東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A methodology for the discrete event...

Clement, Serge.

Linked to FindBook

Google Book

Amazon

博客來

A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems./
Author:	Clement, Serge.
Description:	274 p.
Notes:	Source: Masters Abstracts International, Volume: 45-02, page: 1064.
Contained By:	Masters Abstracts International45-02.
Subject:	Engineering, Mining. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR20165
ISBN:	9780494201657

A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems.
Clement, Serge.

A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems. - 274 p.

Source: Masters Abstracts International, Volume: 45-02, page: 1064.

Thesis (M.A.Sc.)--Laurentian University (Canada), 2006.

The use of simulation as an operations research tool in mining applications is steadily rising. The principals behind computer simulations are to represent real life systems using various mathematical constructions and computer logic. The driving force behind many simulation studies in mining is their ability to explore the performance of various systems over very large time domains within a relatively small time span in "real life". The goal of such simulation studies can be for example: to minimise the cost of production, to verify the feasibility of a proposed mining scheme, to verify design proposals for ore bin sizes, for ore flow and ore haulage throughputs, for cage size versus material handling capacities, and so forth. Finally, most if not all simulation studies in mining to date are all extremely specialised in order to solve or study a specific problem. The constraint may be an orebody's specific shape and size, a set mining development plan or mine architecture, a specific mining method, a specific mining technology such as underground ore conveyors, skip hoists,...or a combination of a few of these elements. Since the performance of most mechanised mining equipment and processes can be measured using well defined statistical methods, an opportunity to standardise the steps involved in the preparation of discrete-event simulation models of mining systems, in general, is observed.

ISBN: 9780494201657Subjects--Topical Terms:

1035560
Engineering, Mining.

A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems.
LDR:03714nam 2200253 a 45 001 970872
005 20110921
008 110921s2006 eng d
020 $a 9780494201657
035 $a (UMI)AAIMR20165
035 $a AAIMR20165
040 $a UMI $c UMI
100 1 $a Clement, Serge. $3 1294911
245 1 2 $a A methodology for the discrete event simulation with three-dimensional animation of vertical retreat mining (VRM) systems.
300 $a 274 p.
500 $a Source: Masters Abstracts International, Volume: 45-02, page: 1064.
502 $a Thesis (M.A.Sc.)--Laurentian University (Canada), 2006.
520 $a The use of simulation as an operations research tool in mining applications is steadily rising. The principals behind computer simulations are to represent real life systems using various mathematical constructions and computer logic. The driving force behind many simulation studies in mining is their ability to explore the performance of various systems over very large time domains within a relatively small time span in "real life". The goal of such simulation studies can be for example: to minimise the cost of production, to verify the feasibility of a proposed mining scheme, to verify design proposals for ore bin sizes, for ore flow and ore haulage throughputs, for cage size versus material handling capacities, and so forth. Finally, most if not all simulation studies in mining to date are all extremely specialised in order to solve or study a specific problem. The constraint may be an orebody's specific shape and size, a set mining development plan or mine architecture, a specific mining method, a specific mining technology such as underground ore conveyors, skip hoists,...or a combination of a few of these elements. Since the performance of most mechanised mining equipment and processes can be measured using well defined statistical methods, an opportunity to standardise the steps involved in the preparation of discrete-event simulation models of mining systems, in general, is observed.
520 $a This thesis work is an attempt to develop a methodology to study a mining system based on discrete-event simulation. This thesis discusses the application of discrete event simulation combined with three-dimensional animation for studying the process of vertical retreat mining typical in Canadian underground hard rock mines. AutoMod(TM) version 9.1 from Brooks Automation Inc. is used to model the mechanised mining methods for the development and subsequent mining of a vertical retreat mining operation. A benchmark model and Microsoft(TM) Excel(TM) spreadsheet based input and output menus are used to feed and extract experimental data to and from the simulation model. The model is capable to simulate both conventional and tele-remote mining operating scenarios including delays incurred due to mining equipment failures as well as ground failures. The benchmark model is verified and validated. A series of simulation experiments are set in order to investigate their impact on development advance footage, daily production tonnage, stope life cycle times, equipment utilization and equipment operator utilization. A variety of output variables are tracked, such as, actual process cycle times, production rates, equipment and operator utilisation, time between failures, time to repair and delays due to ground failures (ground failures occur as discrete events). The same simulation model is used for each experimental run: only input data parameters are changed.
590 $a School code: 1100.
650 4 $a Engineering, Mining. $3 1035560
690 $a 0551
710 2 0 $a Laurentian University (Canada). $3 1019463
773 0 $t Masters Abstracts International $g 45-02.
790 $a 1100
791 $a M.A.Sc.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR20165