Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

The effect of different bed materials was included a as new input into an artificial neural network model to predict the gas composition (CO2, CO, CH(4 )and H-2) and gas yield of a biomass gasification process in a bubbling fluidized bed. Feed and cascade forward back propagation networks with one and two hidden layers and with Levenberg-Marquardt and Bayesian Regulation learning algorithms were employed for the training of the networks. A high number of network topologies were simulated to determine the best configuration. It was observed that the developed models are able to predict the CO2, CO, CH4, H-2 and gas yield with good accuracy (R-2 > 0.94 and MSE < 1.7 x 10(-3)). The results obtained indicate that this approach is a powerful tool to help in the efficient design, operation and control of bubbling fluidized bed gasifiers working with different operating conditions, including the effect of the bed material.