Coarse slime separation equipment and method

Abstract

The invention provides coarse slime separation equipment and method, an ash content-density response relation model is established through prior information of suspension liquid density and clean coal ash content in the coarse slime separation process, and a response deviation coefficient of the coarse slime separation equipment during coarse slime separation is determined based on the ash content-density response relation model; the change situation of the suspension density in the current adjusting period is determined, and the balance control quantity of the suspension density during separation of coarse slime under the change situation is determined according to coal ash content data at a clean coal outlet in coarse slime equipment and the response deviation coefficient; the target suspension density of the coarse slime separation equipment in the next adjustment period is obtained through updating of the sensitivity parameter and the balance control quantity of the coal ash content of the coarse slime in the original state; and coarse slime in the next adjusting period is sorted based on the target suspension density. Based on the target suspension density, the suspension density can dynamically adapt to the change of coal slime characteristics.

Description

Technical Field

[0001] The present application relates to the technical field of mining machinery, and more specifically, to a coarse coal slime sorting device and method. Background Art

[0002] In today's mining operations, efficient, safe and intelligent mining modes have become the mainstream trend. With the deepening of mining resource development, the requirements for ore sorting accuracy are constantly rising, which has prompted mining machinery and equipment control to become an important part of ore sorting. Traditional sorting operations mostly rely on manual experience and simple machinery, which are difficult to meet the needs of large-scale, high-grade ore sorting. With the development of modern science and technology, advanced sensor technology and automatic control technology are widely used in sorting equipment, such as coarse coal slime sorting equipment. Through coarse coal slime sorting equipment, efficient separation of coarse coal slime can be achieved and the sorting effect can be optimized.

[0003] In the control of existing mining machinery and equipment, the core of coarse coal slime sorting is to sort based on the significant difference in surface wettability between coal and impurities such as gangue. During the sorting process, the coarse coal slime is mixed with a suspension, and the suspension promotes a strong adsorption of coal particles and bubbles, and the clean coal floats to the surface along with the bubbles, and the surface foam enriched with coal particles is scraped out through a specific device to complete the collection of clean coal. However, traditional coarse coal slime sorting equipment usually sets a fixed suspension density for sorting based on experience, and the coal slime properties will change with time. The fixed suspension density is adapted to the coal slime properties at a certain moment, and cannot dynamically adapt to the dynamically changing coal slime properties, which leads to a decrease in the coarse coal slime sorting accuracy. Therefore, how to achieve dynamic adaptation of the suspension density to changes in coal slime properties has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a coarse coal slime sorting device and method, which can realize dynamic adaptation of suspension density to changes in coal slime characteristics.

[0005] In a first aspect, the present application provides a coarse coal slime sorting method for a coarse coal slime sorting device, comprising the following steps: The coarse coal slime to be sorted is conveyed to the coarse coal slime sorting equipment, and the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet are monitored in real time; Establishing an ash-density response relationship model according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and determining the response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting based on the ash-density response relationship model; Analyze the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend according to the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient; Determine the sensitivity parameter of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle; The coarse coal slime of the next conditioning cycle is sorted based on the target suspension density.

[0006] In some embodiments, establishing an ash-density response relationship model based on prior information of suspension density and clean coal ash content during the coarse coal slime sorting process specifically includes: Acquiring prior information on the density of the suspension and the ash content of the clean coal during the coarse coal slime separation process; Extract the suspension density data and clean coal ash data at the corresponding timestamp from the prior information, and pair the suspension density data and clean coal ash data according to the timestamp to obtain an ash-density data sample set; Preprocessing the ash-density data sample set to obtain a preprocessed ash-density data sample set; A mathematical response model between ash content and suspension density is established based on the preprocessed ash-density data sample set; The mathematical response model is cross-validated to obtain an ash-density response relationship model.

[0007] In some embodiments, determining the response deviation coefficient of the coarse coal slime separation equipment when performing coarse coal slime separation based on the ash-density response relationship model specifically includes: Collecting the suspension density data in the qualified medium bucket when the coarse coal slime separation equipment performs coarse coal slime separation, and calculating the corresponding predicted ash value by the ash-density response relationship model; Synchronously obtain the clean coal ash data actually collected at the clean coal outlet; Comparing the predicted ash value with the clean coal ash data to obtain an ash deviation value set; A response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting is determined based on the ash deviation value set and a preset sorting influence coefficient.

[0008] In some embodiments, analyzing the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting specifically includes: In the process of coarse coal slime sorting, feedback data of the density of the suspension in the qualified medium barrel is collected; Preprocessing the feedback data to obtain preprocessed feedback data; Calculating the suspension density fluctuation value within the current adjustment cycle according to the pre-processed feedback data; The change trend of the suspension density in the current adjustment cycle is determined by the suspension density fluctuation value and the preset stability threshold.

[0009] In some embodiments, determining the balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation based on the coal ash data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient specifically includes: Collecting coal ash content data at the clean coal outlet of the coarse coal slime equipment; Performing a change trend analysis on the normalized coal ash content data to obtain the fluctuation entropy of the coal ash content data; Based on the fluctuation entropy and the response deviation coefficient, a balance analysis is performed on the suspension density when the coarse coal slime is sorted under the changing situation, so as to obtain a balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation.

[0010] In some embodiments, determining the sensitivity parameters of the raw coal slime coal ash content specifically includes: Sampling ash data of the coarse coal slime in the original state to obtain original coal ash data of the coarse coal slime in the original state; Simultaneously collect key process parameter data related to coal ash content during the current adjustment cycle; Establishing a data sample association matrix from the original coal ash data and the key process parameter data; The sensitivity parameters of the ash content of the coarse coal slime in the original state are determined based on the data sample association matrix.

[0011] In some embodiments, the coarse coal slime separation equipment is a heavy medium cyclone type equipment.

[0012] In a second aspect, the present application provides a coarse coal slime sorting device, the coarse coal slime sorting device comprises a qualified medium barrel, a clean coal outlet and a coarse coal slime sorting unit, the coarse coal slime sorting unit comprises: A monitoring module is used to convey the coarse coal slime to be sorted to the coarse coal slime sorting equipment, and to monitor the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet in real time; A processing module, for establishing an ash-density response relationship model according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and determining a response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting based on the ash-density response relationship model; The processing module is further used to analyze the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend according to the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient; The processing module is further used to determine the sensitivity parameter of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle; An execution module is used to sort the coarse coal slime of the next adjustment cycle based on the target suspension density.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned coarse coal slime sorting method for coarse coal slime sorting equipment.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned coarse coal slime sorting method for coarse coal slime sorting equipment.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the coarse coal slime sorting equipment and method provided in the present application, first, the coarse coal slime to be sorted is conveyed to the coarse coal slime sorting equipment, and the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet are monitored in real time; secondly, an ash-density response relationship model is established based on the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and the response deviation coefficient of the coarse coal slime sorting equipment when sorting the coarse coal slime is determined based on the ash-density response relationship model; further, the suspension density is analyzed based on the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting. The change trend within the current adjustment cycle, and according to the ash content data at the clean coal outlet in the coarse coal slime equipment and the response deviation coefficient, determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend; then, determine the sensitivity parameters of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameters and the balance control amount, and obtain the target suspension density of the next adjustment cycle of the coarse coal slime sorting equipment; finally, sort the coarse coal slime of the next adjustment cycle based on the target suspension density.

[0016] It can be seen that the present application can realize the dynamic adaptation of the suspension density to the changes in the coal slime characteristics; first, an ash-density response relationship model is established based on the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, so as to accurately characterize the intrinsic relationship between the suspension density and the clean coal ash content in the coarse coal slime sorting process, thereby providing an analysis basis for the sorting process; secondly, based on the ash-density response relationship model, the response deviation coefficient of the coarse coal slime sorting equipment when sorting coarse coal slime is determined to quantify the response degree of the coarse coal slime sorting equipment to the ash content change during the sorting process, and provide a basis for the automatic adjustment and optimization control of the subsequent sorting equipment; further, the change trend of the suspension density in the current adjustment cycle is determined, and the suspension density when the coarse coal slime is sorted under this change trend is determined based on the coal ash content data at the clean coal outlet in the coarse coal slime equipment and the response deviation coefficient. The balance control amount of density is used to effectively reflect the sorting effect of the coarse coal slime sorting equipment under different coarse coal slime characteristics, and then the suspension density is adjusted according to the sorting effect under different coarse coal slime characteristics to ensure that the coarse coal slime sorting equipment is always in the best sorting state, thereby avoiding the problem that the fixed suspension density cannot dynamically adapt to the changes in coal slime characteristics; then, the suspension density in the qualified medium barrel is updated through the sensitivity parameters of the coal ash content of the coarse coal slime in the original state and the balance control amount, and the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle is obtained, so as to effectively sort the coarse coal slime in different states, thereby ensuring the stability and effectiveness of the sorting process, and finally, the coarse coal slime in the next adjustment cycle is sorted based on the target suspension density; in summary, the technical solution provided by the present application can realize the dynamic adaptation of the suspension density to the changes in coal slime characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a coarse coal slime sorting method for a coarse coal slime sorting device according to some embodiments of the present application; Figure 2 is an exemplary flow chart of determining a response deviation coefficient according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining a change situation according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a coarse coal slime separation unit according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a coarse coal slime separation method according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a coarse coal slime sorting method for a coarse coal slime sorting device according to some embodiments of the present application. The coarse coal slime sorting method 100 for a coarse coal slime sorting device mainly includes the following steps: In step 101, the coarse coal slime to be sorted is transferred to the coarse coal slime sorting equipment, and the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet are monitored in real time.

[0020] In the specific implementation, first, the coarse coal slime to be sorted is smoothly conveyed from the coarse coal slime storage area to the feed port of the coarse coal slime sorting equipment by a conveyor belt, ensuring that the coarse coal slime can continuously and evenly enter the sorting system; then, a density sensor is installed in the qualified medium barrel of the coarse coal slime sorting equipment to monitor the density of the suspension in the qualified medium barrel in real time. The density sensor can transmit the suspension density data to the control system in real time. At the same time, an online ash meter is set at the clean coal outlet to monitor the clean coal at the clean coal outlet in real time. By real-time monitoring the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet, the sorting system can timely understand the key parameters in the sorting process, thereby realizing effective monitoring and adjustment of the sorting process.

[0021] It should be noted that the coarse coal slime in the present application refers to coal slime with a particle size between 0.5mm and 1.5mm during the coal washing process. The coarse coal slime is an intermediate product produced in the coal processing process, usually fine coal separated during the coal crushing, screening and washing processes; the coarse coal slime sorting equipment in the present application includes a feed inlet, a qualified medium barrel, a clean coal outlet, a conveyor belt, an overflow port, etc. The coarse coal slime sorting equipment is a heavy medium cyclone type equipment, which will not be repeated here.

[0022] In step 102, an ash-density response relationship model is established according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and the response deviation coefficient of the coarse coal slime sorting equipment during coarse coal slime sorting is determined based on the ash-density response relationship model.

[0023] In some embodiments, the ash-density response relationship model is established based on the prior information of the suspension density and the clean coal ash content during the coarse coal slime sorting process, which can be achieved by the following steps, namely: Acquiring prior information on the density of the suspension and the ash content of the clean coal during the coarse coal slime separation process; Extract the suspension density data and clean coal ash data at the corresponding timestamp from the prior information, and pair the suspension density data and clean coal ash data according to the timestamp to obtain an ash-density data sample set; Preprocessing the ash-density data sample set to obtain a preprocessed ash-density data sample set; A mathematical response model between ash content and suspension density is established based on the preprocessed ash-density data sample set; The mathematical response model is cross-validated to obtain an ash-density response relationship model.

[0024] In the specific implementation, first, the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process is obtained from the coarse coal slime sorting database, and the prior information includes the suspension density data and the clean coal ash content data at the same timestamp. The coarse coal slime sorting database refers to a database system for storing, managing and analyzing relevant data in the coarse coal slime sorting process. It includes multidimensional data such as suspension density, clean coal ash content, sorting equipment parameters, sorting effect, etc., and is intended to support sorting process optimization, model building and process control; secondly, the suspension density data and the clean coal ash data at the corresponding timestamp are extracted from the prior information, and the suspension density data and the clean coal ash data are stored in the database through the time of a programming language (such as Python). The sequence alignment function is used to pair the suspension density data with the clean coal ash data according to the timestamp to obtain an ash-density data sample set. In addition, in other embodiments, other methods can be used to pair the suspension density data with the clean coal ash data according to the timestamp, which is not limited here; further, the ash-density data sample set is preprocessed to obtain a preprocessed ash-density data sample set. The specific method includes using a statistical anomaly detection algorithm (such as Z-score) to remove outliers to obtain a preprocessed ash-density data sample set, which will not be repeated here; then, based on the preprocessed ash-density data sample set, an ash-density data sample set is established. The mathematical response model between ash content and suspension density is established by adopting the nonlinear regression in the mathematical modeling method based on the preprocessed ash-density data sample set. For example, the ash-density data sample set is used as input, and a suitable nonlinear regression model (such as polynomial regression) is selected, and then the least squares method is used to fit the ash-density data sample set, and the model parameters are automatically adjusted through programming tools (such as the scikit-learn library in Python) to establish the mathematical response model between ash content and suspension density. It will not be repeated here. Finally, the mathematical response model is cross-validated to obtain the ash-density The ash-density response relationship model is a kind of statistical method for evaluating the generalization ability of a model. The basic process is to divide the entire data set into K non-overlapping subsets, and use one of the subsets as a validation set and the other K-1 subsets as training sets to train the model. The validation set is then used to test the performance of the model.

[0025] It should be noted that the prior information in this application represents the known information about the relationship between the suspension density and the clean coal ash content obtained based on theoretical derivation, and the prior information can be used to guide model construction and optimization. The prior information in this application includes the suspension density data and the clean coal ash data at the corresponding timestamps; the ash-density data sample set in this embodiment represents a paired data set formed by matching the suspension density data and the clean coal ash data. Specifically, the ash-density data sample set in this embodiment refers to the suspension density data and the clean coal ash data recorded in real time by the digital sensor and the data acquisition module during the coarse coal slime sorting process. The two are matched according to their respective timestamps to form a paired data set, and each data sample contains the suspension density value and the corresponding clean coal ash value collected at the same time point; the mathematical response model in this embodiment represents a model for quantitatively expressing the intrinsic relationship between the suspension density and the clean coal ash; the ash-density response relationship model in this application represents a verified model of the intrinsic relationship between the suspension density and the clean coal ash. By determining the ash-density response relationship model, the accurate characterization of the intrinsic relationship between the suspension density and the clean coal ash in the coarse coal slime sorting process can be achieved, thereby providing a scientific and quantitative basis for the sorting process.

[0026] In some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart for determining the response deviation coefficient according to some embodiments of the present application. In this embodiment, the response deviation coefficient of the coarse coal slime separation device when performing coarse coal slime separation based on the ash-density response relationship model can be determined by the following steps: First, in step 1021, the density data of the suspension in the qualified medium bucket when the coarse coal slime separation equipment performs coarse coal slime separation is collected, and the corresponding predicted ash value is calculated by the ash-density response relationship model; Secondly, in step 1022, the clean coal ash data actually collected at the clean coal outlet is synchronously acquired; Then, in step 1023, the predicted ash value is compared with the clean coal ash data to obtain an ash deviation value set; Finally, in step 1024, a response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting is determined based on the ash deviation value set and the preset sorting influence coefficient.

[0027] In specific implementation, first, the density data of the suspension in the qualified medium barrel when the coarse coal slime sorting equipment performs coarse coal slime sorting is collected through a density sensor, the collected suspension density data is input into the ash-density response relationship model, and the ash-density response relationship model is regressed and analyzed by the least squares method to obtain the optimal regression parameters, and then the optimal regression parameters are input into the ash-density response relationship model to calculate the corresponding predicted ash value, which will not be repeated here; secondly, the clean coal ash data actually collected at the clean coal outlet is synchronously obtained through an online ash detector, the clean coal ash data includes multiple clean coal ash values, and the clean coal ash value represents the ash content in the clean coal measured at the clean coal outlet; then, the predicted ash value is compared with the clean coal ash data to obtain an ash deviation value set, that is: the absolute difference between each coal ash value in the clean coal ash data and the predicted ash value is calculated to obtain an ash deviation value set, and the ash deviation value set includes multiple ash deviation values; finally, based on the ash deviation value set and the pre-set separation The response deviation coefficient of the coarse coal slime sorting device when performing coarse coal slime sorting is determined by selecting the influence coefficient, that is, obtaining a preset sorting influence coefficient, calculating the mean of all ash deviation values ​​in the ash deviation value set, and using a weighted algorithm to calculate the mean and the sorting influence coefficient to obtain the response deviation coefficient of the coarse coal slime sorting device when performing coarse coal slime sorting, wherein the response deviation coefficient=(1-e^(−k×deviation value))×sorting influence coefficient, k is a weight adjustment parameter, and the weight adjustment parameter is set between 0 and 1. The sorting influence coefficient can be obtained by statistically analyzing historical data of multiple sorting cycles in the past, recording the ash deviation value and the corresponding suspension density adjustment amount of each cycle, and using a regression analysis method (such as linear regression or multivariate regression) to establish a mathematical relationship between the two. For example, if the regression equation is y=1.5x+0.2 (where x is the ash deviation and y is the suspension density adjustment amount), the regression coefficient 1.5 reflects the degree of influence of the ash deviation on the sorting adjustment, which can be used as the sorting influence coefficient, and will not be repeated here.

[0028] It should be noted that the predicted ash value in this embodiment represents the predicted ash value; the ash deviation value set in this embodiment represents a combination of multiple ash deviation values, and the ash deviation value represents the deviation between the actual ash value and the predicted ash value; the sorting influence coefficient in this embodiment represents a quantitative parameter of the influence of ash deviation on the suspension density adjustment during the coarse coal slime sorting process; the response deviation coefficient in this application represents the parameter of the sorting equipment's response to the ash deviation adjustment, and the response deviation coefficient reflects the equipment's adaptability to changes in coal ash content under current sorting conditions. By determining the response deviation coefficient, the response degree of the coarse coal slime sorting equipment to changes in ash content during the sorting process can be effectively quantified, and a basis can be provided for automatic adjustment and optimization control of subsequent equipment, so that the system can dynamically optimize the sorting process and ensure the stability of the clean coal quality.

[0029] In step 103, the change trend of the suspension density in the current adjustment cycle is analyzed based on the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and the balance control amount of the suspension density when the coarse coal slime is sorted under the changing trend is determined based on the coal ash data at the clean coal outlet in the coarse coal slime equipment and the response deviation coefficient.

[0030] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flow chart of determining the change trend according to some embodiments of the present application. In this embodiment, the change trend of the suspension density in the current adjustment cycle is analyzed according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, which can be achieved by the following steps: First, in step 1031, during the coarse coal slime sorting process, feedback data of the density of the suspension in the qualified medium barrel is collected; Next, in step 1032, the feedback data is preprocessed to obtain preprocessed feedback data; Then, in step 1033, the suspension density fluctuation value in the current adjustment cycle is calculated according to the pre-processed feedback data; Finally, in step 1034, the change trend of the suspension density in the current adjustment cycle is determined by the suspension density fluctuation value and the preset stability threshold.

[0031] In the specific implementation, first, in the process of coarse coal slime sorting, an online ash meter is used to collect feedback data of the suspension density in the qualified medium barrel; secondly, the Z-score standardization method is used to preprocess the feedback data to obtain the preprocessed feedback data, which will not be repeated here; then, the suspension density fluctuation value of the suspension density in the current adjustment cycle is calculated according to the preprocessed feedback data, that is: the standard deviation of the preprocessed feedback data is calculated, and the standard deviation calculation result is used as the suspension density fluctuation value of the suspension density in the current adjustment cycle. In addition, in other embodiments, other calculation methods can be used to calculate the suspension density fluctuation value of the suspension density in the current adjustment cycle, which is not limited here; finally, the change trend of the suspension density in the current adjustment cycle is determined by the suspension density fluctuation value and the preset stability threshold, that is, the absolute difference between the suspension density fluctuation value and the preset stability threshold is calculated, and the size of the absolute difference calculation result is used to measure the change trend of the suspension density in the current adjustment cycle.

[0032] It should be noted that the feedback data in the present application represents the suspension density data fed back in the qualified medium barrel, and the feedback data includes multiple feedback values, and the feedback value is characterized by the suspension density; the suspension density fluctuation value in the present embodiment represents the fluctuation degree value of the suspension density change; the preset stability threshold in the present embodiment represents a pre-set judgment value of the stable change of the suspension density, which can be set according to actual needs and is not limited here; the change trend in the present application represents the state of measuring the change trend of the suspension density. In the present application, by determining the change trend, the stability of the suspension density in the current adjustment cycle can be effectively quantified, providing a scientific basis for the optimization and regulation of the coarse coal slime sorting process. When the change trend is small, it means that the suspension density has tended to balance, which helps to ensure the sorting accuracy and improve the consistency of the clean coal quality; when the change trend is large, it indicates that the system may be disturbed or has not reached a steady state, and it is necessary to further adjust the suspension density or optimize the sorting parameters. In addition, determining the change trend can also reduce unnecessary adjustment operations, reduce system energy consumption and operating costs, and improve the automation level and operating efficiency of the sorting equipment.

[0033] It should also be noted that the adjustment period in the present application refers to the time interval during which the density of the suspension is adjusted once and maintained stable during the coarse coal slime separation process. The adjustment period can be set to a fixed time interval based on historical operating experience, which will not be repeated here.

[0034] In some embodiments, the following steps can be used to determine the balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation based on the coal ash data at the clean coal outlet in the coarse coal slime equipment and the response deviation coefficient, namely: Collecting coal ash content data at the clean coal outlet of the coarse coal slime equipment; Performing a change trend analysis on the normalized coal ash content data to obtain the fluctuation entropy of the coal ash content data; Based on the fluctuation entropy and the response deviation coefficient, a balance analysis is performed on the suspension density when the coarse coal slime is sorted under the changing situation, so as to obtain a balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation.

[0035] In specific implementation, first, the coal ash data at the clean coal outlet of the coarse coal slime equipment is collected through an online sensor, and the coal ash data is normalized by using digital signal processing technology (for example, using the Min-Max normalization algorithm); then, the change trend of the normalized coal ash data is analyzed to obtain the fluctuation entropy of the coal ash data, that is: the normalized coal ash data is input as an input parameter into a preset entropy model, and the entropy model outputs the fluctuation entropy of the coal ash data. The entropy model in this embodiment adopts the Shannon entropy model. In addition, other entropy models can also be used in other embodiments, and the types are not limited; finally, based on the fluctuation entropy and the response deviation coefficient, the suspension density of the coarse coal slime when being sorted under the changing situation is balanced and analyzed to obtain the balance control coefficient of the suspension density when the coarse coal slime is sorted under the changing situation. The control quantity is as follows: based on the fluctuation entropy and the response deviation coefficient, the fuzzy logic control algorithm performs a balance analysis on the suspension density when the coarse coal slime is sorted under the changing situation, and obtains the balance control quantity of the suspension density when the coarse coal slime is sorted under the changing situation, that is, the fluctuation entropy and the response deviation coefficient are respectively input as an input variable into the fuzzy logic control system, and then, according to the preset fuzzy rules (for example, if the fluctuation entropy is high and the response deviation coefficient is large, the adjustment amplitude is large), the two input variables are fuzzified, and the corresponding output variable fuzzy set is obtained through fuzzy reasoning (such as using the Mamdani reasoning method); finally, the output variable fuzzy set is defuzzified (for example, using the center of gravity method) to obtain the balance control quantity of the suspension density when the coarse coal slime is sorted under the changing situation.

[0036] It should be noted that the coal ash data in this embodiment represents a set of multiple coal ash values, and the coal ash value represents the ash content measured in the clean coal; the fluctuation entropy in this embodiment represents a quantitative description of the dynamic volatility of the coal ash data. By determining the fluctuation entropy, an input variable can be provided for the fuzzy logic control algorithm, so as to accurately adjust the balance control amount of the suspension density in the coarse coal slime sorting process; the balance control amount in this application represents the control amount for balancing the suspension density. By determining the balance control amount, the changes in the coarse coal slime characteristics and the operating state of the coarse coal slime sorting equipment can be effectively reflected, ensuring that the coarse coal slime sorting equipment is always in the best sorting state, reducing the impact of coal ash fluctuations on the sorting process, and ensuring that the ash content in the clean coal is maintained in an appropriate range, thereby ensuring the quality of the clean coal.

[0037] In step 104, the sensitivity parameter of the ash content of the coarse coal slime in the original state is determined, and then the suspension density in the qualified medium barrel is feedback updated through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle.

[0038] In some embodiments, determining the sensitivity parameter of the ash content of the raw coal slime can be achieved by the following steps, namely: Sampling ash data of the coarse coal slime in the original state to obtain original coal ash data of the coarse coal slime in the original state; Simultaneously collect key process parameter data related to coal ash content during the current adjustment cycle; Establishing a data sample association matrix from the original coal ash data and the key process parameter data; The sensitivity parameters of the ash content of the coarse coal slime in the original state are determined based on the data sample association matrix.

[0039] In the specific implementation, firstly, an X-ray fluorescence analyzer is used to sample the ash content data of the raw coal slime, so as to obtain the raw coal ash content data of the raw coal slime in the raw state, which is usually in the form of data collected at regular time intervals, wherein the raw coal slime refers to the raw coal slime on the conveyor belt that has not entered the raw coal slime sorting equipment; secondly, key process parameter data related to the coal ash content in the current adjustment cycle are synchronously collected, such as suspension density, suspension flow rate, and suspension temperature, which can be collected in real time through sensors and data acquisition systems, which are not limited here; then, the raw coal ash content data and the key process parameter data are collected. A data sample association matrix is ​​established, that is, the coal ash data at each sampling point and its corresponding key process parameter data are arranged in rows in chronological order to obtain a data sample association matrix; finally, the sensitivity parameters of the coal ash content of the coarse coal slime in the original state are determined based on the data sample association matrix, that is, the data sample association matrix is ​​normalized, the normalized data sample association matrix is ​​processed by principal component analysis, and the process parameters corresponding to the maximum eigenvalues ​​are extracted as sensitivity parameters. In addition, in other embodiments, other methods may be used to determine the sensitivity parameters of the coal ash content of the coarse coal slime in the original state, which are not limited here.

[0040] It should be noted that, in this embodiment, the key process parameter data represents a plurality of process parameters related to coal ash content; in this embodiment, the data sample association matrix represents a matrix composed of a plurality of data samples associated together. Specifically, the data sample association matrix refers to a matrix formed by associating and combining the original coal ash data with the key process parameter data according to the same timestamp. Each row in the matrix represents a set of coal ash data and its corresponding process parameter data, and the element value in the matrix represents the relationship between each process parameter and the coal ash content. By analyzing the matrix, the correlation and influence degree between the coal ash content and the process parameters can be revealed; in this application, the sensitivity parameters represent the key process parameters that have a significant impact on the change of coal ash content. By determining the key process parameters, the suspension density can be dynamically adjusted, which helps to monitor and adjust the key variables in the sorting process, reduce sorting fluctuations, improve the stability of the sorting process, and ensure that the coal slime sorting operates in the best state.

[0041] In some embodiments, the density of the suspension in the qualified medium barrel is updated by feedback through the sensitivity parameter and the balance control amount, and the target suspension density of the next adjustment cycle of the coarse coal slime sorting equipment is obtained by the following steps, namely: Determining the fitting deviation of the sensitivity parameter at different time points during the coarse coal slime separation process; Determine the influence coefficient of the coarse coal slime when it is sorted according to all the fitting deviations; Determine the density correction amount of the coarse coal slime sorting in the current adjustment cycle through the influence coefficient and the balance control amount; The density correction amount is fed back to the current suspension density to obtain the target suspension density of the next adjustment cycle.

[0042] In the specific implementation, first, the fitting deviation of the sensitivity parameter at different time points in the coarse coal slime sorting process is determined, that is: the parameter value of the sensitivity parameter at different time points in the coarse coal slime sorting process is obtained, and the different time points are used as the horizontal axis elements, and the parameter values ​​at different time points are used as the vertical axis elements, and all the fitting deviations are fitted. For each time point, the fitting deviation of the sensitivity parameter at the time point is obtained by the absolute difference between the original parameter value and the fitting parameter value, and then the fitting deviation of the sensitivity parameter at different time points in the coarse coal slime sorting process is obtained; secondly, the influence coefficient of the coarse coal slime sorting is determined according to all the fitting deviations, that is: all the fitting deviations are normalized by using the minimum-maximum normalization, and then all the normalized fitting deviations are averaged, and the average calculation result is used as the coarse coal slime sorting. Influence coefficient during sorting. In addition, in other embodiments, other calculation methods can be used to calculate the influence coefficient of the coarse coal slime during sorting, which is not limited here; then, the density correction amount of the coarse coal slime sorting in the current adjustment cycle is determined by the influence coefficient and the balance control amount, that is: the influence coefficient and the balance control amount are multiplied, and the product calculation result is used as the density correction amount of the coarse coal slime sorting in the current adjustment cycle. In addition, in other embodiments, other calculation methods can be used to calculate the density correction amount of the coarse coal slime sorting in the current adjustment cycle, which is not limited here; the density correction amount is fed back to the suspension density at the current moment to obtain the target suspension density of the next adjustment cycle, that is: the density correction amount is added to the suspension density at the current moment to obtain the target suspension density of the next adjustment cycle.

[0043] It should be noted that the fitting deviation in this embodiment represents the deviation between the parameter value of the sensitivity parameter and the fitting parameter value; the influence coefficient in this embodiment represents the influence index when the coarse coal slime is sorted; the density correction amount in this application represents the amount of suspension density that needs to be adjusted. In the actual sorting process, the characteristics of the coal slime (such as coal ash content, particle size, mineral composition, etc.) and process parameters (such as flow rate, temperature, pressure, etc.) are dynamically changing. The density correction amount can respond to these changes according to the real-time feedback of coal ash content and suspension density data, adjust the suspension density in time, and improve the clean coal sorting efficiency; the target suspension density in this application represents the suspension density required for the next cycle adjustment cycle of the coarse coal slime. Since the coal slime characteristics and process parameters are constantly changing during the sorting process, the target suspension density also needs to be updated and adjusted according to the real-time monitoring data. By determining the target suspension density, the coarse coal slime in different states can be effectively sorted, thereby ensuring the stability and effectiveness of the sorting process.

[0044] It should also be noted that the feedback update in the present application represents a process of updating the suspension density, wherein the suspension density in the qualified medium barrel is feedback updated through the sensitivity parameter and the balance control amount, namely: determining the fitting deviation of the sensitivity parameter at different time points during the coarse coal slime sorting process; determining the influence coefficient of the coarse coal slime sorting based on all the fitting deviations; determining the density correction amount of the coarse coal slime sorting in the current adjustment cycle through the influence coefficient and the balance control amount; feeding back the density correction amount to the suspension density at the current moment to obtain the target suspension density for the next adjustment cycle, i.e. completing the feedback update of the suspension density in the qualified medium barrel.

[0045] In step 105, the coarse coal slime of the next adjustment cycle is sorted based on the target suspension density.

[0046] In the specific implementation, first, the target suspension density is used as the target value for adjusting the suspension density in the qualified medium barrel in the next adjustment cycle, and the suspension density is gradually approached to the target value by adjusting the medium dosage or the clean water replenishment amount. When the suspension density stabilizes to the target value, the coarse coal slime to be sorted is transported to the sorting equipment, so that the coarse coal slime is sorted in a stable suspension environment.

[0047] In addition, in another aspect of the present application, in some embodiments, the present application provides a coarse coal slime sorting device, the device comprising a qualified medium barrel, a clean coal outlet and a coarse coal slime sorting unit, reference Figure 4 , which is a schematic diagram of the structure of a coarse coal slime separation unit according to some embodiments of the present application, the coarse coal slime separation unit 200 includes: a monitoring module 201, a processing module 202 and an execution module 203, which are respectively described as follows: Monitoring module 201, in this application, monitoring module 201 is mainly used to transfer the coarse coal slime to be sorted to the coarse coal slime sorting equipment, and to monitor the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet in real time; Processing module 202, in the present application, the processing module 202 is mainly used to establish an ash-density response relationship model according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and determine the response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting based on the ash-density response relationship model; The processing module 202 is further used to analyze the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend according to the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient; In addition, the processing module 202 is also used to determine the sensitivity parameter of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle; The execution module 203 in the present application is mainly used to sort the coarse coal slime of the next adjustment cycle based on the target suspension density.

[0048] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned coarse coal slime sorting method for the coarse coal slime sorting device.

[0049] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a coarse coal slime separation method according to some embodiments of the present application. The coarse coal slime separation method for a coarse coal slime separation device in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0050] The processor 301 may be a general-purpose central processing unit (CPU), or an application specific integrated circuit (ASIC) or one or more processors for controlling the execution of the coarse coal slime separation method for the coarse coal slime separation device in the present application.

[0051] The communication bus 302 may be used to transmit information between the above-mentioned components.

[0052] The memory 303 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0053] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The determination of the coarse coal slime sorting method for the coarse coal slime sorting device in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0054] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0056] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0057] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the coarse coal slime sorting method for the coarse coal slime sorting device is implemented.

[0058] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A coarse coal slime sorting method for a coarse coal slime sorting device, wherein the coarse coal slime sorting device comprises a qualified medium barrel and a clean coal outlet, characterized in that: The method comprises the following steps: The coarse coal slime to be sorted is conveyed to the coarse coal slime sorting equipment, and the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet are monitored in real time; Establishing an ash-density response relationship model according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and determining the response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting based on the ash-density response relationship model; Analyze the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend according to the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient; Determine the sensitivity parameter of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle; The coarse coal slime of the next conditioning cycle is sorted based on the target suspension density.

2. The method according to claim 1, characterized in that The ash-density response relationship model is established based on the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, specifically including: Acquiring prior information on the density of the suspension and the ash content of the clean coal during the coarse coal slime separation process; Extract the suspension density data and clean coal ash data at the corresponding timestamp from the prior information, and pair the suspension density data and clean coal ash data according to the timestamp to obtain an ash-density data sample set; Preprocessing the ash-density data sample set to obtain a preprocessed ash-density data sample set; A mathematical response model between ash content and suspension density is established based on the preprocessed ash-density data sample set; The mathematical response model is cross-validated to obtain an ash-density response relationship model.

3. The method according to claim 1, characterized in that Determining the response deviation coefficient of the coarse coal slime separation equipment when performing coarse coal slime separation based on the ash-density response relationship model specifically includes: Collecting the suspension density data in the qualified medium bucket when the coarse coal slime separation equipment performs coarse coal slime separation, and calculating the corresponding predicted ash value by the ash-density response relationship model; Synchronously obtain the clean coal ash data actually collected at the clean coal outlet; Comparing the predicted ash value with the clean coal ash data to obtain an ash deviation value set; A response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting is determined based on the ash deviation value set and a preset sorting influence coefficient.

4. The method according to claim 1, characterized in that According to the feedback data of the density of the suspension in the qualified medium barrel during the coarse coal slime sorting, the change trend of the density of the suspension in the current adjustment cycle is analyzed, which specifically includes: In the process of coarse coal slime sorting, feedback data of the density of the suspension in the qualified medium barrel is collected; Preprocessing the feedback data to obtain preprocessed feedback data; Calculating the suspension density fluctuation value within the current adjustment cycle according to the pre-processed feedback data; The change trend of the suspension density in the current adjustment cycle is determined by the suspension density fluctuation value and the preset stability threshold.

5. The method according to claim 1, characterized in that Determining the balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation based on the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient specifically includes: Collecting coal ash content data at the clean coal outlet of the coarse coal slime equipment; Performing a change trend analysis on the normalized coal ash content data to obtain the fluctuation entropy of the coal ash content data; Based on the fluctuation entropy and the response deviation coefficient, a balance analysis is performed on the suspension density when the coarse coal slime is sorted under the changing situation, so as to obtain a balance control amount of the suspension density when the coarse coal slime is sorted under the changing situation.

6. The method according to claim 1, characterized in that The sensitivity parameters for determining the ash content of the coarse coal slime in its original state specifically include: Sampling ash data of the coarse coal slime in the original state to obtain original coal ash data of the coarse coal slime in the original state; Simultaneously collect key process parameter data related to coal ash content during the current adjustment cycle; Establishing a data sample association matrix from the original coal ash data and the key process parameter data; The sensitivity parameters of the ash content of the coarse coal slime in the original state are determined based on the data sample association matrix.

7. The method according to claim 1, characterized in that The coarse coal slime separation equipment is a heavy medium cyclone type equipment.

8. A coarse coal slime sorting device, comprising a qualified medium barrel, a clean coal outlet and a coarse coal slime sorting unit, characterized in that: The coarse coal slime separation unit comprises: A monitoring module is used to convey the coarse coal slime to be sorted to the coarse coal slime sorting equipment, and to monitor the density of the suspension in the qualified medium barrel and the clean coal at the clean coal outlet in real time; A processing module, for establishing an ash-density response relationship model according to the prior information of the suspension density and the clean coal ash content in the coarse coal slime sorting process, and determining a response deviation coefficient of the coarse coal slime sorting equipment when performing coarse coal slime sorting based on the ash-density response relationship model; The processing module is further used to analyze the change trend of the suspension density in the current adjustment cycle according to the feedback data of the suspension density in the qualified medium barrel during the coarse coal slime sorting, and determine the balance control amount of the suspension density when the coarse coal slime is sorted under the change trend according to the coal ash content data at the clean coal outlet of the coarse coal slime equipment and the response deviation coefficient; The processing module is further used to determine the sensitivity parameter of the ash content of the coarse coal slime in the original state, and then feedback update the suspension density in the qualified medium barrel through the sensitivity parameter and the balance control amount to obtain the target suspension density of the coarse coal slime sorting equipment in the next adjustment cycle; An execution module is used to sort the coarse coal slime of the next adjustment cycle based on the target suspension density.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the coarse coal slime sorting method for a coarse coal slime sorting device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the coarse coal slime separation method for a coarse coal slime separation device according to any one of claims 1 to 7 is implemented.

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