Application of agent-based system for bioprocess description and process improvement.
Modeling plays an important role in bioprocess development for design and scale-up. Predictive models can also be used in biopharmaceutical manufacturing to assist decision-making either to maintain process consistency or to identify optimal operating conditions. To predict the whole bioprocess performance, the strong interactions present in a processing sequence must be adequately modeled. Traditionally, bioprocess modeling considers process units separately, which makes it difficult to capture the interactions between units. In this work, a systematic framework is developed to analyze the bioprocesses based on a whole process understanding and considering the interactions between process operations. An agent-based approach is adopted to provide a flexible infrastructure for the necessary integration of process models. This enables the prediction of overall process behavior, which can then be applied during process development or once manufacturing has commenced, in both cases leading to the capacity for fast evaluation of process improvement options. The multi-agent system comprises a process knowledge base, process models, and a group of functional agents. In this system, agent components co-operate with each other in performing their tasks. These include the description of the whole process behavior, evaluating process operating conditions, monitoring of the operating processes, predicting critical process performance, and providing guidance to decision-making when coping with process deviations. During process development, the system can be used to evaluate the design space for process operation. During manufacture, the system can be applied to identify abnormal process operation events and then to provide suggestions as to how best to cope with the deviations. In all cases, the function of the system is to ensure an efficient manufacturing process. The implementation of the agent-based approach is illustrated via selected application scenarios, which demonstrate how such a framework may enable the better integration of process operations by providing a plant-wide process description to facilitate process improvement.
Gao
Ying
Y
Dept. of Biochemical Engineering, University College London, Torrington Place, London, WC1E 7JE, UK.
Kipling
Katie
K
Glassey
Jarka
J
Willis
Mark
M
Montague
Gary
G
Zhou
Yuhong
Y
Titchener-Hooker
Nigel J
NJ
D000426:Alcohol Dehydrogenase / Q000378:metabolism; D057005:Bioengineering / Q000379:methods*; D001702:Biopharmaceutics / Q000379:methods*; D019149:Bioreactors; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods; D005285:Fermentation; D008954:Models, Biological
D000426:Alcohol Dehydrogenase