Continuous-Time Block-Oriented Adaptive on-Line Modeling for Time Varying Systems
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The function of the Department of Chemical and Biological Engineering has been to prepare students for the study and application of chemistry in industry. This focus has included preparation for employment in various industries as well as the development, design, and operation of equipment and processes within industry.Through the CBE Department, Iowa State University is nationally recognized for its initiatives in bioinformatics, biomaterials, bioproducts, metabolic/tissue engineering, multiphase computational fluid dynamics, advanced polymeric materials and nanostructured materials.
History
The Department of Chemical Engineering was founded in 1913 under the Department of Physics and Illuminating Engineering. From 1915 to 1931 it was jointly administered by the Divisions of Industrial Science and Engineering, and from 1931 onward it has been under the Division/College of Engineering. In 1928 it merged with Mining Engineering, and from 1973–1979 it merged with Nuclear Engineering. It became Chemical and Biological Engineering in 2005.
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1913 - present
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- Department of Chemical Engineering (1913–1928)
- Department of Chemical and Mining Engineering (1928–1957)
- Department of Chemical Engineering (1957–1973, 1979–2005)
- Department of Chemical and Biological Engineering (2005–present)
- College of Engineering(parent college)
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Abstract
The development and maintenance of accurate predictive models for dynamic systems are highly challenged by system complexity, limited information (i.e., data), changing cross and time correlation structures and changing model parameters. Thus, for a model or modeling method to achieve long term success in implementation into a real system, it must be phenomenologically sound and adaptive, as well as being capable of immediate update from recently obtained process data (i.e., plant data). A model is phenomenologically sound when its structure accurately captures physical input and output relationships, and the stochastic behavior of process and measurement noise. On-line adaptive methods are critical to success because process variations that cause changes to noise correlation structures and model coefficients are frequent in real systems. A common occurrence in non-adaptive, off-line, model identification is the requirement of a new model by the time the model is ready for implementation due to significant process variations. For a method to have on-line adaptive abilities, it must be capable of using process data (which have a low signal to noise ratio, and limited range over the operating space) to update its fitting performance.
Comments
This is a proceeding from AIChE Annual Meeting, Conference Proceedings (2005): 6502. Posted with permission.