Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network

Background: The relationships between the density of the biomass pellet and the related variables are verycomplicated and highly nonlinear, which make developing a single, general, and accurate mathematical modelalmost impossible. One of the most appropriate methods to solve these problems is the intelligent method.Shankar and Bandyopadhyay and Shankar et al. successfully used genetic algorithms and artificial neural networksto understand and optimize an extrusion process.Results: The results showed that a four-layer perceptron network with training algorithm of back propagation,hyperbolic tangential activation function, and Delta training rule with ten neurons in the first hidden layer and fourneurons in the second hidden layer had the best performance for the prediction of pellet density. The minimumroot mean square error and coefficient of determination for the multilayer perceptron network were 0.01732 and0.972, respectively. Also, the results of statistical analysis indicate that moisture content, speed of piston, and particlesize significantly affected (P < 0.01) the density of pellets while the influence of die length was negligible (P > 0.05).Conclusions: The results indicate that a properly trained neural network can be used to predict effect of inputvariable on pellet density. The ANN model was found to have higher predictive capability than the statistical model.

Keywords:

Extrusion parameters , Biomass pellet , Density , Artificial neural network