CN117138933A - Advanced process control system and method for alumina ore pulp mill - Google Patents
Advanced process control system and method for alumina ore pulp mill Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 54
- PNEYBMLMFCGWSK-UHFFFAOYSA-N aluminium oxide Inorganic materials [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 title claims abstract description 26
- 238000004886 process control Methods 0.000 title claims abstract description 26
- 238000000227 grinding Methods 0.000 claims abstract description 60
- 239000007787 solid Substances 0.000 claims abstract description 31
- 238000004519 manufacturing process Methods 0.000 claims abstract description 30
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 23
- 230000005484 gravity Effects 0.000 claims abstract description 19
- 238000005259 measurement Methods 0.000 claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 238000005516 engineering process Methods 0.000 claims abstract description 9
- 230000004044 response Effects 0.000 claims abstract description 8
- TWNQGVIAIRXVLR-UHFFFAOYSA-N oxo(oxoalumanyloxy)alumane Chemical compound O=[Al]O[Al]=O TWNQGVIAIRXVLR-UHFFFAOYSA-N 0.000 claims abstract description 4
- 239000007788 liquid Substances 0.000 claims description 59
- 238000012937 correction Methods 0.000 claims description 21
- 238000002156 mixing Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 11
- 229910018072 Al 2 O 3 Inorganic materials 0.000 claims description 9
- 238000013499 data model Methods 0.000 claims description 9
- 238000002360 preparation method Methods 0.000 claims description 9
- 238000004090 dissolution Methods 0.000 claims description 7
- 238000012986 modification Methods 0.000 claims description 5
- 230000004048 modification Effects 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 3
- 238000011105 stabilization Methods 0.000 claims description 3
- 230000003750 conditioning effect Effects 0.000 claims 1
- 239000012530 fluid Substances 0.000 claims 1
- 239000002002 slurry Substances 0.000 abstract description 4
- 238000012216 screening Methods 0.000 description 8
- 239000000463 material Substances 0.000 description 7
- 238000007599 discharging Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 230000001276 controlling effect Effects 0.000 description 5
- 238000001914 filtration Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 229910001570 bauxite Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009533 lab test Methods 0.000 description 1
- 239000012669 liquid formulation Substances 0.000 description 1
- 238000003801 milling Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 229910001220 stainless steel Inorganic materials 0.000 description 1
- 239000010935 stainless steel Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
- B02C—CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN
- B02C25/00—Control arrangements specially adapted for crushing or disintegrating
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Abstract
The invention relates to the technical field of ore grinding process control, in particular to an advanced process control system and method for alumina ore pulp grinding. The method comprises the following steps: s1, a data acquisition module acquires data in the process of grinding aluminum oxide; s2, modeling the ore grinding process to obtain the output characteristic and the response curve of the mill; s3, calculating the proportion of the slurry by using a soft instrument technology according to auxiliary variables through a soft instrument algorithm; s4, estimating dominant variables on line by the soft measurement model: alpha K pulp Pulp specific gravity ρ Pulp The value of (2) is combined with an ore grinding quality controller, and the dynamic control of the liquid-solid ratio data realizes the stable control of the production index; s5, utilizing a model predictive control algorithm, and adjusting input parameters of the mill in real time according to a liquid-solid ratio parameter predictive result and an optimization target set by the quality controller. The invention can dynamically control the liquid-solid ratio data to realize the stable control of the production index, and enhance the robustness and adaptability of the control system.
Description
Technical Field
The invention relates to the technical field of ore grinding process control, in particular to an advanced process control system for alumina ore pulp grinding.
Background
The alumina production process involves several links, of which the raw ore grinding links are important factors affecting the quality and cost of the alumina. As shown in fig. 2, in the raw ore grinding process, the operation parameters of the mill, such as ore quantity, flow rate of the blending liquid, frequency of the material transfer pump and the like, need to be adjusted in real time according to different material characteristics, environmental factors and production requirements so as to ensure stable operation and efficient ore discharge of the mill.
For example, the patent with the Chinese authority publication number of CN213000524U discloses a bauxite raw ore pulp grinding system, wherein a bauxite raw ore storage device is input into a feed inlet of a communicating mill, a discharge outlet of the mill is communicated with a middle tank, an outlet pipeline of the middle tank is communicated with a screening machine for feeding through a middle pump, undersize materials of the screening machine are input into a qualified ore pulp tank, and oversize materials are input into the feed inlet of the mill; the screening surface of the screening machine is welded on the side plate at intervals by stainless steel screening strips, and adjacent screening strips form at least two-stage eight-shaped reducing screening slits from top to bottom. Although the classification efficiency is greatly improved, the automation degree and the production stability of the ore grinding process cannot be effectively solved.
However, due to the characteristics of complexity, nonlinearity, time variability and the like in the raw ore grinding process, the traditional control method is difficult to meet the optimization requirement of production, so that the problems of high labor intensity of operators, unstable product indexes and the like are caused.
Disclosure of Invention
The invention aims to solve the technical problems that: the advanced process control system for the alumina ore pulp mill is provided, the advanced control system and the soft instrument technology are adopted to optimize the control system, the control difficulty of raw material mill is effectively solved, and the automation degree and the production stability of the ore milling process are improved. The advanced control system is a control method for modeling, predicting and optimizing the production process by using a prediction model control technology. The soft instrumentation technique is an engineering utility technique that gives an estimate of the measured, i.e., dominant, variable by on-line computation of a soft measurement model.
The technical scheme of the invention is as follows:
an advanced process control method for an alumina ore pulp mill comprises the following steps:
s1, a data acquisition module acquires data in the process of grinding aluminum oxide;
s2, modeling the ore grinding process to obtain the output characteristic and the response curve of the mill;
s3, calculating alpha according to auxiliary variables by using a soft instrument technology through a soft instrument algorithm K pulp Specific gravity ρ of pulp Pulp ；
S4, estimating dominant variables on line by the soft measurement model: alpha K pulp Pulp specific gravity ρ Pulp The value of (2) is combined with an ore grinding quality controller, and the dynamic control of the liquid-solid ratio data realizes the stable control of the production index;
s5, utilizing a model predictive control algorithm, and adjusting input parameters of the mill in real time according to a liquid-solid ratio parameter predictive result and an optimization target set by the quality controller.
Preferably, in the modeling process of step S2, the data of step S1 need to be screened to remove obvious error data, and the data record under the normal working condition is intercepted to perform analysis modeling, so as to obtain the data model of the alumina pulp grinding system.
Preferably, the pulp specific gravity ρ Pulp And alpha K pulp Is calculated by (a) a calculation methodThe formula is:
wherein:
ρ ore ore Ore density in t/m 3 2.35 of reference value;
A ore ore :Al 2 O 3 Content, reference value 49%;
D ore ore :Al 2 O 3 Dissolution rate, reference value 84%;
ρ liquid and its preparation method : density of the mixed liquid, unit t/m 3 1.32 of reference value;
A liquid and its preparation method ：Al 2 O 3 Content, unit g/L, reference value 111;
N liquid and its preparation method : NK, unit g/L, reference 198;
F main mine : the main force is used for grinding ore quantity per unit t/h;
F main liquid : the main mill liquid blending amount is t/h;
F auxiliary ore : auxiliary ore grinding amount per unit t/h;
F auxiliary liquid : auxiliary mill liquid blending amount is given in t/h.
Preferably, the method further comprises a soft meter correction algorithm:
wherein y is out The output value of the soft instrument is Q which is the calculation formula of the soft instrument, K i For the auxiliary variable correction coefficient, i represents the calculated number of auxiliary variables, q fi And B is measured as an artificial measurement data correction deviation value, and f1 is an artificial correction coefficient.
Preferably, the quality controller is capable of controlling the slurry density ρ based on the slurry density ρ provided by the soft meter Pulp And alpha is K pulp And dynamically adjusting the liquid-solid ratio in the production process to realize the prediction and stable control of the production index.
Preferably, the predictive control model generates the flow of the blended liquid, the frequency output of the buffer pump and the rotation speed output of the belt scale, and predicts and optimally controls the flow of the blended liquid, the liquid level of the ore pulp tank and the liquid-solid ratio parameters in the production process.
Preferably, the basic principle of the quality controller is as follows:
for an input-output system:
x(k+1)＝Ax(k)+BuF(k)
y(k+1)＝Cx(k+1)
x (k) and x (k+1) respectively represent the states of the kth moment and the kth+1 moment in the grinding working process; y (k+1) represents the state of the system at the k+1 th time; u (k) represents a control input amount at a kth time of the system; a (k) and B (k) respectively represent two corresponding parameter matrixes at the kth moment in the grinding process; c is a system matrix of appropriate dimensions.
Future output states and outputs can be predicted by the model:
feedback correction is carried out on the estimated input:
wherein the method comprises the steps ofThere is = predicted value of current output from historical inputs and states
For the j-th output in the future P j The predicted value of the step is
For the single value estimation algorithm, the control time domain l=1, i.e., u (k+i) =u (k), i >0. Therefore, it is
Wherein the method comprises the steps of
For the j-th output response at p j Is a value of (2). The optimal control can be obtained by a feedback correction algorithm for the prediction:
wherein the method comprises the steps of
An alumina pulp mill advanced process control system, which applies the alumina pulp mill advanced process control method, comprises a DCS control subsystem, an OPC server and an APC advanced control server;
the DCS control subsystem comprises a data acquisition module, a Central Processing Unit (CPU) and a network communicator, and is used for acquiring mill process control data to an operation station and an OPC server, and executing command output and alarm signals;
the OPC server is a server for collecting DCS signals and is used for establishing a link with the APC server and transmitting all key signals to the APC server;
the APC server comprises a soft instrument, a mill system prediction controller and a mill quality controller, wherein the soft instrument is used for generating soft instrument data according to a measurement model, the mill system prediction controller generates control signals according to a prediction control model to stabilize production states such as the flow rate of the blended liquid, the liquid level of the pulp tank and the like, and the mill quality controller guides dynamic modification of the liquid-solid ratio through the soft instrument data to realize stabilization and optimization of production indexes.
Preferably, the DCS control subsystem is electrically connected with the field instrument through 4-20MA signals, and the signals to be acquired comprise ore deposit quantity, liquid allocation flow, material transfer pump frequency, buffer tank liquid level, ore pulp tank liquid level, conductivity and ore pulp temperature.
According to the technological characteristics of the production system, a soft instrument technology is applied to obtain the pulp specific gravity rho Pulp And alpha is K pulp And controlling the whole mill production flow by using the measured value and the multivariable optimal control model. The multivariable dynamic model predictive control can optimally and rapidly coordinate all the regulating loops of the PLC in real time, so that the production system operates under the optimal 'clamping' condition, the process is stabilized, and economic benefits are generated.
The grinding loop controller adopts a robust multivariable predictive control technology based on a model, and realizes set value or region control on a series of related variables (controlled variables) through adjustment on a series of independent variables (manipulated variables). The grinding loop controller solves all control problems of the grinding loop at the same time. The execution cycle of the grinding loop controller was 30 seconds.
Predictive control algorithms use dynamic models between manipulated and disturbance variables and controlled variables. These models can be identified from the step test data of the device, which will be used by the controller to predict the process response and calculate the control effort needed to achieve the control objective. The linear quadratic optimizer built in the controller can realize linear or quadratic optimization of the local controller through an objective function set by a user. The main purpose of the ore grinding loop controller is to stabilize key product indexes and improve the dissolution rate on the premise of meeting various constraints.
Compared with the prior art, the invention has the following beneficial effects:
on-line estimating dominant variables by using a soft measurement model: alpha k pulp Pulp specific gravity ρ Pulp The value of (2) is combined with an ore grinding quality controller, and the dynamic control of the liquid-solid ratio data realizes the stable control of the production index; the input parameters of the mill, such as the flow rate of the blending liquid, the frequency of the material transfer pump, the rotating speed of the ore discharging belt and the like, are adjusted in real time according to the liquid-solid ratio parameter prediction result and the optimization target set by the quality controller by using a model prediction control algorithm, so that the production process is more stable; the robustness and adaptability of the control system is enhanced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic structural view of the present invention.
Fig. 2 is a flow chart of the mill grinding stock process of the invention.
Fig. 3 is a schematic block diagram of the structural composition of the present invention.
Fig. 4 is a diagram showing the effect of the liquid level control according to the present invention.
FIG. 5 is a graph showing the effect of controlling the throwing of the liquid formulation of the present invention.
Fig. 6 is a graph showing the effect of the mine reduction amount of the present invention.
FIG. 7 is a of the invention k pulp And (5) an index improvement effect graph.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
An advanced process control method for an alumina ore pulp mill comprises the following steps:
s1, a data acquisition module acquires data in the process of grinding aluminum oxide;
s2, modeling the ore grinding process to obtain the output characteristic and the response curve of the mill;
s3, calculating the proportion of the slurry by using a soft instrument technology according to auxiliary variables through a soft instrument algorithm;
s4, estimating dominant variables on line by the soft measurement model: the value of the pulp specific gravity is combined with the ore grinding quality controller, and the dynamic control of the liquid-solid ratio data realizes the stable control of the production index;
s5, utilizing a model predictive control algorithm, and adjusting input parameters of the mill in real time according to a liquid-solid ratio parameter predictive result and an optimization target set by the quality controller.
In the data acquisition process, data are required to be processed, such as a blending liquid flow signal, and a blending liquid total flow filtering calculation block FY101 is added in a DCS;
FY101 is input as total flow FF101 of the blending liquid;
the filtering algorithm is as follows:
wherein T is the filtering time, and the initial filtering time is 20 seconds;
the existing blend flow control circuit FC101 is used in the PLC to change the input to the FF101 filtered FY101.
In step S3, the soft instrument is used for measuring and calculating the ratio of the total blending liquid amount and the ore discharging amount of the main mill, and the purpose of calculation is that when the belt scale frequency of the ore discharging amount is used as a controller control variable for controlling the liquid-solid ratio, the liquid-solid ratio is a controlled variable of the controller, and the controlled variable is also a control variable for the ore grinding quality controller through adjustment of the ore discharging amount.
The formula of the calculation is:
FFY1101＝FF101/XKL
calculating input:
FF101, total amount of main mill blending liquid, m 3 /h；
XKL, the main force grinds ore flow, t/h.
And (3) calculating and outputting:
FFY1101, liquid to solid ratio, m 3 /t。
Calculating the specific gravity rho of the ore pulp passing through the ore pulp tank outlet Pulp And a dissolution solution alpha k pulp Is calculated from soft measurements of (a).
Wherein:
ρ ore ore Ore density in t/m 3 2.35 of reference value;
A ore ore :Al 2 O 3 Content, reference value 49%;
D ore ore :Al 2 O 3 Dissolution rate, reference value 84%;
ρ liquid and its preparation method : density of the mixed liquid, unit t/m 3 1.32 of reference value;
A liquid and its preparation method ：Al 2 O 3 Content, unit g/L, reference value 111;
N liquid and its preparation method : NK, unit g/L, reference 198;
F main mine : the main force is used for grinding ore quantity per unit t/h;
F main liquid : the main mill liquid blending amount is t/h;
F auxiliary ore : auxiliary ore grinding amount per unit t/h;
F auxiliary liquid : auxiliary mill liquid blending amount is given in t/h.
Obtaining the pulp specific gravity rho through the calculation Pulp And alpha k pulp Is a calculated value of (a). And recording the specific gravity ρ of the pulp Pulp Manual measurement value ρ Measuring And alpha is k pulp Laboratory test value alpha k measurement As periodic manual corrections.
The soft instrument correction algorithm is as follows:
wherein y is out The output value of the soft instrument is Q which is the calculation formula of the soft instrument, K i For the auxiliary variable correction coefficient, i represents the calculated number of auxiliary variables, q fi And B is measured as an artificial measurement data correction deviation value, and f1 is an artificial correction coefficient.
After the soft instrument measurement data are acquired through the algorithm, the soft instrument measurement data are further transmitted to the ore grinding loop controller to realize the predictive control function on the ore grinding control loop. In step S4, the grinding circuit controller adopts a model-based predictive control technique to implement set value or zone control on a series of related variables (controlled variables) through adjustment on a series of independent variables (manipulated variables). The controller is also capable of making feed forward adjustments to disturbance variables.
The Manipulated Variable (MV), controlled Variable (CV), and Disturbance Variable (DV) variable tables of the grinding circuit controller are shown in the following table.
Ore grinding loop controller gain matrix:
the ore grinding quality controller controls the specific gravity rho of the ore pulp at the outlet of the ore pulp tank by adjusting the controlled variable-liquid-solid ratio of the ore grinding loop controller Pulp And a dissolution solution alpha K pulp 。
In step S5, the ore grinding quality controller adopts a model-based predictive control technology, can effectively inhibit fluctuation caused by measurable feedforward interference to the process, provides more stable and strict control for the production process, and realizes the edge clamping operation.
Specific optimization and control objectives are defined as follows:
| sequence number | Roll call | Description of the invention | Annotating |
| CV1 | DY101 | Pulp specific gravity of pulp tank outlet | Soft meter calculated value |
| CV2 | AY102 | Alpha K value of dissolution solution | Soft meter calculated value |
| MV1 | FFY1101 | Principal force grinding liquid-solid ratio |
Grinding quality controller gain matrix:
| MV | MV1 | |
| CV | Gain | FFY1101 |
| CV1 | DY101 | - |
| CV2 | AY101 | + |
and screening according to the acquired data to remove obvious error data, intercepting the data record under the normal working condition, and performing analysis modeling to obtain the data model of the alumina pulp grinding system.
The opening data model of the blending liquid and the regulating valve is as follows:
and (3) ore deposit amount and liquid-solid ratio data model:
buffer tank liquid level and buffer pump frequency data model:
ore grinding quality control model ore pulp specific gravity change data model:
αk change data model of ore grinding quality control model:
after a control model is established, model data are input into a model prediction controller for control, the model prediction controller can be in various forms, and the basic principle of the controller used in the project is as follows:
future output states and outputs can be predicted by the model:
feedback correction is carried out on the estimated input:
wherein the method comprises the steps ofThere is = predicted value of current output from historical inputs and states
For the j-th output in the future P j The predicted value of the step is
For the single value estimation algorithm, the control time domain l=1, i.e., u (k+i) =u (k), i >0. Therefore, it is
Wherein the method comprises the steps of
For the j-th output response at p j Is a value of (2). The optimal control can be obtained by a feedback correction algorithm for the prediction:
wherein the method comprises the steps of
Setting up a control platform according to the controller structure and the data model, wherein the whole control system structure is shown in figure 3, and after DCS obtains field measurement values, the liquid-solid ratio, the pulp specific gravity and alpha are calculated through a soft instrument calculation model k pulp And (3) comparing and correcting the calculated value with the pipelining temperature and conductance calculation and the manual detection value, and outputting a soft instrument measured value. Liquid-solid ratio, pulp specific gravity and alpha measured by soft instrument k pulp The value is input as an input variable to a mill quality controller, which dynamically outputs a given value of the liquid-solid ratio according to a model prediction algorithm. And inputting the control instructions into a grinding system controller, and controlling a plurality of inputs by the system controller according to a control model to finally form control output instructions to be output to a DCS, so as to finish the adjustment of the system state and the production index.
Example 2
An alumina pulp mill advanced process control system, which applies the alumina pulp mill advanced process control method, is characterized in that as shown in figure 1, the system comprises a DCS control subsystem, an OPC server and an APC advanced control server;
the control subsystem comprises a data acquisition module, a Central Processing Unit (CPU) and a network communicator, and is used for acquiring mill process control data to an operation station and a server, and executing command output and alarm signals;
the server is a server for collecting DCS signals and is used for establishing a link with the APC server and transmitting all key signals to the APC server;
and the DCS performs final limiting treatment on the output instruction received from the APC server, so that the equipment is ensured to work in a reasonable range. And establishing a heartbeat signal by the DCS and the APC server, and automatically switching the APC output instruction into a manually set safe output instruction once the heartbeat signal is lost for more than a set time.
And after the APC server is installed on site, the APC control system and the DCS system are tested in communication, the data transmitted and received by the APC system are checked, the simulation control instruction is sent by the APC system for testing after confirming that the data is correct, the DCS system receives the data normally, the MV control variable switching test is carried out, and the switching button can be switched normally between the APC controller and the DCS manual value. And testing a soft instrument correction window, and inputting a numerical value to observe whether the data change of the soft instrument is normal. Finally, the alarm function is tested, the controlled variable changes color after exceeding the limit value, the alarm prompt of the popup window is carried out after the communication is interrupted, and the APC system can be cut off and converted into a DCS manual control value.
The server comprises a soft instrument, a mill system prediction controller and a mill quality controller, wherein the soft instrument is used for generating soft instrument data according to a measurement model, the mill system prediction controller generates control signals according to a prediction control model to stabilize production states such as the flow rate of the blended liquid, the liquid level of the pulp tank and the like, and the mill quality controller guides dynamic modification of the liquid-solid ratio through the soft instrument data to realize stabilization and optimization of production indexes.
Preferably, the DCS control subsystem is electrically connected with the field instrument through 4-20MA signals, and the signals to be acquired comprise ore deposit quantity, liquid allocation flow, material transfer pump frequency, buffer tank liquid level, ore pulp tank liquid level, conductivity and ore pulp temperature.
After the liquid level control is put into operation, the liquid level fluctuation is obviously improved as shown in fig. 4. The liquid level of the buffer tank can be automatically controlled by automatically adjusting the pulp pump of the buffer tank through the intelligent ore grinding system. The 3 buffer tank pulp pumps can automatically realize that the frequency of the pulp pumps for use is kept balanced.
The automatic adjustment of the blended liquid after the advanced control system of the mill is started can be realized, as shown in figure 5, the fluctuation of the flow of the blended liquid after the input is greatly reduced, which is beneficial to the liquid-solid ratio and the outlet alpha of the ore pulp tank k pulp Is stable.
As shown in fig. 6, the liquid-solid ratio is controlled by the frequency of the ore discharging belt scale, and the influence of weighing change on the liquid-solid ratio is overcome in time. The frequency of the ore discharging belt scale is automatically adjusted during the load adjustment of the blending liquid, so that the liquid-solid ratio in the load adjustment process is ensured to be stable.
To effectively compare the effect of the intelligent grinding system (i.e., on alpha k pulp Stability effect), 2/15 days 7:27 to 4/6 day 11:57 for about 2 months (run mode starting intelligent ore grinding system, intelligent ore grinding system running substantially continuously, conductivity 3 table output normal) and 5/12 day 0:00 to 7/11 day 23:59 for 2 months (the operation mode is that the intelligent ore grinding system is deactivated, the output of the electric conduction 3 table is normal), and the obvious abnormal value (the correction limit is the + -30% of the average value) of the electric conduction 3 is corrected for data analysis.
From the above table we can see that the stage of running intelligent ore grinding is under the condition of meeting alpha k pulp The fluctuation of the index is greatly slowed down on the basis of the range, and the standard deviation gauge is used for reducing the fluctuation by 42.5 percent. From fig. 7 we can see that the system operating state is significantly smoother after advanced control is enabled.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. An advanced process control method for an alumina pulp mill is characterized by comprising the following steps:
s1, a data acquisition module acquires data in the process of grinding aluminum oxide;
s2, modeling the ore grinding process to obtain the output characteristic and the response curve of the mill;
s3, calculating alpha according to auxiliary variables by using a soft instrument technology through a soft instrument algorithm K pulp Specific gravity ρ of pulp Pulp ；
S4, estimating dominant variables on line by the soft measurement model: alpha K pulp Pulp specific gravity ρ Pulp The value of (2) is combined with an ore grinding quality controller, and the dynamic control of the liquid-solid ratio data realizes the stable control of the production index;
s5, utilizing a model predictive control algorithm, and adjusting input parameters of the mill in real time according to a liquid-solid ratio parameter predictive result and an optimization target set by the quality controller.
2. The advanced process control method for alumina pulp mill according to claim 1, wherein in the modeling process of step S2, the data of step S1 is required to be screened to remove obvious error data, and the data record under normal working conditions is intercepted to be analyzed and modeled to obtain the data model of the alumina pulp mill system.
3. The advanced process control method for alumina pulp mill according to claim 1, wherein the soft meter algorithm calculates pulp specific gravity ρ Pulp And alpha K pulp The calculation mode of (a) is as follows:
wherein:
ρ ore ore : ore density in t/m 3 Reference value: 2.35;
A ore ore ：Al 2 O 3 Content, reference value: 49%;
D ore ore ：Al 2 O 3 Dissolution rate, reference value: 84%;
ρ liquid and its preparation method : density of the mixed liquid, unit t/m 3 Reference value: 1.32;
A liquid and its preparation method ：Al 2 O 3 Content, unit g/L, reference value: 111;
N liquid and its preparation method : NK, unit g/L, reference 198;
F main mine : the main force is used for grinding ore quantity per unit t/h;
F main liquid : the main mill liquid blending amount is t/h;
F auxiliary ore : auxiliary ore grinding amount per unit t/h;
F auxiliary liquid : auxiliary mill liquid blending amount is given in t/h.
4. The alumina pulp mill advanced process control method of claim 3, further comprising a soft meter correction algorithm:
wherein y is out The output value of the soft instrument is Q which is the calculation formula of the soft instrument, K i For the auxiliary variable correction coefficient, i represents the calculated number of auxiliary variables, q fi And B is measured as an artificial measurement data correction deviation value, and f1 is an artificial correction coefficient.
5. An alumina pulp mill advanced process control method according to claim 3, wherein said quality controller is capable of controlling the pulp specific gravity ρ provided by a soft meter Pulp And alpha is K pulp And dynamically adjusting the liquid-solid ratio in the production process to realize the prediction and stable control of the production index.
6. The advanced process control method for alumina pulp mill according to claim 1, wherein the predictive control model generates a blending liquid flow, a buffer pump frequency output and a belt scale rotation speed output, and predicts and optimally controls the blending liquid flow, the pulp tank liquid level and the liquid-solid ratio parameters in the production process.
7. The advanced process control method for alumina pulp mill according to claim 1, wherein the basic principle of the quality controller is:
for an input-output system:
x(k+1)＝Ax(k)+BuF(k)
y(k+1)＝Cx(k+1)
x (k) and x (k+1) respectively represent the states of the kth moment and the kth+1 moment in the grinding working process; y (k+1) represents the state of the system at the k+1 th time; u (k) represents a control input amount at a kth time of the system; a (k) and B (k) respectively represent two corresponding parameter matrixes at the kth moment in the grinding process; c is a system matrix of appropriate dimensions.
Future output states and outputs can be predicted by the model:
feedback correction is carried out on the estimated input:
wherein the method comprises the steps ofThere is = predicted value of current output from historical inputs and states
For the j-th output in the future P j The predicted value of the step is
For the single value estimation algorithm, the control time domain l=1, i.e., u (k+i) =u (k), i >0. Therefore, it is
Wherein the method comprises the steps of
For the j-th output response at p j Is a value of (2). The optimal control can be obtained by a feedback correction algorithm for the prediction:
wherein the method comprises the steps of
8. An alumina pulp mill advanced process control system, applying the alumina pulp mill advanced process control method according to any one of claims 1-7, characterized in that the system comprises a DCS control subsystem, an OPC server, an APC advanced control server;
the DCS control subsystem comprises a data acquisition module, a Central Processing Unit (CPU) and a network communicator, and is used for acquiring mill process control data to an operation station and an OPC server, and executing command output and alarm signals;
the OPC server is a server for collecting DCS signals and is used for establishing a link with the APC server and transmitting all key signals to the APC server;
the APC server comprises a soft instrument, a mill system prediction controller and a mill quality controller, wherein the soft instrument is used for generating soft instrument data according to a measurement model, the mill system prediction controller generates control signals according to a prediction control model to stabilize production states such as the flow rate of the blended liquid, the liquid level of the pulp tank and the like, and the mill quality controller guides dynamic modification of the liquid-solid ratio through the soft instrument data to realize stabilization and optimization of production indexes.
9. The advanced alumina pulp mill process control system according to claim 8, wherein the DCS control subsystem is electrically connected to a site meter via a 4-20MA signal, and the desired acquisition signal comprises a mine down amount, a flow rate of the conditioning fluid, a frequency of the transfer pump, a buffer tank level, a pulp tank level, a conductivity, and a pulp temperature.
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