Control method and system for fuel supply of volatile kiln

Abstract

The invention provides a control method and system for fuel supply of a volatilization kiln, and belongs to the technical field of industrial control. Firstly, real-time working condition data and historical working condition data of the volatilization kiln are obtained, working condition trend prediction processing is carried out based on the real-time working condition data and the historical working condition data of the volatilization kiln, and a working condition trend prediction result is obtained; the method comprises the following steps: constructing a fuel supply plan library according to a working condition trend prediction result, monitoring the deviation between the current working condition and the prediction result in real time, matching and dynamically adapting a target supply adjustment strategy from the fuel supply plan library to obtain an execution scheme, sending the execution scheme to a fuel supply system for execution, and collecting feedback data to update the plan library. Therefore, fuel supply of the volatilization kiln can be intelligently and accurately controlled, production efficiency and product quality are improved, and cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to a control method and system for fuel supply to a volatilization kiln. Background Art

[0002] In the field of industrial production, volatilization kilns, as important thermal equipment, are widely used in metallurgy, chemical industry and other industries. The precise control of their fuel supply plays a key role in ensuring product quality, improving production efficiency and reducing energy consumption.

[0003] Traditional fuel supply control methods for volatilization kilns rely primarily on manual experience and fixed control modes. Under manual experience control, operators adjust fuel supply based on their subjective judgment of kiln operating conditions. However, due to differences in operator experience and the susceptibility of manual judgment to subjective factors, it is difficult to ensure accurate and stable fuel supply. Fixed control modes control fuel supply according to pre-set parameters and lack the flexibility to adapt to the dynamic changes in the kiln's actual operating conditions. During the production process, kiln operating conditions are constantly changing due to factors such as material composition, feed rate, and ambient temperature. Fixed control modes cannot respond to these changes in a timely manner, easily resulting in over- or under-fuel supply. Over-fuel supply wastes energy, increases production costs, and can also cause kiln temperatures to rise, impacting equipment life and product quality. Under-fuel supply leads to insufficient kiln temperatures, resulting in inadequate material reaction, reduced production efficiency, and reduced product quality. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a method for controlling fuel supply for a volatilization kiln, the method comprising: Acquiring real-time operating data and historical operating data of the volatilization kiln, wherein the real-time operating data includes real-time reaction status information in the kiln and real-time material processing progress information, and the historical operating data includes fuel supply adjustment records and operating condition change result information under different operating conditions; Performing working condition trend prediction processing based on the real-time working condition data of the volatilization kiln and the historical working condition data of the volatilization kiln, identifying the potential change direction and potential change period of the working condition in the kiln, and obtaining a working condition trend prediction result; Building a fuel supply plan library based on the operating condition trend prediction result, wherein the fuel supply plan library includes fuel supply adjustment strategies corresponding to different prediction trends, and each fuel supply adjustment strategy is associated with a corresponding operating condition adaptation condition; real-time monitoring of the deviation between the current operating condition data of the volatilization kiln and the operating condition trend prediction result, matching a target supply adjustment strategy from the fuel supply plan library according to the deviation, and dynamically adapting the target supply adjustment strategy to obtain a fuel supply execution plan; The fuel supply execution plan is sent to the fuel supply system for execution, and the kiln operating condition feedback data after execution is collected. The fuel supply adjustment strategy and operating condition adaptation conditions in the fuel supply plan library are updated using the kiln operating condition feedback data.

[0005] On the other hand, an embodiment of the present invention further provides a control system for volatilization kiln fuel supply, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above-mentioned method.

[0006] Based on the above aspects, the embodiment of the present invention obtains real-time operating condition data of the volatilization kiln, including real-time reaction status information and real-time material processing progress information in the kiln, and historical operating condition data of the volatilization kiln, including fuel supply adjustment records and operating condition change result information under different operating conditions. Based on this, operating condition trend prediction processing is performed, and the potential change direction and potential change period of the operating condition in the kiln are identified. After constructing a fuel supply plan library based on the operating condition trend prediction results, the deviation between the current operating condition data and the prediction results is monitored in real time. Based on this, the target supply adjustment strategy is matched from the fuel supply plan library and dynamically adapted to obtain a fuel supply execution plan, thereby achieving precise control and dynamic adjustment of the fuel supply, being able to respond to operating condition changes in a timely manner, sending the fuel supply execution plan to the fuel supply system for execution, and collecting the kiln operating condition feedback data after execution to update the strategy and conditions in the plan library, continuously optimizing the fuel supply control effect, effectively improving the production efficiency and product quality of the volatilization kiln, and reducing energy consumption and production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the execution flow of the control method for volatilization kiln fuel supply provided by an embodiment of the present invention.

[0008] Figure 2 FIG. 1 is a schematic diagram of exemplary hardware and software components of a control system for volatilization kiln fuel supply provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 FIG1 is a flow chart of a method for controlling fuel supply to a volatilization kiln provided by an embodiment of the present invention. The method for controlling fuel supply to a volatilization kiln is described in detail below.

[0010] Step S110: Acquire real-time operating data and historical operating data of the volatilization kiln. The real-time operating data of the volatilization kiln includes real-time reaction status information in the kiln and real-time material processing progress information. The historical operating data of the volatilization kiln includes fuel supply adjustment records under different operating conditions and operating condition change result information.

[0011] This example uses a volatilization kiln for fuel supply control in a mineral roasting process as an application scenario. Data is collected by deploying various monitoring devices throughout the kiln's body, feed system, discharge system, and fuel supply system. Real-time reaction status information within the kiln is acquired by a group of temperature sensors evenly spaced along the kiln's length, an infrared thermal imager mounted on the kiln roof, a gas analyzer on the flue gas duct at the kiln's tail, and a pressure sensor within the kiln. The temperature sensor group collects temperature distribution data across different sections within the kiln, the infrared thermal imager captures the temperature field of the material surface within the kiln, the gas analyzer measures the volume fraction of gases such as oxygen, carbon monoxide, and sulfur dioxide in the flue gas, and the pressure sensor records changes in static and dynamic pressure within the kiln. Real-time material processing progress information is collected by a load cell on the feed conveyor, a material level meter within the kiln, a particle size detector at the discharge port, and a conveyor speed sensor. The load cell records the amount of material fed per unit time, the level meter monitors the material accumulation height within the kiln, the particle size detector analyzes the particle size distribution of the discharged material, and the conveyor speed sensor measures the material's movement speed within the kiln.

[0012] Historical operating data for the volatile kiln is extracted from the production management system's database, covering complete data from multiple past production cycles. Fuel supply adjustment records include fuel flow adjustment values ​​for different time periods, the number and angle settings of fuel nozzle openings, combustion air flow and pressure adjustment parameters, and fuel type switching records. Each adjustment record includes operating condition change results, including temperature curves for each zone within the kiln after adjustment, flue gas composition changes, component analysis results of the calcined material, fuel consumption per unit product, and equipment operational stability indicators.

[0013] During data collection, an industrial-grade data acquisition module is used for real-time operating condition data. This module supports access to multiple signal types, and the data sampling frequency is set based on parameter characteristics, with longer sampling intervals for slowly varying parameters such as temperature and pressure, and shorter sampling intervals for rapidly varying parameters such as gas composition and material flow. Real-time data is transmitted to the data processing center via optical fiber, using an encryption protocol to prevent data leakage or tampering. Historical operating condition data is stored in a redundant disk array, with regular data backup and verification to ensure data integrity. Furthermore, a data access management system is established, with permissions for sensitive data related to process parameters divided, ensuring that only authorized personnel can query and modify it.

[0014] Step S120: performing operating condition trend prediction processing based on the real-time operating condition data and the historical operating condition data of the volatilization kiln, identifying the potential change direction and potential change period of the operating condition in the kiln, and obtaining an operating condition trend prediction result.

[0015] After obtaining real-time and historical operating data, a series of processing steps such as data integration, feature extraction, and historical correlation analysis are used to combine the dynamic changes of real-time data with the empirical laws of historical data to predict future changes in the kiln's operating conditions.

[0016] Step S121: Integrate the real-time operating condition data of the volatilization kiln with the historical operating condition data of the volatilization kiln to obtain a regularized operating condition data set.

[0017] First, preprocess the real-time and historical operating data of the volatilization kiln. The data's time format is unified, with the timestamp of the real-time data and the recording time of the historical data in the same format, accurate to the same time unit. The physical units of the data are standardized, for example, the temperature unit is unified to degrees Celsius, the pressure unit is unified to kilopascals, and the flow unit is unified to cubic meters per hour. Abnormal jump values ​​in the real-time data are identified and eliminated by comparing them with the measured values ​​at adjacent moments using the Laida criterion, and data gaps are then filled using linear interpolation. For missing data segments in the historical data due to equipment failure, trends are supplemented based on complete data from similar operating conditions during the same period.

[0018] The two types of data are then integrated according to their attribute categories. Data reflecting the reaction state within the kiln, such as temperature, pressure, and gas composition, are grouped into the reaction state dataset, while data reflecting the progress of material processing, such as feed rate, material level, particle size, and conveying speed, are grouped into the material progress dataset. The data within each dataset is arranged chronologically, and each data record includes information such as the data collection time, parameter name, parameter value, and data quality indicator. The data quality indicator is used to distinguish data reliability, such as whether the data is collected normally, supplemented by interpolation, or manually corrected.

[0019] Step S122: extracting the variation characteristics of the real-time operating condition data of the volatilization kiln from the regularized operating condition data set, wherein the variation characteristics include the continuous variation trajectory of the reaction state in the kiln and the rate variation characteristics of the material processing progress.

[0020] Real-time operating data from the most recent period was filtered from the regularized operating condition dataset, and variation features were extracted using a time series feature extraction method. The continuous variation trajectory of the reaction state within the kiln was extracted using the following methods: Time series analysis was performed on the measured values ​​of temperature sensors at different locations, calculating the temperature change at each temperature sensor at consecutive time points to obtain the slope of the temperature change over time. The concentration data of each gas component detected by the gas analyzer was smoothed to remove high-frequency noise, and the trend term of the concentration change was extracted to obtain a gas composition variation curve. Spectral analysis was performed on the measured data of the pressure sensor to extract the main frequency components and amplitude variation patterns of the pressure fluctuations. These temperature, gas, and pressure variation features were combined to form a continuous variation trajectory of the reaction state within the kiln, which can reflect the dynamic changes in the reaction intensity within the kiln.

[0021] The rate-varying characteristics of material handling progress are extracted as follows: Based on the measurement data from the feed belt load cell, the rate of change of the feed amount per unit time is calculated to obtain the feed rate variation characteristics. The rate of change of the material filling rate within the kiln is calculated by combining the measurement data from the kiln level meter with the material conveying speed. Statistical analysis is performed on the data from the discharge port particle size detector to calculate the rate of change of the proportion of material in different particle size ranges over time. By integrating these rate-varying characteristics of feed, material level, and particle size, a rate-varying characteristic of material handling progress is formed, which can reflect the efficiency trend of the material handling process within the kiln.

[0022] Step S123: Correlating historical change segments in the historical operating condition data of the volatilization kiln with change characteristics similar to those of the real-time operating condition data of the volatilization kiln, and extracting subsequent operating condition change directions and subsequent operating condition change periods corresponding to the historical change segments.

[0023] Step S1231: Filtering out historical data segments containing a complete process of operating condition change from the historical operating condition data of the volatilization kiln, wherein each historical data segment includes a characteristic change stage and a subsequent change stage.

[0024] Traverse the historical operating data of the volatile kiln and filter out historical data segments with complete operating condition change cycles. Each historical data segment must include the complete process from the beginning of the operating condition change characteristics, through the development and continuation of the characteristics, to the operating condition reaching a new stable state or a turning point. The change characteristic stage refers to the stage in the historical data that is similar to the current real-time operating condition change characteristics in trend, amplitude and frequency. For example, in a certain historical segment, the temperature in the kiln shows a continuous upward trend, and the rate of increase is similar to the current real-time data. The subsequent change stage is the operating condition development stage after the characteristic stage, covering the entire process of the operating condition from the end of the characteristic stage to stability or turning point. Each historical data segment is marked with the start time, duration, key parameter change range of the change characteristic stage, as well as the time span and parameter change pattern of the subsequent change stage.

[0025] Step S1232: performing feature comparison on the change feature of the real-time working condition data of the volatilization kiln and the change feature stage of each historical data segment, and calculating the feature similarity between the change feature and the change feature stage of each historical data segment.

[0026] A multi-dimensional comparison is performed between the currently extracted real-time operating condition change characteristics and the characteristics of each stage of change characteristics in each historical data segment. For the continuous change trajectory of the reaction state in the kiln, the comparison is conducted based on the consistency of temperature change trends (such as rising, falling, or stable), the similarity of temperature change rates, the similarity of gas composition change curves, and the matching degree of pressure fluctuation frequency. For the rate change characteristics of material processing progress, the comparison is conducted based on the consistency of feed rate change direction, the similarity of material level change rates, and the similarity of particle size change rates.

[0027] During the comparison process, a corresponding similarity calculation method is set for each feature dimension. For example, the consistency of temperature change trends is determined by comparing the direction of the trend vector; if the direction is the same, the similarity of that dimension is high. The closeness of the temperature change rate is measured by calculating the relative ratio of the difference between the two rates; the smaller the ratio, the higher the similarity. The similarity scores of each dimension are weighted and summed according to the preset weights to obtain the overall feature similarity. The weight setting is based on the importance of each feature dimension to the change in operating conditions. For example, the weight of the temperature change feature is higher than that of the pressure fluctuation feature.

[0028] Step S1233: Filter out historical data segments whose feature similarity is higher than a similarity threshold, and use the filtered historical data segments as candidate historical change segments.

[0029] Based on the statistical analysis results of historical data, a feature similarity threshold is set. This threshold takes into account the distribution of similar feature segments in historical operating conditions, ensuring that the selected historical segments have a sufficiently high similarity with the current real-time change features. The calculated feature similarity of each historical data segment is compared with this threshold. Historical data segments with feature similarity exceeding the threshold are selected and selected as candidate historical change segments for subsequent analysis.

[0030] Step S1234: extract the operating condition change direction in the subsequent change stage of each candidate historical change segment, determine the key operating condition parameter type associated with the operating condition change direction, and record the change range of each key operating condition parameter under the change direction.

[0031] A detailed analysis of the subsequent change phases of each candidate historical change segment is conducted to determine the direction of the operating condition change. These change directions can include increasing reaction intensity within the kiln, decreasing reaction intensity, maintaining a stable reaction state, or experiencing abnormal reaction fluctuations. For example, if the temperature continues to rise and the concentration of the reaction gas increases during the subsequent change phase, the operating condition change is considered to be increasing reaction intensity; if the temperature gradually decreases and the proportion of unreacted components in the gas increases, the reaction intensity is considered to be decreasing.

[0032] Identify the key operating parameters associated with each operating condition change. Key parameters typically associated with increased reaction intensity include the kiln's high-temperature zone temperature, reaction characteristic gas concentration, and material conversion rate. Key parameters associated with decreased reaction intensity include the low-temperature zone temperature percentage, unreacted gas concentration, and material residence time. Key parameters associated with abnormal reaction fluctuations include temperature fluctuation amplitude, pressure mutation frequency, and gas composition fluctuation range.

[0033] Record the range of change for each key operating parameter in that direction. For example, for an increasing reaction intensity, record the increase in the high-temperature zone temperature from the initial range to the target range, and the increase in the characteristic gas concentration from the initial ratio to the target ratio. For a decreasing reaction intensity, record the increase in the low-temperature zone temperature from the initial ratio to the target ratio, and the increase in the unreacted gas concentration from the initial concentration to the target concentration. These ranges are determined based on actual measurements during subsequent stages of the historical data segment and reflect the typical range of parameter changes under similar change characteristics.

[0034] Step S1235: extracting the time length of the subsequent change phase of each candidate historical change segment, and determining the change period required for the key operating condition parameter to reach a stable state according to the time length.

[0035] Analyze the time series data for the subsequent change phase of each candidate historical change segment to determine the length of time from the end of the characteristic change phase until the key operating condition parameters reach a stable state. A stable state refers to a state in which the rate of change of the key operating condition parameters drops below a preset stability threshold, and the parameter values ​​fluctuate within a small range. For example, if, in the subsequent change phase of a historical segment, the temperature fluctuates within a certain time from the end of the characteristic phase to the amplitude of the fluctuation being less than the stability threshold, this time is the length of the subsequent change phase of that segment.

[0036] Based on this time span, combined with the rate of change of key parameters in the historical segment and the dynamics of parameter changes in the current real-time operating conditions, the predicted change period required for key operating parameters to reach a stable state under the current operating conditions is determined. The change period includes the initial period when the parameters begin to change significantly, the acceleration period when the rate of change is the fastest, the deceleration period when the rate of change gradually slows, and the stabilization period when the parameters reach a stable state. For example, if the subsequent change phase of the historical segment is short and the current real-time parameter change rate is fast, the predicted change period to reach a stable state will be correspondingly shorter; otherwise, it will be longer.

[0037] Step S1236: Record the operating condition change direction, the associated key operating condition parameter type and its change range, the change period in which the key parameter reaches stability under the change direction, and the feature similarity corresponding to each candidate historical change segment to form a candidate historical information list.

[0038] The analysis results of each candidate historical change segment are recorded in a structured manner to form a candidate historical information list. Each record in the candidate historical information list contains the following content: a specific description of the direction of the operating condition change, such as "increased reaction intensity" and "weakened reaction intensity"; the associated key operating condition parameter types, such as "high temperature zone temperature" and "characteristic gas concentration"; the specific change range of each key parameter in the direction of change, expressed in the form of parameter intervals; the change period required for the key parameter to reach a stable state, including the time range of the starting period, acceleration period, deceleration period and stable period; and the feature similarity value between the historical segment and the current real-time change feature. The candidate historical information list can clearly display the key information of each candidate historical segment.

[0039] Step S1237: Deduplication is performed on the operating condition change directions and change periods in the candidate historical information list, and the historical information with the highest feature similarity is retained as core reference information.

[0040] Step S1237 - 1 : Classify the operating condition change directions in the candidate historical information list, and group historical information with the same or similar operating condition change directions into the same direction group.

[0041] The description of the operating condition change direction of each record in the candidate historical information list is compared one by one, and the records are classified according to the core characteristics of the change direction. For example, "slightly increased reaction intensity," "moderately increased reaction intensity," and "significantly increased reaction intensity" are grouped as the "increased reaction intensity" direction group; "slightly decreased reaction intensity" and "significantly decreased reaction intensity" are grouped as the "decreased reaction intensity" direction group; and "local temperature fluctuation" and "slight pressure oscillation" are grouped as the "stable fluctuation" direction group. Each direction group is assigned a unique identifier, recording the number of historical information items contained in the group and the feature similarity of each information item.

[0042] Step S1237-2: In each direction group, statistics are collected on the first stable change period of the key operating condition parameters in the change direction of each historical information record, and the average stable change period is calculated in the same operating condition change direction.

[0043] Extract the period of time during which key operating condition parameters reach stability (i.e., the first change period) from all historical records within each directional group, including parameters such as the start time and duration. Perform statistical analysis on the first change period within the same directional group, calculating the average of the parameters for each period to obtain the average stable change period for that directional group. For example, if a directional group contains three historical records with multiple durations for the first change period, calculate their arithmetic mean as the group's average duration, and also calculate the average deviation of the start time.

[0044] Step S1237 - 3 : comparing the feature similarities of each historical information in the same direction group, screening out the historical information with the highest feature similarity, and extracting the second change period in which the key operating condition parameters in the change direction recorded in the historical information reach stability.

[0045] Within each direction group, the feature similarity values ​​of each historical information item are compared to identify the one with the highest feature similarity. If multiple historical information items have the same and highest feature similarity, the details of these items are further compared with the details of the current real-time operating condition characteristics, and the historical information with the highest match is selected as the high-similarity candidate for that group. The period of time during which the key operating condition parameters of this high-similarity candidate record reach a stable state (i.e., the second change period) is extracted, including detailed information such as the specific start time and the duration of each stage.

[0046] Step S1237-4: If the deviation between the second change period and the average stable change period of the direction group is less than the set deviation, the historical information is directly used as the representative information of the direction group.

[0047] Calculate the deviation between the second change period and the average stable change period for the direction group. The deviation calculation includes both the start time deviation and the duration deviation. Set a deviation threshold based on the statistical fluctuation range of the historical data. If the calculated deviation value is less than the set deviation threshold, it indicates that the change period of the high-similarity candidate is consistent with the average level of the same direction group and has high stability. This historical information is directly determined as the representative information for the direction group.

[0048] Step S1237-5: If the deviation between the second change period and the average stable change period of the direction group is not less than the set deviation, then the stable change period of the key operating condition parameters under the change direction recorded in the historical information is corrected in combination with the average stable change period to obtain the corrected stable change period, and the historical information containing the corrected stable change period is used as the representative information of the direction group.

[0049] When the deviation between the second change period and the average stable change period reaches or exceeds the set deviation threshold, the change period needs to be corrected. The correction method is: take the average stable change period as the benchmark, and perform weighted adjustment in combination with the change trend of the second change period. For example, if the duration of the second change period is much longer than the average value, the duration will be corrected to the weighted value of the average value and the second duration. The weight is set according to the feature similarity. The higher the feature similarity, the greater the weight of the second duration. The revised stable change period replaces the original second change period to form the revised historical information as the representative information of the direction group.

[0050] Step S1237 - 6 : From the representative information of each direction group, the representative information with the highest feature similarity is again screened, and the representative information is used as the core reference information.

[0051] Collect representative information from all direction groups, compare their feature similarity values, and select the representative information with the highest feature similarity as the core reference information. If multiple representative information have the same and highest feature similarity, compare the degree of fit between their corresponding working condition change directions and the overall trend of the current real-time working condition, and select the representative information with the highest fit as the final core reference information.

[0052] Step S124: analyzing the similarity between the change characteristics of the real-time operating condition data of the volatilization kiln and the historical change segments, and determining the reference weight of the historical change segments to the current operating condition.

[0053] The reference weight of each candidate historical change segment is determined based on the numerical value of feature similarity. Historical segments with higher feature similarity are more similar to the change characteristics of the current real-time working conditions and have greater reference value for current trend prediction, so they are assigned a higher reference weight. Historical segments with lower feature similarity have relatively less reference value and are assigned a lower reference weight.

[0054] Reference weights are calculated using a normalization process: the feature similarity value of each historical segment is divided by the sum of the feature similarities of all candidate historical segments to obtain the relative weight of each historical segment. For example, if the feature similarity of a candidate historical segment is a certain value, and the sum of the feature similarities of all candidate segments is another value, the reference weight of that segment is the ratio of the two. This method ensures that the sum of the reference weights of all candidate historical segments is proportional to the overall value, allowing the reference value of different historical segments to be reasonably differentiated.

[0055] Step S125: Based on the reference weight, subsequent operating condition information corresponding to multiple historical change segments is integrated to generate preliminary results of potential change direction and potential change period of the operating condition in the kiln.

[0056] Based on the reference weight of each candidate historical change segment, the corresponding subsequent operating condition information is weighted and fused. For the potential change direction of the kiln operating conditions, the weight of the change direction predicted by each candidate historical segment in the fusion is calculated, and the change direction with the highest weight is selected as the primary candidate for potential change direction. If there are multiple change directions with similar weights, the overall stability of the current real-time operating conditions is considered, and the change direction that aligns with the overall trend of the real-time operating conditions is prioritized.

[0057] For the variation range of key operating parameters, a weighted integration is performed based on the reference weights of each candidate historical segment. For example, if the variation range of a parameter in two candidate segments is interval A and interval B, respectively, and the corresponding reference weights are weight 1 and weight 2, respectively, the fused variation range is a combined range where interval A accounts for weight 1 and interval B accounts for weight 2. For potential variation periods, the variation periods of each candidate segment are similarly weighted based on the reference weights to obtain a combined starting period, acceleration period, deceleration period, and stable period.

[0058] Through the above fusion process, preliminary results of the potential change direction and potential change period of the kiln working conditions are generated, which integrates the empirical information of multiple similar historical fragments.

[0059] Step S126: combining the latest change dynamics of the real-time working condition data of the volatilization kiln, revising the preliminary results of the potential change direction and potential change period to obtain the final working condition trend prediction result.

[0060] Obtain the latest dynamics of the volatilization kiln's real-time operating data, that is, the real-time data collected after the initial results are generated. Analyze the changing characteristics of this latest data to determine whether it continues the previous trend or whether there are new signs of change. For example, if the latest temperature data rises faster than the initial prediction, it indicates a more pronounced trend of increasing reaction intensity; if the latest gas composition data shows unusual fluctuations, it may indicate a change in the direction of operating conditions.

[0061] The direction of potential change is revised based on the latest dynamics. If the latest data supports the initial forecast, the direction remains unchanged. If the latest data indicates a deviation from the initial forecast, the description of the direction is adjusted or the uncertainty of the direction is added. Revisions to the period of potential change include adjusting the timeframe of each period based on the rate of change in the latest data. If the rate of change is accelerating, the time to reach stability is shortened; if the rate of change is slowing, the corresponding timeframe is extended.

[0062] At the same time, corrections are made based on interfering factors in real-time data. For example, the impact of abnormal data caused by temporary sensor failures on the prediction results is eliminated, or the delayed impact of external environmental changes (such as fluctuations in feed composition) on operating conditions is considered. Through this correction process, the final operating condition trend forecast is obtained. This forecast includes the main potential change directions of the kiln operating conditions, the range of changes in related key parameters, the specific time periods for each change stage, and an indicator of the reliability of the forecast results.

[0063] Step S130: constructing a fuel supply plan library according to the operating condition trend prediction result, wherein the fuel supply plan library contains fuel supply adjustment strategies corresponding to different prediction trends, and each fuel supply adjustment strategy is associated with a corresponding operating condition adaptation condition.

[0064] Based on the operating condition trend prediction results, a plan library containing various fuel supply adjustment strategies is established through steps such as scenario classification, strategy construction, condition association, and library structure design.

[0065] Step S131: classifying the potential change directions in the operating condition trend prediction result to divide different operating condition change scenarios, where each operating condition change scenario corresponds to a class of potential change directions.

[0066] A detailed analysis of potential changes in the operating condition trend prediction results is conducted, and different operating condition change scenarios are categorized based on their nature, intensity, and impact. For example, scenarios such as "increased reaction intensity with a faster rate" and "increased reaction intensity with a slower rate" are classified as "increased reaction intensity with material retention" and "increased reaction intensity with abnormal gas composition" are classified as "weakened reaction intensity"; "basically stable reaction state with local temperature fluctuations" is classified as "stable fluctuation"; and "abnormal reaction fluctuations with parameters exceeding the range" is classified as "abnormal risk" scenarios. Each operating condition change scenario is clearly characterized, including the core change direction, the change pattern of key parameters, and the potential impact on kiln reactions and material processing. For example, the core characteristics of the reaction enhancement scenario are an overall upward trend in kiln temperature, increased concentration of reaction-generated gases, and accelerated material processing rate; the core characteristics of the reaction weakening scenario are a decrease in temperature, an increase in the proportion of unreacted materials, and reduced processing efficiency; the core characteristics of the stable fluctuation scenario are small fluctuations of key parameters within the normal range without a clear overall trend; and the core characteristics of the abnormal risk scenario are key parameters exceeding normal thresholds, with a significant increase in the frequency and amplitude of fluctuations. Through the above classification method, complex working condition change trends are converted into easy-to-manage scenario categories.

[0067] Step S132: For each operating condition change scenario, construct an adaptive fuel supply adjustment strategy, wherein the fuel supply adjustment strategy includes an adjustment direction of the fuel supply amount and an adjustment method of the fuel supply rhythm.

[0068] Step S1321: Analyze the changes in the fuel demand for the reaction in the kiln under each operating condition change scenario. If the operating condition change scenario shows an increase in the reaction intensity, the adjustment direction of the fuel supply is determined to be an increase; if the operating condition change scenario shows a decrease in the reaction intensity, the adjustment direction of the fuel supply is determined to be a decrease.

[0069] Analyze the core features of each operating condition change scenario, focusing on the changing trends of key parameters related to reaction intensity. When the reaction intensity enhancement features such as the continuous rise in the kiln temperature, the increase in the concentration of reaction characteristic gases, and the improvement in material conversion rate are obvious in the scenario, it is judged that the reaction in the kiln requires more heat support, so the adjustment direction of the fuel supply is determined to be increased. When the reaction intensity weakens in the scenario, such as the temperature drops, the proportion of unreacted components increases, and the material processing efficiency decreases, it is judged that the heat required for the reaction decreases, and the adjustment direction of the fuel supply is determined to be reduced. For stable fluctuation scenarios, the fuel supply is determined to maintain the current level or make small adjustments based on the fluctuation amplitude and frequency.

[0070] Step S1322: Determine the adjustment method of the fuel supply rhythm based on the change rate of the operating condition change scenario. If the change rate of the operating condition change scenario satisfies the first rate range, the fuel supply rhythm is adjusted to a fast step-by-step manner; if the change rate of the operating condition change scenario satisfies the second rate range, the fuel supply rhythm is adjusted to a slow progressive manner, and the minimum value of the first rate range is greater than the maximum value of the second rate range.

[0071] By calculating the rate of change of key parameters in operating condition change scenarios (such as the temperature change per unit time and the gas composition change rate), the rate range to which they belong is determined. The first rate range corresponds to scenarios with rapid parameter changes. In this case, a rapid step-by-step adjustment rhythm is required. That is, multiple adjustments are made according to the preset step amplitude and interval time. For example, the fuel supply is increased once every short interval, and each adjustment amplitude is large. The second rate range corresponds to scenarios with slow parameter changes. A slow and gradual adjustment rhythm is adopted. That is, the fuel supply is adjusted in small amounts continuously with longer adjustment intervals to ensure smooth changes in the fuel supply. The threshold for dividing the rate range is determined based on the correlation analysis between the parameter change rate and the adjustment effect in historical data.

[0072] Step S1323: In combination with the duration of the operating condition change scenario, the duration period of the fuel supply adjustment is set so that the duration period of the fuel supply adjustment covers the entire period of the operating condition change.

[0073] Based on the potential change period of the scenario in the operating condition trend prediction results, determine the complete duration of the operating condition change, including the entire process from the initial parameter change, acceleration change, deceleration change, and stabilization. Set the duration of the fuel supply adjustment to be no shorter than this complete duration to ensure that the adjustment process can cover the entire stage of the operating condition change. For example, if the complete period of the predicted operating condition change is a certain length of time, set the duration of the fuel supply adjustment to the same or slightly longer length, and reasonably allocate the time proportion of each adjustment stage within the period.

[0074] Step S1324: marking the key adaptation conditions corresponding to each fuel supply adjustment strategy, wherein the key adaptation conditions include the initial parameters of the operating condition change and the change rate threshold of the operating condition change.

[0075] Key adaptation conditions are marked for each constructed fuel supply adjustment strategy as a basis for judging the applicability of the strategy. The initial parameters of the operating condition change include the temperature range in the kiln at the beginning of the scenario, the initial ratio of gas components, the material feed amount and other basic parameters, which clearly define the initial operating condition range for which the strategy is applicable. The rate of change thresholds for operating condition changes include the minimum rate threshold and the maximum rate threshold. The strategy is only applicable when the rate of change of the operating condition is within the threshold range. For example, the adaptation condition of the adjustment strategy for a reaction enhancement scenario is marked as "the initial temperature is within a certain range, and the temperature change rate is within the first rate interval."

[0076] Step S1325: Perform simulation verification on the constructed fuel supply adjustment strategy, apply the fuel supply adjustment strategy to similar historical operating condition scenarios, and observe the simulated operating condition change results after application.

[0077] A historical segment of the operating condition data that resembles the current operating condition change scenario is selected, and the constructed fuel supply adjustment strategy is applied to the corresponding virtual simulation environment. During the simulation, fuel supply parameters are set according to the strategy's adjustment direction, rhythm, and cycle. Changes in parameters such as the simulated kiln temperature, gas composition, and material handling progress are calculated in real time to generate simulated operating condition change results. The simulation verification utilizes a dynamic simulation model based on the heat transfer, mass transfer, and reaction kinetics of the volatilization kiln, which can reflect the impact of fuel supply changes on the kiln's operating conditions.

[0078] Step S1326: If the operating condition change in the simulated operating condition change result meets the expected stability target, the fuel supply adjustment strategy is determined to be effective; if the operating condition change in the simulated operating condition change result does not meet the expected stability target, the adjustment direction of the fuel supply amount or the adjustment method of the fuel supply rhythm is adjusted, and the simulation verification is performed again, and the verified effective fuel supply adjustment strategy is associated with the corresponding operating condition change scenario to form a basic plan unit.

[0079] The results of the simulated operating condition changes are compared with the expected stability targets, which include the temperature in the kiln being stable within the target range, the reaction gas composition meeting the standards, and the material handling efficiency meeting the requirements. If all parameters in the simulation results can reach the expected targets and the fluctuation range is within the allowable range, the fuel supply adjustment strategy is determined to be effective. If the simulation results do not meet expectations, for example, the temperature exceeds the target range or the stabilization time is too long, the reasons are analyzed and the strategy is adjusted, such as changing the adjustment range of the fuel supply, the step interval or the progressive rate of the adjustment rhythm, etc., and the simulation verification is repeated after adjustment. The adjustment strategy that is finally verified to be effective is associated with the corresponding operating condition change scenario and adaptation conditions and stored to form a basic plan unit.

[0080] Step S133: marking corresponding operating condition adaptation conditions for each fuel supply adjustment strategy, wherein the operating condition adaptation conditions include the operating condition change amplitude and operating condition change rate range applicable to the fuel supply adjustment strategy.

[0081] To ensure that fuel supply adjustment strategies accurately adapt to changing operating conditions, each strategy is clearly labeled with its operating condition adaptation conditions. For the "Increased Reaction Intensity and Faster Rate" adjustment strategy in a reaction enhancement scenario, its operating condition adaptation conditions are: the temperature rise in the kiln's high-temperature zone exceeds a set amplitude threshold per unit time, the growth rate of the reaction characteristic gas concentration exceeds a rate threshold, and the material processing rate remains within a high range.

[0082] For the adjustment strategy of "enhanced reaction intensity and slower rate", the operating adaptation conditions are: the temperature rise in the kiln is lower than the above-mentioned amplitude threshold, the growth rate of the reaction characteristic gas concentration is lower than the rate threshold, and the material processing rate changes relatively smoothly.

[0083] In the "weakened reaction intensity with material retention" scenario, the adjustment strategy for "weakened reaction intensity with material retention" requires the following operating conditions: the average temperature drop in the kiln exceeds a set threshold, the material retention time in the kiln exceeds a specified period, and the proportion of unreacted material increases to a certain percentage. The adjustment strategy for "weakened reaction intensity with abnormal gas composition" requires the following operating conditions: the temperature drop in the kiln reaches a set value, the concentration of unreacted components in the flue gas exceeds the normal range, and the frequency of gas composition fluctuations increases.

[0084] The fuel supply adjustment strategy for stable fluctuation scenarios has the following operating conditions: the main temperature parameters in the kiln fluctuate within the normal operating range, the fluctuation amplitude is less than the fluctuation threshold, and the fluctuation frequency is at a low level, and the material processing progress is stable within the planned range.

[0085] In abnormal risk scenarios, the "minor parameter out-of-range" adjustment strategy is based on the following operating conditions: the proportion of key parameters exceeding the normal range is lower than the out-of-range threshold, the parameter fluctuation is small, and there is no trend of continued deterioration. The "serious parameter out-of-range" adjustment strategy is based on the following operating conditions: the proportion of key parameters exceeding the normal range is higher than the out-of-range threshold, the parameter fluctuation is large, there is a trend of continued deterioration, and abnormal equipment operation signals are present.

[0086] Step S134: The operating condition change scenario, the corresponding fuel supply adjustment strategy and the corresponding operating condition adaptation condition are associated and stored to form a basic plan unit.

[0087] Bind each operating condition change scenario with its corresponding fuel supply adjustment strategy and operating condition adaptation conditions to construct a basic plan unit. Each basic plan unit contains the scenario identification, scenario feature description, fuel supply adjustment direction description, fuel supply rhythm adjustment method details, operating condition adaptation condition parameter range, etc. For example, the "rapid enhancement scenario" basic plan unit in the reaction enhancement scenario has the scenario identification "FY-ZQ-KS", the scenario feature description is "the temperature in the kiln rises rapidly and the reaction gas concentration surges", the fuel supply adjustment direction is "step-by-step decrease", the fuel supply rhythm adjustment method is "reducing a certain proportion of fuel supply at certain intervals", and the operating condition adaptation condition parameter range includes the temperature rise range, the gas concentration growth rate range, etc.

[0088] This information is stored in a structured data format, with each basic plan unit having a unique identifier to facilitate subsequent retrieval and access. Furthermore, a data association index is established during the storage process, enabling cross-linked queries between scenarios, strategies, and conditions. For example, scenario identifiers can be used to quickly identify corresponding adjustment strategies and adaptation conditions, and adjustment strategies can be used to reversely query the scope of applicable scenarios.

[0089] Step S135: Classify and organize the basic plan units, and divide the plan modules according to the types of working condition change scenarios, each plan module contains multiple basic plan units for the same type of scenarios.

[0090] The basic plan units are classified and integrated according to the type of working condition change scenario to form different plan modules. All basic plan units for reaction enhancement scenarios are classified as the "reaction enhancement plan module", which contains basic plan units for different sub-scenarios such as "rapid enhancement" and "slow enhancement". The basic plan units for reaction weakening scenarios are classified as the "reaction weakening plan module", which contains plan units for sub-scenarios such as "material retention type weakening" and "gas abnormality type weakening". The basic plan units for stable fluctuation scenarios form the "stable fluctuation plan module", and the basic plan units for abnormal risk scenarios form the "abnormal risk plan module".

[0091] Each emergency plan module has information such as the module name, module description, and a list of included basic emergency plan units. The module description details the operating conditions applicable to the module and the overall adjustment principles. For example, "The reaction enhancement emergency plan module is suitable for various scenarios where the reaction intensity in the kiln is showing an upward trend. The overall adjustment principle is to suppress excessive reaction growth and maintain stable reaction progress." This classification and organization makes the structure of the fuel supply emergency plan library clearer, making it easier to quickly locate the corresponding emergency plan module and basic emergency plan units based on actual operating conditions.

[0092] Step S136: Integrate all the plan modules and establish an index mechanism. The index mechanism is used to quickly locate the corresponding plan module and basic plan unit according to the working condition trend prediction result to form a fuel supply plan library.

[0093] Integrate each plan module into a unified database to build a complete fuel supply plan library. In order to achieve rapid retrieval and call of plans, a multi-dimensional indexing mechanism is established. The indexing mechanism includes scenario feature index, parameter range index, adjustment strategy type index, etc. The scenario feature index is established based on the core feature words of the operating condition change scenario. For example, the relevant basic plan units in the reaction enhancement plan module can be retrieved through keywords such as "reaction enhancement" and "temperature rise". The parameter range index is established based on the parameter range in the operating condition adaptation condition. When the change amplitude and rate of the real-time operating condition parameters are input, the applicable basic plan unit can be matched through this index. The adjustment strategy type index is established according to the adjustment direction of the fuel supply amount and the adjustment method of the supply rhythm, which facilitates the rapid search for the corresponding plan according to the required adjustment strategy type.

[0094] The indexing mechanism utilizes a tree-like structure, with the first-level index representing the plan module type, the second-level index representing the scenario subtype, and the third-level index representing the basic plan unit. Furthermore, an index update mechanism is established to automatically update the index when a basic plan unit is added or modified in the plan library, ensuring consistency between the index and the plan content. By integrating the plan modules and establishing an indexing mechanism, a comprehensive fuel supply plan library has been established, enabling rapid and accurate matching of appropriate fuel supply adjustment strategies based on operating condition trend forecasts.

[0095] Step S140: real-time monitoring of the deviation between the current operating condition data of the volatilization kiln and the operating condition trend prediction result, matching a target supply adjustment strategy from the fuel supply plan library according to the deviation, and dynamically adapting the target supply adjustment strategy to obtain a fuel supply execution plan.

[0096] By real-time monitoring of the deviation between the current operating conditions and the predicted results, the fuel supply strategy is dynamically adjusted to ensure that the fuel supply plan is adapted to the actual operating conditions.

[0097] Step S141: collecting the current working condition data of the volatilization kiln in real time according to a preset working condition deviation monitoring period, wherein the current working condition data of the volatilization kiln includes the real-time data of the reaction state in the kiln and the real-time data of the material processing progress.

[0098] A fixed operating condition deviation monitoring cycle is set, and the cycle length is determined by the rate of operating condition change and the system response time. For example, for scenarios with faster reactions, the monitoring cycle is shorter, and for stable scenarios, the monitoring cycle is longer. During each monitoring cycle, the current operating condition data of the volatilization kiln is collected in real time through data acquisition equipment. Real-time data on the reaction status in the kiln include the latest measurement values ​​of temperature sensors in each area, real-time gas composition data detected by the gas analyzer, the current pressure value of the pressure sensor, etc. Real-time data on the material processing progress include the current feed amount of the feed belt, the real-time material level height of the material level meter in the kiln, the latest particle size analysis results of the particle size detector at the discharge port, the current value of the material conveying speed, etc. The collected data is transmitted to the data processing module in real time.

[0099] Step S142: comparing the current operating condition data of the volatilization kiln with the corresponding predicted data in the operating condition trend prediction result, and calculating the degree of deviation between the current operating condition data of the volatilization kiln and the corresponding predicted data, wherein the degree of deviation includes the deviation of the data change amplitude and the deviation of the data change rate.

[0100] Compare the currently collected real-time data on the kiln's reaction status with the predicted data at the corresponding time point in the operating trend forecast results. For temperature parameters, calculate the difference between the current measured value and the predicted temperature value for each temperature sensor to obtain the deviation in the temperature change amplitude. Calculate the difference between the actual rate of change of the current temperature per unit time and the predicted rate of change to obtain the deviation in the temperature change rate. For gas composition parameters, calculate the ratio of the difference between the current volume fraction and the predicted volume fraction of each gas component to the predicted volume fraction as the deviation in the gas composition change amplitude. Calculate the difference between the actual and predicted gas composition change rates as the deviation in the change rate.

[0101] For material processing progress data, the deviation degree of feed quantity is calculated by the difference between the current feed quantity and the predicted feed quantity and the difference ratio; the deviation degree of material level height is calculated by the height difference between the current material level and the predicted material level and the difference in change rate; the deviation degree of particle size distribution is obtained by the difference between the actual value and the predicted value of the proportion of materials in different particle size ranges; the deviation degree of conveying speed is calculated by the difference between the actual speed and the predicted speed and the difference in speed change rate.

[0102] The change amplitude deviation and change rate deviation of all parameters are combined, and the overall deviation degree is obtained by weighted summation. The weight is set according to the importance of each parameter on the working condition. For example, the weight of the temperature parameter is higher than that of the particle size parameter.

[0103] Step S143: determining a deviation level according to the deviation degree; if the deviation degree is within a preset allowable range, the deviation level is low deviation; if the deviation degree exceeds the preset allowable range, the deviation level is high deviation.

[0104] A preset allowable range for the degree of deviation is set, which is determined based on the range of normal fluctuations in historical operating condition data, while also taking into account the accuracy range required by the process. When the combined value of the overall degree of deviation is less than or equal to the upper limit of the allowable range, the deviation level is determined to be low, indicating that the current operating conditions are basically consistent with the predicted results, and the operating condition change trend is within the expected range. When the combined value of the overall degree of deviation is greater than the upper limit of the allowable range, the deviation level is determined to be high, indicating that the current operating conditions are significantly different from the predicted results, the operating condition change trend deviates from expectations, and more flexible matching and adjustment of the fuel supply adjustment strategy is required.

[0105] Step S144: When the deviation level is low deviation, a basic plan unit directly corresponding to the operating condition trend prediction result is matched from the fuel supply plan library, and the fuel supply adjustment strategy in the basic plan unit is extracted as the initial target strategy.

[0106] In low-deviation scenarios, the current operating conditions generally match the predicted results, so a search is performed directly within the fuel supply plan library based on the predicted operating condition trend. Using the plan library's indexing mechanism, the plan module directly corresponding to the operating condition change scenario in the predicted results is searched for. From this module, basic plan units that match the predicted operating condition change range and rate of change are then matched. For example, if the predicted result is a "slow enhancement" scenario within the reaction enhancement category, and the deviation level is low, the basic plan unit corresponding to "slow enhancement" is extracted from the reaction enhancement plan module.

[0107] The fuel supply adjustment direction and fuel supply rhythm adjustment method in the basic plan unit are extracted as the initial target strategy. The initial target strategy retains the core adjustment logic of the original plan, such as the fuel supply reduction ratio, adjustment interval, and other parameters.

[0108] Step S145: When the deviation level is high, a basic plan unit with similar characteristics to the current operating condition data change of the volatilization kiln is selected from the fuel supply plan library, and the fuel supply adjustment strategies of multiple similar basic plan units are weightedly integrated to obtain an initial target strategy.

[0109] In the case of high deviation, it is necessary to consider the actual change characteristics of the current working conditions more comprehensively and select multiple similar basic plan units from the plan library for fusion.

[0110] Step S1451: extracting core characteristic parameters from the change characteristics of the current working condition data of the volatilization kiln, wherein the core characteristic parameters include the reaction state change rate in the kiln and the material processing progress deviation value.

[0111] The core characteristic parameters that have the greatest impact on fuel supply adjustments are screened from the changing characteristics of the current operating data. The reaction state change rate within the kiln includes the actual temperature rise or fall rate in the high-temperature zone, the rate of change in the concentration of the reaction characteristic gas, and the frequency of pressure fluctuations. Material processing progress deviations include the feed rate deviation ratio, material level deviation, conveying speed deviation rate, and statistical values ​​of particle size distribution deviation.

[0112] Step S1452: searching the fuel supply plan library according to the core characteristic parameters, screening out basic plan units whose operating condition adaptation conditions include the core characteristic parameter range, and using the screened out basic plan units as plan units to be fused.

[0113] Using the parameter range index of the plan library, input the current value of the core characteristic parameter and retrieve the basic plan units in the plan library whose parameter range of the working condition adaptation condition includes these core characteristic parameter values. For example, if the temperature rise rate in the high temperature zone of the current core characteristic parameter is a certain value, retrieve all basic plan units whose temperature rise rate range in the adaptation condition includes this value. Perform a preliminary screening of the retrieved basic plan units, removing obviously irrelevant units, such as plan units whose scenario types change in the opposite direction to the current working condition. Remaining units are retained as plan units to be integrated.

[0114] Step S1453: Calculate the matching degree between the working condition adaptation condition of each plan unit to be integrated and the core characteristic parameters. The higher the matching degree, the greater the weight coefficient corresponding to the plan unit to be integrated.

[0115] For each plan unit to be integrated, calculate the degree of matching between its working condition adaptation conditions and the current core characteristic parameters. For numerical parameters, calculate the degree of proximity between the core characteristic parameter value and the center value of the adaptation condition parameter range. The closer to the center value, the higher the matching degree; for range parameters, calculate the proportion of core characteristic parameter values ​​that fall within the adaptation condition parameter range. The higher the proportion, the higher the matching degree. The matching degree of each core characteristic parameter is weighted and summed according to the preset weights to obtain the overall matching degree of each plan unit to be integrated. The weight coefficient is determined based on the overall matching degree. The plan unit to be integrated with the highest matching degree has the largest weight coefficient, and the weight coefficients of the remaining units decrease in order according to the relative size of the matching degree, and the sum of the weight coefficients of all plan units to be integrated is the overall proportion.

[0116] Step S1454: Extract the fuel supply adjustment direction vector and fuel supply adjustment amplitude in each plan unit to be merged, perform weighted calculation according to the weight coefficient corresponding to each plan unit to be merged, and obtain the fused fuel supply adjustment direction vector and fused fuel supply adjustment amplitude.

[0117] The fuel supply adjustment direction vectors in the pre-merged plan units include direction identifiers such as increase, decrease, and maintain, as well as the adjustment priority for each direction. The direction vectors are converted to numerical values, with increase being a positive value, decrease being a negative value, and maintain being zero. The direction vector values ​​of each pre-merged plan unit are weighted and summed according to the weight coefficients to obtain the fused direction vector value. The positive or negative sign of this value determines the fused fuel supply adjustment direction.

[0118] For the fuel supply adjustment range, we extract adjustment range parameters, such as the adjustment ratio or adjustment amount, from each plan unit to be merged. These adjustment range parameters are weighted according to the weight coefficient to obtain the merged fuel supply adjustment range. This merged fuel supply adjustment range integrates the adjustment experience of multiple similar plan units.

[0119] Step S1455: Extract the fuel supply rhythm adjustment method in each plan unit to be merged, count the frequency of occurrence of each fuel supply rhythm adjustment method in the plan unit to be merged, and select the fuel supply rhythm adjustment method with the highest frequency of occurrence and the largest matching weight value as the fused fuel supply rhythm adjustment method.

[0120] The fuel supply rhythm adjustment methods in the plan units to be integrated include step-by-step, gradual, fine-tuning, and pulse types. Count the number of times each adjustment method appears in the plan units to be integrated to obtain the frequency of occurrence. At the same time, calculate the weighted sum of the matching degrees of all the plan units to be integrated corresponding to each adjustment method, that is, the sum of the matching degrees of each unit under this method multiplied by its weight coefficient. Select the adjustment method with the highest frequency of occurrence. If there are multiple adjustment methods with the same frequency of occurrence, select the adjustment method with the largest weighted matching value as the integrated fuel supply rhythm adjustment method.

[0121] Step S1456: Integrate the fused fuel supply adjustment direction vector, the fused fuel supply adjustment amplitude, and the fused fuel supply rhythm adjustment method to form a preliminary fusion strategy.

[0122] Integrate the merged fuel supply adjustment direction vector, adjustment amplitude, and fuel supply rhythm adjustment method to clearly define the direction of fuel supply adjustment, the specific adjustment amplitude, and the adjustment rhythm. For example, the merged strategy might be "Fuel supply adjustment direction is to decrease, the adjustment amplitude is a certain percentage, and the supply rhythm is to decrease in a step-by-step manner." This initial merged strategy must ensure that all components are logically consistent and that the adjustment direction, amplitude, and rhythm match.

[0123] Step S1457: Detect the coordination between the fuel supply amount and the fuel supply rhythm in the preliminary fusion strategy. If there is a contradiction between the fuel supply amount and the fuel supply rhythm, the fuel supply rhythm adjustment method of the plan unit to be fused with the highest matching degree is preferentially corrected to obtain the initial target strategy.

[0124] The coordination between the fuel supply amount and the fuel supply rhythm in the preliminary fusion strategy was tested, focusing on the consistency of the adjustment direction and the adaptability of the adjustment amplitude and rhythm. The consistency test of the adjustment direction determines whether the adjustment direction of the fuel supply amount (increase or decrease) and the adjustment method of the fuel supply rhythm (rapid step-by-step, slow gradual, etc.) conform to the conventional control logic. For example, when the fuel supply amount is adjusted in the direction of a large increase, if the supply rhythm is slow and gradual, it may cause the reaction intensity to increase lagging, in which case it is judged that there is a contradiction in direction. The adaptability test of the adjustment amplitude and rhythm analyzes whether the size of the adjustment amplitude matches the speed of the rhythm. For example, if the adjustment amplitude is large, using a fast step-by-step rhythm may cause drastic fluctuations in the kiln working conditions, while if the adjustment amplitude is small, using a slow and gradual rhythm may result in too low adjustment efficiency. In these cases, it is judged that there is a contradiction in the adaptability of the amplitude and rhythm.

[0125] If the detection finds that there is a coordination contradiction, the plan unit with the highest matching degree will be selected from the plan units to be merged, and the rhythm parameters in the preliminary fusion strategy will be corrected based on the fuel supply rhythm adjustment method of the unit. For example, if the supply volume in the preliminary fusion strategy needs to be greatly increased but the rhythm is slow and gradual, and the plan unit with the highest matching degree adopts a fast step-by-step rhythm, the rhythm of the preliminary fusion strategy will be corrected to a fast step-by-step rhythm, and the step interval and the amplitude ratio of each adjustment will be adjusted accordingly to ensure that the corrected rhythm can adapt to the supply volume adjustment needs. If the detection does not find a coordination contradiction, the preliminary fusion strategy will be directly determined as the initial target strategy. The initial target strategy combines the advantages of multiple similar plan units and can better adapt to the working conditions under high deviation conditions.

[0126] Step S150: Send the fuel supply execution plan to the fuel supply system for execution, and collect the kiln operating condition feedback data after execution, and use the kiln operating condition feedback data to update the fuel supply adjustment strategy and operating condition adaptation conditions in the fuel supply plan library.

[0127] After determining the fuel supply execution plan, it is converted into executable control instructions and sent to the fuel supply system. At the same time, a closed-loop feedback mechanism is established to continuously optimize the strategies and conditions in the plan library through operating condition feedback data.

[0128] Step S151: Convert the fuel supply execution plan into an executable supply control instruction and send it to the fuel supply system, so that after the fuel supply system executes the supply control instruction, it collects feedback data of the working conditions in the kiln according to the set feedback data collection cycle, and the feedback data of the working conditions in the kiln includes the reaction state change data in the kiln after execution and the material processing progress change data after execution.

[0129] The fuel supply execution plan's parameters, such as the fuel supply adjustment direction, adjustment range, and supply rhythm, are converted into supply control instructions recognizable by the fuel supply system. The instruction format must comply with the system's communication protocol specifications and include information such as the instruction type identifier, execution start time, adjustment parameter sequence, and execution cycle. For example, for a step-by-step adjustment strategy, the control instruction must clearly specify the time node for each step adjustment, the adjusted fuel flow value, or the adjustment range ratio.

[0130] Supply control commands are sent via an industrial bus to the fuel supply system's controller. Upon receiving the commands, the controller drives actuators such as the fuel pump and regulating valve according to the command parameters. A feedback data collection cycle is also established, determined by the response speed to changes in operating conditions. Shorter collection cycles are used for sensitive parameters (such as kiln temperature and gas composition), while longer cycles are used for slowly changing parameters (such as material conversion rate). Sensors and metering equipment deployed within the kiln periodically collect data on changes in the kiln's reaction state after execution. This includes real-time temperature monitoring values ​​for each zone, composition curves of the reaction gases, and fluctuations in kiln pressure. Data on changes in material processing progress after execution is also collected, including changes in material movement speed within the kiln, changes in the particle size distribution of the discharged material, and the equilibrium between feed and discharge. This feedback data is then transmitted in real time to the data processing center.

[0131] Step S152: comparing the expected operating condition change corresponding to the fuel supply execution plan with the actual operating condition change in the feedback data of the kiln operating condition, and calculating the degree of fit between the expected operating condition change and the actual operating condition change.

[0132] Expected operating condition change indicators are extracted from the fuel supply execution plan. These indicators are set based on historical data and theoretical analysis, including the expected temperature change range, gas composition optimization targets, and the extent of material handling efficiency improvement. These expected operating condition change indicators are compared item by item with the actual operating condition change data from the kiln operating condition feedback data. The comparison covers the consistency of parameter change trends, the degree of similarity in change ranges, and the time difference between reaching a stable state.

[0133] The degree of fit between expected and actual operating condition changes is calculated using a multi-dimensional weighted scoring method. A corresponding weight is assigned to each comparison parameter, determined by its importance to the kiln's operating conditions. For example, temperature parameters are weighted higher than pressure parameters. For each parameter, the deviation ratio between the actual change value and the expected change value is calculated. The fit score for that parameter is determined based on the deviation ratio, with the smaller the deviation ratio, the higher the score. The fit scores of all parameters are weighted and summed according to their weights to obtain an overall fit value, which reflects the degree of fit between the actual results of the fuel supply implementation plan and the expected goals.

[0134] Step S153: If the degree of fit is higher than the preset fit threshold, it is determined that the fuel supply adjustment strategy corresponding to the fuel supply execution plan is valid, and the matching relationship between the fuel supply adjustment strategy and the current operating conditions is recorded, and the matching relationship is added to the basic plan unit corresponding to the fuel supply plan library.

[0135] When the calculated degree of fit exceeds the preset fit threshold, it indicates that the fuel supply execution plan can effectively guide the kiln operating conditions in the expected direction and the corresponding fuel supply adjustment strategy is suitable for the current operating conditions. At this time, the specific content of the fuel supply adjustment strategy is recorded in detail, including the supply adjustment direction, adjustment range, and rhythm parameters. At the same time, key parameters of the current operating conditions, such as initial reaction temperature, material characteristics, and feed rate, are recorded to clarify the matching relationship between the two.

[0136] The recorded matching relationships are added to the corresponding basic plan units in the fuel supply plan library, enriching the applicable cases for the basic plan units. This supplementary content includes the timestamp of the successful match, a description of the operating conditions, and data on the strategy execution effect (such as the degree of fit). This allows the basic plan units to accumulate more effective application examples and improve the accuracy of subsequent matches under similar operating conditions. Simultaneously, the supplemented basic plan units are indexed and updated to ensure that the association between the basic plan units and the corresponding operating conditions can be quickly identified when searching the plan library.

[0137] Step S154: If the degree of fit is lower than a preset fit threshold, the reasons for the deviation between the expected operating condition change and the actual operating condition change are analyzed, where the reasons for the deviation include a fuel supply amount setting deviation and a fuel supply rhythm setting deviation.

[0138] For example, when the degree of fit is lower than the preset fit threshold, it is necessary to conduct in-depth analysis of the causes of the deviation from the two dimensions of fuel supply volume and fuel supply rhythm, and locate the root cause of the problem by comparing the actual parameters with the expected parameters.

[0139] Step S1541: If the operating parameter with deviation is the reaction intensity in the kiln, the expected fuel supply quantity parameter corresponding to the fuel supply execution plan is extracted, and the actual fuel supply quantity parameter during the execution of the fuel supply system is collected.

[0140] When the actual value of the kiln reaction intensity operating condition parameter deviates from the expected value, the expected fuel supply parameters set in the fuel supply execution plan for that reaction stage must be clarified, including the fuel injection volume per unit time and the fuel distribution ratio in different combustion stages. At the same time, the actual fuel supply data during the execution process is collected through the flow meter, pressure sensor and other equipment in the fuel supply system, including the real-time fuel flow curve, the cumulative supply volume, and the actual fuel distribution in each combustion stage.

[0141] Step S1542: Compare the actual fuel supply parameter with the expected fuel supply parameter. If the actual fuel supply parameter is smaller than the expected fuel supply parameter, and the actual kiln reaction intensity value is smaller than the expected kiln reaction intensity value, it is determined that the cause of the deviation is the fuel supply setting deviation.

[0142] Compare the collected actual fuel supply parameters against the expected fuel supply parameters item by item, and calculate the difference and relative deviation ratio between the two. If the actual fuel supply is consistently lower than the expected fuel supply, and the corresponding actual reaction intensity in the kiln (such as temperature, reaction gas concentration, etc.) is also lower than the expected reaction intensity, it indicates that the fuel supply is not meeting the reaction requirements. Therefore, the cause of the deviation is determined to be a deviation in the fuel supply setting.

[0143] Step S1543: If the actual fuel supply parameter is greater than the expected fuel supply parameter, and the actual kiln reaction intensity value is greater than the expected kiln reaction intensity value, it is determined that the cause of the deviation is the fuel supply setting deviation.

[0144] If the actual fuel supply continues to be higher than the expected fuel supply, and the actual reaction intensity in the kiln exceeds the expected reaction intensity, resulting in excessive temperature and excessive reaction, it indicates that the fuel supply exceeds the reasonable demand range. At this time, the cause of the deviation is also determined to be the fuel supply setting deviation.

[0145] Step S1544: If the operating condition parameter with the deviation is the operating condition stabilization time, then extract the corresponding expected fuel supply rhythm parameter in the fuel supply execution plan, and the expected fuel supply rhythm parameter includes the time interval of fuel supply adjustment and the frequency of change of the adjustment amplitude.

[0146] When the operating condition stabilization time (i.e., the time from the start of fuel supply adjustment to the kiln reaching a stable state) deviates, the expected fuel supply rhythm parameters set in the fuel supply execution plan are extracted. These parameters include the interval between fuel supply adjustments (e.g., how often the supply adjustment is made), the frequency of each adjustment (e.g., whether the adjustment amplitude changes at a constant speed or in steps), and the sequence of adjustments at different stages.

[0147] Step S1545: Collect the actual fuel supply rhythm parameters during the execution of the fuel supply system, compare the time interval in the actual fuel supply rhythm parameters with the time interval in the expected fuel supply rhythm parameters, and compare the actual adjustment amplitude change frequency with the expected adjustment amplitude change frequency.

[0148] The fuel supply system's control logs and sensor records are used to collect actual fuel supply rhythm parameters, including the actual adjustment interval, the amplitude change between each adjustment, and the fluctuation of the adjustment frequency. The difference between the actual and expected intervals is calculated to analyze the impact of time deviation on operating stability. The actual adjustment amplitude change frequency is compared with the expected frequency to determine the degree of matching of the adjustment rhythm.

[0149] Step S1546: If the actual time interval is greater than the expected time interval, the actual adjustment amplitude change frequency is less than the expected adjustment amplitude change frequency, and the actual operating condition stabilization time value is greater than the expected operating condition stabilization time value, it is determined that the cause of the deviation is the fuel supply rhythm setting deviation.

[0150] When the actual adjustment time interval is longer than expected and the frequency of adjustment amplitude change is lower than expected, resulting in the kiln working conditions taking longer to reach a stable state, and the actual stabilization time exceeds the expected stabilization time, it means that the fuel supply rhythm is too slow and fails to adapt to the changing working conditions in time. Therefore, it is determined that the cause of the deviation is the deviation in the fuel supply rhythm setting.

[0151] Step S1547: If the actual time interval is smaller than the expected time interval, the actual adjustment amplitude change frequency is greater than the expected adjustment amplitude change frequency, and the actual working condition stabilization time value is smaller than the expected working condition stabilization time value, and at the same time, the feedback data of the working condition in the kiln records working condition fluctuations, then it is determined that the cause of the deviation is a deviation in the fuel supply rhythm setting.

[0152] If the actual adjustment time interval is too short and the adjustment amplitude changes too frequently, the working conditions in the kiln will fluctuate frequently in a short period of time. Although the actual stabilization time is shorter than expected, the working conditions fluctuate significantly (such as frequent fluctuations in temperature and pressure). This indicates that the fuel supply rhythm is too fast, resulting in unstable working conditions. The cause of the deviation is also determined to be the deviation in the fuel supply rhythm setting.

[0153] Step S1548: extract the actual value and expected value of the operating condition parameter with deviation, calculate the difference between the actual value and the expected value, and then calculate the ratio of the difference to the expected value, and determine the severity of the deviation based on the ratio.

[0154] For operating parameters with deviations (such as reaction intensity and stabilization time), extract the actual and expected values ​​and calculate the absolute difference between them. This absolute difference is then divided by the expected value to obtain the relative deviation ratio. The severity of the deviation is determined by the relative deviation ratio, with smaller ratios considered minor and larger ratios considered severe.

[0155] Step S1549: Record the cause of the deviation, the operating parameters with deviation, the comparison data between the actual and expected parameters, and the severity of the deviation, and organize them into a deviation analysis report in a preset format.

[0156] Structured records are generated for the cause of the deviation (fuel supply deviation or cadence deviation), the corresponding operating parameter type, detailed comparison data between actual and expected parameters (including differences and ratios), and the severity level of the deviation. Using the pre-set report format, the time of deviation, the process steps involved, the data source, and the analysis process are clearly presented to form a complete deviation analysis report.

[0157] Step S155: Correct the corresponding fuel supply adjustment strategy according to the cause of the deviation, adjust the fuel supply amount or fuel supply rhythm parameters in the fuel supply adjustment strategy, and correct the operating condition adaptation conditions associated with the fuel supply adjustment strategy at the same time, and update the corrected fuel supply adjustment strategy and the corrected operating condition adaptation conditions to the fuel supply plan library.

[0158] Targeted corrections are made to the fuel supply adjustment strategy based on the cause of the deviation identified through analysis. If the cause of the deviation is a deviation in the fuel supply setting, the supply adjustment range is adjusted based on the ratio of the difference between the actual reaction intensity and the expected value. For example, if the reaction intensity is insufficient, the supply adjustment range is proportionally increased. At the same time, the upper and lower limits of the supply are corrected to ensure that the adjusted supply range is within the safe operating range of the equipment. If the cause of the deviation is a deviation in the fuel supply rhythm setting, the step interval or progressive rate in the rhythm parameter is adjusted. For example, if the stabilization time is too long, the adjustment interval is shortened or the initial adjustment range is increased to enable the operating condition to reach a stable state more quickly.

[0159] While revising the fuel supply adjustment strategy, re-evaluate the operating adaptation conditions associated with the strategy. Analyze the differences between the current operating conditions and the original adaptation conditions, and adjust the parameter ranges in the adaptation conditions. For example, if the original adaptation conditions did not consider the impact of material moisture on the reaction, resulting in poor strategy performance, then add a limited range for material moisture to the revised adaptation conditions. Synchronously update the revised fuel supply adjustment strategy and operating adaptation conditions to the corresponding basic plan unit in the fuel supply plan library, replacing the original strategy content. Record the reasons for the revision, the comparison of parameters before and after the revision, and the evaluation of the revised effects to ensure that the strategies in the plan library can continue to adapt to changes in actual operating conditions.

[0160] Step S156: Counting the usage frequency and compatibility of each basic plan unit within a preset time period, deleting basic plan units with extremely low usage frequency and a compatibility continuously lower than a preset compatibility threshold, and optimizing the storage structure of the fuel supply plan library.

[0161] Set a preset time period, such as a production cycle, and count the number of times each basic plan unit in the fuel supply plan library is used during that time period. The usage frequency is calculated as the ratio of the number of uses to the total number of calls. Also, count the compatibility values ​​of each basic plan unit after each use, and calculate the average compatibility and its trend.

[0162] Basic plan units with extremely low usage frequency and an average fit consistently below the preset fit threshold are marked. These units are typically suitable for special or rare working conditions and have poor practical effects. Retaining them increases the redundancy of the plan library and affects retrieval efficiency. After technical review and confirmation, these marked basic plan units are deleted from the plan library, and the reason for deletion, deletion time, and related parameters are recorded.

[0163] The storage structure of the emergency plan library was optimized, and the remaining basic emergency plan units were sorted according to frequency of use and compatibility. Highly used and highly compatible units were prioritized for retrieval, improving the efficiency of emergency plan matching. At the same time, the index system of the emergency plan library was reorganized, and new search keywords were added, such as revised working condition adaptation condition parameters, to ensure a clearer structure for the emergency plan library and more accurate retrieval.

[0164] Figure 2 The following is a schematic diagram illustrating exemplary hardware and software components of a control system 100 for volatilization kiln fuel supply that can implement the concepts of the present application, provided in some embodiments of the present application. For example, a processor 120 can be used in the control system 100 for volatilization kiln fuel supply and perform the functions of the present application.

[0165] For example, the control system 100 for volatilizing kiln fuel supply may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. For example, the control system 100 for volatilizing kiln fuel supply may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of the present application may be implemented based on these program instructions. The control system 100 for volatilizing kiln fuel supply also includes an I / O interface 150 for connecting the computer to other input / output devices.

[0166] In addition, an embodiment of the present invention further provides a readable storage medium having computer executable instructions preset therein. When a processor executes the computer executable instructions, the above-mentioned control method for volatilization kiln fuel supply is implemented.

[0167] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for controlling fuel supply to a volatilization kiln, characterized in that: The method comprises: Acquiring real-time operating data and historical operating data of the volatilization kiln, wherein the real-time operating data includes real-time reaction status information in the kiln and real-time material processing progress information, and the historical operating data includes fuel supply adjustment records and operating condition change result information under different operating conditions; Performing working condition trend prediction processing based on the real-time working condition data of the volatilization kiln and the historical working condition data of the volatilization kiln, identifying the potential change direction and potential change period of the working condition in the kiln, and obtaining a working condition trend prediction result; Building a fuel supply plan library based on the operating condition trend prediction result, wherein the fuel supply plan library includes fuel supply adjustment strategies corresponding to different prediction trends, and each fuel supply adjustment strategy is associated with a corresponding operating condition adaptation condition; real-time monitoring of the deviation between the current operating condition data of the volatilization kiln and the operating condition trend prediction result, matching a target supply adjustment strategy from the fuel supply plan library according to the deviation, and dynamically adapting the target supply adjustment strategy to obtain a fuel supply execution plan; The fuel supply execution plan is sent to the fuel supply system for execution, and the kiln operating condition feedback data after execution is collected. The fuel supply adjustment strategy and operating condition adaptation conditions in the fuel supply plan library are updated using the kiln operating condition feedback data.

2. The method for controlling fuel supply to a volatilization kiln according to claim 1, characterized in that: The operating condition trend prediction processing is performed based on the real-time operating condition data of the volatilization kiln and the historical operating condition data of the volatilization kiln, identifying the potential change direction and potential change period of the operating condition in the kiln, and obtaining the operating condition trend prediction result, including: Integrating the real-time operating condition data of the volatilization kiln with the historical operating condition data of the volatilization kiln to obtain a regularized operating condition data set; Extracting the change characteristics of the real-time working condition data of the volatilization kiln from the regularized working condition data set, wherein the change characteristics include the continuous change trajectory of the reaction state in the kiln and the rate change characteristics of the material processing progress; Associating historical change segments in the historical operating condition data of the volatile kiln with change characteristics similar to those of the real-time operating condition data of the volatile kiln, and extracting subsequent operating condition change directions and subsequent operating condition change periods corresponding to the historical change segments; Analyzing the similarity between the change characteristics of the real-time operating condition data of the volatilization kiln and the historical change segments, and determining the reference weight of the historical change segments to the current operating condition; Based on the reference weight, subsequent operating condition information corresponding to the plurality of historical change segments is integrated to generate preliminary results of potential change direction and potential change period of the operating condition in the kiln; Combined with the latest change dynamics of the real-time operating condition data of the volatilization kiln, the preliminary results of the potential change direction and potential change period are corrected to obtain the final operating condition trend prediction result.

3. The method for controlling fuel supply to a volatilization kiln according to claim 2, wherein: The step of associating historical change segments in the historical operating condition data of the volatilization kiln with change characteristics similar to those of the real-time operating condition data of the volatilization kiln, and extracting subsequent operating condition change directions and subsequent operating condition change periods corresponding to the historical change segments, includes: Filtering out historical data segments containing a complete process of operating condition change from the historical operating condition data of the volatilization kiln, each historical data segment including a characteristic change stage and a subsequent change stage; Comparing the change characteristics of the real-time working condition data of the volatilization kiln with the change characteristic stages of each historical data segment, and calculating the feature similarity between the change characteristics and the change characteristic stages of each historical data segment; Filter out historical data segments whose feature similarity is higher than a similarity threshold, and use the filtered historical data segments as candidate historical change segments; Extract the operating condition change direction in the subsequent change stage of each candidate historical change segment, determine the key operating condition parameter type associated with the operating condition change direction, and record the change range of each key operating condition parameter under the change direction; Extracting the time length of the subsequent change phase of each candidate historical change segment, and determining the change period required for the key operating condition parameter to reach a stable state based on the time length; Record the operating condition change direction, the associated key operating condition parameter type and its change range, the time period during which the key parameter reaches stability under the change direction, and the feature similarity for each candidate historical change segment, to form a candidate historical information list; The working condition change directions and change periods in the candidate historical information list are deduplicated, and the historical information with the highest feature similarity is retained as core reference information.

4. The method for controlling fuel supply to a volatilization kiln according to claim 3, characterized in that: Deduplication is performed on the working condition change direction and change period in the candidate historical information list, The historical information with the highest feature similarity is retained as the core reference information, including: Classifying the operating condition change directions in the candidate historical information list, and grouping historical information with the same or similar operating condition change directions into the same direction group; In each direction group, statistics are collected for the first stable change period of the key operating condition parameters under the change direction of each historical information record, and the average stable change period under the same operating condition change direction is calculated; Compare the feature similarities of each historical information in the same direction group, select the historical information with the highest feature similarity, and extract the second change period in which the key operating condition parameters in the change direction recorded in the historical information reach stability; If the deviation between the second change period and the average stable change period of the direction group is less than the set deviation, the historical information is directly used as the representative information of the direction group; If the deviation between the second change period and the average stable change period of the direction group is not less than a set deviation, then the change period of the key operating condition parameter reaching stability in the change direction recorded in the historical information is corrected in combination with the average stable change period to obtain a corrected stable change period, and the historical information containing the corrected stable change period is used as the representative information of the direction group; From the representative information of each direction group, the representative information with the highest feature similarity is screened again and used as the core reference information.

5. The method for controlling fuel supply to a volatilization kiln according to claim 1, characterized in that: The constructing of a fuel supply plan library according to the operating condition trend prediction result includes: Classifying the potential change directions in the operating condition trend prediction result to divide different operating condition change scenarios, each operating condition change scenario corresponding to a class of potential change directions; For each operating condition change scenario, an adaptive fuel supply adjustment strategy is constructed, which includes the adjustment direction of the fuel supply amount and the adjustment method of the fuel supply rhythm; Marking the corresponding operating condition adaptation conditions for each fuel supply adjustment strategy, wherein the operating condition adaptation conditions include the operating condition change amplitude and operating condition change rate range applicable to the fuel supply adjustment strategy; The operating condition change scenario, the corresponding fuel supply adjustment strategy and the corresponding operating condition adaptation condition are associated and stored to form a basic plan unit; Classify and organize the basic emergency plan units, and divide the emergency plan modules according to the types of working condition change scenarios, each emergency plan module contains multiple basic emergency plan units for the same type of scenarios; All the plan modules are integrated to establish an index mechanism, which is used to quickly locate the corresponding plan modules and basic plan units according to the working condition trend prediction results to form a fuel supply plan library.

6. The method for controlling fuel supply to a volatilization kiln according to claim 5, characterized in that: The adaptive fuel supply adjustment strategy is constructed for each operating condition change scenario, including: Analyze the changes in the fuel demand for the reaction in the kiln under each operating condition change scenario. If the operating condition change scenario shows an increase in the reaction intensity, the fuel supply amount will be adjusted in the direction of increase; if the operating condition change scenario shows a decrease in the reaction intensity, the fuel supply amount will be adjusted in the direction of decrease; Determine the fuel supply rhythm adjustment method based on the change rate of the operating condition change scenario. If the change rate of the operating condition change scenario meets the first rate range, the fuel supply rhythm is adjusted to a fast step-by-step method. If the change rate of the operating condition change scenario meets the second rate range, the fuel supply rhythm is adjusted to a slow gradual method. The minimum value of the first rate range is greater than the maximum value of the second rate range. In combination with the duration of the operating condition change scenario, the duration period of the fuel supply adjustment is set so that the duration period of the fuel supply adjustment covers the entire period of the operating condition change; Mark the key adaptation conditions corresponding to each fuel supply adjustment strategy, where the key adaptation conditions include the initial parameters of the operating condition change and the change rate threshold of the operating condition change; Conduct simulation verification on the constructed fuel supply adjustment strategy, apply the fuel supply adjustment strategy to similar historical operating conditions, and observe the changes in the simulated operating conditions after application; If the operating condition changes in the simulated operating condition change results meet the expected stability target, the fuel supply adjustment strategy is determined to be effective; if the operating condition changes in the simulated operating condition change results do not meet the expected stability target, the adjustment direction of the fuel supply amount or the adjustment method of the fuel supply rhythm is adjusted, and the simulation verification is performed again, and the verified effective fuel supply adjustment strategy is associated with the corresponding operating condition change scenario to form a basic plan sheet.

7. The method for controlling fuel supply to a volatilization kiln according to claim 1, wherein: The real-time monitoring of the deviation between the current operating condition data of the volatilization kiln and the operating condition trend prediction result, matching the target supply adjustment strategy from the fuel supply plan library according to the deviation, and dynamically adapting the target supply adjustment strategy to obtain a fuel supply execution plan, including: According to a preset working condition deviation monitoring period, the current working condition data of the volatilization kiln is collected in real time, wherein the current working condition data of the volatilization kiln includes real-time data of the reaction state in the kiln and real-time data of the material processing progress; Comparing the current operating condition data of the volatilization kiln with the corresponding predicted data in the operating condition trend prediction result, and calculating the degree of deviation between the current operating condition data of the volatilization kiln and the corresponding predicted data, wherein the degree of deviation includes the deviation of the data change amplitude and the deviation of the data change rate; Determining a deviation level according to the deviation degree, if the deviation degree is within a preset allowable range, the deviation level is low deviation; if the deviation degree exceeds the preset allowable range, the deviation level is high deviation; When the deviation level is low, matching a basic plan unit directly corresponding to the operating condition trend prediction result from the fuel supply plan library, and extracting the fuel supply adjustment strategy in the basic plan unit as the initial target strategy; When the deviation level is high, a basic plan unit with similar characteristics to the current operating condition data change characteristics of the volatilization kiln is selected from the fuel supply plan library, and the fuel supply adjustment strategies of multiple similar basic plan units are weightedly integrated to obtain an initial target strategy; The fuel supply rhythm in the initial target strategy is adjusted in combination with the real-time data of the material processing progress in the current operating condition data of the volatilization kiln. The fuel supply amount in the initial target strategy is adjusted in combination with the real-time data of the reaction state in the kiln in the current operating condition data of the volatilization kiln, thereby forming a fuel supply execution plan.

8. The method for controlling fuel supply to a volatilization kiln according to claim 7, characterized in that: When the deviation level is high, a basic plan unit with similar characteristics to the current operating condition data change characteristics of the volatilization kiln is selected from the fuel supply plan library, and the fuel supply adjustment strategies of multiple similar basic plan units are weightedly integrated to obtain an initial target strategy, including: Extracting core characteristic parameters from the change characteristics of the current working condition data of the volatilization kiln, wherein the core characteristic parameters include the reaction state change rate in the kiln and the material processing progress deviation value; Searching the fuel supply plan library according to the core characteristic parameters, screening out basic plan units whose operating condition adaptation conditions include the range of the core characteristic parameters, and using the screened out basic plan units as plan units to be integrated; Calculating the matching degree between the working condition adaptation condition of each plan unit to be integrated and the core characteristic parameters, the higher the matching degree, the greater the weight coefficient corresponding to the plan unit to be integrated; Extract the fuel supply adjustment direction vector and fuel supply adjustment range from each plan unit to be fused, perform weighted calculation according to the weight coefficient corresponding to each plan unit to be fused, and obtain the fused fuel supply adjustment direction vector and fused fuel supply adjustment range; Extract the fuel supply rhythm adjustment method from each plan unit to be merged, count the occurrence frequencies of each fuel supply rhythm adjustment method in the plan unit to be merged, and select the fuel supply rhythm adjustment method with the highest occurrence frequency and the largest matching weight value as the merged fuel supply rhythm adjustment method; Integrating the fused fuel supply adjustment direction vector, the fused fuel supply adjustment amplitude, and the fused fuel supply rhythm adjustment method to form a preliminary fusion strategy; The coordination between the fuel supply amount and the fuel supply rhythm in the preliminary fusion strategy is detected. If there is a contradiction between the fuel supply amount and the fuel supply rhythm, the fuel supply rhythm adjustment method of the plan unit to be fused with the highest matching degree is preferentially used for correction to obtain the initial target strategy.

9. The method for controlling fuel supply to a volatilization kiln according to claim 1, characterized in that: The fuel supply execution plan is sent to the fuel supply system for execution, and at the same time, feedback data on the kiln working condition after execution is collected, and the fuel supply adjustment strategy and working condition adaptation conditions in the fuel supply plan library are updated using the feedback data on the kiln working condition, including: Converting the fuel supply execution plan into an executable supply control instruction and sending it to the fuel supply system, so that after the fuel supply system executes the supply control instruction, it collects feedback data of the working conditions in the kiln according to a set feedback data collection period, wherein the feedback data of the working conditions in the kiln includes data on changes in the reaction state in the kiln after execution and data on changes in the material processing progress after execution; Comparing the expected operating condition change corresponding to the fuel supply execution plan with the actual operating condition change in the feedback data of the kiln operating condition, and calculating the degree of fit between the expected operating condition change and the actual operating condition change; If the degree of fit is higher than a preset fit threshold, the fuel supply adjustment strategy corresponding to the fuel supply execution plan is determined to be valid, a matching relationship between the fuel supply adjustment strategy and the current operating condition is recorded, and the matching relationship is added to the basic plan unit corresponding to the fuel supply plan library; If the degree of fit is lower than a preset fit threshold, analyzing the reasons for the deviation between the expected operating condition change and the actual operating condition change, wherein the reasons for the deviation include a fuel supply amount setting deviation and a fuel supply rhythm setting deviation; modifying the corresponding fuel supply adjustment strategy according to the cause of the deviation, adjusting the fuel supply amount or fuel supply rhythm parameter in the fuel supply adjustment strategy, and modifying the operating condition adaptation condition associated with the fuel supply adjustment strategy, and updating the modified fuel supply adjustment strategy and the modified operating condition adaptation condition to the fuel supply plan library; The usage frequency and compatibility of each basic plan unit within a preset time period are counted, and basic plan units with extremely low usage frequency and compatibility continuously lower than a preset compatibility threshold are deleted to optimize the storage structure of the fuel supply plan library.

10. A control system for fuel supply to a volatilization kiln, characterized in that: The system comprises a processor and a memory, wherein the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the control method for volatilization kiln fuel supply according to any one of claims 1 to 9.

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