Prediction of lignocellulosic biomass structural components from ultimate/proximate analysis

Prathana Nimmanterdwong, Benjapon Chalermsinsuwan and Pornpote Piumsomboon

Energy, 2021, vol. 222, issue C

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.

Keywords: Lignocellulosic biomass; Biomass; Structural component; Self-organizing maps (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001948

DOI: 10.1016/j.energy.2021.119945

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