Relationship between torrefaction severity, product properties, and pyrolysis characteristics of various biomass

Abstract

Summary Torrefaction of biomass improves the fuel quality via mild thermal decomposition of the lignocellulosic structure. Establishing common relationships for key characteristics of various types of torrefied biomass can benefit the process design and reaction severity assessment of samples in a commercial plant. In this study, the properties of torrefied biomass were correlated with the mass yield for the experimental results of five biomass samples (namely, wood chips, wood pellets, kenaf, rice straw, and rice husk) processed between 190°C and 310°C with a mass loss of up to 57.0% on a dry, ash‐free (daf) basis. Compared with existing studies, improved curve‐fit quality (R2 = 0.954‐0.994) was achieved in terms of the volatile matter/fixed carbon ratio, elemental composition, and heating value based on an assessment of the property ratio between raw and torrefied biomass for the mass yield on a daf basis. A new methodology was proposed to derive the torrefaction severity from the pyrolysis characteristics using thermogravimetric analysis, with R2 = 0.951. Additionally, the amount of fixed carbon increased by up to 44% compared to that of raw biomass during torrefaction, indicating cross‐linking and polymerization reactions. The energy density of torrefied biomass varies widely between 1.7 and 10.4 GJ/m3 depending on the compaction degree of the raw biomass and torrefaction severity. The grindability of torrefied biomass was similar to coal when the mass loss reached 13% daf for woody samples and kenaf.

Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm

“…9 Higher heating value (HHV), an important parameter for designing the biomass conversion facilities, is either experimentally tested by a calorimeter bomb or mathematically calculated using Channiwala and Parikh's correlation based on the proximate and ultimate analysis results. 4,10 These characterizations always require repetitive experiments and subsequently instrumental analysis or mathematical method, consuming lots of time, costs and manpower. Therefore, developing a reliable method to predict the properties of torreed biomass based on those of feedstock without various tests and experiments is of great value to save time, costs and manpower.…”

Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction

“…Several ML models, involving artificial neural network (ANN), gradient boosting trees (GBT), random forest (RF), support vector machines (SVM), etc., were employed to predict the properties of torrefied biomass. However, these studies mainly focused on the HHV [8][9][10][11][12] and mass yield [1,10,[13][14][15], while other properties of torrefied biomass which are also important parameters for evaluating the performance of torrefied biomass, including fuel ratio (FR, fixed carbon content divided by volatile content), and O/C and H/C (oxygen or hydrogen content divided by carbon content) ratios [16] were rarely reported. Moreover, nitrogen content, which is the dominant source of NOx and other nitrogen containing pollutants, such as NH 3 and HCN during biomass conversion was also not of concern until now.…”