CDS&E: Catalytic Kinetics of Hydrocarbon Transformations from Dynamic Experimental Approaches Combined with on-line Machine Learning

CDS

• 批准号: 2053826
• 负责人: Robert Rioux
• 金额: $ 52.05万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053826&HistoricalAwards=false
• 关键词: CDS& Catalytic; Kinetics; Hydrocarbon; Transformations

Catalysts and catalytic chemical processes are essential for the manufacture of products we use every day. The rational design of catalytic materials and reactors requires determining the sequence of chemical transformations, and the rates of individual steps, that convert raw materials to desired products. The project develops computational approaches that integrate artificial intelligence methods with systems engineering techniques to accelerate the discovery of improved catalysts and catalytic processes. The developed approach will be validated on well-studied systems, and training activities will facilitate transfer of the approach to other academic and industrial catalysis researchers.Catalytic mechanism and kinetic parameter identification traditionally involve acquiring steady-state reaction rates over multiple experiments producing a limited amount of discrete data. Transient reactor experiments can provide time-resolved data that are richer in mechanistic information. Temporal dynamics of gas-phase concentrations or temperatures perturb surface coverage(s), which will be probed by time-resolved infrared spectroscopy and correlated with gas-phase concentrations measured by mass spectrometry in an operando packed bed reactor-infrared spectrometer set-up. System theoretic concepts will be repurposed to ensure structural identifiability of the kinetic parameters given the reaction mechanism and experimental apparatus. Artificial intelligence will explore the inputs search space to ensure persistent excitation of the inlet reactor conditions that induce a continuous data stream rich in mechanistic information. The proposed effort will thus combine current nonlinear system identification methods and state-of-the-art experimental apparatuses to derive an automated Design of Experiments (DoE) procedure that informs mechanistic models of catalytic processes with minimal experimentalist supervision. Validation will be performed on previously studied ethylene hydrogenation and CO oxidation catalytic systems. The outcome of this project will be a set of software and hardware tools, and an integrated procedure to tailor inlet perturbations, that continuously excite the dynamics of operando packed bed reactors to determine catalytic kinetic parameters. This new transient approach will be useful, both in academic and industrial applications, for the development of catalytic materials and processes. This transient approach allows for rapid mechanism development, comparison of intrinsic kinetics among catalytic materials, and estimation of parameters useful for catalytic reactor system design. In addition to providing this technique to the catalysis community, educational and outreach activities are planned to provide research training to students from STEM underrepresented groups and the catalysis community.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.

催化剂和催化化学工艺对于我们日常使用的产品的制造至关重要。 催化材料和反应器的合理设计需要确定化学转化的顺序，以及将原材料转化为所需产品的各个步骤的速率。 该项目开发了将人工智能方法与系统工程技术相结合的计算方法，以加速发现改进的催化剂和催化过程。 所开发的方法将被验证的良好的研究系统，和培训活动将促进转让的方法，以其他学术和工业催化researchers.Catalytic机制和动力学参数识别传统上涉及获取稳态反应速率在多个实验产生的离散数据量有限。 瞬态反应堆实验可以提供更丰富的机械信息的时间分辨数据。 气相浓度或温度的时间动态扰动表面覆盖率，这将通过时间分辨红外光谱法探测，并与操作填充床反应器-红外光谱仪装置中通过质谱法测量的气相浓度相关。 系统理论的概念将被重新利用，以确保给定的反应机理和实验装置的动力学参数的结构识别。 人工智能将探索输入搜索空间，以确保入口反应器条件的持续激励，从而产生富含机械信息的连续数据流。 因此，所提出的努力将结合联合收割机目前的非线性系统识别方法和国家的最先进的实验装置，以获得一个自动化的实验设计（DoE）的过程，通知机械模型的催化过程与最小的实验监督。将对先前研究的乙烯加氢和CO氧化催化系统进行验证。 该项目的成果将是一套软件和硬件工具，和一个集成的程序，以定制入口扰动，连续激发operando填充床反应器的动态，以确定催化动力学参数。 这种新的瞬态方法将是有用的，无论是在学术和工业应用中，催化材料和工艺的发展。 这种瞬态的方法允许快速的机制开发，比较催化材料之间的本征动力学，和用于催化反应器系统设计的参数的估计。 除了向催化社区提供这种技术外，还计划开展教育和推广活动，为STEM代表性不足的群体和催化社区的学生提供研究培训。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知识价值和更广泛的影响审查标准进行评估来支持。