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By Bhattacharyya A. K

A strata parameter is introduced to include the deformation characteristics of the undermined strata in the prediction of subsidence movement. By using the strata parameter and mining factors as parameters, a nomogram for predicting the maximu

Jan 1, 1994

By R C. A Minnitt

The two methods for calibrating the parameters K and (alpha) for use in Gy’s equation for the fundamental sampling error (FSE), the duplicate sampling analysis (DSA) and the segregation fractionalised analysis (SFA) are described in detail. A case study using identical broken reef material from a Witwatersrand-type orebody is calibrated using the DSA and SFA methods and the results are compared. Classically, the form of Gy’s equation for the FSE raises the nominal size of fragments given by dN to a power of 3. Gy’s formula is a theoretical formula applicable in the case when fragments are randomly collected one by one, which is obviously never the case. This is because the fragment shape factor (f) and the granulometric factor (g) were introduced to take care of this anomaly. More recent formulae, such as those suggested by François-Bongarçon and Lyman, are empirically based formula. A later modification of Gy’s formula raises dN to the power ? (alpha), the latter term being calibrated with the coefficient K in the DSA and SFA methods. The preferred value of ? (alpha) for low-grade gold ores used by sampling practitioners in the mining industry is 1.5. A review of calibration experiments for low-grade gold ores using the DSA and SFA methods has produced values of K that vary between 70 and 170 and values of alpha in the range 0.97 to 1.30. The average value for alpha is found to be 1, rather than 3 as originally proposed in the classic form of Gy’s equation or the industry preference for 1.5. It is suggested that for low-grade gold bearing ores, the equation for the FSE should raise dN to a power of 1. Such an equation for the variance of the combined FSE plus the grouping and segregation error, now referred to as the short-term component of the quality fluctuation error (QFE1), greatly simplifies the characterisation of gold ores, which now only requires the calibration of K for a given mass and established fragment size. The implications for the heterogeneity test from the generalised equation are that provided the fragments have been screened to within a narrow size range, any particular size will return a value for K that is acceptable for use in the sampling nomogram.CITATION:Minnitt, R C A, 2017. A generalised form of Gy’s equation for gold ores – empirical evidence, in Proceedings Eighth World Conference on Sampling and Blending , pp 331–350 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

The strategy of adjusting operating conditions to maintain optimum process performance as the feed quality varies - adaptive optimisation - is being investigated by computer simulation of chemical processes for the treatment of ores. Those ore-process systems that are most likely to yield substantial benefits from adaptive optimisation are being identified as a guide to the selection and development of systems for practical application. The generalised mathematical representation of the cost structure, ore quality variations, process kinetics, reactor characteristics and operating variables for ore-process systems is described. Results are summarised for temperature, reagent concentration or residence time as the manipulated variable. For almost all systems, adaptive optimisation was economically beneficial, although a few exceptions have been identified where there is a significant synergistic effect between poor-performance and good-performance materials in a blended feed. The sensitivity of the benefits of adaptive optimisation to a range of economic and technical factors is summarised.

Jan 1, 1985

By Jackson D. G

Iron ore mineralisation in the Paraburdoo environs is known to extend almost continuously over a strike length of 55 kilometres. Six formations of the Mount Bruce Supergroup con- tain high grade mineralisation in varying degrees of importance. Ore is currently being mined from the Joffre and Dales Gorge Members of the Brockman Iron Formation, with smaller, less significant amounts, occasionally taken from the lowermost units of the Weeli Wolli Formation and the upper parts of the Mount McRae Shale. Regional mapping has demonstrated the existance locally of a major unconformity at the base of the Wyloo Group sediments out- cropping south of the minesite. The basal con- glomerates at the unconformity are sometimes mineralised at Paraburdoo. A supergene enrich- ment process controlled by structure, strati- graphy and topography is the only one which can satisfactorily explain the disposition and chemistry of the Paraburdoo iron orebodies.

Jan 1, 1979

By C. B. Manula

Languages such as SIMSCRIPT and GASP have reduced the programming skills required to build simulation models, but not to a level where these can be used by a busy engineer or manager unless he has extensive programming experience. In an attempt to make simulation a more readily available tool, Sanford (1969) structured a generalized materials handling model (UGMHS)* for underground coal mining. The majority of materials handling systems consist of a set of moves and a set of pieces of equipment required to execute the moves. UGMHS gives a technique for describing and classifying the elements in these sets and then a technique for simulating the interactions of these elements as the materials handling system operates. To apply the model, the user simply furnishes data parameters and a fixed set of orders to the computer for his own problem. This new philosophy of dynamic simulation as applied by UGMHS was demonstrated in the paper by Sanford and Manula (1973). A dynamic model of underground coal mining was presented for the purposes of systems design and selection of equipment for a completely new operation and the analysis of existing ones for possible alterations, additions, and general improvements. The main differences between this application and its predecessors is the ability to transport material from multiple mine faces to multiple mine destinations and the ready adaptability to a wide variety of mine layouts. The second difference that characterizes the model is the deterministic simulation of equipment moves according to their mechanical capabilities and the physical profile of the mining scheme. Here, the computer generates performance data in the absence of time studies to compare equipment rather than operator performance.

Jan 1, 1974

By J. C. Johnson

The problem of estimating pillar capacity in coal mines has motivated development of a variety of empirical equations. Most of these equations have arisen from experience and a very limited set of measurements that have, necessarily, been focused on particular regions or coal fields. Their applicability is often limited to a specific range of pillar sizes. This is of particular concern in the design of large gateroad pillars and small barrier pillars between panels. The result has been a variety of equations and equation coefficients that can calculate load estimates to size pillars. Inclusion of these equations in more sophisticated loading models, such as boundary element methods (e.g. LaModel and MULSIM) has been hampered by absence of a function that describes specific variation of coal strength within the pillar. A singular exception, to our knowledge, is a function corresponding to the Bieniawski pillar strength equation. Because of this, it is the only coal strength criteria inherently available in most boundary element models. This paper presents a general method for deriving local coal strength, as a function of distance from the pillar rib, for an arbitrary pillar strength equation. Integration of this function provides the strength of pillar regions, or cells, needed for boundary element models. The method is demonstrated for the Bieniawski, Maleki and Holland-Gaddy equations. The Maleki and Holland-Gaddy equations contrast strongly with Bieniawski as strengths are extrapolated to large pillars and barriers. This development provides a tool for incorporating any of the proposed empirical equations into a boundary element model, thereby allowing the analyst to choose the most appropriate equation for a particular region, coal seam and/or pillar size.

Feb 23, 2014

By Ralph W. Barbaro

In the mineral industry, the objective of a mining venture is to extract, process, and market a mineral commodity in such a manner that the profit of the venture is maximized. In a mining venture, a company normally has control over mine production scheduling, the selection and location of processing facilities (mineral beneficiation plants, smelting plants, refineries, or blending facilities), processed ore blending, and the selection of markets. The flexibility of the above factors provides many alternatives to satisfy the needs of each market with regard to quantity and quality of ore. The purpose of this paper is to present a mathematical model for determining the alternative that yields the maximum profit. Production planning is generally concerned with the best utilization of fixed resources, more specifically, with the determination of production and inventory levels to meet fluctuating demand requirements. A mine can be any source of ore such as an entire mine, a production unit within a mine, or an independent supplier. The objective of production scheduling is to determine the times at which each mine will be used and to allocate a required level of production. Production planning and scheduling problems are intimately associated with the location of new and existing mines and processing facilities. Although the location of the mines is usually fixed, there can be considerable flexibility in locating new processing facilities, in the selection of the processing plant, and in blending the ore before and after processing.

Jan 1, 1983

By D. J. Shields

The U.S. Bureau of Mines has developed a microcomputer-based mineral modelling system that enhances analytical capabilities of the agency. It draws upon the extensive data base previously developed for the Bureau's Availability studies. The model is first formulated as a generalized network flow optimization problem, the optimal flows from which are fed into a transportation algorithm. The goal of the modelling system is first to fulfill prespecified demand at the minimum opportunity cost and second to identify feasible, optimal trade patterns. The system facilitates the analysis of policy issues on a real-time basis and supports the Bureau's mission to assess mineral resources.

Jan 1, 1987

By A. S. Polubinsky, V. P. Chernyak

A further development of the theory of heat- and mass transfer in the system “strata - mine opening" is necessary for an improvement of the existing technique of heat calculation of mines and other underground constructions. A generalized boundary problem of heat- and mass conduction in strata around an opening as a theoretical ground for that purpose and several particular situations, important from the point of view of an opening and surrounding strata heat state analysis, as well as results of numerical experiments executed for such analysis are presented.

Jan 1, 1997

By Richard L. Sanford

The generalized rail haulage simulator described in this paper is an outgrowth of the need to expand a previously developed single level simulator to permit analysis of production problems where intermediate car storage is used. Many of the ideas of the former single level simulator, which is an integral part of an Underground Generalized Materials Handling Simulator (UGMHS), have been used in model development. In addition, experience gained in applying the earlier model to a variety of operating conditions has been heavily relied on. The generalized rail haulage simulation model can operate as an independent system and, as such, can be used to analyze rail haulage systems using production distributions as data input. In addition, this model is designed to replace the present rail haulage sub-assembly in the UGMHS model and the linking procedure has been integrated into the model. The flow of coal from production sections in UGMHS, for example, fill the mine cars and a condition of no empty cars at a loading station will shut down in by production systems.

Jan 1, 1974

By R. B. Mears, E. H. Dix, R. H. Brown

In the Institute of Metals Division Lecture of 1940, E. H. Dix, J~.(I)~ discussed the acceleration of the rate of corrosion by high constant stresses. Experimental data obtained on this subject at the Aluminum Research Laboratories and a theory compatible with these facts were included in that lecture. The present paper contains data and ideas subsequently developed at the Aluminum Research Laboratories. Dix pointed out that in order for acceleration of corrosion by constant stress to occur: (1) There must exist in the alloy a susceptibility to selective corrosion along more or less continuous paths, as for in- stance at grain boundaries, and (2) There must exist a condition of high stress acting in a direction tending to pull the metal apart along these continuous paths. Acceleration of corrosion rate by stress is not confined to any one alloy system. Table I lists some of the metals and alloys and some of the corroding environments in which acceleration of corrosion by stress was sufficient to cause cracking (stress- corrosion cracking). It will be noted that the variety of alloys in which stress-corrosion cracking has occurred is rather large. Also, it will be observed that

Jan 1, 1945

By Markus A. Reuter, Jacques J. Eksteen

The modelling of bath type furnaces tends to be either fundamental in nature or total empirical "black box models". While models of the first kind are robust, they tend to lack accuracy, in cases where they are to be used for process control, and may take an overly long in development time. On the other hand, black box models tend to lack robustness of prediction for all circumstances and tend to be limited to interpolate based on historical data. While this may very often provide short-term accuracy, the models may have problems with stability in the long term. Moreover, the model structure may not reflect any of the fundamental properties of the system and remains opaque to fundamental analysis. The aim of the model development is in obtaining a model that have robustness, high accuracy, a short development time and which is adaptive to a changing environment. The models are specifically developed for on-line decision support or control of furnaces. The modelling approach is evaluated for a chromite bath smelting furnace. The role of systems analysis, mass balance smoothing, thermodynamic analysis are integrated into one generic algorithm for bath type furnaces. Therefore, this paper argues that to understand complex metallurgical furnaces a hybrid approach is the only method to fully grasp the intricacies of metallurgy, to improve operations, economics as well as environmental performance.

Jan 1, 2003

By Markus A. Reuter, Jacques J. Eksteen

"The modelling of bath type furnaces tends to be either fundamental in nature or total empirical ""black box models"". While models of the first kind are robust, they tend to lack accuracy, in cases where they are to be used for process control, and may take an overly long in development time. On the other hand, black box models tend to lack robustness of prediction for all circumstances and tend to be limited to interpolate based on historical data. While this may very often provide short-term accuracy, the models may have problems with stability in the long term. Moreover, the model structure may not reflect any of the fundamental properties of the system and remains opaque to fundamental analysis. The aim of the model development is in obtaining a model that have robustness, high accuracy, a short development time and which is adaptive to a changing environment. The models are specifically developed for on-line decision support or control of furnaces. The modelling approach is evaluated for a chromite bath smelting furnace. The role of systems analysis, mass balance smoothing, thermodynamic analysis are integrated into one generic algorithm for bath type furnaces. Not only are fundamental process relationships such the mass and energy balances satisfied through a scientific method of data reconciliation; the proposed modelling approach allows adaptive prediction, the first step towards predictive control or metallurgical decision support.It is shown that neither an ARMAX model only, nor a purely equilibrium model only, could predict the dynamics of the process very well. However, a combination of the two approaches more than doubled the accuracy as measured by the R2. It was noted for carbon dissolved in the alloy, that the improvement in R2 in using a hybrid modelling approach was more than the sum of the R2 of the individual traditional approaches. Similar improvements in modelling performance were noted for all other species.It shows further that process dynamics may totally overshadow the effect of thermochemical equilibrium (even when the thermochemical equilibrium prediction includes the recycled alloy and slag heels as additional feeds). The exclusion of process dynamics therefore leads to erroneous results."

Jan 1, 2003

By Nirmal Weerasekara, Lian X. Liu, The University of Queensland, Weiguo Xie, Benjamin Bonfils, Ping Yu

A mill model structure based on the population mass balance framework incorporating breakage characteristics, slurry and solids transport, product classification and discharge and energy consumption was reported in XXVII IMPC and named the Generic Tumbling Mill Model Structure Version I (GTMMS I). In this work, a new 4D (four dimensional) appearance function sub-model based on the experimental results of the JK Rotary Breakage Tester (JK RBT) was developed to describe the breakage characteristics and was applied to this new model structure. The newly developed 4D appearance function model has fewer fitting parameters and is more generic in nature. Most importantly, it is applicable to both high and low energy impact breakage. It is therefore much more versatile in comparison to the existing JKMRC t10- tn appearance function and the size-dependent JK M-p-q t10-tn appearance function. In addition, the Discrete Element Method (DEM) energy distribution model was integrated into the new model structure. With DEM results providing energy distribution information inside the mill directly, the selection function and the back-calculation method used in the existing JKSimMet modelling method are not needed in this model structure. With the above two revolutionary innovations, the previous model structure was upgraded to the Generic Tumbling Mill Model Structure Version II (GTMMS II). Furthermore, the new model structure is dynamic in nature with time-stepping technique for non-steady-state simulation. A case of the dynamic grinding of a SAG mill was studied to validate the GTMMS II. The model simulation results agreed well with the plant data. With the newly developed 4D appearance function model, the incorporation of DEM energy distribution and transport function, the GTMMS II is an important step forward towards mechanistic modelling of tumbling mills.

Jan 1, 2016

"This generic study has been developed in response to the frequently asked question, ""That is the best treatment route for refractory gold ore?"". The study has developed operating and capital costs for many potential treatment routes and they are presented here in a generic form. All costs are in relation to the value of 1.00 given to the base case, a 1000 mtpd bio-oxidation plant, operating at 25% solids and 72 hours residence time.There is unfortunately no simple answer to the above quest ions. The treatment of refractory ores is a complex subject, with many variables, such as mineralogy, chemistry, environment and tonnage. However this study does offer information and relative numbers that we believe will be of assistance to gold metallurgists in obtaining a ""first shot"" at estimating costs.Equally, this technology is a rapidly evolving one. As more plants are commissioned, and test programs completed, better data is constantly being generated. For a generic study of this type to remain useful it will need regular updating and adjustment."

Jan 1, 1989

By C. Musingwini, A. S. Nhleko, P. M. Muke

Open-pit mine planning includes the determination of a long-term open-pit mine production scheduling (OMPS) plan. The OMPS plan is generally a three-dimensional (3D) block extraction sequence that seeks to maximise the net present value (NPV) of future cash flows, while complying with technical and operational constraints. The OMPS problem is computationally complex because it involves the analysis of large datasets, while simultaneously incorporating both multiple hard and soft constraints. The OMPS problem has been solved using deterministic or exact approaches, heuristic, and metaheuristic models. When compared to heuristic and metaheuristic models, deterministic approaches tend to take much longer to solve the OMPS problem when the number of constraints increases. Therefore, this paper presents a metaheuristic model which utilises a genetic algorithm (GA) framework to solve the OMPS problem. The GA-based model was applied to the Newman1 and Zuck Small mining instances, which are publicly available from the online MineLib database. The NPVs for the OMPS plan generated using the GA-based model for the two case studies were compared to the best-known published MineLib solutions obtained using the TopoSort heuristic model at the time of this study. For the Newman1 instance the equivalent annual NPVs were approximately US$6.6 million for the GA-based solution and US$5.1 million for the TopoSort solution. For the Zuck Small mining instance, the equivalent annual NPVs were approximately US$99.8 million for the GA-based solution and US$102.5 mil for the TopoSort solution. These results show that the GA-based model produced comparable solutions to some of the best-known solutions, thus, indicating strong potential application of GA-based models to solve OMPS problems.

Sep 1, 2021

By Biswajit Samanta, Pranjal Pathak

This article presents a customized genetic algorithms (GA) with new crossover and mutation operators to solve the short-term production scheduling problem of multi-mine mineral value chains (MVC). The preceding problem consists of determining the extraction sequence and destination allocation of blocks from all the mines collaboratively while closely meeting the quality and quantity requirements of the processing units subject to relevant technical and operational constraints. The short-term production scheduling is carried out at shorter scales wherein the operations are modeled in a great detail with large number of constraints. This makes the industry-scale instances of the problem computationally intractable for standard mixed-integer programming (MIP) solvers. Thus, a GA-based heuristic approach is developed to obtain nearoptimal solutions to the large-scale instances of the problem in a reasonable amount of computational time. Computational experiments show that the developed GA-based method is a promising way to handle industry-scale instances of the problem. Moreover, the sensitivity analysis on various parameter combinations of crossover and mutation operators indicates that the customized global mutation operator, when used in combination with the customized crossover operators, took on average 12.5% less time than the customized local mutation operator to converge to a solution.

May 21, 2022

By Biswajit Samanta, Pranjal Pathak

A multimine mineral value chain (MVC) consists of multiple mines in the upstream supplying raw materials to multiple destinations in the downstream. The short-term mine production scheduling problem of a multimine MVC aims to determine the optimal extraction sequence and destination allocation of blocks from all the mines collaboratively to meet the quality and quantity requirements of all the processing plants in each period, subject to all relevant technical and operational constraints of the mining system. It is carried out at shorter scales of days/weeks/months under the purview of long-term production scheduling. An optimal short-term production schedule for MVC involving multiple mines that share the same infrastructure (processing units, stockpiles and waste dumps) requires coordination across the network of mines. Optimizing the short-term production schedules independently at each mine fails to capture the benefit of collaboratively generating schedules for all the mines, resulting in suboptimal schedules. Additionally, in short-term production scheduling, the operations are modeled in greater detail with additional decision variables and constraints to model the more complex multimine MVC situations. This makes the large-scale instances of the problem computationally intractable for standard mixed-integer programming (MIP) solvers. This work presents a customized genetic algorithm (GA)-based heuristic approach to obtain near-optimal solutions to the industrial-scale instances of the short-term production scheduling problem of multimine MVC within a reasonable computation time.

Oct 1, 2022

By C D. Rawlings

An assessment is made of the potential for thick seam underground mining of black coal in Australia. A brief summary is given of the geo-mechanical environment and previous mining history existing in a number of areas of particular interest from the point of view of thick seam mining. A classification of thick seam mining methods is presented with emphasis on the critical geo-mechanical parameters influencing the performance of such methods. A summary is made of perceived geo-mechanical research requirements related to thick seam mining in Australian coalfields.

Jan 1, 1980

By Y. Topkaya, Lloyd Petrie, Dianne Marozas, R. E. Johnson, Steven Paulson, J. H. Canterford, B. R. Palmer, R. B. Bhappu, Lorin Redden

This paper describes a fundamental approach to in situ leach mining (ISLM) lixiviant development which emphasizes leaching selectivity through detailed characterization of mineral-solution interfacial chemistry and post-leach geochemistry. This experimentation and modeling approach consists of: (1) petrographic identification of reactive ore and gangue minerals; (2) determination of leaching kinetics of individual minerals through batch leaching tests; (3) evaluation of bulk solution geochemistry through column leaching tests; and (4) ISLM simulation of element species transport and geochemistry through core leaching tests. Recent results confirm good leaching selectivity for manganese, but reveal post- leach precipitation and permeability loss during core leaching experiments.

Jan 1, 1993