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The purpose of this paper is to employ lessons learned from the industrial accidents in Skikda refinery during the period from 2005 to 2016 as input data for the numerical simulation of risk consequences to identify the exposed areas to the various effects of industrial accidents.

In order to assess how the lessons learned can contribute to modeling the accidents effects in the refining activities, this paper presents a combined statistical/dynamic approach that combines two main tools, namely, lessons learned from petroleum refining in Algeria and Areal Locations of Hazardous Atmospheres software.

The results showed that fire is the most frequent accident at Skikda refinery that is mainly caused by equipment failures with a frequent involvement of crude oil and LNG. The NO₂ toxic effects are unacceptable. This means that in the case of a similar accident, the entire population will be exposed to an intolerable concentration of NO₂. Therefore, people must be relocated to a safer place. The results indicate that the concentration threshold can be met beyond the distance of 1 km.

Due to the economic importance of Skikda refinery and the absence of data related to the accidents in the refineries of Algiers, Arzew and Hassi Messaoud, this study is limited to the statistical analysis of accidents related to Skikda refinery.

This approach makes the risk assessment more practical and effective for the appropriate utilization of safety barriers and for the whole decision-making process.

This work presents a review paper of accidents that occurred in the oil-refining sector in Algeria, whose objective is learning lessons from past accidents history, by identifying their immediate causes and effects on personnel, equipment and environment in order to propose prevention measures. The novelty of this work is highlighted by the fact that this statistical analysis of oil and gas refining accident is realized for the first time in Algeria. This is due to the difficulty of obtaining data on accidents in the Algerian refining sector; for this reason, the authors have limited the study to the Skikda refinery.

The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities.

A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model.

A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources.

A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality.

The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

The main objective of safety instrumented systems (SISs) is to maintain a safe condition of a facility if hazardous events occur. However, in some cases, SIS's can be activated prematurely, these activations are characterized in terms of frequency by a Spurious Trip Rate (STR) and their occurrence leads to significant technical, economic and even environmental losses. This work aims to propose an approach to optimize the performances of the SIS by a multi-objective genetic algorithm. The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

The optimization of SIS performances is performed using the multi-objective genetic algorithm by minimizing their probability of failure on demand PFDavg, Spurious Trip Rate (STR) and Life Cycle Costs (LCCavg). A set of constraints related to maintenance costs have been established. These constraints imply specific maintenance strategies which improve the SIS performances and minimize the technical, economic and environmental risks related to spurious shutdowns. Validation of such an approach is applied to an Emergency Shutdown (ESD) of the blower section of an industrial facility (RGTE- In Amenas).

A case study concerning a safety instrumented system implemented in the RGTE facility has shown the great applicability of the proposed approach and the results are encouraging. The results show that the selection of a good maintenance strategy allows a very significant minimization of the PFDavg, the frequency of spurious trips and Life Cycle Costs of SIS.

The maintenance strategy defined by the system designer can be modified and improved during the operational phase, in particular safety systems. It constitutes one of the least expensive investment strategies for improving SIS performances. It has allowed a considerable minimization of the SIS life cycle costs; PFDavg and the frequency of spurious trips.