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Received February 18, 2011
Accepted April 29, 2011
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Dynamic simulation based fault detection and diagnosis for distillation column
College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao, P. R. China 1School of Polymer Science and Engineering, Qingdao University of Science & Technology, Qingdao, P. R. China
tianwd@qust.edu.cn
Korean Journal of Chemical Engineering, January 2012, 29(1), 9-17(9), 10.1007/s11814-011-0117-2
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Abstract
The model-based fault diagnosis approach is characterized by a powerful process supervision capability with a priori knowledge about the system under consideration. Nevertheless, system complexity, high dimensionality, process nonlinearity and/or lack of good data often hamper its application in chemical engineering systems. A nonsteady state model based fault detection and diagnosis method for the distillation process was developed, using dynamic simulation to monitor the distillation process and identify abnormal sources when large deviations among measuring_x000D_
variables occur. It continuously updates the inner distillation parameters via on-line correction and predicts the trend of measuring variables and determines the existence of malfunctions simultaneously. The distillation model is dependent on transfer equilibrium, mass and heat balance, and is simulated by Euler and two-tier approach. This method was demonstrated with simulated data of a stripping tower collected from the Tennessee Eastman chemical plant simulator.
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