关 键 词：

鲍店煤矿3.0 Mta新井设计含5张CAD图 煤矿 3.0 Mta 设计 CAD

资源描述：

鲍店煤矿3.0 Mta新井设计含5张CAD图.zip,鲍店煤矿3.0,Mta新井设计含5张CAD图,煤矿,3.0,Mta,设计,CAD

内容简介：

外文原文：Modeling and Simulation of the Underground Mining Transportation System（XU LEI）Abstract: The system simulation is a study hotspot in large underground mines system engineering field. In view to the fact that the underground transportation is a large and complex system, we set Datun horizontal transportation coal mining system as the research object in Xuzhou district, discreting event simulation theory, a transportation system model for certain transportation parameters. Through adjusting some transportation parameters, confirming the best distribution, thus analyzing the transportation system efficiency, carrying on a scientific evaluation on the transportation capacity system. Keywords: underground mine; the system simulation; transportation system. 1 IntroductionThe system simulation is a new subject which is based on auxiliary system design and management decision-making. The new technology has been widely used in mechanical manufacturing, material processing, transportation, military deployment, flight training, business services, computer, communication, mining engineering system analysis and design works. We use it to carry on reality system test, which can help us accurately evaluate the operation system performance. We can judge various advantages and disadvantages of running options without interfering with the actual system in the cases, making timely solution decisions. The large underground mining transportation system, as a mining arterie, is a large and complex system. Using transportation system simulation technology to study them, we can obtain good economic benefits. Such as one Open-pit mine in U.S, analysing the broken system of forklift trucks under different numbers by SIMAN language simulation; One underground mine in South Africa, using simulation technology to do a feasibility simulation research on the gangue-rock shipment system of a new mine to determine the number of crusher and the capacity of adhesive tape machine, it leads to saving at least 4.4 million dollars investment. Because domestic underground mining level of automation transportation system isnt enough, the application of this technology has not come into widely used. This article takes Datun horizontal transportation coal mining system as the research object which is based on PLC control system, adopting discrete event systems simulation principle to build the system simulation model, so as to demonstrate its transportation capability. 2Profiles of mining transportation systemDatun coal gangue is mainly transferred by each sneak well and transportation flat, finally danger out by different levels. With the decline in the middle of production and limited surface, the transportation level is the lowest of the whole east, most of the mine gangue is transported through this level, and because of its unique geographical position, we hope it can afford a certain amount of transportation capacity of the other mines, and therefgangue we urgently needs to make a scientific evaluation for the entire production system, so as to provide reliable basis for the optimization of transportation line. This level is shown in figure 1 below. Fig. 1. Transportation route schemes.This transportation system is a monorail transportation system, the gangue which is exploited from each stope enter 7, 8, 9, 10 (7 to 10) sneak wells group through their respective transportation level roadway or slipping to level 1360 meters through sneak wells group. Then transferring to fallway by the track transportation. The wasted gangue is uninstalled in the pit position, gangue continue to arrive via tracks mill. According to the scene material, wasted gangue rate is 25%. As can be seen from the graph, whether gangue of 7-10 sneak wells group or 25 to 27 sneak wells groups are required to pass through tracks 2, 3, 4 and rail track rails 5 (tracks 2-5). So, tracks 2-5 are the main bottleneck of the whole transportation system. 3 Model buildingFig. 2. Logic structure.The system is a discrete event system. According to the discrete event simulation principle: We can look the train as sports entity; Each pack, unloading track respectively means competition resources; Wheel-dreven and transfer station stand for queue waiting sites, queuing rules are first come first serve. Producing entities equal to the train number, after the entity is uninstalled, going back to the install mine dot through the original path, and then execute transportation tasks, so as to keep on circulating in the system. Using events at fostering both propulsion simulation clock steps, simulating transportation system operation. According to actual condition, at the same time, transfer station can only stay a train. Every track (between two transfer stations) will only make a train operate. In order to make the whole line operate well, not occuring car accident, Waiting for the heavy truck(or empty car) of transfer station, only when it enter the passage of tracks, and util the next transfer station has no heavy truck (or empty car) ,can it enter the track, or it will have to continue to wait . To simplify the actual conditions, making modeling mgangue convenient, we assums that: 1) 7 to 10 sneak Wells with 25-27 of slip Wells, there is always full of mine. 2) The wheel-dreven can accommodate a sufficient number of trains. Therefgangue, we can build a transportation model, the logic structure model is shown in figure 2. 4Data collection and analysisWhat this model requires most is various transportation links, the time needed for that time is usually random variables, such as loading, unloading time and rail train running time in paragraphs.These datas are collected through large field records, then, after getting data identification, parameter estimation, fitting degree of inspection 4, we can find out the odds of theganguetical distribution density function or experience, in order to generate consistent random variables by computer. The probability density function that we obtain is shown in table 1. Table 1. Distribution density function.ParametersFunctionLoading time/minunloading time /minTrack(1,2,3)running time/minTrack(4,5)running time/min5System simulation and results analysis5. 1 The best vehicle number of various transportation lineThe system is divided into two transportation line: Line 1: From 7 to 10 sneak wells to the mill; Line 2: From 25 to 27 sneak wells to the mill. Supposing the working time of the system is: 3 classes in 1 day, 7 hours every class, every day works for 21 hours. Simulating a single transportation route, changing vehicle number, making the model run 30 days respectively (21 x 30 = 630 hours, simulating the real time), we can get results as figure 3 shows. Fig. 3. Vehicles variation.As can be seen from the graph, with the increase of vehicles number, driving ability increases, but the increasing number gradually reduce to a certain number, and driving ability has no mgangue improvement, even declined. This is because the fact that with the increase of tracks utilization rate, driving ability can not get unlimited increase, meanwhile, heavy traffic may have leaded to waiting time increased, causing driving ability declined. From the graph, we can see that line 1 and 2, as long as there is respectively 6 or 7 trains, they will reach saturation. 5. 2 Distribution of total line vehiclesBefore this, what we get is just the best vehicle number of a single line, line 1 and line 2, they compete to use tracks 2、3、4, there must be certain proportion relationship between them. Distributing the vehicle according to the proportion of stable condition, 6:7, changing the total vehicle number, we obtain the relationship between each vehicle combination and drive number, as shown in table 2. Table 2. The relationship between each vehicle and driving number.Line1/columLine2/columVehicle number552757562823662880672916772949782967From table 2,we can see the maximum number of vehicle distribution is that line 1 distributes 7 column, line 2 distributes 8 column, but considering convenient for management, under the premise of satisfying the production, (6, 7) combination has reached saturation. 5. 3 Transportation efficiencyWe use facilities utilization rate (the ratio of facilities busy time with total time)to measure the efficiency of transportation system. The function of B(t) stands for the facilities state of in moment t : 1 facility is busy.B(t)=0facility is not busy.T: the total systems work time Through the operation model, drawing facilities utilization under different saturated state as we referred to, such as shown in table 3. Table 3. Facilities utilization rate FacLineDatum7-10seak wells25-27seak wellstrack1track 2track 3track 4track 5Waste ratew.house/%group/%group/%/%/%/%/%/%/%Line 14798-5858585816Line 247-98585858575716Line 3747580489292919125From the table we can see, railway track 2-5 which is seen as transportation bottlenecks ,when we simulate line 1、2 respectively, due to restriction from7-10 sneak wells group or 25 to 27 sneak wells group , they can only achieve about 58% function. But in the total transportation, they have already reached mgangue than 90%. The former state did not reach good condition while the latter transportation capability has been well done. 6 Coal production capacity assessment in four yearsAccording to the above results, the system drives 2916 columns in 30 days, 97 columns in a day. Assuming that every day, the car engine several materials is 17 columns, harvesters is 80 columns; each column can bear 63 t; every year, they work 330 days. So, we the production capacity is 63 x 80 x 330 = 166. 3 million t. The actual car number of the transportation system is 84 columns/days or so, and theganguetical values differ a little from it. Therefgangue, the rest transportation capacity of this system remains not so much. If we want to further widen transportation capacity, only to retrofit, such as changing the higher utilization rate of 2 to 5 track into a two-way track. 7 ConclusionThrough the above simulation analysis, we can draw a conclusion that: 1) Using system modeling simulation method, through building model, we can adjust the transportation parameters without interfering with the actual system.To make transportation state achieve the best state, easier to monitor the transportation process, finding out the weak link, thus transforming; 2) Each road transportation route can accommodate limited vehicle number; wed better put quantity control in its saturation point to make the vehicle distribution achieve the best state; 3) The method can accurately calculate the mining system potential carrying capacity, providing the basis for decision-maker. References:1. Li Zhongxue. Foreign Simulation System Technology and Its New Application in Mining Development J. Journal of China Mining, 1998, 7 (2) : 75 79. 2. Jerry Banks, John S Carson, Barry L Nel son, David M Nicol, Systems Simulation of Discrete Event (English Version. The 4th Edition) M. Beijing: China Machine Press, 2005. 3. Lu Ziai, Lin Minbiao. Computer Simulation of Port Service System of J. Journal of Hehai University, 1999, 27 (3) : 17 to 21. 4. Zhang Xiaoping. Logistics System Simulation Principle and Application M. Beijing: China Supplies Press, 2005. 30-43. 5. Zhao Wenguang, Li Zhongxue, Simulation System Technology and Its New Progress in Mining J, Foreign Metal Mines, 2000, 3:51-56. 6. Li Minghe, Lu Weifeng. Production Material Transport System Modeling and Simulation Based on Pet ri Nets J. Journal of Anhui University of Technology, 2004, 21 (1) : 45-48. 7. Zhang Xiaoxia. Computer Simulation of Underground Mines Railway Transport System J. China Mining, 2000, 9 (49) : 579-582. 8. Gu Qitai. Modeling and Simulation of Discrete Event Systems M. Beijing: Tsinghua University Press, 1999.中文译文：地下煤矿运输体系的建模与仿真摘要：系统仿真是系统工程领域的研究热点之一。针对大型地下煤矿的运输这一庞大而复杂的系统，以徐州大屯煤矿水平运输系统为研究对象，应用离散事件仿真原理，建立了运输系统模型。通过对某些运输参数的调整，确定了其最优的车辆分配，分析了运输系统效率，对该运输系统的运输能力做出了科学的评估。关键词：地下煤矿；系统仿真；运输系统；随机系统1引言系统仿真是辅助系统设计和管理决策的一门新兴技术学科。已广泛应用于机械制造、物料处理、交通运输、军事部署、飞行训练、商业服务、计算机与通讯以及采矿工程等系统的分析与设计之中。用它来对现实系统进行试验，能够准确地评价出一个系统的运行性能；可以在不干扰实际系统的情况下比较各种可供选择的运行方案之优劣，并及时作出决策。大型地下煤矿的运输系统，作为一个矿山的动脉，是一个庞大而复杂的系统。采用运输系统仿真技术对其进行研究，可取得良好的经济效益。如美国某露天矿，采用SIMAN 语言模拟分析了不同卡车数量条件下的铲车以及矿石破碎系统；南非某地下金属矿，采用仿真技术进行了一个新矿的矿岩装运系统的可行性模拟研究，以确定LHD 的数量、破碎机和胶带机的能力以及矿仓的容量，结果至少节省了440 万美元的投资1 。由于国内地下煤矿运输系统的自动化水平还不够，这方面的应用还不尽成熟，本文以采用了PLC 控制系统的以徐州大屯煤矿水平运输系统为研究对象，采用离散事件系统仿真原理，建立了系统仿真模型，论证了它的运输能力。2矿山运输系统概况矿山开拓方式主要为平硐- 溜井开拓。矿石主要经由各溜井和运输平硐，最后由不同水平标高的坑口运出地表，随着生产中段的下降以及地表形态的限制,水平是目前整个东区最低的运输水平，该矿山绝大部分矿石通过该水平运出，并且由于其得天独厚的地理位置，希望能够承担其它矿山的部分矿石运输，因此迫切需要对该水平的运输能力做出一个科学的评估，为云锡集团个旧东区整个生产系统的优化提供可靠的依据。该水平的运输线路如图1 所示。该运输系统为单轨运输系统，各采场采出的矿石跟废石通过各自所在中段的运输平巷进入7、8、9、10( 7-10) 溜井群或25、26、27( 25-27) 溜井群下放到水平，然后利用铁轨运输运出坑口，废石在坑口位置卸掉，矿石继续经由铁轨到达选厂。据现场资料，废石率为25% 。图1 运输线路示意图从图中可以看出，无论是7-10 溜井群的矿石还是25-27 溜井群的矿石都需经过铁轨2、铁轨3、铁轨4 以及铁轨5(铁轨2-5) 。因此，铁轨2-5 是整个运输系统的瓶颈所在。3构建模型图2逻辑结构图该系统为一离散事件系统。根据离散事件仿真原理2：把列车看作运动实体；各段铁轨跟各装、卸矿点分别看作是竞争使用的资源；车场跟会让站为队列等侯的场所，排队规则均为先到先服务。产生列车数量的实体，实体卸矿后按原路径返回到装矿点，再执行运输任务，如此一直在系统内循环。采用事件步长法推进仿真时钟3 ，模拟运输系统的运营。根据实际情况，在同一时刻，会让站只能停留一列车，每段铁轨( 两个会让站之间) 只能让一列车运行。为了使整条线路畅通运营，不发生碰车事故，等候在会让站的重车( 或空车) 只有在它要进入的那段铁轨为空闲，并且下一个会让站没有重车( 或空车) 时才能进入该段铁轨，否则继续等待。为了简化实际条件，方便建模，做出如下假设:1)7-10 溜井群与25-27 溜井群始终有矿;2)车场可以容纳足够数量的列车。因此，可建立该运输模型，模型的逻辑结构如图2 所示。4数据的采集分析本模型需要的主要是各运输环节所需的时间，这些时间一般都是随机变量，比如装车时间、卸矿时间以及列车在各段铁轨的运行时间。这些数据通过现场大量记录采集得到，然后,经过数据辨识、参数估计、拟合度检验4 ，找出与之相符的理论分布密度函数或经验分布密度函数，以便用计算机生成一致的随机变量。得出各分布密度函数如表1 所示。表1 分布密度函数表参数函数装车时间/min卸矿时间/min铁轨1、铁轨2、铁轨3运行时间/min铁轨4、铁轨5运行时间/min注：铁轨1、铁轨2、铁轨3的长度相等，铁轨4、铁轨5的长度相等5系统仿真与仿真结果分析5.1各运输线路最优车辆数把系统分为两条运输线路：线路1：从7-10 溜井群至选厂；线路2：从25-27 溜井群至选厂。假设系统的工作时间为：1 天3 班，1 班7 小时，每天的工作时间为21 小时。模拟单条运输线路，改变车辆数目，分别让模型运行30 天( 2130= 630 小时，模拟实际的时间) ，得到结果如图3所示。图3 出车数变化图从图中可以看出出车能力随车辆的增多而增多，但增量逐渐减少，到一定的数量后，出车能力不会再有提高，还会有所下降，这是因为随着铁轨使用率的提高，出车能力不可能无限制的提高，并且车辆过多反而会造成等待时间过长，造成出车能力的下降。从图中可知，线路1、2 中分别只要有6、7 列车就达到饱和状态。5.2总线路车辆的分配前文所得出的只是独立的单条线路的最优车辆数，线路一和线路二竞争使用铁轨2、3、4，它们之间必定有着一定的比例关系。按前文得出的稳定状态下的比例6：7 分配车辆，改变车辆的总数，得到各车辆组合与出车数的关系，如表2 所示：表2 各车辆组合与出车数的关系线路1/列线路2/列出车数/列552757562823662880672916772949782967可以看出出车数最多的车辆分配为线路1 分