Modeling the drying of a high-moisture solid with an artificial neural network

  • Torrecilla J
  • Aragón J
  • Palancar M
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Abstract

The byproduct so-called alperujo (a solid-liquid byproduct generated
from olive oil extraction by two-phase centrifugation) is difficult
to dry due to its high moisture content. An artificial neural network
(ANN) was developed to model the drying of alperujo in fluidized
bed dryers. The ANN, a three layer perceptron (3 inputs, 4 hidden
nodes, and I output) with back-propagation updating, serves to
predict the moisture of the output solid at a time t + T from known
input data at time t. The input data are the actual values of the
input air temperature, fluidized bed temperature, and output solid
moisture. T is the sampling time. The ANN learning, topology optimization,
and verification are described in this paper. The essential data
to design the ANN were taken from runs in a bench-scale dryer,
and the ANN validation was carried out by applying basic statistical
criteria and the standard tests of Mann-Whitney, Kruscal-Wallis,
and Kolmogorov-Smirnov. The results of these tests, which compare
the real with predicted moisture data, demonstrate that the fluidized
bed dryer is well-modeled by the ANN (prediction error of 4.5%).

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Authors

  • José S. Torrecilla

  • José M. Aragón

  • Maria C. Palancar

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