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By L. F. Reinartz

ANOTHER year has rolled by. We are twelve months further away from the start of the depression and. therefore that much nearer to recovery. The accumulated needs and wants 'of our lame, virile population are ready to burst their bonds as soon as confidence returns and credit is stabilized. The steel industry has been harassed by uncertainty of the Government's attitude toward organized labor's de¬mands, and irregularity in volume of production. Steel labor, if left to its own devices, would in most instances, march shoulder to shoulder with the management of steel plants in their effort to pull the industry nut of the ?red.? Cooperation between management and men has improved considerably during; the year.

Jan 1, 1935

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 Carl R. Christiansen, James J. Scott

Advance mine planning, coupled with good mining practice, can be employed to develop underground space in stone mines for future industrial usage. To assure the developer the maximum return on his investment, initial planning should consider all aspects of the commercial operation, including surface layout, mining, and underground industrialization. The modular concept of mining to compartmentalize the space offers several advantages over conventional methods. Some expenditures can be considered as mining costs with a reduction of profit from stone production while lowering the capital requirements for conversion for usable space.

Jan 1, 1977

By Collins J. L, Fytas K

Surface mining accounts for more than 50% of the world's total mineral production. In future years this status is expected to change somewhat in favour of surface mining. The production technology during the past decade has concentrated on greater degree of mechanization with high capacity heavy duty equip- ment. This trend will continue. Next two decades will also see microproces- ,or based equipment, health and performance monitoring and management information systems fully integrated with excavated and material handling systems which in themselves will be amenable to auto- matic and remote control. Computerized mine planning will also be integrated within the system providing a fully integrated planning, excavat- ing and materials handling system. The surface mines of the 1990's and beyond will be so computer dependent that manpower will be reduced to one third of current levels.

Jan 1, 1988

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 Farid Mirahadi

Forecasting productivity of construction operations is a difficult but crucial task in planning construction projects. Over the past decades, many models have been developed to forecast productivity for different construction operations. Models made up of several functional relations and controlled by a specific number of control rules are more in line with human reasoning and logic. Neural-Network-Driven Fuzzy Reasoning (NNDFR) structure as one of these models shows a great performance for modeling datasets among which clear clusters are recognizable. Lack of the compatibility of conventional NNDFR with fuzzy clustering algorithms besides the insufficient attention paid to the optimization of number of clusters in this model, created a potential area for further research. The main contribution of the proposed model is to develop a modified NNDFR system to model construction data. To this end, Fuzzy C-Means (FCM) algorithm is substituted for K-means in NNDFR structure, and its parameters such as the number of clusters and weighting exponent are optimized through genetic algorithm. The proposed model is further verified through simulation of a construction operation in which several qualitative and quantitative factors are considered. Its implementation to the case study shows a considerable improvement of model performance with lower Mean Squared Error (MSE). The developed model assists researchers and practitioners in utilizing historical construction data to forecast productivity of construction operations with a high accuracy that could not be obtained by traditional techniques.

Aug 1, 2013

By Mukherjee R, Bandopadhyay S

Three applications of fuzzy logic in mineral exploration show the possibility of providing feasible solution and allow greater freedom in decision-making processes for geoexploration data which may be ill- defined, biased or where apriori infor

Jan 1, 1998

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

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 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 J. A. Meech, C. A. Harris

"The application of control theory in mineral processing is more difficult than in other industries because of the complex nature of the unit processes involved and because of problems with accurate on stream measurement of process variables. Conventional control is based on proportional, integral and derivative functions of a continuously measured error term. Computer control can enhance these actions by incorporating logical routines to switch from one relationship to another. The success of this logical approach depends on our understanding of the process dynamics and the stability of the process and model under all conditions. Typically the model does not apply at all times in mineral processing because of frequent and unmeasurable changes in the ore parameters.Recent advances in the theory of fuzzy sets have led to the application of this science to process control. Fuzzy Logic can be used to develop heuristic or rule-of-thumb controllers with the strategy based on the actual operating practice of experienced plant operators. Control action has increased flexibility through the adaptation of human reasoning. Acceptance of the control system by operating personnel is significantly higher because of their participation in its formulation. The requirement of an exact model is not essential.A heuristic controller has been designed and tested with a computer simulation model of a secondary crushing plant. The results of this study are discussed and future applications of this control technique to other mineral processes are suggested. IntroductionThe application of control theory in mineral processing is more difficult than in other industries because of the complex nature of the unit processes involved. Mineral processes typically are characterized by non-linear relationships between input and output variables. Many of these variables cannot be monitored accurately on-stream and so long time lags on the measurement loop are features of the system. The processes, themselves, often have long time-varying responses while the process loads fluctuate significantly on a short-term basis.Attempts to control these processes have led to the formulation of mathematical models to describe each unit activity. Adaptive techniques have also been tried to compensate and account for process changes and vageries. These approaches are mathematically rigorous so that their complexity and precision limit their usefulness when applied to ""vague"" processes."

Jan 1, 1987

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

By Alvarez A. R, Garcia H. l

A fuzzy expert system, DIME, has been developed, under Matlab v 5.1. to assess the coal and gas outburst risk in underground mining. Expert systems and fuzzy logic are powerful tools to manage information represented by linguistic statements,

Jan 1, 1998

By J. N. Rivera

El objetivo del texto busca establecer una distribución más equitativa de los bonos mineros a través de un análisis de los factores influenciables en la explotación. El estudio describe un análisis detallado de los costos y eficiencias de una mina particular,diferenciándolos de los costos y constantes de un labor de corte-relleno, para asi determinar un sistema de bonos que tenga por base promedios pesados obtenidos del laboreo.

Nov 20, 1967