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

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.