Purpose

The reliability prediction is among the most important objectives for achieving overall system performance, and this prediction carried out by anticipating system performance degradation. In this context, the purpose of this research paper is to development of methodology for the photovoltaic (PV) modules' reliability prediction taking into account their future operating context.

Design/methodology/approach

The proposed methodology is framed by dependability methods, in this regard, two methods of dysfunctional analysis were used, the Failure Mode and Effects Criticality Analysis (FMECA) method is carried out for identification of the degradation modes, and the Fault Tree Analysis (FTA) method is used for identification the causes of PV modules degradation and the parameters influencing its degradation. Then, based on these parameters, accelerated tests have been used to predict the reliability of PV modules.

Findings

The application of the proposed methodology on PWX 500 PV modules' in different regions of Algeria makes it possible to predict its reliability, taking into account the future constraints on its operation. In this case, the temperature and relative humidity vary from one region to another was chosen as constraints. The results obtained from the different regions confirms the reliability provided by the designer of the Saharan cities Biskra, In Salah, Tamanraset, and affirms this value for the two Mediterranean cities of Oran and Algiers.

Originality/value

The proposed methodology is developed for the reliability prediction of the PV modules taking into account their future operating context and, the choice of different regions confirms or disproves the reliability provided by the designer of the PV modules studied. This application confirms their performance within the framework of the reliability prediction.

In the context of the industrial process safety, the domino effect has become a topical issue for scientists and managers of companies given the diversity of factors that contributed to the aggravation of this phenomenon such as; proximity to industrial facilities, transport networks, development of industrial complexes, storage of hazardous substances and population growth. The purpose of this article is the MICDE method (Method of Identification and Characterization of Domino Effects) application on industrial zone of LPG storage in SONATRACH-Algeria Group for analyzes the domino effects caused by a major industrial accident.

Our study is adopted on the MICDE method which constitutes an aid in the integration of the domino effects problem in hazard studies and safety studies. In our application, it aims to formalize the points relating to the domino effects due to the BLEVE (Boiling Liquid Expanding Vapor Explosion) phenomenon of an LPG storage sphere.

The results obtained show that the hazardous equipment in the vicinity is seriously affected by the thermal and overpressure effect of the main accident, and may be seats in a new accident. The MICDE method is a promising method can be applied in several fields since it studies the phenomenon. This method facilitates decision-making in the prevention of domino effects for the sustainability facilities

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled.

In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

Renewable power generation facilities are constantly expanding due to the expected depletion of fossil fuels and the increasingly demanding policy of pollution control. Having said that, hydrogen is one of the promising energy sources. That said, hydrogen chain safety is an unescapable parameter that should continuously coexist with the development of hydrogen domain. In this context, this article presents a contribution to the risk analysis and evaluation of a complex hydrogen production system 'EGA-9000′ at CIEMAT (Centre for Research on Energy, Environment and Technology - Madrid, Spain). The methodology followed in this study revolves around the risk analysis process through a FAST (Functional Analysis System Technique) functional analysis method and a HAZOP (HAZard and Operability) dysfunctional analysis method. The evaluation of the thirty-three scenarios identified by the risk analysis shows that the studied system is insecure. Indeed, five scenarios at an unacceptable level of risk. And it is noted that the risk of fire and explosion is the major risk for all scenarios studied. To this end, safety measures (recommendation) have been proposed based on the weaknesses detected by the risk analysis carried out.

The industrial risk mapping is a topical problem in the field of risk management that attracts many researchers to develop risk matrices to ensure consultation between their actors. In this context, this paper aims to propose the principal component analysis (PCA) method as support for this consultation. Indeed, the use of PCA method is justified by its robustness for aggregate initial data associated with industrial risks as principal factors and ranking of this risk in terms of their criticalities in risk matrices. However, the aggregation of initial data on industrial risks by the main factors, in some cases, leads to inaccuracies which make it difficult to classify certain risks. This paper proposes two variants of PCA method to solve this inaccuracy and succeeds in classifying risks according to their respective criticalities, namely PCA-Improved (PCA-I) and PCA-I-Fuzzy (PCA-IF). The results come from the PCA application and its proposed variants (PCA-I and PCA-IF) on an example of accident scenarios ranking. We have established a scientific basis for the capitalization of mapping tool for consultation and decision support to industrial risk managers.