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A FERROALOYS CONSUMPTION OPTIMIZATION SYSTEM

NLMK

GOALS AND OBJECTIVES

  • BUSINESS OBJECTIVE

    To improve overall workshop efficiency.

  • IT OBJECTIVE

    To develop and implement a new machine learning-based (ML-based) recommendation service.

SOLUTION

  • A mathematical ML-model.
Solve a similar problem

IMPLEMENTATION

Previously, the composition and quantity of ferroalloys required for a particular grade of steel were determined manually, based on instructions and steelmakers’ own personal experience. This state of affairs led to a situation in which the consumption of ferroalloys was not always optimal.


Jet Infosystems specialists developed and implemented a service which is able to recommend optimal ferroalloy consumption during production. This solution was implemented at Novolipetsk Metallurgical’s first converter workshop.


The system uses a great amount of data to generate recommendations. This data includes: the target chemical composition for steel and ferroalloys, their interchangeability, the melting route, results from intermediate chemical analyses, the presence/absence of certain ferroalloys, as well as their cost and other historical data.

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PROJECT RESULTS

The new ML-model makes it possible to accurately calculate the composition and volume of additives required to create steel with the required chemical composition. As a result, the strength, heat resistance and other parameters of the finished product have been improved.

As present, this service has been implemented for the first converter workshop using an ordinary brand gauge.

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