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Feature selection and bleach time modelling of paper pulp using tree based learners
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2016 (English)In: WISE 2016 / [ed] Springer International Publishing, China - Shanghai, 2016, 385-396 p.Conference paper (Refereed)
Abstract [en]

Paper manufacturing is energy demanding and improvedmodelling of the pulp bleach process is the main non-invasive means ofreducing energy costs. In this paper, time it takes to bleach paper pulpto desired brightness is examined. The model currently used is analysedand benchmarked against two machine learning models (Random Forestand TreeBoost). Results suggests that the current model can be super-seded by the machine learning models and it does not use the optimalcompact subset of features. Despite the differences between the machinelearning models, a feature ranking correlation has been observed for thenew models. One novel, yet unused, feature that both machine learningmodels found to be important is the concentration of bleach agent.

Place, publisher, year, edition, pages
China - Shanghai, 2016. 385-396 p.
Series
Computer Notes in Computer Science, ISSN 0302-9743 ; 10042
Keyword [en]
Feature selection, Machine learning, CFS, Random forest, TreeBoost, XGBoost, Paper manufacturing
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-23410DOI: 10.1007/978-3-319-48743-4_31ISBN: 978-3-319-48742-7ISBN: 978-3-319-48743-4OAI: oai:DiVA.org:du-23410DiVA: diva2:1047928
Conference
Quat 2016 (4th Annual Workshop on Data Quality and Trust in Big Data), Shanghai, November 7-10 2016
Available from: 2016-11-19 Created: 2016-11-19 Last updated: 2016-11-21Bibliographically approved

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