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.
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.
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.
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.
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.
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.
The DIBANET process chain, as a result of its patented pre-treatment stage, has significantly increased the yields of levulinic acid, formic acid, and furfural beyond what was considered to be the state of the art. By fractionating lignocellulosic biomass into its three main polymers (cellulose, hemicellulose, lignin) it has also allowed for lignin to be recovered and sold as a higher-value product. These developments have meant that the amount of acid hydrolysis residues (AHRs) that have been produced are significantly (up to 88%) less than in the Biofine process. These AHRs are required to provide process heat for DIBANET. Direct combustion is the most efficient means for doing this. If such combustion does not occur and the AHRs are instead used in other processes, e.g. pyrolysis and gasification, then more biomass will need to be purchased to fuel the core DIBANET process. The AHRs have not been proven to be superior to virgin biomass when put through these thermochemical processes. Indeed, many of the results from DIBANET Work Package 4 indicate the opposite. Hence, given that DIBANET, and the modelling of its optimal configuration, is designed on the basis of an integrated process, centred on the core element of the acid hydrolysis of biomass, then combustion is the only viable end use for the AHRs.
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 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 Irelandís biofuel demands.