Error Entropy and Mean Square Error Minimization Algorithms for Neural Identification of Supercritical Extraction Process
Error Entropy and Mean Square Error Minimization Algorithms for Neural Identification of Supercritical Extraction Process
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Date
2008
Authors
Rosana Soares
Vladimiro Miranda
Adriana Castro
Roberto Oliveira
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Abstract
In this paper, Artificial Neural Networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and
Technological Biology - IBET in Oeiras - Lisbon - Portugal. A strategy is used to complement the experimental data collected in laboratory during
extraction procedures of useful compositions for the pharmaceutical industry using Black Agglomerate
Residues (BAR) originating from of the cork production as raw material. The strategy involves fitting of data obtained during an operation of
extraction. Two neural models are presented: the neural model trained using a Mean Square Error
(MSE) minimization algorithm and the neural model which the learning was based on the error entropy
minimization. A comparison of the performance of the two models is presented.