Coal slurry concentration detection method, device, equipment and computer readable storage medium

Abstract

The embodiments of the present disclosure provide a method, device, equipment and computer-readable storage medium for detecting coal slurry concentration. The method includes: obtaining a first coal slurry concentration associated parameter, wherein the first coal slurry concentration associated parameter includes: coal mill parameters, coal slurry tank parameters and coal slurry pipeline parameters; obtaining the actual measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter; training a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model; inputting the second coal slurry concentration associated parameter into the target coal slurry concentration model to obtain a coal slurry concentration detection value. In this way, the coal slurry concentration model can be trained to accurately detect the coal slurry concentration detection value, thereby avoiding the need for manual repeated detection of coal slurry concentration, and avoiding the need to use expensive measuring instruments to achieve coal slurry concentration detection, thereby improving the flexibility and accuracy of coal slurry concentration detection.

Description

Technical Field

[0001] The present disclosure relates to the field of coal slurry, and in particular to the field of coal slurry concentration technology. Background Art

[0002] During the production process of the gasifier, the composition of its process gas is closely related to the concentration of coal slurry. When the concentration of coal slurry fluctuates, the ratio of oxygen to dry coal in the slurry changes, resulting in fluctuations in the temperature of the gasifier and the composition of the process gas. At present, the concentration of coal slurry is manually detected with a detection cycle of 2 hours. The detection frequency is high and there is a lag. The amount of human operation is large and the real-time adjustment is poor.

[0003] Therefore, online detection of water-coal slurry concentration is a difficult problem in the gasification industry. Coal slurry concentration measurement also uses measurement methods such as radiation, light waves and sound waves, but these measuring instruments are expensive, have high maintenance costs, and the measurement accuracy cannot meet the needs. Summary of the invention

[0004] The present invention provides a coal slurry concentration detection method, device, equipment and storage medium.

[0005] According to a first aspect of the present disclosure, a method for detecting coal slurry concentration is provided. The method comprises:

[0006] Acquire first coal slurry concentration-related parameters, wherein the first coal slurry concentration-related parameters include: coal mill parameters, coal slurry tank parameters, and coal slurry pipeline parameters;

[0007] Obtaining a real measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter;

[0008] Training a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model;

[0009] The second coal slurry concentration associated parameter is input into the target coal slurry concentration model to obtain a coal slurry concentration detection value.

[0010] According to the above aspects and any possible implementation, there is further provided an implementation, wherein the coal mill parameters include: water flow entering the coal mill and coal weight entering the coal mill;

[0011] The coal slurry tank parameters include: coal slurry tank temperature;

[0012] The coal slurry pipeline parameters include: the temperature on the coal slurry pipeline, the horizontal pressure difference on the coal slurry pipeline, and the vertical pressure difference on the coal slurry pipeline.

[0013] According to the above aspects and any possible implementation, an implementation is further provided, wherein the coal mill parameter is an inlet parameter of the coal mill, wherein one end of the coal mill is connected to a water inlet pipe and a coal pipe, and the other end is connected to the inlet of the first coal slurry tank, and a water flow meter is installed on the water inlet pipe for detecting the water flow, and a feed weighing machine is installed on the coal pipe for detecting the weight of the coal;

[0014] The coal slurry tank temperature is the temperature detected by a first temperature sensor in a first coal slurry tank, wherein the inlet of the first coal slurry tank is connected to the coal mill, and the outlet is connected to a coal slurry pump;

[0015] The coal slurry pipeline parameters are detection parameters on the coal slurry pipeline, wherein the coal slurry pipeline includes a first horizontal coal slurry pipeline, a vertical coal slurry pipeline and a second horizontal coal slurry pipeline, one end of the first horizontal coal slurry pipeline is connected to the coal slurry pump, and the other end is connected to the vertical coal slurry pipeline, one end of the vertical coal slurry pipeline is connected to the first horizontal coal slurry pipeline, and the other end is connected to the second horizontal coal slurry pipeline, one end of the second horizontal coal slurry pipeline is connected to the vertical coal slurry pipeline, and the other end is connected to the second coal slurry tank, and a first pressure gauge is installed on the first horizontal coal slurry pipeline to detect the horizontal pressure difference, a second pressure gauge is installed on the vertical coal slurry pipeline to detect the vertical pressure difference, and a second temperature sensor is installed on the second horizontal coal slurry pipeline to detect the temperature in the second horizontal coal slurry pipeline.

[0016] According to the above aspects and any possible implementation, an implementation is further provided, wherein the preset coal slurry concentration model is: C = k1*PDI1+k2*PDI2+k3*T1+k4*T2+k5*F1+k6*F2+k7

[0017] Among them, PDI1 represents the vertical pressure difference on the coal slurry pipeline, PDI2 represents the horizontal pressure difference on the coal slurry pipeline, T1 represents the coal slurry tank temperature, T2 represents the temperature on the coal slurry pipeline, F1 represents the water flow entering the coal mill, F2 represents the weight of coal entering the coal mill, and k1~k7 represent coal slurry coefficients.

[0018] According to the above aspects and any possible implementation, an implementation is further provided, wherein the actual measured coal slurry concentration is obtained by the following steps:

[0019] sampling the coal slurry on the coal slurry pipeline;

[0020] The coal slurry concentration meter is used to analyze the concentration of the obtained coal slurry samples.

[0021] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the preset coal slurry concentration model is trained according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model, including:

[0022] The first coal slurry concentration associated parameter and the actual measured coal slurry concentration are input into the preset coal slurry concentration model, and the coal slurry coefficient corresponding to the first coal slurry concentration associated parameter in the preset coal slurry concentration model is modified to obtain the target coal slurry concentration model.

[0023] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0024] The first coal slurry concentration associated parameter and the corresponding actual measured coal slurry concentration are screened out from a plurality of groups of candidate coal slurry concentration associated parameters and the corresponding measured coal slurry concentrations.

[0025] According to a second aspect of the present disclosure, a coal slurry concentration detection device is provided. The device comprises:

[0026] A first acquisition module is used to acquire first coal slurry concentration related parameters, wherein the first coal slurry concentration related parameters include: coal mill parameters, coal slurry tank parameters and coal slurry pipeline parameters;

[0027] A second acquisition module, used for acquiring the actual measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter;

[0028] A training module, configured to train a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model;

[0029] The third acquisition module is used to input the second coal slurry concentration associated parameter into the target coal slurry concentration model to obtain the coal slurry concentration detection value.

[0030] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0032] In the present disclosure, an accurate target coal slurry concentration model can be obtained by obtaining the first coal slurry concentration associated parameter and using the measured corresponding actual measured coal slurry concentration to train the preset coal slurry concentration model. In this way, after obtaining the second coal slurry concentration associated parameter, the coal slurry concentration detection value can be accurately detected by inputting it into the target coal slurry concentration model, thereby avoiding the need for manual repeated detection of coal slurry concentration, and avoiding the need to use expensive measuring instruments to realize coal slurry concentration detection, thereby improving the flexibility and accuracy of coal slurry concentration detection.

[0033] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0035] Figure 1 A flow chart of a method for detecting coal slurry concentration according to an embodiment of the present disclosure is shown;

[0036] Figure 2 A system diagram of a coal slurry concentration detection device according to an embodiment of the present disclosure is shown;

[0037] Figure 3 A block diagram of a coal slurry concentration detection device according to an embodiment of the present disclosure is shown;

[0038] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0040] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0041] Figure 1 A flow chart of a method 100 for detecting coal slurry concentration according to an embodiment of the present disclosure is shown. The method 100 may include:

[0042] Step 110, obtaining first coal slurry concentration-related parameters, wherein the first coal slurry concentration-related parameters include: coal mill parameters, coal slurry tank parameters, and coal slurry pipeline parameters;

[0043] Step 120, obtaining the actual measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter;

[0044] Step 130, training a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model;

[0045] Step 140, input the second coal slurry concentration associated parameter into the target coal slurry concentration model to obtain a coal slurry concentration detection value. The first coal slurry concentration associated parameter and the second coal slurry concentration associated parameter are both coal slurry concentration associated parameters, the difference being that the first coal slurry concentration associated parameter is a coal slurry concentration associated parameter closely related to the coal slurry concentration detected in the coal slurry concentration model training phase, while the second coal slurry concentration associated parameter is a coal slurry concentration associated parameter closely related to the coal slurry concentration detected in the coal slurry concentration model application phase.

[0046] By obtaining the first coal slurry concentration associated parameter and using the measured corresponding actual measured coal slurry concentration to train the preset coal slurry concentration model, an accurate target coal slurry concentration model can be obtained. In this way, after obtaining the second coal slurry concentration associated parameter, the coal slurry concentration detection value can be accurately detected by inputting it into the target coal slurry concentration model, thereby avoiding the need for manual repeated detection of coal slurry concentration, and avoiding the need to use expensive measuring instruments to realize coal slurry concentration detection, thereby improving the flexibility and accuracy of coal slurry concentration detection.

[0047] In some embodiments, the coal mill parameters include: water flow entering the coal mill and coal weight entering the coal mill;

[0048] The coal slurry tank parameters include: coal slurry tank temperature;

[0049] The coal slurry pipeline parameters include: the temperature on the coal slurry pipeline, the horizontal pressure difference on the coal slurry pipeline, and the vertical pressure difference on the coal slurry pipeline.

[0050] The coal mill is used to crush the coal and then send the crushed coal to the first coal slurry tank. Therefore, the amount of water and coal entering the coal mill will affect the concentration of the coal slurry produced by the coal mill, and the temperature of the coal slurry tank may affect the viscosity of the coal slurry and then affect the flow rate, resulting in different coal slurry concentrations. The pressure difference on the coal slurry pipeline actually reflects the density of the coal slurry, and is therefore closely related to the coal slurry concentration. Therefore, these coal slurry concentration-related parameters that are closely related to the coal slurry concentration can be used to accurately train the coal slurry concentration model.

[0051] In some embodiments, the coal mill parameters are inlet parameters of the coal mill, wherein one end of the coal mill is connected to a water inlet pipe and a coal pipe, and the other end is connected to the inlet of the first coal slurry tank, and a water flow meter is installed on the water inlet pipe to detect the water flow, and a feed weighing machine is installed on the coal pipe to detect the weight of the coal;

[0052] The coal slurry tank temperature is the temperature detected by the first temperature sensor in the first coal slurry tank, wherein the inlet of the first coal slurry tank is connected to the coal mill, and the outlet is connected to the coal slurry pump; the first coal slurry tank is a small coal slurry tank compared to the second coal slurry tank, and the second coal slurry tank is a large coal slurry tank.

[0053] The coal slurry pipeline parameters are detection parameters on the coal slurry pipeline, wherein the coal slurry pipeline includes a first horizontal coal slurry pipeline, a vertical coal slurry pipeline and a second horizontal coal slurry pipeline, one end of the first horizontal coal slurry pipeline is connected to the coal slurry pump, and the other end is connected to the vertical coal slurry pipeline, one end of the vertical coal slurry pipeline is connected to the first horizontal coal slurry pipeline, and the other end is connected to the second horizontal coal slurry pipeline, one end of the second horizontal coal slurry pipeline is connected to the vertical coal slurry pipeline, and the other end is connected to the second coal slurry tank, and a first pressure gauge is installed on the first horizontal coal slurry pipeline to detect the horizontal pressure difference, a second pressure gauge is installed on the vertical coal slurry pipeline to detect the vertical pressure difference, and a second temperature sensor is installed on the second horizontal coal slurry pipeline to detect the temperature in the second horizontal coal slurry pipeline.

[0054] The installation and connection of instruments used to measure parameters related to coal slurry concentration are as follows: Figure 2 As shown, through Figure 2 The coal slurry concentration-related parameters can be accurately detected, and these coal slurry concentration-related parameters can be used for model training to obtain a target coal slurry concentration model that can accurately predict the coal slurry concentration.

[0055] In some embodiments, the preset coal slurry concentration model is: C = k1*PDI1+k2*PDI2+k3*T1+k4*T2+k5*F1+k6*F2+k7

[0056] Among them, PDI1 represents the vertical pressure difference on the coal slurry pipeline, PDI2 represents the horizontal pressure difference on the coal slurry pipeline, T1 represents the coal slurry tank temperature, T2 represents the temperature on the coal slurry pipeline, F1 represents the water flow entering the coal mill, F2 represents the weight of coal entering the coal mill, and k1~k7 represent coal slurry coefficients.

[0057] In the preset coal slurry concentration model, k1~k7 represent coal slurry coefficients, which are used to be repeatedly modified during the model training process until the difference between the predicted coal slurry concentration corresponding to the first coal slurry concentration associated parameter output by the model and the actual measured coal slurry concentration is less than the preset threshold value. The model training is stopped, indicating that the target coal slurry concentration model has been trained and the output concentration is relatively accurate.

[0058] In some embodiments, the actual measured coal slurry concentration is obtained by the following steps:

[0059] sampling the coal slurry on the coal slurry pipeline;

[0060] The coal slurry concentration meter is used to analyze the concentration of the obtained coal slurry samples.

[0061] There is a sampling port on the coal slurry pipeline. By sampling the coal slurry from the sampling port of the coal slurry pipeline, a special coal slurry concentration meter can be used to measure the measured coal slurry concentration corresponding to the first coal slurry concentration correlation parameter as the actual measured coal slurry concentration, thereby ensuring the training accuracy of the preset coal slurry concentration model.

[0062] In some embodiments, the training of a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model includes:

[0063] The first coal slurry concentration associated parameter and the actual measured coal slurry concentration are input into the preset coal slurry concentration model, and the coal slurry coefficient corresponding to the first coal slurry concentration associated parameter in the preset coal slurry concentration model is modified to obtain the target coal slurry concentration model.

[0064] After the first coal slurry concentration associated parameter is input into the preset coal slurry concentration model, the predicted coal slurry concentration corresponding to the first coal slurry concentration associated parameter can be obtained, and then the predicted coal slurry concentration is compared with the actual measured coal slurry concentration. If the difference is less than the preset threshold, it means that the target coal slurry concentration model has been trained and the output concentration is relatively accurate, and the model training can be stopped.

[0065] In some embodiments, the method further comprises:

[0066] The first coal slurry concentration associated parameter and the corresponding actual measured coal slurry concentration are screened out from a plurality of groups of candidate coal slurry concentration associated parameters and the corresponding measured coal slurry concentrations.

[0067] Since there may be many groups of candidate coal slurry concentration associated parameters and the corresponding measured coal slurry concentrations, in order to shorten the training time and improve the training accuracy, the parameters can be automatically screened according to the changing trend of the coal slurry concentration associated parameters when the concentration changes, so as to select the first coal slurry concentration associated parameter, and use the measured coal slurry concentration corresponding to the screened first coal slurry concentration associated parameter as the actual measured coal slurry concentration.

[0068] The following will be combined Figure 2 The technical solution of the present disclosure is further described in detail:

[0069] The present disclosure aims to provide an online continuous detection method for coal slurry concentration, screens the operating parameters that affect the coal slurry concentration by means of correlation analysis, selects six parameters, including the vertical pipeline pressure difference measurement PDI1 at the inlet of a large coal slurry tank, the horizontal pipeline pressure difference measurement PDI2 at the outlet of a small coal slurry pump, the internal temperature measurement T1 of the coal slurry tank, the coal slurry pipeline ambient temperature measurement T2, the coal grinding water flow rate F1 and the coal grinding machine weighing load F2, as input variables for calculating the coal slurry concentration, establishes a functional relationship between the coal slurry concentration C and the vertical pipeline pressure difference measurement PDI1 at the inlet of a large coal slurry tank, the horizontal pipeline pressure difference measurement PDI2 at the outlet of a small coal slurry pump, the internal temperature measurement T1 of the coal slurry tank, the coal slurry pipeline ambient temperature measurement T2, the coal grinding water flow rate F1 and the coal grinding machine weighing load F2, uses conventional measuring instruments such as pressure difference, flow rate and temperature to calculate the coal slurry concentration, and realizes real-time detection.

[0070] The coal slurry concentration C = k1*PDI1+k2*PDI2+k3*T1+k4*T2+k5*F1+k6*F2+k7

[0071] PDI1: vertical differential pressure transmitter signal (mmH2O) i.e. vertical differential pressure

[0072] PDI2: Horizontal pressure difference transmitter signal (mmH2O) i.e. horizontal pressure difference

[0073] T1: Small coal slurry storage tank coal slurry temperature (℃) i.e. coal slurry tank temperature

[0074] T2: Ambient temperature (℃), i.e. the temperature on the coal slurry pipeline

[0075] F1: Coal grinding water flow (m3 / h), that is, the water flow entering the coal mill

[0076] F2: Mill load (t / h) i.e. the weight of coal entering the mill

[0077] k1~k7: Constants from -1000.000 to +1000.000, modifiable.

[0078] Compared with the prior art, the advantages of the present invention are:

[0079] (1) Compared with online instruments, only two differential pressure gauges are needed, which is low in cost, easy to maintain and has a long service life.

[0080] (2) All measurement parameters can be read from the DCS system (uploaded to the DCS system by sensors, pressure gauges, water flow meters, and feed weighing machines). The performance of conventional instruments is stable and reliable, making it easy to achieve automatic control.

[0081] (3) High measurement accuracy. Within a fixed calibration period (two days), the average calculation deviation of the model is less than 0.3%.

[0082] (4) Compared with online analytical instruments, the model deviation verification method is simple, which makes it easy to make corrections based on the test values ​​in a timely manner to eliminate fixed deviations.

[0083] The gasification furnace of a gasification unit adopts a four-nozzle water-coal slurry process, tracks 60 sets of coal slurry concentration detection data, and records the above 6 operating parameters at the time of adoption. The tracking data are screened, 30 sets of data are selected for modeling, and the coal slurry concentration calculation formula is obtained by linear fitting method.

[0084] C (coal slurry concentration) = 0.1172*[PDI1]-0.1455*[PDI2]-0.0154*T1+0.0371

[0085] *T2-0.4999*F(water)+0.2364*F(coal)+66.632

[0086] [PDI1]: Vertical differential pressure transmitter signal PDT06004A-1, 0~200mmH2O

[0087] [PDI2]: Horizontal differential pressure transmitter signal PDT06004A-2, 0~200mmH2O

[0088] T1: Small coal slurry storage tank coal slurry temperature TI-06003A, 0~100℃

[0089] T2: Ambient temperature TT-07032B, -50~50℃

[0090] F(water): coal grinding water flow rate m3 / h

[0091] F(coal): mill load t / h.

[0092] 70 sets of coal slurry concentration detection data were put into use and tracked. With a calibration frequency of once every two days, the average deviation of the model was 0.24%, which met the coal slurry concentration measurement requirements.

[0093] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0094] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0095] Figure 3 FIG. 3 shows a block diagram of a coal slurry concentration detection device 300 according to an embodiment of the present disclosure. Figure 3 As shown, the device 300 includes:

[0096] A first acquisition module 310 is used to acquire first coal slurry concentration related parameters, wherein the first coal slurry concentration related parameters include: coal mill parameters, coal slurry tank parameters and coal slurry pipeline parameters;

[0097] A second acquisition module 320 is used to obtain the actual measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter;

[0098] A training module 330, configured to train a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model;

[0099] The third acquisition module 340 is used to input the second coal slurry concentration associated parameter into the target coal slurry concentration model to obtain a coal slurry concentration detection value.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0101] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0102] Figure 4A schematic block diagram of an electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0103] The device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0104] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0105] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0111] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0113] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for detecting coal slurry concentration, characterized in that: include: Acquire first coal slurry concentration-related parameters, wherein the first coal slurry concentration-related parameters include: coal mill parameters, coal slurry tank parameters, and coal slurry pipeline parameters; Obtaining a real measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter; Training a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model; Inputting the second coal slurry concentration correlation parameter into the target coal slurry concentration model to obtain a coal slurry concentration detection value; The coal mill parameters include: water flow entering the coal mill and the weight of coal entering the coal mill; The coal slurry tank parameters include: coal slurry tank temperature; The coal slurry pipeline parameters include: the temperature on the coal slurry pipeline, the horizontal pressure difference on the coal slurry pipeline, and the vertical pressure difference on the coal slurry pipeline; The preset coal slurry concentration model is: C = k1*PDI1+k2*PDI2+k3*T1+k4*T2+k5*F1+k6*F2+k7 Among them, PDI1 represents the vertical pressure difference on the coal slurry pipeline, PDI2 represents the horizontal pressure difference on the coal slurry pipeline, T1 represents the coal slurry tank temperature, T2 represents the temperature on the coal slurry pipeline, F1 represents the water flow entering the coal mill, F2 represents the weight of coal entering the coal mill, and k1~k7 represent coal slurry coefficients.

2. The method according to claim 1, characterized in that: The coal mill parameters are the inlet parameters of the coal mill, wherein one end of the coal mill is connected to a water inlet pipe and a coal pipe, and the other end is connected to the inlet of the first coal slurry tank, and a water flow meter is installed on the water inlet pipe to detect the water flow, and a feed weighing machine is installed on the coal pipe to detect the weight of the coal; The coal slurry tank temperature is the temperature detected by a first temperature sensor in a first coal slurry tank, wherein the inlet of the first coal slurry tank is connected to the coal mill, and the outlet is connected to a coal slurry pump; The coal slurry pipeline parameters are detection parameters on the coal slurry pipeline, wherein the coal slurry pipeline includes a first horizontal coal slurry pipeline, a vertical coal slurry pipeline and a second horizontal coal slurry pipeline, one end of the first horizontal coal slurry pipeline is connected to the coal slurry pump, and the other end is connected to the vertical coal slurry pipeline, one end of the vertical coal slurry pipeline is connected to the first horizontal coal slurry pipeline, and the other end is connected to the second horizontal coal slurry pipeline, one end of the second horizontal coal slurry pipeline is connected to the vertical coal slurry pipeline, and the other end is connected to the second coal slurry tank, and a first pressure gauge is installed on the first horizontal coal slurry pipeline to detect the horizontal pressure difference, a second pressure gauge is installed on the vertical coal slurry pipeline to detect the vertical pressure difference, and a second temperature sensor is installed on the second horizontal coal slurry pipeline to detect the temperature in the second horizontal coal slurry pipeline.

3. The method according to claim 1, characterized in that: The actual measured coal slurry concentration is obtained by the following steps: sampling the coal slurry on the coal slurry pipeline; The coal slurry concentration meter is used to analyze the concentration of the coal slurry samples.

4. The method according to claim 1, characterized in that: The step of training a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model includes: The first coal slurry concentration associated parameter and the actual measured coal slurry concentration are input into the preset coal slurry concentration model, and the coal slurry coefficient corresponding to the first coal slurry concentration associated parameter in the preset coal slurry concentration model is modified to obtain the target coal slurry concentration model.

5. The method according to claim 1, characterized in that The method further comprises: The first coal slurry concentration associated parameter and the corresponding actual measured coal slurry concentration are screened out from a plurality of groups of candidate coal slurry concentration associated parameters and the corresponding measured coal slurry concentrations.

6. A coal slurry concentration detection device, characterized in that: include: A first acquisition module is used to acquire first coal slurry concentration related parameters, wherein the first coal slurry concentration related parameters include: coal mill parameters, coal slurry tank parameters and coal slurry pipeline parameters; A second acquisition module, used for acquiring the actual measured coal slurry concentration corresponding to the first coal slurry concentration associated parameter; A training module, configured to train a preset coal slurry concentration model according to the first coal slurry concentration associated parameter and the actual measured coal slurry concentration to obtain a target coal slurry concentration model; A third acquisition module is used to input the second coal slurry concentration associated parameter into the target coal slurry concentration model to obtain a coal slurry concentration detection value; The coal mill parameters include: water flow entering the coal mill and the weight of coal entering the coal mill; The coal slurry tank parameters include: coal slurry tank temperature; The coal slurry pipeline parameters include: the temperature on the coal slurry pipeline, the horizontal pressure difference on the coal slurry pipeline, and the vertical pressure difference on the coal slurry pipeline; The preset coal slurry concentration model is: C = k1*PDI1+k2*PDI2+k3*T1+k4*T2+k5*F1+k6*F2+k7 Among them, PDI1 represents the vertical pressure difference on the coal slurry pipeline, PDI2 represents the horizontal pressure difference on the coal slurry pipeline, T1 represents the coal slurry tank temperature, T2 represents the temperature on the coal slurry pipeline, F1 represents the water flow entering the coal mill, F2 represents the weight of coal entering the coal mill, and k1~k7 represent coal slurry coefficients.

7. An electronic device, characterized in that: include: 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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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