Actively Learned Optimal Sustainable Operation of Plasma-Catalyzed Methane Bireforming on La<sub>0.7</sub>Ce<sub>0.3</sub>NiO<sub>3</sub> Perovskite Catalyst
Diego Alexander Gonzalez‐Casamachin, Qin Tian, Wei-Min Huang, Srinivas Rangarajan, Lihua Zhang, Jonas Baltrušaitis
Abstract
High Resolution Image Download MS PowerPoint Slide Plasma-catalytic bireforming of methane was studied and actively optimized using a La 0.7 Ce 0.3 NiO 3 perovskite catalyst via experimentation in tandem with response surface modeling. Plasma power, inlet flow rate, temperature, CO 2 /CH 4 ratio, and steam concentration were tuned to maximize a variety of process- and sustainability-based metrics. Analysis of the optimal conditions (with respect to different metrics) with and without the catalyst reveals that dry reforming is driven largely via noncatalytic reactions, while steam reforming and water gas shift reactions require the catalyst. The experimental outcome demonstrated that under optimum reaction conditions using the La 0.7 Ce 0.3 NiO 3 catalyst it is possible to minimize global warming potential (GWP), in terms of inferred CO 2 footprint normalized to hydrogen throughput, resulting in maximizing hydrogen yield through steam reforming (and water gas shift reactions) at an SEI of ≈12 eV/molecule. Furthermore, the highest CH 4 conversion reached was 87% while the catalyst showed good activity stability in DBD plasma experiments.The actively learned iterative optimization procedure developed in this work allows for a direct juxtaposition of thermal (heat needed to make steam and heat the plasma reactor) and electrical (power requirement for plasma generation) carbon footprints in a highly nonlinear multivariate process. Furthermore, the corresponding GWP was calculated using a conventional electricity mix, wind electricity, and solar electricity, allowing a direct sustainability assessment in catalyst-assisted plasma conversion of carbonaceous feedstock to H 2 and CO.