Characterization of Biochar Properties Affected by Different Pyrolysis Temperatures Using Visible-Near-Infrared Spectroscopy

Rapid characterization of biochar for energy and ecological purpose utilization is fundamental. In this work, visible and near-infrared (vis-NIR) spectroscopy was used to measure ash, volatile matter, fixed carbon contents, and calorific value of three types of biochar produced from pine wood, cedar wood, and cotton stalk, respectively. The vis-NIR spectroscopy was also used to discriminate biochar feedstock types and pyrolysis temperature. Prediction result shows that partial least squares (PLS) regression calibrating the spectra to the values of biochar properties achieved very good or excellent performance with coefficient of determination () of 0.86~0.91 and residual prediction deviation (RPD) of 2.58~3.32 for ash, volatile matter, and fixed carbon, and good prediction with of 0.81 and RPD of 2.30 for calorific value. Linear discrimination analysis (LDA) of the principal components (PCs) produced from PCA of wavelength matrix shows that three types of biochar can be successfully discriminated with 95.2% accuracy. The classification of biochar with different pyrolysis temperatures can be conducted with 69% accuracy for all three types and 100% accuracy for single type of cotton stalk. This experiment suggests that the vis-NIR spectroscopy is promising as an alternative of traditionally quantitative and qualitative analysis of biochar properties.

[1]  Shihong Zhang,et al.  Biomass-based pyrolytic polygeneration system on cotton stalk pyrolysis: influence of temperature. , 2012, Bioresource technology.

[2]  Hongwei Wu,et al.  Biochar as a Fuel: 1. Properties and Grindability of Biochars Produced from the Pyrolysis of Mallee Wood under Slow-Heating Conditions , 2009 .

[3]  W. Marsden I and J , 2012 .

[4]  R. Ocampo-Pérez,et al.  Adsorption of Fluoride from Water Solution on Bone Char , 2007 .

[5]  Woojin Lee,et al.  Fast pyrolysis of Oil Mallee Woody Biomass : Effect of temperature on the yield and quality of pyrolysis products , 2008 .

[6]  Hyun-Woo Cho,et al.  Enhanced discrimination and calibration of biomass NIR spectral data using non-linear kernel methods. , 2008, Bioresource technology.

[7]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[8]  Jae-Young Kim,et al.  Influence of pyrolysis temperature on physicochemical properties of biochar obtained from the fast pyrolysis of pitch pine (Pinus rigida). , 2012, Bioresource technology.

[9]  Abdul Mounem Mouazen,et al.  In situ Determination of Growing Stages and Harvest Time of Tomato (Lycopersicon Esculentum) Fruits Using Fiber-Optic Visible—Near-Infrared (Vis-NIR) Spectroscopy , 2011, Applied spectroscopy.

[10]  Başak Burcu Uzun,et al.  Composition of products obtained via fast pyrolysis of olive-oil residue: effect of pyrolysis temperature. , 2007 .

[11]  Kevin McDonnell,et al.  Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy. , 2011, Bioresource technology.

[12]  Edward Hodgson,et al.  Measurement of key compositional parameters in two species of energy grass by Fourier transform infrared spectroscopy. , 2009, Bioresource technology.

[13]  Abdul Mounem Mouazen,et al.  Quantitative analysis of soil nitrogen and carbon at a farm scale using visible and near infrared spectroscopy coupled with wavelength reduction , 2011 .

[14]  Matt A. Sanderson,et al.  Compositional analysis of biomass feedstocks by near infrared reflectance spectroscopy , 1996 .

[15]  Sushil Adhikari,et al.  Physiochemical properties of bio-oil produced at various temperatures from pine wood using an auger reactor. , 2010, Bioresource technology.

[16]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[17]  A. F. Mitchell,et al.  The Mahalanobis distance and elliptic distributions , 1985 .

[18]  J. Lehmann Bio-energy in the black , 2007 .

[19]  Rapid characterization of biomass using near infrared spectroscopy coupled with multivariate data analysis: Part 1. Yellow-poplar (Liriodendron tulipifera L.). , 2010, Bioresource technology.

[20]  Vis/Near- and Mid- Infrared Spectroscopy for Predicting Soil N and C at a Farm Scale , 2012 .

[21]  Johannes Lehmann,et al.  A handful of carbon , 2007, Nature.

[22]  T. Theophanides,et al.  Infrared Spectroscopy - Life and Biomedical Sciences , 2012 .

[23]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[24]  Hui Zhou,et al.  Temperature- and duration-dependent rice straw-derived biochar: Characteristics and its effects on soil properties of an Ultisol in southern China , 2011 .

[25]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[26]  P. Nico,et al.  Dynamic molecular structure of plant biomass-derived black carbon (biochar). , 2010, Environmental science & technology.

[27]  Ayhan Demirbas,et al.  Effects of temperature and particle size on bio-char yield from pyrolysis of agricultural residues , 2004 .