Van Kessel

1. Practical experiences of the calorific value sensor and practical issues in optimising the control concept of grate combustion

4. Dynamic model for grate stoker systems Model structure

6. On-line calorific value sensor (2)

7. Calorific value waste , moisture fraction waste a n d calorific value combustible part as functi on of time On-line calorific value sensor (3)

8. On-line calorific value sensor (4) Calculated and measured steam production as a function of time.

9. On-line calorific value sensor (5) Calculated and measured steam production as a function of time.

14. Validation of dynamic models (1) Procedure system identification

15. Validation of dynamic models (2) Comparison dynamic model and plant results

16. Validation of dynamic models (3) Validation of controller model

18. Optimisation of control concept (2) AVR plant, optimisation by tuning of control parameters

19. Optimisation of control concept (3) AVR plant, optimisation by tuning of control parameters

20. Optimisation of control concept (4) AVR plant, optimisation by tuning of control parameters

23. Evolutionary Control Classic control/fuzzy logic/neural networks : based upon fixed optimisation rules models are not able to cope with changes in the plant state Evolutionary model: evolving structure: I.e. self adapting Concept from artificial life. “ Not control rules but autonomous structures are able to generate optimised-control rules”

24. Evolutionary Control Performance model (index): target for optimisation including all the variables and constraints Represents the global performance of the plant to be maximised MSWC performance model is based upon fuzzy theory Parameters in fuzzy set: e.g. O2, steam production, CO, NOX

25. Evolutionary Control Diagram of evolutionary control approach