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灭火剂。在环境和经济考虑的推动下，该行业正在迅速创新，但下一代HHC化合物引发了对消防安全的担忧。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。该项目将使用一种名为机器学习的计算方法来帮助对这些复杂的反应系统进行建模。这一突破将使我们能够开发一种自动反应机理生成工具，以创建HHCs燃烧的详细动力学模型。本文提出的方法不仅具有创新性和必要性，而且还将广泛应用于机构自动生成的其他方面。这个职业项目的综合教育目标是开发一系列计算模块，教学生在整个化学工程课程中解决问题。研究方法是扩展和应用自动反应机理生成器(RMG)软件，为含有任何卤素原子组合的任何碳氢化合物的燃烧创建详细的动力学模型。来自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，以实现必要的参数估计代码的重构。量子化学计算将自动进行，以补充文献搜索，以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现有实验数据进行验证，并用于解释这些化合物的显著燃烧行为。该教育项目与研究相一致，开发了一系列计算模块，这些模块将整合到现有的课程中。这些单元将教会学生如何使用Python和SciPy来解决化学工程问题。将科学计算的教学模块整合到整个本科化学工程课程中，将有助于为数据分析、处理和计算日益重要的工作场所培养新一代研究生工程师。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.

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 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.

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.