Intelligent control method for crushing and screening process of high-temperature electrical-grade magnesium oxide

Abstract

The invention discloses an intelligent control method for the crushing and screening process of high-temperature electrical-grade magnesium oxide, which comprises the following steps: analyzing the technological process, equipment and operation of the crushing and screening process of high-temperature electrical-grade magnesium oxide, determining crushing and separation as granularity influence links, and selecting the rotating speed of a crusher, the rotating speed of a grader and the feeding flow as operating variables; in the off-line link of the production process, with the particle size distribution range of the fused magnesia powder as a target, according to the magnesium oxide content, the hardness, the particle size and the type of the fused magnesia raw material, a mixed intelligent modeling method is adopted to calculate a preset value of an operation variable; in the online link of the production process, adjustment is made in real time in combination with operation feedback and machine load conditions; and finally obtaining an operation variable set value for control.

Description

Technical Field

[0001] The invention relates to the technical field of control of a high-temperature electrical grade magnesium oxide production process, in particular to an intelligent control method for a high-temperature electrical grade magnesium oxide crushing and screening process. Background Art

[0002] The crushing and screening process of high-temperature electrical grade magnesium oxide is an important process in the production process of high-temperature electrical grade magnesium oxide. It crushes the larger blocky fused magnesium oxide raw materials into finer powders, and then screens out the fused magnesium oxide powder with a suitable particle size range for subsequent high-temperature calcination and modification processes.

[0003] High temperature electrical grade magnesium oxide crushing and screening process Figure 1 As shown. The feeding device feeds the blocky fused magnesium oxide into the crusher for crushing. The crushed fused magnesium oxide powder is lifted to a high place by a bucket elevator and sent to a linear vibrating screen. The linear vibrating screen classifies the fused magnesium oxide powder according to different particle sizes. The coarser particles are returned to the crusher for further crushing, and the finer particles are collected by the bag. The particles with appropriate particle sizes enter the airflow classifier for further screening. The airflow classifier further screens the fused magnesium oxide powder with appropriate particle size. The coarser particles are used as fused magnesium oxide powder with appropriate particle size. After being lifted to a high place by a bucket elevator, they are subjected to magnetic separation and iron removal, and then collected and sent to the high-temperature furnace calcination process; the other part of the finer particles are collected after screening by the collector, and the remaining very small part of the finer particles is collected by the electric dust collector.

[0004] The indicator that reflects the quality of the crushing and screening process of high-temperature electrical grade magnesium oxide is the particle size distribution of the fused magnesium oxide powder collected after magnetic separation and iron removal (the mass fraction distributed in the range of +35 mesh, -35 mesh to +325 mesh, -325 mesh). It is tested offline by the inspectors using a standard vibrating screen machine.

[0005] The crushing and screening process of high-temperature electrical grade magnesium oxide has a complex mechanism, and the types and properties of raw materials vary. In actual production, the particle size distribution of fused magnesium oxide powder is controlled by the operator based on subjective experience. The operating behavior is closely related to the operator's experience, sense of responsibility and other factors, and has a certain degree of randomness, which leads to large fluctuations in product quality and poor consistency. Therefore, how to determine the appropriate operating variable setting value in a timely and accurate manner according to the actual production situation and reduce the fluctuation of product quality has become an urgent problem to be solved. Summary of the invention

[0006] In view of the complex mechanism of the crushing and screening process of high-temperature electrical grade magnesium oxide, the changeable types and properties of raw materials, the certain randomness of operation, and the large fluctuations in product quality, the technical problem to be solved by the present invention is to provide an intelligent control method for the crushing and screening process of high-temperature electrical grade magnesium oxide, which can overcome the changes in raw materials and automatically determine the set values ​​of operating variables according to actual production conditions.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0008] A method for intelligently controlling a high-temperature electrical grade magnesium oxide crushing and screening process comprises the following steps:

[0009] Analyze the process flow, equipment and operation of high-temperature electrical grade magnesium oxide crushing and screening, determine crushing and separation as the links affecting particle size, and select crusher speed, classifier speed and feed flow rate as operating variables;

[0010] In the offline stage of the production process, the particle size distribution range of the fused magnesium oxide powder is taken as the target, and the preset values ​​of the operating variables are calculated using a hybrid intelligent modeling method based on the magnesium oxide content, hardness, particle size and type of the fused magnesium oxide raw material. In the online stage of the production process, real-time adjustments are made based on operational feedback and machine load conditions. Finally, the set values ​​of the operating variables are obtained for control.

[0011] The offline process of the production process includes:

[0012] Construct input and output sample sets based on historical data of production processes;

[0013] Consider the degree of similarity between the new situation and existing operational situations;

[0014] Offline construction of hybrid intelligent models to predict pre-set values ​​of operating variables in new situations;

[0015] Set the preset value Y of the manipulated variable new The data is sent to the basic control system for execution. If the particle size distribution cannot be satisfied, the preset value is empirically corrected.

[0016] Delete the historical data with the greatest similarity to the new question and update the stored sample set.

[0017] Constructing the input and output sample set based on the historical data of the production process includes: defining the input data matrix The output data is stored in the output data matrix Among them, X i =[x i1 ,x i2 ,...,x i6 ,x i7 ](i=1,2,...,U) represents the input variable of the i-th sample data, xi1 ~x i7 Respectively represent the magnesium oxide content, hardness and particle size of the fused magnesium oxide raw material, the mass fraction of the desired range of +35 mesh, -35 mesh to +325 mesh, -325 mesh, and the type of the fused magnesium oxide raw material; Y i =[y i1 ,y i2 ,y i3 ](i=1,2,...,U) represents the output variable of the i-th sample data, y i1 ~y i3 They respectively represent the preset values ​​of crusher speed, classifier speed and feed flow rate.

[0018] During production operation, the similarity between the new situation and the existing operation situation is considered as the similarity between the new problem input and the sample input;

[0019]

[0020] Among them, X new =[x new1 ,x new2 ,...,x new6 ,x new7 ] indicates new question input, x newj Represents the jth (j=1, 2, ..., 7) component of the new problem input.

[0021] Consider the similarity between the new situation and the existing operation situation; build a hybrid intelligent model offline, specifically:

[0022] If the similarity is high and the number of similar samples is large, the extreme learning machine ELM method is used to establish a pre-set model of the operating variables;

[0023] If the similarity is high and the number of similar samples is small, the SMOTE algorithm is used to expand the similar samples, and then the Bagging LSSVM method is used to establish the pre-set model of the operating variables;

[0024] If the similarity is low, ask domain experts or excellent operators to directly provide the preset values ​​of the operating variables in the current situation.

[0025] If the similarity D(X new ,X i ) is greater than or equal to the similarity threshold δ s The number of samples N is greater than or equal to the sample number threshold δ sn , the extreme learning machine (ELM) method is used to establish a prediction model for the preset values ​​of the operating variables, as follows:

[0026]

[0027] Among them, Y new Enter X for new questions new The preset values ​​of the operating variables at the time, the weight matrix a i , b i It is the input weight vector and bias value of hidden layer nodes for randomly generating ELM method.

[0028] If the similarity D(X new ,X i ) is greater than or equal to δ s The number of samples N is less than δ sn , the SMOTE algorithm is used to expand similar samples, and then the Bagging LSSVM method is used to establish a preset model of the operating variables. The steps are as follows:

[0029] ① Set the upsampling ratio to AA, and use the SMOTE method to perform linear interpolation to obtain new samples (NSI k ,NSO k );

[0030] (NSI k ,NSO k )=(X i ,Y i )+rand i,z (0,1)×((X z -X i ),(Y z -Y i )) (5)

[0031] Where k = 1, 2, ..., AA, rand i,z (0,1) means using existing samples (X i ,Y i ) and (X z ,Y z )When generating a new sample (NSI k ,NSO k )A random number generated in the interval [0,1];

[0032] The final expanded B = A × AA new samples are put together with the selected samples to form an extended sample set (EX j ,EY j ), where j = 1, 2, ..., (A + B);

[0033] ②Use the Bagging LSSVM method to establish a prediction model for preset values ​​of operating variables:

[0034] a. In the interval [1, (A+B)], the bagging method is used according to the sampling probability Randomly extract s samples from the input and output sample sets, and repeat Z times to obtain Z pre-set sample subsets: {C1, V1}, {C2, V2}, …, {C Z ,V Z};

[0035] Among them, the new sample subset consisting of s samples extracted from the input and output sample sets for the i-th (i=1,2,...,Z) time is set to is the input data of the zth sample in the i-th subset, is the output data of the zth sample in the i-th subset;

[0036] b. Establish the j-th output LSSVM model f based on the i-th subset ij (X new );

[0037] c. Use the average method to synthesize the results of the Z LSSVM sub-models to obtain the preset value of the jth operating variable:

[0038]

[0039] Therefore, the new preset value of the manipulated variable is Y new =[y new1 ,y new2 ,y new3 ].

[0040] In the online production process, based on the difference between the target range and the detection value of the particle size distribution of the fused magnesium oxide powder, a feedback compensation strategy is used to calculate the feedback compensation value ΔY(t) of the operating variable; based on the crusher return flow and bucket elevator current, the crusher load condition is judged, and the intelligent adjustment strategy is used to calculate the adjustment value of the operating variable.

[0041] ΔY(t)=[Δy1(t),Δy2(t),Δy3(t)]

[0042]

[0043] Finally, the manipulated variable is set to

[0044] The present invention has the following advantages and beneficial effects:

[0045] The method of the present invention does not need to analyze the complex mechanism of the crushing and screening process of high-temperature electrical grade magnesium oxide, makes full use of historical data with good operating effects and the operating experience and knowledge of on-site experts, and can automatically give set values ​​of operating variables in a timely manner according to changes in raw materials and products, thereby achieving stable control of the particle size distribution of fused magnesium oxide powder, improving the consistency of the particle size distribution, making the crushing and screening process operation more regularized, and eliminating the arbitrariness and blindness of operators as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is the schematic diagram of the crushing and screening process of high-temperature electrical grade magnesium oxide;

[0047] Figure 2 Flowchart for calculation of operating variables. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation method of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the invention, so the present invention is not limited by the specific implementation disclosed below.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The present invention is further described in detail below in conjunction with the accompanying drawings and examples.

[0050] The technical problem to be solved by the present invention is to provide an intelligent control method for the crushing and screening process of high-temperature electrical grade magnesium oxide, which can overcome the change of raw materials and automatically determine the set value of the operating variable according to the actual production situation. Figure 2 The figure shows the calculation flow chart of the operating variables. The specific steps are as follows.

[0051] 1. Selection of operating variables:

[0052] The process flow, equipment and operation of high-temperature electrical grade magnesium oxide crushing and screening were analyzed. It was found that the links with greater influence on particle size are crushing and separation. The variables affecting the quality and efficiency of the crushing link are feed flow and crusher speed. The main factor affecting the quality and efficiency of the separation link is the classifier speed. Therefore, crusher speed, classifier speed and feed flow were selected as operating variables.

[0053] 2. Calculation of the set value of the operating variable:

[0054] (I) Presetting of operating variables

[0055] During production operations, the preset values ​​of the operating variables are predicted by considering the similarity between the new situation (i.e., new problem) and the existing operating situation. If the similarity is high and the number of similar samples is large, the ELM (extreme learning machine) method is used to establish the preset model of the operating variables; if the similarity is high and the number of similar samples is small, the SMOTE (synthetic minority class oversampling) algorithm is used to expand the similar samples, and then the Bagging LSSVM (Bagging least squares support vector machine) method is used to establish the preset model of the operating variables; if the similarity is low, the domain experts or excellent operators are asked to directly give the preset values ​​of the operating variables in the current situation.

[0056] (1) Constructing input and output sample sets

[0057] The crushing and screening process of high-temperature electrical grade magnesium oxide and the operation of operators were analyzed. It was found that the operators determined the preset values ​​of crusher speed, classifier speed and feed flow rate based on the target value of particle size, the magnesium oxide content, hardness and particle size of the fused magnesium oxide raw material, and the target particle size distribution - the mass fraction distributed in the range of +35 mesh, -35 mesh to +325 mesh, and -325 mesh. Therefore, in the preset algorithm, the input variables are selected as: magnesium oxide content, hardness and particle size of the fused magnesium oxide raw material, the mass fraction of the target range of +35 mesh, -35 mesh to +325 mesh and -325 mesh, and the type of fused magnesium oxide raw material; the output variables are the preset values ​​of crusher speed, classifier speed and feed flow rate.

[0058] Select the input and output variable data with good control effects in history to form the input and output modeling sample set, and store the input variable data in the input modeling data matrix, and store the output variable data in the output modeling data matrix. Suppose a total of U groups of input and output data are found, and the input data among them are stored in the input data matrix (symbol T represents matrix transpose, the same below), the output data is stored in the output data matrix In, X i =[x i1 ,x i2 ,...,x i6 ,x i7 ](i=1,2,...,U) represents the input variable of the i-th sample data, x i1 ~x i7 Respectively represent the magnesium oxide content, hardness and particle size of the fused magnesium oxide raw material, the mass fraction of the desired range of +35 mesh, -35 mesh to +325 mesh, -325 mesh, and the type of the fused magnesium oxide raw material; Y i =[y i1,y i2 ,y i3 ](i=1,2,...,U) represents the output variable of the i-th sample data, y i1 ~y i3 They respectively represent the preset values ​​of crusher speed, classifier speed and feed flow rate.

[0059] (2) Determine the similarity between the new question input and the sample input

[0060] Query whether there is a sample in the input and output sample set that is the same as the 7th component of the new question input. If it exists, retrieve the sample that is the same as the 7th component of the new question input, and then use formula (1) to determine the similarity between the new question input and the sample input; if it does not exist, the similarity between the new question input and the sample input is 0.

[0061]

[0062] Among them, X new =[x new1 ,x new2 ,...,x new6 ,x new7 ] indicates new question input, x newj Represents the jth (j=1, 2, ..., 7) component of the new problem input.

[0063] (3) Establish a prediction model for preset values ​​of operating variables based on the similarity results

[0064] A. Similarity is greater than or equal to the similarity threshold δ s , δ s The value is 0.65, and the similarity is greater than or equal to δ s The number of samples is greater than or equal to the sample size threshold δ sn , δ sn Take it as 80.

[0065] Assume that the similarity is greater than or equal to δ s The number of samples is N, and the sample input matrix is The sample output matrix is X Ii =[x Ii1 ,x Ii2 ,...,x Ii6 ](i=1,2,...,N),Y Oi =[y Oi1 ,y Oi2 ,y Oi3 ]i(1,2,...,N). The extreme learning machine (ELM) method is used to establish a prediction model for the preset values ​​of the operating variables. The steps are as follows:

[0066] ① Randomly generate ELM input weight vector a i and the bias value b of the hidden layer node i , i=1,2,…,L, L represents the number of hidden layer nodes, L=10;

[0067] ② Calculate the ELM hidden layer output matrix H;

[0068]

[0069] Among them, h i (x j )=G(a i ,b i ,X Ij ) represents the output of the i-th hidden layer node, which is the ELM nonlinear feature map. G(a i ,b i ,X Ij )=exp(-b i ||X Ij -a i ||), sample data (X Ij ,Y Oj )∈R 6 ×R 3 , j=1,2,…,N,a i =[a i1 ,a i2 ,…,a i6 ] T represents the input weight connecting the i-th hidden layer node of ELM, β i =[β i1 ,β i2 ,β i3 ] T represents the output weight of the i-th hidden layer node; b i Represents the bias value of the i-th hidden layer node.

[0070] ③ Use formula (3) to calculate the output weight matrix β:

[0071]

[0072] in, represents the Moore-Penrose generalized inverse of the hidden layer output matrix H, which is obtained using the singular value decomposition method.

[0073] ④Use formula (4) to find the new problem input as X new The preset value Y of the manipulated variable at new , where represents the result after the new problem input is standardized.

[0074]

[0075] B. Similarity is greater than or equal to δ s , and the similarity is greater than or equal to δ s The number of samples is less than δ sn The steps are as follows:

[0076] ① Use the SMOTE method to perform linear interpolation to obtain new samples:

[0077] Assume the number of selected samples is A, the upsampling ratio is AA, for each sample

[0078] (X i ,Y i ), i = 1, 2, ..., A, select the W samples with the highest similarity (X z ,Y z ), z = 1, 2, ..., W, W is usually not more than 5, randomly select AA samples from the selected W samples, and use formula (5) to expand AA new samples (NSI k ,NSO k ):

[0079] (NSI k ,NSO k )=(X i ,Y i )+rand i,z (0,1)×((X z -X i ),(Y z -Y i )) (5)

[0080] Where k = 1, 2, ..., AA, rand i,z (0,10 means using existing samples (X i ,Y i ) and (X z ,Y z )When generating a new sample (NSI k ,NSO k ) generates a random number in the interval [0,1].

[0081] After the expansion operation is performed on all A selected samples, B = A × AA new samples are expanded. The expanded new samples are put together with the selected samples to form an expanded sample set (EX j ,EY j ), where j = 1, 2, ..., (A + B).

[0082] ②Use the Bagging LSSVM method to establish a prediction model for preset values ​​of operating variables:

[0083] a. Use the Bagging method to create sample subsets.

[0084] Assign equal sampling probability to each positive integer in the interval [1, (A+B)] With this probability, randomly generate s = (A + B) positive integers k from the interval [1, (A + B)] i ∈]1,(A+B)](i=1,2,...,s), extract the kth i samples form a new sample subset, let in, is the input data of the i-th sample in the first subset, is the output data of the ith sample in the first subset, then the first preset sample subset generated from the extended sample set is {C1, V1}; repeat the above steps Z times, and thus Z preset sample subsets are obtained: {C1, V1}, {C2, V2}, …, {C Z ,V Z}, Z is taken as 50.

[0085] b. Establish LSSVM sub-model for each sample subset.

[0086] The coefficients of the LSSVM model for the jth output built based on the i-th subset are given by equations (6) and (7):

[0087]

[0088] Solve α by formula (8) ij and d ij ,Right now:

[0089]

[0090] Among them, σ ij is the width of the radial basis function, ranging from [0.1, 20], 1 s =[1,1,...,1] T , α ij =[α ij1 ,α ij2 ,...,α ijs ] T , α ij represents the weight parameter vector, V ij Indicates V i The j-th (j=1,2,3) column of (i=1,2,...,Z). Among them, r=1,2,...,s, v=1,2,...,s, i=1,2,...,Z, j=1,2,3. Is is the s-order identity matrix. ij is the regularization parameter, and its value range is in the interval [0.1,10]. ij Represents the bias parameter.

[0091] c. Weighted synthesis of sub-models

[0092] The results of the Z LSSVM sub-models are synthesized using the average method, and the preset value of the jth operating variable is:

[0093]

[0094] Therefore, the new preset value of the manipulated variable is Y new =[y new1 ,y new2 ,y new3 ]

[0095] C. Similarity is less than the threshold δ s .

[0096] Ask domain experts or experienced operators to directly provide solutions Y new .

[0097] (4) Modification of preset values ​​of operating variables

[0098] Set the preset value Y of the manipulated variable new The basic control system is sent to execute and the obtained results are evaluated. If the obtained particle size distribution meets the production requirements, the obtained results are kept unchanged. If the obtained particle size distribution does not meet the requirements, the preset value needs to be corrected. The correction method adopts the correction scheme provided by the field experts (manual correction) until the obtained particle size distribution meets the requirements.

[0099] (5) Operation variable preset result storage

[0100] If the maximum similarity between the new question and the samples in the input and output sample sets is greater than or equal to the similarity threshold SIM max , then delete the historical input and output data with the greatest similarity to the new question from matrices X and Y, and then store the input and output data of the new question in the input and output sample set respectively; otherwise, store the input and output data of the new question directly in the input and output sample set. max Take 0.95.

[0101] (II) Feedback Compensation

[0102] Feedback compensation can correct the preset values ​​of crusher speed, classifier speed and feed flow rate to compensate for the influence of unknown interference, so that the particle size distribution is within the target range, thereby better adapting to changes in working conditions.

[0103] Feedback compensation is implemented using an expert rule method based on rule reasoning, and its form is shown in the following rules:

[0104] 1) If the difference between the expected value and the detected value of the mass fraction of the particle size distribution in the range of +35 mesh is less than or equal to △a1%, the crusher speed increases by △y1(t)=△A11r / min, the classifier speed decreases by △y2(t)=△A21r / min, and the feed flow rate decreases by △y3(t)=△A31t / h;

[0105] 2) If the difference between the expected value and the detected value of the mass fraction of the particle size distribution in the range of +35 mesh is greater than △a1% and less than △a2%, the crusher speed increases by △y1(t)=△A12r / min, the classifier speed decreases by △y2(t)=△A22r / min, and the feed flow rate decreases by △y3(t)=△A32t / h;

[0106] 3) If the difference between the expected value and the detected value of the mass fraction of the particle size distribution in the range of -325 mesh is less than or equal to △b1%, the crusher speed is reduced by △y1(t)=△B11r / min, the classifier speed is increased by △y2(t)=△B21r / min, and the feed flow rate is increased by △y3(t)=△B31t / h;

[0107] 4) If the difference between the expected value and the detected value of the mass fraction in the particle size distribution range of -325 mesh is greater than △b1% and less than △b2%, the crusher speed is reduced by △y1(t)=△B12r / min, the classifier speed is increased by △y2(t)=△B22r / min, and the feed flow rate is increased by △y3(t)=△B32t / h;

[0108] The parameter values ​​in the rule are as follows: △a1=-3; △A11=60; △A21=10; △A31=0.3; △a2=1; △A12=39; △A22=6; △A32=0.1; △b1=-3; △B11=45; △B21=8; △B31=0.5; △b2=1.5; △B12=27; △B22=5; △B32=0.1.

[0109] (III) Crusher load monitoring and adjustment

[0110] Hammer crusher (referred to as crusher) is one of the most critical equipment in the general powder processing process. During production, it is necessary to ensure that the equipment operates under appropriate load, otherwise failure conditions will occur and have a great impact on production. This part monitors the return material volume and crusher current, and comprehensively judges the condition of the crusher load through expert rules. If it is abnormal, the adjustment value of the feed volume and crusher speed is given to keep the crusher away from the failure condition and return to normal condition.

[0111] 1) If the crusher return material volume is greater than M1 and less than or equal to M2t / h, or the crusher current is greater than N1 and less than or equal to N2A, the feed flow adjustment amount

[0112] 2) If the crusher return material volume is greater than M2 and less than or equal to M3t / h, or the crusher current is greater than N2 and less than or equal to N3A, the feed flow adjustment amount

[0113] 3) If the crusher return material volume is greater than M3t / h, or the crusher current is greater than N3A, the feed flow adjustment amount

[0114] The parameter values ​​in the rule are as follows: M1=1.8; M2=2.3; M3=2.6; N1=54.2; N2=57.3; N3=58.5; △K1=-0.3; △K2=-0.5; △K3=-0.6.

[0115] Therefore, the final manipulated variable setting is Where ΔY(t)=[Δy1(t),Δy2(t),Δy3(t)],

[0116] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent control method for the crushing and screening process of high-temperature electrical grade magnesium oxide, characterized in that: The following steps are involved: Analyze the process flow, equipment and operation of high-temperature electrical grade magnesium oxide crushing and screening, determine crushing and separation as the links affecting particle size, and select crusher speed, classifier speed and feed flow rate as operating variables; In the offline stage of the production process, the particle size distribution range of the fused magnesia powder is taken as the target, and the preset values ​​of the operating variables are calculated using a hybrid intelligent modeling method based on the magnesia content, hardness, particle size and type of the fused magnesia raw material; In the online stage of the production process, real-time adjustments are made based on operational feedback and machine load conditions; ultimately, the set values ​​of the operating variables are obtained for control.

2. The intelligent control method for crushing and screening process of high-temperature electrical grade magnesium oxide according to claim 1 is characterized in that: The offline process of the production process includes: Construct input and output sample sets based on historical data of production processes; Consider the degree of similarity between the new situation and existing operational situations; Offline construction of hybrid intelligent models to predict pre-set values ​​of operating variables in new situations; Set the preset value Y of the manipulated variable new Output to the basic control system for execution. When the particle size distribution cannot be satisfied, the preset value is empirically corrected. Delete the historical data with the greatest similarity to the new question and update the stored sample set.

3. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 2 is characterized in that: Constructing the input and output sample set based on the historical data of the production process includes: defining the input data matrix The output data is stored in the output data matrix Among them, X i =[x i1 ,x i2 ,...,x i6 ,x i7 ])i=1,2,...,U) represents the input variable of the i-th sample data, x i1 ~x i7 Respectively represent the magnesium oxide content, hardness and particle size of the fused magnesium oxide raw material, the mass fraction of the desired range of +35 mesh, -35 mesh to +325 mesh, -325 mesh, and the type of the fused magnesium oxide raw material; Y i =[y i1 ,y i2 ,y i3 ](i=1,2,...,U) represents the output variable of the i-th sample data, y i1 ~y i3 They respectively represent the preset values ​​of crusher speed, classifier speed and feed flow rate.

4. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 2 is characterized in that: During production operations, the similarity between the new situation and the existing operation situation is considered, and the similarity between the new problem input and the sample input is calculated; Among them, X new =[x new1 ,x new2 ,...,x new6 ,x new7 ] indicates new question input, x newj Represents the jth (j=1, 2, ..., 7) component of the new problem input.

5. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 2 is characterized in that: Consider the similarity between the new situation and the existing operation situation; build a hybrid intelligent model offline, specifically: If the similarity is high and the number of similar samples reaches the set value, the extreme learning machine ELM method is used to establish a pre-set model of the operating variables; If the similarity is high and the number of similar samples does not reach the set value, the SMOTE algorithm is used to expand the similar samples, and then the Bagging LSSVM method is used to establish the pre-set model of the operation variables; If the similarity is low, the expert gives the preset values ​​of the operating variables in the current situation.

6. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 5 is characterized in that: If the similarity D(X new ,X i ) is greater than or equal to the similarity threshold δ s The number of samples N is greater than or equal to the sample number threshold δ sn , the extreme learning machine (ELM) method is used to establish a prediction model for the preset values ​​of the operating variables, as follows: Among them, Y new Enter X for new questions new The preset values ​​of the operating variables at the time, the weight matrix a i 、b i To randomly generate the input weight vector and bias value of the hidden layer node of the ELM method.

7. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 5 is characterized in that: If the similarity D(X new ,X i ) is greater than or equal to δ s The number of samples N is less than δ sn , the SMOTE algorithm is used to expand similar samples, and then the Bagging LSSVM method is used to establish a preset model of the operating variables. The steps are as follows: ① Set the upsampling ratio to AA, and use the SMOTE method to perform linear interpolation to obtain new samples (NSI k ,NSO k ); (NSI k ,NSO k )=(X i ,AND i )+rand i,z (0,1)×((X z -X i ),(AND z -AND i )) (5) Where k = 1, 2, ..., AA, rand i,z (0,1) means using existing samples (X i ,Y i ) and (X z ,Y z )When generating a new sample (NSI k ,NSO k )A random number generated in the interval [0,1]; The final expanded B = A × AA new samples are put together with the selected samples to form an extended sample set (EX j ,EY j ), where j = 1, 2, ..., (A + B); ②Use the Bagging LSSVM method to establish a prediction model for preset values ​​of operating variables: a. In the interval [1, (A+B)], the bagging method is used according to the sampling probability Randomly extract s samples from the input and output sample sets, and repeat this process Z times to obtain Z pre-set sample subsets: {C1, V1}, {C2, V2}, …, {C Z ,V Z }; Among them, the new sample subset consisting of s samples extracted from the input and output sample sets for the i-th (i=1,2,...,Z) time is set to is the input data of the zth sample in the i-th subset, is the output data of the zth sample in the i-th subset; b. Establish the j-th output LSSVM model f based on the i-th subset ij (X new ); c. Use the average method to synthesize the results of the Z LSSVM sub-models to obtain the preset value of the jth operating variable: Therefore, the new preset value of the manipulated variable is Y new =[y new1 ,y new2 ,y new3 ].

8. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 1, characterized in that: In the online link of the production process, a feedback compensation strategy is adopted based on the difference between the target range and the detected value of the particle size distribution of the fused magnesium oxide powder to calculate the feedback compensation value of the operating variable ΔY(t)=[Δy1(t), Δy2(t), Δy3(t)].

9. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 1, characterized in that: In the online production process, the crusher load condition is judged based on the crusher return flow and bucket elevator current, and the adjustment value of the operating variable is calculated using an intelligent adjustment strategy.

10. The intelligent control method for high temperature electrical grade magnesium oxide crushing and screening process according to claim 2, 8 or 9, characterized in that: Operation variable setting value

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