Torbjörn A. Lestander

High quality biofuel pellet production from pre-compacted low density raw materials

In this study, pre-compaction was evaluated as a method to enhance stable reed canary grass pellet production. An experimental design of the factors raw material moisture content, steam addition, raw material bulk density, and die temperature was used to find production conditions for high quality pellets by multiple linear regression modelling of responses. Response variables being modelled were variability of pelletizer current (as a measurement of uneven production), pellet bulk density, and pellet durability. By pre-compacting the raw material from a bulk density of 150 kg/m3 to 270kg/m3, continuous production could be obtained at minimum raw material moisture content of 13.8%. Bulk density and durability were both highly correlated to raw material moisture content, but showed different optima. Multiple response optimization was used to target process settings for production of high quality reed canary grass pellets with bulk density >650kg/m3 and durability >97.5%.

NIR techniques create added values for the pellet and biofuel industry

A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.

Selection of near infrared wavelengths using genetic algorithms for the determination of seed moisture content

For fast measurements of single seeds using near infrared (NIR) spectra for the prediction of seed moisture content, it may be necessary to reduce the spectra from over 1000 wavelengths to just a few narrow bands. This reduction makes it possible to utilise a few parallel and simultaneous NIR sensor measurements in seed sorting instead of scanning a few NIR bands that are sequential in time. Three different approaches of genetic algorithms (GA) were used to select wavelengths within the range 400–2500 nm. The GA models were compared for two different datasets: single seeds and bulk seeds of Scots pine. It was shown that GA and interval partial least squares combined with GA allowed a meaningful reduction in spectral content without any loss in predictive quality. The three models selected three to six wavelength regions mainly around the peak of the combination of the first O–H stretching overtone and the O–H bending at 1190 nm and on the slopes of the first O–H stretching overtone at 1450 nm. For some of the GA models, the selected regions were subdivided into one to three more regions. In total six to eight narrow regions were used to simulate uniform density filters based on average absorbance within selected regions. The RMSEP values of the filter simulations were of at least the same quality as those for the whole wavelength range or the NIR range. The wavelength bands chosen for the single seeds were also applied for the bulk samples and vice versa with good result. The overall results illustrate the possibility of using GAs to select wavelengths in order to build filter spectrometers based on a few wavelength bands for the determination of seed moisture content.

NIR provides excellent predictions of properties of biocoal from torrefaction and pyrolysis of biomass

When biomass is exposed to high temperatures in torrefaction, pyrolysis or gasification treatments, the enrichment of carbon in the remaining ‘green coal’ is correlated with the temperature. Various other properties, currently measured using wet chemical methods, which affect the materials’ quality as a fuel, also change. The presented study investigated the possibility of using NIR spectrometry to estimate diverse variables of biomass originating from two sources (above-ground parts of reed canary grass and Norway spruce wood) carbonised at temperatures ranging from 240 to 850 °C. The results show that the spectra can provide excellent predictions of its energy, carbon, oxygen, hydrogen, ash, volatile matter and fixed carbon contents. Hence NIR spectrometry combined with multivariate calibration modeling has potential utility as a standardized method for rapidly characterising thermo-treated biomass, thus reducing requirements for more costly, laborious wet chemical analyses and consumables.

Characterization of Scots pine stump-root biomass as feed-stock for gasification.

The main objective was to explore the potential for gasifying Scots pine stump-root biomass (SRB). Washed thin roots, coarse roots, stump heartwood and stump sapwood were characterized (solid wood, milling and powder characteristics) before and during industrial processing. Non-slagging gasification of the SRB fuels and a reference stem wood was successful, and the gasification parameters (synthesis gas and bottom ash characteristics) were similar. However, the heartwood fuel had high levels of extractives (≈19%) compared to the other fuels (2-8%) and thereby ≈16% higher energy contents but caused disturbances during milling, storage, feeding and gasification. SRB fuels could be sorted automatically according to their extractives and moisture contents using near-infrared spectroscopy, and their amounts and quality in forests can be predicted using routinely collected stand data, biomass functions and drill core analyses. Thus, SRB gasification has great potential and the proposed characterizations exploit it.

Cassava stems: a new resource to increase food and fuel production

Given the growing global population, mankind must find new ways to lower competition for land between food and fuel production. Our findings for cassava suggest that this important crop can substantially increase the combined production of both food and fuel. Cassava stems have previously been overlooked in starch and energy production. These food‐crop residues contain about 30% starch (dry mass) mostly in the xylem rather than phloem tissue. Up to 15% starch of the stem dry mass can be extracted using simple water‐based techniques, potentially leading to an 87% increase in global cassava starch production. The integration of biofuel production, using residues and wastewater from starch extraction, may bring added value. The cassava roots on which biofuels and other products are based can be replaced by cassava stems without land use expansion, making root starch available as food for additional 30 million people today.

Near infrared image analysis for online identification and separation of wood chips with elevated levels of extractives.

Forest-based biorefinery feedstocks are usually broken up into wood chips prior to any form of processing. These wood chips have a complex and highly variable composition, although they may look identical to an inexperienced observer. Some chips have high contents of valuable extractives. Therefore, it would be desirable to separate such chips that are rich in extractives. Various fractions of pine and spruce wood were used to acquire near infrared (1000–2498 nm) hyperspectral images in order to explore the usefulness of multivariate image analysis for detection and separation purposes. Multivariate modelling by image principal component analysis detected large variations in extractive content among wood chips of different biomass types, for example, sapwood, heartwood and knotwood. The extractive parts could be classified in the images and their content could be reasonably well predicted. Partial least squares (PLS) regression models could be made between collected spectra and measured extractive contents. These worked better for milled and homogenised bulk samples than for average image spectra. Regression coefficients showed that the C–H bonds in the spectra were responsible for the validity of the models. The average image PLS models could be used to make prediction images showing the location of the regions with high extractive content in knotwood. The results indicate that extremely rapid spectral-based fractionation could be used to separate tailored biomass streams of wood chips.

Analysis, pretreatment and enzymatic saccharification of different fractions of Scots pine.

BackgroundForestry residues consisting of softwood are a major lignocellulosic resource for production of liquid biofuels. Scots pine, a commercially important forest tree, was fractionated into seven fractions of chips: juvenile heartwood, mature heartwood, juvenile sapwood, mature sapwood, bark, top parts, and knotwood. The different fractions were characterized analytically with regard to chemical composition and susceptibility to dilute-acid pretreatment and enzymatic saccharification.ResultsAll fractions were characterized by a high glucan content (38-43%) and a high content of other carbohydrates (11-14% mannan, 2-4% galactan) that generate easily convertible hexose sugars, and by a low content of inorganic material (0.2-0.9% ash). The lignin content was relatively uniform (27-32%) and the syringyl-guaiacyl ratio of the different fractions were within the range 0.021-0.025. The knotwood had a high content of extractives (9%) compared to the other fractions. The effects of pretreatment and enzymatic saccharification were relatively similar, but without pretreatment the bark fraction was considerably more susceptible to enzymatic saccharification.ConclusionsSince sawn timber is a main product from softwood species such as Scots pine, it is an important issue whether different parts of the tree are equally suitable for bioconversion processes. The investigation shows that bioconversion of Scots pine is facilitated by that most of the different fractions exhibit relatively similar properties with regard to chemical composition and susceptibility to techniques used for bioconversion of woody biomass.

NIR spectroscopic measurement of moisture content in Scots pine seeds

When tree seeds are used for seedling production it is important that they are of high quality in order to be viable. One of the factors influencing viability is moisture content and an ideal quality control system should be able to measure this factor quickly for each seed. Seed moisture content within the range 3-34% was determined by near-infrared (NIR) spectroscopy on Scots pine (Pinus sylvestris L.) single seeds and on bulk seed samples consisting of 40-50 seeds. The models for predicting water content from the spectra were made by partial least squares (PLS) and ordinary least squares (OLS) regression. Different conditions were simulated involving both using less wavelengths and going from samples to single seeds. Reflectance and transmission measurements were used. Different spectral pretreatment methods were tested on the spectra. Including bias, the lowest prediction errors for PLS models based on reflectance within 780-2280 nm from bulk samples and single seeds were 0.8% and 1.9%, respectively. Reduction of the single seed reflectance spectrum to 850-1048 nm gave higher biases and prediction errors in the test set. In transmission (850-1048 nm) the prediction error was 2.7% for single seeds. OLS models based on simulated 4-sensor single seed system consisting of optical filters with Gaussian transmission indicated more than 3.4% error in prediction. A practical F-test based on test sets to differentiate models is introduced.

Prediction of Pinus sylvestris clear-wood properties using NIR spectroscopy and biorthogonal partial least squares regression

Thirteen wood parameters were predicted using near infrared (NIR) spectra in the range 780-2380 nm modelled by biorthogonal partial least squares regression. The analysis of parameters and NIR measurements was done on clear- wood samples from the base and midstem of Scots pine (Pinus sylvestris L.) from trees at two sites. Calibrations based on the measured parameters at seven growth rings (cambial age ranging between 6 and 42 years) could be divided into three groups: (i) the best accuracy was found for longitudinal modulus of elasticity (r > 0.9) followed by bending, compression, and cell length (0.8 < r < 0.9); (ii) microfibril angle, longitudinal hardness, proportion of latewood, and creep with correla- tions in the range of 0.7-0.8; and (iii) tangential hardness, cell diameter, and cell wall thickness with 0.4 < r < 0.7. It was also shown that juvenile (cambial age £20 years) and mature wood can be classified using NIR techniques. Resume´ : La regression bi-orthogonale partielle par les moindres carresaeteutilisee pour modeliser 13 parametres du