Predicting Grinding Mill Power Consumption in Mining: A Comparative Study-Reference-Cited by-同舟云学术

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Predicting Grinding Mill Power Consumption in Mining: A Comparative Study

Published:2023-12-16 Issue: Volume:9 Page:395-399
ISSN:
Container-title:2023 7th IEEE Congress on Information Science and Technology (CiSt)
language:
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Author:

Loudari Chaimae¹,Cherkaoui Moha¹,Bennani Rachid²,El Harraki Imad¹,Fares Omar³,El Adnani Mohamed⁴,Abdelwahed El Hassan⁴,Benzakour Intissar³,Bourzeix François⁵,Baina Karim⁶

Affiliation:

1. National School of Mines of Rabat (ENSMR),dept. LMAID Laboratory,Rabat,Morocco

2. Moroccan Foundation for Advanced Science Innovation and Research (MAScIR),dept. Digitalization and Microelectronic Smart Devices,Rabat,Morocco

3. MANAGEM Group,dept. EIA Reminex Research Center,Casablanca,Morocco

4. University of Cadi Ayyad (UCA),dept. LISI Laboratory,Marrakech,Morocco

5. Moroccan Foundation for Advanced Science Innovation and Research (MAScIR),dept. Embedded Systems and Artificial Intelligence Department,Rabat,Morocco

6. National Higher School for Computer Science and System analysis (ENSIAS),dept. Al-Qualsadi Research & Development Team,Rabat,Morocco

Funder

Ministry of Higher Education

EMI

Project Management

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10409867/10409868/10410011.pdf?arnumber=10410011

Reference15 articles.

1. Disruptive Innovation in Mining Industry 4.0

2. Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry

3. Machine learning applications in minerals processing: A review

4. Big data driven predictive production planning for energy-intensive manufacturing industries

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