Practical experiences of the calorific value sensor and practical issues in optimising the control concept of grate combus...
Contents <ul><li>Process modelling of MSWC </li></ul><ul><li>On-line calorific value sensor </li></ul><ul><li>Dynamic mode...
Process modelling <ul><li>At TNO mathematical models are used to optimize  </li></ul><ul><li>thermal (conversion) processe...
Dynamic model for grate stoker    systems Model structure
On-line calorific value sensor (1) <ul><li>Changing calorific value of the fuel is one of the main problems  in MSW combus...
On-line calorific value sensor (2)
Calorific value waste ,  moisture fraction   waste   a n d   calorific value combustible part  as functi on   of time   On...
On-line calorific value sensor (4) Calculated and measured steam production  as  a  function  of time.
On-line calorific value sensor (5) Calculated and measured steam production  as  a  function  of time.
On-line calorific value sensor (6) <ul><li>Possible applications: </li></ul><ul><li>Calorific value sensor as a diagnostic...
Dynamic Model for grate system  (1) <ul><li>How to model waste layer and furnace? </li></ul><ul><li>Different methods for ...
Dynamic Model for grate system  (2) <ul><li>Balances solid phase furnace </li></ul><ul><li>Energy balance gas phase furnac...
Dynamic Model for grate system  (3) <ul><li>Steam system </li></ul>
Validation of dynamic models (1) Procedure system identification
Validation of dynamic models (2) Comparison dynamic model and plant results
Validation of dynamic models (3) Validation of controller model
Optimisation of control concept (1) <ul><li>Different possibilities for optimisation  of control concept </li></ul><ul><li...
Optimisation of control concept (2) AVR plant, optimisation by tuning of control parameters
Optimisation of control concept (3) AVR plant, optimisation by tuning of control parameters
Optimisation of control concept (4) AVR plant, optimisation by tuning of control parameters
EU project ECOTHERM <ul><li>Objective:Development and testing of new advanced control technologies (Model Predictive Contr...
Model Predictive Control <ul><li>MPC: based upon measurements from the past, a model of the plant and the control objectiv...
Evolutionary Control  Classic control/fuzzy logic/neural networks :  based upon fixed optimisation rules models are not ab...
Evolutionary Control  Performance model (index): target for optimisation  including all the variables and constraints  Rep...
Evolutionary Control  Diagram of evolutionary control approach
Conclusions <ul><li>A dynamical first principal model (simulator) of grate firing systems is available </li></ul><ul><li>V...
Upcoming SlideShare
Loading in...5
×

Van Kessel

349

Published on

Van Kessel

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
349
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Van Kessel"

  1. 1. Practical experiences of the calorific value sensor and practical issues in optimising the control concept of grate combustion
  2. 2. Contents <ul><li>Process modelling of MSWC </li></ul><ul><li>On-line calorific value sensor </li></ul><ul><li>Dynamic model for MSWC systems </li></ul><ul><li>Validaton of dynamic models </li></ul><ul><li>Optimisation of control concept </li></ul><ul><ul><li>Conventional tuning </li></ul></ul><ul><ul><li>Advanced control concepts </li></ul></ul><ul><ul><li>EU–project ECOTHERM </li></ul></ul><ul><li>Conclusions </li></ul>
  3. 3. Process modelling <ul><li>At TNO mathematical models are used to optimize </li></ul><ul><li>thermal (conversion) processes </li></ul><ul><li>Examples </li></ul><ul><ul><li>Cement production process </li></ul></ul><ul><ul><li>Biomass gasification </li></ul></ul><ul><ul><li>Biomass combustion </li></ul></ul><ul><ul><li>Municipal Solid Waste Combustion </li></ul></ul><ul><ul><li>Computational Fluid Dynamics </li></ul></ul><ul><ul><li>Sterilization of food products </li></ul></ul><ul><li>Focus on MSWC </li></ul>
  4. 4. Dynamic model for grate stoker systems Model structure
  5. 5. On-line calorific value sensor (1) <ul><li>Changing calorific value of the fuel is one of the main problems in MSW combustion: </li></ul><ul><li>Development of an on-line calorific value sensor </li></ul><ul><li>Requirements: </li></ul><ul><li>No energy balance, and </li></ul><ul><li>No mass flows </li></ul><ul><li>The patented sensor is based upon a model and the </li></ul><ul><li>following measurements: </li></ul><ul><li>H 2 O, O 2 en CO 2 -concentrations (with IR) </li></ul><ul><li>Relative humidity of the ambient air </li></ul>
  6. 6. On-line calorific value sensor (2)
  7. 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. 8. On-line calorific value sensor (4) Calculated and measured steam production as a function of time.
  9. 9. On-line calorific value sensor (5) Calculated and measured steam production as a function of time.
  10. 10. On-line calorific value sensor (6) <ul><li>Possible applications: </li></ul><ul><li>Calorific value sensor as a diagnostic tool </li></ul><ul><li>Continuous determination on-line mass- and energy balances </li></ul><ul><li>Source of additional information for operators </li></ul><ul><li>Integration of the sensor in the control concept in order to reduce fluctuations </li></ul>
  11. 11. Dynamic Model for grate system (1) <ul><li>How to model waste layer and furnace? </li></ul><ul><li>Different methods for modelling waste layer and furnace </li></ul><ul><li>Detailed dynamic models for waste layer are available in literature </li></ul><ul><li>Validation is mostly based upon put furnace experiments </li></ul><ul><li>Good validation of models with real plants is a complicated due to disturbances and control system </li></ul><ul><li>Dynamic modelling: there is no need for a detailed description of the processes in the waste layer to describe well the overall dynamic behaviour of the MSWC </li></ul>
  12. 12. Dynamic Model for grate system (2) <ul><li>Balances solid phase furnace </li></ul><ul><li>Energy balance gas phase furnace </li></ul><ul><li>Reaction rate solid fuel </li></ul>
  13. 13. Dynamic Model for grate system (3) <ul><li>Steam system </li></ul>
  14. 14. Validation of dynamic models (1) Procedure system identification
  15. 15. Validation of dynamic models (2) Comparison dynamic model and plant results
  16. 16. Validation of dynamic models (3) Validation of controller model
  17. 17. Optimisation of control concept (1) <ul><li>Different possibilities for optimisation of control concept </li></ul><ul><li>by using validated model </li></ul><ul><li>Tuning present control concept </li></ul><ul><li>Testing new classical control concepts </li></ul><ul><li>Development of new advanced control concepts </li></ul><ul><li>e.g. Model Predictive Control </li></ul><ul><li>Evolutionary control </li></ul>
  18. 18. Optimisation of control concept (2) AVR plant, optimisation by tuning of control parameters
  19. 19. Optimisation of control concept (3) AVR plant, optimisation by tuning of control parameters
  20. 20. Optimisation of control concept (4) AVR plant, optimisation by tuning of control parameters
  21. 21. EU project ECOTHERM <ul><li>Objective:Development and testing of new advanced control technologies (Model Predictive Control and Evolutionary Control) </li></ul><ul><ul><ul><li>Further development models and new control strategies </li></ul></ul></ul><ul><li>Partners TNO (co-ordinator), ENEA (I), IST(P), Irradiare(P), CS SI (F) and two MSWC installations: AGEA (I) and AVR (NL) </li></ul><ul><li>Integration of control systems and supervision systems </li></ul><ul><li>Construction of a prototype and testing in MSWCs AGEA and AVR </li></ul>
  22. 22. Model Predictive Control <ul><li>MPC: based upon measurements from the past, a model of the plant and the control objectives it predicts the plant behaviour in the near future with respect to the constraints and boundary conditions of the system. </li></ul><ul><li>Based upon the control objectives it calculates at every sample time t, the most optimal control actions for the near future. At every time sample t this is repeated. </li></ul><ul><li>Mathematically: an optimisation problem </li></ul>
  23. 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. 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. 25. Evolutionary Control Diagram of evolutionary control approach
  26. 26. Conclusions <ul><li>A dynamical first principal model (simulator) of grate firing systems is available </li></ul><ul><li>Validation has shown that the model is in good compliance with practical data </li></ul><ul><li>An on-line calorific value sensor has been developed and validated </li></ul><ul><li>Using of the dynamic model has shown good results in improving the performance of a MSWC system </li></ul>

×