Dragline excavation simulation, real-time terrain recognition and object detection

The contribution of coal to global energy is expected to remain above 30% through 2030. Draglines are the preferred excavation equipment in most surface coal mines. Recently, studies toward dragline excavation efficiency have focused on two specific areas. The first area is dragline bucket studies, where the goal is to develop new designs which perform better than conventional buckets. Drawbacks in the current approach include operator inconsistencies and the inability to physically test every proposed design. Previous simulation models used Distinct Element Methods (DEM) but they over-predict excavation forces by 300% to 500%. In this study, a DEM-based simulation model has been developed to predict bucket payloads within a 16.55% error. The excavation model includes a novel method for calibrating formation parameters. The method combines DEM-based tri-axial material testing with the XGBoost machine learning algorithm to achieve prediction accuracies of between 80.6% and 95.54%. The second area is dragline vision studies towards efficient dragline operation. Current dragline vision models use image segmentation methods that are neither scalable nor multi-purpose. In this study, a scalable and multi-purpose vision model has been developed for draglines using Convolutional Neural Networks. This vision system achieves an 87.32% detection rate, 80.9% precision and 91.3% recall performance across multiple operation tasks. The main novelty of this research includes the bucket payload prediction accuracy, formation parameter calibration and the vision system accuracy, precision and recall performance toward improving dragline operating efficiencies --Abstract, page iii

Development of a dragline in-bucket bulk density monitor

This paper details the implementation and trialling of a prototype in-bucket bulk density monitor on a production dragline. Bulk density information can provide feedback to mine planning and scheduling to improve blasting and consequently facilitating optimal bucket sizing. The bulk density measurement builds upon outcomes presented in the AMTC2009 paper titled ‘Automatic In-Bucket Volume Estimation for Dragline Operations’ and utilises payload information from a commercial dragline monitor. While the previous paper explains the algorithms and theoretical basis for the system design and scaled model testing this paper will focus on the full scale implementation and the challenges involved

Computational dynamics and virtual dragline simulation for extended rope service life

The dragline machinery is one of the largest equipment for stripping overburden materials in surface mining operations. Its effectiveness requires rigorous kinematic and dynamic analyses. Current dragline research studies are limited in computational dynamic modeling because they eliminate important structural components from the front-end assembly. Thus, the derived kinematic, dynamic and stress intensity models fail to capture the true response of the dragline under full operating cycle conditions. This research study advances a new and robust computational dynamic model of the dragline front-end assembly using Kane\u27s method. The model is a 3-DOF dynamic model that describes the spatial kinematics and dynamics of the dragline front-end assembly during digging and swinging. A virtual simulator, for a Marion 7800 dragline, is built and used for analyzing the mass and inertia properties of the front-end components. The models accurately predict the kinematics, dynamics and stress intensity profiles of the front-end assembly. The results showed that the maximum drag force is 1.375 MN, which is within the maximum allowable load of the machine. The maximum cutting resistance of 412.31 KN occurs 5 seconds into digging and the maximum hoist torque of 917. 87 KN occurs 10 seconds into swinging. Stress analyses are carried out on wire ropes using ANSYS Workbench under static and dynamic loading. The FEA results showed that significant stresses develop in the contact areas between the wires, with a maximum von Mises stress equivalent to 7800 MPa. This research study is a pioneering effort toward developing a comprehensive multibody dynamic model of the dragline machinery. The main novelty is incorporating the boom point-sheave, drag-chain and sliding effect of the bucket, excluded from previous research studies, to obtain computationally dynamic efficient models for load predictions --Abstract, page iii

Stochastic-optimization of equipment productivity in multi-seam formations

Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii

Performance Appraisal of Equipments in Opencast Mines

Mining is a very capital-intensive industry, and it is well known fact that the equipment availability and precise estimation of this utilization are very important since mine managers want to utilize their equipment as effectively as possible to get an early return on their investments as well reducing total production cost. While a lot of thrust is put on the selection of mining equipments not much consideration is paid towards the measurement of effectiveness of those equipments. The increase in automation, compounded by the increase in the size and capacity of equipment over the years has drastically changed the consequences of equipment ineffectiveness. In the present economic conditions, severe global competition, challenges of environmental and safety considerations, in order to achieve high production and productivity of HEMMs in opencast mines, it is pertinent to have high % availability and % utilization of equipments besides ensure overall equipment effectiveness vis-à-vis established CMPDI norms/global bench marks. This necessitates performance appraisal of various equipments in highly mechanized OCPs, critically analyze the idle/down time of equipments and take ameliorative measures to improve machine productivity and performance. OEE is a hierarchy of matrices which evaluate and indicates how effectively a production operation is utilized The project work was carried out with the following objectives: To estimate % availability, % utilization and analyze idle hours of Dragline (10/70) at Belpahar OCP and Sameleswari OCP. To determine Overall Equipment Effectiveness (OEE) of Dragline and Surface Miner at BOCP and SOCP

Performance Study & Evaluation of Electrical Parameter of Dragline in Open Cast Mines

Draglines are the most expensive piece of excavating equipment at the mine site and it’s operated safely, efficiently and economically. In order to achieve high production and productivity of heavy earth moving machine in opencast mines, it is necessary to have high % availability and % utilization of equipment. The study also propounds the importance of appraisal of dragline productivity parameters, such as, swing angle, availability, seating position, cycle time, utilization, etc., in the field scale. In this contest, calculation of operating cost of dragline operation in various methods has been done and compared critically on case study basis. Principally, it will serve as a guide to the method employed in determining the operating cost in various methods Draglines are versatile and provide a low cost mining method. Each dragline that is considered for an AC Electrical upgrade will be fully analyzed to determine the machines maximum potential productivity increases. The assessment will include review of current dragline productivity practices, installed electrical capability and additional mechanical enhancements that could be included, such as increased suspended load, which will allow us to take full advantage of the AC IGBT/AFE system and BI 348 motor with its increased capability and still operate the machine within Bucyrus recommended criteria. The AC IGBT/AFE System has a fully integrated onboard computer package that allows complete access to the drive application software and PLC software and chopper drive use in regenerative braking. It is called access direct which will be discussed