A Model for Predicting the Auto-ignition Temperature using Quantitative Structure Property Relationship Approach

While flammable materials are operated in process industries, the electric equipments should be explosion-proof to reduce the possibility of a fire or an explosion. Auto-ignition temperature (AIT) of a flammable material is the primary characteristic in determining the specifications of these explosion-proof equipments. However, due to limitations on experiments, the AIT of a compound reported in different data compilations is very diverse, and the difference between different compilations was found to be higher than 300 K in many cases. Thus, an effective method to predict the AIT of flammable materials is indispensible in this regard. In this study, a model to predict the AIT of organic compounds is built by using the quantitative structure property relationship (QSPR) approach. This model is built from a set of 820 organic compounds, which are collected from the DIPPR database supported by American Institute of Chemical Engineer. This model is of four molecular descriptors: mean electrotopological state, the aromatic ratio, rotatable bond fraction, and atom-centered fragments. It is found that the R value of the proposed model is 0.900, the average error in percentage is of 6.0%, and the average absolute error is about 36.0 K. While comparing with other works in the literature, this model is built from the largest data set and gives satisfactory performance. As compared with the known experimental errors in measuring the AIT, the proposed model also offers a reasonable estimate of the AIT. Thus, the proposed model can estimate the AIT of a compound for which its AIT is as yet not readily available within a reasonable accuracy.