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Han, In-Su,Kim, Minjin,Lee, Chang-Hyun,Cha, Woonou,Ham, Byoung-Kyoung,Jeong, Jy-Hyo,Lee, Haksoo,Chung, Chang-Bock,Han, Chonghun 한국화학공학회 2003 Korean Journal of Chemical Engineering Vol.20 No.6
This paper deals with an application of partial least squares (PLS) methods to an industrial terephthalic acid (TPA) manufacturing process to identify and remove the major causes of variability in the product quality. Multivariate statistical analyses were performed to find the major causes of variability in the product quality, using the PLS models built from historical data measured on the process and quality variables. It was found from the PLS analyses that the variations in the catalyst concentrations and the process throughput significantly affect the product quality, and that the quality variations are propagated from the oxidation unit to the digestion units of the TPA process. A simulation-based approach was addressed to roughly estimate the effects of eliminating the major causes on the product quality using the PLS models. Based on the results that considerable amounts of the variations in the product quality could be reduced, we have proposed practical approaches for removing the major causes of product quality variations in the TPA manufacturing process.
Han, In-Su,Han, Chonghun,Chung, Chang-Bock Wiley Subscription Services, Inc., A Wiley Company 2005 Journal of applied polymer science Vol.95 No.4
<P>This article presents the application of three black-box modeling methods to two industrial polymerization processes to predict the melt index, which is considered an important quality variable determining product specifications. The modeling methods covered in this study are support vector machines (SVMs; known as state-of-the-art modeling methods), partial least squares (PLS), and artificial neural networks (ANNs); the processes are styrene–acrylonitrile (SAN) and polypropylene (PP) polymerizations currently operated for commercial purposes in Korea. Brief outlines of the modeling procedure are presented for each method, followed by the procedures for training and validating the models. The SVM models yield the best prediction performances for both the SAN and PP polymerization processes. However, the ANN models fail to accurately predict the melt index when sufficient data are not available for model training in the PP polymerization process. The PLS models are not effective either when applied to the SAN polymerization process, for which the melt index has strong nonlinear functionality with the process variables. The good prediction performance that the SVM models show despite the insufficient data or strong process nonlinearity suggests that SVMs can be effectively used as alternative to PLS or ANNs for modeling the melt indices in other polymerization processes as well. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 967–974, 2005</P>
Modeling and simulation of a simulated moving bed for adsorptive para-xylene separation
Chonghun Han,Jinsuk Lee,Nam Cheol Shin,임영섭 한국화학공학회 2010 Korean Journal of Chemical Engineering Vol.27 No.2
A multi-cell model was developed to analyze the behavior of a simulated moving bed process for adsorptive para-xylene separation from other xylene isomers. A novel technology for a semi-batch mode adsorption experiment was developed and used for fast and accurate data collection. Interaction parameters between different species for a multi-component extended Langmuir isotherm were estimated from single and multi-component adsorption experiments and implemented into the model. The parameters such as porosities, particle density and mass transfer coefficients were obtained from adsorbent analysis and commercial plant operation. To resolve the problem of high dimensionality,a cell-by-cell approach was proposed to solve the model. The recovery and purity of para-xylene as well as the concentration profile calculated from the model were in good agreement with the actual data. The effects of channeling and feed composition change were simulated, and they turned out to be physically meaningful. The simulation model will be used for operation condition optimization, trouble shooting, and productivity enhancement including a configuration change.