scholarly journals An Unmanned Intelligent Transportation Scheduling System for Open-Pit Mine Vehicles Based on 5G and Big Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 135524-135539 ◽  
Author(s):  
Sai Zhang ◽  
Caiwu Lu ◽  
Song Jiang ◽  
Lu Shan ◽  
Neal Naixue Xiong
Keyword(s):  
Big Data ◽  
Intelligent Transportation ◽  
Open Pit Mine ◽  
Open Pit ◽  
Scheduling System ◽  
Transportation Scheduling

scholarly journals Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G

2021 ◽  
Vol 12 (5) ◽  
pp. 827-835
Author(s):  
Mohanad F Jwaid, Husam K Salih Juboori
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Vehicles ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Transport Costs ◽  
Time Data ◽  
Nsga Ii ◽  
Scheduling System ◽  
Unmanned Vehicle

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).

scholarly journals Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Song Jiang ◽  
Minjie Lian ◽  
Caiwu Lu ◽  
Qinghua Gu ◽  
Shunling Ruan ◽  
...  
Keyword(s):  
Big Data ◽  
Evaluation System ◽  
Open Pit Mine ◽  
Open Pit ◽  
Prediction Algorithm ◽  
Ensemble Prediction ◽  
Log Analysis ◽  
Flow Monitoring ◽  
Mine Slope ◽  
Analysis Platform

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.

Management and engineering solutions to improve stripping equipment efficiency in Chernogorksy open pit mine

2018 ◽  
pp. 36-48
Author(s):  
G. N. Shapovalenko ◽  
S. N. Radionov ◽  
V. V. Gorbunov ◽  
V. A. Khazhiev ◽  
V. Yu. Zalyadnov ◽  
...  
Keyword(s):  
Open Pit Mine ◽  
Open Pit ◽  
High Concentration ◽  
Open Pit Mines ◽  
Blasting Operation ◽  
Internal Dump ◽  
Mining Conditions ◽  
Mining Front ◽  
Comparison Of Equipment

Chernogosky open pit mine integrates truck-and-shovel system of mining with overburden rehandling to internal dump with a set of walking excavators for rehandling of overburden to mined-out area of the pit. It is possible to improve efficiency of stripping in the conditions of Chernogorsky OPM by reducing percentage of stripping with more expensive handling system. The relevant research and solutions to this effect are presented in this article. Comparative characterization of mining conditions and parameters of mining systems applied is given for open pit mines Chernogorsky, Turnui, Nazarovsky, Vostochno-Beisky and Izykh. The comparative analysis points at the need to account for difficulty of mining and process sites in comparison of equipment productivity. High concentration of mining machines, which is conditioned by narrow mining front and simultaneous operation of five faces, as well as blasting operation implemented every 1-2 days, are recognized as the main constraints of excavator capacity in mining with direct dumping in Chernogorsky open pit mine. The management and engineering solutions implemented in the mine and resulted in higher efficiency of draglines are described.

Assessment of maintenance schedule uniformity for Belaz dump trucks in Chernogorsky open pit mine

2018 ◽  
pp. 127-133
Author(s):  
E. A. Vakulin ◽  
V. A. Ivashkevich ◽  
E. I.I. Gnitsak ◽  
V. S. Baikin ◽  
S. P. Maslyukov
Keyword(s):  
Open Pit Mine ◽  
Open Pit ◽  
Time Loss ◽  
Maintenance Schedule ◽  
Monthly Average ◽  
Dump Trucks ◽  
Maintenance Personnel ◽  
Productive Time ◽  
Assessment Results

Uniform schedule maintenance of mining and haulage machines is one of the key conditions for increasing productive time of maintenance personnel and decreasing monthly average servicing time. Currently, Russian mines infringe regulated maintenance schedule aimed to improve output per shift. The loss of time of maintenance personnel and equipment as a consequence maintenance irregularity is never assessed. This article presents assessment results on maintenance schedule uniformity in terms of dump trucks BelAZ-7513 and BelAZ-7530 at Chernogorsky open pit mine, SUEK-Khakassia. A variant of calculation of time loss owing to inconsistent maintenance schedule for dump trucks is proposed. The loss of time by maintenance personnel and by mining/haulage machines is assessed. The fleet of dump trucks BelAZ-7513 and BelAZ-7530 is analyzed depending on overtime of operation between maintenance periods. It is recommended to improve uniformity of maintenance schedule for mining and haulage equipment.

Qualitative assessment of equipment failure cause finding and removal

2018 ◽  
pp. 116-126
Author(s):  
E. A. Vakulin ◽  
A. I. Zayats ◽  
V. A. Beklemeshev ◽  
V. A. Ivashkevich ◽  
V. A. Khazhiev ◽  
...  
Keyword(s):  
Production Condition ◽  
Real Life ◽  
Open Pit Mine ◽  
Qualitative Assessment ◽  
Open Pit ◽  
Quality Level ◽  
Equipment Failure ◽  
Operation Conditions ◽  
Failure Investigation

Investigation of failures is one of the critical activities of mining and haulage equipment operability assurance in mining. Maintaining failure investigation at the required quality level, it is possible to identify provisions, rules and procedures that should be revised or changed, operation conditions that should be improved, additional personnel training, if required, etc. Investigation of failures in mines is under responsibility of machine men and electricians of maintenance and operation services. In reality, factory management and setup for production condition weak concernment of these workers in quality investigation aimed at finding of sources of equipment failures. This article describes real-life results achieved in development and use of maintenance service operation, technology and management monitoring. The requirements are substantiated for quality improvement in failure cause finding and removal in mining and haulage equipment at Chernogorsky open pit mine, SUEK-Khakassia. Causes of the present quality of failure investigation by machine men of Chernogorsky Repair and Engineering Works and Chernogorsky open pit mine are revealed. The proposed recommended practices will improve quality of mining and haulage equipment failure investigation.

scholarly journals An Optimum Deployment Algorithm of Camera Networks for Open-Pit Mine Slope Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1148
Author(s):  
Hua Zhang ◽  
Pengjie Tao ◽  
Xiaoliang Meng ◽  
Mengbiao Liu ◽  
Xinxia Liu
Keyword(s):  
Focal Length ◽  
Field Tests ◽  
Mineral Resources ◽  
Video Camera ◽  
Open Pit Mine ◽  
Mine Safety ◽  
Open Pit ◽  
Prototype System ◽  
Mine Slope ◽  
Slope Monitoring

With the growth in demand for mineral resources and the increase in open-pit mine safety and production accidents, the intelligent monitoring of open-pit mine safety and production is becoming more and more important. In this paper, we elaborate on the idea of combining the technologies of photogrammetry and camera sensor networks to make full use of open-pit mine video camera resources. We propose the Optimum Camera Deployment algorithm for open-pit mine slope monitoring (OCD4M) to meet the requirements of a high overlap of photogrammetry and full coverage of monitoring. The OCD4M algorithm is validated and analyzed with the simulated conditions of quantity, view angle, and focal length of cameras, at different monitoring distances. To demonstrate the availability and effectiveness of the algorithm, we conducted field tests and developed the mine safety monitoring prototype system which can alert people to slope collapse risks. The simulation’s experimental results show that the algorithm can effectively calculate the optimum quantity of cameras and corresponding coordinates with an accuracy of 30 cm at 500 m (for a given camera). Additionally, the field tests show that the algorithm can effectively guide the deployment of mine cameras and carry out 3D inspection tasks.

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