Expert Control System for Rougher Flotation of Phosphate

Expert Control System for Rougher Flotation of Phosphate

Copyright © IFAC 11th Triennial World Congress. Tallinn . Estonia. USSR . 1990 EXPERT CONTROL SYSTEM FOR ROUGHER FLOTATION OF PHOSPHATE SoL. Jamsa-Jo...

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Copyright © IFAC 11th Triennial World Congress. Tallinn . Estonia. USSR . 1990

EXPERT CONTROL SYSTEM FOR ROUGHER FLOTATION OF PHOSPHATE SoL. Jamsa-Jounela and E-K. Karki Kemim Oy. Porkkalankatu 3 . P.O. Box 330. SF-OOJOJ H elsinki. Finland

Abstract. The parameters of a detailed phenomenological flotation model for the rougher flotation bank have been evaluated performing large steady state sampling in the plant. The dynamic model has been used in simulation of the operation of a rougher flotation bank, as a step in development of expert control of a larger flotation system. The simulator has been interfaced with the ONSPEC control software package run in the IBM DOS environment, the software for flotation circuit simulation and expert control system design is described. The control strategy tested by simulation aims to maximize the recovery, while the concentrate grade is maintained above a given minimum level. The determination of the manipulated variables hierarchy of the bank is described and the simulated results of expert control are presented. Keyword. Expert Control; Computer Aided Control System Design; Phosphate Flotation Control; Flotation Modelling.

INTRODUCTION

complexity were developed on this basis. Such models could be assembled to form a dynamic model of the flotation plant (Niemi 1966, Niemi and Paakkinen 1969) in order to study the plant operation by simulation. The need for a more detailed understanding of flotation operations resulted in a more sophisticated model of the cell (Bascur 1982).

The flotation plant control system usually consists of conventional single loop controllers. Due to the physical characteristics of flotation process, especially the varying composition of the raw ore as a disturbance variable, their efficiency has been found to be limited. Also the use and maintenance of control systems which are based on modern control theory have proved difficult in the industrial environment.

The results of the control system design using the classical PIcontrollers suggested that some new advanced control methods were required for the control of the flotation process. Niemi and colleagues (1974), Koivo and Cojocariu (1977) have used a single cell model for the development of optimal control algorithms via applications of the maximum principle. Andersen and colleagues (1981) and Zaragoza and Herbst (1987) have reported an application of state feedback control and Kalman filter for rougher flotation control. Hammoude and Smith (1981) have used a linear model to develop a minimum-variance controller for recleaning.

Expert systems or knowledge-based systems are a branch of artificial intelligence which has recently gained increasing interest. Because flotation plants require constant supervision and manual intervention by plant operators in response to usual observations of poor quality concentrates and high tailing losses, the flotation process is an excellent candidate for an expert control system. An expert control strategy may control the set points of process controllers and record the effects of such changes and of similar changes made by the human operator in order to decide on further actions.

However, the use and maintenance of control systems which were based on modern control theory proved difficult in the industrial environment. Therefore expert control strategies for logic control of such situations which were not governed by continuous control were introduced more recently. Expert control study of flotation of sulphide (Jlimsli and Herbst 1988) and phosphate minerals (Jlimsli and Niemi 1989) by simulation with results which proved it a valuable tool.

The expert control strategy tested in this simulation study aims maximizing recovery of phosphates from the rougher flotation. while concentrate grade is maintained above a minimum level. The paper describes large steady state sampling in an industrial environment for the parameter determination of the dynamic flotation model used by the simulator. In addition the determination of the hierarchy of manipulated variables and expert rules by simulation of the operations of the rougher flotation bank, as a step to development of an industrial expert control application are presented. The variables manipulated for control are reagent feed rates, aeration rate and pulp levels in the cells.

DYNAMIC MODEL OF ROUGHER FLOTATION BANK A detailed phenomenological flotation model developed and tested by Bascur (1982), has been shown to represent adequately the behaviour of particles of different mineralogical composition and particle sizes under a wide range of steady state and dynamic operating conditions. In the model, the particle/bubble and water transport description together with the hydrodynamic characteristic of a flotation cell have been linked together to provide a

REVIEW OF LITERATURE Flotation was relatively early understood to be a process of first order kinetics, and models of flotation cells of various degrees of

31

general model which includes all controlled and manipulated variables for control system development. The simulation study described here is based on a simplified dynamic model (Bascur and Herbst 1985).

FLOTATION PLANT AND ITS REGULAR CONTROLS The main minerals of the Siilinjarvi mine and their average contents are apatite 10%, calcite 14% and dolomite 5%, mica 65%, and other silicates 5%. The average phosphate content is only 4% Pps which makes the Siilinjarvi deposit one of the lowest grade phosphate deposit exploited in the world. The distribution of apatite is relatively constant throughout the deposit, whereas the other minerals, especially carbonates and mica, are more variable. Due to the variation of the latter components, the ore processing is complex and requires a systematic variation of the grinding and flotation conditions according to the ore types.

The set of equations for a mineralogical species j are written as follows:

In the concentrator, there are two parallel, two-stage grinding lines with a capacity of 800 t/h. The total annual capacity is 7.0 million tons. Each circuit comprises a rod mill in open circuit and a ball mill in closed circuit with two-stage hydraulic classification including hydrocyclones and cone classifiers.

(1)

The mineral pulp is conducted from conditioning to rougher flotation. Rougher concentrates are pumped to a four-stage cleaner flotation circuit. Cleaner circuits are closed, each cleaner tailing reporting to the preceding flotation stage.

(2)

The concentrator is extensively automated with the fully distributed microprosessor based system Damatic. All measurements and control actions proceed through the Damatic system. The reporting and process studies are based on a PDP 11/23+ process computer.

Equations 1 and 2 represent the mass balance of mineralogical species j in the pulp and froth volumes, V 11' and V LF' respectively. C represents the concentration of the species j while the superJ. SCrIpts P and F refer to the pulp and froth. 0F«d' OE' OR' OTand Oc are the volumetric flow rates of liquid in the feed, entrainment, drainage, tailing, and concentrate. OA is the volumetric f10wrate of air, while a/, a F and kj R are the rate constants of the species in the pulp, froth and drainage.

Flotation reagents, i.e. the emulsifier, calcite depressant and collector are added to the conditioner and the frother to the rougher flotation feed. There are feed forward control loops which relate the reagent addi tions to the ore feed with a delay in order to compensate for the variations in throughput. The s lurry levels in the pump sumps are measured by pressure transducers and controlled by the speed of the variable frequency drive pumps. The cell levels are controlled by tail valves and have an additional feed forward compensation from the level of the first cell to the subsequent tail valves.

In order to quantify the transfer of water at the pulp/froth interface, the f10wrates of entrained water OE' water draining back OR' water leaving the cell with concentrate Oc and tailings 0,., have to be balanced. The volume balance of the liquid in the flotation cell yields

The flow of the rougher concentrate is controlled by aeration and by frother and emulsifier feeds. If a froth overloading appears, the operator increases depressant feed and may decrease collector feed by changing the setpoint of its controller. The final concentrate f10wrate and assay are controlled usually through the air flowrate to the cleaning sections, especially to the fourth cleaner or more seldom through the froth height or reagent feeds. So far, there are no on-line analyzers in the plant. The samples for the analysis in laboratory are taken hourly. The rougher flotation process with its instrumentation is shown in Fig. 1.

for the pulp d

di

(3)

(V 11') = 0F«d - 0T- 0E + OR

and for the froth

(4) where V 11' is the volume of liquid in pulp; V LF is the volume of liquid in froth and 0F«d is water entering the cell with new feed. In order to parameterize the simplified model equations, the general steady state system equations corresponding to Eqs (14) were used in the present study to obtain the parameters and aF. One needed to have a set of data CF«d, CMP, CMF, the values of OR' Oc' 0,., OE and OA obtained foi the ~ater inodel and to solve for a P and aF. The seLs of two nonlinear equations were iteratively ~olved by Newton-Raphson techniques. An initial and guess was made for kt and the system was solved for If the system did not converge the initial guess for kR was modified and the procedure repeated. J

a/

a/

STEADY STATE OF ROUGHER FLOTATION BANK Since most parameters of the dynamic model are also present in the steady state model, the latter state was first submitted to a study. In addition to collection of the physical and mineralogical data which characterized the ore and the flotation devices, the values of the adjustable quantities like the mass flows, feeds of chemicals, pulp levels and aeration rates were recorded and held constant during a test. Samples were taken from the slurry in the rougher flotation bank (Fig. 1) and ana lyzed for their particle size distribution and mineral contents.

ar

In this study the whole rougher flotation bank operation was simulated and models of flotation cells described above were assembled in order to form a dynamic model of the whole bank.

32

Although the chemical variations of the natural minerals cause inaccuracies in the different mineral distribution calculations according to their theoretical equations, the results in Table I of Fig. 2 characterize in detail the behaviour of the rougher flotation bank. The cells were assumed perfect mixers and therefore they were modeled in steady state by

Q CP = T

j

The 6.0 version of ONSPEC control software, used in this study, introduces the use of DOS media and a DOS-type operating environment. Concurrent DOS 6.0 is a multi-tasking, multi user operating system. It features four virtual consoles through user adjustable windows and can also support external user consoles. In this simulation study, for example, the process is displayed in one window, the simulator coded in FORTRAN is running in another one, the trends are displayed in a third window and the real time expert control system is running in the fourth window.

C F"d

Q F"d

j

1+k1:

(3)

J

The flotation rate coefficients were calculated from the above equation (Fig. 3). The residence time of pulp in the individual cells was calculated by assuming perfect mixing within the cells and using the measured cell volume divided by the volumetric flowrate of the tailings.

ONSPEC control software contains programs for design and preparation of diagrams, lists, specifications and reports; for modelling and simulation; for process control displays, alarms, trends and historical reporting. The software is modular in design to allow the user to select the features necessary for a specific application. Software for this simulation study also included the World interface to ONSPEC and the ONSPEC Superintendent. The World interface is a library of routines which allow external programs access to ONSPEC queues and data tables. ONSPEC Superintendent is a real-time expert system. The Superintendent package consists of three programs. The first is called the EYE, that functions to compile a text file into a knowledge base. The second program is the BRAJN, which develops the Expert Diagnostics and helps to develop the knowledge base. The third program is SUPER, the Expert Console. Super is intended to run the knowledge base continuously.

Table I shows that the apatite, and to some degree, calcite and dolomite are concentrated by the cells. The overall apatite recovery of the rougher bank is 85.44, the calcite recovery 26.19 and the dolomite recovery 17.41. The figures in Table I indicate losses of the apatite in coarse size classes +210 I-lm. Dependence on the particular steady state is shown in Fig. 3 by the overall rate coefficients of apatite. The rate coefficient was 0.292 min- 1 in the cell number 2210 and 0.185 min- 1 in the last cell of the bank. The mean residence times were 1.70 min and 2.01 min respectively. The k's of the different size classes of apatite, calcite and dolom ite are also shown in Fig. 3. The curves indicate that the fast floating, optimum size classes of apatite are +37 ... -210 I-lm and the slow floating, fine size class of apatite is -37I-lm. The finest size class of apatite is concentrated mainly by the last cells of the rougher bank.

The flotation process simulator is modular in structure. Software package contains three main modules, which are the steady state and dynamic calculations modules and the control strategies module.

A number of other dependences needed by the stated detailed model were determined through separate tests (Jamsa and Niemi 1989, Karki 1989). Several of these were related to the effects of feed and concentration of frother and collector and to the effects of aeration. After the dependence of the surface tension on the frother added had first been determined, it turned out in another test that the power consumption of the impeller was a decreasing function of the aeration. These correlations for the bank model were determined from the second and fourth rougher flotation cells. The effects of aerat ion and frother on the air hold-up were determined indirectly by changing them stepwise and recording the change of pulp level, while the cell level control was disconnected. Bubble sizes and their distrubution were determined by means of image recording and analysis techniques. Substitution of the test data and the correlations in the model formulae (Jamsa and Herbst 1988) yields the values of their adjustable factors.

The steady state module contains the following submodules data input calculations of the mineral distributions calculations of the mass and water balances calculations of the mineral overall recoveries and recoveries by size interval calculations of the cells residence times calculations of the cells overall and size interval flotation rate coefficients The dynamic calculations module contains the flotation process dynamic simulator described earlier in the text. This module contains for example a submodule which gives the possibility to simulate random and deterministic disturbances added to the flotation process manipulated variables. Control strategies module contains the possibility to simulate different single input single output controllers, expert control and adaptive multi input multi output control strategy. Control and manipulated variables are user adjustable.

HARDWARE CONFIGURATION AND CONTROL SYSTEM SOFTWARE PACKAGE

All modules described above are menu-driven by the ONSPEC control software package, which gives flexibility and simplicity for the user interface.

The control software package ONSPEC has been designed to run on 286/386 personal computers and is also available for the DEC VMS environment. The configuration for the ONSPEC system used in this simulation study is

DYNAMIC RESPONSES OF THE ROUGHER FLOTATION BANK

UNISYS 386 microcomputer 1.0 M bytes RAM memory 40 M bytes hard disk EGA color graphics board EGA monitor

In the dynamic testing phase of the study, step change tests were made for collector addition rate, aeration rate, frother feed rate and cell level setpoints. The initial data used for simulations were collected in the beginning of each experiment from the rougher flotation bank and calculations made for each steady state as described earlier in the text. To compare the results with the practical operation of the flotation bank, measurements were

A math co-prosessor chip and 4 M bytes memory board have been added to the system.

33

made and samples taken and analyzed also during the test. For practical reasons, the samples could be taken at five minutes intervals. Since the flow dynamics of the cells are much faster, only a check of long-term and steady state effects was obtained.

Simulated responses in grade and recovery to a change of +5.5 ml! s in the collector flowrate and displayed in Fig. 6 and 7 . This change is about 25% of the collector addition operating range. The responses of the first rougher flotation cell show that the recovery increases from 35.0 to 36.0% P20 S' and the grade increases from 24.3 to 24.7% pps' The delay between the collector step change to the first rougher flotation cell responses is 15 minutes. After twenty minutes delay the grade response of the second rougher flotation cell increases from 20.0 to 21.8% Pps and the recovery 34.31% Pps to 36.0% pps' Delay between the collector step change and the third cell is 25 minutes, the grade difference being +0.7% Pps and the recovery difference +1.0% P 20S' Responses of the fourth cell after 35 minutes delay show the grade difference +1.4% P2 0 S and the recovery difference of 0.8% P20 S ' Responses of the last rougher flotation cell variables to a step change in collector flowrate show that the grade increases after 45 minutes delay from 6.7 to 7.3% P20S and the recovery increases about +1.5% P2 0 S ' The measured grade responses of the all five cells are similar to the simulated responses. The recovery responses show the similarities of responses reported by Fuerstenau 1976 and Jiimsii and Herbst 1988.

Collectors, activators and depressants affect the chemical environment of the flotation pulp and either enhance or reduce the probability of bubble-mineral aggregates being formed. Variables such as froth depth and air addition rate affect the retention time of mineral particles in the froth. Froth height affects the residence time in the froth phase and consequently the particle drainage from the froth. The latter feature applies particularly to the gangue particles which generally appear in the froth by entrainment. Therefore, the froth height can be used to control concentrate or tailings grade from a particular bank. Increase of the froth height produces a higher concentrate grade, but at the expense of a loss of recovery. The water recovery to the concentrate will also be reduced. The simulated responses to a froth height change produced by a pulp level setpoint change of -3 cm in each cell showed that the grade increased producing a steady state difference of about +0.3% P20S and a sharp decrease in the recovery, about -2% P 0 was 2 S observed.

The stability of the froth depends on the amount of frother added. Iffrother addition rate is increased, the froth becomes more stable and the transfer rate from the froth to the concentrate launder increases. In general the effect of an increase in frother is similar to a decrease in froth height in that it increases the flowrate of water to the concentrate. In Fig. 8 the measured and simulated responses of grade to a change in frother addition rate are represented. Fig. 9 shows the simulated responses of recovery. The frother was decreased from 15.3 10- 6 mol /1 to 9.6 10-6 mol/I , which is about 30% of the [rother addition operating range. The grade differences of the rougher flotation cells are +1.3, +1.2, +1.5, +0.8, +0.5 % Pps and the recovery differences -2.0, -2.2, -1.5, -2.0, -2.0% P20S respectively. Plant test data show the same trend as the simulated grade responses and the pattern of the recovery responses is similar to that obtained at the simulation study by Jiimsii and Niemi 1989.

Change in air addition rate at normal levels of aeration has a similar overall effects as a change in froth depth. Decrease in air flowrate increases concentrate grade and decreases recovery of the valuable minerals, because the air affects the flowrate of water to the concentrate and this results in a proportionally greater reduction of the flowrate of gangue to the concentrate than of that of the valuable mineral. The flotation process responds much faster to changes in aeration rate than to those in froth depth, and aeration rate is often more effective than froth depth in maintaining stable circuit behavior (Fuerstenau 1981). Measured and simulated responses in grade to a change of +720 Nm 3/h in total rougher flotation aeration rate, are displayed in Fig. 4. Fig. 5 represents the simulated responses of recovery respectively. In this experiment the grade decreases about -0.5% P20S in the first three cells and the pattern of the measured grade responses is similar to the simulated results. Measured grade responses of the last two cells show the opposite pattern of the responses of the first cells. The function between the power consumption and the aeration was determined for the first three cells using the measurements taken from the second cell and for the last two cells from the fourth cell. The recovery shows a difference of +3 % Pps for the first three cells and -2.0 and -1.0% P20 S difference for the last two cells. These results show the importance of the possibility to manipulate the airflowrate for control purposes separately for the first cells and the last cells of the bank.

EXPERT CONTROL SYSTEM FOR FLOTATION The expert control strategy, which will be tested at the Siilinjiirvi concentrator, is to maximize rougher flotation recovery while maintaining the rougher grade above a given minimum level. The hierarchies of the manipulated variables to control the recovery and grade have been determined by simulating the changes of the cell level setpoint, the aeration flow rate, the collector and frother addition rates as described earlier. According to the simulation results in these process conditions the air flowrate seems to be the most important manipulated variable to control the phosphate rougher flotation recovery. The frother and collector addition rates are the next in the hierarchy of the manipulated variables. The froth height has a minor effect on the recovery. From the simulation results we can see that the frother has the biggest effect on the concentrate assay, the next important variables are the collector addition rate and the air flow rate. As compared with the results for the sulphide ore published by Jiimsii and Herbst (1988), the hierarchies of the manipulated variables to control the rougher concentrate recovery and assay are different from those of sulphide ore. The dynamic flotation model has thus taken into account the characteristics of the phosphate ore as compared with the sulphide ore. The determinations of the flotation rate coefficients and the correlations described earlier in this paper are the most important factors to describe the differencies between these two ore types. Fig.l 0 shows the flowsheet of the BRAIN-language program designed on the basis of these simulations.

Change in the addition rate of collector may have several effects on the mineral flotation rate depending on the conditions of the pulp and the froth before the change is made. An increase in the addition rate of collector may either increase or decrease recovery and concentrate grade depending on the initial collector addition rate. It has been, however, found experimentally that a linear relationship exists between the collector rate and the slowfloating fraction of valuable mineral. The physical significance of this relationship relates to the fact that the function of collector in flotation is to render the valuable particles hydrophobic and therefore amenable to flotation , and that it does this by transforming the valuable mineral into fast floating particles. As more collector is added to the process, more valuable minerals are transformed to fast-floating particles until the recovery plateau is received. Change in collector addition rate also causes a change in the behaviour of each size range so slow-floating fraction is also a function of particle size.

Expert system, used in this simulation study, is started cyclically

34

REFERENCES

according to the process delays. It supervises the flotation process using the knowledge base which is the array of rules and actions designed above. The knowledge base is coded by BRAIN. The setpoints of the basic control loops of the flotation process are determined and changed according to this knowledge base and the theoretical process simulations occur through ONSPEC data tables. The operator also has the possibility of changing the set points as a terminal input. Simulated responses in recovery and grade for expert control of the second rougher flotation cell are displayed in Fig. 1 O. The initial values used in the simulation are the following: the frother concentration 8.9 1~ mol/I, the collector flow rate 66.0 ml/s, the air flowrate 298 Nml/s and the cell level setpoint 3.215 m. The maximum limit for the air flowrate was 500 Nml/s, for the frother concentration 11 1~ mol/l and for the collector addition rate 70 ml/s. The minimum grade limit was assumed to be 25.5% pps' According to the program displayed in Fig. 9 the air flowrate was changed stepwise by 15 Nml/s, the frother concentration by 2 1~ mol/l and the collector addition rate by 4 ml/s observing the maximum limits mentioned above. The recovery increased by 1.5% Pps and the grade response decreased slowly by 0.5% Pps showing that it is possible to move towards the optimum on the grade-recovery curve where better operating results can be achieved.

DISCUSSION On the basis of this simulation study one may conclude that the described detailed phenomenological flotation model represents adequately the behavior of the phosphate rougher flotation bank in a steady state and under dynamic operating conditions. The model developed gives thus an excellent possibility to design and test different control methods for the flotation process. In this paper the hierarchy of the manipulated variables to control the rougher flotation was determined by simulation. In order to find the optimal operating conditions of the bank the expert control strategy was designed and the simulation results presented. The examination of the results showed the expert control strategy is worthwhile testing at the Siilinjarvi concentrator with the implementation of the information system to automatic collecting and monitoring of the setpoint changes of the operator and feedback from the effects of the changes according to the ore types as described by Kallioinen et al 1987. Data file which includes the different ore types and their grinding and flotation conditions will provide the basis for this research work in a near future.

ACKNOWLEDGEMENTS The writers would like to thank the mine manager Lauri Siirama and mill superintendent Jarmo Aaltonen for the opportunity to perform the experiments at the Siilinjarvi concentrator. The assistance given by technician Simo Parkkinen and the laboratory staff of the concentrator while large sampling for this study was being carried out are gratefully acknowledged.

35

Andersen, R., B. Gronli, T. Olsen, I. Kaggerud, K. Ramslo, and K. Sandvik (1981). An Optimal Control System Of the Flotation at the Folldal Verk Concentrator, Norway. In J. Laskowski (Ed.). Mineral Processing Proc. 13th 1nL Mineral Processing Congress, Vo!. 2. Elsevier, New York. pp. 1517-1540. Bascur, O. A (1982). Modelling and Computer Control of a Flotation Cell. Ph. D. Dessertation. University of Utah, Salt Lake City, Utah, USA Bascur, O. A, and J. A Herbst (1985). On the Development of a Model-Based Control Strategy for Copper Ore Flotation. In E. Forssberg (Ed.). Flotation of Sulphide Minerals. Elsevier, Netherlands. pp. 409-431. Fuerstenau, D. W. (1981). Mineral and Coal Flotation Circuits, Their Simulation and Control. Elsevier, New York. 291 p. Hammoude, A., and H. Smith (1981). Experiments with SelfTuning Control of Flotation. In O'Shea and Polis (Ed.). Proc. 3rd IFAC Symposium on Automation in Mining, Mineral and Metal Processing. Pergamon Press, Oxford. pp. 213-218. Jamsa, S-L., and J. A Herbst (1988). A Simulation Study of Expert Control System for Flotation. In J. M. Macleod and A D. Heher (Ed.). Software for Computer Control 1988. Pergamon Press, New York. pp. 67-75. Jamsa, S-L., and A Niemi (1989). A Simulation Study of Expert Control System for a Phosphate Flotation Process. 6th IFAC Symposium on Automation in Mining, Mineral and Metal Processing. Buenos Aires, Argentina, (to appear). Kallioinen, J., E. Kesti, J. Miettinen, and I. Mullen (1988). A Sophisticated Information System for Process Control. Copper 87. Economics Metallurgy and Process Control. Chile. Koivo, H., and R. Cojocariu (1977). An Optimal Control for a Flotation Circuit. Automatica, 13, 37-45. Karki, E-K. (1989). Dynamics and Simulation of Flotation Cell. Master's thesis. Helsinki University of Technology, Helsinki, Finland, (in Finnish). Niemi, A (1966). A Study of Dynamic and Control Properties of Industrial Flotation Processes. Acta Polytechnica Scandinavica, Chemistry including Metallurgy Series, No 48. The Finnish Academy of Technical Sciences, Helsinki. 111 pp. Niemi, A, and U. Paakkinen (1869). Simulation and Control of Flotation. Circuits. Automatica, 5,551-561. Niemi, A, J. Maijanen, and M. Nihtila (1974). Singular Optimal Feedforward Control of Flotation. 1FAC/1FORS Symp. Optimization Methods - Applied Aspects. Vama, Bulgaria. pp. 277-283. Zaragoza, R., and J. A. Herbst (1987). A Model Based Feedforward Control Scheme for Flotation Plants. 116th AIME Annual Meeting. Denver, Colorado, USA. pp. 23-27.

G .l

I I '",. 1.1. Il I Ht;S

~I <010

1

" ~ '

• sompl ing point TO 1 NO

"

Fig. 1.

Cl[ . II'IH(j

SECTION

The rougher flotation circuit at the Siilinj1irvi concentrator.

TABLE 1. Rougher banks rxrformancc.

Tola! now (Vh) Dry now (Vh) SoI;C!S (%)

Assay (%) Mineral flows (I/h) Apatite Calcite Dolomite

Other

Cell 2220

Cell 2210

Rougher bank

Cell 2230

Cell 2250

'::ell 2240

Feed

Cone.

Tail.

Feed

Cone.

Tail

Cone.

Tail.

Cone.

Tail.

Cone.

Tail.

Cone.

Tail.

3955.5 1230.2 31.1 3.9

704.8 223.4 31.7 18.1

3250.7 1006.8 31.0 0.8

1977.8 615.1 31.1 3.9

92.2 31.1 33.7 25.2

1885.6 584.0 31.0 2.8

73.8 24.0 32.6 20.5

1811.8 560.0 30.9 2.0

78.1 24.1 30.8 15.8

1733.7 535.9 30.9 1.4

52.0 18.3 35.2 12.7

1681.7 517.6 30.8 1.0

84.2 19.0 22.5 6.8

1597.5 498.6 31.2 08

113.57 280.64 184.42 651.54

97.04 73.49 32.10 20.78

16.53 207. 15 152.32 630.76

56.78 140.32 92.21 325.77

18.79 6.53 2.95 2.78

37.99 133.79 89.26 322.99

11.84 6.73 3.35 2.13

26.15 127.06 85.91 320.86

9.16 8.70 3.79 2.42

16.99 118.36 82.12 318.45

5.60 7.53 3.51 1.66

11.39 110.83 78.61 316.79

3.08 6.85 5.49 3.53

8.31 103.98 73.12 313.26

9.23 22.81 14.99 52.97

43.44 32.89 14.37 9.30

1.64 20.58 15.13 62.65

9.23 22.81 14.99 52.97

60.52 21.03 9.50 8.95

6.50 22.91 15.28 55.31

49.23 27.98 13.93 8.86

4 .67

38.07

2.20

36. 14

3.17 22.09 15.32 59.42

30.60

22.69 15.34 57.30

41.15

21.41

15.19 61.20

16.25 36.15 28.97 18.63

167 20.85

19.18 9.07

Mineral distribution (%)

Apatite Calcite Dolomite

Other Recovery (%) Apatite

85.44 26.19 17.41

Calcite Dolomite

Apatite size recovery (%) +500 IJm

-500/+210 ....m -210/+149 "'" -149/ +74 .,un -74/+37 ,....m -37 ,....m

15.75 10.04

1466

62 .82

27 .07 6.18 6.98

33.09 4.65 3.20

31.17 5.03 3.75

35.03 6.84

32.94 6.36

4.41

4.27

0.48 7.96 21.60 37.25 50.04 47.89

0.33 9.02 20.55 3240 45 .71 53.49

0.75 12.92 22.60 29.89

1.79 15.88 21.20 27 .65 31.23 68.48

046 5.46 8.75 1520 38.05

0.07 1.12 3.04 5.24 7.04 6.73

005 1.46 332 5. 23 7.38 8.63

0.10 125 2.00

5.84 8.75 12.8

0. 35 3.07 4.09 5.3' 6.03 13.22

0.05 0.77 2.09 3.61 4.85 4.64

0.04 1.09 2.48 3.90 5.51 644

0.09 1.63 2.85 3.76 5.64 8.25

2.75 3.59 4.05 8.88

3 12 1'1 226 3.92 981 1894

1.70

176

1.84

1.90

2 01

44 .79

65 .50

73 49

Calcite size reoovery (%)

+500 IJm

-500/+210 J.tm -210/+149 J.tm -149/ +74 ~lm -74/+37 f..lm - 37 1lm

Dolomite size recovery (%) +500 J.tm -500/+210 J.tm -210/+1 49Ilm ~ )lm

-149/+74

-74/+37 -37 f.lm

Residence lime (min)

Fig. 2.

Steady state of rougher flotation bank.

36

0.15 2.52 4.42

0.23 2.06

34 7

8.68 1676

0.<0

1.6

1.6

CELL 2210

0.15

1.4

lA

O.JO

1.2

1.2

Q20

0.8

0.8

0.15

0.6

0.10

0.4

OA

0.2

0.2

CELL 2220

0.25

ooo L---,----,----r---,----,----

1.6

l

mo

CELL 1.4

1.2

-.

16

1.6

14

1.4

12

1.2

08

0.8

06

0.6

04

0.4 -

02

Q.2 .500

~.

30

CEll 2220

.

60

gO

60

gO

liI

.

30

60

60

CELL 2250

30

gO

l!I

.

l!I

60

gO

C

30

~I

flo wrot.e

60

gO

120

60

~

gO

~=

30

.

120

.

gO

120

60

gO

120

60

gO

120 (mln)

60

CELL 2250

~ 30

120 All'"

120

CELL 2240

120

~

gO

CELL 2230

120

ill

60

r

30

.

CELL 2240

30

CEll 2220

120

CEl" 22JQ. -.........

gO

/ 30

120

----

30

CEll 2210

l!I

CEll 2210

flow,..ote

NmJ/h·IOJ

Nm)/h· I O)

!:1

/ I

30

Fig. 4 .

SIZE INTERV AL I~ml

Reco .... er-I::! % P20S

• me o sured po lnts

!:1

o

·210/.149 ·74/.37 .500/.210 -149/.74 -37 SIZE INTERVAL I~ml

erode % P20S

III

DOL{)t,IjTE

Apatite, calcite and dolomite overall and size by size flotation rate coefficients.

Fig. 3.

AIr

CE LL 2250 APATITE CALOTE

SIZE INTERVAL I~ml

i! I

0 L-~~6-~~==*=~__

01L-~~~~~~~~--

o

III

0.6 -

60

gO

120

Time

/ • Time

30

(mln]

Fig. 5.

SImulated cnd measured responses In grade t.o ., chang e 1 n the olr flo"'I"' o t8. The cell level s

SImulated responses In ,..ec ovo"'~ to ., ch ange 01,.. flowrote. The cell levels were controlled bl::! the tOlllngs flowrote.

tne

wer-e contre 11 od by the t o III ngs fl ow,..o1.o.

37

In

• meo~u,..ed pOlnt~

lllr~c_E_LL

__2_2_20____

~~

====.

30

60

gO

120

60

gO

120

:it_c_E_L_L_2_23_o_ _ _ _ _

30

:j lr_c_E_L_L_2_2_~-030

B

__

~_--'===:= 60

CELL 2250

7

6 5 30

60

gO

.

120

.==. gO

Recove .. y % P20S

120

E1 "CC "'"

Collecto,.. Flowrote [ml/s)

l!1

/.---------------30

60

gO

T ,me

30

gO

.

120

120 [mln)

37

Fig. 6.

60

t

CELL 2220

~;~t----~C==.

Slmulated and measured responses In g,..ade to a change In the collecto,.. flowrate. The eel} 1eve 1s we,..e contro lIed by the to ll1ngs f lowrate.

30

fil~ "~CC

60

gO

120

L~. "_3_0_____

30

60

gO

120

gO

120

El~--C-E-LL--2-2-~0---------=-~ 30

60

!;~r-C-E-L-L-2-2-5-0----------~~ 30

60

gO

120

60

gO

I Time 120 Cm,n]

Coll ector Flow,..ate (ml/s)

III

/ 30

Fig. 7.

38

SImulated ·responses In ,..ecover-y to 0 change In the collector flowrote. Tho cell levels wer-e controlled by the tOlllngs flowrote.

Recovery

er-ode % PZ 0 5

• meo~ur-ed

% PZ 0 5

pOlnt.s

~I

CELL 2210

HI

~I

il

~. , 30

60

gO

120

~

CELL 2230

60

, 120

.~ 60

gO

120

~,

CELL 2250

60

gO

120

60

gO

120

2220

30

,

\--------, 30

60

gO

120

gO

120

~ 30

60

60

~

HI gO

120

,

30

60

gO

120

~

60

gO

, Time )20 (mln)

~~l;~!~ 1o?r c .

~~l;~~~ 1o~rc,

Fig. S.

120

CELL 2250

...

30

~l

gO

CELL 2240

CELL 2240

30

.

60

CELL 223Q.

30

ilI

gO

~

30

It'''' ~iI j!I

CELL 2220

30

CELL 2210

il]

\ 30

60

gO

Time 120 Cm,n)

30

Fig. 9.

S Imul at.ed and measured r-esponses In grade t.o a change In t.he frot.hor flo",rate. Tho coli lovels ",ore cont.rolled by t.he t.alllngs flowrat.o.

Slmulat.ed responses In rOCovory to a change ,n t.ho frot.he,. flowrot.e. The coli 10'1101 s cont.rollod by t.ho t.0111ngs flo",rot.e.

i ncreosin

Fig.

10 .

Expert Control Strategy for the rougher flotation.

39

Grodl!

jf= Rl!(o'ltry

30

4'0

20

30

40

10

io

30

'0

10

20

30

'0

SO

,"" l_ n!

~ J ':~ ~ ;'Irllo ..ro l t

----

~".,

20

10

"'.PrOs

10

SO

I-I

~,50

r~

'lnl/hl

r:~~r{. ~o?f

CoUtctor Imllsl

C2111t'Vtlsp

=~ 50

r.....

~

10

20

30

'0

SO

.',,.'od

10

20

30

I,()

SO

·r:, ·

Im l

:~

Fig 11.

Simulated responses of the expert contr ol str ategy.

40