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.

In mines, the exhaust gas is mainly discharged from main ventilators in free dissipation state. The effective recovery of its kinetic energy is lucrative from economical and technical standpoints. This study is focused on recovering the kinetic energy of the exhaust gas without deteriorating the main ventilator’s safe operation or increasing power dissipation. The related experiments and numerical simulations were performed for the conical diffusers with diffusing angles of 16° and inlet diameters of 220 and 320 mm, respectively. By examining the start-up and operation states of the wind turbines using inlet collectors with conical, circular arc, and cone-arc shapes, it was found that the wind speed at the inlet of the drainage tube exceeded 4 and 20 m/s at the minimal and maximal distances from the wind turbine, respectively. The wind speed at the overflow tube outlet was no less than 1.8 m/s, proving a certain recovery of kinetic energy by the wind turbine. Thus, the critical distance between the diffuser and the inlet collector should be at least four times the diffuser’s inlet diameter. Finally, the velocity, pressure, and flow fields in the vortex core regions of the kinetic recovery system with different types of inlet collectors were simulated with the FLUENT commercial software package. The simulation results revealed that the circular-arc collector outperformed other three tested configurations in kinetic energy recovery.

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.

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.