Using optimization methods to maintain accuracy in equipment like power-based belt scales. Sampling Design:
: Used to study the effects of several factors on a process and identify interactions between them. Randomized Block Designs
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Mineral assays rarely follow a perfectly normal distribution; they are frequently right-skewed (log-normal), with a long tail representing rare, high-grade ore pockets. Visualization Techniques Visual tools expose patterns that summary statistics hide:
Occurs when particles are incorrectly excluded or included by the sampler head due to bouncing or splashing. Using optimization methods to maintain accuracy in equipment
Monitoring product quality and tailings losses in real-time.
Developing customized water quality monitoring and mineral sampling procedures to minimize variance. Process Optimization: This link or copies made by others cannot be deleted
Minimize J=∑i=1n(xi−x̂iσi)2Minimize cap J equals sum from i equals 1 to n of open paren the fraction with numerator x sub i minus x hat sub i and denominator sigma sub i end-fraction close paren squared Subject to the conservation of mass constraints: ∑Mass In=∑Mass Outsum of Mass In equals sum of Mass Out = Measured value (e.g., feed assay) x̂ix hat sub i = Reconciled estimate σisigma sub i = Standard deviation of the sensor/assay method
When optimizing a metallurgical process—such as determining the ideal flotation reagent regime or leaching temperature—changing one factor at a time (OFAT) is inefficient and misses critical interactions between variables. Design of Experiments (DoE) solves this problem. Factorial Designs Evaluates