Trends in accidents, disasters and risk sources in Europe

Computer Vision for Kinetic Analysis of Lab- and Process-Scale Mixing Phenomena

A software platform for the computer vision-enabled analysis of mixing phenomena of relevance to process scale-up is described. By bringing new and known time-resolved mixing metrics under one platform, hitherto unavailable comparisons of pixel-derived mixing metrics are exemplified across non-chemical and chemical processes. The analytical methods described are applicable using any camera and across an appreciable range of reactor scales, from development through to process scale-up. A case study in nucleophilic aromatic substitution run on a 5 L scale in a stirred tank reactor shows how camera and offline concentration analyses can be correlated. In some cases, it can be shown that camera data hold the power to predict reaction progress.

Proposal of Risk Identification Methodology Using the Prompt List on the Example of an Air Carrier

Risk management and safety are often-mentioned topics that dominate, especially in this period. The Safety Management System (SMS) is based on risk management, the implementation of which is required in air transport as well as in other modes of transportation. The fact is that the safety of passengers and staff is a priority for every airline, and such individuals are exposed to some common groups of risks. Risk identification is vital in successful risk management as only identified risks can be managed. Based on a study of published frameworks and standards, a risk identification methodology was designed using a prompt list of a logically arranged list of risks concerning typical aviation activities and the specific environment in which the methodology operates. The prompt list was compiled based on a context analysis, which focused on internal and external contexts and on which the structure of the prompt list was based so that risks could be moved from the list to a risk register for further processing based on various criteria. The list contains risks from several areas (economic, ecologic, social, individual, business, marketing, etc.). The design of the prompt list itself also includes a proposal for a methodology for its compilation, use, and updating.

Analysis of accidents caused by human factors in the oil and gas industry using the HFACS-OGI framework

Objectives. Human factors have been identified as the most common causes of catastrophic accidents in the oil and gas industry. Therefore, this study aims to analyze human causal factors of accidents in the oil and gas industry using the human factors analysis and classification system for the oil and gas industry (HFACS-OGI) framework. Methods. This study involved quantitative data collection for 184 accident cases in the oil and gas industry that occurred from 2013 to 2017 from the International Association of Oil and Gas Producers (IOGP) database. The causal factors of these accidents were coded using the HFACS-OGI framework. Accident data were analyzed using descriptive statistics and the χ² test. Results. Study findings reveal that 23% of all accidents were recorded in 2013. Thirty-two percent of accidents occurred in Asia, while 69% of accidents were recorded in onshore locations. Contractors were involved in 86% of accidents, while 28% of accidents occurred during drilling, workover and well services. The contractor's work environment was the main human factor in 90% of accident cases. Conclusion. The HFACS-OGI framework proves to be a vital tool for robust accident analysis of human factors in the oil and gas industry.

Statistical analysis of major accidents in petrochemical industry notified to the major accident reporting system (MARS)

The European major accident reporting system (MARS) was created within the framework of European Union (EU) directive 82/501, the so-called "SEVESO" directive, and in order to register all the major industrial accidents notified to the European Union authorities from the member states. Statistical analysis of these accidents offers significant data to the understanding and prevention of industrial accidents. This paper makes an analysis of some characteristics of major accidents in the petrochemical sector included in MARS. The statistical analysis focused on the main categorization fields of the MARS short reports and additionally a refinement of the immediate causes of major accidents with focus on the organizational factors was attempted through the details provided in the full reports of the database.