Statistical Methods For Mineral Engineers

Before complex modeling can begin, engineers must understand the basic behavior of their data.

Highly sensitive to small, persistent shifts in the process mean. CUSUM charts are ideal for detecting gradual changes, such as the slow degradation of grinding media or subtle shifts in ore hardness. Statistical Methods For Mineral Engineers

The digital revolution has brought ML into mainstream mineral processing. ML models, such as Random Forests and Support Vector Machines, are particularly powerful for handling complex, non-linear systems. One common use is for data reconciliation , where ML algorithms are used to clean and impute missing or erroneous data from plant sensors. Another is for predicting key performance indicators (KPIs) in real-time, enabling "soft sensors" to predict a critical variable (e.g., concentrate grade) that is otherwise difficult or expensive to measure directly. Before complex modeling can begin, engineers must understand

Empirical models are vital when fundamental physical laws are too complex to model in real time. Regression analysis allows engineers to predict outputs based on operating conditions. Linear and Multiple Linear Regression (MLR) The digital revolution has brought ML into mainstream

Once a plant is operational, maintaining consistent performance is a primary objective. provides the tools for this task. However, mineral processing data is often autocorrelated—today's feed grade is correlated with yesterday's—violating the independence assumption of traditional SPC.