Boiler intelligent temperature coordination system and method based on machine learning

Abstract

The invention discloses a boiler intelligent temperature coordination system and method based on machine learning, and relates to the field of intelligent temperature control, the system comprises an information sensing module, an information transmission module, an intelligent control module and a response execution module; the information sensing module collects the fuel adding amount, the steam temperature, the attemperation water flow and the steam pressure in the boiler in unit time through a sensor and converts the fuel adding amount, the steam temperature, the attemperation water flow and the steam pressure into digital signals. The information transmission module transmits the collected data to the intelligent control module for intelligent analysis and processing; the collected data information is analyzed and processed through the intelligent control module, and whether an adjustment instruction needs to be generated or not is judged; the response execution module adjusts the fuel adding amount, a steam valve and the attemperation water flow in unit time according to the analysis processing result to achieve the effect of adjusting the steam temperature; the economic benefit of the whole boiler unit is improved by coordinately controlling the boiler steam temperature, and the boiler energy consumption and failure rate are reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature coordination technology, specifically to an intelligent temperature coordination system and method for boilers based on machine learning. Background Technology

[0002] Boilers have a wide range of applications, especially in the industrial sector where they play a crucial role. For example, steam boilers, also known as steam generators, convert the chemical energy of fuel into thermal energy to the maximum extent through the boiler's combustion equipment. Water then absorbs the thermal energy to generate steam, which can directly provide thermal energy or convert thermal energy into mechanical or electrical energy. This is an important component of steam power plants and can be used in industrial fields such as thermal power plants, ships, locomotives, and mining enterprises.

[0003] There are still many problems to be solved in the operation of boilers. Intelligent temperature control of boiler steam is one of the more important ones. When the boiler unit is in boiling water, the heat production of the boiler is unstable and steam overheating may occur. Both excessively high and low boiler steam temperatures will have many adverse effects on the boiler equipment, such as reduced component life, increased equipment failure rate, and high energy consumption, which will reduce the economic efficiency of the entire boiler unit. Moreover, if the control is not timely, steam overheating may even lead to dangerous accidents such as tube rupture. Since steam temperature is affected by multiple factors and the detection and control equipment is in an environment that is not easy to operate manually, an intelligent adjustment method is needed to control the steam temperature by coordinating multiple factors. Summary of the Invention

[0004] The purpose of this invention is to provide a machine learning-based intelligent temperature coordination system and method for boilers to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a machine learning-based intelligent temperature coordination system for boilers, which includes an information sensing module, an information transmission module, an intelligent control module, and a response execution module;

[0006] The information sensing module uses sensors to collect data on fuel addition, steam temperature, desuperheating water flow rate, and steam pressure within the boiler per unit time, and converts the collected analog signals into digital signals.

[0007] The analog signals collected by the sensor are transmitted to the intelligent control module through the information transmission module for intelligent analysis and processing.

[0008] The intelligent control module analyzes and processes the collected data, and determines whether an adjustment command needs to be generated through comparative analysis.

[0009] The response execution module adjusts the fuel addition amount, steam valve, and desuperheating water flow rate per unit time based on the analysis and processing results to achieve the effect of adjusting the steam temperature.

[0010] Furthermore, the information sensing module includes a sensor network unit and a data acquisition unit. The sensor network unit senses the amount of fuel added per unit time through a weighing sensor. The higher the amount of fuel added, the higher the heat generated after combustion, and the higher the heat transferred to the steam. Therefore, by controlling the amount of fuel added, the steam temperature can be controlled from the energy source. Adjusting the amount of fuel added per unit time can also avoid energy waste caused by excessive fuel addition and incomplete combustion. The temperature sensor placed inside the boiler senses the steam temperature. The pressure sensor senses changes in the steam pressure. The greater the degree of opening and closing of the steam valve, the greater the amount of steam entering, the greater the steam pressure, and the higher the steam temperature. A flow rate sensor is placed at the desuperheating water inlet to sense changes in the desuperheating water flow rate. The desuperheating water can adjust the steam temperature in a timely manner through heat transfer. The data acquisition unit converts the fuel added per unit time, steam temperature, steam pressure, and desuperheating water flow rate sensed by the sensors into digital signals and collects them into a memory.

[0011] Furthermore, the intelligent control module includes a data analysis unit, a comparison analysis unit, and an instruction generation unit; the data analysis unit analyzes and processes the collected steam pressure and fuel addition data per unit time, and simultaneously analyzes the data on the change of steam temperature inside the boiler, thereby analyzing the impact of the changes of these two factors on the temperature change; the comparison analysis unit compares the collected steam temperature with the standard temperature range and determines whether the steam exceeds the temperature limit; the instruction generation unit generates adjustment instructions based on the judgment result.

[0012] Furthermore, the data analysis unit further processes the data collected by the sensors, classifies and stores the three types of data collected at the same time, and sets the standard steam temperature range of the boiler as [T0-5, T0+5]. The data analysis unit analyzes the historical data of collected steam temperature and steam pressure, establishes a mathematical model to analyze the relationship between steam temperature and steam pressure, and derives the corresponding steam pressure range [P] from the standard steam temperature value of the boiler. c P d By analyzing historical data on steam temperature and fuel addition per unit time, a mathematical model is established to analyze the relationship between steam temperature and fuel addition per unit time, and the corresponding fuel addition range per unit time is obtained using the boiler standard steam temperature value. a X b ].

[0013] Furthermore, the comparison and analysis unit is equipped with a standard steam temperature T0 and an acceptable error temperature of ±5℃. The comparison and analysis unit compares the collected steam temperature T0 with the set standard temperature to determine whether the steam temperature exceeds the limit; and determines whether the collected fuel addition amount X per unit time falls within the fuel addition amount range per unit time corresponding to the boiler's standard steam temperature range [X]. a X b Within ], and to determine whether the steam pressure P is within the steam pressure range corresponding to the boiler's standard steam temperature [P c P d Within.

[0014] Furthermore, the instruction generation unit determines whether an adjustment instruction needs to be generated based on the comparison analysis results between the collected temperature and the standard temperature range; if the collected temperature is outside the standard temperature range, an adjustment instruction needs to be generated to adjust the flow rate of the desuperheating water to adjust the steam temperature; otherwise, no adjustment instruction is generated; at the same time, it needs to determine whether to generate an instruction to adjust the fuel addition amount and steam valve per unit time based on the comparison results between the fuel addition amount and steam pressure per unit time and the addition amount and pressure range corresponding to the standard temperature range.

[0015] A machine learning-based intelligent temperature coordination method for boilers includes the following steps:

[0016] S1: Collect the steam temperature and pressure in the superheater of the boiler and the flow rate of desuperheating water, as well as the amount of fuel added per unit time in the boiler, and perform preliminary processing on the collected information.

[0017] S2: Analyze and process the changes in fuel addition per unit time, steam temperature, steam pressure and desuperheating water flow rate in the boiler, and the impact of the three factors on steam temperature;

[0018] S3: Analyze the collected steam temperature data, determine whether the steam temperature is too high, and generate adjustment instructions based on the judgment results;

[0019] S4: Adjust the fuel addition per unit time, steam valve and desuperheating water flow rate according to the adjustment instructions to coordinate and control the steam temperature.

[0020] Furthermore, in step S1: the changes in fuel addition amount, steam temperature, steam pressure and desuperheating water flow rate per unit time are collected, and the collected changes in fuel addition amount, steam temperature, pressure and desuperheating water flow rate per unit time are converted into digital signals, classified, transmitted and stored. This preliminary data processing is then used for further data analysis.

[0021] Furthermore, in step S2: the collected data representing the changes in fuel addition, steam temperature, steam pressure, and desuperheating water flow rate per unit time are analyzed and processed; the relationship between fuel addition and steam pressure fluctuations per unit time and the steam temperature fluctuations are analyzed based on the collected data, and the patterns are analyzed; by analyzing the steam temperature collected by the boiler and the steam pressure collected at the same time, a model of the relationship between steam temperature and steam pressure is established.

[0022]

[0023] in, This is a constant coefficient in the formula; the standard steam temperature range inside the boiler is set to [T0-5, T0+5]. Substituting the left endpoint T0-5 of the range into the above formula, i.e., letting y = T0-5, we obtain the corresponding steam pressure value P. c for: Substituting the right endpoint T0+5 of the steam temperature range into the above formula, i.e., letting y = T0+5, we obtain the corresponding steam pressure value Pd as: The steam pressure range [P] corresponding to the standard steam temperature range is obtained. c P d ];

[0024] By analyzing the steam temperature collected from the boiler and the fuel addition amount collected per unit time at the same moment, a model is established to show the relationship between steam temperature and fuel addition amount:

[0025]

[0026] in, It is a constant coefficient in the formula; substituting the left endpoint T0-5 of the standard steam temperature range inside the boiler into the above formula, i.e., letting Y = T0-5, we obtain the corresponding fuel addition value X per unit time. a for: Substituting the right end of the interval, T0+5, into the above formula, i.e., letting Y = T0+5, we obtain the corresponding fuel addition value X per unit time. b for: This yields the range of fuel addition per unit time corresponding to the standard steam temperature [X]. a X b ].

[0027] Furthermore, in step S3: the collected steam temperature is T, the fuel addition per unit time is X, and the steam pressure is P. The collected steam temperature is compared with the standard temperature to analyze whether the steam temperature exceeds the standard. The standard temperature is set as T0, and the acceptable error temperature is ±5℃. If the temperature is within the error range of the standard temperature, no adjustment command needs to be generated. Otherwise, an adjustment command needs to be generated to adjust the desuperheating water flow rate, the fuel addition per unit time, and the steam pressure. The judgment result is divided into the following three cases:

[0028] When T-T0 < -5℃, an adjustment command needs to be generated: first, increase the desuperheating water flow rate; then determine whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required.

[0029] When T-T0 > 5℃, an adjustment command needs to be generated. First, the desuperheating water flow rate should be reduced; then, it should be determined whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required.

[0030] When -5℃≤T-T0≤5℃, there is no need to generate an instruction to adjust the desuperheating water flow rate.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention monitors and analyzes the situation inside the furnace through sensor detection and intelligent data analysis, judges the boiler temperature by comparing the data, and then controls the steam temperature by controlling the flow rate of desuperheating water. At the same time, it coordinates and controls the amount of fuel added per unit time and the steam pressure to control the temperature of superheated steam in the boiler within the standard range, thereby reducing the impact of boiler overheating caused by excessive steam on boiler efficiency and the life of the entire boiler unit. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a schematic diagram of the structure of a machine learning-based intelligent temperature coordination system for boilers according to the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] See also Figure 1 The present invention provides a technical solution: a machine learning-based intelligent temperature coordination system for boilers, which includes an information sensing module, an information transmission module, an intelligent control module, and a response execution module;

[0036] The information sensing module uses sensors to collect data on fuel addition, steam temperature, desuperheating water flow rate, and steam pressure within the boiler per unit time, and converts the collected analog signals into digital signals.

[0037] The analog signals collected by the sensor are transmitted to the intelligent control module through the information transmission module for intelligent analysis and processing.

[0038] The intelligent control module analyzes and processes the collected data, and determines whether an adjustment command needs to be generated through comparative analysis.

[0039] The response execution module adjusts the fuel addition amount, steam valve, and desuperheating water flow rate per unit time based on the analysis and processing results to achieve the effect of adjusting the steam temperature.

[0040] The information sensing module includes a sensor network unit and a data acquisition unit. The sensor network unit senses the amount of fuel added per unit time through a weighing sensor. The higher the amount of fuel added, the higher the heat generated after combustion, and the higher the heat transferred to the steam. Therefore, by controlling the amount of fuel added, the steam temperature can be controlled from the energy source. Furthermore, adjusting the amount of fuel added per unit time can also avoid energy waste caused by excessive fuel addition and incomplete combustion. The temperature sensor placed inside the boiler senses the steam temperature. The pressure sensor senses changes in the steam pressure. The greater the degree of opening and closing of the steam valve, the greater the amount of steam entering, the greater the steam pressure, and the higher the steam temperature. A flow rate sensor is placed at the desuperheating water inlet to sense changes in the desuperheating water flow rate. The desuperheating water can adjust the steam temperature in a timely manner through heat transfer. The data acquisition unit converts the fuel added per unit time, steam temperature, steam pressure, and desuperheating water flow rate sensed by the sensors into digital signals and collects them into a memory.

[0041] The intelligent control module includes a data analysis unit, a comparison analysis unit, and an instruction generation unit. The data analysis unit analyzes and processes the collected steam pressure and fuel addition data per unit time, and simultaneously analyzes the data on the change of steam temperature inside the boiler, thereby analyzing the impact of these changes on temperature change. The comparison analysis unit compares the collected steam temperature with the standard temperature range and determines whether the steam exceeds the temperature limit. The instruction generation unit generates adjustment instructions based on the judgment result.

[0042] The data analysis unit further processes the data collected by the sensors, classifies and stores the three types of data collected at the same time, and sets the standard steam temperature range of the boiler as [T0-5, T0+5]. The data analysis unit analyzes the historical data of collected steam temperature and steam pressure, establishes a mathematical model to analyze the relationship between steam temperature and steam pressure, and derives the corresponding steam pressure range [P] from the standard steam temperature value of the boiler. c P d By analyzing historical data on steam temperature and fuel addition per unit time, a mathematical model is established to analyze the relationship between steam temperature and fuel addition per unit time, and the corresponding fuel addition range per unit time is obtained using the boiler standard steam temperature value. a X b ].

[0043] The comparative analysis unit is set with a standard steam temperature T0 = 541℃ and an acceptable error temperature of ±5℃. The unit compares the collected steam temperature T0 with the set standard temperature to determine if the steam temperature exceeds the limit; and it also determines whether the collected fuel addition amount X per unit time falls within the fuel addition amount range per unit time corresponding to the boiler's standard steam temperature range. a X b Within ], and to determine whether the steam pressure P is within the steam pressure range corresponding to the boiler's standard steam temperature [P c P d Within.

[0044] The instruction generation unit determines whether to generate an adjustment instruction based on the comparison analysis results between the collected temperature and the standard temperature range. If the collected temperature is outside the standard temperature range, an adjustment instruction needs to be generated to adjust the flow rate of the desuperheating water to adjust the steam temperature; otherwise, no adjustment instruction is generated. At the same time, it needs to determine whether to generate an instruction to adjust the fuel addition amount and steam valve per unit time based on the comparison results between the fuel addition amount and steam pressure per unit time and the addition amount and pressure range corresponding to the standard temperature range.

[0045] A machine learning-based intelligent temperature coordination method for boilers includes the following steps:

[0046] S1: Collect the steam temperature and pressure in the superheater of the boiler and the flow rate of desuperheating water, as well as the amount of fuel added per unit time in the boiler, and perform preliminary processing on the collected information.

[0047] S2: Analyze and process the changes in fuel addition per unit time, steam temperature, steam pressure and desuperheating water flow rate in the boiler, and the impact of the three factors on steam temperature;

[0048] S3: Analyze the collected steam temperature data, determine whether the steam temperature is too high, and generate adjustment instructions based on the judgment results;

[0049] S4: Adjust the fuel addition per unit time, steam valve and desuperheating water flow rate according to the adjustment instructions to coordinate and control the steam temperature.

[0050] In step S1: The changes in fuel addition, steam temperature, steam pressure, and desuperheating water flow rate per unit time are collected, and the collected changes in fuel addition, steam temperature, pressure, and desuperheating water flow rate per unit time are converted into digital signals, classified, transmitted, and stored. This preliminary data processing is then used for further data analysis.

[0051] In step S2: The collected data representing the changes in fuel addition, steam temperature, steam pressure, and desuperheating water flow rate per unit time are analyzed and processed. The relationship between fuel addition, steam pressure, and steam temperature fluctuations per unit time is analyzed based on the collected data, and the patterns are analyzed. By analyzing the steam temperature collected from the boiler and the steam pressure collected at the same time, a model of the relationship between steam temperature and steam pressure is established.

[0052]

[0053] in, This is a constant coefficient in the formula; the standard steam temperature range inside the boiler is set to [T0-5, T0+5]. Substituting the left endpoint T0-5 of the range into the above formula, i.e., letting y = T0-5, we obtain the corresponding steam pressure value P. c for: Substituting the right endpoint T0+5 of the steam temperature range into the above formula, i.e., letting y = T0+5, we obtain the corresponding steam pressure value Pd as: The steam pressure range [P] corresponding to the standard steam temperature range is obtained. c P d ];

[0054] By analyzing the steam temperature collected from the boiler and the fuel addition amount collected per unit time at the same moment, a model is established to show the relationship between steam temperature and fuel addition amount:

[0055]

[0056] in, It is a constant coefficient in the formula; substituting the left endpoint T0-5 of the standard steam temperature range inside the boiler into the above formula, i.e., letting Y = T0-5, we obtain the corresponding fuel addition value X per unit time. a for: Substituting the right end of the interval, T0+5, into the above formula, i.e., letting Y = T0+5, we obtain the corresponding fuel addition value X per unit time. b for: This yields the range of fuel addition per unit time corresponding to the standard steam temperature [X]. a X b ].

[0057] Furthermore, in step S3: the collected steam temperature is T, the fuel addition per unit time is X, and the steam pressure is P. The collected steam temperature is compared with the standard temperature to analyze whether the steam temperature exceeds the standard. The standard temperature is set as T0, and the acceptable error temperature is ±5℃. If the temperature is within the error range of the standard temperature, no adjustment command needs to be generated. Otherwise, an adjustment command needs to be generated to adjust the desuperheating water flow rate, the fuel addition per unit time, and the steam pressure. The judgment result is divided into the following three cases:

[0058] When T-T0 < -5℃, an adjustment command needs to be generated: first, increase the desuperheating water flow rate; then determine whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required.

[0059] When T-T0>5℃, an adjustment command needs to be generated, first reducing the desuperheating water flow rate; then determining whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required.

[0060] When -5℃≤T-T0≤5℃, there is no need to generate an instruction to adjust the desuperheating water flow rate.

[0061] Example 1: In step S2: The collected data representing the changes in fuel addition, steam temperature, steam pressure, and desuperheating water flow rate per unit time are analyzed and processed. Based on the collected data, the relationship between fuel addition, steam pressure, and steam temperature fluctuations per unit time is analyzed, and the patterns are analyzed. Here, a medium-sized coal-fired steam boiler is used as an example. By analyzing the steam temperature collected from the boiler and the steam pressure collected at the same time, a model of the relationship between steam temperature and steam pressure is established.

[0062]

[0063] In this model, y and x take multiple values ​​to estimate the coefficients, with y taking the values ​​of y... i ={530, 535, 540, 545, 547, 548}, representing the collected steam temperature values, where x is taken as x. i ={3.7, 3.8, 3.9, 4.0, 4.1, 4.2}, representing the steam pressure values ​​collected at the same time as the steam temperature, where i = 1, 2, 3, 4, 5, 6. These are the constant coefficients in the formula; the least squares method can be used to obtain estimates of the constant coefficients in the formula, according to the formula:

[0064]

[0065]

[0066] get:

[0067]

[0068]

[0069] get:

[0070] y = 393 + 37.4x

[0071] The standard steam temperature range inside the boiler is set to [536, 546]. Substituting the left endpoint of the range, 536, into the above formula, i.e., setting y = 536, we obtain the corresponding steam pressure P. c =3.82, substituting the right endpoint of the steam temperature range, 546, into the above formula, i.e., setting y = 546, yields the corresponding steam pressure P. d =4.09, thus obtaining the steam pressure range [3.82, 4.09] corresponding to the standard steam temperature range, with the unit set as megapascals (MPa);

[0072] By analyzing the steam temperature collected from the boiler and the fuel addition rate per unit time collected at the same time, a model of the relationship between steam temperature and fuel addition rate is established, using the formula:

[0073]

[0074] In this model, Y and X take multiple values ​​to estimate the coefficients, with Y taking the values ​​of Y0 and X taking the values ​​of Y1 and X taking the values ​​of Y2 and X taking the values ​​of Y3 and X taking the values ​​of Y4 and X taking the values ​​of Y5 and X taking the values ​​of Y6 and X taking the values ​​of Y7j ={530, 535, 540, 545, 547, 548}, representing the collected steam temperature values, where X is taken as X. j ={8, 10, 12, 16, 20, 24}, representing the fuel addition per unit time collected simultaneously with the steam temperature, where j = 1, 2, 3, 4, 5, 6. These are the constant coefficients in the formula; the least squares method can be used to obtain estimates of the constant coefficients in the formula, according to the formula:

[0075]

[0076]

[0077] get:

[0078]

[0079] get:

[0080] Y = 524.6 + 1.08X;

[0081] Substituting the left endpoint of the standard steam temperature range in the boiler, 536, into the above formula, i.e., setting Y = 536, we obtain the corresponding fuel addition value per unit time, Xa = 10.6. Substituting the right endpoint of the range, 546, into the above formula, Y = 546, we obtain the corresponding fuel addition value per unit time, Xa. b =19.8, thus obtaining the range of fuel addition per unit time corresponding to the standard steam temperature [10.6, 19.8], with the unit set as tons (t).

[0082] In step S3: The collected steam temperature is T = 550℃, the fuel addition is X = 13t, and the steam pressure is P = 3.9MPa. The collected steam temperature value is compared with the standard temperature value to analyze whether the steam temperature exceeds the limit. The standard temperature is set at T0 = 541℃, and the acceptable fluctuation range is ±5℃. The fuel addition amount judgment results are as follows:

[0083] T-T0=550-541=9℃>5℃;

[0084] It can be determined that the boiler temperature is higher than the standard temperature during normal operation, and the excess exceeds 5°C. Therefore, an adjustment command needs to be generated. First, the desuperheating water flow rate should be increased to directly cool the steam. Then, it should be determined whether the fuel addition amount and steam pressure are within the range corresponding to the standard steam temperature. The standard steam temperature range [536, 546] corresponds to a fuel addition amount range of [10.6, 19.8] per unit time, and a steam pressure range of [3.82, 4.09]. Comparing these, it can be seen that the boiler fuel addition amount X = 13t per unit time is within the range [10.6, 19.8], and the steam pressure P = 3.9MPa is within the range [3.82, 4.09]. Therefore, it is not necessary to adjust the fuel addition amount and steam pressure per unit time; only an instruction to increase the desuperheating water flow rate needs to be generated.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0086] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A machine learning-based intelligent temperature coordination system for boilers, characterized in that, The system includes an information sensing module, an information transmission module, an intelligent control module, and a response execution module; The information sensing module uses sensors to collect data on fuel addition, steam temperature, desuperheating water flow rate, and steam pressure within the boiler per unit time, and converts the collected analog signals into digital signals. The analog signals collected by the sensor are transmitted to the intelligent control module through the information transmission module for intelligent analysis and processing. The intelligent control module analyzes and processes the collected data, and determines whether an adjustment command needs to be generated through comparative analysis. The response execution module adjusts the fuel addition amount, steam valve, and desuperheating water flow rate per unit time based on the analysis and processing results to achieve the effect of adjusting the steam temperature.

2. The intelligent temperature coordination system for boilers based on machine learning according to claim 1, characterized in that: The information sensing module includes a sensor network unit and a data acquisition unit. The sensor network unit senses the amount of fuel added per unit time through a weighing sensor, senses the temperature of steam through a temperature sensor placed inside the boiler, senses the change in steam pressure through a pressure sensor, and senses the change in the flow rate of desuperheating water through a flow velocity sensor. The data acquisition unit converts the amount of fuel added per unit time, steam temperature, steam pressure, and desuperheating water flow rate sensed by the sensors into digital signals and collects them into a memory.

3. The intelligent temperature coordination system for boilers based on machine learning according to claim 1, characterized in that: The intelligent control module includes a data analysis unit, a comparison analysis unit, and an instruction generation unit. The data analysis unit analyzes and processes the collected data on steam pressure and fuel addition per unit time, and simultaneously analyzes the data on changes in steam temperature inside the boiler, thereby analyzing the impact of these changes on steam temperature. The comparison analysis unit compares the collected steam temperature with the boiler's standard steam temperature range and determines whether the steam is overheating. The instruction generation unit generates adjustment instructions based on the determination results.

4. The intelligent temperature coordination system for boilers based on machine learning according to claim 3, characterized in that: The data analysis unit further processes the data collected by the sensors, classifies and stores the three types of data collected at the same time, and sets the standard steam temperature range of the boiler as [T0-5, T0+5]. The data analysis unit analyzes the historical data of collected steam temperature and steam pressure, establishes a mathematical model to analyze the relationship between steam temperature and steam pressure, and derives the corresponding steam pressure range [P] from the boiler standard steam temperature value. c P d By analyzing historical data on collected steam temperature and fuel addition per unit time, a mathematical model is established to analyze the relationship between steam temperature and fuel addition per unit time, and the corresponding fuel addition range per unit time is obtained through the boiler standard steam temperature value [X]. a X b ].

5. The intelligent temperature coordination system for boilers based on machine learning according to claim 3, characterized in that: The comparative analysis unit is set with a standard steam temperature T0 and an acceptable error temperature of ±5℃. The unit compares the collected steam temperature T0 with the set standard temperature to determine if the steam temperature exceeds the limit; and it also determines whether the collected fuel addition amount X per unit time falls within the fuel addition amount range per unit time corresponding to the boiler's standard steam temperature range. a X b Within ], and to determine whether the steam pressure P is within the steam pressure range corresponding to the boiler's standard steam temperature [P c P d Within.

6. The intelligent temperature coordination system for boilers based on machine learning according to claim 3, characterized in that: The instruction generation unit determines whether to generate an adjustment instruction based on the comparison analysis results between the collected temperature and the standard temperature range. If the collected temperature is outside the standard temperature range, an adjustment instruction needs to be generated to adjust the flow rate of the desuperheating water to adjust the steam temperature; otherwise, no adjustment instruction is generated. At the same time, it needs to determine whether to generate an instruction to adjust the fuel addition per unit time and the steam valve based on the comparison results between the fuel addition amount and steam pressure per unit time and the corresponding fuel addition amount and pressure range of the standard temperature range.

7. A machine learning-based intelligent temperature coordination method for boilers, characterized in that: Includes the following steps: S1: Collect the steam temperature and pressure in the superheater of the boiler and the flow rate of desuperheating water, as well as the amount of fuel added per unit time in the boiler, and perform preliminary processing on the collected information. S2: Analyze and process the changes in fuel addition per unit time, steam temperature, steam pressure and desuperheating water flow rate in the boiler, and the impact of the three factors on steam temperature; S3: Analyze the collected steam temperature data, determine whether the steam temperature is too high, and generate adjustment instructions based on the judgment results; S4: Adjust the fuel addition per unit time, steam valve and desuperheating water flow rate according to the adjustment instructions to coordinate and control the steam temperature.

8. The intelligent temperature coordination method for boilers based on machine learning according to claim 7, characterized in that: In step S1: The changes in fuel addition amount, steam temperature, steam pressure and desuperheating water flow rate per unit time are collected, and the collected changes in fuel addition amount, steam temperature, pressure and desuperheating water flow rate per unit time are converted into digital signals and transmitted and stored.

9. The intelligent temperature coordination method for boilers based on machine learning according to claim 7, characterized in that: In step S2: The collected data representing the changes in fuel addition, steam temperature, steam pressure, and desuperheating water flow rate per unit time are analyzed and processed. The relationship between fuel addition, steam pressure, and steam temperature fluctuations per unit time is analyzed based on the collected data, and the patterns are analyzed. By analyzing the steam temperature collected from the boiler and the steam pressure collected at the same time, a model of the relationship between steam temperature and steam pressure is established. in, and This is a constant coefficient in the formula; the standard steam temperature range inside the boiler is set to [T0-5, T0+5]. Substituting the left endpoint T0-5 of the range into the above formula, i.e., letting y = T0-5, we obtain the corresponding steam pressure value P. c for: Substituting the right endpoint T0+5 of the steam temperature range into the above formula, i.e., letting y = T0+5, we obtain the corresponding steam pressure value Pd as: The steam pressure range [P] corresponding to the standard steam temperature range is obtained. c P d ]; By analyzing the steam temperature collected from the boiler and the fuel addition amount collected per unit time at the same moment, a model is established to show the relationship between steam temperature and fuel addition amount: in, and It is a constant coefficient in the formula; substituting the left endpoint T0-5 of the standard steam temperature range inside the boiler into the above formula, i.e., letting Y = T0-5, we obtain the corresponding fuel addition value X per unit time. a for: Substituting the right end of the interval, T0+5, into the above formula, i.e., letting Y = T0+5, we obtain the corresponding fuel addition value X per unit time. b for: This yields the range of fuel addition per unit time corresponding to the standard steam temperature [X]. a X b ].

10. The intelligent temperature coordination method for boilers based on machine learning according to claim 7, characterized in that: In step S3: The collected steam temperature is T, the fuel addition per unit time is X, and the steam pressure is P. The collected steam temperature is compared with the standard temperature to analyze whether the steam temperature exceeds the standard. The standard temperature is set as T0, and the acceptable error temperature is ±5℃. If the temperature is within the error range of the standard temperature, no adjustment command needs to be generated. Otherwise, an adjustment command needs to be generated to adjust the desuperheating water flow rate, the fuel addition per unit time, and the steam pressure. The judgment result is divided into the following three cases: When T-T0 < -5℃, an adjustment command needs to be generated: first, increase the desuperheating water flow rate; then determine whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required. When T-T0>5℃, an adjustment command needs to be generated, first reducing the desuperheating water flow rate; then determining whether the fuel addition amount X per unit time is within the range [X]. a X b If X is within this range, then no instruction to adjust the fuel addition per unit time is needed; otherwise, an instruction to adjust the fuel addition to this range is needed. Then, determine if the steam pressure P is within the range [P]. c P d If P is within this range, then there is no need to generate an instruction to adjust the steam pressure; otherwise, an instruction to adjust the steam pressure to this range is required. When -5℃≤T-T0≤5℃, there is no need to generate an instruction to adjust the desuperheating water flow rate.

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