Type of Document Dissertation Author Zhang, Yongchun Author's Email Address firstname.lastname@example.org URN etd-0710102-121458 Title Dynamic Modeling and Analysis of Oscillatory Bioreactors Degree Doctor of Philosophy (Ph.D.) Department Chemical Engineering Advisory Committee
Advisor Name Title Martin A. Hjortso Committee Chair Armando B. Corripio Committee Member F. Carl Knopf Committee Member Jorge A. Aravena Committee Member Mark L. McLaughlin Dean's Representative Keywords
- model reduction
Date of Defense 2002-06-12 Availability unrestricted AbstractDynamic modeling of bioreactors is a challenging problem. The complexity of first principle models also make model validation and analysis very difficult and model-based controller design practically intractable. This thesis has focused on finding an effective tool for model dynamic analysis, construction of low-dimension model and simple and effective controller design.
The validity of a biochemical reactor model often is evaluated by comparing transient responses to experimental data. Dynamic simulation can be rather inefficient and ineffective for analyzing bioreactor model. Bifurcation analysis is found to be a powerful tool for obtaining a more efficient and complete characterization of the model behavior. Dynamic behaviors of three low-dimension continuous bioreactor models consisting of a small number of ordinary differential equations are investigated. Several important features, as well as potential limitations, that are difficult to ascertain via dynamic simulation are disclosed through the bifurcation analysis. Bifurcation analysis is also successfully used for analysis and validation of more complex population balance models for yeast cultures.
Saccharomyces cerevisiae exhibits sustained oscillations over a wide range of operating conditions when produced in a continuous bioreactor. Transient cell population balance models consist of nonlinear partial differential-integro equations. An accurate discretized approximation which typically requires a large number of nonlinear ordinary differential equations is not well suited for dynamic analysis and controller design purpose. Proper orthogonal decomposition is used to construct nonlinear reduced-order models from spatiotemporal data sets obtained via simulations of an accurate discretized yeast cell population model. The short-term and long-term behaviors of the reduced-order models are evaluated by comparison to the full-order model. Dynamic simulation and bifurcation analysis results demonstrate that reduced-order models with a comparatively small number of differential equations yield accurate predictions over a wide range of operating conditions.
Feedback linearizing control of the yeast bioreactor is also studied. The controller design is based on a low dimensional moment representation of the PBE model. Satisfactory oscillation attenuation results have been achieved. The performance of nonlinear controllers using different input/output variable pairings is also investigated.
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