Abstract Machine learning and artificial intelligence techniques have an ever-increasing
presence and impact on a wide-variety of research and commercial fields. Disappointed by …

The nonferrous metallurgical (NFM) industry is a cornerstone industry for a nation's
economy. With the development of artificial technologies and high requirements on …

Missing data values and differing sampling rates, particularly for important parameters such
as particle size and stream composition, are a common problem in minerals processing …

The selection of an appropriate descriptive system and modeling framework to capture
system dynamics and support process control applications is a fundamental problem in the …

Process controllers are abundant in the industry and require attentive tuning to achieve
optimal performance. While tuning controllers by the most primitive method of trial and error …

A hybrid non-linear model predictive controller (HNMPC) is developed for a run-of-mine ore
grinding mill circuit. A continuous-time grinding mill circuit model is presented with a …

The recently developed reference-command tracking version of model predictive static
programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit …

A dynamic non-linear model was fitted to the grind curve data of an industrial semi-
autogenous grinding (SAG) mill by means of a step-wise procedure. Grind curves give the …

Grinding mills exhibit steady-state behaviour, formalised by quadratic performance functions
called grind curves. Grind curves are predominantly affected by feed-ore characteristics that …

The early detection and root cause identification of abnormal events in industrial processes
is important, to allow for timely corrective actions, ensuring continued economic operation …