Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network

Biomass gasification enables the transformation of biomass feedstock into syngas suitable for further energy conversions. Mathematical models of gasification are not only valuable tool for the design and optimization of the processes, but could be also employed for online prediction and process control. In this work, the potential of using nonlinear autoregressive networks with exogenous inputs (NARX) for predicting the gasification process when a lower amount of experimental data is available was studied. The analysis of using an open-loop NARX network for an online prediction of the syngas composition in a downdraft gasifier at different data recording frequency was performed. The predicted results showed that by decreasing the data recording frequency, the prediction error increases. Furthermore, the possibility of improving the NARX network at lower data recording frequency was analysed by expanding the training dataset to reduce discrepancies between predicted and measured results. Inclusion of four data sets made neural network more robust and flexible for operating with fewer data points. Practical significance of results can be seen in the application of open-loop network for online prediction and control of the gasification process at lower data recording frequency as a part of a model predictive control module.