CONTROL ENGINEERING LABORATORY

Transcription

1 CONTROL ENGINEERING LABORATORY Advanced and sustainable beneficiation of platinum group minerals (PGM) in sulphide poor platinum (PGE) deposits, BEPGE-project Final report Jarmo Kiuttu, Jari Ruuska and Leena Yliniemi Report A No 42, May 2010

2 2 University of Oulu Control Engineering Laboratory Report A No 42, May 2010 Advanced and sustainable beneficiation of platinum group minerals (PGM) in sulphide poor platinum (PGE) deposits, BEPGE-project Jarmo Kiuttu, Jari Ruuska and Leena Yliniemi University of Oulu, Control Engineering Laboratory Abstract: Flotation is a separation process, where valuable minerals and metals are separated from gangue minerals. The separation is based on the difference of the surface chemistry of minerals. Flotation has been used for beneficiation of minerals nearly 100 years, but still the mechanism of the process isn t well known. The aim of this project is to develop the beneficiation process for sulphide poor PGE ores and to increase the knowledge of the flotation process. The developed process will be modelled by using HSC-Sim software. In this report some examples of control applications are presented, simulators for mineral processing industry are mentioned and a developed HSC-model for flotation is introduced. Keywords: Flotation, beneficiation, modeling, control, simulation, mineral processing ISBN ISSN University of Oulu Control Engineering Laboratory P.O. Box 4300 FIN University of Oulu

3 1. INTRODUCTION Flotation Control applications in mineral processing industry; literature review Simulators used in mineral processing industry; literature review Simulation programs and applications in mineral processing industry; literature review JKSimMet JKSimFloat UsimPac SUPASIM HSC HSC Chemistry HSC Sim HSC-Model for flotation Simulation results Future work References

4 4 1. INTRODUCTION This report is written based on the work done in the research project Advanced and sustainable beneficiation of platinum group minerals (PGM) in sulphide poor platinum (PGE) deposits, BEPGE financed by TEKES and companies. The aim of this research is to develop a new, pro-environmental process for the beneficiation of sulphide poor PGE minerals. The main interest is on using carbon dioxide during grinding and flotation. BEPGE-project is carried out in co-operation with GTK (Geological Survey of Finland) and companies. The examined ores come from two Finnish deposits, where the mine pits are under construction. The aim of the Control Engineering Laboratory in this project is to model and to simulate the developed beneficiation process. The aim to develop a new beneficiation process is very challenging. Results should be better than the existing beneficiation alternatives. The new beneficiation process developed during this project could be usable for new opening mines in the future.

5 5 2. Flotation Flotation is a separation process, where valuable minerals and metals are separated form gangue minerals. Differences between minerals surface chemistry is beyond the separation process. This means that surfaces of valuable minerals are made hydrophobic with collector chemicals and the gangue minerals stay hydrophilic or are made hydrophilic with depressant chemicals. The collector chemicals attach into the surfaces of valuable minerals so that their hydrophobic end stays outwards, which makes it possible the attachment of the minerals into the air bubbles. The depressant chemicals create a sheer layer on to the surface of gangue minerals which prevents the flotation of gangue. Frothers are used to create a stable froth on the flotation cell and to lower the surface tension in slurry /Napier-Munn, 2007/. The flotation is carried out in the flotation cell. Before the flotation feed ore is grinded into suitable fineness and elutriated to make the slurry. The slurry is mixed up with chemicals in a conditioner and it s pumped into the flotation cell. The flotation encounters as the air fed into the flotation cell is dispersed into the little bubbles with the rotor and stator and the adhesive force attracts the hydrophobic minerals and air bubbles together in the slurry. After that the air bubbles rise into the froth-zone with the hydrophobic minerals and precious metals. The froth overflows from the flotation cell and drops into the concentrate chute. The principle of flotation is presented in Fig. 1. Figure 1. Principle of flotation /Napier-Munn, 2007/.

6 6 3. Control applications in mineral processing industry; literature review Spencer et. al. (1999) have been studying SAG mill monitoring with acoustic emissions. It has been shown that measurement of acoustic emissions can be used as a part of a system for both process control and condition monitoring of SAG mills. To liberate valuable minerals from gangue minerals, semi-autogenous grinding plays a very important role and that s why it is important to be able to control it. Monitoring of surface vibration (acoustic emission) is commonly used as a non-invasive, low-cost means of monitoring. Results of this study support the view that higher feed rate dynamic steady states correspond to an increased charge mass, an increase in mill rotation speed results in grinding media being lifted higher and more often directly impacting the shell liner, thus increasing acoustic emissions. A control strategy for a column flotation process has been developed by Persechini et. al. (2004). The three variables affecting to the grade and the recovery-% during flotation are the froth layer height, the air holdup in the collection zone and the bias. Three variables, the wash water, air flowrate and tailings flowrate are selected for manipulation to keep the three controlled variables in stable operating conditions. The dynamics of the process is presented in the transfer function matrix. To determine the interaction between the variables and the choice of proper pairing between controlled and manipulated variables an analysis technique based on the relative gain array is done. The controllers are tested in a pilot scale-plant to validate the designed PI-controllers. Liu & MacGregor (2008) carried out a research considering flotation control with machine vision. Proposed method is based on the causal process model predicting future froth appearances from the given values of manipulated variables and observed values of the process variables. Chen et. al. (2007) present a fuzzy logic based on-line optimization control integrated in an expert system of ball mill grinding circuit. Product particle size was controlled to

7 7 enhance ball mills efficiency. Control strategies practicality, reliability and effectiveness were demonstrated by testing it in an industrial operation. Kämpjärvi & Jämsä-Jounela (2003) compared different control strategies to control level in flotation cells. Four control strategies including one SISO (Single Input single Output) and three MIMO (Multiple Input Multiple Output) controllers were tested. Model for six flotation cells in series were used and simulations were performed with Matlab. Results show that level control performances are significantly better with MIMO controllers than with SISO controllers. This is due to high interaction between the control loops, which cannot be taken into account by using SISO systems. Orchard et. al. (2001) present the fuzzy predictive control technique based application to optimize the operation of a grinding plant. Application is specially desingned in order to maximize the ore feed rate and to follow a pre-determined particle size set-point. Simulations done with Matlab under typical disturbances show this control scheme more efficient than a classical one. A multivariable decoupling internal model control for a grinding circuit is presented by Zhou et. al. (2008). Simulations are performed for servo, regulatory, disturbance rejection and robustness problems. Simulations show the better performance of decoupling, setpoint response, load disturbance rejection, fault tolerance and robustness. Maldonado et. al. (2007) present an optimal control of a rougher flotation process. The optimization problem is solved considering phenomenological models for each flotation tank of the circuit, validated by using process data obtained from several sampling surveys carried out in a rougher flotation circuit of a Chilean mineral processing plant. The control objective is the minimization of the Cu tailing grade in each tank given a final Cu concentrate grade. Simulation results show a good correspondence between the proposed optimization strategy and the actual operating practices at the rougher flotation plant under survey.

8 8 Orchard et. al. (2001) presented a fuzzy predictive control application for mineral grinding plant. Controlled variables are solids percentage, particle size and power demand. Manipulated variables are water and ore feed flows. The controller uses linear multivariable models and fuzzy characterization of the controlled variables and calculates the manipulated variables. Simulations performed under typical disturbances show a better performance compared to conventional predictive control. Hamilton & Guy (2001) introduced a level control system developed by CSIRO. This new device is based on the use of a Linear Variable Differential Transformer which measures the change of pulp level as indicated by a floating device at the liquid-air interface. They also present a level control based on pressure measurement. A change in a pulp level produces a corresponding change in the flotation tanks side tube pressure. This change is used as a signal for control. Bouchard et. al (2005) review the recently done work at Universite Laval in the field of column flotation instrumentation and control. The control results presented, rely on froth depth and bias sensors. Results show that control of a flotation column could be improved by using different control methods, such as nonlinear, multivariable and feed-forward control. The emphasis is placed on the available information, which may be used to reach the control objectives. Laboratory and pilot-scale results indicate that integrating knowledge of the process and new measurements available are necessary to reach the control objectives. Chuk & Nuñes (2003) introduce a robust GPC (generalized predictive control) of a flotation column. A robust GPC design is applied to control froth depth and gas hold-up in a laboratory-scale flotation column. Tests are made to demonstrate that the goals are achieved. Froth depth and gas hold-up have a fast and offset-free behavior and controller can manage a wide range of mineralogical changes in feed, such as density, rate, size and grade.

9 9 Bergh & Yianatos (2003) present a monitoring and control application designed for flotation column. PLS (partial least square)-model are used to illustrate the process. Simulations show that these models can be used to provide alarms to operators and startup procedures to remedy the abnormal situation. Ding et. al (2006) present a hybrid intelligent systems for supervisory control of mineral grinding process. System consists of in addition to basic instrumentation, actuator, a process control system, a supervisory control system and a technical performance index decision system, which is done by using CBR (Case Based Reasoning method). Supervisory control system is to adjust the set-points of control loops of the process control system according to the particle size determined by its decision system and production condition. The system is compared to the NN (Neural Networks) method and the results show the validity and efficiency of the system. Jämsä-Jounela et al (2001) developed a monitoring tool for calculating performance indices of the control loops. The structure of the tool is demonstrated via level control of the flotation cells. The simulations and the tests show that the indices were sufficient to provide the necessary information about the control performance.

10 10 4. Simulators in mineral processing industry; literature review Smith et. al (2008) developed base-case model, of a bank of flotation cells at a South African platinum mine on a physics-based froth zone simulator, FrothSim. FrothSim models the three phases (solid, liquid and gas) in flotation froths and model entrainment and drainage of water and unattached solids, allowing the upgrading effect of a froth to be simulated. The overflow rates for minerals were simulated and the entrained minerals and water rates were predicted. After determining the optimum air-profile, it was tested in a froth experimental sampling campaign and confirmed the predicted improvement in flotation performance along the bank. A multistage flotation plant simulator done in Matlab was introduced by Loveday & Hemphill (2006). Simulator is to maximize platinum recovery, while maintaining the mass of final concentrate. Pulp kinetics based conventional models were regressed to steady-state data from a nine stage platinum flotation plant. To characterize the froth in each stage, froth factors were obtained by regression. Also a depressant factor was required for gangue minerals to account for effect of additional depressant in the cleaners. To limit the amount of model parameters, only two pulp kinetic parameters and two recycle parameters were used for three mineral classes. Simulations present an improvement in recovery with the existing circuit and the extra cleaning stage addition caused a further increase in recovery. Plant tests are in the progress to verify the simulations.

11 11 5 Simulation programs and applications in mineral processing industry; literature review Schwarz & Alexander (2006) describe enhancement done in flotation circuit performance by simulation by using JKSimFloat. They describe the simulation principles in details and they show two case studies, where the simulation resulted in substantial improvements to the sites. Vorster et. al (2001) introduce an investigation into the effect of microwave radiation on the processing of a massive sulphide ore. Significant reductions in the Bond work index can be achieved without any discernible adverse effect on the subsequent flotation process. A maximum reduction in work index was achieved after microwave exposure. Process simulation on the package USIMPAC showed significant flowsheet changes are possible as result of exposure to microwave radiation and the subsequent reduction in work index. Three articles by Hay & Rule (2003)/24/, Hay (2005)/25/ and Hay & Schroeder (2005)/26/ are written about the use of SUPASIM program to design, analyse, diagnose, understand and optimise flotation. Klumowsky & Rijkers (1996) describe the experimental work done with small SAG mill. They also describe how to use data to scale-up SAG mill. They also compare different simulation techniques using programs MICROSIM, JKSimMet and USimPac. A conclusion drawn from the work is that small-scale SAG mills can be used to accurately design SAG mill circuits, provided that the ball size and feed size of the samples used is large enough.

12 JKSimMet /26/ JKSimMet is a software package, which is tailored specifically for plant and development metallurgists who wish to plant behaviour and for design engineers who need process simulation models to assess design alternatives. Software is made for the analysis and simulation of comminution and classification circuits in mineral processing operations. JKSimMet is developed in Julius Kruttschnitt Mineral Research Center (JKMRC), Australia. Sofware incorporates models, which are based on a large database of operating plant data and tested in actual plant conditions ( JKSimMet allows the user to: build a graphic-based flowsheet of the prosessing plant assign machine criteria and model parameters to each plant case study simulate the effect caused by changes in operating conditions to predict product flows and size distributions determine optimum conditions, including plant throughput Following models are available: rod and ball mill, autogenous and semi-autogenous mill, crusher, HPGR (high-pressure grinding roll), simple degradation, vibrating screen single and double deck, DSM (Dutch State Mine) screen aka sieve bend, hydrocyclone, effiency curve and splitter. 5.2 JKSimFloat /27/ JKSimFloat is a windows-based software package for the simulation of flotation plant operations. The software is developed for plant metallurgists, operators, researchers and consultants. The models behind the JKSimFloat have been applied to over 50 flotation operations including base-metals (Pb, Zn, Cu, Ni). Simulations of very complex circuits

13 13 with JKSimFloat may only require a few seconds to converge, compared to previous spreadsheet methods which could last several hours ( JKSimFloat allows the user to: build a graphic-based flowsheet of the prosessing plant assign machine criteria and model parameters to each plant case study simulate the effect of changes in the flowsheet to predict the flows, size distributions and element distributions determine optimum grade and recovery via simulation adjust floatability components to estimate the effect of regrinding and reagent addition There are model available for: AMIRA P9 flotation model, conventional, column, Jameson cells, hydrocyclone, size redistribution (regrinding), floatability transfer (reagent), splitter and combiner. 5.3 UsimPac /28/ USimPac is a process simulation software package developed by BRGM and commercialized since BRGM is France's leading public institution in the geoscience field. 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,

14 14 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. A simulator combines the following elements: A flowsheet that describes the process in terms of successive unit operations and material streams. A phase model that describes the materials handled by the plant (raw material, products, reagents, water, wastes). A mathematical model for each unit operation. This model formalizes the current scientific knowledge about the unit operation. A set of algorithms for data reconciliation, model calibration, unit operation sizing, full material balance calculation, power consumption and capital cost calculation. USimPac has over 40 comminution modules used in mineral processing. The complete list can be found from SUPASIM /31/ SUPASIM is a proprietary flotation simulation program and an empirical mill sizing methodology developed in mid 80 s. It also maintains a large database of milling and flotation operations as well as associated laboratory and pilot test data to characterise ores and predict plant performance. The approach of SUPASIM is the ore characterisation, information organising in an understandable way and add structure to the complex flotation situation and by doing so to support the user to interpret the relationship between variables and optimise the flotation process (Hay, 2005). In addition to simulating over 35 operating flotation plants from laboratory data, SUPASIM has been

15 15 used to design and simulate the performance of four flotation plants from laboratory data only ( 5.5 HSC HSC-software is a product developed by Outokumpu Technology. It was originally done for their own calculating purposes, which is still one important area of focus for development work HSC Chemistry /32/ HSC Chemistry software enables the user to simulate chemical reactions and processes on a thermochemical basis. This method does not take into account all the necessary factors, such as rates of reactions, heat and mass transfer issues, etc. However, in many cases a pure thermochemical approach may easily give useful and versatile information for developing new chemical processes and improving existing ones. With HSC Chemistry it is possible to calculate chemical equilibria between pure substances and the ideal and also, to some extent, non-ideal solutions. For these calculations only enthalpy (H), entropy (S) and heat capacity (Cp) data for all prevailing compounds or pure substances is needed. In many cases these calculation results may simulate the real chemical reactions and processes at sufficient accuracy for practical applications. Of course, experimental work is needed to verify the results, because HSC does not take kinetic phenomena into account. However, HSC helps to avoid expensive trial-and-error chemistry, because it quickly and easily gives some kind indication of the effects of process parameters on the reaction products and process conditions. Usually, thermochemical calculations at least show what is physically possible and what is

16 16 impossible, which is highly valuable information when making plans for experimental investigations. The HSC 6.0 contains 21 calculation modules and 11 databases. The name of the program is based on the fact that calculation modules automatically utilize the same extensive thermochemical database which contains enthalpy (H), entropy (S) and heat capacity (Cp) data for more than chemical compounds HSC Sim /33/ Reason for doing HSC Sim was to expand the area of use from the modelling of single chemical reactions to the modelling of whole process. Using HSC Sim module it is possible to connect single reactors using graphic flowsheet. With HSC Sim module it is possible to model and simulate an existing or completely new process. HSC Sim module contains common mineralogical, hydrometallurgical and pyrometallurgical process models. Via simulation it is possible for example to minimize the out coming waste from process by using closed loops of process. Shortly said, waste can be changed often as valuable raw materials, if an appropriate feeding point is from them in process. Experimental testing of these kinds of matters is not often done, as tests can stir up the process and cause danger situations.

17 17 6. HSC-Model for flotation The HSC-model developed in BEPGE is based on the results of laboratory scale flotation tests performed in Autumn Data group consists of 35 flotation tests. These open loop tests include five flotations: three rougher flotations and two cleaner flotations. The three rougher flotations are considered as a one rougher flotation because recoveries of three rougher flotations are summed up. In the model the flows RC1, RC2 and RC3 compose the flow RC. The overview of flowsheet is shown in Figure 2. Figure 2. Overview of flowsheet. Building of HSC-model began with creating a graphical user interface for model. User interface consists of one rougher flotation tank, conditioner, two cleaner flotation tanks and eight flows (Feed, RT1, RC1, CC Feed, CC1, CT1, CC2, CT2). Basic idea was that the user could manipulate three variables: ph-value of flotation, flotation gas (air or air/co 2 -mix) and grinding time (75 or 150 minutes). Figure 3 presents the graphical user interface of HSC-model.

18 18 Figure 3. Graphical user interface of HSC-Model. Flotation model was made to match with the results of actual flotation tests. Therefore it was decided to reduce the amount of minerals in the ore. The minerals used in the model were chalcopyrite (Ccp), pyrrhotite (Po), pentlandite (Pn), gangue (Gan), Platinum (Pt) and palladium (Pd). Mineral composition was set in the mineral set-up. HSC-Sim contains a large database of different minerals. By writing the abbreviation of desired mineral to the right cell, the program pics out the mineral and gives the minerals chemical composition. Overview of mineral setup is presented in Figure 4. Figure 4. HSC-Sim mineral-based model set-up.

19 19 Calculation of the HSC-model is based on different separation of minerals. First the user defines the amount of every mineral and the minerals distribute to concentrate and tails with predetermined ratio. Ratio is based on experimental data, which is used to create the model. First version of the model was based only on one flotation test. This preliminary model had its own ratios for every mineral in every flotation stage. After the minerals are separated to the concentrate and tailings, the program calculates the recovery-% and weigh-% of metal (Pt, Pd, Cu, Ni, Fe, S). About 30 tests were calculated with this preliminary model. The results were collected and diagrams were drawn for observing them visually. Also the differences between the actual results and HSC-model for Pt-, Pd-, Ni- and Cu-recovery-% were counted. Figure 5 presents the simulation result with preliminary model. The results show that preliminary model doesn t match with the experimental data very well. The differences between the calculated recovery percentages and experimental recovery percentages differ usually about 15%. Flotation tests were performed with many different parameters and that s why all flotation tests weren t usable for modelling. Biggest amount of tests (11) were performed with following conditions: mild iron mill as a mill, air as a flotation gas and 75 minutes grinding time. In order to simulate flotation with different conditions the data of these nine tests is used to create and validate functions between variables. It s commonly known that ph-value has significant effect on flotation. Therefore it was decided to create a function between ph-value and separation efficiency of each mineral. Functions were created with datafit application of HSC-Sim. HSC-Sim Data Fit module offers versatile multi-regression, curve fitting and statistical properties for linear and non-linear functionsup to 20 independent variables and 100 parameters can be used in these functions. The user may specify the desired function formula freely or use one of the nearly 400 built-in functions. Data Fit utilizes the robust Levenberg-Marquardt method with double precision to perform nonlinear regression. By converting the function for whole ph-area (4-12) the degree of function would have been too high. To limit the degree of functions it was decided to split the experimental

20 20 data into two sections according the ph-value: acid (2 tests) and alkaline (9 tests). Functions for separation efficiency vs. ph-value were converted for each mineral in acid and alkaline conditions. In alkaline ph data of 6 tests were used for modelling and 3 for testing and in acid ph data of one test for modelling and one for testing were used. Due the lack of data there weren t enough experimental points to convert the functions based only on experimental data. HSC-Sim offers an application to manipulate the results if user knows, on the basis of other information sources, the approximate path of the function curve. Therefore trim points were added to boost the data. Trim points were added near to existing actual points. While converting the function for chalcopyrite, the research done by Göktepe (2002) was utilised. Göktepe studied the effect of ph on pulp potential and sulphide mineral flotation. As a result Göktepe presents a graph (Figure 5) which illustrates the recovery of chalcopyrite as a function of ph with different xantathes. According the experimental data, SIPX was used as a xantathe during tests. While converting functions for other minerals (Platinum, Palladium and Pentlandite) it was supposed that these minerals have same kind of behavior with different ph-values except for neutral zone, which was supposed to decrease the separation efficiency. Function converted for separation efficiency of platinum vs. ph is presented in Figure 6. Figure 5. Recovery of chalcopyrite as a function of ph with different xantathes /Göktepe, 2002/.

21 21 Figure 6. HSC-Sim datafit, curve fitting view. Red points are actual points based on experimental data and green points are the trim points Simulation results Figure 7 presents the simulation result for same test with the preliminary model and advanced model. The results show that preliminary model doesn t match with the experimental data very well. The differences between the calculated recovery percentages and experimental recovery percentages differ usually about 10 to 25%. The same test simulated with advanced model matches a lot better with actual data. Usually differences between actual recoveries and simulated recoveries are below 10%.

22 22 Figure 7. Flotation gas air, test 1(12), left: preliminary model, right: advanced model Functions between separation efficiency of minerals and ph were also converted for air/co 2 -case with same procedure. Figure 8 illustrates the simulation results with preliminary and advanced models for flotations where air/co 2 -mix is used as a flotation gas. Results show that advanced model for air/co 2 -case can forecast the results more efficiently. Figure 8. Flotation gas CO2/air-mix, left: preliminary model, right: advanced model

23 23 Table 1. Average differences and standard deviations of recoveries given by HSC compared to Data. Preliminary model Advanced model Recovery-%_RC1-3 Recovery-%_RC1-3 Data-HSC Data-HSC Average St.Dev. Average St.Dev. Pt Cu Ni Preliminary model Advanced model Recovery-%_CC1 Recovery-%_CC1 Data-HSC Data-HSC Average St.Dev. Average St.Dev. Pt Cu Ni Preliminary model Advanced model Recovery-%_CC2 Recovery-%_CC2 Data-HSC Data-HSC Average St.Dev. Average St.Dev. Pt Cu Ni On the table above are listed the differences and standard deviations between calculated recovery percents and the experimental recoveries. The table shows that datafit is a capable tool to create functions between process variables. Differences between calculated recoveries and experimental data decreased even from 30% to 0,2%. Still, it s important to notice that it cannot be made unquestionable conclusions with these results because the lack of data.

24 24 7. Future work The preliminary model presented in this work is based on predetermined separation ratios for each mineral. The advanced model is based on functions converted between minerals separation efficiency and ph. Because the lack of data the model for flotation performed with air/co2-mix as a flotation gas is unaccurate. The main issue is to perform tests by using air/co2-mix as a flotation gas. As more data from laboratory tests is available, it will be possible to find more accurate functions considering air/co2-case. Also the material of the mill has to be taken into account in the future. The aim is to develop a locked-cycle-model that can be used to predict the behaviour of continous flotation process, with changing conditions.

25 25 References 1. Bergh, L.G.; Yianatos, J.B. (2003). Flotation column monitoring and control based on PLS models, IFAC Workshop on New Technologies for Automation of the Metallurgical Industry, Proceedings, Shanghai, China, October 2003, pp Bouchard, J.; Desbiens, A.; Villar, R. del (2005). Recent advances in bias and froth depth control in flotation columns, Minerals Engineering 18/2005, pp Chen, X.; Zhai, J.; Li, Q.; Fei, S. (2007). Fuzzy Logic Based On-Line Efficiency Optimization Control of a Ball Mill Grinding Circuit, Fuzzy Systems and Knowledge Discovery, FSKD 2007, Fourth International Conference, Proceedings, Haikou, China, August 2007, pp Chuk, O.D.; Nuñez, E. (2003). Robust GPC control of a flotation column using genetic algorithms, IFAC Workshop on New Technologies for Automation of the Metallurgical Industry, Proceedings, Shanghai, China, October 2003, pp Ding, J.; Zhou, P.; Liu, C.; Chai, T. (2006). Hybrid Intelligent System for Supervisory Control of Mineral Grinding Process, Intelligent Systems Design and Applications, 2006, ISDA '06, Sixth International Conference, Proceedings, Jinan, China, October 2006, pp Göktepe, F. (2002). Effect of ph on pulp potential and sulphide mineral flotation, Turkish J. Eng. Env. Sci. 26/2002, pp Hamilton, J.A.; Guy, P.J. (2001). Pulp level control for flotation options and a CSIRO laboratory perspective, Minerals Engineering 14/2001, pp Hay, M.P.; Rule, C.M. (2003). SUPASIM: a flotation plant design and analysis methodology, Minerals Engineering 16/2003, pp Hay, M.P. (2005). Using the SUPASIM flotation model to diagnose and understand flotation behaviour from laboratory through to plant, Minerals Engineering 18/2005, pp Hay, M.P.; Schroeder, G. (2005). Use of the SUPASIM flotation model in optimising Impala s UG2 circuit, Minerals Engineering 18/2005, pp Jämsä-Jounela, S-L; Poikonen, R.; Vatanski, N.; Rantala, A. (2003). Evaluation of control performance: methods, monitoring tool and applications in a flotation plant, Minerals Engineering 16/2003, pp Klymowsky, I.B.; Rijkers, A.L.M. (1996). The use of data from small-scale mills and computer simulation techniques for scale-up and design of SAG mill circuits, Int. J. Miner. Process 44-45/1996, pp Kämpjärvi, P. & Jämsä-Jounela, S-L (2003). Level control strategies for flotation cells, Minerals Engineering 16/2003, pp Liu, J.J. & MacGregor, J.F. (2008). Froth-based modeling and control of flotation processes, Minerals Engineering 21/2008, pp Loveday, B.K.; Hemphill, A.L. (2006). Optimisation of a multistage flotation plant using plant survey data, Minerals Engineering 19/2006, pp

26 Maldonado, M.; Sbarbaro, D.; Lizama, E. (2007). Optimal control of a rougher flotation process based on dynamic programming, Minerals Engineering 20/2007, pp Napier-Munn, T.J. ed., Wills Mineral Processing Technology. 7th ed. Oxford: Butterworth-Heinemann, 2007, 444 p. 18. Orchard, M.; Flores, A.; Munoz, C.; Cipriano, A. (2001). Model-based predictive control with fuzzy characterization of goals and constraints, applied to the dynamic optimization of grinding plants, Fuzzy Systems, 2001, 10 th IEEE International Conference, Proceedings, Melbourne, Australia, 2-5 December 2001, pp Orchard, M.; Flores, A.; Munoz, C.; Cipriano, A. (2001). Predictive control with fuzzy characterization of percentage of solids, particle size and power demand for minerals grinding, Control Applications, 2001, IEEE International Conference, Proceedings, Mexico City, Mexico, 5-7 September 2001, pp Persechini, M.A.M.; Peres, A.E.C.; Jota, F.G (2004). Control strategy for a column flotation process, Control Engineering Practice 12/2004, pp Schwarz, S.; Alexander, D. (2006). JKSimFloat V6.1 PLUS: Improving flotation circuit performance by simulation, Mineral process modelling, simulation and control conference, 2006, International Conference, Proceedings, Ontario, Canada, 6-7 June 2006, pp. xxx-xxx 22. Smith, C.; Neethling, S.; Cilliers, J.J. (2008). Air-rate profile optimisation: From simulation to bank improvement, Minerals Engineering, Article in press, pp. xxx-xxx 23. Spencer, S.J.; Campbell, J.J.; Weller, K.R.; Liu, Y (1999). Acoustic emissions monitoring of SAG mill performance, Intelligent Processing and Manufacturing of Materials, IPMM '99, Second International Conference, Proceedings, Honolulu, HI, July 1999, pp Vorster, W.; Rowson, N.A.; Kingman, S.W. (2001). The effect of microwave radiation upon the processing of Neves Corvo Copper ore, Int. J. Miner. Process 63/2001, pp Zhou, P.; Chai, T.; Wang, H.; Su, C-Y (2008). Multivariable decoupling internal model control for grinding circuit, American Control Conference, Proceedings, Seattle, USA, June 2008, pp _b_ .pdf ( ) _b_ .pdf ( ) 28. ( ) ( ) ( ) 31. iarkisto/viestinta_ja_aktivointi/lehdistotiedotteet/outotec_hsc- Sim_Lehdistotiedote.pdf ( ) 32. HSC-Sim manual available at

27 27 ISBN ISSN University of Oulu Control Engineering Laboratory Series A Editor: Leena Yliniemi 26. Paavola M, Ruusunen M & Pirttimaa M, Some change detection and time-series forecasting algorithms for an electronics manufacturing process. 23 p. March ISBN ISBN (pdf). 27. Baroth R. Literature review of the latest development of wood debarking. August ISBN Mattina V & Yliniemi L, Process control across network, 39 p. October ISBN Ruusunen M, Monitoring of small-scale biomass combustion processes. 28 p. March ISBN X. ISBN (pdf). 30. Gebus S, Fournier G, Vittoz C & Ruusunen M, Knowledge extraction for optimizing monitorability and controllability on a production line. 36 p. March ISBN X 31. Sorsa A & Leiviskä K, State detection in the biological water treatment process. 53 p. November ISBN Mäyrä O, Ahola T & Leiviskä K, Time delay estimation and variable grouping using genetic algorithms. 22 p. November ISBN Paavola M, Wireless Technologies in Process Automation - A Review and an Application Example. 46 p. December ISBN Peltokangas R & Sorsa A, Real-coded genetic algorithms and nonlinear parameter identification. 28 p. April ISBN ISBN (pdf). 35. Rami-Yahyaoui O, Gebus S, Juuso E & Ruusunen M, Failure mode identification through linguistic equations and genetic algorithms. August ISBN , ISBN (pdf). 36. Juuso E, Ahola T & Leiviskä K, Variable selection and grouping. August ISBN ISBN (pdf). 37. Mäyrä O & Leiviskä K, Modelling in methanol synthesis. December ISBN Ohenoja M, One- and two-dimensional control of paper machine: a literature review. October ISBN Paavola M & Leiviskä K, ESNA European Sensor Network Architecture. Final Report. 12 p. December ISBN Virtanen V & Leiviskä K, Process Optimization for Hydrogen Production using Methane, Methanol or Ethanol. ISBN Keskitalo J & Leiviskä K, Mechanistic modelling of pulp and paper mill wastewater treatment plants. January ISBN Kiuttu J, Ruuska J & Yliniemi L, Advanced and sustainable beneficiation of platinum group minerals (PGM) in sulphide poor platinum (PGE) deposits- BEPGE. Final Report. 27 p. May ISBN