• Feedstocks Analysed at Celignis
    Peat

Background on Peat

Peat is a substance that forms from the incomplete degradation of organic matter. This occurs in waterlogged land where the limited availability of oxygen means that the rate of accumulation of plant debris is greater than the rate of its decomposition. Peats can be classified based on the degree of humification, i.e. the degree of decomposition, or transformation, of the original lignocellulosic plant materials. A Von Post Decomposition Scale of Peats rates the degree of humification from H1 (undecomposed) to H10 (where no plant traces remain).

Peat is often used for heat/power generation in rural communities.

Analysis of Peat at Celignis



Celignis Analytical can determine the following properties of Peat samples:



Lignocellulosic Properties of Peat

Cellulose Content of Peat

Celignis founder Daniel Hayes has extensive experience in the collection, preparation, and chemical/infrared analysis of peat samples. He previously carried out a research project, funded by Bord na Mona, that involved the analysis of a number of peat samples.

The composition of peat will vary according to: the type of vegetative matter from which it was formed; the relationship between the water of the peat bog and the ground water system; and the relative degradation of the different chemical groups of the vegetative species. For instance, plant waxes are relatively resistant to degradation while water soluble sugars and starches are highly susceptive.

Hemicellulose, cellulose, and lignin can be resistant to degradation and are found in peats, but to varying degrees. The proportion of cellulose present in peat will typically decrease with increased levels of humification (i.e. increased depth in a peat bog).

Click here to see the Celignis Analysis Packages that determine Cellulose Content

Request a QuoteCellulose Content



Hemicellulose Content of Peat

As with cellulose, the proportion of hemicellulose present in the peat will decrease with depth. However hemicellulose typically decreases more slowly than cellulose with depth, with this trend being more marked in low-moor than high-moor peats.

As well as plant-derived carbohydrates, peats can also contain carbohydrates that are released upon the death of microbes. This can result in the presence of deoxy-sugars such as rhamnose and fucose in the peat samples, sugars that are typically absent (or only present in minute quantities) in the vegetative matter.

Click here to see the Celignis Analysis Packages that determine Hemicellulose Content

Request a QuoteHemicellulose Content



Lignin Content of Peat

While lignin is readily degraded by microbes in aerobic conditions to provide major inputs to the humic layers of soil, it is much more resistant to such degradation in the anaerobic conditions of peat bogs. Hence, while the proportions of hemicellulose and cellulose will decrease with plant depth, the relative amount of lignin will increase, due to a lower rate of degradation.

Click here to see the Celignis Analysis Packages that determine Lignin Content

Request a QuoteLignin Content



Starch Content of Peat



Click here to see the Celignis Analysis Packages that determine Starch Content

Request a QuoteStarch Content



Uronic Acid Content of Peat

Uronic acids have been detected in peats with their abundance and relative contribution to total carbohydrate content varying according to the level of humification.

Click here to see the Celignis Analysis Packages that determine Uronic Acid Content

Request a QuoteUronic Acid Content



Enzymatic Hydrolysis of Peat

We can undertake tests involving the enzymatic hydrolysis of Peat. In these experiments we can either use a commercial enzyme mix or you can supply your own enzymes. We also offer analysis packages that compare the enzymatic hydrolysis of a pre-treated sample with that of the native original material.

Click here to see the Celignis Analysis Packages that determine Enzymatic Hydrolysis

Request a QuoteEnzymatic Hydrolysis



Bioenergy Properties of Peat

Ash Content of Peat

The ash content of peat can be highly variable and can constitute a significant proportion of total mass in some samples.

Click here to see the Celignis Analysis Packages that determine Ash Content

Request a QuoteAsh Content



Heating (Calorific) Value of Peat

The heating value of peat is of particular importance when this feedstock is to be used for heat/power generation. It will vary according to the contents of carbon, moisture, and ash in the sample. Each of these properties can vary significantly between peat samples. In order to improve the energy output in the combustion of peat the resource is land spread and air-dried after harvest and prior to its utilisation in boilers. Peat that has been treated in this way is known as Milled Peat.

Click here to see the Celignis Analysis Packages that determine Heating (Calorific) Value

Request a QuoteHeating (Calorific) Value



Ash Melting Behaviour of Peat

Ash melting, also known as ash fusion and ash softening, can lead to slagging, fouling and corrosion in boilers which may reduce conversion efficiency. We can determine the ash melting behaviour of Peat using our Carbolite CAF G5 BIO ash melting furnace. It can record the following temperatures:

Ash Shrinkage Starting Temperature (SST) - This occurs when the area of the test piece of Peat ash falls below 95% of the original test piece area.

Ash Deformation Temperature (DT) - The temperature at which the first signs of rounding of the edges of the test piece occurs due to melting.

Ash Hemisphere Temperature (HT) - When the test piece of Peat ash forms a hemisphere (i.e. the height becomes equal to half the base diameter).

Ash Flow Temperature (FT) - The temperature at which the Peat ash is spread out over the supporting tile in a layer, the height of which is half of the test piece at the hemisphere temperature.



Click here to see the Celignis Analysis Packages that determine Ash Melting Behaviour

Request a QuoteAsh Melting Behaviour



Major and Minor Elements in Peat

Examples of major elements that may be present in Peat include potassium and sodium which are present in biomass ash in the forms of oxides. These can lead to fouling, ash deposition in the convective section of the boiler. Alkali chlorides can also lead to slagging, the fusion and sintering of ash particles which can lead to deposits on boiler tubes and walls.

We can also determine the levels of 13 different minor elements (such as arsenic, copper, and zinc) that may be present in Peat.

Click here to see the Celignis Analysis Packages that determine Major and Minor Elements

Request a QuoteMajor and Minor Elements



Analysis of Peat for Anaerobic Digestion



Biomethane potential (BMP) of Peat

As peat contains significant amounts of lignin (coupled with elevated ash contents in many samples) it is not a suitable feedstock for anaerobic digestion.

Click here to see the Celignis Analysis Packages that determine BMP

Request a QuoteBMP



Physical Properties of Peat



Bulk Density of Peat

At Celignis we can determine the bulk density of biomass samples, including Peat, according to ISO standard 17828 (2015). This method requires the biomass to be in an appropriate form (chips or powder) for density determination.



Click here to see the Celignis Analysis Packages that determine Bulk Density

Request a QuoteBulk Density



Particle Size of Peat

As peat contains significant amounts of lignin (coupled with elevated ash contents in many samples) it is not a suitable feedstock for anaerobic digestion.

Click here to see the Celignis Analysis Packages that determine Particle Size

Request a QuoteParticle Size

Publications on Peat By The Celignis Team

Hayes, D. J. M., Hayes, M. H. B., Leahy, J. J. (2015) Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions, Fuel 150: 261-268

Link

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.

Hayes, D. J. M. (2011) Analysis of Lignocellulosic Feedstocks for Biorefineries with a Focus on The Development of Near Infrared Spectroscopy as a Primary Analytical Tool, PhD Thesis832 pages (over 2 volumes)

Download

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.

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.





Examples of Other Feedstocks Analysed at Celignis



...