Prediction of lignocellulosic biomass structural components from ultimate/proximate analysis. (1st May 2021) - 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. Graphical abstract: Image 1 Highlights: Model to predict biomass structural component via ultimate/proximate was shown. The self-organizing map was applied for biomass dataset clustering. The biomass characteristic was related to their components. Obtained regression model with data clustering provided more accurate results. The model provided structural component prediction with less time and resource.