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Three books are reviewed: Intuitive Probability and Random Processes Using Matlab by Steven Kay; Relay Feedback: Analysis, Identification and Control by Q.-G. Wang et al.; and Processing Modelling for Control - A Unified Framework Using Standard Black-box Techniques by Benoit Codrons.

Metabolic Flux Analysis was performed to identify the significant metabolic reactions for hybridoma cells during batch and fed-batch culture. Correlation analysis yielded the factors that influence biomass growth and productivity and elucidated the nature of the relationship between them. Consequently, an integrated dynamic model expressing the rate of consumption and production of nutrients/metabolites and the biomass growth and decline was developed.

The occurrence of large-temperature excursions in response to periodic changes in the inlet reactant concentration was demonstrated in a packed-bed reactor for CO oxidation over Pt/Al2O3, a reaction known to be reactant-inhibited. Hysteresis or rate multiplicity was observed by changing the inlet CO concentration. A sufficiently large, sudden, and persistent increase of CO introduced into the reactor resulted in a transient temperature spike that exceeded the adiabatic temperature and was followed by complete extinction. Periodic perturbations of the inlet CO concentration near the hysteresis limit also resulted in high-temperature responses, the amplitude of which near the hot spot depended on the input frequency. A pseudohomogeneous model with modified Langmuir−Hinshelwood reaction kinetics was used to simulate concentration hysteresis effects and extinction waves. Sigmoid functions were used to relate the sticking probabilities of CO and O2 to the CO surface coverage on Pt. With these functions, the ob...

Multi-wavelength fluorescence spectroscopy was investigated as a potential tool for use in monitoring key process variables that include: viable and dead cells, recombinant protein, glucose, and ammonia concentrations for Chinese hamster ovary (CHO) cells during cultivation.For the purpose of calibrating the fluorescence-based empirical model, cells were grown in batch mode with different initial glucose and glutamine concentrations.Spectrofluorometer settings were optimized to ensure reproducibility and accuracy of the acquired spectra. With the purpose of gaining qualitative insight into the evolution of the spectra, the trajectories of individual fluorophore peaks were studied during the cultivation process. Spectral changes related to biomass and secreted proteins were investigated by comparing the spectra at various stages during the downstream processing. A partial least square regression (PLSR) was used to formulate empirical models that related the input data set, i.e., the fluorescence excitation-emission matrix, to the actual state of the system including viable cell and dead cells and recombinant protein, glucose, and ammonia concentrations. The models exhibited accurate prediction ability for the process variables of interest.

A soft-sensor based on an Extended Kalman Filter (EKF) that combines data obtained using a fluorescence-based soft-sensor with a dynamic mechanistic model, was investigated as a tool for continuous monitoring of a Chinese hamster ovary (CHO) cell cultivation process. A standalone fluorescence based soft-sensor, which uses a combination of an empirical multivariate statistical model and measured spectra, was designed for predicting key culture variables including viable and dead cells, recombinant protein, glucose, and ammonia concentrations. The standalone fluorescence sensor was then combined with a dynamic mechanistic model within an EKF framework, for improving the prediction accuracy and generating predictions in-between sampling instances. The dynamic model used for the EKF framework was based on a structured metabolic flux analysis and mass balances. In order to calibrate the fluorescence-based empirical model and the dynamic mechanistic model, cells were grown in batch mode with different initial glucose and glutamine concentrations. To mitigate the uncertainty associated with the model structure and parameters, non-stationary disturbances were accounted for in the EKF by parameter-adaptation. It was demonstrated that the implementation of the EKF along with the dynamic model could improve the accuracy of the fluorescence-based predictions at the sampling instances. Additionally, it was shown that the major advantage of the EKF-based soft-sensor, compared to the standalone fluorescence-based counterpart, was its capability to track the temporal evolution of key process variables between measurement instances obtained by the fluorescence-based soft-sensor. This is crucial for designing control strategies of CHO cell cultures with the aim of guaranteeing product quality.

A nonlinear controller design for a packed-bed reactor is presented. The scheme employs a two-tier approach in which, first, a low-order nonlinear model is developed and, subsequently, a feedback linearizing control law is synthesized. The reduced-order model treats transport mechanisms in the reactor as a nonlinear wave which propagates through the bed. Application of input−output “linearizing” control yields linear dynamics over a wide range of operating conditions. The practical issues of implementing the resultant digital, implicit, nonlinear control law on an actual system are addressed. Closed-loop simulations are used to illustrate the robustness properties of the nonlinear controller. In addition, the relative strengths of this approach over traditional linear control are identified.

Practical guidelines are required for the design and operation of complicated catalytic packed-bed reactors. Primary among design considerations is the avoidance of operating regions of high parametric sensitivity, in which small changes in operating conditions can lead to thermal runaway in the reactor. Existing criteria for predicting these regions rely on complex mathematical formulations for differential sensitivity between input and output variables. The present work centers on the development of practical design criteria for avoiding reactor instability and temperature sensitivity in multitubular packed-bed reactors. A set of simple guidelines is proposed for the sizing of reactors and proper selection of operating conditions. The implications of these open-loop sensitivities for a controlled reactor are investigated. It is shown through simulation studies that violation of the proposed criteria leads to control problems and difficulty in operating at the design point.

This paper compares the performance of two different inferential schemes when applied to an experimental packed-bed reactor. The first scheme, proposed initially by Brosilow, is designed based on Kalman filter estimation. The second less traditional design uses an estimator computed from the Partial Least Squares regression method (PLS). The second approach was found to give superior performance when the nonlinear system under study is operated is a wide range of operating points. Due to the nonlinearity of the system it is essential to address the issue of robustness of the proposed schemes. This is formally done in this work using Structured Singular Value Theory. For the robustness analysis it is crucial to develop a realistic but not overly conservative uncertainty description. Since the PLS estimator uses a large number of measurements, a robust design based on the uncertainty associated with each one of the measurements would be very conservative. To overcome this problem a lumped uncertainty description is proposed which is identified directly from experiments.

An inferential robust control technique is applied to an experimental fixed bed reactor. The controlled variables are the exit concentration and the maximum bed temperature. Both controlled variables are inferred from one single temperature measurement. The location of this measurement is selected to optimize the performance of the closed-loop system when model uncertainty is allowed. Closed-loop experiments are conducted to test the robustness characteristics of the controller. From these experiments, the operating regions most sensitive to modelling uncertainty are determined. The nonlinear system characteristics can cause significant offset in the inferred controlled variables.

A methodology for computing frequency domain uncertainty bounds for single-input single-output systems is presented. This methodology is applied to the identification of a fixed-bed reactor. A robust controller with a single adjustable parameter is designed for the reactor using Internal Model Control (IMC) theory. The computed uncertainty bounds are experimentally validated using the IMC controller.