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By Stanley R. Riggs

Extensive phosphate resources occur within the Miocene Hawthorn and Pungo River Formations which have not been considered in the past since they were poorly known and were not available to conventional mining. These resources include 1) deep phosphates that occur below 75 meters of overburden within major sediment basins throughout the southeastern coastal plain, and 2) offshore phosphates that occur on the continental shelf and within the U.S. Exclusive Economic Zone (EEZ). The future potential of these two vast resources will be realized with development of new mining technologies such as hydraulic slurry mining and integrated with advanced technolgy already being utilized in the offshore petroleum industry.

Jan 1, 1986

By Lynn B. Hales

There is a large economic opportunity associated with improving metallurgical performance in mineral processing plants that use process control. Thus, future control systems will be developed towards more sophisticated user friendly, computer-based systems that use powerful software analysis and control techniques. Auto-tuning, adaptive control and process models will gain expanded use because of their transparent availability to the system designer. Control of individual unit operations will be optimized and coordinated through expert systems that monitor economic conditions and management directives and direct operations accordingly. Plant areas, once considered cost centers, will be integrated into plant-wide control systems for economic control and optimization. This article looks at new control hardware, process sensors, control strategies and software development tools. It also looks at the economics driving the continued development of advanced process control systems. Process control systems routinely used in the mining industry were born when the microprocessor was invented. They have continued along an evolutionary path from vendor specific processing hardware and proprietary operating systems to computer industry standard "open architecture" systems. The many economic forces that drive the development and functionality of these process control systems will cause this evolutionary process to continue. Two of the broadest categories are the needs of the end users and the desire of the system manufacturers to distinguish their products from one another. More

Jan 1, 1994

By Carl R. Christiansen

Excellent underground industrial sites and warehouse facilities have been constructed in Europe and at several locations in the United States. Many quarry and mine operators are considering, or should consider, the possibilities of the development of their mineral properties in such a manner as to make underground space that is suitable for a secondary industrial use. Before proceeding with mine or quarry development, the operator should answer the following questions: 1) Is there a need for underground industrial space in your geographical area? 2) Is your deposit suitable for development of space for industrial usage? 3) If the deposit is suitable, what methods of development can be used? 4) What are the comparative economics of the different methods of space development? An honest analysis of the first two questions may eliminate the need for proceeding to the third. Affirmative answers to questions 1 and 2 should be followed by the engineering and economic studies required in response to questions 3 and 4.

Jan 1, 1975

By R. K. Singhal

The underground coal mining systems have kept pace with the developments that have occurred in other types of mining industry. A diversified group of machines with high horsepower motors, built-in microprocessor technology and with numerous options to satisfy the miners need in a varying geological environment are now available. A brief review of this existing technology and its likely status within the next 21 years is presented. Discussed also are some of the on-going advanced developments in electronic mechanization, remote control and automation together with mine wide data monitoring and management systems and facilities to process and transmit data to remote computerized stations.

Jan 1, 1989

By Ji Changsheng

In a large surface mine design, different kinds of mining equipment can be selected. Even though the major purpose of capital investment is to get more profit from mining activities, there are some other complicated factors which influence the selection of mining equipment must be considered as well. A fuzzy evaluation model with fuzzy set theory is proposed in the paper to make a good selection of mining equipment in which different kinds of factors including quantitative and nonquantitative ones can be taken into account at the same time. With the model, mining equipment selection of a large surface mine can take into consideration not only economic factors but also some other factors which play important role in mining system.

Jan 1, 1996

By W. F. Hahnert, T. L. Ward, K. E. Stoll, P. A. S. Ralston

Fuzzy logic is well suited to the control of non-linear, time-varying, and/or any system for which it is difficult to obtain an exact mathematical model. To design a good fuzzy controller, a thorough understanding of the desired process behavior is required. Many papers have suggested that fuzzy controllers are robust. If they are, it is reasonable to explore whether or not they respond better than conventional controllers to errors in the feedback signal due to imperfect measurement or sensor degradation. It may be the case that simpler or less expensive sensors could be used in some processes. The research presented here relies on an integrated array of computer programs that model and simulate (1) the process to be controlled, (2) common feedback instrumentation, and (3) both fuzzy logic controllers (FLC's) and conventional controllers. Various types of feedback sensor imperfections are imposed upon the system, and the performance of the two controllers is compared. Each controller is tuned to its optimal performance in response to a setpoint change using perfect feedback measurement.

Jan 1, 1993

By M. J. Laurila

On-line analyzers have made possible the use of Fuzzy Logic Control (FLC) for local control applications in coal preparation plants. This includes FLC control of complete circuits such as heavy media vessels, cyclones and froth flotation. These circuits can be controlled in real time in a feedback control loop with an on-line analyzer. Control strategies can be readily developed using Statistical Process Control (SPC) techniques which are first applied off-line in manual mode through the use of control charts and then this process is brought on-line using FLC in conjunction with an on-line analyzer to fully automate the circuit. This paper outlines the types of control strategies employed and presents examples of automatic verses manual control.

Jan 1, 1997

By M. J. Laurila

On-line analyzers have made possible the use of fuzzy logic control (FLC) for local control applications in coal preparation plants. This includes FLC of heavy media vessels, cyclones and froth flotation. These circuits can be controlled in real time in a feedback control loop with an on-line analyzer. Control strategies can be developed using statistical process control (SPC) techniques that are first applied offline in manual mode through the use of control charts. This process is then brought on line using FLC in conjunction with an on-line analyzer to automate the circuit. The advent of programmable logic controllers into preparation plants has enabled the implementation of regulatory control. As shown in Fig. 1, regulatory control is the first level of process control as applied in a processing facility. This involves: ? The logic sequences for start-up and shutdown procedures and all the necessary interlocks for safety purposes. ? The systems to control individual parameters and individual units. Various sensors provide information that is useful to plant operators, while some can be used as signals to simple control loops (Couch, 1996). This is the foundation of process control. It must be performed well if there is to be any success in supervisory control (level 2). Supervisory control involves overseeing and directing the operation of unit operations, or of the whole plant. This includes more complex loops with cascade control. And it may involve feed forward and feedback systems. There have been some applications of this kind of control to thickeners and flotation circuits in coal preparation plants. In addition, there are supervisory controls on some dense medium circuits and, in a few cases, on jigs. These are implemented with proportional integral- derivative (PID) controllers that are available in the PLC or distributed control system (DCS) operating soft ware. In a coal preparation plant, there might exist 200 to 400 PID loops. In a coal-pro¬cessing plant, this number is increased to 2,000 loops or more. It is important to use the correct algorithm or function to control a process variable to the point where automatic control shows a markedly decreased variability over manual mode. This is where fuzzy logic and neural networks have an advantage over traditional model-based expert systems. Because dynamic models of coal preparation unit operations do not exist, model-based expert systems that are prevalent in minerals processing cannot work. Not enough is known about the dynamics of the gravity separation processes employed in coal preparation. Therefore, an artificial intelligence (AI) system consisting of a subset of fuzzy logic, genetic algorithms and neural networks must be applied in coal preparation plants if a supervisory control system is to be successful across a range of feed characteristics and variable set points.

Jan 1, 1997

By K. Rajamani, I. Shah

The development of liquid-level control algorithms, based on sets of linguistic rules expressing human operator's "intuitive" control strategies, have been reported. The main objective of the present paper is to illustrate the implementation methodology. The experimental work was done with a 28.3Lflotation cell. The control variable (height of pulp in the cell) was controlled by manipulating the outflow rate. The controller algorithms were programmed and implemented using an Applelle computer and a Keithley das Series 500 measurement and control interface. It is pointed out that the scheme of fuzzy controllers is simple to use and powerful enough to handle complex and nonlinear systems.

Jan 1, 1987

By Hamed Khodadadi, Hamid Ghadiri

In this study, a fuzzy logic self-tuning PID controller based on an improved disturbance observer is designed for control of the ball mill grinding circuit. The ball mill grinding circuit has vast applications in the mining, metallurgy, chemistry, pharmacy, and research laboratories; however, this system has some challenges. The grinding circuit is a multivariable system in which the high interaction between loops, the variation of the system parameters with time, and time delay are some of the problems that pose a challenge. Since the controller parameters are determined offline in classic PID control, these gains are unchangeable and cannot overcome the mentioned challenges in this system. Hence, a fuzzy logic self-tuning PID controller, which is a combination of fuzzy logic and a conventional PID controller, is proposed to control the grinding system, especially in the presence of model mismatch and disturbances. In addition, in order to solve the system challenges, a multivariable control approach based on an improved disturbance observer is proposed to suppress the effects of the disturbances. The most important feature of the algorithm proposed in this paper as the combination of fuzzy logic self-tuning PID and improved disturbance observer is its ability to eliminate system interaction, attenuate internal and external disturbances, and compensate the effect of system uncertainty on the performance of the ball mill grinding circuit. Simulation results in the cases of the set-point change in particle size and circulating load in the presence of model mismatch and external disturbances confirmed the controller performance compared to other controllers.

By Hamed Khodadadi, Hamid Ghadiri

In this study, a fuzzy logic self-tuning PID controller based on an improved disturbance observer is designed for control of the ball mill grinding circuit. The ball mill grinding circuit has vast applications in the mining, metallurgy, chemistry, pharmacy, and research laboratories; however, this system has some challenges. The grinding circuit is a multivariable system in which the high interaction between loops, the variation of the system parameters with time, and time delay are some of the problems that pose a challenge. Since the controller parameters are determined offline in classic PID control, these gains are unchangeable and cannot overcome the mentioned challenges in this system. Hence, a fuzzy logic self-tuning PID controller, which is a combination of fuzzy logic and a conventional PID controller, is proposed to control the grinding system, especially in the presence of model mismatch and disturbances. In addition, in order to solve the system challenges, a multivariable control approach based on an improved disturbance observer is proposed to suppress the effects of the disturbances. The most important feature of the algorithm proposed in this paper as the combination of fuzzy logic self-tuning PID and improved disturbance observer is its ability to eliminate system interaction, attenuate internal and external disturbances, and compensate the effect of system uncertainty on the performance of the ball mill grinding circuit. Simulation results in the cases of the set-point change in particle size and circulating load in the presence of model mismatch and external disturbances confirmed the controller performance compared to other controllers.

By Hamed Khodadadi, Hamid Ghadiri

The ball mill grinding circuit has widespread application in mining, metallurgy, chemistry, pharmacy and research. It faces some challenges because a grinding circuit is a multivariable system in which high interaction between loops, variation of system parameters with time, and time delays are problems. In this study, a fuzzy logic self-tuning proportional–integral–derivative (FLST-PID) controller based on an improved disturbance observer is designed for control of ball mill grinding circuits. It is combined with an improved disturbance observer (IDOB) to eliminate system interaction, attenuate internal and external disturbances and compensate the effect of system uncertainty on the performance of the ball mill grinding circuit.

By C. L. Karr

Researchers at the University of Alabama have developed a method for producing fuzzy linguistic models of mineral processing equipment directly from engineering data. These fuzzy linguistic models rely exclusively on data collected from the processing systems being modeled and do not presuppose an understanding of the physics of the system. The fuzzy linguistic models use descriptive rules and explicit membership functions that provide a precise, nonmathematical description of the processing equipment. A genetic algorithm is employed to simultaneously select rules and tune membership functions used in the fuzzy models. The result is an extremely flexible approach to modelling that is applicable in a variety of linear and nonlinear systems. The effectiveness of this approach to modelling is demonstrated using a four-input, two-output model of a grinding circuit.

Jan 1, 1996

By C. L. Karr, D. L. Meredith, D. A. Stanley

The U.S. Bureau of Mines is currently investigating ways to combine the learning capabilities of genetic algorithms with the process control capabilities of fuzzy logic. Fuzzy logic has been successfully used for controlling a number of physical systems. However, the selection of acceptable fuzzy membership functions has generally been a subjective decision. In this paper, high-performance fuzzy membership functions are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than the membership functions selected by the authors for the liquid level system considered. The approach to developing genetic algorithm-based fuzzy logic controllers is demonstrated on a liquid level controller.

Jan 1, 1990

By Cong Zhang, Huaying Wang, Yixin Wei

"Water inrush is a serious geological accident in underground coal mines. This paper focuses on the method of the bow tie analysis based on fuzzy reliability to assess the risk of water inrush in coal mines. First, post failure probability and repair probability of initiating events are divided into 5 levels. Then triangular fuzzy numbers are adopted to quantify experts’ language and to acquire the reliability rate which close to the actual values by defuzzification and calculation. According to dynamic bow-tie analysis model, using the method of moments to fit the reliability time of events by Weibull distribution and offering the random sampling rule of failure and repair time for illustrating the feasibility of fuzzy reliability evaluation. To demonstrate the effectiveness of the proposed assessment approach, bow tie analysis model of water inrush in coal mines is taken as an example.Introduction Dynamic bow tie (DBT) analysis is composed of dynamic fault tree and dynamic event tree analysis, and considers the impact of the time factor upon risk assessment of a system. There are many algorithms applied to calculation of the probabilities of node accident and accident consequence of dynamic bow tie analysis: Hidden Markov Model, Bayesian Network (Khakzad et al 2013), Integral Algorithm (Amari et al 2003) and Monte Carlo Simulation (Kim et al 2016). In terms of simulated time and practicability, the Monte Carlo Simulation has an advantage over other methods. DBT requires mass data in quantitative analysis, including the failure rates and the repair rates of the basic events of the Fault Tree (FT) and the subsequent events of the Event Tree (ET) at different time points, i.e., the distribution of failure time and repair time. In practical analysis, it is hard to obtain a large amount of exact historical statistical data, which is a major problem of risk assessment. Due to the difficulty of obtaining the exact data and historical failure data of the basic events and the subsequent events, the FT and ET analysis is stuck with the study of minimal cut sets, minimal diameter sets, structural importance and qualitative analysis (Liu and Zhang 2010, Yan et al 2012, Wang et al 2009, Li and Wang 2011). This paper will use fuzzy numbers and the defuzzification method to transform the experts’ language into the failure rates and repair rates of basic events and subsequent events, solving the problem that it is difficult to obtain a large amount of historical statistical data, and will use the method of moment to conduct Weibull fitting of the reliability time to provide the random sampling criteria of event failure and repair time in Monte Carlo simulation."

Jan 1, 2017

Gaby Copper Mining?s Gabriela Mistral property is located in the Antofagasta region of northern Chile, at an altitude of 2,660 m (8,727 ft) above sea level. The reserve contains an estimated 562 Mt (620 million st) of oxide ore with an average grade of 0.41 percent total copper. The mine went into production in 2008. Gaby Copper faced the problem of having no reliable way to measure the solids level. This is a challenge that exists throughout the mining industry, where dust in the air and accumulation of solids on a sensor make accurate measurements difficult in areas such as transfer chutes, the rock box underneath crushers, silos and anywhere else that information is needed on the level of solids. The next-generation technology in the Sitrans LR560 radar from Siemens addresses this challenge. With a frequency of 78 GHz ? higher than previous generations of radar ? it provides a more robust signal for measuring in areas where there are high levels of dust accumulation. The Sitrans LR560 provides a signal beam angle of four degrees, significantly narrower than any other product on the market can offer.

Jan 1, 2014

By S. Norman Kesten

The Galena mine is located astride the Polaris fault in the Coeur d'Alene district of northern Idaho. The surface installations are about a mile up Lake Gulch, the mouth of which is about 2.4 km (1 112 miles) west of the old town of Wallace. The mine had its beginnings when the first claims were located in the early boom days of the district in the 1880s and when the first underground ore bodies were opened up in 1917. During the ensuing 14 years, ore was mined from which were recovered 5.4 Mkg (12 million lb) of lead and almost 14.2 Mg (112 million oz) of silver. Workings consisted principally of a vertical shaft from surface to the 800 level and an offset underground shaft to the 1600 level, together with extensive lateral openings. High-grade silver-copper ore was discovered near the Silver Standard fault in the Sunshine mine, a few miles to the west, on the 1700 level in 1930. By 1937, the Sunshine and adjacent mines were producing silver-copper ore on a regular basis. In 1943 additional ore of a similar type was found along the Chester fault, to the east of the principal Sunshine workings. The interest of American Smelting and Refining Co. (renamed ASARCO in 1975) was aroused and the company's geologists determined that the Galena mine, idle since 1931, provided a geological environment in which similar ore might be found. After negotiations were initiated with the owners of the property, Callahan Zinc-Lead Co., the property in question was deeded to Callahan's wholly owned subsidiary, Vulcan Silver-Lead.

Jan 1, 1990

By J. W. Breen

ASARCO Incorporated has upgraded or is in the process of upgrading the Galena mine electrical, pumping, refrigeration, compressed air, ventilation, hoisting and rockburst monitoring systems to allow continued mining and development work on the 1 589-m (5,200-ft.) level and to provide services for projected deeper mining and development. Additionally, a separate lead-silver milling circuit has been added to the silver-copper mill to process lead-silver ore reserves. This paper describes completed and planned improvements made to date on these systems.

Jan 1, 1991

By J. C. Judd

The U.S. Department of the Interior, Bureau of Mines investigated solvent extraction methods for recovering gallium from filtrates produced from sulfuric acid leaching of hydrometallurgical zinc residues. These filtrates typically had pH values of 0 to 0.5, too high an acid content for effective gallium recovery by traditional extractants. Of the nine extractants studied in batch tests, 0.3M octyl phenyl acid phosphate (OPAP) in a kerosene diluent was the most effective extractant for gallium recovery, extracting up to 99.9 pct of the gallium. This high extraction was possible when the iron in solution was maintained in the reduced ferrous state. Batch tests also showed that a 1.5M-H2SO4 strip liquor recovered 98 pct of the gallium from the loaded OPAP. A continuous solvent extraction system was designed and operated based on McCabe-Thiele analyses of equilibrium loading and stripping isotherms. When 12 L of leach filtrate containing 0.32 giL Ga and 20 giL Fe were pumped through the system in 32 hr, results showed that 95 pct of the gallium was extracted in four stages at an A:O ratio of 1:1. Stripping the loaded solvent with a 1.5M-H2S04 liquor recovered 94 pct of the gallium in four stages at an A:O ratio of 1:4. A two-stage, 50-pct-phosphoric acid strip successfully removed ferric iron from the OPAP, allowing high gallium extractions to be maintained.

Jan 1, 1990

By R. P. Aldis

A wide range of mineral-derived products are required by the electronics industry for it to function. In one form or another, almost all the known elements have been experimented with and tested for various electrical applications. The two major areas for which minerals are used in electronics are as conductors and semiconductors. Conductors provide the means to transport and distribute electricity, while the main application of semiconductors has been to manipulate electricity. Silicon is the most widely recognized semiconductor material, but gallium, indium, and germanium are rated as the most important metals to the semiconductor industry for future and near future technologies.

Jan 1, 1991