CN115532421B - Intelligent regulation and control method for powder concentrator of large vertical mill - Google Patents
Intelligent regulation and control method for powder concentrator of large vertical mill Download PDFInfo
- Publication number
- CN115532421B CN115532421B CN202210301596.3A CN202210301596A CN115532421B CN 115532421 B CN115532421 B CN 115532421B CN 202210301596 A CN202210301596 A CN 202210301596A CN 115532421 B CN115532421 B CN 115532421B
- Authority
- CN
- China
- Prior art keywords
- control
- vertical mill
- output
- value
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000000843 powder Substances 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000007246 mechanism Effects 0.000 claims abstract description 54
- 230000033001 locomotion Effects 0.000 claims abstract description 18
- 230000001105 regulatory effect Effects 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 22
- 239000002245 particle Substances 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 239000000428 dust Substances 0.000 claims description 19
- 238000000227 grinding Methods 0.000 claims description 15
- 238000012544 monitoring process Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 6
- 230000009467 reduction Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 239000002994 raw material Substances 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 230000001276 controlling effect Effects 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 238000009826 distribution Methods 0.000 abstract description 3
- 230000009471 action Effects 0.000 abstract description 2
- 238000007599 discharging Methods 0.000 abstract description 2
- 238000005065 mining Methods 0.000 abstract description 2
- 238000007873 sieving Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002146 bilateral effect Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- -1 thermal engineering Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Abstract
The invention discloses an intelligent regulation and control system and method for a powder concentrator of a large vertical mill, and relates to the technical field of mining equipment and intelligent manufacturing. The invention aims to utilize a comprehensive intelligent regulation method to regulate a vertical mill in real time: according to working condition data and characteristics of the vertical mill, obtaining state distribution of historical working conditions, predicting the motion state of the moving blade in the adjusting and controlling device by using a model prediction control algorithm, identifying the state by using a predicted value in an auxiliary mode, and adjusting the vertical mill powder selecting machine in real time by using a closed loop feedback control method to realize intelligent adjustment of the vertical mill, so that the vertical mill powder selecting machine can operate efficiently and stably; the regulating device is provided with the wind guide blades, the speed reducing mechanism and the limiting mechanism on the original structure of the vertical mill, the motor drives the speed reducing mechanism to drive the main shaft, the wind guide blades are driven by the main shaft to rotate, and the limiting mechanism plays a limiting role on the device. Under the action of the regulating device, the vertical mill powder selecting machine effectively improves the powder selecting precision, the powder discharging quantity and the powder sieving efficiency.
Description
Technical Field
The invention relates to the technical field of mining equipment and intelligent manufacturing. In particular to an intelligent regulation and control system and method for a powder selecting machine of a large vertical mill.
Background
The large vertical mill is high-end large mine equipment based on intelligent manufacturing and is widely applied to industries such as cement, electric power, metallurgy, chemical industry, nonmetallic mine and the like. It integrates crushing, drying, grinding and classifying conveying into a whole, has high production efficiency, and can grind the block, granular and powdery raw materials into required powdery materials. The large vertical mill mainly comprises a millstone, a motor, a powder selecting machine, a grinding roller, a hydraulic cylinder, a speed reducer, a rocker arm, an energy accumulator and the like, has huge number of parts, has complex multi-source information such as machines, electricity, liquid, thermal engineering, air power and the like, has multiple monitoring operation points (more than 500 monitoring operation points), is subjected to various acting forces among an internal flow field, dust particles, parts and the parts, and has extremely complex stress working conditions. The operation process involves the coupling of multiple physical fields such as gas, solid particles, heat transfer and the like, the operation parameters such as ore feeding quantity, rotation speed, ore discharging quantity, water consumption, material granularity and the like are numerous, the coupling relationship between the operation parameters is complex, and the difficulty in optimizing and regulating the operation parameters is high.
The large vertical mill powder selecting machine is one of the key parts for determining the granularity, granularity level width and granularity distribution of the product and is used in sorting powder ground with large vertical mill. In the traditional high-efficiency combined powder concentrator, a dynamic powder concentrator (rotary cage) and a static powder concentrator (wind guide blade) are combined together, namely, a cylindrical cage is used as a rotor, wind guide blades are uniformly distributed around the cylindrical cage, so that air flow enters a powder concentrator region, and coarse powder and fine powder are separated. However, the traditional vertical mill powder concentrator has complex internal environment, serious mutual interference, shorter service life, more frequent maintenance and less ideal working efficiency because the vertical mill powder concentrator cannot be timely found and adjusted for maintenance when the problems of faults and the like occur. In addition, the powder concentrator has larger resistance, large blade abrasion and large energy consumption. In the traditional powder screening work, mechanisms which can not only adjust the vertical mill powder selecting machine in real time, but also improve the powder screening efficiency and reduce the energy consumption are needed in some vertical mill equipment. Therefore, in order to solve the problems and achieve the purposes of improving efficiency and reducing energy consumption, an intelligent regulation and control system and a regulation and control method for a powder concentrator of a large vertical mill are provided.
Disclosure of Invention
The invention aims to provide an intelligent regulation and control system and method for a powder concentrator of a large vertical mill. The integrated intelligent regulation and control method is utilized to regulate the vertical mill powder concentrator in real time, each state distribution of the historical working conditions is obtained according to the working condition data and characteristics of the vertical mill powder concentrator, the motion state of the moving blades in the regulation and control device is predicted by using a model prediction control algorithm, the state identification is assisted by a predicted value, the compensation blade structure in the vertical mill powder concentrator is regulated in real time by adopting a closed loop feedback control method, the intelligent regulation of the vertical mill powder concentrator is realized, and the vertical mill powder concentrator can operate efficiently and stably.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the utility model provides a large-scale vertical mill selection powder machine intelligent regulation and control system, includes the control unit and stands the mill selection powder machine, and the control unit includes: the system comprises a data preprocessing unit, a vertical mill particle sensor, a motor position sensor, a multivariable model predictive controller and a closed-loop feedback control unit.
The vertical mill powder selecting machine comprises a connecting plate, wherein an inverted cone and a grid are fixedly arranged above the connecting plate, the inverted cone is positioned in the grid, a wind shielding ring is fixedly arranged at the top of the grid, four groups of compensating devices are distributed on the outer surface of the inverted cone in an array manner, each group of compensating devices comprises compensating blades and a dust box, each compensating blade comprises a moving blade, a main shaft and a fixed blade, the dust box is fixedly arranged at the joint of the inverted cone and the connecting plate, the fixed blades in the compensating blades are arranged on the outer surface of the inverted cone, and the fixed blades are connected with the moving blades through the main shafts; a motor is arranged in the dust-proof box,
Four supporting legs are fixedly mounted on the motor, two positioning holes are formed in each supporting leg, the motor is fixed in the dust box through the positioning holes, the end cover is fixed at the output end of the motor through a fixing nut, an output shaft of the motor is fixedly connected with an input gear of a speed reducing mechanism, an output gear is meshed on the input gear, the output gear is fixedly connected with the main shaft, and a limiting hole is formed in the output gear.
The dustproof box is internally provided with a limiting mechanism, the limiting mechanism comprises two supporting plates which are symmetrically arranged left and right, a fixing frame is arranged in the middle of each supporting plate, a locating pin is arranged on each fixing frame, a coil is wound at one end of each locating pin, each locating pin corresponds to a limiting hole on an output gear of the corresponding speed reducing mechanism, scales are marked on the output gear, and a plurality of limiting holes are distributed on the output gears in an array mode.
Further, the invention also provides an intelligent regulation and control system and a regulation and control method for the powder concentrator of the large vertical mill, and the intelligent regulation and control system comprises the following steps:
Step 1, data acquisition and processing
The rotating angle of the moving blade is used as a control output f, and the acquisition characteristic of the data acquired by the vertical mill particle sensor and the motor position sensor after data preprocessing is used as a control input X.
S1.1, in the step, the design variable is X= [ X 1;x2;x3;x4]T ], wherein X 1、x2、x3 and X 4 are respectively expressed as powder output, particle fineness, grinding quality and motor rotation speed;
S1.2, the multivariable model predictive controller reduces the dimension of control output by converting the output into the rotating angle of the moving blade.
Step2, multivariable model predictive control
In the multivariable model predictive control, the corresponding predictive results of the features are collected at different moments, the feature form and the time domain length N of the predictive control are determined, multi-step prediction is carried out through the feature form within the range of the time domain length N, the predictive value f p of the control output at the next moment is obtained, and the control input in the time domain is needed to be pre-advanced when the predictive system outputs in the future along with the time
S2.1, solving a finite time domain open loop optimization problem at each sampling moment according to the obtained current measurement information, and acting the first element of the obtained control sequence on a controlled object; at the next sampling instant, the above process is repeated: the optimization problem is refreshed and solved again with new measurements.
S2.2, in the process of collecting historical monitoring data by each sensor, assuming that the rotation angle of the corresponding output moving blade is mu 1,μ2,μ3 … for a sampling system at each sampling moment I=Ts, 2Ts and 3Ts
f(I)＝μI，(I＝1,2,3…)，
When the system is stabilized and the output is relatively ideal,Considering the influence of the internal environment of the vertical mill on the monitoring error, when the time domain length is N (mu I after I > N and the error level are negligible), then
f(I)＝μI，(I＝1,2,3…，N)，
S2.3, the characteristic form of predictive control is as follows:
x(I+1)＝f(x(I),X(I)) (1)
f(I)＝h(x(I),X(I)) (2)
Wherein X (I), X (I), f (I) are the state of the system at the moment I, control input and control output respectively.
S2.4, calculating the output of the system f (I) in future time according to the two formulas (1) and (2), and marking as: f p(I+1/I),fp(I+2/I),...,fp (I+p/I), p being the prediction horizon.
S2.5, along with the forward direction of time, the future output of the prediction system needs to predict the control input in the time domain in advance This is the design variable for predictive control optimization.
Step 3, optimizing moving blade motion model of vertical mill
Comprehensively analyzing the collected characteristic parameters, and determining the value range of the characteristic parameters by combining the controllability and the actual meaning of the parameters; and analyzing the control output after the dimension reduction, and determining the output constraint by referring to an actual model of the control device. In order to meet the minimum error between the predicted output value and the actual output value of the control system, the control system is required to obtain the optimal control input, and the accumulated error between the predicted output value and the actual output value is reduced. The smaller the cumulative error value, the higher the overall process accuracy of the model prediction.
S3.1, meanwhile, the control input and output constraints which the system should meet are as follows:
0≤X(I+i)≤Xmax,i≥0
0≤f(I+i)≤360°,i≥0
Wherein X (I+i) is the control input at the moment I+i, X max represents the maximum control input; f (i+i) represents the control output at time i+i, and in the multivariate model predictive control, the control output is converted into the rotor blade rotation angle to reduce the dimension of the control output, so that the value of the control output f (i+i) ranges from 0 to 360 °.
S3.2, assuming that the actual output of the system at each current moment is as follows: d (i+1), d (i+2), d (i+p), the control system is required to obtain the optimal control input in order to meet the minimum error between the predicted output value and the actual output value of the control system. The accumulated error between the predicted output value and the actual output value is:
Where d i is the actual output value at I, f p (I/I) represents the predicted output value at I predicted at I in the time domain p by model prediction, and δ represents the accumulated error.
The smaller the accumulated error value, the higher the accuracy of the whole process representing model prediction, and therefore, the convergence criterion of the formula is: delta is less than or equal to epsilon δ, wherein epsilon δ is a minimum value.
Step 4, closed loop feedback control
The method comprises the following steps of predicting the motion state of a moving blade in a control device through a model predictive control algorithm, optimizing a motion model of the moving blade by vertical mill, identifying the motion model by using a predicted value to assist in state identification, and forming a closed-loop feedback control unit for adjusting the rotation angle of a compensating blade of the vertical mill powder concentrator together with a servo motor, a speed reducing mechanism, a limiting mechanism and other adjusting mechanisms:
S4.1, obtaining and optimizing a vertical mill moving blade motion model through multivariable model predictive control, analyzing working condition data by utilizing a comprehensive feature screening method, obtaining a predictive response in an ideal state, and selecting proper control input. And determining the range of the control input according to the value range of the key parameter in the historical data.
S4.2, analyzing real-time data acquired in the opposite grinding running state to obtain an actual value of the control input.
S4.3, comparing the actual rotation angle of the compensating blade of the vertical mill powder concentrator with the predicted angle of the compensating blade at the moment, adjusting the system through closed loop feedback control, judging the running state of the vertical mill according to the state at the moment, and enabling the system to automatically adjust the system according to the measured parameters, the environment and the cost change of raw materials, so that the system is in an optimal state at any time.
Step 5, real-time parameter evaluation of vertical mill
S5.1, detecting null values and abnormal values of the acquired real-time data, discarding the read null values, filling the average value in the read historical data, eliminating the influence of the null values on the sampling interval of the data detection, continuing to read the data of the next sampling interval for detection after filling the null values, and repeating the process until a sufficient sample library is obtained.
S5.2, in the process of recording in the sample library, if difference data exceeding the value range of each parameter in the historical data appears, the data at the moment is considered as an abnormal value, the occurrence times of the abnormal value are recorded, and the average value of the data in the sampling interval is read and replaced.
S5.3, calculating the average value, variance, standard deviation and the occurrence times of abnormal values of each parameter in each sampling interval of the vertical mill, taking the calculated numerical value as a characteristic value for judging the working condition of the vertical mill, evaluating the real-time input parameters of the vertical mill, and judging the running stability of the vertical mill according to the existing running working condition record.
Step 6, identifying the running state of the vertical mill
Based on the state evaluation index of the stable running of the vertical mill, the state of the vertical mill working condition is analyzed, the mean value, variance and abnormal value occurrence times of each parameter under the stable working condition are calculated, and a database of the stable working condition is established.
And S6.1, when the parameters in the stable index are abnormal, starting a control program, searching a control target from a stable working condition database by the program, and returning to the point closest to the current state as a working condition to be selected.
S6.2, comparing the difference between the current state and the working condition to be selected, and adjusting the controllable variable through adjusting the adjustable parameter until the parameter integrally reaches the target value.
And S6.3, judging whether the running condition of the vertical mill in the state meets the requirement or not after the running state of the vertical mill is stable according to the established stable condition database, and otherwise, adjusting the control input to enable the system to enter the optimal condition.
Compared with the prior art, the invention has the beneficial effects that: the control system regulates and controls the limiting mechanism through model prediction and closed loop feedback control, and then controls the rotation angle of the motor, the motor drives the speed reducing mechanism, the speed reducing mechanism drives the main shaft and the moving blades fixedly connected with the main shaft to rotate, corresponding data are obtained through the particle sensor and the motor position sensor and fed back to the control system, the purpose of controlling the flow field in the powder selecting machine is further achieved, the flow field system is regulated in the powder selecting machine, and therefore the fineness and quality of powder selecting are improved.
Drawings
Fig. 1 is a schematic diagram of the structure of a vertical mill.
Fig. 2 is a schematic diagram of the original mechanism of the rotating cage of the powder selecting machine of the vertical mill.
Fig. 3 is a general structure of an intelligent regulation system and a regulation method for a powder concentrator of a large vertical mill.
Fig. 4 is a schematic structural diagram of a compensating blade and dust box in an intelligent regulation system and a regulation method for a powder concentrator of a large vertical mill.
Fig. 5 is a schematic structural diagram of a motor, a speed reducing mechanism and a limiting mechanism in an intelligent regulation system and a regulation method for a powder selecting machine of a large vertical mill.
Fig. 6 is a schematic structural diagram of a motor and a speed reducing mechanism in an intelligent regulation system and a regulation method for a powder selecting machine of a large vertical mill.
Fig. 7 is a schematic structural diagram of a speed reducing mechanism and a limiting mechanism in an intelligent regulation system and a regulation method of a powder selecting machine of a large vertical mill.
Fig. 8 is a general design flow chart of an intelligent regulation system and a regulation method for a powder concentrator of a large vertical mill.
Fig. 9 is a control flow chart of the intelligent regulation system and the intelligent regulation method of the powder selecting machine of the large vertical mill.
Fig. 10 is a schematic diagram of a large-scale vertical mill powder concentrator intelligent regulation system and control method.
In the figure: 1-connecting plate, 2-back taper, 3-grille, 4-wind shielding ring, 5-compensating blade, dust box, 501-moving blade, 502-main shaft, 503-fixed blade, 504-dust box, 6-motor, 601-support leg, 602-locating hole, 603-fixed nut, 7-reducing mechanism, 701-input gear, 702-output gear, 703-limit hole, 8-limit mechanism, 801-supporting plate, 802-magnetic pole, 803-fixed frame, 804-locating pin, 805-coil.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present embodiments disclosed herein as detailed in the accompanying claims.
Referring to fig. 1-6, an intelligent regulation and control system and a regulation and control method for a powder selecting machine of a large vertical mill, wherein the intelligent control system can regulate moving blades on a control device according to data collected by a sensor through a related algorithm and closed loop feedback control, so that the control device is in an optimal state at all times;
including the control unit and stand mill selection powder machine, the control unit includes: the system comprises a data preprocessing unit, a vertical mill particle sensor, a motor position sensor, a multivariable model predictive controller and a closed-loop feedback control unit.
The data preprocessing unit is used for performing outlier processing, null value processing, discretization processing and normalization processing on the data acquired by the vertical grinding particle sensor;
The vertical mill particle sensor adopts a monitoring instrument of the grinding particles of the real-time vertical mill to update the powder output quantity, the particle fineness and the grinding quality of the grinding particles in real time, and the powder output quantity, the particle fineness and the grinding quality are mainly used for updating input parameters in model prediction control.
The motor position sensor adopts a real-time motor rotation monitoring instrument to update the motor rotation angle in real time.
Flow field control system of large-scale vertical mill: the intelligent regulation and control system of the large vertical mill powder concentrator is positioned inside the vertical mill powder concentrator, and the main body of the intelligent regulation and control system of the powder concentrator comprises two parts: a motor part and a limiting mechanism part; the limiting mechanism can control the position of the moving blade in the compensation blade structure by limiting the position of the driven gear in the speed reducing mechanism; the motor is updated in real time through the motor sensor monitoring instrument to the angle of the motor, and the angle of the moving blade is changed through the regulation and control of the controller.
Multivariable model predictive controller: the model predictive control is a finite time domain rolling optimization closed-loop control method based on a predictive model, can better cope with uncertainty of a system and complexity of an internal environment of a vertical mill, can clearly show operation constraint and control targets in an optimization process, and adopts a finite time domain optimization strategy of time forward rolling type in consideration of dynamic response of an intelligent regulation control system of a powder concentrator of a large vertical mill.
Closed loop feedback control unit: comparing the output obtained by optimizing according to the multivariable model predictive control with the actual value output in the running of the vertical mill, judging the running state of the vertical mill, reading the working condition record from the stable working condition mode when the judged state is abnormal to obtain a controlled target value, and then controlling the controllable parameter according to the recommended target value;
the vertical mill powder selecting machine comprises a connecting plate 1, wherein an inverted cone 2 and a grid 3 are fixedly arranged above the connecting plate 1, the inverted cone 2 is positioned in the grid 3, a wind shielding ring 4 is fixedly arranged at the top of the grid 3, four groups of compensating devices 5 are distributed on the outer surface of the inverted cone 2 in an array manner, each group of compensating devices 5 comprises compensating blades and a dust box 504, each compensating blade comprises a moving blade 501, a main shaft 502 and a fixed blade 503, the dust box 504 is fixedly arranged at the joint of the inverted cone 2 and the connecting plate 1, the fixed blades 503 in the compensating blades are arranged on the outer surface of the inverted cone 2, and the fixed blades 503 are connected with the moving blades 501 through the main shafts 502; the dust box 504 has a motor 6 mounted therein,
Four supporting legs 601 are fixedly arranged on the motor 6, two positioning holes 602 are formed in each supporting leg 601, the motor 6 is fixed in the dust box 504 through the positioning holes 602, an end cover is fixed at the output end of the motor 6 through a fixing nut 603, an output shaft of the motor 6 is fixedly connected with an input gear 701 of a speed reducing mechanism 7, an output gear 702 is meshed with the input gear 701, the output gear 702 is fixedly connected with the main shaft 502, and a limiting hole 703 is formed in the output gear 702.
Be provided with stop gear 8 in the dust proof case 504, stop gear 8 includes two bilateral symmetry's backup pad 801 that set up, is provided with mount 803 in the middle of the backup pad 801, install locating pin 804 on the mount 803, locating pin 804 one end winding has coil 805, locating pin 804 corresponds the spacing hole 703 on the output gear 702 of reduction gears 7, the scale has been marked on the output gear 702, spacing hole 703 is equipped with a plurality of and the array distributes on output gear 702.
In practical application, the main shaft 502 on the compensation device 5 can stably run through the reduction mechanism 7, so that the moving blade 501 fixedly installed with the main shaft 502 is easier to fix in position during movement, the limiting mechanism 8 is more accurate in limiting through the limiting hole 703, the amplitude of the variation is controlled within the limiting range, collision caused by overlarge rotating speed is avoided, the device is damaged, and the safety and the service life of the device are improved.
In practical application, the output gear 702 of the speed reducing mechanism 7 drives the main shaft 502 to rotate, the moving blades 501 fixedly connected with the main shaft 502 synchronously rotate, and the fixed blades 503 are fixed on the back taper 2, so that the moving blades 501 rotate more stably when the whole device works, the wind power compensation adjustment process can be performed stably, the powder selecting quality is better, and the powder selecting efficiency is higher.
The motor 6 drives the speed reducing mechanism 7 to rotate, and the limiting mechanism 8 controls the speed reducing mechanism 7 by limiting. The particle sensor monitors the fineness and the powder output of the particle after entering the vertical mill powder separator, the monitored result is sent to the control system, the control system adjusts the servo motor and the existing mechanism through the adjusting device, and the motor position sensor feeds back the control system through the angle through which the monitoring motor rotates.
When the embodiment of the invention is actually applied, the particle sensor and the motor position sensor are used for monitoring and controlling the adjusting device, so that the control of the servo motor and the limiting mechanism is realized, the flow field change in the powder selecting machine of the vertical mill is further controlled, the fineness of powder is adjusted, and the powder selecting quality is improved.
In practical application, the embodiment of the invention can transmit the power of the motor 6 to the main shaft 502 through the reduction mechanism 7, greatly reduce the rotation speed of the output gear 702 through the gear ratio of the input gear 701 and the output gear 702, drive the main shaft 502 to stably operate, and has better powder selecting effect and further improve the powder selecting quality.
In practical application, the output gear 702 is provided with a plurality of limiting holes, and when limiting control is performed, the limiting mechanism 8 controls the movement of the limiting pin 804 through the magnetic pole 802 and the coil 805, so that limiting control can be performed on the speed reducing mechanism 7, and the output gear 702 is provided with scales, so that the adjusting precision of the device is higher, and the positioning is more accurate.
When the embodiment of the invention is practically applied, the dust box 1 can prevent dust from influencing the power driving and controlling structure of the wind power compensation device of the vertical mill powder concentrator, the service life of the device is prolonged, and the compensation blade mechanism 5 is fixedly arranged on the inverted cone, so that the adjustment can be better carried out along with the internal airflow circulation convection field of the vertical mill powder concentrator, and the powder concentration efficiency and effect are further improved.
According to the invention, the motor 6 drives the speed reducing mechanism 7, so that the main shaft 502 fixedly connected with the output gear 702 on the speed reducing mechanism 7 can stably run, the wind power compensation effect of the device is better, the adjusting capability of the device is improved through the limiting mechanism 8 and the limiting hole 703, the stable running of the flow field in the vertical mill is controlled, the motor 6 is fixedly connected with one end of the speed reducing mechanism 7, the speed reducing mechanism 7 is fixedly connected with one end of the main shaft 502, the main shaft 502 is driven to rotate, the main shaft 502 is fixedly connected with the moving blade 501, the moving blade 501 and the fixed blade 503 form a rotating structure through the main shaft 502, the safety of the device is improved, and the powder selecting efficiency is further improved.
Model predictive control is a special class of control. Its current control action is obtained by solving a finite time domain open loop optimal control problem at each sampling instant. The current state of the process is used as the initial state of the optimal control problem, and the solved optimal control sequence only implements the first control effect. This is the biggest difference from those algorithms that use pre-computed control laws. Essentially model predictive control solves an open loop optimal control problem.
Further, the invention also provides an intelligent regulation and control method of the powder concentrator of the large vertical mill, which comprises the following steps:
Step 1, data acquisition and processing
The rotating angle of the moving blade is used as a control output f, and the acquisition characteristic of the data acquired by the vertical mill particle sensor and the motor position sensor after data preprocessing is used as a control input X.
S1.1, in the step, the design variable is X= [ X 1;x2;x3;x4]T ], wherein X 1、x2、x3 and X 4 are respectively expressed as powder output, particle fineness, grinding quality and motor rotation speed;
S1.2, the multivariable model predictive controller reduces the dimension of control output by converting the output into the rotating angle of the moving blade.
Step2, multivariable model predictive control
In the multivariable model predictive control, the corresponding predictive results of the features are collected at different moments, the feature form and the time domain length N of the predictive control are determined, multi-step prediction is carried out through the feature form within the range of the time domain length N, the predictive value f p of the control output at the next moment is obtained, and the control input in the time domain is needed to be pre-advanced when the predictive system outputs in the future along with the time
S2.1, solving a finite time domain open loop optimization problem at each sampling moment according to the obtained current measurement information, and acting the first element of the obtained control sequence on a controlled object; at the next sampling instant, the above process is repeated: the optimization problem is refreshed and solved again with new measurements.
S2.2, in the process of collecting historical monitoring data by each sensor, assuming that the rotation angle of the corresponding output moving blade is mu 1,μ2,μ3 … for a sampling system at each sampling moment I=Ts, 2Ts and 3Ts
f(I)＝μI，(I＝1,2,3…)，
When the system is stabilized and the output is relatively ideal,Considering the influence of the internal environment of the vertical mill on the monitoring error, when the time domain length is N (mu I after I > N and the error level are negligible), then
f(I)＝μI，(I＝1,2,3…，N)，
S2.3, the characteristic form of predictive control is as follows:
x(I+1)＝f(x(I),X(I)) (1)
f(I)＝h(x(I),X(I)) (2)
Wherein X (I), X (I), f (I) are the state of the system at the moment I, control input and control output respectively.
S2.4, calculating the output of the system f (I) in future time according to the two formulas (1) and (2), and marking as: f p(I+1/I),fp(I+2/I),...,fp (I+p/I), p being the prediction horizon.
S2.5, along with the forward direction of time, the future output of the prediction system needs to predict the control input in the time domain in advance This is the design variable for predictive control optimization.
Step 3, optimizing moving blade motion model of vertical mill
Comprehensively analyzing the collected characteristic parameters, and determining the value range of the characteristic parameters by combining the controllability and the actual meaning of the parameters; and analyzing the control output after the dimension reduction, and determining the output constraint by referring to an actual model of the control device. In order to meet the minimum error between the predicted output value and the actual output value of the control system, the control system is required to obtain the optimal control input, and the accumulated error between the predicted output value and the actual output value is reduced. The smaller the cumulative error value, the higher the overall process accuracy of the model prediction.
S3.1, meanwhile, the control input and output constraints which the system should meet are as follows:
0≤X(I+i)≤Xmax,i≥0
0≤f(I+i)≤360°,i≥0
Wherein X (I+i) is the control input at the moment I+i, X max represents the maximum control input; f (i+i) represents the control output at time i+i, and in the multivariate model predictive control, the control output is converted into the rotor blade rotation angle to reduce the dimension of the control output, so that the value of the control output f (i+i) ranges from 0 to 360 °.
S3.2, assuming that the actual output of the system at each current moment is as follows: d (i+1), d (i+2), d (i+p), the control system is required to obtain the optimal control input in order to meet the minimum error between the predicted output value and the actual output value of the control system. The accumulated error between the predicted output value and the actual output value is:
Where d i is the actual output value at I, f p (I/I) represents the predicted output value at I predicted at I in the time domain p by model prediction, and δ represents the accumulated error.
The smaller the accumulated error value, the higher the accuracy of the whole process representing model prediction, and therefore, the convergence criterion of the formula is: delta is less than or equal to epsilon δ, wherein epsilon δ is a minimum value.
Step 4, closed loop feedback control
The method comprises the following steps of predicting the motion state of a moving blade in a control device through a model predictive control algorithm, optimizing a motion model of the moving blade by vertical mill, identifying the motion model by using a predicted value to assist in state identification, and forming a closed-loop feedback control unit for adjusting the rotation angle of a compensating blade of the vertical mill powder concentrator together with a servo motor, a speed reducing mechanism, a limiting mechanism and other adjusting mechanisms:
S4.1, obtaining and optimizing a vertical mill moving blade motion model through multivariable model predictive control, analyzing working condition data by utilizing a comprehensive feature screening method, obtaining a predictive response in an ideal state, and selecting proper control input. And determining the range of the control input according to the value range of the key parameter in the historical data.
S4.2, analyzing real-time data acquired in the opposite grinding running state to obtain an actual value of the control input.
S4.3, comparing the actual rotation angle of the compensating blade of the vertical mill powder concentrator with the predicted angle of the compensating blade at the moment, adjusting the system through closed loop feedback control, judging the running state of the vertical mill according to the state at the moment, and enabling the system to automatically adjust the system according to the measured parameters, the environment and the cost change of raw materials, so that the system is in an optimal state at any time.
Step 5, real-time parameter evaluation of vertical mill
S5.1, detecting null values and abnormal values of the acquired real-time data, discarding the read null values, filling the average value in the read historical data, eliminating the influence of the null values on the sampling interval of the data detection, continuing to read the data of the next sampling interval for detection after filling the null values, and repeating the process until a sufficient sample library is obtained.
S5.2, in the process of recording in the sample library, if difference data exceeding the value range of each parameter in the historical data appears, the data at the moment is considered as an abnormal value, the occurrence times of the abnormal value are recorded, and the average value of the data in the sampling interval is read and replaced.
S5.3, calculating the average value, variance, standard deviation and the occurrence times of abnormal values of each parameter in each sampling interval of the vertical mill, taking the calculated numerical value as a characteristic value for judging the working condition of the vertical mill, evaluating the real-time input parameters of the vertical mill, and judging the running stability of the vertical mill according to the existing running working condition record.
Step 6, identifying the running state of the vertical mill
Based on the state evaluation index of the stable running of the vertical mill, the state of the vertical mill working condition is analyzed, the mean value, variance and abnormal value occurrence times of each parameter under the stable working condition are calculated, and a database of the stable working condition is established.
And S6.1, when the parameters in the stable index are abnormal, starting a control program, searching a control target from a stable working condition database by the program, and returning to the point closest to the current state as a working condition to be selected.
S6.2, comparing the difference between the current state and the working condition to be selected, and adjusting the controllable variable through adjusting the adjustable parameter until the parameter integrally reaches the target value.
And S6.3, judging whether the running condition of the vertical mill in the state meets the requirement or not after the running state of the vertical mill is stable according to the established stable condition database, and otherwise, adjusting the control input to enable the system to enter the optimal condition.
Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (4)
1. The intelligent regulation and control method of the large vertical mill powder concentrator is based on an intelligent regulation and control system of the large vertical mill powder concentrator, the intelligent regulation and control system of the large vertical mill powder concentrator comprises a control unit and a vertical mill powder concentrator, and the control unit comprises: the system comprises a data preprocessing unit, a vertical mill particle sensor, a motor position sensor, a multivariable model predictive controller and a closed-loop feedback control unit;
the vertical mill powder selecting machine comprises a connecting plate (1), wherein an inverted cone (2) and a grid (3) are fixedly arranged above the connecting plate (1), the inverted cone (2) is positioned inside the grid (3), a wind shielding ring (4) is fixedly arranged at the top of the grid (3), four groups of compensating devices (5) are distributed on the outer surface array of the inverted cone (2), each group of compensating devices (5) comprises a compensating blade and a dust box (504), each compensating blade comprises a moving blade (501), a main shaft (502) and a fixed blade (503), the dust box (504) is fixedly arranged at the joint surface of the inverted cone (2) and the connecting plate (1), the fixed blades (503) in the compensating blades are arranged on the outer surface of the inverted cone (2), and the fixed blades (503) are connected with the moving blades (501) through the main shafts (502); a motor (6) is arranged in the dust box (504);
four supporting legs (601) are fixedly arranged on the motor (6), two positioning holes (602) are formed in each supporting leg (601), the motor (6) is fixed in a dust box (504) through the positioning holes (602), an end cover is fixed at the output end of the motor (6) through a fixing nut (603), an output shaft of the motor (6) is fixedly connected with an input gear (701) of a speed reducing mechanism (7), an output gear (702) is meshed on the input gear (701), the output gear (702) is fixedly connected with the main shaft (502), and a limiting hole (703) is formed in the output gear (702);
A limiting mechanism (8) is arranged in the dust box (504), the limiting mechanism (8) comprises two supporting plates (801) which are symmetrically arranged left and right, a fixing frame (803) is arranged in the middle of each supporting plate (801), a positioning pin (804) is arranged on each fixing frame (803), a coil (805) is wound at one end of each positioning pin (804), the positioning pins (804) correspond to limiting holes (703) on an output gear (702) of the reducing mechanism (7), scales are marked on the output gear (702), and the limiting holes (703) are arranged in a plurality and are distributed on the output gears (702) in an array mode;
Characterized in that the method comprises the following steps:
step 1, data acquisition and preprocessing
Taking the rotation angle of the moving blade as a control output f, and taking the acquisition characteristic of data acquired by the vertical mill particle sensor and the motor position sensor after data preprocessing as a control input X;
S1.1, in the step, the design variable is X= [ X 1;x2;x3;x4]T ], wherein X 1、x2、x3 and X 4 are respectively expressed as powder output, particle fineness, grinding quality and motor rotation speed;
S1.2, the multivariable model predictive controller converts output into a rotating angle of a moving blade, so that the dimension of control output is reduced;
Step2, multivariable model predictive control
In the multivariable model predictive control, the corresponding predictive results of the features are collected at different moments, the feature form and the time domain length N of the predictive control are determined, multi-step prediction is carried out through the feature form within the range of the time domain length N, the predictive value f p of the control output at the next moment is obtained, and the control input in the time domain is needed to be pre-advanced when the predictive system outputs in the future along with the time
Step 3, optimizing moving blade motion model of vertical mill
Comprehensively analyzing the collected characteristic parameters, and determining the value range of the characteristic parameters by combining the controllability and the actual meaning of the parameters; analyzing the control output after the dimension reduction, and determining an output constraint by referring to an actual model of the control device; in order to meet the minimum error between the predicted output value and the actual output value of the control system, the control system is required to obtain the optimal control input, and the accumulated error between the predicted output value and the actual output value is reduced; the smaller the accumulated error value is, the higher the accuracy of the whole process of model prediction is;
meanwhile, the control input and output constraints which the system should meet are:
0≤X(I+i)≤Xmax,i≥0
0≤f(I+i)≤360°,i≥0
Wherein X (I+i) is the control input at the moment I+i, X max represents the maximum control input; f (I+i) represents the control output at the moment I+i, and in the multivariate model predictive control, the control output is converted into the rotating angle of the moving blade so as to reduce the dimension of the control output, so that the value range of the control output f (I+i) is 0-360 degrees;
If the actual output of the system at each current moment is: d (i+1), d (i+2), d (i+p), the control system is required to obtain an optimal control input in order to meet the minimum error between the predicted output value and the actual output value of the control system; the accumulated error between the predicted output value and the actual output value is:
Wherein d i is the actual output value at the I moment, f p (I/I) represents the predicted output value at the I moment predicted at the I moment in the time domain p obtained by model prediction, and delta represents the accumulated error;
The smaller the accumulated error value, the higher the accuracy of the whole process representing model prediction, and therefore, the convergence criterion of the formula is: delta is less than or equal to epsilon δ, wherein epsilon δ is a minimum value;
Step 4, closed loop feedback control
Predicting the motion state of a moving blade in a regulating device and optimizing a vertical grinding moving blade motion model through a model prediction control algorithm, and using a predicted value to assist in state identification, wherein a closed-loop feedback control unit for regulating the rotation angle of a compensating blade of the vertical grinding powder selecting machine is formed by the combination of the predicted value, a servo motor, a speed reducing mechanism, a limiting mechanism and other regulating mechanisms;
Step 5, real-time parameter evaluation of vertical mill
Step 6, identifying the running state of the vertical mill
Based on the state evaluation index of the stable running of the vertical mill, analyzing the state of the vertical mill working condition, calculating the mean value, variance and abnormal value occurrence times of each parameter under the stable working condition, and establishing a database of the stable working condition;
the step 2 is specifically as follows:
S2.1, solving a finite time domain open loop optimization problem at each sampling moment according to the obtained current measurement information, and acting the first element of the obtained control sequence on a controlled object; at the next sampling instant, the above process is repeated: refreshing the optimization problem with the new measured value and solving again;
S2.2, in the process of collecting historical monitoring data by each sensor, assuming that the rotation angle of the corresponding output moving blade is mu 1,μ2,μ3 … for a sampling system at each sampling moment I=Ts, 2Ts and 3Ts
f(I)＝μI，(I＝1,2,3…)，
When the system is stabilized and the output is relatively ideal,Considering the influence of the internal environment of the vertical mill on the monitoring error, when the time domain length is N
f(I)＝μI，(I＝1,2,3…，N)，
S2.3, the characteristic form of predictive control is as follows:
x(I+1)＝f(x(I),X(I)) (1)
f(I)＝h(x(I),X(I)) (2)
Wherein X (I), X (I), f (I) are the state of the system at the moment I, control input and control output respectively;
s2.4, calculating the output of the system f (I) in future time according to the two formulas (1) and (2), and marking as: f p(I+1/I),fp(I+2/I),...,fp (I+p/I), p being the prediction horizon;
s2.5, along with the forward direction of time, the future output of the prediction system needs to predict the control input in the time domain in advance This is the design variable for predictive control optimization.
2. The intelligent regulation and control method of the powder concentrator of the large vertical mill as claimed in claim 1, wherein the step 4 is specifically as follows:
S4.1, obtaining a vertical mill moving blade motion model through multivariable model predictive control, optimizing the model, analyzing working condition data by utilizing a comprehensive feature screening method, obtaining a predictive response in an ideal state, and selecting proper control input; determining a control input range according to the value range of the key parameter in the historical data;
S4.2, analyzing real-time data acquired in the opposite grinding running state to obtain an actual value of control input;
S4.3, comparing the actual rotation angle of the compensating blade of the vertical mill powder concentrator with the predicted angle of the compensating blade at the moment, adjusting the system through closed loop feedback control, judging the running state of the vertical mill according to the state at the moment, and enabling the system to automatically adjust the system according to the measured parameters, the environment and the cost change of raw materials, so that the system is in an optimal state at any time.
3. The intelligent regulation and control method of the powder concentrator of the large vertical mill as claimed in claim 2, wherein the step 5 is specifically as follows:
S5.1, detecting null values and abnormal values of the acquired real-time data, discarding the read null values, filling the average value in the read historical data, eliminating the influence of the null values on the sampling interval of the data detection, continuously reading the data of the next sampling interval to detect after filling the null values, and repeating the process until a sufficient sample library is obtained;
S5.2, in the process of recording in a sample library, if difference data exceeding the value range of each parameter in the historical data appears, the data at the moment is considered as an abnormal value, the occurrence times of the abnormal value are recorded, and the average value of the data in the sampling interval at this time is read and replaced;
S5.3, calculating the average value, variance, standard deviation and the occurrence times of abnormal values of each parameter in each sampling interval of the vertical mill, taking the calculated numerical value as a characteristic value for judging the working condition of the vertical mill, evaluating the real-time input parameters of the vertical mill, and judging the running stability of the vertical mill according to the existing running working condition record.
4. The intelligent regulation and control method of the powder concentrator of the large vertical mill as claimed in claim 3, wherein the step 6 is specifically:
s6.1, when the parameters in the stable index are abnormal, starting a control program, searching a control target from a stable working condition database by the program, and returning a point closest to the current state to serve as a working condition to be selected;
S6.2, comparing the difference between the current state and the working condition to be selected, and adjusting the controllable variable through adjusting the adjustable parameter until the parameter integrally reaches a target value;
And S6.3, judging whether the running condition of the vertical mill in the state meets the requirement or not after the running state of the vertical mill is stable according to the established stable condition database, and otherwise, adjusting the control input to enable the system to enter the optimal condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301596.3A CN115532421B (en) | 2022-03-24 | Intelligent regulation and control method for powder concentrator of large vertical mill |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210301596.3A CN115532421B (en) | 2022-03-24 | Intelligent regulation and control method for powder concentrator of large vertical mill |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115532421A CN115532421A (en) | 2022-12-30 |
| CN115532421B true CN115532421B (en) | 2024-06-21 |
Family
ID=
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102151605A (en) * | 2011-03-17 | 2011-08-17 | 浙江大学 | Advanced control method and system for vertical mill based on model identification and predictive control |
| CN103028480A (en) * | 2012-12-10 | 2013-04-10 | 上海凯盛节能工程技术有限公司 | Intelligent control system for vertical mill based on fuzzy PID (proportion integration differentiation) algorithm |
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102151605A (en) * | 2011-03-17 | 2011-08-17 | 浙江大学 | Advanced control method and system for vertical mill based on model identification and predictive control |
| CN103028480A (en) * | 2012-12-10 | 2013-04-10 | 上海凯盛节能工程技术有限公司 | Intelligent control system for vertical mill based on fuzzy PID (proportion integration differentiation) algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101038277B (en) | Soft measurement method for coal power fineness in powdering producer | |
| CN101434045B (en) | Device for calibrating relationship between current of electric machine of numerically controlled machine and cutting load | |
| US11294353B2 (en) | Spindle thermal error compensation method insensitive to cooling system disturbance | |
| CN111443597B (en) | Device and method for controlling granularity of vertical mill mineral powder | |
| CN104117554B (en) | A kind of high-accuracy straightening machine automatic control system and control method | |
| CN102011745A (en) | Neural network control system and method of magnetic suspension molecular pump | |
| CN204535985U (en) | Electrical spindle for machine tool reliability test bench centering adjusting device | |
| CN109443689A (en) | The wind tunnel test measuring device and its measurement method of dynamic aerodynamics when a kind of radar antenna rotary work | |
| CN101623667A (en) | Automatic control method and system for mill load | |
| CN202240898U (en) | Online wearing detection device for grinding wheel based on grinding wheel air-flow field | |
| CN102343549B (en) | Method and device for carrying out online detection on abrasion of grinding wheel by using air flow on grinding wheel | |
| CN115532421B (en) | Intelligent regulation and control method for powder concentrator of large vertical mill | |
| US3960330A (en) | Method for maximizing throughput in an ore grinding system | |
| CN106676934A (en) | Economical and optimal running control method and system for high-concentration grinding system | |
| CN113333070A (en) | Flour mill and control method thereof | |
| JP2013513773A (en) | System and associated method for monitoring and controlling a power plant | |
| CN209302869U (en) | Coal pulverizer automatic regulating system | |
| CN104307886A (en) | Composite rolling mill clad-rolling large-lag AGC (automatic gain control) method | |
| CN108452895B (en) | Energy-saving control method for ball mill | |
| CN115532421A (en) | Intelligent regulation and control system and regulation and control method for powder concentrator of large vertical mill | |
| CN2239020Y (en) | Double-constant feed belt steelyard | |
| CN104439691B (en) | A kind of agitating friction welding upset force and advance drag adaptive controller | |
| Hulthén et al. | Algorithm for dynamic cone crusher control | |
| Zhang et al. | Application of neural network predictive control in cement combined grinding | |
| CN109107744B (en) | Medium-speed mill air-coal ratio and oil pressure dynamic optimization-approaching adjusting method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |