International Journal of Nonferrous Metallurgy

International Journal of Nonferrous Metallurgy

ISSN Print: 2168-2054
ISSN Online: 2168-2062
www.scirp.net/journal/ijnm
E-mail: [email protected]
"Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network"
written by Ebrahim Allahkarami, Omid Salmani Nuri, Aliakbar Abdollahzadeh, Bahram Rezai, Mostafa Chegini,
published by International Journal of Nonferrous Metallurgy, Vol.5 No.3, 2016
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review
Minerals, 2024
[2] Activated carbon adsorbents for the removal of emerging pollutants and its adsorption mechanisms
… for Remediation of Emerging Pollutants from …, 2024
[3] Pulp chemistry variables for gaussian process prediction of rougher copper recovery
Kyeremeh, K Ehrig, C Greet, R Asamoah - Minerals, 2023
[4] Experimental and Machine Learning Studies on Chitosan-Polyacrylamide Copolymers for Selective Separation of Metal Sulfides in the Froth Flotation Process
Colloids and Interfaces, 2023
[5] Artificial neural networks for predicting potentiodynamic tests of brass 70-30
Alvarez… - Materials Today …, 2023
[6] Prediction and Optimisation of Copper Recovery in the Rougher Flotation Circuit
Kyeremeh, C McCamley, M Zanin… - Minerals, 2023
[7] Predictive capability evaluation and mechanism of Ce (III) extraction using solvent extraction with Cyanex 572
Scientific Reports, 2022
[8] Advanced Analytics for Mineral Processing
Advanced Analytics in Mining Engineering, 2022
[9] Comparison of Various Estimation and Simulation Methods for Orebody Grade Variations Modeling
Journal of Mining Science, 2022
[10] Depression of pyrite in polymetallic sulfide flotation using chitosan-grafted-polyacrylamide polymers
2022
[11] АНАЛИЗ СПОСОБОВ И СХЕМ УПРАВЛЕНИЯ ХАРАКТЕРИСТИКАМИ ГИДРОУДАРНЫХ МАШИН ОБЪЕМНОГО ТИПА
ФИЗИКО-ТЕХНИЧЕСКИЕ ПРОБЛЕМЫ РАЗРАБОТКИ ПОЛЕЗНЫХ ИСКОПАЕМЫХ, 2022
[12] Mineralogical Prediction on the Flotation Behavior of Copper and Molybdenum Minerals from Blended Cu–Mo Ores in Seawater
2021
[13] Selectivity index and separation efficiency prediction in industrial magnetic separation process using a hybrid neural genetic algorithm
2021
[14] Estimation and Improvement of Recovery of Low Grade Copper Oxide Using Sulfide Activation Flotation Method Based on GA–BPNN
2021
[15] Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
2020
[16] Hyperspectral signature analysis using neural network for grade estimation of copper ore
IOP Conference Series: Earth and Environmental Science, 2018
[17] Development of a Predictive Model for the Recovery of Rare Earth Elements from the Leaching Process of Chilean Ores
2018
[18] Mitigation of environmental hazards of sulfide mineral flotation with an insight into froth stability and flotation performance
2018
[19] Analysis of kinetic models for chalcopyrite flotation: effect of operating parameters
2018
[20] A Systematic Review on Applications of Artificial Neural Networks in the Extraction Metallurgy Industry
SCIRP Newsletter
Copyright © 2006-2026 Scientific Research Publishing Inc. All Rights Reserved.
Top