Solid Fuel Decomposition Modelling for the Design of Biomass Gasification Systems

A novel equilibrium reaction modelling approach is proposed for the efficient design of biomass gasifiers. Fuels and chars are defined as pseudo species with properties derived from their ultimate analyses; tars as a subset of known molecular species and their distribution determined by equilibrium calculations. Non-equilibrium behaviour for gas, tar, and char formation is explained by reaction temperature differences for a complete set of stoichiometric equations. A nonlinear regression, with an artificial neural network (NN), relates changes in temperature differences to fuel composition and operational variables. This first principles approach, illustrated with fluidised bed reactor data, improves the accuracy of equilibrium calculations, and reduces the amount of required data by preventing the NN from learning atomic and heat balances.