Combustible wood dust explosions and impacts on environments and health - A review

Artificial Neural Network-based Prediction Model to Minimize Dust Emission in the Machining Process

Background

Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards.

Methods

The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons.

Results

The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth.

Conclusion

This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs.

Wood dust and risk of leukemia: Systematic review and meta-analysis

OBJECTIVES

This study aimed to perform a systematic review and meta-analysis to investigate the relationship between wood dust exposure and leukemia. The objectives included synthesizing available evidence, assessing its quality, identifying potential sources of heterogeneity, and drawing conclusions regarding the association between wood dust and leukemia.

METHODS

A systematic literature search was conducted to identify studies meeting that report on the association between wood dust and leukemia. The Joanna Briggs Institute Critical Appraisal tools were employed to ensure robust quality assessment. Meta-analysis, using random-effects models, synthesized evidence from studies with low risk of bias. Overall odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Subgroup analyses explored potential sources of heterogeneity.

RESULTS

The meta-analysis included a comprehensive review of various study types, encompassing 7 studies that examined the association between wood dust exposure and leukemia risk. The analysis revealed a statistically significant positive association, with an overall odds ratio (OR) of 1.56 (95% CI: 1.15-2.12). This indicates that individuals exposed to wood dust are 1.56 times more likely to develop leukemia compared to those not exposed, with the 95% confidence interval ranging from 1.15 to 2.12, highlighting a substantial risk elevation across different study designs. Quality assessment using The Joanna Briggs Institute Critical Appraisal tools demonstrated a low risk of bias across all included studies, enhancing the credibility of the observed association. Subgroup analyses were conducted to explore potential sources of heterogeneity within the studies. Notably, subgroup analysis based on the year of the study revealed significant differences, as indicated by an I^2 value of 87%. The robustness of these results underscores the importance of addressing wood dust exposure as an occupational hazard, particularly in industries related to woodworking and forestry.

CONCLUSION

This meta-analysis provides robust evidence supporting an increased risk of leukemia associated with wood dust exposure implying proactive measures in people exposed to dust.

Effect of storage conditions on lignocellulose biofuels properties

This article examines the effects of different storage conditions on selected physicochemical properties of three types of agro-biomass pellets: sunflower husks, wheat straw and hemp hurds, and wood pellets. The tests were carried out in a climatic chamber, which allows simulation of real storage conditions, i.e. conditions with high air humidity and variable (±) ambient air temperatures. The results showed higher degradability of agro-biomass pellets compared to woody biomass. The pellets degraded to a less extent at varying  ± temperatures than at high humidity (90% RH). After complete moisture saturation, durability decreases for agro-pellets by an average of 9%, while after freezing and defreezing for sunflower husk pellets and woody pellets durability decreases by 2%, and for hemp hurd pellets by 11%. In contrast, strength-by-dropping index for agro-pellets decreased by 20% after being in the environment (30 °C and 90%RH) and 15% under varying temperature conditions. No change in the energy parameters of all pellets in the dry matter was noted. On the other hand, an increase in the moisture content of pellets when they are stored under different environmental conditions results in a decrease in calorific value.

Progress and challenges in valorisation of biomass waste from ornamental trees pruning through pyrolysis processes. Prospects in the bioenergy sector

Nowadays, the scarcity of energy resources is promoting the search for alternative energy sources, boosting interest in the use of forest lignocellulosic residue in the energy sector. In this study, the focus is on the energy recovery from two lignocellulosic residues originated during the pruning of ornamental trees (Horse Chestnut, CI, and False Acacia, FA). Both conventional and flash pyrolysis techniques were applied. The experimental pyrolysis variables were obtained from the study of the thermal behaviour of the pruning residues in thermogravimetric analysis. It was carried out under 5 heating rates and kinetic parameters were estimated using Flynn-Wall-Ozawa method. Results denoted higher maximum mass loss rate values for the same release temperature regions under FA experiments. Also, FA samples had lower final residues for the processes. However, activation energy values were so close for both species. FA was also linked to the faster reactions according frequency factor outcomes. Conventional pyrolysis of pruning residues was carried out in a horizontal oven of original design at a heating rate of 25 °C/min, at 750 °C and 60 min of permanence at that temperature; flash pyrolysis was tested in that oven at 750 and 850 °C. In these pyrolysis processes, three fractions were obtained: bio-char, bio-oil and gas. The physicochemical attributes of the bio-chars suggested their potential utility as biofuels (28.4-29.8 MJ/kg), adsorbent precursors or soil additives. Conventional pyrolysis bio-oils had a dominant monoaromatic hydrocarbons nature, with phenols being the most abundant (≥60%), while flash bio-oils contain mainly polycyclic aromatic hydrocarbons. Conventional pyrolysis gases contained up to 60 vol% of CO2; flash pyrolysis gases had high combustible gas content (CO, CH4, H2) and a low CO2 content (<25 vol%). As a result, their calorific value (18.06 MJ/kg) exhibited a threefold increase compared to the gas produced through conventional pyrolysis (6.04 MJ/kg).

A Review of Approaches for Mitigating Effects from Variable Operational Environments on Piezoelectric Transducers for Long-Term Structural Health Monitoring

Extending the service life of ageing infrastructure, transportation structures, and processing and manufacturing plants in an era of limited resources has spurred extensive research and development in structural health monitoring systems and their integration. Even though piezoelectric transducers are not the only sensor technology for SHM, they are widely used for data acquisition from, e.g., wave-based or vibrational non-destructive test methods such as ultrasonic guided waves, acoustic emission, electromechanical impedance, vibration monitoring or modal analysis, but also provide electric power via local energy harvesting for equipment operation. Operational environments include mechanical loads, e.g., stress induced deformations and vibrations, but also stochastic events, such as impact of foreign objects, temperature and humidity changes (e.g., daily and seasonal or process-dependent), and electromagnetic interference. All operator actions, correct or erroneous, as well as unintentional interference by unauthorized people, vandalism, or even cyber-attacks, may affect the performance of the transducers. In nuclear power plants, as well as in aerospace, structures and health monitoring systems are exposed to high-energy electromagnetic or particle radiation or (micro-)meteorite impact. Even if environmental effects are not detrimental for the transducers, they may induce large amounts of non-relevant signals, i.e., coming from sources not related to changes in structural integrity. Selected issues discussed comprise the durability of piezoelectric transducers, and of their coupling and mounting, but also detection and elimination of non-relevant signals and signal de-noising. For long-term service, developing concepts for maintenance and repair, or designing robust or redundant SHM systems, are of importance for the reliable long-term operation of transducers for structural health monitoring.