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Application of multi regressive linear model and neural network for wear prediction of grinding mill liners
Luleå University of Technology, Sweden.ORCID iD: 0000-0002-8524-3321
Luleå University of Technology, Sweden.
2013 (English)In: International Journal of Advanced Computer Sciences and Applications, ISSN 2156-5570, Vol. 4, no 5, 52-58 p.Article in journal (Refereed) Published
Abstract [en]

The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN) techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.

Place, publisher, year, edition, pages
2013. Vol. 4, no 5, 52-58 p.
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:mdh:diva-26429DOI: 10.14569/IJACSA.2013.040509OAI: oai:DiVA.org:mdh-26429DiVA: diva2:760339
Available from: 2014-11-03 Created: 2014-10-31 Last updated: 2014-11-03Bibliographically approved

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Ahmadzadeh, Farzaneh
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CiteExportLink to record
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Citation style
  • apa
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