QAQC applied to the mining industry - Core Case

QAQC applied to the mining industry

QAQC stands for Quality Assurance and Quality Control. 

Quality assurance (QA) and quality control (QC) are the two major components of any quality management system. 

According to ISO definition, QA is “the set of all planned and systematic actions required to provide appropriate assurance that a product, process or service will comply with certain quality requirements.”

While QC refers to an “actions system to check for efficacy of control activities quality.” 

QA is about issues prevention, while QC is about detecting them, should they occur.

QAQC application to mineral projects

QAQC applies to various types of data management projects, and it is a mining industry standard to be applied in various countries, regarding mineral survey and exploration data – especially associated with sampling, laboratory analyses, and data management.

For all practical purposes, geological quality control procedures aim at monitoring test data precision and accuracy, as well as potential sample contamination during preparation and test. 

QAQC application to the mining industry aims at ensuring the quality of data and information used for decision making associated to the project is solid – thus interfering with relevant aspects of a mineral project that directly affect its development.

A positive QAQC stands for an approval seal to the database. Without such seal, all effort made during complex geological interpretation, mathematical estimation, and resource classification will be jeopardized.

Diagram showing QAQC process in the mining industry Source: Instituto Minere

QAQC applicability to the mining industry

QAQC is more effective if applied during a drilling program to MONITOR data collection and suspend low-performance programs or laboratory testing. 

Performing QAQC upon the completion of a data collection program is an effective way to manage data collection.

A good QAQC process is the one that is active, continuous, and reviewed as data are collected; an easy-to-understand process that makes sense and provides sufficient information for timely corrections to its sampling or laboratory procedures.

QAQC – Major ways to analyze data quality in the mining industry 

  • Statistical process control
  • Develop control graphs
  • Define standards
  • Define control bounds

QAQC process activities for the mining industry are driven by a series of technical standards and analytical procedures. Statistical control and data organization are major QAQC tools.

Photo: Afonso Gonçalves – TOP GEO

QAQC and sampling standards in the mining industry

Sampling in the mining industry is one of the most relevant quality control procedures. It includes some definitions used for control sample description. They include: 

  • Twin samples: Used to assess sampling error and indicate low or high variability of mineralogical sample characteristics. They are samples obtained from the second core drill split. Both samples must be prepared and analyzed at the same laboratory, same lot, however with different identifications, without characterizing which ones will be for quality control. 
  • Coarse whites: Samples used for identification of contamination during preparation stage. They are mine waste material samples (no mineralization), with coarse particle size, which must be subject to the whole preparation process along with original samples of the project. They are recommended to be added right after mineralized samples.
  • Fine whites: This tool is used to identify potential contamination during analysis. They are pulverized material samples free from active components (waste) and must be added and analyzed right after sample mineralization. 
  • Standards: Used to assess analytical accuracy. They are samples prepared under special conditions and must be used as lots for analysis by primary and secondary laboratories. A standard of at least three-degree ranges (low, medium, and high) is recommended. Accreditation criteria are used to assess analytical accuracy.
  • Coarse (or preparation) duplicates: Tool used to assess errors during the preparation stage (quartering). They are duplicates collected right after crushing and quartering stage. They must be added to the same original sample lot, with different numbers, and analyzed at the same laboratory. 
  • Slurry duplicates: Used to access analytical laboratory precision. They are replicates (duplicates) of the original samples, pre-crushed, added to lots for analysis and identified differently from the original samples. Such samples are used to assess the analytical laboratory precision.

QAQC and application to mineral project phases

A Mineral Project has several phases or stages with common data generation or sampling. Therefore, QAQC. procedures must be complied with. In general, these phases/stages include: 

  • Mineral Prospection: This stage is the outset of a mining project, with ore and associated rock characterization being required (thus involving analysis of its mineralogy, grades, and resource and reserve definition). Thus, QAQC will provide data transparency and reliability required for economic feasibility of the project. Sampling and its description (drill cores or manual) and data management are relevant steps for QAQC application.
  • Chemical analyses:  Reliable and transparent results for samples sent over to the laboratory are fundamental for appropriate ore deposit characterization and decision-making. Several sampling control tools (previously referred to) are used for the chemical analysis process, e.g. mass control, preparation white, duplicates, reference materials.
  • Database: All ore deposit information obtained are managed on a database. Thus, it is important for such data to be correct for project development and upcoming stages not to be jeopardized. A responsible person for database management must be appointed, and permissions for each user and backups must be obtained.
  • Geological Modeling: All deposit-related information (drilling data, samples, rock types, chemical analyses, grades, and density, amongst others) can be visualized on a geological model. Thus, it is paramount for all data to be appropriately located and shown on the model, with this being the essential tool for decision-making of the mineral project (e.g., resource definition, economic feasibility, and even mine location for ore exploration). 

Thus, sampling, analyses, and data management stages, along with QAQC procedures, are fundamental for good mineral survey or exploration project performance.

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