Fluidized Beds | Niksa Energy Associates

Overview

One of the primary advantages of fluidized utilization technologies for solid fuels is that they can process an enormous range of fuel quality, including coal wastes and residues and agricultural refuse. This feature, along with the moderate operating temperatures, makes fluidized beds and CFBCs especially well-suited for analysis with NEA’s detailed reaction mechanisms, particularly when the primary goals pertain to gaseous and particulate emissions and tar deposition. To accommodate the detailed reaction mechanisms, NEA’s simulations incorporate semi-empirical expressions for bed hydrodynamics from the chemical engineering literature, rather than explicit particle dynamics. In a variety of applications, this hybrid approach with an emphasis on chemistry yields accurate product gas compositions, including regulated emissions, in only several minutes on ordinary PCs.

The Chemical Structure of CFBCs

The vast majority of CFBC simulators focus on the bed hydrodynamics and particle dynamics at the expense of the process chemistry. NEA took the opposite approach to develop a CFBC simulator based on the fully validated reaction mechanisms in PC Coal Lab^® and elementary reaction mechanisms for chemistry in the gas phase and on soot. The result is an analysis that clearly reveals the underlying chemical structure, from hardly any conversion at all in the dense bottom bed to vigorous conversion of both volatiles and char in the splash zone to staged burnout of char along the riser and into the cyclone separator. This simulator has been used to interpret the most important emissions (NO, N₂O, NO₂, SO₂, unburned carbon) in the literature reports for about two dozen CFBCs at pilot- and full-scale.

Recent Applications in Fluidized Systems

The National Energy Technology Laboratory of the U. S. DoE purchased NEA’s complete suite of chemistry submodels for coal gasification at moderate temperatures, including devolatilization, char oxidation, char gasification, tar conversion, and gas reforming. These mechanisms accurately predicted the product gas compositions from several tests in the riser section in Southern Company Services’ Power System Development Facility (PSDF) for diverse operating conditions with subbituminous and hv bituminous coals.

A Japanese government agency hired NEA to accurately estimate drying times for brown coals in a fluidized stream drying technology. NEA’s drying model explicitly accounts for the three moisture forms – monolayer, multilayer, and bulk – as a basis to predict the distinctive drying behavior and energy requirements of individual low rank coal samples. The mass loadings of the moisture forms are estimated from a database of equilibrium moisture contents that we correlated to parameters derived from a coal’s proximate and ultimate analyses. In accord with data, the predicted drying time is not solely a function of the total moisture content of the coal. One key aspect is whether or not the target moisture level requires removal of any monolayer moisture at all. Coals with little bulk moisture and relatively abundant monolayer moisture will require relatively long drying times simply because monolayer moisture is always released much slower than multilayer moisture. Learn More.

A boiler manufacturer in Japan was concerned about excessive corrosion in its full-scale PFBC. They hired NEA to develop a computer simulator to identify which coals are likely to have excessive alkali vapor emissions. After the predictions satisfied evaluations against lab-scale test data, NEA delivered a software package that accurately predicted the alkali emissions from the pilot-scale PFBC, and was used to screen coals for the 230 MW Karita PFBC.

A Japanese utility OEM routinely uses NEA’s PC Coal Lab^® to develop small, distributed gasifiers for biomass and low-rank coals. These calculations are used to manage the different product gas compositions with various forms of biomass and coal, and also tar-related problems, such as sticky deposits and diminished fuel conversion efficiencies.