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Quick and reliable inorganic elemental chemical analysis of biomass (including solid biofuels) is of importance in the increasing utilization and trade of biomass. In particular, it is important for the exploitation of contaminated/dirty biomass/biomass waste, and potentially also as a tool in ascertaining the type/origin of biomass. X-ray fluorescence (XRF) spectrometry performed directly on the raw biomass with limited prior sample preparation is an attractive method for performing such inorganic elemental analysis. In the present study, we therefore carefully investigate the performance of a commercial multielement standardless XRF method by analyzing five common biomass types (switchgrass, corn stover, eucalyptus, beech, and pine wood). Sample preparation involves milling the raw biomass using cutter and rotor mills (avoiding ball-milling) and coldpressing the powdered samples into pellets using wax binder. XRF users often rely on this type of commercial precalibrated or ‘standardless’ methods delivered with their XRF spectrometer. However, these methods are often sold without any guarantee on performance. We recently demonstrated the quite good performance of a typical commercial precalibrated/standardless method when analyzing biomass in the ideal form of certified reference material. In the present article, we report now on analysis of common raw biomass using the same method purchased with a 4 kW wavelength dispersive (WD) XRF spectrometer. The accuracy (trueness and precision) is determined by comparing the XRF data with the elemental composition obtained by standard elemental analysis (ICP-OES and ion-chromatography). The elements positively detected by the XRF are Na, Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Sr, and maybe Mo. For elements above 25 ppm, the XRF data show a relative systematic error (bias, trueness) typically better than ±15% independent of the concentration. The elements present with >1000 ppm (Mg, Si, Cl, K, Ca) consistently show a positive bias of 3−18% relative. The relative precision (measured as the relative standard error) is better than ±5% (typically ±1%) for concentrations >25 ppm (obtained with 10−30 measurements). Quantifying elements below 25 ppm (Co, Ni, Cu, Zn, Sr, Mo) is possible in some cases, but it requires a more detailed study for each specific element. For example, Cu can be determined down to a few parts per million with an appropriate correction for the method bias. Occasionally, larger relative biases of up to 45−90% can occur for certain elements (Cl, Si) in certain samples, so care should be taken to carefully test the applied method for the particular samples and elements of interest. Quantification of silicon (Si) by XRF works well for concentrations >100 ppm. The XRF method can further be used to estimate the ash yield from biomass combustion with a relative bias better than ±10%. It is shown that the errors on the elemental composition are dominated by systematic errors (biases), and therefore, measuring the two sides of a single pellet combined with correction for any bias is the optimum approach. The five biomass types employed here, combined with the 13 certified reference materials employed in our previous study, span a broad range of biomass types with the XRF method generally producing reliable results (keeping in mind the limitations and needed bias corrections) with errors comparable to the standard reference methods. This suggests that typical standardless/precalibrated XRF methods work well in elemental analysis of raw biomass (keeping in mind the limitations) and therefore could be considered for general usage in, for example, industrial analytical laboratories requiring fast elemental analysis of biomass., JRC.F.2-Energy Conversion and Storage Technologies

Biogas production is generally regarded as a very sustainable practice that can guarantee high GHG savings when used for electricity generation. However, sustainability is strongly influenced by many factors, such as the choice of feedstock and operational practices. This paper analyses the impact of several criticalities on the GHG emissions of biogas production. The analysis is based on a biogas plant producing electricity via a CHP engine. The first critical point is the choice of feedstock. Two substrates are analysed either individually or co¬digested: maize and manure. In the case of manure, the residues of digestion and the avoided emissions from manure storage and field application are taken into account. This results in high GHG savings compared to the reference system of producing electricity from the European mix. When maize is used as a substrate, all emissions from cultivation have to be accounted for. Co¬digestion is combining the advantages of both substrates. A second criticality analysed is the digestate storage. The environmental impact analysis shows that a substantial reduction of GHG emissions can be achieved with closed storage. The biogas pathways, though, have worse environmental performances in eutrophication, terrestrial ecotoxicity and particulate matter potential., Proceedings of the 20th European Biomass Conference and Exhibition, 18-22 June 2012, Milan, Italy, pp. 2229-2234

This work analyzes the environmental impacts ofmilk production inan intensivedairyfarmsituated in theNorthern Italy region of the Po Valley. Three manure management scenarios are compared: in Scenario 1 the animal slurry is stored in an open tank and then used as fertilizer. In scenario 2 the manure is processed in an anaerobic digestion plant and the biogas produced is combusted in an internal combustion engine to produce heat (required by the digester) and electricity (exported). Scenario 3 is similar to scenario 2 but the digestate is stored in a gas-tight tank. In scenario 1 the GHG emissions are estimated to be equal to 1.21 kg CO2 eq. kg−1 Fat and Protein Corrected Milk (FPCM) without allocation of the environmental burden to the by-product meat.With mass allocation, the GHG emissions associated to the milk are reduced to 1.18 kg CO2 eq. kg−1 FPCM. Using an economic allocation approach the GHG emissions allocated to the milk are 1.13 kg CO2 eq. kg−1 FPCM. In scenarios 2 and 3, without allocation, theGHGemissions are reduced respectively to 0.92 (−23.7%) and 0.77 (−36.5%) kg CO2 eq. kg−1 FPCM. If land use change due to soybean production is accounted for, an additionalemission of 0.53 kg CO2 eq. should be added, raising the GHG emissions to 1.74, 1.45 and 1.30 kg CO2 eq kg−1 FPCMin scenarios 1, 2 and 3, respectively. Primary energy from non-renewable resources decreases by 36.2% and 40.6% in scenarios 2 and 3, respectively, with the valorization of the manure in the biogas plant. The other environmental impact mitigated is marine eutrophication that decreases by 8.1% in both scenarios 2 and 3, mostly because of the lower field emissions. There is, however, a trade-off between non-renewable energy and GHG savings and other environmental impacts: acidification (+6.1% and +5.5% in scenarios 2 and 3, respectively), particulate matter emissions (+1.4% and +0.7%) and photochemical ozone formation potential (+41.6% and+42.3%) increase with the adoption of a biogas plant.The causeof the increase ismostlyemissions fromtheCHPengine.These impacts canbe tackledbyimproving biogas combustion technologies to reducemethane andNOx emissions. Freshwater eutrophication slightly increases (+0.8% in both scenarios 2 and 3) because of the additional infrastructures needed. In conclusion, on-farm manure anaerobic digestion with the production of electricity is an effective technology to significantly reduce global environmental impacts of dairy farms (GHG emissions and non-renewable energy consumption), however local impacts may increase as a consequence (especially photochemical ozone formation)., JRC.F.8-Sustainable Transport

With the increasing utilization and trade of biomass, there is a growing need for quick and reliable quantitative chemical analysis of the inorganic elemental composition of biomass. X-ray fluorescence (XRF) spectrometry performed directly on the raw biomass with limited prior sample preparation is an attractive method to this end. However, reliable calibration of XRF spectrometers for universal multi-element analysis can be very hard to implement mainly due to problems with matrix corrections. XRF users thus often rely on commercial precalibrated or standardless methods delivered with their XRF spectrometer. These methods are often sold without any guarantee on performance. Given the actual frequent use of these methods, along with their potential as ready-to-use methods for multi-element analysis of biomass, we investigate here the performance of a typical commercial precalibrated/standardless method recently purchased with a 4 kW wavelength dispersive (WD) XRF spectrometer. The accuracy (trueness and precision) is determined by analyzing the certified inorganic elements in 13 certified reference materials (CRMs) of diverse vegetal/plant origin. The certified elements detected by the XRF are Na, Mg, Al, P, S, Cl, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Rb, Sr, Mo, Ba, and Pb. The relative systematic error (bias, trueness) is typically better than ±20% for elements in the range 25 to 100 ppm, better than ±15% for the range 100 to 1000 ppm, and better than ±10% for concentrations above 1000 ppm. The relative precision (measured as the relative standard error) is better than ±5% (typically ±1−2%) for concentrations >25 ppm. Quantifying elements below 25 ppm is possible in some cases, but it requires a more detailed study for each specific element. Occasionally, larger relative biases of up to ±40% can occur for certain elements in certain samples, so care has to be taken to carefully test the applied method for the particular samples and elements of interest. The XRF method can further be used to estimate the ash yield from biomass combustion with a relative bias (trueness) typically better than ±15%. This involves the determination of silicon (Si) and titanium (Ti) by XRF. The choice of matrix composition in the matrix correction model and the influence of sample moisture and sample grain-size are also addressed. The instrument and method are described in detail allowing for comparisons with other similar XRF spectrometers often already available in industrial analytical laboratories., JRC.F.2-Cleaner energy