(639n) A Microkinetic Model for the Catalytic Upgrading of the Bio-Oil Model Compound Acetic Acid
- Conference: AIChE Annual Meeting
- Year: 2017
- Proceeding: 2017 Annual Meeting
- Group: Topical Conference: Thermal Deconstruction of Biomass
- Time: Wednesday, November 1, 2017 - 6:00pm-8:00pm
In previous work, we developed mechanistic models of pyrolysis of the three constituents of biomass â cellulose, hemicellulose, and lignin â capable of capturing experimental yields and trends of primary products present in bio-oil. The poor quality of this liquid bio-oil can be improved by removal of oxygenated products, typically through catalyzed conversion of the pyrolysis product vapors before condensation into specialty chemicals and fuels. In this work, we developed a microkinetic model for the acid-catalyzed upgrading of a model compound, acetic acid, over a common zeolite, HZSM-5. Using a reaction family approach, we developed plausible elementary reaction families for acid catalysis, which involve the formation of carbenium and oxonium ions. We then utilized automatic network generation to construct a reaction mechanism, considering all possible reactions given the reaction families that were defined. The rate coefficients were calculated via the Arrhenius equation, where the activation energy was related to the heat of reaction by the Evans-Polanyi equation. The heat of reaction on the zeolite surface included the heat of reaction in the gas phase, heats of adsorption for the neutral species, and stabilization energies for the ionic species. The heat of reaction in the gas phase was calculated from the heats of formation of all the reacting species. These heats of formation were, in large part, generated from a group additivity scheme, including values for groups involving C+ and O+. In some cases, these values were missing from the literature, so high-level quantum chemical calculations were used to provide values for regression of the missing groups. The reaction mechanism was coupled with reactor design equations to predict product yields and to explore the effect of various operating parameters (e.g., temperature, acidity) on the product distribution.