Statistical Methods For Mineral — Engineers

Tim Napier-Munn

Statistical Methods for Mineral Engineers is a highly regarded professional resource and monograph written by . It is designed specifically for plant metallurgists and mine site professionals to bridge the gap between academic statistics and the messy, uncertain reality of mineral processing. Why It’s Essential

"If you can’t measure it, you can’t control it. If you can’t control it, you lose money." Statistical Methods For Mineral Engineers

Error Measurement

: Understanding and quantifying the uncertainty inherent in measurement and sampling. Tim Napier-Munn Statistical Methods for Mineral Engineers is

Next came variography: semivariograms, nugget effects, and range. These tools measured how similarity decayed with distance. Lin calculated experimental variograms in multiple directions. The anisotropy was clear: correlation extended farther along strike than down-dip. That mattered for kriging—an interpolator that weights nearby samples according to spatial correlation. Training: Every shift supervisor should understand a CUSUM

It is considered a standard reference text for plant metallurgists and assay chemists to translate vague observations into demonstrable facts. like regression modeling or experimental design in more detail?

6. Design of Experiments (DoE)

1. Training: Every shift supervisor should understand a CUSUM chart.
2. Visualization: Real-time dashboards showing control limits, not just raw data.
3. Documentation: Maintain a “statistical log” of when process adjustments were made, to compare with chart signals.

In a processing plant, dozens of variables (e.g., pH levels, reagent dosage, grind size, and residence time) interact simultaneously. Traditional "one-factor-at-a-time" testing is inefficient and misses these interactions. Instead, engineers use Design of Experiments (DoE) factorial designs Response Surface Methodology (RSM)