Energy consumption control method and system for metallurgical equipment

Abstract

The invention discloses an energy consumption control method and system for metallurgical equipment, and relates to the technical field of metallurgical equipment energy consumption control, and the method comprises the following steps: in the actual operation process of an electric arc furnace, various sensors are installed around an electrode, and the operation state of an electric arc is monitored in real time; key features reflecting the arc drift state are extracted, and complex original data are converted into quantitative indexes reflecting arc drift and the severity of the arc drift; and taking the key quantitative index as a feature vector, training the feature vector by adopting a deep learning method, constructing an arc drift prediction model, and after the construction is completed, outputting an arc state prediction result based on the current input feature vector, and identifying arc drift. According to the method, arc drift is recognized in advance by fusing multiple sensors and deep learning, through intelligent electrode height and response speed self-adaptive regulation and control, the electric energy utilization rate is increased, the smelting time and equipment loss are reduced, and low-energy-consumption, high-efficiency, automatic and green smelting is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption control of metallurgical equipment, and in particular to an energy consumption control method and system for metallurgical equipment. Background Art

[0002] Energy consumption control of metallurgical equipment refers to the management strategy of achieving rational use of energy and reducing overall energy consumption by optimizing process flow, improving equipment energy efficiency, and reducing energy waste during metallurgical production. Specific measures include adopting advanced energy-saving technologies, such as high-efficiency heating systems, waste heat recovery devices, and intelligent control systems, to improve equipment operation efficiency; optimizing production scheduling to reduce no-load and standby energy consumption; promoting clean energy and new energy to replace traditional high-energy-consuming energy; and implementing energy consumption monitoring and intelligent control systems to achieve real-time monitoring, analysis, and optimization of energy consumption. Through these means, not only can production costs be reduced, but carbon emissions can also be reduced, promoting the green and low-carbon development of the metallurgical industry.

[0003] As an important metallurgical equipment, the electric arc furnace monitors the arc state in real time through an intelligent control system, and dynamically adjusts the input power according to key parameters such as arc length, electrode position, furnace temperature, and smelting stage, so that the arc always remains at the optimal power level. The intelligent control system uses sensors and automation algorithms to quickly respond to arc fluctuations, optimize electrode lifting and lowering, adjust current and voltage, and prevent increased energy consumption caused by arc instability. In addition, the system can also combine power load management, optimize peak and valley electricity price strategies, reasonably allocate power use, and reduce ineffective energy consumption. Through these intelligent control methods, the electric arc furnace can not only improve energy utilization efficiency and reduce smelting time, but also reduce electrode consumption and equipment wear, thereby achieving the goals of energy saving and green smelting.

[0004] The prior art has the following deficiencies: During the actual operation of the electric arc furnace, arc drift often occurs in the arc state. Although the existing technology can dynamically adjust the electrode height, the electrode response speed cannot be adaptively adjusted. When the adjustment speed cannot meet the demand, the arc drift cannot be corrected in time, resulting in abnormal fluctuations in the arc length. In this case, the arc may be extinguished (arc broken) or the arc is too long (electric energy diffusion), which seriously reduces the efficiency of electric energy utilization. What's more serious is that the arc cannot effectively transfer heat to the molten pool, and a large amount of input electric energy is consumed ineffectively, resulting in a significant increase in unit energy consumption (kWh / t), which directly increases smelting costs and reduces production efficiency.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an energy consumption control method and system for metallurgical equipment, which realizes accurate monitoring and data collection of arc operation status through multi-sensor fusion technology, and combines deep learning prediction model to identify and predict the severity of arc drift before or just after it occurs, so as to ensure the timeliness and accuracy of adjustment response. At the same time, the introduction of intelligent electrode height adjustment and adaptive response speed optimization can dynamically adjust the electrode lifting rate according to the degree of arc drift, so that the arc can be quickly restored to the ideal state, prevent the arc from being too long or too short, and thus improve the utilization rate of electric energy, reduce the problems of extended smelting time, damage to furnace lining, abnormal electrode consumption, etc. caused by arc instability, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for controlling energy consumption of metallurgical equipment, comprising the following steps: During the actual operation of the arc furnace, various sensors are installed around the electrodes to monitor the arc operation status in real time; Extract key features reflecting arc drift status and transform complex raw data into quantitative indicators reflecting arc drift and its severity; The key quantitative indicators are used as feature vectors, and the feature vectors are trained using deep learning methods to build an arc drift prediction model. After the arc drift prediction model is built, the arc state prediction result is output based on the current input feature vector to identify arc drift; When the arc drift prediction model detects the risk of arc drift, the electrode height is adjusted according to the preset adjustment strategy. By dynamically changing the electrode height, the arc is quickly restored to the ideal position and the arc position is corrected in real time. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity.

[0008] Preferably, key features reflecting the arc drift state are extracted, wherein the extracted features include the deviation between the actual arc position and the ideal position and the temperature distribution around the arc. Within the monitoring window, the deviation between the actual arc position and the ideal position and the temperature distribution around the arc are converted into an arc offset index and an arc hot spot index through feature processing technology. The arc drift and its severity are reflected through the arc offset index and the arc hot spot index.

[0009] Preferably, the arc offset index and the arc hot spot index are used as feature vectors, and the feature vectors are trained using a deep learning algorithm to construct an arc drift prediction model. After the arc drift prediction model is constructed, a drift index is output based on the current input feature vector, and the drift index is used as an arc state prediction result to identify arc drift.

[0010] Preferably, the drift index generated when the arc state is predicted by the arc drift prediction model is compared and analyzed with a preset drift index reference threshold to identify the arc drift. The specific steps are as follows: If the drift index is greater than the drift index reference threshold, it indicates that the arc furnace has an arc drift risk. If the drift index is less than or equal to the drift index reference threshold, it indicates that the arc operation of the arc furnace is stable and there is no arc drift risk.

[0011] Preferably, within the monitoring window, the deviation between the actual arc position and the ideal position is converted into an arc offset index by feature processing technology, and the specific steps are as follows: In order to quantify the degree of arc deviation, a spatial deviation model between the actual arc position and the ideal arc position is first established. The arc position is expressed in three-dimensional space as , the ideal arc position is , then the arc space deviation vector D is defined as: ; The nonlinear normalization method is used to map the spatial offset to the exponential scale. The specific expression is: ,in, represents the normalized spatial offset vector; After obtaining the normalized space offset vector Afterwards, the arc offset index is constructed by weighted geometric mean method, and the constructed expression is: ,in: is the arc offset indicator, , , Respectively represent the weight coefficients along the x, y, and z axes, which are used to adjust the offset contribution in different directions.

[0012] Preferably, within the monitoring window, the temperature distribution around the arc is converted into an arc hot spot index by feature processing technology, and the specific steps are as follows: In the monitoring window, a high-resolution thermal imaging sensor is used to capture the temperature field data around the arc to form a spatial temperature distribution matrix. ,in Represents the pixel coordinates in the thermal image. In order to accurately identify the arc hot spot area, the temperature gradient aggregation function is used to calculate the spatial change rate of the temperature field around the arc. The calculation expression is: ,in, Represents the global gradient aggregation value of the temperature field, and Calculate the second-order derivatives of temperature in the horizontal and vertical directions to reflect the areas with drastic changes in the temperature field. is a weighting function that gives a higher weight to the arc center area to enhance the signal response within the arc influence range; Using the global gradient aggregation value of the temperature field To calculate the arc hot spot index, the calculation expression is: ,in: It is an arc hot spot indicator, used to measure arc drift and its severity. is the adjustment coefficient, which is used to control the influence of the hot spot density on the final arc hot spot index. is the arc hot spot density index, defined as: ,in is the temperature gradient, It is the area of ​​the area around the arc and is used for normalization processing. The arc hot spot density index is used to measure the density of local hot spots in the temperature field.

[0013] Preferably, when the arc drift prediction model detects the arc drift risk, the electrode height is adjusted according to a preset adjustment strategy, and the electrode response speed is intelligently adjusted for arc drifts of different severity. The specific steps are as follows: When the arc drift prediction model detects that the arc drift index exceeds the reference threshold, the electrode height is adjusted immediately. The electrode height is adjusted to maintain the arc length within the optimal range. The adjustment calculation formula is as follows: ,in: is the adjusted electrode height, is the current electrode height, is the electrode height adjustment coefficient, which determines the sensitivity of electrode adjustment after the drift index exceeds the drift index reference threshold. It is the drift index predicted in real time by the arc drift prediction model, which is used to quantify the current arc drift degree. is the drift index reference threshold, which is used to determine the safety limit of drift risk. is the arc length variation trend, is a sign function used to determine the adjustment direction.

[0014] Preferably, on the basis of completing the electrode height adjustment, the electrode response speed is dynamically adjusted to adapt to arc drifts of different severity. The calculation formula for the dynamic adjustment of the electrode response speed is as follows: ,in: The electrode adjustment speed is dynamically adjusted. Adjust the speed of the basic electrode, which is used for the electrode lifting rate under normal conditions. is the speed gain coefficient, which is used to adjust the speed increase as the drift index changes. The degree to which the drift index exceeds the drift index reference threshold is used to dynamically calculate the additional acceleration ratio.

[0015] An energy consumption control system for metallurgical equipment, comprising an arc state monitoring module, an arc drift feature extraction module, an arc drift prediction and identification module, and an electrode intelligent adjustment and dynamic response module; The arc status monitoring module monitors the arc operation status in real time by installing multiple sensors around the electrodes during the actual operation of the arc furnace; The arc drift feature extraction module extracts the key features reflecting the arc drift state and converts the complex raw data into quantitative indicators reflecting the arc drift and its severity; The arc drift prediction and identification module uses key quantitative indicators as feature vectors and uses deep learning methods to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, it outputs the arc state prediction result based on the current input feature vector to identify arc drift; The electrode intelligent adjustment and dynamic response module, when the arc drift prediction model detects the risk of arc drift, adjusts the electrode height according to the preset adjustment strategy, dynamically changes the electrode height, allows the arc to quickly return to the ideal position, and corrects the arc position in real time. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention realizes accurate monitoring and data collection of arc operation status through multi-sensor fusion technology, and combined with deep learning prediction model, it can identify and predict the severity of arc drift in advance before or just when it occurs, ensuring the timeliness and accuracy of adjustment response. At the same time, the introduction of intelligent electrode height adjustment and adaptive response speed optimization can dynamically adjust the electrode lifting rate according to the degree of arc drift, so that the arc can be quickly restored to the ideal state, preventing the arc from being too long or too short, thereby improving the utilization rate of electric energy and reducing problems such as extended smelting time, furnace lining damage, and abnormal electrode consumption caused by arc instability. In addition, through the real-time adjustment of the intelligent control system, the dependence on manual intervention is reduced, and the automation and intelligence level of the smelting process is improved, so that metallurgical enterprises can improve overall production efficiency and equipment safety while reducing production costs, and promote the metallurgical industry to develop in the direction of energy saving, intelligence and green. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1The present invention is a method flow chart of an energy consumption control method for metallurgical equipment.

[0019] Figure 2 The present invention is a schematic diagram of a module of an energy consumption control system for metallurgical equipment. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0021] The present invention provides Figure 1 A method for controlling energy consumption of metallurgical equipment is shown, comprising the following steps: During the actual operation of the arc furnace, various sensors (such as current and voltage sensors, optical cameras, temperature sensors, vibration monitoring devices, etc.) are installed around the electrodes to monitor the arc operation status in real time; The sensor will transmit the collected information such as arc voltage, arc current, arc intensity, furnace temperature distribution and furnace wall vibration to the data acquisition system. Through the real-time acquisition of multi-source data, the dynamic changes of the arc in the arc furnace can be fully reflected, providing a basis for subsequent data analysis and prediction.

[0022] Extract key features that reflect the arc drift state and transform complex raw data into quantitative indicators that can reflect arc drift and its severity; The key features reflecting the arc drift state are extracted, wherein the extracted features include the deviation between the actual arc position and the ideal position and the temperature distribution around the arc. Within the monitoring window, the deviation between the actual arc position and the ideal position and the temperature distribution around the arc are converted into arc offset index and arc hot spot index through feature processing technology. The arc drift and its severity are reflected through the arc offset index and arc hot spot index.

[0023] When the actual arc position deviates greatly from the ideal position, it usually indicates that the arc in the electric arc furnace may have deviated from the target melting area, and there is a potential risk of arc drift. The ideal position is usually the best smelting area set according to process requirements and equipment structure, which can ensure that heat is concentrated and effectively transferred to the molten pool. When the deviation increases, the arc may approach the furnace wall or deviate from the center of the molten pool, resulting in reduced thermal efficiency, prolonged smelting time, and may cause excessive wear of the furnace lining or other unstable factors in the furnace. Therefore, by monitoring and analyzing the deviation between the actual arc position and the ideal position, arc drift can be identified and prevented early, and adjustment measures can be taken in time.

[0024] In the monitoring window, the deviation between the actual arc position and the ideal position is converted into an arc offset index through feature processing technology. The specific steps are as follows: In order to quantify the degree of arc deviation, it is first necessary to establish a spatial deviation model between the actual arc position and the ideal arc position. Assume that the arc position is represented in three-dimensional space as , the ideal arc position is , then the arc space deviation vector D is defined as: ; The nonlinear normalization method is used to map the spatial offset to the exponential scale. The specific expression is: ,in, represents the normalized spatial offset vector, , as well as The exponential transformation of the form can enhance the sensitivity to smaller offsets, so that small offsets will not have too much impact, while larger offsets can be significantly amplified, thereby improving the accuracy of arc drift detection; The role of this step is to convert the spatial information of arc offset into normalized features, making subsequent calculations more robust.

[0025] After obtaining the normalized space offset vector Afterwards, the arc offset index is constructed by weighted geometric mean method, and the constructed expression is: ,in: is the arc offset indicator, , , They represent the weight coefficients along the x, y, and z axes, respectively, and are used to adjust the offset contribution in different directions. They can be adjusted adaptively according to factors such as the magnetic field in the furnace and the molten pool morphology. +1 ensures that even if the offset value in a certain direction is 0, the arc offset index calculation is still valid. Taking the geometric mean can reduce the impact of abnormal values ​​in a single direction and improve the stability of the arc offset index. The final -1 function is to restore the original scale so that when the offset is zero ; The purpose of this step is to aggregate the arc offset data into an overall offset index, which can quantify the degree of arc drift. The larger the value, the more serious the offset. Due to the use of exponential mapping and weighted geometric mean, the arc offset index is more tolerant to small offsets and more sensitive to severe drifts, which helps the intelligent control system make accurate judgments.

[0026] It can be seen from the arc offset index that within the monitoring window, the deviation between the actual arc position and the ideal position is converted into the arc offset index through feature processing technology. The larger the performance value of the arc offset index, the greater the deviation between the actual arc position and the ideal position, indicating that the risk of arc drift is higher, which may lead to uneven heat distribution, reduced energy utilization, and even damage to the furnace lining. On the contrary, when the arc offset index is small, it means that the arc position is relatively stable and the drift risk is low, indicating that the arc furnace is in a good operating state. Therefore, the size of the arc offset index can be used as an important basis for the intelligent control system to identify the stability of the arc, thereby achieving accurate energy consumption optimization and smelting control.

[0027] When the temperature distribution around the arc is uneven and the hot spots are obvious, it usually indicates that there is a potential risk of arc drift in the arc furnace. When the arc is operating normally, its heat transfer should be relatively uniform and mainly concentrated in the molten pool area to ensure that the metal is evenly heated and melted quickly. However, when arc drift occurs, some areas may form local high-temperature hot spots (hot spots) due to heat accumulation, while other areas may not be fully melted due to insufficient heat. This uneven temperature distribution may be caused by the offset of the arc position, abnormal fluctuations in the arc length, or uneven distribution of the charge, further exacerbating the instability of the arc. In addition, the appearance of hot spots may also cause local overheating of the furnace lining, accelerate damage to the furnace lining, increase maintenance costs and the risk of shutdown. Therefore, monitoring the uniformity of temperature distribution around the arc and the hot spot situation can be used as one of the important features to identify arc drift and provide early warning for the intelligent control system.

[0028] In the monitoring window, the temperature distribution around the arc is converted into arc hot spot indicators through feature processing technology. The specific steps are as follows: In the monitoring window, a high-resolution thermal imaging sensor is used to capture the temperature field data around the arc to form a spatial temperature distribution matrix. ,in Represents the pixel coordinates in the thermal image. In order to accurately identify the arc hot spot area, the temperature gradient aggregation function is used to calculate the spatial change rate of the temperature field around the arc. The calculation expression is: ,in, Represents the global gradient aggregation value of the temperature field, and Calculate the second-order derivatives of temperature in the horizontal and vertical directions to reflect the areas with drastic changes in the temperature field. is a weighting function that gives a higher weight to the arc center area to enhance the signal response within the arc influence range; The purpose of this step is to identify the area where the abnormal temperature gradient is concentrated, so as to detect the temperature change trend around the arc and provide a basis for the subsequent calculation of the arc hot spot index.

[0029] Using the global gradient aggregation value of the temperature field To calculate the arc hot spot index, the calculation expression is: ,in: It is an arc hot spot indicator, used to measure arc drift and its severity. is the adjustment coefficient, which is used to control the influence of the hot spot density on the final arc hot spot index. is the arc hot spot density index, defined as: ,in is the temperature gradient, It is the area around the arc and is used for normalization. The arc hot spot density index is used to measure the density of local hot spots in the temperature field. The purpose of this step is to accurately quantify the severity of arc hot spots, where the global gradient aggregation value of the temperature field It reflects the degree of drastic change of the temperature field around the arc and can capture the unevenness of temperature distribution. Measuring the local density of temperature gradients ensures that temperature anomalies are not only identified but also quantified in terms of their concentration.

[0030] It can be seen from the arc hot spot index that within the monitoring window, the temperature distribution around the arc is converted into the arc hot spot index through feature processing technology. The larger the performance value of the arc hot spot index, the greater the arc drift risk of the arc furnace. Conversely, the more stable the arc state of the arc furnace is, the smaller the drift risk is. The arc hot spot index quantifies the unevenness of the temperature field and the density of local hot spots by feature processing the temperature distribution around the arc. When arc drift occurs, the heat transfer path is offset, resulting in abnormally high temperatures (hot spots) in some areas, while other areas have insufficient temperatures. This phenomenon is manifested as a higher value in the arc hot spot index. On the other hand, when the arc is stable, the temperature distribution is more uniform, the hot spot area is less, and the index value is lower, indicating that the path of the arc heat transfer remains reasonable. Therefore, the arc hot spot index can not only reflect the existence of arc drift, but also quantify the severity of drift through its numerical value, and provide real-time feedback for the intelligent control system to dynamically adjust the electrode height and response speed, ensure that the arc is always in the best working state, improve energy utilization and reduce equipment loss.

[0031] The key quantitative indicators are used as feature vectors, and deep learning algorithms (such as recurrent neural networks, RNNs) are used to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, the arc state prediction result is output based on the current input feature vector to identify arc drift; The arc offset index and arc hot spot index are used as feature vectors, and the deep learning method is used to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, the drift index is output based on the current input feature vector. The drift index is used as the arc state prediction result to identify arc drift.

[0032] The arc offset index and arc hot spot index are used as feature vectors, and the feature vectors are trained using a deep learning algorithm to build an arc drift prediction model, specifically: First, it is necessary to systematically acquire and process the data of the arc offset index and the arc hot spot index so that they can be used as feature vectors for the deep learning model. Specifically, the arc offset index is mainly quantified by comparing the ideal position and the actual position of the arc in the molten pool area. When the arc drifts, the arc offset index will increase significantly with the increase of the drift degree; while the arc hot spot index measures the concentration and intensity of the local high-temperature area by extracting the characteristics of the temperature distribution around the arc. If the arc deviates significantly from the normal melting area, it will often form overheating or uneven temperature in a specific area, resulting in a surge in the arc hot spot index. In actual deployment, firstly, the internal data of the arc furnace is collected in real time through high-resolution sensors (such as thermal imaging, optical measurement, electrical signal acquisition, etc.), and these raw data are pre-processed by denoising, filtering, normalization and other operations to ensure the accuracy and consistency of the data. Subsequently, based on a carefully designed algorithm, a multi-dimensional analysis of the temperature field distribution and arc position information is performed to obtain numerical indicators that can directly reflect the arc offset and hot spot degree, and these two numerical indicators are recorded in the form of "arc offset indicator" and "arc hot spot indicator". After combining them into a two-dimensional or multi-dimensional feature vector, other auxiliary parameters that help to judge the arc state (such as arc voltage fluctuations, molten pool surface temperature differences, etc.) can be added as needed to jointly form richer input features. Through such data preparation and feature engineering, a solid foundation can be laid for the subsequent deep learning model training, so that the model can capture abnormal changes in the arc furnace more specifically.

[0033] After the feature vectors are constructed, these feature vectors can be trained using deep learning methods (such as convolutional neural networks, recurrent neural networks, or hybrid architectures that integrate multi-layer perceptrons) to establish an arc drift prediction model. During the training process, the historically collected arc operation data and the corresponding arc drift labels (or discrimination criteria) are divided into training sets and validation sets. Through repeated iterations of forward propagation and back propagation, the model gradually learns the nonlinear mapping relationship between arc offset indicators and arc hot spot indicators and arc drift risks. The multi-layer network structure of deep learning can extract higher-level feature combinations in the hidden layer, so that the model can not only identify simple numerical increases and decreases, but also mine additional information brought about by the interaction or superposition of the two, thereby making more accurate predictions of arc drift. When the training converges and achieves ideal results in the validation set, the model can receive the arc offset indicators and arc hot spot indicators calculated in real time during the online monitoring stage, and output the prediction results of the probability and severity of arc drift after network calculation.

[0034] The arc drift prediction model is not specifically limited here, and can realize the arc drift index and arc hot spot index Perform comprehensive analysis to generate drift index The arc drift prediction model can be used. In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the drift index The generated expression is: , where , Arc offset index and arc hot spot index The preset scaling factor of , Both are greater than 0.

[0035] It can be seen from the drift index that, within the monitoring window, the deviation between the actual arc position and the ideal position is converted into an arc offset index through feature processing technology. The larger the performance value of the arc offset index is, the larger the performance value of the arc hot spot index is, that is, the larger the performance value of the drift index generated when the arc state is predicted by the arc drift prediction model, indicating that the arc drift risk of the arc furnace is greater, and vice versa, the more stable the arc state of the arc furnace is, the smaller the drift risk is.

[0036] The drift index generated when the arc state is predicted by the arc drift prediction model is compared and analyzed with the preset drift index reference threshold to identify the arc drift. The specific steps are as follows: If the drift index is greater than the drift index reference threshold, it indicates that the arc furnace has an arc drift risk. If the drift index is less than or equal to the drift index reference threshold, it indicates that the arc operation of the arc furnace is stable and there is no arc drift risk.

[0037] When the arc drift prediction model detects the risk of arc drift, the electrode height is adjusted according to the preset adjustment strategy. According to the dynamic change of the electrode height, the arc is quickly restored to the ideal position, the arc position is corrected in real time, the arc is prevented from being too long or extinguished, the heat transfer efficiency is improved, and energy waste is reduced. At the same time, the furnace lining and electrode are protected from overheating damage. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity. When a relatively minor drift is detected, the normal response speed is maintained; in the case of severe drift, the response speed of the electrode lifting and lowering is accelerated to ensure faster and more accurate adjustments. When the arc drift prediction model detects the risk of arc drift, the electrode height is adjusted according to the preset adjustment strategy, and the electrode response speed is intelligently adjusted according to arc drifts of different severity. The specific steps are as follows: When the arc drift prediction model detects that the arc drift index exceeds the reference threshold, the electrode height is adjusted immediately to quickly return the arc to the ideal position. The goal of adjusting the electrode height is to maintain the arc length within the optimal range to avoid the arc being too long (energy diffusion) or too short (extinguishing). The calculation formula for adjusting the electrode height is as follows: ,in: is the adjusted electrode height, is the current electrode height, is the electrode height adjustment coefficient, which determines the sensitivity of electrode adjustment after the drift index exceeds the drift index reference threshold. It is the drift index predicted in real time by the arc drift prediction model, which is used to quantify the current arc deviation degree. is the drift index reference threshold, which is used to determine the safety limit of drift risk. The arc length change trend is as follows: if the arc is too long, the electrode height should be lowered; if the arc is too short, the electrode height should be raised. is a sign function that determines the direction of adjustment (positive values ​​indicate lowering the electrode, negative values ​​indicate raising the electrode); The arc length change trend refers to the direction and magnitude of the arc length change over time and operating conditions during the operation of the arc furnace. It can reflect whether the arc is in a stable state or unstable conditions such as elongation or shortening. The arc length is mainly affected by factors such as electrode height, arc voltage, current intensity, and charge distribution. When the arc is offset or disturbed, the arc may be abnormally elongated (energy diffusion, reduced heat transfer efficiency) or suddenly shortened (local overheating, increased risk of arc extinction). In order to quantify this trend, the arc length change trend can be calculated. , its mathematical expression can be defined as: ,in: is the arc length of the current monitoring window, is the arc length of the previous monitoring window; If the arc length changes , indicating that the arc is lengthening, and it may be necessary to adjust the electrode descent to shorten the arc; if the arc length change trend , indicating that the arc is shortening, and the electrode rise may need to be adjusted to extend the arc. By real-time monitoring of the arc length change trend, the electrode position can be adjusted in time before or just after the arc drift occurs to ensure arc stability, optimize energy transmission efficiency, and reduce energy loss and equipment damage during the smelting process.

[0038] The purpose of this step is to correct the arc position in real time to ensure that the arc heat is transferred to the correct area of ​​the molten pool, improve smelting efficiency, reduce energy loss, and reduce overheating damage to electrodes and furnace linings.

[0039] On the basis of completing the electrode height adjustment, it is also necessary to dynamically adjust the electrode response speed to adapt to arc drifts of different severity. When the drift is mild, the system maintains a normal response speed; when the drift is severe, it is necessary to speed up the electrode lifting and lowering to ensure faster and more accurate adjustment. The calculation formula for dynamic adjustment of the electrode response speed is as follows: ,in: The electrode adjustment speed is dynamically adjusted. Adjust the speed of the basic electrode, which is used for the electrode lifting rate under normal conditions. is the speed gain coefficient, which is used to adjust the speed increase as the drift index changes. The degree to which the drift index exceeds the drift index reference threshold is used to dynamically calculate the additional acceleration ratio; The purpose of this step is to improve the sensitivity of the adjustment, ensuring that the greater the arc drift, the faster the electrode adjustment speed, so as to avoid energy waste, extended smelting time or equipment damage caused by slow response. This method ensures dual regulation of electrode height and response speed, so that the arc furnace can maintain the best smelting effect and energy utilization under different operating conditions.

[0040] Through the above scheme, the arc stability during the operation of the arc furnace can be effectively improved, the energy waste caused by arc drift can be reduced, the smelting efficiency can be improved, and the service life of the equipment can be extended. Specifically, the method uses multi-sensor fusion technology to achieve accurate monitoring and data collection of the arc operation status. Combined with the deep learning prediction model, it can identify and predict the severity of arc drift in advance before or just when it occurs, ensuring the timeliness and accuracy of the adjustment response. At the same time, the scheme introduces intelligent electrode height adjustment and adaptive response speed optimization, which can dynamically adjust the electrode lifting rate according to the degree of arc drift, so that the arc can quickly return to the ideal state, prevent the arc from being too long (causing energy diffusion) or too short (causing arc extinction), thereby improving the utilization rate of electric energy (reducing unit energy consumption kWh / t), and reducing problems such as extended smelting time, lining damage, and abnormal electrode consumption caused by arc instability. In addition, the method reduces the dependence on manual intervention through real-time adjustment of the intelligent control system, improves the automation and intelligence level of the smelting process, enables metallurgical enterprises to reduce production costs while improving overall production efficiency and equipment safety, and promotes the metallurgical industry to develop in the direction of energy saving, intelligence and green.

[0041] The present invention provides Figure 2 An energy consumption control system for metallurgical equipment shown includes an arc state monitoring module, an arc drift feature extraction module, an arc drift prediction and identification module, and an electrode intelligent adjustment and dynamic response module; The arc status monitoring module monitors the arc operation status in real time by installing multiple sensors around the electrodes during the actual operation of the arc furnace; The arc drift feature extraction module extracts the key features reflecting the arc drift state and converts the complex raw data into quantitative indicators reflecting the arc drift and its severity; The arc drift prediction and identification module uses key quantitative indicators as feature vectors and uses deep learning methods to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, it outputs the arc state prediction result based on the current input feature vector to identify arc drift; The electrode intelligent adjustment and dynamic response module, when the arc drift prediction model detects the risk of arc drift, adjusts the electrode height according to the preset adjustment strategy, dynamically changes the electrode height, allows the arc to quickly return to the ideal position, and corrects the arc position in real time. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity.

[0042] An energy consumption control method for metallurgical equipment provided in an embodiment of the present invention is realized by the energy consumption control system of the above-mentioned metallurgical equipment. The specific method and process of an energy consumption control system of metallurgical equipment are detailed in the embodiment of the energy consumption control method for the above-mentioned metallurgical equipment, which will not be repeated here.

[0043] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0044] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0045] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0046] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0047] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0048] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0049] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0050] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0051] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for controlling energy consumption of metallurgical equipment, characterized in that: The following steps are involved: During the actual operation of the arc furnace, various sensors are installed around the electrodes to monitor the arc operation status in real time; Extract key features reflecting arc drift status and transform complex raw data into quantitative indicators reflecting arc drift and its severity; The key quantitative indicators are used as feature vectors, and the feature vectors are trained using deep learning methods to build an arc drift prediction model. After the arc drift prediction model is built, the arc state prediction result is output based on the current input feature vector to identify arc drift; When the arc drift prediction model detects the risk of arc drift, the electrode height is adjusted according to the preset adjustment strategy. By dynamically changing the electrode height, the arc is quickly restored to the ideal position and the arc position is corrected in real time. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity.

2. The energy consumption control method of metallurgical equipment according to claim 1, characterized in that: The key features reflecting the arc drift state are extracted, wherein the extracted features include the deviation between the actual arc position and the ideal position and the temperature distribution around the arc. Within the monitoring window, the deviation between the actual arc position and the ideal position and the temperature distribution around the arc are converted into arc offset index and arc hot spot index through feature processing technology. The arc drift and its severity are reflected through the arc offset index and arc hot spot index.

3. The energy consumption control method of metallurgical equipment according to claim 2, characterized in that: The arc offset index and arc hot spot index are used as feature vectors, and the deep learning method is used to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, the drift index is output based on the current input feature vector. The drift index is used as the arc state prediction result to identify arc drift.

4. The energy consumption control method of metallurgical equipment according to claim 3, characterized in that: The drift index generated when the arc state is predicted by the arc drift prediction model is compared and analyzed with the preset drift index reference threshold to identify the arc drift. The specific steps are as follows: If the drift index is greater than the drift index reference threshold, it indicates that the arc furnace has an arc drift risk. If the drift index is less than or equal to the drift index reference threshold, it indicates that the arc operation of the arc furnace is stable and there is no arc drift risk.

5. The energy consumption control method of metallurgical equipment according to claim 2, characterized in that: In the monitoring window, the deviation between the actual arc position and the ideal position is converted into an arc offset index through feature processing technology. The specific steps are as follows: In order to quantify the degree of arc deviation, a spatial deviation model between the actual arc position and the ideal arc position is first established. The arc position is expressed in three-dimensional space as , the ideal arc position is , then the arc space deviation vector D is defined as: ; The nonlinear normalization method is used to map the spatial offset to the exponential scale. The specific expression is: ,in, represents the normalized spatial offset vector; After obtaining the normalized space offset vector Afterwards, the arc offset index is constructed by weighted geometric mean method, and the constructed expression is: ,in: is the arc offset indicator, , , Respectively represent the weight coefficients along the x, y, and z axes, which are used to adjust the offset contribution in different directions.

6. The energy consumption control method of metallurgical equipment according to claim 2, characterized in that: In the monitoring window, the temperature distribution around the arc is converted into arc hot spot indicators through feature processing technology. The specific steps are as follows: In the monitoring window, a high-resolution thermal imaging sensor is used to capture the temperature field data around the arc to form a spatial temperature distribution matrix. ,in Represents the pixel coordinates in the thermal image. In order to accurately identify the arc hot spot area, the temperature gradient aggregation function is used to calculate the spatial change rate of the temperature field around the arc. The calculation expression is: ,in, Represents the global gradient aggregation value of the temperature field, and Calculate the second-order derivatives of temperature in the horizontal and vertical directions to reflect the areas with drastic changes in the temperature field. is a weighting function that gives a higher weight to the arc center area to enhance the signal response within the arc influence range; Using the global gradient aggregation value of the temperature field To calculate the arc hot spot index, the calculation expression is: ,in: It is an arc hot spot indicator, used to measure arc drift and its severity. is the adjustment coefficient, which is used to control the influence of the hot spot density on the final arc hot spot index. is the arc hot spot density index, defined as: ,in is the temperature gradient, It is the area of ​​the area around the arc and is used for normalization processing. The arc hot spot density index is used to measure the density of local hot spots in the temperature field.

7. The energy consumption control method of metallurgical equipment according to claim 4, characterized in that: When the arc drift prediction model detects the risk of arc drift, the electrode height is adjusted according to the preset adjustment strategy, and the electrode response speed is intelligently adjusted according to arc drifts of different severity. The specific steps are as follows: When the arc drift prediction model detects that the arc drift index exceeds the reference threshold, the electrode height is adjusted immediately. The electrode height is adjusted to maintain the arc length within the optimal range. The adjustment calculation formula is as follows: ,in: is the adjusted electrode height, is the current electrode height, is the electrode height adjustment coefficient, which determines the sensitivity of electrode adjustment after the drift index exceeds the drift index reference threshold. It is the drift index predicted in real time by the arc drift prediction model, which is used to quantify the current arc drift degree. is the drift index reference threshold, which is used to determine the safety limit of drift risk. is the arc length variation trend, is a sign function used to determine the adjustment direction.

8. The method for controlling energy consumption of metallurgical equipment according to claim 4, characterized in that: On the basis of completing the electrode height adjustment, the electrode response speed is dynamically adjusted to adapt to arc drifts of different severity. The calculation formula for dynamic adjustment of the electrode response speed is as follows: ,in: The electrode adjustment speed is dynamically adjusted. Adjust the speed of the basic electrode, which is used for the electrode lifting rate under normal conditions. is the speed gain coefficient, which is used to adjust the speed increase as the drift index changes. The degree to which the drift index exceeds the drift index reference threshold is used to dynamically calculate the additional acceleration ratio.

9. An energy consumption control system for metallurgical equipment, used to implement the energy consumption control method for metallurgical equipment as described in any one of claims 1 to 8, characterized in that: It includes arc state monitoring module, arc drift feature extraction module, arc drift prediction and identification module, and electrode intelligent adjustment and dynamic response module; The arc status monitoring module monitors the arc operation status in real time by installing multiple sensors around the electrodes during the actual operation of the arc furnace; The arc drift feature extraction module extracts the key features reflecting the arc drift state and converts the complex raw data into quantitative indicators reflecting the arc drift and its severity; The arc drift prediction and identification module uses key quantitative indicators as feature vectors and uses deep learning methods to train the feature vectors to build an arc drift prediction model. After the arc drift prediction model is built, it outputs the arc state prediction result based on the current input feature vector to identify arc drift; The electrode intelligent adjustment and dynamic response module, when the arc drift prediction model detects the risk of arc drift, adjusts the electrode height according to the preset adjustment strategy, dynamically changes the electrode height, allows the arc to quickly return to the ideal position, and corrects the arc position in real time. At the same time, the electrode response speed is intelligently adjusted for arc drifts of different severity.

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