An adaptive sealing control method, system and device for a rotary kiln sealing system

Abstract

This specification discloses an adaptive sealing control method, system, and device for a rotary kiln sealing system, relating to the field of rotary kiln sealing technology and applied to rotary kiln sealing systems. The rotary kiln sealing system includes a stationary ring connected to the outer cylinder of the rotary kiln and a rotating ring connected to the inner cylinder of the rotary kiln. The stationary ring surrounds both sides of the rotating ring, and packing is provided on both side walls of the rotating ring and the stationary ring, respectively. A pressure ring and a spring are provided on the outer side of the packing, with the spring pressing the pressure ring against the packing. The method includes: acquiring multi-dimensional real-time sensor data; calculating the current comprehensive leakage rate based on the intermediate chamber pressure; performing fuzzy inference to determine the clamping force adjustment coefficient to determine the target clamping force; and calculating the target control quantity using an incremental PID control algorithm based on the target clamping force and the axial clamping force, thereby adjusting the clamping force of the pressure ring on the carbon fiber packing through the target control quantity.

Description

Technical Field

[0001] This specification relates to the field of rotary kiln sealing technology, and in particular to an adaptive sealing control method, system and equipment for a rotary kiln sealing system. Background Technology

[0002] As a core piece of equipment in high-temperature calcination, the sealing performance of a rotary kiln directly affects the stability of the process atmosphere, energy consumption, and equipment lifespan. Currently, the mainstream rotary kiln sealing structures in industrial applications include flexible fish-scale seals, graphite block seals, air-pump seals, and traditional mechanical end-face seals. Each of these sealing structures has its limitations in actual operation. Flexible fish-scale seals are prone to fatigue wear under long-term alternating loads, leading to a gradual increase in the sealing gap. While graphite block seals have self-lubricating properties, their brittle material cannot withstand the intense movement of the kiln body and the erosion of particulate matter. Air-pump seals rely on a complex air system to maintain a non-contact state, resulting in high energy consumption and potential disruption of the kiln's thermal balance. Traditional mechanical end-face seals are extremely sensitive to the dynamic precision of the kiln body, and swaying during operation can easily lead to seal separation.

[0003] Based on the aforementioned sealing structures, existing control methods typically employ static or semi-static clamping force settings. This means that the spring preload or bolt torque is preset based on experience during equipment installation and remains unchanged during operation. For example, utility model patent CN208417534U discloses a graphite block-type sealing structure where a spring applies preload to the outside of the packing. However, in actual operation of a rotary kiln, the ideal clamping force required at the sealing interface dynamically changes due to continuous wear of the carbon-based sealing packing, axial and radial movement caused by kiln thermal expansion, and vibrations caused by changes in material load. Static clamping force settings cannot compensate for these changes in real time. When the clamping force is too low, the sealing gap widens, leading to process gas leakage and external air infiltration, disrupting the required atmospheric stability within the kiln. When the clamping force is too high, friction between the sealing packing and the kiln body intensifies, accelerating material wear and increasing drive energy consumption. Furthermore, existing control methods lack real-time sensing capabilities for the sealing status. Maintenance personnel can only passively intervene after a significant leak occurs or the equipment shuts down abnormally, failing to achieve predictive maintenance.

[0004] During the adaptive control of the rotary kiln sealing system, the clamping force cannot be dynamically adjusted according to the real-time status of the sealing interface. This results in a mismatch between the static clamping force and the dynamic operating conditions, leading to unstable sealing performance and affecting product quality. Summary of the Invention

[0005] This specification provides one or more embodiments of an adaptive sealing control method, system, and device for a rotary kiln sealing system, which addresses the following technical problem: In the adaptive control process of a rotary kiln sealing system, it is impossible to dynamically adjust the clamping force according to the real-time status of the sealing interface, resulting in a mismatch between the static clamping force and the dynamic working conditions, leading to unstable sealing performance and affecting product quality.

[0006] One or more embodiments of this specification employ the following technical solutions: This specification provides one or more embodiments of an adaptive sealing control method for a rotary kiln sealing system. The rotary kiln sealing system includes a stationary ring connected to the outer cylinder of the rotary kiln and a rotating ring connected to the inner cylinder of the rotary kiln. The stationary ring surrounds both sides of the rotating ring. Packing is provided on both side walls of the rotating ring and the stationary ring, respectively. A pressure ring and a spring are provided on the outer side of the packing, and the spring presses the pressure ring against the packing. The method includes: acquiring multi-dimensional real-time sensor data, wherein the multi-dimensional real-time sensor data includes data from the pressure ring and carbon fiber... The axial clamping force between the packing rings, the pressure in the intermediate chamber corresponding to the intermediate sealing chamber, the axial movement of the kiln body, and the radial runout are considered. Based on the intermediate chamber pressure, the current comprehensive leakage rate is calculated. Based on the comprehensive leakage rate, the axial clamping force, the axial movement of the kiln body, and the radial runout, fuzzy reasoning is performed to determine the clamping force adjustment coefficient, thereby determining the target clamping force. Based on the target clamping force and the axial clamping force, a target control quantity is calculated using an incremental PID control algorithm, and the clamping force of the pressure ring on the carbon fiber packing ring is adjusted through the target control quantity.

[0007] This specification provides one or more embodiments of an adaptive sealing control system for a rotary kiln sealing system, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the above-described method.

[0008] This specification provides one or more embodiments of a rotary kiln device that performs the above-described method.

[0009] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: By acquiring axial clamping force, intermediate chamber pressure, kiln body axial movement, and runout, a complete observation system for the sealing interface contact state, leakage flux, and external disturbances is constructed, overcoming the problems of missing operating condition information and inability to trace the root cause of failure caused by single-parameter monitoring in existing technologies. Fuzzy inference is used to transform multi-dimensional inputs into linguistic variables, and nonlinear mapping is performed through an expert rule base to output a continuously adjustable clamping force adjustment coefficient. Fuzzy inference does not require the establishment of a precise mathematical model, yet it can cover multiple combinations of operating conditions such as leakage, wear, and motion, outputting smooth control quantities in the transition zone and avoiding control abrupt changes caused by hard switching. Furthermore, the target clamping force is determined by multiplying the current clamping force by the adjustment coefficient, making the adjustment range proportional to the current pressure level, which conforms to the physical law that stronger compensation is needed in the later stages of wear. The upper-level fuzzy inference is responsible for macroscopic evaluation to generate the target clamping force, while the lower-level PID uses integrals to eliminate steady-state deviations and derivatives to increase system damping, achieving accurate tracking of the target value. This decouples nonlinear decision-making from high-frequency dynamic response tasks, avoiding the limitations of a single algorithm. By implementing real-time leakage feedback closed-loop regulation, the sealing interface is always kept at the optimal contact pressure, which minimizes through-leakage and avoids excessive wear. By using the principle of pressing as needed, frictional power consumption and material wear rate are reduced, extending the service life of the packing and reducing maintenance frequency. By sensing the movement of the kiln body and adjusting the pressing force, dynamic leakage peaks are suppressed, and the continuous stability of the process atmosphere inside the kiln is maintained. Thus, multi-objective optimization is achieved in three dimensions: sealing effect, equipment life and process stability. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating an adaptive sealing control method for a rotary kiln sealing system provided in the embodiments of this specification; Figure 2 This is a schematic diagram of the structure of a rotary kiln sealing system provided in the embodiments of this specification; Figure 3 This is a schematic diagram of the structure of an adaptive sealing control system for a rotary kiln sealing system provided in the embodiments of this specification.

[0011] The components are: 1. Moving ring; 2. Stationary ring; 3. Spring; 4. Pressure ring; 5. Packing; 6. Fixed-distance ring. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0013] This specification provides an adaptive sealing control method for a rotary kiln sealing system. It should be noted that the execution subject in this specification embodiment can be a server or any device with data processing capabilities. Figure 1 A flowchart illustrating an adaptive sealing control method for a rotary kiln sealing system provided in this specification is shown below. Figure 1 As shown, the main steps include the following: Step S101: Obtain multi-dimensional real-time sensor data.

[0014] The multi-dimensional real-time sensor data includes the axial clamping force between the pressure ring and the carbon fiber packing, the intermediate chamber pressure corresponding to the intermediate sealing chamber, the axial displacement of the kiln body, and the radial runout. It should be noted that the methods described in the embodiments of this specification are applied to rotary kiln sealing systems. Figure 2 This is a schematic diagram of a rotary kiln sealing system provided in an embodiment of this specification, serving as a structural example of one applicable rotary kiln sealing system. Figure 2 As shown, the rotary kiln sealing system includes a stationary ring 2 connected to the outer cylinder of the rotary kiln and a rotating ring 1 connected to the inner cylinder of the rotary kiln. The stationary ring 2 surrounds both sides of the rotating ring 1. Packing ferrules 5 are respectively provided on the side walls of the rotating ring 1 and the stationary ring 2. In one example of this specification, the packing ferrules can be carbon fiber packing ferrules. A pressure ring 4 and a spring 3 are provided on the outer side of the packing ferrules 5. The spring 3 presses the pressure ring 4 against the packing ferrules. A thin-film pressure sensor is provided between the pressure ring and the packing ferrules. A hollow bolt is provided at the spring position on the stationary ring 2. The spring extends into the inside of the hollow bolt. The spring pressure can be monitored by a pressure sensor installed inside the hollow bolt. In addition, in the radial direction, a spacer ring 6 is located outside the rotating ring 1. The spacer ring determines the diameter of the overall sealing structure. The spacer ring 6 is connected to the stationary ring by bolts, and there is a certain gap between the spacer ring and the rotating ring.

[0015] Based on the aforementioned rotary kiln sealing system, multi-dimensional real-time sensor data is first acquired. The axial clamping force refers to the pressure value applied by the pressure ring to the carbon fiber packing. This is acquired in real-time by a thin-film pressure sensor or strain gauge pressure sensor installed between the pressure ring and the carbon fiber packing. The sensor output is converted into a standard electrical signal by a signal conditioning circuit and then sent to the controller. The controller performs median filtering on this signal to eliminate instantaneous noise, obtaining the current axial clamping force value. The intermediate chamber pressure refers to the gas pressure value in the chamber formed between the moving ring and the fixed-distance ring. This is acquired by a gas pressure sensor installed on the wall of the intermediate sealing chamber. The sensor directly senses the static pressure inside the chamber and converts it into an electrical signal. After acquisition by the controller, it is also smoothed and filtered to obtain the current intermediate chamber pressure. The axial displacement of the kiln body refers to the reciprocating displacement of the rotary kiln shell along its axial direction. This is acquired by an eddy current displacement sensor or laser displacement sensor installed on a fixed frame with its probe aligned with the end face of the kiln body. The sensor output is an electrical signal proportional to the displacement. After acquisition by the controller, it is linearly calibrated to obtain the real-time axial displacement. Radial runout refers to the sway displacement of the rotary kiln cylinder in the direction perpendicular to the axis. This data is acquired by another set of displacement sensors of the same type, mounted on a fixed frame with their probes aligned with the outer circumference of the kiln. After being collected by the controller, the data undergoes the same calibration process to obtain the real-time radial runout. The data from the above four types of sensors are continuously collected at a fixed sampling frequency and stored in memory after preprocessing by the controller. This data serves as the input for subsequent leakage rate calculation, fuzzy inference, and clamping force adjustment.

[0016] Compared to conventional open-loop control methods in rotary kiln sealing control that rely solely on static preset clamping force or a single sensor (such as pressure or displacement only), this technical solution constructs a complete sensing foundation for the sealing system's operating status by simultaneously acquiring real-time data in four dimensions: axial clamping force, intermediate chamber pressure, kiln body axial movement, and radial runout. Utilizing multi-dimensional data fusion, the controller can synchronously monitor the sealing clamping status, intermediate chamber leakage trends, and kiln body movement characteristics, providing ample information input for subsequent adaptive adjustments. This multi-dimensional data acquisition method overcomes the shortcomings of traditional single-parameter control, upgrading sealing control from passive response to active sensing, significantly improving the control system's adaptability to complex operating conditions, and laying a data foundation for reducing unnecessary over-clamping and extending the life of the sealing packing.

[0017] Step S102: Calculate the current overall leakage rate based on the intermediate chamber pressure. Based on the overall leakage rate, axial clamping force, axial movement of the kiln body, and radial runout, perform fuzzy reasoning to determine the clamping force adjustment coefficient, thereby determining the target clamping force.

[0018] The current overall leakage rate is calculated based on the intermediate chamber pressure, specifically including: collecting the current tracer gas concentration in the intermediate sealed chamber, and calculating the normalized gas concentration leakage rate by using the current missing gas concentration and a preset zero-leakage background concentration; determining the first derivative of the intermediate chamber pressure with respect to time, and determining the pressure change leakage rate by using the ratio of the first derivative to a pre-acquired normal operating condition reference pressure; and weighting the normalized gas concentration leakage rate and the pressure change leakage rate to determine the current overall leakage rate.

[0019] In one embodiment of this specification, after acquiring multi-dimensional real-time sensor data, the current comprehensive leakage rate is calculated based on the intermediate chamber pressure. The comprehensive leakage rate is used to characterize the degree of gas leakage in the rotary kiln sealing system under the current operating conditions.

[0020] Specifically, the intermediate sealing chamber refers to the closed annular space formed between the rotating ring and the spacer ring. This chamber is located between the first seal (the carbon fiber packing on both sides of the rotating ring) and the second seal (the carbon fiber packing within the packing short joint). Therefore, changes in gas pressure and composition within the chamber directly reflect the leakage status of the first seal. When the first seal leaks, the process gas in the kiln will pass through the gap between the rotating ring and the packing into the intermediate sealing chamber, causing the chamber pressure to rise. Simultaneously, the concentration of specific tracer gases within the chamber (such as nitrogen, oxygen, or exhaust components in the process atmosphere) will also change accordingly. Based on this principle, a dual-parameter fusion method of gas concentration and pressure change is used to calculate the overall leakage rate, thereby improving the accuracy and robustness of leak detection.

[0021] First, the current tracer gas concentration in the intermediate sealed chamber is collected. The tracer gas concentration refers to the volume fraction or mass concentration of a characteristic gas within the intermediate sealed chamber. This characteristic gas should be a stable and easily detectable component present in the kiln's process atmosphere. For example, when a rotary kiln is used for lithium battery material sintering, the process atmosphere is typically nitrogen or an inert gas, making nitrogen a suitable tracer gas. This concentration is acquired in real-time using a gas concentration sensor (such as an infrared gas sensor, a thermal conductivity gas sensor, or an electrochemical sensor) installed on the wall of the intermediate sealed chamber. The sensor probe extends directly into the chamber, sensing the concentration of the target component in the gas and converting it into a standard electrical signal. The controller collects this signal at a fixed sampling frequency and performs filtering and smoothing to obtain the current tracer gas concentration value. Simultaneously, a zero-leakage background concentration is pre-stored. The zero-leakage background concentration refers to the equilibrium gas concentration reached by the intermediate sealed chamber after long-term operation under ideal conditions where the sealing system is completely leak-free. This value can be obtained through actual measurement during equipment commissioning when the seal is confirmed to be intact, or determined theoretically based on the ambient air composition and the initial filling atmosphere.

[0022] Then, the normalized gas concentration leakage rate is calculated by subtracting the zero-leakage background concentration from the current tracer gas concentration to obtain the concentration increment. This concentration increment is then divided by the difference between the maximum allowable concentration and the zero-leakage background concentration to obtain a dimensionless ratio between zero and one. The maximum allowable concentration refers to the upper limit of the tracer gas concentration allowed in the intermediate sealed chamber without affecting product quality or process safety. Exceeding this value indicates a leakage serious enough to require intervention. Normalization ensures the comparability of concentration leakage rates under different operating conditions.

[0023] Secondly, the first derivative of the intermediate chamber pressure with respect to time is determined, and the pressure change leakage rate is determined based on the ratio of this first derivative to the reference pressure under normal operating conditions. The intermediate chamber pressure refers to the static pressure of the gas within the intermediate sealed chamber, which has already been acquired by a gas pressure sensor in the aforementioned step of acquiring multi-dimensional real-time sensor data. The intermediate chamber pressure value is continuously recorded at fixed time intervals (i.e., the control cycle), and its first derivative with respect to time is calculated using a numerical differentiation method. Specifically, the first-order backward difference method can be used, subtracting the pressure value from the previous moment from the current pressure value, and then dividing by the time interval between the two samplings to obtain the pressure change rate. The physical meaning of the pressure change rate is the rate at which the chamber pressure increases or decreases per unit time. When the leakage at the first seal worsens, high-pressure gas inside the kiln will enter the intermediate sealed chamber more quickly, leading to an increase in the pressure change rate; conversely, when the seal is good, the pressure change rate tends to be close to zero or negative (because the chamber may slowly release pressure to the outside through the second seal).

[0024] To convert the pressure change rate into a dimensionless leakage rate index, the pressure change leakage rate is further calculated by dividing the first derivative of the pressure with respect to time by a pre-obtained normal operating condition reference pressure. The normal operating condition reference pressure refers to the typical pressure value in the intermediate sealing chamber of the rotary kiln when it is operating stably and in good sealing condition. This value can be obtained through long-term statistical averaging during normal equipment operation or theoretical estimation based on the kiln's working pressure and sealing structure parameters. The result of this division is also a dimensionless value; its positive or negative sign indicates a pressure increase or decrease, and its absolute value reflects the severity of leakage.

[0025] Finally, the normalized gas concentration leakage rate and the pressure change leakage rate are weighted and averaged to determine the current comprehensive leakage rate. Preset weighting coefficients are assigned to each leakage rate, and then the results are summed. These weighting coefficients reflect the reliability or importance of the two leakage rates under different operating conditions. For example, in scenarios where the gas concentration sensor responds slowly but the pressure sensor responds quickly, the pressure change leakage rate can be given a higher weight to quickly detect instantaneous leaks; while in scenarios where the gas concentration sensor has better long-term stability, the normalized gas concentration leakage rate can be given a higher weight to suppress false alarms caused by pressure fluctuations. The specific values ​​of the weights can be obtained through experimental calibration based on the actual response characteristics of the sealing system, and usually the sum of the two weights is equal to one. The weighted average calculation result is the current comprehensive leakage rate, which is a dimensionless value; a larger value indicates a more severe seal leakage.

[0026] If a gas concentration sensor is not installed in the system (e.g., in a simplified implementation), the pressure change leakage rate can be directly used as the current comprehensive leakage rate. In this case, the calculation of the pressure change leakage rate still needs to be performed, while the calculation steps related to gas concentration are omitted accordingly. The comprehensive leakage rate obtained through the above dual-parameter fusion method retains the rapid response characteristics of the pressure signal and utilizes the cumulative characterization capability of the concentration signal, providing a reliable state input for subsequent fuzzy inference.

[0027] This technical solution calculates the comprehensive leakage rate by fusing tracer gas concentration and chamber pressure change rate. By weighting and integrating these two factors, leakage detection possesses both the rapid response capability of pressure signals to capture sudden leaks and the cumulative characterization capability of concentration signals to reflect long-term seal degradation trends, thus significantly improving the accuracy and robustness of leakage detection. Simultaneously, normalization converts physical quantities of different dimensions into dimensionless leakage rate indicators, enabling unified processing by the subsequent fuzzy inference system and avoiding complex data scale transformations. Furthermore, the calculation of the pressure change leakage rate employs a ratio method based on a reference pressure under normal operating conditions. This reference pressure can be adaptively set according to different kiln types and process conditions, exhibiting good adaptability to operating conditions. When two sensors are present in the system, the weighted averaging method allows for flexible adjustment of weights according to actual application scenarios. For example, during start-up and shutdown phases, more reliance can be placed on pressure signals, while during steady-state phases, more reliance on concentration signals can be placed, thereby achieving condition-adaptive leakage assessment.

[0028] Based on the overall leakage rate, the axial clamping force, the axial displacement of the kiln body, and the radial runout, fuzzy reasoning is performed to determine the clamping force adjustment coefficient. Specifically, this includes: fuzzifying the overall leakage rate and mapping it to preset low-leakage, medium-leakage, and high-leakage fuzzy sets to obtain the leakage membership vector of the overall leakage rate; acquiring the friction vibration signal collected by the acceleration sensor installed on the pressure ring, and the sealing friction surface temperature collected by the temperature sensor installed on the outside of the pressure ring; calculating the wear index of the carbon fiber packing based on the historical data of the axial clamping force, the friction vibration signal, and the sealing friction surface temperature; and fuzzifying the wear index and mapping it to a preset light wear... Fuzzy sets, medium wear fuzzy sets, and heavy wear fuzzy sets are used to obtain the wear degree index wear membership vector; the intensity of motion is calculated based on the axial movement and radial runout of the kiln body, and the intensity of motion is fuzzified and mapped to preset steady motion fuzzy sets, fluctuating motion fuzzy sets, and violent motion fuzzy sets to obtain the motion membership vector of the intensity of motion; the leakage membership vector, the wear membership vector, and the motion membership vector are used as inputs, and fuzzy inference is performed based on a preset fuzzy rule base to obtain the fuzzy output membership function of the clamping force adjustment coefficient; the centroid method is used to defuzzify the fuzzy output membership function of the clamping force adjustment coefficient to determine the clamping force adjustment coefficient.

[0029] In one embodiment of this specification, a clamping force adjustment coefficient is determined by fuzzy reasoning based on the comprehensive leakage rate, axial clamping force, axial displacement of the kiln body, and radial runout. The clamping force adjustment coefficient is used to dynamically correct the target clamping force in the future.

[0030] Specifically, the overall leakage rate is first fuzzified. Fuzzification refers to converting precise numerical inputs into membership degrees on fuzzy sets. Three fuzzy sets are preset: a low-leakage fuzzy set, a medium-leakage fuzzy set, and a high-leakage fuzzy set. Each fuzzy set corresponds to a membership function, typically in the form of a triangle or trapezoidal function. The horizontal axis represents the range of the overall leakage rate, and the vertical axis represents the membership degree (between zero and one). The calculated overall leakage rate value is substituted into each of the three membership functions to obtain the degree to which the value belongs to the low, medium, or high leakage state. These three degrees form a three-dimensional vector, the leakage membership vector. For example, when the overall leakage rate is low, its membership degree in the low-leakage fuzzy set is close to one, while its membership degree in the medium and high-leakage fuzzy sets is close to zero. When the overall leakage rate is in the middle range, it may belong to both the low and medium-leakage fuzzy sets simultaneously, exhibiting a fuzzy transition.

[0031] Next, the frictional vibration signal collected by the accelerometer mounted on the pressure ring and the sealing friction surface temperature collected by the temperature sensor mounted on the outside of the pressure ring are acquired. The frictional vibration signal refers to the mechanical vibration generated when the carbon fiber packing slides relative to the moving ring due to surface micro-roughness, fluctuations in the coefficient of friction, and the squeezing action of wear particles. This signal is picked up by a piezoelectric accelerometer, which converts the vibration acceleration into a charge or voltage signal. After charge amplification and anti-aliasing filtering, the signal is sent to the controller for high-speed sampling. The sealing friction surface temperature refers to the temperature rise generated by frictional heat in the contact area between the pressure ring and the carbon fiber packing. This data is collected by a thermocouple or infrared temperature sensor. The sensor probe is placed close to the outer wall of the pressure ring or embedded in the short-circuit wall of the packing, sensing temperature changes in real time and converting them into electrical signals. Based on the above vibration and temperature signals, combined with historical data of the axial clamping force, the wear index of the carbon fiber packing is calculated. The wear index is a dimensionless index used to quantify the degradation process of the packing from a brand-new state to a failed state.

[0032] After calculating the wear index, it is fuzzified and mapped to preset fuzzy sets for light wear, medium wear, and heavy wear to obtain the wear membership vector. Similarly, the intensity of motion is calculated based on the axial movement and radial runout of the kiln body. The dynamic amplitude (difference between maximum and minimum values) of the axial movement and radial runout within a preset time window is calculated respectively. The two dynamic amplitudes are divided by their corresponding maximum allowable axial movement and maximum runout to obtain two normalized values. These two normalized values ​​are then added to obtain the motion intensity index. The larger the index, the more intense the kiln body motion. This index is then fuzzified and mapped to three preset fuzzy sets for steady motion, fluctuating motion, and intense motion to obtain the motion membership vector. Thus, three membership vectors are obtained: leakage membership vector (3D), wear membership vector (3D), and motion membership vector (3D).

[0033] Using the three membership vectors mentioned above as input, fuzzy inference is performed based on a pre-defined fuzzy rule base. The fuzzy rule base is a set of multiple "if-then" conditional statements, each rule corresponding to a decision output under a specific combination of operating conditions. For example, a rule might be stated as: "If leakage is high, wear is medium, and movement is severe, then the clamping force adjustment coefficient should be increased significantly"; another rule might be: "If leakage is low, wear is slight, and movement is stable, then the clamping force adjustment coefficient should be decreased slightly." The rule base typically covers all possible input combinations, and its specific content is determined based on the experience of sealing control experts and system identification experiments. During the inference process, each rule calculates its trigger strength based on the input membership degree (usually taking the minimum value or product of the input membership degrees), and then applies the trigger strength to the rule output fuzzy set to obtain the truncation or scaling results of each rule's output fuzzy set. Finally, the output fuzzy sets of all rules are unioned to obtain the fuzzy output membership function of the clamping force adjustment coefficient, which is a fuzzy set defined on the range of the adjustment coefficient's values.

[0034] Finally, the centroid method is used for defuzzification calculation. The integral of the product of the x-coordinate and y-coordinate of the fuzzy output membership function is divided by the integral of the y-coordinate to obtain the precise value after weighted averaging. This value is the clamping force adjustment coefficient. The centroid method can comprehensively consider the influence of all activation rules, and output a smooth and continuous control quantity.

[0035] It should be noted that the following are examples of membership functions for the input and output variables involved in the fuzzy inference process. The universe of discourse for the overall leakage rate is defined as a dimensionless interval, ranging from the minimum possible leakage (complete sealing) to the maximum permissible leakage (requiring immediate intervention). Three fuzzy sets—low leakage, medium leakage, and high leakage—are defined on this universe of discourse. The low leakage fuzzy set uses a descending half-trapezoidal membership function. When the overall leakage rate is less than or equal to the first threshold, the membership degree is one; when the overall leakage rate is between the first and second thresholds, the membership degree linearly decreases from one to zero; when the overall leakage rate is greater than or equal to the second threshold, the membership degree is zero. This function shape allows for complete membership in extremely low leakage states, with a smooth decrease in the transition region. The medium leakage fuzzy set uses a triangular membership function. Its support interval starts at the first threshold, peaks at the third threshold, and ends at the fourth threshold. When the overall leakage rate equals the third threshold, the membership degree is one; it linearly decreases to zero at both ends. This function shape gives the leakage in the middle range a unimodal response, reflecting the semantics of typical medium leakage. The high-leakage fuzzy set employs an ascending semi-trapezoidal membership function. When the overall leakage rate is less than or equal to the fourth threshold, the membership degree is zero; when the overall leakage rate is between the fourth and fifth thresholds, the membership degree linearly increases from zero to one; when the overall leakage rate is greater than or equal to the fifth threshold, the membership degree is one. This function shape ensures that the high-leakage state quickly achieves complete membership as the leakage rate increases. Specifically, the first threshold is less than the second threshold, the second threshold is less than the third threshold, the third threshold is less than the fourth threshold, and the fourth threshold is less than the fifth threshold. The intervals between each threshold can be experimentally calibrated based on the sensitivity of the actual sealing system.

[0036] The universe of discourse for the wear index is defined as a dimensionless interval, ranging from zero (brand new, no wear) to one (critical failure). Three fuzzy sets—light wear, medium wear, and heavy wear—are defined on this universe. The light wear fuzzy set uses a descending semi-trapezoidal membership function. When the wear index is less than or equal to the first wear threshold, the membership is one; when the index is between the first and second wear thresholds, the membership decreases linearly; and when the index is greater than or equal to the second wear threshold, the membership is zero. This function describes the packing root in the early wear stage, with slight wear. The medium wear fuzzy set uses a triangular membership function. Its support interval starts at the first wear threshold, peaks at the third wear threshold, and ends at the fourth wear threshold. The membership is one at the peak and decreases linearly to zero at the endpoints. This function describes the packing root with moderate wear, in the intermediate degradation stage. The heavy wear fuzzy set uses an ascending semi-trapezoidal membership function. The membership degree is zero when the wear index is less than or equal to the fourth wear threshold; it increases linearly when the index is between the fourth and fifth wear thresholds; and it is one when the index is greater than or equal to the fifth wear threshold. This function describes packing that has undergone severe wear and is approaching or has reached a failure state. The first to fifth wear thresholds increase sequentially, with the fifth wear threshold typically set to one (upper limit of the universe of discourse). The specific values ​​of each threshold are determined based on the packing material properties and accelerated life test results.

[0037] The universe of discourse for the intensity of motion is defined as a dimensionless interval, ranging from zero (complete stillness or extreme stability) to one (the maximum intensity allowed by design). Three fuzzy sets are defined on this universe: steady motion, fluctuating motion, and violent motion. The steady motion fuzzy set uses a descending half-trapezoidal membership function. When the intensity of motion is less than or equal to the first motion threshold, the membership degree is one; when the intensity is between the first and second motion thresholds, the membership degree decreases linearly; when the intensity is greater than or equal to the second motion threshold, the membership degree is zero. This function describes minimal axial movement and radial runout of the kiln body, with almost no relative motion impact on the sealing structure. The fluctuating motion fuzzy set uses a triangular membership function. Its support interval starts at the first motion threshold, peaks at the third motion threshold, and ends at the fourth motion threshold. The membership degree is one at the peak and decreases linearly on both sides. This function describes periodic movement or runout of the kiln body with a certain amplitude, but not reaching a violent level. The violent motion fuzzy set uses an ascending half-trapezoidal membership function. When the intensity of the movement is less than or equal to the fourth movement threshold, the membership degree is zero; when the intensity is between the fourth and fifth movement thresholds, the membership degree increases linearly; when the intensity is greater than or equal to the fifth movement threshold, the membership degree is one. This function describes significant surging or jumping within the kiln body, causing a significant impact on the sealing gap. The first to fifth movement thresholds increase sequentially, with the fifth threshold typically set to one. Each threshold is calibrated after normalization based on the maximum allowable surging and jumping amounts in the kiln design.

[0038] The universe of discourse for the clamping force adjustment coefficient is defined as a dimensionless interval, centered on the reference value 1, extending to the minimum and maximum adjustment coefficients (e.g., a few tenths to a few tenths). Typically, five fuzzy sets are defined on its universe of discourse: significant reduction, slight reduction, no change, slight increase, and significant increase. This can be simplified to three or seven sets; this example uses five fuzzy sets to provide finer adjustment.

[0039] Significantly reducing the fuzzy set uses a descending half-trapezoidal membership function. When the adjustment coefficient is less than or equal to the first output threshold, the membership degree is one; it decreases linearly between the first and second thresholds; and it is zero when it is greater than or equal to the second threshold. This set corresponds to operating conditions requiring a significant reduction in clamping force. Slightly reducing the fuzzy set uses a triangular membership function. The support interval starts at the first output threshold, peaks at the third output threshold, and ends at the fourth output threshold. The membership degree at the peak is one. This set corresponds to operating conditions requiring a moderate reduction in clamping force. Maintaining the fuzzy set uses a triangular membership function. The support interval starts at the second output threshold, peaks at the fifth output threshold (usually one), and ends at the sixth output threshold. The membership degree at the peak is one. This set corresponds to operating conditions where clamping force does not need adjustment. Slightly increasing the fuzzy set uses a triangular membership function. The support interval starts at the fourth output threshold, peaks at the seventh output threshold, and ends at the eighth output threshold. The membership degree at the peak is one. This set corresponds to operating conditions requiring a moderate increase in clamping force. Significantly increasing the fuzzy set uses an ascending half-trapezoidal membership function. When the adjustment coefficient is less than or equal to the eighth output threshold, the membership degree is zero; it increases linearly between the eighth and ninth thresholds; and when it is greater than or equal to the ninth threshold, the membership degree is one. This set corresponds to emergency operating conditions requiring a significant increase in clamping force. The output thresholds increase sequentially, with the ninth threshold representing the upper limit of the output universe of discourse. All thresholds are pre-tuned using expert experience or system optimization methods.

[0040] All the membership functions mentioned above adopt a linear piecewise form (trapezoidal or triangular) because they are simple to calculate, easy to implement in embedded systems, and have clear physical meaning. In practical engineering applications, Gaussian, S-shaped, or other curve forms can also be used, but linear piecewise functions are sufficient to meet the control requirements. Each threshold parameter (such as the first to fifth thresholds, each wear threshold, each motion threshold, and each output threshold) is calibrated through field testing or offline simulation during the equipment commissioning phase and stored in the controller's parameter table. During fuzzy inference, the membership degree is calculated by substituting the precise value of the real-time input into each membership function, thus obtaining the corresponding membership vector. The above function forms ensure a continuous mapping from the input space to the output space, avoiding abrupt changes in the control quantity.

[0041] In the above process, based on the historical data of the axial clamping force, the friction vibration signal, and the temperature of the sealing friction surface, the wear index of the carbon fiber packing is calculated. Specifically, this includes: obtaining a pre-established benchmark clamping force-temperature relationship curve, which includes the steady-state friction temperature of the carbon fiber packing in a brand-new state corresponding to different set clamping forces; obtaining the measured sealing friction surface temperature corresponding to the current axial clamping force, and querying the benchmark clamping force-temperature relationship curve to obtain the benchmark temperature under the same clamping force; calculating the difference between the measured sealing friction surface temperature and the benchmark temperature; when the... When the temperature difference exceeds a preset temperature difference threshold, a temperature correction coefficient is generated based on the magnitude of the difference, wherein the temperature correction coefficient monotonically increases as the difference increases; the friction vibration signal is bandpass filtered to remove frequency components outside the preset effective frequency band, resulting in a filtered vibration signal; a fast Fourier transform is performed on the filtered vibration signal to obtain the spectrum of the vibration signal, and the total energy within the preset wear characteristic frequency band is calculated; the total energy is divided by the pre-calibrated failure threshold energy to obtain the basic wear degree; the basic wear degree is multiplied by the temperature correction coefficient to obtain the wear degree index.

[0042] Specifically, the first step is to obtain a pre-established benchmark clamping force-temperature relationship curve. This curve is a functional relationship obtained through experimental calibration using carbon fiber packing in a brand-new, wear-free state. With the sealing system operating stably, different axial clamping force values ​​are set, and the corresponding temperature values ​​are recorded after the temperature of the sealing friction surface reaches a steady state. Connecting these data points forms a curve reflecting the clamping force-temperature relationship of a brand-new packing under normal friction. Then, the measured sealing friction surface temperature corresponding to the current axial clamping force is obtained, and the benchmark temperature under the same clamping force is obtained by referring to the benchmark curve. Subtracting the current measured temperature from the benchmark temperature yields the difference. The physical meaning of this difference is that when the packing wears, its surface contact condition deteriorates, the friction coefficient increases, or the contact area decreases, leading to increased frictional heat generation under the same clamping force, thus causing the measured temperature to be higher than the benchmark temperature. When this difference exceeds a preset temperature difference threshold (i.e., the normal fluctuation range), wear is determined to have occurred and correction is required. At this point, a temperature correction coefficient is generated based on the magnitude of the difference. This coefficient increases monotonically as the difference increases, and is usually a linear function or a piecewise linear function, so that the correction coefficient is larger when the wear is more severe.

[0043] On the other hand, time-frequency domain analysis is performed on the friction vibration signal. First, the original vibration signal is bandpass filtered to remove frequency components outside the preset effective frequency band. The effective frequency band is predetermined based on the typical vibration characteristics of the carbon fiber packing and metal moving ring friction pair. For example, the low-frequency band may contain overall kiln vibration interference, the high-frequency band may contain electromagnetic noise, while the energy in a specific intermediate frequency band mainly comes from the microscopic friction between the packing and the moving ring and the impact of wear particles. Bandpass filtering can be implemented using digital filters (such as infinite impulse response filters or finite impulse response filters) to retain the signal components within the effective frequency band and suppress irrelevant noise. After obtaining the filtered vibration signal, a fast Fourier transform is performed to convert the time-domain signal into a frequency-domain spectrum. The horizontal axis of the spectrum represents frequency, and the vertical axis represents the amplitude or energy of the corresponding frequency component. In the frequency domain, a wear characteristic frequency band is preset. This wear characteristic frequency band is a narrow frequency range that is most sensitive to packing wear, determined through extensive experiments. The total energy is obtained by calculating the sum of squares or integrals of the amplitudes of all frequency components within the wear characteristic frequency band. Wear causes changes in the amplitude and frequency components of vibration and impact; therefore, the total energy increases as packing wear intensifies.

[0044] Dividing the total energy by the pre-calibrated failure threshold energy yields the basic wear degree. The failure threshold energy refers to the total energy of the wear characteristic frequency band measured under the same test conditions when the carbon fiber packing wears to the point of needing replacement. This value is obtained through accelerated life testing or long-term field monitoring. The result of the division is the basic wear degree, which ranges from zero to one, where zero indicates no wear and one indicates reaching the failure threshold. Multiplying the basic wear degree by the previously generated temperature correction factor yields the final wear degree index. This multiplication operation allows abnormal temperature increases (reflecting abnormal frictional heat effects) to amplify the wear degree reflected by the vibration characteristics, thus providing a more comprehensive assessment of the actual degradation state of the packing. If the temperature difference does not exceed the preset temperature difference threshold, the temperature correction factor is set to one, and the wear degree index is equal to the basic wear degree.

[0045] This solution introduces fuzzy reasoning, converting precise numerical values ​​into fuzzy semantics, and then performing nonlinear mapping through a rule base. This allows the clamping force adjustment coefficient to change smoothly, continuously, and in accordance with physical laws under different operating conditions. Simultaneously, the calculation of the wear index innovatively integrates friction temperature and vibration spectrum characteristics, and introduces a difference correction based on a reference temperature curve. This ensures that wear assessment considers both thermal effects and vibration energy, significantly improving the accuracy and timeliness of wear state perception compared to traditional methods that rely solely on accumulated operating time or manual visual inspection. The calculation of motion intensity allows the control system to detect the impact of kiln body movement and jumping in advance, assigning appropriate weights to them in the fuzzy rules. This proactively increases the clamping force during intense movement, preventing instantaneous leakage peaks. Upgrading sealing control from single-dimensional feedback adjustment to multi-dimensional intelligent decision-making significantly improves the adaptability and robustness of the sealing system under complex operating conditions, effectively reducing the risk of excessive wear or seal failure due to improper adjustment.

[0046] Determining the target clamping force specifically includes: multiplying the axial clamping force by the clamping force adjustment coefficient to determine the target clamping force; and applying a safety constraint to the target clamping force through a preset safety limit, ensuring that it is not lower than the pre-obtained minimum sealing clamping force and not higher than the maximum structural clamping force.

[0047] In one embodiment of this specification, the axial clamping force refers to the value collected in real time by a pressure sensor installed between the clamping ring and the carbon fiber packing, and after filtering. This value reflects the actual pressure currently applied by the clamping ring to the packing. The clamping force adjustment coefficient is a dimensionless multiplier output by the aforementioned fuzzy inference step, and its value represents the proportion that needs to be increased or decreased relative to the current clamping force. The current axial clamping force is multiplied by the clamping force adjustment coefficient to obtain the initial target clamping force. When the adjustment coefficient is greater than one, the target clamping force is higher than the current value, and clamping needs to be increased to suppress leakage; when the adjustment coefficient is less than one, the target clamping force is lower than the current value, and clamping can be appropriately loosened to reduce wear and energy consumption.

[0048] Because multiplication operations may cause the initial target clamping force to exceed the physical safety boundary of the sealing system, it is constrained by a preset safety limit. The safety limit refers to restricting the initial target clamping force within a predetermined closed range. The lower limit of this range is the pre-obtained minimum sealing clamping force, which is the minimum pressure required to ensure a basic sealing effect, calibrated experimentally; below this value, the seal will fail. The upper limit of this range is the maximum structural clamping force, which is the highest permissible pressure determined based on the compressive strength of the carbon fiber packing material, the mechanical bearing capacity of the pressure ring and hollow bolts; exceeding this value may cause excessive packing crushing or structural damage.

[0049] If the initial target clamping force is less than the minimum sealing clamping force, then the target clamping force is set to the minimum sealing clamping force; if the initial target clamping force is greater than the maximum structural clamping force, then the target clamping force is set to the maximum structural clamping force; otherwise, the initial value remains unchanged. The value obtained after the above safety limit is the final target clamping force.

[0050] The proportional adjustment of the current clamping force is achieved through multiplication, maintaining the continuity and smoothness of the control quantity. Furthermore, the preset minimum sealing clamping force and maximum structural clamping force are used for hard constraints, which physically ensures that the target clamping force will not exceed the safe operating range under any circumstances. This not only retains the adaptive capability of fuzzy reasoning but also eliminates the safety risks caused by extreme control commands, effectively improving the robustness and reliability of the sealing control system.

[0051] Step S103: Based on the target clamping force and the axial clamping force, the target control quantity is calculated using an incremental PID control algorithm, and the clamping force of the pressure ring on the carbon fiber packing is adjusted through the target control quantity.

[0052] Based on the target clamping force and the axial clamping force, the target control quantity is calculated using an incremental PID control algorithm. Specifically, this includes: determining the current deviation value corresponding to the target clamping force and the axial clamping force, and determining the basic control quantity based on the current deviation value using the incremental PID control algorithm; calculating the current intensity of motion based on the axial movement and radial runout of the kiln body; and when the current intensity of motion exceeds a preset intensity threshold, calculating the feedforward compensation force increment, and superimposing the feedforward compensation force increment with the basic control quantity to determine the target control quantity.

[0053] In one embodiment of this specification, a target control quantity is calculated using an incremental PID control algorithm based on the target clamping force and the current axial clamping force. This target control quantity is used to drive a miniature electric linear actuator to adjust the clamping force of the pressure ring on the carbon fiber packing. Incremental PID control is a discretized proportional-integral-derivative control algorithm whose output is the increment of the control quantity rather than its absolute value.

[0054] First, determine the current deviation between the target clamping force and the axial clamping force. The target clamping force is the expected pressure value calculated in the previous steps and after safety limits, while the axial clamping force is the actual pressure value collected in real time by the pressure sensor. Both are physical quantities with pressure dimensions. The controller subtracts the axial clamping force from the target clamping force to obtain the current deviation value, denoted as e(t), where t represents the current control cycle. The sign of this deviation value indicates whether the clamping force is too small (needs to be increased) or too large (needs to be decreased), and its absolute value indicates the degree of deviation.

[0055] Then, based on the current deviation value, the basic control quantity is determined using an incremental PID control algorithm. Specifically, this includes: obtaining the historical deviation value of the previous control cycle and calculating the difference between the current deviation value and the historical deviation value as the deviation derivative term, to determine the proportional control component based on the current deviation value and a preset proportional coefficient; multiplying the current deviation value by a preset integral coefficient and then by the control cycle time, and integrating over time to obtain the integral control component; multiplying the deviation derivative term by a preset derivative coefficient and then dividing by the control cycle time to obtain the derivative control component; and adding the proportional control component, the integral control component, and the derivative control component to obtain the basic control quantity.

[0056] Specifically, the incremental PID algorithm does not directly calculate the absolute control quantity, but instead calculates the control increment required for each control cycle. Its mathematical basis is the discretized differential form of the PID control law. To do this, the historical deviation value from the previous control cycle needs to be obtained, denoted as e(t-1). This value is stored in the controller's memory variable and updated at the end of the previous cycle. The difference between the current deviation value and the historical deviation value is calculated, i.e., Δe(t) = e(t) - e(t-1). This difference serves as the input to the deviation derivative term. Based on this, the proportional control component, integral control component, and derivative control component are calculated respectively.

[0057] The proportional control component is determined based on the current deviation value and a preset proportional coefficient. Multiplying the current deviation value e(t) by the preset proportional coefficient Kp yields the proportional control component Pout. The physical meaning of proportional control is that the larger the deviation, the stronger the regulation effect, achieving a fast response. The integral control component is obtained by multiplying the current deviation value by a preset integral coefficient and then by the control cycle time, and then integrating over time. The controller maintains an integral accumulation variable SumI. Each cycle, Ki·e(t)·ΔT is accumulated onto SumI to obtain the integral control component Iout, where Ki is the preset integral coefficient and ΔT is the control cycle time, i.e., the time interval between two adjacent control calculations, set by the controller's timer interrupt. The function of integral control is to eliminate steady-state deviation, enabling the system to ultimately accurately track the target value. The derivative control component is obtained by multiplying the deviation derivative term by a preset derivative coefficient and then dividing by the control cycle time. Multiplying Δe(t) by the preset derivative coefficient Kd and then dividing by ΔT yields the derivative control component Dout. The function of derivative control is to predict the trend of deviation changes, increase system damping, and suppress oscillations. Finally, the proportional control component, integral control component, and derivative control component are added together, i.e., Pout + Iout + Dout, to obtain the basic control quantity. This basic control quantity corresponds to the actuator adjustment increment that needs to be applied in the current control cycle. The unit is Newton of force, and its physical meaning is the additional force increment required to make the actual clamping force approach the target clamping force.

[0058] While calculating the basic control variables, a feedforward compensation mechanism is introduced to address instantaneous disturbances during intense kiln movement. Feedforward compensation pre-generates compensation based on measurable disturbances (kiln body axial movement and runout), forming a composite control strategy with PID feedback control. Specifically, the current intensity of movement is first calculated based on the axial axial movement (Da) and radial runout (Dr) of the kiln body. The axial axial movement (Da) and radial runout (Dr) are data collected in real-time by displacement sensors and then filtered.

[0059] Calculate the dynamic amplitudes (difference between maximum and minimum values ​​within the window) of axial movement and radial runout within a preset time window (e.g., several seconds). Divide each dynamic amplitude by its corresponding maximum allowable axial movement and maximum runout to obtain two normalized ratios. Then, sum these two ratios to obtain the motion intensity index M. This index is dimensionless; a larger value indicates more intense kiln movement. The controller presets a threshold for intense motion. When M exceeds this threshold, it is determined that the kiln is in a state of intense motion, requiring the activation of feedforward compensation.

[0060] When it is determined that feedforward compensation is required, the feedforward compensation force increment is calculated. Specifically, the calculation of the feedforward compensation force increment includes: calculating the first derivative of the axial displacement of the kiln body with respect to time to determine the rate of change of axial displacement based on the axial displacement and the radial runout; calculating the first derivative of the radial runout with respect to time to determine the rate of change of radial runout; and determining the feedforward compensation force increment by using the absolute values ​​of the axial displacement rate of change and the absolute values ​​of the radial runout rate of change.

[0061] Specifically, firstly, the first derivative of the axial runout with respect to time is calculated to obtain the axial runout rate of change, denoted as va. Numerically, the controller uses the first-order backward difference method, subtracting the axial runout from the previous time step from the current time step, and then dividing by the sampling time interval, i.e., va = (Da(t) - Da(t-1)) / Δts, where Δts is the sampling interval of the displacement sensor, which can be the same as or higher than the control period ΔT. Typically, the controller maintains synchronization or interpolation. Similarly, the first derivative of the radial runout with respect to time is calculated to obtain the radial runout rate of change vr. The physical meanings of these two rates of change are the instantaneous velocity of the axial runout and the instantaneous velocity of the radial runout, respectively. The larger their absolute values, the more intense the movement and the stronger the impact on the sealing gap.

[0062] Then, the feedforward compensation force increment is determined by the absolute values ​​of the axial axial displacement rate and the radial runout rate. The absolute value of the axial axial displacement rate is multiplied by a preset first feedforward coefficient k_ff_a to obtain the axial compensation component; the absolute value of the radial runout rate is multiplied by a preset second feedforward coefficient k_ff_r to obtain the radial compensation component; the two are added together to obtain the feedforward compensation force increment Uff. That is, Uff = k_ff_a·|va| + k_ff_r·|vr|. The feedforward compensation force increment has the dimension of force, and its magnitude is proportional to the absolute value of the motion rate, reflecting the additional clamping force required to resist the instantaneous increase in the sealing gap caused by the rapid movement of the kiln body. The preset first and second feedforward coefficients are identified through system identification or calibrated by field tests; their physical meaning is a proportionality factor that converts velocity disturbances into force compensation.

[0063] The feedforward compensation force increment is superimposed with the basic control quantity to determine the final target control quantity. Specifically, the target control quantity Utarget = basic control quantity + Uf. If the current intensity of motion does not exceed the severe motion threshold, then Uff is zero, and the target control quantity is the basic control quantity. The target control quantity is converted into actuator drive commands, such as the number of pulses of a stepper motor or the current increment of a proportional electromagnet, and sent to the driver of the micro electric linear actuator via a fieldbus. The actuator adjusts the position of the pressure ring according to the command, thereby changing the compression of the carbon fiber packing and realizing closed-loop regulation of the axial clamping force. It should be noted that the micro electric linear actuator is used to adjust the position of the pressure ring so that the pressure ring can clamp the packing and increase the sealing effect. The actual installation position of the micro electric linear actuator can be set according to the actual structure. The above-mentioned composite control structure of incremental PID and feedforward compensation allows the sealing system to eliminate steady-state deviations by the integral action of PID when facing slow wear and degradation, and to quickly resist disturbances by feedforward compensation when facing sudden severe motion of the kiln body. The two work together to significantly improve the dynamic response and steady-state accuracy of the clamping force control.

[0064] The above technical solution employs incremental PID control, utilizing integral components to eliminate steady-state deviations, enabling the actual clamping force to accurately track the target clamping force. Derivative components are used to increase system damping, suppressing overshoot and oscillations during clamping force adjustment, resulting in a smoother adjustment process. Furthermore, the incremental algorithm is suitable for incremental actuators such as stepper motors and facilitates anti-integral saturation and disturbance-free switching, improving the reliability of engineering implementation. Feedforward compensation based on the kiln body's axial and radial runout rates is introduced. Conventional PID only responds after a deviation occurs; however, at the moment of violent kiln body movement or runout, the sealing gap rapidly increases, and feedback control cannot act in time before the leakage peak occurs. By real-time monitoring of the kiln body's motion rate, the incremental compensation force is output in advance before the deviation significantly increases, achieving early countermeasures against disturbances and significantly suppressing dynamic leakage peaks. Furthermore, the feedforward compensation and PID feedback control work independently yet collaboratively. The feedforward targets known disturbance models, while the feedback targets unknown deviations and model errors. The superposition of the two achieves wide-bandwidth, high-precision clamping force control, enabling the clamping force adjustment of the sealing system to be upgraded from passive response to a combination of active prediction and feedback. This significantly improves the sealing reliability under dynamic operating conditions while reducing actuator wear and energy consumption caused by frequent and large-scale adjustments.

[0065] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: By acquiring axial clamping force, intermediate chamber pressure, kiln body axial movement, and runout, a complete observation system for the sealing interface contact state, leakage flux, and external disturbances is constructed, overcoming the problems of missing operating condition information and inability to trace the root cause of failure caused by single-parameter monitoring in existing technologies. Fuzzy inference is used to transform multi-dimensional inputs into linguistic variables, and nonlinear mapping is performed through an expert rule base to output a continuously adjustable clamping force adjustment coefficient. Fuzzy inference does not require the establishment of a precise mathematical model, yet it can cover multiple combinations of operating conditions such as leakage, wear, and motion, outputting smooth control quantities in the transition zone and avoiding control abrupt changes caused by hard switching. Furthermore, the target clamping force is determined by multiplying the current clamping force by the adjustment coefficient, making the adjustment range proportional to the current pressure level, which conforms to the physical law that stronger compensation is needed in the later stages of wear. The upper-level fuzzy inference is responsible for macroscopic evaluation to generate the target clamping force, while the lower-level PID uses integrals to eliminate steady-state deviations and derivatives to increase system damping, achieving accurate tracking of the target value. This decouples nonlinear decision-making from high-frequency dynamic response tasks, avoiding the limitations of a single algorithm. By implementing real-time leakage feedback closed-loop regulation, the sealing interface is always kept at the optimal contact pressure, which minimizes through-leakage and avoids excessive wear. By using the principle of pressing as needed, frictional power consumption and material wear rate are reduced, extending the service life of the packing and reducing maintenance frequency. By sensing the movement of the kiln body and adjusting the pressing force, dynamic leakage peaks are suppressed, and the continuous stability of the process atmosphere inside the kiln is maintained. Thus, multi-objective optimization is achieved in three dimensions: sealing effect, equipment life and process stability.

[0066] This specification also provides an adaptive sealing control system for a rotary kiln sealing system, such as... Figure 3 As shown, the system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0067] This specification also provides an embodiment of a rotary kiln device that performs the above-described method.

[0068] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, systems, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0069] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0070] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. An adaptive sealing control method for a rotary kiln sealing system, characterized in that, This invention is applied to a rotary kiln sealing system, which includes a stationary ring connected to the outer cylinder of the rotary kiln and a rotating ring connected to the inner cylinder of the rotary kiln. The stationary ring surrounds both sides of the rotating ring, and packing is provided on both side walls of the rotating ring and the stationary ring, respectively. A pressure ring and a spring are provided on the outer side of the packing, and the spring presses the pressure ring against the packing. The method includes: Acquire multi-dimensional real-time sensor data, including the axial clamping force between the pressure ring and the carbon fiber packing, the intermediate chamber pressure corresponding to the intermediate sealing chamber, the axial displacement of the kiln body, and the radial runout. Based on the intermediate chamber pressure, the current overall leakage rate is calculated. Based on the overall leakage rate, the axial clamping force, the axial displacement of the kiln body, and the radial runout, fuzzy reasoning is performed to determine the clamping force adjustment coefficient, thereby determining the target clamping force. Based on the target clamping force and the axial clamping force, a target control quantity is calculated using an incremental PID control algorithm, and the clamping force of the pressure ring on the carbon fiber packing is adjusted using the target control quantity.

2. The adaptive seal control method of a rotary kiln seal system of claim 1, wherein, Based on the pressure in the intermediate chamber, the current overall leakage rate is calculated, specifically including: The current tracer gas concentration in the intermediate sealed chamber is collected, and the normalized gas concentration leakage rate is calculated by combining the current missing gas concentration with the preset zero-leakage background concentration. The first derivative of the intermediate chamber pressure with respect to time is determined, and the pressure change leakage rate is determined by the ratio of the first derivative to the pre-acquired normal operating condition reference pressure. The current comprehensive leakage rate is determined by taking a weighted average of the normalized gas concentration leakage rate and the pressure change leakage rate.

3. The adaptive seal control method of a rotary kiln seal system of claim 1, wherein, Based on the overall leakage rate, the axial clamping force, the axial displacement of the kiln body, and the radial runout, fuzzy reasoning is used to determine the clamping force adjustment coefficient, specifically including: The overall leakage rate is fuzzified and mapped to preset low leakage fuzzy sets, medium leakage fuzzy sets, and high leakage fuzzy sets to obtain the leakage membership vector of the overall leakage rate. The friction vibration signal is collected by the acceleration sensor installed on the pressure ring, and the temperature of the sealing friction surface is collected by the temperature sensor installed on the outside of the pressure ring; The wear index of the carbon fiber packing is calculated based on the historical data of the axial clamping force, the friction vibration signal, and the temperature of the sealing friction surface. The wear index is fuzzified and mapped to a preset fuzzy set of light wear, medium wear, and heavy wear to obtain the wear membership vector of the wear index. The degree of motion intensity is calculated based on the axial displacement and radial runout of the kiln body, and the degree of motion intensity is fuzzified and mapped to a preset fuzzy set of steady motion, fuzzy motion, and fuzzy motion to obtain the motion membership vector of the degree of motion intensity. Using the leakage membership vector, the wear membership vector, and the motion membership vector as inputs, fuzzy reasoning is performed based on a preset fuzzy rule base to obtain the fuzzy output membership function of the clamping force adjustment coefficient. The centroid method is used to defuzzify the fuzzy output membership function of the clamping force adjustment coefficient to determine the clamping force adjustment coefficient.

4. The adaptive seal control method of a rotary kiln seal system of claim 3, wherein, Based on the historical data of the axial clamping force, the friction vibration signal, and the temperature of the sealing friction surface, the wear index of the carbon fiber packing is calculated, specifically including: Obtain a pre-established benchmark clamping force-temperature relationship curve, which includes the steady-state friction temperature of carbon fiber packing in a brand-new state corresponding to different set clamping forces; Obtain the measured sealing friction surface temperature corresponding to the current axial clamping force, and query the reference clamping force-temperature relationship curve to obtain the reference temperature under the same clamping force. Calculate the difference between the measured sealing friction surface temperature and the reference temperature. When the difference exceeds a preset temperature difference threshold, a temperature correction coefficient is generated based on the magnitude of the difference, wherein the temperature correction coefficient increases monotonically as the difference increases. The friction vibration signal is bandpass filtered to remove frequency components outside the preset effective frequency band, resulting in a filtered vibration signal. The filtered vibration signal is then subjected to a fast Fourier transform to obtain the spectrum of the vibration signal, and the total energy within the preset wear characteristic frequency band is calculated. Divide the total energy by the pre-calibrated failure threshold energy to obtain the basic wear degree, and multiply the basic wear degree by the temperature correction coefficient to obtain the wear degree index.

5. The adaptive seal control method of a rotary kiln seal system of claim 1, wherein, Determine the target clamping force, specifically including: The target clamping force is determined by multiplying the axial clamping force by the clamping force adjustment coefficient. The target clamping force is constrained by a preset safety limit, ensuring that it is not lower than the pre-obtained minimum sealing clamping force and not higher than the maximum structural clamping force.

6. The adaptive seal control method of a rotary kiln seal system of claim 1, wherein, Based on the target clamping force and the axial clamping force, the target control quantity is calculated using an incremental PID control algorithm, specifically including: Determine the current deviation value between the target clamping force and the axial clamping force, and determine the basic control quantity based on the current deviation value using an incremental PID control algorithm; The current intensity of motion is calculated based on the axial displacement and radial runout of the kiln body. When the intensity of the current motion exceeds a preset threshold for intense motion, the feedforward compensation force increment is calculated, and the feedforward compensation force increment is superimposed on the basic control quantity to determine the target control quantity.

7. The adaptive seal control method of a rotary kiln seal system of claim 6, wherein, Based on the current deviation value, the basic control quantity is determined using an incremental PID control algorithm, specifically including: Obtain the historical deviation value of the previous control cycle, and calculate the difference between the current deviation value and the historical deviation value as the deviation differential term, so as to determine the proportional control component based on the current deviation value and the preset proportional coefficient; The current deviation value is multiplied by a preset integral coefficient and then by the control cycle time. The integral control component is obtained by integrating and accumulating the results over time. Multiply the deviation differential term by a preset differential coefficient and then divide by the control cycle time to obtain the differential control component. The basic control quantity is obtained by adding the proportional control component, the integral control component, and the derivative control component.

8. The adaptive seal control method of a rotary kiln seal system of claim 6, wherein, The calculation of the feedforward compensation force increment specifically includes: Based on the axial displacement and radial runout of the kiln body, the first derivative of the axial displacement with respect to time is calculated to determine the rate of change of axial displacement, and the first derivative of the radial runout with respect to time is calculated to determine the rate of change of radial runout. The feedforward compensation force increment is determined by the absolute value of the axial runout rate of change and the absolute value of the radial runout rate of change.

9. An adaptive seal control system for a rotary kiln seal system, the system comprising: The system includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-8.

10. A rotary kiln apparatus characterized by, Perform the method as described in any one of claims 1-8.

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