Shiitake cultivation as biological preprocessing of lignocellulosic feedstocks - Substrate changes in crystallinity, syringyl/guaiacyl lignin and degradation-derived by-products.

Formulation of substrates based on three hardwood species combined with modulation of nitrogen content by whey addition (0-2%) was investigated in an experiment designed in D-optimal model for their effects on biological preproceesing of lignocellulosic feedstock by shiitake mushroom (Lentinula edodes) cultivation. Nitrogen loading was shown a more significant role than wood species for both mushroom production and lignocellulose degradation. The fastest mycelial colonisation occurred with no nitrogen supplementation, but the highest mushroom yields were achieved when 1% whey was added. Low nitrogen content resulted in increased delignification and minimal glucan consumption. Delignification was correlated with degradation of syringyl lignin unit, as indicated by a significant reduction (41.5%) of the syringyl-to-guaiacyl ratio after cultivation. No significant changes in substrate crystallinity were observed. The formation of furan aldehydes and aliphatic acids was negligible during the pasteurisation and fungal cultivation, while the content of soluble phenolics increased up to seven-fold.

Energy-efficient substrate pasteurisation for combined production of shiitake mushroom (Lentinula edodes) and bioethanol.

Hot-air (75-100 °C) pasteurisation (HAP) of birch-wood-based substrate was compared to conventional autoclaving (steam at 121 °C) with regard to shiitake growth and yield, chemical composition of heat-pretreated material and spent mushroom substrate (SMS), enzymatic digestibility of glucan in SMS, and theoretical bioethanol yield. Compared to autoclaving, HAP resulted in faster mycelial growth, earlier fructification, and higher or comparable fruit-body yield. The heat pretreatment methods did not differ regarding the fractions of carbohydrate and lignin in pretreated material and SMS, but HAP typically resulted in lower fractions of extractives. Shiitake cultivation, which reduced the mass fraction of lignin to less than half of the initial without having any major impact on the mass fraction of glucan, enhanced enzymatic hydrolysis of glucan about four-fold. The choice of heating method did not affect enzymatic digestibility. Thus, HAP could substitute autoclaving and facilitate combined shiitake mushroom and bioethanol production.

Multivariate modelling on biomass properties of cassava stems based on an experimental design.

Based on a factorial experimental design (three locations × three cultivars × five harvest times × four replicates) conducted with the objective of investigating variations in fuel characteristics of cassava stem, a multivariate data matrix was formed which was composed of 180 samples and 10 biomass properties for each sample. The properties included as responses were two different calorific values and ash, N, S, Cl, P, K, Ca, and Mg content. Overall principal component analysis (PCA) revealed a strong clustering for the growing locations, but overlapping clusters for the cultivar types and almost no useful information about harvest times. PCA using a partitioned data set (60 × 10) for each location revealed a clustering of cultivars. This was confirmed by soft independent modelling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA), and indicated that the locations gave meaningful information about the differences in cultivar, whereas harvest time was not found to be a differentiating factor. Using the PLS technique, it was revealed that ash, K, and Cl content were the most important responses for PLS-DA models. Furthermore, using PLS regression of fuel and soil variables it was also revealed that fuel K and ash content were correlated with the soil P, Si, Ca, and K content, whereas fuel Cl content was correlated with soil pH and content of organic carbon, N, S, and Mg in the soil. Thus, the multivariate modelling used in this study reveals the possibility of performing rigorous analysis of a complex data set when an analysis of variance may not be successful.

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

BACKGROUND: Forestry 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. RESULTS: All 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. CONCLUSIONS: Since 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.

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.

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.

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%.

Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using bi-orthogonal partial least squares regression.

The multitude of biofuels in use and their widely different characteristics stress the need for improved characterisation of their chemical and physical properties. Industrial use of biofuels further demands rapid characterisation methods suitable for on-line measurements. The single most important property in biofuels is the calorific value. This is influenced by moisture and ash content as well as the chemical composition of the dry biomass. Near infrared (NIR) spectroscopy and bi-orthogonal partial least squares (BPLS) regression were used to model moisture and ash content as well as gross calorific value in ground samples of stem and branches wood. Samples from 16 individual trees of Norway spruce were artificially moistened into five classes (10, 20, 30, 40 and 50%). Three different models for decomposition of the spectral variation into structure and noise were applied. In total 16 BPLS models were used, all of which showed high accuracy in prediction for a test set and they explained 95.4-99.8% of the reference variable variation. The models for moisture content were spanned by the O-H and C-H overtones, i.e. between water and organic matter. The models for ash content appeared to be based on interactions in carbon chains. For calorific value the models was spanned by C-H stretching, by O-H stretching and bending and by combinations of O-H and C-O stretching. Also -C=C- bonds contributed in the prediction of calorific value. This study illustrates the possibility of using the NIR technique in combination with multivariate calibration to predict economically important properties of biofuels and to interpret models. This concept may also be applied for on-line prediction in processes to standardize biofuels or in biofuelled plants for process monitoring.

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.