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  • 1 Halic University Applied Mathematics Department Bomonti Istanbul Turkey
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Abstract  

The aim of this study was to correlate the results of experimental data using DTA method and predictions of artificial neural network (ANN) and multivariate linear regression (MLR). Thermal decomposition of polymers was analyzed by simultaneous DTA method, and kinetic parameters (critical points, the change of enthalpy and entropy) of polymers were investigated. A computer model based on multilayer feed forwarding back propagation and multilayer linear regression model were used for the prediction of critical points, phase transitions of low-density polyethylene (LDPE) and mid-density polyethylene. As a result of our study, we concluded that ANN model is more suitable than MLR about prediction of experimental data.

Manuscript Submission: HERE

  • Impact Factor (2019): 2.731
  • Scimago Journal Rank (2019): 0.415
  • SJR Hirsch-Index (2019): 87
  • SJR Quartile Score (2019): Q3 Condensed Matter Physics
  • SJR Quartile Score (2019): Q3 Physical and Theoretical Chemistry
  • Impact Factor (2018): 2.471
  • Scimago Journal Rank (2018): 0.634
  • SJR Hirsch-Index (2018): 78
  • SJR Quartile Score (2018): Q2 Condensed Matter Physics
  • SJR Quartile Score (2018): Q2 Physical and Theoretical Chemistry

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Journal of Thermal Analysis and Calorimetry
Language English
Size A4
Year of
Foundation
1969
Volumes
per Year
4
Issues
per Year
24
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1388-6150 (Print)
ISSN 1588-2926 (Online)