Separating density forecasting method and forecasting model construction method and device

Abstract

A separating density forecasting method and a forecasting model construction method and device. The forecasting model construction method comprises: performing fluidization operation on dense-medium particles in a fluidized bed to achieve bubbling fluidization; suspending, by means of a dynamometer, minerals to be separated in a dry dense-medium fluidized bed, the minerals to be separated being dense ores; collecting a resultant force borne by the minerals to be separated and the bed layer density; according to the resultant force borne by the minerals to be separated and the bed layer density, the granularity and the real density of the minerals to be separated, and the bulk density of the dense-medium particles, determining a function relationship between the volume of a fluidization dead zone above the minerals to be separated in the fluidized bed and the granularity of the minerals to be separated; and constructing a dry dense-medium fluidized bed separating density forecasting model on the basis of the function relationship.

Description

Sorting density prediction method and prediction model construction method and device Technical Field

[0001] The present invention relates to the technical field of mineral identification, and in particular to a sorting density prediction method and a prediction model construction method and device. Background Art

[0002] The movement of minerals in a fluidized bed for dry separation affects the performance of fluidized dry separation. Obtaining information on the separation behavior of minerals in a fluidized bed for dry separation is crucial for efficient fluidized separation of minerals.

[0003] At present, there is a lack of accurate observation methods for the dynamic behavior of the minerals to be separated in the fluidized bed of dry separation. The research on the separation behavior of the minerals to be separated is mostly in the state of surface observation. There is a lack of in-depth research and verification of the separation process of the minerals to be separated, resulting in low calculation accuracy of the obtained model.

[0004] Summary of the Invention

[0005] In view of the above analysis, the present invention aims to propose a sorting density prediction method and a prediction model construction method and device to solve at least one of the above problems.

[0006] The purpose of the present invention is mainly achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for constructing a fluidized bed separation density prediction model, comprising:

[0008] Fluidizing the weighted particles in the fluidized bed to achieve bubbling fluidization;

[0009] The minerals to be separated are suspended in a dry heavy medium fluidized bed through a dynamometer;

[0010] Collecting the resultant force and bed density of the mineral to be separated;

[0011] Determining a functional relationship between the dead zone volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated based on the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles;

[0012] A dry heavy medium fluidized bed separation density prediction model is constructed based on the functional relationship.

[0013] Furthermore, the dead zone volume of the fluidized bed is determined according to the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles;

[0014] For a plurality of the minerals to be separated, respectively determining the dead zone volume of the fluidized bed and the particle size of each of the minerals to be separated;

[0015] According to the fluidized bed dead zone volumes and particle sizes of the multiple minerals to be separated, a functional relationship between the fluidized bed dead zone volumes and the particle sizes of the minerals to be separated is obtained through dimensional analysis and data fitting.

[0016] Furthermore, the dead zone volume of the fluidized bed is determined according to the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles, including:

[0017] Determining the volume of the mineral to be separated according to the particle size of the mineral to be separated;

[0018] determining the gravity of the mineral to be separated based on the true density and volume of the mineral to be separated;

[0019] determining the buoyancy of the mineral to be separated according to the bed density and the volume;

[0020] Determining the additional gravity exerted by the fluidized dead zone on the mineral to be separated based on the resultant force, the gravity and the buoyancy;

[0021] The volume of the fluidized dead zone is determined according to the additional gravity corresponding to the fluidized dead zone and the bulk density of the weighted particles.

[0022] Furthermore, the sorting density prediction model is: ρ sep =ρ bed +ρ drag -ρ particle

[0023] Among them, ρ sep Characterizing the sorting density, ρ bed Characterizes the bed density, ρ drag Characterizes the change in sorting density caused by drag force, ρ particle Characterizes the change in separation density caused by fluidization dead zone;

[0024] The dry heavy medium fluidized bed separation density prediction model is constructed based on the functional relationship, including:

[0025] According to the fluidization dead zone volume V hood The total volume V of the fluidized dead zone and the mineral to be separated is obtained by tol :

[0026] V hood Characterize the fluidization dead zone volume, d sphCharacterize the particle size of the mineral to be separated, K and α are both constants;

[0027] According to the V tol , determine the separation density change value ρ caused by the fluidization dead zone particle :

[0028] ρ hood Characterizes the bulk density of weighted particles in the fluidized dead zone.

[0029] Furthermore, electrical capacitance tomography (ECT) is used to verify whether the sorting density obtained by the sorting density prediction model is correct.

[0030] Furthermore, the use of electrical capacitance tomography (ECT) to verify whether the sorting density obtained by the sorting density prediction model is correct includes:

[0031] Determining a plurality of calibration minerals according to a preset density range, each calibration mineral corresponding to a density;

[0032] Conducting a floating and sinking test on each of the calibration minerals at the same fluidized bed height, and counting the number of sinking times of each calibration mineral in each test;

[0033] Determining the distribution rate of each of the calibration mineral particles according to the sinking times and the sinking and floating times;

[0034] Determining the standard separation density at the fluidized bed height according to the density and distribution rate corresponding to each of the calibration minerals;

[0035] According to the standard sorting density, it is determined whether the sorting density obtained by the sorting density prediction model is correct.

[0036] Furthermore, the method further comprises:

[0037] The distribution of dielectric constant sensitivity in the fluidized bed is determined by simulation software;

[0038] Determining the optimal conditions for ECT observation based on the distribution of dielectric constant sensitivity in the fluidized bed;

[0039] The optimal conditions include: particle size, dielectric constant, and placement position of the object being observed in the fluidized bed.

[0040] In a second aspect, an embodiment of the present invention provides a fluidized bed separation density prediction method, comprising:

[0041] Obtain the true density and particle size of the minerals to be separated, the bulk density of the weighted particles, the operating gas velocity and fluidizing gas density of the fluidized bed, and the bed density;

[0042] According to the actual density and particle size of the mineral to be separated, the bulk density of the weighted particles, the operating gas velocity and fluidizing gas density of the fluidized bed, and the bed density, the fluidized bed separation density prediction model constructed in the first aspect is used to determine the fluidized bed separation density corresponding to the mineral to be separated.

[0043] In a third aspect, an embodiment of the present invention provides a fluidized bed separation density prediction model construction device, comprising: a combined force detection module, a collection module, and a data processing module;

[0044] The combined force detection module is used to suspend the minerals to be separated in the dry heavy medium fluidized bed through a force gauge after the heavy medium particles in the fluidized bed are fluidized to achieve bubbling fluidization;

[0045] The collection module is used to collect the resultant force and bed density of the mineral to be separated;

[0046] The data processing module is used to determine the functional relationship between the dead zone volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated based on the resultant force exerted on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; and construct a dry heavy medium fluidized bed separation density prediction model based on the functional relationship.

[0047] Furthermore, the data processing module is used to determine the dead zone volume of the fluidized bed based on the resultant force exerted on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; for multiple minerals to be separated, the dead zone volume of the fluidized bed and the particle size of each mineral to be separated are determined respectively; based on the dead zone volume and particle size of the fluidized bed of the multiple minerals to be separated, the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be separated is obtained through dimensional analysis and data fitting.

[0048] In a fourth aspect, an embodiment of the present invention provides a fluidized bed separation density prediction model construction system, comprising: an air supply device, a fluidized bed, an ECT device, a dynamometer, and the apparatus described in the third aspect;

[0049] The air outlet of the air supply device is connected to the bottom of the fluidized bed and blows air toward the top of the fluidized bed chamber;

[0050] The detection end of the ECT device is provided on the outer wall of the fluidized bed;

[0051] The dynamometer is arranged at the top of the fluidized bed;

[0052] The device is in data connection with the ECT equipment and the dynamometer.

[0053] Compared with the prior art, the present invention can achieve at least one of the following technical effects:

[0054] 1. In the fluidization process, the minerals to be sorted will block the gas path, so a fluidization dead zone will be formed in the direction of the minerals to be sorted away from the gas path. The weighted particles with poor fluidization in the fluidization dead zone will generate additional gravity on the minerals to be sorted in the feed layer, thereby affecting the determination of the sorting density. For the minerals to be sorted in the fluidized bed, they are subject to four forces, namely gravity, drag, buoyancy and additional gravity generated by the fluidization dead zone, among which the drag can be ignored, and gravity and buoyancy are easy to obtain. Therefore, the present invention uses a dynamometer to measure the resultant force of the minerals to be sorted, and then combines the bed density, the particle size of the minerals to be sorted, the true density of the minerals to be sorted and the bulk density of the weighted particles to easily obtain the additional gravity of the fluidization dead zone and digitize the additional gravity. This lays a technical foundation for building an accurate sorting density model.

[0055] 2. The sorting density is the sum of the bed density, the density change corresponding to the drag force, and the density change corresponding to the additional gravity. By obtaining the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be sorted, the fluidization dead zone factor is converted into a model parameter to reflect the fluidization dead zone factor in the sorting density prediction model, thereby improving the calculation accuracy of the sorting density prediction model.

[0056] Other features and advantages of the present invention will be described in the following description, and part of them will become obvious from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0058] FIG1 is a flow chart of a method for constructing a fluidized bed separation density prediction model according to an embodiment of the present invention;

[0059] FIG2 is a schematic structural diagram of a fluidized bed separation density prediction model construction system provided by an embodiment of the present invention.

[0060] FIG3 is a data table of floating and sinking test results provided by an embodiment of the present invention;

[0061] FIG4 is a sediment distribution curve provided by an embodiment of the present invention.

[0062] Figure numerals: 1-blower, 2-gas tank, 3-pressure gauge, 4-butterfly valve, 5-rotor flowmeter, 6-fluidized bed, 7-minerals to be separated, 8-electronic dynamometer, 9-dust cover, 10-dust collector, 11-dust collection box, 12-induced draft fan, 13-sensor of electrical capacitance tomography system, 14-electrical capacitance tomography measuring equipment, 15-computer. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein the accompanying drawings constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0064] Existing technologies typically use bed density as the separation density, and continuously improve bed density measurement methods to enhance separation density accuracy. However, for minerals to be separated in a fluidized bed, the presence of fluidized dead zones affects the determination of separation density in dry separation fluidized beds.

[0065] Specifically, since the gas is evenly distributed through the air distribution plate and then flows upward from the bottom of the bed, the mineral to be separated inside the fluidized bed is relatively large in size and its own presence will block part of the upward airflow. Therefore, the mineral to be separated in the fluidized bed will change the direction of the airflow, blocking the original airflow path. In this way, no effective rising airflow is formed in the area near the upper surface of the mineral to be separated for the weighting particles in the area to be fully suspended. This part of the weighting particles is called poorly fluidized weighting material. The area where the poorly fluidized weighting material is located is the fluidization dead zone. The poorly fluidized weighting material within the dead zone will exert its own gravity on the mineral to be separated, making the mineral to be separated become "heavier", thereby affecting the determination of the separation density of the dry separation fluidized bed.

[0066] In order to quantify the effect of the fluidization dead zone on the separation density and construct a separation density model that includes the fluidization dead zone factor, an embodiment of the present invention provides a method for constructing a dry-process heavy medium fluidized bed separation density prediction model, as shown in FIG1 , comprising the following steps:

[0067] Step 1: fluidizing the weighted particles in the fluidized bed to achieve bubbling fluidization.

[0068] Step 2: Suspend the mineral to be separated in a dry heavy medium fluidized bed through a dynamometer.

[0069] In the embodiment of the present invention, the weighted particles are accumulated on the side of the mineral to be separated that does not contact the airflow, and the area where the weighted particles are accumulated is the fluidization dead zone.

[0070] Step 3: Collect the resultant force on the mineral to be separated and the bed density, and obtain the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles.

[0071] In an embodiment of the present invention, the resultant force on the mineral to be separated can be obtained by a dynamometer, the particle size of the mineral to be separated can be directly measured or obtained by screening, and the true density of the mineral to be separated and the bulk density of the weighted particles can be obtained by looking up a table.

[0072] Step 4: Determine the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be separated based on the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles.

[0073] In the embodiment of the present invention, the dead zone volume of the fluidized bed is determined based on the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles. Specifically,

[0074] The volume of the mineral to be separated is determined according to the particle size of the mineral to be separated.

[0075] In the embodiment of the present invention, the mineral to be separated is regarded as a sphere, and its volume calculation formula is:

[0076] Among them, V represents volume and d represents the diameter of the sphere, i.e., particle size.

[0077] The gravity of the mineral to be separated is determined based on its actual density and volume.

[0078] The calculation formula is:

[0079] Where G is gravity, d sph is the particle size of the mineral to be separated, ρ p is the density of the mineral to be separated, and g is the acceleration of gravity.

[0080] Determine the buoyancy of the minerals to be separated based on the bed density and volume;

[0081] The calculation formula is:

[0082] Among them, F f is the buoyancy, ρ bed is the bed density.

[0083] Based on the resultant force, gravity and buoyancy, the additional gravity exerted by the fluidized dead zone on the minerals to be separated is determined.

[0084] The calculation formula is: G+F f +F e =F h

[0085] Among them, F eF is the additional gravity exerted by the fluidized dead zone on the minerals to be separated. h The resultant force, gravity, buoyancy and the additional gravity exerted by the fluidized dead zone on the minerals to be separated are all vectors.

[0086] The volume of the fluidized dead zone is determined based on the additional gravity corresponding to the fluidized dead zone and the bulk density of the weighted particles.

[0087] The calculation formula is: e =ρ hood gV hood

[0088] Among them, V hood Characterizes the fluidization dead zone volume, ρ hood Characterizes the bulk density of weighted particles in the fluidized dead zone. The bulk density of the fluidized dead zone is the bulk density of weighted particles.

[0089] For multiple minerals to be separated, the particle size of each mineral to be separated is obtained respectively, and multiple fluidized bed dead zone volumes are calculated according to the above method. Then, based on the multiple fluidized bed dead zone volumes and the particle size of the multiple minerals to be separated, the functional relationship between the fluidized bed dead zone volume and the particle size of the mineral to be separated is obtained through dimensional analysis and data fitting:

[0090] Among them, V hood Characterize the fluidization dead zone volume, d sph Characterizes the particle size of the mineral to be separated, N is the ratio of the operating gas velocity to the fluidization velocity, and the coefficients K and α are obtained by fitting. It should be noted that the formula corresponding to the functional relationship is obtained by dimensional analysis, and the coefficients K and α are obtained by data fitting.

[0091] Step 5: Construct a dry heavy medium fluidized bed separation density prediction model based on the functional relationship.

[0092] In the embodiment of the present invention, the general formula of the sorting density prediction model is: sep =ρ bed +ρ drag -ρ particle

[0093] Among them, ρ sep Characterizing the sorting density, ρ bed Characterizes the bed density, ρ drag Characterizes the change in sorting density caused by drag force, ρ particle Characterizes the change in separation density caused by the fluidization dead zone.

[0094] ρ particle The calculation process is:

[0095] The volume V of the mineral to be separated is obtained according to the volume of the fluidized dead zone and the volume of the mineral to be separated. tol :

[0096] According to the V tol , determine the separation density change value ρ caused by the fluidization dead zone particle :

[0097] ρ drag Specifically

[0098] Among them, v g is the operating air velocity, μ f The viscosity of air is a constant, ρ f is the gas density in the fluidized bed.

[0099] And ρ bed It can be obtained by the corresponding model with the help of ECT, specifically: ρ bed =ρ s ×ε d +ρ g ×ε b

[0100] ρ s is the true density of the weighted particles, ε d Indicates the distribution ratio of the emulsified phase in the fluidized bed, ε b represents the distribution ratio of the bubble phase in the fluidized bed, ρ g Indicates the density of the gas.

[0101] U is the operating air speed; U mf is the minimum fluidization velocity of the fluidized bed; Y represents the ratio of the volume flow rate of the bubble phase to the volume flow rate of the bubble phase, which is a constant. b is the bubble moving speed. d =(1-ε mf )(1-ε b )

[0102] ε mf The minimum fluidization porosity can be calculated from the true density and bulk density of the weighted particles.

[0103] From this we can see that determining ρ bed The key is to measure the bubble movement speed. The bubble movement speed can be obtained by ECT, and finally calculated according to ρ bed The formula and bubble movement speed are used to obtain ρ bed .

[0104] In summary, the final form of the sorting density model is:

[0105] In the final form of the sorting density model, the construction of the sorting density model can be completed by simply obtaining the coefficients K and α through the fluidization dead zone volume and the particle size of the mineral to be sorted.

[0106] In an embodiment of the present invention, after the sorting density model is constructed, it is only necessary to obtain the bulk density and particle size of the weighted particles, the operating gas velocity and fluidizing gas density of the fluidized bed, and the bed density. Based on the acquired data and the obtained sorting density model, the fluidized bed sorting density corresponding to the mineral to be sorted can be predicted.

[0107] Any model has its own scope of application. For fluidized beds, the application scope of the sorting density model includes the types of minerals and weighting particles to be sorted, their particle sizes, and process conditions. Since sorting density cannot be directly measured, validating the model's scope of application is extremely difficult. To address this technical problem, the present invention verifies the sorting density obtained from the sorting density model using ECT.

[0108] As a non-invasive imaging method, electrical capacitance tomography technology is considered to be the most effective means to solve the imaging of two-phase and multiphase flows in which the continuous phase is a non-conductive substance. It has the potential to characterize the movement behavior of large minerals in three-dimensional fluidized beds, providing good conditions for studying the separation behavior of minerals to be sorted in three-dimensional fluidized beds. It can complete the accurate prediction of the fluidized bed sorting density, which is conducive to the realization of efficient sorting in dry sorting fluidized beds.

[0109] Specifically, based on a preset density range, multiple calibration minerals are determined, each corresponding to a density. A floating and sinking test is performed on each calibration mineral at the same fluidized bed height, and in each test, the number of sinkings of each calibration mineral is counted. Based on the number of sinkings and the number of floating and sinking tests, the distribution rate of each calibration mineral particle is determined. Based on the density and distribution rate corresponding to each calibration mineral, the standard sorting density at the fluidized bed height is determined. Based on the standard sorting density, it is determined whether the sorting density obtained by the sorting density prediction model is correct. With the help of ECT imaging, inspectors can obtain the movement traces of mineral particles in the fluidized bed, providing technical support for confirming whether the mineral particles sink in the fluidized bed. Since the floating and sinking tests are performed on a single particle, multiple tests are required to eliminate contingencies. The distribution rate is then calculated using the ratio of the number of sinkings to the number of tests. Numerical fitting is performed based on multiple densities and multiple distribution rates to obtain a function of the distribution rate and density. Finally, the density corresponding to a 50% distribution rate is selected as the standard sorting density based on this function. For example;

[0110] The test used 30mm diameter ping-pong balls filled with mixed particles to create standard spherical density balls of varying densities. These balls were then subjected to float and sink tests within a gas-solid fluidized bed to measure the fluidized bed separation density. Using a gas-solid fluidized bed of 150-300µm magnetite powder as an example, the density ball float and sink tests were demonstrated. The critical fluidizing gas velocity within the bed was 10cm / s, the superficial gas velocity was 12.4cm / s, the initial bed height was within the 40-300mm range, and the separation time was 60s. The float and sink tests were repeated 30 times for each ball, and the number of sinks was recorded. The results are shown in Figure 3 below.

[0111] The above data are used to draw the sediment distribution curve under different initial bed heights. The horizontal axis corresponding to the distribution rate of 50% in the curve is the fluidized bed separation density under a certain initial bed height. As shown in Figure 4, 2.22g / cm 3 is the standard sorting density.

[0112] Using ECT as a criterion for evaluating the accuracy of model calculations requires ensuring the effectiveness of ECT itself. To ensure this, embodiments of the present invention use simulation software to determine the distribution of dielectric constant sensitivities within the fluidized bed. Based on this distribution of dielectric constant sensitivities within the fluidized bed, optimal conditions for ECT observation are determined. These optimal conditions include the particle size, dielectric constant, and placement of the observed object within the fluidized bed.

[0113] Specifically, the finite element analysis software Comsol was used to simulate the fluidized bed and ECT sensor. The fluidized bed was divided into multiple regions, each corresponding to an ECT detection position. Minerals to be separated with different dielectric constants and particle sizes were then simulated and placed at different detection positions to determine the effects of particle size, dielectric constant, and test position on the test results. Based on the effects of particle size, dielectric constant, and test position on the test results, the optimal conditions for ECT observation can be determined. Before testing, the ECT is calibrated or the particle size, dielectric constant, and test position are adjusted based on the optimal conditions for ECT observation.

[0114] The embodiment of the present invention provides a fluidized bed separation density prediction model construction device, comprising: a combined force detection module, a collection module and a data processing module;

[0115] The combined force detection module is used to suspend the mineral to be separated in the dry heavy medium fluidized bed through a dynamometer and perform fluidization operation on the mineral to be separated. The mineral to be separated includes: the dead zone of the fluidized bed and the mineral to be separated;

[0116] The acquisition module is used to collect the resultant force on the minerals to be separated, bed density, particle size of the minerals to be separated, true density of the minerals to be separated and bulk density of the weighted particles;

[0117] The data processing module is used to determine the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be separated based on the resultant force acting on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; and to construct a dry heavy medium fluidized bed separation density prediction model based on the functional relationship.

[0118] Among them, the data processing module is used to determine the dead zone volume of the fluidized bed based on the resultant force exerted on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; for multiple minerals to be separated, the dead zone volume of the fluidized bed and the particle size of each mineral to be separated are determined respectively; based on the dead zone volume and particle size of the fluidized bed of multiple minerals to be separated, the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be separated is obtained through dimensional analysis and data fitting.

[0119] The present invention provides a fluidized bed separation density prediction model construction system, as shown in FIG2 , comprising: an air outlet device, a fluidized bed, an ECT device, a dynamometer, and a fluidized bed separation density prediction model construction device;

[0120] The outlet end of the gas outlet device is connected to the bottom of the fluidized bed and blows air to the top of the fluidized bed chamber; the detection end of the ECT device is set on the outer wall of the fluidized bed; the dynamometer is set on the top of the fluidized bed; the fluidized bed sorting density prediction model construction device is connected to the ECT device and the dynamometer data.

[0121] Specifically, the fluidized bed sorting density prediction model construction system includes: a blower 1, a gas tank 2, a pressure gauge 3, a butterfly valve 4, a rotor flowmeter 5, a fluidized bed 6, an electronic dynamometer 8, a dust cover 9, a dust collector 10, a dust collecting box 11, an induced draft fan 12, a sensor 13 of the electrical capacitance tomography system, an electrical capacitance tomography measurement device 14, and a computer 15.

[0122] The gas outlet equipment includes: a blower 1, a gas tank 2, a pressure gauge 3, a butterfly valve 4, and a rotameter 5. The gas generated by blower 1 is stored in gas tank 2. During fluidization, the butterfly valve 4 is controlled to transfer the gas from gas tank 2 to the bottom of the fluidized bed 6. The gas flow rate is monitored by the rotameter 5, and the pressure is monitored by the pressure gauge 3. The fluidized gas flows out from the top of the fluidized bed 6, passes through a dust hood 9, and then through a dust collector 10 and a dust box 11, before being discharged into the atmosphere by an induced draft fan 12.

[0123] The ECT equipment includes a capacitance tomography (ECT) system sensor 13 and an ECT measurement device 14. The fluidized bed separation density prediction model construction device is integrated into a computer 15. The ECT system sensor 13 collects dielectric constant distribution information within the fluidized bed 6 and transmits this information to the ECT measurement device 14. The ECT measurement device 14 converts the dielectric constant distribution information into an image and transmits the image to the computer 15. Furthermore, the computer 15 receives the resultant force collected by the electronic dynamometer 8. The electronic dynamometer 8 is positioned at the top of the fluidized bed 6, with the mineral 7 suspended at its bottom.

[0124] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for constructing a fluidized bed separation density prediction model, characterized in that: include: Fluidizing the weighted particles in the fluidized bed to achieve bubbling fluidization; The minerals to be separated are suspended in a dry heavy medium fluidized bed through a dynamometer; Collecting the resultant force and bed density of the mineral to be separated; Determining a functional relationship between the dead zone volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated based on the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles; A dry heavy medium fluidized bed separation density prediction model is constructed based on the functional relationship.

2. The method according to claim 1, characterized in that Determining the functional relationship between the dead zone volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated includes: Determining the dead zone volume of the fluidized bed according to the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles; For a plurality of the minerals to be separated, respectively determining the dead zone volume of the fluidized bed and the particle size of each of the minerals to be separated; According to the fluidized bed dead zone volumes and particle sizes of the multiple minerals to be separated, a functional relationship between the fluidized bed dead zone volumes and the particle sizes of the minerals to be separated is obtained through dimensional analysis and data fitting.

3. The method according to claim 2, characterized in that Determining the dead zone volume of the fluidized bed according to the resultant force on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated, and the bulk density of the weighted particles includes: Determining the volume of the mineral to be separated according to the particle size of the mineral to be separated; Determine the volume of the mineral to be separated based on its true density and volume. subject to gravity; determining the buoyancy of the mineral to be separated according to the bed density and the volume; Determining the additional gravity exerted by the fluidized dead zone on the mineral to be separated based on the resultant force, the gravity and the buoyancy; The volume of the fluidized dead zone is determined according to the additional gravity corresponding to the fluidized dead zone and the bulk density of the weighted particles.

4. The method according to claim 3, characterized in that The sorting density prediction model is: r sep =ρ bed +r drag -r particle Among them, ρ sep Characterizing the sorting density, ρ bed Characterizes the bed density, ρ drag Characterizes the change in sorting density caused by drag force, ρ particle Characterizes the change in separation density caused by fluidization dead zone; The dry heavy medium fluidized bed separation density prediction model is constructed based on the functional relationship, including: According to the fluidization dead zone volume V hood The total volume V of the fluidized dead zone and the mineral to be separated is obtained by tol : V hood Characterize the fluidization dead zone volume, d sph Characterize the particle size of the mineral to be separated, K and α are both constants; According to the V tol , determine the separation density change value ρ caused by the fluidization dead zone particle : ρ hood Characterizes the bulk density of weighted particles in the fluidized dead zone.

5. The method according to claim 4, characterized in that The method further comprises: The sorting density prediction model was verified using electrical capacitance tomography (ECT) to verify the sorting density. Is the density correct? 6. The method according to claim 5, characterized in that The method of using electrical capacitance tomography (ECT) to verify whether the sorting density obtained by the sorting density prediction model is correct includes: Determining a plurality of calibration minerals according to a preset density range, each calibration mineral corresponding to a density; Conducting a floating and sinking test on each of the calibration minerals at the same fluidized bed height, and counting the number of sinking times of each calibration mineral in each test; Determining the distribution rate of each of the calibration mineral particles according to the sinking times and the sinking and floating times; Determining the standard separation density at the fluidized bed height according to the density and distribution rate corresponding to each of the calibration minerals; According to the standard sorting density, it is determined whether the sorting density obtained by the sorting density prediction model is correct.

7. The method according to claim 5, characterized in that The method further comprises: The distribution of dielectric constant sensitivity in the fluidized bed is determined by simulation software; Determining the optimal conditions for ECT observation based on the distribution of dielectric constant sensitivity in the fluidized bed; The optimal conditions include: particle size, dielectric constant, and placement position of the object being observed in the fluidized bed.

8. A fluidized bed separation density prediction method, characterized in that: include: Obtain the true density and particle size of the minerals to be separated, the bulk density of the weighted particles, the operating gas velocity and fluidizing gas density of the fluidized bed, and the bed density; According to the actual density and particle size of the mineral to be separated, the bulk density of the weighted particles The fluidized bed separation density prediction model constructed according to claims 1 to 7 is used to determine the fluidized bed separation density corresponding to the mineral to be separated.

9. A fluidized bed separation density prediction model construction device, characterized in that: include: Combined force detection module, acquisition module and data processing module; The combined force detection module is used to suspend the minerals to be separated in the dry heavy medium fluidized bed through a force gauge after the heavy medium particles in the fluidized bed are fluidized to achieve bubbling fluidization; The collection module is used to collect the resultant force and bed density of the mineral to be separated; The data processing module is used to determine the functional relationship between the dead zone volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated based on the resultant force exerted on the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; and construct a dry heavy medium fluidized bed separation density prediction model based on the functional relationship.

10. The device according to claim 9, characterized in that The data processing module is used to determine the dead zone volume of the fluidized bed based on the resultant force applied to the mineral to be separated, the bed density, the particle size of the mineral to be separated, the true density of the mineral to be separated and the bulk density of the weighted particles; for multiple minerals to be separated, the dead zone volume of the fluidized bed and the particle size of each mineral to be separated are determined respectively; based on the dead zone volume and particle size of the fluidized bed of the multiple minerals to be separated, the functional relationship between the dead zone volume of the fluidized bed and the particle size of the mineral to be separated is obtained through dimensional analysis and data fitting.

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