Ex situ upgrading of pyrolysis vapors over PtTiO2: extraction of apparent kinetics via hierarchical transport modeling. Issue 1 (23rd October 2020)

• Record Type: Journal Article
• Authors: Pecha, M. Brennan; Iisa, Kristiina; Griffin, Michael; Mukarakate, Calvin; French, Richard; Adkins, Bruce; Bharadwaj, Vivek S.; Crowley, Meagan; Foust, Thomas D.; Schaidle, Joshua A.; Ciesielski, Peter N.
• Is Part Of: Reaction chemistry & engineering. Volume 6:Issue 1(2021)
• Page Start: 125
• Page End: 137
• Publication Date: 2020-10-23
• Subjects: Reaction mechanisms (Chemistry) -- Periodicals; Chemical engineering -- Periodicals; Chemical engineering; Reaction mechanisms (Chemistry); Periodicals; 547.705
• DOI: 10.1039/d0re00339e
• Languages: English
• ISSNs: 2058-9883

Abstract

Abstract : Chemical reaction kinetics enable predictive scaling studies and process sensitivity analyses that can substantially accelerate commercial deployment of new catalytic transformation technologies. Abstract : Chemical reaction kinetics enable predictive scaling studies and process sensitivity analyses that can substantially accelerate commercial deployment of new catalytic transformation technologies. The absence of suitable kinetic parameters for catalytic fast pyrolysis (CFP) of biomass feedstocks has precluded such de-risking simulation activities. In this work we consider ex situ CFP using a Pt/TiO2 catalyst in a packed bed vapor phase upgrading reactor (VPU) with co-fed H2 . We develop a multiscale simulation framework to de-couple apparent kinetics from both intraparticle and reactor-scale transport phenomena. The transport model is integrated with a kinetic scheme that predicts (1) lumped yields of product partially deoxygenated compounds, hydrocarbons, light gases, water, and coke, as well as (2) active site concentration and deactivation of the catalyst. We employ recent advancements in mathematical treatments of cascading reaction systems in the context of an axial-dispersion packed bed reactor model to achieve a rapidly-solving simulation framework that is amenable to iterative regression for kinetic parameter extraction. Results demonstrate accurate predictions of CFP yields within 5% for a variety of conditions, including different reaction times, Pt loadings, and variations in feedstock attributes.