Mining Truck Research Articles

Low-cost internet of things (IoT) for monitoring and optimising mining small-scale trucks and surface mining shovels

This paper discusses the design and deployment of low-cost Internet of Things (IoT) in medium-scale open pit mines to optimise the performance of their mining small-scale trucks and surface mining shovels. Low-cost IoT can be implemented in medium-scale operations to automate the collection of process management information that is currently measured manually, replicating part of the results delivered by commercial Fleet Management Systems (FMSs) such as the calculations of the number of truck cycles per shift, the shovel loading time, truck and shovel positioning, tonnes moved per day, truck speed and average fleet efficiency. The process of developing mining benches can also be monitored.By monitoring these tasks, FMSs in the mining industry allow mine operators to maximise productivity, reduce the number of equipment required to accomplish production targets, minimise material re-handling, supply the plant as planned and meet ore blending objectives for better metallurgical recoveries. In the case of large-scale mines, these mining operations are prepared to invest in the high cost of a typical FMS (of the order of $100,000/month depending on fleet size) because they enable mine operators to ensure that their capital-intensive fleets operate at peak productivity, generating maximum return on investment. By contrast, many medium-scale mines cannot afford the installation and ongoing costs associated with a commercial FMS. Medium-scale mines typically have low capitalization, rented mining fleet and are run on a day-to-day basis, with staff being employed or laid off on an almost continuous basis.The emergence of low-cost IoT promises widespread and access to sensors and data that can be used for operational decision-making. This paper presents a trial of a low-cost, under $100, IoT-based Fleet Information System (FIS). The system does not attempt to replicate the functionality of a full FMS but delivers key management information to the mine operators while having low capital and running costs and no requirement for IT or technical skills for installation or maintenance.In a test case in Chile, the FIS was used to inform operational management changes that resulted in a reduction of loading time, optimisation of mining truck routes and truck speed control for better safety without an increase in the mining cost.The low cost of the solution allows medium-scale mines access to tools that can enable them to mirror the performance improvements of their bigger competitors. For medium-scale mines, that means longer life-of-mine, more local employment and a longer positive impact in the community.

Multi-attribute interactive visualization of three-dimensional trajectory sets

Rapidly advancing location-awareness technologies and services have collected and stored massive amounts of moving object trajectory data with attribute information that involves various degrees of spatial scales, timescales, and levels of complexity. Unfortunately, interesting behaviors regarding combinations of attributes are scarcely extracted from datasets. Further, trajectories are typically dependent on the environment of three-dimensional space, and another issue of interest to us is to preserve spatial-location visualization while guaranteeing the description of temporal information. Therefore, we developed a novel analytics tool that combines visual and interactive components to enable a dynamic visualization of three-dimensional trajectory multi-attribute behaviors. Under the context of spatiotemporal analysis, this approach integrates multiple attributes into one view to efficiently explore the attribute visualization problem of multi-attribute combination without over-plotting. To assess the feasibility of our solution, we visualized and analyzed multi-attribute information of moving object trajectories using a real mining truck dataset as a case study.

Case-based reasoning system for prediction of fuel consumption by haulage trucks in open-pit mines

The shovel-truck system is commonly used in open-pit mining operations. Truck haulage cost constitutes about 26% of open-pit mining costs as the trucks are mostly powered by diesel whose cost is escalating annually. Therefore, reducing fuel consumption could lead to a significant decrease in overall mining costs. Various methods have been proposed to improve fuel efficiency in open-pit mines. Case-based reasoning (CBR) can be used to estimate fuel consumption by haulage trucks. In this work, CBR methods namely case-based reasoning using forward sequential selection (CBR-FSS), traditional CBR, and Naïve techniques were used to predict fuel consumption by trucks operating at Orapa Mine. The results show that the CBR method can be used to predict fuel consumption by trucks in open-pit mines; the predicted values of fuel consumption using the CBR-FSS technique gave much lower absolute residual values, higher standardised accuracy values, and effect sizes than those of other prediction techniques on all the datasets used. The system will enable mine planners to know the fuel consumed per trip and allow them to take mitigation measures on trucks with high fuel consumption.

Optimization of truck allocation in open pit mines using differential evolution algorithm

The allocation of trucks in open pit mines is a field with great potential for optimizing resources and applying advanced computer modeling techniques, mainly because many companies still choose to use manual allocation, which is premised on the decisions made by the operator, being subject to common failures and not reaching the maximum potential that the equipment can provide. Therefore, this work focuses on optimizing the allocation of trucks in order to increase production, reducing queue time and keeping ore grades within proper limits. The proposed algorithm was based on the differential evolution technique, where two types of mutation operators were used: rand/1/bin and best/1/bin, thus verifying the most suitable to solve the problem. The trucks were allocated in the ore loading and unloading process, aiming to improve the production capacity in a virtual mine. The results brought a convergence to the maximum global production, in addition to which, the allocation of unnecessary transport equipment to the planned routes was avoided. The two mutation operators compared had certain advantages and disadvantages, each better adapting to certain types of situations. The proposed technique can still be extended to other areas, for example, in the transport of grain on the road network or in the implementation of an allocation in freight cars that transport grain.

Shift Strategy Optimization for Automatic Transmission of Heavy Trucks Based on Dynamic Programming Algorithm

The shift strategy of automatic transmission plays a vital role in the smoothness and economy of heavy-duty mining trucks. In this paper, an engine fuel consumption model, a 6 + 2 speed automatic transmission model, and a vehicle resistance model are built in MATLAB. Combined with the development of smart mining technology, the traditional two-parameter economic shift strategy is corrected based on vehicle load and road slope. The dynamic programming optimization algorithm is used to extract the best economical shift strategy under known working conditions to reduce the fuel consumption and the number of shifts. Finally, simulation experiments of the optimized shift strategy by dynamic programming in a typical mine work cycle are carried out. The simulation results show that the engine speed and output torque are maintained in a relatively stable and efficient working range by using the proposed shift strategy. Compared with the traditional two-parameter shift strategy, dynamic programming has advantages in reducing fuel consumption and shift numbers.

ROAD PERFORMANCE ANALYSIS WITH MEDIAN AND WITHOUT MEDIAN ON THE ROAD OF A.YANI SATUI

The road that is located in the City of Satui is a road that connects cities that are large in the South Kalimantan, the road is often used by mining trucks. From these conditions, there will be a high movement of people, goods, and services. For this reason, it is necessary to conduct a review to determine the performance of the road.The location in this study is on A.Yani Street, City of Satui, South Kalimantan. On this road, there is a part of the road that has a median and does not have a median. Data collected from the results of traffic surveys are primary data which includes geometric data, traffic volume data, and traffic speed data. Secondary data which includes an overview of the study area. The data obtained are then grouped according to the composition of the vehicle. Then the data is made into hourly traffic data to get volume, speed, and density. The data obtained was analyzed to obtain capacity, the appropriate relationship model, and performance changes between roads.The results of data analysis on A.Yani Street, City of Satui are the maximum volumes obtained on the road with a median of 468,75 amp/hour while those on the road without the median are 421,51 amp/hour. The maximum density obtained on the road with a median of 133,67 SMP/km while on the road without a median of 55,86 SMP/km. The maximum speed obtained on the road without the median is 129,2 km/hour while the road with the median maximum speed is 60,87 km/hour. The corresponding equation for A.Yani Street, the City of Satui on the road with the median is the Underwood model, y = 61,161e-0,048x with the R-value of 0,90675 while the road without the median is the Greenberg model, y = -20,41ln(x) + 82,208 with an R-value of 0,90161.

Autonomous Haulage Systems in the Mining Industry: Cybersecurity, Communication and Safety Issues and Challenges

The current advancement of robotics, especially in Cyber-Physical Systems (CPS), leads to a prominent combination between the mining industry and connected-embedded technologies. This progress has arisen in the form of state-of-the-art automated giant vehicles with Autonomous Haulage Systems (AHS) that can transport ore without human intervention. Like CPS, AHS enable autonomous and/or remote control of physical systems (e.g., mining trucks). Thus, similar to CPS, AHS are also susceptible to cyber attacks such as Wi-Fi De-Auth and GPS attacks. With the use of the AHS, several mining activities have been strengthened due to increasing the efficiency of operations. Such activities require ensuring accurate data collection from which precise information about the state of the mine should be generated in a timely and consistent manner. Consequently, the presence of secure and reliable communications is crucial in making AHS mines safer, productive, and sustainable. This paper aims to identify and discuss the relation between safety of AHS in the mining environment and both cybersecurity and communication as well as highlighting their challenges and open issues. We survey the literature that addressed this aim and discuss its pros and cons and then highlight some open issues. We conclude that addressing cybersecurity issues of AHS can ensure the safety of operations in the mining environment as well as providing reliable communication, which will lead to better safety. Additionally, it was found that new communication technologies, such 5G and LTE, could be adopted in AHS-based systems for mining, but further research is needed to considered related cybersecurity issues and attacks.

A method to assess haul roads rolling resistance using dispatch system data

ABSTRACT Haul roads play a significant role in mining, with rolling resistance being one of the main aspects that can affect road quality and consequently the performance of mining trucks. The aim of this study is to estimate the rolling resistance of haul road segments considering longitudinal grade crossed with dispatch system data using back-calculation from Rimpull curves and estimated performance equations developed by Cooper. The use of this method demonstrated that it could assist in quickly identifying issues on mine haul roads and assess road quality locally through rolling resistance estimated maps, which will help to improve maintenance routines and truck performance.

HAULAGE ANALYSIS IN THE AIM OF THE COMBINED SYSTEM APPLICATION ON THE LEAD AND ZINC MINE "SASE" SREBRENICA

<p>One of the most important influencing factors on the economy of mines with surface and underground exploitation is the correct choice of haulage technology of ore and waste.<br><br>The paper analyzes the haulage of ore and waste in the Lead and Zinc Mine “Sase” Srebrenica and determines the optimal haulage length on the four excavation blocks for the application of the new haulage system by using the mine trucks from the aspect of set criteria, increasing capacity and reducing haulage costs through the techno – economic analysis and optimization.The optimal haulage length is determined through the calculations of the loader capacity and the possible choice of the truck<br>depending on the stated criteria.</p>

A New Challenge: Detection of Small-Scale Falling Rocks on Transportation Roads in Open-Pit Mines

In transportation at open-pit mines, rocks dropped as a mining truck is driven will wear out the tires of the vehicle, thus increasing the mining cost. In the case of autonomous vehicles, the vehicle must automatically detect rocks on the transportation roads during the driving process. This will be a new challenge: rough road, rocks of small size and irregular shape, long detection distance, etc. This paper presents a detection method based on light detection and ranging (lidar). It includes two stages: (1) using the modified cloth simulation method to filter out the ground points; (2) using the regional growth method based on grid division to cluster non-ground points. Experimental results show that the method can detect rocks with a size of 20–30 cm at a distance of 40 m in front of the vehicle, and it takes only 0.3 s on an ordinary personal computer (PC). This method is easy to understand, and it has fewer parameters to be adjusted. Therefore, it is a better method for detecting small, irregular obstacles on a low-speed, unstructured and rough road.

Numerical modeling of transport roads in open pit mines

Transport of overburden and mineral raw materials in surface mining is one of the most complex and costly processes today. Good transport roads are essential for successful transportation by trucks in open pit mines. Poor design, and maintenance of the roads has the greatest impact on the high transport costs and possible risks in terms of security. Numerical modeling was performed to analyze the effects of the properties of the built-in materials used in the construction of roads, the thickness of the layers and the interaction of tires with the road surface. The distribution of stress and strain determined in the construction of a road depends on the characteristics of the road and the structure and mass of the truck. For this reason, numerical modeling and computer simulation is considered a very suitable method in the design of roads, because in a short period of time and at a lower cost calculations for the construction of a large number of models can be implemented and the best model available will be chosen. The most favorable construction will be considered to be the one that will, at minimum construction costs and with low costs of maintenance, enable the efficient use of transport equipment for a longer period of exploitation.

Mining truck platooning patterns through massive trajectory data

Truck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements. Properly planning platoons and evaluating the potential of truck platooning are crucial to trucking companies and transportation authorities. This study proposes a series of data mining approaches to learn spontaneous truck platooning patterns from massive trajectories. An enhanced map matching algorithm is developed to identify truck headings by using digital map data, followed by an adaptive spatial clustering algorithm to detect trucks’ instantaneous co-moving sets. These sets are then aggregated to find the network-wide maximum platoon duration and size through frequent itemset mining for computational efficiency. The GPS data were collected from truck fleeting systems in Liaoning Province, China for platooning performance measures and spatiotemporal platooning distribution visualization. Results show that approximately 36% spontaneous truck platoons can be coordinated by speed adjustment without changing routes and schedules. The average platooning distance and duration ratios for these platooned trucks are 9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel consumption. This study also distinguishes the optimal platooning periods and space headways for national freeways and trunk roads, and prioritize the road segments with high possibilities of truck platooning. The derived results are reproducible, providing useful policy implications and operational strategies for large-scale truck platoon planning and roadside infrastructure construction.

Predictive asset availability optimization for underground trucks and loaders in the mining industry

With an increased focus on operational optimization, cost rationalization and increased advances in sensors, data storage and machine learning, mining Original Equipment Manufacturers are in need of intelligent machines to create an unassailable competitive advantage. To address this, Sandvik Mining and Rock Technology (Sandvik) and IBM have developed a customized service for fleet management and predictive maintenance for Sandvik’s mining equipment for the improvement of Overall Equipment Efficiency. In this endeavour, this paper aims to discuss the application of analytics to reduce machine downtime by predicting equipment failure in advance thus improving asset utilization of the fleet used in the mining industry. It details the journey of the analysis using the IoT data, data exploration, statistical approximations employed, various machine learning algorithms considered, and final selection of the techniques based on the industry recommended criteria. Predictive models for component failures (engine, brakes and transmission) and predictive models for equipment time to failure were built for underground mining trucks and loaders. With increased availability of additional sensor data and the need to interpret the outcomes that are actionable, supervised machine learning algorithms like decision trees were considered. Our work highlights various challenges encountered, the workarounds and solutions used to overcome them. The resulting models (built with IBM’s predictive analytics capability) of this work are augmented with Sandvik’s analytical offering, OptiMine® Analytics. This paper also highlights as to how our work has made a significant impact in financial terms and the client testimonials received.

A Method of Mining Truck Loading Volume Detection Based on Deep Learning and Image Recognition.

Detection of the loading volume of mining trucks is an important task in open pit mining. Aiming at the addressing the current problems of low accuracy and high cost of the detection of the loading volume of mining trucks, this paper proposes a mining truck loading volume detection model based on deep learning and image recognition. The training and test data of the model consists of 6000 sets of images taken in a laboratory environment. After image preprocessing, the VGG16 network model is used to pre classify the ore images. The classification results are displayed and the possibility of each category is determined. Then, the loading volume of mining trucks is calculated by using the classification results and the least squares algorithm. By using the labeled image data of five kinds of mining truck loading volume, the arbitrary loading volume detection of mining trucks is realized, which effectively solves the problem of a lack of labeled data types caused by the difficulty in obtaining mine data. Root mean square error (RMSE) and mean absolute error (MAE) are used to evaluate the fitting effect of the model. The experimental results show that the model has high prediction accuracy. The average absolute error is 17.85 . In addition, this paper uses 400 real mining truck images of open-pit mines to verify the model and the average absolute error is 2.53 . The experimental results show that the model has good generality and can be applied well to the actual production of open-pit mines.

Open-pit mine truck fuel consumption pattern and application based on multi-dimensional features and XGBoost

The fuel consumption cost of open-pit mine trucks accounts for up to 22% of the total cost. However, research on the fuel consumption of mine trucks has been hindered by low monitoring accuracy and unclear fuel consumption patterns. Based on the fuel consumption per cycle of mine trucks, this study analysed the fuel consumption of 5179 transportation cycles with three types of mine trucks. First, a regression analysis was applied to reveal the non-linear pattern of the fuel consumption caused by multi-dimensional features, such as haulage distance, lifting height, and operation time per cycle. Then, based on multi-dimensional features and the XGBoost algorithm, a prediction model for the fuel consumption of mine trucks was proposed, and the R-squared and mean absolute percent error index values were determined to be 0.93 and 8.78%, respectively. The experimental results showed that the lifting height, load, and haulage distance were the key factors influencing the fuel consumption of mine trucks. The fuel consumption of mine trucks during queuing cannot be ignored. These results can be applied to formulate reasonable fuel consumption assessment indicators, truck dispatch strategies, and cost budget plans.

Infrastructural requirements for the implementation of autonomous trucks in open-pit mines

The field of study of autonomous trucks especially in open pit mines has been the subject of considerable research over the past three decades. In the last ten years in particular, interest in this area has improved considerably. The implementation of autonomous systems is an integral organizational and cultural change. This work is a guideline for the safe and effective implementation of autonomous trucks in mines. It provides an integral view of the different approaches to implementing autonomous mining trucks with respect to safety criteria, operating costs, etc. It also presents the criteria and measures to be considered when maintaining the haulage routes (vertical/horizontal/combined alignment) that are a vital component for the success of an automation project. Mine site designers and planners should ensure that the design and construction of traffic areas for these trucks are compatible with autonomy and minimize interactions with personnel and non-autonomous equipment. A proposed infrastructural requirement for the autonomous truck zone is presented in this work.

Predictive Equivalent Consumption Minimization Strategy for Power Split Hybrid Electric Mining Truck

Predictive Equivalent Consumption Minimization Strategy for Power Split Hybrid Electric Mining Truck

Research on an energy management strategy and energy optimisation of hydraulic hybrid power mining trucks

Research on an energy management strategy and energy optimisation of hydraulic hybrid power mining trucks

Optimum operational conditions of mining trucks based on the vibrational health risk of the driver

This paper aims to propose the optimum payload and speed limit and load geometry for a heavy duty 60-ton truck in Sungun Copper Mine of Iran as a case study to keep the vibrational health risk at the lowest possible level while maintaining the production rate at the planned level. The root mean square (RMS) of vibrations at different operational conditions was obtained. Then, an optimisation problem was developed to propose the optimum payload and truck speeds at various conditions. The optimum payload was 65 tons. The optimum speed of loaded truck was obtained in the range of 30-35 km/h at good roads and 15 km/h at poor roads. Also, the optimum speed range of empty truck was in the range of 15-20 km/h. Uniform payloads and materials accumulated on the left side can have the lowest and highest effects on the vibrational health risk, respectively.

Optimum operational conditions of mining trucks based on the vibrational health risk of the driver

Optimum operational conditions of mining trucks based on the vibrational health risk of the driver