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Dynamic modelling of biomass gasification processes in fixed bed gasifiers

Gasification is a promising technology for efficient, clean and diverse utilisation of biomass and biomass residues through production of syngas. However, existing technical issues related to process efficiency and environmental standards are preventing the technology to become more economically viable. In order to tackle those issues a lot of attention has been given to biomass gasification process predictive modelling. These models should be robust enough to predict process parameters during variable operating conditions. This could be accomplished either by changes of model input variables such as biomass composition, particle residence time or size or by changes in model structure. This paper analyses the potential of neural network based modelling to predict process parameters during plant operation with variable operating conditions. The dynamic neural network model for gasification purposes will be developed and its performance will be analysed based on measured data. Measurement data for model performance analysis will be derived from a fixed bed biomass gasification plant operated by Technical University Dresden (TU Dreseden).

Biomass gasification; Mathematical modelling; Artificial neural networks; Process analysis

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