- Email: [email protected]
Modern Systems of Automatic Control of Processes of Grinding and Flotation of Copper-molybdenum Ore
16th IFAC Symposium on Automation in Mining, Mineral and Metal Processing August 25-28, 2013. San Diego, California, USA
Modern Systems of Automatic Control of Processes of Grinding and Flotation of Copper-molybdenum Ore V. Morozov*, D. Davaasambuu**, Z. Ganbaatar**, L. Delgerbat**, V. Topchaev***, I. Sokolov***, V. Stolyarov**** *Moscow State University of Mining, Leninsky Prospekt 6, Moscow, 119991, Russia. Tel.+7 (495)230-94-21, Fax.+7 (495) 237-80-33, e-mail: [email protected] ** Erdenet Mining Corporation, Erdenet city, Orkhon aimag, Mongolia Tel.+ 976 (352) 71-557, e-mail: delgerbat@ erdenetmc.mn *** Private Jt.St &R ³6R\X]WVYHWPHWDYWRPDWLND´ 'PLWURYVNRH 6KRVVH 0RVFRZ 1278238, Russia, Tel +7 (495) 489-10-85, Fax +7(495) 489-14-05, e-mail: [email protected] 3ULYDWH -W 6W &R ³(OVFRUW´ 6WDURNDVKLUVNR\H 6KRVVH 0RVFRZ Russia, Tel +7 (495)112-92-92, Fax +7 (495)320-93-93 e-mail: [email protected] Abstract: The processes of grinding and flotation of copper-molybdenum ores are characterised by significant fluctuations of all input, output and intermediate parameters. Instability and non-optimal parameters of grinding and flotation cause between 3% to 6% losses of the valuable component. Under these conditions, it is difficult to apply deterministic mathematical models of processes. However, the use of multi-level adaptive models allows considerable optimisation of the technological processes. Automatic process control grinding uses the principle of maintaining a rational level of loading of the drum mill ore and balls. While the system is operating, energy and acoustic signals are used as input parameters and the results the measurements of the size of the grains of the grinded ore are used as input and output parameters. When managing the flotation, the principle of forward-looking estimation of the grade of ore is used. Additionally, while the system is operating, analyses of the ore in the x-ray and visible spectrum are used as input data. The application of modern systems of automatic control at the ³(UGHQHW´ FRQFHQWUDWRU 0RQJROLD KHOSHG LQFUHDVH WKH UHFRYHU\ RI FRSSHU DQG PRO\EGHQXP
Keywords: mineral processing, grinding, flotation, control, algorithm, optimisation, adaptation, analyser The applied instruments, presented in Fig. 1, provide continuous control of milling and classification.
1. INTRODUCTION Effective control of grinding processes provides facilities with significant improvements of the economic parameters of the entire concentration process. Grinding optimisation is achieved by maintaining maximum productivity in conjunction with the pre-set grain size of the milled ore; thereby providing effective flotation of the valuable mineral (Chanturia et al., 1997). The operational analysis of ore is the basis for modern process control systems. The rapid development of technology for the analysis of the composition and properties of minerals allows us to develop and implement novel methods and systems for automatic control of ore beneficiation.
water FRC 3
PIC 7
GRI 8
ore
LIC 4 ERI 5
in flotation
ERI 6
2. SYSTEM OF AUTOMATIC REGULATION OF THE GRINDING PROCESS
Fig. 1. Schematic structure of the automatic control system for grinding process of copper-molybdenum ore: 1 ± control system for ore feed to the mill; 2 ± control system for water feed to the mill; 3 ± control system for water feed to the sump; 4 ± sump pulp level control system; 5 ± mill drum loading measuring system; 6 ± mill drive wattage measuring system; 7 ± cyclone upstream pulp pressure measuring system; 8 ± cyclone overflow grain size measuring system.
The subject of this research is the grinding complex of the concentrator of the Mongolian-Russian Joint Venture ³(UGHQHW 0LQLQJ &RUSRUDWLRQ´ 7KLV FRPSULVHV D EDOO PLOO DQ overflow that goes to a buffer vessel (sump) and on further to a cyclone battery (using a pump). The cyclone oversize feeds back to the mill, whereas the overflow presents the finished product of the grinding process.
978-3-902823-42-7/2013 © IFAC
FRC 2
FRC 1
166
10.3182/20130825-4-US-2038.00032
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
The milling and classification optimisation system comprises sensors and control units, most of which are traditionally applied at ore concentrators. The concentrator has introduced new methods of grinding control through use of the estimation of mill drum loading degree by acoustic characteristics and output performance (Ulitenko et al., 2005).
Input parameters
The control systems for the ore and water feed to the mill, the water feed to the sump and the sump pulp level are of standard design. The sensors of the mill drum loading (VAZM) and the mill drive wattage (WAM) are standard FRPPHUFLDO SURGXFWV RI -6& ³6R\X]WVYHWPHWDYWRPDWLND´ )RU milling quality control, a test-rod-type instrument for grain size measuring PIC- -6& ³6R\X]WVYHWPHWDYWRPDWLND´) is used.
Criteria of process emergency
Protection from overload and emergency
Criteria of process stabilization
Stabilisation of milling process
Criteria of choosing efficient regimes
Choice of efficient regimes of milling process Output parameters
The grinding process is intended to provide pulp of regulated solid grain size and density (mass share of solid substance). In real production, grinding is controlled by operatortechnologists who have differing skills and experience. In a non-automatic regime, the crushing bay technologists use their own experience. They are guided by the process regulations, the productivity target and information from control instruments regarding the processed ore parameters and processed products. Based on the information available, the technologists choose the required process conditions: setting the ore and water feed, the cyclone upstream pressure and other parameters for local automatic control systems. The major problem in milling and classification process control is the prioritisation in the control, i.e., the choice of regulating actions under conditions of discordant or mutually exclusive recommendations of the individual control circuits.
Fig. 2. Generalised block diagram of succession and implementation conditions of the grinding control algorithms. Table 1. Parameters of the milling and classification process control algorithm ‹
1 2 3 4 5 6 7
3. THE ALGORITHM DESCRIPTION
8 9
In order to provide effective automatic grinding control, the following goals must be achieved: control and optimisation of the upstream ore and water flows and of the performance of the milling and classification facilities. At the same time, an important goal in ore grinding optimisation is a reduction in power consumption. To attain these goals, the algorithm of multi-criterion control with the use of simple and complex criteria, ranked by importance and the principle of effect on the process, was developed. For the control system operation in the optimum process parameters field, the following structure is used, as presented in Fig. 2.
Process parameter (criterion) Ore productivity, tph Mill water feed, tph Sump water feed, tph Sump pulp level, m Cyclone overflow pulp density, solid/liquid Cyclone overflow grain size, % of - —P VL]H Mill drum loading degree, VAZM scale % Mill drive wattage, kWh Cyclone upstream pressure, at
Designati Setting Min. value on Measured Qm Qms Qw/m Qw/s Gs Rc Rcs -74
-74
Qm min Qw/m min Qw/s min Gsmin Rcmin
Qm max Qw/m max Qw/s max Gsmax Rcmax
Fdmin
Fdmax
:mmin Jcmin
:mmax Jcmax
s
Fd :m Jc
Max. value
Jcs
Calculated 10 Mill pulp density, kg/m3 Rm Rms 11 Productivity derivative of GFd/dQm drum loading 12 Productivity derivative of dAm/dQm mill wattage
GFd/dQmmin dAm/dQmmin
7KH DOJRULWKP¶V UHVWULFWLYH EORFNV SURYLGH IXQFWLRQLQJ IRU WKH process in objectively expedient conditions. These conditions are defined by the prevention of critical conditions, for instance, overload drum of mill or cyclone sump overload. At the same time, the restrictive blocks prevent undesirable effects of failure of the automatic control system sensors on the process.
The structure allows prompt changing of the effective performance criteria, stabilisation of the parameters at the required level and protection of the process from failures and overloads.
The stabilising blocks can be arranged both as systems of control by deviation (using setting function for the measured output parameter) and as restrictive systems using values of permissible ranges, restricted by permissible minimal and maximal values of the controlled parameter.
In Table 1, parameters 1±9 of the control algorithm are continuously measured and parameters 10±12 are calculated. The control algorithm contains restrictive, stabilising, regulating (control) and optimisation blocks. The basic blocks (circuits) are presented in Table 2 and positioned in order of decreasing importance (the highest hierarchic level ± 1, the lowest level ± 3). The criteria used in the process control by individual blocks of the algorithm are in the form of a parameter threshold with a permissible parameter variation range or optimum parameter value (Table 1).
For optimisation in blocks, simple parameters of milling and classification processes are used as optimisation criteria, for instance, optimal coarseness of the ground ore, or the relationship of the parameters (e.g., dynamics of mill drum loading or mill drive wattage with an increase of mill productivity) (Morozov et al., 2008). 167
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
For the function of the classification water regime circuit, the technologist defines the permissible limits of the measured classification process parameters in the form of minimal and maximal pulp density values and the cyclone feed sump pulp level. The optimum grinding coarseness (grain size of ore, share of class - —P LQ WKH PLOOHG RUH LV GHILQHG HLWKHU by the technologist or by the first level optimising control system. The pre-set grinding coarseness is maintained by variation of the cyclone sump water feed. To obtain finer grinded material, the water feed increases and to obtain coarser material, the feed decreases. At the same time, the overflow solid grain size is regulated by the cyclone upstream pressure variation.
Table 2. Basic circuits of the milling and classification process control system ‹ Circuit title
1 2
Mill overload prevention Sump overload prevention
3
Sump discharging prevention
4
Grinding water regime failure prevention
5
Mill ore productivity stabilisation
Criteria Restrictive Fd” Fdmax Gs ” G smax Gs • Gsmin Qw/m• 4w/m min Qw/m” 4w/m max
Control principle
Lev el
Prevention of mill overload with ore Prevention of pulp spillover from sump
1
Prevention of complete sump discharging Prevention of transport function infringement
2
Stabilizing Qm • Qm min Qm” 4m max
Grinding and flotation process stabilisation 6 Cyclone overflow Rc• 5 cmin Grinding and density stabilisation flotation process Rc” 5 cmax stabilisation 7 Cyclone upstream Jc • Jcmin Grinding and Jc” Jcmax pressure stabilisation flotation process stabilisation Regulating and optimisation 8 Regulating mill Qw/m= Qp / Rm Stabilising mill ore/water ratio pulp density 9 Solid grain size = -74s Maintaining regulation grinding coarseness in accordance with ore grade 10 Mill productivity GFd/dQm • Mill drum loading optimisation dAm/dQm • degree optimisation
1
3
This control circuit refers to the third level; therefore, this system operates on condition of maintaining the related second- and first-level parameters within the pre-set parameter ranges. The related parameters include the cyclone overflow density, the sump pulp level and the cyclone upstream pressure. When approaching or overrunning the permissible range limits of the parameters, the regulating circuit effect is cancelled.
2 2
2
For the mill productivity optimisation control, the productivity limits are defined initially. The crushing process control is carried out dynamically and allows for incremental variation of the productivity by registering the response of the drum loading or the mill drive wattage change. At VDWLVIDFWRULO\ SRVLWLYH YDOXHV RI GFd/dQm and dAm/dQm derivatives, a further increase of the mill productivity is conducted. In the case of the actuation of the mill productivity or drum loading absolute value restrictions, the next step is cancelled. In the case of the actuation of the LPPHGLDWHO\ FRQWUROOLQJ FLUFXLW UHVWULFWLRQV GFd/dQm•0 and dAm/dQm•0), the system, instead of performing the next forward step, steps backwards and decreases the productivity to the previous value. After a while, the algorithm sets the mill ore productivity again and the control cycle repeats.
2 3
3
4. THE ALGORITHM AND CONTROL SYSTEM FUNCTIONING To maintain milling and classification plant operation in the most efficient sub-critical regimes but excluding the risk of changeover to emergency conditions owing to equipment overload, the control algorithm hierarchical structure is used, which provides interrelated operation of mill productivity and grinding and classification water regime control systems, while maintaining the priority of the restrictive and stabilisation circuits over the optimisation circuits (Ulitenko and Morozov, 2011).
At stable failure of the assignment on cyclone solid coarseness, the algorithm proposes to decrease the mill productivity or decreases it in an unconstrained regime. 5. SYSTEM AND METHOD FOR ADVANCED DIAGNOSTICS OF ORE
The ore load control circuit maintains the pre-set optimal ore feed to the mill and ensures that this value remains within the permitted range. As the circuit refers to the second level, on the one hand, it restricts productivity variation limits via the optimisation circuits and on the other hand, it operates under conditions imposed by the first-level circuits, in this case, the prevention of mill drum overload.
The operational analysis of ore is the basis for modern process control systems. The rapid development of technology for the analysis of the composition and properties of minerals allows us to develop and implement novel methods and systems for automatic control of processes of ore beneficiation. In past years, practically the only method of operational measurement of the composition of ore was Xray fluorescence. Currently, several methods for the operational analysis of ores have been developed and are in use. Most representative mineral composition data give measurements of the ore in the visible part of the spectrum (Ganbaatar et al., 2011).
When operating the regulating circuit, the mill ore/water ratio, the maintained ore/water ratio in the mill feed, is defined by the technologist or the optimisation control systems relying on the processed ore grade, circulating load value and other process parameters. This circuit refers to the second hierarchic level. The system operates under restrictions imposed by the related first-level circuits. Such a related circuit is the mill total water feed stabilisation circuit and the related parameters are the mill minimal and maximal water feed.
$W WKH ³(UGHQHW´ SURFHVVLQJ SODQW D QHZ PHWKRG DQG V\VWHP for advanced diagnostics of ore has been developed and tested, based on the X-ray fluorescence analyser of elemental composition and a video-image analyser of mineral 168
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Absorption,%
composition. The system of video image analysis (Fig. 3) produces integrated digital images of the ore, which are formed with the help of modern telemetric and programtechnical software and hardware. The system facilitates the acquisition of real-time information on the mineralogical composition and type of the ore. The system also provides data on the particle size distribution of the ore entering the milling operation and the nature of the impregnation of minerals.
7
4 700
2
3
51
6
5
6
1
Z 1
3
8
9 2 650
600
550 nm
500
4 450
400
Fig. 4. Colour spectrum of minerals in copper and molybdenum ores (in the visible range): 1 - chalcopyrite, 2 lapis lazuli, 3 - covellite, 4 - bornite, 5 - molybdenite 6 azurite, 7 - cuprite, 8 - malachite, 9 - native copper.
7
Based on the spectral mineralogical analysis performed for the determination of oxidised copper minerals, the primary and secondary copper sulphides, pyrite, quartz, sericite, mica and other minerals are determined. The ratio of these minerals characterises the grade of the ore. The image analysis of lump fractions of crushed ore is used to obtain information on the structure and size of the impregnation of ore minerals.
4
b
The task of evaluating the grade of the processed ore is to determine its similarity to the main technological types of ores (Morozov et al., 2010). Ore is modelled as a mixture of the five types of ores. The initial data used are the content of copper, molybdenum and iron, the mass fraction of minerals of copper and the mass fraction of rock minerals in the ore. The essence of the calculation of the share belonging to a particular type of ore is as follows. In the received ore, the GHJUHH RI ³VLPLODULW\´ WR HDFK RI WKH NQRZQ ILYH W\pes of ore is determined.
Fig. 3. Schematic (a) and image (b) of the ore video image analysis system: 1 - finely crushed ore bin, 2 - feeder 3 conveyor, 4 - hopper mill, 5 - installation for ore preparation for analysis, 6 - source light emission, 7 - receiver reflected light radiation.
The share of each of the five types of ore in the ore delivered to the processing is calculated in proportion to its similarity to each individual grade ore. For this deviation calculated parameters of ore processed from ore taken as typical of the technologist.
The feature of the system for the video image analysis of ores is the lack of complex sampling and delivery to the analyser. This eliminates technical difficulties and improves the overall reliability of the system. To reduce the effects of external factors on video quality and to increase the stability of measurements, a weak intensity sprinkler is installed above the conveyor.
The normalised value of the deviation (Si) of the mixture of the parameters of ore (Zn) to the parameters of ore type (Zni) is calculated as follows: Si = (|Zn-Zni|)/Zni, with i = 1±5
(1)
The normalised values of similarity of parameters of the processed ore to the parameters of standard ore types are computed as follows:
The essence of the method of video image analysis comprises the analysis of mineral composition and estimation of the grade of ore based on the image of the processed ore in the visible spectrum. The database of the system consists of saved video images of all known minerals of deposits ³(UGHQHWL\Q - 2YRR´ &RPSXWHU LPDJHV VWDQGDUGV RI WKHVH minerals were generated by image processing software. The spectral characteristics of copper minerals under visible wavelength radiation (Fig. 4) are a source of information for the video image analysis.
Di = 1/ Si, with i = 1±5
(2)
where Si is the standard deviation of the parameters of the mixture of ore to the parameters of the ore types. The calculation of the mass fraction of a particular type of ore i) in a mixture of ores is performed by the formula: i
169
= kDi ™ N'i), with i = 1±5
(3)
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Estimation of the parameters of ore mined and sent
where k is the coefficient of significance of the measured parameters of the ore. The results of the analysis of the grade of the processed ore are given as time curves, shown in Figure 5. These relationships reflect the change in the composition of processed ore over time.
Creating and maintaining optimal flows of ore Analysis of the composition of coarsely crushed ore
%
Adjustment parameters of the structure of ore flow
1
The analysis of the chemical and the mineral composition of fine crushed and milled ore
4 3 2
The choice of process parameters of grinding and flotation
5
Fig. 6. Algorithm of management of ore quality and control of ore enrichment process.
Time, h
Table 3. Pre-set functions in automatic control system processes for grinding and classification ‹ Process variable MPO MSSO PPO MSO MOO Set function (SF) 1 Size of grinded ore, 67.5 64.5 67.0 66.0 66.0 % cl. -74 microns 2 Consumption of ore 1.65 1.74 1.71 1.75 1.75 to the mill t/m3ch 3 Ratio of the water 1.0 1.05 1.02 1.03 1.03 flow and the ore to the mill units 4 Pulp density in the 43.5 41.0 41.5 40.0 40.0 discharge classification, % solids 5 Pulp density in the 55.0 56.5 56 56.7 56.7 mill discharge, % solids 6 Circulating load, % 275 250 262 246 246 MPO - massive primary ore; MSSO - mixed secondary sulphided ore.; PPO - poor pyritised ore; MSO - mixed sericitised ore; MOO - mixed oxide ores.
Fig. 5. Depending on the composition of ore processed: 1 ± massive primary ore, 2 - mixed secondary sulphide ore. 3 mixed oxide ores, 4 - mixed sericitized ore, 5 - Poor pyritized ore. 6. OPTIMISATION OF GRINDING AND FLOTATION USING ORE GRADE CONTROL Initially, ore quality control is carried out on the stages of production and transportation of ore. The feed stream is of mined ore is separated into two streams, predominantly primary sulphide ores and predominantly mixed oxide ores. (Fig. 6). To calculate the parameters of milling and flotation of various grades of ore, models of these processes were used in combination with the automatic control mode feedback (Morozov et al., 2006). Following the setting of the values of the input parameters chosen in the required capacity grinding circuit, the remainder of the technological parameters of the model provide the required size of the grinded ore. Simulations of flotation reagent consumption were selected to ensure quality concentrate with high recovery of copper and molybdenum.
Table 4. Pre-set functions in automatic control systems in the flotation reagent consumption
The simulation results (Tables 3 and 4) are recommended process parameters for the grinding and flotation technology of standard grades of ore.
‹ Reagents consumption, g / t
MPO MSSO PPO MSO MOO
1 Collector VK-901 11 2 &ROOHFWRU :HUR0;- 10 5140 3 Frother MIBC 13 4 Lime 1100 5 Sodium sulphide 370
The calculated values were used for the control of processes RI JULQGLQJ DQG IORWDWLRQ DW WKH ³(UGHQHW´ FRQFHQWUDWRU
170
12 12
10.5 10
17.5 17.5
14 13
16 1300 410
13 19 16 1100 1300 1300 350 450 430
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
The value of SF for the specified process was calculated as a weighted average of these parameters for standard grades of ore processing (SFi) from the equation: 6)
i SFi ,
(4)
ZKHUH i is the relative mass fraction of the ore i-grade in the mix of ore entering processing. Pre-set functions were used as the base level of the local systems of automatic control of the grinding process and the flotation process. Use of the procedures for determining the grade of ore increases the stability of the automatic control. The application of the developed method provides a productivity growth of 1.5% and the stabilisation of the grinded ore particle size distribution. The results of tests conducted on the industrial process have shown the following. Using the findings of the evaluation the grade ore increased by 5% to 7% and the stability control performance of grinding and flotation increased. The application of the developed method for evaluating the grade of ore provides productivity growth of 1.5% and the stabilisation of the grinded ore particle size distribution. Maintaining an optimal degree of grinding and consumption of reagents during flotation gives an increase in the recovery of copper and molybdenum in the concentrates of 0.3% and 1.1%, respectively. REFERENCES Chanturia, V., Vigdergauz, V. and Lunin, V. (1997) HighEfficient Methods for Ore Preparation and Complex Processing of Complex Ores. Mining Bulletin, 5, 93-102. Ganbaatar. Z. Lodoyravsal, Ch. Delgerbat, L. Duda, O., Morozov, V. (2011) The enrichment of coppermolybdenum ores using integrated radiometric assay grade ore. Mining information-analytical bulletin, 11, 176-182. Morozov, V, Avdokhin, V, Stolyarov, V. and Delgerbat L. (2006) Application of computerized models for optimizing automatic control systems of flotation process. In: Automation in Mining, Mineral and Metal processing, Proceedings of the IFAC Workshop, 222228. IFAC (ed.), Cracow. Morozov, V, Avdokhin V, Topchaev V, Ulitenko K, Stolyarov V, Ganbaatar Z, Delgerbat L, Mergenbaatar N. (2008) Modern Algorithm and system for monitoring and control of milling and flotation process. In: Preprints of the 18th IFAC World Congress. 222-228. IFAC (ed.), Seoul. Morozov, V, Bokani, L, Ulitenko, K, Stolyarov, V, Ganbaatar, Z and Delgerbat L. (2010) Modern systems of control of technological processes. Proceedings of the 18-th Mediterranean conference on control and automation, 237-242. IEEE (ed.), Marrakech. Ulitenko, K., Sokolov, I. and Markin R. (2005) Application of vibro-acoustic analysis for control of volumetric filling of mills. Tsvetnye Metally, 10, 63-66. Ulitenko, K. and Morozov, V. (2011) Increasing Effectiveness of Ore Preparation Process on the Basis of Multi-Level Dynamic Models Application. Mininginformation analytical bulletin, 3, 213-217. 171
Modern Systems of Automatic Control of Processes of Grinding and Flotation of Copper-molybdenum Ore V. Morozov*, D. Davaasambuu**, Z. Ganbaatar**, L. Delgerbat**, V. Topchaev***, I. Sokolov***, V. Stolyarov**** *Moscow State University of Mining, Leninsky Prospekt 6, Moscow, 119991, Russia. Tel.+7 (495)230-94-21, Fax.+7 (495) 237-80-33, e-mail: [email protected] ** Erdenet Mining Corporation, Erdenet city, Orkhon aimag, Mongolia Tel.+ 976 (352) 71-557, e-mail: delgerbat@ erdenetmc.mn *** Private Jt.St &R ³6R\X]WVYHWPHWDYWRPDWLND´ 'PLWURYVNRH 6KRVVH 0RVFRZ 1278238, Russia, Tel +7 (495) 489-10-85, Fax +7(495) 489-14-05, e-mail: [email protected] 3ULYDWH -W 6W &R ³(OVFRUW´ 6WDURNDVKLUVNR\H 6KRVVH 0RVFRZ Russia, Tel +7 (495)112-92-92, Fax +7 (495)320-93-93 e-mail: [email protected] Abstract: The processes of grinding and flotation of copper-molybdenum ores are characterised by significant fluctuations of all input, output and intermediate parameters. Instability and non-optimal parameters of grinding and flotation cause between 3% to 6% losses of the valuable component. Under these conditions, it is difficult to apply deterministic mathematical models of processes. However, the use of multi-level adaptive models allows considerable optimisation of the technological processes. Automatic process control grinding uses the principle of maintaining a rational level of loading of the drum mill ore and balls. While the system is operating, energy and acoustic signals are used as input parameters and the results the measurements of the size of the grains of the grinded ore are used as input and output parameters. When managing the flotation, the principle of forward-looking estimation of the grade of ore is used. Additionally, while the system is operating, analyses of the ore in the x-ray and visible spectrum are used as input data. The application of modern systems of automatic control at the ³(UGHQHW´ FRQFHQWUDWRU 0RQJROLD KHOSHG LQFUHDVH WKH UHFRYHU\ RI FRSSHU DQG PRO\EGHQXP
Keywords: mineral processing, grinding, flotation, control, algorithm, optimisation, adaptation, analyser The applied instruments, presented in Fig. 1, provide continuous control of milling and classification.
1. INTRODUCTION Effective control of grinding processes provides facilities with significant improvements of the economic parameters of the entire concentration process. Grinding optimisation is achieved by maintaining maximum productivity in conjunction with the pre-set grain size of the milled ore; thereby providing effective flotation of the valuable mineral (Chanturia et al., 1997). The operational analysis of ore is the basis for modern process control systems. The rapid development of technology for the analysis of the composition and properties of minerals allows us to develop and implement novel methods and systems for automatic control of ore beneficiation.
water FRC 3
PIC 7
GRI 8
ore
LIC 4 ERI 5
in flotation
ERI 6
2. SYSTEM OF AUTOMATIC REGULATION OF THE GRINDING PROCESS
Fig. 1. Schematic structure of the automatic control system for grinding process of copper-molybdenum ore: 1 ± control system for ore feed to the mill; 2 ± control system for water feed to the mill; 3 ± control system for water feed to the sump; 4 ± sump pulp level control system; 5 ± mill drum loading measuring system; 6 ± mill drive wattage measuring system; 7 ± cyclone upstream pulp pressure measuring system; 8 ± cyclone overflow grain size measuring system.
The subject of this research is the grinding complex of the concentrator of the Mongolian-Russian Joint Venture ³(UGHQHW 0LQLQJ &RUSRUDWLRQ´ 7KLV FRPSULVHV D EDOO PLOO DQ overflow that goes to a buffer vessel (sump) and on further to a cyclone battery (using a pump). The cyclone oversize feeds back to the mill, whereas the overflow presents the finished product of the grinding process.
978-3-902823-42-7/2013 © IFAC
FRC 2
FRC 1
166
10.3182/20130825-4-US-2038.00032
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
The milling and classification optimisation system comprises sensors and control units, most of which are traditionally applied at ore concentrators. The concentrator has introduced new methods of grinding control through use of the estimation of mill drum loading degree by acoustic characteristics and output performance (Ulitenko et al., 2005).
Input parameters
The control systems for the ore and water feed to the mill, the water feed to the sump and the sump pulp level are of standard design. The sensors of the mill drum loading (VAZM) and the mill drive wattage (WAM) are standard FRPPHUFLDO SURGXFWV RI -6& ³6R\X]WVYHWPHWDYWRPDWLND´ )RU milling quality control, a test-rod-type instrument for grain size measuring PIC- -6& ³6R\X]WVYHWPHWDYWRPDWLND´) is used.
Criteria of process emergency
Protection from overload and emergency
Criteria of process stabilization
Stabilisation of milling process
Criteria of choosing efficient regimes
Choice of efficient regimes of milling process Output parameters
The grinding process is intended to provide pulp of regulated solid grain size and density (mass share of solid substance). In real production, grinding is controlled by operatortechnologists who have differing skills and experience. In a non-automatic regime, the crushing bay technologists use their own experience. They are guided by the process regulations, the productivity target and information from control instruments regarding the processed ore parameters and processed products. Based on the information available, the technologists choose the required process conditions: setting the ore and water feed, the cyclone upstream pressure and other parameters for local automatic control systems. The major problem in milling and classification process control is the prioritisation in the control, i.e., the choice of regulating actions under conditions of discordant or mutually exclusive recommendations of the individual control circuits.
Fig. 2. Generalised block diagram of succession and implementation conditions of the grinding control algorithms. Table 1. Parameters of the milling and classification process control algorithm ‹
1 2 3 4 5 6 7
3. THE ALGORITHM DESCRIPTION
8 9
In order to provide effective automatic grinding control, the following goals must be achieved: control and optimisation of the upstream ore and water flows and of the performance of the milling and classification facilities. At the same time, an important goal in ore grinding optimisation is a reduction in power consumption. To attain these goals, the algorithm of multi-criterion control with the use of simple and complex criteria, ranked by importance and the principle of effect on the process, was developed. For the control system operation in the optimum process parameters field, the following structure is used, as presented in Fig. 2.
Process parameter (criterion) Ore productivity, tph Mill water feed, tph Sump water feed, tph Sump pulp level, m Cyclone overflow pulp density, solid/liquid Cyclone overflow grain size, % of - —P VL]H Mill drum loading degree, VAZM scale % Mill drive wattage, kWh Cyclone upstream pressure, at
Designati Setting Min. value on Measured Qm Qms Qw/m Qw/s Gs Rc Rcs -74
-74
Qm min Qw/m min Qw/s min Gsmin Rcmin
Qm max Qw/m max Qw/s max Gsmax Rcmax
Fdmin
Fdmax
:mmin Jcmin
:mmax Jcmax
s
Fd :m Jc
Max. value
Jcs
Calculated 10 Mill pulp density, kg/m3 Rm Rms 11 Productivity derivative of GFd/dQm drum loading 12 Productivity derivative of dAm/dQm mill wattage
GFd/dQmmin dAm/dQmmin
7KH DOJRULWKP¶V UHVWULFWLYH EORFNV SURYLGH IXQFWLRQLQJ IRU WKH process in objectively expedient conditions. These conditions are defined by the prevention of critical conditions, for instance, overload drum of mill or cyclone sump overload. At the same time, the restrictive blocks prevent undesirable effects of failure of the automatic control system sensors on the process.
The structure allows prompt changing of the effective performance criteria, stabilisation of the parameters at the required level and protection of the process from failures and overloads.
The stabilising blocks can be arranged both as systems of control by deviation (using setting function for the measured output parameter) and as restrictive systems using values of permissible ranges, restricted by permissible minimal and maximal values of the controlled parameter.
In Table 1, parameters 1±9 of the control algorithm are continuously measured and parameters 10±12 are calculated. The control algorithm contains restrictive, stabilising, regulating (control) and optimisation blocks. The basic blocks (circuits) are presented in Table 2 and positioned in order of decreasing importance (the highest hierarchic level ± 1, the lowest level ± 3). The criteria used in the process control by individual blocks of the algorithm are in the form of a parameter threshold with a permissible parameter variation range or optimum parameter value (Table 1).
For optimisation in blocks, simple parameters of milling and classification processes are used as optimisation criteria, for instance, optimal coarseness of the ground ore, or the relationship of the parameters (e.g., dynamics of mill drum loading or mill drive wattage with an increase of mill productivity) (Morozov et al., 2008). 167
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For the function of the classification water regime circuit, the technologist defines the permissible limits of the measured classification process parameters in the form of minimal and maximal pulp density values and the cyclone feed sump pulp level. The optimum grinding coarseness (grain size of ore, share of class - —P LQ WKH PLOOHG RUH LV GHILQHG HLWKHU by the technologist or by the first level optimising control system. The pre-set grinding coarseness is maintained by variation of the cyclone sump water feed. To obtain finer grinded material, the water feed increases and to obtain coarser material, the feed decreases. At the same time, the overflow solid grain size is regulated by the cyclone upstream pressure variation.
Table 2. Basic circuits of the milling and classification process control system ‹ Circuit title
1 2
Mill overload prevention Sump overload prevention
3
Sump discharging prevention
4
Grinding water regime failure prevention
5
Mill ore productivity stabilisation
Criteria Restrictive Fd” Fdmax Gs ” G smax Gs • Gsmin Qw/m• 4w/m min Qw/m” 4w/m max
Control principle
Lev el
Prevention of mill overload with ore Prevention of pulp spillover from sump
1
Prevention of complete sump discharging Prevention of transport function infringement
2
Stabilizing Qm • Qm min Qm” 4m max
Grinding and flotation process stabilisation 6 Cyclone overflow Rc• 5 cmin Grinding and density stabilisation flotation process Rc” 5 cmax stabilisation 7 Cyclone upstream Jc • Jcmin Grinding and Jc” Jcmax pressure stabilisation flotation process stabilisation Regulating and optimisation 8 Regulating mill Qw/m= Qp / Rm Stabilising mill ore/water ratio pulp density 9 Solid grain size = -74s Maintaining regulation grinding coarseness in accordance with ore grade 10 Mill productivity GFd/dQm • Mill drum loading optimisation dAm/dQm • degree optimisation
1
3
This control circuit refers to the third level; therefore, this system operates on condition of maintaining the related second- and first-level parameters within the pre-set parameter ranges. The related parameters include the cyclone overflow density, the sump pulp level and the cyclone upstream pressure. When approaching or overrunning the permissible range limits of the parameters, the regulating circuit effect is cancelled.
2 2
2
For the mill productivity optimisation control, the productivity limits are defined initially. The crushing process control is carried out dynamically and allows for incremental variation of the productivity by registering the response of the drum loading or the mill drive wattage change. At VDWLVIDFWRULO\ SRVLWLYH YDOXHV RI GFd/dQm and dAm/dQm derivatives, a further increase of the mill productivity is conducted. In the case of the actuation of the mill productivity or drum loading absolute value restrictions, the next step is cancelled. In the case of the actuation of the LPPHGLDWHO\ FRQWUROOLQJ FLUFXLW UHVWULFWLRQV GFd/dQm•0 and dAm/dQm•0), the system, instead of performing the next forward step, steps backwards and decreases the productivity to the previous value. After a while, the algorithm sets the mill ore productivity again and the control cycle repeats.
2 3
3
4. THE ALGORITHM AND CONTROL SYSTEM FUNCTIONING To maintain milling and classification plant operation in the most efficient sub-critical regimes but excluding the risk of changeover to emergency conditions owing to equipment overload, the control algorithm hierarchical structure is used, which provides interrelated operation of mill productivity and grinding and classification water regime control systems, while maintaining the priority of the restrictive and stabilisation circuits over the optimisation circuits (Ulitenko and Morozov, 2011).
At stable failure of the assignment on cyclone solid coarseness, the algorithm proposes to decrease the mill productivity or decreases it in an unconstrained regime. 5. SYSTEM AND METHOD FOR ADVANCED DIAGNOSTICS OF ORE
The ore load control circuit maintains the pre-set optimal ore feed to the mill and ensures that this value remains within the permitted range. As the circuit refers to the second level, on the one hand, it restricts productivity variation limits via the optimisation circuits and on the other hand, it operates under conditions imposed by the first-level circuits, in this case, the prevention of mill drum overload.
The operational analysis of ore is the basis for modern process control systems. The rapid development of technology for the analysis of the composition and properties of minerals allows us to develop and implement novel methods and systems for automatic control of processes of ore beneficiation. In past years, practically the only method of operational measurement of the composition of ore was Xray fluorescence. Currently, several methods for the operational analysis of ores have been developed and are in use. Most representative mineral composition data give measurements of the ore in the visible part of the spectrum (Ganbaatar et al., 2011).
When operating the regulating circuit, the mill ore/water ratio, the maintained ore/water ratio in the mill feed, is defined by the technologist or the optimisation control systems relying on the processed ore grade, circulating load value and other process parameters. This circuit refers to the second hierarchic level. The system operates under restrictions imposed by the related first-level circuits. Such a related circuit is the mill total water feed stabilisation circuit and the related parameters are the mill minimal and maximal water feed.
$W WKH ³(UGHQHW´ SURFHVVLQJ SODQW D QHZ PHWKRG DQG V\VWHP for advanced diagnostics of ore has been developed and tested, based on the X-ray fluorescence analyser of elemental composition and a video-image analyser of mineral 168
IFAC MMM 2013 August 25-28, 2013. San Diego, USA
Absorption,%
composition. The system of video image analysis (Fig. 3) produces integrated digital images of the ore, which are formed with the help of modern telemetric and programtechnical software and hardware. The system facilitates the acquisition of real-time information on the mineralogical composition and type of the ore. The system also provides data on the particle size distribution of the ore entering the milling operation and the nature of the impregnation of minerals.
7
4 700
2
3
51
6
5
6
1
Z 1
3
8
9 2 650
600
550 nm
500
4 450
400
Fig. 4. Colour spectrum of minerals in copper and molybdenum ores (in the visible range): 1 - chalcopyrite, 2 lapis lazuli, 3 - covellite, 4 - bornite, 5 - molybdenite 6 azurite, 7 - cuprite, 8 - malachite, 9 - native copper.
7
Based on the spectral mineralogical analysis performed for the determination of oxidised copper minerals, the primary and secondary copper sulphides, pyrite, quartz, sericite, mica and other minerals are determined. The ratio of these minerals characterises the grade of the ore. The image analysis of lump fractions of crushed ore is used to obtain information on the structure and size of the impregnation of ore minerals.
4
b
The task of evaluating the grade of the processed ore is to determine its similarity to the main technological types of ores (Morozov et al., 2010). Ore is modelled as a mixture of the five types of ores. The initial data used are the content of copper, molybdenum and iron, the mass fraction of minerals of copper and the mass fraction of rock minerals in the ore. The essence of the calculation of the share belonging to a particular type of ore is as follows. In the received ore, the GHJUHH RI ³VLPLODULW\´ WR HDFK RI WKH NQRZQ ILYH W\pes of ore is determined.
Fig. 3. Schematic (a) and image (b) of the ore video image analysis system: 1 - finely crushed ore bin, 2 - feeder 3 conveyor, 4 - hopper mill, 5 - installation for ore preparation for analysis, 6 - source light emission, 7 - receiver reflected light radiation.
The share of each of the five types of ore in the ore delivered to the processing is calculated in proportion to its similarity to each individual grade ore. For this deviation calculated parameters of ore processed from ore taken as typical of the technologist.
The feature of the system for the video image analysis of ores is the lack of complex sampling and delivery to the analyser. This eliminates technical difficulties and improves the overall reliability of the system. To reduce the effects of external factors on video quality and to increase the stability of measurements, a weak intensity sprinkler is installed above the conveyor.
The normalised value of the deviation (Si) of the mixture of the parameters of ore (Zn) to the parameters of ore type (Zni) is calculated as follows: Si = (|Zn-Zni|)/Zni, with i = 1±5
(1)
The normalised values of similarity of parameters of the processed ore to the parameters of standard ore types are computed as follows:
The essence of the method of video image analysis comprises the analysis of mineral composition and estimation of the grade of ore based on the image of the processed ore in the visible spectrum. The database of the system consists of saved video images of all known minerals of deposits ³(UGHQHWL\Q - 2YRR´ &RPSXWHU LPDJHV VWDQGDUGV RI WKHVH minerals were generated by image processing software. The spectral characteristics of copper minerals under visible wavelength radiation (Fig. 4) are a source of information for the video image analysis.
Di = 1/ Si, with i = 1±5
(2)
where Si is the standard deviation of the parameters of the mixture of ore to the parameters of the ore types. The calculation of the mass fraction of a particular type of ore i) in a mixture of ores is performed by the formula: i
169
= kDi ™ N'i), with i = 1±5
(3)
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Estimation of the parameters of ore mined and sent
where k is the coefficient of significance of the measured parameters of the ore. The results of the analysis of the grade of the processed ore are given as time curves, shown in Figure 5. These relationships reflect the change in the composition of processed ore over time.
Creating and maintaining optimal flows of ore Analysis of the composition of coarsely crushed ore
%
Adjustment parameters of the structure of ore flow
1
The analysis of the chemical and the mineral composition of fine crushed and milled ore
4 3 2
The choice of process parameters of grinding and flotation
5
Fig. 6. Algorithm of management of ore quality and control of ore enrichment process.
Time, h
Table 3. Pre-set functions in automatic control system processes for grinding and classification ‹ Process variable MPO MSSO PPO MSO MOO Set function (SF) 1 Size of grinded ore, 67.5 64.5 67.0 66.0 66.0 % cl. -74 microns 2 Consumption of ore 1.65 1.74 1.71 1.75 1.75 to the mill t/m3ch 3 Ratio of the water 1.0 1.05 1.02 1.03 1.03 flow and the ore to the mill units 4 Pulp density in the 43.5 41.0 41.5 40.0 40.0 discharge classification, % solids 5 Pulp density in the 55.0 56.5 56 56.7 56.7 mill discharge, % solids 6 Circulating load, % 275 250 262 246 246 MPO - massive primary ore; MSSO - mixed secondary sulphided ore.; PPO - poor pyritised ore; MSO - mixed sericitised ore; MOO - mixed oxide ores.
Fig. 5. Depending on the composition of ore processed: 1 ± massive primary ore, 2 - mixed secondary sulphide ore. 3 mixed oxide ores, 4 - mixed sericitized ore, 5 - Poor pyritized ore. 6. OPTIMISATION OF GRINDING AND FLOTATION USING ORE GRADE CONTROL Initially, ore quality control is carried out on the stages of production and transportation of ore. The feed stream is of mined ore is separated into two streams, predominantly primary sulphide ores and predominantly mixed oxide ores. (Fig. 6). To calculate the parameters of milling and flotation of various grades of ore, models of these processes were used in combination with the automatic control mode feedback (Morozov et al., 2006). Following the setting of the values of the input parameters chosen in the required capacity grinding circuit, the remainder of the technological parameters of the model provide the required size of the grinded ore. Simulations of flotation reagent consumption were selected to ensure quality concentrate with high recovery of copper and molybdenum.
Table 4. Pre-set functions in automatic control systems in the flotation reagent consumption
The simulation results (Tables 3 and 4) are recommended process parameters for the grinding and flotation technology of standard grades of ore.
‹ Reagents consumption, g / t
MPO MSSO PPO MSO MOO
1 Collector VK-901 11 2 &ROOHFWRU :HUR0;- 10 5140 3 Frother MIBC 13 4 Lime 1100 5 Sodium sulphide 370
The calculated values were used for the control of processes RI JULQGLQJ DQG IORWDWLRQ DW WKH ³(UGHQHW´ FRQFHQWUDWRU
170
12 12
10.5 10
17.5 17.5
14 13
16 1300 410
13 19 16 1100 1300 1300 350 450 430
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The value of SF for the specified process was calculated as a weighted average of these parameters for standard grades of ore processing (SFi) from the equation: 6)
i SFi ,
(4)
ZKHUH i is the relative mass fraction of the ore i-grade in the mix of ore entering processing. Pre-set functions were used as the base level of the local systems of automatic control of the grinding process and the flotation process. Use of the procedures for determining the grade of ore increases the stability of the automatic control. The application of the developed method provides a productivity growth of 1.5% and the stabilisation of the grinded ore particle size distribution. The results of tests conducted on the industrial process have shown the following. Using the findings of the evaluation the grade ore increased by 5% to 7% and the stability control performance of grinding and flotation increased. The application of the developed method for evaluating the grade of ore provides productivity growth of 1.5% and the stabilisation of the grinded ore particle size distribution. Maintaining an optimal degree of grinding and consumption of reagents during flotation gives an increase in the recovery of copper and molybdenum in the concentrates of 0.3% and 1.1%, respectively. REFERENCES Chanturia, V., Vigdergauz, V. and Lunin, V. (1997) HighEfficient Methods for Ore Preparation and Complex Processing of Complex Ores. Mining Bulletin, 5, 93-102. Ganbaatar. Z. Lodoyravsal, Ch. Delgerbat, L. Duda, O., Morozov, V. (2011) The enrichment of coppermolybdenum ores using integrated radiometric assay grade ore. Mining information-analytical bulletin, 11, 176-182. Morozov, V, Avdokhin, V, Stolyarov, V. and Delgerbat L. (2006) Application of computerized models for optimizing automatic control systems of flotation process. In: Automation in Mining, Mineral and Metal processing, Proceedings of the IFAC Workshop, 222228. IFAC (ed.), Cracow. Morozov, V, Avdokhin V, Topchaev V, Ulitenko K, Stolyarov V, Ganbaatar Z, Delgerbat L, Mergenbaatar N. (2008) Modern Algorithm and system for monitoring and control of milling and flotation process. In: Preprints of the 18th IFAC World Congress. 222-228. IFAC (ed.), Seoul. Morozov, V, Bokani, L, Ulitenko, K, Stolyarov, V, Ganbaatar, Z and Delgerbat L. (2010) Modern systems of control of technological processes. Proceedings of the 18-th Mediterranean conference on control and automation, 237-242. IEEE (ed.), Marrakech. Ulitenko, K., Sokolov, I. and Markin R. (2005) Application of vibro-acoustic analysis for control of volumetric filling of mills. Tsvetnye Metally, 10, 63-66. Ulitenko, K. and Morozov, V. (2011) Increasing Effectiveness of Ore Preparation Process on the Basis of Multi-Level Dynamic Models Application. Mininginformation analytical bulletin, 3, 213-217. 171