Grantee Research Project Results
Elucidating Reaction Chemistry for the Treatment of Phenolic Compounds in Supercritical WaterEPA Grant Number: FP917340
Title: Elucidating Reaction Chemistry for the Treatment of Phenolic Compounds in Supercritical Water
Investigators: Huelsman, Chad M
Institution: University of Michigan
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2011 through August 31, 2014
Project Amount: $126,000
RFA: STAR Graduate Fellowships (2011) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Science & Technology for Sustainability: Green Energy/Natural Resources Production & Use
This project investigates the supercritical water treatment of phenol and its derivatives under various processing conditions to explain the underlying chemistry of supercritical water gasification (SCWG) of biomass. The goal is to construct a reaction network from identified pathways and develop an accompanying chemical kinetic model that accounts for the reaction and formation of all experimentally observed species. Such a model will be essential for optimizing and assessing the environmental impact of this emerging green energy technology.
Phenol and phenol derivatives will be reacted under various processing conditions—temperature, water density, concentration and reaction time—and in quartz batch reactors to avoid any unintended catalysis by metallic reactor walls. Qualitative and quantitative analyses will be conducted post-reaction to determine the identities and yields of gas species and major byproducts. The effect of process variables on yields will be explained by kinetic and thermodynamic principles, which will be used to inform the reaction modeling. Reactant disappearance kinetics, temporal concentration profiles, and the Delplot methodology will be employed to discern the rank of intermediate species and to construct a reaction network comprising reactants, intermediates, and products and the chemical transformations linking them. A kinetic model based on the reaction network will be fit to experimental data to obtain information about the rates of all reactions occurring in the system. Dominant reactions will be identified, and a sensitivity analysis will reveal which reactions are most influential in the formation of a particular species.