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Publisher: Elsevier Ltd
Languages: English
Types: Article
Subjects:
Combustion of solid biomass in large scale power generation has been recognized as a key technology for the transition to a decarbonized electricity sector in the UK by 2050. Much of the near-term forecast capacity is likely to be by the conversion of existing coal-fired pulverized fuel plant (DECC, 2012). In such applications, it will be necessary to ensure that the combustion behaviour of the solid biomass fuels is engineered to match, as far as practical, that of the original plant design. While biomass feedstock characteristics vary considerably, one controllable variable for pulverized fuel is the size of the particles.Useful modelling for adaptation and design of boiler plant can be improved with more detailed measurement of the real behaviour of individual particles of the varying fuels. Typical power plant biomass fuels including pine, eucalyptus and willow with particle sizes ranging from up to 3. mm (Van Loo and Koppejan, 2008) and with differing moisture content and aspect ratios were selected for study. Single particles were supported in a water-cooled cover and then exposed above a flame, simulating biomass combustion in a furnace. Measurements of ignition delay, volatile burning time and char burn-out time were undertaken using high speed image capture. Temperatures of the surrounding environment and near to the particle surface were measured with thermocouples and thermometric imaging. Thermo-gravimetric measurements on separate samples complement the single particle measurements as a means of verifying the demarcation between the different stages of combustion and providing kinetic data.Analysis of the data identified correlations between the biomass fundamental characteristics, particle size, and the observed combustion profiles. Empirical expressions for the duration of each combustion stage have been derived. These have been validated with basic modelling including the predicted devolatilisation stage calculated by the FG-Biomass model (Chen et al.,1998).
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