Mohsen Yahyaei

Mohsen Yahyaei is an expert in modelling, optimising, and controlling mineral processing circuits using novel approaches and tools. He is currently the Director of the Julius Kruttschnitt Mineral Research Centre (JKMRC) and Program Leader for Future Autonomous Systems and Technologies (FAST) at the University of Queensland’s Sustainable Minerals Institute. Mohsen completed his undergraduate studies in Mine Exploration and earned a Master’s degree in Mineral Processing in 2002. His master’s thesis focused on applying column flotation in the Sarcheshmeh Copper Complex, the largest copper mine in the Middle East. After his Master’s, he worked at the R&D centre of the Zarand coal washing plant in Iran for two years before becoming the plant manager. In 2007, he returned to the University of Kerman to pursue a PhD, investigating the effect of liner wear on charge motion and power draw of SAG mills, which he completed in 2010. Since joining JKMRC in 2011, Mohsen has conducted extensive applied research and successfully delivered numerous industry-funded projects. As a comminution specialist, he is dedicated to implementing fundamental understandings in his research to offer practical solutions to the minerals industry and educate engineers and researchers with problem-solving skills for future resource industry challenges. His research focuses on optimising mineral processing techniques to enhance efficiency and sustainability, with a strong emphasis on practical application. Mohsen's research extends to advanced process control, including the development of soft sensors and model-predictive control solutions. His work aims to improve the precision and reliability of industrial processes, contributing significantly to the field of mineral processing. Process Autonomy: Enabling tools and technologies to enable trusted autonomous systems and technologies for mineral processing plants. Dynamic modelling for process control and optimisation: New approach in implementing dynamic modelling of mineral processing circuits for developing process control strategies and accessing process performance Study surface breakage of rock particles: Measuring surface breakage of rocks under different loading mechanisms to inform mechanistic breakage models Mechanistic approach in liner wear modelling: Incorporating factors affecting liner wear in the structure of a mechanistic wear model

[2026] From crack initiation to fragmentation: mineralogical controls on breakage in copper-bearing ore [2026] Developing a PESTLE based model to review the energy transition accelerator parameters in Australia [2026] Soft sensors for grinding circuit control and optimisation: deployment at Newmont operations [2025] Wear-aware reinforcement learning for supervisory control of SABC grinding circuits [2025] Transforming comminution control with soft sensors: the Newmont-Cadia Valley Azure ML experience [2025] Reevaluating system thinking in autonomous decision-making in mineral processing plants [2025] LCA Review for mineral processing units [2024] Application of machine learning for generic mill liner wear prediction in Semi-Autogenous Grinding (SAG) mills [2024] Sustainable resource manage