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Optimal Control of Batch Cooling Crystallizers Using Inversion Approach

Amini, Younes | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40338 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Pishvaie, Mahmoud Reza
  7. Abstract:
  8. In batch cooling crystallization, the crucial control problem is to design a temperature trajectory which produces a desired crystal size distribution. Here, a completely different solution is presented. Designing model-based controller for crystallizer has two steps; first step is determining optimal temperature trajectory which is some kind of feedforward control, second step is adding feedback controller (with manipulated variable of jacket temperature or cooling flow) to track the set-point (determined in first step) and rejecting possible disturbances. Traditionally, determining optimal temperature of crystallizer is done by using calculus of variations of distributed system. In this thesis genetic algorithm is used to find optimal temperature profile in batch cooling crystallizer. Three objective functions are exploited; maximum mean length, closeness to predefined value (desired mean length) and minimum variation coefficient for obtaining optimal temperature trajectory. A different approach is presented. It is neither based on optimization, nor does it just keep one process variable constant. The approach uses nonlinear control methods to determine analytically the feed forward control signal that steers the system into a desired final CSD. More specifically, based on a new state-dependent time scaling, a procedure is developed which gives inversion of the model of system and hence can be used to check whether a desired final CSD is physically possible and, if so, to compute the corresponding temperature signal. The properties of the time-scaled model are further exploited to design trajectories that achieve certain CSD properties (expressed in terms of moments of the CSD) at the end of the batch. Finally, a feedback control scheme to track such a trajectory in the presence of uncertainty is presented.

  9. Keywords:
  10. Population Balance ; Inverse System ; Genetic Algorithm ; Feedforward Control ; Feedback Control

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