Abstract
A control system for set-point control of microbial cultivation process parameters is developed, in which a tendency model is applied for controller adaptation to process nonlinearity and time-varying operating conditions. The tendency model is updated on-line and introduced into control algorithm for prediction of steady-state control action and returning of feedback controller. The control system was tested for controlling dissolved oxygen concentration in batch operating mode bioreactor under extreme operating conditions. In simulation experiments, the control system demonstrates fast adaptation, robust behaviour and significant improvement in control performance compared to that of fixed gain controller.
Highlights
Maintaining in bioreactors a specific state of microorganisms’ culture is commonly implemented by set-point control of key technological parameters, in particular, concentrations of feeding substrates
Because of significant variations in process dynamics over the course of microbial cultivation, the ordinary control systems with fixed gain linear controllers are not adequate to cope with the accurate control task
The controller gain scheduling technique has been applied for design of batch bioreactor controllers with the oxygen uptake rate (OUR) as scheduling variable [4]
Summary
Maintaining in bioreactors a specific state of microorganisms’ culture is commonly implemented by set-point control of key technological parameters, in particular, concentrations of feeding substrates. A relevant objective persists to develop and improve universal and reliable control systems for accurate control of cultivation process parameters that employ a priori knowledge of the controlled process and available on-line measurements This objective is consistent with the Process Analytical Technology (PAT) initiative [10] and the efforts of steering the PAT initiative towards realistic and attainable industrial applications. In this contribution, an adaptive control system is developed based on exploiting a tendency model of the cultivation process dynamics and on-line measurements of process variables applied for updating the model-based control algorithm. The results are compared with those of a standard PI controller and the gain scheduling-based adaptive control system developed for the DOC control in [12]
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