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Using the Stochastic Gradient Descent Optimization Algorithm on Estimating of Reactivity Ratios.

Iosif Sorin Fazakas-Anca1, Arina Modrea2, Sorin Vlase3,4

  • 1AGIMED Sovata, 545500 Sovata, Romania.

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Summary
This summary is machine-generated.

This study introduces a new method using neuronal networks and gradient descent for calculating reactivity ratios in copolymerization. The improved technique demonstrates superior accuracy compared to traditional methods like Fineman-Ross and Kelen-Tüdös.

Keywords:
copolymerizationgradient descentreactivity ratios

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Area of Science:

  • Polymer Chemistry
  • Computational Chemistry

Background:

  • Accurate reactivity ratios are crucial for predicting copolymer composition.
  • Existing methods for calculating reactivity ratios have limitations, especially at varying conversion levels.

Purpose of the Study:

  • To develop and validate an improved method for calculating reactivity ratios.
  • To compare the performance of the new method against established techniques.

Main Methods:

  • Application of neuronal networks optimization algorithm (gradient descent) for reactivity ratio calculation.
  • Comparison with Fineman-Ross, Tidwell-Mortimer, Kelen-Tüdös, extended Kelen-Tüdös, and Error in Variable Methods.
  • Analysis of reactivity ratios at different conversion levels using the Fisher criterion.

Main Results:

  • The proposed gradient descent-based neuronal network method yields improved accuracy in reactivity ratio determination.
  • The new method outperforms the compared traditional and error-in-variable methods.
  • Performance is consistent across various conversion levels.

Conclusions:

  • The neuronal network optimization algorithm offers a more robust and accurate approach to calculating reactivity ratios.
  • This advancement provides a valuable tool for polymer composition prediction and process optimization.