Excess volatilization utilization method for pot-type calcining furnace

Abstract

The invention relates to the technical field of energy recovery, and discloses an excess volatilization utilization method for a pot-type calcining furnace, and the method comprises the following steps: S1, collecting excess volatilization parameter data generated in the operation process of the pot-type calcining furnace, S2, carrying out volatile component analysis processing based on the excess volatilization parameter data to generate volatile component characteristic data, and S3, calculating the volatile component characteristic data according to the volatile component characteristic data. When excess volatilization utilization treatment is carried out on the pot-type calcining furnace, excess volatilization parameter data are collected in real time, volatile component analysis treatment is carried out, volatile component characteristic data are generated, and volatilization availability judgment is completed according to the data, so that the component state and the calorific value stability of volatile matter can be accurately recognized; the problem that in the prior art, due to the fact that volatile matter components and states cannot be detected in real time, a recovery system is unstable is solved, the reliability and safety of the excessive volatilization utilization process are guaranteed, and meanwhile the operation risk caused by component fluctuation is reduced.

Description

Technical Field

[0001] This invention relates to the field of energy recovery technology, specifically a method for utilizing excess volatilization in a tank calciner. Background Technology

[0002] The petroleum coke calciner is a thermal equipment used in the carbon industry, which heat-treats petroleum coke at high temperatures under air-isolated conditions.

[0003] Working mechanism and characteristics Structural composition: The furnace body mainly consists of multiple vertically arranged refractory calcining jars, usually in groups of four. Each calcining jar has heating channels on both sides.

[0004] Heating method: Indirect heating is used, with fuel burning in the fire channel and heat being transferred to the petroleum coke raw material inside the tank through the tank wall.

[0005] Thermal energy utilization: A key feature is the ability to utilize the volatiles released from petroleum coke at high temperatures as a supplementary heat source, which is then directed to the combustion chamber through a specific channel, effectively reducing the consumption of external fuel.

[0006] Temperature and Purpose: Calcination temperatures are typically between 1200-1380℃ or even higher. This process aims to remove moisture and volatiles from petroleum coke, improving its true density, electrical conductivity, and chemical stability to meet the quality requirements for producing carbon products such as prebaked anodes for electrolytic aluminum.

[0007] Currently, due to the complex and varied operating conditions of tank calciners in actual operation, the lack of intelligent algorithms to evaluate and select utilization methods in the multi-pathway conversion of excess volatiles often leads to the adoption of a single and fixed processing mode, which cannot achieve efficient utilization based on quality classification and on-demand allocation, further restricting the overall energy recovery efficiency and economy.

[0008] Therefore, a method for utilizing excess volatilization in a tank-type calcining furnace is proposed to solve the above problems. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method for utilizing excess volatilization in a tank-type calcining furnace. This method solves the problem mentioned in the background technology of lacking intelligent algorithms for evaluating and selecting utilization methods, often employing a single and fixed processing mode, and failing to achieve efficient utilization based on quality classification and on-demand allocation.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for utilizing excess volatilization in a tank-type calcining furnace, the method comprising the following steps: S1. Collect data on excess volatilization parameters generated during the operation of the tank calciner; S2. Perform volatile component analysis based on the excess volatile parameter data to generate volatile component characteristic data; S3. Perform volatile availability judgment processing based on the volatile component characteristic data to generate volatile availability judgment data; S4. When the volatile availability judgment data indicates availability, the calcination parameters of the tank calciner are adjusted to generate calcination parameter adjustment data. S5. Perform a combination processing of volatile component characteristic data and calcination parameter adjustment data to construct volatile utilization combination data; S6. Based on the volatilization utilization combination data and the standard volatilization utilization combination data corresponding to different volatilization utilization algorithms, perform utilization algorithm type matching processing to generate target utilization algorithm type feature data; S7. Construct a summary data of volatilization utilization, process excess volatilization utilization, and generate excess volatilization utilization result data; S8. Perform utilization effect monitoring processing and generate utilization effect monitoring data; S9. Based on the utilization effect monitoring data, output the final utilization report data and complete the excess volatilization utilization process.

[0011] Preferably, the excess volatile parameter data in S1 includes the following steps: S11. Collect excess volatilization parameters of the calciner under stable and high-load operating conditions by a sensor array installed at the outlet of the calciner, including volatile temperature data, volatile pressure data, volatile flow rate data and volatile component concentration data. S12. The collected excess volatilization parameter data is transmitted to the calcining furnace monitoring platform in real time, and outliers are removed using a data cleaning algorithm to generate an initial volatilization dataset. S13. Standardize the initial volatile data set to unify the dimensions of all parameter data and generate standardized excess volatile parameter data for subsequent analysis.

[0012] Preferably, the volatile component characteristic data in S2 includes the following steps: S21. Obtain the excess volatility parameter data and use a component analyzer to separate the chemical components of the volatiles, and identify the main volatile components, including carbon monoxide, methane and uncalcined hydrocarbons. S22. Based on the identified volatile components, a clustering algorithm is used to divide the volatile components into high-energy components and low-energy components, and the proportion distribution of each component is calculated. S23. Generate characteristic data of volatile components based on the proportional distribution, including component energy density characteristics and component stability characteristics, for use in subsequent availability assessment.

[0013] Preferably, the volatile availability judgment data in S3 includes the following steps: S31. Obtain the characteristic data of the volatile components and set the volatile utilization threshold, including the energy recovery threshold and the environmental impact threshold; S32. The characteristic data of the volatile components are compared with the threshold for volatile utilization. When the energy density of the volatile components is higher than the energy recovery threshold and the environmental impact is lower than the environmental impact threshold, the volatile utilization judgment data is output as available. S33. When the data indicating that the volatile substance is available is unavailable, an alarm mechanism is triggered and the reason for unavailability is recorded; otherwise, step S5 is executed directly.

[0014] Preferably, the calcination parameter adjustment data in S4 includes the following steps: S41. When the volatile availability judgment data is available, obtain the current calcination parameters of the tank calciner, including the calcined material supply rate, air flow rate and furnace temperature. S42. The calcination parameters are adjusted according to the volatile component characteristic data using a linear interpolation algorithm, so that the calcination parameters are optimized to a state suitable for the utilization of excess volatilization. S43. Generate calcination parameter adjustment data and update it to the calcination furnace control system in real time to ensure that the adjusted parameters are executed stably.

[0015] Preferably, the combined data on volatilization utilization in S5 includes the following steps: S51. Align the volatile component characteristic data and the calcination parameter adjustment data with the operating status data of the pot furnace according to the time series. S52. Using the time parameter as a reference, arrange and combine the aligned data vertically to construct a volatilization utilization combined data matrix, where the rows of the matrix represent different parameter types and the columns represent time points; S53. Normalize the volatile utilization combined data matrix to eliminate dimensional differences and generate standardized volatile utilization combined data for algorithm matching.

[0016] Preferably, the target utilization algorithm type feature data in S6 includes the following steps: S61. Establish a standard volatile utilization combination data matrix corresponding to different volatile utilization algorithms, including standard data corresponding to heat recovery algorithm, catalytic conversion algorithm and energy storage algorithm; S62. Perform similarity matching between the volatilization utilization combination data and the standard volatilization utilization combination data matrix, and calculate the matching degree using the cosine similarity algorithm; S63. Select the optimal utilization algorithm type based on the matching degree result, and generate target utilization algorithm type feature data; S64. The matching process uses an artificial intelligence optimization algorithm for iterative search to ensure accurate algorithm selection.

[0017] Preferably, the excess volatilization utilization result data in S7 includes the following steps: S71. Combine the volatile utilization combined data and the target utilization algorithm type feature data into volatile utilization summary data; S72. The calcining furnace monitoring platform calls the corresponding application program based on the target application algorithm type feature data to extract key parameters from the volatilization utilization summary data. S73. Perform excess volatilization utilization treatment, including heat recovery, material conversion and energy storage, and generate excess volatilization utilization result data in real time.

[0018] Preferably, the utilization effect monitoring data in S8 includes the following steps: S81. During the utilization and processing, the utilization effect parameters are collected in real time through monitoring sensors, including energy efficiency data, emission reduction data and equipment operating status data. S82. Compare the collected utilization effect parameters with the preset target values ​​and generate a deviation analysis report; S83. When the deviation exceeds the allowable range, the utilization parameters will be automatically adjusted and the optimization mechanism will be triggered.

[0019] Preferably, the final utilization report data in S9 includes the following steps: S91. Integrate the utilization effect monitoring data and the excess volatilization utilization result data to generate a comprehensive utilization report; S92. The utilization report includes the percentage of utilization efficiency, economic benefit assessment and environmental benefit analysis; S93. Output the report to the user interface and storage system, and complete the closed-loop management of the entire excess volatilization utilization method.

[0020] Compared with the prior art, the present invention provides a method for utilizing excess volatilization in a tank calciner, which has the following beneficial effects: 1. In this invention, when processing excess volatilization in a tank calciner, excess volatilization parameter data is collected in real time and volatilization component analysis is performed to generate volatilization component characteristic data. Based on this data, the availability of volatilization is determined, which can accurately identify the composition state and calorific value stability of the volatilized substances. This solves the problem of instability in the recovery system caused by the inability to detect the composition and state of volatilized substances in real time in the prior art, ensuring the reliability and safety of the excess volatilization utilization process, while reducing the operational risks caused by composition fluctuations.

[0021] 2. In this invention, when treating excess volatilization in a tank calciner, dynamic coordination of calcination parameter adjustment and volatilization utilization is achieved. Based on volatile component characteristic data and calcination parameter adjustment data, combined volatilization utilization data is constructed and synchronized with the parameters of the main calcination system. This avoids the calcination state disorder and backfire risk caused by the introduction of volatiles, solves the calcination disorder problem that may be caused by the lack of a dynamic coordination mechanism in the prior art, ensures efficient coordination between volatile utilization and the calcination process, and improves overall operating efficiency.

[0022] 3. In this invention, when treating excess volatilization in a tank calciner, an intelligent algorithm is used to match the utilization algorithm type, generate target utilization algorithm type characteristic data, and combine it with utilization effect monitoring and report output to realize a utilization strategy based on quality classification and on-demand allocation. It can adaptively select the optimal utilization method, such as heat recovery and catalytic conversion, to solve the problem of low utilization efficiency caused by the lack of intelligent algorithm evaluation and selection in the prior art, further improve the accuracy and economy of excess volatilization utilization, and enhance the applicability of the method through closed-loop management. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for utilizing excess volatilization in a tank-type calcining furnace according to the present invention. Detailed Implementation

[0024] 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.

[0025] Please see Figure 1 The method for utilizing excess volatilization in a pot-type calciner includes the following steps: S1. Collect data on excess volatilization parameters generated during the operation of the tank calciner; S2. Perform volatile component analysis and processing based on excess volatile parameter data to generate volatile component characteristic data; S3. Based on the characteristic data of volatile components, perform volatile availability judgment processing to generate volatile availability judgment data; S4. When the volatile matter availability judgment data indicates availability, perform calcination parameter adjustment processing for the tank calciner and generate calcination parameter adjustment data; S5. Based on the volatile component characteristic data and calcination parameter adjustment data, perform combined processing of volatile utilization to construct combined volatile utilization data; S6. Based on the combined data of volatile utilization and the standard combined data of different volatile utilization algorithms, perform utilization algorithm type matching processing to generate target utilization algorithm type feature data; S7. Construct a summary data of volatilization utilization, process excess volatilization utilization, and generate excess volatilization utilization result data; S8. Perform utilization effect monitoring processing and generate utilization effect monitoring data; S9. Output the final utilization report data based on the utilization effect monitoring data, and complete the excess volatilization utilization process; The excess volatility parameter data in S1 includes the following steps: S11. Collect excess volatilization parameters of the calciner under stable and high-load operating conditions by a sensor array installed at the outlet of the calciner, including volatile temperature data, volatile pressure data, volatile flow rate data and volatile component concentration data. S12. The collected excess volatilization parameter data is transmitted to the calcining furnace monitoring platform in real time, and outliers are removed using a data cleaning algorithm to generate an initial volatilization dataset. The specific process is as follows: First, the system receives the initial volatile parameter data stream from the sensor array. At the beginning of the data cleaning, invalid values ​​need to be removed. This step directly filters out values ​​that are physically impossible to exist.

[0026] Secondly, we move to the core outlier detection and handling stage. Here, statistical models are typically used to identify "outliers" that, while within their theoretically possible numerical range, deviate significantly from the normal behavior pattern of most data. A commonly used and effective method is based on interquartile range. The detection algorithm does not rely on the assumption that the data strictly follows a normal distribution and has good robustness to the non-normal characteristics of industrial field data; The specific steps are as follows: Data sorting and quartile calculation: Sort all data points for a specific parameter within a time window, and then find the first quartile of the data sequence. Second quartile and the third and fourth quartiles ; Calculate interquartile range : It is the difference between the upper quartile and the lower quartile, calculated using the following formula: ; This reflects the dispersion of the middle 50% of the data; 3. Determine outlier boundaries: Typically defined as follows: Lower limit of mild outliers: ; Upper limit of mild outliers: ; Lower bound of extreme outliers: ; Upper limit of extreme outliers: ; In practical industrial data processing, 1.5* is often used. According to the rule, any data point below the lower limit or above the upper limit will be initially identified as an outlier; Outlier handling: There are several strategies for handling identified outliers: Delete: Directly removes abnormal data points, suitable for situations where there are few abnormal points and it is confirmed that they are caused by a transient failure; Correction: Replace outliers with a reasonable value. Common methods include replacing them with the median or the moving average within the time window. The median is the preferred robust replacement because it is not sensitive to outliers. This approach can maintain the continuity of the data series and is more beneficial for subsequent time series analysis. Finally, after outlier processing, smoothing filtering is performed. Even if obvious outliers are removed, high-frequency random noise may still exist in the data. At this time, digital filtering techniques such as moving average filter and Savitzky-Golay filter can be applied to smooth the data sequence, so as to preserve the true data trend while suppressing noise. Savitzky-Golay filter can better preserve the local features of the signal while smoothing. S13. Standardize the initial volatile data set to unify the dimensions of all parameter data and generate standardized excess volatile parameter data for subsequent analysis. The specific process includes the following key steps: First, data reading and feature separation are performed. The system reads data from the initial volatile dataset generated in step S12. This dataset is typically represented as a matrix, where each row represents a sample and each column represents a feature parameter. Assume there are... One sample, If there are features, then the dataset can be represented as ; Secondly, calculate the statistic for each feature, which serves as the benchmark for standardization. For the th feature... Feature columns Calculate its: Sample mean: ; It describes the central location of this feature data; Sample standard deviation: ; It describes the degree of dispersion of the feature data; Third, perform a standardization transformation. This is the core computational step, typically using the Z-Score standardization method. Each original data value in each feature column Its standardized value Calculated using the following formula: ; in, The value of the j-th feature is the standardized value of the i-th sample. Let be the original value of the i-th sample for the j-th feature. Let j be the mean of all sample values ​​for the j-th feature. Let $\frac{j}{j}$ be the standard deviation of all sample values ​​for the $j$-th feature. The geometric meaning of this transformation is to convert the original data into a new distribution with a mean of 0 and a standard deviation of 1. After the transformation, all feature parameters are converted to a comparable scale. Finally, a standardized dataset is generated and validated. The results of standardizing all feature columns are combined to form a new standardized dataset. After processing, a simple verification is usually performed to ensure the correctness of the standardization process; The volatile component characteristic data in S2 includes the following steps: S21. Obtain excess volatile parameters and use a component analyzer to separate the chemical components of the volatiles, identifying the main volatile components, including carbon monoxide, methane and uncalcined hydrocarbons. S22. Based on the identified volatile components, a clustering algorithm is used to divide the volatile components into high-energy components and low-energy components, and the proportion distribution of each component is calculated. With the most commonly used Taking the mean clustering algorithm as an example, its specific steps are as follows: Determine the number of clusters In this application, the objective is clearly to divide the components into two categories: high-energy components and low-energy components. Therefore, the number of clusters is pre-set to [value missing]. ; Initialize cluster centers: The algorithm randomly selects two sample points from the standardized feature dataset as initial cluster centers, representing the initial "prototypes" of the high-energy component and the low-energy component, respectively; Iterative optimization: Allocation steps: Calculate the Euclidean distance from each volatile sample to the two centroids. The distance formula is: ; in, It is a sample to the center of mass distance, It is a sample The 1 eigenvalue, It is the center of mass. The One coordinate value; Based on the principle of closest proximity, each sample point is assigned to one of the clusters containing one of the centroids. Thus, all samples are initially divided into two sets. Update steps: Recalculate the centroid of each cluster. The new centroid is the average of all sample points in the cluster. For each feature dimension, the coordinates of the new centroid are obtained by averaging the values ​​of all samples in the cluster for that feature. Convergence judgment: Repeat the assignment and update steps until the position of the centroid no longer changes and the preset maximum number of iterations is reached. At this point, the algorithm considers the clustering result to be stable. S23. Generate volatile component characteristic data based on the proportional distribution, including component energy density characteristics and component stability characteristics, for use in subsequent availability assessment; The data for determining the availability of volatile substances in S3 includes the following steps: S31. Obtain characteristic data of volatile components and set the threshold for volatile utilization, including the energy recovery threshold and the environmental impact threshold. S32. Compare the characteristic data of volatile components with the threshold for volatile utilization. When the energy density of volatile components is higher than the energy recovery threshold and the environmental impact is lower than the environmental impact threshold, output the volatile utilization judgment data as available. S33. When the data for determining the availability of volatile substances is unavailable, an alarm mechanism is triggered and the reason for unavailability is recorded; otherwise, step S5 is executed directly. The calcination parameter adjustment data in S4 includes the following steps: S41. When the volatile availability judgment data is available, obtain the current calcination parameters of the pot furnace, including the calcined material supply rate, air flow rate and furnace temperature. S42. The calcination parameters are adjusted based on the volatile component characteristic data using a linear interpolation algorithm, so that the calcination parameters are optimized to a state suitable for the utilization of excess volatilization. The specific process is as follows.

[0027] First, the system needs to establish a mapping model between the characteristics of volatile components and the target calcination parameters. This model is usually based on historical operating data and experimental calibration, with the volatile component characteristic data as input variables and the calcination parameters as output variables. Secondly, after the system acquires real-time volatile component characteristic data, a linear interpolation algorithm is used to estimate appropriate calcination parameter values ​​between known data points. Its core formula is based on the linear relationship between two points. For the one-dimensional case, the interpolation formula is: ; in, and It is a lookup table with The two closest known heat values, and This corresponds to the calcined material supply rate; This formula calculates the adjusted parameters using a proportional method. The linear transition with calorific value ensures smooth adjustment. In practical applications, the characteristics of volatile components may be multidimensional. In this case, multidimensional linear interpolation and independent processing of each feature dimension are required. However, to simplify implementation, the system often interpolates the main features and uses other features as correction factors. Finally, the algorithm outputs the adjusted calcination parameters and updates them to the calcination furnace control system in real time. S43. Generate calcination parameter adjustment data and update it to the calcination furnace control system in real time to ensure that the adjusted parameters are executed stably. The combined data on volatile utilization in S5 includes the following steps: S51. Align the volatile component characteristic data and calcination parameter adjustment data with the operating status data of the pot furnace according to the time series. S52. Using the time parameter as a reference, arrange and combine the aligned data vertically to construct a volatilization utilization combined data matrix, where the rows of the matrix represent different parameter types and the columns represent time points; S53. Normalize the combined data matrix of volatile utilization to eliminate dimensional differences and generate standardized combined data of volatile utilization for algorithm matching. The target utilization algorithm type feature data in S6 includes the following steps: S61. Establish a standard volatile utilization combination data matrix corresponding to different volatile utilization algorithms, including standard data corresponding to heat recovery algorithm, catalytic conversion algorithm and energy storage algorithm; S62. Perform similarity matching between the combined data of volatile utilization and the standard combined data matrix of volatile utilization, and calculate the matching degree using the cosine similarity algorithm. The specific process of the algorithm is as follows: Cosine similarity assesses the similarity of two vectors by measuring the difference in their directions. It is insensitive to the absolute magnitude of the vectors, making it particularly suitable for comparing the similarity of data patterns rather than their numerical values. Its calculation formula is as follows: ; in: Indexed by vector dimension. For vectors In the Dimension value, For vectors In the Dimension value, Let be the dimension of the vector; Finally, the system calculates the real-time data vector to determine the matching degree and select the algorithm. Compared with all standard algorithm vectors in the database The cosine similarity is calculated, and then the type corresponding to the standard algorithm with the highest cosine similarity value is selected as the matching result. S63. Select the optimal utilization algorithm type based on the matching degree result, and generate target utilization algorithm type feature data; S64. The matching process uses an artificial intelligence optimization algorithm for iterative search to ensure accurate algorithm selection. The excess volatilization utilization results data in S7 include the following steps: S71. Combine the combined data of volatile utilization and the characteristic data of target utilization algorithm type into summary data of volatile utilization; S72. The calcining furnace monitoring platform calls the corresponding utilization program based on the target utilization algorithm type characteristic data to extract key parameters from the volatilization utilization summary data. S73. Perform excess volatilization utilization treatment, including heat recovery, material conversion and energy storage, and generate excess volatilization utilization result data in real time; The utilization effect monitoring data in S8 includes the following steps: S81. During the utilization and processing, the utilization effect parameters are collected in real time through monitoring sensors, including energy efficiency data, emission reduction data and equipment operating status data. S82. Compare the collected utilization effect parameters with the preset target values ​​and generate a deviation analysis report; S83. When the deviation exceeds the allowable range, the parameters will be automatically adjusted and the optimization mechanism will be triggered. The final utilization report data in S9 includes the following steps: S91. Integrate and utilize the monitoring data on utilization effectiveness and the data on the results of excess volatilization utilization to generate a comprehensive utilization report; S92. The utilization report includes the percentage of utilization efficiency, economic benefit assessment and environmental benefit analysis; S93. Output the report to the user interface and storage system, and complete the closed-loop management of the entire excess volatilization utilization method.

[0028] The operating steps of a method for utilizing excess volatilization in a pot-type calciner are as follows: 1. Data Acquisition and Preprocessing Principles: First, volatile parameters at the outlet of the tank calciner are acquired in real time through a sensor array, including temperature, pressure, flow rate, and component concentration data. The principle of this step is to ensure the real-time nature and accuracy of the data source, providing a reliable foundation for subsequent analysis. After data acquisition, statistical methods are used for cleaning and standardization to eliminate outliers and dimensional differences, thereby generating a unified standardized dataset. This ensures data quality, avoids noise interference, and lays the foundation for component analysis.

[0029] 2. Principle of Volatile Component Characteristic Analysis: Based on the preprocessed data, the chemical components of volatiles are identified using a component analyzer, and the components are divided into high-energy and low-energy categories through clustering algorithms. The principle of this step is to quantify the energy density and stability characteristics of volatiles through machine learning technology, thereby accurately assessing their utilization potential. The core of component characteristic analysis is to transform complex volatiles into quantifiable indicators, providing a scientific basis for subsequent judgments.

[0030] 3. Availability Judgment Principle: Based on the characteristic data of volatile components, energy recovery thresholds and environmental impact thresholds are set and numerical comparisons are performed for judgment. The principle is to achieve automated decision-making through threshold comparison: when the energy density is higher than the threshold and the environmental impact is lower than the threshold, it is determined to be available; otherwise, an alarm is triggered. This step reflects the risk control principle, ensuring that only safe and efficient volatiles enter the utilization process and avoiding operational risks.

[0031] 4. Principle of dynamic adjustment of calcination parameters: When the volatiles are determined to be usable, the system obtains the current calcination parameters and adjusts the parameters according to the composition characteristics using a linear interpolation algorithm. The principle is to optimize the parameters through a mathematical model, so that the calcination system can adapt to the introduction of volatiles, prevent the state inside the furnace from becoming disordered, and the dynamic adjustment ensures the synergy between the calcination process and the utilization of volatiles, thereby improving the overall operational stability.

[0032] 5. Volatilization Utilization Combination Construction Principle: The component characteristic data and the adjusted calcination parameters are aligned according to the time series to construct a data matrix and perform normalization processing. The principle is to integrate multi-source parameters into standardized combination data through data fusion technology to eliminate dimensional differences. This step provides a unified input for algorithm matching, reflects the principle of data-driven decision-making, and ensures the accuracy of subsequent matching.

[0033] 6. Utilizing the principle of intelligent matching algorithm: The combined data of volatile utilization is matched with the data matrix of standard algorithm. The matching degree is calculated using the cosine similarity algorithm. The principle is based on the vector space model, which selects the optimal algorithm type to ensure that the utilization strategy is highly consistent with the characteristics of volatiles. Intelligent matching is optimized and iterated through artificial intelligence, which improves the accuracy and adaptability of the utilization method.

[0034] 7. Execution principle of excess volatilization utilization: Based on the matching results, the corresponding utilization program is called to perform actual processing and generate result data. The principle is to transform algorithmic decisions into actual operations and control key parameters in real time through the monitoring platform. This step reflects the principle of automated execution and ensures that the utilization process is efficient and reliable.

[0035] 8. Utilizing the principle of effect monitoring and optimization: During the utilization process, effect parameters are collected in real time and compared with preset targets to generate deviation reports. The principle is to achieve closed-loop control through a feedback mechanism: when the deviation exceeds the limit, the parameters are automatically adjusted and optimization is triggered. The monitoring steps ensure the continuous improvement of the method and reflect the principle of adaptive optimization.

[0036] 9. Report Generation and Closed-Loop Management Principles: Integrate monitoring data and utilization results to generate comprehensive utilization reports, including efficiency percentages, economic benefits, and environmental benefits analysis. The principle is to complete the closed-loop management of the entire method through data aggregation and evaluation, providing decision support for users. This step emphasizes the comprehensiveness and sustainability of the method.

[0037] 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.

[0038] 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 utilizing excess volatilization for a pot calciner, characterized by, The method comprises the following steps: S1, collecting excess volatile parameter data generated by the tank calciner during operation; S2, performing volatile component analysis processing based on the excess volatile parameter data to generate volatile component feature data; S3, performing volatile availability judgment processing according to the volatile component feature data to generate volatile availability judgment data; S4, when the volatile availability judgment data indicates availability, performing calcination parameter adjustment processing of the tank calciner to generate calcination parameter adjustment data; S5, performing volatile utilization combination processing according to the volatile component feature data and the calcination parameter adjustment data to construct volatile utilization combination data; S6, performing utilization algorithm type matching processing based on the volatile utilization combination data and corresponding standard volatile utilization combination data of different volatile utilization algorithms to generate target utilization algorithm type feature data; S7, constructing volatile utilization summary data to perform excess volatile utilization processing to generate excess volatile utilization result data; S8, performing utilization effect monitoring processing to generate utilization effect monitoring data; S9, outputting final utilization report data based on the utilization effect monitoring data and completing the excess volatile utilization process.

2. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The excess volatile parameter data in S1 comprises the following steps: S11, collecting excess volatile parameters of the tank calciner under stable operation state and high load operation state through a sensor array installed at the outlet of the tank calciner, including volatile temperature data, volatile pressure data, volatile flow data and volatile component concentration data; S12, transmitting the collected excess volatile parameter data to the calciner monitoring platform in real time, and removing outliers by using a data cleaning algorithm to generate an initial volatile data set; S13, performing standardization processing on the initial volatile data set to unify the dimensions of all parameter data, and generating standardized excess volatile parameter data for subsequent analysis.

3. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The volatile component feature data in S2 comprises the following steps: S21, obtaining the excess volatile parameter data and using a component analyzer to separate the chemical components of the volatiles to identify the main volatile components, including carbon monoxide, methane and uncalcined hydrocarbons; S22, based on the identified volatile components, using a clustering algorithm to divide the volatile components into high-energy components and low-energy components, and calculating the proportion distribution of each component; S23, generating volatile component feature data according to the proportion distribution, including component energy density features and component stability features, for subsequent availability judgment.

4. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The volatile availability judgment data in S3 comprises the following steps: S31, obtaining the volatile component feature data and setting volatile availability threshold values, including energy recovery threshold values and environmental impact threshold values; S32, performing numerical comparison between the volatile component feature data and the volatile availability threshold values, when the energy density of the volatile components is higher than the energy recovery threshold value and the environmental impact is lower than the environmental impact threshold value, the volatile availability judgment data is output as available; S33, when the volatile availability judgment data is not available, an alarm mechanism is triggered and the reason for unavailability is recorded, otherwise, step S5 is directly executed.

5. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The calcination parameter adjustment data in S4 includes the following steps: S41, when the volatile utilization judgment data is available, obtaining the current calcination parameters of the tank calciner, including the calcine supply rate, air flow and furnace temperature; S42, using a linear interpolation algorithm to adjust the calcination parameters according to the volatile component characteristic data, so that the calcination parameters are optimized to a state suitable for excess volatile utilization; S43, generating calcination parameter adjustment data and updating to the calciner control system in real time to ensure stable execution of the adjusted parameters.

6. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The volatile utilization combination data in S5 includes the following steps: S51, aligning the volatile component characteristic data and the calcination parameter adjustment data with the operation state data of the tank calciner in time sequence; S52, arranging the aligned data up and down to construct a volatile utilization combination data matrix with time parameters as the reference system, wherein the rows represent different parameter types and the columns represent time points; S53, normalizing the volatile utilization combination data matrix to eliminate dimensional differences and generating standardized volatile utilization combination data for algorithm matching.

7. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The target utilization algorithm type characteristic data in S6 includes the following steps: S61, establishing different volatile utilization algorithm corresponding standard volatile utilization combination data matrix, including heat recovery algorithm, catalytic conversion algorithm and energy storage algorithm corresponding standard data; S62, similarity matching the volatile utilization combination data with the standard volatile utilization combination data matrix, and using cosine similarity algorithm to calculate the matching degree; S63, selecting the optimal utilization algorithm type based on the matching degree result and generating the target utilization algorithm type characteristic data; S64, the matching process uses artificial intelligence optimization algorithm for iterative search to ensure accurate algorithm selection.

8. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The excess volatile utilization result data in S7 includes the following steps: S71, combining the volatile utilization combination data and the target utilization algorithm type characteristic data into volatile utilization summary data; S72, the calciner monitoring platform calls the corresponding utilization program according to the target utilization algorithm type characteristic data and extracts the key parameters in the volatile utilization summary data; S73, performing excess volatile utilization processing, including heat recovery, material conversion and energy storage, and generating excess volatile utilization result data in real time.

9. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The utilization effect monitoring data in S8 includes the following steps: S81, during the utilization processing, real-time collection of utilization effect parameters through monitoring sensors, including energy efficiency data, emission reduction data and equipment operation state data; S82, comparing the collected utilization effect parameters with the preset target value to generate a deviation analysis report; S83, when the deviation exceeds the allowed range, automatically adjusting the utilization parameters and triggering the optimization mechanism.

10. The method for utilizing excess volatilization of a shaft kiln according to claim 1, characterized by, The final utilization report data in S9 includes the following steps: S91, integrating the utilization effect monitoring data and the excess volatile utilization result data to generate a comprehensive utilization report; S92, the utilization report includes utilization efficiency percentage, economic benefit evaluation and environmental benefit analysis; S93, output the report to the user interface and storage system, and complete the closed-loop management of the entire excess volatile utilization method.

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