Risk mitigation in production facilities has been an issue of great interest for decades, especially in activities which represent a serious hazard to human health, environment and industrial plants. Dust explosions are a major hazard in many industrial processes: only in the first part of 2019 (January–June) 34 dust explosions, mainly due to organic powders, occurred worldwide. An explosion may take place whenever there is the presence of combustible dusts, which are frequently generated by activities such as grinding, crushing, conveying and storage. Currently, a relatively expensive experimental test, carried out into a 20-L Siwek apparatus, is used to address the order of magnitude (class) of explosive dust: this piece of information is referred to as the deflagration index, Kst. At the current state, only a few pioneering models have been developed in order to predict the value of the Kst as a function of some relevant properties of the dust: e.g. particle size distribution (PSD), humidity, thermal conductivity, etc‥ Most of these models condense the information about the PSD of a given dust into an average value, referred to as D50. In this work, a kinetic free mathematical model aimed at predicting the deflagration index for organic dusts is presented. This model, unlike the older ones, considers the whole particle size distribution for the computation of the deflagration index. In order to be implemented, only a single experimental Kst value (which works as a reference) and a particle size analysis on the dust are required. The model was validated using the whole granulometric distribution of three different organic powders (fosfomycin, sugar and niacin). In addition, the same estimations were done by considering only the D50 data. It was noticed that, for highly polydispersed dusts, results were less accurate with respect to those obtained using the complete PSD, highlighting the importance of considering a complete granulometric distribution for process safety purposes.
Kinetic free mathematical model for the prediction of Kst values for organic dusts with arbitrary particle size distribution
Scotton M. S.;Barozzi M.;Copelli S.
2020-01-01
Abstract
Risk mitigation in production facilities has been an issue of great interest for decades, especially in activities which represent a serious hazard to human health, environment and industrial plants. Dust explosions are a major hazard in many industrial processes: only in the first part of 2019 (January–June) 34 dust explosions, mainly due to organic powders, occurred worldwide. An explosion may take place whenever there is the presence of combustible dusts, which are frequently generated by activities such as grinding, crushing, conveying and storage. Currently, a relatively expensive experimental test, carried out into a 20-L Siwek apparatus, is used to address the order of magnitude (class) of explosive dust: this piece of information is referred to as the deflagration index, Kst. At the current state, only a few pioneering models have been developed in order to predict the value of the Kst as a function of some relevant properties of the dust: e.g. particle size distribution (PSD), humidity, thermal conductivity, etc‥ Most of these models condense the information about the PSD of a given dust into an average value, referred to as D50. In this work, a kinetic free mathematical model aimed at predicting the deflagration index for organic dusts is presented. This model, unlike the older ones, considers the whole particle size distribution for the computation of the deflagration index. In order to be implemented, only a single experimental Kst value (which works as a reference) and a particle size analysis on the dust are required. The model was validated using the whole granulometric distribution of three different organic powders (fosfomycin, sugar and niacin). In addition, the same estimations were done by considering only the D50 data. It was noticed that, for highly polydispersed dusts, results were less accurate with respect to those obtained using the complete PSD, highlighting the importance of considering a complete granulometric distribution for process safety purposes.
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