Activation energy prediction of biomass wastes based on different neural network topologies

Özge Çepelioğullar*, İlhan Mutlu, Serdar Yaman, Hanzade Haykiri-Acma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

The present paper discusses the thermal data prediction performance of ANN for more than one biomass as well as the reliability of this ANN predicted data in the further steps. Lignocellulosic forest residue (LFR) and olive oil residue (OOR) were selected as biomass feedstocks. The thermal data prediction performance of ANN was performed based on two approaches by developing; i) two individual networks for each feedstock, and ii) one-network for both feedstocks. After fixing the main structure of the networks, optimization studies were carried out to determine the best network configuration. In this way, it was also aimed to discuss the effect of internal ANN parameters to the overall prediction capability for more complex problems. At the final step, the predicted data was applied to calculate the activation energies based on three conventional kinetic models and the results were compared with the ones calculated using experimental thermal data. In the end, it was concluded the experimental thermal data fitted quite well to the ANN predicted data (R2 > 0.99) but more complex network topology was required for combined network due to the complexity of the dataset. Most importantly, it is shown that the predicted data can be applicable for the further steps such as in the calculation of the activation energies using different models.

Original languageEnglish
Pages (from-to)535-545
Number of pages11
JournalFuel
Volume220
DOIs
Publication statusPublished - 15 May 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

Funding

The authors would like to thank The Scientific and Technological Research Council of Turkey (TUBITAK 214M403) as well as Istanbul Technical University (ITU)-Scientific Research Project (BAP 39180) for their financial support.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu
Istanbul Teknik ÜniversitesiTUBITAK 214M403

    Keywords

    • Activation energy
    • Artificial Neural Network (ANN)
    • Biomass
    • Pyrolysis
    • TGA

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