Hazard Identification and Risk Analysis in Mining Industry

For any industry to be successful it is to identify the Hazards to assess the associated risks and to bring the risks to tolerable level. Mining activity because of the very nature of the operation, complexity of the systems, procedures and methods always involves some amount of hazards. Hazard identification and risk analysis is carried for identification of undesirable events that can leads to a hazard, the analysis of hazard mechanism by which this undesirable event could occur and usually the estimation of extent, magnitude and likelihood of harmful effects. It is widely accepted within industry in general that the various techniques of risk assessment contribute greatly toward improvements in the safety of complex operations and equipment. Hazard identification and risk analysis involves identification of undesirable events that leads to a hazard, the analysis of hazard mechanism by which this undesirable event could occur and usually the estimation of extent, magnitude and likelihood of harmful effects. The objective of hazards and risk analysis is to identify and analyze hazards, the event sequences leading to hazards and the risk of hazardous events. Many techniques ranging from simple qualitative methods to advanced quantitative methods are available to help identify and analyze hazards. The use of multiple hazard analysis techniques is recommended because each has its own purpose, strengths, and weaknesses. As the part of the project work, hazard identification and risk analysis was carried out for an iron ore mine and a coal mine and the hazards were identified and risk analysis was carried out. The different activities were divided in to high, medium and low depending upon their consequences and likelihood. The high risks activities have been marked in red colour are un-acceptance and must be reduced. The risks which are marked in yellow colour are tolerable but efforts must be made to reduce risk without expenditure that is grossly disproportionate to the benefit gained. The risks which are marked in green have the risk level so low that it is not required for taking actions to reduce its magnitude any further. For the iron ore mine the high risk activities which were recorded were related to face stability and the person blasting the shots. In the coal mine there was problem of fly rocks, iv roads were not proper for haulage purpose, inappropriate use of personal protective equipment and inrushes of water into the mine causing inundation. Hazard identification and risk assessment can be used to establish priorities so that the most dangerous situations are addressed first and those least likely to occur and least likely to cause major problems can be considered later. From the study carried out in the iron ore and coal mine and the risk rating which were made and analyzed shows that the number of high risks in the coal mine was more than that of iron ore mine and same goes for the events in medium risk

Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model

RISK CONTROL IN PRODUCTION SCHEDULING BY CONSIDERING VOLUME AND GRADE UNCERTAINTIES IN RESOURCE ESTIMATION

Mineral deposits are the main assets for the mining industry. Mineral deposits are estimated based on the findings of exploration drilling. Complex host geology with variable grades and geological controls increases difficulty in resource estimation. In these situations, volume (tonnage) and grade are often over- or underestimated, resulting in inaccurate mine plan that leads to costly financial decisions. In this study, a multiple-point geostatistical method, namely Single Normal Equation Simulation (SNESIM) was applied to generate equiprobable orebody models for a copper deposit from Africa that helps to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by generating multiple realization of grade models using sequential Gaussian simulation within each equiprobable orebody models. The results are validated by generating the marginal distribution, and two- and three-point statistics. In addition, a comparative study is performed for the deterministic version, the stochastic version with grade uncertainty, and the stochastic version with volume and grade uncertainty. The results show that the orebody model with the maximum volume is 4.8% more than the average volume and the minimum volume is 5.1% less than the average volume. The grade simulation results demonstrate that the average grade for all simulations is 3.89%, but average grade for different simulations varied from 3.6% to 4.1%. The results also show that the volume and grade uncertainty model overestimated the orebody volume compared to the conventional orebody volume. The long-term production schedule is generated taking into account the volume and grade uncertainties from the orebody models, and satisfying mine production capacity and xi processing capacity constraints. The production schedule results for the volume and grade uncertainty-based model are compared to the production schedule generated from deterministic orebody model, and grade uncertainty-based model. The results demonstrated that the incorporation of both the volume and grade uncertainty significantly reduces the risk of deviation from the target. The results also show that incorporation of volume and grade uncertainty increases the net present value (NPV) of mining project, when compared to the mine plan generated from the deterministic model and stochastic model with only grade uncertainty. The results show that the production schedule generates high revenue over wide range of initial assumptions and the expected NPV is 3% higher than the deterministic version. A sensitivity analysis was also performed to understand the effect of penalty factor for deviating the constraints

GLOBAL OPTIMIZATION OF THE OPEN PIT MINING COMPLEX WITH INTEGRATED CUT-OFF GRADE OPTIMIZATION UNDER UNCERTAINTY

The mining complex refers to an integrated problem where the material is extracted from the mines; the extracted material is passed through a series of processing facilities connected with various material handling methods to generate a set of finished products, which can be sold to the customers. The optimization of the mining complex refers to the simultaneous optimization of the multiple mine production schedules, the destination of the materials, and the method of processing throughout the life of the project. The purpose of optimizing the mining complex is to deal with the effective management of resources and maximize cash flows to generate higher profits over the life of the project. The goal of this dissertation is to develop a global optimization methodology that integrates geological (supply) uncertainty and can manage the risk in the design, mining complex operations, and maximize the cash flows. In this study, a new production schedule approach is presented that integrates geological uncertainty and generate the extraction sequence for the mining complex problem. The extraction sequence is developed to maximize the net present value and provide a consistent quantity of the material to different destinations. To optimize the quantity of materials sent to different destinations, the destination policies are defined based on the cut-off grade optimization and block economic values. This allows to form the destination policies for the mined material into various processing streams and maximize the value of the operation. The production schedule and the destination policies are optimized within a unified solution approach for the mining complex problem. The work presented advances the field through the development of the new model that uses the combination of maximum flow, genetic algorithm, and Lane’s method for the global optimization of the mining complex. The method simultaneously optimizes the production schedule and the cut-off grade while considering uncertainty. The performance advantages and limitations are analyzed and tested on real-world examples. The results show that the models reduce the production risks and increase the net present value of the mining operation

Forecasting time-to-failure of machine using hybrid Neuro-genetic algorithm – a case study in mining machinery

Forecasting of time-to-failure is an important aspect of a mining machine for the performance assessment, fault detection and schedule maintenance. The knowledge of failure time allows more defined arrangement of preventive maintenance. Traditional methods, including lifetime distribution models, fault tree analysis and Markov models, have a limitation of assuming a specific statistical distribution function to fit the failure time data. In this study, a hybrid data-driven method using neural network and genetic algorithm is proposed to forecast failure time. The forecasting model was developed using neural network algorithm and all the neural network parameters, i.e. input nodes, hidden nodes and the learning algorithms, are selected automatically using the genetic algorithm. The developed model was validated using the failure data of a mining machine. A case study was conducted investigating a load-haul-dump machine (LHD) in the mining industry. Failure historical data for the LHD machine were collected, and cumulative failure time was calculated for time-to-failure forecasting. Study results demonstrate that the developed model performs satisfactory in the prediction of next failure time. A comparative study reveals that the proposed method performs better than existing methods

Grade and Tonnage Uncertainty Analysis of an African Copper Deposit Using Multiple-Point Geostatistics and Sequential Gaussian Simulation

© 2017, International Association for Mathematical Geosciences. Spatial uncertainty analysis is a complex and difficult task for orebody estimation in the mining industry. Conventional models (kriging and its variants) with variogram-based statistics fail to capture the spatial complexity of an orebody. Due to this, the grade and tonnage are incorrectly estimated resulting in inaccurate mine plans, which lead to costly financial decision. Multiple-point geostatistical simulation model can overcome the limitations of the conventional two-point spatial models. In this study, a multiple-point geostatistical method, namely SNESIM, was applied to generate multiple equiprobable orebody models for a copper deposit in Africa, and it helped to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by sequential Gaussian simulation within each equiprobable orebody models. The results were validated by reproducing the marginal distribution and two- and three-point statistics. The results show that deviations of volume of the simulated orebody models vary from − 3 to 5% compared to the training image. The grade simulation results demonstrated that the average grades from the different simulation are varied from 3.77 to 4.92% and average grade 4.33%. The results also show that the volume and grade uncertainty model overestimates the orebody volume as compared to the conventional orebody. This study demonstrates that incorporating grade and volume uncertainty leads to significant changes in resource estimates

Open pit mine production schedule optimization using a hybrid of maximum-flow and genetic algorithms

Production scheduling is a critical activity for the long-term production planning of open pit mining operations. It deals with the effective management of resources and maximizes cash flows to generate higher profits over the life of a mine. Production scheduling problems determine that blocks be mined and processed over a number of periods subjected to mining and processing constraints, which makes the problem more complex. The complexity is further increased due to the uncertainty in the input parameters. In this study, the maximum flow algorithm with a genetic algorithm is used to generate the long-term production schedule. The graph structure for maximum flow is created for multiple periods under uncertainty, and the flow in the arcs is controlled by a genetic algorithm to develop a production schedule. Numerical results for realistic instances are provided to indicate the efficiency of the solutions

Developing risk assessment of push-back designs for an Indonesian coal mine under price uncertainty

One of the critical jobs in the mine planning and design is to optimize an open pit mine under many uncertain factors. This paper explains the incorporation process of the volatility of commodity price or market uncertainty into production phase design and ultimate pit limit using a maximum flow minimum cut algorithm. The Ornstein-Uhlenbeck (OU) mean-reversion process was used to generate 50-coal price simulations for 10-years ahead. For implementation, data from an Indonesian coal mining site was integrated into the method and resulted in 42% differences compared to a conventional way

Open-pit mining complex optimization under uncertainty with integrated cut-off grade based destination policies

The aim of a mining complex optimization is to maximize the economic value of the mining project as a whole. To maximize the economic value, it is required to simultaneously optimize the mining extraction sequence and destination of the material into various processing streams. This work presents a global optimization model to simultaneously optimize all aspects of the mining complex under uncertainty. To solve the mining complex problem, the method uses a combination of the maximum flow and a genetic algorithm to define the optimal production sequence, and the flow of extracted material into various destination streams are defined based on the dynamic cut-off grade optimization and block economic values. The dynamic cut-off grade is optimized using Lane\u27s method. An application for a copper-gold mining complex indicates that the optimizer generates results that reduce the risk of not meeting the targets. When compared to commercial deterministic mine planning software, proposed algorithm generates 9.08% higher net present value and the stochastic design generated 13.70% higher expected net present value compared. Two different destination policies are evaluated to study the impact of destination policies on the net present value. Due to change in destination policies, difference of 4.36% is observed in net present value for the stochastic model

Simultaneous stochastic optimization of production sequence and dynamic cut-off grades in an open pit mining operation

An open pit mining operation is a complex system that constitutes several components or processes. An optimal production sequence that defines timing of extraction and a dynamic cut-off grade policy that defines the supply of materials from sources to destinations within the system are crucial to the success of an operation. In current practice, separate sequencing and cut-off grade models achieve these important milestones as part of strategic planning. This paper presents a mathematical model that derives the optimal extraction sequence and cut-off grade policy simultaneously considering grade uncertainty and stockpiling. A framework of genetic, maximum flow and cut-off grade algorithms solves this complex non-linear problem. An application of the method at realistic copper and gold mining operations reveals the value (up to 29% increase in discounted value) of stockpiling as well as risk quantification under uncertainty