Asphyxiation risk due to Oxygen Deficiency Hazard (ODH) can be assessed implementing predictive models of indoor oxygen (O2) level. In the literature, all the models estimating the oxygen concentration by volume (C02) in a working environment are based on a deterministic approach and provide a single-point estimate of C02 as output. However, deterministic model outputs can be uncertain since they can be influenced by a lack of knowledge or incompleteness of information about the exact value of ODH determinants, and/or because mathematical equations could not completely characterize ODH. For these reasons, this paper proposes a study for introducing a stochastic approach in ODH assessment and for performing uncertainty and sensitivity analyses of two existing models. To achieve these objectives, the ODH models were implemented in Microsoft® Excel spreadsheets and @RISK by Palisade was utilized. Some realized cases and simulations are discussed, showing the most interesting results in terms of possible indoor C02 values, their likelihood of occurrence, and the inputs having more effect on the O2 level uncertainty. Attention is also given to the description of implications for safety managers and decision-makers.
Uncertainty and sensitivity analyses of Models for assessing oxygen deficiency hazard: Preliminary results
Stefana E.;Marciano F.;Cocca P.
2020-01-01
Abstract
Asphyxiation risk due to Oxygen Deficiency Hazard (ODH) can be assessed implementing predictive models of indoor oxygen (O2) level. In the literature, all the models estimating the oxygen concentration by volume (C02) in a working environment are based on a deterministic approach and provide a single-point estimate of C02 as output. However, deterministic model outputs can be uncertain since they can be influenced by a lack of knowledge or incompleteness of information about the exact value of ODH determinants, and/or because mathematical equations could not completely characterize ODH. For these reasons, this paper proposes a study for introducing a stochastic approach in ODH assessment and for performing uncertainty and sensitivity analyses of two existing models. To achieve these objectives, the ODH models were implemented in Microsoft® Excel spreadsheets and @RISK by Palisade was utilized. Some realized cases and simulations are discussed, showing the most interesting results in terms of possible indoor C02 values, their likelihood of occurrence, and the inputs having more effect on the O2 level uncertainty. Attention is also given to the description of implications for safety managers and decision-makers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
