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Analysis of an unreplicated 2^2 factorial experiment performed in a continuous process
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
2015 (English)In: Total quality management and business excellence (Online), ISSN 1478-3363, E-ISSN 1478-3371, Vol. 26, no 9-10, p. 1083-1094Article in journal (Refereed) Published
Abstract [en]

This paper presents a tentative analysis method for unreplicated factorial designs where regular statistical experimental analysis cannot be used. The methodology is demonstrated through the analysis of an unreplicated two-level, two-factor factorial experiment performed in a continuous production process where the process was not in statistical control and where changes in the experimental design made conventional experimental analysis impossible. The first step of the analyses included screening of the sampled data. Principal component analysis and factor analysis were then used to create an overview of how the various responses and experimental factors were related. Carbon monoxide efficiency was selected as the most important parameter to be analysed further. Elastic net regression was used as a screening tool to remove non-significant factors, interaction, and covariates. Finally, the carbon monoxide efficiency variation was modelled using an intervention analysis. Two experimental factors were found to actively influence the response. The experiment that from other perspectives can be considered to be unanalysable, did thus reveal causal effects. The results imply that for processes where the process dynamics may be monitored, observations of the process dynamics may reduce the needs for repeated experimental runs, thus reducing the experimental costs.

Abstract [en]

This paper presents a tentative analysis method for unreplicated factorial designs where regular statistical experimental analysis cannot be used. The methodology is demonstrated through the analysis of an unreplicated two-level, two factor factorial experiment performed in a continuous production process where the process was not in statistical control and where changes in the experimental design made conventional experimental analysis impossible. The first step of the analyses included screening of the sampled data. Principal component analysis and factor analysis was then used to create an overview of how the various responses and experimental factors were related. One response, the carbon monoxide efficiency was selected as the most important parameter to be analyzed further. Elastic net regression was used as a screening tool to remove non-significant factors, interaction, and covariates. Finally, the carbon monoxide efficiency variation was modeled using intervention analysis. Two experimental factors were found to actively influence the response. The experiment that from other perspectives can be considered to be unanalyzable, did thus reveal causal effects. The results imply that for processes where the process dynamics may be monitored, observations of the process dynamics may reduce the needs for repeated experimental runs, thus reducing the experimental costs.

Place, publisher, year, edition, pages
2015. Vol. 26, no 9-10, p. 1083-1094
National Category
Reliability and Maintenance
Research subject
Quality Technology & Management; Effective innovation and organisation (AERI); Intelligent industrial processes (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-31576DOI: 10.1080/14783363.2015.1068587ISI: 000362175300012Scopus ID: 2-s2.0-84959215119Local ID: 5cc6d084-cf62-48a9-8e06-e551b84ecaddOAI: oai:DiVA.org:ltu-31576DiVA, id: diva2:1004810
Conference
QMOD conference on Quality and Service Sciences : 03/09/2014 - 05/09/2014
Projects
Statistiska metoder för förbättring av kontinuerliga tillverkningsprocesser
Note

Validerad; 2015; Nivå 1; 20150330 (bjarne); Konferensartikel i tidskrift

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-08-16Bibliographically approved

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Bergquist, Bjarne

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CiteExportLink to record
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