1 research outputs found
Thermal decomposition of rice husk: a comprehensive artificial intelligence predictive model
This study explored the predictive modelling of the pyrolysis of rice husk to
determine the thermal degradation mechanism of rice husk. The study can
ensure proper modelling and design of the system, towards optimising the
industrial processes. The pyrolysis of rice husk was studied at 10, 15 and
20 °C min−1 heating rates in the presence of nitrogen using thermogravimetric
analysis technique between room temperature and 800 °C. The thermal
decomposition shows the presence of hemicellulose and some part of
cellulose at 225–337 °C, the remaining cellulose and some part of lignin were
degraded at 332–380 °C, and lignin was degraded completely at 480 °C. The
predictive capability of artificial neural network model was studied using
different architecture by varying the number of hidden neurone node, learning
algorithm, hidden and output layer transfer functions. The residual mass, initial degradation temperature and thermal degradation rate at the end of the
experiment increased with an increase in the heating rate. Levenberg–
Marquardt algorithm performed better than scaled conjugate gradient
learning algorithm. This result shows that rice husk degradation is best
described using nonlinear model rather than linear model. For hidden and
output layer transfer functions, ‘log-sigmoid and tan-sigmoid', and ‘tansigmoid
and tan-sigmoid' transfer functions showed remarkable results based
on the coefficient of determination and root mean square error values. The
accuracy of the results increases with an increasing number of hidden
neurone. This result validates the suitability of an artificial neural network
model in predicting the devolatilisation behaviour of biomass