WHAT WE DO
MACHINE LEARNING
Leveraging machine learning to uncover correlations between key parameters, optimizing nonlinear and complex systems.
LOWERING ELECTRICITY CONSUMPTION
Identifying and optimizing the most influential factors to lower energy consumption across various metallurgical operations, from ore processing to melting and refining processes.
LOWERING CO2 EMISSIONS
Implementing data-driven strategies to minimize carbon dioxide emissions, enhancing sustainability in metal production.
THE METHOD
We analyze your metallurgical system to identify key parameters for monitoring. Using expert knowledge and machine learning, we uncover meaningful relationships in the data and predict the critical factors that optimize production, reduce energy use, and minimize emissions. Read more
REDUCING ENERGY CONSUMPTION
Our solution has been proven to optimize energy usage, improve material efficiency, and reduce processing time. Operators can use the software to select input materials and set key parameters that influence energy consumption, product quality, and CO2 emissions. Real-time data collection continuously improves the model for even greater efficiency. Read more
OUR TEAM
Vaso Manojlović
Team leader
Jelena Ivanović
Manager
Nigel Phuthi
Metallurgical Engineer
Milan Dotlić
Machine learning scientist
Our team consists of industry professionals and academic experts who bring deep knowledge and practical experience in metallurgical processes and optimization. Vaso Manojlović, a professor at the University of Belgrade’s Department of Metallurgical Engineering, teaches courses on the theory of metallurgical processes, measurements, and control in industry. Jelena Ivanović is completing her PhD in circular economy and waste management, bringing expertise in sustainability. Nigel Phuthi is a seasoned metallurgical engineer with extensive knowledge of various industrial systems. Milan Dotlić, with a PhD in applied mathematics, is a researcher at the Institute for Artificial Intelligence, specializing in machine learning and data-driven solutions for industrial applications.