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  • 1.
    Capaci, Francesca
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Adapting Experimental and Monitoring Methods for Continuous Processes under Feedback Control: Challenges, Examples, and Tools2019Doctoral thesis, comprehensive summary (Other academic)
    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.

  • 2.
    Capaci, Francesca
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Contributions to the Use of Statistical Methods for Improving Continuous Production2017Licentiate thesis, comprehensive summary (Other academic)
    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.

  • 3.
    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

  • 4.
    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.

  • 5.
    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.

  • 6.
    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. 

  • 7.
    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.

  • 8.
    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.

  • 9.
    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.))
  • 10.
    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).

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CiteExportLink to result list
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  • harvard1
  • ieee
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  • Other style
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  • en-GB
  • en-US
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