Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/16072
Title: Modeling and control of chylla haase polymerization reactor
Researcher: Vasanthi D
Guide(s): Pappa A
Keywords: Chylla haase polymerization
Electrical engineering
Polymerization
Unscented Kalman Filter
Upload Date: 21-Feb-2014
University: Anna University
Completed Date: 01/09/2013
Abstract: Polymerization is the process of reacting monomer molecules newlinetogether in chemical reactions to form three dimensional networks of polymer newlinechains. The widely used polymerization reactors in chemical industry for the newlineproduction of fine pigments, chemicals, polymers and pharmaceuticals are newlinebatch and semi batch reactors. Polymerization process exhibits a dynamic newlinebehaviour hence it is complicated to obtain an optimal process model. The newlinemain aim of these processes is to obtain a quality product. The Chylla Haase polymerization reactor taken for this work, an newlineindustrial challenge problem is given by Chylla and Rendall Haase (1992). newlineThis work deals with modeling and control of Chylla Haase polymerization newlinereactor. The corrected model given by Knut Graichen et al (2006) is used for newlinesimulation. In this semi batch polymerization reactor, the tight control of newlinetemperature is required to achieve a high quality product. The neural network newlinemodeling of this semibatch process is based on Yang et al (1999). The self newlinetuning regulator and neural network based controller is also designed. The initial phase of the process will be a heating phase followed newlineby feeding phase and then holding phase. Once the monomer feed is stopped, newlineit behaves as a batch process. Hence to capture the entire dynamics of the newlineprocess it is divided into three phases for polymer A and five phases for newlinepolymer B and individual neural network model is developed. Since it is time newlinedependent in nature, respective neural network model will be active for the newlinespecified time duration. Thus multiple neural network captures the entire newlinedynamics of the process completely. In this process the control task comprises of the heating of the newlinereactor to the operation temperature before the monomer feed starts, and newlinesubsequently keeping the temperature constant throughout the production. newlineThe reactor temperature should be within ±0.6K from the set point during the newlinemonomer feed and for a subsequent hold period.
Pagination: xxiv, 166p.
URI: http://hdl.handle.net/10603/16072
Appears in Departments:Faculty of Electrical and Electronics Engineering

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02_certificate.pdf239.85 kBAdobe PDFView/Open
03_abstract.pdf15.72 kBAdobe PDFView/Open
04_acknowledgement.pdf6.19 kBAdobe PDFView/Open
05_contents.pdf44.59 kBAdobe PDFView/Open
06_chapter 1.pdf22.44 kBAdobe PDFView/Open
07_chapter 2.pdf20.02 kBAdobe PDFView/Open
08_chapter 3.pdf234.46 kBAdobe PDFView/Open
09_chapter 4.pdf488.93 kBAdobe PDFView/Open
10_chapter 5.pdf1.39 MBAdobe PDFView/Open
11_chapter 6.pdf14.69 kBAdobe PDFView/Open
12_references.pdf22.1 kBAdobe PDFView/Open
13_publications.pdf5.49 kBAdobe PDFView/Open
14_vitae.pdf5.11 kBAdobe PDFView/Open


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