(307g) Application of Agent Technique in Bioprocess Modelling for Plant-Wide Process Improvement | AIChE

(307g) Application of Agent Technique in Bioprocess Modelling for Plant-Wide Process Improvement

Authors 

Gao, Y. - Presenter, University College London
Kipling, K. - Presenter, Newcastle University
Glassey, J. - Presenter, Newcastle University
Willis, M. - Presenter, Newcastle University
Montague, G. - Presenter, Newcastle University
Zhou, Y. - Presenter, University College London
Titchener-Hooker, N. J. - Presenter, University College London


In the biopharmaceutical industry, timely development and transfer of cost effective products to the market is of great importance. The industry also requires to move quickly from small-scale data to manufacturing-scale confidence and to improve manufacturing processes so as to achieve optimal process performance. At the heart of this is the need to develop predictive tools such as unit operation models for the description of the whole manufacturing process and the subsequent evaluation of process improvement options. To improve plant-wide efficiency, the strong interactions present in bioprocess must be adequately modelled in order to identify the optimal process operation conditions. Traditionally bioprocess modelling considers the process units separately, which makes it difficult to investigate interactions between units. In this work, a systematic framework is developed to analyse bioprocesses based on a whole process understanding considering the interactions between process operations. This yields a capacity to predict process performance in the case of process variations, and to improve process efficiency within the process design space. An agent-based approach is proposed to provide such an environment for the necessary integration of process models, to handle process interactions, to simulate the overall process behaviour and to lead to the fast evaluation of process improvement options. A multi-agent system comprising of a process knowledge base, process models and a group of functional agents will be introduced in this work. The unit operation models are based on the principle analysis of the process unitizing small scale experimental data. Agents are designed to link the unit operation models, to represent the unit operations, and to simulate the interaction between units. The adoption of an agent-based approach to bioprocess modelling provides a flexible infrastructure for the integration of process operations, supports timely assembling of process models and the updating of process descriptions. Agents have the ability to detect and act on the most up-to-date bioprocess information. In this system, agent components run on top of the process models and datasets, they cooperate with each other in performing their tasks for the description of the whole process behaviour, predict critical process performance, and control of process parameters in a timely manner to maintain the product quality and 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 for the purposes of plant-wide process description and process improvement. An intra-cellular protein production process is used as an example to demonstrate the system application, and in particular the application of agents in detecting of deviation in upstream unit operation and predicting the influences on the subsequent downstream processing whilst providing guidance in terms of possible corrective actions designed to improve process efficiency and ensure product quality.

Key words: Bioprocess modelling, agent-based system, bioprocess interaction, process improvement.

Checkout

This paper has an Extended Abstract file available; you must purchase the conference proceedings to access it.

Checkout

Do you already own this?

Pricing

Individuals

AIChE Pro Members $150.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00