Industry Luncheon Sessions

August 12 
12:00 - 13:30 | Junior Ballroom D

Abstract: Quanser’s role in control systems education is rooted in academia with over 30+ years of leadership in real-time control education.  Today, Quanser focuses on a scaffolded approach that bridges theory to practice by taking students from modeling and simulation to real-world validation. This is done with guided accessible curriculum resources that combine topics such as controls, robotics, and autonomous systems, enabling progression from coursework to research. As the field shifts toward Applied and Physical AI, controls now serve as the foundation for intelligent systems, where the key challenge lies in validating algorithms on real hardware under real-world constraints. This talk will walk though Quanser's academic DNA and how the controls landscape is slowly changing to include AI tools for advanced applications and in class lab assessments to empower the next generation of engineers.

August 13
12:00-13:30 | Junior Ballroom C

Abstract: Mineral processing (crushing, grinding and froth flotation) is a critical and energy intensive part of the mines-to-metals value chain. Effective process control across these unit operations is essential for maximising recovery and throughput within product quality constraints while meeting increasingly stringent environmental and energy efficiency standards. However, the mineral processing industry faces several challenges: declining and highly variable feed grades, rising energy and labour costs, volatile commodity markets, and growing circuit complexity. In addition, there are fundamental measurement difficulties arising from the heterogeneous, multiphase nature of slurry and slurry-air systems, where key quality variables often remain unmeasured or only sparsely observed. Organisational data silos further impede the closed-loop integration necessary for plant-wide optimisation. Panellists will discuss these challenges, along with prospects for improved control. Advances in on-stream sensing are beginning to close long-standing observability gaps. Increased digitisation and historian infrastructure allow real-time access to important control context. Progress in physics-informed machine learning and phenomenological models allow process representations that are both data-efficient and interpretable. Generative AI capabilities present further opportunities for operator decision support and knowledge transfer in an environment of tightening human resources. This panel brings together academic and industry experts to examine where control technology stands today, what is realistically achievable in the near term, and what barriers must be overcome to realise these prospects.