Intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion

Abstract

This invention discloses an intelligent temperature control method for solid waste treatment furnaces based on multi-stage combustion, specifically relating to the field of intelligent furnace temperature control technology. It involves acquiring the initial ignition temperature distribution of solid waste by installing a laser-induced breakdown spectroscopy device at the feed inlet, calculating the temperature distribution coefficient, and obtaining a deflagration risk index to predict the deflagration risk. When a deflagration risk exists, reverse pressure pulse technology is used to promptly release shock waves to suppress the drastic temperature rise caused by deflagration. The method monitors the temperature gradient change of the refractory material, calculates the equivalent thermal stress of the refractory material, promptly identifies thermal stress concentration problems caused by excessive temperature gradients, and obtains the thermal stress safety margin of the refractory material to provide data support for the control system. Based on the thermal stress safety margin of the refractory material and the deflagration risk index, a fuzzy logic algorithm is used to output temperature rise rate adjustment commands and air volume adjustment commands, automatically optimizing the furnace's temperature control behavior. This ensures a slow temperature rise while preventing sudden deflagration.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature control technology for furnaces, and more specifically, to an intelligent temperature control method for solid waste treatment furnaces based on multi-stage combustion. Background Technology

[0002] With increasingly stringent environmental policies, solid waste treatment has become a major social concern. Traditional solid waste treatment methods, such as landfill and incineration, often face environmental pollution and resource waste. In recent years, solid waste treatment furnace technology based on multi-stage combustion has gradually become one of the effective solid waste treatment solutions. This technology uses a multi-stage combustion system to gradually decompose harmful substances in solid waste, improving incineration efficiency and reducing pollutant emissions. However, in practice, the complex composition and uneven thermal characteristics of solid waste pose numerous challenges to the furnace's temperature control system.

[0003] The thermal stress sensitivity of refractory materials is a crucial factor in furnace design. Because refractory materials endure immense thermal stress at high temperatures, their temperature rise rate must be strictly controlled below 50°C / minute to prevent excessive thermal expansion from causing cracks or damage. During the cold start-up phase, the furnace temperature is low; if the temperature rise is too rapid, the refractory material may be damaged due to an excessive temperature gradient. Simultaneously, the complex composition and low initial ignition point of solid waste can lead to sudden combustion at low temperatures, i.e., deflagration. Deflagration causes a dramatic rise in localized temperature, potentially exceeding the thermal stress limit of the refractory material instantaneously, leading to equipment damage and danger. Therefore, ensuring a slow temperature rise in the furnace body to protect the refractory material while avoiding the sudden temperature surge caused by deflagration of solid waste is a major challenge in the current control of solid waste treatment furnaces. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an intelligent temperature control method for solid waste treatment furnaces based on multi-stage combustion, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A smart temperature control method for solid waste treatment furnaces based on multi-stage combustion includes the following steps:

[0007] A laser-induced breakdown spectroscopy device is installed at the feed inlet to obtain the initial ignition temperature distribution of solid waste, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of solid waste. When deflagration risk exists, the shock wave of deflagration is counteracted by a reverse pressure pulse. When deflagration risk does not exist, distributed fiber optic grating sensors are arranged inside the refractory material of the furnace to monitor the temperature gradient change of the refractory material in real time, calculate the equivalent thermal stress of the refractory material, and obtain the thermal stress safety margin of the refractory material. Based on the thermal stress safety margin of the refractory material and the deflagration risk index, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands to optimize the furnace temperature control behavior.

[0008] In a preferred embodiment, a laser-induced breakdown spectroscopy device is installed at the feed inlet to obtain the initial ignition temperature distribution of the solid waste, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of the solid waste, as detailed below:

[0009] Spectral data of solid waste were acquired using a laser-induced breakdown spectroscopy device. Based on the temperature-spectral line conversion relationship, the spectral data of the solid waste was converted into initial ignition temperature distribution data of the solid waste. The temperature-spectral line conversion relationship is as follows: Where I(λ) represents the intensity of the spectral line, B(λ,Tt) represents the radiation intensity of the blackbody radiation, h represents Planck's constant, c represents the speed of light, λ represents light of a specific wavelength, kb represents Boltzmann's constant, and Tt represents the temperature.

[0010] In a preferred embodiment, each obtained temperature data and its corresponding spatial coordinates are treated as a temperature distribution data vector [T]. i WZ i ], construct the initial ignition temperature distribution dataset SH=[T i WZ i ], where T i WZ represents the temperature data point in the i-th temperature distribution data vector. i Represents the spatial coordinates in the i-th temperature distribution data vector;

[0011] A1, randomly select K initial cluster centers C k Where k = {1, 2, 3, ..., K}, K is a positive integer, and each initial cluster center C k A two-dimensional vector is used to store the temperature data points and spatial coordinates in the temperature distribution data vector.

[0012] A2, Calculate the data vector of each temperature distribution in the initial ignition temperature distribution dataset to the center of each initial cluster C. k The Euclidean distance JL is expressed as follows: Among them CT kCWZ represents the temperature data point of the k-th initial cluster center. k Represents the spatial coordinates of the kth initial cluster center; assign each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establish an updated cluster;

[0013] A3, calculate the average value of the temperature data points and the average value of the spatial coordinates within the updated cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as the two new coordinates for the updated cluster;

[0014] A4, repeat A2 and A3 to iteratively update the updated cluster until the updated cluster no longer changes;

[0015] When the update cluster no longer changes, calculate the average value of the temperature data points within each update cluster, and calculate the temperature distribution difference value by subtracting the average values ​​of the temperature data points within every two update clusters. Then, sum up the multiple temperature distribution difference values ​​to obtain the temperature distribution coefficient Cte.

[0016] In a preferred embodiment, the deflagration risk index Rig is calculated based on the temperature distribution coefficient and the chlorine content of the solid waste, as shown in the following expression. Where Gfl represents the chlorine content of solid waste;

[0017] The deflagration risk index is compared with a preset deflagration risk index threshold to assess the deflagration risk of solid waste, as follows:

[0018] If the deflagration risk index is greater than the deflagration risk index threshold, a deflagration risk signal is generated; if the deflagration risk index is less than or equal to the deflagration risk index threshold, no deflagration risk signal needs to be generated.

[0019] In a preferred embodiment, when there is a risk of deflagration, the shock wave of deflagration is counteracted by a reverse pressure pulse, as follows: Where ΔPin represents the pressure of the reverse pressure pulse, ΔPde represents the pressure of the deflagration shock wave, and dj represents the distance between the reverse pressure pulse emission source and the deflagration source.

[0020] In a preferred embodiment, when there is no risk of deflagration, distributed fiber optic grating sensors are arranged inside the furnace refractory material to monitor the temperature gradient change of the refractory material in real time and calculate the equivalent thermal stress of the refractory material, as expressed below: Where tth represents the equivalent thermal stress of the refractory material, Ee represents the elastic modulus, α represents the coefficient of thermal expansion, ξ represents Poisson's ratio, and ΔT represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained based on the currently calculated equivalent thermal stress, as expressed below: Where Ssa represents the thermal stress safety margin of the refractory material, tthMAX represents the maximum equivalent thermal stress that the refractory material can withstand, and tth represents the equivalent thermal stress of the refractory material currently being calculated.

[0021] In a preferred embodiment, based on the thermal stress safety margin and deflagration risk index of the refractory material, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands, as follows:

[0022] The thermal stress safety margin and deflagration risk index of refractory materials are used as input variables:

[0023] The thermal stress safety margin of refractory materials is divided into three fuzzy sets based on its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets based on its value: {low, medium, high}.

[0024] The furnace temperature rise rate adjustment command and air volume adjustment command are used as output variables:

[0025] Define a fuzzy set of furnace temperature rise rate adjustment commands: {decelerate, maintain, accelerate};

[0026] Define a fuzzy set of furnace airflow adjustment commands: {decrease, maintain, increase};

[0027] Fuzzy rules are defined based on the thermal stress safety margin of refractory materials, the deflagration risk index, the furnace temperature rise rate adjustment command, and the air volume adjustment command.

[0028] After fuzzifying the input variables, the activation degree jhd of the fuzzy rule is calculated. n jhd n =μSsa*μRig, where jhd n μSsa represents the activation degree of the nth fuzzy rule, μRig represents the membership degree of the thermal stress safety margin of the refractory material, and μRig represents the membership degree of the deflagration risk index.

[0029] The results of fuzzy inference are defuzzified, and the final output is calculated based on activation weights. Where ΔV represents the furnace temperature rise rate adjustment command, and μΔV represents the membership degree of the furnace temperature rise rate adjustment command; Where ΔQ represents the furnace airflow adjustment command, and μΔQ represents the membership degree of the furnace airflow adjustment command.

[0030] In a preferred embodiment, a temperature control model is constructed based on the furnace's temperature rise rate adjustment command and air volume adjustment command to optimize the furnace's temperature control behavior, as follows: Where Vnew represents the adjusted temperature rise rate, Vcur represents the current temperature rise rate, ΔV represents the furnace temperature rise rate adjustment command, Qnew represents the adjusted air volume, Qcur represents the current air volume, and ΔQ represents the furnace air volume adjustment command.

[0031] In a preferred embodiment, (copyright 10).

[0032] The technical effects and advantages of this invention are as follows:

[0033] 1. This invention, by installing a laser-induced breakdown spectroscopy device at the feed inlet, can acquire the initial ignition temperature distribution of solid waste in real time, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of solid waste. This effectively predicts potential deflagration phenomena at low temperatures and provides early warnings, avoiding localized high temperatures caused by deflagration and reducing safety risks during furnace operation. When deflagration risk exists, reverse pressure pulse technology is used to release shock waves in a timely manner to suppress the drastic temperature rise caused by deflagration, effectively preventing the temperature from exceeding the thermal stress limit of the refractory material, preventing equipment damage, and ensuring the safe operation of the furnace. Distributed fiber optic grating sensors are arranged inside the furnace refractory material to monitor the refractory material in real time. By analyzing the temperature gradient changes in the refractory material, the equivalent thermal stress of the refractory material is calculated to promptly identify thermal stress concentration problems caused by excessive temperature gradients. The thermal stress safety margin of the refractory material is obtained to provide data support for the control system, preventing cracks or damage to the refractory material caused by excessive thermal stress. Based on the thermal stress safety margin and deflagration risk index of the refractory material, fuzzy logic algorithms are used to output temperature rise rate adjustment commands and air volume adjustment commands, automatically optimizing the furnace temperature control behavior. While ensuring a slow temperature rise in the furnace, sudden deflagration is prevented, thereby achieving precise temperature control, reducing energy consumption, improving work efficiency, ensuring the safety of the furnace equipment, and extending the service life of refractory materials and equipment. Attached Figure Description

[0034] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0035] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0036] 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.

[0037] Example: Figure 1The present invention provides an intelligent temperature control method for solid waste treatment furnaces based on multi-stage combustion, comprising the following steps:

[0038] A laser-induced breakdown spectroscopy device is installed at the feed inlet to obtain the initial ignition temperature distribution of solid waste, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of solid waste. When deflagration risk exists, the shock wave of deflagration is counteracted by a reverse pressure pulse. When deflagration risk does not exist, distributed fiber optic grating sensors are arranged inside the refractory material of the furnace to monitor the temperature gradient change of the refractory material in real time, calculate the equivalent thermal stress of the refractory material, and obtain the thermal stress safety margin of the refractory material. Based on the thermal stress safety margin of the refractory material and the deflagration risk index, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands to optimize the temperature control behavior of the furnace.

[0039] A laser-induced breakdown spectroscopy device was installed at the feed inlet to obtain the initial ignition temperature distribution of the solid waste, calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of the solid waste, as detailed below:

[0040] Spectral data of solid waste were acquired using a laser-induced breakdown spectroscopy device. Based on the temperature-spectral line conversion relationship, the spectral data of the solid waste was converted into initial ignition temperature distribution data of the solid waste. The temperature-spectral line conversion relationship is as follows: Where I(λ) represents the intensity of the spectral line, B(λ,Tt) represents the radiation intensity of the blackbody radiation, h represents Planck's constant, c represents the speed of light, λ represents light of a specific wavelength, kb represents Boltzmann's constant, and Tt represents the temperature;

[0041] Each temperature data point and its corresponding spatial coordinates are used as a temperature distribution data vector [T] i WZ i ], construct the initial ignition temperature distribution dataset SH=[T i WZ i ], where T i WZ represents the temperature data point in the i-th temperature distribution data vector. i Represents the spatial coordinates in the i-th temperature distribution data vector;

[0042] A1, randomly select K initial cluster centers C k Where k = {1, 2, 3, ..., K}, K is a positive integer, and each initial cluster center C k A two-dimensional vector distribution corresponds to the storage of temperature data points and spatial coordinates in the temperature distribution data vector;

[0043] A2, Calculate the data vector of each temperature distribution in the initial ignition temperature distribution dataset to the center of each initial cluster C. k The Euclidean distance JL is expressed as follows: Among them CT k CWZ represents the temperature data point of the k-th initial cluster center. k Represents the spatial coordinates of the kth initial cluster center; assign each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establish an updated cluster;

[0044] A3, calculate the average value of the temperature data points and the average value of the spatial coordinates within the updated cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as the two new coordinates for the updated cluster;

[0045] A4, repeat A2 and A3 to iteratively update the updated cluster until the updated cluster no longer changes;

[0046] When the update cluster no longer changes, calculate the average value of the temperature data points in each update cluster, and calculate the difference between the average values ​​of the temperature data points in every two update clusters to obtain the temperature distribution difference value. Then, sum up the multiple temperature distribution difference values ​​to obtain the temperature distribution coefficient Cte.

[0047] In this invention, the temperature distribution coefficient is used to represent the uniformity or concentration of the initial ignition temperature of solid waste. The larger the temperature distribution coefficient, the more uneven the temperature distribution, which means that there is a large local temperature gradient in the solid waste, which may lead to local deflagration or thermal stress concentration; conversely, it indicates that the temperature distribution is more uniform and the system control is more stable.

[0048] The deflagration risk index Rig is calculated based on the temperature distribution coefficient and the chlorine content of the solid waste, as shown in the following expression. Where Gfl represents the chlorine content of solid waste;

[0049] It should be noted that before calculating the deflagration risk index, it is necessary to ensure that the chlorine content and temperature distribution coefficient of the solid waste have been normalized. Commonly used normalization methods include Min-Max normalization and Z-Score standardization.

[0050] The deflagration risk index is compared with a preset deflagration risk index threshold to assess the deflagration risk of solid waste, as follows:

[0051] If the deflagration risk index is greater than the deflagration risk index threshold, it indicates that there is a definite deflagration risk in the solid waste, and a deflagration risk signal is generated; if the deflagration risk index is less than or equal to the deflagration risk index threshold, it indicates that there is no definite deflagration risk in the solid waste, and no deflagration risk signal needs to be generated.

[0052] This invention calculates the deflagration risk index by combining the temperature distribution coefficient with the chlorine content of solid waste. This allows for a more accurate assessment of whether there is a deflagration risk during solid waste treatment. It not only improves the accuracy of deflagration risk prediction but also generates risk signals in a timely manner. This helps the system take emergency measures when there is a high risk of deflagration, avoiding damage to equipment or potential threats to the environment caused by solid waste deflagration. By comparing the index with the deflagration risk index threshold, the system can automatically adjust its operating strategy or take preventive measures when the risk is high, thereby ensuring the safety and stability of the solid waste treatment process.

[0053] When there is a risk of deflagration, the shock wave of deflagration is counteracted by a reverse pressure pulse, as follows: Where ΔPin represents the pressure of the reverse pressure pulse, ΔPde represents the pressure of the deflagration shock wave, and dj represents the distance between the reverse pressure pulse source and the deflagration source.

[0054] It should be noted that the reverse pressure pulse, actively suppressed near the deflagration risk area, is typically located at the feed inlet, furnace surface, or other critical areas to directly influence the propagation path of the deflagration wave. The specific deployment scheme can be optimized based on the furnace structure and airflow distribution to ensure that the reverse pressure pulse can maximally counteract the shock wave caused by deflagration.

[0055] This invention uses reverse pressure pulses to counteract the shock wave of deflagration. When deflagration risk occurs, the reverse suppression of the shock wave significantly reduces the impact on the furnace body and refractory materials, thus reducing the risk of equipment damage. By actively suppressing the shock wave of deflagration, the system can maintain stable operation, avoiding sudden temperature rises and equipment failures caused by deflagration, thereby ensuring the long-term reliability of the furnace. Effective deflagration risk suppression not only extends the service life of equipment but also reduces sudden shutdowns and repairs caused by deflagration, thereby reducing maintenance costs.

[0056] When there is no risk of deflagration, distributed fiber optic grating sensors are arranged inside the refractory material of the furnace to monitor the temperature gradient change of the refractory material in real time and calculate the equivalent thermal stress of the refractory material. The expression is as follows: Where tth represents the equivalent thermal stress of the refractory material, Ee represents the elastic modulus, α represents the coefficient of thermal expansion, ξ represents Poisson's ratio, and ΔT represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained based on the currently calculated equivalent thermal stress, as expressed below: Where Ssa represents the thermal stress safety margin of the refractory material, tthMAX represents the maximum equivalent thermal stress that the refractory material can withstand, and tth represents the equivalent thermal stress of the refractory material currently calculated.

[0057] It should be noted that the elastic modulus, coefficient of thermal expansion, and Poisson's ratio of refractory materials can be obtained by consulting standard material handbooks, and will not be elaborated here.

[0058] This invention, when there is no risk of deflagration, involves arranging distributed fiber optic grating sensors inside the refractory material of the furnace to monitor the temperature gradient changes of the refractory material in real time, calculate the equivalent thermal stress of the refractory material, and obtain the thermal stress safety margin of the refractory material. This helps to assess the thermal stress of the refractory material under different operating conditions and can also effectively obtain the thermal stress safety margin of the refractory material. Through continuous monitoring of thermal stress, dynamic evaluation of the furnace refractory material can be achieved, potential thermal stress accumulation problems can be detected in time, and the temperature control strategy can be automatically adjusted when the thermal stress approaches the critical value to avoid cracks or damage to the refractory material due to excessive thermal stress, thereby improving the safety and stability of the system.

[0059] Based on the thermal stress safety margin and deflagration risk index of the refractory material, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands, as follows:

[0060] The thermal stress safety margin and deflagration risk index of refractory materials are used as input variables:

[0061] The thermal stress safety margin of refractory materials is divided into three fuzzy sets based on its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets based on its value: {low, medium, high}.

[0062] The furnace temperature rise rate adjustment command and air volume adjustment command are used as output variables:

[0063] Define a fuzzy set of furnace temperature rise rate adjustment commands: {decelerate, maintain, accelerate};

[0064] Define a fuzzy set of furnace airflow adjustment commands: {decrease, maintain, increase};

[0065] Fuzzy rules are defined based on the thermal stress safety margin of refractory materials, the deflagration risk index, the furnace temperature rise rate adjustment command, and the air volume adjustment command. For example, if the thermal stress safety margin of the refractory material is high and the deflagration risk index is low, it is permissible to accelerate the furnace temperature rise rate while reducing the furnace air volume to achieve rapid heating and avoid excessive oxygen combustion. If the thermal stress safety margin of the refractory material is medium and the deflagration risk index is medium, it is permissible to maintain the furnace temperature rise rate while increasing the furnace air volume to balance combustion efficiency and thermal stress. If the thermal stress safety margin of the refractory material is low and the deflagration risk index is high, it is permissible to slow down the furnace temperature rise rate while increasing the furnace air volume to prioritize equipment safety.

[0066] After fuzzifying the input variables, the activation degree jhd of the fuzzy rule is calculated. n jhdn =μSsa*μRig, where jhd n μSsa represents the activation degree of the nth fuzzy rule, μRig represents the membership degree of the thermal stress safety margin of the refractory material, and μRig represents the membership degree of the deflagration risk index.

[0067] The results of fuzzy inference are defuzzified, and the final output is calculated based on activation weights. Where ΔV represents the furnace temperature rise rate adjustment command, and μΔV represents the membership degree of the furnace temperature rise rate adjustment command; Where ΔQ represents the furnace air volume adjustment command, and μΔQ represents the membership degree of the furnace air volume adjustment command;

[0068] A temperature control model is constructed based on the furnace's temperature rise rate adjustment command and air volume adjustment command to optimize the furnace's temperature control behavior, as detailed below: Where Vnew represents the adjusted temperature rise rate, Vcur represents the current temperature rise rate, ΔV represents the furnace temperature rise rate adjustment command, Qnew represents the adjusted air volume, Qcur represents the current air volume, and ΔQ represents the furnace air volume adjustment command.

[0069] This invention, by inputting the thermal stress safety margin and deflagration risk index of refractory materials into a fuzzy logic control system, can optimize the temperature rise rate and airflow of the furnace, thereby effectively optimizing the furnace's temperature control behavior. Specifically, by fuzzifying the thermal stress safety margin and deflagration risk index of the refractory materials, temperature rise rate adjustment commands and airflow adjustment commands adapted to different scenarios can be formulated based on their affiliation in different membership sets. Based on these input values, the furnace's temperature rise rate and airflow will be intelligently adjusted according to the fuzzy inference rule set, thereby ensuring that the furnace operates in a highly efficient and safe working state. When the thermal stress safety margin of the refractory materials is high and the deflagration risk index is low, the system will allow an accelerated temperature rise rate and a reduced airflow to heat the furnace more quickly while avoiding the risk of deflagration due to excessively rapid temperature rise. Conversely, when the thermal stress safety margin of the refractory material is low and the deflagration risk index is high, the system will reduce the temperature rise rate and increase the air volume to prioritize the safety of the furnace. These fuzzy rules, through fuzzy reasoning and defuzzification, will ultimately generate adjustment instructions to guide the furnace to adaptively adjust according to safety and efficiency requirements, avoiding equipment damage and dangerous accidents, while improving the furnace's operating efficiency. It can automatically adjust the furnace's operating parameters according to changes in actual working conditions, avoiding the limitations of manual control, improving the safety and production efficiency of furnace operation, and thus achieving more precise and efficient temperature control management while ensuring equipment safety.

[0070] This invention, by installing a laser-induced breakdown spectroscopy device at the feed inlet, can acquire the initial ignition temperature distribution of solid waste in real time, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of solid waste. This effectively predicts potential deflagration phenomena at low temperatures and provides early warnings, avoiding localized high temperatures caused by deflagration and reducing safety risks during furnace operation. When deflagration risk exists, reverse pressure pulse technology is used to promptly release shock waves to suppress the drastic temperature rise caused by deflagration, effectively preventing a sudden temperature increase exceeding the thermal stress limit of the refractory material, preventing equipment damage, and ensuring the safe operation of the furnace. Distributed fiber optic grating sensors are arranged inside the furnace refractory material to monitor the refractory material in real time. By analyzing temperature gradient changes, the equivalent thermal stress of refractory materials is calculated to promptly identify thermal stress concentration problems caused by excessive temperature gradients. The thermal stress safety margin of the refractory materials is obtained to provide data support for the control system, preventing cracks or damage to the refractory materials caused by excessive thermal stress. Based on the thermal stress safety margin and deflagration risk index of the refractory materials, fuzzy logic algorithms are used to output temperature rise rate adjustment commands and air volume adjustment commands, automatically optimizing the furnace's temperature control behavior. While ensuring a slow temperature rise, sudden deflagration is prevented, thereby achieving precise temperature control, reducing energy consumption, improving work efficiency, ensuring the safety of the furnace equipment, and extending the service life of refractory materials and equipment.

[0071] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0072] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes 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 application.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart temperature control method for solid waste treatment furnaces based on multi-stage combustion, characterized in that: Includes the following steps: A laser-induced breakdown spectroscopy device is installed at the feed inlet to obtain the initial ignition temperature distribution of solid waste, calculate the temperature distribution coefficient, and obtain a deflagration risk index to predict the deflagration risk of solid waste. When deflagration risk exists, the shock wave of deflagration is counteracted by a reverse pressure pulse. When deflagration risk does not exist, distributed fiber optic grating sensors are arranged inside the refractory material of the furnace to monitor the temperature gradient change of the refractory material in real time, calculate the equivalent thermal stress of the refractory material, and obtain the thermal stress safety margin of the refractory material. Based on the thermal stress safety margin of the refractory material and the deflagration risk index, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands to optimize the temperature control behavior of the furnace. A laser-induced breakdown spectroscopy device was installed at the feed inlet to obtain the initial ignition temperature distribution of the solid waste, calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of the solid waste, as detailed below: Spectral data of solid waste were acquired using a laser-induced breakdown spectroscopy device. Based on the temperature-spectral line conversion relationship, the spectral data of the solid waste was converted into initial ignition temperature distribution data of the solid waste. The temperature-spectral line conversion relationship is as follows: ,in Indicates the intensity of spectral lines, This represents the intensity of blackbody radiation. Denotes Planck's constant. Represents the speed of light. Represents light of a specific wavelength. Represents the Boltzmann constant. Indicates temperature; Each temperature data point and its corresponding spatial coordinates are treated as a temperature distribution data vector. Construct an initial ignition temperature distribution dataset ,in This represents the temperature data point in the i-th temperature distribution data vector. Represents the spatial coordinates in the i-th temperature distribution data vector; A1, randomly select K initial cluster centers. Where k = {1, 2, 3, ..., K}, K is a positive integer, and each initial cluster center is... A two-dimensional vector is used to store the temperature data points and spatial coordinates in the temperature distribution data vector. A2, Calculate the data vector of each temperature distribution in the initial ignition temperature distribution dataset to the center of each initial cluster. European distance The expression is as follows ,in This represents the temperature data point at the k-th initial cluster center. Represents the spatial coordinates of the kth initial cluster center; assign each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establish an updated cluster; A3, calculate the average value of the temperature data points and the average value of the spatial coordinates within the updated cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as the two new coordinates for the updated cluster; A4, repeat A2 and A3 to iteratively update the updated cluster until the updated cluster no longer changes; When the update cluster no longer changes, calculate the average temperature data points within each update cluster, and then calculate the temperature distribution difference value by subtracting the average temperature data points from every two update clusters. Finally, sum multiple temperature distribution difference values ​​to obtain the temperature distribution coefficient. ; The deflagration risk index is calculated based on the temperature distribution coefficient and the chlorine content of the solid waste. The expression is as follows ,in This indicates the chlorine content of solid waste.

2. The intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion according to claim 1, characterized in that: The deflagration risk index is compared with a preset deflagration risk index threshold to assess the deflagration risk of solid waste, as follows: If the deflagration risk index is greater than the deflagration risk index threshold, a deflagration risk signal is generated; if the deflagration risk index is less than or equal to the deflagration risk index threshold, no deflagration risk signal needs to be generated.

3. The intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion according to claim 2, characterized in that: When there is a risk of deflagration, the shock wave of deflagration is counteracted by a reverse pressure pulse, as follows: ,in Indicates the pressure of the reverse pressure pulse. This indicates the pressure of the deflagration shock wave. This indicates the distance between the reverse pressure pulse emission source and the deflagration source.

4. The intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion according to claim 2, characterized in that: When there is no risk of deflagration, distributed fiber optic grating sensors are arranged inside the refractory material of the furnace to monitor the temperature gradient change of the refractory material in real time and calculate the equivalent thermal stress of the refractory material. The expression is as follows: ,in This represents the equivalent thermal stress of refractory materials. Indicates the elastic modulus. Indicates the coefficient of thermal expansion. Represents Poisson's ratio. This represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained based on the currently calculated equivalent thermal stress, as expressed below: ,in Indicates the thermal stress safety margin of refractory materials. This indicates the maximum equivalent thermal stress that the refractory material can withstand. This represents the equivalent thermal stress of the refractory material currently being calculated.

5. The intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion according to claim 4, characterized in that: Based on the thermal stress safety margin and deflagration risk index of the refractory material, fuzzy logic is used to output furnace temperature rise rate adjustment commands and air volume adjustment commands, as follows: The thermal stress safety margin and deflagration risk index of refractory materials are used as input variables: The thermal stress safety margin of refractory materials is divided into three fuzzy sets based on its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets based on its value: {low, medium, high}. The furnace temperature rise rate adjustment command and air volume adjustment command are used as output variables: Define a fuzzy set of furnace temperature rise rate adjustment commands: {decelerate, maintain, accelerate}; Define a fuzzy set of furnace airflow adjustment commands: {decrease, maintain, increase}; Fuzzy rules are defined based on the thermal stress safety margin of refractory materials, the deflagration risk index, the furnace temperature rise rate adjustment command, and the air volume adjustment command. After fuzzifying the input variables, the activation degree of the fuzzy rules is calculated. : ,in This represents the activation degree of the nth fuzzy rule. Membership degree, representing the thermal stress safety margin of refractory materials. Indicates the degree of membership of the deflagration risk index; The results of fuzzy inference are defuzzified, and the final output is calculated based on activation weights. ,in This indicates a command to adjust the furnace's temperature rise rate. Indicates the membership degree of the furnace temperature rise rate adjustment command; ,in This indicates an instruction to adjust the furnace's airflow. This indicates the membership degree of the furnace airflow adjustment command.

6. The intelligent temperature control method for solid waste treatment furnace based on multi-stage combustion according to claim 5, characterized in that: A temperature control model is constructed based on the furnace's temperature rise rate adjustment command and air volume adjustment command to optimize the furnace's temperature control behavior, as detailed below: ,in This indicates the adjusted rate of temperature rise. This indicates the current rate of temperature rise. This indicates a command to adjust the furnace's temperature rise rate. This indicates the adjusted airflow. This indicates the current airflow. This indicates an instruction to adjust the airflow of the furnace.

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