomial Failure Rate model and compared to those of the complete fault tree implementation.
BOURARECHE M, NAIT-SAID R, Zidani F, OUAZRAOUI N. Improving barrier and operational risk analysis (BORA) using criticality importance analysis case study: oil and gas separator. World Journal of Engineering, Vol. ahead-of-print. 2020, [Internet]. 2020. Publisher's VersionAbstract

Purpose

The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels.

Design/methodology/approach

The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed.

Findings

In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process.

Practical implications

The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately.

Originality/value

In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational

environment.

2019
OUAZRAOUI N, NAIT-SAID R. An alternative approach to safety integrity level determination: results from a case study, . International Journal of Quality & Reliability Management [Internet]. 2019;2019 (36(10):) :1784-1803. Publisher's VersionAbstract

Purpose

The purpose of this paper is to validate a fuzzy risk graph model through a case study results carried out on a safety instrumented system (SIS).

Design/methodology/approach

The proposed model is based on an inference fuzzy system and deals with uncertainty data used as inputs of the conventional risk graph method. The coherence and redundancy of the developed fuzzy rules base are first verified in the case study. A new fuzzy model is suggested for a multi-criteria characterization of the avoidance possibility parameter. The fuzzy safety integrity level (SIL) is determined for two potential accident scenarios.

Findings

The applicability of the proposed fuzzy model on SIS shows the importance and pertinence of the proposed fuzzy model as decision-making tools in preventing industrial hazards while taking into consideration uncertain aspects of the data used on the conventional risk graph method. The obtained results show that the use of continuous fuzzy scales solves the problem of interpreting results and provides a more flexible structure to combine risk graph parameters. Therefore, a decision is taken on the basis of precise integrity level values and protective actions in the real world are suggested.

Originality/value

Fuzzy logic-based safety integrity assessment allows assessment of the SIL in a more realistic way by using the notion of the linguistic variable for representing information that is qualitative and imprecise and, therefore, ensures better decision making on risk prevention.

BOUGHABA A, ABERKANE S, FOURAR Y, DJBABRA M. Study of safety culture in healthcare institutions: case of an Algerian hospital. International Journal of Health Care Quality Assurance [Internet]. 2019;2019 (32(7) :1081-1097. Publisher's VersionAbstract

Purpose

For many years, the concept of safety culture has attracted researchers from all over the world, and more particularly in the area of healthcare services. The purpose of this paper is to measure safety culture dimensions in order to improve and promote healthcare in Algeria.

Design/methodology/approach

The used approach consists of getting a better understanding of healthcare safety culture (HSC) by measuring the perception of healthcare professionals in order to guide promotion actions. For this, the Hospital Survey on Patient Safety Culture questionnaire was used in a pilot hospital setting where it was distributed on a number of 114 health professionals chosen by stratified random sampling.

Findings

The results showed that the identified priority areas for HSC improvement help in establishing a trust culture and a non-punitive environment based on the system and not on the individual.

Originality/value

Safety is recognized as a key aspect of service quality, thus measuring the HSC can help establish an improvement plan. In Algerian health facilities, this study is considered the first to examine perceptions in this particular area. The current results provide a baseline of strengths and opportunities for healthcare safety improvement, allowing the managers of this type of facilities to take steps that are more effective.

2018
MCHEBILA. Simultaneous evaluation of safety integrity’s performance indicators with a generalized implementation of common cause failures. Process Safety and Environmental Protection [Internet]. 2018;2018 (117(7) :214-222. Publisher's VersionAbstract
The average unavailability and the average unconditional failure intensity of safety-instrumented systems represent the main performance indicators of safety integrity. This paper employs an approach based on the exploitation of the availability expression to obtain both performance measures in a simultaneous and straightforward way for any KooN configuration. The implementation of such an approach is generalized to take into account the contribution of common cause failures using any parametric model. The validation of the obtained results is verified through their application using several architectures and using Beta Factor and Binomial Failure Rate models to handle such type of dependent events. Therefore, the contribution of this paper lies in proposing one single formula that can be used to estimate the two main safety integrity’s performance indicators for any KooN architecture using any kind of common cause failures parametric model.
SELLAMI I, NAIT-SAID R, CHETEHOUNA K, IZARRA C, Zidani F. Quantitative consequence analysis using Sedov-Taylor blast wave model. Part II: Case study in an Algerian gas industry. Process Safety and Environmental Protection [Internet]. 2018;2018 (116(5) :771-779. Publisher's VersionAbstract

In the oil and gas industry, it is common to use gas liquefaction that allows storage and transport of large quantities of LNG and LPG. One of the main disadvantages of this storage mode is the BLEVE risk, which remains a major concern for risk decision-makers. In order to prevent the occurrence of this risk and reduce its impact, risk analysts often use quantitative risk analysis (QRA), which is based on the understanding and quantification of the accidental phenomena and their consequences (overpressure, thermal radiation, toxicity dose). QRA is a rigorous and advanced approach that requires reliable data in order to obtain a good estimate and control of risks. The main objective of this paper (Part II) is to integrate the Sedov-Taylor model developed in Part I into the QRA approach in order to evaluate BLEVE blast effect, and illustrate it with a case study on a pressurized LPG accumulator located in the MPP3-plant of SONATRACH company in the Hassi R'Mel gas field (the largest gas field in Algeria). A parametric analysis of the fuel mass, temperature at failure and rupture pressure is carried out to study their influence on the evolution of BLEVE overpressure. In addition, the evaluation of BLEVE thermal effect is performed in order to better realize an exhaustive QRA. Through this application, the results show the great relevance of the Sedov-Taylor model in the consequence analysis and also in the development of process safety recommendations.

SELLAMI I, NAIT-SAID R, IZARRA C, CHETEHOUNA K, Zidani F. Quantitative consequence analysis using Sedov-Taylor blast wave model. Part I: Model description and validation. Process Safety and Environmental Protection [Internet]. 2018;2018 (116(5) :763-770. Publisher's Version
SELLAMI I, MANESCAU B, CHETEHOUNA K, IZARRA C, NAIT-SAID R, Zidani F. BLEVE fireball modeling using Fire Dynamics Simulator (FDS) in an Algerian gas industry. Journal of Loss Prevention in the Process Industries [Internet]. 2018;2018 (54(7) :69-84. Publisher's VersionAbstract

BLEVE is one of major accidents observed in gas industry causing severe damage to people and environment. Its effects are manifested in three ways: shock wave propagation, fireball radiation and fragments projection. To assess these effects, risk decision-makers often use Quantitative Risk Analysis (QRA). In most cases, QRA data are obtained from empirical correlations. However, these correlations are not very satisfactory because they generally overestimate BLEVE effects and do not take into account geometry effects. In order to overcome the limitations of these empirical approaches, CFD modeling appears as a powerful tool able to provide more accurate data to better realize QRA. In this paper, the objective is to develop a CFD methodology in order to predict BLEVE thermal effects. Numerical simulations are carried out using the CFD code FDS. A sensitivity analysis of numerical models is performed in order to choose the right parameters allowing to model the fireball dynamics. The models retained are based on a single-step combustion using EDC model coupled with a LES turbulence model. Predictions show good agreement in comparison with results issued from three large-scale experiments. Furthermore, a case study on a propane accumulator in an Algerian gas processing unit is carried out.

MCHEBILA. Bayesian Networks for Frequency Analysis in Dependability. J Fail. Anal. and Preven [Internet]. 2018;2018 (18) :538–544. Publisher's VersionAbstract
The high suppleness of Bayesian networks has led to their wide application in a variety of dependability modeling and analysis problems. The main objective of this paper is to extend the use of such powerful tool to estimate the occurrence frequency of failures and consequences in a straightforward way. Such extension is based on the employment of a transformation operator to substitute the original terms with matrices that hold the full dependability description of the corresponding element. Two simple case studies in reliability and safety contexts are treated using the suggested method whose results are validated through their comparison to the corresponding results of other classical dependability techniques.
2016
CHIREMSEL Z, NAIT-SAID R, CHIREMSEL R. Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network. J Fail. Anal. and Preven [Internet]. 2016;16 :747–760. Publisher's VersionAbstract
Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS.
2015
MCHEBILA, INNAL F. Generalized analytical expressions for safety instrumented systems' performance measures: PFDavg and PFH”. Journal of Loss Prevention in the Process Industries [Internet]. 2015;34 (3) :167-176. Publisher's VersionAbstract
Safety Instrumented Systems (SIS) constitute an indispensable element in the process of risk reduction for almost all of nowadays' industrial facilities. The main purpose of this paper is to develop a set of generalized and simplified analytical expressions for two commonly employed metrics to assess the performance of SIS in terms of safety integrity, namely: the Average Probability of Failure on Demand (PFDavg) and the Probability of Dangerous Failure per Hour (PFH). In addition to the capability to treat any K-out-of-N architecture, the proposed formulas can smoothly take into account the contributions of Partial Stroke Testing (PST) and Common Cause Failures (CCF). The validity of the suggested analytical expressions is ensured through various comparisons that are carried out at different stages of their construction.
CHIREMSEL Z, NAIT-SAID R, CHIREMSEL R. Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network. J Fail. Anal. and Preven [Internet]. 2015;(16) :747–760. Publisher's VersionAbstract
Safety instrumented systems (SISs) are used in the oil and gas industry to detect the onset of hazardous events and/or to mitigate their consequences to humans, assets, and environment. A relevant problem concerning these systems is failure diagnosis. Diagnostic procedures are then required to determine the most probable source of undetected dangerous failures that prevent the system to perform its function. This paper presents a probabilistic fault diagnosis approach of SIS. This is a hybrid approach based on fault tree analysis (FTA) and Bayesian network (BN). Indeed, the minimal cut sets as the potential sources of SIS failure were generated via qualitative analysis of FTA, while diagnosis importance factor of components was calculated by converting the standard FTA in an equivalent BN. The final objective is using diagnosis data to generate a diagnosis map that will be useful to guide repair actions. A diagnosis aid system is developed and implemented under SWI-Prolog tool to facilitate testing and diagnosing of SIS.