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Open AccessFeature PaperArticle

Surrogate Models for Online Monitoring and Process Troubleshooting of NBR Emulsion Copolymerization

1
Department of Chemical Engineering, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
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Institute for Polymer Research (IPR), Department of Chemical Engineering, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Masoud Soroush
Processes 2016, 4(1), 6; https://doi.org/10.3390/pr4010006
Received: 22 December 2015 / Accepted: 3 March 2016 / Published: 14 March 2016
(This article belongs to the Special Issue Polymer Modeling, Control and Monitoring)
Chemical processes with complex reaction mechanisms generally lead to dynamic models which, while beneficial for predicting and capturing the detailed process behavior, are not readily amenable for direct use in online applications related to process operation, optimisation, control, and troubleshooting. Surrogate models can help overcome this problem. In this research article, the first part focuses on obtaining surrogate models for emulsion copolymerization of nitrile butadiene rubber (NBR), which is usually produced in a train of continuous stirred tank reactors. The predictions and/or profiles for several performance characteristics such as conversion, number of polymer particles, copolymer composition, and weight-average molecular weight, obtained using surrogate models are compared with those obtained using the detailed mechanistic model. In the second part of this article, optimal flow profiles based on dynamic optimisation using the surrogate models are obtained for the production of NBR emulsions with the objective of minimising the off-specification product generated during grade transitions. View Full-Text
Keywords: acrylonitrile butadiene rubber (NBR); emulsion copolymerization; surrogate modeling; artificial neural networks; inverse modeling; dynamic optimisation acrylonitrile butadiene rubber (NBR); emulsion copolymerization; surrogate modeling; artificial neural networks; inverse modeling; dynamic optimisation
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MDPI and ACS Style

Madhuranthakam, C.M.R.; Penlidis, A. Surrogate Models for Online Monitoring and Process Troubleshooting of NBR Emulsion Copolymerization. Processes 2016, 4, 6.

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