Research graph
References from Visible-spectrum image analysis for copper concentration measurement in heap leach solutions: Characterization of photographic and model parameters using a laboratory prototype. Local targets link to admitted publications; unresolved targets remain external evidence.
Estimation of copper grade, acid consumption, and moisture content in heap leaching using extended and unscented Kalman filters
10.3390/min15050521 · 2025 · External reference
Experience on SX/EW pilot plant automation
10.1016/s1474-6670(17)37017-9 · 2000 · External reference
Combining online process measurements and models to empirically test strategies for process monitoring
10.3182/20070821-3-ca-2919.00052 · 2007 · External reference
Unresolved reference
External reference
Computer vision and image-based sensing in mine-to-plant operations and extractive metallurgy: a bibliometric and systematic literature review
10.1016/j.mineng.2026.110256 · 2026 · External reference
Unresolved reference
External reference
Heap leaching. Computer simulation as an alternative technology
2015 · External reference
Training feedforward networks with the Marquardt algorithm
10.1109/72.329697 · 1994 · External reference
A simple and sensitive spectrophotometric method for the determination of copper(II) in aqueous solutions using a novel schiff base reagent
2024 · External reference
Insight into the Bouguer-Beer-Lambert law: a review
10.37256/sce.5220245325 · 2024 · External reference
Single peak parameters technique for simultaneous measurements: Spectrophotometric sequential injection determination of Fe(II) and Fe(III)
10.1016/j.talanta.2015.06.040 · 2016 · External reference
Potential copper production through 2035 in Chile
10.1007/s13563-020-00227-2 · 2020 · External reference
Design of a sensor based on neural networks to determine sludge level of industrial thickeners
10.1177/1550147720933153 · 2020 · External reference
Applying softcomputing for copper recovery in leaching process
2017 · External reference
Optimization of mining–mineral processing integration using unsupervised machine learning algorithms
10.1007/s11053-020-09628-0 · 2020 · External reference
Low-grade chalcopyrite ore, heap leaching or smelting recovery route?
10.1016/j.hydromet.2022.105885 · 2022 · External reference
Enhancing comminution process modeling in mineral processing: a conjoint analysis approach for implementing neural networks with limited data
10.3390/mining4040054 · 2024 · External reference
Spectrophotometric determination of copper (Ii) in soil from ahero rice irrigation schemes using hydroxytriazene
2020 · External reference
10.1117/1.jbo.26.10.100901
10.1117/1.jbo.26.10.100901 · External reference
10.1016/b978-0-443-40294-4.00029-3
10.1016/b978-0-443-40294-4.00029-3 · External reference
Hollow drop bubbles: a preliminary study of simplified prototype for improving copper(II) extraction with ACORGA® M5640 using coated bubble swarm
10.1016/j.hydromet.2024.106340 · 2024 · External reference
Unresolved reference
External reference
Colorimetric measurement of copper(II) in sand using direct electrokinetic extraction into a polymer inclusion membrane
10.1016/j.aca.2025.344262 · 2025 · External reference
Unresolved reference
External reference
A brief note on the heap leaching technologies for the recovery of valuable metals
10.3390/su11123347 · 2019 · External reference
Unresolved reference
External reference
Threshold concentration in the nonlinear absorbance law †
10.1039/c7cp01514c · 2017 · External reference
Unresolved reference
External reference
The evaluation of grinding process using artificial neural network
10.1016/j.minpro.2015.11.013 · 2016 · External reference
Correlation and prediction of saline solution properties for their use in mineral processing using artificial neural networks
10.2166/wrd.2015.132 · 2015 · External reference
Unresolved reference
External reference
Optimization of mining–mineral processing integration using unsupervised machine learning algorithms
10.1007/s11053-020-09628-0 · ExternalCitation · doi-reference
Potential copper production through 2035 in Chile
10.1007/s13563-020-00227-2 · ExternalCitation · doi-reference
10.1016/b978-0-443-40294-4.00029-3
10.1016/b978-0-443-40294-4.00029-3 · ExternalCitation · doi-reference
Colorimetric measurement of copper(II) in sand using direct electrokinetic extraction into a polymer inclusion membrane
10.1016/j.aca.2025.344262 · ExternalCitation · doi-reference
Low-grade chalcopyrite ore, heap leaching or smelting recovery route?
10.1016/j.hydromet.2022.105885 · ExternalCitation · doi-reference
Hollow drop bubbles: a preliminary study of simplified prototype for improving copper(II) extraction with ACORGA® M5640 using coated bubble swarm
10.1016/j.hydromet.2024.106340 · ExternalCitation · doi-reference
Computer vision and image-based sensing in mine-to-plant operations and extractive metallurgy: a bibliometric and systematic literature review
10.1016/j.mineng.2026.110256 · ExternalCitation · doi-reference
The evaluation of grinding process using artificial neural network
10.1016/j.minpro.2015.11.013 · ExternalCitation · doi-reference
Single peak parameters technique for simultaneous measurements: Spectrophotometric sequential injection determination of Fe(II) and Fe(III)
10.1016/j.talanta.2015.06.040 · ExternalCitation · doi-reference
Experience on SX/EW pilot plant automation
10.1016/s1474-6670(17)37017-9 · ExternalCitation · doi-reference
Threshold concentration in the nonlinear absorbance law †
10.1039/c7cp01514c · ExternalCitation · doi-reference
Training feedforward networks with the Marquardt algorithm
10.1109/72.329697 · ExternalCitation · doi-reference
10.1117/1.jbo.26.10.100901
10.1117/1.jbo.26.10.100901 · ExternalCitation · doi-reference
Design of a sensor based on neural networks to determine sludge level of industrial thickeners
10.1177/1550147720933153 · ExternalCitation · doi-reference
Correlation and prediction of saline solution properties for their use in mineral processing using artificial neural networks
10.2166/wrd.2015.132 · ExternalCitation · doi-reference
Combining online process measurements and models to empirically test strategies for process monitoring
10.3182/20070821-3-ca-2919.00052 · ExternalCitation · doi-reference
Estimation of copper grade, acid consumption, and moisture content in heap leaching using extended and unscented Kalman filters
10.3390/min15050521 · ExternalCitation · doi-reference
Enhancing comminution process modeling in mineral processing: a conjoint analysis approach for implementing neural networks with limited data
10.3390/mining4040054 · ExternalCitation · doi-reference
A brief note on the heap leaching technologies for the recovery of valuable metals
10.3390/su11123347 · ExternalCitation · doi-reference
Insight into the Bouguer-Beer-Lambert law: a review
10.37256/sce.5220245325 · ExternalCitation · doi-reference
