Artificial neural network models for biomass gasification in fluidized bed gasifiers

Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine the producer gas composition (CO, CO2, H2, CH4) and gas yield. Published experimental data from other authors has been used to train the ANNs. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO2, H2, CH4) and producer gas yield for a biomass fluidized bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published experimental data used R2 > 0.98. Furthermore a sensitivity analysis has been applied in each ANN model showing that all studied input variables are important.

► We developed two ANN models for fluidized bed biomass gasification process.
► Input variables are biomass composition and a couple of operating parameters.
► Producer gas composition and yield can be determined by an ANN model.
► All input variables have a strong influence in predicting the model outputs.