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A team of specialists from Severstal Digital, part of PAO Severstal, one of the world’s largest steel and steel-related mining businesses, working with experts in flat rolled products from the Cherepovets Steel Mill have successfully increased the productivity of a machine learning model that controls the speed of the Mill’s continuous pickling line #3 (NTA-3).
Adelina, a digital model in use at Severstal’s continuous pickling line #3 (NTA-3) since November 2019, has now been joined by “Ruban”, a new artificial intelligence agent based on a deep - reinforcement learning algorithm. Both products were developed by Severstal in-house using open source applications.
Adelina and Ruban now work in parallel with one another; Adelina controls the speed of the unit, and Ruban adjusts the speed to achieve optimal results. This partnership has made the production process more flexible and secure, as the model and agent are able to adjust the speed of the unit every second and respond instantly to any unforeseen situation.
Evgeny Vinogradov, CEO of Severstal Russian Steel Division, commented:
“The Adelina model had already met our expectations, demonstrating an initial increase in productivity of NTA-3 by more than 5 percent. In March 2020, we produced a record volume of pickled metal at this unit - more than 130 thousand tons. After introducing the Ruban agent, we recorded a further 1.5 percent increase in productivity, and we estimate that using the two technologies in parallel could provide more than 80 thousand tons of additional metal each year. This is a remarkable increase for one of the most significant units in the production of flat rolled products.”
Ruban differs from classic machine learning models, learning not just from historical data, but independently, by exploring the digital twin of NTA-3. The operating speed at the unit largely depends on the parameters of the passing steel strip – the length, width and thickness of the roll, its steel grade and temperature, among other factors. Ruban learns from combinations of different parameters, specifically created for it by a generative adversarial network, which uses two neural networks to generate new data. It also sets a production plan and creates unique situations for training purposes. For effective learning, the agent was assigned a training system based on rewards and penalties; Ruban experiments to find a solution where the reward amount surpasses the penalties as far as possible.
Boris Voskresenskii, Chief Digital Officer of Severstal, commented:
“The use of reinforcement learning to control production units is not widespread, particularly in metallurgy. We believe the use of Artificial Intelligence at NTA-3 to be the first such case in Russian practice. The performance improvement recorded on NTA-3 following the introduction of digital tools proves that a data-driven approach has a great future in the industry, and we are moving in the right direction.”