Optimizing Automatic Control of Flotation with the Use of a Model Based Adaptation-Determination Method

Copyright © IFAC Automation in Mining. Mineral and Metal Processing. Nancy, France, 2004

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OPTIMIZING AUTOMATIC CONTROL OF FLOTATION WITH THE USE OF A MODEL BASED ADAPTATION-DETERMINATION METHOD

v. Morozovt, V. Avdochint, V. Sto)yaroV, N. Konovalov 2 1.Moscow State University ofMining, Leninsky Prospekt 6, Moscow, 119991, Russia. Tel.(095)236-94-21, Fax. (095) 237-80-33, E-mail: [email protected] 2. Private Jt.St.Co. "Elscort", Starokashirskoye Shosse 2/6, Moscow, 115201, Russia, Tel (095)112-92-92, Fax (095)320-93-93 E-mail: [email protected]

Abstract: The basis for effective control of flotation process is operative and reliable measuring composition and physic-chemical parameters of pulp. The used analyzer for measuring parameters of liquid and solid phases of flotation pulp (RA-93 I) includes Xray-fluorescence sensor of metals content and potentiometric block of measuring ionic composition. The analyzer is placed directly into technological flows and allows obtaining information at given moment. Block-scheme of the main operation for controlling and regulating selective flotation includes: collection, storing and processing data on parameters of liquid and solid phases of flotation pulp; estimation of grade of being processed ore and determination of optimum parameters of the process; correction of reagent dosing and parameters of flotation equipment operation. Special studies and tests at pilot automated flotation facility allowed to demonstrate effectiveness and to determine conditions of applicability of the proposed method, system and analyzer in the process of bulk and selective flotation ofpolymetallic ores. Copyright © 2004 IFAC

Keywords: measuring units, model based control, optimization, adaptation.

I. ALGORITHM OF FLOTAnON PROCESS CONTROL and physic-

Algorithm of flotation process control theoretically should account both operative information on standing of the technological process and accumulated information on the main regularities of the process. An attempt to realize such complex approach resulted in elaboration of the proposed algorithm, based on our previous studies in fields of adaptation-determination control of flotation process (Avdokhin, et al., 1998 and Morozov, et al., 1998 ) and flotation process control on the base of monitoring ore grade. At Fig. 1, the proposed algorithm of flotation process control is generally presented.

Figure 1. Algorithm of flotation process control

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and 0.005-0.15 for solutions. Standard relative deviation is I% maximum. Block of electrochemical monitoring of composition of pulp liquid phase in technological flows includes case, filtering and insulating baffles, measurement zone, electrochemical sensor. The block is equipped with fitting device for keeping the case in the technological flow and by shut-off device. Besides, the block is equipped with a device for feed and removing of calibration solutions. Filtered liquid phase (filtrate) of the pulp is taken from the pulp (moving by pipeline) through the filtering membrane and goes to the measurement zone. In the filtrate, parameters of the liquid phase are measured: pH, concentration of ions (collector, depressor), Eh. In preset moments, the sensor calibration is performed. For this purpose, calibration solutions are fed into the measurement zone; on the base of results of the calibration solutions measurements, diagnostics and adjustment of the sensor is conducted (Stolyarov, et al., 2003).

2. OPERATION MONITORING OF A COMPOSITION AND PHYSICO-CHEMICAL PARAMETERS OF PULP The basis for effective control is operative and reliable measuring composition of concentration products and physico-chemical parameters of pulp. Set of the parameters is a basis of both following typing ores and adaptation-determination control of reagent regime of flotation process. General structure of the control system on the base of RA-93 I analyzer is presented in Fig. 2. Design of the measurement zone is in line with construction of parts, forming technological flow (pipe, widening reservoir, etc.), and, for instance, is positioned directly in pipelines of the technological system. to higher-level computer

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3. FLOTATION PROCESS MODEL Mathematic modeling process of copper-zinc flotation is rather difficult task owing to complicity of the object, great variations of ore properties and applying a large quantity of flotation reagents. Complicity of the process is connected, first of all, with considerable interference of parameters that does not allow to use effectively routine methods of statistic analysis for revealing major ties between input and output parameters with obtaining regression stochastic models. Key methodological principle in modeling process of bulk copper-zinc flotation is combining statistic and physic-chemical methods that allows using both theoretical and experimental information on nature of physic-chemical process of flotation. To elaborate adequate model of flotation process, precise data are required on mechanism and dynamics of a large number of physic-chemical processes, occurring in pulp. The model should also take into account parameters, determined by features of mass transfer in flotation cells. The elaborated model, independently of modeling method used, must simulate existing functions of modeled process, which connect final technological parameters of the process with controlled parameters of initial resources, operation of technological equipment and current technological parameters (consumption of reagents, air, etc.). The model must take into account influence of nonregulated or indirectly regulated parameters of ore and technological process on behavior of the technological process. First of all, the model must take into account variations of the parameters with time and influence of the variations both on final technological parameters and on the controlled parameters. The model also must take into account dynamic properties of the object, availability of intra-chamber mixing, transport delay, etc.

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Figure 2. A scheme of RA-931 automatic control system of composition of solid and liquid phase of concentration products I - Pipeline; 2 - Measurement zone; 3 Measuring unit of analyzer of pulp solid phase; 4 - locking device; 5 - measuring unit of sensor of liquid phase composition; 6 - flow rate meter; BCPI - block of control, collection and processing of information; IADC - impulse-analogue-digital converter. The measuring block of the system RA-931 (JSC "Elscort", Russia) includes multi-channel X-rayfluorescence analyzer, intended for control of element composition of solid phase of a pulp, and electrochemical analyzer of pulp liquid phase composition. The block design allows equipping the system with devises for measuring flow rate (consumption), pressure and temperature of the pulp. The X-ray-fluorescent analyzer determines concentrations of elements from Ti to Uranium (without additional devises). A measuring lasts 15300 seconds. Contents of 3 elements and pulp density are measured simultaneously. Threshold of detection (minimal measured content) is 0.01-0.2% for pulp

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4. ESTIMATION OF GRADE OF BEING

5. CALCULATION OF REAGENT DOSING BY ORE GRADE PARAMETERS

PROCESSED ORE In detennining grade of being processed ore, graphical-analytical method of calculation of rating ore to several typical sorts used. Flotation specialists distinguish the typical ores by experimental way and, when solving our task, are fields of finding optimal solution (Pareto field). Before ore flotation, dominating types of the ore should by detennine. The graphical-analytical method consists In detennining of share of the point belonging in specified dots on a plane surface (two-dimension space) or in any other space. An essence of calculation of the shares of the ore belonging into pre-set type consists in the fact that the ore can be classified, in various proportions, into each of 4 known ore types. For this purpose, initially distance from the obtained point to each detennined point is measured. To clarify this point, let us consider an example in twodimension space, and in case, when each type of ore is detennined by 2 parameters only. Along the X-axis, copper content in ore are shown, whereas Y-axis presents zinc content in ore (Fig. 3). 6

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where LD, is lime dosing for ore type I, LD z is lime dosing for ore type 2, LD 3 is lime dosing for ore type 3, LD 4 is lime dosing for ore type 4, and, respectively, CD" CD 2 , CD 3, CD 4 are dosing of collector, and FD" FD 2 , FD 3, FD 4 are dosing of foaming agent. In real case, interaction between ore types, resulting in difference between calculated and actual optimum dosing of each reagent, should be taken into account.

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The above-described block of reagent regime control on the base of detennining typing ore does not take into consideration economic factors. The essence of the adaptation-detennination method of regulation of reagent flotation regime (ADDEFLOT) is realizing two-level automatic control. The lower level provides for control of reagent dosing on the basis of control of ionic composition parameters in the pulp liquid phase. The control goal is to keep the basis relationship among parameters of ionic composition - concentrations of ions A, B, C or relationships between the concentrations - in pulp liquid phase given by equations of type:

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6. ADAPTATION-DETERMINATION CONTROL OF REAGENT DOSING

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Basing on results of analysis of ore grade - i.e. ore type infonnation (described above), we can calculate recommended dosing of reagents for flotation of arrived ore. In the simplest case, the recommended dosing can be detennined as weight-average one (among each type of ore):

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Figure 3. An example of calculation of shares of belonging arrived ore to ore types in twodimension space by graphical-analytical method. 1,2,3,4 - Ore type; 5 (.) - Arrived ore.

pH = const; pS = const; a pC + b pH = const,

(4)

where: pS is negative decimal logarithm of sulfide ions concentration; pC is negative decimal logarithm of ions of a collector. For example, the lower level regulates lime dosing with the use of the model (Avdokhin et al., 2001):

At Fig 3, 4 points (1-4) demonstrate corresponding values of copper and zinc contents for 4 ore types, and point 5 shows copper and zinc content in ore arrived for flotation. Measuring distance from point 5 to other four points (1-4) and following simple calculations allow calculating shares of belonging the arrived ore to each of the ore types (d" d2, d3, ~). Notice that actual classifying arrived ore by types requires calculation of belonging the ore to pre-set types not by 2 parameters (as in the above-given example) but by 5 parameters.

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Principle of operation of the upper level of consists in detennining and including in pre-setting function of the lower level such value of K, parameter, at which maximal economic effectiveness of the process is reached. For copper-zinc flotation, proposed function was applied in fonn:

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Where: E*cuCcuacu - losses, cost and content in initial ore respectively, of the copper;

7. CONCLUSIONS

E*znCznaZn - losses, cost and content in initial ore respectively, of the zinc;

Solution a task of improving effectiveness of flotation process control requires application of multi-level schemes, providing for regulation of both physic-chemical and technological parameters of the process. Algorithm of flotation process control include operation monitoring of composition and physicchemical parameters of pulp, mathematic modeling of flotation, estimation of grade of being processed ore, calculation of reagent dosing by ore grade and adaptation-determination control of reagent dosing. The elaborated algorithm and the control system have been tested at pilot flotation facility of private JSC "Elskort". The tests showed their reasonable reliability and effectiveness.

EPyC pya Py - recovery, cost and content in initial ore, respectively, of the pyrite. This function presents specific, taking into account actual cost, losses of copper and zinc. Figure 4 il1ustrates possibility and expedience of applying the proposed function. The plots de~onstrate ~hat at some set of conditions (floatablhty of mmerals, content of the metals in an ore, prices on the metals), an optimum value of pH exists, at which the best efficiency of the process is achieved. lOO 90

REFERENCES

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Avdokhin, V. and V. Morozov (1998). A system for control of complex ores flotation based on measuring pulp ionic composition. In: Automation in Mining, Mineral and Metal Processing, IFAC (Ed), Cologne, 133-136. Avdokhin, V. and V. Morozov (2001). An adaptation-determination method of automatic control of reagent regime of selective flotation. In: Preprints 100h IFAC Symposium on automation in Mining, Mineral and Metal Processing, Mituhiko Araki (Ed), Tokyo, 92-97. Morozov, V. and V. Avdokhin (1998). Optimization of complex ores concentration on the base of control and regulation of ionic composition of pulp and circulating waters (in Russian). Gomy lnformatsionny-analititceskiy Bulletin, 1, 2732. Morozov, V., V. Stolyarov and N. Konovalov (2003). Increasing efficiency of flotation control with the use of flow analyzers of pulp composition (in Russian). Obogashenie rod, Sankt-Peterburg, 4, 33-36. Stolyarov, V., N. Konovalov and V. Morozov (2003). An operation control of flotation using X-ray analyzers (in Russian). In: Proc. of the rd 4 Congress on mineral Processing of CIS Countries, MISiS (Ed), Moscow, 14-16.

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Fig. 4. Dependence of recovery of copper (I), zinc (2) ferrous (3), and purposeful optimizat~on function Ql (4) of pH in bulk copper-zmc flotation Figure 4 shows that indefinite pH maintenance lea~ to increase of reduced metal losses expressmg m price equivalent as 0.7 USD per tonne of ore. Thus, applying the algorithm and the system of flotation process control al10ws to optimize conditions of flotation process and, on this basis, both increase recovery of valuable components by 12% and decrease cost of the missed components by 3-10% (Morozov, et al., 2003).

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