usim pac 32 starting guide
TRANSCRIPT
Starting Guide
Version 3.2.0.5
Caspeo
3 avenue Claude Guillemin - BP 6009
45060 ORLEANS CEDEX 2 – FRANCE
Tel: +33-238-643615
Fax: +33-238-259742
E-mail: [email protected]
BRGM is the author of USIM PAC
Copyright © BRGM 1986 – 2004, © Caspeo 2004 – 2011
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TABLE OF CONTENT Pages
1 - INTRODUCTION........................................................................................................4 1.1 - The Simulation based Approach....................................................................4 1.2 - Unit operation models....................................................................................6
2 - GENERAL FEATURES OF USIM PAC......................................................................8 3 - INSTALLATION OF USIM PAC .................................................................................8 4 - CASE 6: A DESIGN CASE STUDY ...........................................................................9
4.1 - The Objectives...............................................................................................9 4.2 - The Methodology ...........................................................................................9 4.3 - Step one: enter data ....................................................................................10 4.4 - Step two: define plant performance .............................................................15 4.5 - Step three: design the units of equipment ...................................................20 4.6 - Step four: estimate capital cost....................................................................24
REFERENCES..............................................................................................................26
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1 - INTRODUCTION Since 1986, BRGM has been developing a powerful process simulation software package, USIM PAC (Broussaud 1988; Durance et al. 1993; 1994; Guillaneau et al. 1997, Brochot et al. 2002). It is a user-friendly steady-state simulator that allows mineral processing engineers and scientists to model plant operations with available experimental data and determine optimal plant configuration that meets production targets. The simulator can also assist plant designers with sizing unit operations required to achieve given circuit objectives.
The software package contains functions that can manipulate experimental data, calculate coherent material balances, sizes and settings of unit operations, physical properties of the processed materials, simulate plant operation and display results in tables and graphs. Widely used in industrial plant design and optimization, with more than one hundred fifty licenses sold in thirty countries, this software has been continuously improved, through successive versions, to make it more accurate, powerful and easier to use.
These last years have seen significant developments in mineral processing technologies, particularly in hydrometallurgy, bio-hydrometallurgy (Cézac et al. 1999; Brochot et al. 2000) and mineral liberation. In addition, it is now necessary to take into account the environmental impact at each stage of a mining project, including water and power consumption, waste treatment and disposal (Sandvik et al. 1999; Guillaneau et al. 1999).
This new version of the simulator, USIM PAC 3.2, incorporates these modern developments. Indeed, its structure and tools allow the user to take into account, at the same time, a wide range of technological, economic and environmental aspects (Brochot et al. 2002).
The main features of USIM PAC 3.2 will be presented through the description of the design and optimization methodologies. The significance of these features will be illustrated by an example of design of a gold ore treatment plant.
1.1 - The Simulation based Approach
Process modeling and simulation are used at all stages in the life of a mineral processing plant: from process development to site rehabilitation, including pre-feasibility and feasibility studies, engineering design, plant commissioning, plant operation and upgrading right through to decommissioning. From the beginning, the simulation-based approach describes the behavior and performance of the future plant. This description will be more and more precise owing to the capitalization of knowledge acquired through laboratory tests, pilot plant campaigns and plant operation. There is a continuous exchange between reality and the virtual plant constituted by its steady-state simulator.
A simulator combines the following elements (see Figure 1):
A flowsheet that describes the process in terms of successive unit operations and material streams. This flowsheet encapsulates the experience of the engineers responsible for the plant design or optimization. It can reflect various scenarios so they can be compared against given criteria. It takes into account numerous plant features such as reagents distribution, water recycling or waste treatment.
A phase model that describes the materials handled by the plant (raw material, products, reagents, water, wastes) so that unit operations and plant performance, products and reagents quality (grades and undesirable element level), waste characterization (e.g. long-
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term behavior) for environmental impact can be evaluated. The phase description is critical for analyzing and optimizing the process. This statement reinforces the vital importance of field data and sampling protocols.
A mathematical model for each unit operation. This model formalizes the current scientific knowledge about the unit operation, and its level of complexity depends on the data available and the targeted objectives (i.e. flowsheeting, unit operation sizing, or optimization). The model parameters - dimensions, settings and calibration factors - are calculated or validated from field data.
A set of algorithms for data reconciliation, model calibration, unit operation sizing, full material balance calculation, power consumption and capital cost calculation. These algorithms are interfaced with a set of data representation tools. As a result, the plant simulator constitutes a highly efficient communication vector between the different actors who play a part in the plant life.
STEADY-STATESIMULATOR
MODELSPLANT FEED
Feed rateFeed size distributionFeed mineral distribution
PLANT DESIGN
FlowsheetUnits of equipment
PLANT PERFORMANCE
FlowratesSize distributionsMineral distributionsPower draws
PLANT CAPITAL COST
Figure 1: Main functions of a steady-state simulator
Steady-state simulation does not compete with dynamic simulation: it is not a lower or higher level of simulation. Whereas dynamic simulation is an essential tool for the design of process-control strategies and a key element of advanced process-control systems, steady-state simulation is an essential tool for plant design and pre-control optimization: it is adequate to optimize the circuit design and the dimensions of the units of equipment before the implementation of a process-control system.
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Figure 2: Main Window of the USIM PAC simulator
The simulator offers an array of powerful tools in response to the increasing demand for a multi-criteria, global approach by plant designers. It takes into account a wide spectrum of design criteria, including:
- Economic criteria such as capital cost, reagent and power consumption, production quality in terms of valuable mineral grade or undesirable elements level;
- Technical aspects with the evaluation of various configurations and processing technologies, a complete and detailed description of all material streams and their behavior during process;
- Environmental factors such as water consumption and recycling, pollutant production or waste treatment.
USIM PAC is a very flexible simulator (see Fig. 2). It can be used by process engineers for plant design or optimization, researchers for process model development, as well as academics for teaching process-engineering students.
The previous version, USIM PAC 3.0, already represented a significant milestone towards integrating different industries through a global approach. It was possible to simulate treatment from the mine through the metallurgical plant. Studies on a global approach in urban waste management (Sandvik et al. 1999) or metal life cycle (Reuter 1998) already used steady state process simulation techniques.
Version 3.1 goes further in that way. The material description has been enriched with additional criteria that give capabilities to simulate processes in various field.
1.2 - Unit operation models
The main components of a simulator are:
1. The simulation software, per se, which enables communication between user and simulator and co-ordination of calculations: as this is the only component visible to the user, it is often called the "simulator".
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2. Mathematical models for unit operations, which constitute the core of the system, albeit they are buried inside the simulator as modules.
MODELS
INPUT
FlowratesSize distributionMineral distribution
PHYSICAL PROPERTIES
Output(s)
FlowratesSize distributionsMineral distributionsPower draws
PLANT CAPITAL COST
Figure 3: Unit operation model
Various mathematical models can be associated with each unit operation drawn on the flowsheet. Mathematical models calculate the output streams data from the input stream data and model parameters (see Figure 3). These parameters can be equipment sizes, operating conditions, physical properties, model adjustment parameters or simply performance criteria. Depending upon the simulation objective and the data available, different mathematical models can be used for the same piece of equipment. In USIM PAC, mathematical models are divided into four levels:
- Level 0 models enable the user to specify directly the performance of the units. For example, the performance of a classification unit can be modeled by a partition curve for which the user specifies the bypass, the imperfection and the cut-size (d50). Such models are also called flowsheeting models as they do not take into account any sizing parameters. During the simulation, the performance of the unit will be independent of its dimensions and the flowrate of the ore feeding it.
- Level 1 models take dimensional parameters into account. They require little (sometimes no) experimental data. A typical example is a ball mill model, which uses only the Bond Work Index as its single experimental parameter. If no data is available, it is even possible to estimate the Work Index. Obviously the precision of such models is limited, but they are simple to use.
- Models of higher levels are typically more accurate but they require the estimation of some of their parameters. This estimation can be carried out either on the basis of experimental data obtained from the continuous operation of the unit (level 2 models) or from such data supplemented by information obtained from specific tests, generally carried out in the laboratory (level 3 models).
Over 120 mathematical models are available in USIM PAC 3.1 covering a wide range of unit operations from crushing to refining, from ore dressing to waste management. These include comminution (SAG, Pebble/Rod/Ball mils, Liberation mill, SAM, etc.), classification (Hydrocyclones, Screens, Rake/Spiral classifiers, etc.), concentration (Conventional/Column
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flotation, Gravity/Magnetic separation, etc.), hydrometallurgy (Leaching, Bioleaching, CIP, CIL, Precipitation, Cementation, Solvent extraction, Electrowinning, etc.), solid/liquid separation (Filtration, Sedimentation, etc.), waste treatment (Collection, Sorting, Incineration, composting, etc.).
2 - GENERAL FEATURES OF USIM PAC USIM PAC offers powerful and easy-to-use methods to help engineers reach their objectives. It requires no special training in computing or modeling.
Basic functions of USIM PAC can be divided into plant modeling, data input, data processing and different tools for data and results display. High level functions are also available for configuration and incorporation of user defined functions.
USIM PAC runs under Windows™ 2000/XP. The minimum hardware is:
Pentium-based PC with 256 Mb RAM, 50 Mb of free disk space.
We recommend:
Pentium-based PC with 512 Mb RAM, 128 Mb of free disk space.
The advanced user of USIM PAC can create new icons to represent the devices on a flowsheet, or new equipment simulation models. Models and icons are introduced in the form of FORTRAN functions, which must respect a few simple, well-defined rules. These subroutines must be compiled and linked with an object code module supplied with the USIM PAC Development Kit. FORTRAN compiler and linker are provided with the program.
This flexibility makes it possible to satisfy the need of some USIM PAC users to insert their own models in the software, as well as to provide a legal guarantee on the delivered object code.
The user may also insert into the program completely new functions taking some or all of their data from the USIM PAC files.
3 - INSTALLATION OF USIM PAC The installation of USIM PAC from the CD Rom is done through a specific procedure, designed to copy the disks to your hard disk and to create the required subdirectories.
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4 - CASE 6: A DESIGN CASE STUDY
4.1 - The Objectives
The example given here provides the opportunity to describe a general methodology, by which a preliminary design and capital cost evaluation of a plant can be achieved in a few hours with USIM PAC 3.1. It corresponds to a preliminary design of a comminution circuit in the mineral processing industry. It is delivered with the software as the CASE6 tutorial.
The objective is to make a preliminary design of a gold- grinding/classification/leaching/adsorption plant capable of treating 100 t/h of a gold ore with 95 % recovery.
• Size distribution: 0×8 mm
• Gold content: 7 ppm.
• Specific gravity: 3.
Plant specifications are (see flowsheet fig. 7):
• Primary grinding with a rod mill in open circuit.
• Secondary grinding with a ball mill in closed circuit.
• Classification by hydrocyclone with a circulating load from 150 % to 250 %.
• Leaching tank series with d80 = 75 µm for the feed.
• CIP tank series with 50 ppm of gold in the recycled carbon.
• Dewatering of the barren pulp in a thickener with water recycling for percent solids regulation.
Laboratory tests give:
• Work Index: 14 kWh/st.
• Maximum recovery of gold by cyanidation: 98 %.
• Leaching rate constant: 0.3 h-1 (assuming a first order kinetic).
• Adsorption rate constant: 700, time constant: 0.3 (assuming the kn equilibrium model).
• Maximum percent solids after clarification: 70 %.
The following sections details the succession of steps used in preliminary plant design: plant modeling with flowsheet drawing, phase model description and selection of mathematical models for each unit operation, stream data input, direct simulation and unit sizing algorithms, results display using graphs and sheets.
4.2 - The Methodology
Figure 4 shows the five steps of the methodology followed. Steps one and two aim at defining the way the plant designer wishes the plant to perform. Step three consists of finding units of equipment able to achieve the plant performances defined during step two. Finally steps four and five produce information and documents necessary to present the prefeasibility study. The whole approach, including report generation and printing, requires less than a day for a mineral processing engineer.
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Laboratory experimentation: -measurement of Bond index
- mesurement of flotation kinetics
USIM PACsimulation of the full operation of the plant.
Comparisons between several possible flowsheets
Conventional flowsheethypothesis
USIM PACsimulation of the plant using
simple models without equipmentdimensioning
USIM PACdimensioning the equipment for
the plant USIM PAC technical resultsprintout of a full report
with flowsheet, graphs andtables
USIM PAC economical resultscalculation of the approximate
capital cost of the mainequipment and the overall cost
of the plant
ObjectivePre-feasibility study
Figure 4: Methodology for a preliminary design
4.3 - Step one: enter data
1.1.1.1 Draw plant flowsheet
The Flowsheet Drawing option of USIM PAC is used to draw or modify a flowsheet; it is entirely graphic and icon driven (see Fig. 5).
The arrow is used to select functions in the Toolbox with the mouse (see Fig. 6) and to position equipment icons, material streams or texts on the screen. The Unit button opens the icon library organized in groups of icons, depending on their function.
The created flowsheets are not simply saved as drawings: they are also analyzed and error messages are displayed if the flowsheet is not physically comprehensible. Flowsheets can be displayed on the screen, modified using the Flowsheet Drawing option, or plotted on paper using File\Print... option.
The user can position the equipment on the flowsheet in any order. If required, Automatic Renumbering options can be used to renumber the equipment and/or the streams in a logical order. Manual Renumbering is also available for streams and units in floating menus that can be obtained by a click on the right mouse button over a stream or a unit number.
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Figure 5: Screen drawing plant flowsheet
Figure 6: Tools box of the flowsheet drawing option
Any flowsheet can be copied and pasted through the clipboard. A copy of the described flowsheet is presented on Figure 7.
Recycledwater
Tailings
Barrenpulp
Loadedcarbon
Leached pulp
Unloadedcarbon
OF
UFBall millproduct
Rof millproduct
Ball millfeed
Cyclone feedRod millfeed
Thickener
CIP
LeachingHydrocyclone
Ball millregulator
Ball mill
Hydrocycloneregulator
Rod millRod millregulator
Feeder
Feed
Preliminary designGrinding/Leaching/CIP
1
23
4 5 6
7
8
9
1011
1
2 34 5 67
8
9
10
11
12
13
14
15
1617
18 19
20
Figure 7: Flowsheet of the gold ore grinding circuit
1.1.1.2 Describe phase model
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For this case study, the ore is described in terms of a size distribution for grinding and classification and a global gold content for leaching. The predefined phase named “Gold ore” fits this description. The other phases present in the process are the water used for wet treatment and leaching and the carbon used for the CIP stage. These phases are described by their gold content (see Figures 8, 9 and 10).
Figure 8: Description of the phase model
The units used above can be configured and adapted to the units used in the plant.
For a given project, the user of the program must define a phase model - i.e. he must decide how to represent the material in the streams: the number of particle-size classes and the corresponding mesh sizes, the number and names of the minerals or mineral groups, with their specific gravities. The user can define types of mineral particles, and/or the flowrates of flotation reagents associated with the slurry stream. Specific gravity of gold (19) is not considered in this case as the gold is finely disseminated in the ore. It will behave in the process (grinding, classification etc.) as the ore. That is why its specific gravity has been set up as the same value this of its support.
Figure 9: Description of the phase model - particle size
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Figure 10: Description of the phase model – composition for the three phases
In this case, connections between phases are defined. They represent the capability of gold to transfer from one phase to another (Ore to Solution through leaching, Solution to carbon through adsorption – see fig. 11 & 12).
Figure 11: Connections to represent possible gold transfers
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Gold Ore
Carbon
Leaching water
Gold transfer
Figure 12: Phase model
Solid/liquid pairs of phases can also be defined to describe the streams in terms of pulp flowrate (see fig. 13).
Figure 13: Solid/liquid pair definition
1.1.1.3 Describe plant feed
The feed streams are described using the Stream Description option of the Data menu of the main Window.
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The input interface for the stream data follows the phase model structure. It gives, stream by stream, the list of phases and solid-liquid pairs (see Figure 14).
Available data for each phase are the mass flowrate, the volumetric flowrate and the density; if they are known, the component grade and the size distribution for the ore phase.
Depending on the study, stream density may be required and descriptions such as composition by size classes or floating ability by component can be necessary.
Available data for each solid-liquid pair are the pulp mass flowrate, the pulp volumetric flowrate and density if they are known and the percent solids. For a given solid-liquid pair, only two values among both phase flowrates, pulp flowrate and percent solids are necessary. The other two are calculated. Size distribution can be input using individual % passing or cumulative % passing or retained.
Figure 14: Stream data entry
4.4 - Step two: define plant performance
1.1.1.4 Define performance for each unit operation
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This step obviously requires some expertise from the software user. The time has not yet come when the software is able to define relevant performances for each unit operation from such a general objective as "required circuit performance: d80 = 60 µm at the hydrocyclone overflow". In the present application, the software user (plant designer) suggests the following local objectives, using level 0 models (see Table 1).
Table 1 Level 0 models
Units Models and main parameters Values
#1 – Feeder Mixer (0)
#2 – Rod mill regulator Density Regulator (0)
Percent solids at regulator output (%) 70
#3 – Rod mill Mill (0A)
d80 at the mill discharge (mm) 1
#4 – Hydrocyclone regulator
Density Regulator (0)
Percent solids at regulator output (%) 40
#5 – Hydrocyclone Hydrocyclone (0B)
Short circuit of fines (%) 25
d80 of output fine stream (mm) 0.075
Corrected partition curve imperfection 0.3
#6 – Leaching Leaching (0)
Leached percentage per component of ore and solid phases (%) – Gold
95
#7 – CIP CIP – Carbon-In-Pulp (0)
Adsorbed percentage per component of the liquid phase (%) – Gold
95
#8 – Thickener Solid/Liquid Separator (0)
Percent solids of the slurry stream (mass %) 70
#9 – Splitter Liquid Split (0)
Maximum flowrate of liquid to the specified output (t/h)
180
#10 – Ball mill regulator Density Regulator (0)
Percent solids at regulator output (%) 55
#11 – Ball mill Mill (0A)
d80 at the mill discharge (mm) 0.25
The Equipment Description option of the Data menu is used to enter model selection and parameters for each unit operation (see Fig. 15).
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Figure 15: CIP – Carbon-In-Pulp (0) mathematical model parameters
1.1.1.5 Run a Level 0 simulation
The ideal description of all the streams is calculated by the direct simulation algorithm from the feed description using the selected performance models. This preliminary material balance predicts a first estimate of:
• The circulating load in the grinding circuit,
• The recycling level of water and the fresh water consumption,
• The d80 and the gold content for each stream, and
• The overall gold recovery.
The user can impose a maximum number of iterations and a convergence criterion or he may use default values proposed by the software (see Fig. 16).
Figure 16: Direct simulation starting box
The USIM PAC simulation algorithm is iterative. The output stream(s) from each unit of equipment is(are) calculated by the unit operation model as a function of the feed streams. The number of iterations completed is permanently displayed.
For each iteration, all the calculated flowrates are compared with the values from the previous iteration. Convergence is achieved when the sum of all the least square differences becomes less than the convergence criterion. 1.1.1.6 Verify the validity of simulation results
The Level 0 simulation calculates flowrates and particle-size distributions for all streams of the circuit. The plant designer must check that the values are consistent with the way he anticipates the plant will perform. In the present case, it is important to verify that the circulating load is realistic.
The Simulation Results are displayed in different ways by the options of the Results menu:
The Simulation Results \ Global Sheet option displays the solids and water flowrates and the overall mineral composition of the stream (see Fig. 17). This table is fully configurable and various types of variables can be displayed. We can check on the following table that:
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• circulating load in the grinding circuit is 204%;
• d80 of the cyclone underflow is 75µm
• gold recovery is over 95% (635.6/665)
• addition of water is 35.7 m3/h (53.7+50.1-68.1). Recycling 210 t/h of water to the hydrocyclones (with splitter 9) will optimize water addition.
Figure 17: Display of the global results
There are seven distinct forms of graphical representations: size distribution, size partition, density distribution, density separation and split curves, and stream and component bar graphs. These graphs are entirely configurable. Some predefined graphs can be drawn directly from the flowsheet popup menu. It is possible to draw the size distributions of all solid components directly from a stream submenu. Furthermore, size partition and split curves can be drawn directly from a unit operation submenu.
Figure 18 gives an example of a graph showing the size distribution curves for the hydrocyclone feed, overflow and underflow streams.
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Figure 18: Size distribution curves of the cyclone feed, overflow and underflow
Figure 19 gives the gold partial flowrate in each phase and each stream as a bar graph. It clearly indicates the amount of gold in the grinding circulating load or in the recycled water as well as the phase transfer between ore and water and then between water and carbon.
Figure 19: Stream bar graph of the gold partial flowrates
This ideal material balance will be used as the new objective, called “target”, during the equipment sizing stage. Full stream description can be displayed using the stream data entry interface, the stream overview sheet or more synthetically with graphics and global results.
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4.5 - Step three: design the units of equipment
1.1.1.7 Select Level 1 or Level 2 unit operation models
Target.UP3 file needs to be saved as Design1.UP3. Then, the Equipment Description option is used to replace the Level-0 models with Level-1 or Level-2 predictive models, and to specify some characteristics of the equipment (see Table 2).
Table 2 Level 1 and 2 models
Units Models and main parameters Values
#3 – Rod mill Rod Mill (1)
Number of mills in parallel 1
???? Inside mill diameter (m) 2.7 Length/diameter ratio 2
Percent volumetric loading of rods 35
Fraction of critical speed 0.7
Rod specific gravity 7.8
Work index per component (kWh/st) 14
#5 – Hydrocyclone
Hydrocyclone (2)
Number of hydrocyclones in parallel 2
???? Cyclone diameter: D (m) 0.659 ???? Distance between the underflow and overflow
nozzles / D 2.646
???? Diameter of the feed nozzle / D 0.316 ???? Diameter of the overflow nozzle / D 0.325 ???? Diameter of the underflow nozzle / D 0.204 #6 – Leaching Leaching (1A)
???? Tank volume (m3) 940 Number of tanks in series 6
Maximum recovery per component of ore and solid phases (%) – Gold
98
Rate constant per component (1/h) – Gold 0.3
#7 – CIP CIP – Carbon-In-Pulp (1)
Number of tanks in series 6
Tank volume (m3) 500
Rate constant per component of the liquid (<0 if desorbed) – Gold
700
Time constant per component of the liquids – Gold 0.3
???? Mean residence time of the carbon in one tank (h) 8.1
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#11 – Ball mill Ball Mill (1)
Number of mills in parallel 1
???? Inside mill diameter (m) 3.8 Length/diameter ratio 1.2
Percent volumetric loading of balls 40
Fraction of critical speed 0.7
Ball specific gravity 7.8
Work index per component (kWh/st) 14
Parameters preceded by ???? are calculated by the unit sizing algorithm. The associated value is the result.
The Rod Mill (1) and Ball Mill (1) models are based on the energy-based theories of grinding and in particular on Bond’s law and the Allis Chalmers methods for dimensioning grinding mills (Rowland and Kjos, 1978). The choice was made to use one rod mill and one ball mill with predefined conditions such as mill shape, grinding media loading and speed. Only the diameter will be calculated to fit the target size distribution.
The Hydrocyclone (2) model is based on the empirical equations established as a result of experimental work (Plitt, 1976). This model accounts for the roping effect. The number of cyclones is fixed to 2 regarding the volumetric feed rate of pulp (around 530m3/h). The dimensions of the hydrocyclones are calculated. First, by scanning the diameter is determined. Then, using the Polytope algorithm, all dimensions are adjusted to meet the objective.
The leaching (1A) model uses a first order kinetic equation for the gold transfer in solution using a maximum recovery and a constant rate (McLaughlin and Agar 1991). The leaching tank icon represents a series of 6 tanks for which the volume is determined to achieve the 95 % gold recovery.
The CIP (1) model is derived from the kn model (Fleming, Nicol, and Nicol 1980). The CIP icon represents a series of 6 tanks with a counter-current carbon flow. The tank volume is fixed to an arbitrary volume and the residence time of carbon is adjusted to achieve the 95 % gold recovery. It is then possible to calculate the volume of carbon in the tank and hence the tank volume from the ideal carbon concentration in pulp. 1.1.1.8 Design each unit of equipment
The calculation of equipment size parameters is performed using an optimization algorithm, which finds the set of parameters that gives output streams that most closely match the target data set (see Figure 20). A set of various objective functions is used to compare calculated and target output streams more precisely (Brochot et al. 2002).
After calculation of all unit sizing parameters, a direct simulation is performed to update the material balance with the adjusted models. It is then possible to compare these results with the target using the different graphic tools. A final direct simulation can be performed after changes are made to the unit sizing parameters to reflect available equipment from vendors and the results are compared to the original objectives.
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Figure 20: Unit of equipment sizing
1.1.1.9 Run a Level 1 simulation
It is now possible to run the Direct Simulation again and to predict the detailed operation of the plant, using the Level 1 and Level 2 predictive models, which take into account the selected size(s) of the units of equipment.
As the models are predictive, not only the flowrates and particle-size distributions are calculated for each stream, but also the power draws of the mills and the pressure drop in the hydrocyclones. These values are displayed the Operating parameter Display and the Calculated Power Consumption options (see Fig. 21).
Figure 21: Display of the operating parameter file
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At the end of this step, it is recommended to verify the consistency of the results of the simulation again using the Results and Graphs menu. Figure 22 shows a comparison between the objective and the simulated values for the Gold partial flowrates. Figure 23 shows a comparison of the targeted partition curve and the simulated one.
Figure 22: Comparison of the gold partial flowrates
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Figure 23: Comparison of objective and simulated partition curves.
4.6 - Step four: estimate capital cost
Each situation simulated using the Direct Simulation option can be followed by a calculation of the approximate capital cost of the plant: the user calls the Investment Capital Cost Estimation option and starts by selecting the currency (see Fig. 24), for example 2006 €.
Figure 24: Currency Selection
He may then be required to indicate values for various parameters not entered for the simulation. This option provides a cost report (see Fig. 25) containing:
• A list of the costs of the main units of equipment, and the total purchase cost of the equipment, excluding transport and installation.
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• A table in which various ratios are applied to this purchase cost to provide a budgetary breakdown of the total plant investment.
Figure 25: Cost report
The configuration possibilities are numerous, using the Cost menu, due to the variability of the economic factors. The Marshall and Swift mining equipment index, published in the "Chemical Engineering" review, can be updated (see Fig. 26).
Figure 26: Marshall and Swift index configuration
The cost-calculation models of USIM PAC are based mainly on the work of Mular [1982] updated using the experience of some software users.
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REFERENCES Brochot, S., Wiegel, R.L., Ersayin, S., Touze, S., "Modeling and Simulation of Comminution Circuits
with USIM PAC", 2006, Advances in Comminution, Ed. S.K. Kawatra, SME, pp 495-511.
Brochot, S., Durance, M.-V., Villeneuve, J., Mugabi, M., "Modelling of the bioleaching of sulphide ores: application for the simulation of the bioleaching / gravity section of the Kasese Cobalt Company Ltd process plant", 2003, Processing & Disposal of Mineral Industry Wastes '03 conference, Falmouth, UK, June 18-20.
Brochot, S., Villeneuve, J., Guillaneau, J.-C., Durance, M.-V., Bourgeois, F., "USIM PAC 3: Design and Optimization of Mineral Processing Plants from Crushing to Refining ", 2002, Mineral Processing Plant Design, Practice and Control, Ed. A.L. Mular, D.N. Halbe & D.J. Barratt, SME, pp. 479-494.
Villeneuve, J., Michel, P., Wavrer, Ph., Brochot, S., Lemière, B., "La recherche européenne fabrique les outils d'une meilleure gestion globale des déchets", 2002, Environnement & Technique, juin 2002, n°217, pp 31-34.
Brochot, S., Durance, M.-V., Guillaneau, J.-C., Villeneuve, J., "USIM PAC 3.0: New features for a global approach in mineral processing design", 2002, Application of computers and operations research in the mineral industry, Ed. Sukumar Bandopadhay, SME, pp. 465-478.
Brochot, S., Durance, M.-V., Foucher, S., Guillaneau, J.-C., Morin, D., Villeneuve, J., "Process simulation to enhance complex flowsheet development: examples in biotechnology", 2000, SME-Control 2000 Conference, Salt Lake City, Utah, USA - February 28-March 1.
Brochot, S, Cézac, P., Durance, M.-V., Fourniguet, G., Guillaneau, J.-C., Morin, D., Villeneuve, J. “Simulation of Biotreatments in complex mineral processes” XXI IMPC Rome, July 2000, proceedings pp. A3-108-119. [2000b]
Guillaneau, J.-C., Villeneuve, J. , Bodenan, F., Brochot, S., Durance, M.-V., Hergibo, P.-L., Piantone, P., Sandvik, K., Vedrine, H., Wavrer, P. « Urban Waste Management: From characterization to process evaluation », French-Egyptian Symposium on Water and Waste Management and Treatment , Cairo, Egypt, January 17-18-19, 2000 [2000a]
Cézac, P., Truong-Meyer, X. M., Joulia, X., Brochot, S., Morin, D., “A New Modelling Approach of Bioleaching Process”, 1999, CDROM of the ECCE2, Second European Congress of Chemical Engineering, 5-7 Oct. 1999, 8 pp, Montpellier, France. [1999f].
Guillaneau, J.-C., Morin, D., Durance, M.-V., Morizot, G., “Biotechnology and process simulation: ore processing in ecology friendly conditions”, Proceedings of the third World Mining Environment Congress, pp74-84, 1999, Moscow, Russian Federation. [1999e].
Villeneuve, J., Conil, P., Fourniguet, G., Hergibo, P. L., “Simulation Methods to Analyse the Treatment of a Polluted Soil – Size Classification and Hydrocarbons Recovery”, 1999, CDROM of the ECCE2, Second European Congress of Chemical Engineering, 5-7 Oct. 1999, 8 pp, Montpellier, France. [1999d].
Sandvik, K. L., Villeneuve, J., Durance, M.-V., Védrine, H., “Development of a Mineral Processing Program as a tool for optimal decision in Waste Treatment”, 1999, Proceedings of the REWAS’99, Global Symposium on Recycling, Waste Treatment and Clean Technology, 5-9 Sept. 1999, Vol 1, pp 55-64, San Sebastian, Spain. [1999c].
Guillaneau, J.-C., Brochot, S., Durance, M.-V., Villeneuve, J., Fourniguet, G., Védrine, H., Sandvik, K., Reuter M., “From mineral processing to waste treatment: an open-mind process simulator”, CIM 1999, Québec, Canada [1999b]
Starting Guide 27
USIM PAC 3.2
Guillaneau, J.-C., Morin, D., Durance, M.-V., Morizot, G., “Biotechnology and process simulation: two tools to develop environmentally friendly processes of ores”, 1999, Proceedings of the GME’99, Beijing, China. [1999a]
Durance, M.-V., Villeneuve, J., Guillaneau, J.-C., "Optimisation des ateliers de classification", 1998, Revue de l'Industrie Minérale, Les techniques, Vol. V/98, supplément à décembre 1998, pp. 85-93. [1998.b].
Villeneuve, J., Guillaneau, J.-C., "Méthode d’analyse des performances de la classification granulométrique d’un sol pollué", 1998, Revue de l'Industrie Minérale, Les techniques, Vol. V/98, supplément à décembre 1998, pp. 65-69. [1998.a].
Mouvet, Ch., Artignan, D., Brochot, S., Chiles, J.-P., Coste, B., Negrel, Ph., Pauwels, H., "Variabilité des milieux : illustrations, implications... solution ?", 1997, Les Cahiers des Clubs CRIN, Surveillance de l’Environnement, Paris, 1997. [1997.e].
Fourniguet, G., Villeneuve, J., Rocchia, L., Védrine, H., Brochot, S., Guillaneau, J.-C., "Analyse par bilans matière d’un procédé hydrométallurgique de recyclage de déchets d’aciéries électriques", 1997, 6e Congrès Français de Génie des Procédés, Paris, 24-26 septembre 1997 [1997.d].
Guillaneau, J.-C., Villeneuve, J., Durance, M.-V., Brochot, S., Fourniguet, G., Durand, H., "From Sampling to Simulation: the BRGM range of Software for Process Analysis", 1997, Minerals Engineering Annual Meeting, Santiago, Chile - July 29-August 1, 1997. [1997.c].
Guillaneau, J.-C., Villeneuve, J., Durance, M.-V., Brochot, S., Fourniguet G., Védrine, H., Wavrer, P., Le Guirriec, E., “Logiciels d’aide à l’analyse des procédés”, Memento des Mines et Carrières, nouvelle série n°3, 1997, pp. 376-396. [1997.b]
Guillaneau, J.-C., Villeneuve, J., Durance, M.-V., Brochot, S., Fourniguet, G., Durand, H., "A range of Software for Process Analysis", 1997, SME Annual Meeting, Denver, Colorado - February 24-27, 1997, preprint # 97-202. [1997.a].
Guillaneau, J.-C., Villeneuve, J., Desbiens, A., Hodouin, D., Arnaud, J.-M., Fauvel, M., Charret, B., Maldonado, A., Blot, G., Poiraud, E., Terray, M., Serbon, J.-C., Delubac, G., Broussaud, A., Guyot, O., Soderman, P., Storeng, U., Samsog, P.O., "Optimisation des installations de broyage", 1996, Revue de l'Industrie Minérale, Les techniques, Vol. III-IV/96, supplément à novembre 1996, pp. 176-212. [1996.i].
Pouthier, G., Georgeaux, A., Gourdou, J., Dodds, J., Arigon, Ch., Wavrer, Ph., Clin, F., Védrine, H., "Contrôle de la production", 1996, Revue de l'Industrie Minérale, Les techniques, Vol. III-IV/96, supplément à novembre 1996, pp. 102-116. [1996.h].
Bergounioux, M., Brochot, S., Le Guirriec, E., "Méthodes SQP-Lagrangiennes Appliquées à la réconciliation de données ", mai 1996, Rapport de l’Université d’Orléans, Département de mathématiques, URA 1803 - CNRS, Orléans. [1996.g].
Cézac, P., Truong Meyer, X.M., Joulia, X., Villeneuve, J., Morin, D., "Approche thermodynamique pour la caractérisation du milieu de la lixiviation biologique d'une pyrite cobaltifère", 1996, Colloque SIMO 96, Récents progrès en génie des procédés, Vol 10, n° 49, pp 357-363, Toulouse, Octobre. [1996.f].
Villeneuve, J., Guillaneau, J. -C., Martin, M. A. S., Lopes, G. S., "SAG Mill Modelling in USIM PAC 2: Example of the CVRD Igarapé Bahia Circuit", 1996, Proceedings of the International Autogenous and Semi Autogenous Grinding Technology 1996 Conference (SAG'96), Vancouver, Canada, October 6-9. [1996.e].
Villeneuve, J., Durance, M. V., Guillaneau, J.-C., Brochot, S., Fourniguet, G., "Conception et adaptation de la production de granulats par simulation", 1996, Congrès de la Société de l'Industrie Minérale, Montpellier, 1-4 octobre 1996 [1996.d]
28 Starting Guide
USIM PAC 3.2
Villeneuve, J., Durance, M. V., Guillaneau, J. -C., Santana, A. N., Silva, R. V. G., Martin, M. A. S., "Advanced use of column flotation models for process optimisation", 1996, Proceedings of the International Symposium on Column Flotation (COLUMN'96), Montréal, Canada, August 25-29. [1996.c].
Schena, G., Villeneuve, J., Noël, Y., "A Method for a financially efficient design of cell-based flotation circuits.", International Journal of Mineral Processing, April 1996, Vol 46, Nos 1-2, pp 1-20. [1996.b]
Le Guirriec, E., Bergounioux, M., Brochot, S., "Modélisation et résolution de problèmes de bilan matière statistiquement cohérent", 1996, Communication aux Quatrièmes Journées du groupe MODE de la SMAI, Mars 1996, Limoges, France. [1996.a].
Brochot, S., Durance, M.V., Fourniguet, G., Guillaneau, J.-C., Villeneuve, J., "Approche objet en simulation - application à la simulation des procédés de traitements des matières minérales.", 1995, XVIIème Conférence Internationale des Industries de Procédés,.Interchimie 95, Paris. [1995.h].
Guillaneau, J.-C., Villeneuve, J., Brochot, S., Durance, M.-V., Fourniguet, G., "The Supervisor of Simulation: a step further to meet the Process Engineer Needs.", 1995, Proceedings of the XIX International Mineral Processing Congress, San Francisco, USA, October 22-27. [1995.g].
Brochot, S., Durance, M.-V., Fourniguet, G., Guillaneau, J.-C., Villeneuve J., "Modelling of the Minerals Diversity: a Challenge for Ore Processing Simulation", 1995, Proceedings of the 1995 EUROSIM Conference, EUROSIM’95, pp. 861-866, Vienna, Austria. [1995.f].
Guillaneau, J.-C., Olofsson, O., Durance, M.-V., Villeneuve, J., “Modelling the Sala Agitated Mill (SAM) using BRGM Pilot Plant Data.”, 1995, Proceedings of the APCOM XXV 1995 Conference, July, Brisbane, Australia, pp. 325-331. [1995.e]
Le Guirriec, E., Brochot, S., Bergounioux, M., "An Augmented Lagrangian Method for Problems Arising in Mineral Processing.", 1995, Proceedings of the 17th IFIP TC7 Conference on System modelling and Optimization, Vol. 1, pp. 65-68, July, Prague, Czech Republic. [1995.d].
Villeneuve, J., Guillaneau, J.-C., Durance, M.-V., "Flotation modelling: a wide range of solutions for solving industrial problems.", 1995, Minerals Engineering, Vol. 8, n°4/5, April/May 1995, pp. 409-420, Elsevier Science Ltd. [1995.b]
Morizot, G., Guillaneau, J.-C., "Role of Modelling and Simulation in the development of new Processes.", 1995, International Minerals and Metals Technology 1995, pp. 144-149. [1995.a].
Guillaneau, J.-C., Villeneuve, J., Durance, M. V., Brochot, S., Fourniguet, G., "Modélisation du concassage.", 1994, Revue de l'Industrie Minérale, Les techniques, Vol. 76, supplément à décembre 1994, pp. 67-73. [1994.h].
Guillaneau, J.-C., Villeneuve, J., Durance, M. V., Brochot, S., Fourniguet, G., "Optimisation des installations de concassage : simulation des procédés.", 1994, Revue de l'Industrie Minérale, Les techniques, Vol. 76, supplément à décembre 1994, pp. 114-118. [1994.g].
Guillaneau, J.-C., Durance, M. V., Libaude, J., "Exemple d'optimisation par simulation : concassage secondaire d'un minerai d'or.", 1994, Revue de l'Industrie Minérale, Les techniques, Vol. 76, supplément à décembre 1994, pp. 127-130. [1994.f].
Villeneuve, J., Guillaneau, J.-C., Durance, M.-V., 1994, "Modélisation des séparations solide/liquide dans l'industrie minérale.", Recueil des conférences du congrès Interfiltra 1994, Novembre, Paris, pp. 284-293. [1994.e]
Durance, M.-V., Guillaneau, J.-C., Villeneuve, J., Brochot, S., Fourniguet, G, 1994, "USIM PAC 2 for Windows: advanced simulation of mineral processes.", Proceedings of the 5th International
Starting Guide 29
USIM PAC 3.2
Mineral Processing Symposium, September, Cappadocia, Turkey, A. A. Balkema, pp. 539-547. [1994.d]
Durance, M.-V., Guillaneau, J.-C., Villeneuve, J., Brochot, S., Fourniguet, G., 1994, "USIM PAC 2 for Windows, the new process engineers' partner to design and optimize industrial plants.", Proceedings of the first regional APCOM, June, Bled, Slovenia, pp. 303-312. [1994.c]
Villeneuve, J., Guillaneau, J.-C., 1994, "Modelling and Simulation of Comminution", communication presented at the ICRA workshop, May, Stockholm, Sweden. [1994.b].
Guillaneau, J.-C., Villeneuve, J., Durance, M.-V., Brochot, S., Fourniguet, G., 1994, "Simulation Improvements in Mineral Processing", communication presented at the first International Symposium on Complex Ores Utilization, May, Saint Petersburg, Russia. [1994.a].
Guillaneau, J.-C., Allix, J., Arnaud, J.-M., 1993, "Optimisation des installations de concassage : simulation, commande et réglage automatique des équipements.", communication presented at the SIM Annual Meeting, October, Grenoble. [1993.f].
Durance, M.-V., Guillaneau, J.-C., Libaude, J., 1993, "Optimisation d'une usine de traitement de minerai par simulation du fonctionnement et des caractéristiques des flux : exemple de la mine d'or de Shila (Pérou)", communication presented at the SIM Annual Meeting, October, Grenoble. [1993.e].
Durance, M.-V., Guillaneau, J.-C., Libaude, J., Villeneuve, J., 1993, "Simulation-Based optimization of plants: a practical tool for the mineral industry.", Communication presented at the CIM Metallurgical Society Annual Meeting, August, Québec, Canada. [1993.d].
Durance, M.-V., Guillaneau, J.-C., Villeneuve, J., Fourniguet, G., Brochot, S., 1993, "Computer Simulation of Mineral and Hydrometallurgical Processes: USIM PAC 2.0, a Single Software from Design to Optimization.", Proceedings of the International Symposium on Modelling, Simulation and Control of Hydrometallurgical Processes, August 24-September 2, Québec, Canada, pp. 109-121. [1993.c].
Guillaneau, J.-C., Villeneuve, J., Durance, M.-V., Guyot, O., 1993, "Computer-aided Design and Optimization of Mineral Processing Plant: USIM PAC 2.0 for Windows", Paper to be published in Mining Magazine. [1993.b].
Guillaneau, J.-C., Durance, M.-V., Libaude, J., Ollivier, P., 1993, "Computer-aided Optimization of Mineral Processing Plants, a Case Study: Increasing the Capacity of the Shila Gold Mine, Peru", Paper presented at the SME Annual Meeting, February 15-18, Reno, Nevada, U.S.A.. Preprint # 93-222. [1993.a].
Broussaud, A., Guillaneau, J.-C., Guyot, O., Pastol, J.-F., Villeneuve, J., 1992, "Méthodes et algorithmes pour accroître l'utilité et le réalisme des simulateurs d'installations de traitement de matières minérales.", Revue de l'Industrie Minérale, les Techniques, Vol. 74, n° 5-92, pp. 146-152. [1992.e].
Guillaneau, J.-C., Villeneuve, J., Blot, P., 1992, "Advances in the Design and Optimization of Mineral Processing Plants", Proceedings of the APCOM 92, 23rd International Symposium on the Application of Computers and Operations Research in the Mineral Industry, April 7-11, Tucson, Arizona, U.S.A., Chapter 54, pp. 549-566. [1992.d].
Durance, M.-V., Guillaneau, J.-C., Villeneuve, J, Fourniguet, G., Brochot, S., 1992, "Advances in Research on Steady-state Simulator for Mineral Processing Plants" Paper presented at the Miro Annual Meeting, April, Coventry, United Kingdom. [1992.c].
Libaude, J., Morizot, G., Baudet, G., Morin, D., Guillaneau, J.-C., 1992, "New Technological Developments in Mineral Processing", 3rd Asia-Pacific Mining 92 Conference, March 18-21, Philippines. [1992.b].
30 Starting Guide
USIM PAC 3.2
Villeneuve, J., Guillaneau, J.-C., Pilotte, R., Broussaud, A., 1992, "Objective Driven Simulation: a new Approach to Improving the Efficiency and Usefulness of Steady-state Simulators of Mineral Processing Plants", Paper presented at the SME Annual Meeting, February 24-27, Phoenix, Arizona, U.S.A.. Preprint # 92-168. [1992.a].
Broussaud, A., Herbst, J., 1991, "Succès industriels de la modélisation en minéralurgie", Revue de l'Industrie Minérale, Mines et Carrières, Vol. 73, décembre, pp. 30-36. [1991.h].
Plitt, L.R., Guillaneau, J.-C., Broussaud, A., 1991, "Modélisation mathématique des hydrocyclones avec décharge en boudin", Revue de l'Industrie Minérale, Mines et Carrières, Vol. 73, décembre, pp. 45-47. [1991.g].
Blot, P., Oblad, E., Herbst, J., Villeneuve, J., Guillaneau, J.-C., 1991, "Integrating and Using an Advanced SAG/AG Mill Model in the USIM PAC Mineral Processing Simulator", Proceedings of the Conference on the Computer Applications in the Mineral Industry "Software of the 90's for the Mineral Industry", Vancouver, BC, Canada, September 15-18, pp. 439-449. [1991.f].
Broussaud, A., Guillaneau, J.-C., Guyot, O., Pastol, J.-F., Villeneuve, J., 1991, "Methods and Algorithms to Improve the Usefulness and Realism of Mineral Processing Plant Simulators", Proceedings of the XVII International Mineral Processing Congress, Dresden, Germany, September 23-28, pp. 229-246. [1991.e].
Broussaud, A., Herbst, J.A., 1991, "Optimization de plantas de proceso de minerales", Canteras y explotaciones, Agosto, pp. 52-62. [1991.d].
Guillaneau, J.-C., Broussaud, A., Villeneuve, J., 1991, "Industrial Computer Methods for the Design and off-line Optimization of Mineral Processing Plants", Proceedings of the XIIIrd Symposium on Mineral Processing, Yugoslavia, May, pp. 91-96. [1991.c].
Broussaud, A., Fourniguet, G., Guillaneau, J.-C., Conil, P., Guyot, O., 1991, "Conception et gestion des circuits de broyage assistées par ordinateur", l'Industrie Céramique, n° 860, 5/91, pp. 311-313. [1991.b].
Védrine, H., Broussaud, A., Conil, P., De Matos, C.F., 1991, "Modelling the Flotation Kinetics of a Polymetallic Sulphide Ore", Paper presented at the SME Annual Meeting, February 25-28, Denver, Colorado, U.S.A.. [1991.a].
Guillaneau, J.-C., Villeneuve, J., Broussaud, A., 1990, "Un nouvel outil pour concevoir et/ou optimiser une criblerie", Journées "Carrières, vers une gestion optimale", Haute et Basse Normandie, décembre. [1990.g].
Broussaud, A., Guillaneau, J.-C., Fourniguet, G., 1990, "Practical Computer Methods for the Design and Adaptation of Mineral Processing Plants", Paper presented at the VIth Simposium Sobre Molienda, Chile, November. [1990.f].
Broussaud, A., Fourniguet, G., Guillaneau, J.-C., Conil, P., Guyot, O., 1990, "Conception et gestion assistée par ordinateur des circuits de broyage", Revue de l'Industrie Minérale, les Techniques, Vol. 72, n° 4-90, pp. 144-150. [1990.e].
Broussaud, A., Fourniguet, G., Franck, J., Conil, P., 1990, "Quantitative Analysis of the Accuracy of Steady State Simulation of Mineral Processing Plants", Proceedings of the CONTROL'90, Mineral and Metallurgical Processing, SME, Salt Lake City, Utah, U.S.A., February 26-March 1, pp. 13-21. [1990.d].
Védrine, H., Broussaud, A., Conil, P., De Matos, C.F., 1990, "Modélisation de la cinétique de flottation d'un minerai sulfuré polymétallique", Revue de l'Industrie Minérale, les Techniques, mars-avril, pp. 79-87. [1990.c].
Plitt, L.R., Conil, P., Broussaud, A., 1990, "Technical note, an Improved Method of Calculating the Water-split in Hydrocyclones", Minerals Engineering, Vol. 3, n° 5, pp. 533-535. [1990.b].
Starting Guide 31
USIM PAC 3.2
Broussaud, A., Fourniguet, G., Guillaneau, J.-C., Conil, P., Guyot, O., 1990, "Conception assistée par ordinateur d'un circuit de broyage", Ciments, Bétons, Plâtres, Chaux, n° 784, 3/90, octobre, pp. 205-208. [1990.a].
Ollivier, P., Blot, P., 1989, "Computer Aided Design of a Gold Ore Grinding Circuit", Proceedings of the Randol Wokshop, Phase IV, Sacramento, California, U.S.A., November 10-11, pp. 39-42. [1989.i].
Conil, P., De Matos, C.F., Broussaud, A., Ferrao, C., 1989, "Modelling of the Autogenous Grinding of the Moinho (Portugal) Complex Sulphide Ore", Proceedings of the International Conference on Autogenous/Semi-Autogenous Grinding, Vancouver, BC, Canada, September 25-27, pp. 741-758. [1989.h].
Broussaud, A., Conil, P., Fourniguet, G., Guillaneau, J.-C., 1989, "USIM PAC : premier logiciel intégré d'aide à la conception et à l'optimisation des usines de traitement des minerais", Revue de l'Industrie Minérale, les Techniques, juillet, pp. 1-12. [1989.g].
Broussaud, A., Conil, P., Védrine, H., Ferrao, C.N, Coelho, J., Matos, C.F., 1989, "Modelling of an Autogenous Grinding and Flotation Process. Application to the Industrial Processing of the Moinho Complex Sulphide Ore", CANMET/EEC Meeting on Complex Sulphide Ore, Ottawa, June. [1989.f].
Broussaud, A., Guillaneau, J.-C., Pastol, J.-F, Jourdan, M., Ghibu, C., Perisse, R., 1989, "Development and Application of a Modular Intelligent Control System for Mineral Processing Plants", EEC Seminar on Mineral Processing and Extractive Metallurgy, Warren Spring Laboratories, Stevenage, U.K., February 27th and 28th. [1989.e].
Broussaud, A., Guillaneau, J.-C., Fourniguet, G., Conil, P., Blot, P., 1989, "A revolutionary tool for Mineral Processing Plant Design and Optimization", Paper presented at the SME Annual Meeting, Las Vegas, Nevada, U.S.A.. [1989.d].
Lanthier, R., Hodouin, D., Guillaneau, J.-C., Broussaud, A., 1989, "Real-Time mass Balance Calculation for a Pilot Grinding Circuit", Proceedings of the 21st Annual Meeting of the Canadian Mineral Processor, Ottawa, Ontario, Canada. [1989.c].
Guillaneau, J.-C., Broussaud, A., Lanthier, R., Hodouin, D., 1989, "Mise en oeuvre d'une stratégie de valorisation en ligne des mesures pour la conduite d'un circuit pilote de broyage", Revue de l'Industrie Minérale, les Techniques, novembre-décembre, pp. 8-16. [1989.b].
Morizot, G., Legret, D., Croisé, G., Guillaneau, J.-C., 1989, "Pilotage de la flottation d'un minerai sulfuré polymétallique : le cas Chessy", Revue de l'Industrie Minérale, les Techniques, Vol. 71, n° 3-89, pp. 153-160. [1989.a].
Broussaud, A., Guillaneau, J.-C., Conil, P., Fourniguet, G., Ragot, J., Bloch, G., Hanton, E., 1988, "Poste de contrôle intelligent dédié aux procédés de traitement des minerais", Communication présentée au colloque Ressources du sous-sol organisé par le Ministère de la Recherche, 26-27 septembre. [1988.g].
Broussaud, A., 1988, "Advanced Computer Methods for Mineral Processing: Their Functions and Potential Impact on Engineering Practices", Proceedings of the XVI International Mineral Processing Congress, Stockholm, Sweden, June 5-10, Elsevier, Developments in Mineral Processing, Vol. 10.A, pp. 17-44. [1988.f].
Bourassa, M., Barbery, G., Broussaud, A., Conil, P., 1988, "Flotation Kinetic Scale up: Comparison of Laboratory Batch Test to Pilot Plant Processing", Proceedings of the XVI International Mineral Processing Congress, Stockholm, Sweden, June 5-10, Elsevier. Developments in Mineral Processing, Vol. 10.A, pp. 579-588. [1988.e].
Broussaud, A., Conil, P., Fourniguet, G., Guillaneau, J.-C., 1988, "USIM: an easy to use Simulator for Mineral Processing Plants", Proceedings of the First Canadian Conference on the Computer
32 Starting Guide
USIM PAC 3.2
Applications in the Mineral Industry, March, Québec, Canada, A.A. Balkema, Rotherdam, pp. 137-145. [1988.d].
Broussaud, A., Clin, F., Croisé, G., Fourniguet, G., 1988, "Méthodologie d'échantillonnage et bilans matière cohérents dans un pilotage de minerai sulfuré", Revue de l'Industrie Minérale, les Techniques, mars-avril, pp. 165-170. [1988.c].
Conil, P., Broussaud, A., Niang, S., Delubac, G., 1988, "Utilisation d'un simulateur pour l'aide à l'évaluation d'hypothèses d'évolution d'un atelier de concentration à l'usine de phosphates de Taïba (Sénégal)", Revue de l'Industrie Minérale, les Techniques, janvier-février, pp. 21-29. [1988.b].
Broussaud, A., Guillaneau, J.-C., 1988, "Modèles d'opérations unitaires en minéralurgie", Informatique dans l'Industrie Minérale, Revue de l'Industrie Minérale, les Techniques, numéro spécial, novembre, Vol. 70, pp. 146-172. [1988.a].