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Optimizing process parameters to increase the quality of the output in a separator: An application of Deep Kernel Learning in combination with the Basin-hopping optimizer
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Achieving optimal efficiency of production in the industrial sector is a process that is continuously under development. In several industrial installations separators, produced by Alfa Laval, may be found, and therefore it is of interest to make these separators operate more efficiently. The separator that is investigated separates impurities and water from crude oil. The separation performance is partially affected by the settings of process parameters. In this thesis it is investigated whether optimal or near optimal process parametersettings, which minimize the water content in the output, can be obtained.Furthermore, it is also investigated if these settings of a session can be testedto conclude about their suitability for the separator. The data that is usedin this investigation originates from sensors of a factory-installed separator.It consists of five variables which are related to the water content in theoutput. Two additional variables, related to time, are created to enforce thisrelationship. Using this data, optimal or near optimal process parameter settings may be found with an optimization technique. For this procedure, a Gaussian Process with the Deep Kernel Learning extension (GP-DKL) is used to model the relationship between the water content and the sensor data. Three models with different kernel functions are evaluated and the GP-DKL with a Spectral Mixture kernel is demonstrated to be the most suitable option. This combination is used as the objective function in a Basin-hopping optimizer, resulting in settings which correspond to a lower water content.Thus, it is concluded that optimal or near optimal settings can be obtained. Furthermore, the process parameter settings of a session can be tested by utilizing the Bayesian properties of the GP-DKL model. However, due to large posterior variance of the model, it can not be determined if the process parameter settings are suitable for the separator.

Place, publisher, year, edition, pages
2019. , p. 35
Keywords [en]
Industrial process, Optimization, Gaussian Process, Deep Kernel Learning, Basin-hopping
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-158182ISRN: LIU-IDA/STAT-A--19/010--SEOAI: oai:DiVA.org:liu-158182DiVA, id: diva2:1330940
Subject / course
Statistics
Supervisors
Examiners
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-06-26Bibliographically approved

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CiteExportLink to record
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Citation style
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