Registration Opens July 1, 2021!
About this course…
Poor sampling, compounded by poor laboratory subsampling, leads to questionable geostatistics, and generates severe conciliation problems between the geological model, the mine, and the plant estimates. These problems also affect the price of commodities and the validity of environmental assessments. The result is a huge money loss for the company involved, evolving later in likely litigation. It is of key importance for geologists, miners, metallurgists, chemists, and environmental specialists to extract maximum information from the available data, as large investments and crucial decisions depend on it. False evaluations lead to devastating scenarios such as:
- Abandonment of viable properties,
- Exploitation of unprofitable properties,
- Mismanagement of viable properties, and
- Incompetence in fraud detection.
It is critically important to quantify the heterogeneity of important constituents in any new property. Failure to do appropriate testing leads to invalid sampling and subsampling protocols, excess drilling, and a biased database that would later lead to false geostatistics. The following sequence is part of inescapable practice:
- How is the constituent of interest distributed in the material to be sampled?
- Conduct Heterogeneity Tests to quantify the sampling characteristics of the constituent of interest.
- Optimize sampling protocols and the way they are implemented, according to the results from the Heterogeneity Test.
- Implement protocols using valid sampling equipment: 75% of the sampling equipment available on the market will never do the job.
- Implement a comprehensive, systematic quality control program to monitor sampling precision and accuracy.
The staggering cost of data irrelevant variability is not easy to detect, quantify, or correct. A strategy for effective management of variability will enable managers to identify and minimize annoying conciliation problems between theoretical models and reality: Your decisions are only as good as your samples!
The course offers simple ways to quantify money losses for a given sampling precision, and it provides a good strategy to prevent catastrophic sampling inaccuracy for which there is no statistical cure. Unless sampling precision and accuracy are clearly connected to economic issues, it is unlikely that any manager would understand the reason for improving sampling protocols and the way they are implemented. At the end of the course, the attendee will be better equipped to present the economic advantages of good sampling to company executives. Therefore, the course is pre-requisite for bank investment: Bankers must listen, and trust the Sampling Theory.
Continuing education credits
Colorado School of Mines will award 3.5 Continuing Education Units (CEUs) upon successful completion of this course. This course is offered as part of the Statewide Extended Studies Program of the Colorado Commission on Higher Education.
What you will learn
- You will become familiar with the nine different kinds of sampling errors, how they take place, and how to minimize them; most people can list only two!
- You will become familiar with sampling correctness, so you can eliminate or reject sampling systems offered to you that will never perform a satisfactory job.
- You will become familiar with necessary tests to be performed at mines and plants to optimize all your sampling protocols.
- You will be in a position to select appropriate Data Quality Objectives for operating parameters, which are worth continuous monitoring, to minimize your operating cost.
- You will better appreciate the value of existing chronological data that allow you to better control any process. This data has great value for management, who should use them to identify structural problems leading to unnecessary financial losses.
- Variography is the key to identify the various sources of variability affecting routine chronological data. You will discover the power of Chronostatistics.
- Using existing data, variability from sampling and measurement must be clearly separated from process trends and process cycles. Unless this is well done, continuous process improvement will remain elusive.
- The careful use of the Moving Average and especially its auxiliary functions can greatly help you to minimize the effect of poor sampling and measurement precision.
- The Relative Difference Plots can show, in an unambiguous way, the presence of conditional biases from sampling and from laboratories.
- You will finally realize the weakness of today’s standards on sampling: They are obsolete and not in line with the Sampling Theory.
- You will be updated on the new developments in the world of sampling that were exposed during the first World Conference on Sampling and Blending held in Demark in 2003, and the second WCSB held in Australia in 2005.
- Workshop with software included in progressing lectures.
Who should attend?
This course is designed for individuals responsible for optimizing the performance of mines, metallurgical plants, chemical plants, and environmental assessments. The course also applies to many other areas where someone must collect samples to make important decisions. The course is highly recommended for managers to optimize their operations. You should attend this course if you are:
- Exploration and ore grade control geologists
- President,Vice Presidents, and operations managers
- Geostatisticians and laboratory supervisors
- Miners, metallurgists and chemists
- Quality Assurance and Quality Control managers
- Environmental engineers and pollution control specialists
- Concerned investors and company shareholders
Dr. Francis F. Pitard is a consulting expert in Sampling, Statistical Process Control, and Total Quality Management. He is President of Francis Pitard Sampling Consultants, and Technical Director of Mineral Stats Inc. in Broomfield, Colorado. Learn more…
Max Pitard is the founder and CTO of HonuaTek LLC, a company focused on developing digital solutions for the mining, manufacturing and utilities industries. Learn more…
The in-person section of the course will be taught on the campus of Colorado School of Mines in Golden, Colorado USA. Learn more…