Statistical Methods For - Mineral Engineers ((link))

Statistical Methods for Mineral Engineers: A Practical Guide to Data-Driven Decision Making

Gy’s Formula for Fundamental Sampling Error: Statistical Methods For Mineral Engineers

Amaya also insisted they look beyond grade. Bulk density varied with lithology. Recovery rates depended on mineral liberation characteristics the assays didn’t capture. She introduced multivariate techniques: principal component analysis to summarize correlated geochemical indicators and co-kriging to incorporate secondary variables where appropriate. For zones with scarce sample density, they used indicator kriging to estimate the probability of crossing critical thresholds rather than trying to estimate a precise mean. Statistical Methods for Mineral Engineers: A Practical Guide

Key Objective: It provides tools to determine if process changes (e.g., new collectors or cyclone configurations) actually improve performance or if the observed variations are just "noise". : Used to compare a "new" versus "old"

: Used to compare a "new" versus "old" approach under similar operating conditions to isolate the effect of the change. Time Series Modeling

Control Charts (CUSUM): Monitoring plant performance over time to detect subtle shifts in process efficiency. Review of the Primary Resource: JKMRC Monograph

Developing customized water quality monitoring and mineral sampling procedures to minimize variance. Process Optimization: