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Document Description
TitleAn integrated approach for assessing human health risk in process facility
AuthorSaif, Sadia.
DescriptionThesis (M.Eng.)--Memorial University of Newfoundland, 2010. Engineering and Applied Science
Paginationxx, 92 leaves : col. ill.
SubjectChemical plants--Risk assessment; Chemical processes--Safety measures--Planning; Chemical workers--Health risk assessment; Health risk assessment
Degree GrantorMemorial University of Newfoundland. Faculty of Engineering and Applied Science
DisciplineEngineering and Applied Science
NotesIncludes bibliographical references (leaves 79-78)
AbstractChemical process industries are often prone to undesired incidences and accidents. Release of toxic chemicals is one of such incidences which may lead to human health hazard resulting in potential loss in process facility. In order to prevent these unwanted health effects process safety management programmes (PSM) are adopted. Process safety management involves a systematic evaluation of hazards and necessary measures to mitigate them. Continuous monitoring and effective approaches for risk modeling may prevent these catastrophic situations. The present study is conducted by developing the methodology to assess the human health risk in process facility using quantitative methods, available data and standards. -- Quantitative Risk Assessment (QRA) is a process of identifying and evaluating the risk. The application of QRA in process facility involves development of methods and techniques to assess and minimize the risk as well as to help analyzing the undesired incidences together with the related consequences. Two types of approaches of QRA are presently being used for human health risk assessment. One is deterministic approach and the other is probabilistic approach. Probabilistic approach provides better estimates in certain cases where uncertainties are involved. -- Probabilistic Risk Assessment (PRA) is a reliable method to quantify human health risk. This involves characterization of human health risk considering the uncertainty and variability of exposure parameters. Probabilistic analysis allows to gather information about the range and likelihood of exposure and helps decision makers to take further decision. In addition to that, Bayesian probability analysis has also been used for developing a risk model to characterize the human health risk. -- In this thesis an integrated approach to assess human health risk is described and applied for past and current exposure data directly extracted from secondary sources. First, the hazards were identified and represented based on chronic studies. Again, the mixed chemical exposure is analyzed using two established statistical methods and available epidemiological information. Two exposure-response models are developed applying these data. Subsequently, the toxicity of the chemicals are assessed applying BMD approach to derive the toxicity values, the toxicity score of the chemicals as well as a safe exposure level for workplace using experimental animal data. And, finally a risk model has been developed to quantify the human health risk applying the Bayesian Monte Carlo Analysis. This risk model predicts risk using past and current exposure data. The past exposure data is the mortality data of worker from the Clydach Wales nickel refinery and the current exposure considers the high risk operations (High temperature operations and feed preparation) in process facility. The risk model compares the human health risks from past and present nickel exposure. The sensitivity report is represented using the risk models and Advanced Monte Carlo Simulation of Latin Hypercube Sampling (LHS) which describes the relative importance of exposure parameters quantifying risk.
FormatImage/jpeg; Application/pdf
SourcePaper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifiera3301961
RightsThe author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
CollectionElectronic Theses and Dissertations
Scanning StatusCompleted
PDF File(11.21 MB) --
CONTENTdm file name103653.cpd