The normal distribution applies to highly controlled, steady-state processes with symmetric variations, such as the final product moisture content or chemical reagent additions controlled by automated loops. Log-Normal Distribution Crushed particle size distributions ( P80cap P sub 80
Statistics help identify whether a high-grade sample is a legitimate part of the ore body or a measurement error that needs to be "capped" to prevent biasing the model. 4. Process Optimization: Design of Experiments (DoE) Statistical Methods For Mineral Engineers
A copper porphyry deposit. Inverse distance weighting might over-weight a single high-grade assay near a fault. Kriging detects the anisotropy (directionality) and assigns weights based on the continuity along the ore body vs. across it. Process Optimization: Design of Experiments (DoE) A copper
These reveal whether data is unimodal, bimodal (indicating a shift in ore types), or heavily skewed. across it
Advanced deposits require the joint simulation of multiple geometallurgical attributes (e.g., grade, hardness, recovery, and deleterious element content) while respecting their complex correlations. High‑order statistics and multipoint simulation techniques offer powerful frameworks for generating realistic multi‑attribute orebody models. Production data – such as mill throughput or metal recovery – can subsequently be used to update these models adaptively, supporting short‑term planning decisions with the most current information.
Error caused by the distributional heterogeneity of the material, such as heavy minerals settling to the bottom of a conveyor belt or stockpile. Regular mixing and taking many small increments can mitigate GSE. Increment Materialization Errors
For the practising mineral engineer, the key message is not to treat these methods as a menu of techniques to be applied mechanically. Rather, statistical methods are ways of thinking quantitatively about uncertainty, variability, and risk – essential skills for a profession that must deliver reliable estimates and efficient operations in the face of imperfect information. The engineer who masters these tools will be well equipped to navigate the challenges of modern mining, from exploration through to final product.