Corn dust is a highly energetic substance and frequently found in the food manufacturing industries. It not only poses occupational safety hazards such as suffocation or lung disorders for exposed persons but is often extremely explosible in ignition sensitive environment. This probability of explosion can be assessed and minimised with in-depth knowledge of controlling parameters/physical properties that trigger the ignition. This research takes into account 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. In this study, the ignition of corn dust clouds was analysed using a Godbert Greenwald furnace for different combinations of dispersion pressure and concentrations. Test findings revealed that the minimum ignition temperature rises with a decrease in particle size. However, the minimum ignition temperature decreases with increased dispersion pressure and concentration until a specific value known as 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. As soon as an appropriate estimation of this minimum ignition temperature of the combustible dust is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that point to prevent the explosion. The predictive results obtained from ANN were found to be good when compared with the polynomial surface fit. Several models with different numbers of neurons have been trained with different transfer functions. For the training, validation, and test phases, 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. The findings were considered to be acceptable as the overall value of R² was close to 1.0. The network obtained sufficiently comparable findings with the research conducted by Kasalova and Balog.

玉米粉尘是一种高能物质，经常在食品制造业中发现。它不仅对暴露的人员造成职业安全危害，例如窒息或肺部疾病，而且在着火敏感的环境中通常极易爆炸。可以通过深入了解触发点火的参数/物理特性来评估爆炸的可能性并将其最小化。本研究考虑了最低着火温度（MIT），这是爆炸风险评估的控制参数。麻省理工学院依靠多种因素，例如水分含量，粒径，粉尘浓度，分散压力，湿度和环境温度。在这个研究中，使用戈伯特·格林瓦尔德（Godbert Greenwald）炉分析了分散压力和浓度的不同组合对玉米尘云的着火。测试结果表明，最小着火温度随颗粒尺寸的减小而升高。然而，最小点火温度随着分散压力和浓度的增加而降低，直到一个已知的最佳值即最佳点火值。此外，这项工作集中在多项式表面拟合的统计方法上，该方法基于浓度和分散压力对玉米粉尘中MIT的MIT的综合影响来预测MIT。由于其真实性和良好的声誉，使用贝叶斯信息准则（BIC）的最小值来选择最合适的多项式模型。人工神经网络（ANN）也被用作开发模型的预测工具，该模型可以通过定义的分散压力和玉米粉尘浓度组合来预测MIT。一旦确定了可燃粉尘的最低起燃温度的适当估计值，就可以确保周围热表面的温度不会上升到该点以防止爆炸。与多项式曲面拟合相比，发现从ANN获得的预测结果很好。已经用不同的传递函数训练了具有不同数量神经元的几种模型。对于培训，验证和测试阶段，R 一旦确定了可燃粉尘的最低起燃温度的适当估计，便可以确保周围热表面的温度不会升高到该点以防止爆炸。与多项式曲面拟合相比，发现从ANN获得的预测结果很好。已经用不同的传递函数训练了具有不同数量神经元的几种模型。对于培训，验证和测试阶段，R 一旦确定了可燃粉尘的最低起燃温度的适当估计值，就可以确保周围热表面的温度不会上升到该点以防止爆炸。与多项式曲面拟合相比，发现从ANN获得的预测结果很好。已经用不同的传递函数训练了具有不同数量神经元的几种模型。对于培训，验证和测试阶段，R 已经用不同的传递函数训练了具有不同数量神经元的几种模型。对于培训，验证和测试阶段，R 已经用不同的传递函数训练了具有不同数量神经元的几种模型。对于培训，验证和测试阶段，R^2个值分别约为1.0，即0.9863、0.9930和0.9893。拟议网络的总R ²值为0.9875。由于R ^2的总值接近1.0，因此该发现被认为是可以接受的。该网络获得了与Kasalova和Balog进行的研究相当可比的发现。