Low-nitrogen combustion method, low-nitrogen combustion device and low-nitrogen combustion equipment for smelting furnace based on Internet of Things

Abstract

According to the internet-of-things smelting furnace low-nitrogen combustion method, the flow data of the combustion improver and the fuel agent are obtained in real time through the sensor network and compared with the preset proportion value, and the flow of the combustion improver can be adjusted. In the low-nitrogen combustion process, the combustion temperature can be higher and more stable through the accurate proportion, combustion can be more sufficient, emission of unburned gas is reduced, and the thermal efficiency is optimized.

Description

Technical Field

[0001] The present application relates to the field of furnaces, and in particular to a method, device and equipment for low-nitrogen combustion in an Internet of Things furnace. Background Art

[0002] As a common high-temperature processing equipment in industrial production, furnaces are widely used in metal smelting, glass production and other high-temperature processing processes. The combustion process in the furnace is mainly achieved through the mixed combustion of fuel and combustion aid, and sufficient heat is provided to achieve efficient heating effect. With the improvement of environmental protection requirements, the emission of nitrogen oxides (NOx) in the combustion process has become an urgent problem to be solved.

[0003] In traditional furnace combustion control systems, there is a lack of automatic feedback mechanisms. This means that during the process of adjusting the gas flow and ratio, the system lacks real-time sensor data support and cannot adjust the supply of the oxidant and fuel agent in time according to the actual combustion conditions. For example, there may be problems such as incomplete combustion, uneven temperature, or excessive nitrogen oxide emissions during combustion, but the traditional system cannot monitor the gas flow or mixing ratio in real time, resulting in an inability to respond quickly. If the flow ratio of the oxidant and fuel agent is not appropriate, the NOx emissions during the combustion process may exceed the standard, the combustion will be uneven, and the heating efficiency of the furnace cannot be effectively improved.

[0004] Therefore, it is necessary to provide an IoT furnace low-nitrogen combustion method, device and equipment that monitors gas flow in real time to adjust the supplied combustion aid to improve the combustion of the furnace. Summary of the invention

[0005] In view of this, it is necessary to provide an IoT furnace low-nitrogen combustion method, device and equipment that monitors gas flow in real time to adjust the supplied combustion aid to solve the above problems.

[0006] An embodiment of the present application provides a low-nitrogen combustion method for an IoT furnace, the method comprising the steps of:

[0007] Connect the furnace to the low nitrogen equipment and establish a sensor network connection;

[0008] The flow meter records the flow value of the combustion aid supplied by the low nitrogen equipment;

[0009] Obtain the flow value of the fuel agent, and perform proportional data analysis with the flow value of the combustion-supporting agent, and the analysis result is recorded as the actual proportional value A;

[0010] Setting a preset ratio value B of the combustion aid and the fuel;

[0011] The actual ratio value A is compared with the preset ratio value B. If A is not equal to B, the sensor network controls the flow value of the vacuum pump to supply the combustion aid.

[0012] In at least one embodiment of the present application, "Compare the actual ratio value A with the preset ratio value B. If A is not equal to B, the sensor network controls the flow value of the combustion aid supplied by the vacuum pump", an adjustment deviation value is formed by the difference between the preset ratio value and the actual ratio value, and the rotation speed of the vacuum pump is controlled according to the adjustment deviation value.

[0013] In at least one embodiment of the present application, in “connecting the furnace to the low nitrogen device and establishing a sensor network connection”,

[0014] Acquire sensor data sets of low-nitrogen equipment and transmit them to the sensor network;

[0015] The operating condition of the low-nitrogen equipment is identified based on the sensor data set.

[0016] In at least one embodiment of the present application, the method comprises another step:

[0017] Constructing an Internet of Things sensor, wherein the Internet of Things sensor includes a sensor module disposed on the furnace, and the Internet of Things sensor monitors the combustion state of the furnace in real time and generates sensor data;

[0018] Processing the sensor data according to the prediction model of the furnace and generating a prediction result;

[0019] According to the prediction results, it is determined whether the excess air coefficient a in the furnace is equal to b, where b satisfies the relationship:

[0020] 1.0≤b≤1.2;

[0021] If the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace, where;

[0022] The low-nitrogen combustion aid is oxygen-enriched air.

[0023] In at least one embodiment of the present application, in “processing the sensor data and generating a prediction result according to the prediction model of the furnace”, the control system records the data of previous combustion and forms a combustion data set, and trains the combustion data set according to a machine learning algorithm to obtain the prediction model of the furnace.

[0024] In at least one embodiment of the present application, the step of “processing the sensor data and generating a prediction result according to the prediction model of the furnace” comprises the following steps:

[0025] The sensor data is transmitted to a prediction model, and the prediction model receives the sensor data and performs prediction processing;

[0026] According to the prediction process, the prediction model outputs a prediction result.

[0027] In at least one embodiment of the present application, in the “if the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace, wherein the low-nitrogen combustion aid is oxygen-enriched air”:

[0028] Obtain low-nitrogen combustion aid and fuel agent and transfer them to the furnace through pipelines.

[0029] In at least one embodiment of the present application, in “obtaining a low-nitrogen combustion aid and a fuel agent, and transmitting them to the furnace through a pipeline”:

[0030] A separation membrane and a vacuum pump are provided, wherein one side of the separation membrane is connected to the vacuum pump and forms a low-pressure chamber, and the other side of the separation membrane forms a high-pressure chamber;

[0031] The gas mixture passes through the high-pressure chamber, the separation membrane and the low-pressure chamber in sequence to form a low-nitrogen combustion aid on one side of the low-pressure chamber.

[0032] An embodiment of the present application provides an Internet of Things furnace low-nitrogen combustion device, which is used to implement any of the Internet of Things furnace low-nitrogen combustion methods described above.

[0033] An embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the methods.

[0034] The IoT furnace low-nitrogen combustion method provided above can obtain the flow data of the combustion aid and the fuel agent in real time through the sensor network, and compare it with the preset ratio value, so as to automatically adjust the flow rate of the combustion aid. It ensures real-time feedback and adjustment of the combustion process, and improves the accuracy and stability of combustion. In the low-nitrogen combustion process, accurate ratio can make the combustion temperature higher and more stable, which helps to burn more fully, reduce the emission of unburned gas, and optimize thermal efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flowchart of a low-nitrogen combustion method for an IoT furnace in an embodiment of the present application.

[0036] Figure 2 This is a structural block diagram of the separation membrane and vacuum pump for obtaining a low-nitrogen combustion aid in an embodiment of the present application.

[0037] Figure 3 FIG. 4 is a structural block diagram of a computer device in one embodiment.

[0038] Main component symbols

[0039] 100. A low-nitrogen combustion method for an IoT furnace; 10. furnace; 20. separation membrane; 30. vacuum pump; 40. pipeline; 20a. high-pressure chamber; 20b. low-pressure chamber; 50. oxygen molecules; 60. nitrogen molecules. DETAILED DESCRIPTION

[0040] The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0041] It should be noted that when a component is considered to be "connected" to another component, it may be directly connected to the other component or there may be a central component at the same time. When a component is considered to be "located on" another component, it may be directly located on the other component or there may be a central component at the same time. The terms "top", "bottom", "upper", "lower", "left", "right", "front", "back", and similar expressions used herein are for illustrative purposes only.

[0042] An embodiment of the present application provides a low-nitrogen combustion method for an IoT furnace, the method comprising the steps of:

[0043] Connect the furnace to the low nitrogen equipment and establish a sensor network connection;

[0044] The flow meter records the flow value of the combustion aid supplied by the low nitrogen equipment;

[0045] Obtain the flow value of the fuel agent, and perform proportional data analysis with the flow value of the combustion-supporting agent, and the analysis result is recorded as the actual proportional value A;

[0046] Setting a preset ratio value B of the combustion aid and the fuel;

[0047] The actual ratio value A is compared with the preset ratio value B. If A is not equal to B, the sensor network controls the flow value of the vacuum pump to supply the combustion aid.

[0048] The above sensor network obtains the flow data of the combustion aid and the fuel agent in real time, and compares it with the preset ratio value, which can automatically adjust the flow of the combustion aid. This closed-loop control system ensures real-time feedback and adjustment of the combustion process, and improves the accuracy and stability of combustion. In the low-nitrogen combustion process, accurate ratios can make the combustion temperature higher and more stable, which helps to burn more fully, reduce the emission of unburned gases, and optimize thermal efficiency.

[0049] In conjunction with the accompanying drawings Figure 1-Figure 3 , some embodiments of the present application are described in detail. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0050] Embodiment 1:

[0051] In an embodiment of the present application, a low-nitrogen combustion method 100 of an IoT furnace 10 is provided, and the method comprises the following steps:

[0052] S10, connecting the furnace 10 with the low nitrogen equipment and establishing a sensor network connection;

[0053] S20, the flow meter records the flow value of the combustion-supporting agent supplied by the low-nitrogen equipment;

[0054] S30, obtaining the flow value of the fuel agent, and performing proportional data analysis with the flow value of the combustion-supporting agent, and recording the analysis result as the actual proportional value A;

[0055] S40, setting a preset ratio value B of the combustion aid and the fuel;

[0056] S50, comparing the actual ratio value A with the preset ratio value B, if A is not equal to B, the sensor network controls the vacuum pump 30 to supply the flow value of the combustion-supporting agent.

[0057] Specifically, the low nitrogen device is connected to the furnace 10 and is responsible for transmitting the combustion aid to the furnace 10 through the low nitrogen device. The low nitrogen device includes a vacuum pump 30, a pressure sensor, a content sensor and a flow meter. The pressure sensor, the content sensor and the flow meter form a sensor network to detect the operating state of the vacuum pump 30. The pressure sensor is used to detect the pressure of the vacuum pump, the content sensor is used to record the oxygen content value in the combustion aid, and the flow meter is used to record the flow value of the current combustion aid transmission.

[0058] The combustion aid separates low-nitrogen air through the separation membrane 20 and transmits it to the furnace 10 through the pipeline 40, so that the combustion in the furnace is more complete and the combustion in the furnace is more energy-efficient. The oxygen content in the low-nitrogen air can range from 20% to 50%, which can be adjusted according to the combustion effect. In the present application, the oxygen content in the low-nitrogen air can be 30%.

[0059] By recording the flow rate of the combustion aid in real time, we can ensure that the supply amount is always kept within the ideal range. This provides data support for subsequent flow comparison and adjustment, and can avoid incomplete combustion or temperature fluctuations caused by excessive or insufficient supply of combustion aid.

[0060] The fuel flow rate is a fixed value and the fuel flow rate data needs to be read manually.

[0061] The preset ratio value is a ratio value determined by multiple manual data adjustments to ensure that the fuel agent can burn more fully when the furnace burns. In the embodiment of the present application, the preset ratio value of the combustion aid to the fuel agent is set to 1:3. The specific ratio value can be adjusted according to the data of the fuel agent flow rate, so that the combustion in the furnace can achieve better energy-saving effect.

[0062] By comparing the actual ratio value A and the preset ratio value B, it is determined whether the gas flow rate during the combustion process reaches the predetermined standard. If A is not equal to B, the sensor network will adjust the combustion aid supply flow rate of the vacuum pump 30 until the actual ratio value reaches the preset value. This process keeps the combustion in the best state at all times. Since different fuel and combustion aid ratios will affect the combustion efficiency, temperature and pollutant emissions, the accurate ratio of the combustion aid and the fuel agent is ensured by adjusting the flow ratio in real time, thereby improving the stability, efficiency and environmental protection of the combustion process.

[0063] Furthermore, by adjusting the flow rate of the combustion aid, it is possible to avoid unnecessary energy waste and pollution emissions caused by excessive combustion aid while ensuring combustion efficiency. This process can maximize the performance of the furnace 10 and ensure its stability under different working conditions.

[0064] In a specific embodiment, “the actual ratio value A is compared with the preset ratio value B. If A is not equal to B, the sensor network controls the flow value of the combustion aid supplied by the vacuum pump 30”, and an adjustment deviation value is formed by the difference between the preset ratio value and the actual ratio value, and the rotation speed of the vacuum pump 30 is controlled according to the adjustment deviation value.

[0065] Specifically, the deviation is caused by the difference between the actual ratio and the preset ratio. The pressure change of the pressure sensor can reflect the insufficient or excessive flow of the combustion aid, thereby generating a deviation value. This deviation value is the value required for adjustment, indicating that the system needs to change the flow of the combustion aid to restore to the ideal ratio.

[0066] Further, according to the flow rate of the preset proportional value, the vacuum pump 30 starts to run. In this case, the operation of the vacuum pump 30 is started based on the set flow rate, but it does not directly set the flow rate to be constant, but adjusts the output of the vacuum pump 30 in real time through feedback from the pressure sensor. The operating state of the pump is then automatically adjusted according to the feedback signal. If the set flow rate is 100L / min, the system starts running the pump and monitors the pressure through the pressure sensor. If the flow rate is insufficient, the pump speed will be increased to reach the set flow rate; if the flow rate is too high, the system will reduce the pump speed to prevent the system from overloading. In Example 1 of the present application, the flow value of the transported combustion aid can be adjusted by manually turning the flow valve.

[0067] In a specific embodiment, “connecting the furnace 10 to the low nitrogen equipment and establishing a sensor network connection” specifically includes the following steps:

[0068] Acquire sensor data sets of low-nitrogen equipment and transmit them to the sensor network;

[0069] The operating condition of the low-nitrogen equipment is identified based on the sensor data set.

[0070] Specifically, various sensors of low-nitrogen equipment (such as temperature sensors, flow meters, pressure sensors, etc.) will collect the operating data of the equipment in real time and transmit the data set to the sensor network. There are multiple combustion units in a furnace 10, and each combustion unit has a flow meter, etc. According to the transmitted flow data, etc., it can be judged that a certain combustion unit is in working state and the operating state. Identifying the working condition of low-nitrogen equipment includes the maintenance cycle of the vacuum pump 30 and the service life of the low-nitrogen equipment.

[0071] Embodiment 2:

[0072] In an embodiment of the present application, a low-nitrogen combustion method 100 of an IoT furnace 10 is provided, and the method comprises the following steps:

[0073] S100, constructing an Internet of Things sensor, wherein the Internet of Things sensor includes a sensor module disposed on the furnace 10, and the Internet of Things sensor monitors the combustion state of the furnace 10 in real time and generates sensor data;

[0074] S200, processing the sensor data according to the prediction model of the furnace 10, and generating a prediction result;

[0075] S300. According to the prediction result, it is determined whether the excess air coefficient a in the furnace 10 is equal to b, where b satisfies the relationship:

[0076] 1.0≤b≤1.2;

[0077] S400, if the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace 10, wherein the low-nitrogen combustion aid is oxygen-enriched air.

[0078] Specifically, in S100, by setting sensor modules inside and outside the furnace 10, including temperature sensors, pressure sensors, oxygen concentration sensors and flow sensors, Internet of Things sensing is established.

[0079] The IoT sensor monitors the combustion state of the furnace 10 in real time and collects key operating data in the furnace 10, including temperature, pressure, oxygen concentration, and excess air coefficient. Through real-time data collection by the sensor, the system can accurately understand the combustion state inside the furnace 10. This provides basic data support for subsequent decision-making.

[0080] The sensor module is connected to the control system wirelessly or wired to achieve real-time transmission and processing of IoT data. The sensor network transmits data to the central control system, allowing operators to view the operating status of the furnace 10 in real time through a visual interface and discover potential problems in a timely manner. Automatic data collection reduces dependence on operators and improves the automation and safety of the system.

[0081] In S200, the prediction model is trained based on historical data and real-time data in combination with a machine learning algorithm, and can simulate and predict changes in the combustion process of the furnace 10. The prediction model processes data and generates prediction results through a control system.

[0082] Based on the prediction results, the control system can adjust the parameters of the combustion process, optimize combustion efficiency, and reduce energy waste. Traditional manual adjustments are often limited by the operator's experience, while the prediction model can provide a more accurate and efficient adjustment strategy to avoid errors caused by human factors.

[0083] In S300, the smaller the excess air coefficient is, the more accurate the air supply during the combustion process is, which helps to improve the combustion efficiency, reduce the generation of nitrogen oxides (NOx), and reduce pollutant emissions. The combustion efficiency and emissions in the furnace 10 are determined according to the excess air coefficient, so that the control system controls the action of the adjustment module.

[0084] In S400, oxygen-enriched air is a gas mixture with a nitrogen concentration lower than the nitrogen concentration in the air under natural conditions (about 78%). By adding oxygen-enriched air as a low-nitrogen combustion aid, it helps to reduce the combustion temperature, thereby reducing the generation of nitrogen oxides (NOx) and achieving low-nitrogen combustion. The fuel can be hydrocarbon fuels such as natural gas and coal.

[0085] In summary, by building IoT sensors, combining prediction models and automatic adjustment systems, it is possible to achieve intelligent control of the combustion process of the furnace 10, optimize combustion efficiency, reduce harmful gas emissions, and reduce manual intervention. This method can not only improve the level of automation in industrial production, but also meet increasingly stringent environmental protection requirements, and has broad application prospects. Especially in industries such as metal smelting, glass manufacturing, and ceramic firing, it can significantly improve production efficiency, reduce energy consumption, and reduce pollution, providing strong support for the sustainable development of industrial production.

[0086] In a specific embodiment, in “processing the sensor data and generating a prediction result according to the prediction model of the furnace 10”, the control system records the data of previous combustion and forms a combustion data set, and trains the combustion data set according to a machine learning algorithm to obtain the prediction model of the furnace 10.

[0087] Specifically, by recording data from past combustion processes, including key parameters such as burner temperature, fuel flow, and oxygen concentration, the control system can accumulate a large amount of historical data. These data provide the basis for subsequent analysis and prediction models. The machine learning algorithm can be one of regression analysis, neural network, and support vector machine. By training historical data with a machine learning algorithm, the system can learn the regularity and potential patterns in the combustion process. This enables the furnace 10 to make intelligent predictions and adjustments based on real-time data, significantly improving the accuracy of combustion control. The machine learning model can adjust its prediction capabilities based on continuously updated historical data, continuously improve the accuracy of the prediction, and thus better adapt to the ever-changing combustion environment.

[0088] The machine learning model obtained through training can accurately predict the future combustion state. This prediction model provides the control system with real-time combustion trend prediction based on historical combustion data and environmental parameters. The prediction model can predict possible abnormalities in the combustion process based on the current input sensor data. The prediction model can alert the control system in advance so that the control parameters can be adjusted in time.

[0089] In a specific embodiment, the step of “processing the sensor data and generating a prediction result according to the prediction model of the furnace 10” includes the following steps:

[0090] The sensor data is transmitted to a prediction model, and the prediction model receives the sensor data and performs prediction processing;

[0091] According to the prediction process, the prediction model outputs a prediction result.

[0092] Specifically, the sensor network collects key combustion data (such as temperature, pressure, oxygen concentration, fuel flow) of the furnace 10 in real time and transmits these data to the central control system. The collection and transmission of these data are real-time, so they can reflect the dynamic changes of the combustion process of the furnace 10.

[0093] The sensor data is transmitted to the prediction model as input information, and the corresponding prediction results are made within the information of the sensor data through the combustion data set of previous combustion data. The control logic based on machine learning is provided to avoid the deviation of manual intervention and subjective judgment.

[0094] In a specific embodiment, in “obtaining a low-nitrogen combustion aid and transmitting it to the furnace 10 through the pipeline 40”:

[0095] A separation membrane 20 and a vacuum pump 30 are provided, one side of the separation membrane 20 is connected to the vacuum pump 30 to form a low-pressure chamber 20b, and the other side of the separation membrane 20 forms a high-pressure chamber 20a; the gas mixture sequentially passes through the high-pressure chamber 20a, the separation membrane 20 and the low-pressure chamber 20b to form a low-nitrogen combustion aid on one side of the low-pressure chamber 20b.

[0096] Specifically, the separation membrane 20 is used to separate oxygen and nitrogen in a gas mixture such as air. In this system, the role of the separation membrane 20 is to utilize the selective permeability of the membrane material. Under the action of the pressure difference, oxygen molecules pass through the membrane faster, while nitrogen molecules pass through slower, thereby reducing the amount of nitrogen permeation. The membrane assembly is usually composed of a multi-layer membrane, and the nitrogen concentration is further reduced by multi-stage separation. The vacuum pump 30 is a device for extracting gas from the container to form a negative pressure, which is used in this invention to help the separation membrane 20 assembly produce low-nitrogen air. In this way, the separation membrane 20 can extract oxygen-enriched air, i.e., a low-nitrogen combustion aid, from ordinary air.

[0097] Oxygen-enriched air is generated by using separation membrane 20 technology and vacuum pump 30 driven by pressure difference. The use of this oxygen-enriched air greatly improves combustion efficiency and reduces the emission of harmful gases such as nitrogen oxides. Under the action of different pressure differences when the gas mixture passes through the separation membrane 20, oxygen molecules 50 in the air pass through the separation membrane 20 faster to generate oxygen-enriched air. The vacuum pump 30 reduces the pressure on the low-pressure side to a certain pressure, so that the gas that passes through the membrane becomes oxygen-enriched air. The oxygen-enriched air is sent into the furnace 10 through the pipeline 40, which improves combustion efficiency and reduces the emission of harmful gases. The non-permeated gas (including dust in the air) discharged from the separation membrane 20 is directly released into the atmosphere through another exhaust pipe, which simplifies the system structure and reduces additional processing equipment and maintenance costs.

[0098] The present application also provides a low-nitrogen combustion device for an Internet of Things furnace 10, characterized in that it is used to implement any of the low-nitrogen combustion methods for the Internet of Things furnace 10.

[0099] Embodiment 1:

[0100] The low nitrogen device is connected to the furnace 10 and is responsible for transmitting the flame retardant to the furnace 10 through the low nitrogen device. The low nitrogen device includes a vacuum pump 30, a pressure sensor, a content sensor and a flow meter. The pressure sensor, the content sensor and the flow meter form a sensor network to detect the operating state of the vacuum pump 30. The flame retardant is separated into low nitrogen air with an oxygen content of 30% through the separation membrane 20 and transmitted to the furnace 10 through the pipeline 40.

[0101] Embodiment 2:

[0102] The device includes a furnace 10, a separation membrane 20, a vacuum pump 30, a proportional valve, a frequency converter, a combustion controller and an Internet of Things sensor. The Internet of Things sensor includes a temperature sensor, a pressure sensor, a nitrogen concentration sensor and a flow sensor.

[0103] Furthermore, under the effect of different pressure differences across the separation membrane 20, the gas mixture causes oxygen molecules 50 in the air to pass through the separation membrane 20 faster, generating low-nitrogen air with an oxygen content of 30%. The vacuum pump 30 reduces the pressure on the low-pressure side to a certain pressure, so that the gas that permeates the membrane becomes oxygen-enriched air. The low-nitrogen air is sent into the furnace 10 through the pipeline 40. The IoT sensor on the furnace 10 monitors the combustion process in the furnace 10 in real time. The IoT sensor is connected to the control system wirelessly or wired to realize real-time transmission and processing of data. The control system analyzes the received data and performs adjustments through the adjustment module to control the state of the proportional valve, inverter, and combustion controller.

[0104] Furthermore, the remote monitoring system and user interface are seamlessly connected to the control system via the Internet, allowing users to view system status, receive alarm information, and make necessary interventions anytime and anywhere. The remote monitoring system includes a cloud server and client software, and users can access the system status through a computer or mobile device. The user interface includes a touch screen display and a mobile application, providing an intuitive operating interface for users to perform manual control and status monitoring.

[0105] Furthermore, the control system is equipped with a data storage unit that can store historical data for subsequent analysis and optimization.

[0106] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the methods.

[0107] Embodiment 1: In the embodiment of the present application, a low-nitrogen combustion method 100 of an IoT furnace 10 is provided, and the method comprises the following steps:

[0108] S10, connecting the furnace 10 with the low nitrogen equipment and establishing a sensor network connection;

[0109] S20, the flow meter records the flow value of the combustion-supporting agent supplied by the low-nitrogen equipment;

[0110] S30, obtaining the flow value of the fuel agent, and performing proportional data analysis with the flow value of the combustion-supporting agent, and recording the analysis result as the actual proportional value A;

[0111] S40, setting a preset ratio value B of the combustion aid and the fuel;

[0112] S50, comparing the actual ratio value A with the preset ratio value B, if A is not equal to B, the sensor network controls the vacuum pump 30 to supply the flow value of the combustion-supporting agent.

[0113] Embodiment 2: The steps include: S100, constructing an Internet of Things sensor, wherein the Internet of Things sensor includes a sensor module disposed on the furnace 10, and the Internet of Things sensor monitors the combustion state of the furnace 10 in real time and generates sensor data;

[0114] S200, processing the sensor data according to the prediction model of the furnace 10, and generating a prediction result;

[0115] S300. According to the prediction result, it is determined whether the excess air coefficient a in the furnace 10 is equal to b, where b satisfies the relationship:

[0116] 1.0≤b≤1.2;

[0117] S400, if the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace 10, wherein the low-nitrogen combustion aid is oxygen-enriched air.

[0118] A sensor module is installed on the furnace 10, and the combustion state data (such as temperature, pressure, excess air coefficient, fuel flow rate, etc.) of the furnace 10 is collected in real time through the Internet of Things sensor. The prediction model predicts the combustion state of the furnace 10 through a machine learning algorithm based on historical combustion data and real-time sensor data. According to the prediction results, if the excess air coefficient a of the furnace 10 is inconsistent with the preset target b value, the adjustment module will automatically adjust the mass ratio M of the low-nitrogen combustion aid (oxygen-enriched air) and the fuel, as well as other combustion parameters based on the prediction results. The ratio of the combustion aid and the fuel is adjusted by a proportional valve to finally optimize the combustion process.

[0119] The computer device may specifically be a terminal or a server. The computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program, which, when executed by the processor, enables the processor to implement the low-nitrogen combustion method of the Internet of Things furnace 10. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to execute the low-nitrogen combustion method of the Internet of Things furnace 10. Those skilled in the art will appreciate that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0120] Thus, the low-nitrogen combustion method 100 of the IoT furnace 10 provided above can obtain the flow data of the combustion aid and the fuel agent in real time through the sensor network, and compare it with the preset ratio value, so as to adjust the flow of the combustion aid. It ensures the real-time feedback and adjustment of the combustion process, and improves the accuracy and stability of the combustion. In the low-nitrogen combustion process, the accurate ratio can make the combustion temperature higher and more stable, which helps to make the combustion more complete, reduce the emission of unburned gas, and optimize the thermal efficiency.

[0121] The above is only an implementation method of the present application. It should be pointed out that a person skilled in the art can make improvements without departing from the inventive concept of the present application, but these improvements are within the scope of protection of the present application.

Claims

1. A low nitrogen combustion method for an Internet of Things furnace, characterized in that: The method comprises the steps of: Connect the furnace to the low nitrogen equipment and establish a sensor network connection; The flow meter records the flow value of the combustion aid supplied by the low nitrogen equipment; Obtain the flow value of the fuel agent, and perform proportional data analysis with the flow value of the combustion-supporting agent, and the analysis result is recorded as the actual proportional value A; Setting a preset ratio value B of the combustion aid and the fuel; The actual ratio value A is compared with the preset ratio value B. If A is not equal to B, the sensor network controls the flow value of the vacuum pump to supply the combustion aid.

2. The low nitrogen combustion method for an IoT furnace according to claim 1, characterized in that: "Comparing the actual ratio value A with the preset ratio value B, if A is not equal to B, the sensor network controls the flow value of the vacuum pump to supply the combustion aid", An adjustment deviation value is formed by a difference between the preset ratio value and the actual ratio value, and a rotation speed of the vacuum pump is controlled according to the adjustment deviation value.

3. The low nitrogen combustion method for an Internet of Things furnace according to claim 1, characterized in that: "Connecting furnaces to low nitrogen equipment and establishing sensor network connections", Acquire sensor data sets of low-nitrogen equipment and transmit them to the sensor network; The operating condition of the low-nitrogen equipment is identified based on the sensor data set.

4. The method according to any one of claims 1 to 3, comprising the further step of: Constructing an Internet of Things sensor, wherein the Internet of Things sensor includes a sensor module disposed on the furnace, and the Internet of Things sensor monitors the combustion state of the furnace in real time and generates sensor data; Processing the sensor data according to a prediction model of the furnace and generating a prediction result; According to the prediction results, determine whether the excess air coefficient a in the furnace is equal to b, where b satisfies the relationship: 1.0≤b≤1.2; If the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace, where; The low-nitrogen combustion aid is oxygen-enriched air.

5. The low nitrogen combustion method for an Internet of Things furnace according to claim 4, characterized in that: In "processing the sensor data and generating a prediction result according to the prediction model of the furnace", the control system records the data of previous combustion and forms a combustion data set, and trains the combustion data set according to the machine learning algorithm to obtain the prediction model of the furnace.

6. The low nitrogen combustion method for an Internet of Things furnace according to claim 4, characterized in that: The step of "processing the sensor data and generating a prediction result according to the prediction model of the furnace" comprises the following steps: The sensor data is transmitted to a prediction model, and the prediction model receives the sensor data and performs prediction processing; According to the prediction process, the prediction model outputs a prediction result.

7. The low nitrogen combustion method for an IoT furnace according to claim 4, characterized in that: The above “if the excess air coefficient a is not equal to b, the regulating module controls the mass ratio M of the low-nitrogen combustion aid and the fuel entering the furnace, wherein the low-nitrogen combustion aid is oxygen-enriched air” includes the following steps: Obtain low-nitrogen combustion aid and fuel agent and transfer them to the furnace through pipelines.

8. The low nitrogen combustion method for an Internet of Things furnace according to claim 7, characterized in that: In "Obtaining low-nitrogen combustion aid and fuel, and transferring them to the furnace through pipelines": A separation membrane and a vacuum pump are provided, wherein one side of the separation membrane is connected to the vacuum pump and forms a low-pressure chamber, and the other side of the separation membrane forms a high-pressure chamber; The gas mixture passes through the high-pressure chamber, the separation membrane and the low-pressure chamber in sequence to form a low-nitrogen combustion aid on one side of the low-pressure chamber.

9. An IoT furnace low nitrogen combustion device, characterized in that: Used to implement the low-nitrogen combustion method for an Internet of Things furnace as described in any one of claims 1-8.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 8.

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