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Prediction of Lignocellulosic Biomass Structural Components from Ultimate/proximate Analysis by Prathana Nimmanterdwong; Benjapon Chalermsinsuwan; Pornpote Piumsomboon is a Engineering article available to read on EtoBox.

What is Prediction of Lignocellulosic Biomass Structural Components from Ultimate/proximate Analysis about?

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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Author

Prathana Nimmanterdwong; Benjapon Chalermsinsuwan; Pornpote Piumsomboon

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Engineering (Physical Sciences)