To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process devel...
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To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, controlsystemdesign for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-basedcontrol algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses. In continuous cell culture processes such as perfusion, "State of control" is especially important, but the setup of the controlsystem is often experimental, iterative, and costly. We developed a novel control strategy that systematically tunes cell culture-specific control parameters. The proposed scheme can automatically and adaptively update control parameters in response to dynamic fluctuations in cell culture without relying on qualitative decisions by the ***
The model-based Karlsburg Diabetes Management system (KADIS (R)) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabol...
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The model-based Karlsburg Diabetes Management system (KADIS (R)) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS (R) was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS (R) lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
The model-based Karlsburg Diabetes Management system (KADIS®) has been developed as a decision support for physicians in their efforts to optimize metabolic control in diabetes care. For this purpose, KADIS®...
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The model-based Karlsburg Diabetes Management system (KADIS®) has been developed as a decision support for physicians in their efforts to optimize metabolic control in diabetes care. For this purpose, KADIS® was evaluated under different conditions by conducting open-label mono and polycentric trials, a case-control study and, last but not least, an observational study in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. It is concluded that this model-based decision support system provides an excellent tool to effectively guide physicians in decision making to achieve optimal metabolic control for their patients.
A mathematical model that describes the dynamic behaviour of the steam pressure inside a fire-tube boiler has been obtained by using system identification techniques, due to the complexity of the first-principles-mode...
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A mathematical model that describes the dynamic behaviour of the steam pressure inside a fire-tube boiler has been obtained by using system identification techniques, due to the complexity of the first-principles-model ling of this process. A complete system identification procedure has been carried out, from experimental design to model validation using a laboratory-scale boiler and then ratified in an industrial-scale boiler. In both cases, the identified model is characterized by a second order linear ARMAX structure and time delay which describes with very high precision the steam pressure variation process. This model will be used for model-basedcontrol and prediction applications. The reported results show that system identification and model-basedcontrol have important roles to play in the management and intelligent use of available energetic resources. (C) 2008 Elsevier Ltd. All rights reserved.
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