Energy-saving diagnosis method and device for gas boiler

Abstract

The present disclosure relates to the technical field of gas-fired boilers, and provides an energy-saving diagnosis method and device for a gas-fired boiler. The method comprises: obtaining boiler data of the gas-fired boiler in a target time period, wherein the boiler data comprises: running state data, flue gas temperature data, flue gas oxygen content data, pollutant discharge data, evaporation data and boiler tail-end steam pressure data; performing data preprocessing on the boiler data, and calculating target data according to the boiler data after data preprocessing, wherein the target data comprises: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average pollutant discharge rate and hourly average steam pressure data; performing energy-saving diagnosis on the gas-fired boiler by using a diagnosis knowledge base according to the target data to obtain a diagnosis result, wherein the diagnosis result comprises: flue gas heat loss, incomplete combustion heat loss, pollutant discharge heat loss and excessive heat output; and optimizing the operation of the gas-fired boiler according to the diagnosis result.

Description

Technical Field

[0001] This disclosure relates to the field of gas-fired boiler technology, and in particular to an energy-saving diagnostic method and apparatus for gas-fired boilers. Background Technology

[0002] Traditional energy-saving diagnostics for gas-fired steam boilers rely on manual, offline methods. This involves retrieving data from local control systems or databases and then analyzing it using local analytics tools. The real-time data stored in these systems is typically at the minute or second level. Considering minute-level data, a single monitoring point can generate over 40,000 data points per month, or over 500,000 per year. Energy-saving diagnostics require analyzing data from multiple monitoring points, resulting in a massive dataset and consuming significant time. Furthermore, there are numerous points on which energy-saving diagnostics can be performed on gas-fired steam boilers, making manual analysis extremely labor-intensive. Moreover, individual experience varies, making it impossible to analyze and diagnose all energy-saving points, and each person's conclusions may differ.

[0003] In the process of realizing the present invention, the inventors discovered at least the following technical problems in the related technology: the current methods for energy-saving diagnosis of gas boilers are inefficient and costly. Summary of the Invention

[0004] In view of this, the present disclosure provides an energy-saving diagnostic method, apparatus, electronic device, and computer-readable storage medium for gas-fired boilers, in order to solve the problems of low efficiency and high cost in the existing methods for energy-saving diagnostics of gas-fired boilers.

[0005] A first aspect of this disclosure provides an energy-saving diagnostic method for a gas-fired boiler, comprising: acquiring boiler data of the gas-fired boiler within a target time period, wherein the boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, blowdown data, evaporation data, and boiler terminal steam pressure data; performing data preprocessing on the boiler data, and calculating target data based on the preprocessed boiler data, wherein the target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data; performing energy-saving diagnostics on the gas-fired boiler using a diagnostic knowledge base based on the target data, and obtaining diagnostic results, wherein the diagnostic results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excess heat output; and optimizing the operation of the gas-fired boiler based on the diagnostic results.

[0006] A second aspect of this disclosure provides an energy-saving diagnostic device for a gas-fired boiler, comprising: an acquisition module configured to acquire boiler data of the gas-fired boiler within a target time period, wherein the boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, blowdown data, evaporation data, and boiler terminal steam pressure data; a calculation module configured to preprocess the boiler data and calculate target data based on the preprocessed boiler data, wherein the target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data; a diagnostic module configured to perform energy-saving diagnostics on the gas-fired boiler using a diagnostic knowledge base based on the target data, and obtain diagnostic results, wherein the diagnostic results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excess heat output; and an optimization module configured to optimize the operation of the gas-fired boiler based on the diagnostic results.

[0007] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0008] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The beneficial effects of this disclosed embodiment compared to the prior art are as follows: It acquires boiler data of a gas-fired boiler within a target time period, including: operating status data, flue gas temperature data, flue gas oxygen content data, blowdown volume data, evaporation rate data, and boiler terminal steam pressure data; it preprocesses the boiler data and calculates target data based on the preprocessed data, including: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data; based on the target data, it performs energy-saving diagnosis of the gas-fired boiler using a diagnostic knowledge base, obtaining diagnostic results, including: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excess heat output; and it optimizes the operation of the gas-fired boiler based on the diagnostic results. By employing the above technical means, the problems of low efficiency and high cost in current methods for energy-saving diagnosis of gas-fired boilers can be solved, thereby reducing the cost and improving the efficiency of energy-saving diagnosis of gas-fired boilers. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure;

[0012] Figure 2 This is a schematic flowchart of an energy-saving diagnostic method for a gas-fired boiler provided in an embodiment of this disclosure;

[0013] Figure 3 This is a schematic diagram of the structure of an energy-saving diagnostic device for a gas-fired boiler provided in an embodiment of this disclosure;

[0014] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0015] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0016] The following will describe in detail, with reference to the accompanying drawings, an energy-saving diagnostic method and apparatus for a gas-fired boiler according to an embodiment of the present disclosure.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 101, 102, and 103, server 104, and network 105.

[0018] Terminal devices 101, 102, and 103 can be hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays that support communication with server 104, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. Terminal devices 101, 102, and 103 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not impose any limitations on this. Furthermore, various applications can be installed on terminal devices 101, 102, and 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0019] Server 104 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 104 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This embodiment of the disclosure does not impose any limitations on these aspects.

[0020] It should be noted that server 104 can be either hardware or software. When server 104 is hardware, it can be various electronic devices that provide various services to terminal devices 101, 102, and 103. When server 104 is software, it can be multiple software programs or software modules that provide various services to terminal devices 101, 102, and 103, or it can be a single software program or software module that provides various services to terminal devices 101, 102, and 103. This disclosure does not limit the scope of the embodiments.

[0021] Network 105 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), Infrared, etc. This disclosure does not limit the scope of the network.

[0022] Users can establish a communication connection with server 104 via network 105 through terminal devices 101, 102, and 103 to receive or send information, etc. It should be noted that the specific types, quantities, and combinations of terminal devices 101, 102, and 103, server 104, and network 105 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any limitations on this.

[0023] Figure 2This is a schematic flowchart of an energy-saving diagnostic method for a gas-fired boiler provided in an embodiment of this disclosure. Figure 2 The energy-saving diagnostic method for gas-fired boilers can be derived from... Figure 1 The terminal device or server executes the command. For example... Figure 2 As shown, the energy-saving diagnostic method for this gas-fired boiler includes:

[0024] S201, Obtain boiler data of gas-fired boiler within the target time period, including: operating status data, flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data;

[0025] S202, perform data preprocessing on the boiler data, and calculate the target data based on the preprocessed boiler data. The target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data.

[0026] S203. Based on the target data, use the diagnostic knowledge base to perform energy-saving diagnosis on the gas boiler and obtain the diagnostic results. The diagnostic results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss and excessive heat output.

[0027] S204, Optimize the operation of the gas-fired boiler based on the diagnostic results.

[0028] Boiler data can be collected using devices such as DTUs, DCSs, and SCADA systems. A DTU (Data Transfer Unit) is a wireless terminal device specifically designed to convert serial data to IP data or vice versa for transmission over a wireless communication network. A DCS (Distributed Control System) is a distributed control system. A SCADA (Supervisory Control and Data Acquisition) system is a data acquisition and monitoring control system. The boiler terminal is the end where the boiler discharges steam.

[0029] According to the technical solution provided in this disclosure, boiler data of a gas-fired boiler within a target time period is obtained. The boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, blowdown volume data, evaporation rate data, and boiler terminal steam pressure data. The boiler data is preprocessed, and target data is calculated based on the preprocessed data. The target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data. Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results. These results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excess heat output. The operation of the gas-fired boiler is optimized based on the diagnostic results. By employing the above technical means, the problems of low efficiency and high cost in current methods for energy-saving diagnosis of gas-fired boilers can be solved, thereby reducing the cost and improving the efficiency of energy-saving diagnosis of gas-fired boilers.

[0030] In step S202, the boiler data is preprocessed, including: determining the time the gas-fired boiler has been in a shutdown state based on the operating status data; and based on the time the gas-fired boiler has been in a shutdown state, removing data from the flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data respectively; identifying null values ​​and data exceeding the preset range from the flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data respectively, and removing null values ​​and data exceeding the preset range from the original data.

[0031] Based on the operating status data, the operating status of the gas-fired boiler is determined. Generally, 0 indicates shutdown, and a value greater than 0 indicates operation. If the boiler is in a shutdown state, the corresponding data is discarded. The preset range for flue gas temperature data is 0℃-200℃; the preset range for flue gas oxygen content data is 0-21%; and the preset range for boiler terminal steam pressure data is 0MPa-the boiler's rated evaporation pressure.

[0032] In step S202, the target data is calculated based on the preprocessed boiler data, including: dividing the preprocessed flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data into hourly intervals; averaging the flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data within each hour to obtain hourly average flue gas temperature data, hourly average flue gas oxygen content data, and hourly average steam pressure data; and determining the average blowdown rate based on the preprocessed wastewater discharge data and evaporation data.

[0033] Flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data are all in minute or second increments. For example, if the flue gas oxygen content data is in second increments, then after data preprocessing, the flue gas oxygen content data is divided into hourly intervals, and the average value of the second-level flue gas oxygen content data within each hour is calculated as the hourly average flue gas oxygen content data. Blowout discharge data represents the cumulative amount of blowout discharged by the gas-fired boiler within the target time period, and evaporation data represents the cumulative amount of evaporation by the gas-fired boiler within the target time period. The ratio of blowout discharge data to evaporation data is the average blowout discharge rate.

[0034] In step S203, based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: based on the hourly average flue gas temperature data, calculating the total number of hours the gas-fired boiler operates within the target time period and the number of hours the hourly average flue gas temperature exceeds a preset temperature, wherein the hourly average flue gas temperature data includes multiple hourly average flue gas temperatures; when the number of hours in the second period is greater than the first target number, it is determined that the gas-fired boiler has flue gas heat loss within the target time period, wherein the first target number is determined by the number of hours in the first period; or based on the hourly average flue gas oxygen content data, calculating the number of hours the hourly average flue gas oxygen content exceeds a first preset threshold within the target time period, wherein the hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents; when the number of hours in the third period is greater than the second target number, it is determined that the gas-fired boiler has flue gas heat loss within the target time period, wherein the second target number is determined by the number of hours in the first period.

[0035] It should be noted that the first hour can also be calculated based on the hourly average flue gas oxygen content data or the average blowdown rate and hourly average steam pressure data, which represents the total number of hours the gas-fired boiler operates within the target time period (the first hour calculated from the three types of data is the same).

[0036] The terms "first," "second," and "third" in terms of the first hour, second hour, and third hour are merely for distinction; in reality, the first hour, second hour, and third hour all refer to the number of hours under various circumstances.

[0037] For example, if the target quantity is 60% of the first hour's count, and the first hour's count is 100, then the first target quantity is 60. If the second hour's count is 70, then the second hour's count is greater than the first target quantity, indicating that the gas-fired boiler has flue gas heat loss within the target time period. This flue gas heat loss actually indicates excessively high flue gas temperature (this information is present in the diagnostic results), and flue gas waste heat recovery treatment can be implemented. Flue gas waste heat recovery treatment fully utilizes the heat energy carried in the flue gas of the gas-fired boiler, which can be achieved using heat exchangers or absorption heat pumps. Flue gas waste heat recovery treatment is a step in optimizing the operation of the gas-fired boiler based on the diagnostic results. It should be noted that the first target quantity can also be adjusted to determine whether the flue gas waste heat recovery treatment is deep flue gas waste heat recovery treatment or normal flue gas waste heat recovery treatment.

[0038] For example, if the second target quantity is 30% of the first hour's count (100), then the second target quantity would be 30. The first preset threshold is 6%, and the third hour's count (40) exceeds this threshold. Since the third hour's count is greater than the second target quantity, it's determined that the gas-fired boiler has flue gas heat loss within the target time period. This flue gas heat loss is caused by excessively high oxygen content in the flue gas. Therefore, the fuel and air intake in the gas-fired boiler's burner can be adjusted, i.e., the air-fuel ratio. The air-fuel ratio is the ratio of fuel to air volume. Adjusting the fuel and air intake in the gas-fired boiler's burner is a step in optimizing the operation of the gas-fired boiler based on diagnostic results.

[0039] In step S203, based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: based on the hourly average flue gas oxygen content data, the number of fourth hours in which the hourly average flue gas oxygen content of the gas-fired boiler is lower than the second preset threshold within the target time period is calculated, wherein the hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents; when the fourth hour number is greater than the third target number, it is determined that there is incomplete combustion heat loss in the gas-fired boiler within the target time period, wherein the third target number is determined by the first hour number, which is obtained by statistically analyzing the hourly average exhaust gas temperature data.

[0040] For example, if the third target quantity is 30% of the first hour's count, and the first hour's count is 100, then the third target quantity is 30. The second preset threshold is 3%, and the fourth hour's count, which is below the second preset threshold, is 50. If the fourth hour's count is greater than the third target quantity, then the gas-fired boiler is determined to have incomplete combustion heat loss within the target time period. In this case, the incomplete combustion heat loss is caused by the low oxygen content in the flue gas, so the fuel and air intake in the gas-fired boiler's burner can be adjusted.

[0041] In step S203, based on the target data, the gas boiler is used to perform energy-saving diagnosis using a diagnostic knowledge base to obtain diagnostic results, including: when the average blowdown rate is greater than the third preset threshold, it is determined that the gas boiler has blowdown heat loss within the target time period.

[0042] The diagnostic knowledge base contains various diagnostic tools needed for gas-fired boilers. If blowdown heat loss exists, the problem can be addressed by improving water quality or optimizing the boiler's operating mechanism.

[0043] In step S203, based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: based on the hourly average steam pressure data, the number of fifth hours in which the hourly average steam pressure data of the gas-fired boiler is lower than the fourth preset threshold within the target time period is calculated, wherein the hourly average steam pressure data includes multiple hourly average steam pressure data; when the fifth hour number is greater than the fourth target number, it is determined that the gas-fired boiler has excessive heat output within the target time period, wherein the fourth target number is determined by the first hour number, which is obtained by statistically analyzing the hourly average flue gas temperature data.

[0044] For example, if 40% of the first hour's count is the fourth target quantity, and the first hour's count is 100, then the fourth target quantity is 40. The fourth preset threshold is 20. If the fourth hour's count is below the fourth preset threshold (40), then the fourth hour's count is greater than the fourth target quantity, indicating that the gas-fired boiler has excessive heat output within the target time period. In this case, the excessive heat output is caused by excessive steam pressure, so the pressure setting at the gas-fired boiler outlet or terminal can be optimized.

[0045] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0046] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0047] Figure 3 This is a schematic diagram of an energy-saving diagnostic device for a gas-fired boiler provided in an embodiment of this disclosure. Figure 3 As shown, the energy-saving diagnostic device for this gas-fired boiler includes:

[0048] The acquisition module 301 is configured to acquire boiler data of the gas-fired boiler within a target time period. The boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data.

[0049] The calculation module 302 is configured to perform data preprocessing on the boiler data and calculate the target data based on the preprocessed boiler data. The target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate and hourly average steam pressure data.

[0050] The diagnostic module 303 is configured to perform energy-saving diagnosis on the gas-fired boiler based on the target data and using a diagnostic knowledge base to obtain diagnostic results, including: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss and excessive heat output.

[0051] Optimization module 304 is configured to optimize the operation of the gas-fired boiler based on diagnostic results.

[0052] Boiler data can be collected using devices such as DTUs, DCSs, and SCADA systems. A DTU (Data Transfer Unit) is a wireless terminal device specifically designed to convert serial data to IP data or vice versa for transmission over a wireless communication network. A DCS (Distributed Control System) is a distributed control system. A SCADA (Supervisory Control and Data Acquisition) system is a data acquisition and monitoring control system. The boiler terminal is the end where the boiler discharges steam.

[0053] According to the technical solution provided in this disclosure, boiler data of a gas-fired boiler within a target time period is obtained. The boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, blowdown volume data, evaporation rate data, and boiler terminal steam pressure data. The boiler data is preprocessed, and target data is calculated based on the preprocessed data. The target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data. Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results. These results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excess heat output. The operation of the gas-fired boiler is optimized based on the diagnostic results. By employing the above technical means, the problems of low efficiency and high cost in current methods for energy-saving diagnosis of gas-fired boilers can be solved, thereby reducing the cost and improving the efficiency of energy-saving diagnosis of gas-fired boilers.

[0054] Optionally, the calculation module 302 is further configured to determine the time during which the gas-fired boiler is in a shutdown state based on the operating status data; and based on the time during which the gas-fired boiler is in a shutdown state, to remove data from the flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data respectively; and to identify null values ​​and data exceeding the preset range from the flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data respectively, and to remove null values ​​and data exceeding the preset range from the original data.

[0055] Based on the operating status data, the operating status of the gas-fired boiler is determined. Generally, 0 indicates shutdown, and a value greater than 0 indicates operation. If the boiler is in a shutdown state, the corresponding data is discarded. The preset range for flue gas temperature data is 0℃-200℃; the preset range for flue gas oxygen content data is 0-21%; and the preset range for boiler terminal steam pressure data is 0MPa-the boiler's rated evaporation pressure.

[0056] Optionally, the calculation module 302 is further configured to divide the pre-processed flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data into hourly intervals; to calculate the average of the flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data within each hour, thereby obtaining hourly average flue gas temperature data, hourly average flue gas oxygen content data, and hourly average steam pressure data; and to determine the average wastewater discharge rate based on the pre-processed wastewater discharge data and evaporation data.

[0057] Flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data are all in minute or second increments. For example, if the flue gas oxygen content data is in second increments, then after data preprocessing, the flue gas oxygen content data is divided into hourly intervals, and the average value of the second-level flue gas oxygen content data within each hour is calculated as the hourly average flue gas oxygen content data. Blowout discharge data represents the cumulative amount of blowout discharged by the gas-fired boiler within the target time period, and evaporation data represents the cumulative amount of evaporation by the gas-fired boiler within the target time period. The ratio of blowout discharge data to evaporation data is the average blowout discharge rate.

[0058] Optionally, the diagnostic module 303 is further configured to, based on the hourly average flue gas temperature data, calculate the total number of hours the gas-fired boiler operates within a target time period, specifically the first hour and the second hour when the hourly average flue gas temperature exceeds a preset temperature. The hourly average flue gas temperature data includes multiple hourly average flue gas temperatures. If the second hour is greater than a first target number, it is determined that the gas-fired boiler experiences flue gas heat loss within the target time period, where the first target number is determined by the first hour. Alternatively, based on the hourly average flue gas oxygen content data, it can calculate the total number of hours within the target time period where the hourly average flue gas oxygen content exceeds a first preset threshold. The hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents. If the third hour is greater than the second target number, it is determined that the gas-fired boiler experiences flue gas heat loss within the target time period, where the second target number is determined by the first hour.

[0059] It should be noted that the first hour can also be calculated based on the hourly average flue gas oxygen content data or the average blowdown rate and hourly average steam pressure data, which represents the total number of hours the gas-fired boiler operates within the target time period (the first hour calculated from the three types of data is the same).

[0060] The terms "first," "second," and "third" in terms of the first hour, second hour, and third hour are merely for distinction; in reality, the first hour, second hour, and third hour all refer to the number of hours under various circumstances.

[0061] For example, if the target quantity is 60% of the first hour's count, and the first hour's count is 100, then the first target quantity is 60. If the second hour's count is 70, then the second hour's count is greater than the first target quantity, indicating that the gas-fired boiler has flue gas heat loss within the target time period. This flue gas heat loss actually indicates excessively high flue gas temperature (this information is present in the diagnostic results), and flue gas waste heat recovery treatment can be implemented. Flue gas waste heat recovery treatment fully utilizes the heat energy carried in the flue gas of the gas-fired boiler, which can be achieved using heat exchangers or absorption heat pumps. Flue gas waste heat recovery treatment is a step in optimizing the operation of the gas-fired boiler based on the diagnostic results. It should be noted that the first target quantity can also be adjusted to determine whether the flue gas waste heat recovery treatment is deep flue gas waste heat recovery treatment or normal flue gas waste heat recovery treatment.

[0062] For example, if the second target quantity is 30% of the first hour's count (100), then the second target quantity would be 30. The first preset threshold is 6%, and the third hour's count (40) exceeds this threshold. Since the third hour's count is greater than the second target quantity, it's determined that the gas-fired boiler has flue gas heat loss within the target time period. This flue gas heat loss is caused by excessively high oxygen content in the flue gas. Therefore, the fuel and air intake in the gas-fired boiler's burner can be adjusted, i.e., the air-fuel ratio. The air-fuel ratio is the ratio of fuel to air volume. Adjusting the fuel and air intake in the gas-fired boiler's burner is a step in optimizing the operation of the gas-fired boiler based on diagnostic results.

[0063] Optionally, the diagnostic module 303 is further configured to, based on the hourly average flue gas oxygen content data, count the number of fourth hours in which the hourly average flue gas oxygen content of the gas-fired boiler is lower than the second preset threshold within the target time period, wherein the hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents; when the fourth hour number is greater than the third target number, it is determined that there is incomplete combustion heat loss in the gas-fired boiler within the target time period, wherein the third target number is determined by the first hour number, which is obtained by counting the hourly average exhaust gas temperature data.

[0064] For example, if the third target quantity is 30% of the first hour's count, and the first hour's count is 100, then the third target quantity is 30. The second preset threshold is 3%, and the fourth hour's count, which is below the second preset threshold, is 50. If the fourth hour's count is greater than the third target quantity, then the gas-fired boiler is determined to have incomplete combustion heat loss within the target time period. In this case, the incomplete combustion heat loss is caused by the low oxygen content in the flue gas, so the fuel and air intake in the gas-fired boiler's burner can be adjusted.

[0065] Optionally, the diagnostic module 303 is also configured to determine that the gas-fired boiler has heat loss due to blowdown within a target time period when the average blowdown rate is greater than a third preset threshold.

[0066] The diagnostic knowledge base contains various diagnostic tools needed for gas-fired boilers. If blowdown heat loss exists, the problem can be addressed by improving water quality or optimizing the boiler's operating mechanism.

[0067] Optionally, the diagnostic module 303 is further configured to, based on the hourly average steam pressure data, count the number of fifth hours in which the hourly average steam pressure data of the gas boiler is lower than a fourth preset threshold within a target time period, wherein the hourly average steam pressure data includes multiple hourly average steam pressure data; when the fifth hour number is greater than the fourth target number, it is determined that the gas boiler has excessive heat output within the target time period, wherein the fourth target number is determined by the first hour number, which is obtained by counting the hourly average flue gas temperature data.

[0068] For example, if 40% of the first hour's count is the fourth target quantity, and the first hour's count is 100, then the fourth target quantity is 40. The fourth preset threshold is 20. If the fourth hour's count is below the fourth preset threshold (40), then the fourth hour's count is greater than the fourth target quantity, indicating that the gas-fired boiler has excessive heat output within the target time period. In this case, the excessive heat output is caused by excessive steam pressure, so the pressure setting at the gas-fired boiler outlet or terminal can be optimized.

[0069] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this disclosure.

[0070] Figure 4 This is a schematic diagram of the electronic device 4 provided in an embodiment of this disclosure. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the various method embodiments described above. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the various device embodiments described above.

[0071] For example, computer program 403 may be divided into one or more modules / units, which are stored in memory 402 and executed by processor 401 to perform the present disclosure. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 403 in electronic device 4.

[0072] Electronic device 4 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 4 may include, but is not limited to, processor 401 and memory 402. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0073] Processor 401 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0074] The memory 402 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM. The memory 402 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 402 can include both internal and external storage units of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device. The memory 402 can also be used to temporarily store data that has been output or will be output.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0078] In the embodiments provided in this disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0079] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0081] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0082] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. An energy-saving diagnostic method for a gas-fired boiler, characterized in that, include: Obtain boiler data of a gas-fired boiler within a target time period, wherein the boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data; The boiler data is preprocessed, and target data is calculated based on the preprocessed boiler data. The target data includes: hourly average flue gas temperature, hourly average flue gas oxygen content, average blowdown rate, and hourly average steam pressure. Based on the target data, an energy-saving diagnosis is performed on the gas-fired boiler using a diagnostic knowledge base to obtain diagnostic results, wherein the diagnostic results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excessive heat output. Optimize the operation of the gas-fired boiler based on the diagnostic results.

2. The method according to claim 1, characterized in that, The boiler data is preprocessed, including: Determine the duration during which the gas-fired boiler is in a shutdown state based on the aforementioned operating status data; Based on the time the gas-fired boiler was in the shutdown state, the data of the gas-fired boiler in the shutdown state were removed from the flue gas temperature data, the flue gas oxygen content data, the wastewater discharge data, the evaporation data, and the boiler terminal steam pressure data, respectively. Null values ​​and out-of-limit data exceeding the preset range are determined from the flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data, respectively, and the null values ​​and out-of-limit data are removed from the original data.

3. The method according to claim 1, characterized in that, Based on the boiler data after the aforementioned data preprocessing, the target data is calculated, including: The flue gas temperature data, flue gas oxygen content data, and boiler terminal steam pressure data after the aforementioned data preprocessing are divided into hourly segments. The hourly average data of flue gas temperature, flue gas oxygen content, and boiler terminal steam pressure are calculated to obtain the hourly average flue gas temperature, the hourly average flue gas oxygen content, and the hourly average steam pressure. The average discharge rate is determined based on the preprocessed wastewater discharge data and evaporation data.

4. The method according to claim 1, characterized in that, Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: Based on the hourly average flue gas temperature data, the total number of hours the gas boiler operated within the target time period and the number of hours the hourly average flue gas temperature exceeded the preset temperature are calculated. The hourly average flue gas temperature data includes multiple hourly average flue gas temperatures. When the second number of hours is greater than the first target quantity, it is determined that the gas-fired boiler experiences flue gas heat loss within the target time period, wherein the first target quantity is determined by the first number of hours; or Based on the hourly average flue gas oxygen content data, the number of third hours in which the hourly average flue gas oxygen content of the gas boiler exceeds the first preset threshold within the target time period is calculated, wherein the hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents. When the third hour is greater than the second target number, it is determined that the gas boiler has flue gas heat loss during the target time period, wherein the second target number is determined by the first hour.

5. The method according to claim 1, characterized in that, Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: Based on the hourly average flue gas oxygen content data, the number of fourth hours in which the hourly average flue gas oxygen content of the gas boiler is lower than the second preset threshold within the target time period is calculated. The hourly average flue gas oxygen content data includes multiple hourly average flue gas oxygen contents. When the fourth hour is greater than the third target number, it is determined that the gas boiler has incomplete combustion heat loss during the target time period. The third target number is determined by the first hour, which is obtained by statistical analysis of the hourly average flue gas temperature data.

6. The method according to claim 1, characterized in that, Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: When the average wastewater discharge rate is greater than a third preset threshold, it is determined that the gas-fired boiler has wastewater heat loss during the target time period.

7. The method according to claim 1, characterized in that, Based on the target data, an energy-saving diagnosis of the gas-fired boiler is performed using a diagnostic knowledge base to obtain diagnostic results, including: Based on the hourly average steam pressure data, the number of fifth hours in which the hourly average steam pressure data of the gas boiler is lower than the fourth preset threshold within the target time period is calculated. The hourly average steam pressure data includes multiple hourly average steam pressure data. When the fifth hour is greater than the fourth target number, it is determined that the gas boiler has excessive heat output during the target time period. The fourth target number is determined by the first hour, which is obtained by statistical analysis of the hourly average flue gas temperature data.

8. An energy-saving diagnostic device for a gas-fired boiler, characterized in that, include: The acquisition module is configured to acquire boiler data of the gas-fired boiler within a target time period, wherein the boiler data includes: operating status data, flue gas temperature data, flue gas oxygen content data, wastewater discharge data, evaporation data, and boiler terminal steam pressure data. The calculation module is configured to perform data preprocessing on the boiler data and calculate target data based on the preprocessed boiler data. The target data includes: hourly average flue gas temperature data, hourly average flue gas oxygen content data, average blowdown rate, and hourly average steam pressure data. The diagnostic module is configured to perform energy-saving diagnosis on the gas-fired boiler based on the target data using a diagnostic knowledge base, and obtain diagnostic results, wherein the diagnostic results include: flue gas heat loss, incomplete combustion heat loss, blowdown heat loss, and excessive heat output. An optimization module is configured to optimize the operation of the gas-fired boiler based on the diagnostic results.

9. 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 computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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