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黄金科学技术 ›› 2024, Vol. 32 ›› Issue (4): 666-674.doi: 10.11872/j.issn.1005-2518.2024.04.138

• 采选技术与矿山管理 • 上一篇    下一篇

矿山多级机站通风系统风机优化选型方法

钟德云1,2(),刘雨龙2(),王李管1,2   

  1. 1.长沙迪迈科技股份有限公司,湖南 长沙 410221
    2.中南大学资源与安全工程学院,湖南 长沙 410083
  • 收稿日期:2024-05-21 修回日期:2024-06-17 出版日期:2024-08-31 发布日期:2024-08-27
  • 通讯作者: 刘雨龙 E-mail:deyizhiyun@163.com;225512106@csu.edu.cn
  • 作者简介:钟德云(1990-),男,福建长汀人,博士后,从事智能通风和地质建模研究工作。deyizhiyun@163.com
  • 基金资助:
    国家重点研发计划项目“超大型深井矿山高效绿色开采技术与智能装备”(2022YFC2904105)

Optimization of Fan Selection for Multi-stage Fan Station Ventilation System in Mines

Deyun ZHONG1,2(),Yulong LIU2(),Liguan WANG1,2   

  1. 1.Changsha DIMINE Co. , Ltd. , Changsha 410221, Hunan, China
    2.School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
  • Received:2024-05-21 Revised:2024-06-17 Online:2024-08-31 Published:2024-08-27
  • Contact: Yulong LIU E-mail:deyizhiyun@163.com;225512106@csu.edu.cn

摘要:

为了确定多级机站风机的最优不平衡风压分配方式,提出了一种以通风风机功率最小、最佳匹配风机风量和最佳匹配风机风压为目标的多级机站风机优选多目标优化模型,可用于确定各装机点所需的优选风机型号及风机安装角度。该模型既获得了近似满足风量要求的优选方案,也避免了非线性模型求解不收敛的问题,并在大规模多级机站通风系统中表现出高效的求解能力。此外,提出的多风机优选方法通过数学模型构建各级机站之间的相互约束关系,在风机工况范围内约束各装机点的不平衡风压,以解决各级机站风机风压合理分配的问题,避免因风机风压分配不合理导致风机选型失败。实例验证结果表明本研究的多级机站通风系统风机选型方案是可靠的。

关键词: 矿井通风, 多级机站通风系统, 风机优选, 通风优化, 多风机选型, 多目标优化

Abstract:

The utilization of multi-stage fan station ventilation technology is crucial in the ventilation system of non-coal mines,particularly as mining depths increase.Traditional large main fan ventilation methods may struggle to adequately meet the ventilation requirements of deep mining operations,highlighting the significance of this technology.The multi-stage fan station ventilation system allows for the precise control of air volume and pressure in individual partitions by adjusting the operational status of fans at each level of the station.This method enhances the precision of adjustment and flexibility of control in the ventilation system,increases its efficiency,and decreases energy consumption.The conventional fan optimization approach assumes that each circuit accommodates only one air volume branch.In contrast,the ventilation network solution method utilizes the fan as a residual tree branch,enabling the direct allocation of unbalanced air pressure (fan pressure) to the residual tree branches of each autonomous circuit for fan optimization.In a multi-fan multi-stage station ventilation system,a single circuit may contain multiple installed air volume branches,rendering the traditional fan optimization method ineffective.Therefore,it is necessary to develop a fan optimization method suitable to multi-stage fan stations.Furthermore,due to the mutual influence between the installed branches in the multi-fan multi-stage fan station ventilation fan system,it is necessary to further consider the logical problem of air volume between the installed branches and the optimal allocation of unbalanced wind pressure(fan pressure).In order to determine the optimal unbalanced wind pressure distribution method for multi-stage fan station,this study proposed a multi-objective optimization model for fan selection of multi-stage fan stations,aimed at achieving the minimum fan power consumption while optimizing the fan air volume and pressure.The proposed model assists in determining the most suitable fan model and installation angle for each installation point.The model successfully achieves the optimal solution that closely aligns with the air volume requirements,while also circumventing the issue of non-convergence in nonlinear model solutions.Additionally,it demonstrates efficient solution capabilities in large-scale,multi-stage fan station ventilation systems.Furthermore,the multi-fan optimization method proposed in this study establishes a mutual constraint relationship between stations at each level using a mathematical model.This approach ensures that the unbalanced wind pressure at each installation point remains within the operational range of the fan,thereby addressing the issue of distributing fan pressure effectively across all levels of the station and preventing fan selection failure due to improper pressure distribution.The reliability of the fan selection scheme for the multi-stage fan station ventilation system is confirmed through example verification in this research.

Key words: mine ventilation, multi-stage fan station ventilation system, fan optimization, ventilation optimization, multi-fan selection, multi-objective optimization

中图分类号: 

  • TD72

图1

多级机站风机通风示意图"

图2

风机优选流程图"

图3

多级机站风机优选实例"

表 1

多级机站通风网络风量分配结果"

风机编号安装位置风机类型通风方式长度/m风阻/(N·s2·m-8解算风量/(m3·s-1调节风压/Pa
1出风装机抽出式300.033150-3 232.50
2进风装机压入式300.0361500.00
6内部装机抽出式300.030500.00
12内部装机抽出式300.03050157.50
14内部装机抽出式300.0305015.00

表2

多级机站通风网络风压分配结果"

风机编号安装位置风机类型长度风阻/(N·s2·m-8解算风量/(m3·s-1装机风量/(m3·s-1装机风压/Pa
1出风装机300.0331501501 378.92
2进风装机300.0361501501 378.92
3内部非装机300.030150--
4内部非装机300.030150--
5内部非装机300.02450--
7内部非装机300.03650--
8内部非装机300.03350--
9内部非装机300.02750--
10内部非装机300.04250--
11内部非装机300.03950--
13内部非装机300.03650--
6内部装机300.0305050474.64
12内部装机300.0305050317.14
14内部装机300.0305050459.64

表3

多级机站通风网络风机优选结果"

风机编号安装位置风机类型

通风

方式

风机型号叶片角度串联并联
1出风装机抽出式DK40-8No26-5°11
2进风装机压入式DK40-8No26-5°11
6内部装机抽出式K40-8No2120°11
12内部装机抽出式K40-8No2023°11
14内部装机抽出式K40-8No2120°11
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