GENERAL AND SPECIFIC OBJECTIVES
The course aims to present the great opportunities that exist in the mining industry for the integration of data from mining dispatch systems, process control, laboratory, climate monitoring and planning information mining and geo metallurgical available. Attendees will be trained in how to transform data into information for operational management. Classification, flotation, and thickening grinding models will be shown that provide the knowledge for their analysis with operational data. Examples of industrial cases will be shown. It is suggested that attendees have the plant process diagrams so that their modeling in blocks for their management.
Process diagrams in PowerPoint will be used to show the concepts and methodology. Practical examples are shown using a dynamic simulator running in a historian to analyze the data and its transformation into information for the process model. Examples.
Process Engineers, Mining Engineers, Plant Managers, Technical Support, Students of Chemical, Metallurgical, Electrical, Environmental Engineering, etc.
- Welcome and Introduction to Digital Transformation in the Mining Industry.
- The secrets of digital transformation.
- Process Control and Dynamic Management of Operations.
- Sensors, Control Loops and Decision-Making Strategies.
- The 4 stages of value creation.
- Cloud based strategies using PI System and Seeq Advanced Analytics.
- Digital Twin Plant Model – Steps 1 and Step 2.
- Operational states, ore types, work teams, production planning and environmental constraints.
- Analysis of the global effectiveness of production.
- Net Metal Production Rate, Recovery and Metal Losses.
- Questions and discussion Module 1.
- Steps 3 Predictive Process Models.
- Machine Learning integrated Data Cleansing and Event Classification.
- Hybrid Grinding Classification Dynamic Models.
- Hybrid Flotation Dynamic Models.
- Hybrid Water Recovery Thickener Models.
- Questions and discussion Module 2.
- Pyrometallurgy Smelter and Refining Examples.
- Hydrometallurgical Process Models and Examples.
- Advanced Monitoring of Process Control Systems.
- Predictive Models for Critical Equipment.
- External Remote Support..
- Questions and discussion Module 3.
- Digital Transformation Step 4.
- Operational Integration Mines, Plants and Dispatches.
- Net Copper Production and Recovery Maximization.
- Operational Center of Mine Plants.
- Production, Energy and Water Operational Center.
- Center for Monitoring and Diagnosis of electricity generation.
- Conclusion and Future Work.
- Questions and discussion Module 4.
- Conclusions and Closure of the Course.
EXPERT INSTRUCTOR : Dr. OSVALDO A. BASCUR
Principal Digital Transformation – OSB Digital, LLC in Houston, TX.
Dr. Bascur is currently Principal Digital Transformation at OSB Digit, LLC and he is Consultant Fellow for Seeq Advanced Analytics. He worked as a principal at OSISOFT for 25 years. He was a staff engineer for Pennzoil and before he was process control engineer working for Duval Corporation, Tucson, Az (now Freeport McMoRan).
He has recently designed a template for the digital transformation of Plants for dynamic operational management, quality, asset and energy/water optimization. Today, the Digital Plant Strategy transform sensor data into Operational Insights (Data + Operational Events) to enable Overall Production Effectiveness and Predictive Analytics. The strategy identifies the Hidden Production/Energy/Water Losses in an Industrial Plant enabling Production Maximization while eliminating losses. This template strategy enables the transformation of huge amounts of unusable data into InFORMation to generate additional Operating Insights, Predictive Models, and integrates with Business Intelligence Tools such as MS PowerBI, Seeq Advanced Analytics, and AWS Quick sight.
Dr. Bascur wrote the chapter on Process Control and Operational Intelligence for the SME Mineral and Extractive Metallurgy Processing Handbook in 2019. He contributed with a chapter Measuring, Managing and Transforming Data for Operational Insights for the Smart Manufacturing Concepts and Methods led by the University of Texas and Drexel University. He edited the Latin American Mining Perspectives: Exploration, Mining and Processing for the SME. In addition, he has written more than 95 technical papers and contributed to numerous chapters in several books. He is editor of the MEI and IFAC publications.
He received the most prestigious SME Antoine Gaudin Award in 2014. He is a member of the AIChE, SME, AIST, IFAC MMM and the IMPC.
He is a Chemical Engineer and Metallurgical Engineer from the University of Concepción. He received his PhD in Metallurgical Engineering from the University of Utah, Salt Lake City, USA.