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  • 1.
    Avelin, Anders
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik.
    Widarsson, Björn
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Dahlquist, Erik
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Lilja, Reijo
    VTT, Espoo, Finland.
    Time based data reconciliation and decision support for a CFB boiler2009Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline), 2009 / [ed] Yrjö Majanne, Tampere: Tampere University Press , 2009, s. 338-343Konferensbidrag (Refereegranskat)
    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.
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Widarsson, Björn
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Avelin, Anders
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik.
    MODELBASED DIAGNOSTICS, MAINTENANCE ON DEMAND AND DECISION SUPPORT ON BOILERS2009Ingår i: SIMS, Scandinavian Modelling and Simulation  Society 50, conference in Fredrice, Denmark, October 7-8 (2009)), Fredrice: SIMS electronic , 2009Konferensbidrag (Refereegranskat)
    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.

  • 3. Widarsson, Björn
    et al.
    Dotzauer, Erik
    Mälardalens högskola, Akademin för hållbar samhälls- och teknikutveckling.
    Bayesian network-based early-warning for leakage in recovery boilers2008Ingår i: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 28, nr 7, s. 754-760Artikel i tidskrift (Refereegranskat)
    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.
    Mälardalens högskola, Institutionen för samhällsteknik.
    Karlsson, Christer
    Mälardalens högskola, Institutionen för samhällsteknik.
    Dahlquist, Erik
    Mälardalens högskola, Institutionen för samhällsteknik.
    Bayesian Network for Decision Support on Soot Blowing Superheaters in a Biomass Fuelled Boiler2004Ingår i: 2004 International Conference on Probabilistic Methods Applied to Power Systems, 2004, s. 212-217Konferensbidrag (Övrigt vetenskapligt)
    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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