Global market developments of wooden pellets have led to an increased attention towards pellet quality. ISO 17829 defines the procedure to assess pellets’ geometrical parameters, which play a key role in pellet overall quality. For instance, pellet length influences the spatial arrangement within the stove brazier, affecting the interaction between combustion air and solid biofuel, thus affecting CO emissions. The ISO 17829 method is time-consuming and affected by the operator’s accuracy. Recent studies have investigated the application of new methods, such as image processing, for monitoring the aforementioned parameter. While also assessing the representativeness of ISO 17829’s method, this paper proposes an alternative measuring tool based on image processing named Pellet Length Detector (PLD). Samples were obtained from Italian pellet suppliers and subjected to a multiple dimensional analysis via PLD and caliper. The PLD’s overall performance led to satisfactory results, with only 10% of the samples having a bias between replicates of >2 mm. Compared to caliper, PLD led to an average bias of 0.5 mm. Moreover, a one-way ANOVA highlighted that increasing the sample size between caliper and PLD leads to a greater statistical similarity of the data obtained for different replicates. Given the prototype status of the device, a further performance upgrade is possible, especially through error modeling.

The relevant growth of the wood pellet market in Europe in the last decade led to an increased focus on solid biofuel as a necessary and available renewable resource for energy production. Among biofuels, wooden pellets are among the most widespread for domestic heating. Therefore, monitoring the qualitative properties of commercialized pellets is crucial in order to minimize the amount of harmful emissions in residential areas. Standard ISO 17225 sets threshold values for the chemical and physical properties that commercialized biofuels must fulfil. Specifically, ISO 17225-2 defines that pellets for residential use must be produced from virgin wood, but no method is proposed to assess the actual origin of the material, leading to the risk of the commercialization of pellets made up from chemically treated materials. This study proposes a model obtained via near infrared spectroscopy analyses and chemometrics methods, such as classification, to rapidly assess whether pellets are made up of virgin or chemically treated wood. The result suggests the effectiveness of NIRs for the detection of non-virgin pellets with an accuracy greater than 99%. Furthermore, the model appeared to be accurate in the assessment of both milled and intact pellets, making it a potential in-line instrument for assessments of pellets’ quality.

Biomass continues to play a key role as an alternative to fossil fuels. Woody biomass produces lower greenhouse gas emissions than fossil fuels. However, in order to consider biomass as ‘green energy’, a number of factors should be taken into account, including the characterization of the quality of the resource. Therefore, monitoring quality parameters, such as moisture, ash, N content, is essential to assess the sustainability of biomass for energy production. This paper presents the results of laboratory analyses performed on wood chip samples from four Italian regions over a five-year period (2019–2023). In particular, all quality parameters defined by ISO 17225-9 for industrial wood chips were assessed. Data were analyzed using descriptive, parametric, non-parametric statistics, and multivariate analysis. An interest in quality monitoring has been observed, indicated by an increase in the number of samples received from suppliers and an enhancement in the average values of quality parameters. Moreover, an overall decrease in moisture and N content has been observed, while ash content and heating value have undergone non-linear variations. Statistically significant quality differences between samples from different regions may be the result of different practices, such as outdoor or indoor storage, climate differences, different biomass growth conditions.

Residential barbecuing is becoming increasingly popular worldwide, especially in cities, where it is not only a leisure activity but also an important social and cultural practice. Consequently, the number of grills and smokers in use continues to grow. This study evaluated the environmental performance of a household wood-pellet barbecue dual-function smoker/grill using a life cycle assessment (LCA) approach. The functional units selected were per cooking time (1 h) and per unit of energy delivered (1 kWh) at different cooking settings on the smoker. The results show that most of the impacts, including global warming potential (GWP) and resource use, originate from the production of the smoker itself, whereas emissions released during combustion, especially NO_x, are the main contributors to impacts such as acidification and smog formation. The GWP per hour of operation ranged from 0.44 to 0.63 kg CO₂ eq. From an operational perspective, cooking at intermediate temperatures (between 110 and 175 °C) generally leads to lower impacts per hour than very low-temperature smoking. When considering entire meals, meat typically accounts for most of the total impact, with the smoker’s contribution comparatively small. Overall, the study provides a useful reference and shows that both equipment design and food choices play a role in barbecue sustainability.

Bulk density (BD) is a key quality parameter of wood pellets, influencing handling, storage and volumetric energy density. The standard procedure (ISO 17828) is accurate but time-consuming and subject to operator variability. This preliminary study introduces the mass flow rate (MFR) as a new variable, not included in the standard, and presents a prototype built for its controlled measurement. Thirty commercial wood-pellet samples were characterized for MFR, pellet length distribution metrics and BD. After multivariate analysis, the relationships between MFR, dimensional descriptors and BD were explored through correlation analysis. As a first exploratory test, Support Vector Regression, Extreme Gradient Boosting and a weighted ensemble were evaluated by nested repeated cross-validation, with Gaussian noise-based augmentation applied to training data only. The mean out-of-fold MAE was 17.5 kg/m³ for the ensemble, 17.8 kg/m³ for SVR and 18.3 kg/m³ for XGB, with overlapping confidence intervals and considerable variability across data partitions. Nevertheless, the errors are contained and comparable to those attributable to operator variability and lower than the features standard deviation (24.3 kg/m³). The results suggest that MFR, combined with dimensional descriptors, can support BD estimation, although larger datasets are needed to establish a robust relationship.