TY - JOUR
ID - 669913
TI - Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network
JO - International journal of recycling organic waste in agriculture
JA - IJROWA
LA - en
SN - 2195-3228
AU - Zafari, Abedin
AU - Hossein Kianmehr, Mohammad
AU - Abdolahzadeh, Rahman
AD - University of Tehran
Y1 - 2013
PY - 2013
VL - 2
IS - 1
SP - 9
EP - 0
KW - Extrusion parameters. Biomass pellet. Density. Artificial neural network
DO - 10.1186/2251-7715-2-9
N2 - Background: The relationships between the density of the biomass pellet and the related variables are very complicated and highly nonlinear, which make developing a single, general, and accurate mathematical model almost 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 networks to 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 four neurons in the second hidden layer had the best performance for the prediction of pellet density. The minimum root mean square error and coefficient of determination for the multilayer perceptron network were 0.01732 and 0.972, respectively. Also, the results of statistical analysis indicate that moisture content, speed of piston, and particle size 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 input variable on pellet density. The ANN model was found to have higher predictive capability than the statistical model.
UR - http://ijrowa.khuisf.ac.ir/article_669913.html
L1 - http://ijrowa.khuisf.ac.ir/article_669913_4c80e40d263ed179e72715ed058212f8.pdf
ER -