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Applied Soft Computing on Virgin Olive Oil Elaboration Process Fuzzy Control of Grinding Stage

by A. Jiménez Márquez, G. Beltrán Maza
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 28
Year of Publication: 2020
Authors: A. Jiménez Márquez, G. Beltrán Maza
10.5120/ijca2020920300

A. Jiménez Márquez, G. Beltrán Maza . Applied Soft Computing on Virgin Olive Oil Elaboration Process Fuzzy Control of Grinding Stage. International Journal of Computer Applications. 176, 28 ( Jun 2020), 14-20. DOI=10.5120/ijca2020920300

@article{ 10.5120/ijca2020920300,
author = { A. Jiménez Márquez, G. Beltrán Maza },
title = { Applied Soft Computing on Virgin Olive Oil Elaboration Process Fuzzy Control of Grinding Stage },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 28 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number28/31375-2020920300/ },
doi = { 10.5120/ijca2020920300 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:40.575523+05:30
%A A. Jiménez Márquez
%A G. Beltrán Maza
%T Applied Soft Computing on Virgin Olive Oil Elaboration Process Fuzzy Control of Grinding Stage
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 28
%P 14-20
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A decisional system on fuzzy logic based (DFS) was built for automatization of the olives fruit grinding stage, on extra virgin olive oils elaboration process, for modulation of 'biophenols' content (BPH) in the oils obtained. This Fuzzy model was integrated in a `Simulink' laboratory simulator of this stage which consists on a PI feedback controller type. The input to controller was constituted by the fruits temperature, the pore size of sieve, the rotational speed of hammer mill and the error, which this is the difference between BPH levels predicted at simulator output versus the setpoint BPH level. The importance of these variable, on end level of BPH in oils, was established by 'data-mining' analysis which enabled defined the behavior rules and decisional tables for built the membership functions of Fuzzy system. The temperature, sieve and error were constituted the antecedent function for fuzzification and the hammer speed the consequent function for desfuzzification. A total of 27 rules ‘if..the..and..then..’ were extracted. To work with this laboratory simulator and verify the performance of the fuzzy model an EVOO process model an Artificial Neural Networks based (ANN) was used which was built by employing the Near Infrared (NIR) spectral properties of olives fruit and technological variables of process for prediction of BPH level in oils (r=0.937, RMSE=354 mg kg-1) at horizontal decanter output. This laboratory simulator was checked by application of different BPH setpoint step and monitorization of adjust of the BPH prediction to setpoint for the different hammer speeds established by de Fuzzy system, showing a good response in short times and the ability to modulate the BPH content within a wide range of these (± 450 mg kg-1) with respect to the initial value.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy System Data Mining Artificial Neural Network Soft computing Artificial Intelligence Olive Oil Biophenols

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