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  • 1.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Garvare, Rickard
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Eriksson, Henrik
    Chalmers University of Technology.
    Hallencreutz, Jacob
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.
    Langstrand, Jostein
    Linköpings universitet.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Zobel, Thomas
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Alive and kicking – but will Quality Management be around tomorrow?: A Swedish academia perspective2012In: Quality Innovation Prosperity, ISSN 1335-1745, E-ISSN 1338-984X, Vol. 16, no 2, p. 1-18Article in journal (Refereed)
    Abstract [en]

    The purpose of this article is to describe how Quality Management (QM) is perceived today by scholars at three Swedish universities, and into what QM is expected to develop into in twenty years. Data were collected through structured workshops using affinity diagrams with scholars teaching and performing research in the QM field. The results show that QM currently is perceived as consisting of a set of core of principles, methods and tools. The future outlook includes three possible development directions for QM are seen: [1] searching for a “discipline X” where QM can contribute while keeping its toolbox, [2] focus on a core based on the traditional quality technology toolbox with methods and tools, and [3] a risk that QM, as it is today, may seize to exist and be diffused into other disciplines.

  • 2.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Garvare, Rickard
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Eriksson, Henrik
    Chalmers tekniska högskola, Teknikens ekonomi och organisation.
    Hallencreutz, Jacob
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.
    Langstrand, Jostein
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Avdelningen för Kvalitetsteknik.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Zobel, Thomas
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Alive and kicking–but will Quality Management be around tomorrow?: A Swedish academia perspective2012Conference paper (Refereed)
    Abstract [en]

    Purpose: There is a lack of a recognized conception of quality management (QM) comprises of, as well as a clear roadmap of where QM is heading. The purpose of this article is to investigate how QM is perceived today by scholars at three Swedish universities, but also how and into what QM is expected to develop into in twenty years.Methodology: Data have been collected through three structured workshops using affinity diagrams with scholars teaching and performing research in the QM field affiliated with three different Swedish universities.Findings: The results indicate that current QM is perceived similarly among the universities today, although the taxonomy differs slightly. QM is described as a fairly wide discipline consisting of a set of core of principles that in turn guide which methods and tools that currently by many are perceived as the core of the discipline. The outlook for the future differs more where three possible development directions for QM are seen: [1] searching for a “discipline X” where QM can contribute while keeping its toolbox, [2] focus on a core based on the traditional quality technology toolbox with methods and tools, and [3] a risk that QM, as it is today, may seize to exist and be diffused into other disciplines. Originality/value: This article contributes with a viewpoint on QM today and its future development from the academicians’ perspective.

  • 3.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    In-situ measurement in the iron ore pellet distribution chain using active RFID technology2019In: Powder Technology, ISSN 0032-5910, E-ISSN 1873-328XArticle in journal (Refereed)
    Abstract [en]

    The active radio frequency identification (RFID) technique is used for in-situ measurement of acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based on two experiments, in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to process and product quality improvement, to better understand the transportation process, and to reduce environmental impacts due to dusting.

  • 4.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Power Analysis of Methods for Analysing Unreplicated Factorial Experiments2013Conference paper (Refereed)
    Abstract [en]

    Several methods for formal analysis of unreplicated factorial type experiments have been proposed in the literature. Based on a simulation study, five formal methods found in the literature based on the effect sparsity principle have been studied. The simulation included 23 and 24 type factorials with one, two, or four active effects. The simulated signal-to-noise ratios for the effects were all between two and four, and the Type I and Type II errors of the analysis methods were analysed. Preliminary results show that Bayesian models are more powerful in these contexts, especially if informative priors based on the effect heredity and effect hierarchy principles are used.

  • 5.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Using Active RFID for In-Situ Measurement of Temperature and Acceleration in the Distribution Chain of Iron Ore PelletsIn: Article in journal (Refereed)
    Abstract [en]

    This article describes how active radio frequency identification (RFID) technique can be used to measure accelerations and temperature that iron ore pellets endure along parts of their distribution chain. Increased knowledge about the accelerations and temperature can help the mining company reduce pellets breakage and dusting. In this work we have studied the distribution chain between the pelletizing plant and the harbour. The article reports on two experiments with active RFID transponders released into the product flow after the pelletizing plant. The transponders measured the temperature and peak accelerations. Transponder data were then captured by RFID readers in the harbour. The results show that active RFID technique can be used for in-situ measurements of accelerations and temperature in the pellets distribution chain although further research is needed to study the segregation behaviour of the active transponders

  • 6.
    Bergquist, Bjarne
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Bayesian analysis of unreplicated two-level factorials using effects sparsity, hierarchy, and heredity2011In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 2, p. 152-166Article in journal (Refereed)
    Abstract [en]

    This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.

  • 7.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Exploring the Use of Design of Experiments in Industrial Processes Operating Under Closed-Loop Control2017In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 33, no 7, p. 1601-1614Article in journal (Refereed)
    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

  • 8.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Simulating and Analyzing Experiments in the Tennessee Eastman Process Simulator2015In: ENBIS-15, 2015Conference paper (Refereed)
    Abstract [en]

    In many of today’s continuous processes, the data collection is usually performed automatically yielding exorbitant amount of data on various quality characteristics and inputs to the system. Moreover, such data are usually collected at high frequency introducing significant serial dependence in time. This violates the independent data assumption of many industrial statistics methods used in process improvement studies. These studies often involve controlled experiments to unearth the causal relationships to be used for robustness and optimization purposes.

    However real production processes are not suitable for studying new experimental methodologies, partly because unknown disturbances/experimental settings may lead to erroneous conclusions. Moreover large scale experimentation in production processes is frowned upon due to consequent disturbances and production delays. Hence realistic simulation of such processes offers an excellent opportunity for experimentation and methodological development.

    One commonly used process simulator is the Tennessee Eastman (TE) challenge chemical process simulator (Downs & Vogel, 1993)[1]. The process produces two products from four reactants, containing 41 measured variables and 12 manipulated variables. In addition to the process description, the problem statement defines process constraints, 20 types of process disturbances, and six operating modes corresponding to different production rates and mass ratios in the product stream.

    The purpose of this paper is to illustrate the use of the TE process with an appropriate feedback control as a test-bed for the methodological developments of new experimental design and analysis techniques.

    The paper illustrates how two-level experimental designs can be used to identify how the input factors affect the outputs in a chemical process.

    Simulations using Matlab/Simulink software are used to study the impact of e.g. process disturbances, closed loop control and autocorrelated data on different experimental arrangements.

    The experiments are analysed using a time series analysis approach to identify input-output relationships in a process operating in closed-loop with multivariate responses. The dynamics of the process are explored and the necessary run lengths for stable effect estimates are discussed.

  • 9.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    A two-step procedure for fault detection in the Tennessee Eastman Process simulator2016Conference paper (Refereed)
    Abstract [en]

    High-technological and complex production processes and high availability and sample frequencies of data in large scale industrial processes need the concurrent development of appropriate statistical control tools and monitoring techniques. Therefore, multivariate control charts based on latent variables are essential tools to detect and isolate process faults.Several Statistical Process Control (SPC) charts have been developed for multivariate and megavariate data, such as the Hotelling T2, MCUSUM and MEWMA control charts as well as charts based on principal component analysis (PCA) and dynamic PCA (DPCA). The ability of SPC procedures based on PCA (Kourti, MacGregor 1995) or DPCA (Ku et al. 1995) to detect and isolate process disturbances for a large number of highly correlated (and time-dependent in the case of DPCA) variables has been demonstrated in the literature. However, we argue that the fault isolation capability and the fault detection rate for processes can be improved further for processes operating under feedback control loops (in closed loop).The purpose of this presentation is to illustrate a two-step method where [1] the variables are pre-classified prior to the analysis and [2] the monitoring scheme based on latent variables is implemented. Step 1 involves a structured qualitative classification of the variables to guide the choice of which variables to monitor in Step 2. We argue that the proposed method will be useful for many practitioners of SPC based on latent variables techniques in processes operating in closed loop. It will allow clearer fault isolation and detection and an easier implementation of corrective actions. A case study based on the data available from the Tennessee Eastman Process simulator under feedback control loops (Matlab) will be presented. The results from the proposed method are compared with currently available methods through simulations in R statistics software.

  • 10.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Simulating Experiments in Closed-Loop Control Systems2016In: ENBIS-16 in Sheffield, 2016Conference paper (Refereed)
    Abstract [en]

    Design of Experiments (DoE) literature extensively discusses how to properly plan, conduct and analyze experiments for process and product improvement. However, it is typically assumed that the experiments are run on processes operating in open-loop: the changes in experimental factors are directly visible in process responses and are not hidden by (automatic) feedback control. Under this assumption, DoE methods have been successfully applied in process industries such as chemical, pharmaceutical and biological industries.

    However, the increasing instrumentation, automation and interconnectedness are changing how the processes are run. Processes often involve engineering process control as in the case of closed-loop systems. The closed-loop environment adds complexity to experimentation and analysis since the experimenter must account for the control actions that may aim to keep a response variable at its set-point value.  The common approach to experimental design and analysis will likely need adjustments in the presence of closed-loop controls. Careful consideration is for instance needed when the experimental factors are chosen. Moreover, the impact of the experimental factors may not be directly visible as changes in the response variables (Hild, Sanders, & Cooper, 2001). Instead other variables may need to be used as proxies for the intended response variable(s).

    The purpose of this presentation is to illustrate how experiments in closed-loop system can be planned and analyzed. A case study based on the Tennessee Eastman Process simulator run with a decentralized feedback control strategy (Matlab) (Lawrence Ricker, 1996) is discussed and presented. 

  • 11.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Managerial implications for improvingcontinuous production processes2017Conference paper (Refereed)
    Abstract [en]

    Data analytics remains essential for process improvement and optimization. Statistical process control and design of experiments are among the most powerful process and product improvement methods available. However, continuous process environments challenge the application of these methods. In this article, we highlight SPC and DoE implementation challenges described in the literature for managers, researchers and practitioners interested in continuous production process improvement. The results may help managers support the implementation of these methods and make researchers and practitioners aware of methodological challenges in continuous process environments.

  • 12.
    Capaci, Francesca
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Technical university of Denmark .
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    The Revised Tennessee Eastman Process Simulator as Testbed for SPC and DoE Methods2019In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 31, no 2, p. 212-229Article in journal (Refereed)
    Abstract [en]

    Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process.

  • 13.
    Englund, Stefan
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Granular Flow and Segregation Behavior2016Conference paper (Refereed)
    Abstract [en]

    1. Purpose of the presentation. Granular materials such as grain, gravel, powder or pellets can be thought as intermediate state of matter: They can sustain shear like a solid up to a point, but they can also flow (Behringer 1995). However, differences in particulate sizes, shapes or densities have been known to cause segregation when granular materials are flowing. Surface segregation has often been studied. The mechanisms of segregation on a surface are described in many articles (Makse 1999)(Gray, Gajjar et al. 2015)(Lumay, Boschini et al. 2013). Descriptions of segregation behaviour of granular flow below surfaces are less common. Literature related to bulk flow mostly describe a bulk containing a variety of granular sizes (Engblom, Saxén et al. 2012)(Jaehyuk Choi and Arshad Kudrolli and Martin,Z.Bazant 2005). Warehouses such as silos or binges constitute major segregation and mixing points in many granular material transport chains. Such warehouses also subject the granular media to flow or impact induced stresses. Traceability in these kind of continues or semi continues granular flow environments face many challenges. Adding in-situ sensors, so called PATs, is one way to trace material in a granular flow. It is, however, difficult to predict if the sensors experience the same physical stresses as the average granules do if the PATs segregate. To contain required electronics, these sensors with casings may need to be made larger than the bulk particles it is supposed to follow. It is therefore important to understand when larger particles segregate and how to design sensor casings to prevent segregation. However segregation of larger sized or different shaped particles added as single objects to homogeny sized particle flow has, to our knowledge not yet been studied and that is the purpose of this study.2. Results. We show the significant factors which affect segregation behaviour and how these modify segregation behaviour. Depending on shape on silo and type of flow during discharge we also show how shape, size and density on individual grains is depending on velocity rate in granular flow. 3. Research Limitations/Implications. The time consuming method of manually retrieving data of each individual particle and surrounding bulk material limit the volume of data that can be retrieved. Further research will implement Particle Image Velocimetry technology (PIV) and customised software to analyse metadata from experiments in a much more efficient way.4. Practical implications. Practical outcome as a result of this research is connected to the ability to trace batches in continues and semi continues supply chains in time and space. The possibility to design a decision model to a specific supply chain for more customized controlled quality and, as far as we know, completely new possibilities related root cause analyses of quality issues in the production or supply chain.5. Value of presentation. Even if the research is made in relation to local mining industry and the supply chain related to iron ore pellets, based on their value of this research, the greatest value is expected to pharmaceutical or any law and regulation controlled industry where it is such efficient traceability of any product on the market is essential.2. Method. Experiments have been performed using granules of different shapes and densities to study flow and segregation behaviour. The experiments have been performed in a transparent 2D model of a silo, designed to replicate warehouses along an iron ore pellets distribution chain. Bulk material consisting of granules representing iron ore have been discharged together with larger objects of different sizes representing sensors or RFID tags. Shape, size and density are modified on the larger objects while studying mixing, flow behaviour and segregation tendencies using video. Video analyses have been used to measure the flow speed and flow distribution of the bulk and of the larger objects. The video material and individual particles is then statistically analysed to clarify significant factors in segregation behaviours related to the size, form and density of the particles. The results are based on Design Expert, Minitab and customized Matlab software.

  • 14.
    Holmbom, Martin
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Performance-based logistics: an illusive panacea or a concept for the future?2014In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786, Vol. 25, no 7, p. 958-979Article in journal (Refereed)
    Abstract [en]

    Purpose– The purpose of this paper is to summarize previously reported benefits, drawbacks and important aspects for implementation of performance-based logistics (PBL), and to identify knowledge gaps.Design/methodology/approach– This is a literature review based on 101 articles. The reviewed articles are relevant to PBL in particular, but also to performance contracting, product-service systems (PSS) and servitization in general. The research method involved database searches, filtering results and reviewing publications.Findings– PBL is a business concept that aims to reduce the customer's total costs for capital-intensive products and increase the supplier's profit. The design of the contract, performance measurements and payment models are important aspects for successful implementation. However, the authors find a reason for concern to be the lack of empirical evidence of the profitability of PBL for the customer and the supplier.Originality/value– This literature review of PBL also includes publications from the related research areas: performance contracting, PSS and servitization. Developing PBL can benefit from results in these research areas.

  • 15.
    Kulahci, Murat
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Capaci, Francesca
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Projekt: Statistiska metoder för förbättring av kontinuerliga tillverkningsprocesser2015Other (Other (popular science, discussion, etc.))
  • 16. Kvarnström, Björn
    et al.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Using RFID to improve traceability in process industry: experiments in a distribution chain for iron ore pellets2010In: Journal of Manufacturing Technology Management, ISSN 1741-038X, E-ISSN 1758-7786, Vol. 21, no 1, p. 139-154Article in journal (Refereed)
    Abstract [en]

    Purpose: The purpose of the article is to explore the application of Radio Frequency Identification (RFID) to improve traceability in a flow of granular products and to illustrate examples of special issues that need to be considered when using the RFID technique in a process industry setting.Design/methodology/approach: The article outlines a case study at a Swedish mining company including experiments to test the suitability of RFID to trace iron ore pellets (a granular product) in parts of the distribution chain.Findings: The results show that the RFID technique can be used to improve traceability in granular product flows. A number of special issues concerning the use of RFID in process industries are also highlighted, for example, the problems to control the orientation of the transponder in the read area and the risk of product contamination in the supply chain.Research limitations/implications: Even though only a single case has been studied, the results are of a general interest for industries that have granular product flows. However, future research in other industries should be performed to validate the results.Practical implications: The application of RFID described in this article makes it possible to increase productivity and product quality by improving traceability in product flows where traceability normally is problematic. Originality/value: Prior research has mainly focused on RFID applications in discontinuous processes. By contrast, this article presents a novel application of the RFID technique in a continuous process together with specific issues connected to the use of RFID.

  • 17.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Statistical Methods - Still Ignored?: The Testimony of Swedish Alumni2018In: Total Quality Management and Business Excellence, ISSN 1478-3363, E-ISSN 1478-3371Article in journal (Refereed)
    Abstract [en]

    Researchers have promoted statistical improvement methods as essential for product and process improvement for decades. However, studies show that their use has been moderate at best. This study aims to assess the use of statistical process control (SPC), process capability analysis, and design of experiments (DoE) over time. The study also highlights important barriers for the wider use of these methods in Sweden as a follow-up study of a similar Swedish study performed in 2005 and of two Basque-based studies performed in 2009 and 2010. While the survey includes open-ended questions, the results are mainly descriptive and confirm results of previous studies. This study shows that the use of the methods has become more frequent compared to the 2005 study. Larger organisations (>250 employees) use the methods more frequently than smaller organisations, and the methods are more widely utilised in the industry than in the service sector. SPC is the most commonly used of the three methods while DoE is least used. Finally, the greatest barriers to increasing the use of statistical methods were: insufficient resources regarding time and money, low commitment of middle and senior managers, inadequate statistical knowledge, and lack of methods to guide the user through experimentations.

  • 18.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Identifying Process Dynamics through a Two-Level Factorial Experiment2014In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 26, no 2, p. 154-167Article in journal (Refereed)
    Abstract [en]

    Industrial experiments are often subjected to critical disturbances and in a small design with few runs the loss of experimental runs may dramatically reduce analysis power. This article considers a common situation in process industry where the observed responses are represented by time series. A time series analysis approach to analyze two-level factorial designs affected by disturbances is developed and illustrated by analyzing a blast furnace experiment. In particular, a method based on transfer function-noise modeling is compared with a ‘traditional’ analysis using averages of the response in each run as the single response in an analysis of variance (ANOVA).

  • 19.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Contributions to the use of designed experiments in continuous processes: a study of blast furnace experiments2007Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Design of Experiments (DoE) contains techniques, such as factorial designs, that help experimenters maximize the information output from conducted experiments and minimize the amount of experimental work required to reach statistically significant results. The use of DoE in industrial processes is frequently and thoroughly described in literature. However, continuous processes in industry, frequently found in, for example, the mining and steel industries, highlight special issues that are typically not addressed in the DoE literature. The purpose of this research is to contribute to an increased knowledge of the use of DoE in continuous processes and aims to investigate if factorial designs and other existing techniques in the DoE field are effective tools also in continuous processes. Two studies have been performed. The focus of the first study, a case study of an industrial blast furnace operation, is to explore the potential of using factorial designs in a continuous process and to develop an effective analysis procedure for the experiments in a continuous process. The first study includes, for example, interviews, experiments, and large elements of action research. The focus of the second study is to explore how a-priori process knowledge can be used to increase the analysis sensitivity for unreplicated experiments. The second study includes a metastudy of experiments in literature as well as an experiment. The results show that it is possible to use factorial designs in a continuous process even though it is not straightforward and special considerations by the experimenter will be required. For example, the dynamic nature of continuous processes affects the minimum time required for each run in an experiment since a transient time period is needed between each run to allow the experimental treatments to reach full effect in the process. Therefore, the use of split-plot designs is recommended since it can be hard to completely randomize the experimental run order. It is also found that process control, during the conduction of the experiment, may be unavoidable in continuous processes. Thus, developing a process control strategy during the planning phase is found to be an important experimental success factor. Furthermore, the results indicate that the multitude of cross-correlated response variables typical for continuous processes can be problematic during the planning phase of the experiment. The many and cross-correlated response variables are also reasons to why multivariate statistical techniques, such as principal component analysis, can make an important contribution during the analysis. Moreover, a-priori process knowledge is confirmed to have a positive effect on analysis sensitivity for unreplicated experiments. Since experimental effects in continuous processes can be expected to be small compared to noise, a-priori process knowledge can also make a valuable contribution during analysis of experiments in continuous processes. Furthermore, activities like coordination of people, information and communication as well as logistics planning are found as important parts of the experimental effort in continuous processes.

  • 20.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Multivariate process monitoring of an experimental blast furnace2010In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 26, no 5, p. 495-508Article in journal (Refereed)
    Abstract [en]

    Process monitoring by use of multivariate projection methods has received increasing attention as it can reduce the monitoring problem for richly instrumented industrial processes with many correlated variables. This article discusses the monitoring and control of a continuously operating experimental blast furnace (EBF). A case study outlines the need for monitoring and control of the EBF and the use of principal components (PCs) to monitor the thermal state of the process. The case study addresses design, testing and online application of PC models for process monitoring. The results show how the monitoring problem can be reduced to following just a few PCs instead of many original variables. The case study highlights the problem of multivariate monitoring of a process with frequently shifting operating modes and process drifts and stresses the choice of a good reference data set of normal process behavior. Possible solutions for adaptations of the multivariate models to process changes are also discussed.

  • 21.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    On design of experiments in continuous processes2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Design of Experiments (DoE) includes powerful methods, such as factorial designs, to help maximize the information output from conducted experiments while minimizing the experimental work required for statistically significant results. The benefits of using DoE in industry are thoroughly described in the literature although the actual use of the methods in industry is far from being pervasive. Continuous processes, frequently found in the process industry, highlight special issues that are typically not addressed in the DoE literature. The overall objective of this research is to increase the knowledge of DoE in continuous processes. More specifically, the aims of this research are [1] to identify, explore, and describe potential problems that can occur when planning, conducting, and analyzing experiments in continuous processes, and [2] to propose methods of analysis that help the experimenter in continuous processes tackle some of the identified problems.This research has focused on developing analysis procedures adapted for experiments in continuous processes using a combination of existing DoE methods and methods from the related fields: multivariate statistical methods and time series analysis. The work uses real industrial data as well as simulations. The method is dominated by the study of the practical use of DoE methods and the developed analysis procedures using an industrial case - the LKAB Experimental Blast Furnace plant.The results are presented in six appended papers. Paper A provides a tentative overview of special considerations that the experimenter needs to consider in the planning phase of an experiment in a continuous process. Examples of important experimental complications further discussed in the papers are: their multivariate nature, their dynamic characteristics, the need for randomization restrictions due to experimental costs, the need for process control during experimentation, and the time series nature of the responses. Paper B develops a method to analyze factorial experiments with randomization restrictions using principal components combined with analysis of variance. Paper C shows how the use of the multivariate projection method principal component analysis can reduce the monitoring problem for a process with many and correlated variables. Paper D focuses on the dynamic characteristic of continuous processes and presents a method to determine the transistion time between experimental runs combining principal components and transfer function-noise models and/or intervention analysis. Paper E further addresses the time series aspects of responses from continuous processes and illustrates and compares different methods to analyze two-level factorials with time series responses to estimate location effects. In particular, Paper E shows how multiple interventions with autoregressive integrated moving average models for the noise can be used to effectively analyze experiments in continuous processes. Paper F develops a Bayesian procedure, adapted from Box and Meyer (1986), to calculate posterior probabilities of active effects for unreplicated twolevel factorials, successively considering the sparsity, hierarchy, and heredity principles.

  • 22.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Special considerations when planning experiments in a continuous process2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 3, p. 155-169Article in journal (Refereed)
    Abstract [en]

    Discontinuous processes dominate experimental applications in practice as well as in literature. Continuous processes constitute a significant part of goods production, and the need to gain knowledge using experiments are as relevant in such environments as in, for example, parts production. We argue that the characteristics of continuous processes affect the prerequisites for experimental efforts to such an extent that they need special attention. To describe considerations when planning experiments in a continuous process, experiments performed in a blast furnace process are studied. We propose a tentative list of special considerations, which are discussed and summarized in a thirteen-step check list.

  • 23.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Towards improved analysis methods for two-level factorial experiments with time series responses2013In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 29, no 5, p. 725-741Article in journal (Refereed)
    Abstract [en]

    Dynamic processes exhibit a time delay between the disturbances and the resulting process response. Therefore, one has to acknowledge process dynamics, such as transition times, when planning and analyzing experiments in dynamic processes. In this article, we explore, discuss, and compare different methods to estimate location effects for two-level factorial experiments where the responses are represented by time series. Particularly, we outline the use of intervention-noise modeling to estimate the effects and to compare this method by using the averages of the response observations in each run as the single response. The comparisons are made by simulated experiments using a dynamic continuous process model. The results show that the effect estimates for the different analysis methods are similar. Using the average of the response in each run, but removing the transition time, is found to be a competitive, robust, and straightforward method, whereas intervention-noise models are found to be more comprehensive, render slightly fewer spurious effects, find more of the active effects for unreplicated experiments and provide the possibility to model effect dynamics.

  • 24.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Impact of Autocorrelation on Principal Components and Their Use in Statistical Process Control2016In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 32, no 4, p. 1483-1500Article in journal (Refereed)
    Abstract [en]

    A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in statistical process control (SPC) is that the data are independent in time. In many industrial processes frequent sampling and process dynamics make this assumption unrealistic rendering sampled data autocorrelated (serially dependent). PCA can be used to reduce data dimensionality and to simplify multivariate SPC. Although there have been some attempts in the literature to deal with autocorrelated data in PCA, we argue that the impact of autocorrelation on PCA and PCA-based SPC is neither well understood nor properly documented.This article illustrates through simulations the impact of autocorrelation on the descriptive ability of PCA and on the monitoring performance using PCA-based SPC when autocorrelation is ignored. In the simulations cross- and autocorrelated data are generated using a stationary first order vector autoregressive model.The results show that the descriptive ability of PCA may be seriously affected by autocorrelation causing a need to incorporate additional principal components to maintain the model’s explanatory ability. When all variables have the same autocorrelation coefficients the descriptive ability is intact while a significant impact occurs when the variables have different degrees of autocorrelation. We also illustrate that autocorrelation may impact PCA-based SPC and cause lower false alarm rates and delayed shift detection, especially for negative autocorrelation. However, for larger shifts the impact of autocorrelation seems rather small.

  • 25.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Technical University of Denmark, Department of Applied Mathematics and Computer Science.
    The Effect of Autocorrelation on the Hotelling T2 Control Chart2015In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 31, no 8, p. 1779-1796Article in journal (Refereed)
    Abstract [en]

    One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter in many cases the data is serially dependent (autocorrelated) and cross-correlated due to, for example, frequent sampling and process dynamics. It is well-known that the autocorrelation affects the false alarm rate and the shift detection ability of the traditional univariate control charts. However, how the false alarm rate and the shift detection ability of the Hotelling 2T control chart are affected by various auto- and cross-correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T2 control chart for different shift sizes and various auto- and cross-correlation structures are compared based on the average run length (ARL) using simulated data. Three different approaches in constructing the Hotelling T2 chart are studied for two different estimates of the covariance matrix: [1] ignoring the autocorrelation and using the raw data with theoretical upper control limits; [2] ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and [3] constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity we use a first-order vector autoregressive process, VAR(1), and focus mainly on bivariate data.

  • 26.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Technical University of Denmark.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    On the structure of dynamic principal component analysis used in statistical process monitoring2017In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 167, p. 1-11Article in journal (Refereed)
    Abstract [en]

    When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time-dependent data. In DPCA the input matrix is augmented by adding time-lagged values of the variables. In building a DPCA model the analyst needs to decide on (1) the number of lags to add, and (2) given a specific lag structure, how many principal components to retain. In this article we propose a new analyst driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using simulated vector autoregressive and moving average data, and tested on Tennessee Eastman process data.

  • 27.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Capaci, Francesca
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Lag Structure in Dynamic Principal Component Analysis2016Conference paper (Refereed)
    Abstract [en]

    Purpose of this PresentationAutomatic data collection schemes and abundant availability of multivariate data increase the need for latent variable methods in statistical process control (SPC) such as SPC based on principal component analysis (PCA). However, process dynamics combined with high-frequency sampling will often cause successive observations to be autocorrelated which can have a negative impact on PCA-based SPC, see Vanhatalo and Kulahci (2015).Dynamic PCA (DPCA) proposed by Ku et al. (1995) has been suggested as the remedy ‘converting’ dynamic correlation into static correlation by adding the time-lagged variables into the original data before performing PCA. Hence an important issue in DPCA is deciding on the number of time-lagged variables to add in augmenting the data matrix; addressed by Ku et al. (1995) and Rato and Reis (2013). However, we argue that the available methods are rather complicated and lack intuitive appeal.The purpose of this presentation is to illustrate a new and simple method to determine the maximum number of lags to add in DPCA based on the structure in the original data. FindingsWe illustrate how the maximum number of lags can be determined from time-trends in the eigenvalues of the estimated lagged autocorrelation matrices of the original data. We also show the impact of the system dynamics on the number of lags to be considered through vector autoregressive (VAR) and vector moving average (VMA) processes. The proposed method is compared with currently available methods using simulated data.Research Limitations / Implications (if applicable)The method assumes that the same numbers of lags are added for all variables. Future research will focus on adapting our proposed method to accommodate the identification of individual time-lags for each variable. Practical Implications (if applicable)The visualization possibility of the proposed method will be useful for DPCA practitioners.Originality/Value of PresentationThe proposed method provides a tool to determine the number of lags in DPCA that works in a manner similar to the autocorrelation function (ACF) in the identification of univariate time series models and does not require several rounds of PCA. Design/Methodology/ApproachThe results are based on Monte Carlo simulations in R statistics software and in the Tennessee Eastman Process simulator (Matlab).

  • 28.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kvarnström, Björn
    Bergquist, Bjarne
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    A method to determine transition time for experiments in dynamic processes2009Conference paper (Other academic)
    Abstract [en]

    Planning, conducting, and analyzing experiments performed in dynamic processes, such as continuous processes, highlight issues that the experimenter needs to consider, for example, process dynamics (inertia) and the multitude of responses. Dynamic systems exhibit a delay (transition time) the change of an experimental factor and when the response is affected. The transition time affects the required length of each experimental run in dynamic processes and long transition times may call for restrictions of the randomization of runs. By contrast, in many processes in parts production this change is almost immediate. Knowledge about the transition time helps the experimenter to avoid experimental runs that are either too short for a new steady-state to be reached, and thus incorrect estimation of treatment effects, or unnecessarily long and costly. Furthermore, knowing the transition time is important during analysis of the experiment.Determining the transition time in a dynamic process can be difficult since the processes often are heavily instrumented with a multitude of responses. The process responses are typically correlated and react to the same underlying events. Hence, multivariate statistical tools such as principal component analysis (PCA) are often beneficial during analysis. Furthermore, the responses are often highly positively autocorrelated due to frequent sampling. We propose a method to determine the transition time between experimental runs in a dynamic process. We use PCA to summarize the systematic variation in a multivariate response space. The time series analysis techniques ‘transfer function-noise modeling' or ‘intervention analysis' are then used to model the dynamic relation between an input time series event and output time series response using the principal component scores. We illustrate the method by estimating the transition time for treatment changes in an experimental blast furnace. This knowledge provides valuable input to the planning and analysis phase of the experiments in the process.

  • 29.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kvarnström, Björn
    Bergquist, Bjarne
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    A method to determine transition time for experiments in dynamic processes2011In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 1, p. 30-45Article in journal (Refereed)
    Abstract [en]

    Process dynamics is an important consideration during the planning phase of designed experiments in dynamic processes. After changes of experimental factors, dynamic processes undergo a transition time before reaching a new steady state. To minimize experimental time and reduce costs and for experimental design and analysis, knowledge about this transition time is important. In this article, we propose a method to analyze process dynamics and estimate the transition time by combining principal component analysis and transfer function-noise modeling or intervention analysis. We illustrate the method by estimating transition times for a planned experiment in an experimental blast furnace.

  • 30.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Using factorial design and multivariate analysis when experimenting in a continuous process2008In: Quality and Reliability Engineering International, ISSN 0748-8017, E-ISSN 1099-1638, Vol. 24, no 8, p. 983-995Article in journal (Refereed)
    Abstract [en]

    This article discusses the design and analysis of an experiment performed in a continuous process (CP). Three types of iron ore pellets are tested on two levels of a process variable in an experimental blast furnace process, using a full factorial design with replicates. A multivariate approach to the analysis of the experiment in the form of principal component analysis combined with analysis of variance is proposed. The analysis method also considers the split-plot-like structure of the experiment. The article exemplifies how a factorial design combined with multivariate analysis can be used to perform product development experiments in a CP. CPs also demand special considerations when planning, performing and analyzing experiments. The article highlights and discusses such issues and considerations, for example, the dynamic characteristic of CPs, a strategy to handle disturbances during experimentation and the need for process control during experimentation.

  • 31.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Hyllander, Gunilla
    LKAB, Technology and Business Development.
    A designed experiment in a continuous process2007In: Proceedings from the 10th QMOD Conference: Our Dreams of Excellence, Lunds University, Campus Helsingborg , 2007Conference paper (Refereed)
    Abstract [en]

    This paper discusses the design and analysis of an experiment performed in a continuous process (CP). A full factorial design with replicates is used to test three types of pellets on two levels of a process variable in an experimental blast furnace process. Issues and considerations concerning the experimental design and analysis are discussed. For example, an adaptive experimental design is used. We propose a multivariate approach to the analysis of the experiment, in form of principal component analysis combined with analysis of variance. The factorial design in CPs is found to have a promising potential. However, CPs also demand special considerations when planning, performing and analyzing experiments, and therefore further development of experimental strategies and connected methods of analysis for CPs is needed.

1 - 31 of 31
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