Energy Fuels 2010, 24, 5148–5152 Published on Web 08/17/2010
: DOI:10.1021/ef1004682
Prediction of Resin and Fatty Acid Content of Biorefinery Feedstock by On-line Near-Infrared (NIR) Spectroscopy Torbj€ orn A. Lestander* and Robert Samuelsson Swedish University of Agricultural Sciences, Unit of Biomass Technology and Chemistry, SE-901 83 Umea, Sweden Received April 13, 2010. Revised Manuscript Received July 7, 2010
Extractives in biorefinery feedstock are a source of precursor chemicals and biofuel products. Resin and fatty acids (RFAs) in such extractives constitute an interesting fraction, which may contain both chemically attractive precursors and also problematic volatile organic compounds. On-line near-infrared (NIR) spectra were collected from a process stream, designed experimentally and involving softwood lignocelluloses; the data were regressed using partial least-squares to give RFA concentrations that varied between 0.1 and 0.5% (dry weight basis). At-line NIR models were also constructed using spectral data from pelletized samples from the process stream. In addition, off-line NIR modeling was conducted using softwoods with a wider RFA variation range (0.1-1.0% dry weight basis). All of the calibration models obtained exhibited good predictive abilities. The laboratory-based off-line NIR model explained 94.5% of the variation in concentrations and had a prediction error of 0.070% for the RFA content. The coefficient of variation (CV), representing the percentage of the ratio between the prediction error and the average concentration, was 17.8%. The on-line and at-line models explained 71.3-79.6% of the variation in the RFA concentrations and had prediction errors within the range of 0.026-0.041% and CVs of 13.7-18.1%. This was excellent in comparison to the ca. 10% error in accuracy when determining the RFA reference values. The results illustrate that on-line NIR spectroscopy provides a valid method for real-time predictions of RFA concentrations in biomaterials. This should facilitate better monitoring and process control as well as targeted pretreatments to obtain tailor-made biorefinery feedstock, thus adding value to the production process.
phenomenon of auto-oxidation, which may result in selfheating and spontaneous ignition of stored biomass.4-9 The multitude of biomass sources highlights the need to characterize incoming feedstock to optimize pretreatments and to direct fractions toward process flows with specific endproducts. Sanderson et al., Axrup et al., and Poke and Raymond have shown that the composition of extractives in biomass feedstock can be determined by near-infrared (NIR) spectroscopy.10-12 Biomass extractives also play an important roll in determining the calorific value of a feedstock. Lestander and Rhen found that the NIR overtone vibrations associated with carbon double bonds, frequently found in
Introduction Biomass from plants, in the form of biorefinery feedstock, contains a wide variety of biochemical and bioenergy carriers, mainly consisting of celluloses, hemicelluloses, lignins, and extractives. Softwood lignocellulose extractives contain resin and fatty acids (RFAs) but also phenols, terpenes, sterols, and other chemical compounds that are a source of bio-oils. The fraction containing RFAs is interesting because it contains valuable precursor chemicals and light-phase bio-oils that can be extracted, for example, in supercritical carbon dioxide and in the low-temperature range of thermochemical processes, such as torrefaction and pyrolysis. The RFA fraction is also a source of volatile organic compounds that can be problematic or hazardous during biomass storage and industrial processes.1-3 These compounds are also associated with the
(5) Springer, E. L.; Hajny, G. J. Spontaneous heating in piled wood chips. 1. Initial mechanism. Tappi 1970, 53 (1), 85–86. (6) Kubler, H. Air convection in self-heating piles of wood chips. Tappi 1982, 65 (8), 79–83. (7) Kubler, H. Heat generation processes as cause of spontaneous ignition in forest products. Forest Prod. Abstr. 1987, 10, 300–327. (8) Blomqvist, P.; Persson, H. Self-heating in storages of wood pellets. Proceedings of World Bioenergy; J€onk€oping, Sweden, 2008; pp 138-142. (9) Lestander, T. A. Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy. Holzforschung 2008, 62, 429–434. (10) Sanderson, M. A.; Agblevor, F.; Collins, M.; Johnson, D. K. Compositional analysis of biomass feed stocks by near infrared reflectance spectroscopy. Biomass Bioenergy 1996, 11 (5), 365–370. (11) Axrup, L.; Markides, K.; Nilsson, T. Using miniature diode array NIR spectrometers for analysing wood chips and bark samples in motion. J. Chemom. 2000, 14 (5-6), 561–572. (12) Poke, F. S.; Raymond, C. A. Predicting extractives, lignin, and cellulose contents using near infrared spectroscopy on solid wood in Eucalyptus globulus. J. Wood Chem. Technol. 2006, 26 (2), 187–199.
*To whom correspondence should be addressed. Telephone: þ46-907868795. E-mail:
[email protected]. (1) Svedberg, U. R. A.; Hogberg, H. E.; Hogberg, J.; Galle, B. Emission of hexanal and carbon monoxide from storage of wood pellets, a potential occupational and domestic health hazard. Ann. Occup. Hyg. 2004, 48 (4), 339–349. (2) Arshadi, M.; Gref, R.; Geladi, P.; Dahlqvist, S.-A.; Lestander, T. A. The influence of raw material characteristics on the industrial pelletizing process and pellet quality. Fuel Process. Technol. 2008, 89, 1442–1447. (3) Hagstr€ om, K. Occupational exposure during production of wood € pellets in Sweden. Doctoral Thesis, Orebro Studies in Environmental € Science, Orebro, Sweden, 2008; ISBN 978-91-7668-571-6. (4) Svedberg, U.; Samuelsson, J.; Melin, S. Hazardous off-gassing of carbon monoxide and oxygen depletion during ocean transportation of wood pellets. Ann. Occup. Hyg. 2008, 52 (8), 675–683. r 2010 American Chemical Society
5148
pubs.acs.org/EF
Energy Fuels 2010, 24, 5148–5152
: DOI:10.1021/ef1004682
Lestander and Samuelsson
Sweden was used; it was stored 0 or 140 days and then dried to moisture contents of 9 or 12%. The other variable factors included steam addition to the process flow. More information about the experiment is given by Samuelsson et al., who also showed that the RFA content is influenced by different pretreatments.14 In all, 20 batches were used in the experiment. The second material, examined in the on-line NIR studies (see Figure 1), was the 1.5 h feedstock process stream (ca. 300 kg of dry weight) of each batch fed into a pellet press (SPC 300, Swedish Power Chippers, Ljungby, Sweden). Samples of (1) dried (9 or 12% moisture content), (2) milled (over a 4 mm sieve), and (3) steam-treated (2 or 6 kg of water vapor/h) sawdust (ca. 1 kg of each), respectively, were collected during the process flow on three occasions for each batch. This resulted in 180 (3 3 20) samples. Finally, the third material was the pellets (ca. 6 kg/sample), which were examined in the at-line NIR studies (see Figure 1). In this case, three samples were collected per batch, resulting in a total of 60 (1 3 20) samples. All samples from the different materials were sealed in gastight plastic bags and stored cold (ca. þ5 °C) until analysis. Off-line Collection of NIR Spectra. The first set of materials was analyzed using a sequential scanning NIR spectrometer (LAB-NIR) (Foss NIRSystems, H€ ogan€ as, Sweden). This spectrometer recorded reflectance spectra between 1100 and 2498 at 2 nm intervals from samples in a rotating cup. Average spectra of 32 scans were collected from three replicates per sample. On-line Collection of NIR Spectra. An on-line NIR (OL-NIR) spectrometer (Foss NIRSystems 6500 Industrial, Foss, H€ ogan€ as, Sweden), which collected reflectance spectra every 1.7 min within the range of 780-2380 at 2 nm intervals, was used. Each reflectance spectrum was the average of 32 scans. In total, 491 spectra (mean per batch = 24.6 ( 1.1) were collected during the 4 3 20 sampling periods. The detector, connected by optic fibers to the instrument, was mounted on a tilted steel plate of the pelletizer. A layer, more than 0.5 m thick, of noncompressed, dried and milled sawdust moved smoothly and slowly past the Si-glass detector window (45 45 mm). This NIR measurement position, about 2 min upstream of pellet formation, was situated just before the point of steam addition and the inlet to the pelletizer press die. At-line Collection of NIR Spectra. At-line measurements of pellets were conducted using two different NIR instruments: (i) a 128 diode array NIR spectrometer (DA-NIR) (model MCS 511, Carl Zeiss AG, Oberkochen, Germany, and probe manufactured by VTT, Espo, Finland) that collected reflectance spectra interpolated to every wavelength from 950 to 1700 nm and (ii) a sequential scanning NIR spectrometer (S-NIR) (Foss NIRsystems, H€ ogan€ as, Sweden) that recorded reflectance spectra for every second wavelength between 780 and 2380 nm. To simulate on-line measurements, each of the pellet sub-samples collected (three per batch) was placed on a disk (about 30 cm in diameter) that rotated at ca. 0.1 Hz. The pellets were then spread in a 10-15 cm wide band about 5 cm thick. The probe measurement distance was about 8 cm for the DA-NIR and 18 cm for the S-NIR probe. In total, 922 DA-NIR (mean per batch = 46.0 ( 3.8) and 181 S-NIR (mean per batch = 9.0 ( 0.8) spectra were collected. All reflectance values were transformed into absorbance values, i.e., minus logarithms of the inverse reflectance values. The absorption spectra were then arranged in a spectral matrix (X), with I observations as the rows and K wavelength absorbance values as the columns. Reference Variable. The reference variable was the RFA concentration in the following samples: (i) dry milled sawdust used in the laboratory experiment and, from the factorial
Figure 1. Overview of used materials and NIR instruments called LAB-NIR in the off-line study, OL-NIR in the on-line study, and finally, DA- and S-NIR in the at-line study. The bold arrow indicates prediction of RFA content on the same material as used to collect NIR spectra, whereas the thin arrow indicates RFA predictions on other materials based on PLS models, using the observed RFA contents in those materials as reference variables.
RFAs, were associated with higher calorific values.13 Therefore, it may be possible to predict the RFA fraction in extractives by NIR spectroscopy, which is a fast and nondestructive technique for obtaining real-time predictions in process streams. To use fully the economies of scale in integrated biorefineries and energy plants, producing valuable multi-products, biofuels, electricity, and heat, it may be necessary to transport biomass over long distances, i.e., between regions, nations, or even continents. Such feedstocks must, therefore, have low transportation and handling costs as well as a capacity to be stored safely. Today, this role is filled by biomass pellets, the global market for which is expected to grow rapidly. Techniques for characterizing the RFA content of pellets are, therefore, of great interest. This study focuses on evaluating the possibility of using online NIR techniques in the pelletizing process to monitor the RFA content of biorefinery feedstock in real time. Specifically, sawdust and pellets from softwoods were examined. Experimental Section Biomaterials. Three sets of materials were used to evaluate off-line, on-line, and at-line applications of NIR spectroscopy (see Figure 1 for an overview of used materials and NIR instruments). First, fresh sawdust samples (about 2 kg) from Scots pine (Pinus sylvestris L.; 12 samples) and Norway spruce [Picea abies Karst (L.); 10 samples] produced in sawmills across Sweden were collected for off-line NIR studies. The samples were dried to dryness (constant weight) at 105 °C for 16 h before grinding and passing through a 1 mm sieve. A fractional factorial screening experiment formed the basis for selecting the other two materials collected from a continuous biopellet production process. The main factors in this experiment were species, region, and storage time, giving eight different lots of sawdust. Sawdust of Scots pine and Norway spruce from trees that had grown in regions about 7° of latitude apart in (13) Lestander, T. A.; Rhen, C. Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using biorthogonal partial least squares regression. Analyst 2005, 130, 1182– 1189.
(14) Samuelsson, R.; Thyrel, M.; Sj€ ostr€ om, M.; Lestander, T. A. Effect of biomaterial characteristics on pelletizing properties and biofuel pellet quality. Fuel Process. Technol. 2009, 90, 1129–1134.
5149
Energy Fuels 2010, 24, 5148–5152
: DOI:10.1021/ef1004682
Lestander and Samuelsson
experiment, (ii) dried sawdust, (iii) dried and milled sawdust, (iv) dried, milled, and steam-treated sawdust, and (v) pellets (see Figure 1). The chemical analysis for the factorial design was based on three pooled samples of each material for each batch. The amounts of RFAs in the dry matter were determined using two sub-samples, according to the method described by Arshadi and Gref.15 The concentrations were expressed as percentages (%) and calculated as the total weight of 21 RFAs divided by the weight of the dry substance. The main compounds in the chemical analyses were the acids of abietine, dehydroabietine, and 7-oxodehydroabietine that have 20 carbon atoms. Each reference variable was arranged in a column vector (y) matching the series of the spectral matrices. In all, 102 (22 þ 4 20) RFA double analyses were conducted. Calibration Modeling and Diagnostics. The spectral data and the concentrations of RFAs were calibrated by partial leastsquares (PLS) regression using SIMCA 11.0 software (Umetrics, Umea, Sweden).16,17 The J residuals, arranged in a vector f (f = y - yp), between model-centered observed (y) and PLS model predicted (yp) reference values in a test set (about 1/3 of the observations in this study) were used to calculate the following model diagnostics: root mean squared error of prediction [RMSEP = (fTfJ -1)0.5] as a measure of the prediction error, multiple determination [Q2 = 1 - fTf(yTy)-1] as a measure of the variation explained by the model, and bias (bias = 1TfJ -1) as a measure of the systematic error.17 The coefficient of variation (CV) was calculated as the RMSEP divided by the mean value of the observed reference value and was expressed as a percentage. All variables were centered before modeling, and the number of PLS model components was based on the first or second local minimum of the RMSEP. NIR spectra from dried and milled sawdust in the factorial experiment were also used to predict RFA content in dried and steam-treated sawdust and pellets, respectively, by PLS modeling. The observed RFA contents in respective material were then reference variables for those models. This was also the case when NIR spectra from pellets were used to predict RFA contents in other materials (see Figure 1). Principal component analysis (PCA) was used to provide an overview of the data.18 The used orthogonal PLS algorithm decomposes the spectral and centered matrix X of I observations and K wavelengths into TWT þ E, where T is the matrix (I A) of PLS scores, A is the model rank, W is the matrix of (K A) PLS weights, and E is the residual (I K). When all data for the common wavelengths of all used NIR instruments were pooled (950-1700 nm), the resulting PLS weights were used to calculate variable influence in projection (VIP) for each wavelength k on RFA content at the model rank A as follows:19
Figure 2. Two components of the PCA based only on spectral online data separated the observations into those with high (b, g0.3%) and those with low (O,
