A Simulation Model of an Underground Mine Haulage System

A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

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Logistic evaluation of an underground mine using simulation

Rem Revista Escola de Minas ◽

10.1590/0370-44672014670181 ◽

2014 ◽

Vol 67 (4) ◽

pp. 447-454 ◽

Cited By ~ 1

Author(s):

Marcelo Moretti Fioroni ◽

Letícia Cristina Alves dos Santos ◽

Luiz Augusto G. Franzese ◽

Josiane Cordeiro Seixas ◽

Bruno Salomão Penna ◽

...

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Discrete Event ◽

Underground Mine ◽

Gold Mine ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Transportation Capacity ◽

Traffic Rules

This paper describes a logistic study about an underground gold mine, belonging to AngloGold Ashanti, where four different layout options could be applied to the tunnels, and also different transportation strategies. Each evaluated layout had its own configuration for shaft and truck fleets. The study was made individually for each year of the mine operation life, determining the necessary transportation capacity to achieve the planned production for that year. Due to the very restrictive traffic options in the tunnels, a framework was developed to represent the tunnels and traffic rules in a discrete-event simulation model. The results pointed the scenario with the lowest necessary transportation capacity to achieve the planned production.

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An Intelligent Supervising System for the Operation of an Underground Mine

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2008.3.2394 ◽

2008 ◽

Vol 3 (3) ◽

pp. 259 ◽

Cited By ~ 4

Author(s):

Felisa M. Cordova ◽

Lucio Canete ◽

Luis E. Quezada ◽

Fernando Yanine

Keyword(s):

Simulation Model ◽

Intelligent System ◽

Underground Mine ◽

Master Plan ◽

Knowledge Based System ◽

Feasibility Assessment ◽

Knowledge Based ◽

Operation Rules ◽

Proposed Model ◽

Different Types

This paper presents a conceptual model for an Intelligent System built to support the scheduling for an underground mine in order to supervise its operation. The system is composed by a Simulation Model linked to a Knowledge Based System designed by means of hierarchical, colored and temporal Petri Nets. Simulation Model allows simulating the operation of the production, reduction and transport levels in the mine. Knowledge Based System is activated by events produced in daily operations and yields the results of registered events and the actions taken to solve the problem, generating operation rules. The proposed model allows different types of mine operations and scenarios providing data for decision-making. The system helps to evaluate different policies for programming the activities in the mine thus seeking to enlarge the equipment productivity. The model also allows the feasibility assessment of the Daily Master Plan based on the input data of the simulation model.

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