CN110658050B - Self-adaptive mixing division method for multi-mineral characteristic - Google Patents
Self-adaptive mixing division method for multi-mineral characteristic Download PDFInfo
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Abstract
The invention discloses a self-adaptive mixing and division method for multi-mineral characteristics, which comprises the steps of firstly, constructing a multi-mineral characteristic basic characteristic library to obtain characteristic parameter values of different minerals such as stacking density, loosening coefficient, crushing characteristic and the like; based on a mineral characteristic feature library, a division opening-speed model is constructed, the trained division opening-speed model (D, theta, Ar, v) is solidified into a chip as N (D, K, phi), a self-adaptive mixed division controller is obtained, and real-time opening size D, opening angle theta, opening section radian Ar and division machine motor speed v of the mineral mixed division machine are obtained through real-time calculation according to real-time data of feature vectors (D, K, phi) such as bulk density, loose coefficient and crushing characteristic after mineral crushing, so that self-adaptive control of mineral mixed division is achieved. Thereby improved the precision that the machine of division system appearance was reserved to the reduction, reduced the bias between the sample of reserving of system and the reference sample, improved extension ware ejection of compact rate, strengthened the stability of the machine of division quality division ratio.
Description
Technical Field
The invention is suitable for the field of intelligent manufacturing, and particularly relates to mineral substance sample preparation mixing division.
Background
The automatic division equipment and the system for mineral particles such as coal, iron ore, sinter, pellet, coke, building materials, chemical industry and the like are widely applied to industries and departments such as coal, metallurgy, building materials, electric power, third-party inspection and scientific research institutions and the like. The method of division directly influences the division precision and the division efficiency of the division equipment and the system so as to ensure the accurate measurement of the quality of the mineral substances.
At present, the improvement of the reduction precision and the reduction efficiency is mainly focused on the improvement of a reduction device mechanism, and a patent 201510657376.4 discloses a rotary reduction machine which mixes a sample by a mixer and then sends the sample to a rotary material receiving barrel. Patent 201620949806.X invented a robot system appearance with rotatory division machine, realized many proportions division. Patent 201610947574.9 discloses a rotary dividing machine with adjustable and controllable rotation speed, which automatically adjusts the feeding speed of the feeding device by preset rules. Patent 201820995296.9 discloses a rotary splitter based on a vibrating device to uniformly scatter the sample into the barrel. Patent 201811080535.9 discloses a uniform division machine which combines constant proportion division and constant quality division, and according to the uniform division machine for division of a sample, the uniform mixing is carried out before each division, thereby ensuring that the division precision meets the requirement.
However, the improvement of these division methods and systems does not consider the characteristics of minerals, and there is an inherent limitation to the improvement of division precision and division efficiency of division equipment and systems, and designing an adaptive mixed division method and system oriented to the characteristics of multiple minerals is an urgent problem to be solved for further improving the division precision and division efficiency.
Disclosure of Invention
Since the minerals are abundant and highly heterogeneous, it is very difficult to extract representative samples. The invention provides a self-adaptive mixing division method for detecting the characteristics of multiple minerals, which has strict specifications of number, weight and distribution of sub-samples in different occasions, needs, types and batches, has the advantages that sampling holes of a division device, the granularity of the minerals and the rotating speed of a motor can influence the division precision, improves the precision of sample preparation and sample reservation of a division machine in order to ensure the representativeness of a sample to be collected, reduces the bias between the sample reservation of the system and a reference sample, improves the discharge rate of a separator and enhances the stability of the quality division ratio of the division machine.
The specific scheme is as follows: an adaptive mixing and division method for multi-mineral characteristics comprises the following steps:
(1) multi-mineral characteristic graded sampling measurement and acquisition;
(2) constructing a multi-mineral characteristic basic feature library: storing the measured different mineral bulk densities, loosening coefficients and breaking characteristic feature vectors (D, K, phi) into a database, and constructing a multi-mineral characteristic basic feature knowledge base for training to obtain a division opening-speed model;
(3) constructing a self-adaptive mixed division controller: and (3) solidifying the trained reduction opening-speed model (D, theta, Ar, v) to a chip to obtain a self-adaptive mixed reduction controller, and calculating real-time opening size D, opening angle theta, opening section radian Ar and reduction machine motor speed v of the mineral mixed reduction machine in real time according to the real-time data of the characteristic vectors (D, K, phi) such as the bulk density, the loose coefficient, the crushing characteristic and the like after the mineral is crushed, so that the self-adaptive control of the mineral mixed reduction is realized.
In the method, firstly, a multi-mineral characteristic basic characteristic library is constructed to obtain characteristic parameter values of different minerals such as stacking density, loosening coefficient, crushing characteristic and the like; constructing a division opening-speed model based on a mineral characteristic feature library, and further designing a self-adaptive mixed division controller; the self-adaptive mixing division controller realizes the uniform mixing of the mixer, the uniform feeding without grain segregation of the conveyor and the high-precision rotary division of the rotary sample dividing barrel.
Wherein, in the construction of the multi-mineral characteristic basic characteristic library, the characteristic vectors (D, K, phi) of the bulk density, the loose coefficient and the crushing characteristic of different minerals are calculated according to the following formula
Wherein h iss,maxThe void volume fraction of the largest base particle, calculated by the Reschke T bulk density theory,
K0is the void volume fraction of the powder with single particle size, as,iParticle interference coefficient of i fraction, SiI fraction of the whole mineral volume, n fraction of the fraction, as,iSiThe base particle size is reflected; coefficient of bulk after crushing of minerals
V1Is the pre-crushing volume of the mineral, V2Is the post-fracture volume of the mineral; the crushing characteristics of the mineral substances with different particle diameters are described by a specific surface shape coefficient phi and are calculated by a C.Aschenbrenner true sphericity theory
a. b and c are half distances of the longest diameter, the next longest diameter and the shortest diameter of the three-axis diameter of the particles in sequence. The eigenvector (D, K, phi) values of the different minerals form a multi-mineral characteristic basis feature library.
Preferably, a multi-mineral characteristic basic feature library is trained on the basis of a deep learning neural network to obtain a reduction opening-speed model (D, theta, Ar, v) ═ N (D, K, phi), the opening size of a D reduction machine, theta is the opening angle of the reduction machine, Ar is the opening section radian of the reduction machine, v is the motor speed of the reduction machine, and N is the deep learning neural network, wherein the deep learning neural network N is based on a multi-layer convolutional neural network architecture, and the activation function of the deep learning neural network N adopts a Sigmoid function
Preferably, the specific method for constructing and training the division opening-speed model comprises the following steps:
1) mineral segregation influencing factor acquisition: acquiring a characteristic vector (D, K, phi) value of a current mineral division influence factor in real time;
2) initializing self-adaptive mixed division control;
3) and (3) performing double convolution-sampling feature extraction to finish the training of the self-adaptive mixed reduction control to obtain a self-adaptive mixed reduction control model with accurate predictive control capability, namely completing the initial construction of a reduction opening-speed model.
4) Fuzzy optimization of quantum neural network nodes: and (3) adding a quantum neural network node fuzzy optimization layer in front of an output layer in the self-adaptive hybrid reduction control model to optimize uncertain data.
5) And (3) completing the construction of a division opening-speed model: and after the mineral self-adaptive mixed division control model is trained through feedback, training of the division opening-speed model is completed.
The invention has the beneficial effects that:
compared with the existing division method, the self-adaptive mixing division method for the multi-mineral characteristics, which is provided by the invention, is characterized in that a division opening-speed model is constructed through a multi-mineral characteristic basic characteristic library, a self-adaptive mixing division controller is constructed through the division opening-speed model, a self-adaptive mixing division system is realized through the self-adaptive mixing division controller, and the self-adaptive control on the opening size, the opening angle, the opening section radian and the speed of a motor of a division machine is completed, so that the precision of sample preparation and sample retention of the division machine is improved, the bias between the sample retention and a reference sample of the system is reduced, the discharge rate of the division machine is improved, and the stability of the mass division ratio of the division machine is enhanced.
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FIG. 1 is a flow chart of an adaptive mixing and scoring method for multi-mineral properties. This patent provides a multi-mineral property oriented adaptive mixing and scoring method, comprising: step 1: multi-mineral property measurement and acquisition (step S01); step 2: constructing a multi-mineral characteristic basic feature library (step S02); and step 3: division opening-speed model construction and training (step S03); and 4, step 4: constructing an adaptive mixing division controller (step S04); and 5: constructing an adaptive mixed division system (step S05); step 6: adaptive rotation division (step S06).
FIG. 2 is a flow chart of a method for adaptive hybrid score control. The patent provides a self-adaptive mixing division control method, comprising: step 1: acquiring mineral contraction influence factors (step S31); step 2: initializing the adaptive hybrid division control (step S32); and step 3: double convolution-sampling feature extraction (step S33); and 4, step 4: fuzzy optimization of quantum neural network nodes (step S34); and 5: mineral adaptive mixing and division (step S35).
FIG. 3 is a schematic diagram of a training of a subdivision opening-speed model deep learning neural network;
FIG. 4 is a schematic diagram of fuzzy optimization of quantum neural network nodes;
Detailed Description
The invention relates to a self-adaptive mixing and splitting method and a self-adaptive mixing and splitting system for multi-mineral characteristics, which are used for mixing and splitting mineral samples.
The invention is further described with reference to the following figures and specific examples.
As shown in fig. 1, the method of the present invention comprises the steps of:
(1) multimineral property measurement and acquisition S01: the multi-mineral characteristics are determined by classifying and sampling crushed powder of the minerals; the mineral in this example is coal, and has a packing density D in the eigenvector (D, K, phi) of 0.8259 to 1.040, a bulk modulus K of 1.145 to 1.358, and a specific surface shape modulus phi of 0.571 to 1.000.
(2) The multi-mineral characteristic basic feature library is constructed S02: storing the measured multi-mineral characteristic vector (D, K, phi) values into a database, and constructing a multi-mineral characteristic basic characteristic knowledge base for training a division opening-speed model; the multi-mineral characteristic base feature knowledge base in this embodiment is a coal characteristic base feature knowledge base.
(3) Division opening-velocity model construction and training S03: a division opening-speed model is constructed, a self-adaptive mixed division control method is designed, learning training of a self-adaptive mixed division controller is carried out, self-adaptive mixed division control of learning training completion is achieved, and the flow of the self-adaptive mixed division control method is shown in fig. 2.
1) Mineral segregation influence factor acquisition S31: acquiring a characteristic vector (D, K, phi) value of a current mineral division influence factor in real time; the feature vectors in this embodiment are measured by a bulk density flow meter, a loose coefficient flow meter, and a loose coefficient flow meter.
2) The adaptive mixing division control initialization S32, in this embodiment, the default rotating speed of the mixer is 36r/min, the model of the adopted reducing motor is NMRV75-60-1.1-4P-B6-2, the mixing time is set to 3 minutes from factory, and after the initialization start, the adaptive control is carried out according to the division opening-speed model.
3) And (4) extracting the double convolution-sampling characteristics S33 to finish the training of the self-adaptive mixed division control, thereby obtaining the self-adaptive mixed division controller with accurate predictive control capability.
4) And (8) quantum neural network node fuzzy optimization S34, wherein the quantum neural network node fuzzy optimization layer is added before an output layer in the self-adaptive hybrid reduction control model, so that the uncertain data are optimized.
5) And after the mineral self-adaptive mixing division predictive control model is completed through feedback training, a controller consisting of a division opening-speed model is obtained and is used for realizing mineral self-adaptive mixing division control.
Deep learning neural network for a subdivision opening-velocity model, bias of N bk(k ═ 0,1,2,3) value of 0.5; real-time data of characteristic vectors (D, K, phi) such as bulk density, loose coefficient, crushing characteristic and the like after mineral crushing are used as input layers; cij-Sij(i ═ 1, 2; j ═ 1,2,3,4,5) is a double convolution-sampled feature layer; qj (j ═ 1,2,3,4,5) is a quantum neural network node fuzzy optimization layer;
real-time opening size d, opening angle theta, opening section radian Ar and speed v of a motor of the division machine are output layers when mineral substances are mixed and divided, and each quantity of each layer is a node NiAny node Ni、NjW of the network betweeni,jForming a deep learning neural network N ═ { W at the current timei,jThe deep learning neural network training and learning process is a process of mapping each group of input and output data and iterating to obtain a new deep learning neural network weight, and the weight iteration in the deep learning neural network is performed by an activation function
Calculating and realizing; as shown in fig. 3, an input vector is (0.890, 1.124, 0.678), an output vector is (120,43,0.5,36), and a current set of weights N ═ W of the deep learning neural network is obtained through trainingi,j}。
Energy level transitions in the nodes of the fuzzy optimization layer of the quantum neural network nodes are shown in fig. 4.
The input values of the current node of the optimization layer are blurred for the nodes of the quantum neural network,
and fuzzily optimizing the output value of the current node of the layer for the quantum neural network node. Number n of jump positions per node of the fuzzy analysis layersIs 10, quantum spacing
The initial value is 0.1.
After a large amount of iterative training learning, the final deep learning neural network N ═ W is obtainedi,jThe method is characterized in that a controller formed by a division opening-speed model is input with characteristic vectors (D, K, phi) such as stacking density, loose coefficient and crushing characteristic after mineral crushing in real time, and a deep learning neural network (N ═ W)i,jAnd (4) calculating to obtain a group of real-time opening size d, opening angle theta, opening section radian Ar and division machine motor speed v output results.
(4) The adaptive hybrid division controller is constructed as S04: and (3) solidifying the trained reduction opening-speed model (D, theta, Ar, v) as N (D, K, phi) into a chip to obtain a self-adaptive mixed reduction controller, and calculating the real-time opening size D, the opening angle theta, the opening section radian Ar and the reduction machine motor speed v of the mineral mixed reduction machine in real time to realize the self-adaptive control of the mineral mixed reduction machine. In this embodiment, the adaptive hybrid-subdivision predictive control model algorithm is implemented by using an ARM architecture.
(5) The self-adaptive mixed division system is constructed by S05: the self-adaptive mixing division system comprises a self-adaptive mixer and a controller thereof, a belt conveyor, a rotary sample division barrel, a self-adaptive controller, a rotary motor, an overrunning clutch, a rack and an outer cover, wherein the sample feeding granularity of the division system is less than or equal to 25mm, the division ratio is 1/2-1/8, the capacity of a feeding bin is 80kg, the capacity of sample division barrels is 15kg, the number of the sample division barrels is 8, the production rate is 800kg/h, the total power is 2.02kw, and a power supply is a three-phase 380V.
(6) Adaptive rotation reduction S06: after the sample is added into the mixing barrel, a start button is pressed, and the self-adaptive mixing division controller controls the mixer to start self-adaptive stirring and mixing in real time; arrival mixAfter the set time is closed, the belt conveyor and the rotary sample dividing barrel are automatically started, and the self-adaptive controller, the rotary motor and the overrunning clutch begin to perform self-adaptive rotary division on the coming samples; after the division is finished, sampling is finished by a sample dividing barrel, in the embodiment, the stability fluctuation range of the mass division ratio of the adaptive mixing division machine is between 1/(m-1) -1/(m +1) (1/m is the average value of multiple times of division ratio measurement), and the precision Total Variance (Total Variance) sigma of sample preparation reserved samples of the adaptive mixing division machine<0.05A2(A is sampling precision specified by the standard), the self-adaptive mixing and dividing machine does not have system bias which is obviously different from 0, and the discharging rate of the self-adaptive mixing and dividing machine>99.9％。
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.
Claims (1)
1. An adaptive mixing and division method for multi-mineral characteristics is characterized by comprising the following steps:
(1) multi-mineral characteristic graded sampling measurement and acquisition: crushing mineral substances, then carrying out grading sampling on the powder particles, and measuring to obtain the characteristics of the multi-mineral substances, wherein the characteristics of the multi-mineral substances comprise bulk density D, a loosening coefficient K and a specific surface shape coefficient phi;
(2) constructing a multi-mineral characteristic basic feature library: storing the measured bulk density D, the measured loose coefficient K and the measured specific surface shape coefficient phi of different minerals as mineral division influence factors into a database, and constructing a multi-mineral characteristic basic characteristic knowledge base for training a division opening-speed model;
bulk density in mineral segregation influence factor
Wherein h iss,maxIs the largest void volume fraction of the base particle,
K0is the void volume fraction of the powder with single particle size, as,iParticle interference coefficient of i fraction, SiI fraction of the whole mineral volume, n fraction of the fraction, as,iSiThe base particle size is reflected; the loosening coefficient of minerals in the mineral segregation influence factor
V1Is the pre-crushing volume of the mineral, V2Is the post-fracture volume of the mineral; specific surface shape coefficient in mineral segregation influence factor
Wherein
a. b, c are the longest, the next longest and the shortest half distances of the three-axis diameters of the particles in sequence, p is the ratio of the shortest half distance to the next longest half distance in the three-axis diameters of the particles, and q is the ratio of the next longest half distance to the longest half distance in the three-axis diameters of the particles;
(3) constructing and training a division opening-speed model:
acquisition of mineral segregation influence factors S31: acquiring the bulk density D, the loosening coefficient K and the specific surface shape coefficient phi of the current mineral in real time;
initializing the self-adaptive mixed division control S32: starting initialization, after the initialization is started, carrying out self-adaptive control according to a division opening-speed model, wherein the division opening-speed model adopts the bias b of a deep learning neural network N, NkThe value is 0.5, K is 0,1,2,3, and real-time data of the stacking density D, the loosening coefficient K and the specific surface shape coefficient phi of the mineral are input layers;
③ double convolution-sampling feature extraction S33: extraction of Cij-SijIs a double convolution-sampling characteristic layer, i is 1, 2; j is 1,2,3,4,5, wherein CijFor the jth convolution feature node of the ith layer,Sijis the jth sampling characteristic node of the ith layer;
quantum neural network node fuzzy optimization S34: the fuzzy optimization layer of the quantum neural network nodes is added before an output layer in the self-adaptive hybrid division control model, the data of uncertainty is optimized, Qj is the fuzzy optimization layer of the quantum neural network nodes, j is 1,2,3,4,5, the real-time opening size d, the opening angle theta, the opening section radian Ar and the speed v of a motor of the division machine in the mineral hybrid division are output layers, and each quantity of each layer is a node NiAny node Ni、NjW of the network betweeni,jForming a deep learning neural network N ═ { W at the current timei,jThe deep learning neural network training and learning process is a process of mapping each group of input and output data and iterating to obtain a new deep learning neural network weight, and the weight iteration in the deep learning neural network is performed by an activation function
Calculating and realizing;
mineral self-adaptive mixing and condensation S35: after the mineral self-adaptive mixing division control model is subjected to feedback training, a controller consisting of a division opening-speed model is obtained and used for realizing mineral self-adaptive mixing division control;
(4) and solidifying the trained reduction opening-speed model (D, theta, Ar, v) as N (D, K, phi) into a chip to obtain a self-adaptive mixed reduction controller, inputting the stacking density D, the loosening coefficient K and the specific surface shape coefficient phi of the mineral in real time, and calculating to obtain the real-time opening size D, the opening angle theta, the opening section radian Ar and the reduction machine motor speed v of the mineral mixed reduction machine so as to realize the self-adaptive control of the mineral mixed reduction.
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