Energy-saving control method and system for heat accumulating type aluminum melting furnace

Abstract

The invention relates to the field of aluminum melting furnace energy-saving control, and discloses an energy-saving control method and system for a heat accumulating type aluminum melting furnace, and the method comprises the steps: carrying out the clustering of a plurality of historical operation data, obtaining a plurality of typical operation conditions of the heat accumulating type aluminum melting furnace, and each typical operation condition comprises a plurality of historical operation data; determining an air preheating correction coefficient according to a plurality of historical operation data of the typical operation condition; acquiring current operation data of the heat accumulating type aluminum melting furnace; determining a target typical operation condition to which the current operation data belongs, and obtaining a target air preheating correction coefficient corresponding to the target typical operation condition; the current air preheating temperature, the fuel type and the fuel flow are obtained; determining a target air-fuel ratio according to the fuel type; according to the fuel flow and the target air-fuel ratio, the basic air flow is determined; and the product of the basic air flow and the target air preheating correction coefficient is determined to serve as the corrected air flow. According to the invention, the production requirements of high efficiency and energy conservation under different working conditions can be met.

Description

Technical Field

[0001] This application relates to the field of energy-saving control technology for aluminum melting furnaces, and more specifically, to an energy-saving control method and system for regenerative aluminum melting furnaces. Background Technology

[0002] Regenerative aluminum melting furnaces, as key equipment in the aluminum smelting and processing field, have been widely used. These furnaces utilize a regenerator to recover and utilize the waste heat from high-temperature flue gas. By alternating combustion and exhaust conditions, the regenerator can fully absorb the heat from the high-temperature flue gas, thereby preheating air or fuel, effectively improving fuel combustion efficiency and reducing energy consumption. Existing regenerative aluminum melting furnaces typically include core components such as a furnace chamber, regenerator, combustion system, exhaust system, and switching mechanism. During the combustion stage, fuel and air preheated by the regenerator burn fully in the furnace chamber, providing the necessary heat for melting aluminum ingots. During the exhaust stage, the switching mechanism activates, allowing high-temperature flue gas to enter the regenerator chamber for heat exchange, completing waste heat recovery before being discharged through the exhaust system.

[0003] In actual aluminum smelting production, operating conditions often vary significantly. For example, the feed rate, smelting temperature requirements, and production load fluctuations all create different operating scenarios. However, the core operating parameters of existing regenerative aluminum melting furnaces can only be adjusted manually, and cannot be adaptively adjusted according to dynamic changes in actual operating conditions. This fixed operating mode leads to problems such as decreased waste heat recovery efficiency, increased fuel consumption, and uneven furnace temperature distribution when the furnace operates under non-optimal design conditions. This not only fails to fully utilize the energy-saving advantages of the regenerative structure but may also affect the quality of aluminum smelting, increase production energy costs, and make it difficult to meet the high-efficiency and energy-saving production requirements under different operating conditions. Summary of the Invention

[0004] The purpose of this application is to provide an energy-saving control method and system for regenerative aluminum melting furnaces, which solves the technical problem of not being able to meet the high-efficiency and energy-saving production requirements under different operating conditions, and achieves the technical effect of meeting the high-efficiency and energy-saving production requirements under different operating conditions.

[0005] In a first aspect, embodiments of this application provide an energy-saving control method for a regenerative aluminum melting furnace. The method includes: acquiring multiple historical operating data of the regenerative aluminum melting furnace; clustering the multiple historical operating data to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, each typical operating condition including multiple historical operating data; determining an air preheating correction coefficient for a typical operating condition based on the multiple historical operating data of the typical operating conditions; wherein, the historical operating data includes material state data, energy input data, actual combustion data, and heat exchange efficiency data; acquiring current operating data of the regenerative aluminum melting furnace; determining the target typical operating condition to which the current operating data belongs, and acquiring the target air preheating correction coefficient corresponding to the target typical operating condition; acquiring the current air preheating temperature, fuel type, and fuel flow rate of the current operating data; determining the target air-fuel ratio based on the fuel type; determining the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determining the product of the base air flow rate and the target air preheating correction coefficient as the corrected air flow rate; and sending the corrected air flow rate to the fan control system to adjust the fan frequency to the corrected air flow rate.

[0006] In one possible implementation, the air preheating correction coefficient for typical operating conditions is determined based on multiple historical operating data of typical operating conditions. This includes: obtaining the target air preheating temperature corresponding to the fuel type; obtaining the current air preheating temperature of the current operating data; determining the preheating temperature fluctuation range of typical operating conditions; determining the absolute value of the difference between the target air preheating temperature and the current air preheating temperature as the preheating temperature deviation value; determining the ratio of the preheating temperature deviation value to the preheating temperature fluctuation range as the preheating temperature deviation proportion value; and determining the sum of 1 and the preheating temperature deviation proportion value as the air preheating correction coefficient.

[0007] In another possible implementation, the method further includes: acquiring multiple historical melting stage data of the regenerative aluminum melting furnace; clustering the multiple historical melting stage data to obtain multiple typical melting stage operating conditions of the regenerative aluminum melting furnace, each typical melting stage operating condition including multiple historical melting stage data; determining the air preheating correction coefficient for the typical melting stage operating condition based on the multiple historical melting stage data of the typical melting stage operating condition; wherein, the historical melting stage data includes material state data, energy input data, heat exchange efficiency data, and actual combustion data; when the regenerative aluminum melting furnace is in melting... During the current melting stage, acquire the current melting stage data; determine the target typical melting stage operating condition to which the current melting stage data belongs, and acquire the target air preheating correction coefficient corresponding to the target typical melting stage operating condition; acquire the current air preheating temperature, fuel type, and fuel flow rate of the current melting stage data; determine the target air-fuel ratio based on the fuel type; determine the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determine the product of the base air flow rate and the target air preheating correction coefficient as the corrected air flow rate; send the corrected air flow rate to the fan control system, and adjust the fan frequency to the corrected air flow rate.

[0008] In another possible implementation, based on multiple historical melting stage data of typical melting stage conditions, the air preheating correction coefficient for typical melting stage conditions is determined, including: obtaining the target air preheating temperature corresponding to the fuel type; obtaining the current air preheating temperature and current aluminum feed rate of the current melting stage data; determining the preheating temperature fluctuation range, target aluminum feed rate, and aluminum feed fluctuation range of typical melting stage conditions; determining the absolute value of the difference between the target air preheating temperature and the current air preheating temperature as the preheating temperature deviation value; determining the absolute value of the difference between the target aluminum feed rate and the current aluminum feed rate as the aluminum feed rate deviation value; determining the ratio of the preheating temperature deviation value to the preheating temperature fluctuation range as the preheating temperature deviation proportion value; determining the ratio of the aluminum feed rate deviation value to the aluminum feed fluctuation range as the feed rate deviation proportion value; and determining 1 plus the sum of the preheating temperature deviation proportion value and the feed rate deviation proportion value as the air preheating correction coefficient.

[0009] In another possible implementation, multiple historical operating data are clustered to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, including: determining the material state weight corresponding to the material state data as 0.2, the energy input weight corresponding to the energy input data as 0.3, the actual combustion weight corresponding to the actual combustion data as 0.3, and the heat exchange efficiency weight corresponding to the heat exchange efficiency data as 0.2; clustering multiple historical operating data according to the material state weight, energy input weight, actual combustion weight, and heat exchange efficiency weight to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, each typical operating condition including multiple historical operating data.

[0010] In another possible implementation, multiple historical melting stage data are clustered to obtain multiple typical melting stage operating conditions of the regenerative aluminum melting furnace. This includes: determining the material state weight corresponding to the material state data as 0.4, the energy input weight corresponding to the energy input data as 0.2, the actual combustion weight corresponding to the actual combustion data as 0.2, and the heat exchange efficiency weight corresponding to the heat exchange efficiency data as 0.2; clustering multiple historical melting stage data according to the material state weight, energy input weight, actual combustion weight, and heat exchange efficiency weight to obtain multiple typical melting stage operating conditions of the regenerative aluminum melting furnace, with each typical melting stage operating condition including multiple historical operating data.

[0011] In another possible implementation, the method further includes: obtaining the heat storage coefficient of the heat storage body; determining the historical average heat absorption of the heat storage body under typical operating conditions based on the historical heat absorption of the heat storage body under typical operating conditions of the regenerative aluminum melting furnace; determining the target typical operating condition to which the current operating data belongs, and obtaining the target historical average heat absorption corresponding to the target typical operating condition; determining the difference between the flue gas inlet temperature and the flue gas outlet temperature and multiplying it by the heat storage coefficient as the current heat absorption; triggering the airflow reversal operation of the heat storage body when the current heat absorption is greater than or equal to a preset heat absorption ratio of the target historical average heat absorption; and not triggering the airflow reversal operation of the heat storage body when the current heat absorption is less than the preset heat absorption ratio of the target historical average heat absorption.

[0012] In another possible implementation, the method further includes: determining the airflow reversal cycle of the heat storage body under typical operating conditions; determining the target typical operating condition to which the current operating data belongs, and obtaining the target airflow reversal cycle and the target historical average heat absorption corresponding to the target typical operating condition; obtaining the temperature drop value of the flue gas outlet temperature in the current operating data; when the temperature drop value in the current operating data is greater than or equal to a preset temperature drop value, determining the difference between the current heat absorption and the target historical average heat absorption as the heat absorption difference value; determining the ratio of the heat absorption difference value to the target historical average heat absorption value as the reversal cycle adjustment coefficient; determining the product of the target airflow reversal cycle and the reversal cycle adjustment coefficient as the adjusted airflow reversal cycle; and controlling the airflow reversal operation of the heat storage body according to the adjusted airflow reversal cycle.

[0013] In another possible implementation, the method further includes: determining the quotient of the adjusted airflow reversal period and the target air preheating correction coefficient as the corrected airflow reversal period; and controlling the airflow reversal operation of the heat storage body according to the corrected airflow reversal period.

[0014] Secondly, embodiments of this application provide an energy-saving control system for a regenerative aluminum melting furnace, including units for implementing the above-described method.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are:

[0016] This application provides an energy-saving control method for a regenerative aluminum melting furnace. The method includes: acquiring multiple historical operating data of the regenerative aluminum melting furnace; clustering the multiple historical operating data to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, each typical operating condition including multiple historical operating data; determining the air preheating correction coefficient of the typical operating condition based on the multiple historical operating data of the typical operating condition; acquiring the current operating data of the regenerative aluminum melting furnace; determining the target typical operating condition to which the current operating data belongs, and acquiring the target air preheating correction coefficient corresponding to the target typical operating condition; acquiring the current air preheating temperature, fuel type, and fuel flow rate of the current operating data; determining the target air-fuel ratio based on the fuel type; determining the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determining the product of the base air flow rate and the target air preheating correction coefficient as the corrected air flow rate; and sending the corrected air flow rate to the fan control system to adjust the fan frequency to the corrected air flow rate. In this embodiment, typical operating conditions are constructed by clustering historical data and corresponding air preheating correction coefficients are determined based on historical data within the operating conditions. This provides an accurate basis for subsequent airflow adjustment that fits the operating conditions, avoiding the problem that a single correction coefficient cannot adapt to diverse operating conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart illustrating the first energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0019] Figure 2 A schematic diagram illustrating the workflow of the first energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0020] Figure 3 A schematic flowchart illustrating a second energy-saving control method for a regenerative aluminum melting furnace provided in an embodiment of this application;

[0021] Figure 4 A schematic diagram illustrating the workflow of a second energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0022] Figure 5 A schematic flowchart illustrating the third energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0023] Figure 6 A schematic diagram illustrating the workflow of the third energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0024] Figure 7 A schematic flowchart illustrating the fourth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0025] Figure 8 A schematic diagram illustrating the workflow of the fourth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0026] Figure 9 A schematic flowchart illustrating the fifth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0027] Figure 10 A schematic flowchart illustrating the sixth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0028] Figure 11 A schematic flowchart illustrating the seventh energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0029] Figure 12 A schematic diagram illustrating the workflow of the seventh energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0030] Figure 13 A schematic flowchart illustrating the eighth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0031] Figure 14 A schematic diagram illustrating the workflow of the eighth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment;

[0032] Figure 15 This is a schematic diagram of the logic structure of an energy-saving control system for a regenerative aluminum melting furnace, provided as an embodiment of this application. Detailed Implementation

[0033] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0034] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0036] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0037] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0038] The core operating parameters of existing regenerative aluminum melting furnaces can only be adjusted manually, and cannot be adaptively adjusted according to the dynamic changes in actual working conditions, thus failing to fully realize the energy-saving advantages of the regenerative structure.

[0039] Based on the above reasons, this application provides an energy-saving control method for a regenerative aluminum melting furnace. The method includes: acquiring multiple historical operating data of the regenerative aluminum melting furnace; clustering the multiple historical operating data to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, each typical operating condition including multiple historical operating data; determining the air preheating correction coefficient of the typical operating condition based on the multiple historical operating data of the typical operating condition; acquiring the current operating data of the regenerative aluminum melting furnace; determining the target typical operating condition to which the current operating data belongs, and acquiring the target air preheating correction coefficient corresponding to the target typical operating condition; acquiring the current air preheating temperature, fuel type, and fuel flow rate of the current operating data; determining the target air-fuel ratio based on the fuel type; determining the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determining the product of the base air flow rate and the target air preheating correction coefficient as the corrected air flow rate; and sending the corrected air flow rate to the fan control system to adjust the fan frequency to the corrected air flow rate. In this embodiment, typical operating conditions are constructed by clustering historical data and corresponding air preheating correction coefficients are determined based on historical data within the operating conditions. This provides an accurate basis for subsequent airflow adjustment that fits the operating conditions, avoiding the problem that a single correction coefficient cannot adapt to diverse operating conditions.

[0040] In some scenarios, the energy-saving control method for regenerative aluminum melting furnaces according to the embodiments of this application can be applied to the energy-saving control of regenerative aluminum melting furnaces, thereby improving the energy-saving control effect of regenerative aluminum melting furnaces.

[0041] The following describes in detail an energy-saving control method for a regenerative aluminum melting furnace provided in this application, using specific examples.

[0042] Figure 1 A schematic flowchart of the first energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 1 As shown in the embodiment of this application, an energy-saving control method for a regenerative aluminum melting furnace is provided. The method includes steps S110 to S130, which are described in detail below.

[0043] S110. Obtain multiple historical operating data points for the regenerative aluminum melting furnace. Cluster the multiple historical operating data points to obtain multiple typical operating conditions for the regenerative aluminum melting furnace. Each typical operating condition includes multiple historical operating data points. Based on the multiple historical operating data points for each typical operating condition, determine the air preheating correction coefficient for that typical operating condition. The historical operating data includes material state data, energy input data, actual combustion data, and heat exchange efficiency data.

[0044] Figure 2 A schematic diagram of the workflow of the first energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 2As shown in this implementation, historical data of the regenerative aluminum melting furnace at different operating stages can be collected. This data covers multi-dimensional information reflecting the state of materials in the furnace, energy input, actual combustion process and heat exchange efficiency, providing a comprehensive data foundation for subsequent analysis of operating characteristics.

[0045] It should be noted that the material status data includes furnace temperature, furnace pressure, molten pool level, and aluminum ingot feed rate; the energy input data includes fuel flow rate, fuel pressure, fan frequency, and air flow rate; the actual combustion data includes air preheating temperature, flue gas temperature, and regenerator switching time; and the heat exchange efficiency data includes the actual operating air-fuel ratio and combustion cycle.

[0046] In this implementation, a clustering algorithm can be used to group the collected multi-dimensional historical operating data, group historical data with similar operating characteristics into one category, forming multiple typical operating conditions. Each operating condition contains multiple groups of historical data with similar materials, energy inputs, and combustion characteristics.

[0047] For example, the K-means algorithm can be used to cluster historical data on furnace temperatures of 700-800℃ and aluminum ingot feeding amounts of 5-10 tons into "medium load heating conditions", and furnace temperatures of 900-1000℃ and aluminum ingot feeding amounts of 10-15 tons into "high load melting conditions". The historical data under each condition have similar operating characteristics.

[0048] In this implementation, multiple sets of historical data for each typical operating condition can be statistically analyzed. Combined with the influence of air preheating temperature on combustion efficiency under this condition, an air preheating correction coefficient that reflects the air preheating requirement under this condition can be determined.

[0049] For example, for medium-load heating conditions, the correlation between air preheating temperature and actual combustion efficiency in historical data within this condition is statistically analyzed. When the air preheating temperature increases by 10°C, the combustion efficiency increases by 2%. Based on this, the air preheating correction factor corresponding to this condition is calculated to be 1.05, which is used to compensate for the impact of changes in air preheating temperature when adjusting the air flow rate in the future.

[0050] S120. Obtain the current operating data of the regenerative aluminum melting furnace. Determine the target typical operating condition to which the current operating data belongs, and obtain the target air preheating correction coefficient corresponding to the target typical operating condition. Obtain the current air preheating temperature, fuel type, and fuel flow rate of the current operating data.

[0051] In this implementation, the sensor and data acquisition system on the aluminum melting furnace can collect data such as the current material status, energy input, actual combustion and heat exchange efficiency in real time as the current operating data. This real-time data is used to match the typical operating conditions to which the current operating state belongs.

[0052] In this implementation, the currently collected operating data can be compared with the feature data of each typical operating condition obtained by previous clustering, and the typical operating condition that is closest to the current data features can be found as the target operating condition.

[0053] For example, if the current operating data shows a furnace temperature of 750℃ and an aluminum ingot feeding amount of 8 tons, which best matches the characteristic range of "medium load heating condition" (furnace temperature 700-800℃, aluminum ingot feeding amount 5-10 tons), then the target typical operating condition is "medium load heating condition".

[0054] In this implementation, after determining the target typical operating condition, the air preheating correction coefficient corresponding to the target operating condition can be directly retrieved from the pre-established operating condition-correction coefficient correspondence table.

[0055] For example, if the target typical operating condition is "medium load heating condition", the corresponding correction factor is 1.05, then this 1.05 is directly obtained as the target air preheating correction factor.

[0056] In this implementation, the current air preheating temperature can be detected by a temperature sensor installed on the air preheating pipe of the aluminum melting furnace, the fuel type (such as natural gas or diesel) can be determined by the identification or detection module of the fuel supply system, and the current fuel flow rate can be measured by a flow sensor on the fuel pipe. These parameters are key inputs for subsequent calculation of air flow rate.

[0057] S130. Determine the target air-fuel ratio based on the fuel type. Determine the base air flow rate based on the fuel flow rate and the target air-fuel ratio. Calculate the product of the base air flow rate and the target air preheating correction factor as the corrected air flow rate. Send the corrected air flow rate to the fan control system and adjust the fan frequency to the corrected air flow rate.

[0058] In this implementation, different fuel types have different optimal air-fuel ratios, so the target air-fuel ratio can be selected from the preset fuel parameter table according to the currently used fuel type.

[0059] For example, the optimal air-fuel ratio is 10:1 when the fuel type is natural gas and 14:1 when the fuel type is diesel.

[0060] In this implementation, the basic airflow rate refers to the theoretical amount of air required to completely burn the current fuel flow rate according to the target air-fuel ratio, which can be calculated by multiplying the fuel flow rate by the target air-fuel ratio.

[0061] For example, if the current fuel flow rate is 50 m³ / h and the target air-fuel ratio is 10:1, then the basic air flow rate is 50 × 10 = 500 m³ / h.

[0062] In this implementation, since changes in air preheating temperature affect air density and combustion efficiency, the base air flow rate can be adjusted using the target air preheating correction coefficient. The adjusted flow rate is the corrected air flow rate. Specifically, the product of the base air flow rate and the target air preheating correction coefficient can be used as the corrected air flow rate.

[0063] For example, if the base airflow is 500 m³ / h and the target air preheating correction factor is 1.05, then the corrected airflow is 500 × 1.05 = 525 m³ / h.

[0064] In this implementation, the calculated corrected airflow signal can be transmitted to the fan control system. The control system adjusts the fan's operating frequency according to the flow demand, so that the actual airflow output by the fan matches the corrected airflow. This enables a rapid response to the current air demand of the aluminum melting furnace, avoids energy waste caused by excessively high or low fan output, and improves the fan's operating efficiency and the overall energy-saving effect of the aluminum melting furnace.

[0065] This implementation method constructs typical operating conditions by clustering historical data and determines the corresponding air preheating correction coefficient based on historical data within the operating conditions. It can accurately reflect the actual differences in air preheating requirements under different operating conditions, providing an accurate basis for subsequent airflow adjustment that fits the operating conditions, and avoiding the problem that a single correction coefficient cannot adapt to diverse operating conditions.

[0066] This implementation method matches the current operating data to the target typical operating condition and applies the corresponding correction coefficient. Combined with the fuel type, it dynamically calculates and corrects the air flow, which can achieve precise adjustment of air flow under different operating conditions. This avoids air-fuel ratio imbalance caused by changes in operating conditions or differences in fuel type, and ensures stable combustion efficiency.

[0067] Figure 3 A schematic flowchart of a second energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 3 As shown, in some implementations, the above-mentioned S110, which determines the air preheating correction coefficient for typical operating conditions based on multiple historical operating data of typical operating conditions, also includes S111 to S112. S111 to S112 will be explained in detail below.

[0068] S111. Obtain the target air preheating temperature corresponding to the fuel type. Obtain the current air preheating temperature from the current operating data. Determine the preheating temperature fluctuation range under typical operating conditions.

[0069] Figure 4 A schematic diagram of the workflow of the second energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 4As shown, in this implementation, when determining the air preheating correction coefficient based on multiple historical operating data under typical operating conditions, the target air preheating temperature corresponding to the fuel type currently used in the regenerative aluminum melting furnace can be obtained from a pre-built correspondence between fuel type and target air preheating temperature. Different fuels have different combustion characteristics, and the target temperature is the ideal preheating temperature to ensure efficient fuel combustion, providing a benchmark for subsequent temperature comparisons.

[0070] In this implementation, the current air preheating temperature entering the combustion system can be collected in real time by a temperature sensor installed on the air preheating pipe. This temperature is the current air preheating temperature in the current operation data and is used to compare with the target temperature to determine the degree of deviation.

[0071] In this implementation, the target air preheating temperature determined according to the fuel type under typical operating conditions can be extracted from historical data of typical operating conditions, and the difference between the maximum and minimum values ​​of historical air preheating temperatures can be calculated as the preheating temperature fluctuation range. The preheating temperature fluctuation range reflects the normal range of temperature change under this operating condition.

[0072] For example, the historical maximum temperature under a certain typical operating condition is 350℃ and the minimum temperature is 310℃, with a fluctuation range of 40℃.

[0073] S112. Determine the absolute value of the difference between the target air preheating temperature and the current air preheating temperature, as the preheating temperature deviation value. Determine the ratio of the preheating temperature deviation value to the preheating temperature fluctuation amplitude, as the preheating temperature deviation proportion value. Determine the sum of 1 and the preheating temperature deviation proportion value, as the air preheating correction coefficient.

[0074] In this implementation, the difference between the target air preheating temperature and the current air preheating temperature can be calculated, and the absolute value can be taken to obtain the preheating temperature deviation value. This value reflects the degree of deviation between the current actual temperature and the ideal temperature, eliminating the influence of positive and negative directions.

[0075] For example, if the target temperature is 330℃ and the current temperature is 320℃, the absolute value of the difference is 10℃, which is the deviation value.

[0076] In this implementation, the preheating temperature deviation value can be divided by the preheating temperature fluctuation range under typical operating conditions to obtain the preheating temperature deviation ratio value. This ratio value converts the absolute deviation into a relative proportion relative to the normal fluctuation range.

[0077] For example, a deviation of 10℃ and a fluctuation range of 40℃, with a ratio of 0.25, indicates that the current deviation accounts for one-quarter of the normal fluctuation.

[0078] In this implementation, 1 and the preheating temperature deviation ratio can be added together to obtain the air preheating correction coefficient. When the deviation ratio is 0, the correction coefficient is 1, indicating that the temperature matching does not need to be adjusted; when a deviation exists, the correction coefficient changes with the ratio value.

[0079] For example, a scale of 0.25 and a correction factor of 1.25 are used to correct the base airflow to fit the current temperature deviation.

[0080] This implementation method combines the deviation between the target air preheating temperature and the current temperature with the temperature fluctuation range under typical operating conditions to dynamically reflect the relative degree of temperature deviation, making the corrected airflow more in line with the actual operating conditions and improving the accuracy of airflow adjustment; relative quantification avoids over- or under-correction caused by absolute deviation, making the correction coefficient more in line with the fluctuation characteristics of typical operating conditions and improving the adaptability of airflow adjustment to temperature changes under different operating conditions.

[0081] Through this implementation, the fluctuation range of typical operating conditions reflects the normal range of temperature changes under that condition. Combining this parameter can accurately reflect the degree of the current temperature deviation relative to the normal fluctuation, making the air preheating correction coefficient more in line with the operating characteristics of typical operating conditions. The adjusted corrected airflow is better adapted to the temperature deviation under typical operating conditions, thereby improving the pertinence of energy-saving control.

[0082] Figure 5 A schematic flowchart illustrating the third energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 5 As shown, in some implementations, the above method also includes S210 to S230, which will be explained in detail below.

[0083] S210. Obtain historical melting stage data for the regenerative aluminum melting furnace. Cluster the historical melting stage data to obtain multiple typical melting stage operating conditions for the regenerative aluminum melting furnace. Each typical melting stage operating condition includes multiple historical melting stage data. Based on the multiple historical melting stage data of the typical melting stage operating conditions, determine the air preheating correction coefficient for each typical melting stage operating condition. The historical melting stage data includes material state data, energy input data, heat exchange efficiency data, and actual combustion data.

[0084] Figure 6 A schematic diagram of the workflow of the third energy-saving control method for a regenerative aluminum melting furnace provided in the embodiments of this application is shown below. Figure 6 As shown in this implementation, the operating data of the regenerative aluminum melting furnace in the past melting stage can be collected. This historical melting stage data covers material state, energy input, heat exchange efficiency and actual combustion-related data, providing a basis for subsequent analysis.

[0085] It should be noted that the material status data in the historical melting stage data includes furnace temperature, furnace pressure, molten pool level, and aluminum ingot feed rate; energy input data includes fuel flow rate, fuel pressure, fan frequency, and air flow rate; heat exchange efficiency data includes actual operating air-fuel ratio and combustion cycle; and actual combustion data includes air preheating temperature, flue gas temperature, and regenerator switching time.

[0086] In this implementation, the collected historical melting stage data can be clustered to group data with similar operating characteristics into one category, forming multiple typical melting stage conditions. Each typical condition contains multiple historical data of that category, which facilitates subsequent control of different conditions.

[0087] For example, when clustering data from multiple historical melting stages, the K-means algorithm is used. Three cluster centers (low, medium, and high aluminum ingot feeding amounts) are set according to the different loads of the melting stages. The distance between each historical data point and the cluster center is calculated, and data that are close to each other are grouped into one category. Finally, three typical melting stage conditions of low load, medium load, and high load are obtained, and each condition contains multiple historical data points under the corresponding load.

[0088] In this implementation, the air preheating correction coefficient corresponding to each typical melting stage can be calculated based on multiple historical data, so that the correction coefficient fits the operating characteristics of that operating condition.

[0089] For example, when determining the air preheating correction factor for a typical melting stage, first obtain the target air preheating temperature of 350℃ and the preheating temperature fluctuation range of 20℃ for a typical high-load operating condition; then calculate the absolute value of the difference between the current temperature of 340℃ and the target temperature of a certain historical data within the operating condition, which is 10℃; then calculate the ratio of the deviation value to the fluctuation range, which is 0.5; finally, add 1 to the ratio to obtain the correction factor of 1.5, that is, the air preheating correction factor for the high-load operating condition is 1.5.

[0090] S220. When the regenerative aluminum melting furnace is in the melting stage, acquire the current melting stage data. Determine the target typical melting stage operating condition to which the current melting stage data belongs, and acquire the target air preheating correction coefficient corresponding to the target typical melting stage operating condition. Acquire the current air preheating temperature, fuel type, and fuel flow rate of the current melting stage data.

[0091] In this implementation, when the regenerative aluminum melting furnace enters the melting stage (the stage from when the aluminum ingot begins to melt to when it is completely melted into molten aluminum), the operating data of the current melting stage can be collected in real time, including data on materials, energy, heat exchange, and actual combustion.

[0092] For example, when acquiring data for the current melting stage, the furnace temperature is collected by a furnace temperature sensor, the molten pool level is collected by a level gauge, the fuel flow rate is collected by a flow meter, and the air preheating temperature is collected by a temperature sensor. These data are then integrated to obtain the data for the current melting stage.

[0093] In this implementation, the current melting stage data and several previously obtained typical melting stage conditions can be matched by feature matching to find the most similar typical condition as the target typical melting stage condition, and then the air preheating correction coefficient corresponding to the target condition can be obtained.

[0094] For example, when matching the target typical melting stage conditions, the similarity between the characteristic values ​​of the current data (such as 5 tons of aluminum ingots, furnace temperature of 1200℃, and fuel flow rate of 80m³ / h) and the characteristic centers of each typical condition is calculated. If the characteristic center of the high-load condition is 5-6 tons of aluminum ingots, furnace temperature of 1150-1250℃, and fuel flow rate of 75-85m³ / h, and the current data falls within this range, then the high-load condition is matched, and its correction coefficient of 1.5 is obtained.

[0095] In this implementation, the air preheating temperature, fuel type (such as natural gas or propane), and fuel flow rate of the current melting stage can be collected. These data are key parameters for calculating the corrected air flow rate.

[0096] S230. Determine the target air-fuel ratio based on the fuel type. Determine the base air flow rate based on the fuel flow rate and the target air-fuel ratio. Calculate the product of the base air flow rate and the target air preheating correction factor as the corrected air flow rate. Send the corrected air flow rate to the fan control system and adjust the fan frequency to the corrected air flow rate.

[0097] In this implementation, the target air-fuel ratio can be determined according to the type of fuel used, so that the fuel can burn more completely.

[0098] It should be noted that the target air-fuel ratio can be determined through an empirical value table. The empirical value table is based on the combustion characteristics of different fuels, such as a target air-fuel ratio of 10:1 for natural gas and 15:1 for propane.

[0099] In this implementation, the basic air flow rate can be calculated based on the fuel flow rate and the target air-fuel ratio. That is, the fuel flow rate is multiplied by the target air-fuel ratio to obtain the basic air volume required for complete combustion of the fuel.

[0100] In this implementation, the base airflow rate and the target air preheating correction coefficient can be multiplied to obtain the corrected airflow rate, so that the airflow rate is adapted to the temperature deviation of the current melting stage.

[0101] For example, if the base airflow is 800 m³ / h (fuel flow 80 m³ / h × target air-fuel ratio 10:1) and the target air preheating correction factor is 1.5, then the corrected airflow is 800 × 1.5 = 1200 m³ / h.

[0102] In this implementation, the calculated corrected airflow can be sent to the fan control system. The fan control system adjusts the frequency of the fan according to the corrected airflow so that the airflow output by the fan reaches the corrected value, thus quickly responding to the air demand during the melting stage.

[0103] This implementation method separately divides typical operating conditions for the melting stage and determines corresponding correction coefficients, making the airflow adjustment for the melting stage more in line with the operating characteristics of this stage, improving the pertinence of airflow control during the melting stage, and effectively optimizing the energy consumption level during the melting stage. By establishing a separate correction coefficient system for the melting stage, the air preheating correction coefficient for the melting stage is more in line with the actual operating characteristics of this stage, avoiding deviations caused by overall operating condition correction, improving the accuracy of airflow control during the melting stage, and optimizing combustion efficiency.

[0104] This implementation method, based on the precise matching of characteristic data of the melting stage, enables the current operating status of the melting stage to more accurately correspond to the typical working conditions of the same stage. The correction coefficient is more in line with the actual needs of the stage, improving the accuracy of airflow control, optimizing combustion effect, and saving energy.

[0105] Figure 7 A schematic flowchart illustrating the fourth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 7 As shown, in some implementations, in the above-mentioned S210, the air preheating correction coefficient for the typical melting stage is determined based on multiple historical melting stage data of the typical melting stage, including S211 to S212. S211 to S212 will be explained in detail below.

[0106] S211. Obtain the target air preheating temperature corresponding to the fuel type. Obtain the current air preheating temperature and current aluminum feed rate for the current melting stage. Determine the preheating temperature fluctuation range, target aluminum feed rate, and aluminum feed fluctuation range for typical melting stage conditions.

[0107] Figure 8 A schematic diagram of the workflow of the fourth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 8 As shown, in this implementation, the target air preheating temperature corresponding to the fuel type can be obtained, which is the preheating temperature with optimal fuel combustion efficiency; at the same time, the current air preheating temperature and the current amount of aluminum material fed in the current melting stage data can be obtained.

[0108] In this implementation, the preheating temperature fluctuation range, the target aluminum material feeding amount (i.e., the average aluminum material feeding amount under this condition) and the aluminum material feeding fluctuation range can be determined for typical melting stage conditions.

[0109] It should be noted that there are specific values ​​for the target air preheating temperature corresponding to the fuel type. For example, the target air preheating temperature for natural gas is 350℃, and for liquefied petroleum gas it is 320℃.

[0110] For example, natural gas is used in the current melting stage, and the target air preheating temperature is 350℃; the current air preheating temperature is 330℃, and the current aluminum material input is 2 tons; the preheating temperature fluctuation range of a typical working condition is 20℃, the target aluminum material input is 2 tons, and the aluminum material input fluctuation range is 0.4 tons.

[0111] S212. Determine the absolute value of the difference between the target air preheating temperature and the current air preheating temperature as the preheating temperature deviation value. Determine the absolute value of the difference between the target aluminum material feeding amount and the current aluminum material feeding amount as the aluminum material feeding amount deviation value. Determine the ratio of the preheating temperature deviation value to the preheating temperature fluctuation range as the preheating temperature deviation proportion value. Determine the ratio of the aluminum material feeding amount deviation value to the aluminum material feeding fluctuation range as the feeding amount deviation proportion value. Determine 1, the sum of the preheating temperature deviation proportion value and the feeding amount deviation proportion value, as the air preheating correction coefficient.

[0112] In this implementation, the absolute value of the difference between the target air preheating temperature and the current air preheating temperature can be calculated as the preheating temperature deviation value; the absolute value of the difference between the target aluminum material feeding amount and the current aluminum material feeding amount can be calculated as the aluminum material feeding amount deviation value.

[0113] For example, the target air preheating temperature is 350℃, the current temperature is 330℃, and the preheating temperature deviation is 20℃; the target aluminum material input is 2 tons, the current input is 1.8 tons, and the aluminum material input deviation is 0.2 tons.

[0114] In this implementation, the preheating temperature deviation value can be divided by the preheating temperature fluctuation range under typical working conditions to obtain the preheating temperature deviation ratio value; the aluminum material feeding deviation value can be divided by the aluminum material feeding fluctuation range under typical working conditions to obtain the feeding amount deviation ratio value.

[0115] For example, the preheating temperature deviation is 20℃, the fluctuation range is 20℃, and the deviation ratio is 1; the aluminum material feeding amount deviation is 0.2 tons, the fluctuation range is 0.4 tons, and the deviation ratio is 0.5.

[0116] In this implementation, 1 can be added to the preheating temperature deviation ratio and the material feeding deviation ratio to obtain the air preheating correction coefficient. This coefficient reflects the correction requirements of the air flow rate for the deviations in preheating temperature and aluminum material feeding.

[0117] For example, if the preheating temperature deviation ratio is 1 and the feeding amount deviation ratio is 0.5, the correction factor is 2.5; if the preheating temperature deviation ratio is 0.5 and the feeding amount deviation ratio is 0.3, the correction factor is 1.8.

[0118] This implementation integrates the aluminum material feeding deviation factor into the correction coefficient calculation process, allowing the air preheating correction coefficient to simultaneously reflect changes in both preheating temperature and aluminum material feeding. Compared to considering only preheating temperature deviation, this approach more comprehensively matches the actual production conditions during the melting stage, improving the correction coefficient's adaptability to the melting stage conditions. The combination of multi-dimensional parameters allows the correction coefficient to simultaneously adapt to the differences in operating conditions caused by changes in preheating temperature and aluminum material feeding during the melting stage. Compared to considering only a single temperature factor, this approach more accurately corresponds to fluctuations in actual production conditions, improving the matching accuracy between the melting stage conditions and the correction coefficient.

[0119] This implementation method allows the correction of airflow to simultaneously respond to the combustion conditions required by changes in preheating temperature and aluminum material input. Compared to considering only preheating temperature, it more comprehensively covers the key factors affecting combustion efficiency in the melting stage, and improves the targeting of airflow adjustment to the melting stage conditions.

[0120] Figure 9 A schematic flowchart illustrating the fifth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 9 As shown, in some implementations, S110 above clusters multiple historical operating data to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, and also includes S113 to S114. S113 to S114 will be explained in detail below.

[0121] S113. Determine the material state weight corresponding to the material state data as 0.2, the energy input weight corresponding to the energy input data as 0.3, the actual combustion weight corresponding to the actual combustion data as 0.3, and the heat exchange efficiency weight corresponding to the heat exchange efficiency data as 0.2.

[0122] In this implementation, the weights of various types of data can be determined based on the degree of influence of different types of historical operating data on the operating conditions of the regenerative aluminum melting furnace.

[0123] In this implementation, material status data includes furnace temperature, furnace pressure, molten pool level, and aluminum ingot feeding amount, reflecting the real-time status of the material. While it has some impact on the operating conditions, it is not the core factor, so the weight of material status is set to 0.2. Energy input data includes fuel flow rate, fuel pressure, fan frequency, and air flow rate, directly related to energy consumption and fan operation, and is one of the core factors, so the weight of energy input is set to 0.3. Actual combustion data includes air preheating temperature, flue gas temperature, and heat storage body switching time, directly reflecting combustion efficiency, and is also a core factor, so the weight of actual combustion is set to 0.3. Heat exchange efficiency data includes actual operating air-fuel ratio and combustion cycle, reflecting the heat exchange effect, but its impact is slightly weaker, so the weight of heat exchange efficiency is set to 0.2. This allocation can take into account the impact of various data types, highlight the core factors, and reduce interference from secondary data.

[0124] S114. Cluster multiple historical operating data according to material state weight, energy input weight, actual combustion weight, and heat exchange efficiency weight to obtain multiple typical operating conditions of the regenerative aluminum melting furnace. Each typical operating condition includes multiple historical operating data.

[0125] In this implementation, multiple historical operating data can be clustered based on the aforementioned weights to obtain typical operating conditions. The four categories of data for each historical data are standardized (e.g., data with different dimensions are converted to the 0-1 interval) to eliminate dimensional differences. The standardized data are then multiplied by the corresponding weights and fused to obtain a comprehensive feature value, which comprehensively reflects the overall operating characteristics of the data. Subsequently, clustering algorithms (such as K-means) can be used to cluster the comprehensive feature value, grouping historical data with similar characteristics into one category to form multiple typical operating conditions. Each operating condition contains historical data with similar operating characteristics.

[0126] For example, 1,000 historical data points for a regenerative aluminum melting furnace were collected over one year. Each data point included material status (furnace temperature 800-1200℃, aluminum ingot feeding amount 300-800kg), energy input (fuel flow rate 50-200kg / h, fan frequency 20-50Hz), actual combustion (air preheating temperature 300-600℃, flue gas temperature 150-300℃), and heat exchange efficiency (air-fuel ratio 1.05-1.2, combustion cycle 30-60min).

[0127] For example, when standardizing each type of data, such as furnace temperature of 800℃ being 0 and 1200℃ being 1, the standardized value of a certain data point with furnace temperature of 900℃ is 0.25; fuel flow rate of 50kg / h being 0 and 200kg / h being 1, the standardized value of a certain data point with fuel flow rate of 125kg / h is 0.5.

[0128] For example, when calculating the comprehensive characteristic value, it is obtained by 0.25×0.2 + 0.5×0.3 + 0.5 (normalized value of air preheating temperature)×0.3 + 0.5 (normalized value of air-fuel ratio)×0.2 = 0.45.

[0129] For example, after calculating the comprehensive feature value of 1000 data points, K-means clustering was used to divide them into 3 categories, resulting in three typical operating conditions: "low load heat preservation", "medium load melting", and "high load refining". Among them, the "medium load melting" operating condition contains 350 data points, with standardized values ​​of fuel flow rate (0.4-0.6), standardized values ​​of air preheating temperature (0.4-0.6), and comprehensive feature value (0.4-0.5). The core data are similar, accurately reflecting the typical state of medium load melting.

[0130] This implementation method quantifies the contribution of each data type to the operating condition by setting weights, avoiding the situation where critical information is overwhelmed by treating all data equally. It extracts effective information from historical data more efficiently and makes more efficient use of key information in historical operating data. This makes typical operating conditions more reflective of the essential characteristics of aluminum melting furnace operation, providing more effective historical data support for the subsequent determination of the target air preheating correction coefficient, and improving the effectiveness and reliability of the entire energy-saving control method.

[0131] This implementation method highlights the role of core data by setting weights and reduces interference from secondary data, making the clustering results more focused on key factors. The typical operating conditions obtained by clustering are more focused on the core data affecting the energy consumption and combustion effect of aluminum melting furnace, improving the pertinence of subsequent energy-saving control strategies, and helping to more accurately adjust the fan frequency to correct the air flow, thereby improving energy-saving effect.

[0132] Figure 10 A flowchart illustrating the sixth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 10 As shown, in some implementations, in S210 above, multiple historical melting stage data are clustered to obtain multiple typical melting stage conditions of the regenerative aluminum melting furnace, including S213 to S214. S213 to S214 will be explained in detail below.

[0133] S213. Determine the material state weight corresponding to the material state data as 0.4, the energy input weight corresponding to the energy input data as 0.2, the actual combustion weight corresponding to the actual combustion data as 0.2, and the heat exchange efficiency weight corresponding to the heat exchange efficiency data as 0.2.

[0134] In this implementation, weights can be assigned to different types of historical melting stage data based on the operating characteristics of the regenerative aluminum melting furnace during the melting stage. The key to the melting stage is the melting progress of aluminum ingots. Material state data (such as molten pool level and furnace temperature) directly reflects the degree of melting and distribution of materials and has the most significant impact on the operating conditions. Therefore, the material state weight corresponding to the material state data is set to 0.4. Although energy input data, actual combustion data, and heat exchange efficiency data affect the melting process, their correlation with material state is relatively weak. Therefore, the weights of energy input, actual combustion, and heat exchange efficiency are set to 0.2, respectively.

[0135] S214. Cluster the data of multiple historical melting stages according to the weight of material state, energy input, actual combustion, and heat exchange efficiency to obtain multiple typical melting stage operating conditions of the regenerative aluminum melting furnace. Each typical melting stage operating condition includes multiple historical operating data.

[0136] In this implementation, the collected historical melting stage data can be weighted. First, each data set is categorized by material state, energy input, actual combustion, and heat exchange efficiency. Then, the corresponding weight is multiplied by the value of each data set to obtain comprehensive data. The comprehensive data is then input into a clustering algorithm (such as the K-means algorithm). The algorithm groups the historical melting stage data based on data similarity, grouping historical data with similar operating characteristics into one category. This results in multiple typical melting stage operating conditions, with each condition containing multiple sets of historical melting stage data.

[0137] For example, if 100 sets of historical melting stage data for a regenerative aluminum melting furnace are collected, each set of data includes molten pool level (material state data), fuel flow rate (energy input data), air preheating temperature (actual combustion data), and combustion cycle (heat exchange efficiency data), first calculate the weighted composite value of each set of data: molten pool level × 0.4 + fuel flow rate × 0.2 + air preheating temperature × 0.2 + combustion cycle × 0.2. Input these composite values ​​into the K-means algorithm, setting the cluster size to 3. The algorithm will group data with similar composite values ​​into one category, resulting in three typical melting stage conditions: the first group corresponds to the initial stage of aluminum ingot feeding (low molten pool level, high fuel flow rate), the second group corresponds to the middle stage of melting (medium molten pool level, stable fuel flow rate), and the third group corresponds to the final stage of melting (high molten pool level, reduced fuel flow rate).

[0138] In this implementation, the core of the melting stage is the melting process of the material. The material state has the greatest impact on the operation of the melting stage. Therefore, the technology increases the weight of material state data and decreases the weight of other data, making the clustering process more prominent in the key factors of the melting stage. This setting improves the relevance and accuracy of typical operating conditions in the melting stage, providing a more realistic basis for subsequent calculation of air preheating correction coefficients based on melting stage operating conditions, and improving the precision of melting stage control. It also improves the accuracy of classifying typical operating conditions in the melting stage, making the air preheating correction coefficients determined based on these conditions more closely match the actual needs of material state changes in the melting stage, optimizing airflow adjustment in the melting stage, and improving energy utilization efficiency.

[0139] Figure 11 A schematic flowchart illustrating the seventh energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 11 As shown, in some implementations, the above method also includes S310 to S320, which will be described in detail below.

[0140] S310. Obtain the heat storage coefficient of the heat storage body. Based on the historical heat absorption of the heat storage body under typical operating conditions of the regenerative aluminum melting furnace, determine the historical average heat absorption of the heat storage body under typical operating conditions. Determine the target typical operating condition to which the current operating data belongs, and obtain the target historical average heat absorption corresponding to the target typical operating condition. Determine the difference between the flue gas inlet temperature and the flue gas outlet temperature and multiply it by the heat storage coefficient to obtain the current heat absorption.

[0141] Figure 12 A schematic diagram of the workflow for the seventh energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 12 As shown, in this implementation, the heat storage coefficient of the heat storage body can be obtained. The heat storage coefficient is the amount of heat that the heat storage body can absorb or release per unit temperature change. It is determined by the material (such as corundum mullite, cordierite) and structural parameters of the heat storage body and is used to quantify the heat storage capacity of the heat storage body.

[0142] For example, the heat storage coefficient of a certain corundum mullite heat storage body can be calculated by the material's specific heat capacity (1.1 kJ / (kg·℃)), density (2800 kg / m³), and porosity, and the value is 0.45 kJ / (kg·℃).

[0143] In this implementation, the historical average heat absorption of the heat storage body under typical operating conditions can be determined. Specifically, all historical heat absorption data under the typical operating conditions are summed and then divided by the number of data points to obtain the average heat absorption.

[0144] For example, there are 15 sets of historical heat absorption data for a certain "full load insulation condition", with a total of 3000kJ / kg. The historical average heat absorption is 200kJ / kg, which serves as the heat storage benchmark for this condition.

[0145] In this implementation, the target typical operating condition to which the current operating data belongs can be determined, and then the target historical average heat absorption corresponding to the operating condition can be obtained. Specifically, by comparing the current operating material state (such as furnace temperature and molten pool level), energy input data (such as fuel flow rate) and the characteristics of each typical operating condition, the operating condition with the highest matching degree is the target operating condition.

[0146] For example, the current furnace temperature is 1200℃ and the fuel flow rate is 80m³ / h, which is consistent with the characteristics of "high load melting condition". Therefore, the historical average heat absorption of 220kJ / kg under this condition is obtained as the target value.

[0147] In this implementation, the current heat absorption of the heat storage body can be calculated. Specifically, the flue gas inlet temperature and outlet temperature of the heat storage body can be detected first, the difference between the two can be calculated, and then multiplied by the heat storage coefficient.

[0148] It should be noted that the flue gas temperature difference reflects the degree of heat absorbed by the heat storage body from the flue gas. Multiplying this by the heat storage coefficient yields the actual heat absorption. For example, if the flue gas inlet temperature is 580℃ and the outlet temperature is 260℃, the difference is 320℃. Multiplying this by the heat storage coefficient of 0.45kJ / (kg·℃), the current heat absorption is 144kJ / kg.

[0149] S320. When the current heat absorption is greater than or equal to the preset heat absorption ratio of the target historical average heat absorption, the airflow reversal operation of the heat storage body is triggered. When the current heat absorption is less than the preset heat absorption ratio of the target historical average heat absorption, the airflow reversal operation of the heat storage body is not triggered.

[0150] In this implementation, the airflow reversal can be triggered based on a preset ratio of the current heat absorption to the target historical average heat absorption. The preset ratio is set based on the heat absorption efficiency and operational stability of the heat storage body, for example, 85%. When the current heat absorption reaches or exceeds this ratio, it indicates that the heat storage body has absorbed enough heat, triggering the reversal; otherwise, heat absorption continues.

[0151] For example, if the target historical average heat absorption is 200 kJ / kg and the preset ratio is 85%, which is 170 kJ / kg, if the current heat absorption is 175 kJ / kg, the airflow reversal of the heat storage body will be triggered; if it is 160 kJ / kg, it will not be triggered.

[0152] It should be noted that the airflow reversal operation of the regenerator is a crucial step in regenerative aluminum melting furnaces. Traditional fixed-time reversal may lead to insufficient heat storage or energy waste. This implementation dynamically adjusts the reversal timing based on the ratio of current heat absorption to historical averages. For example, when the current heat absorption reaches 85% of the target, it indicates that the regenerator is close to saturation, and reversing at this point maximizes the utilization of heat storage; if it has not reached this level, continuing heat absorption avoids heat loss caused by premature reversal.

[0153] This implementation method provides a more accurate reference for monitoring the heat storage benchmark based on typical operating conditions, which better meets the heat storage requirements under different operating conditions. This enhances the adaptability of the heat storage system to different operating conditions and allows for precise control of the reversal timing. It also avoids insufficient heat absorption or energy loss caused by reversing too early or too late, thereby improving the energy utilization efficiency and operational stability of the regenerative aluminum melting furnace.

[0154] With this implementation, when the current heat absorption is greater than or equal to the preset heat absorption ratio of the target historical average heat absorption, the airflow reversal operation of the heat storage body is triggered; when the current heat absorption is less than the preset heat absorption ratio, the airflow reversal operation is not triggered. The reversal timing is dynamically adjusted based on the actual heat absorption state of the heat storage body to avoid unnecessary reversal operations, improve the heat storage utilization efficiency of the heat storage body, and reduce energy waste.

[0155] Figure 13 A flowchart illustrating the eighth energy-saving control method for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 13 As shown, in some implementations, the above method also includes S330 to S340, which will be described in detail below.

[0156] S330. Determine the airflow reversal cycle of the heat storage medium under typical operating conditions. Determine the target typical operating condition to which the current operating data belongs, and obtain the target airflow reversal cycle and target historical average heat absorption corresponding to the target typical operating condition. Obtain the temperature drop value of the flue gas outlet temperature in the current operating data. When the temperature drop value in the current operating data is greater than or equal to the preset temperature drop value, determine the difference between the current heat absorption and the target historical average heat absorption, and use it as the heat absorption difference value.

[0157] Figure 14 A schematic diagram of the workflow of the eighth energy-saving control method for a regenerative aluminum melting furnace provided in the embodiments of this application is shown below. Figure 14 As shown, in this implementation, the airflow reversal cycle of the heat storage body under typical operating conditions can be determined. The difference in operating parameters under different typical operating conditions will lead to different heat absorption rates of the heat storage body. Therefore, each typical operating condition needs to be matched with a specific airflow reversal cycle.

[0158] It should be noted that the airflow reversal cycle of the regenerator under typical operating conditions can be determined by an empirical value table. The empirical value table is based on historical operating data of the regenerator aluminum melting furnace and covers empirical values ​​of the airflow reversal cycle under different typical operating conditions.

[0159] For example, a typical operating condition corresponds to an aluminum ingot feed rate of 5 tons and a fuel flow rate of 100 m³ / h. The airflow reversal cycle for this condition is 30 minutes according to the empirical value table.

[0160] In this implementation, the target typical operating condition to which the current operating data belongs can be determined, the target airflow reversal cycle and the target historical average heat absorption corresponding to the target typical operating condition can be obtained, the characteristic data of the current operating data and each typical operating condition can be compared, the operating condition with the highest matching degree can be found as the target typical operating condition, and then the predetermined target airflow reversal cycle and the target historical average heat absorption can be extracted from the historical data of the operating condition.

[0161] For example, the current operating data shows the highest matching degree between the aluminum ingot feeding rate of 4.8 tons and the fuel flow rate of 95 m³ / h, and the typical operating condition of 5 tons of aluminum ingots and 100 m³ / h of fuel flow rate. The target airflow reversal cycle for this operating condition is 30 minutes, and the target historical average heat absorption is 1.2 × 10⁻⁶ m³ / h. 6 kJ.

[0162] In this implementation, the temperature drop value of the flue gas outlet temperature in the current operating data can be obtained. The temperature data of the flue gas outlet of the heat storage body is continuously collected by the temperature sensor, the difference between the flue gas outlet temperature at the current moment and the previous moment is calculated, and the absolute value is taken as the temperature drop value.

[0163] For example, if the flue gas outlet temperature was 350°C at the previous moment and the flue gas outlet temperature is 320°C at the current moment, then the temperature decrease is 30°C.

[0164] In this implementation, when the temperature drop value in the current operating data is greater than or equal to the preset temperature drop value, the difference between the current heat absorption and the target historical average heat absorption is determined as the heat absorption difference. First, a preset temperature drop value is set. When the temperature drop value reaches or exceeds this value, the difference between the current heat absorption and the target historical average heat absorption is calculated to quantify the degree of deviation.

[0165] For example, the preset temperature drop is 25°C, and the current temperature drop is 30°C, which is greater than the preset value; the current heat absorption is 1.3 × 10⁻⁶. 6 kJ, with a target historical average heat absorption of 1.2 × 10 kJ. 6 If the heat absorption is kJ, then the difference in heat absorption is 0.1 × 10⁻⁶ kJ. 6 kJ.

[0166] S340. Determine the ratio of the heat absorption difference to the target historical average heat absorption as the reversing cycle adjustment coefficient. Determine the product of the target airflow reversing cycle and the reversing cycle adjustment coefficient as the adjusted airflow reversing cycle. Control the airflow reversing operation of the heat storage body according to the adjusted airflow reversing cycle.

[0167] In this implementation, the ratio of the heat absorption difference to the target historical average heat absorption can be determined as the commutation cycle adjustment coefficient. The coefficient obtained by dividing the heat absorption difference by the target historical average heat absorption reflects the proportion of the current heat absorption deviation relative to the typical operating condition benchmark.

[0168] For example, the difference in heat absorption is 0.1 × 10⁻⁶. 6 kJ, with a target historical average heat absorption of 1.2 × 10 kJ. 6 If kJ, then the commutation period adjustment coefficient is 0.1 / 1.2≈0.083.

[0169] In this implementation, the product of the target airflow reversal period and the reversal period adjustment coefficient can be determined as the adjusted airflow reversal period. The target airflow reversal period is multiplied by the reversal period adjustment coefficient, and the resulting adjustment period can be adapted to the current heat absorption deviation.

[0170] For example, if the target airflow reversal period is 30 minutes and the reversal period adjustment coefficient is 0.083, then the adjusted airflow reversal period is 30 × 0.083 ≈ 2.5 minutes, that is, the target airflow reversal period is extended to 32.5 minutes.

[0171] In this implementation, the airflow reversal operation of the heat storage body can be controlled according to the adjusted airflow reversal cycle. The adjusted cycle is sent to the airflow reversal control system, and the control system triggers the reversal valve to switch the airflow direction of the heat storage body according to the cycle.

[0172] For example, if the airflow reversal cycle is adjusted to 32.5 minutes, the airflow reversal operation of the heat storage body will be triggered once every 32.5 minutes, replacing the original fixed cycle of 30 minutes.

[0173] This implementation combines historical experience data from typical operating conditions with current real-time operating parameters, making the reversing cycle adjustment more closely match the actual operating state of the regenerative aluminum melting furnace and improving the working stability of the regenerator. It can dynamically adjust the reversing cycle according to the current flue gas temperature changes and heat absorption deviations, avoiding insufficient or excessive heat absorption by the regenerator due to a fixed cycle, and improving the heat storage utilization efficiency.

[0174] This implementation method determines whether the airflow reversal cycle needs to be adjusted based on the temperature drop value of the current flue gas outlet temperature. When the temperature drop value is greater than or equal to the preset temperature drop value, the difference between the current heat absorption and the target historical average heat absorption is further calculated to obtain the reversal cycle adjustment coefficient. The target airflow reversal cycle is then multiplied by the adjustment coefficient to obtain the adjusted airflow reversal, thus improving the energy-saving control effect.

[0175] In some implementations, the above method further includes: determining the quotient of the adjusted airflow reversal period and the target air preheating correction coefficient as the corrected airflow reversal period; and controlling the airflow reversal operation of the heat storage body according to the corrected airflow reversal period.

[0176] In this implementation, the adjusted airflow reversal cycle, which was previously calculated based on the heat absorption deviation and temperature reduction value, can be divided with the target air preheating correction coefficient, which reflects the deviation of air preheating temperature and aluminum material feeding amount, to obtain the corrected airflow reversal cycle. Through this calculation, the correction logic of airflow can be extended to the adjustment of airflow reversal cycle, so that the reversal cycle can simultaneously adapt to changes in heat absorption, air preheating status and feeding amount differences, and achieve linkage between the two.

[0177] For example, if the airflow reversal cycle is adjusted to 60 seconds under the typical melting stage of a target, and the target air preheating correction factor is 1.2, then the corrected airflow reversal cycle is 60 seconds divided by 1.2, resulting in 50 seconds.

[0178] In this implementation, a periodic command can be sent to the airflow reversal control system of the heat storage body according to the calculated corrected airflow reversal period to adjust the time interval of airflow reversal in the heat storage body. This allows the reversal operation of the heat storage body and the correction of airflow to be coordinated and consistent, avoiding timing mismatch caused by independent control of the two.

[0179] By linking the adjustment of the airflow reversal cycle with the air preheating correction coefficient, the airflow reversal of the heat storage body not only considers the change in heat absorption, but also takes into account the deviation of air preheating temperature and aluminum material feeding amount, thereby improving the correlation between airflow reversal control and air preheating correction, and making the reversal operation more in line with the actual air preheating state in operation.

[0180] This implementation method allows the adjustment of airflow and the reversing operation of the regenerator to work together, avoiding the mismatch problem that may occur when the two are controlled independently. This improves the consistency of the overall control of the regenerator aluminum melting furnace and helps to further enhance the energy-saving effect.

[0181] This application also provides an energy-saving control system for a regenerative aluminum melting furnace, including a unit for implementing the method described above.

[0182] Figure 15A schematic diagram of the logic structure of an energy-saving control system for a regenerative aluminum melting furnace provided in this application embodiment is shown below. Figure 15 As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above-described method. The beneficial effects of the embodiments of this application have been described in the above-described method and will not be repeated here.

[0183] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0184] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0185] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0186] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0187] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0188] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An energy-saving control method for a regenerative aluminum melting furnace, characterized in that, The method includes: Acquire multiple historical operating data of the regenerative aluminum melting furnace; cluster the multiple historical operating data to obtain multiple typical operating conditions of the regenerative aluminum melting furnace, each typical operating condition including multiple historical operating data; determine the air preheating correction coefficient for the typical operating condition based on the multiple historical operating data of the typical operating condition; wherein, the historical operating data includes material state data, energy input data, actual combustion data and heat exchange efficiency data; Acquire the current operating data of the regenerative aluminum melting furnace; determine the target typical operating condition to which the current operating data belongs, and obtain the target air preheating correction coefficient corresponding to the target typical operating condition; obtain the current air preheating temperature, fuel type and fuel flow rate of the current operating data; Determine the target air-fuel ratio based on the fuel type; determine the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determine the product of the base air flow rate and the target air preheating correction factor as the corrected air flow rate; send the corrected air flow rate to the fan control system and adjust the fan frequency to the corrected air flow rate.

2. The method according to claim 1, characterized in that, Based on multiple historical operating data points under typical operating conditions, the air preheating correction factor for typical operating conditions is determined, including: Obtain the target air preheating temperature corresponding to the fuel type; obtain the current air preheating temperature from the current operating data; determine the preheating temperature fluctuation range under typical operating conditions; Determine the absolute value of the difference between the target air preheating temperature and the current air preheating temperature as the preheating temperature deviation value; determine the ratio of the preheating temperature deviation value to the preheating temperature fluctuation amplitude as the preheating temperature deviation proportion value; determine the sum of 1 and the preheating temperature deviation proportion value as the air preheating correction coefficient.

3. The method according to claim 2, characterized in that, The method further includes: Acquire historical melting stage data for a regenerative aluminum melting furnace; cluster the historical melting stage data to obtain multiple typical melting stage operating conditions for the regenerative aluminum melting furnace, each typical melting stage operating condition including multiple historical melting stage data; determine the air preheating correction coefficient for the typical melting stage operating condition based on the multiple historical melting stage data of the typical melting stage operating conditions; wherein, the historical melting stage data includes material state data, energy input data, heat exchange efficiency data, and actual combustion data; When the regenerative aluminum melting furnace is in the melting stage, acquire the current melting stage data; determine the target typical melting stage operating condition to which the current melting stage data belongs, acquire the target air preheating correction coefficient corresponding to the target typical melting stage operating condition; acquire the current air preheating temperature, fuel type and fuel flow rate of the current melting stage data; Determine the target air-fuel ratio based on the fuel type; determine the base air flow rate based on the fuel flow rate and the target air-fuel ratio; determine the product of the base air flow rate and the target air preheating correction factor as the corrected air flow rate; send the corrected air flow rate to the fan control system and adjust the fan frequency to the corrected air flow rate.

4. The method according to claim 3, characterized in that, Based on historical melting stage data from multiple typical melting stage conditions, the air preheating correction factor for typical melting stage conditions is determined, including: Obtain the target air preheating temperature corresponding to the fuel type; obtain the current air preheating temperature and current aluminum feed rate of the current melting stage data; determine the preheating temperature fluctuation range, target aluminum feed rate, and aluminum feed fluctuation range of typical melting stage conditions; The absolute value of the difference between the target air preheating temperature and the current air preheating temperature is determined as the preheating temperature deviation value; the absolute value of the difference between the target aluminum material feeding amount and the current aluminum material feeding amount is determined as the aluminum material feeding amount deviation value; the ratio of the preheating temperature deviation value to the preheating temperature fluctuation range is determined as the preheating temperature deviation proportion value; the ratio of the aluminum material feeding amount deviation value to the aluminum material feeding fluctuation range is determined as the feeding amount deviation proportion value; and the sum of 1, the preheating temperature deviation proportion value, and the feeding amount deviation proportion value is determined as the air preheating correction coefficient.

5. The method according to claim 4, characterized in that, Clustering multiple historical operating data sets yielded several typical operating conditions for regenerative aluminum melting furnaces, including: The weights for the material state data, energy input data, actual combustion data, and heat exchange efficiency data are determined to be 0.2, 0.3, 0.3, and 0.2 respectively. Clustering was performed on multiple historical operating data according to material state weight, energy input weight, actual combustion weight, and heat exchange efficiency weight to obtain multiple typical operating conditions of the regenerative aluminum melting furnace. Each typical operating condition includes multiple historical operating data.

6. The method according to claim 5, characterized in that, Clustering data from multiple historical melting stages yielded several typical melting stage operating conditions for regenerative aluminum melting furnaces, including: The weights for material state data, energy input data, actual combustion data, and heat exchange efficiency data are determined to be 0.4, 0.2, 0.2, and 0.2 respectively. Clustering was performed on multiple historical melting stage data according to material state weight, energy input weight, actual combustion weight, and heat exchange efficiency weight to obtain multiple typical melting stage operating conditions of the regenerative aluminum melting furnace. Each typical melting stage operating condition includes multiple historical operating data.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the heat storage coefficient of the heat storage body; determine the historical average heat storage of the heat storage body under typical operating conditions based on the historical heat absorption of the heat storage body under typical operating conditions of the regenerative aluminum melting furnace; determine the target typical operating condition to which the current operating data belongs, and obtain the target historical average heat absorption corresponding to the target typical operating condition; determine the difference between the flue gas inlet temperature and the flue gas outlet temperature and multiply it by the heat storage coefficient to obtain the current heat absorption. When the current heat absorption is greater than or equal to the preset heat absorption ratio of the target historical average heat absorption, the airflow reversal operation of the heat storage body is triggered; when the current heat absorption is less than the preset heat absorption ratio of the target historical average heat absorption, the airflow reversal operation of the heat storage body is not triggered.

8. The method according to claim 7, characterized in that, The method further includes: Determine the airflow reversal cycle of the heat storage body under typical operating conditions; determine the target typical operating condition to which the current operating data belongs, and obtain the target airflow reversal cycle and target historical average heat absorption corresponding to the target typical operating condition; obtain the temperature drop value of the flue gas outlet temperature in the current operating data; when the temperature drop value in the current operating data is greater than or equal to the preset temperature drop value, determine the difference between the current heat absorption and the target historical average heat absorption, and use it as the heat absorption difference value; Determine the ratio of the heat absorption difference to the target historical average heat absorption as the reversal cycle adjustment coefficient; determine the product of the target airflow reversal cycle and the reversal cycle adjustment coefficient as the adjusted airflow reversal cycle; control the airflow reversal operation of the heat storage body according to the adjusted airflow reversal cycle.

9. The method according to claim 8, characterized in that, The method further includes: Determine the quotient of the adjustment airflow reversal cycle and the target air preheating correction coefficient as the corrected airflow reversal cycle; control the airflow reversal operation of the heat storage body according to the corrected airflow reversal cycle.

10. An energy-saving control system for a regenerative aluminum melting furnace, characterized in that, Includes units for implementing the method of any one of claims 1 to 9.

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