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Experiences in applying data‐driven modelling technology to steelmaking processes
Author(s) -
Miletic Ivan,
Boudreau François,
Dudzic Michael,
Kotuza Greg,
Ronholm Laura,
Vaculik Vit,
Zhang Yale
Publication year - 2008
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20090
Subject(s) - steelmaking , multivariate statistics , computer science , principal (computer security) , software , partial least squares regression , missing data , data mining , industrial engineering , engineering , machine learning , metallurgy , programming language , operating system , materials science
Experience has shown that data‐driven modelling methods are useful for improving steelmaking processes. In particular, principal components analysis and partial least squares are well‐suited for industrial implementation because they address practical issues such as colinearity and missing data. In the course of applying these multivariate methods on‐line, a need for a flexible computer infrastructure to better support data handling and model implementation was identified and met with an internally developed software calculation platform. Multivariate methods have been found useful for monitoring and for prediction and can also be applied as a foundation for other methods such as optimization.