• Energy Research

The European target of ensuring reliable and sustainable energy has led to the increase in biofuel demand. This growth makes necessary the check of the product quality in order to prevent environmental and technical problems during combustion. Technical standard EN ISO 17225 divided the different biofuels into quality classes on the basis of their chemico-physical characteristics and the origin and source. In addition, they define the laboratory methodologies to be performed. These conventional analyses can determine these quality parameters but they are lengthy and expensive compared to the real need of the market. In this study, Vis-NIR spectroscopy coupled with partial least squares regression was used to predict the most important chemical-physical parameters of woodchip and pellet samples as a possible alternative to the conventional laboratory analysis. The results showed the possibility to use spectroscopy to obtain information about biofuel quality. In detail, moisture content and net calorific value of woodchip samples were predicted with RMSEP of 3.78% (r2(pred) = 0.97) and RMSECV of 0.37 MJ/kg (r2(CV) = 0.92), respectively. Ash content and gross calorific value of pellet samples were predicted with RMSECV of 0.44% (r2(CV) = 0.81) and 0.20 MJ/kg (r2(CV) = 0.78), respectively, while ash content and gross calorific value on ground pellet samples were predicted with RMSECV of 0.47% (r2(CV) = 0.78) and 0.19 MJ/kg (r2(CV) = 0.80), respectively. The best results were obtained considering only the near infrared region of the electromagnetic spectrum, suggesting that the visible part is not influential for the prediction of the parameters of this study. Having such a rapid and economic tool will be fundamental for the biofuel processors in order to check different quality characteristics of the products directly in real time without the time delay of the laboratory analysis and complications of sampling representation.

AbstractThe use of woodchip for energy use is expected to increase in the next years because of the European targets for mitigating climate change and reducing greenhouse gas emissions. The technical standard EN ISO 17225‐4 determines the woodchip quality classes based on different chemical–physical parameters. Among them, moisture content is one of the most important, and its real‐time monitoring could improve the product quality, increase combustion efficiency, and consequently provide a potential decrease of pollutant emissions. Although the lab procedure to determine moisture content is quite simple, it is too long compared to the real needs of power plant operators. A rapid and simple solution may be represented by near‐infrared spectroscopy coupled with chemometric techniques. In detail, two regression methods, that is, Partial Least Squares (PLS) and Rational function Ridge Regression (RRR), have been compared in order to develop a good model for the prediction of moisture content of woodchip samples as arrived at the lab or directly in the power plant. In addition, the prediction performance has been studied as a function of the number of the spectral measurements of the sample. The results showed that handheld instruments could give reliable results by taking enough replicated spectra. According to the estimated measurement uncertainty of moisture measurements, both methods suffer from lack of fit, and the performance could be better if all unknown sources of errors could be eliminated. In general, RRR performs significantly better than PLS, although, as with many nonlinear methods, the risk for outliers in predictions with RRR is higher than with PLS. This could be overpassed performing predictions with both methods, and in cases of big differences, PLS prediction could be chosen.

Sunflower is one of the most important oilseed crops cultivated in the world for different purposes. In Italy, the production is mostly located in the central area, representing 70% of Italian production. The market demand for sunflower oil is higher than the national production. There is an increasing request for cold pressed sunflower oil for food application. The success of this activity is linked to a correct setting up and management of the production and supply chain with a valorization of products and by-products. To this aim, information is needed, and this paper is focused on the cultivation of sunflower in central Italy using suitable hybrids, as well as on the study of the cold extraction performance of the sunflower seed produced and the quality of by-products and residues. Results indicate that, on average, a range of about 1.0–1.5 t ha−1 of cold pressed oil and different amounts of by-products can be obtained. According to a proposed scenario, 30 ha cultivated with sunflower are needed to create a complete supply chain within the farm, avoiding many additional passages and maintaining all the value for the farmer. It is important to use suitable hybrids for obtaining good yield, but also the cold extraction performances are important because they also affect the quality of by-products and residues that can be valorized themselves to improve sustainability.

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 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 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.