Journal
RENEWABLE ENERGY
Volume 182, Issue -, Pages 443-451Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.10.029
Keywords
CO2 waste valorization; CO2-Rich residues; Reverse water gas shift; CO2 reduction; Cu-catalysts; Manganese oxide
Funding
- Junta de Castilla y Leon (Consejeria de Educaci?on) fellowship [Orden EDU/1100/2017]
- European Social Fund
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This study investigated the design of effective catalytic systems for upgrading real CO2-rich residual streams derived from biomass valorization, showing that improved catalyst performance can be achieved through Mn incorporation to enhance Cu dispersions and surface basic concentrations. The optimal Cu to Mn ratios were found to vary under different RWGS reaction conditions. Additional MnOx promoted Cu dispersion, making the (5 wt%) Cu-(10 wt%) Mn/Al2O3 catalyst the best performing sample in CO2-rich residual feedstock.
This study delivers useful understanding towards the design of effective catalytic systems for upgrading real CO2-rich residual streams derived from biomass valorization. Within this perspective, a catalysts' series based on (5 wt%) Cu-(X wt%) Mn/Al2O3 with X 1/4 0, 3, 8 and 10 is employed. The improved catalyst performance achieved through Mn incorporation is ascribed to enhanced Cu dispersions and promoted surface basic concentrations. Under standard RWGS conditions, the highest reaction rates achieved by (5 wt%) Cu-(8 wt%) Mn/Al2O3 catalyst were associated to improved Cu dispersions along with the constitution of highly active Cu-MnOx domains. Remarkably, variations on the optimal Cu to Mn ratios were detected as a function of the RWGS reaction conditions. Thus, under simulated CO2-rich residual feedstock's, i.e., in presence of CO and CH4, the further promotion on the Cu dispersion attained by the larger amounts of MnOx rendered the (5 wt%) Cu-(10 wt%) Mn/Al2O3 catalyst as the best performing sample. Overall, the presented outcomes underline operative strategies for developing catalytic systems with advanced implementation potentialities. (C) 2021 Elsevier Ltd. All rights reserved.
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