Modern Sampling and Quality Management of Sampling Data (QAQC)

  • AUSTRALIA (Brisbane): 1-5/8/22

Course Brief

MODERN SAMPLING (EXPLORATION AND MINING)

This course will be completed using a mix of theoretical concepts, best practice case studies, and practical activity work, to reinforce knowledge and concepts explored during the course. Participants are assumed to be familiar with a mining environment and are comfortable with basic calculations and statistics.

The course has been developed for professionals involved in mine sampling programs (geologists, mining engineers, metallurgists and chemists) and quality control systems in mineral projects/operating mines. It provides to participants an overview of technical sampling theory, sampling techniques, analytical methods used in the mining industry and reviews best practice quality assurance and control systems.  The intent is to help the participants understand the systems they work with.

Mine sampling, which provides data which forms the basis for most of the costly mine decisions, is a highly technical activity that focusses on collecting data to facilitate informed decision making on available and/or potential orebodies within the organisation. It is expensive and usually there is a single opportunity to complete it properly. As errors in mine sampling processes can lead to erroneous business decisions, it is imperative that sampling is completed the right way the first time, … every time! It should be managed by competent, technical personnel who understand what they are doing and what the business is trying to achieve.

Attendees will learn to appreciate the value of the various mine samples within their organisations, as well as the level of risk that is carried over to decisions when relying on information derived from mine sampling activities. Potential sources of sampling error will be reviewed, as well as how to manage/eliminate the magnitude of sampling error. Methods on quantifying sampling error will be reviewed, and ultimately, robust sampling campaign design parameters will be proposed.

Access to mine sampling data of the highest integrity is key to evaluating and exploiting mineral resources and reserves.

Course Content

  • Mine Sampling defined
  • Rationale and Getting on the same Page
  • Mine Sampling important attributes
  • Sampling program managers and Sampling Designs
  • Understanding Inherent Heterogeneity
  • Working on common misconceptions
  • Sampling Errors
  • The Gaussian Distribution
  • The evolution of the Theory of Sampling
  • Mine Sampling Methods and Selection
  • Sub-sampling & splitting technologies
  • Sampling Nomograms

Key Learning Objectives

  • Sampling Distributions
  • Designing Sampling Protocols
  • Understanding Fundamental Sampling Error
  • Sampling Nomograph

 

QUALITY MANAGEMENT OF SAMPLING DATA (QAQC) 

This course will be completed using a mix of theoretical concepts, best practice case studies, and practical activity work, to reinforce knowledge and concepts explored during the course. Participants are assumed to be familiar with a mining environment and are comfortable with basic calculations and statistics. The course is designed to run over 5 days, is self-paced, inclusive of live online lectures, online practical work and offline assignments.

The course has been developed for professionals involved in quality assurance and quality control (QAQC) of mine sampling programs for minerals projects/operating mines. It provides to participants an overview of quality management systems, protocols, analytical methods used in the mining industry and reviews best practice QAQC. The intent is to work on common misconceptions while exploring evolution of best practice QAQC. Hopefully, it will help participants understand the systems they work with.

To ensure that reliable sampling and assaying data are obtained, a robust QAQC program is required, and should be an important part of any mine sampling program. There are many stages a sample must go through in order to obtain a result, whether it be an assay, specific gravity, moisture content etc.  Errors and faults can occur at any of these stages, so it is critical to have systems in place that minimise these errors and faults, and action procedures for when errors are identified.

The aim of this course is to provide an understanding of the importance of QAQC in mine sampling programs. The module provides an insight into the principles behind QAQC, describes the different types of QAQC data and why they are collected, and provides the necessary statistical tools to analyse QAQC data. Access to high quality mine sampling data is key to evaluating and exploiting mineral resources and reserves.

Course Content

  • QAQC defined
  • Quality Control Concepts
  • Accuracy and the Use of Standard Reference Materials
  • Precision and the Use of Duplicates and Replicates
  • Managing sampling bias
  • Quality Control monitoring
  • Descriptive Statistics
  • Database Management
  • Control Charts and Acceptance/Rejection Criteria
  • Identification of Anomalies
  • Managing QC problems in an inclusive manner
  • Setting up an appropriate Quality Control programme.
  • Commercial software applications vs Excel spreadsheets

Key Learning Objectives

  • Descriptive Statistics And Precision Bias Monitoring
  • Quantify Precision Using The Ranked Hard Plot
  • Duplicate Data Processing And Commentary
  • Accuracy/Contamination Monitoring, And Commentary On CRM/Blanks Data
  • Determining Action Procedures For Dealing With QAQC Anomalies
  • Mapping Out An Effective Qaqc System For Your Operation