Group of Integration, Modeling, Bioprocess Control: S imulation, from Sensor Selection to Control, and Optimization of Optimal Control Action Processes Prof. Dr. Jorge Otávio Trierweiler Jorge@enq.ufrgs.br Chemical Engineering Department (DEQUI) Federal University of Rio Grande do Sul (UFRGS)
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Models are required to improve the control of bioreactors • The models are used for: ▫ Operating point definition – Bifurcation Diagram Analysis ▫ State Estimation – Virtual Analyzer ▫ Optimal control action – DRTO + NMPC • Feedback – advantages ▫ It is robust – survive against model uncertainties ▫ Compensate process disturbances • Basic components of a feedback loop ▫ Sensor ▫ Controller ▫ Actuator PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 2
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion S teps to S olve the Control and Optimization Problem for Bioreactors 1. Model selection = parameter estimation, model 1. Model selection = parameter estimation, model discrimination, and experimental design discrimination, and experimental design 2. Nominal Analysis = Bifurcation Diagram and Steady State 2. Nominal Analysis = Bifurcation Diagram and Steady State Multiplicity Multiplicity 3. Nominal Optimal Operating Point 3. Nominal Optimal Operating Point 4. Control Structure Design = definition and selection of the 4. Control Structure Design = definition and selection of the manipulated and controlled variables manipulated and controlled variables 5. State Estimator Design and Sensor Selection 5. State Estimator Design and Sensor Selection 6. Uncertain Optimal Operating Point = considering the 6. Uncertain Optimal Operating Point = considering the uncertainty in the parameters uncertainty in the parameters 7. Optimal Control Action 7. Optimal Control Action PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 3
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Zymomonas mobilis has interesting dynamic behaviors PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 4
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Bacteria Zymomonas mobilis Bacteria Zymomonas mobilis instead of the classic Yeast Saccharomyces cerevisiae Ethanol production using Zymomonas Zymomonas Mobilis Mobilis: : Ethanol production using • • Anaerobia Anaerobia • • High conversion per S UBS TRAT High conversion per S UBS TRAT • • High tolerance to high ethanol concentration High tolerance to high ethanol concentration • • High fermentation velocity High fermentation velocity PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 5
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Drawbacks of Zymomonas mobilis • A major drawback of this microorganism is that it exhibits • A major drawback of this microorganism is that it exhibits sustained oscillations over a wide range of operating sustained oscillations over a wide range of operating conditions when grown in continuous culture. conditions when grown in continuous culture. • This leads to decreased ethanol productivity and less • This leads to decreased ethanol productivity and less efficient use of available substrate. efficient use of available substrate. • Various models have been proposed to describe the • Various models have been proposed to describe the oscillatory dynamics of continuous Zymomonas mobilis oscillatory dynamics of continuous Zymomonas mobilis cultures: Daugulis et al. (1997) and Jöbses et al. (1985) cultures: Daugulis et al. (1997) and Jöbses et al. (1985) Daugulis, A. J.; McLellan, P. J.; Li, J. Experimental investigation and modeling of oscillatory behavior Daugulis, A. J.; McLellan, P. J.; Li, J. Experimental investigation and modeling of oscillatory behavior in the continuous culture of Zymomonas mobilis. Biotechnol. Bioeng., 1997, 56, 99 ‐ 105 in the continuous culture of Zymomonas mobilis. Biotechnol. Bioeng., 1997, 56, 99 ‐ 105 I.M.L. Jöbses, G.T.C. Egberts, A.V. Ballen, J.A. Roels, Mathematical modeling of growth and I.M.L. Jöbses, G.T.C. Egberts, A.V. Ballen, J.A. Roels, Mathematical modeling of growth and substrateconversion of Zymomonas mobilis at 30 and 35 ◦ C, Biotechnol. Bioeng., 1985, 27, 984–995 . substrateconversion of Zymomonas mobilis at 30 and 35 ◦ C, Biotechnol. Bioeng., 1985, 27, 984–995 . PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 6
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Jöbses’ s Model Biomass concentration Substrate concentration ( Zym om onas m obilis ) (glucose) Internal key compound Product concentration concentration (Ethanol) PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 7
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion I.M.L. Jöbses, G.T.C. Egberts, K.C.A.M. Luyben, J.A. Roels, Fermentation kinetics of Zymomonas mobilis at high ethanol concentrations: oscillations in continuous cultures, Biotechnol. Bioeng. 1986,28, 868– 877 . PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 8
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Understanding the possible dynamic behaviors and defining the possible operating points PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 9
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion s t a b l e unstable stable Cs 0 = 200 kg/m³ PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 10
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Experiment al Verificat ion Elnashaine et al. (2006) “ Practical implications of bifurcation chaos in chemical and biological reaction engineering” , Int ernat ional Journal of Chemical React or Engineering 4 PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 11
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 12
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 13
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Optimized Operating Point PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 14
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Cs 0 = 220 kg/m³ Cs 0 = 200 kg/m³ Cs 0 = 180 kg/m³ Cp ( t ) Cp 0 ( t ) Df ( t ) PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 15
Introduction ZM-Model Nominal Estimators Uncertainty Sensors Conclusion Feedback Performance Limitations • Typical limitations • Typical limitations ▫ Nonminimum phase effects ▫ Nonminimum phase effects � Pure time delay (deadtime) � Pure time delay (deadtime) � Right Half Plane Zeros (RHP ‐ zeros) � Right Half Plane Zeros (RHP ‐ zeros) � RHP ‐ Poles � RHP ‐ Poles ▫ General ▫ General � MV saturation and saturation rate � MV saturation and saturation rate � Noise � Noise � model uncertainty – stability problems � model uncertainty – stability problems • Measurement not available – sensor problem • Measurement not available – sensor problem PAS I 2008 - Mar del Plat a - Argentina - Prof. Dr. Jorge Otávio Trierweiler - Federal University of Rio Grande do S ul (UFRGS ) 16
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