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Disasters caused by large-scale ammonium nitrate (AN) detonations initiated by fires are well known. The Beirut explosion is again a very sad event with disastrous consequences, but it is not a surprise in the sense of a new phenomenon. It is tragic that calamities with AN occur despite ample guidelines for prevention. Knowledge of the decomposition of AN over the years has greatly progressed, as well how to initiate a detonation with a strong shock wave from a high explosive charge. However, to reproduce the initiation of a detonation by a fire under controlled laboratory conditions never succeeded. The details of the transition of decomposing AN to a detonation remained an open question. Is it a deflagration to detonation mechanism, or is it a shock to detonation one? Knowledge of this may further help process safety measures for the product. The paper will bring research contributions from over a century together, it will develop a scenario how this disaster could happen in Beirut, how much of the AN contributed to the blast, and how further research effort could be beneficial. In addition, the paper estimates the TNT mass equivalent of the AN detonation in Beirut employing three different methods.

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In this study, machine learning algorithms, such as support vector machine (SVM), k-nearest-neighbors (KNN), and rndom forest (RF), are applied to improve the accuracy of the quantitative structure–property relationship (QSPR) models to predict the upper flammability limit (UFL) of pure organic compounds. Ten molecular descriptors are utilized to develop the QSPR model. The experimental data set contains 79 chemicals and is split into 70% training and 30% test set in order to conduct cross-validation. The multiple linear regression (MLR) QSPR model of denary logarithms of the UFL obtained in this study has six molecular descriptors and an overall root-mean-square error (RMSE) of 0.145. The other four descriptors are eliminated based on statistical insignificance. The QSPR models aided by SVM and RF improve the prediction of the UFL as indicated by their overall RMSEs of 0.118 and 0.095, respectively. However, the QSPR model aided by KNN demonstrated the least performance with the overall RMSE of 0.163.

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