Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers

•2 Different equilibrium models are developed and their performance is analysed.•Neural network prediction models for 2 different fixed bed gasifier types are developed.•The influence of different input parameters on neural network model performance is analysed.•Methodology for neural network model development for different gasifier types is described.•Neural network models are verified for various operating conditions based on measured data.

The number of the small and middle-scale biomass gasification combined heat and power plants as well as syngas production plants has been significantly increased in the last decade mostly due to extensive incentives. However, existing issues regarding syngas quality, process efficiency, emissions and environmental standards are preventing biomass gasification technology to become more economically viable. To encounter these issues, special attention is given to the development of mathematical models which can be used for a process analysis or plant control purposes. The presented paper analyses possibilities of neural networks to predict process parameters with high speed and accuracy. After a related literature review and measurement data analysis, different modelling approaches for the process parameter prediction that can be used for an on-line process control were developed and their performance were analysed. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed. Measurement data for the model development, verification and performance analysis were derived from biomass gasification plant operated by Technical University Dresden.