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Prediction of lignocellulosic biomass structural components from ultimate/proximate analysis

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  • Nimmanterdwong, Prathana
  • Chalermsinsuwan, Benjapon
  • Piumsomboon, Pornpote

Abstract

In order to reduce time and resource consumption, the mathematical model was developed to predict lignocellulosic biomass structural components including cellulose, hemicellulose and lignin from ultimate/proximate dataset. Self-organizing maps (SOMs) were integrated with a regression model to obtain more precise results than the procedure without data clustering. In SOMs, the 149-biomass dataset from literatures, expressed by the ratios of VM/C, VM/H, VM/O, FC/C, FC/H, FC/O and ASH/O, were employed for training and clustered into 4 groups. The result indicated that each group had its own characteristics. The regression model with pre-analyzed by SOMs provided better results compared to the model without pre-analyzed by SOMs. The model obtained in this study can be applied to further researches in many fields; e.g. biomass characterization and utilization.

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  • Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2021. "Prediction of lignocellulosic biomass structural components from ultimate/proximate analysis," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001948
    DOI: 10.1016/j.energy.2021.119945
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    4. Pablo García Estévez & Antonio Carballo, 2015. "Qualitative judgement in public credit ratings: A proposed supporting approach using Self-Organising Maps (SOMs)," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 38(108), pages 181-190, Septiembr.
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    2. Yang, Yuhan & Wang, Tiancheng & Hu, Hongyun & Yao, Dingding & Zou, Chan & Xu, Kai & Li, Xian & Yao, Hong, 2021. "Influence of partial components removal on pyrolysis behavior of lignocellulosic biowaste in molten salts," Renewable Energy, Elsevier, vol. 180(C), pages 616-625.
    3. Małgorzata Sieradzka & Cezary Kirczuk & Izabela Kalemba-Rec & Agata Mlonka-Mędrala & Aneta Magdziarz, 2022. "Pyrolysis of Biomass Wastes into Carbon Materials," Energies, MDPI, vol. 15(5), pages 1-12, March.
    4. Kartal, Furkan & Dalbudak, Yağmur & Özveren, Uğur, 2023. "Prediction of thermal degradation of biopolymers in biomass under pyrolysis atmosphere by means of machine learning," Renewable Energy, Elsevier, vol. 204(C), pages 774-787.
    5. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).

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