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  • 1.
    Atta, Khalid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Guay, Martin
    Department of Chemical Engineering, Queens University, Kingston, ON K7L 3N6, Canada.
    Adaptive amplitude fast proportional integral phasor extremum seeking control for a class of nonlinear system2019In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a modification of the fast phasor extremum-seeking control for the fast optimization of a class of Wiener-Hammerstein nonlinear dynamical systems. The proposed technique provides a significant improvement of the closed-loop system's performance. This study introduces a new adaptive amplitude technique that is used to adaptively adjust the perturbation amplitude to a small predetermined value in a neighbourhood of the system's unknown optimal equilibrium. An analysis of the system demonstrates that semi-global practical stability analysis of the overall system to the unknown optimum is achieved. The effectiveness of the proposed approach is illustrated using numerical examples. The approach is also implemented for the optimal operation of a lean burn combustion system.

  • 2.
    Castano, Miguel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    New methods for interaction analysis of complex processes using weighted graphs2012In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 22, no 1, p. 280-295Article in journal (Refereed)
    Abstract [en]

    The selection of the structure of a controller in large scale industry processes usually requires extensive process knowledge. The aim of this paper is to report new results on recently suggested methods for the analysis of complex processes. These methods aid the designers in comprehending a process by representing structural and functional relationships from actuators and process disturbances to measured or estimated variables. The methods are formulated in a flexible framework based on graph theory, which can also be used for closed-loop analysis. Additionally, the sensitivity of the methods to scaling and time delays are discussed and resolved. It is also proposed how filtering can be used to restrict the analysis to a frequency region of interest. The feasibility of the methods is shown by the use of three case studies. A quadruple tank process is used to exemplify the methods and their use. Then the methods are applied on a real-life process, the stock preparation plant of a pulp and paper mill. The third study case analyzes a previously published example in closed loop. It is shown that the methods can be used to take efficient decisions on decentralized and sparse control structures, as well as assessing the channel interactions in a closed-loop system.

  • 3.
    Castaño Arranz, Miguel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the selection of control configurations for uncertain systems using gramian-based Interaction Measures2016In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 47, p. 213-225Article in journal (Refereed)
    Abstract [en]

    critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action.

    A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment.

    In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration.

  • 4. Ding, Limei
    et al.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Johansson, Andreas
    Model parameter estimation of simplified linear models for a continuous paper pulp digester2007In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 17, no 2, p. 115-127Article in journal (Refereed)
    Abstract [en]

    A physical model of a continuous paper pulp digester is simplified and two subprocesses selected from the digester are modelled by coupled linear partial differential equations. This study focuses on the parameter identification of the simplified linear models. Finite-dimensional approximation of the model is made and a software package developed for identification of distributed parameter processes is applied. This identification system is developed for flexibility to allow identification for different choices of subprocesses and process variables. Unknown parameters of the subprocess models are estimated and the results are illustrated by process simulation and model validation.

  • 5. Ding, Limei
    et al.
    Johansson, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Application of reduced models for robust control and state estimation of a distributed parameter system2009In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 19, no 3, p. 539-549Article in journal (Refereed)
    Abstract [en]

    This paper studies the application of reduced models of a distributed parameter system for robust process control and state estimation. We take the approach of integrating model reduction, parameter identification, and model uncertainty analysis, in purpose to find an appropriate trade-off between complexity and robust performance. The application example is the temperature system in a continuous paper pulp digester. Physical modeling of this process results in coupled linearized partial differential equations which are then reduced into low-order nominal process models using an orthogonal collocation approximation method.Two different approaches to obtaining a model uncertainty description are adapted for use on a distributed parameter system with low-order nominal model and shown to produce similar results when tested with measurement data. It is also demonstrated how this uncertainty description, in combination with the reduced model, may be used for robust control design and verification of the control performance on the distributed parameter system.Finally, the possibility of estimating the distributed process state using a state observer for the reduced process is demonstrated. Measurements of the process state in a certain position is available and is shown to agree with the estimated state at the same position.

  • 6.
    Gajjar, Shriram
    et al.
    Department of Chemical Engineering, University of California, Davis.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Department of Informatics and Mathematical Modeling, Technical University of Denmark.
    Palazoglu, Ahmet
    Department of Chemical Engineering, University of California, Davis.
    Real-time fault detection and diagnosis using sparse principal component analysis2018In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 67, p. 112-128Article in journal (Refereed)
    Abstract [en]

    With the emergence of smart factories, large volumes of process data are collected and stored at high sampling rates for improved energy efficiency, process monitoring and sustainability. The data collected in the course of enterprise-wide operations consists of information from broadly deployed sensors and other control equipment. Interpreting such large volumes of data with limited workforce is becoming an increasingly common challenge. Principal component analysis (PCA) is a widely accepted procedure for summarizing data while minimizing information loss. It does so by finding new variables, the principal components (PCs) that are linear combinations of the original variables in the dataset. However, interpreting PCs obtained from many variables from a large dataset is often challenging, especially in the context of fault detection and diagnosis studies. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing PCs with sparse loadings via variance-sparsity trade-off. Using SPCA, some of the loadings on PCs can be restricted to zero. In this paper, we introduce a method to select the number of non-zero loadings in each PC while using SPCA. The proposed approach considerably improves the interpretability of PCs while minimizing the loss of total variance explained. Furthermore, we compare the performance of PCA- and SPCA-based techniques for fault detection and fault diagnosis. The key features of the methodology are assessed through a synthetic example and a comparative study of the benchmark Tennessee Eastman process.

  • 7.
    Kirtania, Kawnish
    et al.
    Bangladesh University of Engineering & Technology.
    Choudhury, M. A A Shoukat
    A novel dead time compensator for stable processes with long dead times2012In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 22, no 3, p. 612-625Article in journal (Refereed)
    Abstract [en]

    This paper presents a new and simplified approach for the design of dead time compensators for processes with long dead times. The approach is based on a modified structure of the Smith predictor that allows the user to isolate the disturbance and set-point responses and thereby, provide a two-degrees-of- freedom control scheme. The proposed structure is easy to analyze and tune. Using an estimation of the dead time and process model of the plant, the proposed compensator is left with two tuning parameters that determine the closed-loop performance and robustness. The performance of the proposed compensator is compared with the most recent dead time compensator appeared in the literature. The method is evaluated on two simulated processes and a computer-interfaced pilot-scale two tank heating system to demonstrate the practicality and utility of the proposed scheme. © 2012 Elsevier Ltd. All rights reserved.

  • 8.
    Lucchese, Riccardo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Computing the allowable uncertainty of sparse control configurations2019In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771Article in journal (Refereed)
  • 9.
    Thurley, Matthew
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Automated online measurement of limestone particle size distributions using 3D range data2011In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 21, no 2, p. 254-262Article in journal (Refereed)
    Abstract [en]

    Fully automated online measurement of the size distribution of limestone particles on conveyor belt is presented based on 3D range data collected every minute during 13 hours of production. The research establishes the necessary measurement technology to facilitate automatic control of rock crushing or particle agglomeration processes to improve both energy efficiency and product quality. 3D data from laser triangulation is used to provide high resolution data of the surface of the stream of rocks. The 3D data is unaffected by color variation in the material and is not susceptible to scale or perspective distortion common in 2D imaging. Techniques are presented covering; sizing of particles, determination of non-overlapped and overlapped particles, and mapping of sizing results to distributions comparable to sieving. Detailed variations in the product sieve-size are shown with an abrupt change when the size range of the limestone particles was changed.

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