Tao Wei

Welding technology, extensively utilized in modern industry, poses significant health risks due to metal dust exposure, which can lead to respiratory discomfort, neurological issues, and an increased risk of lung cancer and pneumoconiosis. Enhancing ventilation within factory buildings has proven to be an economical approach to mitigating these risks. This study employs computational fluid dynamics (CFD) to model the airflow and dust transport within a large welding workshop measuring 300 m in length, 28 m in width, and 21 m in height. The impact of the exhaust-to-supply air ratio (ESR) and the height of the side exhaust port (SEP) on dust removal efficiency is investigated. Comparative analysis of transport dynamics between low-density aluminum alloy welding fume and high-density carbon steel welding fume reveals optimal dust exhaust designs. The study identifies two peaks in workshop air velocity at 0–2 m and 8–12 m above the ground, with the top exhaust port (TEP) outperforming the SEP in dust removal. An increased ESR accelerates the upward migration of welding fume, reducing lateral dispersion. An improperly set SEP height can lead to airflow short-circuiting or excessive lateral dispersion, hindering effective dust removal. Optimal SEP height for aluminum alloy and carbon steel dust are determined to be 5 m and 6 m, respectively.

Large mineral flotation workshops in severe cold regions represent special industrial indoor environments characterized by the coexistence of limited ventilation and intense heat release. Such conditions generate pronounced spatial thermal stratification and localized heat accumulation within the workshop, leading to uneven worker thermal exposure and increased thermal discomfort and heat stress risk. However, conventional thermal comfort models were primarily developed for ordinary buildings with relatively stable thermal environments. Their applicability to large industrial workshops remains insufficiently validated. Nine representative monitoring points were arranged in the summer operating areas of the workshop, and thermal comfort surveys were conducted among 35 workers who had adapted to the local climate and working environment. The predicted mean vote (PMV) model was used as the baseline assessment framework, while an adaptive predicted mean vote (aPMV) model was further calibrated using field-based thermal sensation information. Monte Carlo simulation was employed to evaluate uncertainty propagation under field-data constraints, and Sobol sensitivity analysis was conducted to identify the dominant factors affecting thermal comfort predictions. The results demonstrated that the conventional PMV model exhibited a clear warm prediction bias under the investigated industrial conditions. After adaptive correction, the deviation from the field-based TSV was reduced by 82.93%, indicating improved agreement with workers’ actual thermal perception. Sensitivity analysis identified metabolic rate as the dominant contributor to aPMV output variance, with first-order and total-effect Sobol indices of 0.530 and 0.535. The proposed framework provides a scenario-specific approach for thermal comfort assessment in the investigated flotation workshop and offers preliminary methodological references for similar large-scale flotation workshops.

Interelectrode distance is a key structural parameter of passive air purifiers based on the particle sink effect, but its influence on particulate matter (PM) dynamics remains unclear. This study experimentally investigates the concentration evolution of PM in four size ranges (≤1, 1–2.5, 2.5–10, >10 μm) under different interelectrode distances in a confined space, combined with multifractal detrended fluctuation analysis (MF-DFA) and coupling detrended fluctuation analysis (CDFA). Results show a non-monotonic effect of interelectrode distance on PM removal, with an optimal range of 3–4 cm. Submicron PM (≤1 μm) exhibits a short-term rise followed by a decline; the rising phase duration increases with larger interelectrode distance, revealing a competition between fragmentation-induced release of large particles and their capture. MF-DFA indicates that concentration series of each PM size range display typical multifractal behavior, and the skewness direction of the multifractal spectrum varies with interelectrode distance and particle size. CDFA further reveals strong coupled multifractal features among concentration series of the same particle size at different distances, with coupling strength increasing with particle size. The skewness evolution of the coupled multifractal spectrum quantitatively uncovers how the interelectrode distance, through non-monotonic matching between electric field intensity and dust collection area, regulates PM concentrations toward higher or lower values. This study provides a mechanistic basis for optimizing passive air purifier structures.

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes.