The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130912
PDF

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Author 1: Manuel Alejandro Ospina-Alarcón
Author 2: Ismael E. Rivera-M
Author 3: Gabriel Elías Chanchí-Golondrino

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a two-month field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage. Results showed that the model (R2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

Keywords: Empirical modeling; dynamic gravimetric concentration model; gravimetric concentration; machine learning for recovery percentage modelling; mineral processing

Manuel Alejandro Ospina-Alarcón, Ismael E. Rivera-M and Gabriel Elías Chanchí-Golondrino, “Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130912

@article{Ospina-Alarcón2022,
title = {Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130912},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130912},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Manuel Alejandro Ospina-Alarcón and Ismael E. Rivera-M and Gabriel Elías Chanchí-Golondrino}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org