Abstract
The elemental composition of biomass is a significant property, which determines the energy content of biomass feedstock. This article develops a prediction model based on a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized with genetic algorithm (GA). The model inputs were the proximate constituents of biomass which are ash, fixed carbon, and volatile matter. These were used to predict the hydrogen (H), oxygen (O) and carbon (C) content of biomass fuels. The proposed algorithm was evaluated based on some known performance metrics. The root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of correlation (CC), mean absolute error (MAE) are 3.673, 2.4609, 5.1757, 0.9464, 0.309 at computation time (CT) of 33.65 secs for carbon (C); 0.6293, 0.4168, 8.3011, 0.75581, 0.0716 at CT of 40.21 secs for hydrogen (H); 4.4538, 3.1042, 13.3983,0.9167, 0.9899 at CT of 33.57 secs for oxygen (O), respectively. Regression analysis was also carried out to determine the level of dependence among the correlated variables. The model performance shows that GA-ANFIS can be applied in the computation of the elemental composition of biomass for strategic decision-making.
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