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Models for on-line control of batch polymerization processes

Models for on-line control of batch polymerization processes. State and parameter estimation for a semi -batch free-radical emulsion copolymerization process. Student: Fredrik Gjertsen Supervisor, NTNU: Prof. Sigurd Skogestad Supervisor, external : Peter Singstad, Cybernetica AS.

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Models for on-line control of batch polymerization processes

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  1. Models for on-line controlof batch polymerizationprocesses State and parameter estimation for a semi-batch free-radicalemulsioncopolymerizationprocess Student: Fredrik Gjertsen Supervisor, NTNU: Prof. Sigurd Skogestad Supervisor, external: Peter Singstad, Cybernetica AS Trondheim, 13. desember 2013

  2. Agenda • Motivation and overview: MPC • Model description • The need for estimation • Results from off-line parameter estimation • Conclusions from thework • Looking forward: A proposal for an extension to thework

  3. Components of an MPC implementation

  4. Typicaldevelopmentprocess (In my case: Approximatelyoneyear)

  5. Components of an MPC implementation • Initially: A processofinterest – Free-radicalemulsioncopolymerization • Step 1: Acquire a processmodel • Step 2: Verify and improvetheprocessmodelthrough parameter fitting • Ultimate goal: A completepackageincluding all thenecessarycomponents

  6. Model description • Free-radicalemulsioncopolymerization • Monomers: Styrene, Butyl acrylate • Multi-component, multi-phase, reactivechemical system • Semi-batch reactorsetup • The model is formulated in lab-scale • The modelwasformulatedusingtheModelicaprogramminglanguage and implementedusingtheDymolasoftware. • Parameter fitting wasperformedusingtheCyberneticaModelFitsoftware.

  7. Estimator algorithms • States and parameters ofthemodel is onlyknownwith a certainaccuracy • Off-line parameter fitting prior to on-line implementation • On-line state and parameter estimation (filtering) • The estimator is a keycomponent in themodel-basedcontrollerimplementation • H∞-methods, MovingHorizon Estimator, etc. • Kalman Filter estimator has beenchosen • Extended to apply for nonlinear systems

  8. Components of an MPC implementation

  9. Strategy for parameter fitting • Off-line parameter estimation is done usingexperimental data • Typicaloptimization problem: fk: Model output at time k ym,k: Measurement at time k θ: The entirecollectionof parameters φ: Parameters chosen for optimization • Similar to themethodofleastsquares. • lsqcurvefitin MATLAB

  10. Results – Reactortemperature (Initial behavior)

  11. Results – Conversion of monomer (Initial behavior)

  12. Results – Conversion of monomer (With optimallyfitted parameters)

  13. Results – Molecularweightdistribution (Initial behavior)

  14. Results– Molecularweightdistribution (With optimallyfitted parameters)

  15. Conclusions • Some parameters have beenadjusted to improvethemodel • Factorsgoverningthechemicalreaction rates • Factorsgoverningterminationofgrowing polymer chains • Factorsgoverning heat transfer remainuntouched • Demand for computationalpower is high • Maybetoohigh? • Includesimplifications for on-line implementation? • The establishedformulations for on-line estimationcan be applied in the on-line controllerimplementation

  16. Suggestions for furtherwork • Complete the MPC setup for a semi-batch case • Tune estimator basedonprojectwork • Design and tune controlleralgorithm • Extendtheestablishedwork to includecontinuousreactor cases • «Smart-scale» tubularreactors • This willrequire more modelingwork, but most ofthetheory is reapplicable • Design and tune both estimator and controller, usingexperimental data for tubularreactors

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