Thermal Properties of Multiwalled Carbon Nanotubes Toughened Quartz Nanocomposite Using Artificial Neural Network (ANN)

Yusuf Tijjani

Abstract


Sequel to the experimental results acquisition using the established test procedures as outlined in the previous study on the mechanical and thermal properties of CNT-Reinforced quartzite Nanocomposite for Furnace Lining, the present research has been centered on the implementation and evaluation of an artificial intelligence model for the characterization of the multiwalled carbon nanotubes (MWNTs) reinforced quartz ceramic nanocomposite. A multi input and multi output Artificial Neural Network (ANN) model was developed using the Levenberg Marquardt Back Propagation (LMBP) algorithm to predict the thermal properties of the MWNTs/quartz nanocomposite bricks developed in the previous study. The predicted model was compared with the experimental test results in order to evaluate the power and the accuracy of the artificial intelligence model for the characterization of the entire series of the nanocomposite bricks developed. The developed model adequately predicts the thermal properties; specifically specific heat capacity and thermal conductivity of MWNTs/quartz nanocomposite with a coefficient of determination (R2) of 1.00.


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References


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