Modeling a biogas upgrading PSA unit with a sustainable activated carbon derived from pine sawdust. Sensitivity analysis on the adsorption of CO2 and CH4 mixtures

Pressure Swing Adsorption (PSA) is one of the implemented technologies for removing carbon dioxide in biogas streams. Different adsorbents, mostly zeolite-based, and process configurations have been patented and commercially demonstrated. In this study, we have developed a numerical model to successfully describe the dynamic performance of biomass-derived activated carbon in biogas purification. It is the first step in designing a biomass-based carbon capture unit within the bioenergy and circular economy context. Microporous activated carbon pellets prepared from pine sawdust by physical activation with CO2 was the adsorbent material choice. The model was built with the fittings of single-component adsorption isotherms of CO2 and CH4 at different temperatures to the Langmuir-Freundlich model and the Ideal Adsorbed Solution Theory (IAST) to account for multicomponent adsorption. The kinetics of mass transfer in the solid phase was described by the Linear Driving Force model (LDF). The dynamic simulations were performed with the aid of the commercial software Aspen Adsorption and experimental data previously obtained in the laboratory used for the model validation [1]. The model was applied to address the separation performance of a biogas upgrading biomass-based PSA process by running a parametric study to determine the influence of key performance parameters. The sensitivity analysis concluded that a single stage 4-step PSA can produce methane with a purity above 95% and a recovery of around 60% in a configuration with P/F ratios (quotients of molar flows of CH4 in the purge and the feed streams) between 0.67 and 1 for an adsorption pressure of 3 bar.This work was carried out with financial support from the Gobierno del Principado de Asturias (PCTI, Ref. IDI/2018/000115), co-financed by the European Regional Development Fund (ERDF), and from the CSIC (Project PIE, Ref. 202080E115).Peer reviewe