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.
Publication details
- Authors: Nimmanterdwong, P., Chalermsinsuwan, B., Piumsomboon, P.
- Published in: (2021) Energy, 222, art. no. 119945.
- Year: 2021
- DOI: 10.1016/j.energy.2021.119945
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