Experimental Investigation and Data-driven Modeling of the Minimum Ignition Temperature of Corn and Iron Dust

Combustible dust is often a highly energetic substance and frequently found in the process industries. It not only poses occupational safety hazards such as suffocation or lung disorders for exposed people but is often extremely explosible in ignition sensitive environment. This probability of ignition and subsequent explosion can be assessed and minimized with in-depth knowledge of controlling parameters/physical properties that trigger the ignition. This research considers the minimum ignition temperature (MIT), which is the control parameter for explosion risk assessment. MIT relies on multiple factors, such as moisture content, particle size, dust concentration, dispersion pressure, humidity, and environmental temperature. However, this research is focused on experimental analysis of particle size along with the data-driven modelling of the MIT based on the synergistic effect of dispersion pressure and concentration. In this study, the ignition of corn (organic) and iron (inorganic) dust clouds were analyzed using a Godbert Greenwald furnace for different combinations of dispersion pressure and concentrations. Test findings revealed that the minimum ignition temperature rises with the increasing particle size. However, the minimum ignition temperature decreases with increased dispersion pressure and concentration until a specific value known as the optimal value for ignition. Moreover, this work focuses on a statistical approach of polynomial surface fitting to forecast the MIT based on the combined impact of concentration and dispersion pressure on MIT for corn dust in a real-time experiment. The minimum value of the Bayesian information criterion (BIC) was used to select the most appropriate polynomial model due to its authenticity and strong reputation. An artificial neural network (ANN) is also used as a predictive tool to develop a model that can forecast the MIT with a defined combination of dispersion pressures and corn dust concentrations. For the training, validation, and test phases involving the corn dust, R2 values are around 1.0, i.e., 0.9863, 0.9930, and 0.9893, respectively. The overall R2 value was 0.9875 for the proposed network