A model to assess dust explosion occurrence probability.
Dust handling poses a potential explosion hazard in many industrial facilities. The consequences of a dust explosion are often severe and similar to a gas explosion; however, its occurrence is conditional to the presence of five elements: combustible dust, ignition source, oxidant, mixing and confinement. Dust explosion researchers have conducted experiments to study the characteristics of these elements and generate data on explosibility. These experiments are often costly but the generated data has a significant scope in estimating the probability of a dust explosion occurrence. This paper attempts to use existing information (experimental data) to develop a predictive model to assess the probability of a dust explosion occurrence in a given environment. The pro-posed model considers six key parameters of a dust explosion: dust particle diameter (PD), minimum ignition energy (MIE), minimum explosible concentration (MEC), minimum ignition temperature (MIT), limiting oxygen concentration (LOC) and explosion pressure (Pmax). A conditional probabilistic approach has been developed and embedded in the proposed model to generate a nomograph for assessing dust explosion occurrence. The generated nomograph provides a quick assessment technique to map the occurrence probability of a dust explosion for a given environment defined with the six parameters.
Safety and Risk Engineering Group (SREG), Faculty of Applied Science, Memorial University, St. Johns, NL, Canada A1B 3X5.
Junaid
J
Hassan
Safety and Risk Engineering Group (SREG), Faculty of Applied Science, Memorial University, St. Johns, NL, Canada A1B 3X5. Electronic address: fikhan@mun.ca.
Faisal
F
Khan
Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Canada B3J2X4.
Paul
P
Amyotte
Safety and Risk Engineering Group (SREG), Faculty of Applied Science, Memorial University, St. Johns, NL, Canada A1B 3X5.
Refaul
R
Ferdous
D004391:Dust; D005107:Explosions; D008956:Models, Chemical; D015233:Models, Statistical; D010316:Particle Size; D011336:Probability; D011379:Prognosis; D017751:Safety Management; D013696:Temperature
D004391:Dust
Dust explosion; Dust explosion parameters; Dust explosion probability; Probabilistic approach