High-frequency induction heating torch ignition energy consumption optimization system

Abstract

The present invention belongs to the field of adaptive control technology, and specifically relates to a high-frequency induction heating torch ignition energy consumption optimization system. The system comprises: a system modeling portion, an energy consumption optimization portion, and a dynamic frequency adaptive control portion. The system modeling portion is used to calculate total energy consumption by modeling the high-frequency impedance during torch heating with skin effect correction. The energy consumption optimization portion is used to find the current peak value, frequency, and upper limit of heating time corresponding to the minimum value of the comprehensive energy consumption optimization objective function based on the high-frequency effect and skin effect during torch heating. The dynamic frequency adaptive control portion is used to heat the torch based on the current peak value, frequency, and upper limit of heating time corresponding to the minimum value of the comprehensive energy consumption optimization objective function. The present invention can minimize energy consumption and parasitic losses.

Description

Technical Field

[0001] The invention belongs to the technical field of adaptive control, and in particular relates to a high-frequency induction heating torch ignition energy consumption optimization system. Background Art

[0002] With the continuous improvement of industrial automation and intelligent manufacturing levels, high-frequency induction heating technology, as an efficient and energy-saving heating method, has been widely used in aerospace, metallurgy, automobile manufacturing and other fields. Among them, torch ignition technology is a key link in the high-frequency induction heating system. Its working efficiency and energy consumption control are directly related to the performance and operating cost of the entire equipment. In the existing technology, the torch ignition system is mainly designed based on the traditional low-frequency electromagnetic heating principle, and usually adopts a fixed current, a fixed frequency and a preset heating time to achieve the ignition operation. Although this technical solution can meet the basic ignition requirements to a certain extent, it ignores the dynamic changes of electromagnetic parameters under high-frequency working conditions and their nonlinear effects, resulting in obvious energy waste, uneven heating, slow response and other problems in the actual application of the system.

[0003] In the existing technology, some researchers have improved high-frequency induction heating technology and proposed a torch ignition system based on feedback control. The basic idea is to achieve dynamic control of the ignition process by real-time monitoring of physical quantities such as temperature and power during the torch heating process, and then using a closed-loop control strategy to adjust the input parameters. For example, relevant literature mentions the use of PID controllers or fuzzy control algorithms to adjust the heating current. Although this method improves the response speed and stability of the system to a certain extent, its control algorithm is relatively simple and cannot accurately reflect the complex physical phenomena such as electromagnetic field distribution, skin effect, and resonance effect in high-frequency induction heating. As a result, there is still a large deviation between the actual energy consumption calculation and theoretical prediction under high-frequency working conditions. In addition, traditional control methods often only consider a single energy loss indicator, such as direct thermal energy consumption, while ignoring the additional energy loss caused by eddy current loss, non-ideal coupling, and other high-frequency effects, resulting in unsatisfactory overall energy consumption optimization of the system. In addition, in the existing technology, the torch heating system mostly uses a low-frequency approximate model to model high-frequency electromagnetic parameters. This model ignores the current concentration phenomenon caused by the skin effect under high-frequency conditions, resulting in large differences between the measured resistance and inductance parameters and the actual working state. For example, traditional inductance measurement methods are usually based on the assumption of uniform current distribution. In a high-frequency environment, since the current is mainly concentrated on the surface of the conductor, the effective resistance increases significantly and the equivalent inductance also changes. However, these factors are often not fully considered in the existing models. As a result, during the ignition process of the high-frequency induction heating torch, the system cannot accurately calculate the instantaneous ignition energy consumption and eddy current loss, which in turn affects the overall energy consumption optimization strategy. In addition, the existing technology mostly adopts fixed preset values ​​for the current control of the high-frequency induction heating torch, lacks a real-time dynamic adjustment mechanism, and cannot be adaptively adjusted according to changes in the actual working state of the torch. It is easy to cause excessive current fluctuations or insufficient response during the ignition process, reducing the heating efficiency and ignition stability of the system. Summary of the Invention

[0004] In light of this, the primary purpose of the present invention is to provide a high-frequency induction heating torch ignition energy consumption optimization system. This system utilizes a target optimization function to globally and jointly optimize key parameters during the ignition process, such as input current, operating frequency, and upper heating time limit. Combined with real-time closed-loop feedback and a dynamic adaptive adjustment mechanism, this system can minimize energy consumption and parasitic losses. By comprehensively characterizing the energy transmission, storage, and loss processes, the system can effectively address issues such as uneven electromagnetic field distribution, non-ideal coupling, and environmental interference in high-frequency environments, thereby significantly improving ignition efficiency and system stability and reducing overall operating costs.

[0005] The technical solution adopted in the present invention is as follows:

[0006] A high-frequency induction heating torch ignition energy consumption optimization system, the system comprising: a system modeling part, an energy consumption optimization part and a dynamic frequency adaptive control part; the system modeling part is used to model the high-frequency impedance during torch heating under skin effect correction, separate the equivalent resistance and equivalent inductance from the modeled high-frequency impedance, and thereby establish an ignition instantaneous energy consumption and eddy current loss model to calculate the total energy consumption; the energy consumption optimization part is used to establish a comprehensive energy consumption optimization objective function during torch heating based on the high-frequency effect and skin effect during torch heating, combined with the ignition instantaneous energy consumption and eddy current loss model, and find the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function; the dynamic frequency adaptive control part is used to heat the torch and dynamically adaptively adjust the current based on the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function.

[0007] Furthermore, the high-frequency impedance after modeling is expressed as:

[0008] ;

[0009] in, The frequency is High frequency impedance when is the imaginary number symbol; is the phase; The frequency is The equivalent inductance when The frequency is The equivalent resistance when .

[0010] Furthermore, the frequency is separated from the high-frequency impedance after modeling. The equivalent resistance is expressed as follows:

[0011] ;

[0012] in, is the reference resistance of the torch; is the conductor thickness of the torch; is skin depth; is the resistivity of the torch; is the magnetic permeability of the torch; is the weight parameter; is the index weight parameter; is the modulation parameter; is the resonant frequency of the torch; is the equivalent capacitance.

[0013] Furthermore, the frequency is separated from the high-frequency impedance after modeling. The equivalent inductance is expressed as follows:

[0014] ;

[0015] in, For low frequency inductance.

[0016] Furthermore, the ignition instantaneous energy consumption and eddy current loss model are expressed using the following formula:

[0017] ;

[0018] in, is the instantaneous energy consumption and eddy current loss during ignition; is the average cross-sectional area of ​​the flare; is the material conductivity of the torch; For time The current when is the current attenuation coefficient; is the average radius of the average cross-sectional area of ​​the torch.

[0019] Furthermore, the total energy consumption is calculated using the following formula:

[0020] ;

[0021] in, The upper limit of heating time; The energy deviation compensation term caused by non-ideal coupling and high-frequency effects is calculated using the following formula:

[0022] ;

[0023] in, is the peak current.

[0024] Furthermore, the objective optimization function Use the following formula to express it:

[0025] ;

[0026] The optimization goal is: ; The following constraints are met:

[0027] ;

[0028] ;

[0029] ;

[0030] By solving the optimization objective optimization function Current peak under optimization objectives and constraints ,frequency and upper limit of heating time , heating the torch.

[0031] Furthermore, the current is dynamically and adaptively adjusted using the following formula:

[0032] ;

[0033] in, is the initial current; Adaptive adjustment of the amplitude for the set current; For time The current when For time The current when .

[0034] Furthermore, the current adaptively adjusts the amplitude The value range is .

[0035] The above technical solution achieves the following beneficial effects: By meticulously modeling the electromagnetic response characteristics of the torch during heating, fully accounting for energy losses caused by current concentration, surface effects, and resonance under high-frequency operating conditions, the present invention achieves precise calculation and optimized control of the torch ignition process's energy consumption. Utilizing advanced modeling techniques, the system organically integrates the torch's internal resistive losses with magnetic energy storage to establish an energy consumption model that reflects the actual heating state, providing a solid data foundation for subsequent energy optimization and dynamic adaptive control. This system not only theoretically overcomes the limitations of traditional low-frequency heating models but also, in practical application, can capture subtle changes in the torch's operating state in real time, thereby dynamically balancing energy input and output during the ignition process. By utilizing a multi-parameter comprehensive optimization design, the system ensures that the torch rapidly achieves the desired heating effect while significantly reducing overall energy consumption. By meticulously calculating the instantaneous energy consumption of the torch ignition and the additional energy consumption caused by high-frequency effects, the system achieves comprehensive management of the heating process's energy consumption, enabling the equipment to maximize energy input during operation and reducing operating costs due to energy waste. The system also optimizes current and frequency control in real time through an adaptive adjustment mechanism, improving ignition efficiency while also effectively mitigating system instability caused by temperature fluctuations and material property changes. This adaptive control strategy enables the torch to maintain excellent operating conditions under a variety of complex operating conditions, significantly enhancing system reliability and durability. Furthermore, the present invention leverages advanced mathematical models and optimization algorithms to optimize energy consumption, achieving coordinated control of multiple key parameters such as heating time, operating frequency, and input current. Through closed-loop feedback and real-time data monitoring, the system dynamically adjusts operating conditions within a short period of time, effectively controlling every detail of the ignition process. This not only ensures optimal energy release at the moment of ignition, but also achieves unprecedented energy conversion efficiency throughout the heating process. Thanks to this multi-factor coupled optimization control strategy, the total energy consumption during operation is significantly lower than that of traditional heating systems, while also significantly improving the torch's ignition response speed and heating uniformity. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the system structure of a high-frequency induction heating torch ignition energy consumption optimization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] All features disclosed in this specification, or all steps in the disclosed methods or processes, except mutually exclusive features and / or steps, can be combined in any manner.

[0038] Any feature disclosed in this specification (including any appended claims and abstract), unless otherwise stated, may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0039] Example 1: Reference Figure 1 , a high-frequency induction heating torch ignition energy consumption optimization system, the system includes: a system modeling part, an energy consumption optimization part and a dynamic frequency adaptive control part; the system modeling part is used to model the high-frequency impedance during torch heating under the skin effect correction, separate the equivalent resistance and equivalent inductance from the modeled high-frequency impedance, and use this to establish an ignition instantaneous energy consumption and eddy current loss model to calculate the total energy consumption; the energy consumption optimization part is used to establish a comprehensive energy consumption optimization objective function during torch heating based on the high-frequency effect and skin effect during torch heating, combined with the ignition instantaneous energy consumption and eddy current loss model, and find the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function; the dynamic frequency adaptive control part is used to heat the torch according to the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function, and dynamically adaptively adjust the current.

[0040] Specifically, during high-frequency induction heating of a torch, the current density is unevenly distributed within the conductor, exhibiting a surface concentration phenomenon known as the skin effect. This results in significant differences in the impedance parameters under actual operating conditions compared to traditional low-frequency theories. Therefore, to accurately describe the electromagnetic characteristics of the torch during heating, the modeling component of this system first monitors the high-frequency impedance in real time. A skin effect correction method is then applied to match the measured raw impedance data with the theoretical model. Through mathematical derivation and experimental correction, a quantitative description of the actual current distribution within the torch is achieved. This modified modeling process isolates two core parameters from the overall high-frequency impedance: equivalent resistance and equivalent inductance. The former primarily reflects the energy lost as heat during the torch heating process, while the latter reflects the energy stored by the torch due to changes in the magnetic field during heating. This process not only considers the inherent physical properties of the material, such as resistivity and permeability, but also incorporates the effect of the actual operating frequency on skin depth. This ensures that the isolated equivalent resistance and equivalent inductance accurately reflect the dynamic changes of the torch under high-frequency operating conditions. Through this refined parameter extraction, the system modeling component was able to establish a comprehensive energy consumption model for the instantaneous energy consumption and eddy current losses during torch ignition. In practical applications, the calculation of instantaneous energy consumption during torch ignition is affected not only by the peak input current and operating frequency, but also by the magnetic field distribution within the torch and its dynamic changes over time. Furthermore, eddy current losses, a secondary energy loss, arise from the localized current distribution induced by the high-frequency magnetic field in the torch and its surrounding metal components, resulting in ineffective energy loss as heat.

[0041] To this end, the system modeling component comprehensively considers the two aforementioned energy consumption components during the torch heating process, organically combining the mathematical descriptions of instantaneous energy consumption and eddy current losses to develop an energy balance equation applicable to the ignition process of a high-frequency induction heating torch. This equation theoretically fully captures the complex coupling relationship between energy input, energy storage, and energy loss during torch heating. During the modeling process, not only were parameters such as input current peak value, frequency, and heating time derived in detail, but advanced correction algorithms were also employed to account for current density variations caused by the skin effect in the high-frequency electromagnetic field, incorporating the distribution characteristics of electromagnetic parameters into the energy consumption calculation to ensure accurate results. Furthermore, the system fully accounts for potential temperature fluctuations during the torch ignition process, the nonlinear effects of material properties with temperature, and local magnetic field interference. An adaptive correction mechanism was introduced into the model, enabling continuous parameter updates and corrections in practice to ensure the torch heating process is always in optimal working condition. By correcting the high-frequency impedance of the torch for the skin effect during heating, this method can separate the equivalent resistance and equivalent inductance. Obtaining these two parameters provides a key foundation for subsequent energy optimization. The equivalent resistance primarily describes the heat loss caused by uneven current distribution on the conductor surface during torch heating, and its value is typically higher than the traditional theoretical value under high-frequency conditions. The equivalent inductance, on the other hand, reflects the ability of the high-frequency magnetic field to store energy within the torch, and changes in its value directly affect the energy conversion efficiency during the torch ignition process.

[0042] This invention precisely measures the impedance of the torch under high-frequency operation and applies skin effect correction theory to the data, extracting equivalent resistance and equivalent inductance that reflect the true physical state. This approach not only eliminates deviations caused by uneven current distribution but also provides a reliable physical parameter basis for subsequent energy optimization. Based on this precise modeling result, the present invention further constructs a mathematical model linking the instantaneous energy consumption of the torch ignition and eddy current losses. This model theoretically describes the energy input, storage, and loss during the torch heating process. The instantaneous energy consumption primarily reflects the heat generated by resistive losses when current passes through the torch, while the eddy current losses arise from the additional heat generated by localized closed currents induced by the high-frequency magnetic field in the torch and surrounding metal components. Based on this quantitative analysis of these two energy consumption components, the energy optimization component proposes a comprehensive energy optimization objective function. This objective function organically integrates all energy loss factors during the torch heating process, taking into account both the electromagnetic energy transmission and conversion caused by high-frequency effects and the impact of the skin effect on energy consumption distribution in actual operation. Specifically, during the construction of the objective function, the coupling relationship between peak current, operating frequency, and the upper limit of heating time is mathematically described, enabling the precise quantification of the contribution of different parameters to energy loss. Establishing this objective function requires not only a deep understanding of the physical meaning of each parameter, but also the consideration of the dynamic changes caused by various factors during the actual heating process, such as ambient temperature, material properties, and electromagnetic field distribution.

[0043] To this end, the present invention utilizes advanced numerical simulation techniques and optimization algorithms to perform a global search and solution for the objective function, ultimately determining the peak current, operating frequency, and upper limit of the heating time that minimize the objective function. This minimizes overall energy consumption while ensuring that the torch ignites quickly and reaches the desired temperature. It is noteworthy that in this energy consumption optimization process, the system does not simply adjust a single parameter, but rather comprehensively considers the energy transfer and loss mechanisms throughout the entire torch heating process, striving to establish a dynamic feedback mechanism between the theoretical model and actual operation. This allows the optimization component to recalculate and adjust the objective function through real-time data updates, thus maintaining the system in its optimal operating state. This optimization method theoretically breaks through the limitations of traditional single energy consumption control strategies. By introducing high-frequency effects and skin effects into the energy consumption optimization objective function, it systematically and comprehensively characterizes the complex physical phenomena of energy transmission during the torch heating process. At the same time, the construction process of the comprehensive energy consumption optimization objective function embodies the advanced concept of multi-physics field coupling modeling. It not only considers the mutual influence between electromagnetic field distribution and thermal energy conversion, but also introduces a feedback regulation mechanism into the objective function, achieving an organic combination of theoretical model and practical control strategy. By solving the key parameters corresponding to the minimum value of the objective function, the present invention can provide an optimal energy consumption configuration scheme for the high-frequency induction heating torch ignition process, thereby ensuring that the torch can quickly achieve the expected heating effect at the moment of ignition, while effectively reducing energy waste throughout the heating process, achieving the dual goals of minimizing energy consumption and maximizing heating efficiency.

[0044] Example 2: The high-frequency impedance after modeling is expressed as:

[0045] ;

[0046] in, The frequency is High frequency impedance when is the imaginary number symbol; is the phase; The frequency is The equivalent inductance when The frequency is The equivalent resistance when .

[0047] Specifically, in the actual operation of the high-frequency induction heating torch, since the frequency changes involved in the heating process have dynamic characteristics, As a function of time, it reflects the real-time regulation of the operating frequency during the torch heating process, which is closely related to the skin effect. The skin effect causes the current to be mainly concentrated on the surface of the conductor, thereby making the equivalent resistance The impedance value under high frequency conditions is significantly higher than that under low frequency conditions. At the same time, the magnetic field formed by high frequency current inside the conductor will also cause equivalent inductance. Therefore, by introducing This conversion relationship not only achieves a direct response to frequency changes, but also tightly couples the magnetic field energy storage effect with the frequency, so that the formula can reflect the electromagnetic characteristics caused by the combined action of high-frequency effect and skin effect during the torch ignition process. In other words, the real and imaginary parts of the complex impedance in the formula respectively describe the two basic phenomena of energy loss in the form of heat energy and storage and periodic release in the form of magnetic energy during the torch heating process. This unified description provides an effective means for the accurate calculation of the torch heating energy consumption. In addition, the complex impedance modeling method used in this embodiment theoretically breaks through the limitations of the traditional real number model for heating system analysis. By considering both resistive and inductive elements, the model is more in line with the complex electromagnetic phenomena encountered in the actual operation of high-frequency induction heating. The large amount of induced current generated by the high-frequency induction at the moment of torch ignition makes the current distribution in the conductor no longer uniform, which in turn causes the local temperature to rise sharply. It is difficult to accurately describe this phenomenon if only the traditional model is used. The complex impedance model is precisely the equivalent parameters obtained by mathematically converting actual measurements. and Combined with the above, the dynamic characteristics of high-frequency impedance are accurately characterized. This characterization not only theoretically reveals the inherent mechanism of energy conversion during the torch heating process, but also provides the necessary data support for constructing a comprehensive energy consumption objective function in the energy consumption optimization part, thereby enabling the torch to achieve optimal energy utilization at the moment of ignition.

[0048] At the same time, the application of this formula is also highly adaptive. During the ignition process of the high-frequency induction heating torch, due to the influence of factors such as ambient temperature, material properties and heating time, the resistance and inductance parameters in actual operation often show a trend of dynamic change. This requires the system to be able to update its internal model in real time in order to accurately predict and control the energy consumption of the torch. Introducing the formula as a time function, the system can recalculate the current working status at each moment and The numerical value of the high-frequency impedance is thus highly consistent with actual operating conditions. This dynamic adjustment mechanism not only improves the system's robustness under actual operating conditions but also provides real-time feedback data for the dynamic frequency adaptive control component, enabling the entire high-frequency induction heating torch ignition energy optimization system to maintain optimal operation in rapidly changing environments. More importantly, the complex impedance representation in the formula enables the system to analyze the energy consumption of the torch heating process beyond simple DC or low-frequency energy consumption calculations. Instead, it can comprehensively consider the complex relationship between current distribution, heat loss, and magnetic energy storage under high-frequency conditions. This mathematical description not only enables accurate theoretical prediction of the energy consumption distribution during torch heating, but also, in practical applications, effectively adjusts heating parameters through real-time measurement and calculation, reducing the additional energy consumption caused by the skin effect, improving heating efficiency, and optimizing energy consumption during the ignition process. This complex impedance modeling approach enables the high-frequency induction heating torch ignition energy optimization system to achieve higher energy utilization and control accuracy, while also promoting the application of high-frequency electromagnetic theory in industrial heating.

[0049] Example 3: Separate the frequency from the high-frequency impedance after modeling The equivalent resistance is expressed as follows:

[0050] ;

[0051] in, is the reference resistance of the torch; is the conductor thickness of the torch; is skin depth; is the resistivity of the torch; is the magnetic permeability of the torch; is the weight parameter; is the index weight parameter; is the modulation parameter; is the resonant frequency of the torch; is the equivalent capacitance.

[0052] Specifically, The reference resistance of the torch reflects the inherent resistance of the torch under low frequency or ideal conditions. However, in actual operation, due to the high-frequency electromagnetic field, the internal current of the conductor is mainly concentrated on the surface (i.e., the skin effect), which greatly reduces the effective conductive cross-sectional area and causes the actual resistance to be much greater than the reference value. Represents the thickness of the torch conductor, which is related to the skin depth The ratio between them directly reflects the unevenness of current distribution; the skin depth according to Given, where is the resistivity of the torch material, is the magnetic permeability of the torch material, and This reflects the direct effect of the operating frequency on the skin depth. As the frequency increases, decreases, resulting in a ratio Increase, further amplifying the resistance increase caused by the skin effect. The weight parameter in the formula and exponential parameters It is used to adjust the contribution of this ratio to the equivalent resistance correction part. Its significance lies in reasonably adjusting the influence of high-frequency effect on resistance change according to actual measurement data and engineering experience, so that the model can more accurately reflect the distribution state of current on the conductor surface during torch heating and the additional energy consumption caused by it. Specifically, the exponential parameter reflects the strength of the nonlinear effect, and the weight parameter is used to quantify the amplitude of this nonlinear correction. At the same time, the modulation parameter in the formula and resonant frequency The resonance effect of the torch under high-frequency working state is further considered; in the high-frequency induction heating process, due to the equivalent inductance in the torch and equivalent capacitance The resonant circuit formed has a resonant frequency Determines the resonance state of the torch, and the torch operating frequency The deviation from the resonant frequency directly affects the amplitude and phase distribution of the current, which in turn has a significant impact on energy consumption. By introducing the transcendental function in the formula , the system can smoothly modulate the resistance change caused by the frequency deviation from the resonance point, The nonlinear characteristics of the function make the correction effect smaller when the torch operating frequency is close to the resonant frequency, while when the operating frequency deviates from the resonant state, the resistance correction effect increases rapidly, so that the system can reflect the dynamic characteristics of the electromagnetic parameters changing with the resonant state in real time during the high-frequency heating process.

[0053] Overall, the formula is obtained by the reference resistor The coupling with multiple correction factors fully reveals the dynamic change mechanism of the actual equivalent resistance caused by the skin effect and resonance effect under high-frequency conditions. Its physical significance lies in summarizing the complex energy transmission and loss phenomena in high-frequency electromagnetic induction heating into a controllable and predictable mathematical model, which provides theoretical support for the accurate calculation of energy consumption during the torch ignition process. Through this expression, the system can dynamically calculate the equivalent resistance according to the actual working state of the torch, thereby providing accurate parameters for subsequent energy consumption optimization, adaptive adjustment and other links, ensuring that the torch reaches the optimal energy utilization state during high-frequency heating. Furthermore, due to the close coupling relationship between the electromagnetic field and heat conduction under high-frequency working conditions, the model not only takes into account the additional heat energy loss caused by the current concentration on the conductor surface, but also incorporates the influence of magnetic energy storage under resonant conditions on resistance correction into the calculation, making the overall model more valuable for engineering applications. By continuously adjusting the parameters 、 as well as The value of, engineers can calibrate and optimize the model according to the actual measurement results, so that the calculated equivalent resistance can accurately match the performance of the torch in actual high-frequency induction heating. This model is not only highly innovative in theory, but also can adaptively update parameters through real-time feedback data in actual applications, thereby realizing dynamic monitoring of the electromagnetic characteristics of the torch and real-time optimization of energy consumption. In this way, the system can minimize energy loss and improve overall work efficiency while ensuring rapid ignition and uniform heating. Through the equivalent resistance expression described in this embodiment, the high-frequency induction heating torch ignition energy consumption optimization system can theoretically fully describe the resistive characteristics of the torch during operation and its dynamic changes with factors such as frequency and temperature, thereby providing accurate and real-time parameter input for the adaptive control of the system, ensuring that the torch can always maintain the best working state under actual high-frequency heating conditions.

[0054] Example 4: Separate the frequency from the high-frequency impedance after modeling The equivalent inductance is expressed as follows:

[0055] ;

[0056] in, For low frequency inductance.

[0057] Specifically, represents the inductance of the torch at low frequency or in ideal state, which reflects the basic ability of the torch to store magnetic energy without significant skin effect interference; and is the skin depth, and its expression is , which reflects the phenomenon that the effective conductive cross-sectional area decreases due to the current distribution tending to concentrate on the conductor surface under high-frequency working conditions; parameter Represents the thickness of the torch conductor, which is used as a scale parameter to measure the proportional relationship between the actual geometric size of the torch conductor and the skin depth; in this formula, the ratio It is a dimensionless parameter, and its value directly reflects the degree of influence of the skin effect on the electromagnetic distribution inside the conductor at the current working frequency. When the torch is in a lower frequency working state, the skin depth is relatively large. Higher values ​​of Approaches a smaller value, so that the correction factor in the bracket approaches 1, that is, Close to When the operating frequency increases and the skin depth decreases, the dimensionless parameter decreases, making the exponential term gradually approach 1. At this time, the minus term in the correction factor is close to 0.2, which ultimately reduces the actual measured equivalent inductance to This variation is of great significance in the high-frequency induction heating torch ignition energy consumption optimization system because it reveals that under high-frequency working conditions, since the current is mainly distributed along the surface of the conductor, the energy stored in the torch magnetic field shows a significant downward trend, resulting in a decrease in the inductance value, which in turn affects the system's energy consumption and heating efficiency.

[0058] Through this formula, the system can dynamically and in real time calculate the equivalent inductance at the current operating frequency, thereby providing accurate parameter input for subsequent energy consumption calculations, objective function construction, and adaptive control modules. Furthermore, this formula not only reflects the trend of inductance value changes with frequency, but also effectively incorporates the non-uniformity of electromagnetic field distribution inside the torch into the calculation scope by introducing the ratio of skin depth to conductor thickness, making the model more accurate and adaptable. Especially in practical applications, the heating process of the torch may involve factors such as temperature changes, material aging, and fluctuations in the external electromagnetic environment, all of which will affect the resistivity of the conductor. and magnetic permeability This indirectly affects the depth of the skin, and the formula is updated dynamically To reflect this change, the calculation result of equivalent inductance is always consistent with the actual working condition. After correction, the formula can accurately describe the actual energy storage capacity under high-frequency conditions, provide a theoretical basis for the transmission and storage of magnetic energy at the moment of torch ignition, and also provide key parameters for optimizing system energy consumption. It is worth noting that the model adopts the form of an exponential decay function, which reflects the nonlinear characteristics of the physical process, that is, as the ratio of skin depth to conductor thickness changes, the correction effect of the inductance value does not change linearly, but shows a certain attenuation trend, which is consistent with the situation that the current distribution in actual high-frequency electromagnetic phenomena gradually tends to saturation. By continuously calibrating the coefficients in the formula (such as 0.2 and 0.3), the adaptability of the model to different torch materials and geometric dimensions can be further improved, ensuring that the equivalent inductance of the torch can be accurately predicted under different working conditions.

[0059] Example 5: The ignition instantaneous energy consumption and eddy current loss model is expressed using the following formula:

[0060] ;

[0061] in, is the instantaneous energy consumption and eddy current loss during ignition; is the average cross-sectional area of ​​the flare; is the material conductivity of the torch; For time The current when is the current attenuation coefficient; is the average radius of the average cross-sectional area of ​​the torch.

[0062] Specifically, in the formula, the first term uses This part reflects the high frequency current at the moment of torch ignition. Flows through the equivalent resistance corrected for skin effect The heat dissipation caused by The square of the current represents the decisive role played by the current in heat loss, and After the accurate extraction of the aforementioned modeling part, the torch can dynamically reflect the working frequency The actual resistance change caused by the uneven distribution of the electromagnetic field is calculated. At the same time, the exponential function introduced in the denominator plays a role in smooth attenuation modulation. The function uses the current attenuation coefficient To control the actual ignition time Resonant frequency with the torch The sensitivity of energy consumption changes when the deviation between the determined moments is determined, thereby realizing the dynamic regulation of the energy transmission efficiency change of the torch near the resonant state. Its essence is to describe the nonlinear change of energy input during the torch ignition process in a mathematical way, so that the energy consumption at the ignition moment can be corrected accordingly with the change of the actual working state of the system. At the same time, the second item describes the energy consumption caused by the eddy current effect, where The electrical conductivity of the torch material directly determines the material's ability to respond to eddy currents. is the magnetic permeability, which reflects the ability of the torch material to store and release magnetic energy under high-frequency magnetic field; in addition, The average radius represents the average cross-sectional area of ​​the torch, and its geometric dimensions have a decisive influence on the magnetic field distribution. is the skin depth, and its value is determined by the relationship between the resistivity, magnetic permeability and angular frequency of the material under high-frequency conditions. It reflects the physical property that the current is mainly distributed on the surface of the conductor under high-frequency induction heating conditions. is the average cross-sectional area of ​​the torch, which determines the energy transfer capacity of the torch in the cross-sectional area direction. Therefore, this part of the energy consumption model mathematically adopts the square relationship between the magnetic energy density and the material parameters, reflecting the quadratic coupling effect between the energy density in the magnetic field and the current and the magnetic parameters of the material. The entire model comprehensively considers the complex coupling relationship between the current, impedance, skin effect and eddy current loss during the ignition process of the torch. It not only considers the direct heat energy loss generated by the flow of current, but also takes into account the eddy current effect induced by the high-frequency magnetic field in the material, so that the model can accurately reflect the energy consumption characteristics of the torch under the actual high-frequency working state. In practical applications, this model not only provides theoretical support for energy consumption optimization, but also provides real-time data support for the subsequent dynamic frequency adaptive control part, so that the system can dynamically monitor and regulate the input current and related parameters at the moment of ignition to achieve the best balance between energy consumption and heating efficiency. It's worth noting that the exponential function modulation term used in the formula is designed to smooth the current decay process. When the torch's operating state is close to resonance, energy loss changes minimally, but increases rapidly as it deviates further from resonance, effectively preventing instability caused by drastic fluctuations in system parameters. The second term, eddy current loss, quantitatively describes the non-ideal dissipation of energy in the high-frequency magnetic field by coupling with skin depth and cross-sectional area. This dual energy consumption model demonstrates the present invention's profound understanding of complex electromagnetic effects in the high-frequency induction heating torch ignition energy optimization system. It transcends the limitations of traditional single energy consumption calculation methods while fully accounting for the interactions between current distribution, magnetic field energy storage, and heat loss under high-frequency operating conditions. Consequently, through in-depth research and optimization of the ignition instantaneous energy consumption and eddy current loss models, the system can minimize energy losses while ensuring the torch rapidly achieves the desired heating effect, improving overall heating efficiency and system stability, and ultimately achieving the goal of efficient and energy-saving torch ignition.

[0063] Example 6: Calculate the total energy consumption using the following formula:

[0064] ;

[0065] in, The upper limit of heating time; The energy deviation compensation term caused by non-ideal coupling and high-frequency effects is calculated using the following formula:

[0066] ;

[0067] in, is the peak current.

[0068] Specifically, when calculating the instantaneous energy consumption, the present invention adopts the basic heat energy loss theory based on the product of the square of the current and the equivalent resistance, and supplements it with a modulation factor to smoothly describe the sensitivity of the energy consumption change of the torch when it is close to the resonance state. This modulation effect can be used to calculate the difference between the torch operating frequency and its resonant frequency. When there is a large deviation between the energy consumption and the time, it will quickly reflect the reduction of energy transmission efficiency. By integrating the instantaneous energy consumption, the system can accurately calculate the time from the start of heating to the end of heating. The energy consumption accumulated during the period is dynamically reflected through continuous integration, reflecting the time-varying characteristics of the energy consumption of the torch during the entire ignition process, thereby providing a reliable data basis for overall energy consumption optimization. However, in practical applications, due to the complex non-ideal coupling phenomenon involved in the high-frequency induction heating process and the local electromagnetic field imbalance caused by the high-frequency effect, there is often a certain deviation between the theoretically calculated instantaneous energy consumption and the actual energy consumption. In order to overcome this shortcoming, this embodiment introduces a compensation term The main purpose of the compensation is to correct the energy loss caused by non-ideal coupling and high frequency effects. The design of the compensation item fully considers the current peak during the torch ignition process. The operating frequency is also introduced. The degree of deviation from the resonant frequency is nonlinearly amplified using an exponential decay function. This automatically increases the compensation amplitude when the flare operating frequency is far from the resonant frequency, while minimizing the compensation effect as the frequency approaches the resonant state. This design concept not only ensures that the compensation term reflects the difference between actual energy consumption and theoretical value in real time under different operating conditions, but also provides an effective correction mechanism for local energy waste caused by high-frequency effects during the flare ignition process, making the calculated total energy consumption more accurate to actual operation.

[0069] In the entire energy consumption calculation model, the integral term and the compensation term work together to form a closed-loop feedback mechanism. This ensures that the system not only dynamically accumulates instantaneous energy consumption but also automatically corrects for energy deviations caused by external disturbances, material parameter changes, frequency fluctuations, and other high-frequency effects. This method not only comprehensively monitors energy transfer and loss during the flare ignition process but also maintains the accuracy and stability of the overall energy consumption calculation through real-time adjustment of the compensation term when encountering nonlinear fluctuations within the system or changes in the external environment. This method theoretically overcomes the shortcomings of traditional single-integration methods in high-frequency energy consumption calculations, enabling energy consumption assessment to move beyond the simple addition of instantaneous data and comprehensively consider the additional energy consumption caused by complex electromagnetic phenomena, thereby achieving the goal of optimizing energy consumption control during the flare ignition process. Furthermore, the total energy consumption calculation formula proposed in this embodiment has extremely high adaptability and practicality in engineering practice. Because torches are often affected by temperature fluctuations, material aging, and electromagnetic environment fluctuations during actual operation, their high-frequency electromagnetic characteristics undergo subtle changes over time. The combination of integral calculations and compensation mechanisms is designed to address this dynamic characteristic. By continuously integrating instantaneous energy consumption data at each moment during the heating process, the system can capture subtle differences in energy consumption during the initial, middle, and final stages of ignition. The compensation term automatically adjusts the system's energy compensation by providing real-time feedback on the relationship between the torch's operating frequency and resonant frequency, thereby achieving dynamic balance and control of overall energy consumption.

[0070] Example 7: Target Optimization Function Use the following formula to express it:

[0071] ;

[0072] The optimization goal is: ; The following constraints are met:

[0073] ;

[0074] ;

[0075] ;

[0076] By solving the optimization objective optimization function Current peak under optimization objectives and constraints ,frequency and upper limit of heating time , heating the torch.

[0077] Specifically, the construction of the objective optimization function fully considers that during the high-frequency induction heating process of the torch, the current in the conductor is mainly distributed on the surface under the influence of the skin effect, resulting in a significant difference in the actual effective current flowing through the conductor compared to that under low-frequency conditions. Therefore, an equivalent resistance corrected by the skin effect is introduced into the model, and its expression includes the reference resistance And by the weight parameter , index parameters and modulation parameters The correction factor is determined by the transcendental function Torch operating frequency and resonant frequency The deviation between the two is smoothly modulated, so that when the torch operating frequency is close to the resonant state, the resistance correction effect is weak, and when it deviates from the resonant state, the correction effect is rapidly enhanced, thus truly reflecting the energy loss characteristics of the torch under high-frequency heating conditions. At the same time, the second part describing the eddy current loss is introduced into the objective function, which is calculated by the conductivity of the torch material. , magnetic permeability and the cross-sectional area of ​​the flare and the mean radius The energy loss phenomenon caused by eddy current in high-frequency magnetic field is described in the form of secondary coupling, and combined with the skin depth This parameter reflects the energy loss induced by the magnetic field under high frequency conditions. In order to fully reflect the dynamic characteristics of instantaneous energy consumption and eddy current loss during the torch ignition process, the target optimization function adopts an integral method to integrate the entire heating cycle from the initial moment to the upper limit of the heating time. The energy consumption in the whole heating cycle is accumulated, and the unevenness of the instantaneous energy consumption over time is taken into account in the integral expression. By continuously summing the integral expression, the system can obtain a theoretical value representing the total energy consumption in the whole heating cycle.

[0078] However, since the actual heating process is affected by non-ideal coupling and high-frequency effects, which leads to a deviation between the theoretical model and the actual energy consumption, an additional compensation term is set in the objective function. , the compensation term is related to the current peak , operating frequency And it is closely related to the degree of deviation between the torch and the resonant state. Its design idea is to use the exponential decay function to nonlinearly amplify and correct the energy consumption error caused by non-ideal factors, so that when the operating frequency of the torch is far away from the resonant frequency, the compensation term increases rapidly, thereby compensating for the additional energy loss caused by the high-frequency effect; and when the operating frequency is close to the resonant state, the influence of the compensation term is weakened accordingly, ensuring that the system energy consumption calculation is more in line with the actual operating conditions. Therefore, this target optimization function not only organically integrates the theoretical calculation of instantaneous energy consumption and eddy current loss, but also corrects the energy consumption deviation caused by the non-ideality of the system through the compensation mechanism, so that the overall model has higher accuracy and adaptability in theory. In this optimization model, the optimization goal of the system is to make Get the minimum value, that is, by solving To determine the optimal parameter combination, the specific implementation process is to convert the objective function to the current peak value , operating frequency And the upper limit of heating time By calculating the partial derivative and setting it equal to zero, the parameter solution that satisfies the optimization conditions is obtained. This method mathematically embodies the extreme value principle and optimal control theory. It dynamically adjusts key parameters through continuous feedback from actual system data, achieving closed-loop adaptive control. In practice, due to the time-varying and nonlinear characteristics of the electromagnetic parameters exhibited by the torch during high-frequency induction heating, the system requires not only a rigorous theoretical energy optimization model but also the ability to obtain dynamic data on the torch's operating state in real time. Using sensors and feedback mechanisms, the model parameters can be updated to ensure that the optimal parameters obtained truly reflect the current torch operating conditions. Through this global optimization method, the torch can obtain the optimal combination of current, frequency, and upper limit of heating time at the moment of ignition. This minimizes overall energy consumption and energy losses caused by high-frequency effects while meeting the requirements of rapid ignition and heating. Furthermore, the parameters in the objective optimization function not only have clear physical meanings in their mathematical expression but have also been validated by extensive experimental data in engineering applications. The model's fitting accuracy can be further improved by adjusting the weight parameters and modulation coefficients, ensuring that the system maintains excellent energy control under various operating conditions. Especially in actual application environments, the operating state of the torch may undergo slight changes due to the influence of external temperature, material aging, and other uncertain factors. The objective function can quickly respond to these changes through the dynamic integration of instantaneous energy consumption and eddy current loss and real-time correction of compensation terms, thereby achieving adaptive control through feedback regulation. This energy consumption optimization method based on the optimization objective function not only enables the high-frequency induction heating torch ignition process to achieve a comprehensive description of various energy consumption factors in theory, but also significantly improves the ignition efficiency and reduces energy waste through the joint optimization of key parameters in the actual control strategy, thereby improving the overall performance of the system.

[0079] Example 8: Dynamically and adaptively adjust the current using the following formula:

[0080] ;

[0081] in, is the initial current; Adaptive adjustment of the amplitude for the set current; For time The current when For time The current when .

[0082] Specifically, in the formula, The adjustment is not only affected by the current working frequency, but also combined with the historical time points and The current change trend is to ensure that the current regulation process will not cause system instability due to sudden changes. Specifically, This item ensures that the current regulation can keep pace with the current operating frequency of the torch, thereby reducing the electromagnetic field mismatch problem caused by frequency drift. The term "follows" applies a logarithmic transformation to the rate of change of the historical current difference, modifying the current amplitude in a nonlinear manner. This results in a gentle adjustment when the current rate of change is low, while the system responds promptly when the current rate of change is high, thus ensuring smooth and optimized energy input during the ignition process. The key benefit of this mechanism lies in the introduction of dynamic feedback of current changes, enabling the system to adapt to actual operating conditions and improve the stability and energy efficiency of the entire torch ignition process. The theoretical basis of this method stems from the current distribution characteristics within the conductor during high-frequency induction heating and the nonlinear dynamic response of the electromagnetic induction system. Under high-frequency operating conditions, the current in the torch conductor is affected by the skin effect, causing it to be primarily concentrated on the conductor surface. This phenomenon causes the current density to exhibit significant dynamic variations in time and space. Current control strategies that rely solely on fixed amplitude or linear adjustment methods are difficult to adapt to the complex electromagnetic environment during torch ignition, potentially leading to localized overheating and reduced energy utilization efficiency. Therefore, the present invention proposes a dynamic adaptive adjustment method based on the historical current change rate. The uniqueness of this method is that it uses the data of the first two time steps as input to calculate the current change trend and performs nonlinear correction on its influence through logarithmic transformation, so that the current adjustment process of the system is smoother and has adaptive characteristics.

[0083] Especially in practical applications, the working state of the torch may be affected by factors such as changes in material properties, ambient temperature fluctuations, and system electromagnetic field interference. The adaptive adjustment strategy adopted by this method can maintain the stability and optimality of the current input under these external conditions. For example, when the torch enters the high-frequency resonance state, the current change rate of the system is relatively stable. The value of is small, so the adjustment item The influence of the current is weakened, and the current adjustment tends to be stable, thus ensuring that the system does not generate additional energy fluctuations when operating efficiently. When the torch frequency shifts, or the electromagnetic field distribution of the system changes due to a sudden change in the current input, this method can respond quickly by increasing the The influence weight of the term is increased, so that the current adjustment amplitude is enhanced, thereby timely correcting the energy input state of the system and enabling the torch to quickly return to the optimal working state. In addition, the mathematical structure of this method fully considers the nonlinear characteristics of the high-frequency induction heating process. In particular, by introducing the logarithmic correction term, the rate of change of the current adjustment is not a simple linear relationship, but is dynamically adjusted according to the magnitude of the historical rate of change of the current. This mechanism ensures that the system can maintain a strong adaptability under different working conditions, while avoiding the system oscillation problem that may be caused by an excessively fast adjustment rate. In fact, the application of this method in engineering practice shows that compared with traditional fixed current control or simple linear adjustment methods, the adaptive current adjustment method proposed in the present invention can effectively reduce the energy loss during the ignition process and improve the stability of the system, so that the torch can complete the ignition process under the optimal energy consumption conditions, thereby improving the overall heating efficiency and extending the life of the equipment. Furthermore, the advantage of this method is also reflected in its high compatibility with the high-frequency induction heating torch ignition energy consumption optimization system. Since the system's energy consumption optimization objective function has already been solved based on the coupled relationship between current, frequency, and time, this adaptive adjustment method ensures that the actual current input is consistent with the optimization calculation results while ensuring optimal energy consumption, avoiding deviations caused by changes in the external environment or internal system state. In other words, this method not only achieves dynamic adjustment of current input, but also constructs a real-time control mechanism that matches the overall energy consumption optimization framework, making energy management of the torch ignition process more precise and ensuring that the system always operates on the optimal control curve.

[0084] Example 9: Current Adaptive Adjustment Amplitude The value range is .

[0085] Specifically, choose This range, first of all, reflects the requirement for extremely high-precision control of the torch current input. The adjustment range cannot be too large, causing the current to change too quickly and cause system oscillation or energy waste, nor can the adjustment range be too small, failing to respond in time to instantaneous deviations caused by external environment or internal parameter fluctuations, thereby failing to achieve real-time optimization of energy consumption and heat distribution under high-frequency electromagnetic field conditions. During the torch ignition process, the current generated by high-frequency electromagnetic induction is mainly concentrated on the surface of the conductor due to the skin effect, resulting in the actual effective current distribution showing a local amplification phenomenon, and this phenomenon is summarized as parameters such as corrected equivalent resistance and equivalent inductance in the instantaneous energy consumption calculation. Through precise measurement of these parameters and model correction, the accurate current response curve of the torch under different working conditions can be obtained. Therefore, the lower limit of ΔI is set to , in fact, is to ensure that within a very small range of changes, the system can capture the weak signal of current fluctuations, so as to make fine adjustments to the electromagnetic field distribution and local heat conduction; and the upper limit is limited to This is to prevent instabilities caused by excessive current adjustments. For example, rapid current fluctuations can cause the internal temperature of the torch to rise sharply, leading to local overheating or degradation of material properties. It is precisely this scientific setting of upper and lower limits that enables the entire system to maintain a high dynamic response speed when facing complex actual operating conditions, while ensuring smooth and continuous changes in energy consumption during the ignition process, thereby achieving optimal control of global energy consumption.

[0086] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these specific embodiments are merely illustrative, and that those skilled in the art may omit, substitute, and modify the details of the methods and systems described above without departing from the principles and spirit of the present invention. For example, combining the above method steps to perform substantially the same functions in substantially the same manner to achieve substantially the same results falls within the scope of the present invention. Accordingly, the scope of the present invention is limited solely by the appended claims.

Claims

1. High-frequency induction heating torch ignition energy consumption optimization system, characterized by: The system includes: a system modeling part, an energy consumption optimization part and a dynamic frequency adaptive control part; the system modeling part is used to model the high-frequency impedance during torch heating under skin effect correction, separate the equivalent resistance and equivalent inductance from the modeled high-frequency impedance, and use this to establish an ignition instantaneous energy consumption and eddy current loss model to calculate the total energy consumption; the energy consumption optimization part is used to establish a comprehensive energy consumption optimization objective function during torch heating based on the high-frequency effect and skin effect during torch heating, combined with the ignition instantaneous energy consumption and eddy current loss model, and find the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function; the dynamic frequency adaptive control part is used to heat the torch and dynamically adaptively adjust the current based on the current peak value, frequency and heating time upper limit corresponding to the minimum value of the comprehensive energy consumption optimization objective function; the modeled high-frequency impedance is expressed as: ； in, The frequency is High frequency impedance when is the imaginary number symbol; is the phase; The frequency is The equivalent inductance when The frequency is The equivalent resistance when The frequency is separated from the modeled high-frequency impedance to The equivalent resistance is expressed as follows: ； in, is the reference resistance of the torch; is the conductor thickness of the torch; is skin depth; is the resistivity of the torch; is the magnetic permeability of the torch; is the weight parameter; is the index weight parameter; is the modulation parameter; is the resonant frequency of the torch; is the equivalent capacitance; The frequency is separated from the modeled high-frequency impedance to The equivalent inductance is expressed as follows: ； in, is a low-frequency inductor; The ignition instantaneous energy consumption and eddy current loss model are expressed using the following formula: ； in, is the instantaneous energy consumption and eddy current loss during ignition; is the average cross-sectional area of ​​the flare; is the material conductivity of the torch; For time The current when is the current attenuation coefficient; is the average radius of the average cross-sectional area of ​​the torch.

2. The high-frequency induction heating torch ignition energy consumption optimization system according to claim 1, characterized in that: The total energy consumption is calculated using the following formula: ； in, The upper limit of heating time; The energy deviation compensation term caused by non-ideal coupling and high-frequency effects is calculated using the following formula: ； in, is the peak current.

3. The high-frequency induction heating torch ignition energy consumption optimization system according to claim 2, characterized in that: Objective optimization function Use the following formula to express it: ； The optimization goal is: ; The following constraints are met: ； ； ； By solving the optimization objective optimization function Current peak under optimization objectives and constraints ,frequency and upper limit of heating time , heating the torch.

4. The high-frequency induction heating torch ignition energy consumption optimization system according to claim 3, characterized in that: The current is dynamically and adaptively adjusted using the following formula: ； in, is the initial current; Adaptive adjustment of the amplitude for the set current; For time The current when For time The current when .

5. The high-frequency induction heating torch ignition energy consumption optimization system according to claim 4, characterized in that: Current adaptive adjustment range The value range is .

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