Sealing method for sand sealing cover of pot-type calcining furnace

Abstract

The invention relates to the technical field of industrial furnace equipment sealing, and discloses a pot-type calcining furnace sand sealing cover sealing method which comprises the following steps: performing pretreatment before pot-type calcining furnace sand sealing cover sealing, generating sand sealing cover sealing pretreatment data, performing sand sealing cover sealing material proportioning treatment based on the sand sealing cover sealing pretreatment data, and obtaining the pot-type calcining furnace sand sealing cover sealing material. Generating matching data of the sealing material of the sand sealing cover; when the sealing treatment of the sand sealing cover of the pot calciner is carried out, the sealing pretreatment data of the sand sealing cover is generated, and the matching treatment of the sealing material of the sand sealing cover is carried out based on the sealing pretreatment data of the sand sealing cover, so that the accurate matching of the sealing material with the surface roughness data and the operation environment temperature data is ensured; and meanwhile, the uniformity of the coating data of the sealing layer of the sand sealing cover is detected in real time in combination with an image recognition technology, the deviation problem in the coating process can be recognized in real time, the coating accuracy and consistency of the sealing layer of the sand sealing cover are guaranteed, and sealing errors caused by improper material matching and uneven coating are reduced.

Description

Technical Field

[0001] This invention relates to the field of sealing technology for industrial furnace and kiln equipment, specifically a method for sealing a sand seal cover for a tank-type calcining furnace. Background Technology

[0002] The sand seal method for calcining furnaces is a core process for maintaining the critical atmosphere and pressure in the furnace during operation. The sealing reliability and long-term stability of the sand seal directly determine the stability of the internal thermal environment, energy utilization efficiency, and production safety of the calcining furnace, and thus have a decisive impact on the quality of calcined products, energy consumption costs, and continuous service life of the equipment.

[0003] Currently, due to various dynamic factors affecting the sand sealing process of tank calcining furnaces, including ambient temperature fluctuations, surface condition changes, and inconsistent material ratios, traditional sealing methods cannot detect in real time whether the uniformity of the sealing layer coating, the rationality of the pressure distribution, and the deviation of the heat curing effect have occurred. When the sealing layer has uneven thickness, local leakage, or insufficient curing, it will cause a large error in the reliability of the seal, and cannot guarantee the stability of the internal atmosphere of the tank calcining furnace and the safety of long-term equipment operation.

[0004] Therefore, a sand-sealing method for tank-type calcining furnaces is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a sand seal method for a tank-type calcining furnace, which solves the problems mentioned in the background art, such as large errors in sealing reliability, and the inability to guarantee the stability of the internal atmosphere of the tank-type calcining furnace and the safety of long-term equipment operation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for sealing a sand-sealed lid of a tank-type calcining furnace, the method comprising the following steps: S1. Perform pretreatment before sealing the sand cover of the tank calciner and generate sand cover sealing pretreatment data. S2. Based on the sand seal pre-processing data, perform sand seal material ratio processing to generate sand seal material ratio data. S3. Based on the sand sealant material ratio data, perform sand sealant coating treatment to generate sand sealant coating data. S4. Based on the sand seal cover coating data, perform sand seal cover sealing pressure regulation processing to generate sand seal cover sealing pressure regulation data. S5. Perform heat curing treatment on the sand seal cap according to the sand seal cap sealing pressure control data to generate sand seal cap heat curing data. S6. Based on the aforementioned sand seal cover thermosetting data, perform sand seal cover sealing performance testing to generate sand seal cover sealing performance testing data. S7. Based on the sand seal cover sealing performance test data, perform sand seal cover sealing optimization and adjustment processing to generate sand seal cover sealing optimization and adjustment data. S8. Based on the sand seal cover sealing optimization and adjustment data, perform sand seal cover sealing durability test processing to generate sand seal cover sealing durability test data; S9. Based on the sand seal cover sealing durability test data, perform final output processing of the sand seal cover sealing to generate sand seal cover sealing completion data for the tank calciner.

[0007] Preferably, the generation of sand seal cap sealing pretreatment data in S1 includes the following steps: S11. Obtain the initial state parameters of the sand seal cover through the tank calciner monitoring platform, including the surface roughness data of the sand seal cover, the size data of the sand seal cover, and the operating environment temperature data. S12. Based on the initial state parameters of the sand seal cover, perform sand seal cover cleaning treatment, use ultrasonic cleaning equipment to remove surface impurities, and generate sand seal cover cleanliness data. S13. Based on the cleanliness data of the sand seal cover, perform sand seal cover drying treatment, and use a hot air drying device to dry at a controllable temperature to generate sand seal cover dryness data. S14. Combine the cleanliness data of the sand seal cover, the dryness data of the sand seal cover, and the operating environment temperature data, and generate sand seal cover sealing pretreatment data through a data fusion algorithm.

[0008] Preferably, the step S2 of generating the sand sealant material ratio data includes the following steps: S21. Obtain the surface roughness data and operating environment temperature data from the sand seal pretreatment data; S22. Based on the surface roughness data, determine the particle size parameters of the sealing material, use an optimization algorithm to calculate the optimal particle size matching value, and generate the particle size data of the sealing material. S23. Select the sealing material composition based on the operating environment temperature data, including the ratio of silicate-based sealant and high-temperature resin, and generate sealing material composition data through a component analysis model. S24. Combine the particle size data of the sealing material with the composition data of the sealing material, and use a weighted average algorithm to generate the ratio data of the sand sealant.

[0009] Preferably, the generation of sand seal cover coating data in step S3 includes the following steps: S31. Receive the sealing material composition data and particle size data from the sand seal cover sealing material ratio data; S32. Configure coating equipment parameters based on the sealing material composition data, including coating nozzle diameter and coating speed, and generate coating equipment setting data; S33. Adjust the coating thickness according to the particle size data of the sealing material, and use an adaptive control algorithm to monitor the coating process in real time to generate coating thickness data. S34. The coating uniformity is detected by image recognition technology, and the coating equipment setting data and coating thickness data are combined to generate the sand seal cover sealing layer coating data.

[0010] Preferably, the step S4 of generating the sand seal cap sealing pressure control data includes the following steps: S41. Obtain the coating thickness data and uniformity data from the coating data of the sand seal cover sealing layer; S42. Calculate the required sealing pressure value based on the coating thickness data, apply pressure using a hydraulic control system, and generate initial sealing pressure data. S43. Adjust the pressure distribution based on the uniformity data, monitor the pressure uniformity through a pressure sensor array, and generate pressure distribution data; S44. Combining the initial sealing pressure data and pressure distribution data, the pressure value is optimized using a feedback control algorithm to generate sand seal cap sealing pressure regulation data.

[0011] Preferably, the step S5 of generating the sand seal cap thermosetting data includes the following steps: S51. Receive the pressure value and pressure distribution data from the sand seal cover sealing pressure control data; S52. Based on the pressure value, set a thermosetting temperature curve, including heating rate, holding temperature and cooling rate, and generate thermosetting temperature data; S53. Determine the heat curing time based on the pressure distribution data, calculate the optimal curing time using a heat conduction model, and generate heat curing time data. S54. The heat curing process is performed by an infrared heating device, and temperature and time parameters are monitored to generate heat curing data for the sand seal cap.

[0012] Preferably, the step S6 of generating sand seal cap sealing performance test data includes the following steps: S61. Obtain the curing temperature data and curing time data from the heat curing data of the sand seal cover. S62. Based on the curing temperature data, perform a hardness test on the sealing layer, measure the hardness value using a hardness tester, and generate hardness data for the sealing layer. S63. Based on the curing time data, the airtightness of the sealing layer is tested, and the leakage rate is detected by a leak detector to generate airtightness data of the sealing layer. S64. Combine the hardness data and air tightness data of the sealing layer, and use the evaluation algorithm to generate sand seal cover sealing performance test data.

[0013] Preferably, the step S7 of generating sand seal cover sealing optimization adjustment data includes the following steps: S71. Receive the hardness data and airtightness data from the sand seal cover sealing performance test data; S72. Determine the degree of curing of the sealing layer based on the hardness data. If it is below the threshold, adjust the thermal curing parameters and generate curing optimization data. S73. Based on the airtightness data, identify the leak point, use image technology to locate the weak area, and generate leak point location data; S74. Combining the solidification optimization data and the leakage point location data, the sand seal cover sealing optimization adjustment data is generated through an iterative optimization algorithm.

[0014] Preferably, the generation of sand seal cap sealing durability test data in S8 includes the following steps: S81. Obtain the optimized parameters from the sand seal cover sealing optimization adjustment data; S82. Based on the optimized parameters, perform accelerated aging tests to simulate long-term operating conditions and generate aging test data. S83. Evaluate the sealing durability based on the aging test data, including fatigue cycle and thermal shock resistance, and generate durability evaluation data; S84. Generate sand seal cover sealing durability test data through statistical analysis.

[0015] Preferably, the step S9 of generating the data indicating the completion of the sand seal on the calciner furnace includes the following steps: S91. Receive the durability evaluation data from the sand seal cover sealing durability test data; S92. Based on the durability assessment data, perform final parameter calibration, including adjusting the material ratio and pressure value, and generating calibration parameter data; S93. Perform the final sealing operation based on the calibration parameter data, and generate data on the completion of the sand seal of the calcining furnace. S94. The data of completing the sealing of the sand seal cover of the calcining furnace is output to the monitoring platform.

[0016] Compared with the prior art, the present invention provides a sand seal method for a tank-type calcining furnace, which has the following beneficial effects: 1. In this invention, during the sealing treatment of the sand seal cover of the calcining furnace, sand seal cover pretreatment data is generated, and the sand seal cover sealing material ratio is processed based on the sand seal cover pretreatment data to ensure accurate matching of the sealing material with surface roughness data and operating environment temperature data. At the same time, image recognition technology is combined to detect the uniformity of the sand seal cover sealing layer data in real time, which can identify deviation problems in the coating process in real time, ensure the accuracy and consistency of the sand seal cover sealing layer coating, and reduce sealing errors caused by improper material ratio and uneven coating.

[0017] 2. In this invention, when performing sand seal pressure regulation, the coating thickness and properties data in the sand seal layer coating data are obtained, and the pressure distribution data is monitored by a pressure sensor array. The sand seal pressure regulation data is optimized by a feedback control algorithm, which can dynamically adjust the pressure value to adapt to the sealing layer state, reduce local pressure insufficiency and excessive pressure, and correct abnormal pressure distribution in real time, ensuring the uniformity and reliability of sand seal pressure regulation and improving the bonding strength of the sealing interface.

[0018] 3. In this invention, during the testing and optimization of the sand seal cover's sealing performance, the effect of the sand seal cover's thermosetting data is judged in real time by combining the hardness data and airtightness data of the sealing layer. Based on an iterative optimization algorithm, optimization data for the sand seal cover is generated. Combined with accelerated aging tests to verify the durability test data of the sand seal cover, multi-dimensional optimization and long-term stability assessment of the sealing parameters are achieved. This enables closed-loop control for different operating conditions, reduces the risk of seal failure, and improves the overall accuracy and durability of the sand seal cover for the tank calciner. Attached Figure Description

[0019] Figure 1 This is a flowchart of a sand-sealing method for a tank-type calcining furnace according to the present invention. Detailed Implementation

[0020] 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.

[0021] For specific implementation examples, please refer to: Figure 1 A method for sealing a sand-sealed lid on a tank-type calcining furnace, characterized by the following steps: S1. Perform pretreatment before sealing the sand cover of the tank calciner and generate sand cover sealing pretreatment data. S2. Based on the sand seal pre-processing data, process the sand seal material ratio to generate sand seal material ratio data. S3. Apply sand sealant coating data to the sand sealant layer according to the sand sealant material ratio data. S4. Based on the sand seal cover coating data, perform sand seal cover sealing pressure regulation processing to generate sand seal cover sealing pressure regulation data. S5. Perform heat curing treatment on the sand seal cap according to the sand seal pressure control data, and generate heat curing data for the sand seal cap. S6. Based on the thermal curing data of the sand seal cap, perform sand seal cap sealing performance testing and generate sand seal cap sealing performance testing data. S7. Based on the sand seal cover sealing performance test data, optimize and adjust the sand seal cover sealing to generate sand seal cover sealing optimization and adjustment data. S8. Based on the sand seal cover sealing optimization and adjustment data, perform sand seal cover sealing durability test processing to generate sand seal cover sealing durability test data; S9. Based on the sand seal durability test data, perform final output processing of the sand seal to generate the sand seal completion data for the tank calciner.

[0022] The steps involved in generating sand seal cap pretreatment data in S1 are as follows: S11. Obtain the initial state parameters of the sand seal cover through the tank calciner monitoring platform, including the surface roughness data of the sand seal cover, the size data of the sand seal cover, and the operating environment temperature data. S12. Based on the initial state parameters of the sand seal cover, perform sand seal cover cleaning treatment, use ultrasonic cleaning equipment to remove surface impurities, and generate sand seal cover cleanliness data. S13. Based on the cleanliness data of the sand cover, dry the sand cover by using a hot air drying device at a controlled temperature to generate sand cover dryness data. S14. Combine the sand seal cover cleanliness data, sand seal cover dryness data, and operating environment temperature data, and generate sand seal cover sealing pretreatment data using a data fusion algorithm. The data fusion algorithm includes the following steps: S141. Receive the cleanliness data of the sand seal cover, the dryness data of the sand seal cover, and the operating environment temperature data; S142. Normalize the sand cover cleanliness data, sand cover dryness data, and operating environment temperature data to generate a normalized dataset. S143. Weighted fusion of the normalized dataset based on preset weight coefficients to generate fused data; Weighted fusion is achieved through the following formula: ; in, Indicates data fusion. Indicates the first Each weighting coefficient Indicates the first A normalized data value, This indicates the number of data items in the normalized dataset; S144. Verify the reliability of the fused data through a consistency check algorithm and output the sand seal cap pre-processing data. The core calculations are as follows: ; in, Represents variance. Indicates sample size. Indicates the first Each sample data value, This represents the mean.

[0023] The steps involved in generating the sand sealant formulation data for S2 are as follows: S21. Obtain surface roughness data and operating environment temperature data from the sand seal pretreatment data; S22. Determine the particle size parameters of the sealing material based on surface roughness data, calculate the optimal particle size matching value using an optimization algorithm, and generate sealing material particle size data. The optimization algorithm includes the following steps: S221. Initialize the search space and optimization objective function for the particle size parameters of the sealing material; The search space initialization expression is: ; in, Indicates the current granularity parameter. and These represent the minimum and maximum values ​​of the parameter, respectively. The optimized initialization expression of the objective function is as follows: ; in, Describe the objective function. Indicates the current granularity parameter. This represents the ideal particle size parameter value; S222. The optimal solution for the granularity parameters is calculated iteratively using the gradient descent method to generate candidate granularity data; Gradient descent is implemented using the following formula: ; in, Indicates the first Parameter values ​​at the next iteration This indicates the updated parameter value. Indicates the learning rate. Describe the objective function exist gradient at; S223. Evaluate the matching degree of candidate granularity data through the objective function, and select the granularity parameter with the highest matching degree. The formula for matching degree evaluation is: ; in, Indicates the current granularity parameter The degree of matching, Describe the objective function. The better the parameter; The formula for selecting the parameter with the highest matching degree is: ; in, This represents the granularity parameter with the highest matching degree. The parameter that represents the maximum value. Indicates the current granularity parameter The degree of matching; S224. Output the optimal particle size matching value as the particle size data of the sealing material; S23. Select the sealing material composition based on the operating environment temperature data, including the ratio of silicate-based sealant and high-temperature resin. Generate sealing material composition data through a component analysis model. The component analysis model includes the following steps: S231. Input the operating environment temperature data and call the pre-stored sealing material composition database; S232. Based on temperature data, query the database to match candidate ratios of silicate-based sealants and high-temperature resins; S233. Analyze the correlation between temperature and component ratio using a linear regression model to generate predicted component ratio values; The linear regression model is expressed by the following formula: ; in, This represents the predicted component proportions. This indicates the ambient temperature data. and Represents the regression coefficient; S234. Perform boundary constraint processing on the predicted values ​​and output the sealing material composition data; S24. Combine the particle size data of the sealing material with the composition data of the sealing material, and use a weighted average algorithm to generate the sand sealant ratio data. The weighted average algorithm includes the following steps: S241. Receive the particle size data and composition data of the sealing material; S242. Assign preset weighting coefficients to particle size data and composition data, wherein the weighting coefficients are determined based on historical experimental data; S243. Calculate the weighted average of particle size data and component data to generate initial proportion data; The weighted average is calculated using the following formula: ; in, This represents the initial proportioning data. This represents the weighting coefficients for granular data. This indicates the particle size data of the sealing material. This represents the weighting coefficients of the component data. This indicates the composition data of the sealing material; S244. Standardize the initial mixing ratio data and output the mixing ratio data of the sand sealant.

[0024] The steps involved in generating sand seal cap coating data in S3 are as follows: S31. Receive the sealing material composition data and particle size data from the sand seal cover sealing material ratio data; S32. Configure coating equipment parameters based on sealing material composition data, including coating nozzle diameter and coating speed, and generate coating equipment setting data; S33. Adjust the coating thickness based on the particle size data of the sealing material, and use an adaptive control algorithm to monitor the coating process in real time to generate coating thickness data. The adaptive control algorithm includes the following steps: S331. Real-time monitoring of coating thickness data during the coating process; S332. Compare the deviation between the current thickness and the target thickness, and generate a deviation signal; S333. Adjust the coating equipment parameters, including coating speed and nozzle diameter, according to the deviation signal; Deviation signal adjustment is achieved through a PID control algorithm, the formula of which is: ; in, Indicates control output. Indicates the deviation signal. , , These represent the proportional, integral, and differential coefficients, respectively. Indicates time, Represents the integral variable. Indicates time An infinitesimal increment; S334. Parameters are dynamically optimized and adjusted using a PID controller; S34. The uniformity of coating is detected using image recognition technology. Combined with coating equipment settings and coating thickness data, coating data for the sand seal cap is generated. Image recognition technology includes the following steps: S341. Acquire surface image data of the coating layer; S342. Preprocess the image data, including grayscale conversion and filtering for noise reduction; S343. Identify the boundary and uniformity features of the coated area using an edge detection algorithm; Edge detection is implemented using the Sobel operator, with the following formula: ; in, Indicates the gradient magnitude. and They represent Spatial location and Gradient value of spatial location; S344. Calculate the uniformity index and compare it with the threshold to generate the coating uniformity result.

[0025] The steps involved in generating sand seal cap sealing pressure control data in S4 are as follows: S41. Obtain the coating thickness and uniformity data from the sand seal cover coating data; S42. Calculate the required sealing pressure value based on the coating thickness data, apply pressure using a hydraulic control system, and generate initial sealing pressure data; S43. Adjust the pressure distribution based on the uniformity data, monitor the pressure uniformity through a pressure sensor array, and generate pressure distribution data; S44. Combining the initial sealing pressure data and pressure distribution data, the pressure value is optimized using a feedback control algorithm to generate sand seal cap sealing pressure regulation data. The feedback control algorithm includes the following steps: S441. Receive initial sealing pressure data and pressure distribution data; S442. Calculate the pressure distribution uniformity index and compare it with the ideal uniformity. S443. Generate a pressure adjustment signal based on the comparison results, and fine-tune the pressure value through the hydraulic control system; The pressure adjustment signal is calculated and generated based on the following deviation-gain control formula: ; in, Indicates the amount of control adjustment. Indicates feedback gain. Indicates uniformity deviation; S444, Iterate through the pressure adjustment until the uniformity index reaches the preset threshold.

[0026] The steps involved in generating the heat-curing data for the sand seal cap in S5 are as follows: S51. Receive the pressure value and pressure distribution data from the sand seal cover sealing pressure control data; S52. Set the thermosetting temperature curve based on the pressure value, including the heating rate, holding temperature and cooling rate, and generate thermosetting temperature data; S53. Determine the heat curing time based on the pressure distribution data, calculate the optimal curing time using the heat conduction model, and generate heat curing time data. The heat conduction model includes the following steps: S531, Input pressure distribution data and material thermophysical parameters; S532. Establish a one-dimensional heat conduction equation to simulate the temperature distribution within the sealing layer; The one-dimensional heat conduction equation is: ; in, Indicates temperature. Indicates time, Indicates spatial location, Indicates the thermal diffusivity. Indicates the sign of partial derivatives; S533. Solve the equations using the finite difference method to predict the curing time at different locations; The formula for the finite difference method is: ; in, Indicates temperature. Indicates spatial location and time step Temperature value at that time Indicates spatial location and the next time step The temperature value calculated at that time, Indicates the thermal diffusivity. Indicates the time step used in the calculation. Indicates the spatial step size used in the calculation; S534. Select the maximum curing time as the optimal curing duration and generate thermosetting time data; S54. The heat curing process is performed by an infrared heating device, and temperature and time parameters are monitored to generate heat curing data for the sand seal cap.

[0027] The steps involved in generating sand seal cap sealing performance test data in S6 are as follows: S61. Obtain the curing temperature data and curing time data from the heat curing data of the sand seal cap; S62. Based on the curing temperature data, perform hardness testing on the sealing layer, use a hardness tester to measure the hardness value, and generate hardness data for the sealing layer. S63. Based on the curing time data, the airtightness of the sealing layer is tested, and the leakage rate is detected by a leak detector to generate airtightness data of the sealing layer. S64. Combine the sealing layer hardness data and airtightness data, and use an evaluation algorithm to generate sand seal cap sealing performance test data. The evaluation algorithm includes the following steps: S641, Receive sealing layer hardness data and airtightness data; S642. Standardize the hardness data and air tightness data to generate hardness score and air tightness score. S643. Calculate the overall performance score based on the weighted summation formula; The weighted summation formula is: ; in, This indicates the overall performance score. This represents the weighting coefficient for the hardness score. Indicates hardness score. The weighting coefficients representing the airtightness score. Indicates the airtightness score; S644. Determine the sealing performance level based on the scoring threshold and output the sand seal cover sealing performance test data.

[0028] The steps involved in generating sand seal cover sealing optimization adjustment data in S7 are as follows: S71. Receive the hardness data and airtightness data from the sand seal cover sealing performance test data; S72. Determine the curing degree of the sealing layer based on hardness data. If it is below the threshold, adjust the heat curing parameters to generate curing optimization data. S73. Identify leak points based on airtightness data, locate weak areas using image technology, and generate leak point location data; S74. Combining the solidification optimization data and leak point location data, generate sand seal cap sealing optimization adjustment data through an iterative optimization algorithm. The iterative optimization algorithm includes the following steps: S741. Initialize the solidification optimization data and leak point location data as input; S742. Set the optimization objective to minimize leakage points and curing deviation; S743. Generate candidate parameter combinations iteratively using a genetic algorithm and evaluate their fitness. In genetic algorithms, the selection probability formula is: ; in, Indicates the first The probability of selecting each candidate parameter Indicates the first The fitness values ​​of each candidate parameter This represents the fitness metric for each candidate parameter. Indicates the number of candidate parameters; S744: Select the parameter combination with the highest adaptability and output the sand seal cover sealing optimization adjustment data.

[0029] The steps involved in generating sand seal cap durability test data in S8 are as follows: S81. Obtain the optimized parameters from the sand seal cover sealing optimization adjustment data; S82. Accelerated aging test is performed based on the optimized parameters to simulate long-term operating conditions and generate aging test data. S83. Evaluate the sealing durability based on aging test data, including fatigue cycle and thermal shock resistance, and generate durability evaluation data; S84. Generate sand seal cover sealing durability test data through statistical analysis. The statistical analysis includes the following steps: S841. Collect fatigue cycle and thermal shock resistance data from aging test data; S842. Calculate the mean, variance, and confidence interval of the data; S8421. Calculate the mean of the aging test data to reflect the central location characteristics of the data. The calculation formula is as follows: ; in, This represents the mean. Indicates the first Each sample data value, Indicates sample size; S8422. Calculate the variance and standard deviation of the aging test data to quantify the data volatility. The calculation formula is as follows: ; in, Represents variance. Indicates standard deviation, Indicates the first Each sample data value, This represents the mean. Indicates sample size; S8423. Calculate the confidence interval of the mean aging test data to assess the reliability of the data. The calculation formula is as follows: ; in, Indicates the confidence interval. This represents the mean. This indicates that in a region with 1 degree of freedom The significance level is time Distribution critical value, Indicates standard deviation, Indicates sample size; S843. Fit a durability trend model through regression analysis; The regression analysis uses a linear model, and the formula is: ; in, This indicates durability assessment data. This indicates aging test data. and Represents the regression coefficient; S844. Generate durability assessment data based on the model and output sand seal cover sealing durability test data.

[0030] The steps involved in generating the complete sealing data for the sand seal cover of the calcining furnace in S9 are as follows: S91. Receive durability assessment data from the sand seal cover sealing durability test data; S92. Based on the durability assessment data, perform final parameter calibration, including adjusting the material ratio and pressure value, and generating calibration parameter data. The final parameter calibration includes the following steps: S921. Receive durability assessment data and extract key parameters, including material ratio and pressure value; S922. Compare the deviations of key parameters with standard values ​​and generate a deviation report; S923. Calculate calibration parameters using interpolation. The interpolation method uses a linear interpolation formula: ; in, Indicates the calibration parameter value. Indicates the input parameters, ( , )and( , () represents a known data point; S924. Verify the accuracy of the calibration parameters and output the calibration parameter data; S93. Perform the final sealing operation based on the calibration parameter data and generate data on the completion of the sand seal of the tank calciner. S94. After the sand seal of the calcining furnace is completed, the data is output to the monitoring platform.

[0031] The operating steps of this sand-sealing method for a tank-type calcining furnace are as follows: Step 1: Pre-processing First, a pre-sealing treatment of the sand seal cover is performed before sealing the sand seal cover of the calcining furnace. Initial state parameters of the sand seal cover are obtained through the calcining furnace monitoring platform, including sand seal cover surface roughness data, sand seal cover size data, and operating environment temperature data. Then, based on these parameters, the cover is cleaned and the sand seal cover is dried, generating sand seal cover cleanliness data and sand seal cover dryness data respectively. Finally, these data are integrated with the operating environment temperature data through a data fusion algorithm to generate sand seal cover sealing pre-treatment data.

[0032] Step 2: Material Proportioning Process Based on the above sand seal pretreatment data, the process moves to the sand seal material formulation stage. In this stage, surface roughness data and operating environment temperature data are extracted from the pretreatment data. The optimal particle size data of the sealing material is calculated using an optimization algorithm, and the composition data of the sealing material is determined through a component analysis model. Finally, a weighted average algorithm is used to combine the particle size and composition data to generate sand seal material formulation data, providing a formula basis for the preparation of the sealing layer.

[0033] Step 3: Layer Coating Treatment Based on the sand sealant material ratio data, the sand sealant sealing layer coating process is performed. The coating equipment parameters are configured according to the material characteristics, and the coating process is monitored and adjusted in real time based on the particle size data of the sealing material to generate coating layer thickness data that meets the requirements. At the same time, image recognition technology is used to detect the coating uniformity. Combining the equipment settings and thickness data, the sand sealant sealing layer coating data is generated.

[0034] Step 4: Pressure Regulation and Treatment Next, based on the sand seal cap sealing layer data, the sand seal cap sealing pressure is regulated. The initial pressure is calculated based on the coating thickness and uniformity data, and the pressure distribution data is monitored using a pressure sensor array. The initial sealing pressure data is dynamically optimized and adjusted through a feedback control algorithm to generate sand seal cap sealing pressure regulation data.

[0035] Step 5: Heat curing treatment Based on the pressure control data of the sand seal cap, the sand seal cap is subjected to heat curing treatment. In this stage, the heat curing temperature curve is set according to the pressure value, and the optimal heat curing time data is calculated using the pressure distribution data and the heat conduction model. The curing process is executed by an infrared heating device and the parameters are monitored, and finally the heat curing data of the sand seal cap is generated.

[0036] Step Six: Performance Testing and Processing Based on the thermal curing data of the sand seal cap, the sealing performance of the sand seal cap is tested. By testing the hardness and air tightness of the sealing layer, the sealing layer and air tightness data are obtained respectively. Then, the two data are combined and evaluated using an evaluation algorithm to generate the sealing performance test data of the sand seal cap.

[0037] Step 7: Optimization and Adjustment Based on the sand seal cap sealing performance test data, the sand seal cap sealing optimization and adjustment process is initiated. If the hardness data does not meet the standard, the thermosetting parameters are adjusted to generate curing optimization data. Based on the airtightness data, the leak point is located and leak point location data is generated. Finally, the data are comprehensively processed through iterative optimization algorithms to generate the sand seal cap sealing optimization and adjustment.

[0038] Step 8: Durability Testing Treatment Based on the sand seal optimization adjustment data, sand seal seal durability test processing was carried out. Accelerated aging test was conducted using the optimized parameters to simulate long-term operating conditions and generate aging test data. Fatigue cycle and thermal shock resistance indicators were evaluated through statistical analysis methods to generate sand seal seal durability test data.

[0039] Step 9: Final Output Processing Finally, based on the sand seal durability test data, the final output processing of the sand seal is performed. First, the final parameters are calibrated to generate calibration parameter data, and then the final sealing operation is performed accordingly. Finally, data representing the successful completion of the entire sealing method for the tank calciner sand seal is generated and output to the monitoring platform, marking the end of the entire sealing process.

[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for sealing a sand-sealed lid on a tank-type calcining furnace, characterized in that: The method includes the following steps: S1. Perform pretreatment before sealing the sand cover of the tank calciner and generate sand cover sealing pretreatment data. S2. Based on the sand seal pre-processing data, perform sand seal material ratio processing to generate sand seal material ratio data. S3. Based on the sand sealant material ratio data, perform sand sealant coating treatment to generate sand sealant coating data. S4. Based on the sand seal cover coating data, perform sand seal cover sealing pressure regulation processing to generate sand seal cover sealing pressure regulation data. S5. Perform heat curing treatment on the sand seal cap according to the sand seal cap sealing pressure control data to generate sand seal cap heat curing data. S6. Based on the aforementioned sand seal cover thermosetting data, perform sand seal cover sealing performance testing to generate sand seal cover sealing performance testing data. S7. Based on the sand seal cover sealing performance test data, perform sand seal cover sealing optimization and adjustment processing to generate sand seal cover sealing optimization and adjustment data. S8. Based on the sand seal cover sealing optimization and adjustment data, perform sand seal cover sealing durability test processing to generate sand seal cover sealing durability test data; S9. Based on the sand seal cover sealing durability test data, perform final output processing of the sand seal cover sealing to generate sand seal cover sealing completion data for the tank calciner.

2. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 1, characterized in that: The process of generating sand seal cap sealing preprocessing data in S1 includes the following steps: S11. Obtain the initial state parameters of the sand seal cover through the tank calciner monitoring platform, including the surface roughness data of the sand seal cover, the size data of the sand seal cover, and the operating environment temperature data. S12. Based on the initial state parameters of the sand seal cover, perform sand seal cover cleaning treatment, use ultrasonic cleaning equipment to remove surface impurities, and generate sand seal cover cleanliness data. S13. Based on the cleanliness data of the sand seal cover, perform sand seal cover drying treatment, and use a hot air drying device to dry at a controllable temperature to generate sand seal cover dryness data. S14. Combine the cleanliness data of the sand seal cover, the dryness data of the sand seal cover, and the operating environment temperature data, and generate sand seal cover sealing pretreatment data through a data fusion algorithm.

3. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 2, characterized in that: The steps involved in generating the sand sealant formulation data in S2 are as follows: S21. Obtain the surface roughness data and operating environment temperature data from the sand seal pretreatment data; S22. Based on the surface roughness data, determine the particle size parameters of the sealing material, use an optimization algorithm to calculate the optimal particle size matching value, and generate the particle size data of the sealing material. S23. Select the sealing material composition based on the operating environment temperature data, including the ratio of silicate-based sealant and high-temperature resin, and generate sealing material composition data through a component analysis model. S24. Combine the particle size data of the sealing material with the composition data of the sealing material, and use a weighted average algorithm to generate the ratio data of the sand sealant.

4. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 3, characterized in that: The steps involved in generating the sand seal coating data in S3 are as follows: S31. Receive the sealing material composition data and particle size data from the sand seal cover sealing material ratio data; S32. Configure coating equipment parameters based on the sealing material composition data, including coating nozzle diameter and coating speed, and generate coating equipment setting data; S33. Adjust the coating thickness according to the particle size data of the sealing material, and use an adaptive control algorithm to monitor the coating process in real time to generate coating thickness data. S34. The coating uniformity is detected by image recognition technology, and the coating equipment setting data and coating thickness data are combined to generate the sand seal cover sealing layer coating data.

5. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 4, characterized in that: The steps involved in generating the sand seal cap sealing pressure control data in S4 are as follows: S41. Obtain the coating thickness data and uniformity data from the coating data of the sand seal cover sealing layer; S42. Calculate the required sealing pressure value based on the coating thickness data, apply pressure using a hydraulic control system, and generate initial sealing pressure data. S43. Adjust the pressure distribution based on the uniformity data, monitor the pressure uniformity through a pressure sensor array, and generate pressure distribution data; S44. Combining the initial sealing pressure data and pressure distribution data, the pressure value is optimized using a feedback control algorithm to generate sand seal cap sealing pressure regulation data.

6. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 5, characterized in that: The steps involved in generating the sand seal thermosetting data in S5 are as follows: S51. Receive the pressure value and pressure distribution data from the sand seal cover sealing pressure control data; S52. Based on the pressure value, set a thermosetting temperature curve, including heating rate, holding temperature and cooling rate, and generate thermosetting temperature data; S53. Determine the heat curing time based on the pressure distribution data, calculate the optimal curing time using a heat conduction model, and generate heat curing time data. S54. The heat curing process is performed by an infrared heating device, and temperature and time parameters are monitored to generate heat curing data for the sand seal cap.

7. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 6, characterized in that: The step of generating sand seal cap sealing performance test data in S6 includes the following steps: S61. Obtain the curing temperature data and curing time data from the heat curing data of the sand seal cover. S62. Based on the curing temperature data, perform a hardness test on the sealing layer, measure the hardness value using a hardness tester, and generate hardness data for the sealing layer. S63. Based on the curing time data, the airtightness of the sealing layer is tested, and the leakage rate is detected by a leak detector to generate airtightness data of the sealing layer. S64. Combine the hardness data and air tightness data of the sealing layer, and use the evaluation algorithm to generate sand seal cover sealing performance test data.

8. The sealing method for a sand-sealed lid of a tank-type calcining furnace according to claim 7, characterized in that: The steps involved in generating the sand seal cover sealing optimization adjustment data in S7 are as follows: S71. Receive the hardness data and airtightness data from the sand seal cover sealing performance test data; S72. Determine the degree of curing of the sealing layer based on the hardness data. If it is below the threshold, adjust the thermal curing parameters and generate curing optimization data. S73. Based on the airtightness data, identify the leak point, use image technology to locate the weak area, and generate leak point location data; S74. Combining the solidification optimization data and the leakage point location data, the sand seal cover sealing optimization adjustment data is generated through an iterative optimization algorithm.

9. A method for sealing a sand-sealed lid of a tank-type calcining furnace according to claim 8, characterized in that: The steps involved in generating sand seal cap sealing durability test data in S8 are as follows: S81. Obtain the optimized parameters from the sand seal cover sealing optimization adjustment data; S82. Based on the optimized parameters, perform accelerated aging tests to simulate long-term operating conditions and generate aging test data. S83. Evaluate the sealing durability based on the aging test data, including fatigue cycle and thermal shock resistance, and generate durability evaluation data; S84. Generate sand seal cover sealing durability test data through statistical analysis.

10. A method for sealing a sand-sealed lid of a tank-type calcining furnace according to claim 9, characterized in that: The steps involved in generating the data for completing the sealing of the sand seal cover of the calcining furnace in S9 are as follows: S91. Receive the durability evaluation data from the sand seal cover sealing durability test data; S92. Based on the durability assessment data, perform final parameter calibration, including adjusting the material ratio and pressure value, and generating calibration parameter data; S93. Perform the final sealing operation based on the calibration parameter data, and generate data on the completion of the sand seal of the calcining furnace. S94. The data of completing the sealing of the sand seal cover of the calcining furnace is output to the monitoring platform.

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