An improved layer of protection analysis based on a cloud model: Methodology and case study

Environmental risk assessment and corrective measures for the metal rolling industry

Identification, assessment, elimination, and control of hazards in every workplace represent the key to success as well as a critical part of any health and safety programme. Due to the complexity of the operations, equipment, and procedures in the rolling industry, this industry always involves some degree of hazard. Hence, it is necessary to apply an effective risk assessment method to manage hazards. In this research, environmental failure mode and effect analysis (E-FMEA) was used to identify possible risks, analyze environmental risks, and determine the highest risk priorities in an aluminium rolling industry. For this purpose, the production process, equipment, and tasks were examined. Stack emissions, as well as factory sewage, were analyzed according to the standard methods. Considering the results of the analysis and according to the expert panel comments, potential harmful factors were identified and classified regarding the possibility of occurrence, capability for discovery, pollution level, and intensity of their effect on the environment. Using the E-FMEA method, 210 aspects and environmental consequences were identified where 27 cases had a low risk, 148 had a moderate risk, and 35 had a high risk. For those classes of risks with a priority number above 118, correction and control operations were accomplished. The results of the repeated risk assessment approved that the protective measures have been satisfactorily effective; the control operations were performed well and the risk priority number (RPN) was mitigated to the low- or moderate-risk levels.

Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects

The construction of large renewable energy projects is characterized by the great uncertainties associated with their administrative complexity and their constructive characteristics. For proper management, it is necessary to undertake a thorough project risk assessment prior to construction. The work presented in this paper is based on a hierarchical risk structure identified by a group of experts, from which a Probabilistic Fuzzy Sets with Analysis Hierarchy Process (PFSAHP) was applied. This probabilistic analysis approach used expert opinion based on the Monte Carlo Method that allows for extracting more information from the original data. In addition, the coherence of the experts' opinions is assessed using a novel parameter known as Confidence Level, which allows for adjusting the opinions of experts and weighting their judgments regarding impact and probability according to their coherence. This model has the advantage of offering a risk analysis in the early stages of the management of renewable energy projects in which there is no detailed information. This model is also more accurate than the classic fuzzy methodology when working with complete distribution functions, whilst it avoids the loss of information that results from the traditional mathematical operations with Fuzzy numbers. To test the model, it was applied to a 250 MW photovoltaic solar plant construction project located in southeast of Spain (Region of Murcia). As a result of the application of the proposed method, risk rankings are obtained with respect to the cost, the time, the scope and from a general point of view of the project.