This study proposes easy-to-apply machine learning-developed models, which predict four flammability properties of pure organic compounds: the flash point, heat of combustion, lower flammability limit (LFL), and upper flammability limit (UFL). These flammability properties pose a strong impact on the inherently safer design of industrial processes. Similar to quantitative structure-property relationship (QSPR) or group contribution models, machine learning algorithms are utilized in this study to establish predictive models. Compared to previous models, this study uses readily available variables (i.e., the numbers of atomic elements, molecular weights, and normal boiling points) as default variables without the analysis of detailed molecular structures or the in-depth knowledge of chemistry. This study consists of two steps: Step (1) building multiple linear regression (MLR) models by incorporating default input variables and Step (2) building MLR models by incorporating interaction and transformed variables to improve the predictions from the models in Step 1. In Step 1, an optimal subset of predictors is identified by constructing an MLR model via the sequential floating backward selection (SFBS) algorithm. As a result of Step 1, the two constructed models of the flash point and heat of combustion are found to be adequate, while the predictability of LFL and UFL are insufficient. In Step 2, MLR models incorporating nonlinearity and interaction terms are constructed via the sequential floating forward selection (SFFS) algorithm by selecting the optimal subset of default variables. The results show that all the constructed models in Step 2 are adequate as predictive models; the mean absolute errors (MAEs) of the flash point, heat of combustion, LFL, and UFL are 7.31 (5.67 via the SFBS) [K], 60.6 (61.87 via the SFBS) [kJ/mol], 0.21 (0.19 via the SFBS) [vol.%], and 2.44 (2.33 via the SFBS) [vol.%], respectively. Compared to previous models, the approved models in this study provide highly competitive performance with enhanced simplicity and interpretability.

本研究提出了易于应用的机器学习开发模型，可预测纯有机化合物的四种可燃性特性：闪点、燃烧热、可燃性下限 (LFL) 和可燃性上限 (UFL)。这些可燃性特性对工业过程本质上更安全的设计产生了重大影响。类似于定量结构-性质关系（QSPR）或群体贡献模型，本研究利用机器学习算法来建立预测模型。与之前的模型相比，本研究使用现成的变量（即原子元素的数量、分子量和正常沸点）作为默认变量，而没有分析详细的分子结构或深入的化学知识。本研究包括两个步骤：步骤 (1) 通过合并默认输入变量构建多元线性回归 (MLR) 模型和步骤 (2) 通过合并交互和转换变量来构建 MLR 模型以改进步骤 1 中模型的预测。 在步骤 1 中，一个最优子集通过顺序浮动反向选择 (SFBS) 算法构建 MLR 模型来识别预测变量。作为步骤 1 的结果，发现闪点和燃烧热的两个构建模型是足够的，而 LFL 和 UFL 的可预测性不足。在步骤 2 中，通过选择默认变量的最佳子集，通过顺序浮动前向选择 (SFFS) 算法构建包含非线性和交互项的 MLR 模型。结果表明，步骤2中所有构建的模型都足以作为预测模型；闪点、燃烧热、LFL 和 UFL 的平均绝对误差 (MAE) 为 7.31（通过 SFBS 为 5.67）[K]、60.6（通过 SFBS 为 61.87）[kJ/mol]、0.21（通过 SFBS 为 0.19） SFBS) [vol.%] 和 2.44（通过 SFBS 为 2.33）[vol.%]。与以前的模型相比，本研究中批准的模型提供了极具竞争力的性能，并具有增强的简单性和可解释性。