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Modeling Tools for Development of Tailings Management Plans Terry Eldridgea, Marco Barrientosb, Marcelo Musséc and Matías Silvad * a Principal, Golder Associates Ltd.,Vancouver, Canada; bTailings Area Manager, Golder Associates S.A., Santiago, Chile; cAssociate, Golder Associates S.A., Santiago, Chile; dMSc (candidate) (Recherche) Mécanique Energétique et Ingéniere spécialité Modélisation et Expérimentation en Mécanique des solides, Universite Joseph Fourier, Grenoble, France Abstract Proper management of a tailings impoundment facility during mine operation can significantly reduce the environmental impact of the facility during operation, reduce operating effort and reduce the effort required to close and manage the facility after operations are complete. Variables that must be considered in development of the tailings management plan are the properties of the tailings, which may change over time as new ore types are processed or the process itself is changed, the slope of the tailings both on a beach and underwater, and how this slope may change with the season and thickening technology used, and desired location, area and depth of the pond to maximize reclamation and to provide flood management capacity within the impoundment. Sensitivity analyses for each of the variables can be made to determine the key parameters that will govern the operation of a specific tailings basin over time, and allow operating rules to be developed and incorporated into the management plan. This paper presents the concept and application of a new computational tool which is specific to modeling tailing impoundments. Keywords: tailings, modeling
*
Corresponding author. Email:
[email protected],
[email protected],
[email protected],
[email protected]
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1
Introduction
Experience acquired through the development of various tailing impoundment projects, specifically in the simulation of disposal and/or distribution of tailings within the impoundment, has shown that the accurate modeling of geometrical parameters is a greatly valued contribution in all stages of a tailings storage facility (TSF). In the engineering stages, it provides basic information for the design of important works comprising the facilities, such as tailings classification and distribution systems, surface water management and water recovery systems, besides providing data for programming deferred works. On the other hand, it allows carrying out during operation a much more controlled impoundment filling planning and its subsequent follow-up, making possible any necessary adjustment. This results in a more efficient management of the facilities. Presently, there is a number of computing tools (software) that indirectly allows modeling a wide range of geometrical conditions. Nevertheless, when performing the modeling of realistic tailings conditions, in which multiple variables must be included such as, for example, the superposition of discharge points, variation of the slope along the tailings surface (near the discharge point, on the beach and underwater), these tools evidence a lack of “specialization,” resulting in the inability to model efficiently and rapidly the most representative variables existing in a TSF. Based on these constantly increasing needs, an initiative was promoted some years ago, to approach the “exclusive” modeling of TSF, both in their design stages as well as during their operation. This publication summarizes the development of a tool created for modeling, on a realistic basis (but at the same time, easily and rapidly) the multiple specific variables that condition the TSF staging, and which are not necessarily being assessed with the other tools presently available in the market. One of the main functions that a computing tool must fulfil is to be in charge of complexity and “heavy” work, to allow the user to direct his/her efforts towards design with all necessary resources in hand. The adequate design of an interface in an integrated environment is showing only the access to available resource. Nevertheless, it is necessary at this point to emphasize the great number of algorithms that underlie these resources, which have been developed for several years as a product of experience in a number of projects, each of which has its own particularities. Among the most important algorithms we may name the following: (i) mesh creation starting from a cloud of points, by means of geostatistical and mathematical algorithms; (ii) surface modification, (iii) surface visualization; (iv) discharge laws characterization; (v) calculation of volume and areas of deposited tailings; (vi) calculations of resulting surfaces; (vii) creations of a deposit’s stage curve, (ix) creation of graphic files (Metafile, JPEG, bitmap) and (x) graph and results export and other applications.
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General aspects of tailings storage facilities
In general, a TSF is understood as a work sited on natural ground, whose purpose is containing two types of elements: (i) a solid element, conformed by tailings as such, and (ii) a fluid element, corresponding to water associated with the tailings, and originated in the flotation process. The solid element is retained and/or stored in its totality in the facilities, either as material for retaining wall construction, if required, or stored totally within the impoundment. The fluid element, by contrast, is characterized by not being contained in its totality within the basin, as different losses take place, such as seepage, evaporation and retention. Moreover, another part is recovered and returned back to the process plant, if required. Tailings represent the non-commercial value material and, thus, an optimization of the storage facilities is widely required. Of course, the final footprint and general layout will depend heavily on the final configuration of the deposit. Moreover, different structures or components that take place within a TSF, as well as their construction schedule, will depend on the filling plan, such as: • Tailings transport, classification, distribution and disposition systems • Embankments • Water management (process, surface and underground) • Access roads • Closure 3
Necessity of computational tools
In the past, tailings productions were relatively small, dilutions were relatively high, the storage facilities were not large and, therefore, there was a negligible impact of the beach slopes on the final configuration of the impoundment. For this reason, it was feasible to run accurate enough filling simulations by manual methods. Currently (and during recent years), tailings productions have considerably increased at the same time that solids concentrations are much higher (due to new techniques and water-saving procedures). Deposits have become larger and, so the impact of the beach slopes becomes dramatically more important on the configuration of the facilities. Moreover, the properties of the tailings may change considerably during the operation (different type of ore, mill process, thickening, seasonal effects, etc.), and a sensitivity analysis is needed to cover the several configurations the facilities may have. The use of computational methods is, then, a must. 4
The model
The first task in developing a model in general is to create a concept of the system (the facilities), identify the variables and define the general criteria. Then, the model can be
221 implemented. Successive iterations and interpretations must take place in order to obtain an adequate database for design. 5
Tailings management plan
Tailings management plans (TMP) are useful for new deposits and for existing ones. 5.1
New deposits
A comprehensive TMP provides tools to: • Define the design criteria • Design the facilities (for operation and closure) • Identify interferences • Define the construction schedule • Develop water balances • Prepare closure plans 5.2
Existing deposits (currently in operation)
A TMP gives proper tools to take into account operational impacts originated by: • Changes in the tailings beach slopes (due to changes on mineralogy, milling, solids concentration, etc.) • Changes in tailings production • Adjustments to the location and parameters of the supernatant pond • Changes to the general configuration, mainly for closure 6 6.1
Models and variables General
A Model is defined as information related to a System (a set of objects grouped in some regular interaction or interdependence), which has been obtained to be studied in order to be able to predict its behaviour under different conditions. With most studies, it is not necessary to bear in mind all the details pertaining to a system. Therefore, a model is not only a “substitute” of a system, but also its “simplification.” A model must comply with the following basic principles: Realism and simplicity: consists of finding an adequate description for the system, not too simplified nor too sophisticated. The model must be simple, with hypothesis and parameters that may be experimentally controlled and calibrated. Economy: this characteristic, points out the need to solve the problem with the less demanding model.
222 Furthermore, it must be kept in mind that the model must be easily understood by the user, must have clear goals and objectives, must not give absurd replies, must be easy to use favouring communication with the user, must contain, at least the most important parts or functions of the system, must be flexible (that is, it must be modifiable, adaptable or updatable) and must evolve (simple at first, but capable of being made more complex depending on the user’s needs). Any modeling work requires, at least, the definition of the system to be modeled, data collection, model implementation, validation, and the experiencing and interpretation of results and documentation. 6.2
Identifying the variables
The main variables related to a TSF are, amongst others: • Tailings production • Tailings beach slopes • Tailings dry density • Supernatant pond • Tailings solids concentration • Grain size • Specific gravity • Mass distribution In order to simplify the analysis, following variables can be considered as “basic” or “critical” to simulate a TMP: • Tailings slopes • Tailings dry density • Location and required volume of the supernatant pond Many times, these parameters are not accurate or they are not known at all (mostly, at the first stages of the design or studies). It becomes, thus, extremely important that models can be run in a relatively quick way, to predict the impact of the variability of the key parameters on the configuration of the TSF (sensitivity analyses). 7 7.1
The GoldTail software The skills
The GoldTail software allows the simulation of: • Earthworks (excavations and fillings) • Creation of dams and embankments • Capacity curves (considering horizontal layers) • Filling of impoundments (considering tailings slopes) • Tailings beach parameters • Location (migration) and parameters of supernatant pond • Stage curve
223 • • 7.2
Proportion of total tailings flows through each discharge point GoldTail offers compatibility with other software (import/export files). Example
A simple example (gold TSF) is shown as follows. Figure 1 includes es the general arrangement; the tailings discharge paths as well as the expected migration of the supernatant pond.
Migration of pond
Tailings discharge paths
Figure 1: General arrangement
A complete sequence of filling is shown in Figure 2, as follows. Active discharges, changes to tailings beach slopes and location of the supernatant pond for each of the considered stages can be identified.
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Figure 2: Tailings filling sequence
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7.3
The results
The following results can be obtained from the model: • • • • • • • •
Volume of embankments Tailings stage curve Active/Inactive discharge points for each stage Tailings mass distribution for each stage Migration of the supernatant pond Tailings area for each stage Parameters of supernatant pond for each stage 3-D figures
These results provide tools and adequate data bases to carry on other studies (i.e., water balances) and the design of the different facilities of the TSF. 8
Conclusions
This software represents a “personalized” solution to specific problems: it has been developed to simulate tailings deposition plans. The development process has been performed over several years and has never been considered completed, as at any time new challenges are being identified (showing the flexibility and evolution of the model). The advantage of continuing with the application source code makes updating activities easier and also facilitates the incorporation of new capabilities. Update and calibration are continuous tasks for us.
References • • • •
James R. Carr, Prentice Hall, Numerical Analysis for the Geological Sciences, 1995. Roy A. Plastock, McGraw-Hill, Gráficas por Computadora, 1987. Xavier Emery, Departamento Ingeniería de Minas, Universidad de Chile, Geoestadística Líneal, 2000. Edwards H. Isaaks, R. Mohan Srivastava, Oxford University Press, An Introduction to Applied Geostatistics, 1989.
