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Stable catalyst design for the activation of methane to syngas, hydrogen, and chemicals


    Problem Statement

    Methane and light alkanes are species with relatively poor economic interest. Our goal is to activate C–H σ-bond to produce hydrogen, olefins, carbon monoxide, and carbon nanofibers, following different process strategies such as oxidative coupling (for olefins), CO2 dry reforming (for syngas), cracking or catalytic decomposition (for hydrogen-free of COx and sequestrated carbon nanotubes/nanofibers), cracking/co-cracking with CO or methanol. We work on developing, synthesizing, characterizing, and testing innovative catalysts with a twist of reaction engineering concepts, with a focus on multi-scale implications.

    We delve into the mechanistic insights into a series of in-house-synthesized metal-supported heterogeneous catalysts by combining them with dynamic reactors and ab initio calculations. We explore catalysts with extended lifetimes, enhanced activity, selectivity, and heat transfer. These catalysts are based on alloys-intermetallics, high entropy alloys, exsolved perovskites, and SiC, among others.

    We investigate novel reactor designs based on forced-dynamic, operando, and fluidized-bed reactors to amplify kinetic information and improve selectivity.

    CHA2023

    Goals

    • Develop a microkinetic-based modeling framework to analyze the catalyst performance
    • Scale the technical catalyst for its application in demanding exothermic (oxidative coupling of methane using SiC and spray drying) or fluidized-bed (catalytic decomposition of methane) conditions
    • Develop new catalytic concepts based on Ni-alloys (Ni-Fe, -Co, -Zn…)
    • Improve the catalyst structure-function correlations using in-situ, operando, and dynamic techniques and reactors

    Related People

    Abdulrahman Mubarak Alsaiari
    Seba Alareeqi
    Vijay V. Kumar

    Related Publications

    Robust data curation for improved kinetic modeling in oxidative coupling of methane using high-throughput reactors

    by Lezcano, Gobouri, Realpe, Kulkarni, Velisoju, Castaño
    Chem. Eng. Sci. Year: 2024 DOI: https://doi.org/10.1016/j.ces.2023.119412

    Abstract

    High-throughput catalytic reactors are useful for developing large datasets of kinetic results and discovering faster new catalysts and models. However, these large datasets are often challenging to curate, and here we present a methodology for reinforced outlier detection using a robustified principal component analysis with a minimum covariance determinant. We estimate the kinetic parameters of three Mn-Na-W catalysts for the oxidative coupling of methane using datasets curated in different ways. These catalysts differ in their support composition (SiO2 and SiC), preparation method (impregnation or spray-dying), and performance. The kinetic model subsequently trained consists of six global reactions, and the obtained fit demonstrates that the proposed data curation method is a useful tool for enhancing the predictions of subsequent models and gaining further insight into underlying correlations within the data, especially when the aesthetic curation of data is too resource-demanding and/or biased.

    Keywords

    MKM CHA