Prediction of autoignition temperatures of organic compounds by the structural group contribution approach

A model to predict the autoignition temperatures (AIT) of organic compounds is proposed based on the structural group contribution (SGC) approach. This model has been built up using a 400-compound training set; the fitting ability for these training data is 0.8474, with an average error of 32K and an average error percentage of 4.9%. The predictive capability of the proposed model has been demonstrated on an 83-compound validation set; the predictive capability for these validation data is about 0.5361, with an average error of 70K and an average error percentage of 11.0%. The proposed model is shown to be more accurate than those of other published works. This improvement is largely attributed to the modifications of the group definitions for estimating the AIT instead of the type of empirical model chosen. Through the Q(2) value and hypothesis testing, it was found that the empirical model should be chosen as a polynomial of degree 3. As compared to the known errors in experimentally determining the AIT, the proposed method offers a reasonable estimate of the AIT for the organic compounds in the training set, and can also approximate the AIT for compounds whose AIT is as yet unknown or not readily available to within a reasonable accuracy.     

An accurate model for prediction of autoignition temperature of pure compounds

Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient of 0.984, root mean square error of 15.44K, and average percent error of 1.6% for the experimental values.     

多硝基含能化合物5 s爆发点的定量构效关系

为精确预测含能材料的5 s爆发点,解决大量新型含能材料实验测试难度大、安全数据不全等问题,基于定量构效关系(QSPR)原理,研究多硝基含能材料分子结构与5 s爆发点(ln T_E)间的内在定量关系。应用集成学习算法随机森林(RF)筛选出8个对5 s爆发点具有显著影响的分子描述符;采用人工神经网络(ANN)建立90种多硝基含能材料5 s爆发点的预测模型。73种训练集的复决定系数为0.918,均方根误差为0.036,平均绝对误差为0.027。17个检验样本的复决定系数为0.903,均方根误差为0.061,平均绝对误差为0.053。对模型进行了验证以及应用域评价。结果表明：模型具备较好的预测性和泛化性能,可用于对多硝基含能材料的5 s爆发点进行精度较高的预测,有效解决现有含能材料的爆发点数据不够全面的问题,为相关产品研制与生产安全提供参考。     

Prediction of reactive hazards based on molecular structure

There is considerable interest in prediction of reactive hazards based on chemical structure. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. This paper reviews some of the commonly employed theoretical hazard evaluation techniques, including the oxygen-balance method, ASTM CHETAH, and calculated adiabatic reaction temperature (CART). It also discusses the development of a study table to correlate and predict calorimetric properties of pure compounds. Quantitative structure-property relationships (QSPR) based on quantum mechanical calculations can be employed to correlate calorimetrically measured onset temperatures, T(o), and energies of reaction, -deltaH, with molecular properties. To test the feasibility of this approach, the QSPR technique is used to correlate differential scanning calorimeter (DSC) data, T(o) and -deltaH, with molecular properties for 19 nitro compounds.