Functional use of carbon dioxide for the sustainable valorization of orange peel in the pyrolysis process

Although biomass is carbon-neutral, its use as a primary feedstock faces challenges arising from inconsistent supply chains. Therefore, it becomes crucial to explore alternatives with reliable availability. This study proposes a strategic approach for the thermochemical valorization of food processing waste, which is abundantly generated at single sites within large-scale processing plants. As a model biomass waste from the food industry, orange peel waste was particularly chosen considering its substantial consumption. To impart sustainability to the pyrolysis system, CO₂, a key greenhouse gas, was introduced. As such, this study highlights elucidating the functionality of CO₂ as a reactive feedstock. Specifically, CO₂ has the potential to react with volatile pyrolysates evolved from orange peel waste, leading to CO formation at ≥490 °C. The formation of chemical constituents, encompassing acids, ketones, furans, phenols, and aromatics, simultaneously decreased by 15.1 area% in the presence of CO₂. To activate the efficacy of CO₂ at the broader temperature spectrum, supplementary measures, such as an additional heating element (700 °C) and a nickel-based catalyst (Ni/Al₂O₃), were implemented. These configurations promote thermal cracking of the volatiles and their reaction kinetics with CO₂, representing an opportunity for enhanced carbon utilization in the form of CO. Finally, the integrated process of CO₂-assisted catalytic pyrolysis and water-gas shift reaction was proposed. A potential revenue when maximizing the productivity of H₂ was estimated as 2.62 billion USD, equivalent to 1.11 times higher than the results from the inert (N₂) environment. Therefore, utilizing CO₂ in the pyrolysis system creates a promising approach for enhancing the sustainability of the thermochemical valorization platform while maximizing carbon utilization in the form of CO. Copyright © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.