Analysis of Acid Insoluble Residue

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.

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.

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 ( ) 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 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.