Gas-fired steam boiler energy consumption diagnostic system and method

Abstract

The present disclosure relates to the technical field of energy, and provides a gas-steam boiler energy consumption diagnosis system and method. The gas-steam boiler energy consumption diagnosis system comprises: a data acquisition module configured to acquire energy consumption data and influence factor data of a diagnosis project; a steam unit consumption analysis module configured to statistically analyze steam unit consumption of the diagnosis project, and acquire hourly steam unit consumption of the diagnosis project in different steam unit consumption intervals; a trend analysis module configured to acquire hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content and hourly average boiler outlet pressure corresponding to the hourly steam unit consumption in the different steam unit consumption intervals; and a data fitting module configured to fit a change relationship between any two parameters in the hourly steam unit consumption, the hourly average boiler load rate, the hourly average flue gas temperature, the hourly average flue gas oxygen content and the hourly average boiler outlet pressure, and obtain a fitting result.

Description

Technical Field

[0001] This disclosure relates to the field of energy technology, and in particular to an energy consumption diagnosis system and method for gas-fired steam boilers. Background Technology

[0002] Gas-fired steam boilers are commonly used steam production equipment in daily life and production, and are a major energy production equipment. If the energy consumption of gas-fired steam boilers is high, they will consume a lot of energy, which will not only affect the ecological environment, but also increase operating costs.

[0003] Boiler operation and maintenance providers are responsible for the operation and management of multiple gas-fired steam boiler projects. The equipment conditions and operating conditions vary between these projects. Many factors influence the energy consumption of gas-fired steam boilers. Equipment energy efficiency, operating conditions, and control levels all affect energy consumption. Changes in parameters such as boiler load rate, flue gas temperature, flue gas oxygen content, boiler start-up and shutdown frequency, boiler outlet steam pressure, and blowdown rate also impact the energy consumption of the gas-fired steam boiler.

[0004] For projects with high energy consumption, quickly identifying the causes of high energy consumption is a crucial aspect of energy conservation. Current technologies for analyzing the causes of high energy consumption in gas-fired steam boilers require obtaining relevant data from the local control system or database, processing the data, and then using local analysis tools. This process involves large amounts of data, high analysis difficulty, poor timeliness, and low efficiency. Summary of the Invention

[0005] In view of this, the present disclosure provides an energy consumption diagnosis system and method for gas-fired steam boilers to solve the problems of difficulty and low efficiency in analyzing high energy consumption of gas-fired steam boilers in the prior art.

[0006] A first aspect of this disclosure provides an energy consumption diagnosis system for a gas-fired steam boiler. The system includes: a data acquisition module for acquiring energy consumption data and influencing factor data for a diagnostic item; the energy consumption data includes the steam unit consumption of the diagnostic item, and the influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure; a steam unit consumption analysis module for statistically analyzing the steam unit consumption of the diagnostic item to obtain the hourly steam unit consumption of the diagnostic item within different steam unit consumption ranges; a trend analysis module for obtaining the hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure corresponding to the hourly steam unit consumption within different steam unit consumption ranges; and a data fitting module for fitting the variation relationship between any two parameters among the hourly steam unit consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure to obtain a fitting result.

[0007] A second aspect of this disclosure provides a method for diagnosing the energy consumption of a gas-fired steam boiler. This method includes: acquiring energy consumption data and influencing factor data for a diagnostic item; the energy consumption data includes the steam consumption per unit of the diagnostic item; and the influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure; performing statistical analysis on the steam consumption per unit of the diagnostic item to obtain the hourly steam consumption per unit of the diagnostic item within different steam consumption ranges; obtaining the hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure corresponding to the hourly steam consumption per unit of the diagnostic item within different steam consumption ranges; and fitting the relationship between any two parameters among the hourly steam consumption per unit of the hourly steam consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure to obtain a fitting result.

[0008] The beneficial effects of this disclosed embodiment compared with the prior art are as follows: by calculating the hourly steam consumption of the diagnostic project in different steam consumption ranges and the hourly values ​​of the corresponding influencing factors, and fitting the relationship between any two parameters in the calculated data, the influencing factors that have a significant impact on energy consumption can be identified, thereby quickly locating the cause of high energy consumption in the diagnostic project and improving the timeliness and efficiency of high energy consumption analysis of gas-fired steam boilers. Attached Figure Description

[0009] 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.

[0010] Figure 1 This is a schematic diagram of the structure of a gas-fired steam boiler energy consumption diagnosis system provided in an embodiment of this disclosure;

[0011] Figure 2 This is a schematic diagram of another gas-fired steam boiler energy consumption diagnosis system provided in an embodiment of this disclosure;

[0012] Figure 3 This is a schematic flowchart of an energy consumption diagnosis method for a gas-fired steam boiler provided in an embodiment of this disclosure;

[0013] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0014] 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.

[0015] The following will describe in detail, with reference to the accompanying drawings, the energy consumption diagnosis system and method for gas-fired steam boilers according to embodiments of the present disclosure.

[0016] Figure 1 This is an energy consumption diagnostic system for a gas-fired steam boiler provided in an embodiment of this disclosure. For example... Figure 1 As shown, the energy consumption diagnostic system for gas-fired steam boilers includes:

[0017] The data acquisition module 101 is used to acquire energy consumption data and influencing factor data of the diagnostic project. The energy consumption data includes the steam consumption per unit of the diagnostic project, and the influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content and boiler outlet pressure.

[0018] The steam consumption analysis module 102 is used to perform statistical analysis on the steam consumption of the diagnostic items and obtain the hourly steam consumption of the diagnostic items in different steam consumption ranges.

[0019] The trend analysis module 103 is used to obtain the hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure corresponding to the hourly steam consumption in different steam consumption ranges.

[0020] The data fitting module 104 is used to fit the variation relationship between any two parameters among hourly steam consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure, and obtain the fitting result.

[0021] According to the energy consumption diagnosis scheme for gas-fired steam boilers provided in this disclosure, energy consumption data and influencing factors of the diagnosis items can be automatically obtained to determine the influencing factors of high energy consumption of gas-fired steam boilers and thus identify the problems of the diagnosis items.

[0022] Specifically, the data acquisition module 101 can determine the time and time granularity of the data analysis, such as month, quarter, or year, and acquire energy consumption data and influencing factor data for diagnostic projects, median projects, and benchmark projects.

[0023] Among them, diagnostic items are those requiring analysis, benchmarking items are manually selected items, and median items are those whose energy consumption is at the median level among all items. Median items are those whose steam consumption per unit area is automatically obtained based on the selected time period, representing the median value among all items. Energy consumption data includes fuel consumption, steam production data, and the steam consumption per unit area for each item that can be calculated based on the energy consumption data.

[0024] Steam consumption per unit of steam output is the amount of gas consumed, and it is an indicator for evaluating gas-fired steam boilers. A lower steam consumption per unit of steam output indicates better energy efficiency; a higher steam consumption per unit of steam output indicates poorer energy efficiency. The formula for calculating steam consumption per unit of steam output (q) is as follows:

[0025]

[0026] Among them, Q 燃气 Gas consumption is measured in cubic meters over the time period analyzed; Q 蒸汽 Steam production is measured in tons within the time frame used for analysis.

[0027] In this embodiment of the disclosure, the influencing factor data includes IoT influencing factor data and non-IoT influencing factor data. The IoT influencing factor data includes: steam consumption per unit, boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure. Specifically, the boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure are the average boiler load rate, average flue gas temperature, average flue gas oxygen content, and average boiler outlet pressure, respectively, representing the average values ​​under the boiler's operating conditions within the query time range. Steam consumption per unit can be calculated using the steam consumption per unit calculation formula.

[0028] Non-IoT influencing factors data include: boiler start-up and shutdown frequency, blowdown rate, inspection frequency, maintenance frequency, repair frequency, and remaining defects. Among these, boiler start-up and shutdown frequency, inspection frequency, maintenance frequency, repair frequency, and remaining defects are all statistical data. The blowdown rate K can be calculated using the blowdown rate formula:

[0029]

[0030] Among them, Q 污 The cumulative discharge volume over the diagnostic time period is expressed in tons; Q 汽 The cumulative evaporation over the diagnostic time range is expressed in tons.

[0031] The steam unit consumption analysis module 102 can also be used for progressive steam unit consumption statistical analysis, specifically including steam unit consumption statistics and steam unit consumption distribution.

[0032] Steam consumption statistics are compiled by counting the cumulative hours of hourly steam consumption in different steam consumption intervals for diagnostic items, median items, and benchmark items. Steam consumption q can be divided into five steam consumption intervals: q<70, 70≤q<80, 80≤q<90, 90≤q<100, and 100≤q, where q is the steam consumption.

[0033] The steam unit consumption analysis module 102 can also be used to compare the hourly steam unit consumption distribution of diagnostic items, median items, and benchmark items, including the upper and lower limit range of steam unit consumption, the median value of steam unit consumption, and the distribution density of steam unit consumption.

[0034] By comparing and analyzing steam consumption statistics, the differences in steam consumption distribution among diagnostic items, median items, and benchmark items can be identified. The steam consumption analysis module 102 can statistically analyze the cumulative time for diagnostic items, median items, and benchmark items across different steam consumption intervals, and compare their steam consumption distributions.

[0035] The steam unit consumption analysis module 102 can also be used to statistically analyze the hourly steam unit consumption of diagnostic projects, median projects, and benchmark projects, and the cumulative number of hours in different steam unit consumption ranges.

[0036] Specifically, the steam consumption analysis module can perform hourly steam consumption analysis on diagnostic items to obtain the upper and lower limit range of steam consumption, the median value of steam consumption, and the distribution density of steam consumption.

[0037] The trend analysis module 103 can also analyze the hourly values ​​of IoT influencing factor data, including: hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure. All four indicators can be the average values ​​of data under boiler operating conditions.

[0038] In this embodiment of the disclosure, the trend analysis module 103 can also analyze and compare the distribution of influencing factors of diagnostic items, median items and benchmark items, including the upper and lower limit range of influencing factors, the median value of influencing factors and the distribution density of influencing factors.

[0039] Specifically, the trend analysis module 103 can analyze and diagnose the changing trends of the project's hourly steam consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure over time.

[0040] The trend analysis module 103 can also analyze and diagnose the changing trends of various influencing factors of the project over time, and view the distribution of hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content and hourly average boiler outlet pressure corresponding to hourly steam consumption in different steam consumption ranges, as well as the time of occurrence.

[0041] In this embodiment of the disclosure, when the data fitting module 104 analyzes the relationship between energy consumption and influencing factors through data fitting, it can specifically analyze the relationship between any two parameters such as hourly steam consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure, with each hour corresponding to a set of data.

[0042] When selecting two parameters, you can choose one energy consumption index or influencing factor as the analysis parameter and the other energy consumption index or influencing factor as the variable parameter.

[0043] When analyzing the relationship between the analytical parameter and the variable parameter, a cubic equation in one variable is fitted: y = ax 3 +bx 2 +cx+d means fitting the relationship to the variable using this cubic equation as the fitting equation, and obtaining the fitting result. Here, y corresponds to the analysis parameter, x corresponds to the variable parameter, and a, b, c, and d are constant coefficients.

[0044] Commonly used analytical combinations that include analytical parameters and variable parameters include: hourly steam consumption and hourly average boiler load rate, hourly steam consumption and hourly average flue gas temperature, hourly steam consumption and hourly average flue gas oxygen content, hourly steam consumption and hourly average boiler outlet pressure, hourly average boiler load rate and hourly average flue gas temperature, and hourly average boiler load rate and hourly average flue gas oxygen content.

[0045] like Figure 2 As shown, the gas-fired steam boiler energy consumption diagnosis system may also include an over-limit analysis module 201, which is used to determine whether the data of the influencing factors of the diagnosis item are within the set range.

[0046] The over-limit analysis module 201 can determine whether the IoT influencing factor data of the diagnostic project is within a reasonable range, thereby analyzing whether each influencing factor of the diagnostic project exceeds the limit.

[0047] The IoT influencing factors data can include: average boiler load rate, average flue gas temperature, average flue gas oxygen content, and average boiler outlet pressure. The reasonable ranges are defined as follows: reasonable boiler load rate range of 30%-100%; reasonable flue gas temperature range of 60℃-80℃; reasonable flue gas oxygen content range of 3%-6%; and reasonable boiler outlet pressure range defined based on end-user pressure requirements. If the IoT influencing factor data exceeds the reasonable range, further analysis is required through the influencing factor analysis module.

[0048] like Figure 2As shown, the gas-fired steam boiler energy consumption diagnostic system may also include an influencing factor analysis module 202, used to analyze the hourly values ​​of IoT influencing factor data to obtain the upper and lower limit ranges, median values, and distribution density of the IoT influencing factor data. The IoT influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure. The influencing factor analysis module 202 can compare the distribution of influencing factors for diagnostic items, median items, and benchmark items, including the upper and lower limit ranges, median values, and distribution density of the influencing factors.

[0049] In related technologies, traditional analysis methods only analyze a single project, lacking horizontal comparisons between similar projects. They fail to identify differences in influencing factors and energy consumption between projects, and cannot reveal the energy-saving potential and potential problems of a project through inter-project comparison. The gas-fired steam boiler energy consumption diagnostic system in this disclosure can compare projects with median energy consumption levels with benchmark projects. By comparing the differences in energy consumption and influencing factors between projects, it can promptly identify project problems.

[0050] like Figure 2 As shown, the gas-fired steam boiler energy consumption diagnosis system may also include a project overview module 203, which is used to compare the steam unit consumption, IoT influencing factor data and non-IoT influencing factor data of the diagnosed project, median project and benchmark project, to obtain the differences in steam unit consumption between projects, as well as the differences in IoT influencing factor data and non-IoT influencing factor data.

[0051] Comparing data across projects reveals differences in steam consumption per unit area and the influencing factors. By analyzing the magnitude of these differences, we can preliminarily identify the factors contributing to the high energy consumption of the diagnosed project.

[0052] like Figure 2 As shown, the gas-fired steam boiler energy consumption diagnostic system may also include a display module 204, which is used to display the fitting results as well as the analysis results and comparison differences of the out-of-limit analysis module, the influencing factor analysis module, and the project overview module.

[0053] The display module enables user interaction with the system and showcases the business interface. This module supports clients across different terminal devices, operating systems, language environments, and network environments. These clients include large-screen terminals, PC (personal computer) terminals, and APP (smart terminal application) terminals for management departments and enterprises. Based on this display module, the gas-fired steam boiler energy consumption diagnostic system can provide PC and APP terminals for boiler hosting and maintenance companies, thereby supporting enterprises in analyzing the causes of high energy consumption in gas-fired steam boilers.

[0054] The gas-fired steam boiler energy consumption diagnosis system in this embodiment calculates the hourly steam consumption of the diagnostic item within different steam consumption ranges and the hourly values ​​of the corresponding influencing factors. By fitting the relationship between any two parameters in the calculated data, it can identify the influencing factors that have a significant impact on energy consumption, thereby quickly locating the cause of high energy consumption in the diagnostic item and improving the timeliness and efficiency of high energy consumption analysis of gas-fired steam boilers.

[0055] The following are embodiments of the method disclosed herein, which are executed by the system in the system embodiments of this disclosure. The energy consumption diagnosis method for gas-fired steam boilers described below and the energy consumption diagnosis system for gas-fired steam boilers described above can be referred to in correspondence. For details not disclosed in the embodiments of the method disclosed herein, please refer to the system embodiments of this disclosure.

[0056] Figure 3 This is a flowchart illustrating a method for diagnosing the energy consumption of a gas-fired steam boiler according to an embodiment of this disclosure. The method provided in this embodiment can be executed by any electronic device with computer processing capabilities, such as a terminal or server. Figure 3 As shown in the embodiments of this disclosure, the energy consumption diagnosis method for gas-fired steam boilers includes:

[0057] Step S301: Obtain energy consumption data and influencing factor data for the diagnostic items. The energy consumption data includes the steam consumption per unit of the diagnostic items, and the influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure.

[0058] Step S302: Perform statistical analysis on the steam consumption of the diagnostic items to obtain the hourly steam consumption of the diagnostic items in different steam consumption ranges.

[0059] Specifically, steam consumption statistical analysis can also be performed, including steam consumption statistics and steam consumption distribution. Steam consumption statistics involve calculating the cumulative hours of hourly steam consumption for diagnostic items, median items, and benchmark items across different steam consumption intervals. By comparing steam consumption statistical analyses, differences in the steam consumption distributions of diagnostic items, median items, and benchmark items can be identified. For example, the cumulative time for diagnostic items, median items, and benchmark items across different steam consumption intervals can be calculated and their distributions compared.

[0060] The formula for calculating steam consumption q is as follows:

[0061]

[0062] Among them, Q 燃气 Gas consumption is measured in cubic meters over the time period analyzed; Q 蒸汽 Steam production is measured in tons within the time frame used for analysis.

[0063] Step S303: Obtain the hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure corresponding to the hourly steam consumption in different steam consumption ranges.

[0064] Specifically, the hourly values ​​of IoT influencing factors data can also be analyzed, including: hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure. All four indicators can be the average values ​​of data under boiler operating conditions.

[0065] Step S304: Fit the relationship between any two parameters among hourly steam consumption, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure to obtain the fitting result.

[0066] Specifically, when analyzing the relationship between energy consumption and influencing factors through data fitting, we can analyze the relationship between any two parameters: hourly steam consumption per unit, hourly average boiler load rate, hourly average flue gas temperature, hourly average flue gas oxygen content, and hourly average boiler outlet pressure. Each hour corresponds to a set of data.

[0067] When selecting two parameters, one energy consumption index or influencing factor can be chosen as the analysis parameter, and the other as the variable parameter. Commonly used combinations of analysis parameters and variable parameters include: hourly steam consumption per unit area and hourly average boiler load rate, hourly steam consumption per unit area and hourly average flue gas temperature, hourly steam consumption per unit area and hourly average flue gas oxygen content, hourly steam consumption per unit area and hourly average boiler outlet pressure, hourly average boiler load rate and hourly average flue gas temperature, and hourly average boiler load rate and hourly average flue gas oxygen content.

[0068] According to the energy consumption diagnosis scheme for gas-fired steam boilers provided in this disclosure, energy consumption data and influencing factors of the diagnosis items can be automatically obtained to determine the influencing factors of high energy consumption of gas-fired steam boilers and thus identify the problems of the diagnosis items.

[0069] In step S301, the time and time granularity of the data analysis can be determined, such as month, quarter, year, and energy consumption data and influencing factor data of diagnostic items, median items and benchmark items can be obtained.

[0070] Among them, diagnostic items are those requiring analysis, benchmarking items are manually selected items, and median items are those whose energy consumption is at the median level among all items. Median items are those whose steam consumption per unit area is automatically obtained based on the selected time period, representing the median value among all items. Energy consumption data includes fuel consumption, steam production data, and the steam consumption per unit area for each item that can be calculated based on the energy consumption data.

[0071] Before step S302, it can be determined whether the data on the influencing factors of the diagnostic project are within the set range. These set ranges include: a reasonable range for boiler load rate of 30%-100%; a reasonable range for flue gas temperature of 60℃-80℃; a reasonable range for flue gas oxygen content of 3%-6%; and a reasonable range for boiler outlet pressure defined according to the pressure requirements of the end user. If the IoT influencing factor data exceeds the reasonable range, further analysis is required through the influencing factor analysis module.

[0072] In step S302, hourly steam consumption analysis can also be performed on the diagnostic items to obtain the upper and lower limit range of steam consumption, the median value of steam consumption, and the distribution density of steam consumption.

[0073] By comparing and analyzing the statistical data of steam consumption per unit, the differences in the distribution of steam consumption per unit among diagnostic items, median items, and benchmark items can be identified.

[0074] Before step S303, the hourly values ​​of the IoT influencing factor data can be analyzed to obtain the upper and lower limit ranges of the IoT influencing factor data, the median value of the influencing factors, and the distribution density of the influencing factors. The IoT influencing factor data includes boiler load rate, flue gas temperature, flue gas oxygen content, and boiler outlet pressure.

[0075] In this embodiment of the disclosure, the influencing factor data also includes non-IoT influencing factor data, which includes: boiler start-up and shutdown frequency, blowdown rate, inspection frequency, maintenance frequency, repair frequency, and remaining defects. The blowdown rate K can be calculated according to the blowdown rate formula:

[0076]

[0077] Among them, Q 污 The cumulative discharge volume over the diagnostic time period is expressed in tons; Q 汽 The cumulative evaporation over the diagnostic time range is expressed in tons.

[0078] In related technologies, traditional analysis methods only analyze a single project, lacking horizontal comparisons between similar projects. They fail to identify differences in influencing factors and energy consumption between projects, and cannot reveal the energy-saving potential and potential problems of a project through inter-project comparison. The gas-fired steam boiler energy consumption diagnostic system in this disclosure can compare projects with median energy consumption levels with benchmark projects. By comparing the differences in energy consumption and influencing factors between projects, it can promptly identify project problems.

[0079] Specifically, before step S304, the steam consumption per unit of the diagnostic project, the median project, and the benchmark project, as well as the data on IoT influencing factors and the data on non-IoT influencing factors, can be compared to obtain the differences in steam consumption per unit of each project, and the differences in IoT influencing factor data and non-IoT influencing factor data.

[0080] Comparing data across projects reveals differences in steam consumption per unit area and the influencing factors. By analyzing the magnitude of these differences, we can preliminarily identify the factors contributing to the high energy consumption of the diagnosed project.

[0081] In this embodiment of the disclosure, the hourly steam consumption of diagnostic items, median items, and benchmark items can be statistically analyzed for the cumulative number of hours in different steam consumption intervals. Different steam consumption intervals include: q < 70, 70 ≤ q < 80, 80 ≤ q < 90, 90 ≤ q < 100, and 100 ≤ q, where q is the steam consumption per unit of steam.

[0082] In step S304, the following cubic equation can also be used as the fitting equation to fit the changing relationship and obtain the fitting result:

[0083] y = ax 3 +bx 2 +cx+d

[0084] Where y is the analysis parameter, x is the variable parameter, and a, b, c, and d are constant coefficients.

[0085] In this embodiment of the disclosure, the energy consumption diagnosis scheme for gas-fired steam boilers can also display the fitting results, as well as the analysis results and comparison differences of over-limit analysis, influencing factor analysis, and project overview, thereby providing support for enterprises to analyze the reasons for high energy consumption of gas-fired steam boilers.

[0086] Since the steps of the gas-fired steam boiler energy consumption diagnosis method in the example embodiments of this disclosure correspond to the modules of the above-described gas-fired steam boiler energy consumption diagnosis system, for details not disclosed in the method embodiments of this disclosure, please refer to the above-described embodiments of the gas-fired steam boiler energy consumption diagnosis system.

[0087] 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.

[0088] 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.

[0089] The energy consumption diagnosis method for gas-fired steam boilers in this embodiment calculates the hourly steam consumption of the diagnostic item within different steam consumption ranges and the hourly values ​​of the corresponding influencing factors. By fitting the relationship between any two parameters in the calculated data, the influencing factors that have a significant impact on energy consumption can be identified, thereby quickly locating the cause of high energy consumption in the diagnostic item and improving the timeliness and efficiency of high energy consumption analysis of gas-fired steam boilers.

[0090] This disclosure also 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 above-described gas-fired steam boiler energy consumption diagnosis method.

[0091] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described gas-fired steam boiler energy consumption diagnosis method.

[0092] 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 in the various device embodiments described above.

[0093] 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 different components.

[0094] The processor 401 may 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.

[0095] The memory 402 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM of the electronic device 4. 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, Flash Card, etc., equipped on the electronic device 4. The memory 402 can also 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.

[0096] 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.

[0097] If the integrated module 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. The 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 the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0098] 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. A gas-steam boiler energy consumption diagnostic system, characterized in that, The gas-steam boiler energy consumption diagnosis system comprises: A data acquisition module is configured to acquire energy consumption data and influence factor data of a diagnosis project, wherein the energy consumption data comprises steam unit consumption of the diagnosis project, and the influence factor data comprises a boiler load rate, a flue gas temperature, a flue gas oxygen content, and a boiler outlet pressure; A steam unit consumption analysis module is configured to statistically analyze the steam unit consumption of the diagnosis project, and acquire hourly steam unit consumption of the diagnosis project in different steam unit consumption intervals; A trend analysis module is configured to acquire hourly average boiler load rates, hourly average flue gas temperatures, hourly average flue gas oxygen contents, and hourly average boiler outlet pressures corresponding to the hourly steam unit consumption in the different steam unit consumption intervals; A data fitting module is configured to fit a change relationship between any two parameters of the hourly steam unit consumption, the hourly average boiler load rates, the hourly average flue gas temperatures, the hourly average flue gas oxygen contents, and the hourly average boiler outlet pressures, and obtain a fitting result; A project overview module is configured to compare steam unit consumption, internet-of-things (IoT) influence factor data, and non-IoT influence factor data of the diagnosis project, a median project, and a benchmark project, acquire differences in steam unit consumption among the projects, and differences in the IoT influence factor data and the non-IoT influence factor data.

2. The gas and steam boiler energy consumption diagnostic system according to claim 1, characterized in that, Further comprising an out-of-limit analysis module configured to determine whether the influence factor data of the diagnosis project is located in a set interval.

3. The gas and steam boiler energy consumption diagnostic system according to claim 1, characterized in that, The steam unit consumption analysis module is further configured to perform hourly steam unit consumption analysis on the diagnosis project, and obtain upper and lower limit range intervals of the steam unit consumption, a median value of the steam unit consumption, and a distribution density of the steam unit consumption.

4. The gas and steam boiler energy consumption diagnostic system according to claim 1, characterized in that, Further comprising an influence factor analysis module configured to analyze hourly values of the IoT influence factor data, and obtain upper and lower limit range intervals of the IoT influence factor data, a median value of the influence factor, and a distribution density of the influence factor, wherein the IoT influence factor data comprises the boiler load rate, the flue gas temperature, the flue gas oxygen content, and the boiler outlet pressure.

5. The gas and steam boiler energy consumption diagnostic system of claim 4, wherein The influence factor data further comprises non-IoT influence factor data, and the non-IoT influence factor data comprises: A number of times of starting and stopping of the boiler, a blowdown rate, a number of times of inspection, a number of times of maintenance, a number of times of repair, and a remaining defect.

6. The gas and steam boiler energy consumption diagnostic system of claim 1, wherein The different steam unit consumption intervals comprise: q<70, 70≤q<80, 80≤q<90, 90≤q<100, and 100≤q, wherein q is the steam unit consumption.

7. The gas and steam boiler energy consumption diagnostic system of claim 1, wherein The fitting of the change relationship to obtain the fitting result comprises: fitting the change relationship to obtain the fitting result by using a monomial cubic equation as a fitting equation: y = ax 3 + bx 2 + cx + d wherein y is an analysis parameter, x is a variable parameter, a, b, c, and d are constant coefficients.

8. The gas and steam boiler energy consumption diagnostic system of claim 1, wherein Further comprising a display module configured to display the fitting result.

9. A gas-steam boiler energy consumption diagnostic method, characterized by, The gas-steam boiler energy consumption diagnosis method comprises: acquiring energy consumption data and influence factor data of a diagnosis project, wherein the energy consumption data comprises steam unit consumption of the diagnosis project, and the influence factor data comprises a boiler load rate, a flue gas temperature, a flue gas oxygen content, and a boiler outlet pressure; statistically analyzing the steam unit consumption of the diagnosis project, and acquiring hourly steam unit consumption of the diagnosis project in different steam unit consumption intervals; acquire the hourly average boiler load rate, the hourly average flue gas temperature, the hourly average oxygen content of flue gas and the hourly average boiler outlet pressure corresponding to the hourly steam consumption in the different steam consumption intervals; fit the change relationship between any two parameters of the hourly steam consumption, the hourly average boiler load rate, the hourly average flue gas temperature, the hourly average oxygen content of flue gas and the hourly average boiler outlet pressure to obtain a fitting result; compare the steam consumption, the data of the material-communication influencing factors and the data of the non-material-communication influencing factors of the diagnosis project, the median project and the benchmarking project, acquire the difference of the steam consumption among the projects, and the difference of the data of the material-communication influencing factors and the data of the non-material-communication influencing factors.

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