FIND OUT MORE
ABOUT OUR SOLUTION

SERVICE

Expert analysis of the current state of data collection, with the goal of improving data infrastructure and obtaining more data for our models based on our knowledge. More data improves model accuracy and opens up new possibilities, such as generating a CO2 report for each process stage and identifying manufacturing weak points.

  • Analysis of the current state
  • Standardization of all data types
  • Formation of the main data collection server
  • Expert analysis of the data from a metallurgical point of view

PRODUCT

Online software based on data-driven machine learning methods for predicting important process parameters, reducing energy consumption and CO2 emissions, improving product quality, and making products more competitive in large steel industries.

Online software for controllingimportant process parameters.

Software that looks for the influence of impurities and the quality of the input raw materials in the material on the quality of the product.

For process monitoring and control, we use cutting-edge data-driven machine-learning methods. Our service is founded on a multidisciplinary team and scientific principles, and includes the following components:
1. Client consultation regarding data infrastructure adaptation;
2. Creation of a model unique to each customer(because different customers have a combination of different equipment);
3. Real-time monitoring of process parameters with indicators for the main items essential to the client (energy efficiency, product quality, saving raw materials, CO2 emission report). Also, our model links parameters between different processing units, so that causal parameters can be found (e.g., chemical composition with tensile strength).

THE METHOD

We use an agile method to develop our software:

  • We take data from the industry, process the data and connect the most important parameters for the set goal. We look for connections between data, clean data, remove outliers.
  • We build a model to predict the use of the most suitable machine learning model.
  • We influence the process in real time, check the model, install new sensors to collect data.

With our research, it was demonstrated that machine learning can be used to discover correlations between many processing parameters within nonlinear and complex systems, such as melting steel scrap in an electric arc furnace. Using a data-centric approach with machine learning analysis, we found the relationship between the most influential parameters and the electricity consumption of melting steel scrap in electric arc furnace. In order to manage such data-centric models, expert knowledge is essential, because data management can better increase model accuracy than model tuning alone. This concept has been proven in terms of energy consumption, steel utilization, melting time, and it can also be implemented in terms of CO2 emissions and product quality.
Operators would use the online software to choose scrap materials for charging the furnace (of which there are more than 14 types), and set key process parameters that affect energy consumption, steel quality, and carbon dioxide emissions. Data would be collected in real-time and sent to the server so that the model could be further improved. Furthermore, we found that the addition of new sensors will improve the model's weaknesses by gathering new data. Deploying such sensors can be simple to install in existing infrastructure and useful for improving model performance.

Our multidisciplinary team does not view machine learning as a black box, but carefully analyzes the relationships between parameters from a technological, economic, and environmental point of view. Once the MVP is established (online software), our team can successfully incorporate new user requirements in an agile way, which implies, a circular process of: data processing, model tuning, and analysis of results. In this way, the software would make the best possible combination of steel scrap to enhance the steel making process for energy conservation and production efficiency and obtain steel of better quality.

Production data is very scattered and difficult to obtain correlation using conventional methods.

We find the connection of the influence of certain parameters on the target parameters in the process.

We predict the target parameter with high accuracy (and low mean absolute error).

TEAM OF EXPERTS AND KNOWLADGE

This multidisciplinary but complementary team consists of an assistant professor who specializes in metallurgy and who cooperates with Metalfer, which is where the idea for the software that is the subject of this project came from. Vaso Manojlovic is a participant in a large number of projects of the Ministry of Science and Technological Development. A concrete confirmation of the subject innovation is the published work on the same topic in a top scientific journal. This also indicates the novelty in this sector of industry. Jelena dealt with waste and scrap iron, has 3 years of work experience in a managerial position in the company with the main focus on efficient management of waste streams, environmental methods of waste recycling, studies.
Jelena is also a PhD student at the Faculty of Technology and Metallurgy with the topic of circular economics in the metallurgical industry. Jelena and Vaso won second place in the competition for the best technological innovation in Serbia in 2016, so the team members got to know each other's work.
Milan Dotlić, PhD, specialist in applied mathematics and machine learning. For many years he worked at the Jaroslav Černi Institute, participated in a large number of science projects and published a large number of scientific papers in the field. What drives us are the preliminary data obtained for the model as well as their initial confirmation in practice, ie the model follows a real system. The technical side of the story is led by assistant professor Vaso Manojlovic, and each member of the team has done so far in his segment: problem, solution, simulation model, negotiations with users, testing. What will keep the team together is the common faith in the future of machine learning and artificial intelligence in large systems, the great savings that are being made, the environmental side of the story and the encouragement of such initiatives in the Republic of Serbia.

MILAN DOTLIĆ

Machine learning scientist

VASO MANOJLOVIĆ

Team leader

JELENA IVANOVIĆ

Manager

What drives us are the preliminary data for the model as well as their initial confirmation in practice, ie the model follows a real system.

References

METALFER Steel Mill is group of companies involved inmetallurgy, energy and trading. Metalfer steel mill owns and operates EAF basedsteel mill producing construction steel products. Metalfer invest is responsible for development of new projects concentrating on energy generation, environmental projects and new technologies.
Research article: Manojlović Vaso, Željko Kamberović, Marija Korać, and Milan Dotlić. "Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters." Applied Energy 307 (2022): 118209.
Other projects: MATERIAL DESIGN. Currently we are working on design of biocompatible Titan alloys using machine learning methods.

We have one Ph.D. researcher working on this issue. We found the influence of certain elements on the modulus of elasticity in Titanium alloys.