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  • 1.
    Avelin, Anders
    et al.
    Mälardalen University, School of Business, Society and Engineering.
    Widarsson, Björn
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Dahlquist, Erik
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Lilja, Reijo
    VTT, Espoo, Finland.
    Time based data reconciliation and decision support for a CFB boiler2009In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2009 / [ed] Yrjö Majanne, Tampere: Tampere University Press , 2009, p. 338-343Conference paper (Refereed)
    Abstract [en]

    This paper covers a method for operator decision support, where physical simulation models are used to connect different physical variables to each other. By comparing energy and material balances for a larger process area inconsistencies in single process parts and sensor measurements can be detected, by following the development between single measurements and values predicted from the simulations. This information then can be used as input to e.g. a BN, Bayesian Network, for decision support. The application has been for a CFB boiler at Mälarenergi AB. The simulators have been made in Modelica respectively a more advanced model in APROS.

  • 2.
    Dahlquist, Erik
    et al.
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Widarsson, Björn
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering.
    MODELBASED DIAGNOSTICS, MAINTENANCE ON DEMAND AND DECISION SUPPORT ON BOILERS2009In: SIMS, Scandinavian Modelling and Simulation  Society 50, conference in Fredrice, Denmark, October 7-8 (2009)), Fredrice: SIMS electronic , 2009Conference paper (Refereed)
    Abstract [en]

    At a CFB boiler a system has been tested based on a Modelica model together with a decision support system. The model is a physical model including energy and material balances, chemical reactions like combustion and gasification reactions. For the combustion system we primarily consider equilibrium conditions while for gasification the kinetics is important and thus PLS-models built on experimental data in a pilot plant are combined with literature data and a physical model. The simulation model is first developed in Modelica, but then placed as an object in Simulink/Matlab, from which data is communicated to and from the data base through OPC-server. Measured data are collected from the process data base and inserted as initial data into the simulation model, including the boiler, separator, heat exchangers and steam system. A simulation during 300 seconds is performed and the data after this is compared to the initial data. If we have steady state conditions, the values after the simulation will be the same as the initial data, while if the data are not balanced, the difference will correspond to a balanced state between all measured data and the physical correlations in the boiler. This procedure is repeated on a regular basis and the trend of the difference between the measured and the balanced data is plotted and analyzed with respect to slope respectively variance. These data are combined with other type of information like standard deviation of sensors, which corresponds to noise; is the data value changing at all? Input of manual information like lab-data, unexpected events like noise; maintenance actions; activities like how many times a valve has been opening and closing; combination of data like Energy and Mass balances combined with conductivity in blow down from steam drum to detect possible leakages in piping or boiler systems;

    All this information is introduced into a BN, Bayesian Net, which has been built from known relations, but where the quantitative data is built from experience and statistics. In this way we can then detect possible faults or probable faults coming up. This information is used by both the operators and maintenance staff. The mathematical simulation model over the CFB boiler and results from the utilization is presented in this paper.

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  • 3. Widarsson, Björn
    et al.
    Dotzauer, Erik
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Bayesian network-based early-warning for leakage in recovery boilers2008In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 28, no 7, p. 754-760Article in journal (Refereed)
    Abstract [en]

    Early-warning for leakage in a recovery boiler can help the process operator to detect faults and take action when a dangerous situation is developing. By analysing the mass-balances on both the steam and combustion side of the boiler in a Bayesian network, the probability of leakage can be determined and used as an early-warning. The method is tested with real plant data combined with leakage simulations. The results show that it is possible to detect considerably smaller leakages using this method than using the type of simple steam-side mass-balance method that is in use today. Bayesian network is an efficient tool to combine information from measurement signals and calculations giving an early-warning system that is robust to signal faults

  • 4.
    Widarsson, Björn
    et al.
    Mälardalen University, Department of Public Technology.
    Karlsson, Christer
    Mälardalen University, Department of Public Technology.
    Dahlquist, Erik
    Mälardalen University, Department of Public Technology.
    Bayesian Network for Decision Support on Soot Blowing Superheaters in a Biomass Fuelled Boiler2004In: 2004 International Conference on Probabilistic Methods Applied to Power Systems, 2004, p. 212-217Conference paper (Other academic)
    Abstract [en]

    In a process for combined heat and power generation there is a need for fault detection, decision support and risk assessment to prevent operational disturbances and reduction in performance. A method to achieve decision support is to use Bayesian networks, where knowledge about the process is combined with operational experience. The network covers the convectional superheaters in the flue gas train, which is a major problem domain in biomass-fuelled boilers. The superheaters are exposed to fouling from flue gases. Fouling reduces the heat transfer and result in a decreased power plant performance. The Bayesian network is constructed to give decision support on preventive action to reduce abnormal fouling. Validation of the Bayesian network show that the prediction of hard fouling works well under uncertainty.

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