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Published in:
2009 | OriginalPaper | Chapter
Bioprocess Modelling for Learning Model Predictive Control (L-MPC)
Authors : María Antonieta Alvarez, Stuart M. Stocks, S. Bay Jørgensen
Published in: Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control
Publisher: Springer Berlin Heidelberg
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Batch and Fed-Batch cultivation processes are used extensively in many industries where a major issue today is to reduce the production losses due to sensitivity to disturbances occurring between batches and within batches. In order to ensure consistent product quality by eliminating the influence of process disturbances it is very important to consider implementation of monitoring and control and thereby significantly improve the economic impact for these industries. A data driven modeling methodology is described for batch and fed batch processes which is based upon data obtained from operating processes. The chapter illustrates how additional production experiments may be designed to improve model quality for control. The chapter also describes how the developed models may be used for process monitoring, for ensuring process reproducibility through control and for optimizing process performance by enforcing learning from previous batch runs through Learning Model Predictive Control (L-MPC).