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Exploring the Use of Design of Experiments in Industrial Processes Operating Under Closed-Loop Control
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-3911-8009
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-4222-9631
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.ORCID-id: 0000-0003-1473-3670
Rekke forfattare: 42017 (engelsk)Inngår i: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 33, nr 7, s. 1601-1614Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Industrial manufacturing processes often operate under closed-loop control, where automation aims to keep important process variables at their set-points. In process industries such as pulp, paper, chemical and steel plants, it is often hard to find production processes operating in open loop. Instead, closed-loop control systems will actively attempt to minimize the impact of process disturbances. However, we argue that an implicit assumption in most experimental investigations is that the studied system is open loop, allowing the experimental factors to freely affect the important system responses. This scenario is typically not found in process industries. The purpose of this article is therefore to explore issues of experimental design and analysis in processes operating under closed-loop control and to illustrate how Design of Experiments can help in improving and optimizing such processes. The Tennessee Eastman challenge process simulator is used as a test-bed to highlight two experimental scenarios. The first scenario explores the impact of experimental factors that may be considered as disturbances in the closed-loop system. The second scenario exemplifies a screening design using the set-points of controllers as experimental factors. We provide examples of how to analyze the two scenarios

sted, utgiver, år, opplag, sider
John Wiley & Sons, 2017. Vol. 33, nr 7, s. 1601-1614
HSV kategori
Forskningsprogram
Kvalitetsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-61872DOI: 10.1002/qre.2128ISI: 000413906100024Scopus ID: 2-s2.0-85012952363OAI: oai:DiVA.org:ltu-61872DiVA, id: diva2:1072527
Merknad

Validerad;2017;Nivå 2;2017-11-03 (andbra)

Tilgjengelig fra: 2017-02-08 Laget: 2017-02-08 Sist oppdatert: 2019-06-18bibliografisk kontrollert
Inngår i avhandling
1. Contributions to the Use of Statistical Methods for Improving Continuous Production
Åpne denne publikasjonen i ny fane eller vindu >>Contributions to the Use of Statistical Methods for Improving Continuous Production
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Complexity of production processes, high computing capabilities, and massive datasets characterize today’s manufacturing environments, such as those of continuous andbatch production industries. Continuous production has spread gradually acrossdifferent industries, covering a significant part of today’s production. Commonconsumer goods such as food, drugs, and cosmetics, and industrial goods such as iron,chemicals, oil, and ore come from continuous processes. To stay competitive intoday’s market requires constant process improvements in terms of both effectivenessand efficiency. Statistical process control (SPC) and design of experiments (DoE)techniques can play an important role in this improvement strategy. SPC attempts toreduce process variation by eliminating assignable causes, while DoE is used toimprove products and processes by systematic experimentation and analysis. However,special issues emerge when applying these methods in continuous process settings.Highly automated and computerized processes provide an exorbitant amount ofserially dependent and cross-correlated data, which may be difficult to analyzesimultaneously. Time series data, transition times, and closed-loop operation areexamples of additional challenges that the analyst faces.The overall objective of this thesis is to contribute to using of statisticalmethods, namely SPC and DoE methods, to improve continuous production.Specifically, this research serves two aims: [1] to explore, identify, and outlinepotential challenges when applying SPC and DoE in continuous processes, and [2] topropose simulation tools and new or adapted methods to overcome the identifiedchallenges.The results are summarized in three appended papers. Through a literaturereview, Paper A outlines SPC and DoE implementation challenges for managers,researchers, and practitioners. For example, problems due to process transitions, themultivariate nature of data, serial correlation, and the presence of engineering processcontrol (EPC) are discussed. Paper B further explores one of the DoE challengesidentified in Paper A. Specifically, Paper B describes issues and potential strategieswhen designing and analyzing experiments in processes operating under closed-loopcontrol. Two simulated examples in the Tennessee Eastman (TE) process simulatorshow the benefits of using DoE techniques to improve and optimize such industrialprocesses. Finally, Paper C provides guidelines, using flow charts, on how to use thecontinuous process simulator, “The revised TE process simulator,” run with adecentralized control strategy as a test bed for developing SPC and DoE methods incontinuous processes. Simulated SPC and DoE examples are also discussed.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2017. s. 109
Serie
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Emneord
Process industry, Continuous process, Statistical process control, Design of experiments, Process improvements, Simulation tool, Engineering process control
HSV kategori
Forskningsprogram
Kvalitetsteknik
Identifikatorer
urn:nbn:se:ltu:diva-66256 (URN)978-91-7583-996-7 (ISBN)978-91-7583-997-4 (ISBN)
Presentation
2017-11-27, A109, Luleå University of Technology, Luleå, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Research Council, 4731241
Tilgjengelig fra: 2017-10-25 Laget: 2017-10-25 Sist oppdatert: 2017-11-27bibliografisk kontrollert
2. Adapting Experimental and Monitoring Methods for Continuous Processes under Feedback Control: Challenges, Examples, and Tools
Åpne denne publikasjonen i ny fane eller vindu >>Adapting Experimental and Monitoring Methods for Continuous Processes under Feedback Control: Challenges, Examples, and Tools
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Continuous production covers a significant part of today’s industrial manufacturing. Consumer goods purchased on a frequent basis, such as food, drugs, and cosmetics, and capital goods such as iron, chemicals, oil, and ore come through continuous processes. Statistical process control (SPC) and design of experiments (DoE) play important roles as quality control and product and process improvement methods. SPC reduces product and process variation by eliminating assignable causes, while DoE shows how products and processes may be improved through systematic experimentation and analysis. Special issues emerge when applying these methods to continuous process settings, such as the need to simultaneously analyze massive time series of autocorrelated and cross-correlated data. Another important characteristic of most continuous processes is that they operate under engineering process control (EPC), as in the case of feedback controllers. Feedback controllers transform processes into closed-loop systems and thereby increase the process and analysis complexity and application of SPC and DoE methods that need to be adapted accordingly. For example, the quality characteristics or process variables to be monitored in a control chart or the experimental factors in an experiment need to be chosen considering the presence of feedback controllers.

The main objective of this thesis is to suggest adapted strategies for applying experimental and monitoring methods (namely, DoE and SPC) to continuous processes under feedback control. Specifically, this research aims to [1] identify, explore, and describe the potential challenges when applying SPC and DoE to continuous processes; [2] propose and illustrate new or adapted SPC and DoE methods to address some of the issues raised by the presence of feedback controllers; and [3] suggest potential simulation tools that may be instrumental in SPC and DoE methods development.

The results are summarized in five appended papers. Through a literature review, Paper A outlines the SPC and DoE implementation challenges for managers, researchers, and practitioners. For example, the problems due to process transitions, the multivariate nature of data, serial correlation, and the presence of EPC are discussed. Paper B describes the issues and potential strategies in designing and analyzing experiments on processes operating under closed- loop control. Two simulated examples in the Tennessee Eastman (TE) process simulator show the benefits of using DoE methods to improve these industrial processes. Paper C provides guidelines on how to use the revised TE process simulator under a decentralized control strategy as a testbed for SPC and DoE methods development in continuous processes. Papers D and E discuss the concurrent use of SPC in processes under feedback control. Paper D further illustrates how step and ramp disturbances manifest themselves in single-input single-output processes controlled by variations in the proportional-integral-derivative control and discusses the implications for process monitoring. Paper E describes a two-step monitoring procedure for multivariate processes and explains the process and controller performance when out-of-controlprocess conditions occur.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2019
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Emneord
Continuous process, Statistical process control, Design of experiments, Engineering process control, Quality improvement, Simulation tools.
HSV kategori
Forskningsprogram
Kvalitetsteknik och logistik; Kvalitetsteknik
Identifikatorer
urn:nbn:se:ltu:diva-74661 (URN)978-91-7790-411-3 (ISBN)978-91-7790-412-0 (ISBN)
Disputas
2019-09-27, A109, Luleå University of Technology, Lulea, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-06-19 Laget: 2019-06-18 Sist oppdatert: 2019-09-03bibliografisk kontrollert

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