Dynamic modelling of biomass gasification in a co-current fixed bed gasifier

Highlights: • Dynamic neural network model for biomass gasification has been developed for changing operating conditions. • Process temperature and syngas composition prediction have been used for performance analysis. • Data has been extracted from a co-current, fixed bed gasifier operated by TU Dresden. • Dynamic neural network model has a higher prediction accuracy than multiple linear regression models. • Dynamic neural network model is suitable for online prediction of process parameters. - Abstract: Existing technical issues related to biomass gasification 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 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. Dynamic neural network based model for gasification purposes will be developed and its performance will be analysed based on measured data derived from a fixed bed biomass gasification plant operated by Technical University Dresden (TU Dresden). Dynamic neural network can predict process temperature with an average error less than 10% and in those terms performs better than multiple linear regression models. Average prediction error of syngas quality is lower than 30%. Developed model is applicable for online analysis of biomass gasification process under variable operating conditions. The model is automatically modified when new operating conditions occur.