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dc.contributor.authorWang, Jin
dc.date.accessioned2013-09-19T20:04:30Z
dc.date.available2013-09-19T20:04:30Z
dc.date.issued2013-09-04
dc.identifier.urihttp://hdl.handle.net/1853/49005
dc.descriptionPresented on September 4, 2013 from 4-5 pm in room G011 of the Molecular Science and Engineering Building.en_US
dc.descriptionDr. Jin Wang is B. Redd Associate Professor in the Department of Chemical Engineering at Auburn University. She obtained her BS and PhD degrees in chemical engineering (specialized in biochemical engineering) from Tsinghua University in 1994, and 1999 respectively. She then obtained a PhD degree (specialized in control engineering) from the University of Texas at Austin in 2004. From 2002 to 2006 she was a development engineer and senior development engineer at Advanced Micro Devices, Inc. During her tenure at AMD, her R&D yielded 12 patents granted by USPTO. In addition, she received several prestigious corporate awards for being instrumental in developing effective advanced control solutions.
dc.descriptionRuntime: 54:25 minutes
dc.description.abstractLarge-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.en_US
dc.format.extent54:25 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesSchool of Chemical and Biomolecular Engineering Seminar Seriesen_US
dc.subjectGenome-scaleen_US
dc.subjectMetabolic network modelingen_US
dc.subjectProcess monitoringen_US
dc.subjectSystems engineeringen_US
dc.titleSystems Engineering Approaches to the Study of Industrial Processes and Biological Systemsen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Chemical and Biomolecular Engineeringen_US
dc.contributor.corporatenameAuburn Universityen_US
dc.embargo.termsnullen_US


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