Properly designed experiments are necessary to ensure that trial results are definitive and cost-effective: Factorial Experiments
Instructions on how to properly design and run plant trials to boost recovery or mill throughput. Data Analysis: Techniques for error analysis, outlier detection, and regression modeling Process Control: Sampling theory, mass balancing, and multivariate analysis. Risk Management: Statistical Methods For Mineral Engineers
: Establishing ranges within which the "true" value of a parameter likely falls, allowing engineers to report results with a defined level of certainty. 5. Advanced & Emerging Methods Properly designed experiments are necessary to ensure that
utilizes control charts (like Shewhart or CUSUM charts) to monitor performance in real-time. By distinguishing between "common cause" variation (inherent noise) and "assignable cause" variation (a mechanical failure or change in ore grade), engineers can intervene before a process drifts out of specification, preventing significant metal loss. 4. Regression Analysis and Predictive Modeling 4. Regression Analysis and Predictive Modeling