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Apeldoorn, Netherlands

Bellamy L.J.,White Queen Safety Strategies | Damen M.,RIGO Research and Advies | Jan Manuel H.,National Institute for Public Health and Environment RIVM | Aneziris O.N.,Greek National Center For Scientific Research | And 2 more authors.
Reliability Engineering and System Safety | Year: 2014

The risk of a serious occupational accident per hour exposure was calculated in a project to develop an occupational risk model in the Netherlands (WebORCA). To obtain risk rates, the numbers of victims of serious occupational accidents investigated by the Dutch Labour inspectorate 1998-Feb 2004 were divided by the number of hours exposure for each of 64 different types of hazards, such as contact with moving parts of machines and falls from various types of height. The exposures to the occupational accident hazards were calculated from a survey of a panel of 30,000 from the Dutch working population. Sixty risk rates were then used to predict serious accidents for activity sectors and jobs in the Netherlands where exposures to the hazards for sectors or jobs could be estimated from the survey. Such predictions have been called "horoscopes" because the idea is to provide a quick look-up of predicted accidents for a particular sector or job. Predictions compared favourably with actual data. It is concluded that predictive data can help provide information about accidents in cases where there is a lack of data, such as for smaller sub groups of the working population. © 2014 Elsevier Ltd. Source

Bellamy L.J.,White Queen Safety Strategies
International journal of occupational safety and ergonomics : JOSE | Year: 2014

Since 2003, a project has been underway to analyse the most serious occupational accidents in The Netherlands. All the serious occupational accidents investigated by the Dutch Labour Inspectorate for the 12 years of 1998-2009 inclusive have been entered into a database, a total of 20 030 investigations. This database uses a model of safety barriers supported by barrier tasks and management delivery systems such that, when combined with sector and year information, trends in the data can be analysed for their underlying causes. The trend analyses show that while the number of victims of serious reportable accidents is significantly decreasing, this is due to specific sectors, hazards and underlying causes. The significant results could not easily be directly associated with any specific regulation or action undertaken in The Netherlands although there have been many different approaches to reducing accidents during the period analysed, which could be contributing to the effect. Source

Papazoglou I.A.,Greek National Center For Scientific Research | Aneziris O.,Greek National Center For Scientific Research | Bellamy L.,White Queen Safety Strategies | Ale B.J.M.,Bosboom Toussaint Plein 117 | Oh J.I.H.,Ministry of Social Affairs and Employment SZW
Risk Analysis | Year: 2015

Occupational risk rates per hour of exposure have been quantified for 63 occupational accident types for the Dutch working population. Data were obtained from the analysis of more than 9,000 accidents that occurred over a period of six years in the Netherlands and resulted in three types of reportable consequences under Dutch law: (a) fatal injury, (b) permanent injury, and (c) serious recoverable injury requiring at least one day of hospitalization. A Bayesian uncertainty assessment on the value of the risk rates has been performed. Annual risks for each of the 63 occupational accident types have been calculated, including the variability in the annual exposure of the working population to the corresponding hazards. The suitability of three risk measures-individual risk rates, individual annual risk, and number of accidents-is examined and discussed. © 2015 Society for Risk Analysis. Source

Bellamy L.J.,White Queen Safety Strategies | Mud M.,RPS | Manuel H.J.,Dutch National Institute for Public Health and the Environment RIVM | Oh J.I.H.,Ministry of Social Affairs and Employment SZW
Journal of Loss Prevention in the Process Industries | Year: 2013

In the Netherlands there are around 400 "Seveso" sites that fall under the Dutch Major Hazards Decree (BRZO) 1999. Between 2006 and 2010 the Dutch Labour Inspectorate's Directorate for Major Hazard Control completed investigations of 118 loss of containment incidents involving hazardous substances from this group. On the basis of investigation reports the incidents were entered in a tailor-made tool called Storybuilder developed for the Dutch Ministry of Social Affairs and Employment for identifying the dominant patterns of technical safety barrier failures, barrier task failures and underlying management causes associated with the resulting loss of control events. The model is a bow-tie structure with six lines of defence, three on either side of the central loss of containment event. In the first line of defence, failures in the safety barriers leading to loss of control events were primarily equipment condition failures, pre start-up and safeguarding failures and process deviations such as pressure and flow failures. These deviations, which should have been recovered while still within the safe envelope of operation, were missed primarily because of inadequate indication signals that the deviations have occurred. Through failures of subsequent lines of defence they are developing into serious incidents. Overall, task failures are principally failures to provide adequate technical safety barriers and failures to operate provided barriers appropriately. Underlying management delivery failures were mainly found in equipment specifications and provisions, procedures and competence. The competence delivery system is especially important for identifying equipment condition, equipment isolation for maintenance, pre-start-up status and process deviations. Human errors associated with operating barriers were identified in fifty per cent of cases, were mostly mistakes and feature primarily in failure to prevent deviations and subsequently recover them. Loss of control associated with loss of containment was primarily due to the containment being bypassed (72% of incidents) and less to material strength failures (28%). Transfer pipework, connections in process plant and relief valves are the most frequent release points and the dominant release material is extremely flammable. It is concluded that the analysis of a large number of incidents in Storybuilder can support the quantification of underlying causes and provide evidence of where the weak points exist in major hazard control in the prevention of major accidents. © 2013 Elsevier Ltd. Source

Manuel H.J.,National Institute of Public Health and the Environment RIVM | Kooi E.S.,National Institute of Public Health and the Environment RIVM | Bellamy L.J.,White Queen Safety Strategies | Mud M.L.,RPS Advies en Ingenieursbureau BV | Oh J.I.H.,Ministry of Social Affairs and Employment
Global Congress on Process Safety 2012 - Topical Conference at the 2012 AIChE Spring Meeting and 8th Global Congress on Process Safety | Year: 2012

Quantitative Risk Assessment (QRA) is used in the Netherlands and the UK for site permitting and land-use planning around industries with dangerous substances. For example, individual risk contours resulting from a QRA determine where houses and other types of constructions may exist. Therefore, both industry and regulators ask for models and data that are up to date. Data used for input includes the failure frequency of failure scenarios. In the past, updating frequencies required costly one-time investigations. As major hazard accidents are investigated by the Labor Inspectorate by decree, a possibility arises to use these accident investigation data for a continuing updating scheme. Recent major accidents in the UK and in the Netherlands were analyzed with the Storybuilder method. Underlying failure causes at operational and management levels were investigated and stored in a structured database. The amount of equipment present at plants was explored using various paths. Failure scenarios and frequencies can be updated on a regular basis by combining the accident data with the equipment data. Source

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