Carbon monoxide adjustment optimization control method, system and equipment based on big data

Abstract

The present invention provides a carbon monoxide adjustment optimization control method, system and equipment based on big data, which determine the coal type, load and operation mode of the pulverizing system of the boiler in the thermal power plant; input the coal type, load and operation mode of the pulverizing system into the carbon monoxide concentration control curve, output the carbon monoxide concentration adjustment optimization parameters, and correct the boiler oxygen content control instruction through thermal logic based on the carbon monoxide concentration adjustment optimization parameters; monitor the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control instruction; based on the difference between the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value, feedback adjust the carbon monoxide concentration adjustment optimization parameters, determine the currently required carbon monoxide concentration adjustment optimization parameters through the carbon monoxide concentration control curve, and realize reasonable control of carbon monoxide concentration through correction of oxygen content combined with feedback adjustment of specific boiler combustion parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon monoxide concentration control, and in particular to a carbon monoxide adjustment optimization control method, system and equipment based on big data. Background Art

[0002] The control of carbon monoxide concentration in thermal power plants is related to the economy of the boiler. In daily operation, the carbon monoxide concentration is controlled by controlling the oxygen content. If the carbon monoxide concentration is controlled too high, the heat loss of incomplete combustion of the boiler will increase. If the carbon monoxide concentration is controlled too low, it will cause a series of impacts such as increased fan power consumption, increased flue gas temperature, increased liquid ammonia consumption, and increased carbon content in fly ash. At the same time, increased CO concentration emissions will also affect boiler safety and easily cause secondary combustion in the tail flue.

[0003] Therefore, how to optimize and adjust the control of carbon monoxide concentration has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] The present invention provides a carbon monoxide adjustment optimization control method, system and equipment based on big data, which are used to solve the defect of low carbon monoxide concentration control efficiency in the prior art.

[0005] The present invention provides a carbon monoxide adjustment optimization control method based on big data, comprising:

[0006] Determine the type of coal burned in the boilers of thermal power plants, the load and the operation mode of the pulverizing system;

[0007] Input the coal type, load and operation mode of the pulverizing system into the carbon monoxide concentration control curve, and output the carbon monoxide concentration adjustment optimization parameter, wherein the carbon monoxide concentration control curve is obtained by pre-training based on the coal type sample, load sample, operation mode sample of the pulverizing system and carbon monoxide concentration adjustment optimization parameter sample;

[0008] Based on the carbon monoxide concentration, the optimization parameters are adjusted and the boiler oxygen content control instruction is corrected through thermal logic;

[0009] monitoring the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control command;

[0010] Based on the differences between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target values, the carbon monoxide concentration is adjusted and optimized parameters are adjusted through feedback.

[0011] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, before determining the type of coal burned by the boiler in the thermal power plant, the load and the operation mode of the pulverizing system, the method further includes:

[0012] Retrieve coal type data, load data, and pulverizing system operation mode data of a preset number of boilers within a preset time period;

[0013] Counting the actual carbon monoxide concentration adjustment optimization parameter data under the coal type data, load data, and pulverizing system operation mode data;

[0014] Determine the standard carbon monoxide concentration adjustment optimization parameter data of a preset number of boilers within the preset time period under the coal type data, the load data, and the operation mode data of the pulverizing system;

[0015] The actual carbon monoxide concentration adjustment optimization parameter data is merged with the standard carbon monoxide concentration adjustment optimization parameter data to obtain a carbon monoxide concentration adjustment optimization parameter sample and a corresponding coal type sample, load sample, and pulverizing system operation mode sample;

[0016] Based on the coal type sample, load sample, pulverizing system operation mode sample and the carbon monoxide concentration adjustment optimization parameter sample, a carbon monoxide concentration control curve is constructed.

[0017] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, the input of the coal type, load and operation mode of the pulverizing system to the carbon monoxide concentration control curve and the output of the carbon monoxide concentration adjustment optimization parameters include:

[0018] Determine the structural composition ratio of the coal type;

[0019] Based on the structural composition ratio of the coal type, a corresponding target carbon monoxide concentration curve is selected from the carbon monoxide concentration curve set;

[0020] The coal type, load and operating mode of the pulverizing system are input into the corresponding target carbon monoxide concentration control curve, and the carbon monoxide concentration adjustment optimization parameters are output.

[0021] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, the determining of the structural composition ratio of the coal type includes:

[0022] Collect pictures of coal types;

[0023] Input the coal type picture into the coal type detection model, and output the coal type;

[0024] The coal structure composition ratio is determined based on different coal types.

[0025] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, after determining the coal structure composition ratio based on different coal types, it also includes:

[0026] Accepting manual confirmation information, wherein the manual confirmation information is obtained through a quality relationship;

[0027] When the manual confirmation information indicates that the proportion of the coal type structure is wrong, the proportion in the manual information is used as the new proportion of the coal type structure.

[0028] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, the optimization parameter is adjusted based on the carbon monoxide concentration, and the boiler oxygen content control instruction is corrected through thermal logic, including:

[0029] Determine current boiler oxygen levels;

[0030] Determining the target boiler oxygen content corresponding to the carbon monoxide concentration adjustment optimization parameter through the oxygen control curve;

[0031] Based on the magnitude relationship between the target boiler oxygen content and the current boiler oxygen content, the boiler oxygen content control instruction is corrected.

[0032] According to a carbon monoxide adjustment optimization control method based on big data provided by the present invention, the carbon monoxide concentration adjustment optimization parameter is feedback-adjusted based on the difference between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value, including:

[0033] Determining the combustion loss under the fan power consumption rate, the exhaust gas temperature, the liquid ammonia consumption and the fly ash carbon content;

[0034] determining a difference between the combustion loss and an optimal combustion loss;

[0035] Based on the difference, the carbon monoxide concentration is adjusted to optimize the parameters so as to reduce the combustion loss.

[0036] A carbon monoxide adjustment optimization control method based on big data provided by the present invention also includes:

[0037] Determine the combustion efficiency and economic indicators under different coal types, different loads and different pulverizing system operation modes;

[0038] Based on the combustion efficiency and the economic index, determining the optimal combination of coal type, load and operation mode of the pulverizing system;

[0039] The optimal combination of coal type, load and operation mode of the pulverizing system is pushed to different thermal power plants.

[0040] The present invention also provides a carbon monoxide adjustment optimization control system based on big data, comprising:

[0041] A determination module is used to determine the type of coal burned by the boiler in the thermal power plant, the load and the operation mode of the pulverizing system;

[0042] A big data module, used for inputting the coal type, load and operation mode of the pulverizing system into a carbon monoxide concentration control curve, and outputting carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is obtained by pre-training based on coal type samples, load samples, operation mode samples of the pulverizing system and carbon monoxide concentration adjustment optimization parameter samples;

[0043] A correction module, used to adjust the optimization parameters based on the carbon monoxide concentration and correct the boiler oxygen content control instruction through thermal logic;

[0044] A monitoring module, used for monitoring the power consumption rate of the fan, the exhaust gas temperature, the liquid ammonia consumption and the carbon content of the fly ash under the oxygen content control instruction of the boiler;

[0045] The regulating module is used to feedback and regulate the carbon monoxide concentration adjustment optimization parameters based on the difference between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value.

[0046] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing and controlling carbon monoxide adjustment based on big data as described above is implemented.

[0047] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the carbon monoxide adjustment optimization control method based on big data as described in any one of the above is implemented.

[0048] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned carbon monoxide adjustment optimization control methods based on big data.

[0049] The present invention provides a carbon monoxide adjustment optimization control method, system and equipment based on big data. The method determines the type of coal burned in the boiler of a thermal power plant, the load and the operating mode of the pulverizing system; inputs the type of coal, the load and the operating mode of the pulverizing system into the carbon monoxide concentration control curve, and outputs the carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operating mode samples and carbon monoxide concentration adjustment optimization parameter samples; based on the carbon monoxide concentration adjustment optimization parameters, the boiler oxygen content control instruction is corrected through thermal logic; monitoring The fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control instruction; based on the differences between the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target values, the carbon monoxide concentration adjustment optimization parameters are feedback adjusted, and the carbon monoxide concentration adjustment optimization parameters currently required are accurately determined by means of the carbon monoxide concentration control curve, and then the carbon monoxide concentration adjustment optimization parameters are realized through the correction control of the oxygen content, and then combined with the specific boiler combustion parameters, the carbon monoxide concentration adjustment optimization parameters are feedback adjusted, which not only ensures the combustion efficiency, but also ensures the carbon monoxide concentration and rationality. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a flow chart of a carbon monoxide adjustment optimization control method based on big data provided by an embodiment of the present invention;

[0052] Figure 2 It is a structural schematic diagram of a carbon monoxide adjustment optimization control system based on big data provided by the present invention;

[0053] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Combine the following Figure 1-Figure 3 The present invention describes a carbon monoxide adjustment optimization control method, system and equipment based on big data.

[0056] Figure 1 It is a flow chart of a carbon monoxide adjustment optimization control method based on big data provided in an embodiment of the present invention.

[0057] like Figure 1 As shown, this embodiment provides a carbon monoxide adjustment optimization control method based on big data, including:

[0058] 101. Determine the type of coal burned in the boiler of the thermal power plant, the load and the operating mode of the pulverizing system.

[0059] In a specific implementation process, we first determine the type of coal burned in the boiler, the load, and the operating mode of the pulverizing system under the current working state of the thermal power plant. The required carbon monoxide concentration is different for different types of coal, different loads, and different operating modes of the pulverizing system.

[0060] Among them, there are many different combinations of coal types, loads and operating modes of the pulverizing system. Therefore, it is also necessary to determine the corresponding combination content to ensure more accurate subsequent carbon monoxide concentration control.

[0061] The specific combination method can be determined automatically through image recognition or machine parameter recognition to automatically obtain coal type parameters, load parameters and corresponding pulverizing system operation mode parameters, thereby realizing automatic determination of the combination method. It can also be determined manually by manually inputting the specific combination method according to the on-site conditions.

[0062] 102. Input the coal type, load and operation mode of the pulverizing system into the carbon monoxide concentration control curve, and output the carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is pre-trained based on the coal type samples, load samples, operation mode samples of the pulverizing system and the carbon monoxide concentration adjustment optimization parameter samples.

[0063] After obtaining the type of coal, load, and operating mode of the pulverizing system, input them into the carbon monoxide concentration control curve, and the carbon monoxide concentration control curve can automatically calculate the optimal carbon monoxide concentration adjustment optimization parameters. Among them, the carbon monoxide concentration control curve is obtained through big data training, that is, there are corresponding carbon monoxide concentration adjustment optimization parameters for any type of coal, load, and operating mode of the pulverizing system. The carbon monoxide concentration control curve directly reflects the corresponding relationship between the type of coal, load, and operating mode of the pulverizing system and the carbon monoxide concentration. It can be understood that the horizontal axis is the type of coal, load, and operating mode of the pulverizing system, and the vertical axis is the carbon monoxide concentration, so there is a corresponding value for each different situation.

[0064] The carbon monoxide concentration control curve can be used to quickly and accurately determine the optimal carbon monoxide concentration adjustment optimization parameters, which is efficient and fast. The construction process of the carbon monoxide concentration control curve can be through a large number of data samples. The more data samples there are, the more accurate the final carbon monoxide concentration control curve will be.

[0065] 103. Adjust the optimization parameters based on the carbon monoxide concentration and correct the boiler oxygen content control instructions through thermal logic.

[0066] After obtaining the precise carbon monoxide concentration adjustment optimization parameters, the system needs to be adjusted to achieve the purpose of adjusting the carbon monoxide concentration optimization parameters. The method of controlling the carbon monoxide concentration is to control the boiler oxygen content and adjust the carbon monoxide concentration to the target value by correcting the boiler oxygen content.

[0067] 104. Monitor the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control command.

[0068] Real-time monitoring of boiler operating status parameters under the current carbon monoxide concentration, i.e. boiler oxygen content control command, including fan power consumption rate, flue gas temperature, liquid ammonia consumption, fly ash carbon content, etc., so as to determine the boiler combustion efficiency, heat loss, etc. through the size of the status parameters.

[0069] Among them, the specific method of monitoring can be through various different sensors. The monitoring method through sensors is fast and has high accuracy.

[0070] 105. Based on the difference between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target values, the carbon monoxide concentration is adjusted and optimized parameters are adjusted through feedback.

[0071] After determining and calculating the fan power consumption rate, exhaust temperature, liquid ammonia consumption and fly ash carbon content, compare them with the corresponding target values ​​to determine whether the state parameters are within a reasonable and efficient range. If they are within the range, maintain the current carbon monoxide concentration adjustment optimization parameters. If they are not within the range, it indicates that the current carbon monoxide concentration adjustment optimization parameters are not the optimal values. At this time, feedback is required to adjust the carbon monoxide concentration adjustment optimization parameters to ensure that the carbon monoxide concentration is in the optimal proportion to the fan power consumption rate, exhaust temperature, liquid ammonia consumption and fly ash carbon content.

[0072] Furthermore, on the basis of the above-mentioned embodiments, before determining the type of coal burned in the boilers in the thermal power plant, the load and the operating mode of the pulverizing system, the present embodiment also includes: retrieving the coal type data, load data and operating mode data of the pulverizing system of a preset number of boilers within a preset time period; counting the actual carbon monoxide concentration adjustment optimization parameter data under the coal type data, load data and operating mode data of the pulverizing system; determining the standard carbon monoxide concentration adjustment optimization parameter data under the coal type data, load data and operating mode data of the pulverizing system of a preset number of boilers within a preset time period; fusing the actual carbon monoxide concentration adjustment optimization parameter data with the standard carbon monoxide concentration adjustment optimization parameter data to obtain a carbon monoxide concentration adjustment optimization parameter sample and the corresponding coal type sample, load sample and operating mode sample of the pulverizing system; constructing a carbon monoxide concentration control curve based on the coal type sample, load sample, operating mode sample of the pulverizing system and the carbon monoxide concentration adjustment optimization parameter sample.

[0073] Specifically, in order to make the carbon monoxide concentration control curve more accurate, a large number of coal samples, load samples, pulverizing system operation mode samples and carbon monoxide concentration adjustment optimization parameter samples will be used during the construction. The larger the number of samples, the more accurate the final carbon monoxide concentration curve. Moreover, by fusing the actual carbon monoxide concentration adjustment optimization parameter data corresponding to the coal type data, load data, and pulverizing system operation mode data of a preset number of boilers within a preset time period with the corresponding standard carbon monoxide concentration adjustment optimization parameter data, the various samples obtained, and the final constructed curve can not only ensure the authenticity of the results, but also ensure that they fit the theoretical values. By combining the actual values ​​with the theoretical values, the actual carbon monoxide concentration adjustment optimization parameter data corresponding to the coal type samples, load samples, and pulverizing system operation mode samples can be corrected to avoid inaccurate carbon monoxide concentration adjustment optimization parameter samples due to statistical errors, thereby better improving the accuracy of the carbon monoxide concentration control curve.

[0074] Furthermore, on the basis of the above-mentioned embodiments, the input coal type, load and operation mode of the pulverizing system in this embodiment are connected to the carbon monoxide concentration control curve, and the carbon monoxide concentration adjustment optimization parameters are output, including: determining the structural composition ratio of the coal type; based on the structural composition ratio of the coal type, selecting the corresponding target carbon monoxide concentration curve in the carbon monoxide concentration curve set; inputting the coal type, load and operation mode of the pulverizing system to the corresponding target carbon monoxide concentration control curve, and outputting the carbon monoxide concentration adjustment optimization parameters.

[0075] Among them, determining the structural composition ratio of coal types includes: collecting coal type pictures; inputting coal type pictures into a coal type detection model, and outputting coal type types; and determining the structural composition ratio of coal types based on different coal types.

[0076] Specifically, in order to more accurately determine the carbon monoxide concentration adjustment optimization parameters, different carbon monoxide concentration curves corresponding to different coal structure composition ratios must be determined. Therefore, when making the determination, first collect coal type pictures and use image recognition to accurately determine the coal structure composition ratio, thereby screening out the corresponding carbon monoxide concentration control curve, thereby accurately determining the target carbon monoxide concentration adjustment optimization parameters.

[0077] Among them, the carbon monoxide concentration curve set contains a variety of carbon monoxide concentration curves, which are respectively for different coal ratios, that is, each coal ratio corresponds to a carbon monoxide concentration curve, thereby ensuring that the final carbon monoxide concentration parameter is optimal and is determined according to different actual situations. The obtained carbon monoxide concentration value is more real and accurate.

[0078] After determining the coal structure composition ratio based on different coal types, it also includes: accepting manual confirmation information, which is obtained through quality relationship; when the manual confirmation information indicates that the coal structure composition ratio is wrong, the ratio in the manual information is used as the new coal structure composition ratio.

[0079] After determining the coal structure ratio through big data, in order to ensure the accuracy of the data, the coal structure ratio can also be manually verified to ensure the accuracy of the coal structure ratio. When the two are wrong, the manual confirmation method shall prevail, thereby better ensuring the accuracy of the carbon monoxide concentration control curve selection, and further better ensuring the accuracy of the carbon monoxide concentration adjustment optimization parameters.

[0080] Furthermore, based on the above-mentioned embodiments, the present embodiment adjusts the optimization parameters based on the carbon monoxide concentration and corrects the boiler oxygen content control instruction through thermal logic, including: determining the current boiler oxygen content; determining the target boiler oxygen content corresponding to the carbon monoxide concentration adjustment optimization parameters through the oxygen control curve; and correcting the boiler oxygen content control instruction based on the relationship between the target boiler oxygen content and the current boiler oxygen content.

[0081] Specifically, after obtaining the carbon monoxide concentration adjustment optimization parameters, it is necessary to control the required carbon monoxide concentration adjustment optimization parameters through oxygen control. At this time, the corresponding target boiler oxygen content under the carbon monoxide concentration adjustment optimization parameters is obtained through the oxygen control curve, and then the current oxygen content is compared with the target boiler oxygen content. When it is greater than the target value, the oxygen content is reduced, and if it is less than the target value, the oxygen content is increased. The oxygen content curve can be understood as a one-to-one correspondence, that is, the independent variable is controlled by the dependent variable, and the carbon monoxide concentration is changed by controlling the boiler oxygen content, thereby ensuring that the carbon monoxide concentration meets the standard.

[0082] Furthermore, on the basis of the above embodiments, the present embodiment provides feedback adjustment of the carbon monoxide concentration optimization parameters based on the difference between the fan power consumption rate, the exhaust temperature, the liquid ammonia consumption and the fly ash carbon content and the preset target values, including: determining the combustion loss under the fan power consumption rate, the exhaust temperature, the liquid ammonia consumption and the fly ash carbon content; determining the difference between the combustion loss and the optimal combustion loss; and based on the difference, adjusting the carbon monoxide concentration optimization parameters to reduce the combustion loss.

[0083] Specifically, in order to ensure the relationship between the carbon monoxide concentration value and the combustion loss and ensure that it works at the optimal ratio, it is necessary to determine the combustion loss under the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content in real time. The combustion loss can be determined by numerical calculation through a preset formula, and then the difference between the combustion loss and the optimal combustion loss is compared. The size of the difference can determine whether the current carbon monoxide concentration is the optimal value. If it is not the optimal value, the carbon monoxide concentration can be adjusted again through feedback, so that the carbon monoxide concentration adjustment optimization parameters under the optimal combustion loss value can be successfully obtained. Through feedback adjustment, not only the optimal state parameters are guaranteed, but also the rationality of the heat loss value is guaranteed, which effectively improves the boiler combustion efficiency.

[0084] Furthermore, based on the above embodiments, the present embodiment also includes: determining the combustion efficiency and economic indicators under different coal types, different loads and different pulverizing system operating modes; based on the combustion efficiency and economic indicators, determining the optimal combination of coal type, load and pulverizing system operating mode; pushing the optimal combination of coal type, load and pulverizing system operating mode to different thermal power plants.

[0085] Specifically, after determining the CO concentration adjustment optimization parameters, the combustion efficiency and economic indicators under different coal types, different coals and different pulverizing system operation modes can also be determined, thereby ensuring the most efficient boiler combustion. The optimal combination method is pushed to different thermal power plants, thereby improving the efficiency of different thermal power plants and reducing energy loss and waste.

[0086] By accurately determining the carbon monoxide concentration, it is possible to ensure the safety of the unit while reducing the operating intensity of operators, improving the overall economy of the unit, and ultimately maximizing the economic benefits of the boiler.

[0087] Based on the same general inventive concept, the present invention also protects a carbon monoxide adjustment optimization control system based on big data. The carbon monoxide adjustment optimization control system based on big data provided by the present invention is described below. The carbon monoxide adjustment optimization control system based on big data described below and the carbon monoxide adjustment optimization control method based on big data described above can be referenced to each other.

[0088] Figure 2 It is a structural schematic diagram of the carbon monoxide adjustment optimization control system based on big data provided by the present invention.

[0089] like Figure 2 As shown, this embodiment provides a carbon monoxide adjustment optimization control system based on big data, including:

[0090] The determination module 201 is used to determine the type of coal burned by the boiler in the thermal power plant, the load and the operation mode of the pulverizing system;

[0091] The big data module 202 is used to input the coal type, load and operation mode of the pulverizing system into the carbon monoxide concentration control curve, and output the carbon monoxide concentration adjustment optimization parameter, wherein the carbon monoxide concentration control curve is obtained by pre-training based on the coal type sample, load sample, operation mode sample of the pulverizing system and carbon monoxide concentration adjustment optimization parameter sample;

[0092] A correction module 203, for adjusting the optimization parameters based on the carbon monoxide concentration and correcting the boiler oxygen content control instruction through thermal logic;

[0093] Monitoring module 204, used to monitor the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control command;

[0094] The regulating module 205 is used to feedback and adjust the carbon monoxide concentration to adjust the optimization parameters based on the difference between the fan power consumption rate, the exhaust gas temperature, the liquid ammonia consumption and the fly ash carbon content and the preset target values.

[0095] Furthermore, this embodiment also includes a construction module for:

[0096] Retrieve coal type data, load data, and pulverizing system operation mode data of a preset number of boilers within a preset time period;

[0097] Counting the actual carbon monoxide concentration adjustment optimization parameter data under the coal type data, load data, and pulverizing system operation mode data;

[0098] Determine the standard carbon monoxide concentration adjustment optimization parameter data of a preset number of boilers within the preset time period under the coal type data, the load data, and the operation mode data of the pulverizing system;

[0099] The actual carbon monoxide concentration adjustment optimization parameter data is merged with the standard carbon monoxide concentration adjustment optimization parameter data to obtain a carbon monoxide concentration adjustment optimization parameter sample and a corresponding coal type sample, load sample, and pulverizing system operation mode sample;

[0100] Based on the coal type sample, load sample, pulverizing system operation mode sample and the carbon monoxide concentration adjustment optimization parameter sample, a carbon monoxide concentration control curve is constructed.

[0101] Furthermore, the big data module 202 in this embodiment is specifically used for:

[0102] Determine the structural composition ratio of the coal type;

[0103] Based on the structural composition ratio of the coal type, a corresponding target carbon monoxide concentration curve is selected from the carbon monoxide concentration curve set;

[0104] The coal type, load and operating mode of the pulverizing system are input into the corresponding target carbon monoxide concentration control curve, and the carbon monoxide concentration adjustment optimization parameters are output.

[0105] Furthermore, the big data module 202 in this embodiment is also specifically used for:

[0106] Collect pictures of coal types;

[0107] Input the coal type picture into the coal type detection model, and output the coal type;

[0108] The coal structure composition ratio is determined based on different coal types.

[0109] Furthermore, the big data module 202 in this embodiment is also specifically used for:

[0110] Accepting manual confirmation information, wherein the manual confirmation information is obtained through a quality relationship;

[0111] When the manual confirmation information indicates that the proportion of the coal type structure is wrong, the proportion in the manual information is used as the new proportion of the coal type structure.

[0112] Furthermore, the correction module 203 in this embodiment is specifically used for:

[0113] Determine current boiler oxygen levels;

[0114] Determining the target boiler oxygen content corresponding to the carbon monoxide concentration adjustment optimization parameter through the oxygen control curve;

[0115] Based on the magnitude relationship between the target boiler oxygen content and the current boiler oxygen content, the boiler oxygen content control instruction is corrected.

[0116] Furthermore, the adjustment module 205 in this embodiment is specifically used for:

[0117] Determining the combustion loss under the fan power consumption rate, the exhaust gas temperature, the liquid ammonia consumption and the fly ash carbon content;

[0118] determining a difference between the combustion loss and an optimal combustion loss;

[0119] Based on the difference, the carbon monoxide concentration is adjusted to optimize the parameters so as to reduce the combustion loss.

[0120] Furthermore, this embodiment also includes a push module, which is used to:

[0121] Determine the combustion efficiency and economic indicators under different coal types, different loads and different pulverizing system operation modes;

[0122] Based on the combustion efficiency and the economic index, determining the optimal combination of coal type, load and operation mode of the pulverizing system;

[0123] The optimal combination of coal type, load and operation mode of the pulverizing system is pushed to different thermal power plants.

[0124] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0125] like Figure 3As shown, the electronic device may include: a processor (processor) 310, a communication interface (Communications Interface) 320, a memory (memory) 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logic instructions in the memory 330 to execute a carbon monoxide adjustment optimization control method based on big data, which includes: determining the type of coal burned in the boiler in the thermal power plant, the load and the operating mode of the pulverizing system; inputting the coal type, load and the operating mode of the pulverizing system into the carbon monoxide concentration control curve, and outputting the carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operating mode samples and carbon monoxide concentration adjustment optimization parameter samples; based on the carbon monoxide concentration adjustment optimization parameters, correcting the boiler oxygen content control instruction through thermal logic; monitoring the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control instruction; based on the difference between the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value, feedback adjusting the carbon monoxide concentration adjustment optimization parameters.

[0126] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0127] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the carbon monoxide adjustment optimization control method based on big data provided by the above methods, which includes: determining the type of coal burned in the boiler in the thermal power plant, the load and the operating mode of the pulverizing system; inputting the coal type, load and the operating mode of the pulverizing system into the carbon monoxide concentration control curve, and outputting the carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operating mode samples and carbon monoxide concentration adjustment optimization parameter samples; based on the carbon monoxide concentration adjustment optimization parameters, the boiler oxygen content control instruction is corrected through thermal logic; the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control instruction are monitored; based on the difference between the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value, the carbon monoxide concentration adjustment optimization parameters are feedback adjusted.

[0128] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the carbon monoxide adjustment optimization control method based on big data provided by the above-mentioned methods, the method comprising: determining the type of coal burned in the boiler in the thermal power plant, the load and the operating mode of the pulverizing system; inputting the type of coal, the load and the operating mode of the pulverizing system into the carbon monoxide concentration control curve, and outputting the carbon monoxide concentration adjustment optimization parameters, wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operating mode samples and carbon monoxide concentration adjustment optimization parameter samples; based on the carbon monoxide concentration adjustment optimization parameters, correcting the boiler oxygen content control instruction through thermal logic; monitoring the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control instruction; and feedback-adjusting the carbon monoxide concentration adjustment optimization parameters based on the difference between the fan power consumption rate, flue gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value.

[0129] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbon monoxide adjustment optimization control method based on big data, characterized in that: include: Determine the type of coal burned in the boilers of thermal power plants, the load and the operation mode of the pulverizing system; Input the coal type, load and operation mode of the pulverizing system to the carbon monoxide concentration control curve, and output the carbon monoxide concentration adjustment optimization parameters, including: determining the structural composition ratio of the coal type, including collecting coal type pictures; inputting the coal type pictures to the coal type detection model, and outputting the coal type type; determining the coal type structural composition ratio based on different coal types, and accepting manual confirmation information, wherein the manual confirmation information is obtained through quality relationship; when the manual confirmation information indicates that the coal type structural composition ratio is wrong, the ratio in the manual confirmation information is used as the new coal type structural composition ratio; based on the coal type structural composition ratio, selecting the corresponding target carbon monoxide concentration curve in the carbon monoxide concentration curve set; inputting the coal type, load and operation mode of the pulverizing system to the corresponding target carbon monoxide concentration control curve, and outputting the carbon monoxide concentration adjustment optimization parameters; wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operation mode samples and carbon monoxide concentration adjustment optimization parameter samples; Based on the carbon monoxide concentration, the optimization parameters are adjusted and the boiler oxygen content control instruction is corrected through thermal logic; monitoring the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content under the boiler oxygen content control command; Based on the differences between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target values, the carbon monoxide concentration is adjusted and optimized parameters are adjusted through feedback.

2. The carbon monoxide adjustment optimization control method based on big data according to claim 1 is characterized in that: Before determining the type of coal burned by the boiler in the thermal power plant, the load and the operation mode of the pulverizing system, the following steps are also included: Retrieve coal type data, load data, and pulverizing system operation mode data of a preset number of boilers within a preset time period; Counting the actual carbon monoxide concentration adjustment optimization parameter data under the coal type data, load data, and pulverizing system operation mode data; Determine the standard carbon monoxide concentration adjustment optimization parameter data of a preset number of boilers within the preset time period under the coal type data, the load data, and the operation mode data of the pulverizing system; The actual carbon monoxide concentration adjustment optimization parameter data is merged with the standard carbon monoxide concentration adjustment optimization parameter data to obtain a carbon monoxide concentration adjustment optimization parameter sample and a corresponding coal type sample, load sample, and pulverizing system operation mode sample; Based on the coal type sample, load sample, pulverizing system operation mode sample and the carbon monoxide concentration adjustment optimization parameter sample, a carbon monoxide concentration control curve is constructed.

3. The carbon monoxide adjustment optimization control method based on big data according to claim 1 is characterized in that: The step of adjusting the optimization parameters based on the carbon monoxide concentration and correcting the boiler oxygen content control instruction through thermal logic includes: Determine current boiler oxygen levels; Determining the target boiler oxygen content corresponding to the carbon monoxide concentration adjustment optimization parameter through the oxygen control curve; Based on the magnitude relationship between the target boiler oxygen content and the current boiler oxygen content, the boiler oxygen content control instruction is corrected.

4. The carbon monoxide adjustment optimization control method based on big data according to claim 1 is characterized in that: The method of adjusting the carbon monoxide concentration optimization parameters based on the difference between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value includes: Determining the combustion loss under the fan power consumption rate, the exhaust gas temperature, the liquid ammonia consumption and the fly ash carbon content; determining a difference between the combustion loss and an optimal combustion loss; Based on the difference, the carbon monoxide concentration is adjusted to optimize the parameters so as to reduce the combustion loss.

5. The carbon monoxide adjustment optimization control method based on big data according to claim 1 is characterized in that: Also includes: Determine the combustion efficiency and economic indicators under different coal types, different loads and different pulverizing system operation modes; Based on the combustion efficiency and the economic index, determining the optimal combination of coal type, load and operation mode of the pulverizing system; The optimal combination of coal type, load and operation mode of the pulverizing system is pushed to different thermal power plants.

6. A carbon monoxide adjustment optimization control system based on big data, characterized in that: include: A determination module is used to determine the type of coal burned by the boiler in the thermal power plant, the load and the operation mode of the pulverizing system; A big data module is used to input the coal type, load and operating mode of the pulverizing system into a carbon monoxide concentration control curve, and output carbon monoxide concentration adjustment optimization parameters, including: determining the structural composition ratio of the coal type, including collecting coal type pictures; inputting the coal type pictures into a coal type detection model, and outputting the coal type; determining the coal type structural composition ratio based on different coal types, and accepting manual confirmation information, wherein the manual confirmation information is obtained through a quality relationship; when the manual confirmation information indicates that the coal type structural composition ratio is wrong, using the ratio in the manual confirmation information as a new coal type structural composition ratio; based on the coal type structural composition ratio, selecting a corresponding target carbon monoxide concentration curve from a carbon monoxide concentration curve set; inputting the coal type, load and operating mode of the pulverizing system into a corresponding target carbon monoxide concentration control curve, and outputting carbon monoxide concentration adjustment optimization parameters; wherein the carbon monoxide concentration control curve is pre-trained based on coal type samples, load samples, pulverizing system operating mode samples, and carbon monoxide concentration adjustment optimization parameter samples; A correction module, used to adjust the optimization parameters based on the carbon monoxide concentration and correct the boiler oxygen content control instruction through thermal logic; A monitoring module, used for monitoring the power consumption rate of the fan, the exhaust gas temperature, the liquid ammonia consumption and the carbon content of the fly ash under the oxygen content control instruction of the boiler; The regulating module is used to feedback and regulate the carbon monoxide concentration adjustment optimization parameters based on the difference between the fan power consumption rate, exhaust gas temperature, liquid ammonia consumption and fly ash carbon content and the preset target value.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the carbon monoxide adjustment optimization control method based on big data as described in any one of claims 1 to 5 is implemented.

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