APPLICATION OF NEURAL NETWORK FOR ESTIMATION OF PISTACHIO POWDER SORPTION ISOTHERMS

Authors

  • H. TAVAKOLIPOUR
  • M. MOKHTARIAN

DOI:

https://doi.org/10.52292/j.laar.2014.440

Keywords:

Pistachio, Modeling, Sorption isotherm, Neural network approach, Isosteric heat of sorption

Abstract

Moisture sorption isotherms for pistachio powder were determined by gravimetric method at temperatures of 15, 25, 35 and 40ºC. Some mathematical models were tested to measure the amount of fitness of experimental data. The mathematical analysis proved that Caurie model was the most appropriate one. As well, adsorptiondesorption moisture content of pistachio powder were predicted using artificial neural network (ANN) approach. The results showed that, MLP network was able to predict adsorption-desorption moisture content with R2 values of 0.998 and 0.992, respectively. Comparison of ANN results with classical sorption isotherm models revealed that ANN modeling had greater accuracy in predicting equilibrium moisture content of pistachio powder.

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Published

2014-07-31

Issue

Vol. 44 No. 3 (2014): Latin American Applied Research

Section

Chemical Engineering

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