Analysis of Ash

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 paper and pulp industry is one of the major industries that generate large amount of solid waste with high moisture content. Numerous opportunities exist for valorisation of waste paper sludge, although this review focuses on primary sludge with high cellulose content. The most mature options for paper sludge valorisation are fermentation, anaerobic digestion and pyrolysis. In this review, biochemical and thermal processes are considered individually and also as integrated biorefinery. The objective of integrated biorefinery is to reduce or avoid paper sludge disposal by landfilling, water reclamation and value addition. Assessment of selected processes for biorefinery varies from a detailed analysis of a single process to high level optimisation and integration of the processes, which allow the initial assessment and comparison of technologies. This data can be used to provide key stakeholders with a roadmap of technologies that can generate economic benefits, and reduce carbon wastage and pollution load.

Paper sludge (PS) from the paper and pulp industry consists primarily of cellulose and ash and has significant potential for ethanol production. Thirty-seven PS samples from 11 South African paper and pulp mills exhibited large variation in chemical composition and resulting ethanol production. Simultaneous saccharification and fermentation (SSF) of PS in fed-batch culture was investigated at high solid loadings and low enzyme dosages. Water holding capacity and viscosity of the PS influenced ethanol production at elevated solid loadings of PS. High viscosity of PS from virgin pulp mills restricted the solid loading to 18% (w/w) at an enzyme dosage of 20 FPU/gram dry PS (gdPS), whereas an optimal solid loading of 27% (w/w) was achieved with corrugated recycle mill PS at 11 FPU/gdPS. Ethanol concentration and yield of virgin pulp and corrugated recycle PS were 34.2 g/L at 66.9% and 45.5 g/L at 78.2%, respectively.

Paper sludge samples collected from recycling mills exhibited high ash content in the range of 54.59%–65.50% and glucose concentrations between 21.97% and 31.11%. Washing the sludge reduced the total ash content to between 10.7% and 19.31% and increased the concentration of glucose, xylose and lignin. Samples were screened for ethanol production and fed-batch simultaneous saccharification and fermentation (SSF) was optimised for the washed samples that resulted in highest and lowest ethanol concentrations. Maximum ethanol concentrations of 57.31 g/L and 47.72 g/L (94.07% and 85.34% of the maximum theoretical yield, respectively) was predicted for high and low fermentative potential samples, respectively, and was experimentally achieved with 1% deviation. A generic set of process conditions were established for the conversion of high ash-containing paper sludge to ethanol. Techno-economic analysis based on three different revenue scenarios, together with Monte Carlo analysis revealed 95% probability of achieving IRR values in excess of 25% at a paper sludge feed rate of 15 t/d. Feed rates of 30 t/d and 50 t/d exhibited a cumulative probability of 100%. This study presents the technical feasibility and economic viability of paper mills expansion towards bioethanol production from paper sludge.

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.

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.

Rice straw is an attractive lignocellulosic material for bioethanol production since it is one of the most abundant renewable resources. It has several characteristics, such as high cellulose and hemicelluloses content that can be readily hydrolyzed into fermentable sugars. But there occur several challenges and limitations in the process of converting rice straw to ethanol. The presence of high ash and silica content in rice straw makes it an inferior feedstock for ethanol production. One of the major challenges in developing technology for bioethanol production from rice straw is selection of an appropriate pretreatment technique. The choice of pretreatment methods plays an important role to increase the efficiency of enzymatic saccharification thereby making the whole process economically viable. The present review discusses the available technologies for bioethanol production using rice straw.