Brief Overview of Current Control Methods

BRIEF OVERVIEW OF CURRENT CONTROL METHODS A. Bioreactor Control 

Bioseparation practitioners can probably benefit from the control experience of bioreactor operation. Several extensive articles on the instrumentation of bioreactors discuss issues and approaches to measuring temperature, pressure, flow, and volume pH, dissolved oxygen and C02, redox potentials, and specific ions or chemicals.9 11 Control of bioreactors has been a fertile research area for a number of years, particularly with respect to fermentor control.12 

A key problem in bioreactor control is the difficulty in obtaining reliable sensors and consequently of reliable on-line process information. Demands for product consistency and process productivity produce requirements for more process information.13 Especially in the case of fermentors, rapid, accurate on-line measurement of process variables is often a complex task. As a result, much research effort has focused on methods for quantitatively estimating compositions within reactors and on using model-based control techniques. 

An estimator (or more specifically an optimal state estimator ) in this usage is an algorithm for obtaining approximate values of process variables which cannot be directly measured. It does this by using knowledge of the system and measurement dynamics, assumed statistics of measurement noise, and initial condition information to deduce a minimum error state estimate. The basic algorithm is usually some version of the Kalman filter.14 In extremely simple terms, a stochastic process model is compared to known process measurements, the difference is minimized in a least-squares sense, and then the model values are used for unmeasurable quantities. Estimators have been tested on a variety of processes, including mycelial fermentation and fed-batch penicillin production,13 and baker s yeast fermentation.15 The 

A second approach to the problem of difficult to obtain measurements is knowledge-based or model-based control. Knowledge-based systems attempt to use various types of knowledge of the biological process (rules etc.) to supplement traditional mathematical control approaches.16 Expert systems are one type of knowledge-based control. Model-based control systems use a model of the process as part of the control algorithm their reliability depends on the accuracy of the model. 

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