Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction-Reference-Cited by-同舟云学术

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Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction

Published:2024-06-30 Issue: Volume:6 Page:1-9
ISSN:
Container-title:2024 IEEE Congress on Evolutionary Computation (CEC)
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Author:

Ghasemi Zahra¹,Neshat Mehdi¹,Aldrich Chris²,Karageorgos John³,Zanin Max⁴,Neumann Frank⁵,Chen Lei¹

Affiliation:

1. School of Electrical and Mechanical Engineering, The University of Adelaide,Adelaide,Australia

2. Western Australian School of Mines, Curtin University,Perth,Australia

3. Manta Controls Pty Ltd,Adelaide,Australia

4. School of Chemical Engineering, The University of Adelaide,Adelaide,Australia

5. School of Computer and Mathematical Sciences, The University of Adelaide,Adelaide,Australia

Funder

Australian Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609966/10611750/10611827.pdf?arnumber=10611827

Reference35 articles.

1. Wills’ mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery;Wills;Butterworth-Heinemann,2015

2. Wills' Mineral Processing Technology

3. Nonlinear Model Predictive Control of Industrial Grinding Circuits using Machine Learning

4. A novel approach to the geometallurgical modelling of the Collahuasi grinding circuit

5. Hardness model and reconciliation of throughput models to plant results at Minera Escondida Ltda., Chile;Flores;Technical Bulletin,2005

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