CN114758196A - Clean coal ash content on-line soft measurement method in lump coal shallow slot sorting process - Google Patents
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Abstract
The invention discloses an on-line soft measurement method for clean coal ash content in a lump coal shallow slot sorting process, relates to the technical field of coal sorting processing, and solves the problems of complexity, poor adaptability and higher cost of a clean coal ash content prediction method in the existing lump coal shallow slot sorting process. The method comprises the following steps: collecting data in real time, wherein the data comprises clean coal yield, tail coal yield, qualified medium density and dual-energy X-ray images of clean coal; inputting the R value of each pixel point in the real-time collected dual-energy X-ray image of the clean coal into the trained main clean coal ash prediction model, and processing to obtain a predicted value of the clean coal ash; processing the clean coal output and the tail coal output which are collected in real time to obtain the clean coal yield; inputting the clean coal yield and the qualified medium density after the normalization treatment into a trained clean coal ash compensation model, and obtaining a clean coal ash error predicted value after the treatment; and compensating the predicted value of the clean coal ash content by using the error predicted value of the clean coal ash content to obtain a final clean coal ash content prediction result.
Description
Technical Field
The invention relates to the technical field of coal sorting processing, in particular to a clean coal ash content online soft measurement method in a lump coal shallow slot sorting process.
Background
With the continuous promotion of the double-carbon strategy, the low-carbon clean utilization of coal resources becomes the central importance of energy economy. The traditional lump coal shallow slot sorting detection technology has serious hysteresis, and cannot guide the adjustment of production indexes in time, so that the product quantity is reduced, and a large amount of coal resources are lost.
Lump coal shallow slot separation is commonly used for pre-gangue discharge and classification entry, and the existing separation process comprises the following steps: raw coal enters a shallow slot separator through a raw coal belt, and two products obtained through separation enter a clean coal belt and a tail coal belt through dehydration and medium removal respectively to become clean coal products and tail coal products. The existing clean coal ash content detection means is a sampling and sample class test, the mode has serious hysteresis, the operation state of production equipment, production process parameters, product quality and the like cannot be sensed in real time, the quality is usually ensured through the loss of yield, a large amount of clean coal is lost in tailings, the tailings ash content is low, the calorific value is high, and coal resources are wasted.
Therefore, the development of a clean coal ash online prediction method which is simple, wide in adaptability, low in cost and suitable for the lump coal shallow slot sorting process is urgently needed.
Disclosure of Invention
In view of the above analysis, the embodiment of the present invention aims to provide an online soft measurement method for clean coal ash content in a lump coal shallow slot sorting process, so as to solve the problems of complexity, poor adaptability and high cost of the existing clean coal ash content prediction method in the lump coal shallow slot sorting process.
The invention discloses a clean coal ash content on-line soft measurement method in a lump coal shallow slot sorting process, which comprises the following steps:
collecting data in real time, wherein the data comprises clean coal yield, tail coal yield, qualified medium density and dual-energy X-ray images of clean coal;
inputting the R value of each pixel point in the real-time collected dual-energy X-ray image of the clean coal into the trained main clean coal ash prediction model, and processing to obtain a predicted value of the clean coal ash;
processing the clean coal output and the tail coal output collected in real time to obtain the clean coal yield; inputting the clean coal yield and the qualified medium density after the normalization treatment into a trained clean coal ash compensation model, and obtaining a clean coal ash error predicted value after the treatment;
and compensating the predicted value of the clean coal ash content by using the error predicted value of the clean coal ash content to obtain a final clean coal ash content prediction result.
On the basis of the scheme, the invention also makes the following improvements:
Further, training the clean coal ash prediction main model by:
obtaining a first set of samples, each set of first sample data in the first set of samples comprising: the dual-energy X-ray image of clean coal and the measured value of ash content of the clean coal;
and taking the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of first sample data as input and the measured value of the ash content of the clean coal as a label, training the main model of the clean coal ash content prediction, determining the structure and the parameters of the main model of the clean coal ash content prediction, and obtaining the trained main model of the clean coal ash content prediction.
Further, training the clean coal ash compensation model by:
obtaining a second sample set, each set of second sample data in the second sample set comprising: clean coal yield, tail coal yield, qualified medium density, dual-energy X-ray image of clean coal and clean coal ash measured value;
processing the clean coal yield and the tailing coal yield in each group of second sample data to obtain the clean coal yield; inputting the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of second sample data into the trained main model for predicting the ash content of the clean coal to obtain a predicted value of the ash content of the clean coal;
And taking the cleaned coal yield corresponding to each group of second sample data and the qualified medium density after the normalization processing as input, taking the difference value between the corresponding cleaned coal ash content measured value and the cleaned coal ash content predicted value as a label, and training the cleaned coal ash content compensation model to obtain the trained cleaned coal ash content compensation model.
Further, each set of second sample data satisfies: the deviation between the theoretical clean coal ash value obtained by matching the clean coal yield, the tail coal yield and the raw coal selectivity curve and the actual clean coal ash value of the current second sample data does not exceed the set deviation threshold value.
Further, the method further comprises:
in the process of collecting data in real time, a real-time measured value of the ash content of the clean coal is also collected regularly;
and if the error between the real measured value of the clean coal ash content acquired regularly and the final prediction result of the clean coal ash content obtained based on the real acquired data at the moment is lower than the set error, the real acquired data and the real measured value of the clean coal ash content at the moment are used as a group of correction data, a plurality of groups of correction data form a correction data set, and the clean coal ash content compensation model is corrected on line based on the correction data set.
Further, the R value of each pixel point in the dual-energy X-ray image of the clean coal is as follows:
Wherein, mulRepresenting the attenuation coefficient, mu, of a pixel in a dual-energy X-ray image under low-energy X-rayshAnd the attenuation coefficient of a pixel point in the dual-energy X-ray image under the high-energy X-ray is represented.
Further, the processing of the clean coal output and the tail coal output collected in real time to obtain the clean coal yield comprises:
wherein r is1Represents the yield of clean coal, Q1Represents the yield of clean coal, Q2Indicating the tailing coal yield.
Further, the main prediction model of the clean coal ash content is realized based on a convolutional neural network.
Further, the clean coal ash compensation model is realized based on least square support vector regression.
Further, the clean coal yield is measured by a clean coal belt scale;
the yield of the tailings is measured by a tailings belt weigher;
the qualified medium density is measured by a densimeter in a qualified medium barrel;
the dual-energy X-ray image of the clean coal is acquired by an industrial X-ray machine arranged above the clean coal belt.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
the invention discloses an on-line soft measurement method for clean coal ash content in a lump coal shallow slot sorting process, which has the following advantages:
firstly, determining a clean coal ash predicted value based on an R value of each pixel point in a real-time collected dual-energy X-ray image of clean coal, determining a clean coal ash error predicted value based on a clean coal yield, a tail coal yield and qualified medium density collected in real time, and then compensating the clean coal ash predicted value by using the clean coal ash error predicted value to obtain a final clean coal ash prediction result; the method can realize continuous measurement, and can realize effective, accurate and rapid detection of clean coal ash content in the lump coal shallow slot sorting process, so as to guide the setting of feeding amount, circulating suspension liquid amount and suspension liquid density based on the prediction result, reduce system fluctuation and improve clean coal yield.
And secondly, real-time online continuous detection of the ash content of the sorted clean coal can be realized, certain self-adaptive capacity is realized, and a predicted value can be corrected through production conditions. And the separation working condition can be regulated and controlled in time according to the predicted value so as to meet new working condition conditions.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings, in which like reference numerals refer to like parts throughout, are for the purpose of illustrating particular embodiments only and are not to be considered limiting of the invention.
FIG. 1 is a schematic diagram of an on-line soft measurement process of clean coal ash content in a lump coal shallow slot sorting process disclosed in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a clean coal ash prediction main model training process of a sorting process provided by an embodiment of the invention;
fig. 3 is a schematic diagram of a clean coal ash prediction compensation model training process of the sorting process according to the embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The embodiment of the invention discloses a clean coal ash online soft measurement method in a lump coal shallow slot sorting process, which is shown in a flow diagram in figure 1 and comprises the following steps:
step S1: collecting data in real time, wherein the data comprises clean coal yield, tail coal yield, qualified medium density and dual-energy X-ray images of clean coal;
step S2: inputting the R value of each pixel point in the real-time collected dual-energy X-ray image of the clean coal into the trained main clean coal ash prediction model, and processing to obtain a predicted value of the clean coal ash;
step S3: processing the clean coal output and the tail coal output collected in real time to obtain the clean coal yield; inputting the clean coal yield and the qualified medium density after the normalization treatment into a trained clean coal ash compensation model, and obtaining a clean coal ash error predicted value after the treatment;
step S4: and compensating the predicted value of the clean coal ash content by using the error predicted value of the clean coal ash content to obtain a final clean coal ash content prediction result.
Specifically, the data in this embodiment is obtained by the following method: the yield of the clean coal is measured by a clean coal belt weigher; the yield of the tail coal is measured by a tail coal belt weigher; the qualified medium density is measured by a densimeter in the qualified medium barrel; the dual-energy X-ray image of the clean coal is acquired by an industrial X-ray machine arranged above the clean coal belt.
There is a direct relationship between the R-value of the dual energy X-ray image of clean coal and the clean coal ash prediction. Specifically, the R value of the material property is related to the effective atomic number of the material, and the R value of each pixel point in the dual-energy X-ray image of the clean coal is calculated according to the formula (1):
wherein, mulRepresenting the attenuation coefficient, mu, of a pixel in a dual-energy X-ray image under low-energy X-rayshRepresenting the attenuation coefficient of a pixel point in the dual-energy X-ray image under the high-energy X-ray;
the Z value of the material property satisfies:
Z＝-6.596×105×e-9.815R+4.685×e0.6783R (2)
and the relationship between the Z value and the substance classification is shown in Table 1:
TABLE 1 relationship between Z-value and substance class
In the cleaned coal obtained by sorting, the higher the ash content of the cleaned coal is, the more inorganic substances (such as gangue) are contained in the cleaned coal; conversely, the lower the clean coal ash content is, the more organic matter is contained therein; therefore, a direct relation exists between the R value of each pixel point in the dual-energy X-ray image of the clean coal and the clean coal ash, and the clean coal ash can be predicted based on the R value of each pixel point in the dual-energy X-ray image of the clean coal.
In addition, the clean coal yield and the qualified medium density have different degrees of influence on the prediction of the clean coal ash, and the prediction result of the clean coal ash can be compensated based on the clean coal yield and the qualified medium density.
Before implementing the above scheme, training of a main clean coal ash prediction model and a compensation clean coal ash prediction model is completed, and a specific implementation manner is introduced as follows:
(1) training the clean coal ash prediction main model by the following method, wherein the training process is as shown in FIG. 2:
step A1: obtaining a first set of samples, each set of first sample data in the first set of samples comprising: the dual-energy X-ray image of clean coal and the measured value of ash content of the clean coal;
step A2: and taking the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of first sample data as input and the measured value of the ash content of the clean coal as a label, training the main model of the clean coal ash content prediction, determining the structure and the parameters of the main model of the clean coal ash content prediction, and obtaining the trained main model of the clean coal ash content prediction.
In the process of training the main clean coal ash prediction model, a mapping relation between the R value of each pixel point in the dual-energy X-ray image of the clean coal and the actual measured value of the clean coal ash can be established, and the mapping relation is embodied by the structure and the parameters of the main clean coal ash prediction model. Therefore, in the real-time processing process, the R value of each pixel point in the real-time acquired dual-energy X-ray image of the clean coal can be input into the trained clean coal ash prediction main model, and the predicted value of the clean coal ash is obtained after processing.
Preferably, the main clean coal ash prediction model is a main clean coal ash prediction model based on a convolutional neural network. In the training process of the clean coal ash content prediction main model, the convolutional neural network carries out back propagation on errors obtained through gradient descent, parameters of all layers of the convolutional neural network are updated layer by layer, and the structure and the parameters of the clean coal ash content prediction main model are finally determined through multiple rounds of iterative training.
The clean coal ash content prediction main model based on the convolutional neural network can preliminarily predict sorted clean coal ash content, but high-precision prediction results cannot be obtained due to the factors of complex sorting conditions, errors in the dual-energy X-ray image acquisition process, limited information reflected by images and the like. Meanwhile, other influence factors of the sorted clean coal ash content are analyzed, so that the clean coal ash content prediction compensation model is trained according to the influence factors of the sorted clean coal ash content to predict the deviation of the clean coal ash content prediction value of the clean coal ash content prediction main model.
(2) Training the clean coal ash prediction compensation model by the following method, wherein the training process is shown in FIG. 3:
step B1: obtaining a second sample set, each set of second sample data in the second sample set comprising: clean coal yield, tail coal yield, qualified medium density, dual-energy X-ray image of clean coal and a measured value of clean coal ash;
Step B2: processing the clean coal yield and the tail coal yield in each group of second sample data to obtain the clean coal yield; in particular, the amount of the solvent to be used,
wherein r is1Denotes the yield of clean coal, Q1Denotes the yield of clean coal, Q2Indicating the tailing coal yield.
Step B3: inputting the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of second sample data into the trained main model for predicting the ash content of the clean coal to obtain a predicted value of the ash content of the clean coal;
step B4: and taking the clean coal yield and the qualified medium density after normalization corresponding to each group of second sample data as input, taking the difference value between the corresponding measured clean coal ash content value and the predicted clean coal ash content value as a label, and training the clean coal ash content compensation model to obtain a trained clean coal ash content compensation model. Preferably, a maximum and minimum normalization method is selected to perform normalization processing on the qualified medium density. After normalization processing, all data are converted into a [0,1] interval, so that all indexes belong to the same magnitude.
Illustratively, the clean coal ash compensation model is implemented based on least squares support vector regression.
It should be noted that, in order to ensure the accuracy of the data of the training model, each set of second sample data satisfies: the deviation between the theoretical clean coal ash value obtained by matching the clean coal yield, the tail coal yield and the raw coal selectivity curve and the actual measured clean coal ash value of the current second sample data is not more than a set deviation threshold value.
Preferably, the method in this embodiment further includes:
in the process of collecting data in real time, a real-time measured value of the ash content of the clean coal is also collected regularly;
if the error between the regularly acquired clean coal ash actual measurement value and the final clean coal ash prediction result obtained based on the actual acquired data at the moment is lower than the set error, the actual acquired data and the clean coal ash actual measurement value at the moment are used as a group of correction data, a plurality of groups of correction data form a correction data set, and the clean coal ash compensation model is corrected on line based on the correction data set, so that the model parameter self-correction is realized, and the self-adaptive capacity of the model under various working conditions is enhanced.
In summary, compared with the prior art, the online soft measurement method for ash content in clean coal in the lump coal shallow slot sorting process provided in this embodiment has the following advantages: firstly, determining a clean coal ash predicted value based on an R value of each pixel point in a real-time acquired dual-energy X-ray image of clean coal, determining a clean coal ash error predicted value based on a clean coal yield, a tail coal yield and qualified medium density acquired in real time, and then compensating the clean coal ash predicted value by using the clean coal ash error predicted value to obtain a final clean coal ash predicted result; the method can realize continuous measurement, and can realize effective, accurate and rapid detection of clean coal ash content in the lump coal shallow slot sorting process, so as to guide the setting of feeding amount, circulating suspension liquid amount and suspension liquid density based on the prediction result, reduce system fluctuation and improve clean coal yield. And secondly, real-time online continuous detection of the ash content of the sorted clean coal can be realized, certain self-adaptive capacity is realized, and a predicted value can be corrected through production conditions. And the separation working condition can be regulated and controlled in time according to the predicted value so as to meet new working condition conditions.
Those skilled in the art will appreciate that all or part of the processes for implementing the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, for instructing the relevant hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. An on-line soft measurement method for clean coal ash content in a lump coal shallow slot sorting process is characterized by comprising the following steps:
collecting data in real time, wherein the data comprises clean coal yield, tail coal yield, qualified medium density and dual-energy X-ray images of clean coal;
inputting the R value of each pixel point in the real-time collected dual-energy X-ray image of the clean coal into the trained main clean coal ash prediction model, and processing to obtain a predicted value of the clean coal ash;
processing the clean coal output and the tail coal output which are collected in real time to obtain the clean coal yield; inputting the clean coal yield and the qualified medium density after the normalization treatment into a trained clean coal ash compensation model, and obtaining a clean coal ash error predicted value after the treatment;
And compensating the clean coal ash prediction value by using the clean coal ash error prediction value to obtain a final clean coal ash prediction result.
2. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process as claimed in claim 1, wherein the clean coal ash content prediction main model is trained by:
obtaining a first set of samples, each set of first sample data in the first set of samples comprising: the dual-energy X-ray image of the clean coal and the measured value of the ash content of the clean coal;
and taking the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of first sample data as input and the measured value of the ash content of the clean coal as a label, training the main model for predicting the ash content of the clean coal, determining the structure and the parameters of the main model for predicting the ash content of the clean coal, and obtaining the trained main model for predicting the ash content of the clean coal.
3. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process as claimed in claim 1, wherein the clean coal ash content compensation model is trained by:
obtaining a second sample set, each set of second sample data in the second sample set comprising: clean coal yield, tail coal yield, qualified medium density, dual-energy X-ray image of clean coal and a measured value of clean coal ash;
Processing the clean coal yield and the tail coal yield in each group of second sample data to obtain the clean coal yield;
inputting the R value of each pixel point in the dual-energy X-ray image of the clean coal in each group of second sample data into the trained main model for predicting the ash content of the clean coal to obtain a predicted value of the ash content of the clean coal;
and taking the cleaned coal yield corresponding to each group of second sample data and the qualified medium density after the normalization processing as input, taking the difference value between the corresponding cleaned coal ash content measured value and the cleaned coal ash content predicted value as a label, and training the cleaned coal ash content compensation model to obtain the trained cleaned coal ash content compensation model.
4. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process according to claim 3, wherein each group of second sample data satisfies: the deviation between the theoretical clean coal ash value obtained by matching the clean coal yield, the tail coal yield and the raw coal selectivity curve and the actual clean coal ash value of the current second sample data does not exceed the set deviation threshold value.
5. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process as claimed in claim 3, wherein the method further comprises:
in the process of collecting data in real time, a real-time measured value of the ash content of the clean coal is also collected regularly;
And if the error between the real measured value of the clean coal ash content acquired regularly and the final prediction result of the clean coal ash content obtained based on the real acquired data at the moment is lower than the set error, the real acquired data and the real measured value of the clean coal ash content at the moment are used as a group of correction data, a plurality of groups of correction data form a correction data set, and the clean coal ash content compensation model is corrected on line based on the correction data set.
6. The on-line soft measurement method for the ash content of the clean coal in the lump coal shallow slot sorting process according to any one of claims 1 to 5, wherein the R value of each pixel point in the dual-energy X-ray image of the clean coal is as follows:
wherein, mulRepresenting the attenuation coefficient, mu, of a pixel in a dual-energy X-ray image under low-energy X-rayshAnd the attenuation coefficient of a pixel point in the dual-energy X-ray image under the high-energy X-ray is represented.
7. The on-line soft measurement method for the clean coal ash content in the lump coal shallow slot sorting process according to any one of claims 1 to 5, wherein the processing of the clean coal yield and the tail coal yield acquired in real time to obtain the clean coal yield comprises:
wherein r is1Represents the yield of clean coal, Q1Represents the yield of clean coal, Q2Indicating the tailing coal yield.
8. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process according to any one of claims 1 to 5, wherein the clean coal ash content prediction main model is implemented based on a convolutional neural network.
9. The on-line soft measurement method for clean coal ash in the lump coal shallow slot sorting process as claimed in any one of claims 1 to 5, wherein the clean coal ash compensation model is implemented based on least squares support vector regression.
10. The on-line soft measurement method for clean coal ash content in the lump coal shallow slot sorting process as claimed in any one of claims 1 to 5,
the clean coal yield is measured by a clean coal belt weigher;
the tailing yield is measured by a tailing belt scale;
the qualified medium density is obtained by measuring a densimeter in the qualified medium barrel;
the dual-energy X-ray image of the clean coal is acquired by an industrial X-ray machine arranged above the clean coal belt.
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| PCT/CN2022/138267 WO2023221473A1 (en) | 2022-05-18 | 2022-12-12 | Online soft measurement method for clean coal ash content during lump coal shallow trough sorting process |
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| WO2023221473A1 (en) * | 2022-05-18 | 2023-11-23 | 中国矿业大学 | Online soft measurement method for clean coal ash content during lump coal shallow trough sorting process |
| CN117288783A (en) * | 2023-11-24 | 2023-12-26 | 深圳翱翔锐影科技有限公司 | X-ray-based gangue sorting method, computer equipment and storage medium |
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| CN106546697B (en) * | 2016-10-28 | 2018-11-23 | 中南大学 | A kind of on-line prediction method of bauxite flotation process mash acid alkalinity |
| CN111753912A (en) * | 2020-06-28 | 2020-10-09 | 中国矿业大学 | Coal slime flotation clean coal ash content prediction method based on deep learning |
| CN113191452B (en) * | 2021-05-21 | 2022-03-01 | 中国矿业大学(北京) | Coal ash content online detection system based on deep learning and detection method thereof |
| CN114758196A (en) * | 2022-05-18 | 2022-07-15 | 中国矿业大学 | Clean coal ash content on-line soft measurement method in lump coal shallow slot sorting process |
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| WO2023221473A1 (en) * | 2022-05-18 | 2023-11-23 | 中国矿业大学 | Online soft measurement method for clean coal ash content during lump coal shallow trough sorting process |
| CN117288783A (en) * | 2023-11-24 | 2023-12-26 | 深圳翱翔锐影科技有限公司 | X-ray-based gangue sorting method, computer equipment and storage medium |
| CN117288783B (en) * | 2023-11-24 | 2024-02-20 | 深圳翱翔锐影科技有限公司 | X-ray-based gangue sorting method, computer equipment and storage medium |
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