Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines

WANG Kai; PEI Xiao-dong; YANG Tao; CHEN Rui-ding; HAO Hai-qing; JIANG Shu-guang; SUN Yong

doi:10.13374/j.issn2095-9389.2022.05.05.003

Volume 45 Issue 7

Jul. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2023 > 45(7): 1214-1224

WANG Kai, PEI Xiao-dong, YANG Tao, CHEN Rui-ding, HAO Hai-qing, JIANG Shu-guang, SUN Yong. Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines[J]. Chinese Journal of Engineering, 2023, 45(7): 1214-1224. doi: 10.13374/j.issn2095-9389.2022.05.05.003

Citation:

WANG Kai, PEI Xiao-dong, YANG Tao, CHEN Rui-ding, HAO Hai-qing, JIANG Shu-guang, SUN Yong. Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines[J]. Chinese Journal of Engineering, 2023, 45(7): 1214-1224. doi: 10.13374/j.issn2095-9389.2022.05.05.003

Citation:

WANG Kai, PEI Xiao-dong, YANG Tao, CHEN Rui-ding, HAO Hai-qing, JIANG Shu-guang, SUN Yong. Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines[J]. Chinese Journal of Engineering, 2023, 45(7): 1214-1224. doi: 10.13374/j.issn2095-9389.2022.05.05.003

PDF( 5210 KB)

Study on intelligent ventilation linkage control theory and supply–demand matching experiment in mines

doi: 10.13374/j.issn2095-9389.2022.05.05.003

1.
State Key Laboratory of Coal Mine Safety Technology, China Coal Technology & Engineering Group Shenyang Research Institute, Shenyang 113122, China
2.
School of Safety Engineering, China University of Mining & Technology, Xuzhou 221116, China
3.
School of School of Mine Safety, North China Institute of Science and Technology, Langfang 065201, China

More Information

Corresponding author: E-mail: yaotang585@163.com
Received Date: 2022-05-05
Available Online: 2022-08-29
Publish Date: 2023-07-25

Abstract

Abstract

To determine the dynamic matching of a mine ventilation system to onsite demands of automatic adjustment, we analyze the principle of air volume supply and demand matching and a linkage control method. Subsequently, we establish a mathematical model of main ventilator frequency adjustment, associate branch resistance adjustment, and joint adjustment with multi-feature fusion. We also propose a matching regulation model and a stability determination method for a ventilation network’s branch supply and demand. Based on the monitoring of harmful gases, intelligent emergency control software is developed by a mine ventilation supply and demand model. We realize the automatic calculation of the best working frequency of a ventilator when an unbalanced supply and ventilation demand is selected for frequency conversion adjustment. When selecting the associated branch wind resistance adjustment, we use a cellular automata model to calculate the optimal adjustment roadway. We obtain the adjusted wind resistance value using a winding network inversion calculation model. When a single adjustment method fails, a joint control scheme of fan frequency conversion and branch resistance adjustment is generated. A reliable adjustment of air volume supply and demand matching is realized through an advanced simulation analysis of the air network. A typical mine ventilation system is used to establish an experimental model for the automatic adjustment of the air demand of a branch of a winding network. The air demand adjustment and dilution experiment are carried out with the statistical law of onsite gas overrun as the guidance model of branch air demand control. The following results are obtained. The branch air volume changes according to the adjustment theory model under three adjustment methods. Further, the CO₂ concentration change is evidently delayed in the air adjustment process. In the process of fan frequency conversion regulation, the air volume of each branch of the air network changes according to the ventilation network sensitivity, and the fluctuation of the air network is minimal. When a single associated branch resistance adjustment method is used to regulate the wind, the local wind network has great influence on air volume and thus fluctuates greatly. When the fan frequency and associated branch wind resistance are combined, the fluctuation of the branch air volume of the entire air network is the largest, and the system stability and security are the lowest. Therefore, the fan frequency and combined regulation methods of multiple associated branches are recommended to use in practical applications of mines. The experiment verified the practicability and feasibility of the deviation of mine ventilation supply and demand from intelligent control systems. It also provided theoretical and application guidance for mine ventilation linkage control.
- intelligent ventilation,
- air regulation model,
- frequency conversion regulation,
- branch resistance regulation,
- cellular automata1

FullText(HTML)

References(27)

References

[1]	王國法, 劉峰, 龐義輝, 等. 煤礦智能化——煤炭工業高質量發展的核心技術支撐. 煤炭學報, 2019, 44(2):349 doi: 10.13225/j.cnki.jccs.2018.2041 Wang G F, Liu F, Pang Y H, et al. Coal mine intellectualization: The core technology of high quality development. J China Coal Soc, 2019, 44(2): 349 doi: 10.13225/j.cnki.jccs.2018.2041
[2]	Wang K, Jiang S G, Wu Z Y, et al. Intelligent safety adjustment of branch airflow volume during ventilation-on-demand changes in coal mines. Process Saf Environ Prot, 2017, 111: 491 doi: 10.1016/j.psep.2017.08.024
[3]	裴曉東, 王凱, 李曉偉, 等. 基于元胞自動機的集約化礦井調風模型分析與仿真. 中國礦業大學學報, 2017, 46(4):755 doi: 10.13247/j.cnki.jcumt.000697 Pei X D, Wang K, Li X W, et al. Analysis and simulation of intensive mine air regulation model based on cellular automaton. J China Univ Min &Technol, 2017, 46(4): 755 doi: 10.13247/j.cnki.jcumt.000697
[4]	魏連江, 周福寶, 梁偉, 等. 礦井通風網絡特征參數關聯性研究. 煤炭學報, 2016, 41(7):1728 Wei L J, Zhou F B, Liang W, et al. Correlation of mine ventilation network characteristic parameters. J China Coal Soc, 2016, 41(7): 1728
[5]	黃光球, 孫鵬, 陸秋琴. 風窗對井下通風系統的影響及其調節與定位優化. 中國安全生產科學技術, 2014, 10(3):160 doi: 10.11731/j.issn.1673-193x.2014.03.028 Huang G Q, Sun P, Lu Q Q. Influence of air windows on underground ventilation system and its adjusting and locating optimization. J Saf Sci Technol, 2014, 10(3): 160 doi: 10.11731/j.issn.1673-193x.2014.03.028
[6]	李偉. 全斷面通道式自動風窗研究與應用. 工礦自動化, 2016, 42(12):15 Li W. Research of automatic passageway type air regulator with full section and its application. Ind Mine Autom, 2016, 42(12): 15
[7]	盧新明. 礦井通風智能化技術研究現狀與發展方向. 煤炭科學技術, 2016, 44(7):47 Lu X M. Study status and development orientation of mine ventilation intelligent technology. Coal Sci Technol, 2016, 44(7): 47
[8]	吳奉亮, 高佳南, 常心坦, 等. 礦井風網雅可比矩陣對稱特性及并行求解模型. 煤炭學報, 2016, 41(6):1454 Wu F L, Gao J N, Chang X T, et al. Symmetry property of Jacobian matrix of mine ventilation network and its parallel calculation model. J China Coal Soc, 2016, 41(6): 1454
[9]	劉劍, 李雪冰, 宋瑩, 等. 無外部擾動的均直巷道風速和風壓測不準機理實驗研究. 煤炭學報, 2016, 41(6):1447 Liu J, Li X B, Song Y, et al. Experimental study on uncertainty mechanism of mine airvelocity and pressure with non-external disturbance. J China Coal Soc, 2016, 41(6): 1447
[10]	劉劍, 郭欣, 鄧立軍, 等. 基于風量特征的礦井通風系統阻變型單故障源診斷. 煤炭學報, 2018, 43(1):143 Liu J, Guo X, Deng L J, et al. Resistance variant single fault source diagnosis of mine ventilation system based on air volume characteristic. J China Coal Soc, 2018, 43(1): 143
[11]	方博, 馬恒. 運用監控數據的礦井通風網絡動態解算及應用. 遼寧工程技術大學學報(自然科學版), 2016, 35(12):1439 doi: 10.11956/j.issn.1008-0562.2016.12.010 Fang B, Ma H. Mine ventilation network application monitoring database and application dynamic solver. J Liaoning Tech Univ Nat Sci, 2016, 35(12): 1439 doi: 10.11956/j.issn.1008-0562.2016.12.010
[12]	李雨成, 李俊橋, 鄧存寶, 等. 基于角聯子網的風量反演風阻病態改良算法. 煤炭學報, 2019, 44(4):1147 doi: 10.13225/j.cnki.jccs.2018.0741 Li Y C, Li J Q, Deng C B, et al. Improved algorithm of air quantity calculating resistance based on diagonal subnetwork. J China Coal Soc, 2019, 44(4): 1147 doi: 10.13225/j.cnki.jccs.2018.0741
[13]	Suvar M C, Lupu C, Arad V, et al. Computerized simulation of mine ventilation networks for sustainable decision making process. Environ Eng Manag J, 2014, 13(6): 1445 doi: 10.30638/eemj.2014.159
[14]	Widiatmojo A, Sasaki K, Sugai Y, et al. Assessment of air dispersion characteristic in underground mine ventilation: Field measurement and numerical evaluation. Process Saf Environ Prot, 2015, 93: 173 doi: 10.1016/j.psep.2014.04.001
[15]	Nel A J H, Arndt D C, Vosloo J C, et al. Achieving energy efficiency with medium voltage variable speed drives for ventilation-on-demand in South African mines. J Clean Prod, 2019, 232: 379 doi: 10.1016/j.jclepro.2019.05.376
[16]	Chatterjee A, Zhang L J, Xia X H. Optimization of mine ventilation fan speeds according to ventilation on demand and time of use tariff. Appl Energy, 2015, 146: 65 doi: 10.1016/j.apenergy.2015.01.134
[17]	張卅卅, 任高峰, 張聰瑞, 等. 深部開采礦井通風智能感知及風機遠程集中安全監控系統. 武漢理工大學學報, 2015, 37(1):104 Zhang S S, Ren G F, Zhang C R, et al. Intellisense and remote centralized security monitoring system for the ventilation system in deep mining. J Wuhan Univ Technol, 2015, 37(1): 104
[18]	陳贊成, 楊鵬, 呂文生, 等. 高寒礦井穿脈巷道掘進炮煙擴散規律的數值模擬. 北京科技大學學報, 2011, 33(5):521 doi: 10.13374/j.issn1001-053x.2011.05.003 Chen Z C, Yang P, Lü W S, et al. Numerical simulation on the diffusion law of blasting fume during roadway tunneling across a vein in an alpine mine. J Univ Sci Technol Beijing, 2011, 33(5): 521 doi: 10.13374/j.issn1001-053x.2011.05.003
[19]	郭對明, 李國清, 胡乃聯, 等. 基于文本挖掘的礦山安全隱患大數據分析與可視化. 工程科學學報, 2022, 44(3):328 doi: 10.3321/j.issn.1001-053X.2022.3.bjkjdxxb202203002 Guo D M, Li G Q, Hu N L, et al. Big data analysis and visualization of potential hazardous risks of the mine based on text mining. Chin J Eng, 2022, 44(3): 328 doi: 10.3321/j.issn.1001-053X.2022.3.bjkjdxxb202203002
[20]	張慶華. 我國煤礦通風技術與裝備發展現狀及展望. 煤炭科學技術, 2016, 44(6):146 doi: 10.13199/j.cnki.cst.2016.06.024 Zhang Q H. Development and prospect of mine ventilation technology and equipment. Coal Sci Technol, 2016, 44(6): 146 doi: 10.13199/j.cnki.cst.2016.06.024
[21]	王國法, 王虹, 任懷偉, 等. 智慧煤礦2025情景目標和發展路徑. 煤炭學報, 2018, 43(2):295 doi: 10.13225/j.cnki.jccs.2018.0152 Wang G F, Wang H, Ren H W, et al. 2025 scenarios and development path of intelligent coal mine. J China Coal Soc, 2018, 43(2): 295 doi: 10.13225/j.cnki.jccs.2018.0152
[22]	郭奇峰, 蔡美峰, 吳星輝, 等. 面向2035年的金屬礦深部多場智能開采發展戰略. 工程科學學報, 2022, 44(4):476 doi: 10.3321/j.issn.1001-053X.2022.4.bjkjdxxb202204002 Guo Q F, Cai M F, Wu X H, et al. Technological strategies for intelligent mining subject to multifield couplings in deep metal mines toward 2035. Chin J Eng, 2022, 44(4): 476 doi: 10.3321/j.issn.1001-053X.2022.4.bjkjdxxb202204002
[23]	Chen K Y, Si J H, Zhou F B, et al. Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method. Int J Min Sci Technol, 2015, 25(1): 79 doi: 10.1016/j.ijmst.2014.11.001
[24]	李翠平, 胡磊, 侯定勇, 等. 基于元胞自動機的井巷火災仿真. 北京科技大學學報, 2013, 35(12):1546 doi: 10.13374/j.issn1001-053x.2013.12.006 Li C P, Hu L, Hou D Y, et al. Cellular automata-based tunnel fire simulation. J Univ Sci Technol Beijing, 2013, 35(12): 1546 doi: 10.13374/j.issn1001-053x.2013.12.006
[25]	司俊鴻, 陳開巖. 基于Tikhonov正則化的礦井通風網絡測風求阻法. 煤炭學報, 2012, 37(6):994 doi: 10.13225/j.cnki.jccs.2012.06.025 Si J H, Chen K Y. Measuring airflow & evaluating resistance model of the mine ventilation network based on Tikhonov regularization. J China Coal Soc, 2012, 37(6): 994 doi: 10.13225/j.cnki.jccs.2012.06.025
[26]	周福寶, 魏連江, 夏同強, 等. 礦井智能通風原理、關鍵技術及其初步實現. 煤炭學報, 2020, 45(6):2225 doi: 10.13225/j.cnki.jccs.zn20.0338 Zhou F B, Wei L J, Xia T Q, et al. Principle, key technology and preliminary realization of mine intelligent ventilation. J China Coal Soc, 2020, 45(6): 2225 doi: 10.13225/j.cnki.jccs.zn20.0338
[27]	Wang K, Jiang S G, Ma X P, et al. Abnormal gas emission in coal mines and a method for its dilution using ventilator control. J Nat Gas Sci Eng, 2016, 33: 355 doi: 10.1016/j.jngse.2016.05.021