Objective The conventional modelling of solid fuel devolalilisation fails in the case of biomass to depict its diversity from coal as well as between different types of biomass. To alleviate this problem, extensive, repetitive and most of all very expensive combustion trials for each single type of biomass must be performed at large and full industrial scale furnaces in order to characterise its combustion behaviour. An appealing alternative can be the use of CFD mathematical models; they are becoming increasingly popular while at the same time they receive the scepticism of the power generation and high temperature process industry. This scepticism is a result of the fact, among others, that each type of biomass fuel has a different and very complicated chemical structure thus its simulated behaviour by the relatively simple devolatilisation and combustion models currently in use by CFD codes is associated with a great degree of uncertainty. To address this problem, there are current trends to incorporate more elaborate sub-models into the codes for the thermal decomposition and combustion processes (devolatilisation, secondary cracking, soot formation etc.) for different types of coal or coal blends but they are cumbersome, and computationally uneconomical. Furthermore, biomass has been much less investigated due to the relatively recent interest in its industrial utilisation as a fuel. This project applies the newly emerging neural network technology to enable the more generalised prediction of biomass devolatilisation behaviour. The technology will be used to predict the volatile release rate from selected key biomass characterisation parameters and the local physical and thermal conditions in the near burner field. The new formulation will be tested against bench-top data and then incorporated in a reputed in-house CFD combustion model where it will be validated against data taken in a large scale furnace. Fields of science engineering and technologyenvironmental engineeringenergy and fuelsfossil energycoalagricultural sciencesagricultural biotechnologybiomassnatural sciencesmathematicsapplied mathematicsmathematical modelnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programme(s) FP4-NNE-JOULE C - Specific programme for research and technological development, including demonstration in the field of non-nuclear energy, 1994-1998 Topic(s) 03050103 - Development of simulation tools, tests and trials of complete energy from biomass systems in rural areas, qualification and development of preliminary norms and standards for feedstock, processes and systems Call for proposal Data not available Funding Scheme CSC - Cost-sharing contracts Coordinator ABB Combustion Services Ltd. EU contribution No data Address Sinfin Lane DE24 9GH Derby United Kingdom See on map Total cost No data Participants (3) Sort alphabetically Sort by EU Contribution Expand all Collapse all CINAR LTD. United Kingdom EU contribution No data Address 11,Elvaston Place 11 SW7 5QG LONDON See on map Links Website Opens in new window Total cost No data Coaltec e Ambiente - Associação para a Formação, Estudos e Projectos no âmbito do Carvao e Outros Combustiveis Portugal EU contribution No data Address Estrada do Paço do Lumiar 1699 Lisboa See on map Total cost No data UNIVERSITA DEGLI STUDI DI PISA Italy EU contribution No data Address Via Diotisalvi 2 56126 Pisa See on map Total cost No data
