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Introduction-Statistical verification of operations used earlier for kinetics experimental data treatment of the styrene suspension polymerization performed on the one of known kinetics model. Internal standard times matrix for different conversions found to have only two statistically significant eigenvalues. Therefore, they may be considerate as plains and easily described to give equation for its dependence from initial condition needed for dimensionless time scale definition. Conversion vectors dependence on the later forms sets of very closely curves. They may be considerate as stochastic realization of the one common curve what follows from its non parametric statistical comparison. Final single curve finds by uniting and sorting operation of its values. That operation gives one plain common curve described by empirical equation with small set of the empirical parameters needed, considerably less than in existing models. Procedure is convenient and robust.
V.P. Savelvanov. 2015. \u201cFactor Analysis of the Styrene Polymerization Kinetics. Statistical Verification of the Procedure\u201d. Global Journal of Science Frontier Research - A: Physics & Space Science GJSFR-A Volume 15 (GJSFR Volume 15 Issue A1): .
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
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Total Score: 101
Country: Unknown
Subject: Global Journal of Science Frontier Research - A: Physics & Space Science
Authors: V.P. Savelvanov (PhD/Dr. count: 0)
View Count (all-time): 130
Total Views (Real + Logic): 4269
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Publish Date: 2015 02, Fri
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Introduction-Statistical verification of operations used earlier for kinetics experimental data treatment of the styrene suspension polymerization performed on the one of known kinetics model. Internal standard times matrix for different conversions found to have only two statistically significant eigenvalues. Therefore, they may be considerate as plains and easily described to give equation for its dependence from initial condition needed for dimensionless time scale definition. Conversion vectors dependence on the later forms sets of very closely curves. They may be considerate as stochastic realization of the one common curve what follows from its non parametric statistical comparison. Final single curve finds by uniting and sorting operation of its values. That operation gives one plain common curve described by empirical equation with small set of the empirical parameters needed, considerably less than in existing models. Procedure is convenient and robust.
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