CAREER: Predictive kinetic modeling of halogenated hydrocarbon combustion

卤代烃（HHC）被广泛用作制冷剂和灭火剂。在环境和经济考虑的推动下，该行业正在快速创新，但下一代HHC化合物引发了消防安全问题。在设计过程的早期预测这些新型HHC的燃烧行为将节省大量的时间、精力和费用。描述HHC燃烧的化学动力学模型非常复杂，包括涉及数百种化学物种的数千个基元反应。为了有效地预测这些燃烧行为，我们必须自动构建动力学模型。该项目将使用一种称为机器学习的计算方法来帮助模拟这些复杂的反应系统。这一突破将使我们能够开发一个自动化的反应机制生成工具，为HHCs的燃烧创建详细的动力学模型。本文提出的方法不仅新颖、必要，而且在机构自动生成的其他方面也具有广泛的应用价值。该CAREER项目的综合教育目标是开发一系列的计算模块，教学生解决整个化学工程课程中的问题。研究方法是扩展和应用自动化反应机理生成器（RMG）软件，以创建详细的动力学模型，用于包含任何卤素原子组合的任何碳氢化合物混合物的燃烧。机器学习领域的优化决策树和新型卷积神经网络算法将被扩展，以实现参数估计代码的必要重组。量子化学计算将自动化，以补充文献检索，从而生成必要的训练数据。模型生成工具将根据关键示例化合物的可用实验数据进行验证，并用于解释这些化合物的显着燃烧行为。该教育计划与研究保持一致，开发了一系列计算模块，将集成到现有课程中。这些模块将教学生使用Python和SciPy来解决化学工程问题。在本科化学工程课程中整合科学计算的教学模块将有助于培养一代毕业工程师，以适应数据分析、处理和计算日益重要的工作场所。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Halogenated hydrocarbons (HHCs) are widely used as both refrigerants and fire suppressants. Driven by environmental and economic considerations, there is rapid innovation in the industry, but the next generation of HHC compounds raise fire safety concerns. Predicting the combustion behavior of these novel HHCs earlier in the design process will save much time, effort, and expense. The chemical kinetic models for describing HHC combustion are highly complex, comprising thousands of elementary reactions involving hundreds of chemical species. To effectively predict these combustion behaviors, we must automate the construction of kinetic models. This project will use a computational approach known as machine learning to help model these complex reacting systems. This breakthrough will enable us to develop an automated reaction mechanism generation tool to create detailed kinetic models for combustion of HHCs. The methodology proposed in this work are not only novel and necessary, but will be widely applicable in other aspects of automated mechanism generation. The integrated educational objective of this CAREER project is to develop a series of computational modules teaching students to solve problems throughout their chemical engineering curriculum.The research approach is to extend and apply automated Reaction Mechanism Generator (RMG) software to create detailed kinetic models for combustion of any mix of hydrocarbons containing any combination of halogen atoms. Optimized decision-tree and novel convolutional neural network algorithms from the field of machine learning will be extended to enable the necessary restructuring of parameter estimation codes. Quantum chemistry calculations will be automated to supplement literature searches to generate the necessary training data. The model-generating tool will be validated against available experimental data from key example compounds, and used to explain the remarkable combustion behavior of these compounds. The educational program is aligned with the research, developing a series of computational modules that will be integrated into existing classes. These modules will teach students to use Python and SciPy to solve chemical engineering problems. The integration of teaching modules for scientific computing throughout the undergraduate chemical engineering curriculum will help prepare a generation of graduate engineers for a workplace in which data analysis, processing, and computation are increasingly important.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.