Research Article Open Access

MODELING ISOSTERIC HEAT OF BANANA FOAM MAT USING NEURAL NETWORK APPROACH

Preeda Prakotmak1, Hataitep Wongsuwan1, Somchart Soponronnarit2 and Somkiat Prachayawarakorn3
  • 1 Department of Mechanical Engineering, Faculty of Engineering at Khamphengsean, Kasetsart University, Khamphengsean Campus, Nakornpathom 73140, Thailand
  • 2 School of Energy, Environment and Materials, Thailand
  • 3 Department of Chemical Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand

Abstract

Information on the adsorption isotherm and the thermodynamic properties can assist in optimizing food processing operations such as drying, packaging and storage in the assessment of the quality of food. In this study, an Artificial Neural Network (ANN) was used for modelling the water activity/Equilibrium Relative Humidity (ERH) of banana foam mat under a range of values of the Experimental Equilibrium Moisture Content (EMC) to calculate the isosteric heat of sorption (qst) by applying the Clausius-Clapeyron equation. The EMC of three dry banana foam samples at different densities of 0.21, 0.26 and 0.30 g/cm3 was determined by a standard gravimetric method over a temperature range of 35-45°C and a relative humidity range of 32-83%. The modified-GAB model best fitted the EMC data. However, the modified-GAB model was not acceptable for predicting the heat sorption behaviour. A negative value of qst estimated using the modified-GAB equation was found at a moisture content above 0.24 kg/kg d.b., showing the poor fit of the model. A multilayer feed-forward ANN trained by back-propagation algorithms was developed to correlate the output ERH to three exogenous inputs (foam density, EMC and temperature). The developed ANN models could predict the ERH more accurately than the modified-GAB model. The predictions from the ANN models produced R2 values higher than 0.97. No negative qst values were found using the ANN method."

American Journal of Applied Sciences
Volume 11 No. 8, 2014, 1279-1291

DOI: https://doi.org/10.3844/ajassp.2014.1279.1291

Submitted On: 30 March 2014 Published On: 13 May 2014

How to Cite: Prakotmak, P., Wongsuwan, H., Soponronnarit, S. & Prachayawarakorn, S. (2014). MODELING ISOSTERIC HEAT OF BANANA FOAM MAT USING NEURAL NETWORK APPROACH. American Journal of Applied Sciences, 11(8), 1279-1291. https://doi.org/10.3844/ajassp.2014.1279.1291

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Keywords

  • ANN
  • Adsorption Isotherm
  • Banana Foam
  • Back-Propagation
  • Isosteric Heat