A Multi-Method Machine Learning Framework for Simulating Power Dynamics in an Iron Ore Cone Crusher Plant-Reference-Cited by-同舟云学术

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A Multi-Method Machine Learning Framework for Simulating Power Dynamics in an Iron Ore Cone Crusher Plant

Published:2024-09-04 Issue: Volume: Page:1-16
ISSN:0882-7508
Container-title:Mineral Processing and Extractive Metallurgy Review
language:en
Short-container-title:Mineral Processing and Extractive Metallurgy Review

Author:

Salehzadeh Gharaei Roohollah¹,Gholami Alireza²,Khoshdast Hamid³⁴,Biglari Hossein⁵

Affiliation:

1. Department of Mining Engineering, Islamic Azad University, Sirjan, Iran

2. The Robert M. Buchan Department of Mining, Queen’s University, Kingston, Ontario, Canada

3. Department of Mining Engineering, Higher Education Complex of Zarand, Shahid Bahonar University of Kerman, Zarand, Iran

4. Mineral Industries Research Center, Shahid Bahonar University of Kerman, Kerman, Iran

5. Department of Architectural Engineering, Higher Education Complex of Zarand, Shahid Bahonar University of Kerman, Zarand, Iran

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08827508.2024.2395828

Reference61 articles.

1. Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach

2. Using Artificial Neural Networks for the Prediction of Bond Work Index from Rock Mechanics Properties

3. Support vector regression;Basak D.;Neural Information Processing-Letters and Reviews,2007

4. Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth

5. CatBoost-SHAP for modeling industrial operational flotation variables – A “conscious lab” approach

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