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POWERFLAM2<br />

Objectives<br />

The programme aims to coordinate and<br />

provide synergy between results obtained<br />

from a range of different complimentary<br />

techniques to allow substantial increases<br />

in the co-firing of biomass and bio-waste<br />

materials with coal in large utility boilers.<br />

The techniques to be used range from<br />

measurements obtained on large utility<br />

boilers, to those on pilot and simulation<br />

rigs and various laboratory techniques.<br />

These measurements will be used to<br />

correlate and calibrate computational fluid<br />

dynamic (CFD) simulations of the various<br />

complex processes occurring (see figure<br />

1). This will be followed by neural network<br />

analysis of the plethora of data obtained to<br />

provide working tools for the utility<br />

operators. When integrated together, it will<br />

allow operators of large pulverised coal<br />

boilers to evaluate the effects of different<br />

levels of substituting / co-firing upon the<br />

whole performance of the boiler.<br />

PowerFlam2 programme<br />

Background<br />

Fossil fuel is still the main source of energy<br />

conversion for the power industry. It is likely to<br />

be dominant for many years to come since the<br />

development of renewable energies, in terms of<br />

affordable and reliable technology, is low.<br />

Electricity generation via fossil fuel is in the<br />

100’s MW per installation whereas equivalent<br />

biomass output is generally in the 10’s of MW<br />

range. Europe has to face the fact that<br />

generations using traditional fuels are<br />

fundamental to the increasing demand for energy.<br />

The use of coal within this sector is also vital to<br />

meet the needs of supply and demand. The<br />

infrastructure is well established and the<br />

technology is mature, but the environmental<br />

consequences are also known. Fuel substitution<br />

offers a route to use the existing <strong>European</strong><br />

power plant infrastructure to reduce the<br />

equivalent amount of available carbon for energy<br />

conversion, thereby having a considerable<br />

influence on CO2 reduction from this crucial<br />

industrial sector.<br />

Challenges<br />

Although co-firing of biomass and bio-waste has<br />

been practised in a number of plants, the practice<br />

is not widespread. A recent survey by a trade<br />

association representing large utilities, VGB<br />

Powertech E.V. (formerly VGB Technishe<br />

Vereinigung Der Grosskraftwerksbetreiber E.V.),<br />

showed that only 29 out of 353 power plants<br />

surveyed employed any form of co-firing. Barriers<br />

to substantially increased co-firing arise from a<br />

number of technical, economic and operational<br />

factors. A number of critical technical factors have<br />

been identified, including the effects of the co-<br />

148<br />

firing fuel on slagging and fouling in<br />

the system, downstream deNOx systems,<br />

environmental impact and the quality /<br />

marketability of the ash. There are no models or<br />

techniques at the moment that would allow an<br />

operator of a large utility boiler to ascertain<br />

whether a particular form of biomass material or<br />

waste can be successfully co-fired.<br />

Programme structure<br />

The programme will be delivered by a consortium,<br />

including three universities, two major research<br />

laboratories, three large utility operators and<br />

the VGB Powertech E.V. The core of the work<br />

involves the use of several novel laboratory and<br />

pilot scale rigs that provide different experimental<br />

information concerning the behaviour of the fuel<br />

blends in different conditions. Complimentary<br />

modelling work with CFD and neural networks will<br />

be undertaken to better describe the processes<br />

occurring both in the rigs and large boilers (see<br />

figures 2 and 3).<br />

Expected impact<br />

New test methods will be created for fuel blends<br />

using several different laboratory / pilot scale rigs<br />

that will individually give different information on,<br />

for example, ignition, volatile evolution and<br />

slagging. Extensive modelling results using CFD<br />

and neural network analysis will be correlated and<br />

calibrated using results from laboratory and<br />

large co-fired plant experiments. The information<br />

obtained will be in a format that can be integrated<br />

with systems that utility operators are able to use<br />

to make judgments on realistic fuel blends and<br />

levels of substitution for optimum performance.

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