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This paper aims to investigate the failure modes present in the dangerous operations and modify processes. After the identification of potential failures, prioritization will be made to rank them for quick attention and immediate correction in the operation/modification.

The methodology utilizes the strength of hazard and operability (HAZOP) study and failure mode and effect analysis (FMEA) to identify and prioritize the hidden potential failures present in the system. Fuzzy linguistics approach has been applied to enhance the performance of the study. This study correlates HAZOP study, FMEA and fuzzy system.

This proposed technique is used to find the better ranking of the failure modes. Risk priority number and fuzzy weighted geometric mean of the risk factors are used to improve the performance of the risk evaluation. This makes the assessment easier to be carried out effectively for critically operated systems.

This proposed approach could be very useful for the systematic and rational risk assessment of passive systems.

A new decision‐making approach is used to prioritize the failure modes present in the process industry with use of a case study.

This paper aims to conduct a detailed analysis of the industrial practices currently being used in the geothermal energy industry and to determine whether they are contributing to any limitations. A HAZOP-based upgradation model for improvement in existing industrial practices is proposed to ensure the removal of inefficient conventional practices. The HAZOP-based upgradation model examines the setbacks, identifies its causes and consequences and suggests improvement methods comprising of modern-day technology.

This paper proposed a HAZOP-based upgradation model for improvement in existing industrial practices. The proposed HAZOP model identifies the drawbacks brought on by conventional practices and suggests improvements.

The study reviewed the challenges geothermal power plants currently face due to conventional practices and suggested a total of 22 upgradation recommendations. From those, a total of 11 upgradation modules comprising modern digital technology and Industry 4.0 elements were proposed to improve the existing practices in the geothermal energy industry. Autonomous robots, augmented reality, machine learning and Internet of Things were identified as useful methods for the upgradation of the existing geothermal energy system.

If proposed recommendations are incorporated, the efficiency of geothermal energy generation will increase as cumulating setbacks will no longer degrade the work output.

The proposed recommendation by the study will make way for Industry 4.0 integration with the geothermal energy sector.

The paper uses a proposed HAZOP-based upgradation model to review issues in existing industrial practices of the geothermal energy sector and recommends solutions to overcome operability issues using Industry 4.0 technologies.

Algeria occupies an important place in all of the oil-producing countries. However, in recent years, industrial facilities have experienced a rapid increase in the number of major industrial accidents and calamities, where fires and explosions have caused impacts and severe effects on people, property and the environment. Therefore, this paper aims to analyze the risks involved and assess the performance of the technical barriers at the thermo-hydraulic level of facilities at the level of the refinery of Skikda.

The most effective methods in this field of activity are the systematic analyses of deviations or failures. As working tools, hazard and operability (HAZOP) method and fault tree analysis (FTA) were used to identify the various possible risks that could lead to undesirable phenomena, with the Topping unit. Using this type of analysis, it is possible to understand what the most risky process steps are and, where appropriate, to propose appropriate corrective and preventive measures. HAZOP is dedicated to the analysis of the risks of thermo-hydraulic systems and to control drift parameters such as pressure, temperature and flow. Unfortunately, with its limitations to the detection of leaks, the authors completed the study by FTA, working with a predetermined set of process parameters associated with system operation (compression, temperature, flow, etc.). Each of these parameters are combined with a series of possible potential deviations (too much, little, inverse, etc). For each analyzed equipment, the relevant parameters are selected and combined with the relevant deviations. For each parameter of the deviant process, the causes and consequences are determined.

The combination of these two methods has allowed to highlight the various possible risks that could lead to undesirable phenomena, to respond to industrial expectations or even to manage the operation process safely. Thus, it enabled the authors to detect the weaknesses of the process. This allowed the authors to reinforce the technical safety barriers of the functional links of the process and propose appropriate, corrective and preventive measures.

The work intends to reflect the real situation in which companies face risks. The originality of the work is represented by the combination of two methods: HAZOP and the FTA. This association allowed the authors to identify areas of weakness to set priorities for action by the company, through organizational, technical and human solutions, while engaging in a process of continuous improvement.

The international nuclear community continues to face the challenge of managing both the legacy waste and the new wastes that emerge from ongoing energy production. The UK is in the early stages of proposing a new convention for its nuclear industry, that is: waste minimisation through closely managing the radioactive source which creates the waste. This paper proposes a new technique (called waste and source material operability study (WASOP)) to qualitatively analyse a complex, waste‐producing system to minimise avoidable waste and thus increase the protection to the public and the environment.

WASOP critically considers the systemic impact of up and downstream facilities on the minimisation of nuclear waste in a facility. Based on the principles of HAZOP, the technique structures managers' thinking on the impact of mal‐operations in interlinking facilities in order to identify preventative actions to reduce the impact on waste production of those mal‐operations.'

WASOP was tested with a small group of experienced nuclear regulators and was found to support their qualitative examination of waste minimisation and help them to work towards developing a plan of action.

Given the newness of this convention, the wider methodology in which WASOP sits is still in development. However, this paper communicates the latest thinking from nuclear regulators on decision‐making methodology for supporting waste minimisation and is hoped to form part of future regulatory guidance. WASOP is believed to have widespread potential application to the minimisation of many other forms of waste, including that from other energy sectors and household/general waste.

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.

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.

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.

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.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The objective of this study is to evaluate the equipment risk before and after SEMI S2‐93A implementation, thus providing a guideline for safety improvement. Semiconductor Plant A located in Taiwan’s Hsinchu Science Based Industrial Park with 147 manufacturing machines was used for risk assessment. The study was carried out in three steps. First, a preliminary hazard analysis was conducted. A detailed process safety evaluation was conducted (Hazard and Operability Study, HAZOP); and finally, the equipment risk comparison before and after Semiconductor Equipment Manufacturing Instruction (SEMI S2‐93A) implementation. The preliminary hazard analysis results showed high risk in 21.77 per cent of the manufacturing machines under risk assessment at Plant A. The largest percentage existed in the Diffusion Department. The machine types specified by the hazardous work site review and inspection according to Article 26 of Labor Inspection Regulation (the machines that use such chemicals as, SiHi, HF, HCL, etc. and that are determined to be highly hazardous through preliminary hazard analysis) were added to the detailed process analysis and evaluation. In the third part of this evaluation, the machines at Plant A used for detailed process safety assessment were divided into two groups based on the manufacturing data before and after 1993. The severity, possibility, and actual accident analysis before and after SEMI S2‐93A implementation were compared. The semiconductor Equipment Manufacturing Instruction (SEMI S2‐93A) implementation can reduce the severity and possibility of hazard occurrence.