Cellulose Nanowhiskers from Moso Bamboo Residues: Extraction and Characterization

To take full advantage of moso bamboo processing waste, bamboo cellulose nanowhiskers were isolated from bamboo residues using sulfuric acid hydrolysis. Changes in bamboo cellulose at different stages of processing, as well as the roles of acid concentrations (55 wt% and 65 wt%) and hydrolysis times (1 h to 5 h) on the characteristics of nanowhiskers were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermogravimetry-Fourier transform infrared spectroscopy analysis (TGA-FTIR), synchrotron radiation wide angle X-ray scattering (WAXS), composition analysis, and Brunauer-Emmett Teller (BET) analysis. Both rod-like and network-like nanowhiskers were observed. Alkaline pretreatment removed impurities and part of the hemicellulose. Cellulose content increased to nearly 85%, and specific surface area improved as well after bleaching. Nanowhiskers had an average length of 455 nm, diameter of 12 nm, and an aspect ratio of about 37. Cellulose I was the dominant composition in bamboo cellulose; the transformation of cellulose Iα to cellulose Iβ was observed. Nanowhiskers presented greater crystallinity and crystallite size than those of cellulose without hydrolysis, but lower thermal stability. These bionanowhiskers might be used as reinforcements in nanocomposites

Determination and Visualization of pH Values in Anaerobic Digestion of Water Hyacinth and Rice Straw Mixtures Using Hyperspectral Imaging with Wavelet Transform Denoising and Variable Selection

Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874–1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algorithm (SPA), random frog (RF) and variable importance in projection (VIP) were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS) and a back propagation neural network (BPNN) were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp) of 0.911 and root mean square error of prediction (RMSEP) of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

Abstract Background In the pursuit of sources of energy, biofuel pellet is emerging as a promising resource because of its easy storage and transport, and lower pollution to the environment. The composition of biomass has important implication for energy conversion processing strategies. Current standard chemical methods for biomass composition are laborious, time-consuming, and unsuitable for high-throughput analysis. Therefore, a reliable and efficient method is needed for determining lignocellulose composition in biomass and so to accelerate biomass utilization. Here, near-infrared hyperspectral imaging (900–1700 nm) together with chemometrics was used to determine the lignocellulose components in different types of biofuel pellets. Partial least-squares regression and principal component multiple linear regression models based on whole wavelengths and optimal wavelengths were employed and compared for predicting lignocellulose composition. Results Out of 216 wavelengths, 20, 10 and 17 were selected by the successive projections algorithm for cellulose, hemicellulose and lignin, respectively. Three simple and satisfactory prediction models were constructed, with coefficients of determination of 0.92, 0.84 and 0.71 for cellulose, hemicellulose and lignin, respectively. The relative parameter distributions were quantitatively visualized through prediction maps by transferring the optimal models to all pixels on the hyperspectral image. Conclusions Hence, the overall results indicated that hyperspectral imaging combined with chemometrics offers a non-destructive and low-cost method for determining biomass lignocellulose components, which would help in developing a simple multispectral imaging instrument for biofuel pellets online measurement and improving the production management

MOESM1 of Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

Additional file 1: Figure S1. Profiles of reference spectra for biofuel pellets extracted from raw near-infrared hyperspectral images

Thermal properties of biochars derived from waste biomass generated by agricultural and forestry sectors

Waste residues produced by agricultural and forestry industries can generate energy and are regarded as a promising source of sustainable fuels. Pyrolysis, where waste biomass is heated under low-oxygen conditions, has recently attracted attention as a means to add value to these residues. The material is carbonized and yields a solid product known as biochar. In this study, eight types of biomass were evaluated for their suitability as raw material to produce biochar. Material was pyrolyzed at either 350 °C or 500 °C and changes in ash content, volatile solids, fixed carbon, higher heating value (HHV) and yield were assessed. For pyrolysis at 350 °C, significant correlations (p < 0.01) between the biochars’ ash and fixed carbon content and their HHVs were observed. Masson pine wood and Chinese fir wood biochars pyrolyzed at 350 °C and the bamboo sawdust biochar pyrolyzed at 500 °C were suitable for direct use in fuel applications, as reflected by their higher HHVs, higher energy density, greater fixed carbon and lower ash contents. Rice straw was a poor substrate as the resultant biochar contained less than 60% fixed carbon and a relatively low HHV. Of the suitable residues, carbonization via pyrolysis is a promising technology to add value to pecan shells and Miscanthus