WO2022037500A1 - 一种基于料层厚度预测的布料控制系统及方法 - Google Patents
一种基于料层厚度预测的布料控制系统及方法 Download PDFInfo
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- 239000000463 material Substances 0.000 title claims abstract description 390
- 238000009826 distribution Methods 0.000 title claims abstract description 166
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000005245 sintering Methods 0.000 claims abstract description 214
- 238000005457 optimization Methods 0.000 claims abstract description 16
- 238000005096 rolling process Methods 0.000 claims abstract description 16
- 239000000203 mixture Substances 0.000 claims description 82
- 238000012549 training Methods 0.000 claims description 40
- 238000001514 detection method Methods 0.000 claims description 33
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B21/00—Open or uncovered sintering apparatus; Other heat-treatment apparatus of like construction
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0028—Regulation
- F27D2019/0075—Regulation of the charge quantity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27M—INDEXING SCHEME RELATING TO ASPECTS OF THE CHARGES OR FURNACES, KILNS, OVENS OR RETORTS
- F27M2003/00—Type of treatment of the charge
- F27M2003/04—Sintering
Definitions
- the present application relates to the technical field of iron and steel smelting, and in particular, to a distribution control system and method based on material layer thickness prediction.
- Sintering refers to the process of converting powdery materials into dense bodies. In the iron and steel production process, it refers to mixing iron ore powder, coal powder and lime in a certain proportion, and sintering to obtain sintered ore with blast furnace production process requirements. , transporting raw materials for blast furnace ironmaking.
- the thickness of the material layer is appropriate is one of the important parameters affecting the output and quality of sintering production.
- it is usually used to adjust the opening of the auxiliary door to adjust the material in the area corresponding to the auxiliary door. The thickness of the layer is adjusted, or the rotation speed of the round roller feeder is adjusted to change the material layer in the width range of the trolley as a whole. If the opening of the auxiliary door is adjusted to a large extent, the flow of the corresponding cloth device to the sintering trolley will be large, and the thickness of the corresponding material layer will be thick, otherwise, the thickness of the material layer will be small.
- the material layer thickness of the sintering trolley is generally detected in real time to control the material layer.
- the specific process is, in the process of material distribution by the sintering trolley, at the downstream of the moving direction of the sintering trolley, the thickness of the material layer of the sintering trolley is monitored, and the feedback control of the material distribution is carried out according to the detected material layer thickness.
- this material layer thickness detection method detects the material layer thickness of the sintered trolley that has been clothed. It takes about 2 to 3 minutes from the auxiliary door adjustment point to the layer thickness detection point, and the feedback control has a large hysteresis. . If the thickness of the material layer is found to be inappropriate at this time, it cannot be changed, which is not conducive to realizing the timely and stable control of the material for the sintering system.
- the present application provides a distribution control system and method based on material layer thickness prediction to solve the problem of hysteresis in the distribution control of the sintering system by detecting the material layer thickness of the sintering trolley in the prior art.
- a first aspect of the present application provides a material layer thickness prediction-based material distribution control system
- the material distribution control system includes a round roller feeder, a roller type material distribution machine and a sintering trolley, and the round roller feeder is used to feed all materials
- the roller feeder supplies mixed material
- the roller feeder is used for material distribution to the sintering trolley
- the material distribution control system further includes a mixture detection mechanism, a feeding roller controller connected with the round roller feeder, and The distribution roller controller and auxiliary door controller connected with the roller distributor, and the sintering trolley controller connected with the sintering trolley; and the mixture detection mechanism, the feeding roller controller, the distribution roller controller, and the auxiliary door control central processing unit connected to the sintering trolley controller; of which:
- the central processing unit is configured to perform the following steps:
- the thickness of the material layer is predicted, and the predicted value of the thickness of the material layer is obtained;
- the feeding roller controller to adjust the speed of the feeding roller to the speed of the feeding roller to be adjusted; drive the distribution roller controller to adjust the speed of the distribution roller to the speed of the distribution roller to be adjusted; drive the auxiliary door controller Adjust the opening of the auxiliary door to the opening of the auxiliary door to be adjusted; drive the sintering trolley controller to adjust the speed of the sintering trolley to the speed of the sintering trolley to be adjusted;
- the rolling optimization model is used to calculate the corresponding feed roller speed, distribution roller speed, auxiliary door opening when the variance of the thickness deviation value of the material layer is the smallest under the condition that the bulk density of the mixture is unchanged. and sintering trolley speed.
- the variance of the material layer thickness deviation value is obtained in the following manner:
- ⁇ is the variance of the thickness deviation value of the material layer
- E(k) refers to the deviation value of the material layer thickness
- R(k) the preset target value of the material layer thickness
- Y(k) the predicted value of the material layer thickness
- the thickness of the material layer is predicted to obtain the predicted value of the thickness of the material layer, and the following steps are specifically performed:
- Data reduction is performed on the characteristic value of the thickness of the material layer to obtain the predicted value of the thickness of the material layer.
- the material distribution control system system further includes a material layer thickness detection device arranged above the sintering trolley, the material layer thickness detection device is connected to the central processing unit, and the material layer thickness detection device is used for pre-setting.
- the time interval of the sintering trolley is to detect the material layer thickness of the sintering trolley, and obtain the measured value of the material layer thickness; input the eigenvector of the predicted material layer thickness into the pre-established material layer thickness dynamic prediction model to generate the material layer thickness eigenvalues ,Also includes:
- the material layer thickness dynamic prediction model is updated online by using the learning sample, and the updated material layer thickness dynamic prediction model is obtained.
- hi is the obtained predicted value of the material layer thickness at time i
- ki is the generated characteristic value of the material layer thickness at time i
- H is the maximum allowable thickness of the material layer on the sintering trolley.
- the ratio of the speed of the sintering trolley to the maximum rotational speed of the sintering trolley is the ratio of the speed of the sintering trolley to the maximum rotational speed of the sintering trolley.
- the material layer thickness dynamic prediction model is generated based on neural network model training, and established according to the following steps:
- the bulk density, feed roller speed, distribution roller speed and sintering trolley speed of the N groups of independent mixtures are quantified to the same interval according to a certain shrinkage ratio, and combined with the opening of the auxiliary door, as the input of the N groups of training samples;
- the actual material layer thickness corresponding to the input of the above N groups of training samples on the sintering trolley is detected, and the actual characteristic value of the actual material layer thickness is calculated, and the actual characteristic value is used as the N groups of output training samples;
- the neural network model is trained by the time backpropagation method
- the material layer thickness dynamic prediction model can also be a material layer thickness prediction table, and is established according to the following steps:
- the preset time interval detect the actual material layer thickness corresponding to the above N groups of input training samples on the sintering trolley, and calculate the actual characteristic value of the actual material layer thickness;
- a second aspect of the present application provides a method for controlling distribution based on prediction of material layer thickness, the method for controlling distribution includes:
- the thickness of the material layer is predicted, and the predicted value of the thickness of the material layer is obtained;
- the rolling optimization model is used to calculate the corresponding feed roller speed, distribution roller speed, auxiliary door opening when the variance of the thickness deviation value of the material layer is the smallest under the condition that the bulk density of the mixture is unchanged. and sintering trolley speed.
- the step of predicting the thickness of the material layer according to the bulk density of the mixture, the rotating speed of the feeding roller, the rotating speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley, and obtaining the predicted value of the thickness of the material layer includes: :
- Data reduction is performed on the characteristic value of the thickness of the material layer to obtain the predicted value of the thickness of the material layer.
- the distribution control system includes a round roller feeder, a roller type distributor and a sintering cart.
- the mixed material is supplied to the roller distributor, which is used for material distribution to the sintering trolley;
- the distribution control system also includes a mixture detection mechanism, a feeding roller controller connected to the round roller feeder, and a roller distributor.
- the connected distribution roller controller and auxiliary door controller, and the sintering trolley controller connected with the sintering trolley; and the mixture detection mechanism, the feeding roller controller, the distribution roller controller, the auxiliary door controller and the sintering table
- the central processing unit to which the vehicle controller is connected.
- the sintering trolley is quantified to the same interval according to a certain shrinkage ratio, and combined with the opening of the auxiliary door, the eigenvector of the predicted material layer thickness is generated; then the eigenvector of the predicted material layer thickness is input into the pre-established material layer thickness dynamic prediction
- the characteristic value of the thickness of the material layer is generated; the data of the characteristic value of the material layer thickness is restored to obtain the predicted value of the material layer thickness, and then the material layer is calculated according to the predicted value of the material layer thickness and the target value of the material layer thickness.
- the layer thickness prediction realizes the distribution control of the sintering system.
- the material layer thickness prediction-based material distribution control system provided in the embodiment of the present application can predict the material layer thickness on the sintering trolley in advance through the collected mixture parameters and sintering system state parameters, so that the predicted material layer thickness and real-time Adjust the relevant key parameters that affect the cloth, and realize the timely and stable control of the cloth process of the sintering system.
- FIG. 1 is a schematic structural diagram of a distribution control system based on material layer thickness prediction provided by an embodiment of the application;
- Fig. 2 is the working flow chart of the cloth control system based on material layer thickness prediction provided by the embodiment of the application;
- FIG. 3 is a flow chart of predicting the thickness of the material layer by the cloth control system provided by the embodiment of the present application.
- Fig. 4 is the flow chart of the online update material layer thickness dynamic prediction model provided by the embodiment of this application.
- FIG. 5 is a flow chart of generating a dynamic prediction model of material layer thickness provided by an embodiment of the present application.
- FIG. 6 is a schematic structural diagram of an LSTM neural network model provided by an embodiment of the present application.
- FIG. 7 is another flowchart of generating a dynamic prediction model of layer thickness according to an embodiment of the present application.
- 1- batching room 2- mixer, 3- round roller feeder, 4- roller distributor, 5- sintering trolley, 6- ignition fan, 7- ignition fan, 8- single roller crusher, 9-ring cooler, 10-material layer thickness detection device, 101-mixing material detection mechanism, 102-feeding roller controller, 103-distributing roller controller, 104-auxiliary door controller, 105-sintering trolley controller , 106-CPU.
- the sintering system mainly includes a sintering trolley, a mixer, a main shaft fan, a ring cooler and other equipment.
- the overall process flow chart is shown in Figure 1: various raw materials are proportioned in the batching chamber 1 to form a mixture. After entering the mixer 2 for mixing and pelletizing, it is evenly distributed on the sintering trolley 5 through the round roller feeder 3 and the roller distributor 4 to form a mixed material layer, and the ignition fan 6 and the ignition fan 7 start mixing. The material is ignited to start the sintering process. After the sintering is completed, the obtained sintered ore is crushed by the single-roller crusher 8 and then enters the ring cooler 9 for cooling, and finally is screened and granulated before being sent to the blast furnace or the finished product silo.
- FIG. 1 it is a schematic structural diagram of a distribution control system based on material layer thickness prediction provided in an embodiment of the present application.
- the distribution control system includes a round roller feeder 3, a roller type distributor 4 and a sintering trolley 5.
- the round roller feeder 3 is used to supply the mixed material to the roller type distributor 4.
- the distribution machine 4 is used to distribute material to the sintering cart 5 .
- the material layer thickness prediction system also includes a mixture detection mechanism 101, the mixture detection mechanism 101 includes a sampling device and an offline detection device; the sampling device is used to obtain the pelletized mixture from the sintering system, and to The obtained mixture is input to the offline detection device, and the offline detection device is used to measure the density of each component of the test sample and the bulk density of the mixture, and the mixture detection mechanism 101 executes step S201, and the obtained mixture is The bulk density is sent to the central processing unit 106 .
- the mixture detection mechanism 101 includes a sampling device and an offline detection device; the sampling device is used to obtain the pelletized mixture from the sintering system, and to The obtained mixture is input to the offline detection device, and the offline detection device is used to measure the density of each component of the test sample and the bulk density of the mixture, and the mixture detection mechanism 101 executes step S201, and the obtained mixture is The bulk density is sent to the central processing unit 106 .
- Feeding roller controller 102 the feeding roller controller 102 is connected with the round roller feeder 3, used to control the feeding roller rotation speed of the round roller feeder 3, and can obtain the round roller feeding
- the rotation speed of the feeding roller of machine 3 for example, the rotation speed sensor is used to measure the rotation speed of the feeding roller.
- the rotation speed sensor is a sensor that converts the rotation speed of the rotating object into the power output. Install an encoder, or use a proximity switch combined with a high-speed counter for digital-to-analog conversion speed measurement.
- the feeding roller controller 102 is further configured to execute step S202, and send the obtained rotational speed of the feeding roller to the central processing unit 106.
- FIG. 2 it is a working flow chart of the cloth control system based on material layer thickness prediction provided by the embodiment of the present application.
- the distribution roller controller 103 the distribution roller controller 103 is connected with the roller distributor 4, is used to control the distribution roller rotation speed of the roller distributor 4, and can obtain the distribution roller rotation speed of the roller distributor 4, so
- the distribution roller controller 103 is further configured to execute step S203, and send the acquired rotation speed of the distribution roller to the central processing unit 106.
- the auxiliary door controller 104 is arranged above the round roller feeder 3, used to control the opening degree of the auxiliary door of the roller distributor 4, and can obtain the auxiliary door of the roller distributor 4 door opening degree, and is configured to execute step S204 , and send the acquired auxiliary door opening degree to the central processing unit 106 .
- auxiliary door controllers 104 there are at least four auxiliary doors under each silo, the number of auxiliary door controllers 104 used in the embodiment of the present application is the same as the number of auxiliary doors, and one auxiliary door controller 104 controls the opening of one auxiliary door.
- the sintering trolley controller 105 which is arranged on the sintering trolley 5, is used to control the running speed of the sintering trolley 5, and can obtain the running speed of the sintering trolley 5, and is configured to execute the steps 205. Send the acquired speed of the sintering trolley to the central processing unit 106.
- the central processing unit 106 is connected to the mixture detection mechanism 101 , the feeding roller controller 102 , the distributing roller controller 103 , the auxiliary door controller 104 and the sintering trolley controller 105 .
- steps S201 to S205 are not in order, and may be performed in any order or simultaneously.
- the central processing unit 106 is configured to perform the following steps S206 to S213.
- Step S206 receiving the bulk density of the mixture, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley.
- Step S207 Predict the thickness of the material layer according to the bulk density of the mixture, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley, and obtain the predicted value of the thickness of the material layer.
- Step S301 quantify the bulk density of the mixture, the rotational speed of the feeding roller, the rotational speed of the distribution roller, and the speed of the sintering trolley at the same time into the same interval according to a certain shrinkage ratio, and combine with the opening of the auxiliary door to generate a feature for predicting the thickness of the material layer vector.
- the bulk density, the rotation speed of the feeding roller, the rotation speed of the distribution roller and the speed of the sintering trolley at the same time are quantified into the same interval according to a certain shrinkage ratio, and the same interval after quantification is the interval (0,1).
- the quantitative model of bulk density is:
- the quantitative model of the feed roller speed is:
- the quantitative model of the rotation speed of the distribution roller is:
- the quantitative model of the speed of the sintering trolley is:
- Norm( ⁇ ) represents the quantized bulk density, ⁇ represents the bulk density, Represents the density of the component with the highest density among the components;
- Norm(n 1 ) represents the quantified feeding roller speed, n 1 represents the feeding roller speed, Represents the rated speed of the feeding roller;
- Norm(n 2 ) represents the quantized distribution roller speed, n 2 represents the distribution roller speed, Represents the rated speed of the distribution roller;
- Norm(s) represents the quantized sintering trolley speed, v represents the sintering trolley speed, Indicates the maximum speed of the sintering trolley.
- the feature vector for predicting the thickness of the material layer is to integrate the influencing factors of the thickness of the material layer according to a certain rule, for example:
- X(k) (x1(k), x2 (k), x3 (k), x4 (k), x5 (k))
- X(k) represents the feature vector of the predicted material layer thickness
- x 1 (k), x 2 (k), x 3 (k), x 4 (k), x 5 (k) represent the bulk density, Feed roller speed, distribution roller speed, auxiliary door opening and sintering trolley speed.
- Step S302 input the eigenvector of predicting the thickness of the material layer into the pre-established dynamic prediction model of the material layer thickness, and generate the characteristic value of the material layer thickness, and the dynamic prediction model of the material layer thickness includes the eigenvector of the predicted material layer thickness and the The mapping relationship between the eigenvalues of the layer thickness.
- the mapping relationship includes the eigenvector of the predicted material layer thickness and the corresponding eigenvalue of the material layer thickness, namely:
- y( k ) is the eigenvalue that affects the thickness of the material layer
- fk is the mapping relationship between the eigenvector of the predicted material layer thickness and the eigenvalue of the material layer thickness.
- Step S303 performing data restoration on the characteristic value of the material layer thickness to obtain a predicted value of the material layer thickness.
- the characteristic value of the material layer thickness output by the dynamic prediction model of material layer thickness certain data processing is required to obtain the final predicted value of the material layer thickness.
- the specific operation is to calculate the characteristic value of the material layer thickness and sintering.
- the product of the maximum allowable thickness of the material layer of the trolley is obtained to obtain the predicted value of the material layer thickness.
- hi is the obtained predicted value of the thickness of the material layer at time i
- ki is the generated characteristic value of the thickness of the material layer at time i
- H is the maximum allowable thickness of the material layer on the sintering trolley.
- Step S208 Calculate the deviation value of the thickness of the material layer according to the predicted value of the thickness of the material layer and the target value of the thickness of the material layer.
- Y(k) refers to the predicted value sequence of the layer thickness obtained at time k
- R(k) is the set target value sequence of the layer thickness at time k
- E(k) is the thickness deviation of the layer at time k sequence of values.
- Step S209 input the thickness deviation value of the material layer into the rolling optimization model, and obtain the speed of the feed roller to be adjusted, the speed of the distribution roller to be adjusted, the opening of the auxiliary door to be adjusted and the speed of the sintering trolley to be adjusted.
- the rolling optimization model is used to calculate the corresponding feed roller speed, distribution roller speed, auxiliary door opening when the variance of the thickness deviation value of the material layer is the smallest under the condition that the bulk density of the mixture is unchanged. and sintering trolley speed.
- the variance of the material layer thickness deviation value is obtained in the following manner:
- ⁇ is the variance of the material layer thickness deviation
- E(k) is the material layer thickness deviation value at time k
- R(k) is the preset target value sequence of material layer thickness at time k
- Y(k) is the material layer thickness at time k. Sequence of predicted values for layer thickness.
- the central processing unit 106 obtains the bulk density of the mixture, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley, and predicts the Sequence of predicted values for layer thickness at time k:
- Y( k ) fk(x1(k), x2 (k), x3 (k), x4 (k), x5 (k))
- f k represents the mapping relationship between the eigenvectors of the predicted layer thickness at the kth moment and the eigenvalues of the layer thickness; at the kth moment, the sequence of predicted deviation values is expressed as follows:
- the variance of the thickness deviation value of the material layer is expressed as follows:
- Step S210 sending the speed of the sintering trolley to be adjusted.
- the sintering trolley controller 105 is driven to adjust the speed of the sintering trolley to the speed of the sintering trolley to be adjusted.
- Step S211 sending the opening degree of the auxiliary door to be adjusted.
- the auxiliary door controller 104 is driven to adjust the opening degree of the auxiliary door to the opening degree of the auxiliary door to be adjusted.
- Step S212 sending the rotation speed of the cloth roller to be adjusted.
- the distribution roller controller 103 is driven by sending the rotation speed of the distribution roller to be adjusted to the distribution roller controller 103 .
- Step S213 sending the rotation speed of the feeding roller to be adjusted.
- the feeding roller controller 102 is driven by sending the rotational speed of the feeding roller to be adjusted to the feeding roller controller 102 .
- steps S210 to S213 are not in order, and may be performed in any order or simultaneously.
- the speed of the sintering trolley is adjusted to the speed of the sintering trolley to be adjusted
- the opening of the auxiliary door is adjusted to the opening of the auxiliary door to be adjusted
- the speed of the distribution roller is adjusted to the speed of the distribution roller to be adjusted
- the feeding The adjustment of the roller speed to the speed of the feed roller to be adjusted must be done at the same time.
- the central processing unit 106 is connected to the mixture detection mechanism 101 , the feeding roller controller 102 , the distributing roller controller 103 , the auxiliary door controller 104 and the sintering trolley controller 105 .
- the embodiment of the present application provides a distribution control system based on the prediction of material layer thickness. and the speed of the sintering trolley; then quantify the bulk density, feeding roller speed, distribution roller speed and sintering trolley speed at the same time to the same interval according to a certain shrinkage ratio, and combine the auxiliary door opening to generate the predicted layer thickness Then input the eigenvector of predicting the thickness of the material layer into the pre-established dynamic prediction model of the material layer thickness to generate the characteristic value of the material layer thickness; and restore the data of the characteristic value of the material layer thickness to obtain the material layer thickness.
- the predicted value of the thickness then according to the predicted value of the thickness of the material layer and the target value of the thickness of the material layer, the deviation value of the material layer thickness is calculated, and finally the deviation value of the material layer thickness is input into the rolling optimization model, and the speed of the feeding roller to be adjusted is obtained.
- the material layer thickness prediction-based material distribution control system provided in the embodiment of the present application can predict the material layer thickness on the sintering trolley in advance through the collected mixture parameters and the sintering system state parameters, so that the predicted material layer thickness can be realized. Precise cloth control of the sintering system.
- the parameters of the mixture refer to the bulk density of the mixture
- the state parameters of the sintering system refer to the rotation speed of the feeding roller, the rotation speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley.
- the thickness of the material layer of the sintering trolley there are many factors that affect the thickness of the material layer of the sintering trolley, such as the opening of the main door, the inclination of the feeding roller, the width of the sintering trolley and the number of auxiliary doors, etc., but these factors are generally fixed values, such as the width of the sintering trolley and the number of auxiliary doors ; or there are changes, but there will not be frequent changes, which are stable values for a relatively long time, such as the opening of the main door and the inclination of the feeding roller, so the cloth control system based on the prediction of the thickness of the material layer in the embodiment of the present application does not use these values.
- the influencing factors are taken into consideration, that is, these influencing factors have not changed in the actual application process and in the establishment of the dynamic prediction model of material layer thickness.
- the material layer thickness prediction system further includes a material layer thickness detection device 10.
- the layer thickness detection device The material layer thickness on the sintering trolley can be detected in real time.
- the material layer thickness detection device 10 is arranged on the upper position of the sintering trolley to detect the material layer thickness of the sintering trolley, for example, a material level gauge is used to detect the material layer thickness.
- a method for online updating a dynamic prediction model of layer thickness is provided.
- step S302 the feature vector of the predicted material layer thickness is input into the pre-established material layer thickness dynamic prediction model, and before the step of generating the characteristic value of the material layer thickness, the method further includes:
- Step S401 acquiring learning samples in time series adjacent to the feature vector of the predicted material layer thickness, wherein the learning samples include input samples and feature values of the material layer thickness corresponding to the input samples.
- the learning sample includes not only the mixture parameters of the sintering system and the state parameters of the sintering system, but also the corresponding measured value of the thickness of the material layer.
- the specific acquisition method of the learning sample is: acquiring and predicting the characteristics of the thickness of the material layer.
- the normalization process adopts the following models:
- hi is the obtained predicted value of the material layer thickness at time i
- ki is the generated characteristic value of the material layer thickness at time i
- H is the maximum allowable thickness of the material layer on the sintering trolley.
- the historical prediction samples collected here are used as learning samples. Since the sintering process of the sintering system is a long-term continuous process, the sintering system has been performing mixed operations before collecting the feature vector for predicting the thickness of the material layer.
- the historical prediction samples within the nearest preset time interval of the eigenvectors for predicting the thickness of the material layer can guarantee the working conditions of the sintering system corresponding to the obtained learning samples and the working conditions of the sintering system at the time when the eigenvectors for predicting the thickness of the material layers are collected. Consistent.
- Step S402 using the learning sample to update the material layer thickness dynamic prediction model online, and obtain the updated material layer thickness dynamic prediction model.
- the dynamic prediction model of the material layer thickness can be updated online, which further ensures that the characteristic value of the material layer thickness output by the material layer thickness dynamic prediction model is more accurate.
- the prediction deviation value between the eigenvalues of the material layer thickness corresponding to the measured value is determined.
- the dynamic prediction model has two update methods. The deviation value is small and relatively stable, and is within the allowable error range of the model quality index, then the predicted deviation value is directly added to the characteristic value of the material layer thickness of the dynamic prediction model, and the result is used as the updated material layer thickness. Eigenvalues.
- the mapping included in the dynamic prediction model of the material layer thickness is updated according to the learning sample and the characteristic value of the material layer thickness corresponding to the measured value. relation.
- the mean square error of the predicted deviation value can be generally used as the quality index, and then according to the statistical distribution law of the quality index, a statistical confidence limit is preset to determine whether an update needs to be triggered and the required update method.
- the characteristic value of the thickness of the material layer corresponding to the measured value although it is not suitable for the closed-loop control problem of the hybrid process due to the hysteresis problem, it can be used as a reference for the steady state, that is, when the sintering system reaches a steady state in a stable state, the thickness of the material layer will be maintained. At a certain level, under normal circumstances, the measured value fluctuates, but its distribution usually does not deviate from the confidence interval.
- the model update mechanism is triggered. If it is judged that the process characteristics belong to the gradual change according to the index analysis results, the model recursion method is selected, and the moving window recursion method is used to update the dynamic prediction model of the material layer thickness. The steps are as follows:
- a new measurement value [X m , Y m ] is obtained, it is added to the sample set, and the oldest sample is eliminated, the new sample set (learning sample) is:
- the dynamic prediction model of the material layer thickness is updated online by using the learning sample to obtain a new dynamic prediction model of the material layer thickness. If it is judged that the process characteristics belong to sudden changes according to the index analysis results, the real-time learning method is selected to reconstruct the dynamic prediction model of material layer thickness.
- the material layer thickness dynamic prediction model is established by using multiple sets of known mixture parameters, sintering system state parameters and corresponding material layer thickness measurement values.
- a method for training a neural network model is provided to generate the dynamic prediction model of the material layer thickness.
- the specific operation is to use multiple sets of known mixture parameters and sintering system state parameters as the input of the neural network model, and use the corresponding material layer thickness measurement value as the output of the neural network model to train the neural network model, that is, adjust the neural network model.
- the weight matrix and bias term corresponding to the middle layer of the network model are used to establish the mapping relationship between the mixture parameters and the corresponding mixer state parameters and the degree of mixing. Referring to FIG. 5 , a flow chart of generating a dynamic prediction model of material layer thickness provided by an embodiment of the present application, and the specific generating steps include:
- step S501 the bulk density, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley of the N groups of independent mixtures are acquired.
- the N groups of independent mixture parameters and the corresponding state parameters of the sintering system can be the data of the same sintering system or the data of multiple sintering systems, and are divided into a group according to the corresponding relationship, that is, the same mixer and The data at the same time is regarded as a unified group of data.
- Step S502 quantify the N groups of independent bulk density, feed roller rotational speed, distribution roller rotational speed and sintering trolley speed into the same interval according to a certain shrinkage ratio, and combine with the auxiliary door opening to generate input of N groups of training samples.
- the bulk density in the parameters of the mixture, and the speed of the feed roller, the speed of the distribution roller and the speed of the sintering trolley in the state parameters of the sintering system need to be quantified to the interval (0,1), where the value of the auxiliary door opening is percentage data.
- Step S503 Obtain N groups of independent layer thickness measurement values sent by the material layer thickness detection device 10, and quantify the material layer thickness measurement values according to a certain shrinkage ratio to generate output of N groups of training samples.
- the measured value of the thickness of the material layer is quantified according to a certain shrinkage ratio, and the specific operation is to calculate the ratio of the measured value of the material layer thickness to the maximum allowable thickness of the material layer of the sintering trolley.
- Step S504 using the input of the training sample and the output of the training sample, the neural network model is trained by the time back-propagation method.
- the dynamic prediction training module uses the input of the training sample and the output of the training sample to train the neural network model by the time backpropagation method; wherein, the time backpropagation method is a learning algorithm suitable for multi-layer neuron networks, Excitation propagation and weight update are repeated in a loop to guide the response (output) of the multi-layer neuron network to the input until it reaches a predetermined target range.
- the time backpropagation method is a learning algorithm suitable for multi-layer neuron networks, Excitation propagation and weight update are repeated in a loop to guide the response (output) of the multi-layer neuron network to the input until it reaches a predetermined target range.
- Step S505 the weight parameters, bias parameters and learning factors of the neural network model are continuously updated through iterative training.
- Step S506 if the eigenvalues of the predicted material layer thickness of the neural network model and the measured eigenvalues of the material layer thickness reach the set tolerance range, or the neural network model reaches the set maximum number of iterations, the training ends, and Save the last updated weight parameters, bias parameters and learning factors to obtain a dynamic prediction model of layer thickness.
- a mapping relationship between the eigenvectors of the predicted layer thickness and the eigenvalues of the layer thickness is established in the neural network model, until the predicted layer thickness according to the mapping relationship is established.
- the eigenvalues of thickness meet production requirements.
- the specific judgment method is to judge whether the eigenvalues of the predicted material layer thickness and the detected eigenvalues of the material layer thickness of the neural network model reach the set tolerance range, and judge whether the neural network model reaches the set maximum number of iterations , if the eigenvalues of the predicted layer thickness of the neural network model and the detected eigenvalues of the layer thickness reach the set tolerance range, or the neural network model reaches the set maximum number of iterations, the training ends and saves the final
- the updated weight parameters, bias parameters and learning factors are used to obtain the dynamic prediction model of material layer thickness.
- the embodiment of the present application provides the neural network model training to generate the dynamic prediction model for the thickness of the material layer.
- RNN Recurrent Neural Network
- LSTM Long Short-Term Memory
- FIG. 6 it is a schematic diagram of the structure based on the LSTM neural network model.
- the structure of the middle layer mainly includes forget gate,
- the input gate and the output gate are composed of input gates and output gates, each gate has a corresponding weight matrix and bias term.
- the multi-layer neuron network is trained by training samples, and the weight parameters, bias parameters and learning factors are continuously updated to obtain the data.
- Layer thickness dynamic prediction model is trained by training samples, and the weight parameters, bias parameters and learning factors are continuously updated to obtain the data.
- the neural network model divides multiple groups of training samples into two parts, including 2/3 groups of training data and 1/3 groups of test data.
- the input of 3 sets of training samples and the output of training samples are used as the training data of the neural network model, and the weight parameters, bias parameters and learning factors are continuously updated; and the input of 1/3 sets of training samples and the output of training samples are used as neural network Tolerance test data for the network model.
- the dynamic prediction model of material layer thickness can also be a mixing degree prediction table, and established according to the following steps:
- step S701 the bulk density, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening degree of the auxiliary door and the speed of the sintering trolley of the N groups of independent mixtures are obtained.
- Step S702 acquiring N groups of independent material layer thickness measurement values sent by the material layer thickness detection device.
- Step S703 Statistical analysis is performed on the bulk density of the N groups of independent mixtures, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door, the speed of the sintering trolley, and the measured values of the corresponding material layer thickness, and a layer thickness prediction is established. surface.
- layer thickness prediction table and the material layer thickness prediction table mentioned in this application have the same meaning, and all refer to the material layer thickness prediction table.
- the statistical analysis includes data preprocessing.
- the data preprocessing first quantifies the bulk density, the speed of the feeding roller, the speed of the distribution roller and the speed of the sintering trolley to the same interval according to a certain shrinkage ratio, and then quantifies the parameters of the mixture and the state of the sintering system.
- the same multiple sets of data are extracted, and the measured values of the corresponding material layer thicknesses in different groups are compared. If the corresponding measured values of the material layer thickness are also the same, only one set of data is retained, and the same data in other groups are removed.
- the average mixing degree corresponding to multiple sets of data is used as the corresponding material layer thickness, and only one set of data is retained.
- the range is set to 0.5%-2%.
- the corresponding measurement values of mixing degree are different, and the deviation exceeds the allowable range, and the source of multiple sets of data is marked and kept in the isolation area.
- the data source here refers to the data Collect the corresponding sintering system and collection time
- the isolation area refers to a separate area in the layer thickness prediction table, which is used to record abnormal data as reference data for equipment maintenance.
- the measured value of the material layer thickness here also needs to be preprocessed.
- the specific operation is to calculate the ratio of the measured value of the material layer thickness to the maximum allowable thickness of the material layer of the sintering trolley.
- sintering system state parameters and corresponding material layer thickness measurement values that have been preprocessed, they are sorted according to the set multiple index items, and each index item is sorted from small to large. Among them, at least 5 index items are included.
- the first-level index item is the bulk density of the mixture
- the second-level index item is the rotation speed of the feeding roller.
- the third-level index item is the speed of the cloth roller
- the fourth-level index item is the opening of the auxiliary door
- the fifth-level index item is the speed of the sintering trolley.
- the establishment of the mixing degree prediction table is completed according to the above rules.
- the mixing degree prediction table includes the mapping relationship between the feature vector of the predicted material layer thickness and the eigenvalue of the material layer thickness. For example, as shown in Table 1, it is the mixing degree prediction part of the table data.
- An embodiment of the present application provides a method for controlling distribution based on prediction of material layer thickness, and the method for controlling distribution includes:
- the thickness of the material layer is predicted, and the predicted value of the thickness of the material layer is obtained.
- the step of predicting the thickness of the material layer according to the bulk density of the mixture, the rotational speed of the feeding roller, the rotational speed of the distribution roller, the opening of the auxiliary door and the speed of the sintering trolley, and obtaining the predicted value of the thickness of the material layer includes:
- the material layer thickness dynamic prediction model includes the predicted material layer thickness eigenvector and material layer thickness The mapping relationship between the eigenvalues;
- Data reduction is performed on the characteristic value of the thickness of the material layer to obtain the predicted value of the thickness of the material layer.
- the deviation value of the layer thickness is calculated.
- the rolling optimization model is used to calculate the corresponding feed roller speed, distribution roller speed, auxiliary door opening when the variance of the thickness deviation value of the material layer is the smallest under the condition that the bulk density of the mixture is unchanged. and sintering trolley speed.
- the material distribution control system includes a round roller feeder 3, a roller type material distribution machine 4 and a sintering trolley 5,
- the round roller feeder 3 is used to supply the mixed material to the roller distributor 4, and the roller distributor 4 is used to distribute the material to the sintering trolley 5;
- the material distribution control system further includes a mixture detection mechanism 101 , the feeding roller controller 102 connected with the round roller feeder 3, the distribution roller controller 103 and the auxiliary door controller 104 connected with the roller distributor 4, and the sintering trolley controller connected with the sintering trolley 5 105 ; and the central processing unit 106 connected with the mixture detection mechanism 101 , the feeding roller controller 102 , the distribution roller controller 103 , the auxiliary door controller 104 and the sintering trolley controller 105 .
- the opening of the auxiliary door and the speed of the sintering trolley are quantified to the same interval according to a certain shrinkage ratio, and the eigenvector of the predicted layer thickness is generated; then the eigenvector of the predicted layer thickness is input into the pre-established dynamic prediction model of the layer thickness
- the characteristic value of the material layer thickness is generated; and the data of the characteristic value of the material layer thickness is restored to obtain the predicted value of the material layer thickness, and then the material layer thickness is calculated according to the predicted value of the material layer thickness and the target value of the material layer thickness.
- Layer thickness prediction enables cloth control of the sintering system.
- the material layer thickness prediction-based material distribution control system provided by the embodiment of the present application can predict the material layer thickness on the sintering trolley in advance through the collected mixture parameters and the sintering system state parameters, so that the predicted material layer thickness can be realized. Timely and stable feedback cloth control for sintering system.
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Abstract
Description
Claims (10)
- 一种基于料层厚度预测的布料控制系统,所述布料控制系统包括圆辊给料机、辊式布料机和烧结台车,所述圆辊给料机用于向所述辊式布料机供给混合料,所述辊式布料机用于向烧结台车布料;其特征在于,所述布料控制系统还包括混合料检测机构、与圆辊给料机连接的给料辊控制器、与辊式布料机连接的布料辊控制器和辅门控制器,以及与烧结台车连接的烧结台车控制器;以及与混合料检测机构、给料辊控制器、布料辊控制器、辅门控制器和烧结台车控制器连接的中央处理器;其中:所述中央处理器被配置为执行以下步骤:接收所述混合料检测机构发送的混合料堆密度,接收给料辊控制器发送的给料辊转速,接收布料辊控制器发送的布料辊转速,接收辅门控制器发送的辅门开度,接收烧结台车控制器发送的烧结台车速度;根据混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度,对料层厚度进行预测,获得料层厚度的预测值;根据料层厚度的预测值和料层厚度的目标值,计算料层厚度偏差值;将料层厚度偏差值输入滚动优化模型,得到待调整的给料辊转速、待调整的布料辊转速、待调整的辅门开度和待调整的烧结台车速度;驱动所述给料辊控制器将给料辊转速调整为待调整的给料辊转速;驱动所述布料辊控制器将布料辊转速调整为待调整的布料辊转速;驱动所述辅门控制器将辅门开度调整为待调整的辅门开度;驱动所述烧结台车控制器将烧结台车速度调整为待调整的烧结台车速度;其中,所述滚动优化模型用于在所述混合料的堆密度不变的条件下,计算出料层厚度偏差值的方差最小时,对应的给料辊转速、布料辊转速、辅门开度和烧结台车速度。
- 根据权利要求1所述的基于料层厚度预测的布料控制系统,其特征在于,根据混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度,对料层厚度进行预测,获得料层厚度的预测值,具体执行以下步骤:将同一时刻的混合料的堆密度、给料辊转速、布料辊转速和烧结台车速度,按照一定收缩比例量化到同一区间,并结合辅门开度,生成预测料层厚度的特征向量;将预测料层厚度的特征向量输入到预先建立的料层厚度动态预测模型中,生成料层厚度的特征值,所述料层厚度动态预测模型中包含预测料层厚度的特征向量与料层厚度的特征值之间的映射关系;对料层厚度的特征值进行数据还原,获得料层厚度的预测值。
- 根据权利要求3所述的基于料层厚度预测的布料控制系统,其特征在于,所述布料控制系统还包括设置在烧结台车上方的料层厚度检测装置,所述料层厚度检测装置连接中央处理器,所述料层厚度检测装置用于按照预先设定的时间间隔,检测烧结台车的料层厚度,并获得料层厚度的测量值;将预测料层厚度的特征向量输入到预先建立的料层厚度动态预测模型中,生成料层厚度的特征值,还包括:获取与所述预测料层厚度的特征向量邻近时序上的数据并作为样本,其中,所述样本包括输入样本,以及所述输入样本对应的料层厚度的特征值;利用所述学习样本在线更新所述料层厚度动态预测模型,获取更新后的料层厚度动态预测模型。
- 根据权利要求3所述的基于料层厚度预测的布料控制系统,其特征在于,采用以下方法对料层厚度的特征值进行数据还原:h i=k i×H其中,h i为获得的i时刻料层厚度的预测值,k i为生成的i时刻料层厚度的特征值,H为烧结台车上的料层最大允许厚度。
- 根据权利要求3所述的基于料层厚度预测的布料控制系统,其特征在于,将同一时刻的堆密度、给料辊转速、布料辊转速和烧结台车速度,按照一定收缩比例量化到同一区间,具体执行以下步骤:计算堆密度与各原料中密度最大的原料的密度的比值;计算给料辊转速与给料辊的最大转速的比值;计算布料辊转速与布料辊的最大转速的比值;计算烧结台车速度与烧结台车的最大转速的比值。
- 根据权利要求3所述的基于料层厚度预测的布料控制系统,其特征在于,所述料层厚度动态预测模型是基于神经网络模型训练生成,并按照以下步骤建立:按照预先设定的时间间隔,获取N组独立的混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度;将N组独立的混合料的堆密度、给料辊转速、布料辊转速和烧结台车速度,按照一定收缩比例量化到同一区间,并结合辅门开度,作为N组训练样本的输入;按照预先设定的时间间隔,检测烧结台车上与上述N组训练样本输入对应的实际料层厚度,并计算实际料层厚度的实际特征值,将实际特征值作为N组输出训练样本;利用输入训练样本以及输出训练样本,采用时间反向传播法训练神经网络模型;通过迭代训练不断更新神经网络模型的权重参数、偏置参数以及学习因子;若神经网络模型的预测值与测量值达到设定的允差范围,或神经网络模型达到设定的最大迭代次数,则训练结束,并保存最后更新的权重参数、偏置参数以及学习因子,获得料层厚度动态预测模型。
- 根据权利要求3所述的基于料层厚度预测的布料控制系统,其特征在于,所述料层厚度动态预测模型还可以是料层厚度预测表,并按照以下步骤建立:按照预先设定的时间间隔,获取N组独立的混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度;按照预先设定的时间间隔,检测烧结台车上与上述N组输入训练样本对应的实际料层厚度,并计算实际料层厚度的实际特征值;对N组独立的混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度以及对应的实际特征值,进行统计分析,建立料层厚度预测表。
- 一种基于料层厚度预测的布料控制方法,其特征在于,所述布料控制方法包括:获取混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度;根据混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度,对料层厚度进行预测,获得料层厚度的预测值;根据料层厚度的预测值和料层厚度的目标值,计算料层厚度偏差值;将料层厚度偏差值输入滚动优化模型,得到待调整的给料辊转速、待调整的布料辊转速、待调整的辅门开度和待调整的烧结台车速度;将给料辊转速调整为待调整的给料辊转速;将布料辊转速调整为待调整的布料辊转速;将辅门开度调整为待调整的辅门开度;将烧结台车速度调整为待调整的烧结台车速度;其中,所述滚动优化模型用于在所述混合料的堆密度不变的条件下,计算出料层厚度偏差值的方差最小时,对应的给料辊转速、布料辊转速、辅门开度和烧结台车速度。
- 根据权利要求9所述的基于料层厚度预测的布料控制方法,其特征在于,所述根据混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度,对料层厚度进行预测,获得料层厚度的预测值的步骤,包括:将同一时刻的混合料的堆密度、给料辊转速、布料辊转速、辅门开度和烧结台车速度,按照一定收缩比例量化到同一区间,并结合辅门开度,生成预测料层厚度的特征向量;将预测料层厚度的特征向量输入到预先建立的料层厚度动态预测模型中,生成料层厚度的特征值,所述料层厚度动态预测模型中包含预测料层厚度的特征向量与料层厚度的特征值之间的映射关系;对料层厚度的特征值进行数据还原,获得料层厚度的预测值。
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
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Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IT1139918B (it) * | 1980-12-08 | 1986-09-24 | Bostroem Olle | Metodo di sinterizzazione per aspirazione e apparecchiatura relativa |
KR100530081B1 (ko) * | 2002-12-12 | 2005-11-22 | 주식회사 포스코 | 고로용 소결광 장입 제어방법 |
CN1776338A (zh) * | 2005-11-24 | 2006-05-24 | 广东韶钢松山股份有限公司 | 一种烧结自动布料方法 |
CN101560599A (zh) * | 2009-04-17 | 2009-10-21 | 中冶长天国际工程有限责任公司 | 一种混合料层厚的控制方法及控制系统 |
CN102072657A (zh) * | 2010-12-30 | 2011-05-25 | 中南大学 | 一种基于多目标遗传算法的烧结布料过程优化控制方法 |
CN102072658A (zh) * | 2010-12-30 | 2011-05-25 | 中南大学 | 一种稳定料层厚度的烧结偏析布料控制方法 |
CN204665915U (zh) * | 2015-03-23 | 2015-09-23 | 宝钢不锈钢有限公司 | 一种烧结机料层厚度控制系统 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP3950244B2 (ja) * | 1998-11-13 | 2007-07-25 | 新日本製鐵株式会社 | 焼結原料の装入制御方法 |
CN101339115B (zh) * | 2008-08-20 | 2010-11-03 | 中冶长天国际工程有限责任公司 | 一种混合料密度的检测方法及检测系统 |
JP5400555B2 (ja) * | 2009-03-31 | 2014-01-29 | 株式会社神戸製鋼所 | 高炉の操業条件導出方法、及びこの方法を用いた高炉の操業条件導出装置 |
TWI513948B (zh) * | 2013-03-29 | 2015-12-21 | China Steel Corp | 燒結機佈料控制系統與方法 |
JP2014201827A (ja) * | 2013-04-10 | 2014-10-27 | Jfeスチール株式会社 | 焼結鉱の冷却制御方法 |
CN104180659B (zh) * | 2013-05-22 | 2016-03-30 | 宝山钢铁股份有限公司 | 烧结机头部组合偏析布料方法 |
KR101719516B1 (ko) * | 2015-11-05 | 2017-03-24 | 주식회사 포스코 | 소결광 제조 방법 |
-
2020
- 2020-08-20 CN CN202010844822.3A patent/CN113295000B/zh active Active
-
2021
- 2021-08-13 BR BR112022023796A patent/BR112022023796A2/pt unknown
- 2021-08-13 WO PCT/CN2021/112557 patent/WO2022037500A1/zh active Application Filing
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IT1139918B (it) * | 1980-12-08 | 1986-09-24 | Bostroem Olle | Metodo di sinterizzazione per aspirazione e apparecchiatura relativa |
KR100530081B1 (ko) * | 2002-12-12 | 2005-11-22 | 주식회사 포스코 | 고로용 소결광 장입 제어방법 |
CN1776338A (zh) * | 2005-11-24 | 2006-05-24 | 广东韶钢松山股份有限公司 | 一种烧结自动布料方法 |
CN101560599A (zh) * | 2009-04-17 | 2009-10-21 | 中冶长天国际工程有限责任公司 | 一种混合料层厚的控制方法及控制系统 |
CN102072657A (zh) * | 2010-12-30 | 2011-05-25 | 中南大学 | 一种基于多目标遗传算法的烧结布料过程优化控制方法 |
CN102072658A (zh) * | 2010-12-30 | 2011-05-25 | 中南大学 | 一种稳定料层厚度的烧结偏析布料控制方法 |
CN204665915U (zh) * | 2015-03-23 | 2015-09-23 | 宝钢不锈钢有限公司 | 一种烧结机料层厚度控制系统 |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114807596A (zh) * | 2022-05-07 | 2022-07-29 | 北京首钢自动化信息技术有限公司 | 一种矿堆的配料控制方法和装置 |
CN114807596B (zh) * | 2022-05-07 | 2023-11-07 | 北京首钢自动化信息技术有限公司 | 一种矿堆的配料控制方法和装置 |
CN115061427A (zh) * | 2022-06-28 | 2022-09-16 | 浙江同发塑机有限公司 | 吹塑机的料层均匀性控制系统及其控制方法 |
CN115061427B (zh) * | 2022-06-28 | 2023-04-14 | 浙江同发塑机有限公司 | 吹塑机的料层均匀性控制系统及其控制方法 |
CN115478159A (zh) * | 2022-09-01 | 2022-12-16 | 马鞍山钢铁股份有限公司 | 一种适用于超厚料层烧结的梯形布料装置 |
CN115478159B (zh) * | 2022-09-01 | 2023-11-21 | 马鞍山钢铁股份有限公司 | 一种适用于超厚料层烧结的梯形布料装置 |
CN117213260A (zh) * | 2023-10-13 | 2023-12-12 | 湖南科技大学 | 一种节能降耗的环冷机分布式智能协调控制方法 |
CN117213260B (zh) * | 2023-10-13 | 2024-05-24 | 湖南科技大学 | 一种节能降耗的环冷机分布式智能协调控制方法 |
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