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  • 1.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Hansson, Karl
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Feature selection and bleach time modelling of paper pulp using tree based learners2016In: Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I / [ed] Wojciech CellaryMohamed F. MokbelJianmin WangHua WangRui ZhouYanchun Zhang, China - Shanghai: Springer, 2016, Vol. 10042, p. 385-396Conference 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.

  • 2.
    Hansson, Karl
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Machine Learning Algorithms in Heavy Process Manufacturing2016In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, no 1, p. 1-13Article in journal (Refereed)
    Abstract [en]

    In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

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