School of Electrical and Control Engineering, Heilongjiang University of Science and Technology
CCTEG Chongqing Research Institute
Assessment Center, Air Force Communication NCO Academy

针对传统长压短抽式通风除尘系统易形成涡流和风流死角的问题，结合康达效应对长压短抽式通风除尘系统进行结构优化，将抽风管与压风管进行嵌套处理，使负压风筒中的风流在抽风筒管口产生康达效应，确保气流紧贴巷道壁面，减少了空气中粉尘的扩散，并显著降低了能耗。通过流场和离散相模型（DPM）仿真得到最佳压抽比为2∶3，在最佳压抽比下的仿真结果显示，与传统长压短抽式通风除尘系统相比，优化系统除尘后司机处及下风侧的粉尘浓度分别降低了5.56%和55.41%。确定通风除尘系统整体结构及压抽比后，通过参数调控可进一步提高除尘效率。选择风筒与产尘面的距离、风筒中轴线与地面的距离及抽压风筒之间的距离作为通风除尘系统的优化调控参数，通过卷积神经网络（CNN）对优化通风除尘系统的除尘参数进行智能调控，匹配出不同初始粉尘浓度下的最优参数，实现智能除尘。通过等比例缩小的通风除尘实验平台对45组参数调控方案进行实验，结果表明：对比BP神经网络，采用CNN模型进行粉尘浓度预测的准确性和稳定性更优；司机处和下风侧初始粉尘浓度为300～900 mg/m³时，采用优化通风除尘系统后，平均粉尘浓度分别下降了51.49%～83.88%，验证了参数调控的有效性。

This study addressed the challenges of vortex formation and dead air zones in traditional long-pressure short suction ventilation and dust removal systems. By leveraging the Coanda effect, the system's structure was optimized through the nesting of the exhaust and pressure ducts. This design enhanced airflow in the negative pressure duct, promoting adherence to the tunnel walls, reducing dust dispersion, and significantly lowering energy consumption. Simulations utilizing flow field analysis and discrete phase model (DPM) revealed an optimal pressure-extraction ratio of 2∶3. Under this ratio, results indicated that the optimized system reduced dust concentrations at the driver's position and downwind side by 5.56% and 55.41%, respectively, compared to traditional systems. With the structure and pressure-extraction ratio established, further improvements in dust removal efficiency were achievable through parameter regulation. Key parameters included the distance between the duct and the dust-producing surface, the distance from the duct's central axis to the ground, and the distance between the pressure and extraction ducts. Convolutional neural network (CNN) was employed for intelligent parameter control, enabling the identification of optimal parameters for varying initial dust concentrations. Experiments conducted on 45 parameter regulation schemes using a scaled-down experimental platform demonstrated that the CNN model outperformed BP neural networks in accuracy and stability for dust concentration predictions. When initial dust concentrations at the driver's position and downwind side ranged from 300 to 900 mg/m3, the optimized system achieved an average dust concentration reduction of 51.49% to 83.88%, thereby validating the effectiveness of parameter control.

国家重点研发计划项日(2020YFF01015000ZL)。

刘丹丹，沈琪翔，王威廉，等. 综掘工作面通风除尘系统结构优化及参数智能调控[J]. 工矿自动化，2024，50（10）：152-159.

LIU Dandan, SHEN Qixiang, WANG Weilian, et al. Structural optimization and intelligent parameter control of ventilation and dust removal systems for comprehensive excavation workface[J]. Journal of Mine Automation，2024，50（10）：152-159.