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By Nick Barton

A recent analysis of 145 TBM tunnels of more than 1000km total length has helped to quantify the time dependence of utilization (U). In fact U = Tm, where T is time in hours and m is a negative gradient of deceleration. In poor quality ground, gradient m has a strong dependence on rock mass quality Q, for example m ˜ -0.5when Q = 0.1. Good investigation is critical for ensuring faster TBM tunneling then drill-and-blasting, which tends not to slow down (in m/hr) as the tunnel length increases. Anew design model called QTBM allows more reliable prognoses be made, and occasionally will disqualify the TBM solution.

Jan 1, 2001

By Jackson M. Langton

INTRODUCTION The Morenci-Metcalf district is located on the southern slope of the white Mountains in Greenlee County, Arizona. Both porphyry copper deposits are four to six miles northwest of Clifton, the county seat, and the new Metcalf mine is situated approximately one mile northeast of the Morenci open pit (Figure 1). Chase Creek, a south-flowing tributary of the San Francisco River, dissects the district and separates the two ore bodies. More than 1,200 exploration and development drill holes have been completed in the district and the Morenci-Metcalf ore bodies have been delineated on a 400-foot grid. Recently initiated drilling programs have revealed new data about deeper protore and associated hypogene alteration. This information, correlated with surface mapping and petrographic studies, was utilized to propose a plausible geochronologic sequence for this region. The reasoning behind this study has been helpful in locating new ore beneath leached capping indicative of only protore mineralization. The scope of this paper is to establish a practical solution to a highly theoretical problem of ore genesis and to familiarize the reader with stratigraphic, structural, and mineralogic events responsible for localizing ore in this district. Continued research will possibly alter the proposed time span and relative displacements, but it is doubtful that the order of events will vary significantly. Geochronologic conclusions are therefore presented as general hypotheses and additional geologic mapping,

Jan 1, 1972

By M. L. Harris

Coal dust produced during the mining process is a prime explosion hazard in underground coal mines. To inert the coal dust and prevent a catastrophic propagating dust explosion, rock dust is applied to the roof, ribs, and floor surfaces of mine entries. Currently, one determination of whether a mine is adequately protected from the coal dust explosion hazard is made by analyzing collected mine dust samples for percent total incombustible content (% TIC). A minimum of 80% TIC is required per 30 CFR 75.403. The as-received moisture of the dust sample is included in the % TIC. Moisture and humidity within a coal mine vary greatly with the seasons, with explosions occurring more frequently during dry winter months. This paper will discuss the fluctuations of the as-received moisture content of the mine dust samples collected by U.S. Mine Safety and Health Administration inspectors in 2010 and the observed fluctuations of the moisture content of rock dust within the U.S. National Institute for Occupational Safety and Health Office of Mining Safety and Health Research Bruceton Experimental Mine and the Safety Research Coal Mine over a one-year period.

By S. A. McGhee

Introduction ASARCO's Ray Operations include an open-pit copper mine in the Mineral Creek Mining District of Pinel County. The district is located in east central Arizona, 132 km (82 miles) southeast of Phoenix, AZ, and 126 km (78 miles) north of Tucson, AZ. The mine lies 8 km (5 miles) north of the Gila River in Mineral Creek Valley, between the Tortilla Mountains to the west and the Dripping Springs Range to the east. The rich copper deposits of the Mineral Creek mining district were first noted by a US Army Officer in 1847. The Ray Copper Company was the first major operation in the area. The company was named after the Ray mining claim, which was one of the earliest recorded claims. After decades of sporadic and unsuccessful mining ventures, the Ray Consolidated Mining Company was formed in 1910. The company introduced one of the first successful block caving operations in the world. In 1933, the Kennecott Copper Corporation acquired the Ray Consolidated Mining Company and continued operation of the mine under the same name until 1943, when the property was renamed the Ray Mines Division of Kennecott. Underground mining continued until 1955, when the mine was converted to an open pit operation. In November 1986, ASARCO Inc. purchased the Ray Mines Division and related facilities from Kennecott. The purchase also included the Hayden concentrator and the Ray Smelter, located approximately 32 km (20 miles) from the Ray Mine. This paper summarizes the 54,400 tpd (60,000 stpd) in-pit crusher and overland-conveying system and the 24,200 tpd (30.000 stpd) copper SAG concentrator and tailings disposal system constructed at ASARCO's Ray Complex - Ray Operations. General description of facilities The Ray concentrator includes a sulfide SAG concentrator, a "relocatable" in-pit primary-crushing plant and overland-conveyor system, a concentrator tailings-disposal system and plant ancillary facilities, The major elements of the project include: • A relocatable Fuller-Traylor in-pit 1524-mm (60-in) x 2261-mm (89-in) primary gyratory crusher at the 442 m (1450 ft) mine elevation. The crushing plant has a nominal capacity of 54,400 tpd (60,000 stpd) of sulfide ore. • A 1524-mm (60-in) wide x 1253-m (4110-ft) long overland belt conveyor, which runs from the in-pit crushing facility to a transfer station serving both the Ray Concentrator and the existing Hayden concentrator railroad load-out facility. • A 1524-mm (60-in) wide x 152-m (500-ft) long stackerbelt conveyor, which delivers a nominal 27,200 tpd (30,000 stpd) of ore from the transfer station to a 23,600 t (26,000 st) live-capacity coarse-ore stockpile serving the Ray Concentrator. • A nominal 27,200-tpd (30,000-stpd) concentrator, including semiautogenous grinding, flotation, copper concentrate filtration and associated plant ancillary facilities. • A tailings disposal system. Flotation tailings are pumped from the Ray Concentrator to a 122-m (400-ft) diam. thickener, located 4.8 km (3 miles) southeast of the Ray Concentrator site. The thickener overflow is pumped to two 76.2-m (250-ft.) diam. concrete process-water reservoirs. The thickener underflow, at 45% solids, is pumped to a new tailings disposal site located at Elder Gulch. • An HDPE-lined temporary-containment area to collect material from Ray Concentrator plant upsets and local storm-water run-off from a 100-year, 24-hour rain event. •A reclaim-water system from the Elder Gulch tailing pond area to the Ray Concentrator. • A 610-mm (24-in.) diam. x 31.9-km (19.2 mile) long fresh-water pipeline from the Hayden fresh-water-well field system to the Ray Concentrator. The overall process design criteria for the new Ray Concentrator is based on a nominal throughput of 27,200 tpd (30,000 stpd) of ore at 90% plant availability. However, based on the maximum performance capacity of the installed grinding equipment, it may be possible to achieve a sustained operating level in excess of 36,300 tpd (40,000 stpd) when processing soft ores. The balance of this paper will cover the major process systems including:

Jan 1, 1995

By D. Brunner, G. R. Baiden, H. J. Yazici

System simulation is an increasingly popular technique for analyzing and improving existing as well as newly designed underground mining operations. An engineering firm that specializes in simulation helped a major metals producer build a long-range planning model of an existing underground mine. Subsequently the team adapted this model to assess technology alternatives in the design of a new mine. The two models differ in the level of detail used for modeling the development process. This paper discusses the differences in approach between the models along with the experiences gained and challenges faced during the construction and use of the models.

Jan 1, 1999

By Jonathan K. Hrica, Brianna M. Eiter

The ability to identify hazards during a workplace examination is a critical skill for mineworkers to have in order to maintain a safe workplace. While research suggests that being able to successfully recognize hazards requires a complex set of competencies, it is unclear which competencies are most critical to adequately perform a workplace examination. This paper presents a hazard recognition competencies framework as an explanation of the competencies that affect mineworker hazard recognition. To better understand how the industry is selecting and preparing workplace examiners, researchers fromthe National Institute for Occupational Safety and Health (NIOSH) interviewed nine health and safety (H&S) professionals at surface stone, sand, and gravel mine sites throughout the USA. A primary purpose of these interviews was to verify the competencies included in the hazard recognition competencies framework and to understand why these competencies are critical. Finally, the results of this study are presented within the context of current NIOSH research findings related to hazard recognition.

Jul 27, 2020

By Y. V. Vasylchuk

An algorithm is presented for optimizing the classification of surface mine material subject to excavating constraints. High-resolution, expected-profit models are optimized to classification maps subject to site-specific excavating constraints. This optimization problem defies traditional closed-form analytical solutions. A practical heuristic algorithm quickly determines the optimum final destination for material subject to realistic selectivity constraints. The expected profit could be calculated from an estimated model if the value calculations are all linear; otherwise, simulation could be used. In both cases, a single expected profit for all destinations on a high-resolution grid is the starting point for dig limits optimization. Maximizing expected profit in a risk-neutral manner is correct given the repeated nature of grade-control decisions over relatively short timeframes.

By Steve Wiig

In 1987, the Rock Springs, WY district office of the US Bureau of Land Management (BLM) began to develop an automated system to store, maintain and manipulate more than 14,000 drill hole logs contained in the district's office files for various solid leasable minerals. The development of this system was based predominantly on a trial-and-error basis, with many procedural changes incorporated over time. The current Automated Drill Hole Database and Analysis System will continue to evolve with new software developments and increased personal expertise in this field. The mission of the BLM's Branch of Solid Minerals is mandated by a series of federal laws. The most important are The Mineral Leasing Act, the Federal Land Policy and Management Act, the Federal Coal Lease Amendments Act and the Surface Mining Control and Reclamation Act. Several regulations, promulgated from these laws, dictate that BLM must perform certain tasks and functions as part of its specific duties. The regulations that apply to the BLM Solid Minerals program are codified at 43 Code of Federal Regulations (CFR) 3400 for coal, and at 43 CFR 3500 for non-energy solid leasable minerals, such as sodium, phosphate and potash. Specific functions required of the Branch of Solid Minerals necessitates the use of geologic and drill hole data analyses. These functions can be divided into pre-lease and post-lease. Examples of pre-lease functions that normally require some level of drill hole data analyses include:

Jan 1, 1992

By E. Siami-Irdemoosa, S. R. Dindarloo

Ease of excavation or rippability, also called diggability or excavatibility, is a critical decision-making parameter in the selection of both the overburden removal method and the required equipment in surface mining and geotechnical engineering projects. The most widely used method of estimating rippability is rock mass classification. Although there are different classification schemes in the literature, all of them have the two major limitations of sharp transitions at class boundaries and subjective uncertainties in decision making. In this study, the decision trees method was employed as a classification tool for estimating ground rippability, using the four rock mass parameters of weathering degree, uniaxial compressive strength, joint spacing and bedding spacing. The results were compared with those of the widely used method of diggability index rating (DIR) and a fuzzy-based DIR. The problem of subjective uncertainty was resolved in the proposed decision trees method, as the tree rules were derived automatically from the training data sets. Compared with fuzzy-DIR, a decision tree classifier is simpler, needs less computational time and is more appreciable by industry practitioners. Nevertheless, the proposed method is not flawless and, similar to both the conventional and fuzzy DIR methods, yielded poor estimations in particular instances. The limitations of the three methods are discussed.

Jan 1, 2015

By S. Que

Local community acceptance is a key aspect of planning and managing mining projects, whether through sustainability assessment and reporting or corporate social responsibility programs. However, there has been very little research to understand the determinants of community acceptance. This paper seeks to identify key mine characteristics and demographic factors that influence individual acceptance of mining projects. Six demographic factors and 16 mine characteristics were selected based on literature review. Four of the six demographics factors were confirmed to be significantly (p < 0.05) correlated with people?s attitude of the importance of the mine characteristics. All sixteen mine characteristics are identified as important factors. The most important mining factors are Job opportunities, water shortage or pollution, air pollution and land pollution. Both the mining and control groups have same opinion of 12 mining characteristics and differ on four (infrastructure improvement, labor shortage for other businesses, noise pollution, and mine life). During design and management of mines, this data can be used as a reference. This result will facilitate better choice experiment (survey) design for discrete choice modeling. Such discrete choice models can provide a viable framework for data-driven community engagement and sustainable mine management.

Feb 23, 2014

By Turker Hudaverdi, Yasar Agan

This study tries to predict blast-induced flyrock using intelligent modelling techniques. Flyrock is a site-specific phenomenon. Design variables and rock properties highly influence flyrock throw distance. Site investigations were conducted in a sandstone quarry. Fundamental operational parameters, blast dimensions, bench face condition, and rock mass structure were monitored. Stepwise regression technique was applied for variable selection. Burden–hole diameter ratio, in situ block size, and powder factor were determined as the most significant parameters. Considering output of stepwise regression, artificial neural network, ANFIS, and Gaussian process regression techniques were applied to predict flyrock throw. A comprehensive validation was performed using twelve different performance indices. Pre-determination of input parameters supported development of successful soft computing applications. Rock block size was found to be an appropriate input variable for flyrock modelling. In addition to classical performance indices, standardized and symmetric accuracy metrics were quite useful in model validation process. The intelligent models predict flyrock range with an error less than 6 m. The mean percentage errors are lower than 10%. ANFIS seems to be the best model for flyrock prediction. The calculated mean absolute error is 5.36 for ANFIS model.

Nov 12, 2023

Arsenic (As) removal behavior from Changgoon mine tailings was investigated using Acidithiobacillus ferrooxidans (A. ferrooxidans) in well-controlled batch reactors by varying pulp density (0.5?4%, pH=1.8, temp=30oC), reaction initial pH (1.8?2.2, pulp density=0.5%, temp=30oC), and temperature (25?35oC, pulp density=0.5%, pH=1.8). The initial concentration of As in the mine tailings used for bioleaching tests was about 30000 ppm, and the size range of tailings used in this study was 63?74 µm. The reaction speed and initial cell concentration were fixed to 150 rpm and 107 cells/mL, respectively. To complement leaching experiments, pH, oxidation/reduction potential (ORP), and amount of ferrous/ferric ions in leachate were analyzed. Scanning electron microscopy and x-ray diffraction analyses were also conducted for the samples before and after leaching to see if any morphological and compositional changes in tailings took place during leaching process. The As leaching efficiency increased with decreasing pulp density, decreasing pH, and increasing temperature in the presence of A. ferrooxidans. Specifically, no significant change in As removal rate was observed when pulp density d 2% while the removal rate dramatically decreased at pulp density of 4%. Regarding pH effect, As removal rate was much more enhanced at lower pHs (i.e., 1.8 and 2.0) compared to pH 2.2. Increasing temperature was observed to tend to enhance As removal rate, showing about 20% enhanced leaching rate under 35oC compared to 25oC and 30oC at the elapsed time of 5 days. The results for As removal behavior at different conditions (i.e., pH, temperature, pulp density) were consistent with the change in pH, ORP, and ferric ion concentration in leachate, showing greater removal rate at the condition with lower pH, and higher ORP and ferric ion concentration in leachate. Furthermore, decreasing removal efficiency was likely in part due to the extent of the formation of by-product (i.e., jarosite) of ferric ions.

Feb 27, 2013

By T. Grammatikopoulos, C. Gibson, M. Aghamirian

"Lithium is a silver-white alkali metal that has recently become a target commodity for many junior mining companies. Lithium has a wide range of applications including ceramics and glass, lubricating greases, air treatment, continuous cast mold flux powders, polymer production, and primary aluminium production, but the current popularity of lithium can be attributed to an anticipated increase in demand for lithium ion batteries. The most common lithium-bearing minerals occur globally in pegmatites and granites. Spodumene [LiAl(Si2O6)] is the most desirable economic lithium-bearing mineral due to its high lithium content (theoretical Li2O content of about 8 wt%). However, lithium can also be hosted in petalite, micas, tourmaline, and phosphate minerals. In Canada there are a number of spodumene deposits in Ontario and Quebec at development stage that have the potential to help narrow the forecast lithium supply gap stemming from the widespread acceptance and use of electric vehicles. This paper discusses the beneficiation of hard rock lithium deposits, including an in depth review of the use of flotation to upgrade spodumene. A review of the use of dense media separation (DMS), magnetic separation, and roasting as means to process spodumene bearing ores is also discussed. INTRODUCTION Lithium is a soft, silver-white alkali metal with atomic number 3. As of 2015, the end use of lithium was estimated to be as follows: lithium-ion batteries (35%), ceramics and glass (32%), lubricating greases (9%), air treatment (5%), continuous casting mould flux powders (5%), polymer production (4%), primary aluminium production (1%) and other uses (9%) (Jaskula, 2014). Lithium is found in brine lake deposits containing lithium chloride and in hard rock pegmatite deposits. It can be sold in a range of product types; as mineral concentrate, brine, compound (lithium carbonate or lithium hydroxide) and metal depending on its intended end use (Goonan, 2012; Jaskula 2014). In the 1990s the United States and Chile were the largest producers of lithium globally, primarily from brine operations (Ober, 1994). Most of the lithium compounds produced over this period were consumed in the glass, ceramics and aluminium industries. In the 1980s and 1990s there were two major lithium operations in the United States: brine operations at Silver Peak, Nevada, and a spodumene mine and lithium carbonate/lithium hydroxide plant in Kings Mountain, North Carolina. The Kings Mountain operations ceased production in 1991 and were eventually dismantled in 1994. Brine operations were also active in Argentina and Chile, while the largest lithium ore deposit in the world was operated at Greenbushes, Western Australia. During this period it was predicted that major shifts in demand for lithium would come from the success and widespread use of electric vehicles (Ober, 1994)."

Jan 1, 2017

By Hemant Agrawal, Arvind Kumar Mishra

Rock breakage using blasting is one of the important operations of the mining industry. The delay timing decides the initiation sequence of holes in the blasts and is a crucial factor to improve the overall blast performance. Blast-induced ground vibration is an undesired yet unavoidable outcome of blasting. Peak particle velocity (PPV) is commonly used to measure the magnitude of blast-induced ground vibration. Channelization and better utilization of explosive energy delay are provided between blast holes using initiating systems. The use of different initiation systems even on the same site results in a very significant difference in the values of PPV generated. Due to scattered delay timings of pyrotechnic detonators, the scaled distance predictor equations obtained become unreliable. The error/scatter in delay timing leads to overlapping of holes and hence increases in maximum charge per delay. In this paper, an analytical approach has been discussed to measure the overlapping of holes during blasting with initiation systems having inherent cap scatter. The analytical approach will be calculating the probable overlapping of holes due to scatter in delay timing and will help blasting engineers to understand the variation in maximum charge per delay due to pyrotechnic based initiating systems. In this study, a total number of 102 blasts were conducted using different initiation systems at two different opencast coal mine and 221 corresponding blast vibrations events were recorded.

Nov 10, 2020

By A. K. Martin

Adaptive management can be described as a structured, iterative process of robust decision making in the face of uncertainty, with a goal of reducing uncertainty over time through system monitoring. Adaptive management is a tool which can be applied to not only change a system, but learn about the system (Holling, 1978). Adaptive management has been used in ecosystem management for years and guidance is provided in a variety of handbooks and publications. Adaptive management can be applied to industrial facilities and mines through the use of an adaptive environmental management plan (AEMP) and associated practices. The goal of this paper is to provide an explanation of how the plans and practices support regulatory agency approvals and permitting, span the life-cycle of mines, manage uncertainty, and assist in addressing mining impact and legacy issues. AEMPs and associated practices are useful tools to address the uncertainties in identifying, evaluating, and managing impacts of proposed new and modified mining facilities undergoing environmental review. Once a proposed project is approved, the AEMP continues as a living document, being informed, revised, and adapted by the operations. The AEMP can serve as an ongoing management tool for describing and evaluating environmental impacts, continuing in operations into post reclamation. If initiated during planning and environmental review of a project (and in some locations such as the United States [U.S.], the preparation of an Environmental Impact Statement (EIS)), the AEMP becomes a basis to identify, monitor, and manage potential project environmental impacts. Development of an AEMP for an operating mine is also beneficial since there are generally developing and ongoing environmental issues at those facilities as well. Over time, the AEMP is adapted to changing requirements and conditions including those anticipated and unanticipated. Ever increasing scrutiny of mining projects and the complexities of the EIS process highlight the uncertainties inherent in impact predictions. In making the momentous decision of initial project approval, these uncertainties can discourage the regulatory agencies? actions and create stakeholder anxiety and controversy about a project. A well-designed AEMP can support the decision to approve an EIS and enable a project to move forward. Additionally, an AEMP can inform financial assurance estimates and revisions over the life of a mine providing all parties including the citizens of the surrounding communities with greater confidence that potential environmental impacts and risks will be well managed.

Feb 23, 2014

By A. D. Davis, J. L. Sorensen, C. J. Webb

Environmental impacts from acidic mine drainage are growing national concerns that are particularly acute in the headwaters of the Rocky Mountain region. In the Black Hills of South Dakota, approximately one thousand mines are inactive or abandoned, most of them in the headwaters of major drainages. This paper examines the Bear Butte Creek basin, a heavily mineralized watershed with significant historical mining impacts and a surface gold mine permitted in 1997. The watershed loses its surface discharge to swallow holes in the karstic Madison Limestone. The integrity of the aquatic ecosystem was monitored with a suite of sitespecific bio-indicators. Stream surveys covered the entire reach of Bear Butte Creek from near its headwaters to the swallow holes in the Madison aquifer. Data from the stream surveys, water-quality sampling, and bio-indicator surveys clearly showed that water-quality impacts are limited mainly to Strawberry Creek and to the area of Bear Butte Creek near and downstream of the town of Galena.

Jan 1, 1999

The 18th annual American Mining Hall of Fame Awards Banquet sponsored by the Mining Foundation of the Southwest will be held on Saturday, Dec. 2, 2000 at The Westin La Paloma in Tucson, AZ. The 2000 inductee, guest of honor and featured speaker at the banquet will be Ronald C. Cambre, chairman and chief executive officer of Newmont Mining. He will be honored for his role in positioning Newmont as an innovative and key player in the gold mining industry.

Jan 1, 2000

By V. T. McLemore

"Production of industrial minerals remains important to the rural economy of New Mexico. In 2014, 226 active mines and mills were registered in New Mexico and included four coal, eight potash, 11 metal, 33 industrial mineral and 170 aggregate and stone operations. New Mexico leads U.S. domestic production of potash, perlite, zeolite and travertine. Other production includes aggregate, humate, pumice, gypsum, salt, common and fire clay, scoria, limestone, fly ash, cement, magnetite, silica and decorative stone. New Mexico also has the potential for additional production of industrial minerals. One company is exploring for garnet. Cretaceous black sandstones in the San Juan Basin have drawn interest for titanium, rare earth elements (REEs), niobium and zircon, and other REE deposits are being explored. REE-thoriumuranium veins have been found in the Gallinas, Capitan and Cornudas Mountains and Laughlin Peak-Chico Hills. Exploration for beryllium has occurred in the Ojo Caliente No. 2, Cuchillo Negro and Victorio mining districts. Companies also are examining high-calcium limestone and high-magnesium dolomite for potential development, while aggregate continues to be important in urban areas and along highways. IntroductionIndustrial minerals and rocks are literally the building blocks of our way of life. They are an exceptionally diverse and vital group of raw materials that underpin almost all aspects of human activity, infrastructure and standard of living. Industrial minerals and rocks are used in the manufacture of many products, from ceramics to plastics to refractories to paper. Although industrial minerals permeate nearly every aspect of daily life, their presence and their roles are typically invisible. A widely used definition of industrial minerals and rocks is “any rock, mineral, or other naturally occurring substance of economic value, exclusive of metal ores, mineral fuels, and gemstones: one of the nonmetallics” (Bates, 1975). This definition includes the important construction materials sector as well as specialized industrial minerals, but complex interactions between consumer industries produce a number of apparent anomalies to this definition. Some metals are included as industrial minerals because their use is industrial rather than as a metal. Sulfur is now predominantly extracted as a byproduct of cleaning natural gas, one of the world’s major energy fuels.Production of industrial minerals has been and remains important to the rural economy of New Mexico. Industrial minerals constitute nearly 41 percent of the more than $3.1 billion generated by mineral production in New Mexico in 2014 (Table 1). In 2014, 226 active mines and mills were registered in New Mexico and included four coal, eight potash, 11 metal, 33 industrial mineral and 170 aggregate and stone operations (New Mexico Mining and Minerals Division, 2015a). New Mexico leads U.S. domestic production in potash, perlite, zeolite and travertine. Other industrial minerals production from New Mexico includes aggregate, humate, pumice, gypsum, salt, common and fire clay, scoria, limestone, fly ash, cement, magnetite, silica and decorative stone. Additional production statistics can be found in Harben et al. (2008). Major industrial mineral districts are shown in Fig. 1."

Jan 6, 2017

By K. F. Bailey

Naturally occurring asbestos in aggregate and other mineral deposits, and the release of asbestos fibers into the ambient environment, is a public health concern. Determining the presence of asbestos in an ore body also presents a considerable sampling and analytical challenge. Improper and inaccurate identification of asbestos can result in unwarranted public and worker panic leading to significant costs to mine operators both in terms of dollars and company image. Some companies have been forced out of business simply because they had the non-asbestiform, prismatic variety of one of the six asbestos minerals. In the late 70’s, asbestos was found as a contaminate in a trap rock quarry in Rockville, Maryland. This discovery led to a massive overreaction by federal, state and local governments. In 1986, the Occupational Safety and Health Administration (OSHA) promulgated a final rule that regulated the common non-asbestiform mineral habits of actinolite, tremolite, and anthophyllite as if they were asbestos. The rule was reversed after a six-year struggle. Recently, the Libby, Montana, W. R. Grace vermiculite - asbestos, as well as the California chrysotile in serpentinite, and the New Jersey Southdown quarry – asbestos events have rekindled the entire issue of naturally occurring asbestos. It is time to get this concern addressed with sound science so that real asbestos is properly identified and controlled.

Jan 1, 2002

By Manoj Khandelwal, Jian Zhou, Yingui Qiu, Peixi Yang, Pingan Peng

Rockburst prediction is crucial in deep hard rock mines and tunnels to make safer working conditions. Due to the complex interaction of many factors involved in rockburst prediction, such as multi-variable and multi-interference factors, three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL). The RHL is determined according to four kinds of microseismic characteristic parameters including angular frequency ratio, total energy, apparent stress, and convexity radius. Then, six types of microseismic characteristic parameters are taken as input variables in 343 sets of data, including angular frequency ratio and total energy, etc.. And the RHL is taken as the output target of rockburst prediction. The classification performance of PSO-SVM, HHO-SVM, and MFO-SVM hybrid models is evaluated by accuracy (ACC), precision (PRE), and kappa coefficient. Findings reveal that the MFO-SVM model performs best in terms of accuracy, with ACC, PRE, and kappa coefficients reaching 0.9559, 0.9063, and 0.9094 respectively, while PSO-SVM and HHO-SVM have similar performances. However, the PSO-SVM, HHO-SVM, and MFO-SVM all perform better than the unoptimized SVM model. This confirms that the three optimization algorithms significantly enhance the rockburst prediction capacity of the SVM model to help mine practitioners apply machine learning methods to rockburst prediction problems appropriately.

Feb 3, 2023