Simplified empirical model to predict biomass thermal conversion products. (November 2020)

• Record Type: Journal Article
• Authors: Nimmanterdwong, Prathana; Chalermsinsuwan, Benjapon; Piumsomboon, Pornpote
• Is Part Of: Energy reports. Volume 6(2020)Supplement 6
• Page Start: 286
• Page End: 292
• Publication Date: 2020-11
• Subjects: Biomass thermal conversion -- Biorefinery -- Regression model -- Aspen plus; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.08.051
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: The objective of this study was to develop empirical models for predicting the obtained products from lignocellulosic biomass through alternative technologies. The model can be applied for further studies to provide the insight information for bio-refinery optimization and planning where suitable biomass characteristics are necessary for the conversion pathways to achieve the target products. To build the regression model, 330 case studies including the use of 66 lignocellulosic biomasses in 5 technologies were simulated using Aspen plus 11.0 software. Five correlations between biomass characteristics and five product yields were then successfully developed with R 2 equals to 0.99. The results indicated that almond shell with high VM, C, H contents and low FC, O, N contents produced the highest syngas, hydrogen, power and liquid fuels product yields (7121 kJ/kg biomass, 0.051 kg/kg biomass, 6474 kJ/kg biomass and 0.12 L/kg biomass, respectively). Among the local biomass candidates, palm empty fruit bunch (PEFB) was the preferable choice.