The Data Science of Steel, or Data Factory to Help Steel Factory

The Data Science of Steel

Applying Machine Learning to steel production is really hard! Here are some lessons from Yandex researchers on how to balance the need for findings to be accurate, useful, and understandable at the same time.

Steel production is an area that has been studied for decades, and as such the industry has remained very conservative. Despite the big data revolution beginning in the early 2000s, “old-school” industries like steel-making have largely shunned any form of data-driven applications.

Fortunately, things change, and here’s an example of how data analytics technologies, born within the internet industry, can be applied to an offline practice like turning pig iron into steel.

When we began work with Magnitogorsk Iron and Steel Works (MMK), one of the world’s largest steel producers and a leading steel company in Russia, a lot of time was spent looking for a challenge that if solved, could (a) positively impact business revenues, and (b) be completed in reasonable time.The challenge that was eventually uncovered and able to meet these criteria, is one well-known to all metallurgists: how much of each ferroalloy to add during steel-making process in order to ensure the required chemistry of the steel at the lowest possible cost.

This chemistry is dictated by the international standards for steel – a list of required ranges for the amounts of each element in the final mix. However, uncertainties in the steel-making process make determining the optimal amount of ferroalloys a complicated task. As ferroalloys participate in numerous chemical reactions, the final absorption of the added elements depends on many factors, which measurements are rough or even unknown.On top of that, ferroalloys are rather expensive –they can cost steelmakers hundreds of millions of dollars per year. As a result, metallurgy companies must balance two competing demands: keeping the use of costly ferroalloys to a minimum during production, and making sure that the resulting chemical composition complies with all requirements.

To help steelmakers with this challenge, we developed a recommendation service based on Machine Learning technologies and a history of 200,000 smeltings over the past years – luckily stored by MMK.

In a nutshell, the solution we developed consists of two parts: (1) the ferroalloy absorption model and (2) the optimization. However, before we discovered success there were a number of failed attempts – each of which provided interesting insights for us to learn from. The first part of the solution is the “absorption model”. Such model takes all available parameters of smeltings – the mass of scrap and crude iron, records of material consumption and chemical measurements, – as well as the amounts of the added ferroalloys, and returns the expected chemical composition of steel.

Fig. 1: These two-dimensional examples illustrate the restrictions applied to the percentages of certain chemical elements. The areas in yellow reflect the domains where the requirements are satisfied.

Chemical composition is a vector of percent shares of all chemical elements. Our first attempt was to build a model that would predict the whole vector at once, using a deep learning approach. Despite the recent hype around deep learning, it does not provide the best solution to every problem. In this instance, for example, the neural net proved to be not as good as expected. It is still not quite clear why the neural net failed; one of the reasons could be the various nature of input factors we used. This is far from the traditional deep learning tasks which use homogeneous factors such as computer vision, speech recognition or natural language processing.

We then switched to the element-by-element model.

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