Statistical Methods For Mineral Engineers 〈5000+ Premium〉

Statistical Methods for Mineral Engineers is a highly regarded professional resource and monograph written by Tim Napier-Munn. 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

Hypothesis Testing: Applying t-tests, F-tests, and chi-square tests to compare different reagents, equipment configurations, or circuit designs. Statistical Methods For Mineral Engineers

Design of Experiments (DOE): Systematically varying process parameters (e.g., pH, reagent dosage, grind size) to determine their effect on recovery. This is far more efficient than "one-factor-at-a-time" testing.

4.2 Detecting Autocorrelation: The Durbin-Watson Test

Before fitting a regression model (e.g., recovery = a·grade + b·grind + error), run a Durbin-Watson test. If the statistic is near 0 or 4 (strong autocorrelation), switch to time-series models like ARIMA or use differencing. Statistical Methods for Mineral Engineers is a highly

The book covers a wide range of statistical methods, from basic descriptive statistics to advanced techniques such as multivariate analysis, geostatistics, and simulation modeling. The authors have structured the book into 10 chapters, each focusing on a specific aspect of statistical analysis: variograms) is a field unto itself

Part 1: Foundational Concepts – Describing the Orebody

Before any processing occurs, the resource must be quantified. Traditional geostatistics (kriging, variograms) is a field unto itself, but here we focus on practical statistical descriptors.

1.1 Measures of Central Tendency and Spread

Mean grade is deceptive in mineral processing because high-grade outliers can pull the arithmetic mean upward, while the median better represents what the plant actually sees.