Apply statistical data validation methods to ensure measurement reliability.
Utilize PCA to manage, visualize, and interpret high-dimensional, noisy data.
) to evaluate model fit, ensuring they do not over-interpret weak correlations. Diagnostic Testing Statistical Methods For Mineral Engineers
Compares the means of two groups. A paired t-test evaluates the same circuit before and after a specific change (e.g., changing a frother type). An independent t-test compares two parallel flotation banks running different reagents.
Useful in particle counting statistics, automated mineralogy (e.g., QEMSCAN/TIMA data), and evaluating liberation states. 2. Pierre Gy’s Sampling Theory Diagnostic Testing Compares the means of two groups
Statistical Methods For Mineral Engineers " is most notably the title of a widely used monograph by Emeritus Professor Tim Napier-Munn , published by the Julius Kruttschnitt Mineral Research Centre (JKMRC) Core Purpose and Scope The text is designed as a practical guide for metallurgists and plant engineers
Mineral processing is inherently variable. Ore bodies are heterogeneous, crushers wear, flotation circuits drift, and assays contain error. Statistics is the bridge between noisy data and confident decisions. Ore bodies are heterogeneous
These allow engineers to study the interaction between variables. For example, a certain reagent might only work effectively when the pH is above 10.
Identifies the middle value, providing a measure of central tendency less affected by extreme assay outliers.
Utilizing designs like the Central Composite Design (CCD) or Box-Behnken, engineers can map out the multi-dimensional operating window of a circuit. This generates a mathematical contour map showing the precise "sweet spot" where recovery and concentrate grade are simultaneously maximized. 7. Statistical Process Control (SPC) and Control Charts