Coal quality index information determination method and device, electronic equipment and storage medium
By performing linear fitting on multiple indicators of coal samples, a coal quality indicator prediction model was established, which solved the problems of high cost and low efficiency in existing technologies and achieved efficient determination of coal quality indicator information.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CCTEG CHINA COAL RES INST
- Filing Date
- 2023-09-22
- Publication Date
- 2026-05-12
AI Technical Summary
In existing technologies, determining coal quality indicators requires sophisticated instruments and cumbersome experiments, resulting in high costs and low efficiency.
By acquiring indicators such as Gibbs freeness, Oya total expansion, plastic layer thickness, bonding index, softening temperature, flow temperature and solidification temperature of multiple first coal samples, linear fitting is performed to establish a coal quality index prediction model, which is used to predict the target coal quality index of the coal sample to be predicted.
It effectively reduced the cost of determining coal sample index information and improved the efficiency of determining coal quality index information.
Smart Images
Figure CN117405852B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, electronic device and storage medium for determining coal quality index information. Background Technology
[0002] Coal quality parameters (e.g., Gibbs freeness, Oya total expansion, plastic layer thickness, bonding index, softening temperature, flow temperature, solidification temperature, and plasticity temperature) can be used to evaluate the combustion and processing properties of coal, as well as in the coke preparation process. Furthermore, they can be used to assess the suitability and economic value of coal in different application areas.
[0003] In related technologies, it is necessary to rely on precision instruments and tedious experimental calculations to determine the coal quality index information of coal samples.
[0004] In this approach, determining the coal quality indicators of coal samples requires significant costs and is inefficient. Summary of the Invention
[0005] This disclosure aims to at least partially address one of the technical problems in the related art.
[0006] Therefore, the purpose of this disclosure is to propose a method, apparatus, electronic device and storage medium for determining coal quality index information, which can effectively reduce the cost of determining coal sample index information and effectively improve the efficiency of determining coal quality index information.
[0007] The method for determining coal quality index information proposed in the first aspect of this disclosure includes: acquiring multiple first coal samples and coal samples to be predicted; determining the first Gibbs freeness, first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first solidification temperature, and first plasticity temperature of each first coal sample; sequentially performing linear fitting processing on each of the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first solidification temperature, and first plasticity temperature with the first Gibbs freeness to obtain a coal quality index prediction model; and predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
[0008] The apparatus for determining coal quality index information according to the second aspect of this disclosure includes: an acquisition module for acquiring multiple first coal samples and a coal sample to be predicted; a determination module for determining a first Gibbs freeness, a first Oahu-Asia total expansion, a first plastic layer thickness, a first bonding index, a first softening temperature, a first flowing temperature, a first solidification temperature, and a first plasticity temperature for each first coal sample; a processing module for sequentially performing linear fitting processing on each of the first Oahu-Asia total expansion, the first plastic layer thickness, the first bonding index, the first softening temperature, the first flowing temperature, the first solidification temperature, and the first plasticity temperature and the first Gibbs freeness to obtain a coal quality index prediction model; and a prediction module for predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
[0009] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a method for determining coal quality index information as proposed in a first aspect of this disclosure.
[0010] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for determining coal quality index information as proposed in the first aspect of this disclosure.
[0011] The fifth aspect of this disclosure provides a computer program product that, when executed by an instruction processor, performs a method for determining coal quality index information as described in the first aspect of this disclosure.
[0012] The method, apparatus, electronic device, and storage medium for determining coal quality index information proposed in this disclosure have at least the following beneficial effects: By acquiring multiple first coal samples and coal samples to be predicted, and determining the first Gibbs freeness, first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature of each first coal sample, and then sequentially performing linear fitting processing on each of the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature with the first Gibbs freeness to obtain a coal quality index prediction model, and predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model, the cost of determining coal sample index information can be effectively reduced, and the efficiency of determining coal quality index information can be effectively improved.
[0013] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description
[0014] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:
[0015] Figure 1 This is a flowchart illustrating a method for determining coal quality index information according to an embodiment of this disclosure;
[0016] Figure 2A This is a schematic diagram of the function curve of the first coal quality index prediction model proposed in an embodiment of this disclosure;
[0017] Figure 2B A schematic diagram of the function curve of the second coal quality index prediction model proposed in one embodiment of this disclosure;
[0018] Figure 2C This is a schematic diagram of the function curve of the third coal quality index prediction model proposed in an embodiment of this disclosure;
[0019] Figure 2D This is a schematic diagram of the function curve of the fourth coal quality index prediction model proposed in an embodiment of this disclosure;
[0020] Figure 2E A schematic diagram of the function curve of the fifth coal quality index prediction model proposed in one embodiment of this disclosure;
[0021] Figure 2F This is a schematic diagram of the function curve of the sixth coal quality index prediction model proposed in an embodiment of this disclosure;
[0022] Figure 2G This is a schematic diagram of the function curve of the seventh coal quality index prediction model proposed in an embodiment of this disclosure;
[0023] Figure 3 This is a flowchart illustrating a method for determining coal quality index information according to another embodiment of this disclosure;
[0024] Figure 4 This is a schematic diagram of the structure of a device for determining coal quality index information according to an embodiment of this disclosure;
[0025] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation
[0026] Embodiments of this disclosure are described in detail below, with examples of embodiments illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.
[0027] Figure 1 This is a flowchart illustrating a method for determining coal quality index information according to an embodiment of this disclosure.
[0028] It should be noted that the execution subject of the coal quality index information determination method in this embodiment is the coal quality index information determination device. This device can be implemented by software and / or hardware. The device can be configured in an electronic device, which may include, but is not limited to, a terminal, a server, etc.
[0029] like Figure 1 As shown, the method for determining this coal quality index information includes:
[0030] S101: Obtain multiple first coal samples and coal samples to be predicted.
[0031] Among them, the multiple coal samples obtained in the initial stage of the implementation of the method for determining coal quality index information are the first coal samples.
[0032] Among them, the coal sample whose corresponding coal quality index information is to be determined is the coal sample to be predicted.
[0033] Among them, coal quality indicators can include, for example, the total expansion of the coal sample, the thickness of the plastic layer, the bonding index, the softening temperature, the flow temperature, the solidification temperature, and the plasticity temperature.
[0034] In this embodiment of the disclosure, multiple initial coal samples of different coal types may be selected. For example, 16 coal samples of 6 coal types may be selected, including 2 first coal samples of fat coal type, 4 first coal samples of coking coal type, 6 first coal samples of 1 / 3 coking coal type, 2 first coal samples of lean coal type, 1 first coal sample of weakly caking coal type, and 1 first coal sample of 1 / 2 medium caking coal type. There is no limitation on this.
[0035] In this embodiment of the disclosure, in a coking configuration business scenario, any coal sample whose coal quality index information needs to be determined is selected as the coal sample to be predicted. Then, the coal quality index information of the coal sample to be predicted can be determined by combining the first coal sample. For details, please refer to the subsequent embodiments, which will not be repeated here.
[0036] S102: Determine the first Gibbs freeness, first Oya total expansion, first plastic layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature for each first coal sample.
[0037] In this embodiment of the disclosure, the first caking index (G) of each first coal sample can be determined using a caking index tester according to GB / T 5447 "Method for Determination of Caking Index of Bituminous Coal". R.I (%), using a plastic layer index tester, according to GB / T479 "Method for Determination of Plastic Layer Index of Bituminous Coal", the thickness of the first plastic layer (Y value (mm)) of each first coal sample was determined; using a Gibbs flowability tester, according to GB / T 25213 "Determination of Plasticity of Coal by Constant Torque Gibbs Plasticity Tester", the first Gibbs flowability lgMF (dd / min) of each first coal sample was determined; using an O-A expansion tester, according to GB / T 5450 "Test of O-A Expansion Gauge for Bituminous Coal", the first O-A expansion degree a+b (%) of each first coal sample was determined.
[0038] In this embodiment of the disclosure, the first softening temperature T of each first coal sample can also be determined based on the methods of Differential Thermal Analysis (DTA), Thermogravimetric Analysis (TGA), or Differential Scanning Calorimetry (DSC). s (°C), First flow temperature T max (°C), First curing temperature T r (°C) and the first plastic temperature ΔT (°C).
[0039] In this embodiment of the disclosure, the measurement results of the first Gibbs freeness, first Oerlikon total expansion, first plastic layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature of the 16 first coal samples are shown in Table 1:
[0040] Table 1
[0041]
[0042] S103: Linear fitting is performed on each of the following parameters in sequence: first Oya total expansion, first gel layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature, and first plastic temperature, along with the first Gibbs flowability, to obtain a coal quality index prediction model.
[0043] In this embodiment of the disclosure, after determining the first Gibbs flowability, first Oerlikon total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature for each first coal sample, a linear fitting process can be performed on each of the first Oerlikon total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature with the first Gibbs flowability to obtain a coal quality index prediction model.
[0044] Optionally, in some embodiments, a linear fitting process is performed sequentially on each of the first Oaurya's total expansion, the first gel layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first curing temperature, and the first plasticity temperature, and the first Gibbs flow rate, to obtain a coal quality index prediction model. Alternatively, a polynomial regression process can be performed sequentially on each of the first Oaurya's total expansion, the first gel layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first curing temperature, and the first plasticity temperature, and the first Gibbs flow rate, to obtain a first coal quality index prediction model, a second coal quality index prediction model, a third coal quality index prediction model, a fourth coal quality index prediction model, a fifth coal quality index prediction model, a sixth coal quality index prediction model, and a seventh coal quality index prediction model. The first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model, and the seventh coal quality index prediction model are collectively used as the coal quality index prediction model.
[0045] Among them, the first coal quality index prediction model is used to predict the total expansion of the Oya, the second coal quality index prediction model is used to predict the thickness of the plastic layer, the third coal quality index prediction model is used to predict the bonding index, the fourth coal quality index prediction model is used to predict the softening temperature, the fifth coal quality index prediction model is used to predict the flow temperature, the sixth coal quality index prediction model is used to predict the solidification temperature, and the seventh coal quality index prediction model is used to predict the plasticity temperature.
[0046] The linear fitting process can include first-order curve regression and second-order curve regression, and there are no restrictions on this.
[0047] In this embodiment of the disclosure, the lgMF of the first coal sample may be subjected to quadratic curve regression with a+b, Y, and GR.I respectively to obtain the first coal quality index prediction model, the second coal quality index prediction model, and the third coal quality index prediction model.
[0048] The first coal quality index prediction model can be expressed as: a+b=12.248(lgMF)2-8.460lgMF+14.988.
[0049] The second coal quality index prediction model can be expressed as: Y = 1.332(lgMF)2 - 1.490lgMF + 9.763.
[0050] The third coal quality index prediction model can be expressed as: G R.I =-2.694(lgMF)2+21.055lgMF+48.584.
[0051] In this embodiment of the disclosure, the schematic diagram of the function curve of the first coal quality index prediction model can be shown as follows: Figure 2A As shown: The schematic diagram of the function curve of the second coal quality index prediction model can be seen as follows. Figure 2B As shown in the diagram, the function curve of the third coal quality index prediction model can be illustrated as follows: Figure 2C As shown, see Figure 2A , Figure 2B and Figure 2C , Figure 2A This is a schematic diagram of the function curve of the first coal quality index prediction model proposed in an embodiment of this disclosure. Figure 2B A schematic diagram of the function curve of the second coal quality index prediction model proposed in one embodiment of this disclosure. Figure 2C This is a schematic diagram of the function curve of the third coal quality index prediction model proposed in one embodiment of this disclosure.
[0052] In this embodiment of the disclosure, the lgMF of the first coal sample may be subjected to quadratic curve regression with each of the first softening temperature, the first flow temperature, the first solidification temperature and the first plastic temperature to obtain the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model and the seventh coal quality index prediction model.
[0053] The fourth coal quality index prediction model can be expressed as: Ts=-1.096(lgMF)2-8.079lgMF+443.395.
[0054] The fifth coal quality index prediction model can be expressed as: T max =-0.966(lgMF)2+2.810lgMF+454.660.
[0055] The sixth coal quality index prediction model can be expressed as: T f =0.455(lgMF)2+4.227lgMF+473.109.
[0056] The seventh coal quality index prediction model can be expressed as: △T=1.551(lgMF)2+12.307lgMF+29.714.
[0057] In this embodiment of the disclosure, the schematic diagram of the function curve of the fourth coal quality index prediction model can be shown as follows: Figure 2D As shown: The schematic diagram of the function curve of the fifth coal quality index prediction model can be seen as follows. Figure 2E As shown in the diagram, the function curve of the sixth coal quality index prediction model can be illustrated as follows: Figure 2F As shown, the function curve diagram of the seventh coal quality index prediction model can be illustrated as follows: Figure 2G As shown, see Figure 2D , Figure 2E , Figure 2F and Figure 2G , Figure 2D This is a schematic diagram of the function curve of the fourth coal quality index prediction model proposed in one embodiment of this disclosure. Figure 2E A schematic diagram of the function curve of the fifth coal quality index prediction model proposed in one embodiment of this disclosure. Figure 2F This is a schematic diagram of the function curve of the sixth coal quality index prediction model proposed in one embodiment of this disclosure. Figure 2G This is a schematic diagram of the function curve of the seventh coal quality index prediction model proposed in an embodiment of this disclosure.
[0058] S104: Predict the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
[0059] In this embodiment of the present disclosure, after performing linear fitting processing on each of the first Oya total expansion, the first gel layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first curing temperature, and the first plastic temperature and the first Gibbs flowability to obtain a coal quality index prediction model, the target coal quality index of the coal sample to be predicted can be predicted based on the coal quality index prediction model.
[0060] The target coal quality indicators include: second Oya total expansion degree, second plastic layer thickness, second bonding index, second softening temperature, second flow temperature, second solidification temperature, and second plasticity temperature.
[0061] Optionally, in some embodiments, predicting the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model may involve obtaining the second Gibbs mobility of the coal sample to be predicted and substituting the second Gibbs mobility into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted.
[0062] In other words, in this embodiment of the present disclosure, after measuring the second Gibbs flowability of the coal sample to be predicted, the second Gibbs flowability can be sequentially substituted into a coal quality index prediction model, a second coal quality index prediction model, a third coal quality index prediction model, a fourth coal quality index prediction model, a fifth coal quality index prediction model, a sixth coal quality index prediction model, and a seventh coal quality index prediction model to obtain the second Oerlikon total expansion, the second plastic layer thickness, the second bonding index, the second softening temperature, the second flow temperature, the second solidification temperature, and the second plasticity temperature. Thus, multiple target coal quality indicators of the coal sample to be predicted can be determined by measuring the second Gibbs flowability of the coal sample to be predicted, thereby effectively reducing the cost of measuring the coal quality indicators of the coal sample to be predicted and effectively improving the efficiency of determining coal quality indicator information.
[0063] In this embodiment of the disclosure, by acquiring multiple first coal samples and coal samples to be predicted, and determining the first Gibbs freeness, first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature for each first coal sample, and then sequentially performing linear fitting processing on each of the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature with the first Gibbs freeness, a coal quality index prediction model is obtained. Based on the coal quality index prediction model, the target coal quality index of the coal sample to be predicted is predicted. This can effectively reduce the cost of determining coal sample index information and effectively improve the efficiency of determining coal quality index information.
[0064] Figure 3 This is a flowchart illustrating a method for determining coal quality index information according to another embodiment of this disclosure.
[0065] like Figure 3 As shown, the method for determining this coal quality index information includes:
[0066] S301: Obtain multiple first coal samples and coal samples to be predicted.
[0067] S302: Determine the first Gibbs flowability, first Oya total expansion, first plastic layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature for each first coal sample.
[0068] S303: Linear fitting is performed sequentially on each of the first Oya total expansion, first gel layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature and first plastic temperature and the first Gibbs flowability to obtain a coal quality index prediction model.
[0069] S304: Predict the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
[0070] For a detailed description of S301-S304, please refer to the above embodiments, which will not be repeated here.
[0071] S305: Based on the target coal quality indicators, the first Oya total expansion degree, the first plastic layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first solidification temperature, and the first plasticity temperature of the first coal sample of the first coal sample type, determine the target coal sample type of the second coal sample.
[0072] In this embodiment of the disclosure, each first coal sample has a corresponding coal sample type. Referring to the above embodiments, the first coal sample type may include: fat coal type, coking coal type, 1 / 3 coking coal type, lean coal type, weakly caking coal type, and 1 / 2 medium caking coal type.
[0073] Optionally, in some embodiments, the target coal sample type of the second coal sample is determined based on the target coal quality indicators, namely, the first total expansion of the first coal sample of the first coal sample type, the first plastic layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first solidification temperature, and the first plasticity temperature. This can be achieved by weighted summation of the first total expansion of the first coal sample of the first coal sample type to obtain a first sum, and by weighted summation of the second total expansion of the first coal sample type, the second plastic layer thickness, the second bonding index, the second softening temperature, the second flow temperature, the second solidification temperature, and the second plasticity temperature to obtain a second sum. If the difference between the first sum and the second sum is less than a difference threshold, the target coal sample type of the second coal sample is determined to be the first coal sample type.
[0074] The first coal sample type can be any coal sample type.
[0075] In other words, in this embodiment of the present disclosure, a first sum can be obtained by weighting and summing the first total expansion degree, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plastic temperature of a first coal sample of a certain coal sample type based on a preset first weighting coefficient. Then, a second sum can be obtained by weighting and summing the second total expansion degree, second plastic layer thickness, second bonding index, second softening temperature, second flow temperature, second curing temperature, and second plastic temperature based on a preset second weighting coefficient. Finally, the first sum and the second sum are compared, and if the difference between the first sum and the second sum is less than the difference threshold, the target coal sample type of the second coal sample is determined to be the first coal sample type.
[0076] In this embodiment of the disclosure, by acquiring multiple first coal samples and coal samples to be predicted, and determining the first Gibbs freeness, first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature of each first coal sample, and then sequentially performing linear fitting processing on each of the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature with the first Gibbs freeness, a coal quality index prediction model is obtained. Based on the coal quality index prediction model, the target coal quality index of the coal sample to be predicted is predicted. This can effectively reduce the cost of determining coal sample index information and effectively improve the efficiency of determining coal quality index information. Then, based on the target coal quality index, the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature of the first coal sample of the first coal sample type, the target coal sample type of the second coal sample is accurately determined.
[0077] Figure 4 This is a schematic diagram of the structure of a device for determining coal quality index information according to an embodiment of this disclosure.
[0078] like Figure 4 As shown, the coal quality index information determination device 40 includes:
[0079] The acquisition module 401 is used to acquire multiple first coal samples and coal samples to be predicted;
[0080] The determination module 402 is used to determine the first Gibbs flowability, the first Oerlikon total expansion, the first plastic layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first solidification temperature, and the first plasticity temperature for each first coal sample.
[0081] The processing module 403 is used to sequentially perform linear fitting processing on each of the first Oya total expansion, the first gel layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first curing temperature and the first plasticity temperature and the first Gibbs flow rate, so as to obtain a coal quality index prediction model.
[0082] The prediction module 404 is used to predict the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model.
[0083] In some embodiments of this disclosure, the processing module 403 is further configured to:
[0084] Polynomial regression was performed sequentially on each of the following: first total expansion of Oya, first gel layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature, along with the first Gibbs flowability, to obtain the first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model, and the seventh coal quality index prediction model. The first coal quality index prediction model is used to predict the total expansion of Oya; the second coal quality index prediction model is used to predict the gel layer thickness; the third coal quality index prediction model is used to predict the adhesion index; the fourth coal quality index prediction model is used to predict the softening temperature; the fifth coal quality index prediction model is used to predict the flow temperature; the sixth coal quality index prediction model is used to predict the curing temperature; and the seventh coal quality index prediction model is used to predict the plasticity temperature.
[0085] The first, second, third, fourth, fifth, sixth, and seventh coal quality index prediction models are collectively used as the coal quality index prediction models.
[0086] In some embodiments of this disclosure, the prediction module 404 is further configured to:
[0087] Obtain the second Gibbs mobility of the coal sample to be predicted;
[0088] The second Gibbs flowability is substituted into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted. The target coal quality index includes: second Oya total expansion, second plastic layer thickness, second bonding index, second softening temperature, second flow temperature, second solidification temperature, and second plasticity temperature.
[0089] In some embodiments of this disclosure, each first coal sample has a corresponding coal sample type; wherein, the prediction module 404 is further configured to:
[0090] After substituting the second Gibbs flowability into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted, the target coal sample type of the second coal sample is determined based on the target coal quality index, the first Oya total expansion degree, the first plastic layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first solidification temperature, and the first plasticity temperature of the first coal sample of the first coal sample type.
[0091] In some embodiments of this disclosure, the prediction module 404 is further configured to:
[0092] The first total expansion degree, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first solidification temperature and first plasticity temperature of the first coal sample of the first coal sample type are weighted and summed to obtain the first sum value;
[0093] The second total expansion, the second adhesive layer thickness, the second adhesion index, the second softening temperature, the second flow temperature, the second curing temperature, and the second plasticity temperature are weighted and summed to obtain the second sum value.
[0094] If the difference between the first sum and the second sum is less than the difference threshold, the target coal sample type of the second coal sample is determined to be the first coal sample type.
[0095] With the above Figures 1 to 3 Corresponding to the method for determining coal quality index information provided in the embodiments, this disclosure also provides a device for determining coal quality index information. Since the device for determining coal quality index information provided in the embodiments of this disclosure is similar to the one described above... Figures 1 to 3 The method for determining coal quality index information provided in the embodiments corresponds to the method for determining coal quality index information provided in the embodiments of this disclosure. Therefore, the implementation method for determining coal quality index information is also applicable to the device for determining coal quality index information provided in the embodiments of this disclosure, and will not be described in detail in the embodiments of this disclosure.
[0096] In this embodiment, by acquiring multiple first coal samples and coal samples to be predicted, and determining the first Gibbs freeness, first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature for each first coal sample, and then sequentially performing linear fitting processing on each of the first Ouar-Asia total expansion, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first curing temperature, and first plasticity temperature with the first Gibbs freeness, a coal quality index prediction model is obtained. Based on the coal quality index prediction model, the target coal quality index of the coal sample to be predicted is predicted. This can effectively reduce the cost of determining coal sample index information and effectively improve the efficiency of determining coal quality index information.
[0097] To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for determining coal quality index information as proposed in the foregoing embodiments of this disclosure.
[0098] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for determining coal quality index information as proposed in the foregoing embodiments of this disclosure.
[0099] To implement the above embodiments, this disclosure also proposes a computer program product, which, when executed by an instruction processor, performs a method for determining coal quality index information as proposed in the foregoing embodiments of this disclosure.
[0100] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0101] like Figure 5 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).
[0102] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.
[0103] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0104] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 5 Not shown; usually referred to as a "hard drive".
[0105] although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.
[0106] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.
[0107] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0108] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the method for determining coal quality index information mentioned in the foregoing embodiments.
[0109] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0110] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
[0111] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.
[0112] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.
[0113] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0114] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.
[0115] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0116] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.
[0117] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0118] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.
Claims
1. A method for determining coal quality index information, characterized in that, The method includes: Obtain multiple primary coal samples and coal samples to be predicted; Determine the first Gibbs flowability, first Oya total expansion, first plastic layer thickness, first adhesion index, first softening temperature, first flow temperature, first solidification temperature, and first plasticity temperature for each of the first coal samples. The first total expansion of the first Oya, the first gel layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first curing temperature and the first plasticity temperature, and the first Gibbs flow rate are sequentially subjected to polynomial regression processing to obtain the first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model and the seventh coal quality index prediction model. The first coal quality index prediction model is used to predict the total expansion of the Oya coal. The first coal quality index prediction model is expressed as: a+b=12.248(lgMF)²-8.460 lgMF+14.988; The second coal quality index prediction model is used to predict the thickness of the plastic layer. The second coal quality index prediction model is expressed as: Y=1.332(lgMF)²-1.490lgMF+9.763; The third coal quality index prediction model is used to predict the bonding index. The third coal quality index prediction model is expressed as: G R.I =-2.694(lgMF)²+21.055lgMF+48.584; The fourth coal quality index prediction model is used to predict the softening temperature. The fourth coal quality index prediction model is expressed as: Ts = -1.096(lgMF)² - 8.079 lgMF + 443.395; The fifth coal quality index prediction model is used to predict the flow temperature. The fifth coal quality index prediction model is expressed as: T max =-0.966(lgMF)²+2.810 lgMF+454.660; The sixth coal quality index prediction model is used to predict the solidification temperature. The sixth coal quality index prediction model is expressed as: T f =0.455(lgMF)²+4.227 lgMF+473.109; The seventh coal quality index prediction model is used to predict the plasticity temperature. The seventh coal quality index prediction model is expressed as: △T=1.551(lgMF)²+12.307lgMF+29.714; The first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model, and the seventh coal quality index prediction model are collectively used as the coal quality index prediction model. The target coal quality index of the coal sample to be predicted is predicted based on the coal quality index prediction model.
2. The method as described in claim 1, characterized in that, The prediction of the target coal quality index of the coal sample to be predicted based on the coal quality index prediction model includes: Obtain the second Gibbs mobility of the coal sample to be predicted; The second Gibbs flowability is substituted into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted. The target coal quality index includes: second Oya total expansion, second plastic layer thickness, second bonding index, second softening temperature, second flow temperature, second solidification temperature, and second plasticity temperature.
3. The method as described in claim 2, characterized in that, Each of the first coal samples has a corresponding coal sample type; The step of substituting the second Gibbs mobility into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted further includes: Based on the target coal quality indicators, the first Oya total expansion degree, first plastic layer thickness, first bonding index, first softening temperature, first flow temperature, first solidification temperature, and first plasticity temperature of the first coal sample of the first coal sample of the first coal sample type, the target coal sample type of the second coal sample is determined.
4. The method as described in claim 3, characterized in that, The step of determining the target coal sample type of the second coal sample based on the target coal quality indicators, the first Oya total expansion degree, the first plastic layer thickness, the first bonding index, the first softening temperature, the first flow temperature, the first solidification temperature, and the first plasticity temperature of the first coal sample of the first coal sample type, includes: The first total expansion degree, first plastic layer thickness, first adhesion index, first softening temperature, first flow temperature, first curing temperature and first plasticity temperature of the first coal sample of the first coal sample type are weighted and summed to obtain the first sum value. The second total expansion of the second Oya, the second adhesive layer thickness, the second adhesion index, the second softening temperature, the second flow temperature, the second curing temperature and the second plasticity temperature are weighted and summed to obtain the second sum value; If the difference between the first sum and the second sum is less than the difference threshold, the target coal sample type of the second coal sample is determined to be the first coal sample type.
5. A device for determining coal quality index information, characterized in that, include: The acquisition module is used to acquire multiple first coal samples and coal samples to be predicted; The determination module is used to determine the first Gibbs freeness, the first Oerlikon total expansion, the first plastic layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first curing temperature, and the first plasticity temperature for each of the first coal samples. The processing module is used to sequentially perform polynomial regression processing on each of the first Oya's total expansion, the first gel layer thickness, the first adhesion index, the first softening temperature, the first flow temperature, the first curing temperature, and the first plasticity temperature, along with the first Gibbs flowability, to obtain a first coal quality index prediction model, a second coal quality index prediction model, a third coal quality index prediction model, a fourth coal quality index prediction model, a fifth coal quality index prediction model, a sixth coal quality index prediction model, and a seventh coal quality index prediction model. The first coal quality index prediction model is used to predict the Oya's total expansion, and is expressed as: a+b=12.248(lgMF)²-8.460lgMF+14.988; the second coal quality index prediction model is used to predict the gel layer thickness, and is expressed as: Y=1.332(lgMF)²-1.490lgMF+9.763; the third coal quality index prediction model is used to predict the adhesion index, and is expressed as: G R.I =-2.694(lgMF)²+21.055lgMF+48.584; The fourth coal quality index prediction model is used to predict the softening temperature, and the fourth coal quality index prediction model is expressed as: Ts=-1.096(lgMF)²-8.079 lgMF+443.395; The fifth coal quality index prediction model is used to predict the flow temperature, and the fifth coal quality index prediction model is expressed as: T max =-0.966(lgMF)²+2.810 lgMF+454.660; The sixth coal quality index prediction model is used to predict the solidification temperature, and the sixth coal quality index prediction model is expressed as: T f =0.455(lgMF)²+4.227lgMF+473.109; The seventh coal quality index prediction model is used to predict the plastic temperature, and the seventh coal quality index prediction model is expressed as: ΔT=1.551(lgMF)²+12.307lgMF+29.714; The first coal quality index prediction model, the second coal quality index prediction model, the third coal quality index prediction model, the fourth coal quality index prediction model, the fifth coal quality index prediction model, the sixth coal quality index prediction model, and the seventh coal quality index prediction model are used together as the coal quality index prediction model; The prediction module is used to predict the target coal quality indicators of the coal sample to be predicted based on the coal quality indicator prediction model.
6. The apparatus as claimed in claim 5, characterized in that, The prediction module is also used for: Obtain the second Gibbs mobility of the coal sample to be predicted; The second Gibbs flowability is substituted into the coal quality index prediction model to determine the target coal quality index of the coal sample to be predicted. The target coal quality index includes: second Oya total expansion, second plastic layer thickness, second bonding index, second softening temperature, second flow temperature, second solidification temperature, and second plasticity temperature.
7. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
8. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.