Industrial fire simulation and uncertainty associated with the Emission Dispersion Model

Chettouh, Samia; Hamzi, Rachida; Innal, Fares; Haddad, Djamel

doi:10.1007/s10098-014-0792-x

Cited by 9 publications

(11 citation statements)

References 22 publications

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“…The estimate of the contents in liquefied petroleum gas (LPG) is 3 percent mole with 0.75 kg/cm 2 and 5 percent mole with 0.95 kg/cm 2 (Tseng et al , 2012; Chettouh et al , 2014; Cochet, 2005). Data used to feed ALOHA are given in Table VIII.…”

Section: Resultsmentioning

confidence: 99%

Contribution of the lessons learned from oil refining accidents to the industrial risks assessment

Chettouh

Hamzi

Chebila

2018

MEQ

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Purpose The purpose of this paper is to employ lessons learned from the industrial accidents in Skikda refinery during the period from 2005 to 2016 as input data for the numerical simulation of risk consequences to identify the exposed areas to the various effects of industrial accidents. Design/methodology/approach In order to assess how the lessons learned can contribute to modeling the accidents effects in the refining activities, this paper presents a combined statistical/dynamic approach that combines two main tools, namely, lessons learned from petroleum refining in Algeria and Areal Locations of Hazardous Atmospheres software. Findings The results showed that fire is the most frequent accident at Skikda refinery that is mainly caused by equipment failures with a frequent involvement of crude oil and LNG. The NO2 toxic effects are unacceptable. This means that in the case of a similar accident, the entire population will be exposed to an intolerable concentration of NO2. Therefore, people must be relocated to a safer place. The results indicate that the concentration threshold can be met beyond the distance of 1 km. Research limitations/implications Due to the economic importance of Skikda refinery and the absence of data related to the accidents in the refineries of Algiers, Arzew and Hassi Messaoud, this study is limited to the statistical analysis of accidents related to Skikda refinery. Practical implications This approach makes the risk assessment more practical and effective for the appropriate utilization of safety barriers and for the whole decision-making process. Originality/value This work presents a review paper of accidents that occurred in the oil-refining sector in Algeria, whose objective is learning lessons from past accidents history, by identifying their immediate causes and effects on personnel, equipment and environment in order to propose prevention measures. The novelty of this work is highlighted by the fact that this statistical analysis of oil and gas refining accident is realized for the first time in Algeria. This is due to the difficulty of obtaining data on accidents in the Algerian refining sector; for this reason, the authors have limited the study to the Skikda refinery.

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Section: Resultsmentioning

confidence: 99%

Contribution of the lessons learned from oil refining accidents to the industrial risks assessment

Chettouh

Hamzi

Chebila

2018

MEQ

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“…In this work, uncertainties are introduced to the system by utilizing a Monte Carlo simulation technique, which is widely used for uncertainty analysis (Chettouch et al 2014;Kazantzi et al 2013). The simulation will then re-evaluate the sustainability performance of each candidate scenario after the uncertainties have been introduced.…”

Section: Uncertainty Incorporation and Optimal Strategy Developmentmentioning

confidence: 99%

Sustainability enhancement under uncertainty: a Monte Carlo-based simulation and system optimization method

Liu

Huang

2015

Clean Techn Environ Policy

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Known methods for sustainability enhancement are typically scenario based, and the uncertainty of surrounding available data and information is usually not addressed holistically, due to inherent problem complexity. Thus the solutions identified by those methods could be not sufficiently effective in many industrial applications. In this paper, we introduce a Monte Carlo-based simulation and system optimization method for deriving sustainability enhancement strategies, where uncertainties are systematically taken into account. The methodological efficacy is illustrated through the study of an industrial sustainability enhancement problem involving a number of sectors.

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“…The obtained result is a hierarchization of the input parameters depending on their contribution to the overall uncertainty. The SA, then allows model users to be more informed about the confidence that can be placed in model results and hence becomes a quality insurance factor (Chettouh et al, 2014).…”

Section: Global and Local Sensitivity Analysis Of Edmmentioning

confidence: 99%

“…The effects of these pollutants have increased significantly and can be short-and long-term with high concentrations significantly affecting human health and air quality (Dadashzadeh et al, 2014;Poudyal et al, 2012;Srinivas et al, 2016). To assess these effects, the so-called emission dispersion model (EDM) was developed by (Chettouh et al, 2014;Hamzi, 2008). The EDM is a deterministic model that takes into account all the phenomena associated with a pollutant in an atmosphere during fire, including diffusion, transport and chemical kinetics in order to estimate its concentration over a given area (Chettouh et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…To assess these effects, the so-called emission dispersion model (EDM) was developed by (Chettouh et al, 2014;Hamzi, 2008). The EDM is a deterministic model that takes into account all the phenomena associated with a pollutant in an atmosphere during fire, including diffusion, transport and chemical kinetics in order to estimate its concentration over a given area (Chettouh et al, 2014). The results are deterministic estimates and the analysis does not provide accurate information about the modeled event due to the non-consideration of the associated uncertainty and the sensitivity analysis (SA) to the various involved parameters.…”

Section: Introductionmentioning

confidence: 99%

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Global and local sensitivity analysis of the Emission Dispersion Model input parameters

Chettouh

2021

WJSTSD

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PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

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Industrial fire simulation and uncertainty associated with the Emission Dispersion Model

Cited by 9 publications

References 22 publications

Contribution of the lessons learned from oil refining accidents to the industrial risks assessment

Contribution of the lessons learned from oil refining accidents to the industrial risks assessment

Sustainability enhancement under uncertainty: a Monte Carlo-based simulation and system optimization method

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

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