Data-driven models for predicting fire and explosion-related properties have been improved greatly in recent years using machine-learning algorithms. However, choosing the best machine learning approach is still a challenging task. Therefore, in this study, the predictability comparisons have been made with the different machine learning methods used to model the MIT for iron dust. The MIT of iron dust was determined using the Godbert-Greenwald furnace for seventy unique combinations of dispersion pressures and dust concentrations. The data has been divided into 'Training Set' and 'Testing Set'. The implementation and efficacy of machine learning and statistical approaches have been demonstrated through real-time experimental results. The support vector machines (SVM) regression models were trained with various kernel functions to enhance the performance of the resultant model. The cubic kernel function was found suitable for training SVMs. Besides, a feed-forward artificial neural network with the backpropagation algorithm and a polynomial surface fit model have also been developed to predict the MIT. For statistical phenomena, such as MIT, predictive modelling based on real-time experimental data is critical. If an accurate estimate of the combustible dust's minimum ignition temperature is confirmed, it is possible to ensure that the temperatures of the surrounding hot surfaces do not rise to that level, preventing an explosion. An overall comparison of predictive models has been given with unseen test data set. All the trained models yielded comparable results with unseen test data set. However, the SVM model with Bayesian optimizer approach can effectively assess the risk of ignition based on dust MIT under the influence of dispersion pressure and dust cloud concentration among all the approaches adopted in this study.

近年来，使用机器学习算法，预测火灾和爆炸相关属性的数据驱动模型得到了极大改进。然而，选择最好的机器学习方法仍然是一项具有挑战性的任务。因此，在本研究中，对用于模拟 MIT 铁尘的不同机器学习方法进行了可预测性比较。铁粉的 MIT 是使用 Godbert-Greenwald 炉对 70 种独特的分散压力和粉尘浓度组合确定的。数据被分为“训练集”和“测试集”。机器学习和统计方法的实施和有效性已通过实时实验结果得到证明。支持向量机 (SVM) 回归模型使用各种核函数进行训练，以提高最终模型的性能。发现三次核函数适合训练 SVM。此外，还开发了具有反向传播算法和多项式曲面拟合模型的前馈人工神经网络来预测 MIT。对于统计现象，如麻省理工学院，基于实时实验数据的预测建模至关重要。如果确定可燃粉尘的最低着火温度的准确估计值，则可以确保周围热表面的温度不会上升到该水平，从而防止爆炸。预测模型的总体比较已与未见的测试数据集进行了比较。所有经过训练的模型都产生了与看不见的测试数据集相当的结果。然而，在本研究采用的所有方法中，采用贝叶斯优化器方法的 SVM 模型可以有效地评估在分散压力和粉尘云浓度影响下基于粉尘 MIT 的点火风险。