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Machine learning prediction of hydrocarbon mixture lower flammability limits using quantitative structure-property relationship models

机译：使用定量结构 - 性能关系模型的烃混合物烃混合物降低燃烧性限制的机器学习预测

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摘要

Lower flammability limit (LFL) of hydrocarbon mixture is a critical property for fire and explosion hazards. In this study, by using experimental LFL data of hydrocarbon mixture from a single reference, quantitative structure-property relationship (QSPR) models have been established using four machine learning methods, namely, k-nearest neighbors, support vector machine, random forest, and boosting tree. The K-fold cross-validation method, which has significant advantages over the traditional validation set approach, is implemented for QSPR model evaluation. Prediction errors and accuracy are assessed and compared with traditional multiple linear regression. The results show that models generated by machine learning methods have a significantly lower root mean square error than traditional methods in both training and test data sets. This is the first time that machine learning-based QSPR models are developed for prediction of hydrocarbon mixture LFL, and the models are proven to be highly predictable and reliable.
机译：烃混合物的降低可燃性极限（LFL）是火灾和爆炸危害的关键性质。在本研究中，通过使用来自单个参考的烃混合物的实验性LFL数据，使用四台机器学习方法，即K-Indection邻居，支持向量机，随机森林以及随机林和QSPR）模型已经建立了定量结构 - 性质关系（QSPR）模型升压树。 QSPR模型评估实现了与传统验证集方法相比具有显着优势的K折叠交叉验证方法。评估预测误差和准确度，并与传统的多个线性回归进行比较。结果表明，机器学习方法生成的模型比训练和测试数据集中的传统方法具有明显较低的均方根误差。这是第一次开发基于机器学习的QSPR模型以预测烃混合物LFL，并且经过证明该模型是高度可预测和可靠的。

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  • 来源
    《Process safety progress》 |2020年第2期|e12103.1-e12103.9|共9页
  • 作者

    Zeren Jiao; Shuai Yuan; Zhuoran Zhang; Qingsheng Wang;

  • 作者单位

    Mary Kay O'Connor Process Safety Center Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas;

    Mary Kay O'Connor Process Safety Center Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas;

    Mary Kay O'Connor Process Safety Center Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas;

    Mary Kay O'Connor Process Safety Center Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas;

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  • 正文语种 eng
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  • 关键词

    hydrocarbon mixture; lower flammability limit; machine learning; quantitative structure-property relationship;

    机译：烃混合物;降低易燃项;机器学习;定量结构 - 财产关系;

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