Quality prediction for polypropylene production process based on CLGPR model
Ge, Z, Chen, T and Song, Z (2011) Quality prediction for polypropylene production process based on CLGPR model Control Engineering Practice, 19 (5). pp. 423-432.
zge11-contEngPract-web.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
Online measurement of the melt index is typically unavailable in industrial polypropyleneproductionprocesses, soft sensing models are therefore required for estimation and prediction of this important quality variable. Polymerization is a highly nonlinear process, which usually produces products with multiple quality grades. In the present paper, an effective soft sensor, named combined local Gaussian process regression (CLGPR), is developed for prediction of the melt index. While the introduced Gaussian process regression model can well address the high nonlinearity of the process data in each operation mode, the local modeling structure can be effectively extended to processes with multiple operation modes. Feasibility and efficiency of the proposed soft sensor are demonstrated through the application to an industrial polypropyleneproductionprocess.
|Divisions :||Faculty of Engineering and Physical Sciences > Chemical and Process Engineering|
|Identification Number :||https://doi.org/10.1016/j.conengprac.2011.01.002|
|Additional Information :||NOTICE: this is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 19(5), May 2011, DOI 10.1016/j.conengprac.2011.01.002.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||04 May 2012 15:58|
|Last Modified :||23 Sep 2013 19:13|
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