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

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.