Flotation Circ uit Simulatio n as a Tool t o Evaluate Benefits o f Flot ation Cell Cell Modernization Toni Mattsson, Antti Remes, Miika Tirkkonen Minerals Processing - Flotation, Outotec (Finland) Oy, Espoo, Finland
stages are defined. The kinetic rate constants and mass portions of different floatability classes are used in the simulation to scale-up from the laboratory batch test to the continuously operating industrial flotation circuit. The main outcomes of the simulation are residence times of flotation stages, flotation cell sizes and types, the number of flotation cells and banks, element grades and recoveries, and a mass balance of the complete circuit - all of which are present in a full-scale continuously operating circuit.
Abstract The scope of supply for an existing commercial flotation cell modernization consists typically of flotation cell drive mechanism, mixing mechanism, dart valves, cell automation and/or launders modernization. The objective of the modernization is to enhance both the mechanical and metallurgical performance of the flotation cell, and simultaneously increase availability and energy efficiency.
When a simulation is used as a tool to evaluate the performance of an existing flotation circuit, the flotation model is build on the basis of laboratory scale kinetic hot flotation tests. Feed samples for the laboratory tests are collected directly from the process streams. Overlapping the laboratory tests process survey is carried ou t giving current performance of the circuit.
In order to evaluate the possible metallurgical benefits of flotation cell modernization, a new approach to simulate the circuit is presented in this paper. In this approach the flotation circuit model is implemented based on kinetic flotation test data from an on-site laboratory ‘hot flotation’ test work. *Test work is carried out with a process sample, thus giving equal feed properties for the model and industrial circuit during the sampling period. In the kinetic tests, the floatability kinetics of the main minerals are defined for each flotation stage. The kinetic parameters are used as a basis to simulate the existing flotation circuit characterized by the current flotation cell performance. In other words, the number and size of flotation cells, throughput and solids content can be input to the simulator from the existing flotation circuit. Finally, the achievable circuit performance with mechanically proper working flotation cells is evaluated on the basis of the modeled and measured performance.
From the simulation grades and recoveries in the full-scale flotation circuit, retention times of existing cells can be estimated, and plant capacity required to achieve metallurgical targets can be assessed. The measured and simulated performance comparison can be used as a tool to evaluate metallurgical benefits of flotation cell modernizations.
Flotation modernizations Flotation is a key process in concentrator plants. If it is not operating at the optimum level due to old or outdated technologies, overall plant performance can suffer, which has significant financial implications. Similarly, changes in ore deposits may require flowsheet reconfiguration, and constant improvement and optimization of the flotation process which can lead to vast economic and environmental benefits.
Introduction Traditionally, flotation circuits have been designed on the basis of kinetic laboratory tests, where results are confirmed from pilot scale test work. From the kinetic laboratory tests, the kinetics of main minerals for different flotation
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Figure 1 Flotation modernization options The scope of existing commercial flotation cell modernizations can consist of updating drive mechanisms, mixing mechanisms, dart valves, cell and process automation and launders (see Figure 1). The objective of the modernization is to enhance both the mechanical and metallurgical performance of the flotation cell, and simultaneously increase the availability and energy efficiency of the cell. In an ideal case the modernization can lead to improved metallurgical performance, better availability, shorter residence times and a decrease of energy consumption.
assessment is the first step towards improved beneficiation and better equipment control. A flotation assessment is an expert evaluation of the current process and flotation equipment conditions, including auxiliary equipment. The goal is to identify the most appropriate modernization path for each process and piece of equipment. A decent assessment covers process metallurgical, process controls and automation and also mechanical condition assessments. The disciplines or flotation assessment are presented in Figure 2.
Everything starts with a decent assessment There is no “one size fits all flotation modernization” available because setups, conditions and targets vary from concentrator to concentrator. Therefore possible improvement areas should be assessed case by case. Proper
Figure 2 Flotation assessment disciplines
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When the metallurgical performance of the plant is investigated, an on-site test campaign needs to be performed. The on-site test campaign consists of a plant survey and laboratory hot flotation tests. The objective of this stage is to simultaneously study how the plant is currently performing and generate the kinetic laboratory test data required for the flotation circuit simulation. From the on-site survey and simulation, possible benefits of modernization are estimated. Based on the estimation return of investment and other economical figures can be calculated to back-up investment decision making. A step-by-step modernization procedure is presented in Figure 3.
A laboratory batch flotation test is an important tool to screen and evaluate optimum grind sizes and reagent schemes for a certain ore sample. This helps to determine the most beneficial flowsheet to process the ore, and most importantly, to assess feasibility of processing the ore. The main outcomes of batch laboratory flotation tests are grades and recoveries of both valuables and gangue minerals, residence times of flotation stages, and flotation kinetics of main minerals, often expressed as flotation rate constant and proportion of mineral in different floatability classes. A common method to assess the floatability of an operating flotation circuit is “hot flotation” tests. The objective of the hot flotation tests is to transfer process slurry properties to a laboratory scale flotation cell by collecting a test feed sample directly from the process stream. The main outcomes of hot flotation tests are recoveries, grades and kinetics of the main minerals from a certain flotation stage. The kinetic parameters can be used for performance comparison, and also to evaluate flotation behavior of the ore on operating industrial scale circuit.
Laboratory flotation cells There is a vast variety of commercial laboratory flotation cell designs. These designs vary from round to square cell, from self to force aerated, and from bottom to top driven mixing mechanism. Each laboratory flotation cell design produces unique test data and therefore test trial need to be carried out throughout with the same design by using a standardized test procedure and operating parameters (Mattsson et al. 2013, O’Connor et al. 1987, Runge, K. 2010).
Figure 3 step-by-step path to modernize flotation circuit through assessments, modeling and evaluation of most economic options.
An example of a laboratory flotation machine is presented in Figure 4. The presented OutotecGTK LabCell laboratory flotation cell is equipped with automatic froth scrapers adjustable according to the size of the rectangular cell. With this machine, the froth scraping cycle, impeller speed and air flow rate can all be adjusted.
Process survey and laboratory flotation testing A process survey gives the analysis of the current process performance. The survey consists of sampling of process streams, sample treatment and chemical analysis, data reconciliation and mass balancing. On the basis of the established solids and water balances, process performance can be evaluated and the process bottle-necks identified.
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rate and solids contents in the feed at each stage continuously operating industrial scale circuit can be simulated.
Simulation based evaluation The flotation circuit is modeled with HSC Chemistry© Sim process flowsheet simulation module. The HSC-Sim module consists of graphical flowsheet and spreadsheet type process unit models. In minerals processing simulation mode the unit operation calculations are based on particle distributions. This enables versatile simulation of all concentrantrator unit processes. The flotation circuit model is based on the laboratory flotation testwork. The feed material is defined as particles, consisting of minerals. The laboratory model fitted flotation kinetics is assigned for each particle. Here, a three component flotation model is applied. The particles are defined to be fast-, slow- and non-floating components. The recovery of a mineral is calculated from the following laboratory batch (1) or plant continuous time (2) formulas:
(1) Figure 4 Outotec-GTK LabCell equipped with automatic froth scraping mechanism
(2)
Hot flotation test procedure
Where k is kinetic flotation rate constant (1/min) and t is residence time (min), m is a mass fraction of floatability component; subscripts F, S and N stands for the fast- slow and non-floating floatability components. In continuous time the average cell residence time (min) is calculated 3 based on the effective cell size V (m ) and the 3 volumetric input flow rate Q (m /h). The effective size V is obtained from the modernized flotation cell geometry and gas dispersion characteristics.
Kinetic hot flotation tests describe flotation behavior of the slurry sample at sampled flotation stages. During the test, typically 5 to 7 separate concentrate samples are collected in a cumulative time series. The collected concentrates and tailings are dry weighted and analyzed for the main elements enabling element to mineral conversion according to the feed mineralogy. As a result of kinetic laboratory tests, recovery profiles of different minerals at different flotation stages can be defined.
T=
From the laboratory test data, kinetic parameters can be derived with mathematical models. The kinetic parameters, rate constants and mass portions of different floatability classes, generate ground for the simulation with feed mineralogy. On the basis of the kinetics of main minerals at different flotation stages, feed mineralogy, flowsheet, sizes of modernized cells, feed flow
V Q
∙ 60
(3)
Also, the froth recovery R f and entrainment Ent parameters are taken into account, thus the continuous time flotation recovery equation of a single floatability class of a mineral has the form (Savassi, 2005),
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R=
ktR (1−Rw )+EntRw
(1+ktR )(1−Rw )+EntRw
measured performance can be observed – the relative error median for Cu grade and Cu recovery were 6.6 % and 3.6 %, respectively. In Figure 6, modeling based on the process slurry hot flotation test work and the corresponding plant sampling mass balance for a rougherscavenger bank are compared. The model prediction accuracy is very good; here the bank 3 consists of six 160 m TankCells modeled with TankCell simulation characteristics and scale up methods.
(4)
In addition, to take into account the Outotec ® FloatForce mixing mechanism gas dispersion characteristics, the kinetic flotation factor is further decomposed to mineral floatability P and bubble surface area flux Sb (1/min): k
=
PS b
.
(5)
Sb is calculated using superficial gas velocity Jg and bubble Sauter diameter d32, which are specific for the TankCell characteristics in the plant operating conditions:
a)
Cu Grade %
25 20
S b
=
6 J g
d 32
.
d 15 e t a l u m i S 10
(6)
Metallurgical evaluation 5
A step-by-step modernization assessment provides mechanical and metallurgical information from the circuit. The final outcomes of the hot flotation tests, metallurgical sampling and simulation are recoveries and grades of main minerals from each flotation stage for both the operating circuit and simulated circuit. On the basis of the measured and simulated performances it can be assessed whether the plant is operated at optimal metallurgical parameters and whether the performance can be enhanced through modernization.
0 0
5
10
15
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25
Plant Measured
Cu Recovery %
b) 100 80 d e t a l u m i S
The accuracy of a simulation is evaluated in Figure 5 and Figure 6. The data plotted to these figures is from a certain Cu circuit survey. The surveyed Cu circuit consists of a rougher, scavenger and four cleaning stages. The laboratory hot flotation test data, on which basis the simulator was build, was produced with Outotec-GTK LabCell simultaneously with the process survey. The modeled flotation cell was an Outotec TankCell© equipped with a FloatForce© mixing mechanism.
60 40 20 0 0
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60
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Plant Measured
Figure 5 Measured correlations between simulated and measured a) Cu grade and b) Cu recovery
In Figure 5 correlations between simulated and measured grades and recoveries at different process stages are shown. From this figure positive correlation between simulated and
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100
) % ( y r e v o c e R e v i t a l u m u C
Cell 6
Cell 5
Cell 4
90
Cell 3
80
Cell 2
70 Cell 1 60 50 Continuous Model
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Plant Mass Balance
30 20 10 0 0
5
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Cumulative flotation time (min)
Figure 6 Cumulative Cu recovery of a rougher ba nk; simulation and mass plant measured mass balance Note that the accuracy of the simulation is strongly dependent on the quality of the laboratory and on-site work. Sample treatment, chemical analysis, sampling points and methods can have significant impact on the results.
visual observations experiences.
and
previous
on-site
The results of the previous flotation cell modernization studies will be reviewed next. The reviews are divided to cover the equipment and operating performance of the flotation cell so that the equipment performance encompasses both the mechanical and operating performance of the flotation cell as well as operating performance automation and control of adjustable parameters.
Modernization benefits A comparison of the simulated and measured metallurgical performances gives an indication of how well the full-scale flotation circuit performs and how well it is operated. If a significantly higher metallurgical performance is reached in the simulation as compared to the process survey, hen a higher metallurgical performance could also be achieved in the full-scale circuit. This basically means that the cells are not working properly or that they are not operating correctly. On the other hand, the comparison can also show equal or lower performance for the simulation. This indicates that the plant operates well and that the modernization should focus for example to increase energy efficiency or availability of the plant.
Equipment performance Flotation cell metallurgical performance is defined by solids suspension, gas dispersion, froth transportation distance and cell hydrodynamics. In order to enhance the cell performance, typically the cell drive mechanism, mixing mechanism, dart vales and launders need to be modernized. Studies published by Gamez et al., (2011), Cesni, F., (2009) and Yianatos et al., (2012) compared the performance of a flotation bank equipped with old insufficiently performing or worn mechanisms to the performance of a flotation bank with an upgraded mixing mechanism. These studies showed significant metallurgical improvements when the old
The plant modernization approach on the basis of the plant survey and simulation varies from concentrator to concentrator. The actions taken after simulation are usually backed-up with
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mechanisms were upgraded. For example, Gamez et al. (2011) showed a 7 % copper recovery increase with the new mixing mechanism as compared to the old one. In the study done by Yianatos et al. (2012) it was observed 0.7-3.6 % higher copper recovery and 1.5-5.5 % higher molybdenum recovery for a rougher flotation line equipped with an Outotec ® FloatForce mixing mechanism, as compared to a line equipped with a traditional mixing mechanism.
measurement instrument automation system.
and
process
In 2001 Brown et al. reported a study where FrothMaster™ control solution was used to control level, air and frother dosing according to the speed of the froth over the lip. In this study 5.28 % Cu recovery improvement was measured at a 99% confidence level and 2.3 % Au recovery improvement at a 95 % confidence level for a line operated by a vision-based control solution than for a line operated with human vision and problem solving abilities. Rantala et al. (2014) reported over 1.3 % Cu recovery increase and better selectivity at Kevitsa mine when a rougher flotation cell was operated with ACT™ advanced process control as compared to when it the control was turned off.
The energy efficiency of the flotation cell can also be enhanced with a mixing mechanism upgrade. The Outotec TankCell 300 test work carried out at Codelco Chuquicamata has proven that with more pumping and by utilizing an energy efficient mechanism, there’s possibility to reduce the power input to the flotation cells without affecting metallurgical performance (Rinne, A. and Peltola A. 2008, and Colemen, R., and Rinne A., 2011).
Furthermore, control of large industrial flotation cells was discussed in paper published by Carr, D., et al. (2009). This work showed significantly higher operating stability when the cell was operated with control applications.
The flotation cell energy consumption is a very important factor since it represents two-thirds of the flotation cell lifecycle costs over a lifespan of 25 years (Grönstrand et al., 2006).
Monetary benefits of modernization Operating performance
Existing flotation circuit modernization can result in significant benefits with an extremely short payback period. Figure 7 illustrates an example of economic and environmental benefits that modernization can bring for an average size concentrator (throughput of 400 tons/hour). In the presented calculation example, 1% improvement in recovery and 10% reduction in the mixing-mechanism rotation speed are used.
Level, air and reagent dosing are the adjustable operating parameters affecting flotation performance. These variables are usually controlled by human vision and adjustment of the variable requires problem solving abilities from the operator. Control of these parameters can also be carried out using a vision-based
Figure 7 Economic and environmental Flotation m odernization options
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Grönstrand, S., Niitti, T., Rinne, A., and Turunen, J. 2006. Enhancement of Flow Dynamics of th Existing Flotation Cells. Proceedings of the 38 Annual Meeting of the Canadian Mieral Processors. pp. 403-422.
Conclusions The flotation plant modernization phase presents the most significant phase of the plant life span. This phase can consist of step-by-step approaches basically through the life span of the flotation plant to achieve optimal performance. The modernization steps can result in metallurgical performance enhancement as well as reduction in operating costs.
Mattsson, T., Grau, R., Leppinen, J. and Heiskanen K. 2013. Effect of Operating Conditions and Machine Parameters on the Flotation Kinetics in a New Laboratory Cell. Proceedings Flotation Conference 2013 Cape Town,
In this paper, a new approach to simulate existing flotation in order to evaluate the benefits of modernization was presented. The approach consists of simultaneously carried out process survey and kinetic hot flotation tests with process slurry sample. The data produced generates the basis for simulation and gives performance comparison for measured and simulated flotation circuit. On the basis of the comparison and visual on-site observations next steps towards more efficient flotation circuit can be taken.
O’Connor C.T., Dunne, R.C. and Duncan, R.B., Comparison of performance of various laboratory flotation cells. Trans. Intn Min. Metall. (Sect. C: Mineral Process. Extr. Metall.), 96, September 1987, pp. C168-170. Rantala, A., Muzinda, I., Timperi, J., Cruickshank, C., and Haavisto, O. 20 14. Implementaiton of Advanced Flotation Control at th First Quantum Minerals’ Kevitsa Mine. 12 Ausimm Mill Operators’ Conference. Townsville, QLD, 1-3 September 2014.
References
Rinne, A. and Peltola, A. 2008. On Lifetime of Flotation Operations. Minerals Engineering, Vol. 21. pp. 846-850.
Brown, N.M., Dioses, J. and Van Olst, M. 2001. “Advances in Flotation Process Control at Cadia Hill Gold Mine Using Froth Im aging Technology”, 2001 SME Annual Meeting Denver.
Runge, K. 2010. Laboratory flotation testing – An Essential Tool for Ore Characterisation. Flotation Plant Optimisation – A Metallurgical Guide to Indentifying and Soliving Problems in Flotation Plants – Chapter 9. Edited by C J Greet. AusIMM 2010.
Carr, D., Dixon, A. and Tiili, O. 2009. Optimising Large Flotation Cell Performance Through th Advanced Instrumentation and Control. 10 Mill Operators’ Conference, Adelaide, SA, 12-14 October 2009.
Savassi, O.N. 2005. A Compartment Model for the Mass Transfer Inside a Conventional Flotation Cell. International Journal of Mineral Processing, 2005, Vol. 77, pages 65 – 79.
Cesnik, F., 2009. Improvements in Flotation Cell Operation and Maintenance at Newcrest Cadia th Valley Operations. Proceedings of the 10 Mill Operators’ Conference. pp. 183-188.
Yinatos, J., Bergh, L., Pino, C., Vinnett, L., Munoz, C. and Yanez, A. 2012. Industrial Evaluation of a New Flotation Mechanism for Large Flotation Cells. Minerals Engineering 3638. 2012. pp. 262-271.
Coleman, R. and Rinne, A. 2011. Flotation Mechanism Design for Improved Metallurgical and Energy Performance. Proceedings MetPlant 2011, pp 405-418 Gamez, A., Saltijeral, F., Lopez, O. and Grönstrand, S. 2011. Respect Equals Recovery in Flotaiton. SME Annual Meeting 2011 (Society for Mining, Metallurgy and Exploration: Denver)
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