Saman Energy Giti Co, Postal Code 3331619636, Tehran, Iran.
J Hazard Mater. 2011 May 15;189(1-2):211-21. doi: 10.1016/j.jhazmat.2011.02.014. Epub 2011 Feb 15.
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.
准确预测纯净化合物的自燃温度（AIT）非常重要。在本研究中，应用人工神经网络-基团贡献（ANN-GC）方法来评估纯净化合物的 AIT。研究了来自不同化学家族的 1025 种纯净化合物，以提出一个全面和可预测的模型。得到的结果表明，实验值的平方相关系数为 0.984，均方根误差为 15.44K，平均百分比误差为 1.6%。
