The Application of On-line Estimation to a Double Effect Evaporator

  • Stephen G. Payne

Student thesis: Doctoral ThesisDoctor of Philosophy


For the implementation of on-line process control, it is often necessary to determine the true state of the plant, in real time, from insufficient and noisy measurements. Further information may be available in the form of a mathematical model: the measurements and model predictions can be combined to give a 'best' estimate of the process state. One such technique is the minimum variance recursive estimator or Kalman filter.

This research is primarily an on-line application of the Kalman filter to the estimation of the temperatures, flows and overall heat transfer coefficients of a double effect evaporator. Two dynamic models are derived - a comprehensive eighteen order system and a fourth order reduced model.

Two major software packages are developed - ASP, for interactive digital simulation and BASELINE for interactive data logging. Both packages are not confined to the double effect evaporator system as they are specifically designed for any programmer with a knowledge of BASIC.

From on-line steady state experiments, accurate heat transfer coefficient correlations are derived which provide supporting equations for dynamic simulation. The results of comprehensive model simulation sive that the system response cannot be determined without a knowledge of the vapour phase dynamics. The response of the reduced model linulgtan closely follows the
plant response to an identical disturbance and so this model is adopted for Kalman filter experiments.
Date of Award1974
Original languageEnglish


  • on-line estimation
  • double effect evaporator

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