WO2023193656A1 - 分选密度预测方法及预测模型构建方法和装置 - Google Patents

分选密度预测方法及预测模型构建方法和装置 Download PDF

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WO2023193656A1
WO2023193656A1 PCT/CN2023/085316 CN2023085316W WO2023193656A1 WO 2023193656 A1 WO2023193656 A1 WO 2023193656A1 CN 2023085316 W CN2023085316 W CN 2023085316W WO 2023193656 A1 WO2023193656 A1 WO 2023193656A1
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density
separated
fluidized bed
minerals
mineral
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English (en)
French (fr)
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段晨龙
周晨阳
陈建强
刘锡波
柴学森
王程
高忠林
王丹
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中国矿业大学
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07B—SEPARATING SOLIDS FROM SOLIDS BY SIEVING, SCREENING, SIFTING OR BY USING GAS CURRENTS; SEPARATING BY OTHER DRY METHODS APPLICABLE TO BULK MATERIAL, e.g. LOOSE ARTICLES FIT TO BE HANDLED LIKE BULK MATERIAL
    • B07B7/00—Selective separation of solid materials carried by, or dispersed in, gas currents
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Definitions

  • the 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.
  • the movement behavior of the minerals to be separated in the dry separation fluidized bed affects the fluidized dry separation effect. Obtaining information on the separation behavior of minerals to be separated in dry separation fluidized beds is of great significance for the efficient fluidized separation of minerals.
  • 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.
  • the present invention provides a method for constructing a fluidized bed separation density prediction model, including:
  • the minerals to be separated are suspended in the dry heavy medium fluidized bed through a dynamometer;
  • the mineral to be sorted is determined according to the resultant force on the mineral to be sorted, the bed density, the particle size of the mineral to be sorted, the true density of the mineral to be sorted, and the packing density of the weighted particles.
  • the volume of the fluidized bed dead space above the mineral is a function of the particle size of the mineral to be sorted. relation;
  • the fluidized bed is determined based on the resultant force on the minerals to be separated, the bed density, the particle size of the minerals to be separated, the true density of the minerals to be separated, and the packing density of the weighted particles. dead space volume;
  • the functional relationship between the fluidized bed dead space volume and the particle size of the mineral to be separated is obtained through dimensional analysis and data fitting.
  • the dead space volume of the chemical bed includes:
  • the gravity and the buoyancy force determine the additional gravity exerted by the fluidization dead zone on the minerals to be sorted;
  • the volume of the fluidized dead zone is determined based on the additional gravity corresponding to the fluidized dead zone and the packing density of the weighted particles.
  • ⁇ sep represents the separation density
  • ⁇ bed represents the bed density
  • ⁇ drag represents the change in separation density caused by drag force
  • ⁇ particle represents the change in separation density caused by the fluidization dead zone
  • the dry process heavy medium fluidized bed separation density prediction model is constructed based on the functional relationship, including:
  • V hood represents the fluidized dead zone volume
  • d sph represents the particle size of the mineral to be sorted
  • K and ⁇ are both constants
  • ⁇ hood represents the packing density of heavy particles in the fluidization dead zone.
  • the electrical capacitance tomography technology ECT is used to verify whether the sorting density obtained by the sorting density prediction model is correct.
  • the sorting density obtained by the sorting density prediction model is determined. Is the degree correct?
  • the method also includes:
  • the optimal conditions include: the particle size of the observed object, the dielectric constant, and the placement position in the fluidized bed.
  • embodiments of the present invention provide a fluidized bed separation density prediction method, including:
  • the fluidization system constructed by the first aspect Bed separation density prediction model determines the fluidized bed separation density corresponding to the mineral to be separated.
  • embodiments of the present invention provide a fluidized bed separation density prediction model construction device, including: a combined force detection module, an acquisition module and a data processing module;
  • the combined force detection module is used to perform a fluidization operation on the heavy-weight particles in the fluidized bed to achieve bubbling fluidization, and then suspend the minerals to be sorted in the dry heavy-medium fluidized bed through a dynamometer;
  • the collection module is used to collect the resultant force and bed density of the minerals to be sorted;
  • the data processing module is used to calculate the resultant force and bed density of the minerals to be separated, as well as the particle size of the minerals to be separated, the true density of the minerals to be separated, and the packing density of the weighted particles. , determine the functional relationship between the dead space volume of the fluidized bed above the mineral to be separated and the particle size of the mineral to be separated; build a dry heavy medium fluidized bed separation density prediction model based on the functional relationship.
  • the data processing module is used to calculate the result 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 weighted particles.
  • the bulk density is used to determine the fluidized bed dead space volume; for multiple minerals to be separated, the fluidized bed dead space volume of each mineral to be separated and the particle size of each mineral to be separated are determined; according to the Describe the fluidized bed dead space volume and particle size of multiple minerals to be separated, and obtain the functional relationship between the fluidized bed dead space volume and the particle size of the mineral to be separated through dimensional analysis and data fitting.
  • embodiments of the present invention provide a fluidized bed separation density prediction model construction system, including: air supply equipment, fluidized bed, ECT equipment, dynamometer and the device described in the third aspect;
  • the air outlet end of the air supply equipment is connected to the bottom of the fluidized bed and blows air to the top of the inner chamber of the fluidized bed;
  • the detection end of the ECT equipment is provided with the outer wall of the fluidized bed
  • the force gauge is arranged at the top of the fluidized bed
  • the device is in data connection with the ECT device and the load cell.
  • the present invention can achieve at least one of the following technical effects:
  • the minerals to be separated will block the gas path, so a fluidization dead zone will be formed in the direction of the minerals to be separated away from the gas path.
  • the poorly fluidized weighted particles in the fluidized dead zone will generate additional gravity for the minerals to be sorted in the feed layer, thus affecting the determination of the sorting density.
  • For the minerals to be sorted in the fluidized bed they are subject to four additional gravity forces: gravity, drag force, buoyancy force and fluidization dead zone.
  • the drag force is negligible, and gravity and buoyancy force are easy to obtain.
  • the present invention uses a dynamometer to measure the resultant force of the minerals to be separated, and then combines the bed density, the particle size of the minerals to be separated, the true density of the minerals to be separated and the packing density of the weighted particles,
  • the additional gravity of the fluidization dead zone can be easily obtained and the additional gravity can be digitized. This lays a technical foundation for building an accurate sorting density model.
  • the separation 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.
  • Figure 1 is a flow chart of a method for building a fluidized bed separation density prediction model according to an embodiment of the present invention
  • Figure 2 is a schematic structural diagram of a system for building a fluidized bed separation density prediction model according to an embodiment of the present invention.
  • Figure 3 is a data table of floating and sinking test results provided by the embodiment of the present invention.
  • Figure 4 is a sediment distribution curve provided by an embodiment of the present invention.
  • the existing technology usually uses the bed density as the sorting density, and continuously improves the measurement methods to increase the bed density to improve the accuracy of the sorting density.
  • the existence of fluidized dead zone affects the determination of the separation density of the dry separation fluidized bed.
  • embodiments of the present invention provide a method for constructing a dry heavy-medium fluidized bed separation density prediction model , as shown in Figure 1, including the following steps:
  • Step 1 Fluidize the heavy particles in the fluidized bed to achieve bubbling fluidization.
  • Step 2 Suspend the minerals to be separated in the dry heavy medium fluidized bed through a dynamometer.
  • the weighted particles will accumulate on the side of the mineral to be separated that is not in contact with the air flow, and the area where the weighted particles accumulate is the fluidization dead zone.
  • Step 3 Collect the resultant force and bed density of the minerals to be sorted, and obtain the particle size of the minerals to be sorted, the true density of the minerals to be sorted, and the packing density of the weighted particles.
  • the resultant force on the minerals to be sorted can be obtained by a dynamometer, the particle size of the minerals to be sorted can be directly measured or obtained through a screening method, and the true density of the minerals to be sorted and the packing density of the weighted particles can be Obtained through table lookup.
  • Step 4 According to the resultant force on the minerals to be separated, bed density, and the properties of the minerals to be separated The particle size, the true density of the minerals to be separated and the packing density of the weighted particles determine the functional relationship between the dead space volume of the fluidized bed and the particle size of the minerals to be separated.
  • the dead space volume of the fluidized bed is determined based on the resultant force on the minerals to be separated, the density of the bed, the particle size of the minerals to be separated, the true density of the minerals to be separated, and the packing density of the weighted particles. specifically,
  • the volume of the mineral to be separated is determined.
  • the mineral to be sorted is regarded as a sphere, and its volume calculation formula is:
  • V represents the volume
  • d represents the diameter of the sphere, which is the particle size.
  • G is the gravity
  • d sph is the particle size of the mineral to be sorted
  • ⁇ p is the density of the mineral to be sorted
  • g is the gravity acceleration.
  • F f is the buoyancy force
  • ⁇ bed is the bed density
  • the additional gravity exerted by the fluidization dead zone on the minerals to be sorted is determined.
  • F e is the additional gravity exerted by the fluidized dead zone on the minerals to be sorted
  • F h is the force gauge obtained Together.
  • the resultant force, gravity, buoyancy force and the additional gravity exerted by the fluidization dead zone on the minerals to be sorted are all vector quantities.
  • the volume of the fluidized dead zone is determined based on the additional gravity corresponding to the fluidized dead zone and the packing density of the weighted particles.
  • V hood represents the fluidization dead zone volume
  • ⁇ hood represents the packing density of weighted particles in the fluidization dead zone.
  • the packing density of the fluidized dead zone is the packing density of the weighted particles.
  • V hood represents the fluidization dead zone volume
  • d sph represents the particle size of the mineral to be sorted
  • N is the ratio of the operating gas speed to the fluidization speed
  • the coefficient K and coefficient ⁇ are obtained through fitting. It should be noted that dimensional analysis obtains the formula corresponding to the functional relationship, and the coefficient K and coefficient ⁇ are obtained through data fitting.
  • Step 5 Construct a dry heavy-medium fluidized bed separation density prediction model based on functional relationships.
  • ⁇ sep represents the separation density
  • ⁇ bed represents the bed density
  • ⁇ drag represents the change in separation density caused by drag force
  • ⁇ particle represents the change in separation density caused by the fluidization dead zone.
  • the volume V tol of the mineral to be separated is obtained according to the volume of the fluidized dead space and the volume of the mineral to be separated:
  • v g is the operating gas velocity
  • ⁇ f is the constant air viscosity
  • ⁇ f is the gas density in the fluidized bed.
  • ⁇ bed ⁇ s ⁇ d + ⁇ g ⁇ b
  • ⁇ s is the true density of weighted particles
  • ⁇ d represents the distribution proportion of the emulsified phase in the fluidized bed
  • ⁇ b represents the distribution proportion of the bubble phase in the fluidized bed
  • ⁇ g represents the density of the gas.
  • U is the operating gas speed
  • U mf is the minimum fluidization speed of the fluidized bed
  • Y represents the ratio of the bubble phase volume flow rate to the bubble phase volume flow rate, which is a constant.
  • U b is the bubble moving speed.
  • ⁇ mf minimum fluidized porosity can be obtained from the true density and packing density of weighted particles.
  • the key to determining ⁇ bed is to measure the bubble movement speed.
  • the bubble moving speed can be obtained through ECT, and finally ⁇ bed is obtained according to the formula for calculating ⁇ bed and the bubble moving speed.
  • the coefficient K and coefficient ⁇ only need to be obtained from the fluidization dead zone volume and the particle size of the mineral to be sorted, and the sorting density model can be constructed.
  • the separation density model is constructed, only the bulk density and particle size of the weighted particles, the operating gas velocity of the fluidized bed, the density of the fluidized gas, and the bed density can be obtained.
  • the data and the obtained sorting density model are used to predict the fluidized bed sorting density corresponding to the minerals to be sorted.
  • any model has its own scope of application.
  • the application scope of the separation density model includes the types of minerals and weighted particles to be separated, the particle sizes and process conditions of the minerals and weighted particles to be separated. Since sorting density cannot be measured directly, this brings great difficulty to verify the applicable scope of the model.
  • the present invention uses ECT to verify the sorting density obtained by the sorting density model.
  • capacitance tomography technology is considered to be the most effective means of imaging two-phase flow and multi-phase flow in which the continuous phase is non-conductive material. It has the ability to characterize the movement of bulk minerals in a three-dimensional fluidized bed. The potential of this behavior provides good conditions for the study of the separation behavior of minerals to be separated in a three-dimensional fluidized bed. It can accurately predict the separation density of the fluidized bed and is conducive to the realization of high-efficiency dry separation fluidized bed separation. select.
  • multiple calibration minerals are determined according to the preset density range, and each calibration mineral corresponds to a density; a floating and sinking test is performed on each calibration mineral at the same fluidized bed height, and in each test , count the number of sinkings of each calibration mineral respectively; determine the distribution rate of each calibration mineral particle based on the number of sinkings and the number of sinking and floating tests; determine the distribution rate of each calibration mineral particle at the height of the fluidized bed based on the density and distribution rate corresponding to each calibration mineral.
  • Standard sorting density based on the standard sorting density, determine whether the sorting density obtained by the sorting density prediction model is correct.
  • the density corresponding to the distribution rate of 50% is selected as Standard sorting density. For example;
  • the test uses 30mm diameter table tennis balls filled with mixed particles to prepare standard spherical density balls with different densities, and conduct floating and sinking tests in the gas-solid fluidized bed to measure the fluidized bed separation density.
  • the critical fluidizing gas velocity in the bed is 10cm/s
  • the superficial gas velocity is 12.4cm/s
  • the initial bed height is 40
  • the sorting time is 60s.
  • the floating and sinking test of a single density ball is repeated 30 times, and the number of sinking times is recorded. The results of the floating and sinking test are shown in 3 below.
  • the abscissa in the curve corresponding to a distribution rate of 50% is the fluidized bed separation density at a certain initial bed height.
  • 2.22g/ cm3 is the standard sorting density.
  • the embodiment of the present invention determines the distribution of dielectric constant sensitivity in the fluidized bed through simulation software. According to the distribution of dielectric constant sensitivity in the fluidized bed, the optimal conditions for ECT observation are determined. The optimal conditions include: the particle size of the observed object, the dielectric constant, and the placement position in the fluidized bed.
  • the finite element analysis software Comsol was used to simulate the fluidized bed and ECT sensor respectively. And the fluidized bed is divided into multiple areas, each area corresponds to an ECT detection position. Then simulate the minerals to be sorted with different dielectric constants and different particle sizes, and place the simulated minerals to be sorted at different detection positions to determine the impact of particle size, dielectric constant and test position on the detection results.
  • the optimal conditions for ECT observation can be determined based on the impact of particle size, dielectric constant and test position on the detection results. Before testing, according to the optimal conditions for ECT observation, either calibrate the ECT, or adjust the particle size, dielectric constant and test position.
  • Embodiments of the present invention provide a fluidized bed separation density prediction model construction device, which includes: a combined force detection module, an acquisition module and a data processing module;
  • the combined force detection module is used to suspend the minerals to be sorted through a dynamometer and dry heavy medium fluidization In the bed, fluidization operation is performed on the minerals to be separated.
  • the minerals to be separated include: the dead zone of the fluidized bed and the minerals to be separated;
  • the acquisition module is used to collect the resultant force on the minerals to be sorted, bed density, particle size of the minerals to be sorted, the true density of the minerals to be sorted and the packing density of the weighted particles;
  • the data processing module is used to determine the dead space volume of the fluidized bed and the volume of the fluidized bed to be separated based on the resultant force on the minerals to be separated, the density of the bed, the particle size of the minerals to be separated, the true density of the minerals to be separated, and the packing density of the weighted particles.
  • the functional relationship of the particle size of minerals; a dry heavy-medium fluidized bed separation density prediction model is constructed based on the functional relationship.
  • the data processing module is used to determine the dead zone volume of the fluidized bed based on the resultant force on the minerals to be separated, the bed density, the particle size of the minerals to be separated, the true density of the minerals to be separated, and the packing density of the weighted particles; For multiple minerals to be sorted, determine the fluidized bed dead space volume and particle size of each mineral to be sorted respectively; according to the fluidized bed dead zone volume and particle size of multiple minerals to be sorted, the throughput The functional relationship between the dead space volume of the fluidized bed and the particle size of the minerals to be sorted was obtained through class analysis and data fitting.
  • the invention provides a fluidized bed separation density prediction model construction system, as shown in Figure 2, including: gas outlet equipment, fluidized bed, ECT equipment, dynamometer and fluidized bed separation density prediction model construction device;
  • the outlet end of the air outlet equipment is connected to the bottom of the fluidized bed and blows air to the top of the inner chamber of the fluidized bed; the detection end of the ECT equipment is set on the outer wall of the fluidized bed; the dynamometer is set on the top of the fluidized bed; the fluidized bed separation density prediction model is constructed
  • the device has data connections with ECT equipment and force gauges.
  • the fluidized bed separation density prediction model construction system includes: blower 1, gas tank 2, pressure gauge 3, butterfly valve 4, rotor flowmeter 5, fluidized bed body 6, electronic force gauge 8, dust cover 9 , dust collector 10, dust collection box 11, induced draft fan 12, sensor 13 of the capacitance tomography system, capacitance tomography measurement equipment 14, computer 15.
  • the gas outlet equipment includes: blower 1, gas tank 2, pressure gauge 3, butterfly valve 4, and rotor flowmeter 5.
  • the gas produced by blower 1 is stored with gas tank 2.
  • the butterfly valve 4 is controlled to transport the gas in the gas tank 2 to the bottom of the fluidized bed 6 .
  • the air flow rate is monitored through the rotameter 5, and the air pressure is monitored through the pressure gauge 3.
  • the fluidized gas flows out from the upper end of the fluidized bed 6, passes through the dust cover 9, passes through the dust collector 10, and the dust collecting box 11 in sequence, and is finally discharged into the atmosphere by the induced draft fan 12.
  • the ECT device includes a sensor 13 of the capacitance tomography system and a capacitance tomography measurement device 14 .
  • the fluidized bed separation density prediction model building device is integrated on the computer 15 .
  • the sensor 13 of the capacitance tomography system collects the dielectric constant distribution information in the fluidized bed 6 and transmits the dielectric constant distribution information to the capacitance tomography measurement device 14 .
  • the capacitance tomography measurement device 14 converts the dielectric constant distribution information into an image and transmits the image to the computer 15 .
  • the computer 15 will also receive the resultant force collected by the electronic force gauge 8 .
  • the electronic load cell 8 is arranged at the top of the fluidized bed 6, and the mineral 7 is suspended from its bottom end.

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Abstract

一种分选密度预测方法及预测模型构建方法和装置，预测模型构建方法包括：对流化床内加重质颗粒进行流化操作达到鼓泡流态化；通过测力计将待分选矿物悬挂于干法重介流化床中，待分选矿物为重密度矿石；采集待分选矿物受到的合力、床层密度；根据待分选矿物受到的合力、床层密度，以及待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆密度，确定流化床内待分选矿物上方流化死区体积和待分选矿物的粒度的函数关系；基于函数关系构建干法重介流化床分选密度预测模型。

Description

分选密度预测方法及预测模型构建方法和装置 技术领域
本发明涉及矿物识别技术领域，尤其涉及一种分选密度预测方法及预测模型构建方法和装置。
背景技术
待分选矿物在干法分选流化床内的运动行为影响着流态化干法分选效果。获取待分选矿物在干法分选流化床中的分离行为信息，对矿物的流态化高效分选具有重要意义。
目前干法分选流化床内待分选矿物的动力学行为缺乏准确的观测手段,待分选矿物分离行为的研究多处于表面观测状态，缺乏对待分选矿物分离过程的深入研究与验证，导致得到的模型的计算精度不高。
发明内容
鉴于上述的分析，本发明旨在提出一种分选密度预测方法及预测模型构建方法和装置，解决上述问题中的至少一个。
本发明的目的主要是通过以下技术方案实现的：
第一方面，本发明提供了一种流化床分选密度预测模型构建方法，包括：
对流化床内加重质颗粒进行流化操作达到鼓泡流态化；
通过测力计将待分选矿物悬挂于干法重介流化床中；
采集所述待分选矿物受到的合力和床层密度；
根据所述待分选矿物受到的合力、床层密度，以及所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定所述待分选矿物上方流化床死区体积和所述待分选矿物的粒度的函数 关系；
基于所述函数关系构建干法重介流化床分选密度预测模型。
进一步地，根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积；
针对多个所述待分选矿物，分别确定各所述待分选矿物的流化床死区体积和各所述待分选矿物的粒度；
根据所述多个待分选矿物的流化床死区体积和粒度，通过量纲分析和数据拟合得到所述流化床死区体积和所述待分选矿物的粒度的函数关系。
进一步地，所述根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积，包括：
根据所述待分选矿物的粒度，确定所述待分选矿物的体积；
根据所述待分选矿物的真实密度和所述体积，确定所述待分选矿物受到重力；
根据所述床层密度和所述体积，确定所述待分选矿物受到的浮力；
根据所述合力、所述重力和所述浮力，确定所述流化死区对所待分选矿物施加的附加重力；
根据所述流化死区对应的附加重力和所述加重质颗粒的堆积密度，确定所述流化死区的体积。
进一步地，所述分选密度预测模型为：
ρsep＝ρbed+ρdrag-ρparticle
其中，ρsep表征分选密度，ρbed表征床层密度，ρdrag表征曳力造成的分选密度变化值，ρparticle表征流化死区所造成的分选密度变化值；
所述基于所述函数关系构建干法重介流化床分选密度预测模型，包括：
根据流化死区体积Vhood和待分选矿物体积得到所述流化死区与待分选矿物的总体积Vtol：
Vhood表征流化死区体积，dsph表征所述待分选矿物的粒度，K和α均为常数；
根据所述Vtol，确定流化死区所造成的分选密度变化值ρparticle：
ρhood表征流化死区中加重质颗粒的堆积密度。
进一步地，利用电容层析成像技术ECT校验所述分选密度预测模型得到的分选密度是否正确。
进一步地，所述利用电容层析成像技术ECT校验所述分选密度预测模型得到的分选密度是否正确，包括：
根据预设密度范围，确定多个校验矿物，每一个所述校验矿物对应一个密度；
分别在同一流化床高度下，对每一个所述校验矿物进行浮沉试验，并在每一次试验中，分别统计各所述校验矿物的下沉次数；
根据所述下沉次数和沉浮试验次数，确定各所述校验矿物颗粒的分配率；
根据各所述校验矿物对应的密度和分配率，确定所述流化床高度下的标准分选密度；
根据所述标准分选密度，确定所述分选密度预测模型得到的分选密 度是否正确。
进一步地，所述方法还包括：
通过模拟软件确定流化床中介电常数灵敏度的分布；
根据所述流化床中介电常数灵敏度的分布，确定ECT观测的最优条件；
所述最优条件包括：被观测物的粒度、介电常数、在流化床中的投放位置。
第二方面，本发明实施例提供了一种流化床分选密度预测方法，包括：
获取待分选矿物的真实密度和粒径、加重质颗粒的堆积密度、流化床的操作气速和流化气体密度、床层密度；
根据所述待分选矿物的真实密度和粒径、所述加重质颗粒的堆积密度、所述流化床的操作气速和流化气体密度、床层密度，通过第一方面构建的流化床分选密度预测模型，确定所述待分选矿物对应的流化床分选密度。
第三方面，本发明实施例提供了一种流化床分选密度预测模型构建装置，包括：合力检测模块、采集模块和数据处理模块；
所述合力检测模块用于在对流化床内加重质颗粒进行流化操作达到鼓泡流态化后，通过测力计将待分选矿物悬挂于干法重介流化床；
所述采集模块用于采集所述待分选矿物受到的合力和床层密度；
所述数据处理模块用于根据所述待分选矿物受到的合力、床层密度，以及所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定所述待分选矿物上方流化床死区体积和所述待分选矿物的粒度的函数关系；基于所述函数关系构建干法重介流化床分选密度预测模型。
进一步地，所述数据处理模块用于根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积；针对多个所述待分选矿物，分别确定各所述待分选矿物的流化床死区体积和各所述待分选矿物的粒度；根据所述多个待分选矿物的流化床死区体积和粒度，通过量纲分析和数据拟合得到所述流化床死区体积和所述待分选矿物的粒度的函数关系。
第四方面，本发明实施例提供了一种流化床分选密度预测模型构建系统，包括：供风设备、流化床、ECT设备、测力计和第三方面所述的装置；
所述供风设备出气端连接所述流化床底部，向所述流化床内室顶部吹气；
所述ECT设备的检测端设置所述流化床外壁；
所述测力计设置在所述流化床顶端；
所述装置与所述ECT设备和所述测力计数据连接。
与现有技术相比，本发明至少能实现以下技术效果之一：
1.在流化工艺中，待分选矿物会阻断气路，因此在待分选矿物远离气路方向上会形成流化死区。流化死区的中流化不良的加重质颗粒会对入料层的待分选矿物产生附加重力，从而影响分选密度的确定。对于流化床中的待分选矿物，其受到四个分别是重力、曳力、浮力和流化死区产生的附加重力，其中曳力可忽略不计，重力和浮力又容易求得。因此本发明利用测力计测得待分选矿物的合力，再结合床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，可以很容易求得流化死区的附加重力，使附加重力数据化。为构建精准的分选密度模型奠定技术基础。
2.分选密度为床层密度、曳力对应密度变化量和附加重力对应的密度变化之和，通过得到流化床死区体积和待分选矿物的粒度的函数关系，将流化死区这一因素转变成模型的参数，以实现在分选密度预测模型中体现流化死区这一要素，从而提高了分选密度预测模型的计算精度。
本发明的其他特征和优点将在随后的说明书中阐述，并且，部分的从说明书中变得显而易见，或者通过实施本发明而了解。本发明的目的和其他优点可通过在所写的说明书以及附图中所特别指出的结构来实现和获得。
附图说明
附图仅用于示出具体实施例的目的，而并不认为是对本发明的限制，在整个附图中，相同的参考符号表示相同的部件。
图1为本发明实施例提供一种流化床分选密度预测模型构建方法的流程图；
图2为本发明实施例提供一种流化床分选密度预测模型构建系统的结构示意图。
图3为本发明实施例提供的浮沉试验结果的数据表；
图4为本发明实施例提供的沉物分配曲线。
附图标记：1-鼓风机、2-气罐、3-压力表、4-蝶阀、5-转子流量计、6-流化床体、7-待分选矿物、8-电子测力计、9-防尘罩、10-除尘器、11-集尘箱、12-引风机、13-电容层析成像系统的传感器、14-电容层析成像测量设备、15-计算机。
具体实施方式
下面结合附图来具体描述本发明的优选实施例，其中，附图构成本发明一部分，并与本发明的实施例一起用于阐释本发明的原理，并非用 于限定本发明的范围。
现有技术通常以床层密度为分选密度，并不断改进提高床层密度的测量方法，以提高分选密度的精度。但对于流化中床中的待分选矿物，流化死区的存在影响干法分选流化床分选密度的确定。
具体地，由于气体是通过布风板均匀布风后由床层底部向上流动的，由于在流化床内部的待分选矿物自身体积较大，自身的存在会阻挡一部分的向上气流。所以流化床中的待分选矿物会改变气流的走向，使原有气流通路被阻断。如此，在待分选矿物的上表面附近区域内没有形成有效的上升气流供该区域内的加重质颗粒充分悬浮，这部分加重质颗粒被称为流化不良的加重质。流化不良的加重质所在的区域即为流化死区。死区范围内的流化不良的加重质会将自身的重力施加于待分选矿物，使得待分选矿物变的更“重”了，从而影响干法分选流化床分选密度的确定。
为了将流化死区对分选密度的影响量化，并构建包含流化死区因素的分选密度模型，本发明实施例提供了一种干法重介流化床分选密度预测模型构建方法，如图1所示，包括以下步骤：
步骤1、对流化床内加重质颗粒进行流化操作达到鼓泡流态化。
步骤2、通过测力计将待分选矿物悬挂于干法重介流化床中。
在本发明实施例中，加重质颗粒会堆积在待分选矿物不接触气流的一侧，而加重质颗粒堆积的区域即为流化死区。
步骤3、采集待分选矿物受到的合力、床层密度，并获取待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆积密度。
在本发明实施例中，待分选矿物受到的合力可由测力计获取，待分选矿物的粒度可以直接测量或者通过筛选法获得，待分选矿物的真实密度和加重质颗粒的堆积密度可以通过查表获得。
步骤4、根据待分选矿物受到的合力、床层密度，以及待分选矿物的 粒度、待分选矿物的真实密度和加重质颗粒的堆积密度，确定流化床死区体积和待分选矿物的粒度的函数关系。
在本发明实施例中，根据待分选矿物受到的合力、床层密度、待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆积密度，确定流化床死区体积。具体地，
根据待分选矿物的粒度，确定待分选矿物的体积。
在本发明实施例中，待分选矿物视为球体，其体积计算公式为：
其中，V代表体积，d代表球体的直径即粒度。
根据待分选矿物的真实密度和体积，确定待分选矿物受到重力。
计算公式为：
其中，G为重力，dsph为待分选矿物的粒度，ρp为待分选矿物密度，g为重力加速度。
根据床层密度和体积，确定待分选矿物受到的浮力；
计算公式为：
其中，Ff为浮力，ρbed为床层密度。
根据合力、重力和浮力，确定流化死区对待分选矿物施加的附加重力。
计算公式为：
G+Ff+Fe＝Fh
其中，Fe为流化死区对待分选矿物施加的附加重力，Fh为测力计得到 合力。合力、重力、浮力和流化死区对待分选矿物施加的附加重力均为矢量。
根据流化死区对应的附加重力和加重质颗粒的堆积密度，确定流化死区的体积。
计算公式为：
Fe＝ρhoodgVhood
其中，Vhood表征流化死区体积，ρhood表征流化死区中加重质颗粒的堆积密度。流化死区的堆积密度即为加重质颗粒的堆积密度。
针对多个待分选矿物，分别获取各待分选矿物的粒度，并按照上述方法计算得到多个流化床死区体积。之后根据多个流化床死区体积多个待分选矿物的粒度，通过量纲分析和数据拟合得到流化床死区体积和待分选矿物的粒度的函数关系：
其中，Vhood表征流化死区体积，dsph表征所述待分选矿物的粒度，N为操作气速与流化速度的比值，系数K和系数α通过拟合得到。需要说明的是，量纲分析得到了函数关系对应的公式，通过数据拟合得到系数K和系数α。
步骤5、基于函数关系构建干法重介流化床分选密度预测模型。
在本发明实施例中，分选密度预测模型的通式为：
ρsep＝ρbed+ρdrag-ρparticle
其中，ρsep表征分选密度，ρbed表征床层密度，ρdrag表征曳力造成的分选密度变化值，ρparticle表征流化死区所造成的分选密度变化值。
ρparticle的计算过程为：
根据流化死区体积和待分选矿物体积得到所述待分选矿物的体积Vtol：
根据所述Vtol，确定流化死区所造成的分选密度变化值ρparticle：
ρdrag具体为
其中，vg为操作气速，μf为空气粘度为常数，ρf为流化床中气体密度。
而ρbed可以借助ECT通过相应的模型求出，具体地：
ρbed＝ρs×εd+ρg×εb
ρs为加重质颗粒真实密度，εd表示乳化相在流化床中的分布比例，εb表示气泡相在流化床中的分布比例，ρg表示气体的密度。
U为操作气速；Umf为流化床的最小流化速度；Y代表气泡相体积流率与气泡相体积流率的比值，为常数。Ub为气泡移动速度。
εd＝(1-εmf)(1-εb)
εmf最小流化孔隙率可以由加重质颗粒的真实密度和堆积密度求得。
由此可知，确定ρbed的关键在于测量气泡移动速度。气泡移动速度可以通过ECT得到，最后根据计算ρbed的公式和气泡移动速度得到ρbed。
综上，分选密度模型通式的最终形式为：
在分选密度模型的最终形式中，只需要通过流化死区体积和待分选矿物的粒度得到系数K和系数α，就能完成构建分选密度模型。
在本发明实施例中，分选密度模型构建完成之后，只需获取加重质颗粒的堆积密度和粒径、流化床的操作气速和流化气体密度、床层密度，就可以根据获取到的数据和得到的分选密度模型，预测待分选矿物对应的流化床分选密度。
任何模型都有自己的适用范围，对于流化床而言，分选密度模型的应用范围包括待分选矿物与加重质颗粒的种类，待分选矿物与加重质颗粒的粒径和工艺条件。由于分选密度不能直接测量，这给验证模型的适用范围带来极大的困难。为了解决上述技术问题，本发明通过ECT对分选密度模型得到的分选密度进行验证。
电容层析成像技术作为一种非侵入性的成像手段，被认为是解决连续相为非导电物质的两相流及多相流成像的最有效手段，具有表征三维流化床内大块矿物运动行为的潜力，为三维流化床中待分选矿物分离行为的研究提供良好的条件，能够完成对流化床分选密度的精准的预测，有利于实现干法分选流化床的高效分选。
具体地，根据预设密度范围，确定多个校验矿物，每一个校验矿物对应一个密度；分别在同一流化床高度下，对每一个校验矿物进行浮沉试验，并在每一次试验中，分别统计各校验矿物的下沉次数；根据下沉次数和沉浮试验次数，确定各校验矿物颗粒的分配率；根据各校验矿物对应的密度和分配率，确定流化床高度下的标准分选密度；根据标准分选密度，确定分选密度预测模型得到的分选密度是否正确。借助ECT成像，检测人员可以获取到矿物颗粒在流化床中的移动踪迹，为确认矿物颗粒是否在流化床中下沉提供技术支持。由于是针对单个颗粒进行浮沉试验，为了排除偶然性，需要进行多次测试，之后利用下沉次数和测试次数的比值，求得分配率。并根据多个密度和多个分配率进行数值拟合得到分配率和密度的函数，最后根据该函数选择分配率50％对应的密度为 标准分选密度。例如；
试验采用直径为30mm乒乓球填充混合颗粒，以配制成不同密度的标准球形密度球，并分别在气固流化床内进行浮沉试验，以测量流化床分选密度。以150-300um磁铁矿粉气固流化床为示例，展示密度球浮沉试验，床内临界流化气速为10cm/s,表观气速为12.4cm/s,初始床层高度为40-300mm范围内，分选时间为60s,单个密度球浮沉试验重复测试30次，并记录其下沉次数，该浮沉试验结果如下3所示。
利用上述数据绘制不同初始床高下的沉物分配曲线，曲线中分配率为50％所对应的横坐标即为某一初始床高下的流化床分选密度。如图4所示，2.22g/cm3为标准分选密度。
以ECT作为衡量模型计算结果正确与否的标准，需要保证ECT自身的检测效果。为了保证ECT的检测效果，本发明实施例通过模拟软件确定流化床中介电常数灵敏度的分布。根据所述流化床中介电常数灵敏度的分布，确定ECT观测的最优条件，所述最优条件包括：被观测物的粒度、介电常数、在流化床中的投放位置。
具体地，利用有限元分析软件Comsol分别模拟出流化床和ECT传感器。并将流化床分割成多个区域，每一个区域对应一个ECT的检测位置。之后模拟具有不同介电常数、不同粒度的待分选矿物，并将模拟出的待分选矿物放在不同的检测位置，以确定粒度、介电常数和测试位置对检测结果的影响。根据粒度、介电常数和测试位置对检测结果的影响，可以确定ECT观测的最优条件。检测前根据ECT观测的最优条件，或对ECT进行校准，或调整粒度、介电常数和测试位置。
本发明实施例提供了一种流化床分选密度预测模型构建装置，包括：合力检测模块、采集模块和数据处理模块；
合力检测模块用于通过测力计将待分选矿物悬挂于与干法重介流化 床中，并对待分选矿物进行流化操作，待分选矿物包括：流化床死区和待分选矿物；
采集模块用于采集待分选矿物受到的合力、床层密度、待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆积密度；
数据处理模块用于根据待分选矿物受到的合力、床层密度、待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆积密度，确定流化床死区体积和待分选矿物的粒度的函数关系；基于函数关系构建干法重介流化床分选密度预测模型。
其中，数据处理模块用于根据待分选矿物受到的合力、床层密度、待分选矿物的粒度、待分选矿物的真实密度和加重质颗粒的堆积密度，确定流化床死区体积；针对多个待分选矿物，分别确定各待分选矿物的流化床死区体积和各待分选矿物的粒度；根据多个待分选矿物的流化床死区体积和粒度，通过量纲分析和数据拟合得到流化床死区体积和待分选矿物的粒度的函数关系。
本发明提供了一种流化床分选密度预测模型构建系统，如图2所示，包括：出气设备、流化床、ECT设备、测力计和流化床分选密度预测模型构建装置；
出气设备出气端连接流化床底部，向流化床内室顶部吹气；ECT设备的检测端设置流化床外壁；测力计设置在流化床顶端；流化床分选密度预测模型构建装置与ECT设备和测力计数据连接。
具体地，流化床分选密度预测模型构建系统包括：鼓风机1、气罐2、压力表3、蝶阀4、转子流量计5、流化床体6、电子测力计8、防尘罩9、除尘器10、集尘箱11、引风机12、电容层析成像系统的传感器13、电容层析成像测量设备14、计算机15。
出气设备包括：鼓风机1、气罐2、压力表3、蝶阀4、转子流量计5。 鼓风机1产生的气体存储与气罐2。流化时，控制蝶阀4将气罐2中的气体输送至流化床体6的底部。并通过转子流量计5监测气流量，通过压力表3监测气压。流化后的气体从流化床体6的上端流出，经防尘罩9依次通过除尘器10、集尘箱11，最后被引风机12排入大气。
ECT设备包括电容层析成像系统的传感器13、电容层析成像测量设备14。流化床分选密度预测模型构建装置集成在计算机15上。电容层析成像系统的传感器13采集流化床体6内的介电常数分布信息，并将介电常数分布信息传输至电容层析成像测量设备14。电容层析成像测量设备14将介电常数分布信息转换成图像，并将图像传输至计算机15。此外，计算机15还会接收电子测力计8采集到的合力。电子测力计8设置在流化床体6顶端，其底端悬挂矿物7。
以上所述，仅为本发明较佳的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。

Claims (10)

  1. 一种流化床分选密度预测模型构建方法，其特征在于，包括：
    对流化床内加重质颗粒进行流化操作达到鼓泡流态化；
    通过测力计将待分选矿物悬挂于干法重介流化床中；
    采集所述待分选矿物受到的合力和床层密度；
    根据所述待分选矿物受到的合力、床层密度，以及所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定所述待分选矿物上方流化床死区体积和所述待分选矿物的粒度的函数关系；
    基于所述函数关系构建干法重介流化床分选密度预测模型。
  2. 根据权利要求1所述方法，其特征在于，所述确定所述待分选矿物上方流化床死区体积和所述待分选矿物的粒度的函数关系，包括：
    根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积；
    针对多个所述待分选矿物，分别确定各所述待分选矿物的流化床死区体积和各所述待分选矿物的粒度；
    根据所述多个待分选矿物的流化床死区体积和粒度，通过量纲分析和数据拟合得到所述流化床死区体积和所述待分选矿物的粒度的函数关系。
  3. 根据权利要求2所述方法，其特征在于，所述根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积，包括：
    根据所述待分选矿物的粒度，确定所述待分选矿物的体积；
    根据所述待分选矿物的真实密度和所述体积，确定所述待分选矿物 受到重力；
    根据所述床层密度和所述体积，确定所述待分选矿物受到的浮力；
    根据所述合力、所述重力和所述浮力，确定所述流化死区对所待分选矿物施加的附加重力；
    根据所述流化死区对应的附加重力和所述加重质颗粒的堆积密度，确定所述流化死区的体积。
  4. 根据权利要求3所述方法，其特征在于，
    所述分选密度预测模型为：
    ρsep＝ρbed+ρdrag-ρparticle
    其中，ρsep表征分选密度，ρbed表征床层密度，ρdrag表征曳力造成的分选密度变化值，ρparticle表征流化死区所造成的分选密度变化值；
    所述基于所述函数关系构建干法重介流化床分选密度预测模型，包括：
    根据流化死区体积Vhood和待分选矿物体积得到所述流化死区与待分选矿物的总体积Vtol：
    Vhood表征流化死区体积，dsph表征所述待分选矿物的粒度，K和α均为常数；
    根据所述Vtol，确定流化死区所造成的分选密度变化值ρparticle：
    ρhood表征流化死区中加重质颗粒的堆积密度。
  5. 根据权利要求4所述的方法，其特征在于，所述方法还包括：
    利用电容层析成像技术ECT校验所述分选密度预测模型得到的分选 密度是否正确。
  6. 根据权利要求5所述的方法，其特征在于，
    所述利用电容层析成像技术ECT校验所述分选密度预测模型得到的分选密度是否正确，包括：
    根据预设密度范围，确定多个校验矿物，每一个所述校验矿物对应一个密度；
    分别在同一流化床高度下，对每一个所述校验矿物进行浮沉试验，并在每一次试验中，分别统计各所述校验矿物的下沉次数；
    根据所述下沉次数和沉浮试验次数，确定各所述校验矿物颗粒的分配率；
    根据各所述校验矿物对应的密度和分配率，确定所述流化床高度下的标准分选密度；
    根据所述标准分选密度，确定所述分选密度预测模型得到的分选密度是否正确。
  7. 根据权利要求5所述的方法，其特征在于，
    所述方法还包括：
    通过模拟软件确定流化床中介电常数灵敏度的分布；
    根据所述流化床中介电常数灵敏度的分布，确定ECT观测的最优条件；
    所述最优条件包括：被观测物的粒度、介电常数、在流化床中的投放位置。
  8. 一种流化床分选密度预测方法，其特征在于，包括：
    获取待分选矿物的真实密度和粒径、加重质颗粒的堆积密度、流化床的操作气速和流化气体密度、床层密度；
    根据所述待分选矿物的真实密度和粒径、所述加重质颗粒的堆积密 度、所述流化床的操作气速和流化气体密度、床层密度，通过权利要求1-7构建的流化床分选密度预测模型，确定所述待分选矿物对应的流化床分选密度。
  9. 一种流化床分选密度预测模型构建装置，其特征在于，包括：合力检测模块、采集模块和数据处理模块；
    所述合力检测模块用于在对流化床内加重质颗粒进行流化操作达到鼓泡流态化后，通过测力计将待分选矿物悬挂于干法重介流化床；
    所述采集模块用于采集所述待分选矿物受到的合力和床层密度；
    所述数据处理模块用于根据所述待分选矿物受到的合力、床层密度，以及所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定所述待分选矿物上方流化床死区体积和所述待分选矿物的粒度的函数关系；基于所述函数关系构建干法重介流化床分选密度预测模型。
  10. 根据权利要求9所述的装置，其特征在于，
    所述数据处理模块用于根据所述待分选矿物受到的合力、床层密度、所述待分选矿物的粒度、所述待分选矿物的真实密度和所述加重质颗粒的堆积密度，确定流化床死区体积；针对多个所述待分选矿物，分别确定各所述待分选矿物的流化床死区体积和各所述待分选矿物的粒度；根据所述多个待分选矿物的流化床死区体积和粒度，通过量纲分析和数据拟合得到所述流化床死区体积和所述待分选矿物的粒度的函数关系。
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