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dc.contributor.advisorHepsø, Vidar
dc.contributor.authorGuldhav, Gard Marius
dc.date.accessioned2024-03-30T18:19:36Z
dc.date.available2024-03-30T18:19:36Z
dc.date.issued2023
dc.identifierno.ntnu:inspera:169899243:171075528
dc.identifier.urihttps://hdl.handle.net/11250/3124361
dc.descriptionFull text not available
dc.description.abstract
dc.description.abstractThe steel industry is changing and demands for cleaner raw materials is pressuring suppliers to develop newer, more effective and sustainable methods for iron ore beneficiation. This thesis investigates the possibility of developing a new capability for automating process control of gravity spirals not only for better product quality but also for balancing between yield and quantity, as those two have a somewhat inverse relationship. The research begins by exploring methods of computer vision and machine learning in one of the main beneficiation processes of the plant, combined with time series data and a collaborative interdisciplinary effort to empower machine learning models to deliver real-time decision support. While still providing operators the illusion of control. Additionally, the thesis investigates the benefits of remote operations, leveraging cloud-based computing and data analysis by migrating computing and storage resources to the cloud, the plant gains agility and scalability, essential for adjusting to the iterative nature of capability development. Remote operations not only streamline processes, but also offer environmental advantages by reducing on-site infrastructure. Sustainability remains a core theme and recognizes the importance of aligning production goals with environmental regulations.
dc.languageeng
dc.publisherNTNU
dc.titleCapability Development in Ore Beneficiation: A Machine Learning Approach to Spiral Monitoring
dc.typeMaster thesis


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