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Prediction of torrefied biomass properties from raw biomass

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  • Kartal, Furkan
  • Özveren, Uğur

Abstract

The torrefaction process enhances the quality of raw biomass and has gained widespread attention as an effective technique in energy production. Therefore, the estimation of torrefied biomass characteristics at certain operating conditions is critical to obtain desired solid products. In this study, the carbon, hydrogen, oxygen content and higher heating value (HHV) of torrefied biomass were estimated based on the results of proximate analysis (the fixed-carbon, volatile matter and ash values) of raw biomass and experimental conditions (torrefaction temperature and time). A total of 448 input and output sets belonging to lignocellulosic biomass were collected from 61 different works in the literature. Subsequently, the feedforward backpropagation algorithm based artificial neural network (ANN) model and adaptive neuro-fuzzy inference system (ANFIS) were developed as a machine learning approach for modeling the torrefaction process. The estimation capability of the developed models was examined with evaluation indicators such as mean squared error, mean absolute percentage error, and coefficient of determination. The method developed in this study provided acceptable accuracies for both elemental composition and heating value estimates. Moreover, the ANN model provided slightly better performance than ANFIS. The results show that the developed ANN model is a useful tool to obtain the desired torrefied biomass.

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  • Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.
    • Handle: RePEc:eee:renene:v:182:y:2022:i:c:p:578-591
    DOI: 10.1016/j.renene.2021.10.042
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    2. Sui, Haiqing & Chen, Jianfeng & Cheng, Wei & Zhu, Youjian & Zhang, Wennan & Hu, Junhao & Jiang, Hao & Shao, Jing'ai & Chen, Hanping, 2024. "Effect of oxidative torrefaction on fuel and pelletizing properties of agricultural biomass in comparison with non-oxidative torrefaction," Renewable Energy, Elsevier, vol. 226(C).
    3. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    4. Mohd Fuad, Muhammad Ariff Hanaffi & Hasan, Mohd Faizal & Chong, William Woei Fong & Ani, Farid Nasir & Ngadiman, Nor Hasrul Akhmal, 2024. "A novel oxidative microwave torrefaction approach for producing empty fruit bunch-starch binder briquettes as a potential biomass-based energy," Renewable Energy, Elsevier, vol. 228(C).
    5. Zhao, Zhong & Feng, Shuo & Zhao, Yaying & Wang, Zhuozhi & Ma, Jiao & Xu, Lianfei & Yang, Jiancheng & Shen, Boxiong, 2022. "Investigation on the fuel quality and hydrophobicity of upgraded rice husk derived from various inert and oxidative torrefaction conditions," Renewable Energy, Elsevier, vol. 189(C), pages 1234-1248.
    6. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.

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