@inproceedings{8f1b8eaa0c484a778634428056b58b3a,
title = "Digital optimization of refractory maintenance",
abstract = "Refractory condition monitoring and maintenance are key to extending the lifetime of a vessel lining and therefore increase steelmaking efficiency. We present an approach for optimizing refractory maintenance by employing statistics and machine learning methods. The scope of this work is to provide intelligent decision support for planning and performing maintenance on refractory material in order to improve its lifetime. We tackle this challenge by contriving a recommendation system for refractory maintenance, e.g. for proposing hot repair intervals, based on historic process data and lining measurements.",
keywords = "Automated maintenance, Industry 4.0, Refractory optimization",
author = "Nikolaus Mutsam and Franz Pernkopf and Gregor Lammer",
note = "Publisher Copyright: {\textcopyright} 2021 by the Association for Iron & Steel Technology.; Iron and Steel Technology Conference and Exposition : AISTech 2021 ; Conference date: 29-06-2021 Through 01-07-2021",
year = "2021",
doi = "10.33313/382/169",
language = "English",
series = "AISTech - Iron and Steel Technology Conference Proceedings",
publisher = "Association for Iron and Steel Technology",
pages = "1657--1666",
booktitle = "AISTech 2021 - Proceedings of the Iron and Steel Technology Conference",
}