Abstract
The objective of this study was to investigated the use of chemometric modeling of thermogravimetric (TG) data as an alternative approach to estimate the chemical and proximate (i.e. volatile matter, fixed carbon and ash contents) composition of lignocellulosic biomass. Since these properties affect the conversion pathway, processing costs, yield and / or quality of products, a capability to rapidly determine these for biomass feedstock entering the process stream will be useful in the success and efficiency of bioconversion technologies. The 38-minute long methodology developed in this study enabled the simultaneous prediction of both the chemical and proximate properties of forest-derived biomass from the same TG data. Conventionally, two separate experiments had to be conducted to obtain such information. In addition, the chemometric models constructed with normalized TG data outperformed models developed via the traditional deconvolution of TG data. PLS and PCR models were especially robust in predicting the volatile matter (R2–0.92; RPD– 3.58) and lignin (R2–0.82; RPD– 2.40) contents of the biomass. The application of chemometrics to TG data also made it possible to predict some monomeric sugars in this study. Elucidation of PC loadings obtained from chemometric models also provided some insights into the thermal decomposition behavior of the chemical constituents of lignocellulosic biomass. For instance, similar loadings were noted for volatile matter and cellulose, and for fixed carbon and lignin. The findings indicate that common latent variables are shared between these chemical and thermal reactivity properties. Results from this study buttresses literature that have reported that the less thermally stable polysaccharides are responsible for the yield of volatiles whereas the more recalcitrant lignin with its higher percentage of elementary carbon contributes to the yield of fixed carbon.
Highlights
The use of lignocellulosic biomass for energy and to replace other products derived from fossil fuel will reduce net greenhouse gas emissions and persistent toxic materials that result during the extraction and processing of fossil fuels
This study has demonstrated that chemometric modeling of thermogravimetric (TG) data can be used as an alternative approach to rapidly estimate the chemical and proximate composition of lignocellulosic biomass
PLS and PCR models calibrated with normalized TG data performed very well in predicting especially the lignin (R2–0.82; ratio of preformance to deviation (RPD)– 2.37) and volatile matter (R2–0.92; RPD– 3.58) contents of forest-derived biomass
Summary
The use of lignocellulosic biomass for energy and to replace other products derived from fossil fuel will reduce net greenhouse gas emissions and persistent toxic materials that result during the extraction and processing of fossil fuels. The chemical composition of lignocellulosic biomass to a large extent determines the optimal conversion methodology and affects the distribution and yield of products. When biomass is to be used as a source of energy or fuel, information about its proximate composition is necessary. It is used to measure the mass fraction of water, volatile matter, ash and fixed carbon (by difference) in lignocellulosic biomass. Biomass with high volatile matter content are easier to ignite and yield higher quantities of liquid products; whereas a higher fixed carbon gives more solid products. The chemical and proximate characteristics of a fuel feedstock impact the kinetics of degradation, the efficiency and emission parameters of a processing plant
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