Assessment of producer gas composition in air gasification of biomass using artificial neural network model - George, Joel; Arun, P.; Muraleedharan, C. (International journal of hydrogen energy, 2018)
Energy generation from renewable and carbon-neutral biomass is significant in the context of a sustainable energy framework. Hydrogen can be conveniently extracted from biomass through thermo-chemical conversion process of gasification. In the present work, an artificial neural network (ANN) model is developed using MATLAB software for gasification process simulation based on extensive data obtained from experimental investigations. Experimental investigations on air gasification are conducted in a bubbling fluidised bed gasifier with different locally available biomasses at various operating conditions to obtain the producer gas. The developed artificial neural network consists of seven input variables, output layer with four output variables and one hidden layer with fifteen neurons. The multi-layer feed-forward neural network is trained employing Levenberg–Marquardt back-propagation algorithm. Performance of the model appraised using mean squared error and regression analysis shows good agreement between the output and target values with a regression coefficient, R = 0.987 and mean squared error, MSE = 0.71. The developed model is implemented to predict the producer gas composition from selected biomasses within the operating range. This model satisfactorily predicted the effect of operating parameters on producer gas yield, and is thus a useful tool for the simulation and performance assessment of the gasification system. [Display omitted] •Robust ANN model for fluidised bed gasification with generalization capability.•ANN model formulated based on extensive in-house experimental data.•Reasonable prediction of producer gas yield and composition.•Parametric studies on biomass gasification process using developed ANN model.•Illustration of ANN as useful tool for performance assessment of gasification.