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  • 1.
    Samuelsson, Pär
    et al.
    Dalarna University, School of Technology and Business Studies, Electrical Engineering.
    Sohlberg, Björn
    Dalarna University, School of Technology and Business Studies, Electrical Engineering.
    ODE-based modelling and calibration of temperatures in steelmaking ladles2010In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 18, no 2, p. 474-479Article in journal (Refereed)
    Abstract [en]

    This brief considers modelling of steel and wall temperatures in steelmaking ladles used as liquid steel transportation vessels. Two measurement campaigns in order to collect data for model calibration and validation were conducted at the facilities of a Swedish stainless steel manufacturer. The process data were utilized in order to calibrate and validate an ordinary differential equation (ODE) model using a grey-box approach. The grey-box approach is justified since model structure and parameters are only partially known and few data are available for calibration. It is concluded that even though the model structure is non-complicated, the model shows promising accuracy for the steel temperature estimation. The model will be further evaluated in online simulations at the steel mill.

  • 2.
    Sohlberg, Björn
    Dalarna University, School of Technology and Business Studies, Electrical Engineering.
    Monitoring and Failure Diagnosis of a Steel Strip Process1998In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 6, no 2, p. 294-303Article in journal (Refereed)
    Abstract [en]

    This paper deals with condition monitoring and failure diagnosis of a steel strip rinsing process. Modelling and identification of the process is based on a priori knowledge about the process and data from the process. Some parts of the process wear out slowly. It is not possible to measure the wear with any transducers. In the model, the worn parts are modelled explicitly and estimated on-line by an Extended Kalman Filter. The parameter estimation is used for supervision and as an advisory system for the process operators to decide which worn parts should be changed at the next planned stop. In addition to the normal wear, other types of abrupt failures may suddenly occur. It is not possible to detect these failures directly and the failures will give a biased parameter estimate and mislead the process operators into thinking, that a part subject to wear should be changed although it is performing well. Therefore, the condition monitoring system is complemented with a fault detection and diagnosis system, which distinguishes normal wear from sudden abrupt failures.

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