A Kinetic Approach to the Catalytic Oxidation of Mercury in Flue Gas

Four mercury oxidation catalysts were tested in a packed bed reactor in the presence of flue gas generated by the NETL 500 lb/h coal combustor. The four catalysts tested were Ir, Ir/HCl, Darco FGD activated carbon, and Thief/HCl. The Thief/HCl and Darco converted the highest percentage of the inlet...

Full description

Saved in:
Bibliographic Details
Published in:Energy & fuels 2006-09, Vol.20 (5), p.1941-1945
Main Authors: Presto, Albert A, Granite, Evan J, Karash, Andrew, Hargis, Richard A, O'Dow, William J, Pennline, Henry W
Format: Article
Language:English
Subjects:
01 COAL, LIGNITE, AND PEAT
ACTIVATED CARBON
Air pollution caused by fuel industries
Applied sciences
CATALYSTS
CATALYTIC EFFECTS
Energy
Energy. Thermal use of fuels
Exact sciences and technology
FLUE GAS
HYDROCHLORIC ACID
IRIDIUM
MERCURY
OXIDATION
Pollution reduction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Four mercury oxidation catalysts were tested in a packed bed reactor in the presence of flue gas generated by the NETL 500 lb/h coal combustor. The four catalysts tested were Ir, Ir/HCl, Darco FGD activated carbon, and Thief/HCl. The Thief/HCl and Darco converted the highest percentage of the inlet mercury; however, the high conversion in these experiments was aided by larger catalyst loadings than in the Ir and Ir/HCl experiments. We propose a method for analyzing mercury oxidation catalyst results in a kinetic framework using the bulk reaction rate for oxidized mercury formation normalized by either the catalyst mass or surface area. Results reported for fractional mercury oxidation are strongly influenced by the specific experimental conditions and are therefore difficult to translate from experiment to experiment. The catalyst-normalized results allow for more quantitative analysis of mercury oxidation catalyst data and are the first step in creating a predictive model that will allow for efficient scaling up from laboratory-scale to larger-scale studies.
ISSN:0887-0624
1520-5029
DOI:10.1021/ef060207z