Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), ash, and ethanol-soluble extractives, were obtained for 53 samples of paper and cardboard. These samples were mostly the type of materials typically found in domestic wastes (e.g. newspapers, printing paper, glossy papers, food packaging). A number of the samples (48) were obtained by separating a sample, after milling, into two particle size fractions. It was found that the fractions containing the smaller particles typically had higher ash and Klason lignin contents and lower glucose and xylose contents that the larger particle size fractions. Nevertheless, all of the sample types had attractive total sugars contents (>50%) indicating that these could be suitable feedstocks for the production of biofuels and chemicals in hydrolysis-based biorefining technologies. NIR models of a high predictive accuracy (R2 of > 0.9 for the independent validation set) were obtained for total sugars, glucose, xylose, Klason lignin, and ash and with values for the Root Mean Square Error of Prediction (RMSEP) of 2.36%, 2.64%, 0.56%, 1.98%, and 4.87%, respectively. Good NIR models (R2 of > 0.8) were also obtained for mannose, arabinose, and galactose. These results suggest that NIR is a suitable method for the rapid, low-cost, analysis of the major lignocellulosic components of waste paper/cardboard samples.

Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), moisture, and ash were obtained for 53 peat samples. These included samples with high, medium, and low degrees of humification. Klason lignin was the main constituent and was greatest in the samples classified as being highly humified, with structural sugars the lowest in this class. The total sugars contents of all samples were considered to be insufficient to allow for their use in biorefining hydrolysis processes for the production of chemicals and biofuels. NIR models were developed for spectral datasets obtained from the samples in their unprocessed (wet), dry and unground, and dry and ground states. Typically the most accurate models were based on the spectra of dry and ground samples. However the NIR models for the wet samples still offered reasonable predictive capabilities. All models were suitable at least for sample screening, with the models for total sugars, glucose, xylose, galactose, and moisture suitable for quantitative analyses.

The ability of using novel method of near infrared (NIR) spectra to predict the composition and higher heating value (HHV) of dry pig manure was examined. Number of pig manure solid fractions variously pre-treated samples were collected in Denmark, from different pig slurry treatment plants (using mechanical or chemical???mechanical separation) and then analysed for their energy values. These values were determined by conventional method using bomb calorimetry and also calculated based on ultimate analysis. NIR spectra method was successfully applied and reasonable R2 values were obtained for the independent prediction set for nitrogen, ash, and the HHV. NIR also showed ability for predicting which type of treatment plants the samples came from. In addition, new empirical equations, based on ultimate analyses of pig manure solids used for prediction of the HHV was established.

There has been a significant degree of hype regarding the commercial potential of second-generation biofuels (2GBs; biofuels sourced from lignocellulosic materials). In 2007, ambitious targets for the mass substitution of fossil-fuel-derived transport fuels by 2GBs were put forward in the United States and similar targets exist for other countries. However, as of May 2012, no commercial-scale 2GB facilities are currently operating. The technical and financial obstacles that have delayed the deployment of these facilities are discussed, as are recent advancements in research that may help to overcome some of these. There are six commercial-scale facilities currently (May, 2012) in construction and many more are planned in the near term. The prospects for 2GBs are more promising now than in the past but the delays in getting to this point mean that the ambitious targets of several years ago are unlikely to be reached in the near term.

Miscanthus plants were sampled from several plantations in Ireland over the harvest window (October-April). These were separated into their anatomical components and the loss of leaves monitored. Three distinct phases were apparent: there was minimal loss in the "Early" (October to early December) and "Late" (March and April) phases, and rapid leaf loss in the interim period. Samples were analysed for constituents relevant to biorefining. Changes in whole-plant composition included increases in glucose and Klason lignin contents and decreases in ash and arabinose contents. These changes arose mostly from the loss of leaves, but there were some changes over time within the harvestable plant components. Although leaves yield less biofuel than stems, the added biomass provided by an early harvest (31.9-38.4%) meant that per hectare biofuel yields were significantly greater (up to 29.3%) than in a late harvest. These yields greatly exceed those from first generation feedstocks.

Biomass feedstocks for the production of biofuels and chemicals vary greatly in their chemical compositions. These differences affect which technologies are used for processing. First generation technologies focus on the conversion of sugars, starches, and oils whilst second generation technologies process lignocellulose. While the conversion in first generation processes is relatively facile, the processing of lignocellulose is hindered by the complexity of the biomass matrix. Lignocellulosic feedstocks, however, tend to be significantly less costly, in economic, environmental, and energy terms, to produce. The effects of the various constituents on the conversion of biomass by either hydrolytic or thermochemical means are discussed, as are the logistical considerations needed when sourcing feedstocks. Biomass can be classified as a specifically grown energy crop, an agricultural residue, or a waste resource. Several examples of lignocellulosic feedstocks are discussed for each of these types and representative chemical data for a variety of materials presented.

Miscanthus samples were scanned over the visible and near infrared wavelengths at several stages of processing (wet-chopped, air-dried, dried and ground, and dried and sieved). Models were developed to predict lignocellulosic and elemental constituents based on these spectra. The dry and sieved scans gave the most accurate models; however the wet-chopped models for glucose, xylose, and Klason lignin provided excellent accuracies with root mean square error of predictions of 1.27%, 0.54%, and 0.93%, respectively. These models can be suitable for most applications. The wet models for arabinose, Klason lignin, acid soluble lignin, ash, extractives, rhamnose, acid insoluble residue, and nitrogen tended to have lower R(2) values (0.80+) for the validation sets and the wet models for galactose, mannose, and acid insoluble ash were less accurate, only having value for rough sample screening. This research shows the potential for online analysis at biorefineries for the major lignocellulosic constituents of interest.

This document is the result of the evaluation of biomass feedstocks, from Europe and Latin America, that took place as part of the DIBANET project. That project is co-financed from the 7 th Framework Programme for Research and Technological Demonstration of the European Union. (Title: Enhancing international cooperation between the EU and Latin America in the field of biofuels; Grant Agreement No: 227248-2).

The work in Task 2.1 of Work Package 2 (WP2) at DIBANET partners UL, CTC, and UNICAMP involved evaluating, on a number of levels, potential feedstocks for utilisation in the DIBANET acid-hydrolysis process (WP3). In the early stage of the project a wide number of feedstocks were examined and relevant secondary compositional data were sought from the literature. Selected feedstocks were analysed at the laboratories of UL, CTC, and UNICAMP and, from these, a limited number of feedstocks were subjected to more in-depth analysis/evaluation.

Work at UL focused on Miscanthus, cereal straws, and waste papers. The wet-chemical and spectroscopic analysis that was carried out on a wide number of Miscanthus samples have allowed for in-depth understandings to be reached regarding the changes in lignocellulosic composition, and potential biomass/biofuel yields that could be realised over the harvest window. Straws present much less chemical variation but have enough structural carbohydrates to warrant their processing in the DIBANET technology. Waste papers can have amongst the highest total carbohydrate contents of any of the feedstocks studied.

Work at CTC focused on the residues of the sugarcane industry ??? sugarcane bagasse and sugarcane trash (field residues from harvesting). A large number of samples were collected from a variety of sugar mills and plantations. It has been seen that there can be a significant variation in the composition of different bagasse samples, particularly with regards to the ash content. Sugarcane trash has lower total carbohydrates contents than bagasse but is still a suitable feedstock for DIBANET.

Work at UNICAMP focused on the evaluation of residues from the banana, coffee, and coconut industries. It was found that these also have potential for utilisation in the DIBANET process, however the value of the residues for this end-use is dependent on which part of the plant is utilised. For instance, coffee husks have sufficient structural carbohydrates to allow for decent yields of levulinic acid, formic acid, and furfural in DIBANET, however the leaves of the coffee plant do not. Leaves from the banana plant are also of less value for DIBANET than the other parts of the plant (e.g. stem).

A major output of this Deliverable is the downloadable electronic database that contains all of the WP2 analytical data obtained during the course of the project. It contains analytical data and predicted biorefining yields for a total of 1,281 samples. It can be obtained, free of charge, from the DIBANET website and will be a valuable tool for stakeholders in biorefining projects.

This document presents the data and evaluations that were made regarding biomass feedstocks, and also puts forward ???guidelines of best practice??? in terms of making the best use of these resources. A shortened version of this document can also be downloaded from the DIBANET website.

The processing of lignocellulosic materials in modern biorefineries will allow for the production of transport fuels and platform chemicals that could replace petroleum-derived products. However, there is a critical lack of relevant detailed compositional information regarding feedstocks relevant to Ireland and Irish conditions. This research has involved the collection, preparation, and the analysis, with a high level of precision and accuracy, of a large number of biomass samples from the waste and agricultural sectors. Not all of the waste materials analysed are considered suitable for biorefining; for example the total sugar contents of spent mushroom composts are too low. However, the waste paper/cardboard that is currently exported from Ireland has a chemical composition that could result in high biorefinery yields and so could make a significant contribution to Ireland???s biofuel demands.

Miscanthus was focussed on as a major agricultural feedstock. A large number of plants have been sampled over the course of the harvest window (October to April) from several sites. These have been separated into their anatomical fractions and analysed. This has allowed observations to be made regarding the compositional trends observed within plants, between plants, and between harvest dates. Projections are made regarding the extents to which potential chemical yields may vary. For the DIBANET hydrolysis process that is being developed at the University of Limerick, per hectare yields of levulinic acid from Miscanthus could be 20% greater when harvested early compared with a late harvest.

The wet-chemical analysis of biomass is time-consuming. Near infrared spectroscopy (NIRS) has been developed as a rapid primary analytical tool with separate quantitative models developed for the important constituents of Miscanthus, peat, and (Australian) sugarcane bagasse. The work has demonstrated that accurate models are possible, not only for dry homogenous samples, but also for wet heterogeneous samples. For glucose (cellulose) the root mean square error of prediction (RMSEP) for wet samples is 1.24% and the R2 for the validation set (R_val^2) is 0.931. High accuracies are even possible for minor analytes; e.g. for the rhamnose content of wet Miscanthus samples the RMSEP is 0.03% and the R_val^2 is 0.845. Accurate models have also been developed for pre-treated Miscanthus samples and are discussed. In addition, qualitative models have been developed. These allow for samples to be discriminated for on the basis of plant fraction, plant variety (giganteus/non-giganteus), harvest-period (early/late), and stand-age (one-year/older).

Quantitative NIRS models have also been developed for peat, although the heterogeneity of this feedstock means that the accuracies tend to be lower than for Miscanthus. The development of models for sugarcane bagasse has been hindered, in some cases, by the limited chemical variability between the samples in the calibration set. Good models are possible for the glucose and total sugars content, but the accuracy of other models is poorer. NIRS spectra of Brazilian bagasse samples have been projected onto these models, and onto those developed for Miscanthus, and the Miscanthus models appear to provide a better fit than the Australian bagasse models