CAREER: Predictive kinetic modeling of halogenated hydrocarbon combustion

职业：卤代烃燃烧的预测动力学模型

• 批准号: 1751720
• 负责人: Richard West
• 金额: $ 50.39万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751720&HistoricalAwards=false
• 关键词: CAREER; Predictive; kinetic; modeling; halogenated

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.

卤代烃（HHC）被广泛用作制冷剂和抑制剂。在环境和经济考虑的推动下，该行业有迅速的创新，但是下一代HHC化合物引起了消防安全的关注。在设计过程中预测这些新型HHC的燃烧行为将节省大量时间，精力和费用。描述HHC燃烧的化学动力学模型是高度复杂的，包括数千种涉及数百种化学物种的基本反应。为了有效预测这些燃烧行为，我们必须自动化动力学模型的构建。该项目将使用称为机器学习的计算方法来帮助建模这些复杂的反应系统。这一突破将使我们能够开发自动反应机理生成工具，以创建详细的动力学模型以燃烧HHC。这项工作中提出的方法不仅是新颖和必要的，而且将广泛适用于自动化机制的其他方面。该职业项目的综合教育目标是开发一系列的计算模块，教学学生在整个化学工程课程中解决问题。该研究方法是扩展和应用自动化反应机制生成器（RMG）软件，以创建详细的动力学模型，以燃烧任何包含卤素原子原子质组合的水合物组合。从机器学习领域进行了优化的决策树和新型的卷积神经网络算法，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搜索以生成必要的培训数据。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。教育计划与研究一致，开发​​了一系列将集成到现有类中的计算模块。这些模块将教学生使用Python和Scipy解决化学工程问题。整个本科化学工程课程的科学计算教学模块的整合将有助于为一代研究生工程师做好准备工作场所的工作场所，在该工作场所中，数据分析，处理和计算越来越重要。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务。

项目成果

Automating the generation of detailed kinetic models for halocarbon combustion with the Reaction Mechanism Generator

使用反应机制生成器自动生成卤化碳燃烧的详细动力学模型

• DOI: 10.1016/j.proci.2022.07.204
• 发表时间: 2023
• 期刊: Proceedings of the Combustion Institute
• 影响因子: 3.4
• 作者: Farina, David S.;Sirumalla, Sai Krishna;West, Richard H.
• 通讯作者: West, Richard H.

Extensive High-Accuracy Thermochemistry and Group Additivity Values for Halocarbon Combustion Modeling

• DOI: 10.1021/acs.iecr.1c03076
• 发表时间: 2021-10-22
• 期刊: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
• 影响因子: 4.2
• 作者: Farina, David S., Jr.;Sirumalla, Sai Krishna;West, Richard H.
• 通讯作者: West, Richard H.

Automated Kinetic Models to Predict the Flame Speeds of Halocarbons

预测卤化碳火焰速度的自动动力学模型

• 发表时间: 2023
• 期刊: 13th U.S. National Combustion Meeting
• 作者: Khalil, Nora;Harris, Sevy;West, Richard H.
• 通讯作者: West, Richard H.

Extensive High-Accuracy Thermochemistry and Group Additivity Values for Automated Generation of Halocarbon Combustion Models

用于自动生成卤化碳燃烧模型的广泛高精度热化学和基团加和值

• 发表时间: 2021
• 期刊: 12th U.S. National Combustion Meeting
• 作者: Farina Jr., David;Sirumalla, Sai Krishna;West, Richard H.
• 通讯作者: West, Richard H.

RMG Database for Chemical Property Prediction

• DOI: 10.1021/acs.jcim.2c00965
• 发表时间: 2022-10-12
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Johnson，Matthew S.;Dong，Xiaorui;Green，William H.
• 通讯作者: Green，William H.