[1]
|
Sustainable Technologies for Remediation of Emerging Pollutants from Aqueous Environment
2024
DOI:10.1016/B978-0-443-18618-9.00011-5
|
|
|
[2]
|
Artificial neural networks for predicting potentiodynamic tests of brass 70-30
Materials Today: Proceedings,
2023
DOI:10.1016/j.matpr.2023.01.287
|
|
|
[3]
|
Pulp Chemistry Variables for Gaussian Process Prediction of Rougher Copper Recovery
Minerals,
2023
DOI:10.3390/min13060731
|
|
|
[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
DOI:10.3390/colloids7020041
|
|
|
[5]
|
Artificial neural networks for predicting potentiodynamic tests of brass 70-30
Materials Today: Proceedings,
2023
DOI:10.1016/j.matpr.2023.01.287
|
|
|
[6]
|
Advanced Analytics in Mining Engineering
2022
DOI:10.1007/978-3-030-91589-6_15
|
|
|
[7]
|
Comparison of Various Estimation and Simulation Methods for Orebody Grade Variations Modeling
Journal of Mining Science,
2022
DOI:10.1134/S1062739122010197
|
|
|
[8]
|
Predictive capability evaluation and mechanism of Ce (III) extraction using solvent extraction with Cyanex 572
Scientific Reports,
2022
DOI:10.1038/s41598-022-14528-9
|
|
|
[9]
|
Selectivity index and separation efficiency prediction in industrial magnetic separation process using a hybrid neural genetic algorithm
SN Applied Sciences,
2021
DOI:10.1007/s42452-021-04361-6
|
|
|
[10]
|
Estimation and Improvement of Recovery of Low Grade Copper Oxide Using Sulfide Activation Flotation Method Based on GA–BPNN
Processes,
2021
DOI:10.3390/pr9040583
|
|
|
[11]
|
Mineralogical Prediction on the Flotation Behavior of Copper and Molybdenum Minerals from Blended Cu–Mo Ores in Seawater
Minerals,
2021
DOI:10.3390/min11080869
|
|
|
[12]
|
Soft Computing Techniques in Solid Waste and Wastewater Management
2021
DOI:10.1016/B978-0-12-824463-0.00008-2
|
|
|
[13]
|
Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
Engineering Reports,
2020
DOI:10.1002/eng2.12167
|
|
|
[14]
|
Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
Engineering Reports,
2020
DOI:10.1002/eng2.12167
|
|
|
[15]
|
Analysis of kinetic models for chalcopyrite flotation: effect of operating parameters
Geosystem Engineering,
2018
DOI:10.1080/12269328.2018.1560367
|
|
|