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Journals / Politeknik Dergisi / 2020 / Cilt: 23 Sayı: 4

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

Günay Özbay(AKSARAY ÜNİVERSİTESİ)0000-0002-7951-8421
Erkan Sami Kökten(KARABUK UNIVERSITY)0000-0003-3428-4534
Pages
1255–1264
Publication Date
2020-12-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The relative impact results revealed that the most important parameter that affects the bio-oil yield was catalyst type (11.4%).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study is dedicated to developing a reliable artificial neural network (ANN) model to model the pyrolysis liquid product (bio-oil).
📊
Key Findings & Data: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.
💡
Conclusion & Impact:The relative impact results revealed that the most important parameter that affects the bio-oil yield was catalyst type (11.4%).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:catalyst · modeling · pyrolysis

Abstract

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%).

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%).

🔖

Cite This Article

Özbay, G., Kökten, E. S. (2020). Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach. Politeknik Dergisi, Cilt: 23 Sayı: 4, 1255–1264. https://doi.org/10.2339/politeknik.659136

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