A combined phenomenological artificial neural network approach for determination of pyrolysis and combustion kinetics of polyvinyl chloride

ÖZSİN G., ALPASLAN TAKAN M., Takan A., Pütün A. E.

International Journal of Energy Research, cilt.46, sa.12, ss.16959-16978, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 12
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/er.8361
  • Dergi Adı: International Journal of Energy Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.16959-16978
  • Anahtar Kelimeler: artificial neural network (ANN), combustion, kinetics, polyvinyl chloride (PVC) polymer, pyrolysis
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2022 John Wiley & Sons Ltd.As a widely used plastic material polyvinyl chloride (PVC) accounts for a significant amount of plastic waste but also offers great potential in conversion to chemical feedstock via pyrolysis process. However, development of a sensitive mathematical approach is required for proper process design and monitoring of thermochemical conversion processes. In this work, we attempt to develop an artificial neural network (ANN) model for estimation of mass loss as a function of temperature and heating rate during pyrolysis and combustion of PVC. For this purpose, pyrolysis and combustion characteristics were quantified using thermogravimetric analysis, then non-isothermal kinetics were analysed by iso-conversional models. The results of ANN models show that this method helps predict complex systems with high regression coefficient (R2) values. The best performed model analysed by ANN for pyrolysis was NN 7 with R2 = 0.9993, the best performed model for combustion was NN 10 with R2 = 0.9982. Comparison of experimental results to ANN predictions indicates that ANNs with a quick propagation algorithm can be an effective approach for modelling complex non-linear systems such as thermal degradation of thermoplastics.