Version 1
: Received: 7 December 2023 / Approved: 12 December 2023 / Online: 12 December 2023 (03:15:22 CET)
How to cite:
Puliyanda, A. Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane. Preprints2023, 2023120761. https://doi.org/10.20944/preprints202312.0761.v1
Puliyanda, A. Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane. Preprints 2023, 2023120761. https://doi.org/10.20944/preprints202312.0761.v1
Puliyanda, A. Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane. Preprints2023, 2023120761. https://doi.org/10.20944/preprints202312.0761.v1
APA Style
Puliyanda, A. (2023). Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane. Preprints. https://doi.org/10.20944/preprints202312.0761.v1
Chicago/Turabian Style
Puliyanda, A. 2023 "Model-Based Catalyst Screening and Optimal Experimental Design for the Oxidative Coupling of Methane" Preprints. https://doi.org/10.20944/preprints202312.0761.v1
Abstract
The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)), and deploys it to locate optimal conditions that maximize C2 yield for a catalyst. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions, while the predictions of methane conversion are driven by the reaction conditions. The machine learning (ML) regressor is used as a surrogate to develop performance curves for each of the catalysts via a multi-objective optimization routine that seeks to maximize the KPIs in the decision space of reaction conditions, is seen to locate optimal conditions at which the maximum C2 yields for catalysts are predicted to be 15%, higher on average. Analyzing the catalysts in the space of their performance curves with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, reveals distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
