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Processes 2018, 6(1), 8; https://doi.org/10.3390/pr6010008

Computational Package for Copolymerization Reactivity Ratio Estimation: Improved Access to the Error-in-Variables-Model

Institute for Polymer Research (IPR), Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Received: 12 December 2017 / Revised: 9 January 2018 / Accepted: 13 January 2018 / Published: 19 January 2018
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Abstract

The error-in-variables-model (EVM) is the most statistically correct non-linear parameter estimation technique for reactivity ratio estimation. However, many polymer researchers are unaware of the advantages of EVM and therefore still choose to use rather erroneous or approximate methods. The procedure is straightforward but it is often avoided because it is seen as mathematically and computationally intensive. Therefore, the goal of this work is to make EVM more accessible to all researchers through a series of focused case studies. All analyses employ a MATLAB-based computational package for copolymerization reactivity ratio estimation. The basis of the package is previous work in our group over many years. This version is an improvement, as it ensures wider compatibility and enhanced flexibility with respect to copolymerization parameter estimation scenarios that can be considered. View Full-Text
Keywords: copolymerization kinetics; copolymer composition; design of experiments; error-in-variables-model (EVM); parameter estimation; polymer reaction engineering; reactivity ratios copolymerization kinetics; copolymer composition; design of experiments; error-in-variables-model (EVM); parameter estimation; polymer reaction engineering; reactivity ratios
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Scott, A.J.; Penlidis, A. Computational Package for Copolymerization Reactivity Ratio Estimation: Improved Access to the Error-in-Variables-Model. Processes 2018, 6, 8.

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