vi
Management of Thiocyanate and Thiosalts in Gold Extraction Project. . . . . . . . . . . . . . . . . . . . . . 762
Effect of Makeup Water Quality and Future Minerals on Flotation Plant Performance. . . . . . . . . . . . . . 776
Human Capital
The Changing Environment for Mineral Processing Education
EMJM PROMISE—A Contribution to Sustainable Mineral Processing. . . . . . . . . . . . . . . . . . . . . 787
Understanding the Industry Perspective on the Current U.S. Hardrock Mining Engineering Education. . . . . 795
Swedish School of Mines (Education, Research, and Infrastructure) . . . . . . . . . . . . . . . . . . . . . . . 806
Mineral Processing Education in the Philippines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 810
Alternative Pathways to Mineral Processing Skills
Empowering Small-Scale Mining Communities Through Education and Technology ..............814
Providing Authentic Learning Experiences in Process Mineralogy in an Evolving Learner Landscape. ......820
Rebuilding the Lost Mineral Process Engineering Competency Base. . . . . . . . . . . . . . . . . . . . . . . 827
Process Control, Modeling, and Simulation
Advanced Modeling and Control of Comminution
A Computational Method to Determine the Real Time Wear Rate of Grinding Media in Ball Mill .......839
A Data-Driven Approach for Optimizing Dry Classification Grinding Circuit Efficiency. ...........848
Digitalizing Conveyed Process Feed Flows for Improved Ore Quality and Process Control. . . . . . . . . . . . ⤀ 861
Optimization of a Grinding Circuit Using Physics-Based and Machine Learning Models ............870
A Preliminary Non-Linear Model Predictive Control for Pressing Iron Ore Concentrates in an
Industrial HPGR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 881
Advanced Modeling and Control of Flotation
Copper Recovery Predictive Performance for Selected Machine Learning Algorithms. .............893
Development and Trial of an Online Modal Mineralogy Module for Process Plant Slurries. ..........901
Modelling of Mineral Dissolution Occurring During Flotation: Simulation of Water Recycling
Scenarios in an Industrial Plant. ........................................915
Real-Time Flotation Optimization Using Scientific AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927
Robust Expert Control of Entire Pb/Zn/Cu Flotation Plant for Complex Ores based on
Dynamic Multivariant Response Surface Methodology, Thermodynamics and Kinetics of Flotation and
Reagents at Doe Run’s Buick Mill ........................................934
Simulation of Flotation Circuits Using a Model Derived from First Principles. . . . . . . . . . . . . . . . . . 947
Circuit Simulation, Design, and Process Optimization 1
Application of Circuit Analysis to the Simulation of Rare Earth Element Solvent Extraction Circuits � � � � � � 955
Paper Withdrawn � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 964
PrOMMiS: Applying Novel Modeling Methods to Accelerate Critical Minerals Research, Development,
Demonstration, and Deployment (RD3) � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 980
Remote Integration of Autoclave APC at Pueblo Viejo Mine Amidst the COVID-19 Pandemic � � � � � � � � � 991
Circuit Simulation, Design, and Process Optimization 2
Defining a Quanitative “Framework” for ESG Optimization of Mineral Processing Plants. . . . . . . . . . . 1002
Entropy Analysis for Raw Material Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011
Management of Thiocyanate and Thiosalts in Gold Extraction Project. . . . . . . . . . . . . . . . . . . . . . 762
Effect of Makeup Water Quality and Future Minerals on Flotation Plant Performance. . . . . . . . . . . . . . 776
Human Capital
The Changing Environment for Mineral Processing Education
EMJM PROMISE—A Contribution to Sustainable Mineral Processing. . . . . . . . . . . . . . . . . . . . . 787
Understanding the Industry Perspective on the Current U.S. Hardrock Mining Engineering Education. . . . . 795
Swedish School of Mines (Education, Research, and Infrastructure) . . . . . . . . . . . . . . . . . . . . . . . 806
Mineral Processing Education in the Philippines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 810
Alternative Pathways to Mineral Processing Skills
Empowering Small-Scale Mining Communities Through Education and Technology ..............814
Providing Authentic Learning Experiences in Process Mineralogy in an Evolving Learner Landscape. ......820
Rebuilding the Lost Mineral Process Engineering Competency Base. . . . . . . . . . . . . . . . . . . . . . . 827
Process Control, Modeling, and Simulation
Advanced Modeling and Control of Comminution
A Computational Method to Determine the Real Time Wear Rate of Grinding Media in Ball Mill .......839
A Data-Driven Approach for Optimizing Dry Classification Grinding Circuit Efficiency. ...........848
Digitalizing Conveyed Process Feed Flows for Improved Ore Quality and Process Control. . . . . . . . . . . . ⤀ 861
Optimization of a Grinding Circuit Using Physics-Based and Machine Learning Models ............870
A Preliminary Non-Linear Model Predictive Control for Pressing Iron Ore Concentrates in an
Industrial HPGR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 881
Advanced Modeling and Control of Flotation
Copper Recovery Predictive Performance for Selected Machine Learning Algorithms. .............893
Development and Trial of an Online Modal Mineralogy Module for Process Plant Slurries. ..........901
Modelling of Mineral Dissolution Occurring During Flotation: Simulation of Water Recycling
Scenarios in an Industrial Plant. ........................................915
Real-Time Flotation Optimization Using Scientific AI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927
Robust Expert Control of Entire Pb/Zn/Cu Flotation Plant for Complex Ores based on
Dynamic Multivariant Response Surface Methodology, Thermodynamics and Kinetics of Flotation and
Reagents at Doe Run’s Buick Mill ........................................934
Simulation of Flotation Circuits Using a Model Derived from First Principles. . . . . . . . . . . . . . . . . . 947
Circuit Simulation, Design, and Process Optimization 1
Application of Circuit Analysis to the Simulation of Rare Earth Element Solvent Extraction Circuits � � � � � � 955
Paper Withdrawn � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 964
PrOMMiS: Applying Novel Modeling Methods to Accelerate Critical Minerals Research, Development,
Demonstration, and Deployment (RD3) � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 980
Remote Integration of Autoclave APC at Pueblo Viejo Mine Amidst the COVID-19 Pandemic � � � � � � � � � 991
Circuit Simulation, Design, and Process Optimization 2
Defining a Quanitative “Framework” for ESG Optimization of Mineral Processing Plants. . . . . . . . . . . 1002
Entropy Analysis for Raw Material Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011