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

Abstract

The invention discloses an intelligent temperature control method for a solid waste treatment smelting furnace based on multi-stage combustion, and particularly relates to the technical field of intelligent temperature control of smelting furnaces. Laser-induced breakdown spectroscopy equipment is installed at a feeding port to obtain initial ignition point temperature distribution of solid waste, a temperature distribution coefficient is calculated, a detonation risk index is obtained, and the detonation risk of the solid waste is predicted; when a detonation risk exists, shock waves are released in time by adopting a reverse pressure pulse technology to inhibit severe temperature rise caused by detonation, the temperature gradient change of the refractory material is monitored, the equivalent thermal stress of the refractory material is calculated, and the problem of thermal stress concentration caused by overlarge temperature gradient is found in time; the thermal stress safety margin of the refractory material is obtained to provide data support for a control system, according to the thermal stress safety margin and the deflagration risk index of the refractory material, a temperature rise rate adjusting instruction and an air volume adjusting instruction are output through a fuzzy logic algorithm, the temperature control behavior of the smelting furnace is automatically optimized, and the temperature control behavior of the smelting furnace is automatically optimized while it is ensured that the temperature rise of the smelting furnace is slow. And the sudden deflagration phenomenon can be prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent temperature control of furnaces, and more specifically, to an intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion. Background Art

[0002] With increasingly stringent environmental protection policies, solid waste treatment has become a focus of current social attention. Traditional solid waste treatment methods, such as landfill and incineration, often face problems of environmental pollution and waste of resources. In recent years, solid waste treatment furnace technology based on multi-stage combustion has gradually become one of the effective solid waste treatment solutions. Through the multi-stage combustion system, harmful substances in solid waste are gradually decomposed, incineration efficiency is improved, and pollutant emissions are reduced. However, in actual operation, the complex composition and uneven thermal characteristics of solid waste bring many challenges to the temperature control system of the furnace.

[0003] Among them, the thermal stress sensitivity of refractory materials is an important factor in furnace design. Since refractory materials are subjected to huge thermal stress in high temperature environments, their temperature rise rate needs to be strictly controlled below 50°C / minute to prevent excessive thermal expansion from causing cracks or damage. During the cold start phase of the furnace, the furnace body temperature is relatively low. At this time, if the temperature rises too quickly, the refractory materials may be damaged due to excessive temperature gradients. At the same time, solid waste has a complex composition and a low initial ignition point, which may lead to sudden combustion at low temperatures, i.e., deflagration. Deflagration can cause a sharp rise in local temperature, and may even instantly exceed the thermal stress limit of the refractory material, causing equipment damage and danger. Therefore, how to ensure that the furnace body temperature rises slowly to protect the refractory material while avoiding the sudden temperature rise caused by the deflagration of solid waste is a major problem 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, an embodiment of the present invention provides an intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The intelligent temperature control method of a solid waste treatment furnace 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 the solid waste and calculate the temperature distribution coefficient, and the deflagration risk index is obtained to predict the deflagration risk of the solid waste; when there is a deflagration risk, a reverse pressure pulse is used to offset the shock wave of the deflagration; when there is no deflagration risk, a distributed fiber grating sensor is 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; according to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic to optimize the temperature control behavior of the furnace.

[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 to calculate the temperature distribution coefficient, and to obtain the deflagration risk index to predict the deflagration risk of the solid waste, as follows:

[0009] The spectrum data of solid waste is obtained by laser induced breakdown spectroscopy equipment, and the spectrum data of solid waste is converted into the initial ignition temperature distribution data of solid waste according to the temperature-spectral line conversion relationship. 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 blackbody radiation, h represents Planck's constant, c represents the speed of light, λ represents light of a specific wavelength, kb represents the Boltzmann constant, and Tt represents temperature.

[0010] In a preferred embodiment, each obtained temperature data and the corresponding spatial coordinates are taken as a temperature distribution data vector [T i ,WZ i ], construct the initial ignition temperature distribution data set SH = [T i ,WZ i ], where T i represents the temperature data point in the ith temperature distribution data vector, WZ i Represents the spatial coordinates of 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 is a two-dimensional vector that stores the temperature data points and spatial coordinates in the temperature distribution data vector;

[0012] A2, calculate each temperature distribution data vector in the initial ignition temperature distribution data set to each initial cluster center C k The Euclidean distance JL is expressed as follows CT krepresents the temperature data point at the center of the kth initial cluster, CWZ k Represents the spatial coordinates of the center of the kth initial cluster; assigns each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establishes an updated cluster;

[0013] A3, calculate the average value of the temperature data points and the average value of the spatial coordinates in the update cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as two new coordinates of the update cluster respectively;

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

[0015] When the update cluster no longer changes, the average value of the temperature data points in each update cluster is calculated, and the average values ​​of the temperature data points in every two update clusters are calculated to obtain a temperature distribution difference value, and multiple temperature distribution difference values ​​are accumulated to obtain a 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, and the expression is as follows: Where Gfl represents the chlorine content of solid waste;

[0017] The deflagration risk index is compared with the 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, there is no need to generate a deflagration risk signal.

[0019] In a preferred embodiment, when there is a risk of deflagration, the shock wave of the deflagration is offset 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, a distributed fiber Bragg grating sensor is 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 thermal expansion coefficient, ξ represents the Poisson's ratio, and ΔT represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained according to the equivalent thermal stress of the refractory material currently calculated, and the expression is as follows: 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.

[0021] In a preferred embodiment, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic according to the thermal stress safety margin and the deflagration risk index of the refractory material, 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 according to its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets according to its value: {low, medium, high};

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

[0025] Define the furnace temperature rise rate adjustment instruction fuzzy set: {deceleration, maintenance, acceleration};

[0026] Define the fuzzy set of furnace air volume adjustment instructions: {reduce, maintain, increase};

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

[0028] After fuzzifying the input variables, the activation degree jhd of the fuzzy rules is calculated n :jhd n =μSsa*μRig, where jhd n represents the activation degree of the nth fuzzy rule, μSsa 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 result of fuzzy reasoning is defuzzified, and the final output result is calculated according to the activation weight: Wherein ΔV represents the temperature rise rate adjustment instruction of the furnace, and μΔV represents the membership degree of the temperature rise rate adjustment instruction of the furnace; Wherein ΔQ represents the air volume adjustment instruction of the furnace, and μΔQ represents the membership degree of the air volume adjustment instruction of the furnace.

[0030] In a preferred embodiment, a temperature control model is constructed according to the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace to optimize the temperature control behavior of the furnace, as follows: Wherein Vnew represents the adjusted temperature rise rate, Vcur represents the current temperature rise rate, ΔV represents the temperature rise rate adjustment instruction of the furnace, Qnew represents the adjusted air volume, Qcur represents the current air volume, and ΔQ represents the air volume adjustment instruction of the furnace.

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

[0032] Technical effects and advantages of the present invention:

[0033] 1. The present invention can obtain the initial ignition temperature distribution of solid waste in real time, calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of solid waste by installing a laser induced breakdown spectroscopy device at the feed inlet. It can effectively predict the possible deflagration of solid waste at low temperature and issue early warnings to avoid local high temperatures caused by deflagration, reduce safety risks during furnace operation, and when there is a risk of deflagration, use reverse pressure pulse technology to release shock waves in time to suppress the sharp rise in temperature caused by deflagration, effectively avoid a sudden temperature rise exceeding the thermal stress limit of refractory materials, prevent equipment damage, and ensure the safe operation of the furnace. Distributed fiber grating sensors are arranged inside the refractory materials of the furnace to monitor the refractory materials in real time. The temperature gradient of the material is changed, the equivalent thermal stress of the refractory material is calculated, and the thermal stress concentration problem caused by excessive temperature gradient is discovered in time, and the thermal stress safety margin of the refractory material is obtained to provide data support for the control system, so as to avoid cracks or damage of the refractory material caused by excessive thermal stress. According to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction are output through the fuzzy logic algorithm, and the temperature control behavior of the furnace is automatically optimized. While ensuring that the temperature rise of the furnace is slow, it can prevent sudden deflagration, thereby realizing precise adjustment of temperature control, reducing energy consumption and improving work efficiency, ensuring the safety of furnace equipment, and extending the service life of refractory materials and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0035] Figure 1 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0038] A laser-induced breakdown spectroscopy device is installed at the feed port to obtain the initial ignition temperature distribution of the solid waste and calculate the temperature distribution coefficient, and the deflagration risk index is obtained to predict the deflagration risk of the solid waste; when there is a deflagration risk, the shock wave of the deflagration is offset by a reverse pressure pulse; when there is no deflagration risk, a distributed fiber grating sensor is 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; according to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic to optimize the temperature control behavior of the furnace;

[0039] Install a laser induced breakdown spectroscopy device at the feed inlet to obtain the initial ignition temperature distribution of the solid waste and calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of the solid waste, as follows:

[0040] The spectrum data of solid waste is obtained by laser induced breakdown spectroscopy equipment, and the spectrum data of solid waste is converted into the initial ignition temperature distribution data of solid waste according to the temperature-spectral line conversion relationship. 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 blackbody radiation, h represents Planck's constant, c represents the speed of light, λ represents light of a specific wavelength, kb represents the Boltzmann constant, and Tt represents the temperature;

[0041] Each temperature data and the corresponding spatial coordinates obtained are taken as a temperature distribution data vector [T i ,WZ i ], construct the initial ignition temperature distribution data set SH = [T i ,WZ i ], where T i represents the temperature data point in the ith temperature distribution data vector, WZ i Represents the spatial coordinates of 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 corresponding to the temperature data points and spatial coordinates in the temperature distribution data vector;

[0043] A2, calculate each temperature distribution data vector in the initial ignition temperature distribution data set to each initial cluster center C k The Euclidean distance JL is expressed as follows CT k represents the temperature data point at the center of the kth initial cluster, CWZ k Represents the spatial coordinates of the center of the kth initial cluster; assigns each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establishes an updated cluster;

[0044] A3, calculate the average value of the temperature data points and the average value of the spatial coordinates in the update cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as two new coordinates of the update cluster respectively;

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

[0046] When the update cluster no longer changes, the average value of the temperature data points in each update cluster is calculated, and the average values ​​of the temperature data points in every two update clusters are calculated to obtain the temperature distribution difference value, and multiple temperature distribution difference values ​​are accumulated to obtain the temperature distribution coefficient Cte;

[0047] The temperature distribution coefficient in the present invention is used to indicate the uniformity or concentration of the initial ignition temperature of the 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; on the contrary, 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. The expression is as follows: 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 are normalized. Commonly used normalization methods include Min-Max normalization and Z-Score normalization.

[0050] The deflagration risk index is compared with the 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 means that the solid waste has a definite deflagration risk, and a deflagration risk signal is generated; if the deflagration risk index is less than or equal to the deflagration risk index threshold, it means that the solid waste does not have a definite deflagration risk, and there is no need to generate a deflagration risk signal;

[0052] The present invention calculates the deflagration risk index by combining the temperature distribution coefficient with the chlorine content of the solid waste, and can more accurately evaluate whether there is a deflagration risk in the solid waste treatment process, which not only improves the prediction accuracy of the deflagration risk, but also can generate risk signals in time to help the system take emergency measures when a high deflagration risk occurs, thereby avoiding damage to equipment or potential threats to the environment caused by solid waste deflagration. By comparing with the deflagration risk index threshold, the system can automatically adjust the operation 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 the deflagration is offset 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;

[0054] It should be noted that the reverse pressure pulse is deployed near the risk area of ​​deflagration. The active suppression is usually located at the feed inlet, furnace surface or other key areas to directly affect the propagation path of the deflagration wave. The specific deployment plan can be optimized based on the structure and airflow distribution of the furnace to ensure that the reverse pressure pulse can maximize the impact of the shock wave caused by the deflagration.

[0055] The present invention offsets the shock wave of the deflagration through a reverse pressure pulse and can, when the risk of deflagration occurs, significantly reduce the impact on the furnace body and refractory materials by reversely suppressing the shock wave, thereby reducing the risk of equipment damage. By actively suppressing the shock wave of the deflagration, the system can maintain stable operation and avoid temperature rise and equipment failure caused by the deflagration, thereby ensuring the long-term reliability of the furnace. Effective suppression of the risk of deflagration can not only extend the service life of the equipment, but also reduce sudden shutdowns and repairs caused by the deflagration, thereby reducing maintenance costs.

[0056] When there is no risk of deflagration, a distributed fiber Bragg grating sensor is 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. The expression is as follows: Where tth represents the equivalent thermal stress of the refractory material, Ee represents the elastic modulus, α represents the thermal expansion coefficient, ξ represents the Poisson's ratio, and ΔT represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained according to the equivalent thermal stress of the refractory material currently calculated, and the expression is as follows: Where Ssa represents the thermal stress safety margin of the refractory material, tthMAX represents the maximum equivalent thermal stress of the refractory material, and tth represents the equivalent thermal stress of the refractory material currently calculated;

[0057] It should be noted that the elastic modulus, thermal expansion coefficient and Poisson's ratio of refractory materials can be obtained by referring to the standard material manual and will not be described in detail here;

[0058] When there is no risk of deflagration, the present invention arranges a distributed fiber grating sensor 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, which is helpful to evaluate the thermal stress of the refractory material under different working 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 refractory material of the furnace can be achieved, potential thermal stress accumulation problems can be discovered in time, and the temperature control strategy can be automatically adjusted when the thermal stress approaches the critical value, so as to avoid cracks or damage of the refractory material due to excessive thermal stress, thereby improving the safety and stability of the system;

[0059] According to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic, 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 according to its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets according to its value: {low, medium, high};

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

[0063] Define the furnace temperature rise rate adjustment instruction fuzzy set: {deceleration, maintenance, acceleration};

[0064] Define the fuzzy set of furnace air volume adjustment instructions: {reduce, maintain, increase};

[0065] Fuzzy rules are defined according to the thermal stress safety margin of refractory materials, the deflagration risk index, the temperature rise rate adjustment instructions of the furnace, and the air volume adjustment instructions; for example, if the thermal stress safety margin of refractory materials is high and the deflagration risk index is low, it is allowed to accelerate the temperature rise rate of the furnace while reducing the air volume of the furnace to quickly heat up and avoid excessive oxygen combustion; if the thermal stress safety margin of refractory materials is medium and the deflagration risk index is medium, it is allowed to maintain the temperature rise rate of the furnace while increasing the air volume of the furnace to balance the combustion efficiency and thermal stress; if the thermal stress safety margin of refractory materials is low and the deflagration risk index is high, it is allowed to reduce the temperature rise rate of the furnace while increasing the air volume of the furnace to give priority to equipment safety;

[0066] After fuzzifying the input variables, the activation degree jhd of the fuzzy rules is calculated n :jhdn =μSsa*μRig, where jhd n represents the activation degree of the nth fuzzy rule, μSsa 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 result of fuzzy reasoning is defuzzified, and the final output result is calculated according to the activation weight: Wherein ΔV represents the temperature rise rate adjustment instruction of the furnace, and μΔV represents the membership degree of the temperature rise rate adjustment instruction of the furnace; Where ΔQ represents the air volume adjustment command of the furnace, and μΔQ represents the membership degree of the air volume adjustment command of the furnace;

[0068] According to the temperature rise rate adjustment instructions and air volume adjustment instructions of the furnace, a temperature control model is constructed to optimize the temperature control behavior of the furnace, as follows: Wherein Vnew represents the adjusted temperature rise rate, Vcur represents the current temperature rise rate, ΔV represents the temperature rise rate adjustment instruction of the furnace, Qnew represents the adjusted air volume, Qcur represents the current air volume, and ΔQ represents the air volume adjustment instruction of the furnace;

[0069] The present invention can achieve optimal adjustment of the temperature rise rate and air volume of the furnace by inputting the thermal stress safety margin and deflagration risk index of refractory materials into the fuzzy logic control system, thereby effectively optimizing the temperature control behavior of the furnace. Specifically, by fuzzifying the thermal stress safety margin and deflagration risk index of refractory materials, temperature rise rate adjustment instructions and air volume adjustment instructions adapted to different situations can be formulated according to their affiliation in different membership sets. Based on these input values, the temperature rise rate and air volume of the furnace will be intelligently adjusted according to the fuzzy reasoning rule set, thereby ensuring that the furnace operates in an efficient and safe working state; when the thermal stress safety margin of the refractory material is high and the deflagration risk index is low, the system will allow the temperature rise rate to be accelerated and the air volume to be reduced so as to heat the furnace more quickly while avoiding the risk of deflagration caused by excessively rapid temperature increases. On the contrary, when the thermal stress safety margin of refractory materials is low and the deflagration risk index is high, the system will reduce the temperature rise rate and increase the air volume to give priority to the safety of the furnace. These fuzzy rules will be processed through fuzzy reasoning and defuzzification, and finally generate adjustment instructions to guide the furnace to make adaptive adjustments according to safety and efficiency requirements, avoid equipment damage and dangerous accidents, and improve the operating efficiency of the furnace; it can automatically adjust the furnace operating parameters according to changes in actual working conditions, avoid the limitations of human control, and improve the safety and production efficiency of furnace operation, thereby achieving more accurate and efficient temperature control management while ensuring equipment safety.

[0070] The present invention installs a laser induced breakdown spectroscopy device at the feed inlet, so as to obtain the initial ignition temperature distribution of the solid waste in real time, calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of the solid waste, effectively predict the possible deflagration of the solid waste at low temperature and issue an early warning, avoid local high temperature caused by deflagration, and reduce the safety risk during the operation of the furnace. When there is a risk of deflagration, the reverse pressure pulse technology is used to release the shock wave in time to suppress the sharp temperature rise caused by the deflagration, effectively avoid the sudden temperature rise exceeding the thermal stress limit of the refractory material, prevent equipment damage, and ensure the safe operation of the furnace. Distributed fiber grating sensors are arranged inside the refractory material of the furnace to monitor the refractory material in real time. The temperature gradient changes are calculated, the equivalent thermal stress of the refractory material is calculated, and the thermal stress concentration problem caused by excessive temperature gradient is discovered in time, and the thermal stress safety margin of the refractory material is obtained to provide data support for the control system, so as to avoid cracks or damage of the refractory material caused by excessive thermal stress. According to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction are output through the fuzzy logic algorithm, and the temperature control behavior of the furnace is automatically optimized. While ensuring that the temperature rise of the furnace is slow, it can prevent sudden deflagration, thereby realizing precise adjustment of temperature control, reducing energy consumption and improving work efficiency, ensuring the safety of furnace equipment, and extending the service life of refractory materials and equipment.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0073] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion, characterized in that: The steps include: A laser induced breakdown spectroscopy device is installed at the feed inlet to obtain the initial ignition temperature distribution of the solid waste and calculate the temperature distribution coefficient, and the deflagration risk index is obtained to predict the deflagration risk of the solid waste; when there is a deflagration risk, a reverse pressure pulse is used to offset the shock wave of the deflagration; when there is no deflagration risk, a distributed fiber grating sensor is 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; according to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic to optimize the temperature control behavior of the furnace.

2. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 1 is characterized in that: Install a laser induced breakdown spectroscopy device at the feed inlet to obtain the initial ignition temperature distribution of the solid waste and calculate the temperature distribution coefficient, and obtain the deflagration risk index to predict the deflagration risk of the solid waste, as follows: The spectrum data of solid waste is obtained by laser induced breakdown spectroscopy equipment, and the spectrum data of solid waste is converted into the initial ignition temperature distribution data of solid waste according to the temperature-spectral line conversion relationship. 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 blackbody radiation, h represents Planck's constant, c represents the speed of light, λ represents light of a specific wavelength, kb represents the Boltzmann constant, and Tt represents temperature.

3. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 2 is characterized in that: Each temperature data and the corresponding spatial coordinates obtained are taken as a temperature distribution data vector [T i ,WZ i ], construct the initial ignition temperature distribution data set SH = [T i ,WZ i ], where T i represents the temperature data point in the ith temperature distribution data vector, WZ i Represents the spatial coordinates of the i-th temperature distribution data vector; 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 is a two-dimensional vector that stores the temperature data points and spatial coordinates in the temperature distribution data vector; A2, calculate each temperature distribution data vector in the initial ignition temperature distribution data set to each initial cluster center C k The Euclidean distance JL is expressed as follows CT k represents the temperature data point at the center of the kth initial cluster, CWZ k Represents the spatial coordinates of the center of the kth initial cluster; assigns each temperature distribution data vector to the cluster with the closest Euclidean distance to it, and establishes an updated cluster; A3, calculate the average value of the temperature data points and the average value of the spatial coordinates in the update cluster, and use the average value of the temperature data points and the average value of the spatial coordinates as two new coordinates of the update cluster respectively; A4, repeat A2 and A3 to iteratively update the update cluster until the update cluster no longer changes; When the update cluster no longer changes, the average value of the temperature data points in each update cluster is calculated, and the average values ​​of the temperature data points in every two update clusters are calculated to obtain a temperature distribution difference value, and multiple temperature distribution difference values ​​are accumulated to obtain a temperature distribution coefficient Cte.

4. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 3 is characterized in that: The deflagration risk index Rig is calculated based on the temperature distribution coefficient and the chlorine content of the solid waste. The expression is as follows: Where Gfl represents the chlorine content of solid waste; The deflagration risk index is compared with the 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, there is no need to generate a deflagration risk signal.

5. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 4 is characterized in that: When there is a risk of deflagration, the shock wave of the deflagration is offset 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.

6. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 4 is characterized in that: When there is no risk of deflagration, a distributed fiber Bragg grating sensor is 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. The expression is as follows: Where tth represents the equivalent thermal stress of the refractory material, Ee represents the elastic modulus, α represents the thermal expansion coefficient, ξ represents the Poisson's ratio, and ΔT represents the temperature gradient change; and the thermal stress safety margin of the refractory material is obtained according to the equivalent thermal stress of the refractory material currently calculated, and the expression is as follows: 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.

7. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 6 is characterized in that: According to the thermal stress safety margin of the refractory material and the deflagration risk index, the temperature rise rate adjustment instruction and the air volume adjustment instruction of the furnace are output through fuzzy logic, 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 according to its value: {low, medium, high}; the deflagration risk index is divided into three fuzzy sets according to its value: {low, medium, high}; The temperature rise rate adjustment instruction and air volume adjustment instruction of the furnace are used as output variables: Define the furnace temperature rise rate adjustment instruction fuzzy set: {deceleration, maintenance, acceleration}; Define the fuzzy set of furnace air volume adjustment instructions: {reduce, maintain, increase}; Fuzzy rules are defined based on the thermal stress safety margin of refractory materials, the deflagration risk index, the temperature rise rate adjustment instruction of the furnace, and the air volume adjustment instruction; After fuzzifying the input variables, the activation degree jhd of the fuzzy rules is calculated n :jhd n =μSsa*μRig, where jhd n represents the activation degree of the nth fuzzy rule, μSsa 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; The result of fuzzy reasoning is defuzzified, and the final output result is calculated according to the activation weight: Wherein ΔV represents the temperature rise rate adjustment instruction of the furnace, and μΔV represents the membership degree of the temperature rise rate adjustment instruction of the furnace; Wherein ΔQ represents the air volume adjustment instruction of the furnace, and μΔQ represents the membership degree of the air volume adjustment instruction of the furnace.

8. The intelligent temperature control method for a solid waste treatment furnace based on multi-stage combustion according to claim 7 is characterized in that: According to the temperature rise rate adjustment instructions and air volume adjustment instructions of the furnace, a temperature control model is constructed to optimize the temperature control behavior of the furnace, as follows: Wherein Vnew represents the adjusted temperature rise rate, Vcur represents the current temperature rise rate, ΔV represents the temperature rise rate adjustment instruction of the furnace, Qnew represents the adjusted air volume, Qcur represents the current air volume, and ΔQ represents the air volume adjustment instruction of the furnace.

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