Title:
Systems Engineering Approaches to the Study of Industrial Processes and Biological Systems

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Author(s)
Wang, Jin
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Abstract
Large-scale industrial processes and biological systems share many similarities at the systems level: they both consist of many individual components; they both have built-in feedback control/regulation mechanisms; and the properties of the overall systems are determined by the complex interaction among different components. Their complex nature makes the integrative systems approaches essential in understanding, controlling and optimizing these systems. As a result, many process systems engineering principles and techniques have been extended into the emerging field of systems biology. However, despite their commonalities at the system level, large-scale industrial processes and biological systems have their unique characteristics and challenges that existing systems approaches cannot fully address. In this talk, our most recent progress in both research areas (process systems engineering and systems biology) will be presented. For large-scale industrial processes, one of our focuses is process monitoring. The specific challenge we aim to address is how to effectively handle process nonlinear dynamics, non-Gaussianity, frequent process changes driven by manufacturing on-demand, but without the heavy computational burden of available nonlinear methods. The solution we developed is a new multivariate framework named statistics pattern analysis (SPA) and we use the benchmark Tennessee Eastman Process to demonstrate the effectiveness of the new framework. For biological systems, one specific challenge we aim to address is how to effectively utilize genome-wide metabolic network models and extract biological meaningful information from them. The solution we developed is a system identification based approach where we use the metabolic models as a high fidelity simulator to conduct carefully designed in silico experiments. We will use scheffersomyces stiptis (the yeast with the strongest native capability to ferment xylose) as the model system to illustrate our developed approach.
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Date Issued
2013-09-04
Extent
54:25 minutes
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Moving Image
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Lecture
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