In underground coal mines, miners face the hazard of being struck or pinned by a piece of mobile mining machinery. Proximity detection systems have been developed and are used by the industry to protect miners around these machines by detecting the presence of the miners and automatically issuing warnings or disabling machine motion when a miner is in potentially dangerous proximity. These systems were originally developed for continuous mining machines, slow-moving machines that move on bulldozer-style tracks, and are now mandated by the Mine Safety and Health Administration (MSHA) to be used on continuous mining machines. These systems are now being adapted to other underground vehicles, such as shuttle cars, scoops, and battery haulers — vehicles that move on rubber tires at much higher speeds. There are concerns that the detection range of these systems may not provide for an adequate stopping distance on these faster moving machines. To address these concerns, researchers have developed a dynamic modeling system to determine the stopping distance of mobile underground coal equipment. This model can be used in conjunction with worker escapability data and/or information on interaction with other vehicles to provide insight into whether or not proximity detection systems will be adequate for the underground mining workplace. This paper details the background, development, and operation of the resulting application software, focusing on the utility of the graphical user interface to visualize the generated data. The refined data developed by this process can then be utilized by mine operators and proximity detection system manufacturers to more accurately determine the detection range needed to provide effective protection for miners working in an underground mining environment.

The National Institute for Occupational Safety and Health (NIOSH) Pittsburgh Mining Research Division (PMRD) conducts a wide variety of mining-related health and safety research. As part of this research, PMRD’s Workplace Health Branch maintains a Noise Control Team tasked with developing noise controls to reduce future incidences of noise-induced hearing loss (NIHL) among the nation’s mining workforce. A noise control project that PMRD is currently investigating is the development of noise controls to reduce the noise emissions from jumbo drills. Operators of jumbo drills are frequently overexposed to noise, putting them at risk of NIHL. A key contributor to the noise at the operator location is the noise radiated from the jumbo drill string, or drill rod. Jumbo drilling is rotary-percussive in nature, and the drill string is mechanically excited by the cutting of the media as well as by a percussive hammer. These excitations travel from the bit/rock interface and from the drifter hammer into the drill string, vibrating the structure and causing it to radiate noise. The development of an instrumented drill string will allow NIOSH to quantify the forces within the drill string during drilling, providing critical information for the development of jumbo drill noise controls.

Radioactive waste resulting from production of weapons is stored at five U.S. Department of Energy (DOE) locations. The waste characteristics range from fluid to sludge to granular salts with radiation levels exceeding 10,000 rad/h. Innovative tools are used to remotely remove the solidified radioactive waste from underground storage tanks. When available, commercial systems are evaluated for waste dislodging and retrieval applications. A waste dislodging and sluicing system developed to dislodge and fracture deposits of ore in underground mining, called a borehole miner, was evaluated by Pacific Northwest National Laboratory (PNNL) and Waterjet Technology, Inc., for removing solidified nuclear waste stored in underground tanks. This compact system may be installed in tanks via small diameter risers and has the capability to both dislodge and retrieve in a single unit. The borehole miner arm includes an extendible nozzle that operates at high pressure using either water or slurry as the dislodging fluid, while providing a focused high-pressure jet to dislodge solidified material. The sluicer nozzle is attached to a retractable arm that can extend and angle to enhance dislodging in specific areas of the tank by changing the standoff distance. This paper describes the borehole miner system and presents results of experiments to evaluate its ability to dislodge solidified saltcake and sludge materials. Tests were conducted with a stationary jet to evaluate the potential to develop an extendible-nozzle borehole miner system for deployment to dislodge radioactive saltcake and sludge wastes stored in underground storage tanks. The tests were successful and identified ranges of parameters for jet diameter and standoff distance applicable for waste remediation. For saltcake simulants, erosion models were developed that represent the data.

Sustainable energy supply and environmental impact caused by burning of fossil fuels are two of the major worldwide concerns. Regarding sustainable energy supply, several non-conventional alternatives and technologies are currently under study to be implemented from short- to mid-term. On the other hand, CO 2 capture and sequestration (CCS) has been considered as a means for controlling anthropogenic greenhouse gas. Based on these considerations, CCS along with non-conventional heat mining and use of geothermal energy is one of the mid-term concepts that is subject to an in-depth research. One of those research lines involves injection of supercritical carbon dioxide (sCO 2 ) in a naturally high-permeability hydrothermal reservoir in order to mine geothermal energy for subsequent power production. This requires to understand widely the behavior of sCO 2 inside the geothermal reservoir, as well as to study feasible options for sustainable power production using sCO 2 as a working fluid. The present work refers to CO 2 captured from a fossil fuel plant that is compressed and injected into a geothermal reservoir forming a geothermal plume for extracting geothermal energy for power production. Assuming that hot sCO 2 is available for power production, a power system is proposed and analyzed using a process simulation software. The plant configuration allows sCO 2 to circulate through a direct expansion turbine for production of electricity, then sCO 2 at the outlet of the turbine passes through a heat exchanger to provide heat to an organic Rankine cycle for additional power production. Supercritical CO 2 leaving the heat exchanger is compressed and reinjected back into the geothermal reservoir for further heat mining. Based on a pre-selection stage, Isobutane, R245fa and CO 2 were selected as working fluids to analyze the performance of these working fluids on the entire power system. The results show that the combined system makes use of geothermal heat mined by sCO 2 more effectively than a pure ORC, increasing the overall energy efficiency of the system.

Deep sea mining is mineral retrieval process that takes place on the ocean floor wherein global industries are actively exploring and experimenting of different techniques in this relatively new concept of mining for extracting it economically from depths of 5000–5500 m below the ocean’s surface. National Institute of Ocean Technology (NIOT), India has been working on a mining concept for ∼6000 m water depth where a crawler based mining machine collects, crushes and pumps nodules to the mother ship using a positive displacement pump through a flexible riser (umbilical) system. The umbilical also serve as the weight supporting member for the miner and pump. In this paper, static and dynamic analysis of the umbilical system in steep wave configuration and the miner is carried out using ORCAFLEX for launching and touchdown conditions. Three different materials are considered and the best suitable material for umbilical is selected as the first step based on the tension. Then umbilical with Single Miner System is analyzed for the launching and touchdown conditions. Based on the analysis the optimum number and spacing of buoyancy tanks that will keep the stresses within the allowable limits in the umbilical cable are recommended.

To address concerns of how mobile proximity detection systems will adapt to underground mobile haulage vehicles, researchers have collected and categorized data on the parameters of 145 mine haulage vehicles in 5 categories including load-haul-dump, shuttle car, roof bolter, haul truck, and mobile coal haulage (face drill, production drill, and others.) Statistical methods were used to determine the appropriate representative vehicle for each category. These representative vehicles’ parameters and characteristics could then be used to develop a dynamic model that predicts their dynamic behavior on an underground haulageway surface. These models can be used in conjunction with worker escapability data and/or interaction with other vehicles to provide insight as to whether or not the proximity detection systems will be adequate for the underground mining workplace.

Since 1984, remote controlled continuous mining machines (CMM) have caused 40 crushing and pinning fatalities in the United States. Due to limited space in the underground environment and visibility needs, CMM operators typically work close to the machine which exposes them to the danger of being struck or pinned by it. Because of these fatalities, the Mine Safety and Health Administration (MSHA) has published a rule requiring proximity detection systems (PDSs) on all CMMs except for full-face machines. To test PDS performance, researchers at the National Institute for Occupational Safety and Health (NIOSH) conducted a series of field tests in underground coal mines throughout the United States on CMMs equipped with PDSs. The field tests collected data under a variety of conditions to evaluate the warning and shutdown zone performance of these systems. A baseline test condition was measured when the machine was operating in non-mining mode. Three additional conditions discussed in this paper include testing of the PDS while the machine was operating in mining mode, examining the possibility of parasitic coupling to the trailing cable, and examining the effects of the presence of a shuttle car. The results of this study indicate that the average warning and stop zones vary minimally between non-mining mode and trailing cable influence measurements, as well as between the mining mode and shuttle car presence tests. A majority of the measurements for warning and stop zones showed repeatability within +/− 5 inches (12.7 cm). Additionally, parasitic coupling to the trailing cable was not experienced during this field testing. However, these results show that the range of stop zone measurements varied by 4.7 ft on average and as much as 11.7 ft in different field sites. This is most likely due to individual preferences by operators during installation when the warning and stop zone distances are set. While a PDS should effectively stop a CMM when an operator gets too close to the machine, the large variations between field test measurements indicate that there is a wide variation of performance established during system installation.

Fourier transform based frequency representation makes an underlying assumption of stationarity and linearity for the target signal whose spectrum is to be computed, and thus it is unable to track time varying characteristics of non-stationary signals that also widely exist in the physical world. Time-frequency representation (TFR) is a technique to reveal useful information included in the signals, and thus the TFR methods are very attractive to the scientific and engineering world. Local mean decomposition (LMD) is a TFR technique used in many fields, e.g. machinery fault diagnosis. Similar to Hilbert-Huang transform, it is an alternative approach to demodulate amplitude-modulation (AM) and frequency-modulation (FM) signals into a set of components, each of which is the product of an instantaneous envelope signal and a pure FM signal. TFR can then be derived by the instantaneous envelope signal and the pure FM signal. However, LMD based TFR technique still has two limitations, i.e. the end effect and the mode mixing problems. Solutions for the two limitations greatly depend on three critical parameters of LMD that are boundary condition, envelope estimation, and sifting stopping criterion. Most reported studies aiming to improve performance of LMD have focused on only one parameter a time, and thus they ignore the fact that the three parameters are not independent to each other, and all of them are needed to address the end effect and the mode mixing problems in LMD. In this paper, a robust optimization approach is proposed to improve performance of LMD through an integrated framework of parameter selection in terms of boundary condition, envelope estimation, and sifting stopping criterion. The proposed optimization approach includes three components. First, the mirror extending method is employed to deal with the boundary condition problem. Second, moving average is used as the smooth algorithm for envelope estimation of local mean and local magnitude in LMD. The fixed subset size is the only parameter that usually needs to be predefined with a prior knowledge. In this step, a self-adaptive method based on the statistics theory is proposed to automatically determine a fixed subset size of moving average for accurate envelope estimation. Third, based on the first and the second steps, a soft sifting stopping criterion is proposed to enable LMD to achieve a self-adaptive stop for each sifting process. In this last step, we define an objective function that considers both global and local characteristics of a target signal. Based on the objective function, a heuristic mechanism is proposed to automatically determine the optimal number of sifting iterations in the sifting process. Finally, numerical simulation results show the effectiveness of the robust LMD in terms of mining time-frequency representation information.

Carbon dioxide (CO 2 ) emitted from various sources, mainly fossil fuel power plants, is considered responsible of the global warming effect. Many processes and techniques are still under research for CO 2 capture and sequestration. On the other hand, it is proposed that the geothermal heat be mined from geothermal reservoirs using captured CO 2 . In this sense, some theoretical studies show feasibility of using supercritical carbon dioxide (sCO 2 ) as a heat mining media in such geothermal reservoirs. In this work, it is carried out a set of numerical simulations to determine the most effective distance between injection and production wells for extracting geothermal energy utilizing sCO 2 (Water is used for comparison). In the study, the permeability is considered in the range of 0.5 mD to 3.5 mD, with the aim of determining also the critical point in which sCO 2 works better than water (H 2 O) as a working fluid. The remaining properties such as volume, density and other thermal properties remain fixed. Afterwards, it is constructed a numerical model which is implemented in TOUGH2 and PETRASIM 5 software to simulate the cases established. In the model, it is considered a simplified control volume, i.e. only one well for injection and one for production, assuming a constant flow rate at the inlet and at the outlet, meaning that sequestration is not taken into account. A length of 300 meter is defined for reservoir thickness, considering also a pressure and temperature of 100 bar and 200 °C, respectively. The energy mined is estimated for a period of twenty-five years. As typically, the sensitivity analysis is performed by varying only one property and keeping the remaining properties constant, isolating in this way the effect of such variable. Results show that for small permeabilities H 2 O works better than sCO 2 , but it is possible to assure that for permeabilities greater than 1 mD, sCO 2 presents more advantages as extracting heat media instead of water. Both, H 2 O and sCO 2 show a linear behavior. A deep analysis is necessary to carry out, because results shows that sCO 2 works better in an intermediate zone (greater than 200 meter length, but smaller than 800 meter length). An unusual behavior is presented when the distances between the wells are varied; water shows a linear behavior increasing monotonically, while sCO 2 shows a nonlinear behavior for some distances sCO 2 works better. As expected, the more the distance, the greater the amount of the energy mined due to the volume related with each one of the distances.

Currently in the United States, agencies responsible for regulations related to worker or public exposures to dust set rules based on a few general categories determined by gross particle size categories like PM10 (particles < 10 μm) and PM2.5 (particles < 2.5 μm) and the total mass of certain specific compounds (e.g., 3.5 mg/m 3 of carbon black). Environmental health researchers however, have begun to focus on a new category of ultrafine particles (PM0.1; particles < 100 nm) as being more indicative of actual health risks in people. The emerging field of nanotoxicology meanwhile is providing new insights into how and why certain particles cause damage in the lungs by investigating the effects of exposure in animals to very well characterized engineered nanomaterials. Based on this recent research the National Institute of Occupational Safety and Health (NIOSH) has issued new recommended exposure limits (RELs) for carbon nanotubes (CNTs) and titanium dioxide nanoparticles that are 2–3 orders of magnitude more stringent than RELs for larger particles of the same or similar substance. It remains unclear at present how stringent future regulations may be for engineered and inadvertently created nanoparticles or ultrafine dusts. Nor is it clear whether verification methods to demonstrate compliance with these rules could or should be devised to differentiate between engineered and inadvertently created nanoparticles. This study presents a review of the history of dust regulation in the United States, how emerging data on the health risks of ultrafine particles and engineered nanoparticles is changing our understanding of the risks of inhaled dust, and how future rulemaking in regards to these and similar particulate materials may unfold. This review shows the extent to which rules on dust have become more stringent over time specifically in the case of diesel emissions and silica exposure, and indicates that new rules on worker exposure to ultrafine dusts or engineered nanomaterials may be expected in the United States within 5–10 years based on past experience on the time delay in connecting research on new hazards to regulatory intervention. Current research suggests there will be several challenges to compliance with these rules depending on the structure of the final rule and the development of detection technologies. Although the research on ultrafine dust control technologies appears to indicate that once rulemaking begins there may be no serious feasibility limits to controlling these exposures. Based on ongoing exposure studies, those industries likely to be most affected by a new rule on ultrafine dusts not specific to engineered nanomaterials will include transportation, mining, paper and wood products, construction, and manufacturing.

Data Mining has tremendous potential and usefulness in improving the effectiveness of decision-making in manufacturing. Tools and techniques of data mining can be intelligently applied from product design analysis to the product repair and maintenance. Vast amount of data in the form of documents (text), graphical formats (CAD-file), audio/video, numbers, figures and/or hypertext are available in any typical manufacturing system. Our ultimate goal is to develop data-driven methodologies to solve manufacturing problems using data mining techniques. As a precursor, based on a literature study, this paper investigates selective manufacturing areas to identify the requirements for applying data mining techniques in solving potential manufacturing problems. The reviewed manufacturing areas are: (i) the “Design Intent” retrieval process for the product design and manufacturing, (ii) selection of materials, (iii) performance evaluations of manufacturing process design and operation management, and (iv) product inspection, and after-sales services (repair and maintenance). Industrial efforts towards addressing “Big Data” issues have also been briefly narrated in this paper. Lastly, the paper discusses two important data–related issues that may affect any applications of the data mining tools and techniques — (i) uncertainty involved in data collection, and (ii) interoperability of data collected at different levels of an enterprise.

Flexible multistage rotating systems that are supported by water-lubricated rubber bearings (WLRB) have many engineering applications, including power generation, mining, water services, waste treatment, oil and gas industries, and industrial processes. Due to the flexibility of rotating shafts in these applications, dynamic modeling and vibration analysis is essentially important to optimal design and reliable operation of this kind of rotor systems. This paper presents a new model of WLRBs, with the focus on determination of bearing dynamic coefficients. Conventional bearing models are normally of pointwise type, and are invalid for WLRBs that have a large length-to-diameter ratio. The bearing model proposed in this work considers spatially distributed bearing forces, and for the first time, addresses the issue of mixed lubrication, which involves interaction effects of shaft vibration, elastic deformation of rubber material and fluid film pressure. With the bearing model, the dynamic response of a flexible multistage rotating system with WLRBs is described by a Distributed Transfer Function Method (DTFM). The steady-state response of the rotor system due to unbalanced mass is then computed by the DTFM, and compared with experimental data, yielding the dynamic stiffness coefficients of WLRBs. It is shown that the proposed bearing model and DTFM formulation is useful in vibration analysis and optimal design of WLRB-supported flexible multistage rotor systems.

The safety of mining in the United States has improved significantly over the past few decades, although it remains one of the more dangerous occupations. Following the Sago mine disaster in January 2006, federal legislation (The Mine Improvement and New Emergency Response {MINER} Act of 2006) tightened regulations and sought to strengthen the authority and safety inspection practices of the Mine Safety and Health Administration (MSHA). While penalties and inspection frequency have increased, understanding of what types of inspection findings are most indicative of serious future incidents is limited. The most effective safety management and oversight could be accomplished by a thorough understanding of what types of infractions or safety inspection findings are most indicative of serious future personnel injuries. However, given the large number of potentially different and unique inspection findings, varied mine characteristics, and types of specific safety incidents, this question is complex in terms of the large number of potentially relevant input parameters. New regulations rely on increasing the frequency and severity of infraction penalties to encourage mining operations to improve worker safety, but without the knowledge of which specific infractions may truly be signaling a dangerous work environment. This paper seeks to inform the question, what types of inspection findings are most indicative of serious future incidents for specific types of mining operations? This analysis utilizes publicly available MSHA databases of cited infractions and reportable incidents. These inspection results are used to train machine learning Classification and Regression Tree (CART) and Random Forest (RF) models that divide the groups of mines into peer groups based on their recent infractions and other defining characteristics with the aim of predicting whether or not a fatal or serious disabling injury is more likely to occur in the following 12-month period. With these characteristics available, additional scrutiny may be appropriately directed at those mining operations at greatest risk of experiencing a worker fatality or disabling injury in the near future. Increased oversight and attention on these mines where workers are at greatest risk may more effectively reduce the likelihood of worker deaths and injuries than increased penalties and inspection frequency alone.

The fluidized bed reactors are widely used in chemical, mining and pharmaceutical industries and energy applications because of the low pressure drop, the uniform distribution of temperature and of high-speed mass transfer of energy and speed. Fluidization behavior depends on the reactor geometry and internals as well as the particle size distribution and physical properties of the process material. This paper presents a 3D fluid dynamic simulation of a fluidized bed reactor for the pharmaceutical processing of powder, such as mixing, granulation and drying. Firstly, sensitivity analyses based on a literature test-case were performed, for the validation of the computational model and the development of the additional components required for the simulation of a real fluidized bed reactor. Then an unsteady URANS 3D simulation of a modular laboratory-scale fluid bed reactor, product of IMA S.p.A. Active Division, was performed to evaluate the velocity field and particle distribution of the powder involved in the mixing process.

We previously proposed data-mining system to derive useful information from the data and attempt to determine the ball-nose end-milling conditions in a rapid prototype manufacturing system. Practical end-milling condition decision equations are derived to determine the end-milling conditions at each cluster by using response surface method. End-millings of hardened die steel JIS SKD61 (HRC53) and DH31S were carried out to investigate the practicability of derived mining conditions under contour line milling. As a result, our catalog-mining system was found to be effective in selecting the end-milling conditions as the first trial condition. Catalog mining can derive indicative cutting conditions for unskilled engineers.

We have developed a process that uses both hierarchical and non-hierarchical clustering methods to mine data in tool catalogs. Principal component regression is used for quantifying the correlation between the predictor and criterion variables, and multiple regression analysis is used for creating an end-milling condition determinant matrix for each cluster. We fixed the outside diameter of the tool shape parameter as a constant trivial value and examined the correlation between the other tool shape parameters and the end-milling conditions. We thereby extracted valuable new knowledge hidden in trivial parameters and built a hypothesis in regards to data-mining effect. We found that cutting speed is the most important of the criterion variables and that the number of determination coefficient is no less important for determining prediction accuracy of end-milling condition decision equations. End-milling condition decision determinants derived from our data-mining process are important indicators for adjusting end-milling conditions on the basis of end-milling efficiency and tool life.

Granular materials are processed in many industries including agricultural, pharmaceutical, mining, and oil-sand and several geophysical processes such as landslides and avalanches. There are few models capable of predicting the flow of granular materials and particularly their collapse. Theoretical studies of the topic usually encounter difficulties in accurately predicting the collapse dynamics and final stable heaps. The two-dimensional gravitational collapse of cohesionless rectangular granular piles is numerically investigated in this paper. Piles surrounded by either air (dry case) or an oil-water mixture (wet case) undergo a dam-break collapse onto a horizontal base. The granular material is modeled as a perfectly plastic substance based on the Mohr-Coulomb law. The constitutive relations represent the granular material as a fluid, with a shear viscosity as a function of solids pressure, the second invariant of the deviatoric strain-rate tensor, and the internal angle of friction of the granular material. This two-phase flow problem (grains and liquid or air) is then formulated accordingly and solved by the mixture model method for the wet collapse and the level-set method for the dry collapse using COMSOL finite-element software. In both air and the liquid, stable heaps are achieved. The results are compared with experimental measurements of Balmforth & Kerswell [1] and Rondon et al. [2]. The model can closely predict the final shape of the collapsed dry pile. The final shape of the collapsed wet pile is also well-predicted when its initial packing concentration is relatively low. Further developments are needed to model the wet collapse of high-initial-concentration piles.

The prediction method of the experimental stress wave at the welded joints of the body under actual operations was developed, and the strength evaluation flow based on the method was proposed. We conducted running tests using a dump truck prototype, the EH3500ACII, as an experimental approach. The high accelerations in longitudinal, vertical, and lateral directions were measured for a sudden stop, bump override and sharp turn test, respectively. The tendencies of the stress waveforms at the welded joints in each running test are in good agreement with those of the acceleration waveforms. The distinct element (DE) and finite element (FE) methods were used to predict the experimental stress waves. First, the pressure loads of soil were analyzed using DE method. Kanto loam (Japanese soil) was modeled using a large number of particles, and filled a container modeled using rigid plates to form a shape similar to that of actual loads. The soil pressure distributions were formulated with a variety of acceleration loads. Next, the obtained load pressures were applied to the FE model as the load conditions. The stresses at the evaluation points were calculated under a 1G acceleration in the vertical, lateral, and longitudinal directions, respectively. Finally, those stress results and the measured time series acceleration data were multiplied, and the stress waveforms under running conditions were predicted by using a linear superposition method. When the analytical stresses were compared with the experimental results, the tendencies of the analytical stress variations corresponded well with those of the experimental stress waveforms. On the other hand, the analytical stresses were smaller than the actual experimental values. This was because the weld geometries were not modeled in FE model, and the stress concentrations on the weld toes were not precisely estimated. The strength evaluation flow was developed by using the soil pressure distribution formulated using the DE analysis, the database of the time series acceleration data obtained from the running tests, the ratio of the stress ranges of the experiment and that of the analysis, K stress_ratio , and the strength criteria for welded joints. It is possible to evaluate the durability of a new structure model in a short period of time by using this evaluation flow.

Steel roller discs are an efficient mechanical tool for cutting soft to medium strength rocks in both civil and mining projects. However, their application for hard rock cutting has been hindered since steel discs wear quickly and fail prematurely due to high concentrated stresses generated at the sharp corners and thin elements of the disc. To overcome this problem, the current generation roller discs comprises a steel shaft connected to a tungsten carbide (WC) disc. In other words, while the material of the disc itself is replaced by a stronger WC material, steel continues to be used for the shaft body. In this paper, to comprehend the influence of material parameters on the induced stresses inside different parts of a roller cutter, an analytical closed-form solution and a series of finite element (FEM) numerical studies are performed. By considering the problem as a traction boundary-value problem in an elastic domain, the analytical solution is based on the Airy stress formulation in Cartesian coordinates with a Fourier series representing the boundary conditions. The analytical result is developed for both plane stress and plane strain conditions, encompassing all possible loading configurations and shaft geometries. To verify the numerical models, these results are also compared with laboratory experimental data obtained from cutting granite using mini discs for the penetration range 1 to 3 mm.

One of the oldest industrial diseases, silicosis, results from exposures to respirable crystalline silica, also known as silica dust. Workers worldwide exposed to silica dust span a variety of industries from construction, mining, sandblasting, to masonry, and machinery. In the United States alone 1.7 million workers are exposed to crystalline silica, which can also lead to lung cancer, tuberculosis, and other chronic airway diseases [1]. This paper will examine the health effects of silica dust on the worker, discussing exposure paths, work groups affected, occupational safety measures, worker health policies, and compare these among the developed and developing world. Implementing worker safety programs, which include wearing Personal Protective Equipment (PPE) and health monitoring and developing exposure limits, can help mitigate the adverse health risks of working with silica dust. A comparison of the successes and limitations of several programs from around the world will show that strong national occupational safety programs can reduce the mortality and illness rates of silicosis due to occupational silica dust exposure. Without a strong national occupational safety program history shows that workers will remain unprotected until they rally together under tragedy to fight for safety. Before more workers suffer and die tragically and unnecessarily, global safety policy makers can take a lesson from history, examine and compare current programs, and implement strong national occupational safety programs that save lives.