A. J. N. Akel, R. Patriarca, G. D. Gravio, G. Antonioni, N. Paltrinieri
{"title":"Business Intelligence for the Analysis of Industrial Accidents Based on Mhidas Database","authors":"A. J. N. Akel, R. Patriarca, G. D. Gravio, G. Antonioni, N. Paltrinieri","doi":"10.3303/CET2186039","DOIUrl":null,"url":null,"abstract":"Reducing the frequency and severity of accidents in industrial processes is a continuous open challenge. Learning from previous events represents a crucial instrument to ensure an improved design of industrial plants, especially considering the complexity arising in everyday operations. This article is grounded on a database of industrial accidents involving hazardous substances and materials. The Major Hazard Incident Data Service (MHIDAS) was developed in 1986 by the Health and Safety Executive (HSE) to provide a reliable source of data on major hazard incidents and to learn for the past accidents. The database has more than 9000 accident reports covering the periods from 1950 to the end of the 1990s caused by hazardous substances/materials. This paper aims are to provide an understanding of MHIDAS data through quantitative analyses that can be obtained by exploiting the information collected through appropriate data management tools. Therefore, Information Technology (IT) services such as Business Intelligence (BI) tools have been used in this research. The paper describes the process of creating a BI model for data management on MHIDAS database to generate useful information on previous industrial safety events, allowing a detailed search engine as well through any event stored in MHIDAS.","PeriodicalId":9695,"journal":{"name":"Chemical engineering transactions","volume":"9 1","pages":"229-234"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemical engineering transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3303/CET2186039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 4
Abstract
Reducing the frequency and severity of accidents in industrial processes is a continuous open challenge. Learning from previous events represents a crucial instrument to ensure an improved design of industrial plants, especially considering the complexity arising in everyday operations. This article is grounded on a database of industrial accidents involving hazardous substances and materials. The Major Hazard Incident Data Service (MHIDAS) was developed in 1986 by the Health and Safety Executive (HSE) to provide a reliable source of data on major hazard incidents and to learn for the past accidents. The database has more than 9000 accident reports covering the periods from 1950 to the end of the 1990s caused by hazardous substances/materials. This paper aims are to provide an understanding of MHIDAS data through quantitative analyses that can be obtained by exploiting the information collected through appropriate data management tools. Therefore, Information Technology (IT) services such as Business Intelligence (BI) tools have been used in this research. The paper describes the process of creating a BI model for data management on MHIDAS database to generate useful information on previous industrial safety events, allowing a detailed search engine as well through any event stored in MHIDAS.
期刊介绍:
Chemical Engineering Transactions (CET) aims to be a leading international journal for publication of original research and review articles in chemical, process, and environmental engineering. CET begin in 2002 as a vehicle for publication of high-quality papers in chemical engineering, connected with leading international conferences. In 2014, CET opened a new era as an internationally-recognised journal. Articles containing original research results, covering any aspect from molecular phenomena through to industrial case studies and design, with a strong influence of chemical engineering methodologies and ethos are particularly welcome. We encourage state-of-the-art contributions relating to the future of industrial processing, sustainable design, as well as transdisciplinary research that goes beyond the conventional bounds of chemical engineering. Short reviews on hot topics, emerging technologies, and other areas of high interest should highlight unsolved challenges and provide clear directions for future research. The journal publishes periodically with approximately 6 volumes per year. Core topic areas: -Batch processing- Biotechnology- Circular economy and integration- Environmental engineering- Fluid flow and fluid mechanics- Green materials and processing- Heat and mass transfer- Innovation engineering- Life cycle analysis and optimisation- Modelling and simulation- Operations and supply chain management- Particle technology- Process dynamics, flexibility, and control- Process integration and design- Process intensification and optimisation- Process safety- Product development- Reaction engineering- Renewable energy- Separation processes- Smart industry, city, and agriculture- Sustainability- Systems engineering- Thermodynamic- Waste minimisation, processing and management- Water and wastewater engineering