• Energy Research

Abstract Considering the focus of the current European policy in promoting the reuse of waste products and increasing the share of renewable energies, waste wood is becoming an appealing resource rather than a product to dispose of. End-life waste wood products could be used for the production of panel board or as feedstock in combustion units. In this study, waste wood samples have been collected in a big panel board company, and have been analyzed by means of Near Infrared Spectroscopy. Principal Component Analysis has been used in order to investigate the variability of the material, and Partial-Least Squares regression models have been developed for the prediction of moisture content and net calorific value. The results indicate that both models could be used in quality control applications, and Near Infrared Spectroscopy can be considered as a tool for the rapid evaluation of waste wood parameters for energy applications. Considering the high correlation between the two parameters it is also possible to analyze only the moisture content and have indications about the net calorific value using a simple linear regression, with positive effects in terms of quality control and the reuse of the waste wood material.

AbstractScattering effect is a really common physical phenomenon during near‐infrared analysis. It is an undesired variation in the spectral data due to a deviation of light from a straight trajectory into different paths. The nonlinearities introduced can be handled by using spectral preprocessing techniques. The situation is completely different when the parameter of interest is physical by nature, such as ash content, in this case removing the physical artifacts of scattering would be negative for the final model. In this study, we have decided to investigate the ash content parameter trying to figure out if the information useful for its prediction is related to the scattering effects, the chemical features, or a mixture of them. To this aim, two near‐infrared spectral datasets were taken into consideration: woodchip for energy sector and pellet samples for feed sector. A new regression model (CORR‐PLS) was developed by including principal components analysis scores and extended multiplicative scatter correction (EMSC) factors as physical parameters into the partial least squares (PLS) regression model. The prediction performance of the regular PLS models (PLS on the raw data and MSC pre‐treated data) were compared with that of the CORR‐PLS model both with regard to prediction uncertainty and model complexity in order to evaluate which is the relevant information for prediction of the ash content.

The conference organized by the European Society for Agricultural Engineers has been postponed one year and at the end held online because of the pandemic situation. In particular this year the main focus was 'New Challenges for Agricultural Engineering towards a Digital World'. The audience consists in agricultural engineers, scientists, technicians, academics and industry and the main topics are ways for improving food and feed production systems as also the distribution channels in sustainable and safety conditions. Our study on the investigation of waste wood quality for assessing it potential reuse for energy use perfectly fits with the theme of circular economy and sustainable future of agriculture of this conference.

Abstract The use of woodchip, and biofuels in general, is a fundamental step towards the transition from fossil fuel to renewable energy. The growth in the demand for wood fuels and the inherent variability in the properties of woody material lead to the need to verify its quality. EN ISO 17225-4 divides woodchip in different quality classes according to chemical-physical parameters and quality attributes. In this study, we have coupled near infrared spectroscopy with Partial Least Square regression to model gross calorific value and ash content of woodchip samples. Moreover, variables selection methods were tested in order to improve the models and get better prediction. Gross calorific value and ash content were predicted with a standard error of 234 J/g and 0.44%, respectively. The results indicate that the models could be used in screening applications and near infrared spectroscopy is a promising tool in the evaluation of biomass quality.

The pellet energy market is expanding rapidly in Europe and also at the global level, in response to the continuously growing energy demand and because of the high degree of reliability, the easy handling, and the cheap and simple logistics, in comparison to other solid biomasses. The fast growth of this market has highlighted the problem of product quality, which has strong repercussions for technical, environmental, and economic aspects. The biomass quality is defined by several chemical–physical parameters that are directly measurable in the laboratory. In addition, there are quality attributes related to origin and source, difficult to investigate through traditional analyses, such as the type of wood (hardwood/softwood) and the presence of bark. The development of a rapid technique able to provide this information could be an advantageous tool for the energy sector proving indications on biofuel traceability and sustainability. More than 90 samples belonging to three of the most common European speci...

Abstract Biogenic volatile organic compound (BVOC) emissions are important in the global atmospheric chemistry and their feedbacks to global warming are uncertain. Global warming is expected to trigger vegetation changes and water table drawdown in boreal peatlands, such changes have only been investigated on isoprene emission but never on other BVOCs. We aimed at distinguishing the BVOCs released from vascular plants, mosses and peat in hummocks (dry microsites) and hollows (wet microsites) of boreal peatland microcosms maintained in growth chambers. We also assessed the effect of water table drawdown (−20 cm) on the BVOC emissions in hollow microcosms. BVOC emissions were measured from peat samples underneath the moss surface after the 7-week-long experiment to investigate whether the potential effects of vegetation and water table drawdown were shown. BVOCs were sampled using a conventional chamber method, collected on adsorbent and analyzed with GC–MS. In hummock microcosms, vascular plants increased the monoterpene emissions compared with the treatment where all above-ground vegetation was removed while no effect was detected on the sesquiterpenes, other reactive VOCs (ORVOCs) and other VOCs. Peat layer from underneath the surface with intact vegetation had the highest sesquiterpene emissions. In hollow microcosms, intact vegetation had the highest sesquiterpene emissions. Water table drawdown decreased monoterpene and other VOC emissions. Specific compounds could be closely associated to the natural/lowered water tables. Peat layer from underneath the surface of hollows with intact vegetation had the highest emissions of monoterpenes, sesquiterpenes and ORVOCs whereas water table drawdown decreased those emissions. The results suggest that global warming would change the BVOC emission mixtures from boreal peatlands following changes in vegetation composition and water table drawdown.

The three datasets contain the spectral data acquired on waste wood samples using a handheld spectrophotometer (MicroNIR™ OnSite instrument). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 24 randomly distributed increments have been collected from 16 static lots, resulting in a total of 384 samples (we note these DT-SamTot). All the samples have been analyzed by Near-Infrared (NIR) spectrophotometer directly on site. In addition, four of the 24 increments for each lot - resulting in a total of 64 samples - have been sent to the lab for further analysis (DT-Lab). Additionally, another dataset has been created based on a reduced DT-SamTot dataset, where we only consider the four of 24 increments for each lot that were sent to the lab (DT-SamRed). It is important for having more accurate indications about the differences in variability between DT-Lab and DT-SamTot samples. We provide three CSV files: DT-Sam_Tot_270521_v01.csv: spectral data and information of DT-SamTot.; DT-Sam_Red_270521_v01.csv: spectral data and information of DT-SamRed. DT-Lab_270521_v01.csv: spectral data and information of DT-Lab. The three CSV files contain similar information in the columns: Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of sample (from 1 to 24) and the last number the NIR replicate. E.g. S4-13-1.sam: lot number 4, sample number 13, NIR replicate number 1. Please note that for DT-Lab dataset we have a different coding where labA and labB are the two sample replicates for the moisture content analysis. Rep: number indicating the NIR replicates for each sample. Please note that for DT-Lab dataset we have also rep2 column reporting the sample replicates for the moisture content analysis. Lot: number of lot to which the sample belongs (from 1 to 16). Day: day in which the sample has been collected (1 = 18/02/2020; 2 = 19/02/2020). Mois: moisture content of the sample (%). PCN: net calorific value of the sample (J/g). Spectral data: absorbance values for each sample from 908.1 nm to 1676.2 nm. The aim behind this dataset is to investigate the variability of the waste wood (WP1 of WoodSpec project) and this information is essential for increasing the reuse of the material and guarantee an accurate and successful use of a NIR sensor into real industrial applications. A second aim is the development of regression models for predicting the moisture content and net calorific value of the samples (WP3 of WoodSpec project). First indications about the variability and the chemical-physical characteristics of the material are essential for determining the suitability in energy applications. If you would like know more about the data, or to use these data, please refer to our article in Renewable Energy, doi: https://doi.org/10.1016/j.renene.2021.05.137 Funding: The project leading to this application has received funding from theEuropean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.

The combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000 samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.