Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Günay ÖZBAY; Erkan Sami KÖKTEN

doi:https://doi.org/10.2339/politeknik.659136

Makale detayları

Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Yazarlar:

Günay ÖZBAY | Erkan Sami KÖKTEN

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Polytechnic 2020 Vol.23 Issue 4, pp.1255-1264

Belge Tipi:

Makale

Yayın Tarihi:

11/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000589455

Sıklık:

Aylık

eISSN:

2147-9429

DOI:

https://doi.org/10.2339/politeknik.659136

Özet:

 


This study is dedicated to
developing a reliable artificial neural network (ANN) model to model the
pyrolysis liquid product (bio-oil). Some related parameters with the bio-oil
yield such as the pyrolysis temperature, duration, catalyst type, catalyst ratio,
particle size, proximate, and ultimate analysis of the biomass were tested. Due
to the different characteristics of different biomass types and pyrolysis
methods, only slow and intermediate pyrolysis data from woody biomass were used
in modeling. The correlation coefficients (R) were 0.992, 0.933, and 0.951 for
training, validation, and testing, respectively. In order to evaluate the
predictability of the ANN model, the predicted results were compared with the
experimental results that were not introduced before. The simulated data were
in good agreement with the experimental results indicating the reliability of
the developed model. The relative impact results revealed that the most
important parameter that affects the bio-oil yield was catalyst type (11.4%).  


.

 


This study is dedicated to
developing a reliable artificial neural network (ANN) model to model the
pyrolysis liquid product (bio-oil). Some related parameters with the bio-oil
yield such as the pyrolysis temperature, duration, catalyst type, catalyst ratio,
particle size, proximate, and ultimate analysis of the biomass were tested. Due
to the different characteristics of different biomass types and pyrolysis
methods, only slow and intermediate pyrolysis data from woody biomass were used
in modeling. The correlation coefficients (R) were 0.992, 0.933, and 0.951 for
training, validation, and testing, respectively. In order to evaluate the
predictability of the ANN model, the predicted results were compared with the
experimental results that were not introduced before. The simulated data were
in good agreement with the experimental results indicating the reliability of
the developed model. The relative impact results revealed that the most
important parameter that affects the bio-oil yield was catalyst type (11.4%).  


.

Makale detayları

Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Yazarlar: Günay ÖZBAY | Erkan Sami KÖKTEN

Yayıncı: Gazi University

Ana Başlıklar: Uygulamalı fizik

Dergi: Journal of Polytechnic 2020 Vol.23 Issue 4, pp.1255-1264

Belge Tipi: Makale

Yayın Tarihi: 11/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000589455

Sıklık: Aylık

eISSN: 2147-9429

DOI: https://doi.org/10.2339/politeknik.659136

Sayı

Yazarlar:

Günay ÖZBAY | Erkan Sami KÖKTEN

Yayıncı:

Gazi University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Journal of Polytechnic 2020 Vol.23 Issue 4, pp.1255-1264

Belge Tipi:

Makale

Yayın Tarihi:

11/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000589455

Sıklık:

Aylık

eISSN:

2147-9429

DOI:

https://doi.org/10.2339/politeknik.659136