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Quick and reliable inorganic elemental chemical analysis of biomass (incl. 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 multi-element standardless XRF method by analyzing 5 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 cold-pressing the powdered samples into pellets using binder (vax).XRF users often rely on this type of commercial pre-calibrated 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 pre-calibrated / 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 ionchromatography). The certified elements consistently detected by the XRF are: Na, Mg, Al, Si, P, S, Cl, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Sr, 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. E.g. Cu can be determined down to a few ppm with an appropriate correction for the method bias. Occasionally, larger relative biases of up to 45% to 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 corre...

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The main goal of this work is to understand the effect of thermal runaway initiation conditions on the severity of thermal runaway (TR) of Graphite—NMC (111) cells. A coupled electrical-thermal model is developed, which includes the initial energy input, the chemical decomposition processes of the anode, cathode and the electrical energy released by an internal short circuit. 780 different thermal runaway events are simulated and the output is analysed by machine learning techniques such as principal component analysis and clustering. It was found that TR events form 5 clusters between no thermal runaway and severe thermal runaway. Sensitivity analysis is applied on the 39 input invariants and the triggering energy input, resistance ratio, the heat convection coefficient, the ratio of activation energy of oxygen liberation and electrolyte evaporation are found to be the most important parameters. The later one determines the amount of electrolyte combustion. The probability of thermal runaway is calculated taking into account the most important parameters and their interactions. Finally, a combination of initiation parameters is suggested, which most likely results in a repeatable and reproducible outcome.