Advances in Mineral Processing Using Empirical Modeling

Dear Colleagues,

Many unit operations in mineral processing, such as comminution, flotation, leaching, and classification, are difficult to describe through purely analytical models. The physicochemical phenomena involved are complex, ore feeds vary considerably, and the interaction between process stages adds further difficulty. Empirical modeling addresses this by constructing mathematical representations directly from experimental data, achieving the accuracy needed for engineering applications without requiring a full mechanistic description of the system. This has been a common approach in mineral processing for decades.

In recent years, greater access to plant data and improved computational tools have significantly expanded what empirical methods can do. Design of experiments (DoE) techniques, including factorial designs, response surface methods, and optimal strategies, allow researchers to characterize processes systematically while keeping experimental effort manageable. Stochastic simulation makes it possible to account for the uncertainty introduced by ore variability. Empirical models are also at the core of process control schemes and soft sensors, and increasingly serve as the foundation for machine learning and deep learning applications, which handle complex, high-dimensional data in ways that traditional regression methods cannot. Digital twins have taken this further, incorporating empirical models into dynamic plant representations that support real-time monitoring and optimization.

This Special Issue covers advances in empirical modeling across the full mineral processing chain. Relevant topics include the design of experiments and response surface modeling, stochastic simulation, process control, soft sensors, machine learning and deep learning for prediction and classification, digital twins, data reconciliation, and post-mortem process analysis. Works focused on specific unit operations, such as grinding, flotation, leaching, gravity separation, classification, solvent extraction, or dewatering, are welcome, as are contributions dealing with integrated circuit modeling. Papers that combine experimental or plant data with validated predictive models are of particular interest.

Dr. Felipe Sepulveda
Dr. Daniel Calisaya-Azpilcueta
Prof. Dr. Mauricio Sales-Cruz
Prof. Dr. Peng Gao
Guest Editors

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