Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform

• Autores: Walas Mateo, Federico; Redchuk, Andrés
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Lean Startup methodology; OEE; AI/ML; Low code platform; Net zero emissions; Industry 4.0.
• Institución: Universidad Nacional de La Plata

Traditional industries like steelmaking, are in the spotlight for the need of improving processes towards net zero emissions. This article presents a case on a new business model to ease the adoption of Machine Learning (ML) to optimize industrial processes, applied to a blast furnace at a steel company. The focus of the paper is to illustrate the way a ML platform with a Low Code solution approach can give results in two months to optimize a production process at a steel mill. The methodology used in the case allows obtaining a data model to be validated in less time than conventional approaches. This work pretends to give more light to the use of industrial data and the way traditional industries can evolve towards the industry 4.0 paradigm. The adoption of the low code solution is based on lean startup methodology. The cycle to obtain valid results includes the involvement of people from the process as well as analytics experts. At the end it can be seen that the solution contribute to improve Operational Equipment Effectiveness (OEE) and lower energy consumption. Besides process operators became empowered with the predictions that give the platform.
Instituto de Investigación en Informática