鮑店煤礦3.0 Mta新井設(shè)計(jì)含5張CAD圖.zip,鮑店煤礦3.0,Mta新井設(shè)計(jì)含5張CAD圖,煤礦,3.0,Mta,設(shè)計(jì),CAD 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 Introduction The 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 isn't 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 system Datun 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 building Fig. 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 analysis What 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. Parameters Function Loading time/min unloading time /min Track(1,2,3)running time/min Track(4,5)running time/min 5System simulation and results analysis 5. 1 The best vehicle number of various transportation line The 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 vehicles Before 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/colum Line2/colum Vehicle number 5 5 2757 5 6 2823 6 6 2880 6 7 2916 7 7 2949 7 8 2967 From 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 efficiency We 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)= 0 facility 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 Fac Line Datum 7-10 seak wells 25-27 seak wells track1 track 2 track 3 track 4 track 5 Waste rate w.house/% group/% group/% /% /% /% /% /% /% Line 1 47 98 - - 58 58 58 58 16 Line 2 47 - 98 58 58 58 57 57 16 Line 3 74 75 80 48 92 92 91 91 25 From 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 years According 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 Conclusion Through 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; we’d 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. 中文譯文： 地下煤礦運(yùn)輸體系的建模與仿真 摘要：系統(tǒng)仿真是系統(tǒng)工程領(lǐng)域的研究熱點(diǎn)之一。針對大型地下煤礦的運(yùn)輸這一龐大而復(fù)雜的系統(tǒng)，以徐州大屯煤礦水平運(yùn)輸系統(tǒng)為研究對象，應(yīng)用離散事件仿真原理，建立了運(yùn)輸系統(tǒng)模型。通過對某些運(yùn)輸參數(shù)的調(diào)整，確定了其最優(yōu)的車輛分配，分析了運(yùn)輸系統(tǒng)效率，對該運(yùn)輸系統(tǒng)的運(yùn)輸能力做出了科學(xué)的評估。 關(guān)鍵詞：地下煤礦；系統(tǒng)仿真；運(yùn)輸系統(tǒng)；隨機(jī)系統(tǒng) 1引言 系統(tǒng)仿真是輔助系統(tǒng)設(shè)計(jì)和管理決策的一門新興技術(shù)學(xué)科。已廣泛應(yīng)用于機(jī)械制造、物料處理、交通運(yùn)輸、軍事部署、飛行訓(xùn)練、商業(yè)服務(wù)、計(jì)算機(jī)與通訊以及采礦工程等系統(tǒng)的分析與設(shè)計(jì)之中。用它來對現(xiàn)實(shí)系統(tǒng)進(jìn)行試驗(yàn)，能夠準(zhǔn)確地評價出一個系統(tǒng)的運(yùn)行性能；可以在不干擾實(shí)際系統(tǒng)的情況下比較各種可供選擇的運(yùn)行方案之優(yōu)劣，并及時作出決策。大型地下煤礦的運(yùn)輸系統(tǒng)，作為一個礦山的動脈，是一個龐大而復(fù)雜的系統(tǒng)。采用運(yùn)輸系統(tǒng)仿真技術(shù)對其進(jìn)行研究，可取得良好的經(jīng)濟(jì)效益。如美國某露天礦，采用SIMAN 語言模擬分析了不同卡車數(shù)量條件下的鏟車以及礦石破碎系統(tǒng)；南非某地下金屬礦，采用仿真技術(shù)進(jìn)行了一個新礦的礦巖裝運(yùn)系統(tǒng)的可行性模擬研究，以確定LHD 的數(shù)量、破碎機(jī)和膠帶機(jī)的能力以及礦倉的容量，結(jié)果至少節(jié)省了440 萬美元的投資[1] 。由于國內(nèi)地下煤礦運(yùn)輸系統(tǒng)的自動化水平還不夠，這方面的應(yīng)用還不盡成熟，本文以采用了PLC 控制系統(tǒng)的以徐州大屯煤礦水平運(yùn)輸系統(tǒng)為研究對象，采用離散事件系統(tǒng)仿真原理，建立了系統(tǒng)仿真模型，論證了它的運(yùn)輸能力。 2礦山運(yùn)輸系統(tǒng)概況 礦山開拓方式主要為平硐- 溜井開拓。礦石主要經(jīng)由各溜井和運(yùn)輸平硐，最后由不同水平標(biāo)高的坑口運(yùn)出地表，隨著生產(chǎn)中段的下降以及地表形態(tài)的限制,水平是目前整個東區(qū)最低的運(yùn)輸水平，該礦山絕大部分礦石通過該水平運(yùn)出，并且由于其得天獨(dú)厚的地理位置，希望能夠承擔(dān)其它礦山的部分礦石運(yùn)輸，因此迫切需要對該水平的運(yùn)輸能力做出一個科學(xué)的評估，為云錫集團(tuán)個舊東區(qū)整個生產(chǎn)系統(tǒng)的優(yōu)化提供可靠的依據(jù)。該水平的運(yùn)輸線路如圖1 所示。 該運(yùn)輸系統(tǒng)為單軌運(yùn)輸系統(tǒng)，各采場采出的礦石跟廢石通過各自所在中段的運(yùn)輸平巷進(jìn)入7、8、9、10( 7-10) 溜井群或25、26、27( 25-27) 溜井群下放到水平，然后利用鐵軌運(yùn)輸運(yùn)出坑口，廢石在坑口位置卸掉，礦石繼續(xù)經(jīng)由鐵軌到達(dá)選廠。據(jù)現(xiàn)場資料，廢石率為25% 。 圖1 運(yùn)輸線路示意圖 從圖中可以看出，無論是7-10 溜井群的礦石還是25-27 溜井群的礦石都需經(jīng)過鐵軌2、鐵軌3、鐵軌4 以及鐵軌5(鐵軌2-5) 。因此，鐵軌2-5 是整個運(yùn)輸系統(tǒng)的瓶頸所在。 3構(gòu)建模型 圖2邏輯結(jié)構(gòu)圖 該系統(tǒng)為一離散事件系統(tǒng)。根據(jù)離散事件仿真原理[2]：把列車看作運(yùn)動實(shí)體；各段鐵軌跟各裝、卸礦點(diǎn)分別看作是競爭使用的資源；車場跟會讓站為隊(duì)列等侯的場所，排隊(duì)規(guī)則均為先到先服務(wù)。產(chǎn)生列車數(shù)量的實(shí)體，實(shí)體卸礦后按原路徑返回到裝礦點(diǎn)，再執(zhí)行運(yùn)輸任務(wù)，如此一直在系統(tǒng)內(nèi)循環(huán)。采用事件步長法推進(jìn)仿真時鐘[3] ，模擬運(yùn)輸系統(tǒng)的運(yùn)營。 根據(jù)實(shí)際情況，在同一時刻，會讓站只能停留一列車，每段鐵軌( 兩個會讓站之間) 只能讓一列車運(yùn)行。為了使整條線路暢通運(yùn)營，不發(fā)生碰車事故，等候在會讓站的重車( 或空車) 只有在它要進(jìn)入的那段鐵軌為空閑，并且下一個會讓站沒有重車( 或空車) 時才能進(jìn)入該段鐵軌，否則繼續(xù)等待。 為了簡化實(shí)際條件，方便建模，做出如下假設(shè): 1)7-10 溜井群與25-27 溜井群始終有礦; 2)車場可以容納足夠數(shù)量的列車。 因此，可建立該運(yùn)輸模型，模型的邏輯結(jié)構(gòu)如圖2 所示。 4數(shù)據(jù)的采集分析 本模型需要的主要是各運(yùn)輸環(huán)節(jié)所需的時間，這些時間一般都是隨機(jī)變量，比如裝車時間、卸礦時間以及列車在各段鐵軌的運(yùn)行時間。這些數(shù)據(jù)通過現(xiàn)場大量記錄采集得到，然后,經(jīng)過數(shù)據(jù)辨識、參數(shù)估計(jì)、擬合度檢驗(yàn)[4] ，找出與之相符的理論分布密度函數(shù)或經(jīng)驗(yàn)分布密度函數(shù)，以便用計(jì)算機(jī)生成一致的隨機(jī)變量。得出各分布密度函數(shù)如表1 所示。 表1 分布密度函數(shù)表 參數(shù) 函數(shù) 裝車時間/min 卸礦時間/min 鐵軌1、鐵軌2、鐵軌3 運(yùn)行時間/min 鐵軌4、鐵軌5 運(yùn)行時間/min 注：鐵軌1、鐵軌2、鐵軌3的長度相等，鐵軌4、鐵軌5的長度相等 5系統(tǒng)仿真與仿真結(jié)果分析 5.1各運(yùn)輸線路最優(yōu)車輛數(shù) 把系統(tǒng)分為兩條運(yùn)輸線路：線路1：從7-10 溜井群至選廠；線路2：從25-27 溜井群至選廠。 假設(shè)系統(tǒng)的工作時間為：1 天3 班，1 班7 小時，每天的工作時間為21 小時。模擬單條運(yùn)輸線路，改變車輛數(shù)目，分別讓模型運(yùn)行30 天( 21×30= 630 小時，模擬實(shí)際的時間) ，得到結(jié)果如圖3所示。 圖3 出車數(shù)變化圖 從圖中可以看出出車能力隨車輛的增多而增多，但增量逐漸減少，到一定的數(shù)量后，出車能力不會再有提高，還會有所下降，這是因?yàn)殡S著鐵軌使用率的提高，出車能力不可能無限制的提高，并且車輛過多反而會造成等待時間過長，造成出車能力的下降。從圖中可知，線路1、2 中分別只要有6、7 列車就達(dá)到飽和狀態(tài)。 5.2總線路車輛的分配 前文所得出的只是獨(dú)立的單條線路的最優(yōu)車輛數(shù)，線路一和線路二競爭使用鐵軌2、3、4，它們之間必定有著一定的比例關(guān)系。按前文得出的穩(wěn)定狀態(tài)下的比例6：7 分配車輛，改變車輛的總數(shù)，得到各車輛組合與出車數(shù)的關(guān)系，如表2 所示： 表2 各車輛組合與出車數(shù)的關(guān)系 線路1/列 線路2/列 出車數(shù)/列 5 5 2757 5 6 2823 6 6 2880 6 7 2916 7 7 2949 7 8 2967 可以看出出車數(shù)最多的車輛分配為線路1 分配7 列，線路2 分配8 列，但如果考慮到便于管理，在滿足生產(chǎn)的前提下，( 6，7) 組合已經(jīng)達(dá)到了飽和。 5.3運(yùn)輸效率 用設(shè)施使用率( 設(shè)施的忙碌時間跟總時間的比值)來衡量運(yùn)輸系統(tǒng)的效率。用函數(shù)B (t) 表示設(shè)施t 時刻的狀態(tài): B (t)=1 設(shè)施忙 0 設(shè)施閑 通過運(yùn)行模型，得出前文所述各線路飽和狀態(tài)下設(shè)施利用率如表3 所示。從表中看出作為運(yùn)輸瓶頸的鐵軌2-5 的使用率在單獨(dú)模擬線路1、2 時，由于受到裝礦點(diǎn)7-10 溜井群或25-27溜井群的制約只能達(dá)到58%左右，而在總線路中都已達(dá)90%以上，前者的運(yùn)輸狀態(tài)沒有達(dá)到良好狀態(tài)，后者的運(yùn)輸能力已得到很好的發(fā)揮。 表3 設(shè)施使用率表 設(shè)施 線路 大屯 選廠 7-10 溜井 25-27 溜井 鐵軌1 鐵軌2 鐵軌3 鐵軌4 鐵軌5 坑口費(fèi) 礦倉/% 群/% 群/% /% /% /% /% /% 石場/% 線路1 47 98 - - 58 58 58 58 16 線路2 47 - 98 58 58 58 57 57 16 總線路 74 75 80 48 92 92 91 91 25 5.4年出礦能力評估 按前文所得的結(jié)果，30 天出車數(shù)為2916 列，每天97 列，設(shè)每天的材料車、人車數(shù)為17列，礦車80 列；每列礦車的礦量為63 t ；年工作日330 天?？傻贸瞿瓿龅V能力為63×80×330= 166。3 萬t 。 該運(yùn)輸系統(tǒng)目前實(shí)際的出車數(shù)為84 列/ 天左右，與理論值相差不大。因此，該系統(tǒng)剩余的運(yùn)輸能力已經(jīng)不多，如想再擴(kuò)大運(yùn)輸能力，只有對其進(jìn)行改造，如把使用率較高的鐵軌2-5由單軌改成雙軌。 6結(jié)論 通過前文的模擬分析，可以得出如下結(jié)論： 1) 用系統(tǒng)建模仿真的方法，通過建立模型，可以在不干擾實(shí)際系統(tǒng)的情況下調(diào)整運(yùn)輸參數(shù)，使運(yùn)輸狀態(tài)達(dá)到最優(yōu)，并可以監(jiān)視運(yùn)輸?shù)娜^程，找出薄弱環(huán)節(jié)，進(jìn)行改造； 2) 每條運(yùn)輸線路所能容納的車輛數(shù)是有限的，應(yīng)把數(shù)量控制在飽和點(diǎn)，使用該方法可以使車輛的分配達(dá)到最優(yōu)； 3) 采用該方法可以準(zhǔn)確的計(jì)算出礦山系統(tǒng)潛在的運(yùn)輸能力，為決策者提供可靠的依據(jù)；本文得出的結(jié)果跟實(shí)際基本吻合，因此，用該方法對地下礦運(yùn)輸系統(tǒng)進(jìn)行研究優(yōu)化是可行的。其中分布密度函數(shù)的取得對仿真結(jié)果有較大影響，需嚴(yán)格按步驟收集、處理原始數(shù)據(jù)。 參考文獻(xiàn): [1]李仲學(xué). 國外系統(tǒng)模擬技術(shù)及其在礦業(yè)中應(yīng)用的新進(jìn)展[ J] . 中國礦業(yè), 1998, 7( 2) : 75—79. 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