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Predicting the concentration of residual methanol in industrial formalin using machine learning
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Forutspå koncentrationen av resterande metanol i industriell formalin med hjälp av maskininlärning (Swedish)
Abstract [en]

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to provide the best results and was used to create the predictive model. The model was compiled to a stand-alone application with a graphical user interface using the MATLAB CompilerTM.

Place, publisher, year, edition, pages
2016. , 51 p.
Keyword [en]
Machine learning, Predictive modeling, Formalin, MATLAB
National Category
Engineering and Technology
URN: urn:nbn:se:kau:diva-46997OAI: diva2:1043898
External cooperation
Akzo Nobel Adhesives AB; The MathWorks AB
Educational program
Engineering: Engineering Physics (300 ECTS credits)
Available from: 2016-11-07 Created: 2016-11-01 Last updated: 2016-11-07Bibliographically approved

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