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Reseach Article

Machine Learning Application in Process on Extra Virgin Olive Oil Elaboration Disk Stack Vertical Centrifuge Modeling

by A. Jiménez Márquez, G. Beltrán Maza
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 37
Year of Publication: 2020
Authors: A. Jiménez Márquez, G. Beltrán Maza
10.5120/ijca2020920511

A. Jiménez Márquez, G. Beltrán Maza . Machine Learning Application in Process on Extra Virgin Olive Oil Elaboration Disk Stack Vertical Centrifuge Modeling. International Journal of Computer Applications. 176, 37 ( Jul 2020), 30-35. DOI=10.5120/ijca2020920511

@article{ 10.5120/ijca2020920511,
author = { A. Jiménez Márquez, G. Beltrán Maza },
title = { Machine Learning Application in Process on Extra Virgin Olive Oil Elaboration Disk Stack Vertical Centrifuge Modeling },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 37 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number37/31446-2020920511/ },
doi = { 10.5120/ijca2020920511 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:28.141547+05:30
%A A. Jiménez Márquez
%A G. Beltrán Maza
%T Machine Learning Application in Process on Extra Virgin Olive Oil Elaboration Disk Stack Vertical Centrifuge Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 37
%P 30-35
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine learning through the Artificial Neural Networks Supervised Learning technique was applied for modelling the clarification process of Virgin Olive Oil by a disk stack centrifuge equipment. The neural network obtained was trained and validated by a real database generated from different experiments to analyze the performance of the centrifuge. Compositional variability of oil, the water/oil relations flow rates, and both temperatures to the centrifuge inlet were the technological variables that were checked about the oil loss in the waters process outlet as parameter indicator of the effectiveness on solid-liquid-liquid separation. The results obtained in validation indicate a good predictive capacity, with a correlation coefficient r> 0.85 and an error of 0.30 kg h-1, which allows this neural model to become an excellent tool to optimize the centrifuge. A ‘Simulink’ model was designed for performance verification of the Network by checking the predictions obtained from another data set that not intervened in its build. A t-Test was applied and the results indicate they are not a significant difference between predicted and real mean values for P = 0.05 and 60 degrees of freedom.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Neuronal Network Optimization Olive oil elaboration process Disk Stack Centrifuge.