Predictive Models for the Estimation of the Minimum Ignition Energy of Polydisperse Organic Dusts
Vitaloni, Linda
Melchiore, Alessandra
Scotton, Martina
Barozzi, Marco
Busini, Valentina
Copelli, Sabrina

How to Cite

Vitaloni L., Melchiore A., Scotton M., Barozzi M., Busini V., Copelli S., 2023, Predictive Models for the Estimation of the Minimum Ignition Energy of Polydisperse Organic Dusts, Chemical Engineering Transactions, 104, 103-108.


The process industry is a sector characterized by the sale of 50 % of its products in the form of powder and in which 80 % of the goods generated are made through a production system that involves the use of a powder. This sector massively employs solid materials and, using operations such as material transport, crushing, screening, sanding, trimming, feeding tanks and bins, storage of granular materials and many other activities, is very often characterized by the collateral emission of dusts. A similar scenario makes the risk of a dust explosion one of the major concerns of the process industry. In this context, to ensure the safety of people and infrastructures, it is crucial to obtain the parameters that characterize the explosiveness of the dust. Actually, these parameters are all determined experimentally, involving large economic costs, technical difficulties, and long dead times. This work focused on the estimation of one of these parameters, the Minimum Ignition Energy (MIE), which is considered to be one of the most important to assess the probability of having a dust explosion. Therefore, starting from the experimental test within a 1.2 L Hartmann tube, two new versions of a mathematical model capable of predicting the MIE for an organic powder were proposed. The models characterize the powder analysed through its particle size distribution and a few chemical-physical characteristics obtained from literature. Six organic powders were selected to validate the model (aspirin, cork, corn starch, sugar d50=135 µm, sugar d50=34 µm and wheat flour), with the intention of comparing the theoretical data obtained with literature experimental ones.