Using a Virtual Flotation Process to Track a Real Flotation Circuit

Using a Virtual Flotation Process to Track a Real Flotation Circuit

16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing August 25-28, 2013. San Diego, California, USA Using a Virtual Flotation Pr...

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16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing August 25-28, 2013. San Diego, California, USA

Using a Virtual Flotation Process to Track a Real Flotation Circuit J. Kaartinen**, J. Pietilä*, A. Remes**, S. Torttila* *Aalto University, Automation and Systems Technology, Espoo, Finland (e-mail: [email protected], [email protected]) **Outotec (Finland) Oy, Espoo, Finland (e-mail: [email protected], [email protected])

Abstract: This paper presents a novel way of using a dynamic flotation process simulation environment for concurrent simulation alongside a real flotation process. The simulation environment is actively adapted to a real flotation process. This yields new possibilities on the usability of the simulation model. For example, virtual measurements can be introduced that provide completely new variables or increased fault tolerance through redundancy. Another option is to provide feedback for the process operators in the form of textual reports. These reports contain e.g. shift-specific performance indices that can be compared against reference data. Furthermore, by speeding up the simulation, it is possible to make prognosis on the effects of different control actions before actually making them. Keywords: process simulators, operators, process models, adaptation, process automation. (2006) propose an application of on-line tracking simulator to detect membrane degradation in Polymer Electrolyte Membrane Fuel Cells (PEMFC).

1. INTRODUCTION Froth flotation is a complex process and some parts of it are still not fully understood. This is mainly due to complex nature of the physical details of the flotation phenomenon, and also due to internal circulation loops that are needed for efficient operation of the process.

This kind of technique can be applied to flotation process as well so that non-measurable parameters, such as flotation factors, can be estimated. However, if the simulator can be run faster than the actual process, new intriguing opportunities arise. An accurate simulator could be used to make a prognosis of the future state of the process. When implemented to a real plant, operators could use the simulation environment to see the effects of different control actions in advance and choose the one that is most appropriate in the given situation.

One way to increase the knowledge on the flotation phenomenon, and on the behavior of complex flotation circuits, is the use of simulation techniques. There are commercial simulators available, such as Outotec HSC (see e.g. Roine 2009, Outotec 2013) and JKSimFloat (see e.g. JKSimFloat 2013), but they are often mainly used as an aid in designing flotation circuits and process controls. Recently Bergh and Yianatos (Bergh and Yianatos, 2013) have described a simulation system that can be utilized in control design to take into account the changes in feed properties. However, due to the complexity of the mineral flotation process it is extremely difficult to develop an off-line simulator that is highly accurate and could describe a real flotation process precisely. Recent advances in computing have made it possible to design dynamic on-line simulators that can be run in parallel with a real process and adapt the simulation parameters so that the accuracy of the simulators can be significantly improved.

The research group has previously developed a general structure of a virtual environment intended for training purposes in off-line simulation scenarios (Roine et al., 2011). This work has been continued in order to be able to facilitate on-line simulations, where the simulation environment is actively adapted to a real flotation process. An overview of the simulator setup is given in section 2 and the application of the simulation system to Pyhäsalmi Mine copper circuit is described in section 3. Section 4 shows another use case for the simulator; how the system is utilized as a stand-alone simulation.

There are many applications to such simulators and a common use case would be to track a variable that is not directly measurable from the process. One such study has been published by Jaklic et al. in 2007. They propose a simulator that can give accurate estimates of steel slab temperature fields that cannot be directly measured in a computer controlled hot-rolling process. The simulator is connected to the process database and current process data is used to update the simulator. Similarly, Kawaguchi et al. 978-3-902823-42-7/2013 © IFAC

2. PROCESS SIMULATION SETUP The simulation environment contains all the necessary components needed for simulating a full flotation circuit (see Fig. 1). The whole system is further packaged inside a simulated operating system, i.e. inside a virtual machine. The structure is similar to the actual flotation process and to the automation system connected to it. The physical flotation 116

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kinetics are obtained by model fitting them to the plant circuit sampling data. Calculation of mineral recoveries is based on compartment model presented by Savassi (2005) in steady state form. Description of the applied calculation equations for true flotation, entrainment and froth recovery are summarized in Roine et al. (2011). True flotation recovery R (%), with residence time t, for fast-, slow- and non-floating particles with corresponding flotation kinetic rates (kF, kS and kNon) and mass proportions of them (mF, mS and mNon) in steady state is obtained by Equation (1). Here, the recovery by flotation is calculated dynamically,

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The Pyhäsalmi copper flotation circuit, shown in Fig. 2, consists of rougher ± scavenger bank, followed by four cleaning stages and middling flotation. The average head grade of Cu was 1.0% with 95% mill recovery in 2012, the milling feed capacity is roughly 160 t/h (Inmet, 2013). The dynamic flotation circuit model (Fig. 2) was set up with HSC Chemistry® 7 Sim simulation module (Roine, 2009, Outotec 2013). 2.2 Simulated PLC and Automation System If the only target would be to track the real process, one would get by with a much simpler setup than the one depicted in Fig. 1. In that setting the logic emulation and the simulated automation system could be replaced by simply reading values from the plants automation system. This would already give many benefits like completely new virtual measurements and increased fault tolerance, for example. However, this would not enable accurate prognosis capabilities.

Fig. 1. Components of the simulation setup. Since the simulated process is not a single software package, but a collection of separate pieces of software each performing a dedicated task, there must be some way to synchronize the execution and to transfer the necessary data between the different components. This is accomplished by custom made communication software (marked with orange color in Fig. 1). The idea is similar to routing solution used in the Internet. Each node in the system is connected to the central hub acting as the router. The router handles incoming messages from each node by putting them into queue and subsequently delivering each message into output queue based on the desired target address. The messaging protocol is designed solely for this application.

In order to carry out the prognosis as described in section 3, the simulated system contains an installation of the automation system, as well as, a logic emulator realized with Matlab®. Both the automation system and the logic emulator are initialized with the parameter values of their real life counterparts. As the tracking simulation progresses, the simulated system is kept in check by Matlab® based adaptation routine, as described in the following subsection. Once the prognosis capabilities are needed, the simulated logic and automation system can continue independently.

2.1 Dynamic Flotation Circuit Model

2.3 Adaptation Scheme to Track the Real Circuit Response

The simulation model of the flotation circuit (i.e. the blue rectangle in the lower left corner of Fig. 1) was set up as a property based simulation. It means that the feed material is described with particles, each of them having their own size classes, mineralogy and specific gravities. A brief description of property based simulators for minerals and metals processing can be found in Lamberg 2010.

Adaptation algorithms are also coded with Matlab®, and are thus integrated into logic emulation software. Several adaptation approaches have been tested and the most promising ones are currently under investigation. The results reported in this paper are obtained with a simple implementation of PID based adaptation. This is a good starting point for verifying correct operation.

Recoveries in the flotation units are modeled based on flotation kinetic rates for each mineral and size fraction. The

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Fig. 2. Dynamic HSC simulation flowsheet of the Pyhäsalmi copper flotation circuit. line laser diffraction analyzer), feed tonnage belt scales, and slurry density transmitters.

The general idea of the adaptation process is depicted in Fig. 3, where it is shown that some measurements are merely fed through the adaptation system. These include, for example, the necessary variables describing the changes in the ore flow that is fed to the flotation circuit, as they can be used directly in the simulation model. The flotation kinetics rates, however, do not have a direct physical interpretation (i.e. cannot be measured directly) and thus their values have to be estimated indirectly in the Parameter Estimator. The parameter estimation concept is covered in detail in Pietilä et al. (2013).

In addition, the flotation model parameters are updated based on the plant measurements to track the key measurements e.g. when the ore type is changing. Each flotation stage in the model (Fig. 2) has its own flotation kinetic rates. The recoveries are tracked based on rougher-scavenger response to the final tails recovery, by manipulating the kinetic factors (kF and kS in Eq. 1) of all flotation stages through the circuit. The final concentrate Cu grade (%) is tracked by manipulating the pyrite kinetics, since it is the major sulphide mineral tending to recover to the concentrate, thus affecting to the Cu grade. The variable pairings for the adaptation are listed in Table 1. Still, the calculation of the measured recoveries involves assays of feed F (%), concentrate C (%) and tails T (%) streams as follows (Wills and Napier-Munn, 2006), 4 L srr

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The physical meaning of the fast floating components of the minerals, listed in Table 1, is related to the particles having highest degree of mineral liberation in contrast to slow and non-floating particles. This physical property relationship has been reported e.g. in Welsby et al. 2010.

Fig. 3. Principle of the adaptation of the simulator parameters for tracking the real process. As mentioned, the model is run with similar feed composition as the real concentrator plant. The feed mineralogy and capacity are updated continuously based on plant on-line elemental assays (XRF analyzer), particle size analysis (on118

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Savassi O.N. (2005). A Compartment Model for the Mass Transfer Inside a Conventional Floation Cell, Int. J. Miner. Process, 77, pp. 65-79. Welsby S.D.D., Vianna S.M.S.M., Franzidis J.-P. (2010). Assigning physical significance to floatability components, Int. J. Miner. Process, 97, pp. 59-67. Wills B.A., and Napier-Munn T.J. (2006). :LOOV¶ 0LQHUDO Processing Technology, Seventh Edition. ButterworthHeinemann, Oxford, United Kingdom.

the improvement of the simulation model with regard of the effects of reagent dosage changes. ACKNOWLEDGEMENTS The work reported in this paper is done in the SOREX project, which is a part of the Green Mining program funded by TEKES ± the Finnish Funding Agency for Technology and Innovation. The authors would like to thank Outotec Oyj and Pyhäsalmi Mine Oy for the fruitful collaboration. REFERENCES Bergh L., Yianatos J. (2013). Control of rougher flotation circuits aided by industrial simulator, Journal of Process Control, 23, pp. 140-147. Inmet (2013). Pyhäsalmi Mine. , referred Feb 11, 2013. Jaklic A., Vode F., Kolenko T. (2007). Online simulation model of the slab-reheating process in a pusher-type furnace, Applied Thermal Engineering, 27, pp. 11051114. JKSimFloat (2013). , referred Feb 13, 2013. Kawaguchi K., Onoe Y., Nakaya M. (2006). An application of On-Line Tracking Simulator to a PEMFC, In Proceedings of the SICE-ICASE International Joint Conference, Oct. 18 ± 21, 2006, Bexco, Busan, Korea. Lamberg, P. (2010). Structure of a Property Based Simulator for Minerals and Metallurgical Industry, In Proceedings of the 51st SIMS Conference on Simulation and Modelling, SIMS 2010 October 14 to 15, 2010, Oulu, Finland. Moilanen, J., and Lamberg, P. (2010). Virtual Experience for Operator Training. In Proceedings of the 7 th International Mineral Processing Seminar (PROCEMIN2010), Santiago, Chile. Nakaya M., Xinchun L. (2013). On-line tracking simulator with a hybrid of physical and Just-In-Time models, Journal of Process Control, 23, pp. 171-178. Outotec (2013). , referred Feb 13, 2013. Pietilä J., Kaartinen J., Reinsalo A-M. (2013). Parameter Estimation for a Flotation Process Tracking Simulator, 15th IFAC symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing, 25-28 August 2013, San Diego, California, USA (submitted). Roine, A. (2009). New simulation tool for process engineers and research scientists, Outotec press release 19th of Oct. 2009. Roine T., Kaartinen J., Lamberg P. (2011). Training simulator for flotation process operators, In Proceedings of the 18th IFAC World Congress, August 28 ± September 2, 2011, Milano, Italy.

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