Method, device and equipment for controlling density of dense medium suspension, medium and product

Abstract

The invention relates to a dense medium suspension density control method, device and equipment, a medium and a product, and is applied to the technical field of dense medium coal separation. According to the method, state data of middling coal are obtained, and the state data at least comprise ash content data; based on the ash content data, the quality deviation degree of the middling coal is determined, and the quality deviation degree is used for measuring the raw material quality of the middling coal; determining a density adjustment parameter based on the quality deviation degree; and regulating and controlling the density of the dense medium suspension based on the density regulation parameter. According to the quality deviation degree of the middling coal, the influence of different raw material quality conditions of the middling coal on the dense medium separation result can be considered, and therefore the density of the dense medium suspension liquid can be accurately regulated and controlled. Therefore, the density control accuracy of the dense medium suspension can be improved.

Description

Technical Field

[0001] This application belongs to the field of heavy medium coal preparation technology, specifically relating to a method, device, equipment, medium, and product for controlling the density of a heavy medium suspension. Background Technology

[0002] With increasing global environmental awareness and growing energy demand, the coal industry faces increasingly stringent environmental requirements and resource constraints. Green coal preparation technology, by reducing environmental pollution, improving resource utilization efficiency, and promoting cleaner production, fosters the coordinated development of the coal industry and environmental protection, and is an important pathway for the coal industry to achieve sustainable development. Heavy media coal preparation is one of the key methods of green coal preparation technology. Its core principle is to separate coal and gangue in the coal feed by adjusting the density of the heavy media suspension.

[0003] In related technologies, the current density of a heavy medium suspension can be collected using sensors, and the error of this density can be evaluated to achieve density control. However, in actual sorting processes, a single density sensor cannot cope with the influence of complex working conditions and fluctuations caused by coal mining factors, resulting in errors in the collected data. Therefore, the accuracy of density control for heavy medium suspensions is relatively low. Summary of the Invention

[0004] This disclosure addresses some deficiencies mentioned in the background art by providing a method, apparatus, equipment, medium, and product for controlling the density of heavy medium suspensions, which helps improve the accuracy of heavy medium suspension density control.

[0005] In a first aspect, embodiments of this disclosure provide a method for controlling the density of a heavy medium suspension, comprising: Obtain the state data of Zhongmei Coal, wherein the state data includes at least ash content data; Based on the ash content data, the quality deviation of the middlings is determined, and the quality deviation is used to measure the raw material quality of the middlings. Based on the aforementioned quality deviation, the density adjustment parameters are determined; The density of the heavy medium suspension is adjusted based on the density adjustment parameters.

[0006] Optionally, the state data of the middlings may include at least one of the following: particle size parameters, clean coal images, and gangue images.

[0007] Optionally, determining the density adjustment parameters based on the quality deviation includes: Based on the particle size parameters, the uniformity of particle size distribution of the middlings is determined; Based on the image of clean coal and the image of gangue, determine the image grayscale deviation; Based on the ash content data and the preset standard ash content data, the difference in ash content data of the middlings coal is determined; The density adjustment parameter is determined based on the quality deviation, the particle size distribution uniformity, the image grayscale deviation, and the gray data difference.

[0008] Optionally, determining the quality deviation of the middlings based on the ash content data includes: By combining historical ash content data from historical periods, a discrete analysis is performed on the ash content data of the middlings coal to obtain the degree of dispersion of the ash content data. The degree of dispersion of the ash content data includes at least the standard deviation of the ash content data. Obtain the gray data difference between each adjacent sampling time in the historical period; The quality deviation is obtained by multiplying the dispersion of the ash data by the difference between multiple ash data values.

[0009] Optionally, determining the uniformity of particle size distribution of the middlings based on the particle size parameter includes: Obtain the standard normal distribution result of the middlings coal; The particle size and the number of particles corresponding to the particle size in the particle size parameter are fitted to obtain the particle size distribution result; Based on the standard normal distribution results and the particle size distribution results, the degree of particle size distribution deviation and the verification statistic of the middlings are determined; The uniformity of the particle size distribution is determined based on the degree of particle size distribution deviation and the verification statistics.

[0010] Optionally, determining the density adjustment parameter based on the quality deviation, the particle size distribution uniformity, the image grayscale deviation, and the gray data difference includes: Obtain the sign parameter of the particle size distribution result; The first result is obtained based on the ratio of the particle size distribution uniformity to the sum of the quality deviation and the difference in ash content data; The ratio of the particle size distribution uniformity to the difference in ash content data is obtained to get a second result; Obtain the normalized result of the product of the first result and the second result; The density adjustment parameter is obtained by multiplying the sign parameter and the normalization result.

[0011] Optionally, determining the image grayscale deviation based on the clean coal image and the gangue image includes: Based on the average grayscale value of the clean coal image and the average grayscale value of the gangue image, the image grayscale difference between the clean coal image and the gangue image is determined. The absolute value of the image grayscale difference is taken as the image grayscale deviation.

[0012] Optionally, adjusting the density of the heavy medium suspension based on the density adjustment parameter includes: Obtain the current density of the heavy media suspension; Based on the current density of the heavy medium suspension and the density adjustment parameters, the target density of the heavy medium suspension is determined. The density of the heavy medium suspension is adjusted based on the target density and the current density.

[0013] Optionally, determining the target density of the heavy medium suspension based on its current density and the density adjustment parameter includes: The target density adjustment amount is obtained by multiplying the density adjustment parameter by the preset density adjustment amount. The target density is obtained by summing the target density adjustment amount with the current density.

[0014] Optionally, adjusting the density of the heavy medium suspension based on the target density and the current density includes: If the target density is less than the current density, the opening of the diversion water supply valve in the heavy medium separation tank is increased. The diversion water supply valve is used to control the replenishment of clean water in the heavy medium separation tank to reduce the current density. If the target density is greater than the current density, the opening of the diversion and replenishment valve in the heavy medium sorting tank is increased. The diversion and replenishment valve is used to control the replenishment of high-density medium in the heavy medium sorting tank to increase the current density. If the target density is equal to the current density, maintain the opening of the diversion and replenishment valves.

[0015] In a second aspect, embodiments of this disclosure provide a device for controlling the density of a heavy medium suspension, comprising: The acquisition module is used to acquire the state data of the coal, wherein the state data includes at least ash content data; The first determining module is used to determine the quality deviation of the middlings based on the ash content data, wherein the quality deviation is used to measure the raw material quality of the middlings; The second determining module is used to determine the density adjustment parameters based on the mass deviation. The control module is used to control the density of the heavy medium suspension based on the density adjustment parameters.

[0016] In a third aspect, embodiments of this disclosure provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for controlling the density of a heavy media suspension.

[0017] In a fourth aspect, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the above-described method for controlling the density of a heavy media suspension.

[0018] In a fifth aspect, embodiments of this disclosure provide a computer program product including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the above-described method for controlling the density of a heavy medium suspension.

[0019] This disclosure involves acquiring middlings state data, including at least ash content data; determining the middlings quality deviation based on the ash content data, whereby the quality deviation measures the raw material quality of the middlings; determining density adjustment parameters based on the quality deviation; and controlling the density of the heavy medium suspension based on these parameters. By utilizing the middlings quality deviation, the impact of different raw material quality conditions on the heavy medium separation results can be considered, thereby enabling precise control of the heavy medium suspension density. Therefore, the accuracy of heavy medium suspension density control can be improved.

[0020] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for controlling the density of a heavy medium suspension provided in this disclosure.

[0022] Figure 2 Another flowchart of a method for controlling the density of a heavy medium suspension provided in this disclosure.

[0023] Figure 3 This is a schematic diagram of a device for controlling the density of a heavy medium suspension provided in this disclosure.

[0024] Figure 4 This is a hardware block diagram of an electronic device provided in this disclosure.

[0025] Figure 5 This is a schematic diagram of a computer program product provided in this disclosure. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solution of this application, the application scenario of this application will be described first below.

[0027] With the increasing global awareness of environmental protection and the growth of energy demand, the coal industry is facing increasingly stringent environmental protection requirements and resource constraints. Green coal preparation technology promotes the coordinated development of the coal industry and environmental protection by reducing environmental pollution, improving resource utilization efficiency, and promoting clean production. It is an important way for the coal industry to achieve sustainable development. Heavy media coal preparation is one of the important means of green coal preparation technology. Its core is to separate coal and gangue in coal materials by adjusting the density of heavy media suspension. The heavy media separation in coal preparation plants mainly includes the following main process flow: (1) raw coal pretreatment, (2) desliming treatment, (3) heavy media separation, (4) desliming and dewatering, and (5) magnetic separation and recovery. The process flow is described in detail below: (1) Raw coal pretreatment: The raw coal blocks mined from coal mines are of different sizes, which is not conducive to coal block separation. Therefore, crushing and screening pretreatment is required in coal preparation plants. Specifically, crushing is carried out by crusher and screening is carried out by grading and screening into coal blocks of different particle sizes. In the preferred embodiment, the coal is screened into large lumps of 50mm to 150mm, small lumps of 13mm to 50mm, and fine coal less than 13mm. (2) Desliming treatment: Due to the changes in the strata where the raw coal is mined and the transportation process, it is inevitable that fine coal or gangue particles will be mixed in the raw coal, so coal of different particle sizes will also carry fine particles, and desliming treatment is required. The methods that can be used are wet desliming and dry desliming. Among them, wet desliming uses a desliming screen to remove fine mud from the coal. For lump coal, a 6mm screen hole is used, and for fine coal, a 1mm screen hole is used; for dry desliming, a vibrating mixed flow dryer can be used for desliming treatment. The raw coal after desliming treatment is medium coal. (3) Heavy medium separation: The medium coal of the three particle sizes is fed into three heavy medium shallow tank separators. Heavy medium suspension is added in the heavy medium shallow tank. In this disclosure, magnetite powder suspension can be used. Taking a single sorting line as an example, the fine coal with a lower density floats up as a residue, while the gangue with a higher density sinks as a bottom flow, thus achieving the sorting of middlings. (4) Dewatering: The coal products and gangue separated after heavy media separation carry some heavy media particles, so dewatering is required. The coal is dewatered by passing the heavy media particles through the medium. At the same time as dewatering, water is sprayed to wash the heavy media adhering to the surface of the coal and gangue, and the dilute medium is further recovered. The dewatered coal or gangue is further dewatered by a centrifuge. (5) Magnetic separation and recovery: The dilute medium after dewatering, which contains coal slime and magnetite powder, is recovered by a magnetic separator and can be returned to the heavy media system, which is convenient for resource recycling.

[0028] In related technologies, sensors can be used to collect the current density of heavy medium suspensions and construct a property model. Based on the heavy medium separation data output by the model and preset density data, the current density is evaluated for error, thereby achieving intelligent density control. However, in actual separation processes, a single density acquisition sensor cannot cope with the influence of complex working conditions, and the quality of coal may fluctuate due to geological and mining factors, resulting in errors in the collected data. Therefore, the accuracy of density control for heavy medium suspensions is relatively low.

[0029] To address the aforementioned technical problems, this disclosure provides a method, apparatus, equipment, medium, and product for controlling the density of a heavy medium suspension. In this disclosure, the state data of middlings, including at least ash content data, is acquired; based on the ash content data, the quality deviation of the middlings is determined, which measures the raw material quality of the middlings; based on the quality deviation, a density adjustment parameter is determined; and based on the density adjustment parameter, the density of the heavy medium suspension is adjusted. By using the quality deviation of the middlings, the influence of different raw material quality conditions on the heavy medium separation results can be considered, thereby enabling precise control of the density of the heavy medium suspension. Therefore, the accuracy of heavy medium suspension density control can be improved.

[0030] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present disclosure and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the drawings, not the entire structure.

[0031] Figure 1 A flowchart illustrating a method for controlling the density of a heavy media suspension provided in this disclosure. Figure 1 As shown, the method includes: S101: Obtain the status data of China Coal.

[0032] Specifically, after raw coal pretreatment and desliming, the raw coal is now processed into intermediate coal, or middlings. Middlings contain clean coal and gangue. The purpose of heavy media separation is to distinguish the clean coal and gangue in the middlings based on their density differences. In subsequent separation processes, different particle size grades are separated through multiple process flows. In this embodiment, a single heavy media separation production line is used as an example for detailed explanation.

[0033] Middlings, after desliming, are conveyed to the feed end of a shallow heavy media separator via a conveyor belt. The heavy media separator mainly consists of a tank body, a water pump, horizontal and upward flow devices, a hanging plate, and an overflow weir. Within the heavy media separator, the horizontal flow direction of the heavy media suspension is consistent with the direction of the middlings feed end. Simultaneously, an upward flow device at the bottom of the tank body generates an upward water flow, achieving the effect of stratifying the middlings in the heavy media suspension. The denser gangue in the middlings sinks to the bottom, is flushed out by the hanging plate, and enters the gangue discharge port, while the less dense clean coal is discharged through the horizontal flow overflow weir.

[0034] In this embodiment, the status data includes at least ash content data. Ash content data of the coal at the feed end can be obtained by deploying an online ash analyzer directly above the feed end of the heavy medium separator. The sampling interval of the online ash analyzer sensor can be 10 seconds.

[0035] It is understood that this embodiment does not impose special restrictions on the process flow of heavy media separation in coal preparation plants. In actual application scenarios, implementers can make adjustments according to the actual situation.

[0036] S102: Determine the quality deviation of middlings based on ash content data.

[0037] Specifically, in the heavy media separation process of coal preparation plants, the quality of raw materials is a key factor affecting the density of the heavy media suspension. If the quality of the raw materials is poor and they contain other impurities such as iron, aluminum, silicon, and calcium, the clean coal and gangue will be intermingled and embedded in the raw materials, making the boundary between the separation densities between the two unclear. This will further lead to a less than ideal separation result when the density of the fixed heavy media suspension is fixed.

[0038] In practice, the quality of raw materials fluctuates, which will be reflected in the fluctuating data detected by the online ash analyzer at the feed end. Large fluctuations in the ash analyzer data indicate poor stability in the quality of the raw materials. Therefore, the quality deviation of middlings can be determined using ash content data; this quality deviation is used to measure the raw material quality of middlings.

[0039] S103: Determine density adjustment parameters based on mass deviation.

[0040] Specifically, as mentioned earlier, the quality of the raw materials is a key factor affecting the density of the heavy medium suspension. Therefore, based on the quality deviation, the influence of the raw material quality itself during the mining process can be corrected, thereby obtaining the density adjustment parameter to adjust the density of the heavy medium suspension.

[0041] S104: Adjust the density of the heavy medium suspension based on the density adjustment parameter.

[0042] Specifically, the current density of the heavy medium suspension can be adjusted based on density adjustment parameters to obtain the target density. The relevant equipment in the heavy medium separation tank is then controlled and adjusted to ensure the heavy medium suspension reaches the target density, thus achieving density control of the heavy medium suspension.

[0043] This disclosure involves acquiring middlings state data, including at least ash content data; determining the middlings quality deviation based on the ash content data, whereby the quality deviation measures the raw material quality of the middlings; determining density adjustment parameters based on the quality deviation; and controlling the density of the heavy medium suspension based on these parameters. By utilizing the middlings quality deviation, the impact of different raw material quality conditions on the heavy medium separation results can be considered, thereby enabling precise control of the heavy medium suspension density. Therefore, the accuracy of heavy medium suspension density control can be improved.

[0044] In one possible embodiment, the state data of the middlings coal includes at least one of the following: particle size parameters, clean coal images, and gangue images.

[0045] Specifically, in this embodiment, an ultrasonic particle size analyzer is deployed at the same location as the online ash analyzer at the feed end to acquire the particle size parameters of the middlings coal during the conveying process. Simultaneously, visual sensors are deployed at the gangue discharge port and the clean coal discharge port to acquire images of the clean coal and gangue at the discharge end.

[0046] Based on the aforementioned coal condition data, an exemplary method for determining density adjustment parameters based on quality deviation includes: Based on particle size parameters, the uniformity of particle size distribution of middlings is determined; based on clean coal images and gangue images, image grayscale deviation is determined; based on ash content data and preset standard ash content data, the difference in ash content data of middlings is determined; based on mass deviation, particle size distribution uniformity, image grayscale deviation, and ash content data difference, density adjustment parameters are determined.

[0047] Specifically, based on the particle size parameters in the heavy medium separator, the uniformity of particle size distribution is analyzed, which can standardize the quality of the coal at the input end. The more ideal the result of the raw coal after crushing and desliming, the greater the uniformity of particle size distribution, which can also reflect the ideal impurity content in the raw material. The density of the heavy medium suspension in the heavy medium separator can be dynamically adjusted based on the uniformity of particle size distribution. The grayscale difference between the clean coal image and the gangue image can characterize the separation result of heavy medium separation, and the density of the heavy medium suspension in the heavy medium separator can be dynamically adjusted based on the image grayscale deviation. The difference between the ash content data of the middlings and the preset standard data can characterize the compatibility between the current density of the heavy medium suspension and the raw material quality, and the density of the heavy medium suspension in the heavy medium separator can be dynamically adjusted based on the ash content data difference. Among them, if the ash content data difference is larger, it indicates that the current density of the heavy medium suspension does not meet the current quality of the raw material in the heavy medium separation process.

[0048] It should be noted that the preset standard ash content data range is set to 9%~14%, and in the preferred embodiment it is set to 9%.

[0049] In one possible embodiment, an exemplary method for determining the quality deviation of middlings based on ash content data includes: By combining historical ash content data from different historical periods, a discrete analysis is performed on the ash content data of China Coal Group to obtain the degree of dispersion of the ash content data; the difference in ash content data between adjacent sampling times in the historical period is obtained; and the product of the degree of dispersion of the ash content data and the differences in multiple ash content data is obtained to obtain the quality deviation.

[0050] Specifically, when measuring ash content at the feed end, only the ash content of the surface layer of middlings can be determined. However, during the transport of deslimed middlings on the conveyor belt, smaller particles are screened to the bottom of the belt through movement and vibration, while the surface layer consists of larger coal lumps. Therefore, the actual ash content data collected may deviate from the actual values, thus affecting the suspension separation density.

[0051] In this embodiment, historical ash content data from different historical periods will be used to analyze the dispersion of ash content data at the feed end at each sampling time within those periods. This dispersion includes at least the standard deviation of the ash content data. Furthermore, the quality deviation will be obtained by combining the differences in ash content data between different sampling times.

[0052] In one exemplary embodiment, the quality deviation can satisfy the following relationship:

[0053] in, For quality deviation, To determine the dispersion of gray data, This represents the difference in gray data.

[0054] It should be noted that, to assess the quality deviation under the current sampling conditions, in this embodiment, the historical period can be composed of N sampling moments forming a time window. The length of this time window is determined by the length of the heavy medium separation tank and the horizontal flow velocity of the heavy medium suspension. The method for measuring the horizontal flow velocity of the heavy medium suspension is a conventional technique for those skilled in the art and will not be elaborated upon in this embodiment. For example, the value of N is the ratio of the length of the heavy medium separation tank to the horizontal flow velocity of the heavy medium suspension, which is converted with the sampling frequency to determine the number of sampling points N in the time window. If the amount of data is less than N during the initial operation of the equipment, no analysis is performed. The implementer can also set the value of N as they see fit; this embodiment does not impose any special restrictions on this.

[0055] In one possible embodiment, an exemplary method for determining the uniformity of particle size distribution in middlings based on particle size parameters includes: Obtain the standard normal distribution of middlings; fit the particle size and the corresponding number of particles in the particle size parameter to obtain the particle size distribution result; based on the standard normal distribution result and the particle size distribution result, determine the degree of particle size distribution deviation and the verification statistic of middlings; based on the degree of particle size distribution deviation and the verification statistic, determine the uniformity of particle size distribution.

[0056] Specifically, the particle size distribution is obtained based on an ultrasonic particle size analyzer, and the particle size distribution result is fitted. In this embodiment, for each sampling time, the proportion of each particle size in the middlings coal at the sampling time during the transportation process is obtained by using an ultrasonic particle size analyzer. The proportion of particle size is used as the vertical axis and the particle size is used as the horizontal axis. The particle size distribution result is obtained by fitting the data.

[0057] To obtain the standard normal distribution result of medium coal under ideal conditions, the actual crushing process is affected by the impurity content in the raw coal raw material, which makes the crushing effect of the raw coal raw material poor. This causes the ideal standard normal distribution result of raw coal, that is, the standard normal distribution result of medium coal, to be deviated, which may be positively or negatively skewed, or even the particle size distribution result obtained by detection may have multiple peaks.

[0058] Therefore, the uniformity of particle size distribution is determined based on the degree of deviation between the particle size distribution results and the standard normal distribution results, as well as the verification statistic.

[0059] In one exemplary embodiment, the particle size distribution uniformity can satisfy the following relationship:

[0060] in, For uniform particle size distribution, To determine the degree of particle size distribution deviation, To avoid positive numbers with a denominator of zero, the value range is from 0 to 0.1; in this example, the value is 0.01. To test the statistic.

[0061] In this embodiment, the Shapiro-Wilk test can be used. The larger the test statistic value, the closer it is to a normal distribution.

[0062] In one possible embodiment, an exemplary method for determining density adjustment parameters based on quality deviation, particle size distribution uniformity, image grayscale deviation, and gray data difference includes: Obtain the sign parameter of the particle size distribution result; obtain the first result based on the ratio of the particle size distribution uniformity to the sum of the differences between the quality deviation and the ash content data; obtain the second result based on the ratio of the particle size distribution uniformity to the differences between the ash content data; obtain the normalized result of the product of the first result and the second result; obtain the density adjustment parameter based on the product of the sign parameter and the normalized result.

[0063] Specifically, during the adjustment process, if the particle size distribution uniformity of middlings shows a positive bias, it indicates that the overall particle size distribution has a smaller number of large particles and a larger number of small particles. For middlings with small particle sizes, a higher suspension density is often required. Therefore, the sign parameter obtained through the sign function is positive, meaning that the density needs to be adjusted upwards from the current density. Conversely, if the particle size distribution uniformity shows an inverse bias, the current density needs to be adjusted downwards.

[0064] In one exemplary embodiment, the density adjustment parameter can satisfy the following relationship:

[0065] in, For density adjustment parameters, To determine the degree of particle size distribution deviation, This is a sign function used to obtain the positive or negative sign of the degree of particle size distribution deviation. For normalization function, For quality deviation, For uniform particle size distribution, Image grayscale deviation, This represents the difference in gray data. For the normalized result, As the first result, This is the second result.

[0066] In one possible embodiment, an exemplary method for determining image grayscale deviation based on images of clean coal and gangue includes: Based on the mean grayscale values ​​of the clean coal image and the gangue image, the image grayscale difference between the clean coal image and the gangue image is determined; the absolute value of the image grayscale difference is taken as the image grayscale deviation.

[0067] Specifically, the separated clean coal mainly appears as black or dark brown coal lumps, while the separated gangue exhibits a variety of colors due to differences in impurity content, but overall appears gray or grayish-white. Therefore, the greater the deviation in the average grayscale value between the two at the current sampling time, the better the separation effect of the heavy medium suspension; conversely, the smaller the deviation in the average grayscale value, the worse the separation effect. Therefore, the image grayscale difference between the clean coal image and the gangue image can be calculated, and the density of the heavy medium suspension can be dynamically adjusted based on this image grayscale difference.

[0068] In one possible embodiment, an exemplary method for adjusting the density of a heavy medium suspension based on a density adjustment parameter includes: Obtain the current density of the heavy medium suspension; determine the target density of the heavy medium suspension based on the current density and density adjustment parameters; and adjust the density of the heavy medium suspension based on the target density and the current density.

[0069] Specifically, the preset suspension separation density varies depending on the particle size of the middlings coal being separated. The preset initial density parameter for the heavy medium suspension is set to 1.501 g / cm³ for large coal lumps ranging from 50 mm to 150 mm. 3 For small coal lumps of 13mm to 50mm, the initial suspension density was set to 1.646 g / cm³. 3 The initial suspension density for coal fines smaller than 13 mm was set to 1.761 g / cm³. 3 This yields the initial density of the heavy medium suspension of coal at each particle size. This initial density serves as the initial density value of the suspension. Subsequently, the current density of the heavy medium suspension in the heavy medium separation tank can be acquired using a density sensor. Based on the current density and density adjustment parameters, the target density can be determined.

[0070] An exemplary method for determining the target density of a heavy medium suspension based on its current density and density adjustment parameters includes: The target density adjustment amount is obtained by multiplying the density adjustment parameter by the preset density adjustment amount; the target density is obtained by summing the target density adjustment amount with the current density.

[0071] Specifically, the preset density adjustment amount is used to control the amount of density adjustment per cycle; in this embodiment, it is 0.05 g / cm³. 3 If the target density adjustment value is less than 0.01 g / cm³ 3If the system is stable, no adjustments will be made to avoid frequent adjustments that could lead to low system stability.

[0072] In one exemplary embodiment, the target density can satisfy the following relationship:

[0073] in, Let the target density be at time t+1. Let be the current density at time t. The density adjustment parameter at time t. This is the preset density adjustment amount. This represents the target density adjustment amount at time t.

[0074] During the adjustment process, based on the density of the heavy medium suspension at each sampling time, the suspension separation density can be adjusted upwards or downwards according to the quality of the middlings coal, thereby improving the separation of clean coal and gangue.

[0075] In one possible embodiment, an exemplary method for regulating the density of a heavy medium suspension based on a target density and the current density includes: If the target density is less than the current density, increase the opening of the diversion and water supply valve in the heavy medium separation tank; if the target density is greater than the current density, increase the opening of the diversion and water supply valve in the heavy medium separation tank; if the target density is equal to the current density, keep the opening of the diversion and water supply valves constant.

[0076] Specifically, the target density for the next time step and the current density at the current sampling time, obtained from the density sensor, can be obtained based on the above process. The opening degrees of the diversion and replenishment valves are adjusted based on the difference between the target and current densities. The diversion and replenishment valves control the replenishment of clean water into the heavy medium separation tank, which reduces the current density; the diversion and replenishment valves control the replenishment of high-density media into the heavy medium separation tank, which increases the current density.

[0077] It should be noted that the specific process of adjusting the valve opening at the next moment based on the difference between the target density and the current density, and the opening degree of the diversion and water supply valves at the current moment, is a well-known technique to those skilled in the art, and no special restrictions are placed on the specific adjustment in this embodiment. Through result calculation and analysis, it can be obtained that the average coal-carrying rate of gangue after sorting in this embodiment is less than 1%, while the average calorific value of clean coal is greater than 5500 kcal / kg.

[0078] Figure 2 Another flowchart is provided for a method of controlling the density of a heavy medium suspension according to this disclosure. Figure 2 As shown, the method includes: S201: Obtain the particle size parameters of middlings, ash content data of middlings, clean coal image, gangue image, and current density of heavy medium suspension.

[0079] Specifically, the middlings coal, after desliming, is conveyed to the feed end of the heavy media separator via a conveyor belt. An online ash analyzer is deployed directly above the feed end of the heavy media separator to acquire ash content data of the middlings coal at the feed end; an ultrasonic particle size analyzer is deployed at the same location to acquire particle size parameters during the conveying process. A density sensor collects the current density of the heavy media suspension in the heavy media separator, while visual sensors are deployed at the gangue discharge port and the clean coal discharge port to acquire separation effect images, including images of clean coal and gangue at the discharge end.

[0080] S202: Based on the ash content data of middlings, determine the quality deviation of middlings, and based on the ash content data of middlings and the preset standard ash content data, determine the difference in ash content data of middlings.

[0081] Specifically, as mentioned earlier, the quality of the raw materials is a key factor affecting the density of the heavy medium suspension. Therefore, based on the ash content data of middlings coal, the quality deviation is determined to correct the impact of the raw material's inherent quality during mining, thereby obtaining density adjustment parameters to adjust the density of the heavy medium suspension. The difference between the ash content data of middlings coal and the preset standard data can characterize the compatibility between the current density of the heavy medium suspension and the raw material quality, and can be used to dynamically adjust the density of the heavy medium suspension.

[0082] S203: Determine the uniformity of particle size distribution of middlings based on the particle size parameters of middlings.

[0083] Specifically, based on the particle size parameters in the heavy medium separator, the uniformity of particle size distribution can be analyzed, which can be used to standardize the quality of coal at the input end. The more ideal the result of crushing and desliming of the raw coal, the greater the uniformity of particle size distribution, which can also reflect that the impurity content in the raw material is relatively ideal, and can be used to dynamically adjust the density of the heavy medium suspension.

[0084] S204: Determine the image grayscale deviation based on the clean coal image and gangue image.

[0085] Specifically, the grayscale difference between clean coal images and gangue images can characterize the sorting results of heavy medium separation and can be used to dynamically adjust the density of the heavy medium suspension.

[0086] S205: Determine the density adjustment parameters based on the middlings quality deviation, middlings particle size distribution uniformity, image grayscale deviation, and middlings ash content data difference.

[0087] Specifically, the calculation method for the density adjustment parameter mentioned earlier is the same as here, and will not be repeated here.

[0088] S206: Adjust the heavy medium suspension based on its current density and density adjustment parameters.

[0089] Specifically, the target density is calculated based on the density adjustment parameters and the current density. Then, the heavy medium suspension is regulated. If the target density is less than the current density, the opening of the diversion and water supply valve in the heavy medium separation tank is increased; if the target density is greater than the current density, the opening of the diversion and water supply valve in the heavy medium separation tank is increased; if the target density is equal to the current density, the opening of both the diversion and water supply valves is maintained.

[0090] In related technologies, the density of the heavy medium suspension is adjusted to approach a preset fixed density value based on the difference between the real-time suspension density value collected by a density sensor and the preset fixed suspension density value. However, this ignores the impact of actual raw coal quality differences and sensor interference on the suspension density adjustment. This disclosure focuses on analyzing the quality differences of coal at the feed end of the sorting tank based on an ash analyzer and a particle size sensor, and analyzing the differences in sorting results at the discharge end based on a graphic sensor, thereby dynamically adjusting the density of the heavy medium suspension. This disclosure can adjust the sorting density of the heavy medium suspension according to different coal quality conditions, improving the accuracy of heavy medium suspension density control, thereby improving the sorting effect of clean coal and gangue.

[0091] Figure 3 This is a schematic diagram of a device for controlling the density of a heavy medium suspension provided in this disclosure. Figure 3 As shown, the device 300 includes: an acquisition module 310, a first determination module 320, a second determination module 330, and a control module 340.

[0092] The acquisition module 310 is used to acquire the state data of the middlings coal, wherein the state data includes at least ash content data; The first determining module 320 is used to determine the quality deviation of the middlings based on the ash content data, wherein the quality deviation is used to measure the raw material quality of the middlings. The second determining module 330 is used to determine the density adjustment parameters based on the mass deviation. The control module 340 is used to control the density of the heavy medium suspension based on the density adjustment parameters.

[0093] Optionally, the second determining module is used to: Based on the particle size parameters, the uniformity of particle size distribution of the middlings is determined; Based on the image of clean coal and the image of gangue, determine the image grayscale deviation; Based on the ash content data and the preset standard ash content data, the difference in ash content data of the middlings coal is determined; The density adjustment parameter is determined based on the quality deviation, the particle size distribution uniformity, the image grayscale deviation, and the gray data difference.

[0094] Optionally, the first determining module is used to: By combining historical ash content data from historical periods, a discrete analysis is performed on the ash content data of the middlings coal to obtain the degree of dispersion of the ash content data. The degree of dispersion of the ash content data includes at least the standard deviation of the ash content data. Obtain the gray data difference between each adjacent sampling time in the historical period; The quality deviation is obtained by multiplying the dispersion of the ash data by the difference between multiple ash data values.

[0095] Optionally, the second determining module is used to: Obtain the standard normal distribution result of the middlings coal; The particle size and the number of particles corresponding to the particle size in the particle size parameter are fitted to obtain the particle size distribution result; Based on the standard normal distribution results and the particle size distribution results, the degree of particle size distribution deviation and the verification statistic of the middlings are determined; The uniformity of the particle size distribution is determined based on the degree of particle size distribution deviation and the verification statistics.

[0096] Optionally, the second determining module is used to: Obtain the sign parameter of the particle size distribution result; The first result is obtained based on the ratio of the particle size distribution uniformity to the sum of the quality deviation and the difference in ash content data; The ratio of the particle size distribution uniformity to the difference in ash content data is obtained to get a second result; Obtain the normalized result of the product of the first result and the second result; The density adjustment parameter is obtained by multiplying the sign parameter and the normalization result.

[0097] Optionally, the second determining module is used to: Based on the average grayscale value of the clean coal image and the average grayscale value of the gangue image, the image grayscale difference between the clean coal image and the gangue image is determined. The absolute value of the image grayscale difference is taken as the image grayscale deviation.

[0098] Optionally, the control module is used for: Obtain the current density of the heavy media suspension; Based on the current density of the heavy medium suspension and the density adjustment parameters, the target density of the heavy medium suspension is determined. The density of the heavy medium suspension is adjusted based on the target density and the current density.

[0099] Optionally, the control module is used for: The target density adjustment amount is obtained by multiplying the density adjustment parameter by the preset density adjustment amount. The target density is obtained by summing the target density adjustment amount with the current density.

[0100] Optionally, the control module is used for: If the target density is less than the current density, the opening of the diversion water supply valve in the heavy medium separation tank is increased. The diversion water supply valve is used to control the replenishment of clean water in the heavy medium separation tank to reduce the current density. If the target density is greater than the current density, the opening of the diversion and replenishment valve in the heavy medium sorting tank is increased. The diversion and replenishment valve is used to control the replenishment of high-density medium in the heavy medium sorting tank to increase the current density. If the target density is equal to the current density, maintain the opening of the diversion and replenishment valves.

[0101] This application also provides an electronic device for executing the above-described method for controlling the density of heavy medium suspensions. Please refer to... Figure 4 It illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 4 As shown, the electronic device 40 includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected via the bus 402. The memory 401 stores a computer program that can run on the processor 400. When the processor 400 runs the computer program, it executes the method for controlling the density of heavy media suspension provided in any of the foregoing embodiments of this application.

[0102] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0103] Bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Memory 401 is used to store programs. After receiving an execution instruction, processor 400 executes the program. The method for controlling the density of the heavy medium suspension disclosed in any of the foregoing embodiments of this application can be applied to processor 400, or implemented by processor 400.

[0104] The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 400 or by instructions in software form. The processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 401. The processor 400 reads the information in memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0105] The electronic device provided in this application embodiment and the method for controlling the density of heavy medium suspension provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0106] This application also provides a computer-readable storage medium corresponding to the method for controlling the density of heavy medium suspensions provided in the foregoing embodiments. The computer-readable storage medium shown can be an optical disc, on which a computer program is stored. When the computer program is run by a processor, it executes the method for controlling the density of heavy medium suspensions provided in any of the foregoing embodiments.

[0107] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0108] The computer-readable storage medium provided in the above embodiments of this application and the method for controlling the density of heavy medium suspensions provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0109] This application also provides a computer program product 500, such as... Figure 5 As shown. This computer program product carries a computer program 501. The instructions included in the program code can be used to execute the steps of the method for controlling the density of the heavy medium suspension described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0110] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0111] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0112] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0113] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0114] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0115] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0116] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0117] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for controlling the density of a heavy medium suspension, characterized in that, include: Obtain the state data of Zhongmei Coal, wherein the state data includes at least ash content data; Based on the ash content data, the quality deviation of the middlings is determined, and the quality deviation is used to measure the raw material quality of the middlings. Based on the aforementioned quality deviation, the density adjustment parameters are determined; The density of the heavy medium suspension is adjusted based on the density adjustment parameters.

2. The method according to claim 1, characterized in that, The state data of the medium coal also includes at least one of the following: particle size parameters, clean coal images, and gangue images.

3. The method according to claim 2, characterized in that, The determination of density adjustment parameters based on the quality deviation includes: Based on the particle size parameters, the uniformity of particle size distribution of the middlings is determined; Based on the image of clean coal and the image of gangue, determine the image grayscale deviation; Based on the ash content data and the preset standard ash content data, the difference in ash content data of the middlings coal is determined; The density adjustment parameter is determined based on the quality deviation, the particle size distribution uniformity, the image grayscale deviation, and the gray data difference.

4. The method according to claim 1, characterized in that, The determination of the quality deviation of the middlings based on the ash content data includes: By combining historical ash content data from historical periods, a discrete analysis is performed on the ash content data of the middlings coal to obtain the degree of dispersion of the ash content data. The degree of dispersion of the ash content data includes at least the standard deviation of the ash content data. Obtain the gray data difference between each adjacent sampling time in the historical period; The quality deviation is obtained by multiplying the dispersion of the ash data by the difference between multiple ash data values.

5. The method according to claim 3, characterized in that, Determining the uniformity of particle size distribution of the middlings based on the particle size parameter includes: Obtain the standard normal distribution result of the middlings coal; The particle size and the number of particles corresponding to the particle size in the particle size parameter are fitted to obtain the particle size distribution result; Based on the standard normal distribution results and the particle size distribution results, the degree of particle size distribution deviation and the verification statistic of the middlings are determined; The uniformity of the particle size distribution is determined based on the degree of particle size distribution deviation and the verification statistics.

6. The method according to claim 5, characterized in that, The determination of the density adjustment parameters based on the quality deviation, the particle size distribution uniformity, the image grayscale deviation, and the gray data difference includes: Obtain the sign parameter of the particle size distribution result; The first result is obtained based on the ratio of the particle size distribution uniformity to the sum of the quality deviation and the difference in ash content data; The ratio of the particle size distribution uniformity to the difference in ash content data is obtained to get a second result; Obtain the normalized result of the product of the first result and the second result; The density adjustment parameter is obtained by multiplying the sign parameter and the normalization result.

7. The method according to claim 3, characterized in that, The determination of image grayscale deviation based on the clean coal image and the gangue image includes: Based on the average grayscale value of the clean coal image and the average grayscale value of the gangue image, the image grayscale difference between the clean coal image and the gangue image is determined. The absolute value of the image grayscale difference is taken as the image grayscale deviation.

8. The method according to claim 1, characterized in that, Based on the density adjustment parameters, the density of the heavy medium suspension is controlled, including: Obtain the current density of the heavy media suspension; Based on the current density of the heavy medium suspension and the density adjustment parameters, the target density of the heavy medium suspension is determined. The density of the heavy medium suspension is adjusted based on the target density and the current density.

9. The method according to claim 8, characterized in that, Determining the target density of the heavy medium suspension based on its current density and the density adjustment parameters includes: The target density adjustment amount is obtained by multiplying the density adjustment parameter by the preset density adjustment amount. The target density is obtained by summing the target density adjustment amount with the current density.

10. The method according to claim 8, characterized in that, The method of adjusting the density of the heavy medium suspension based on the target density and the current density includes: If the target density is less than the current density, the opening of the diversion water supply valve in the heavy medium separation tank is increased. The diversion water supply valve is used to control the replenishment of clean water in the heavy medium separation tank to reduce the current density. If the target density is greater than the current density, the opening of the diversion and replenishment valve in the heavy medium sorting tank is increased. The diversion and replenishment valve is used to control the replenishment of high-density medium in the heavy medium sorting tank to increase the current density. If the target density is equal to the current density, maintain the opening of the diversion and replenishment valves.

11. A device for controlling the density of a heavy medium suspension, characterized in that, include: The acquisition module is used to acquire the state data of the coal, wherein the state data includes at least ash content data; The first determining module is used to determine the quality deviation of the middlings based on the ash content data, wherein the quality deviation is used to measure the raw material quality of the middlings; The second determining module is used to determine the density adjustment parameters based on the mass deviation. The control module is used to control the density of the heavy medium suspension based on the density adjustment parameters.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-10.

14. A computer program product, characterized in that, Includes computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the method as described in any one of claims 1-10.

Images