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

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 com...

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Detalles Bibliográficos
Autores principales: Walas Mateo, Federico, Redchuk, Andrés
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2022
Materias:
Ciencias Informáticas
Lean Startup methodology
OEE
AI/ML
Low code platform
Net zero emissions
Industry 4.0.
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/140656
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-140656
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Lean Startup methodology
OEE
AI/ML
Low code platform
Net zero emissions
Industry 4.0.
spellingShingle Ciencias Informáticas
Lean Startup methodology
OEE
AI/ML
Low code platform
Net zero emissions
Industry 4.0.
Walas Mateo, Federico
Redchuk, Andrés
Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
topic_facet Ciencias Informáticas
Lean Startup methodology
OEE
AI/ML
Low code platform
Net zero emissions
Industry 4.0.
description 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.
format Objeto de conferencia
Objeto de conferencia
author Walas Mateo, Federico
Redchuk, Andrés
author_facet Walas Mateo, Federico
Redchuk, Andrés
author_sort Walas Mateo, Federico
title Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
title_short Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
title_full Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
title_fullStr Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
title_full_unstemmed Process Optimization in the Steel Industry using Machine Learning adopting an Artificial Intelligence Low Code Platform
title_sort process optimization in the steel industry using machine learning adopting an artificial intelligence low code platform
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/140656
work_keys_str_mv AT walasmateofederico processoptimizationinthesteelindustryusingmachinelearningadoptinganartificialintelligencelowcodeplatform
AT redchukandres processoptimizationinthesteelindustryusingmachinelearningadoptinganartificialintelligencelowcodeplatform
bdutipo_str Repositorios
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