Development of computer based equipment performance monitoring systems in open cast mines

Truck haulage is the most common means used for moving ore/waste in open-pit mining operations, but it is usually the most expensive unit operation in a truck shovel mining system. The state-of-the-art in computing technology has advanced to a point where there are several truck dispatching systems which offer the potential of improving truck-shovel productivity and subsequent savings. Introducing a dispatching system in a mine can achieve operational gains by reducing waiting times and obtain other benefits through better monitoring, optimal routing and grade control. Efficiency of the employed truck-shovel fleet depends on the dispatching strategy in use, the complexity of the truckshovel system and a variety of other variables. It is a common situation in mining that considerable analysis of the available strategies is undertaken before dispatching is adopted. In most cases, computer simulation is the most applicable and effective method of comparing the alternative dispatching strategies. To develop a computer based equipment performance monitoring systems in open cast mines. We have made a choice to make it on the shovel dumper combination using GPS. The computer monitors the location and status (full or empty, heading, and velocity) of each vehicle in the fleet. The system analyzes production statistics, such as haul routes, historic data about drive time to a specific shovel location, and cycle time how long it takes to make a round trip from the shovel to the dump site and back. The system then correlates these data to most efficiently route all the vehicles. The computer based equipment performance monitoring of equipments of open cast coal mine on an offline monitoring basis. This system starts from the counting of the number of trips dumpers. It has very good features such as it is easy to learn, very good user interface capability. The success of the system is totally dependent on the availability and incorporation of the data into the system .If the data will no be available then the system cannot provide good result. The data which has been incorporated into the system also should be correct, other wise it will provide wrong information to the management. There is a huge scope for further development of this kind of system such as incorporation of other equipment such as dragline, dozer, etc. It can also be used for equipment maintenance system and also for inventory control. The program written in C compiler has been written on the basis of GPS data of the time, loading, unloading points in the mine. It gives us the availability, utilization, idle time and breakdown time for the shovel dumper system. The data taken is on the basis of the data selected for a whole shift

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

Evaluation of Truck Dispatch System and its Application using GPS in Opencast Mines- a Case Study of Indian Mines

Truck haulage now a days is the most common means which is used for moving ore/waste in open-cast mining operations. The truck haulage is usually the costliest unit operation in a truck shovel open cast mining. The advancement in computer coding technology has advanced to a point where there are many truck dispatching systems which will give the potential of advancing truck-shovel productivity and future savings. By trying a dispatching system in any mine can give operational increase in production by minimizing waiting times and can give other beneficial advantages and can also be obtained through good monitoring, optimal routing. The capacity of the employed truck-shovel fleet counts on the dispatching methodology in use, the intricacy of the truck shovel system and a number of other variables. It is a very common situation in mining that considerable number of analysis of the available techniques is undertaken before dispatching is done. In many number of cases, computer simulation is the better applicable and effective method of relating the alternative dispatching strategies. Keeping this in mind computer programs are developed using C++ language for the monitoring of the equipment performance in truck dispatch system in opencast mines. To study about the truck dispatch system (TDS), we have made a choice to make it on the shovel dumper combination using GPS. In TDS system the computer monitors the location and status whether the dumper is full or empty and its heading, velocity of each vehicle in the fleet. The system analyses production numbers, such as haul routes, historic data about drive time to a specific shovel location and the cycle time and time taken to make a complete trip, trip from the shovel to the dump site and back

Computational intelligent impact force modeling and monitoring in HISLO conditions for maximizing surface mining efficiency, safety, and health

Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem. This research study developed a rigorous mathematical model and a 3D virtual simulation model to capture the dynamic impact force for a multi-pass shovel loading operation. The research further involved the application of artificial intelligence and machine learning for implementing the impact force detection in real time. Experimental results showed the impact force magnitudes of 571 kN and 422 kN, for the first and second shovel pass, respectively, through an accurate representation of HISLO with continuous flow modelling using FEA-DEM coupled methodology. The novel ‘DeepImpact’ model, showed an exceptional performance, giving an R2, RMSE, and MAE values of 0.9948, 10.750, and 6.33, respectively, during the model validation. This research was a pioneering effort for advancing knowledge and frontiers in addressing the WBV challenges in deploying heavy mining machinery in safe and healthy large surface mining environments. The smart and intelligent real-time monitoring system from this study, along with process optimization, minimizes the impact force on truck surface, which in turn reduces the level of vibration on the operator, thus leading to a safer and healthier working mining environments --Abstract, page iii

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Performance Evaluation of Surface Mining Equipment with Particular Reference to Shovel-Dumper Mining

Surface mining is the most well-known mining around the world, and open pit mining accounts for more than 60% of all surface yield. Haulage costs represent as much as 60% of the aggregate working expense for these type of mines, so it is desirable to keep up an effective haulage framework. Equipment availability and estimation of utilization very precisely which is very important since mine manager wants to utilize their equipment as effectively as possible to get an early return of their investment as well as reducing total production cost. In present situation to achieve high production and productivity of HEMMs in opencast mines, it is desired to have high % availability and % utilization of equipment besides ensure overall equipment effectiveness as per CMPDI norms/global bench marks. OEE shows that how an equipment is utilized with its maximum effectiveness. It uses parameters like availability, performance and utilization for the estimation of equipment effectiveness. One method for effectively use of equipment in haul cycle is queuing theory. Queuing theory was developed to model systems that provide service for randomly arising demands and predict the behaviour of such systems. A queuing system is one in which customers arrive for service, wait for service if it is not immediately available, and move on to the next server or exit the system once they have been serviced. Most mining haul routes consist of four main components: loading, loaded hauling, dumping, and unloaded hauling to return to the loader. These components can be modelled together as servers in one cyclic queuing network, or independently as individual service channels. Data from a large open pit mine are analysed and applied to a multichannel queuing model representative of the loading process of the haul cycle. The outputs of the model are compared against the actual dumper data to evaluate the validity of the queuing model developed

Optimasi Kemampuan Produksi Alat Berat dalam Rangka Produktifitas dan Keberlanjutan Bisnis Pertambangan Batubara: Studi Kasus Area Pertambangan Kalimantan Timur

Coal mining business is now faced with various challenges such as export restrictions policy, an increase in value added products, and the decline in market prices of products. To be able to compete, mining companies are expected to increase productivity and efficiency and make continuous improvements in the production process. In the mining process, the availability of equipment and dump truck unloading tool will determine the sustainability of production that have an impact on productivity and efficiency. The purpose of this study was to optimize the production of coal mining in the context of the efficient use of equipment using the match factor, queues, and linear programming. The research location is in the area of the mining concession contractor PT KTD is in the village of Embalut, District Tenggarong Seberang, Kertanegara Kutai Regency, East Kalimantan in October-November 2015. Unloading equipment used backhoe excavator is 5 units and 32 units of dump trucks. The simulation results match factor generated by the method optimal dump truck needs 26 units, while the queuing method and linear programming as much as 25 units of dump truck. The results of production optimization with linear programming method produced mining productivity of 1,208 BCM of overburden per hour with the optimum cost of $ 0909/BCM

A simulation model for truck-shovel operation

A truck-shovel mining system is a flexible mining method commonly used in surface mines. Both simulation and queuing models are commonly used to model the truckshovel mining operation. One fundamental problem associated with these types of models is that most of the models handle the truck haulage system as macroscopic simulation models, which ignore the fact that a truck as an individual vehicle unit dynamically interacts not merely with other trucks in the system but also with other elements of the traffic network. Some important operational factors, such as the bunching effect and the influence of the traffic intersections, are either over simplified or ignored in such a macroscopic model. This thesis presents a developed discrete-event truck-shovel simulation model, referred to as TSJSim (Truck and Shovel JaamSim Simulator), based on a microscopic traffic and truck-allocation approach. The TSJSim simulation model may be used to evaluate the Key Performance Indicators (KPIs) of the truck-shovel mining system in an open pit mine. TSJSim considers a truck as an individual traffic vehicle unit that dynamically interacts with other trucks in the system as well as other elements of the traffic network. TSJSim accounts for the bunching of trucks on the haul routes, practical rules at intersections, multiple decision points along the haul routes as well as the influence of the truck allocation on the estimated queuing time. TSJSim also offers four truck-allocation modules: Fixed Truck Assignment (FTA), Minimising Shovel Production Requirement (MSPR), Minimising Truck Waiting Time (MTWT) and Minimising Truck Semi-cycle Time (MTSCT) including Genetic Algorithm (GA) and Frozen Dispatching Algorithm (FDA)

The Application of Artificial Intelligence to Reduce Greenhouse Gas Emissions in the Mining Industry

Mining industry consumes a significant amount of energy and makes greenhouse gas emissions in various operations such as exploration, extraction, transportation and processing. A considerable amount of this energy and gas emissions can be reduced by better managing the operations. The mining method and equipment used mainly determine the type of energy source in any mining operation. In surface mining operations, mobile machines use diesel as a source of energy. These machines are haul trucks excavators, diggers and loaders, according to the production capacity and site layout and they use a considerable amount of fuel in surface mining operation; hence, the mining industry is encouraged to conduct some research projects on the energy efficiency of mobile equipment. Classical analytics methods that commonly used to improve energy efficiency and reduce gas emissions are not sufficient enough. The application of artificial intelligence and deep learning models are growing fast in different industries, and this is a new revolution in the mining industry. In this chapter, the application of artificial intelligence methods to reduce the gas emission in surface mines with some case studies will be explained