Unlike chemical plants that process homogeneous fluids, a mineral processing plant feeds on heterogeneous rock. A single assay result from a shift composite might be 2.5% Cu, but the next hour’s feed could be 1.8% or 3.2%. Is the change real? Is the flotation tank failing? Or did you just pick a weird rock?
The Mineral Engineer’s Dilemma:
“If I take two samples from the same conveyor belt, why don’t they give me the same grade?”
The Statistical Answer:
Every measurement = True Value + Sampling Error + Preparation Error + Analysis Error. Statistical Methods For Mineral Engineers
Statistics provides the tools to quantify those errors and act on signal, not noise.
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. Unlike chemical plants that process homogeneous fluids, a
Key parameter: The control limits are not arbitrary. For mineral processes, use three-sigma limits (99.7% confidence), but warn operators that false alarms will occur approximately 0.3% of the time.
"Pierre Gy’s Theory of Sampling" is critical for mineral engineers, as it statistically quantifies the errors inherent in collecting a sample from a moving stream or a stockpile. Before any processing occurs, the resource must be
Today’s mineral engineer has access to automated mineralogy (QEMSCAN, MLA), NIR sensors, and laser diffraction. This creates high-dimensional data.
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.