An intelligent control system for ladle baking

Abstract

The present invention relates to the field of metal casting technology, and discloses an intelligent control system for ladle baking, comprising: an exhaust gas monitoring unit that collects the humidity, temperature, and flow parameters of the exhaust gas in the exhaust duct in real time; a feature extraction and stage identification unit that calculates the characteristic index of the baking entropy growth rate based on the coupling of the above parameters, and dynamically identifies the stages of intense evaporation, slowed evaporation, pure heating, and uniform heat saturation; a baking control unit that sets a target entropy growth rate interval according to the current stage, and achieves homogenized drying and heating inside the ladle by adjusting the heat output of the heating device. The present invention dynamically analyzes the thermodynamic state inside the ladle through the exhaust gas entropy flow characteristics, realizes adaptive and precise control of the baking process, avoids the problems of uneven baking and low energy efficiency caused by black box operation of traditional methods, and has the ability to verify the penetration of residual moisture, which significantly improves the baking quality and energy efficiency.

Description

Technical Field

[0001] The invention relates to an intelligent control system for ladle baking, belonging to the technical field of metal casting. Background Art

[0002] In the ladle pretreatment process, traditional baking methods generally adopt a control strategy based on a fixed temperature-time curve, or rely on the operator's experience to adjust the gas supply. The core logic is to indirectly infer the internal drying state by monitoring the surface temperature of the ladle.

[0003] However, this apparent parameter control mode has inherent limitations when dealing with complex working conditions: 1. The moisture distribution, heat capacity characteristics and structural differences of the refractory materials lining the ladle make it difficult for a unified heat input strategy to adapt to individual needs, and there are often significant deviations in the drying process of newly built ladles and circulating ladles; 2. The coupling effect of moisture evaporation and material temperature rise has not been effectively decoupled. Excessive heat input in the early stage of baking can easily cause surface overburning and internal moisture residue, and unbalanced heat energy distribution in the later stage leads to low heat distribution efficiency.

[0004] While the industry has attempted to introduce multi-point temperature monitoring and fuzzy control algorithms for optimization, they are still constrained by the black-box nature of refractory internal state perception and are unable to capture the dynamic relationship between moisture phase change and heat transfer. This is especially true for ladles with complex wall thicknesses and structures. Residual moisture is difficult to completely remove under conventional positive pressure baking, and transitioning to high-temperature stages can easily lead to localized vapor pressure surges, compromising the structural integrity of the refractory material. Therefore, avoiding the cognitive limitations of apparent parameter control and establishing a precise control mechanism based on real-time feedback from the ladle's internal thermodynamic state, while ensuring thorough drying and energy efficiency under complex operating conditions, have become key technical challenges in improving the quality of pretreatment for metal casting containers. Summary of the Invention

[0005] The present invention provides an intelligent control system for ladle baking, the main purpose of which is to solve the problems of uneven baking, low energy efficiency and incomplete residual moisture detection caused by the traditional baking method due to the inability to perceive the thermodynamic state inside the ladle in real time.

[0006] To achieve the above objectives, the present invention provides an intelligent control system for ladle baking, the system comprising:

[0007] The exhaust gas monitoring unit is configured in the exhaust duct during the ladle baking process and is used to collect the humidity, temperature and flow parameters of the exhaust gas in real time;

[0008] The feature extraction and stage identification unit is connected to the exhaust gas monitoring unit and is configured to: obtain a baking entropy growth rate characteristic index characterizing the baking process inside the ladle based on exhaust gas humidity parameters, exhaust gas temperature parameters, and exhaust gas flow parameters through coupled calculation; and dynamically identify at least one physical and chemical stage of the current baking process among the intense evaporation stage, evaporation slowing stage, pure heating stage, and uniform heat saturation stage by continuously analyzing the temporal changes of the baking entropy growth rate characteristic index;

[0009] The baking control unit is connected to the feature extraction and stage identification unit and to the heating device of the ladle, and is configured to: set the corresponding target baking entropy growth rate interval based on the current physical and chemical stage identified by the feature extraction and stage identification unit; dynamically adjust the total heat output of the heating device based on the deviation between the baking entropy growth rate characteristic index monitored in real time and the target baking entropy growth rate interval to guide the baking process so that the inside of the ladle achieves homogenized drying and heating.

[0010] Preferably, the exhaust gas monitoring unit includes a humidity sensor configured to monitor exhaust gas humidity parameters, a temperature sensor configured to monitor exhaust gas temperature parameters, and a flow sensor or flow estimation module configured to monitor exhaust gas flow parameters.

[0011] Preferably, the characteristic index of the baking entropy growth rate includes the water evaporation per unit time obtained by calculating the exhaust gas humidity parameter, the exhaust gas temperature parameter and the exhaust gas flow parameter, wherein the water evaporation per unit time is higher than the preset water evaporation initial threshold at the initial stage of baking, and shows a downward trend as the free water and part of the bound water in the refractory material are removed and forms a characteristic inflection point.

[0012] Preferably, the feature extraction and stage identification unit is configured to determine that the baking process transitions from a violent evaporation stage dominated by water evaporation or an evaporation slowdown stage dominated by water evaporation to a pure heating stage dominated by refractory matrix heating by detecting the decreasing inflection point of the water evaporation amount per unit time in the time series.

[0013] Preferably, the baking control unit is configured as follows: in the intense evaporation stage, if the baking entropy growth rate characteristic index is lower than the lower limit of the target baking entropy growth rate interval, the heat output of the heating device is increased; if the baking entropy growth rate characteristic index is higher than the upper limit of the target baking entropy growth rate interval, the heat output of the heating device is reduced; in the pure heating stage, the heat output strategy is adjusted according to the changes in the exhaust gas temperature parameters to effectively increase the internal temperature of the refractory material; when the baking entropy growth rate characteristic index tends to a preset stable value, indicating that the inside of the ladle tends to a thermal equilibrium and dry state, the baking control unit automatically enters the insulation stage or the programmed cooling stage.

[0014] Preferably, the system also includes an auxiliary temperature monitoring unit configured to monitor the surface temperature or near-surface temperature of the ladle lining; when adjusting the total heat output of the heating device, the baking control unit refers to the feedback of the auxiliary temperature monitoring unit to ensure that the surface temperature or near-surface temperature of the ladle lining does not exceed the preset safety temperature upper limit.

[0015] Preferably, the baking control unit is configured to select different control strategies according to preset baking target types, wherein the baking target types include completely drying a newly built ladle, rapidly heating a turnover ladle, or energy-saving baking.

[0016] Preferably, the feature extraction and stage identification unit is configured to, when it is determined that the ladle baking process has reached a preset final drying stage or an initial pure heating stage, enable a residual water evaporation response enhancement discrimination mechanism under transient negative pressure perturbation; the exhaust gas monitoring unit is configured to, during the application of the transient negative pressure perturbation, monitor the transient changes in the exhaust gas humidity parameters in the exhaust duct at a sampling frequency higher than the conventional sampling frequency; the feature extraction and stage identification unit is further configured to, by determining whether the transient changes in the exhaust gas humidity parameters present a specific humidity pulse response indicating that the residual water inside the ladle has accelerated evaporation under negative pressure induction, thereby making a final confirmation on whether the ladle has reached a completely dry state; wherein the detection logic of the specific humidity pulse response satisfies:

[0017] ,

[0018] in, is the humidity change, is the peak humidity during the negative pressure perturbation, is the baseline humidity value before negative pressure perturbation, It is the preset humidity pulse recognition threshold.

[0019] Preferably, the system further comprises a negative pressure applying unit, which applies a short negative pressure disturbance to the inner cavity of the ladle under the instruction of the feature extraction and stage identification unit by controlling the suction of the exhaust system connected to the ladle exhaust duct or a bypass valve combination.

[0020] Preferably, if the feature extraction and stage identification unit detects a specific humidity pulse response, the baking control unit is configured to adjust the subsequent baking strategy, including extending the current low-temperature drying stage or starting to increase the temperature at a gentler rate, until there is no specific humidity pulse response when the residual moisture evaporation response enhancement discrimination mechanism under transient negative pressure perturbation is executed again.

[0021] Compared with the background technology problems, the beneficial effects of the present invention are:

[0022] 1. By coupling and analyzing the temporal evolution of exhaust gas humidity, temperature, and flow parameters, the system can construct core characteristic indicators that reflect the state of moisture migration and heat absorption inside the ladle, and accurately divide the physical and chemical stages of the baking process accordingly. This mechanism avoids the limitations of traditional apparent parameter control, allowing the heat output strategy to automatically switch with the inherent needs of the baking stage. In the intense evaporation stage, it prioritizes efficient moisture removal, while in the pure heating stage, it focuses on promoting a uniform increase in the internal temperature of the refractory material, achieving dynamic adaptation of energy efficiency distribution and material thermodynamic response, and effectively avoiding the industry-wide contradiction between over-baking and under-baking.

[0023] 2. The system captures the implicit impact of different ladles' initial states (such as residual moisture and ladle age differences) on the baking process through real-time entropy flow characteristic curves, and automatically generates personalized control trajectories that match their heat capacity characteristics. This mechanism transforms traditional experience-driven fixed process parameters into dynamic decision-making logic based on feedback from the ladle's own state, enabling differentiated scenarios such as newly built ladles and turnover ladles to obtain the optimal baking path, significantly improving process adaptability and quality consistency.

[0024] 3. At the critical stage of the final drying stage, the system actively stimulates the migration response of deep pore moisture through transient negative pressure perturbations, and verifies the penetration of residual moisture based on high-sensitivity humidity pulse detection. This mechanism forms a closed-loop verification with the stage identification module of the main control system. It not only inherits the main scheme's accurate judgment of the macro process, but also strengthens the ability to ensure the thoroughness of drying by capturing the microscopic response under physical disturbances. The synergistic effect of the two effectively solves the industry problem of the difficulty in detecting moisture in dead corners inside complex structure ladles.

[0025] 4. By integrating exhaust gas temperature trend analysis, real-time surface temperature monitoring and dynamic threshold of entropy growth rate, the system has built a multi-layer redundant temperature safety protection mechanism. In the intense evaporation stage, the coupled analysis of exhaust gas heat loss rate and surface temperature can predict the risk of local overheating in advance; in the pure heating stage, the feedback of entropy growth rate on the overall heat absorption efficiency of refractory materials can suppress invalid heat input. This multi-dimensional signal fusion mechanism avoids the hysteresis defect of single temperature monitoring and realizes active prediction and precise intervention of thermal shock protection.

[0026] 5. The system uses a baking target type selection module to convert differentiated requirements such as complete drying and rapid heating into dynamic control rules for entropy increase characteristic indicators. For newly built ladles, the system automatically extends the stability criterion of the low-temperature evaporation stage to ensure that the bound water is fully removed; for turnover ladles, rapid reuse is achieved by optimizing the heating rate and soaking time. This flexible architecture enables the same hardware system to inherently support multiple process paradigms, greatly improving the efficiency of equipment reuse and the level of intensive process management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a schematic diagram of the hierarchical module structure of the present invention;

[0028] Figure 2 This is a schematic diagram of the network connection structure between the central control room and the local system of the present invention;

[0029] Figure 3 This is a data flow structure diagram of multi-sensor collaborative perception and feature recognition in the exhaust gas monitoring unit of the present invention;

[0030] Figure 4 Schematic diagram of the evolution trend of each stage of the ladle baking process of the present invention.

[0031] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0032] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] The present application provides an intelligent control system for ladle baking, the system comprising:

[0034] The exhaust gas monitoring unit is configured in the exhaust duct during the ladle baking process and is used to collect the humidity, temperature and flow parameters of the exhaust gas in real time;

[0035] The feature extraction and stage identification unit is connected to the exhaust gas monitoring unit and is configured to: obtain a baking entropy growth rate characteristic index characterizing the baking process inside the ladle based on exhaust gas humidity parameters, exhaust gas temperature parameters, and exhaust gas flow parameters through coupled calculation; and dynamically identify at least one physical and chemical stage of the current baking process among the intense evaporation stage, evaporation slowing stage, pure heating stage, and uniform heat saturation stage by continuously analyzing the temporal changes of the baking entropy growth rate characteristic index;

[0036] The baking control unit is connected to the feature extraction and stage identification unit and to the heating device of the ladle, and is configured to: set the corresponding target baking entropy growth rate interval based on the current physical and chemical stage identified by the feature extraction and stage identification unit; dynamically adjust the total heat output of the heating device based on the deviation between the baking entropy growth rate characteristic index monitored in real time and the target baking entropy growth rate interval to guide the baking process so that the inside of the ladle achieves homogenized drying and heating.

[0037] Preferably, the exhaust gas monitoring unit includes a humidity sensor configured to monitor exhaust gas humidity parameters, a temperature sensor configured to monitor exhaust gas temperature parameters, and a flow sensor or flow estimation module configured to monitor exhaust gas flow parameters.

[0038] Preferably, the characteristic index of the baking entropy growth rate includes the water evaporation per unit time obtained by calculating the exhaust gas humidity parameter, the exhaust gas temperature parameter and the exhaust gas flow parameter, wherein the water evaporation per unit time is higher than the preset water evaporation initial threshold at the initial stage of baking, and shows a downward trend as the free water and part of the bound water in the refractory material are removed and forms a characteristic inflection point.

[0039] Preferably, the feature extraction and stage identification unit is configured to determine that the baking process transitions from a violent evaporation stage dominated by water evaporation or an evaporation slowdown stage dominated by water evaporation to a pure heating stage dominated by refractory matrix heating by detecting the decreasing inflection point of the water evaporation amount per unit time in the time series.

[0040] Preferably, the baking control unit is configured as follows: in the intense evaporation stage, if the baking entropy growth rate characteristic index is lower than the lower limit of the target baking entropy growth rate interval, the heat output of the heating device is increased; if the baking entropy growth rate characteristic index is higher than the upper limit of the target baking entropy growth rate interval, the heat output of the heating device is reduced; in the pure heating stage, the heat output strategy is adjusted according to the changes in the exhaust gas temperature parameters to effectively increase the internal temperature of the refractory material; when the baking entropy growth rate characteristic index tends to a preset stable value, indicating that the inside of the ladle tends to a thermal equilibrium and dry state, the baking control unit automatically enters the insulation stage or the programmed cooling stage.

[0041] Preferably, the system also includes an auxiliary temperature monitoring unit configured to monitor the surface temperature or near-surface temperature of the ladle lining; when adjusting the total heat output of the heating device, the baking control unit refers to the feedback of the auxiliary temperature monitoring unit to ensure that the surface temperature or near-surface temperature of the ladle lining does not exceed the preset safety temperature upper limit.

[0042] Preferably, the baking control unit is configured to select different control strategies according to preset baking target types, wherein the baking target types include completely drying a newly built ladle, rapidly heating a turnover ladle, or energy-saving baking.

[0043] Preferably, the feature extraction and stage identification unit is configured to, when it is determined that the ladle baking process has reached a preset final drying stage or an initial pure heating stage, enable a residual water evaporation response enhancement discrimination mechanism under transient negative pressure perturbation; the exhaust gas monitoring unit is configured to, during the application of the transient negative pressure perturbation, monitor the transient changes in the exhaust gas humidity parameters in the exhaust duct at a sampling frequency higher than the conventional sampling frequency; the feature extraction and stage identification unit is further configured to, by determining whether the transient changes in the exhaust gas humidity parameters present a specific humidity pulse response indicating that the residual water inside the ladle has accelerated evaporation under negative pressure induction, thereby making a final confirmation on whether the ladle has reached a completely dry state; wherein the detection logic of the specific humidity pulse response satisfies:

[0044] ,

[0045] in, is the humidity change, is the peak humidity during the negative pressure perturbation, is the baseline humidity value before negative pressure perturbation, It is the preset humidity pulse recognition threshold;

[0046] In addition, it should be noted that the amount of water evaporation per unit time in the system is the basic parameter for characterizing the dehumidification intensity in the initial stage of baking, and is mainly used to reflect the dynamic migration process of free water and bound water. The characteristic index of the entropy growth rate is a characteristic quantity of the system thermodynamic state obtained by comprehensive calculation based on multiple parameters such as water evaporation, heat input and exhaust flow. Although the two are related in calculation logic, they are used for working condition identification and control decision-making at different stages respectively. Regarding the detection logic of specific humidity pulse response, in order to enhance the adaptability and engineering practicality of the system, two types of equivalent criterion expressions are introduced, among which The form is suitable for standardized modeling scenarios, while the 3% humidity surge is derived from the empirical judgment value under large-sample statistical analysis. Both are reliable and substitutable in actual operation. In terms of the correspondence between the entropy growth rate and the heat regulation strategy, the system uses a dynamic feedback loop to map and adjust the power output of the heating device according to the degree of deviation between the entropy growth rate index and the set target interval. The mapping logic combines real-time sensor feedback and stage identification results in the control module to form a multivariable adaptive control path to ensure the responsiveness and stability of heat input at each stage. The threshold parameters used in various physical and chemical stage criteria in the present invention, including the water evaporation rate limit, the entropy growth rate fluctuation amplitude, the temperature rise rate lower limit, etc., can be derived from the statistical results of measured data of different types of ladles in multiple furnace types and different ambient temperatures. They are all extended implementation methods known to ordinary technicians in this field.

[0047] Preferably, the system further comprises a negative pressure applying unit, which applies a short negative pressure disturbance to the inner cavity of the ladle under the instruction of the feature extraction and stage identification unit by controlling the suction of the exhaust system connected to the ladle exhaust duct or a bypass valve combination.

[0048] Preferably, if the feature extraction and stage identification unit detects a specific humidity pulse response, the baking control unit is configured to adjust the subsequent baking strategy, including extending the current low-temperature drying stage or starting to increase the temperature at a gentler rate, until there is no specific humidity pulse response when the residual moisture evaporation response enhancement discrimination mechanism under transient negative pressure perturbation is executed again.

[0049] Example 1: In this example, combined with the typical operating environment of a steelmaking plant, the implementation path of an intelligent control system for ladle baking is specifically described. Its core goal is to achieve dynamic adaptive regulation of the ladle drying and heating process through real-time perception and analysis of the thermodynamic characteristics of exhaust gas, thereby ensuring uniform and thorough baking effects, reasonable energy consumption, and further protecting the integrity of the refractory material structure and the service life of the ladle.Before the steel bags are put into the baking area, the composite high-precision sensor device embedded in the exhaust system is used to monitor the parameters to be collected in the exhaust duct in real time. The humidity parameter is collected by a capacitive micro-humidity sensor, which has a response time of less than two seconds and has the ability to operate stably for a long time under high-temperature water vapor impact environment; the temperature parameter is measured by an integrated thermocouple module, and the probe position is fixed slightly below the central axis of the main stream of the exhaust gas after multiple rounds of debugging to ensure that the measured value can truly reflect the temperature rise trend of the central area of ​​the furnace body; the flow parameter is collected by a thermal mass flow meter, and the wind pressure fluctuation calibration module is used to correct the error, so as to obtain the accurate trend of the exhaust volume flow change, to ensure The representativeness and validity of the data, and the core function of the feature extraction and stage identification unit is to continuously analyze the joint change trend of the above three parameters to construct an entropy growth rate characteristic index reflecting the internal hydrothermal state. This index is intended to characterize the phase change intensity and heat transfer effect of free water and bound water driven by heat inside the ladle per unit time; in order to enhance the practicality of this index in engineering applications, the system sets the reference evaporation section as the judgment baseline for entropy increase changes in the initial stage, and then monitors the rate of change of water evaporation per unit time. When the system observes that the fluctuation amplitude of this value is less than the median of a stable interval obtained by statistical analysis of the measured data for five consecutive minutes, it can be determined that a severe evaporation stage has occurred. The stage has ended and entered the stage of evaporation slowdown or pure heating; the calculation of the entropy growth rate characteristic index adopts the fusion function modeling result based on the three parameters of exhaust gas humidity, temperature and flow rate. Its physical meaning can be regarded as a comprehensive representation of the moisture migration and phase change intensity driven by thermal energy in the system per unit time. The acquisition and processing of various parameters have been carried out through engineering modeling and training and verification of multiple groups of actual sample data to ensure rationality and feasibility. In addition, the relevant numerical thresholds in the stage judgment (such as evaporation rate range, entropy growth rate fluctuation amplitude, etc.) are all derived from the measured statistical results of typical new steel ladles and turnover ladles under different furnace types, which are representative and repeatable. Although there is data overlap in the various functional modules of the system, such as auxiliary The temperature monitoring unit is mainly used for independent temperature control protection and heat loss warning, and forms functional complementarity with the heat input adjustment strategy based on entropy increase feedback in the main control module, enhancing system safety and response sensitivity from multiple dimensions; although the auxiliary temperature monitoring unit operates independently as a thermal safety assurance component, when the main control system adjusts the heating power, its feedback data is also used as an auxiliary reference input to correct or verify the thermal control trend calculated by the entropy growth rate characteristic indicator, thereby realizing dynamic regulation under multi-source information fusion. Its role is not to directly intervene in the main control logic, but to provide necessary supplements and redundant protection to the main control strategy by monitoring boundary constraints and response delays, forming coordination and complementarity between the inner and outer thermal control mechanisms.

[0050] During actual operation, if the system identifies that the current ladle is a newly built structure, the control module will automatically call the steady-state extension control strategy because of its high initial humidity and large heat capacity. This strategy mainly extends the heat input time in the low-temperature section and limits the initial heating rate to prevent sealing sintering due to excessively rapid surface temperature increase. At this stage, the heat input corresponding to the target range of entropy growth rate set by the system is controlled between 80% and 95% of the basic setting, and the heat input intensity is dynamically corrected according to the temporal change trend of the exhaust humidity to ensure that moisture is released layer by layer from the inside of the ladle and achieves uniform drying; after entering the pure heating stage, the entropy growth rate shows a trend of change from dynamic fluctuation to monotonic slowdown. , the exhaust gas temperature also begins to rise steadily. At this time, the control module adjusts the heat input strategy according to the average rate of change of the exhaust gas temperature. When the temperature rise slope is not less than three degrees Celsius per minute for ten consecutive minutes, the system gradually increases the heat source output to the full power level of the basic setting. At the same time, an auxiliary temperature monitoring mechanism is introduced, and a non-contact infrared temperature measuring device installed near the surface of the ladle lining is used to collect temperature data in real time. When the lining temperature reaches the preset safety upper limit, the system will automatically trigger the output adjustment logic to reduce the heat source input power to prevent the surface material from burning or cracking due to thermal shock; and near the end of the drying process, the system starts the transient negative pressure perturbation response mechanism, which cooperates with the exhaust fan system to At the same time, a small vacuum operation is applied inside the ladle for a short time to form a low-amplitude negative pressure disturbance, which stimulates the evaporation response of moisture that may remain in the deep pores of the refractory material. The duration of the disturbance operation is controlled within 30 seconds, and the vacuum intensity does not exceed 150% of the conventional exhaust state, so as to maintain the stability of the system operation while ensuring the effectiveness of the disturbance. Before and after the disturbance, the humidity sensor samples at a frequency of once per second. The system detects whether a rapid humidity rise of more than 3% occurs within ten seconds after the start of the disturbance. If the humidity pulse response feature is detected, the system determines that there is still residual moisture and will test again after maintaining the current stage for ten to fifteen minutes; if no humidity rise is detected for two consecutive times, the system will detect the residual moisture again. If there is a pulse response, the system considers that the drying is completed and can enter the programmed cooling or insulation stage. At the same time, in order to ensure the safety of transient negative pressure disturbance operation and system stability, the control module will synchronously lock the output state of the heating device before applying the disturbance to avoid system fluctuations caused by the superposition of heat input and disturbance process. The system also has upper limits on disturbance intensity and duration to ensure that the negative pressure operation does not cause significant interference or sudden temperature changes to the furnace temperature field. In addition, the auxiliary temperature monitoring module continuously tracks the near-surface temperature changes of the ladle lining during the disturbance. Once an abnormal fluctuation in the heating rate is detected, the safety protection logic will be automatically triggered, the heating program will be suspended and the system will be checked to achieve dynamic suppression of disturbance side effects and controllable whole process.

[0051] Throughout operation, all control logic and parameter settings underlying the system are based on multiple sets of experimental verification and the accumulation and induction of field operation data. For example, the reference threshold for the entropy growth rate is a key criterion extracted through statistical analysis of more than 20 sets of field-tested samples with different ladle ages, different lining structures, and different furnace types. Parameters such as the temperature control upper limit, heat flux adjustment range, and disturbance intensity limit are also selected based on actual testing on a high-temperature refractory material test bench. Key factors such as thermal gradient, water vapor diffusion, material specific heat, and porosity are comprehensively considered to ensure that each parameter setting has a high degree of engineering feasibility and safety margins. At the same time, the system fully considers the adaptability required when multiple process objectives coexist. When receiving a rapid temperature increase command, the control module automatically adjusts the control strategy, weakening the time constraints of the dehydration stage and shifting the system response priority to improving heat penetration efficiency, thereby increasing the heat input rate while ensuring safety. When switching to energy-saving baking mode, the control module reduces the total heat input, extends the residence time of each stage, and relaxes the stage transition criteria to achieve the control goal of meeting the drying effect requirements with the lowest energy consumption.

[0052] Example 2: In a typical production process of a steel plant, there are significant differences in the state of the ladle lining. Taking a newly built ladle as an example, because the initial moisture content of its refractory material is relatively high, if the traditional unified temperature rise control curve is adopted, it is very easy to cause rapid sintering of the surface layer while the deep moisture cannot be fully removed. On the other hand, if the long cycle process of the newly built ladle is still used for the turnover ladle, it is easy to cause energy waste. Therefore, an intelligent control system is constructed that can dynamically identify the baking state of the ladle based on the exhaust gas parameters and adjust the heat input in real time accordingly, so as to achieve personalized control of different ladle structures and ensure the uniformity of the baking effect, thermal energy utilization efficiency and operational safety.

[0053] In this experiment, six ladles with the same structure, consistent lining material and rated capacity of 30 tons were selected as test objects, of which three were newly built ladles (their initial refractory moisture content was about 15%) and three were turnover ladles (with a moisture content of less than 3%). All ladles completed the full process baking independently in the same gas-fired heating furnace in turn to ensure that the data were representative and reproducible; humidity parameters were collected by a capacitive high-temperature micro-humidity sensor with a sampling frequency of 1Hz; temperature parameters were collected by a K-type thermocouple arranged on the lower axis of the mainstream beam of the exhaust pipe, with sampling every 2 seconds; exhaust flow rate was collected by a thermal mass flowmeter, supplemented by a wind pressure fluctuation correction module to improve measurement accuracy; an auxiliary infrared temperature measurement module was used to non-contact monitor the near-surface temperature of the ladle lining to provide safe temperature control support. The entropy growth rate characteristic index is calculated by comprehensively calculating the water evaporation intensity per unit time (unit: g / min), the trend of exhaust gas enthalpy change, and the exhaust flow rate. It is used to characterize the dynamic characteristics of water migration and heat conduction state under heat drive. To achieve stable identification of each stage, this system sets the following physical and chemical stage criteria:

[0054]

[0055] Near the end of the pure heating phase, the system automatically activates the transient negative pressure disturbance mechanism to identify the presence of deep residual moisture. During the disturbance, if a typical humidity pulse response is collected (i.e., a sudden increase in humidity exceeding 3% within 10 seconds after the disturbance), it is determined that drying is not yet complete. Taking the newly built ladle numbered #N1 as an example, its key operating data are shown in the following table:

[0056]

[0057] Among them, the newly built ladle sample needs to undergo two rounds of negative pressure disturbance before it can be confirmed to be completely dried; while the turnover ladle sample entered the pure heating stage at the 80th minute, the entropy growth rate stabilized below 0.45, and no humidity surge characteristics appeared. The entropy growth rate collected during the test was highly consistent with the trend of moisture migration intensity changes, the stage switching nodes were clear, and the system control strategy responded reasonably, verifying the control system's adaptability to different ladle states. Through the test process and data analysis, the following conclusions were drawn: the entropy growth rate characteristic index constructed based on the three parameters of exhaust gas can effectively reflect the moisture migration and heat absorption state inside the ladle; the physicochemical properties of different baking stages can be stably and reliably identified through the trend of this indicator; the control system can dynamically adjust the heat output according to this indicator, and achieve effective adaptation to differentiated ladles such as newly built ladles and turnover ladles; the transient negative pressure disturbance mechanism has obvious penetration verification capability at the end of the drying stage, and can form a closed-loop confirmation of residual moisture; all parameter settings are derived from the statistical analysis of a large number of actual operation samples and high-temperature experimental device tests, with good engineering feasibility and safety margins.

[0058] Example 3: This example combines Figures 1 to 4 , the realization of an intelligent control system for ladle baking is explained. Figure 1As shown in the figure, the system structure is divided into three parts: perception layer, control layer and monitoring layer. Among them, the perception layer includes hardware components such as valve matrix, ignition device, alarm, multi-source sensor and detection instrument, which is responsible for real-time perception of key physical quantities in the ladle baking process. The control layer is composed of multiple functional modules. The first is the data acquisition and processing module, which specifically includes multi-source sensor acquisition and data preprocessing, a state switching control module that supports manual switching and fully automatic operation of the system, and an equipment fault diagnosis module that realizes fault diagnosis and alarm, and safety protection functions; in addition, it also includes an advanced baking process control strategy module with temperature-flow cascade control, APC temperature control algorithm and combustion optimization control capabilities. The monitoring layer is composed of an intelligent interaction module, a remote monitoring module and a process optimization management module. The intelligent interaction module provides functions such as baking parameter management, multi-device management and query system logs. The remote monitoring module supports remote operation and safety monitoring, and can generate energy consumption reports. The process optimization management module coordinates the overall efficiency improvement of the system operation process.

[0059] like Figure 2 As shown in the figure, the central control room includes the client, main server, core switch and photoelectric converter. The client is used for operating the control interface, the main server is responsible for the management and processing of system operation data, the core switch realizes high-speed network exchange between various control devices, and the photoelectric converter serves as the medium for communication between the central control room and the local area, completing the conversion of photoelectric signals. The local control part covers the baking field control units of 1# baking machine, 2# baking machine and 3# baking machine. Each baking machine is equipped with an access switch, HMI, intelligent controller and corresponding PLC module (1# PLC, 2# PLC, 3# PLC); HMI is used for human-machine interface display and operation, the intelligent controller processes the collected data and executes the control strategy, the PLC implements the underlying control logic of the field actuators, and the access switch is connected to the aggregation switch to ensure smooth communication between the unit modules. The aggregation switch serves as the communication aggregation point of the local network and transmits data to the central control room through the optoelectronic converter to achieve unified scheduling and centralized monitoring of the system; the entire system architecture supports the parallel management of multiple ladle baking equipment, and builds a highly reliable remote and on-site collaborative control network through optoelectronic conversion, network switching and intelligent control to ensure the real-time, accuracy and stability of the system operation.

[0060] like Figure 3As shown, the exhaust gas monitoring unit includes a temperature sensor, a humidity sensor, a flow sensor or a flow estimation module, a collaborative sensing unit and a feature extraction and stage identification unit. The temperature sensor is used to collect exhaust gas temperature parameters in real time, the humidity sensor is used to collect exhaust gas humidity parameters in real time, and the flow sensor or the flow estimation module is used to collect exhaust gas flow parameters in real time. The data collected by all sensors will be uniformly input into the collaborative sensing unit for fusion processing. The collaborative sensing unit performs real-time parameter integration processing on the data transmitted by the temperature sensor, humidity sensor and flow sensor or flow estimation module, and outputs exhaust gas temperature parameters, exhaust gas humidity parameters and exhaust gas flow parameters, and further transmits these real-time parameters to the feature extraction and stage identification unit; the feature extraction and stage identification unit is based on the above three types of exhaust gas parameters, and couples and calculates the baking entropy growth rate characteristic index of the baking process inside the ladle, and identifies the physical and chemical process stages such as the intense evaporation stage, the evaporation slowdown stage, the pure heating stage and the uniform heat saturation stage accordingly, so as to realize the accurate dynamic identification of the drying and heating state of the ladle.

[0061] like Figure 4 As shown in the figure, the dynamic change trend and indicator evolution of humidity curve A, temperature curve B and entropy increase curve C in the four stages of ladle baking process from intense evaporation, evaporation slowdown, pure heating to uniform heat saturation. The horizontal axis is baking time (min), and the vertical axis is the standard value of humidity, temperature and entropy increase characteristic indicators. Humidity curve A represents the change of humidity in the ladle exhaust duct. It drops rapidly in the intense evaporation stage, slows down in the evaporation slowdown stage, and gradually stabilizes in the pure heating stage. Temperature curve B shows the trend of temperature increase in the center of the furnace body over time, which continues to rise throughout the baking process. Entropy increase curve C is the change of entropy growth rate calculated by the feature extraction and stage identification unit. It fluctuates violently in the early stage, then gradually stabilizes and enters a platform state in the uniform heat saturation stage. In addition, two disturbance events are marked in the figure, namely disturbance 1 (ΔH=11%) and disturbance 2 (ΔH=0%). The corresponding humidity responses are used to determine whether there is residual moisture in the ladle, thereby assisting the system in determining whether the drying is thorough.

[0062] Example 4: In a heavy-duty continuous casting workshop of a steel plant with an annual output of more than three million tons, the present intelligent control system for ladle baking is implemented for the typical application scenario where the humidity of the newly built ladle is high in winter, the turnover cycle is tight, and the continuity of the production line is strictly required. In this scenario, the initial moisture content of the ladle lining material is usually more than 15%, and due to the tight production organization rhythm, there is a practical need to shorten the baking cycle. If improperly controlled, it is very easy to produce a phenomenon of dry surface and wet inside during the heating stage, and then cause the refractory material to explode during the first molten steel impact process. This embodiment realizes an adaptive control strategy through dynamic feedback of the thermal state inside the ladle, thereby ensuring that the goal of homogenized drying and heating of the ladle is achieved within a limited time; the system automatically starts sensing after the ladle is moved into the baking position Layer, through the humidity sensing unit, thermal enthalpy sensing module and dynamic flow estimation component arranged in the smoke exhaust system, a three-parameter collaborative sensing matrix of exhaust gas humidity parameter, exhaust gas temperature parameter and exhaust gas flow parameter is constructed. The collected data is input into the core computing platform at a frequency of once per second, and the feature extraction module generates a thermal state change trend map. The module continuously tracks the changes in water evaporation intensity and exhaust gas heat content per unit time within the initial ten minutes, and thus forms the first batch of entropy growth rate sample data. The system compares the sample data with the entropy increase characteristic curve under similar ladle working conditions in the historical database, and matches the current ladle's heat capacity level and moisture-thermal decoupling characteristics, and identifies it as a high-water and high-heat capacity type object, and then automatically calls the steady-state extended control strategy.

[0063] During the initial stages of intense evaporation, the system sets a target entropy growth rate range based on the lower bound of the stable evaporation characteristic curve for a typical newly built ladle. Taking into account the relatively low furnace temperature and the ambient temperature of approximately -5°C, the control module proactively adjusts the initial heat input to between 75% and 85% of the standard full-load setting. It also periodically adjusts the burner injection angle to achieve spatial uniformity in heat distribution, thereby reducing the risk of localized overburning. Simultaneously, an auxiliary temperature monitoring device simultaneously collects near-surface temperature data from the ladle. If the system detects that the correlation deviation between the surface temperature curve and the entropy growth rate curve exceeds the set warning threshold (i.e., 3%), the slope correction logic is immediately triggered, automatically reducing the heat input rate and extending the duration of the intense evaporation phase. If the system continuously monitors that the water evaporation rate per unit time drops below 35 grams per minute and the entropy growth rate fluctuates below 5% for more than five minutes, the system determines that the evaporation has entered the slowdown phase and simultaneously activates the judgment node reinforcement mechanism. This mechanism further verifies the validity of the trend inflection point by fitting the first-order derivative of the entropy growth curve, ensuring that the phase transition is not misjudged due to short-term disturbances.

[0064] During the evaporation slowdown phase, the control module stabilizes heat input at approximately 65 percent of the full-load setting and adjusts the nozzle array opening ratio based on the dynamic trend of the exhaust gas enthalpy to maintain a balanced and stable furnace temperature field. When the exhaust gas humidity parameter stabilizes below the baseline value and the exhaust gas temperature continues to rise at a rate of more than 2.5 degrees Celsius per minute, the entropy growth rate shows a downward trend. The system therefore determines that the ladle is in a pure heating phase. During this phase, considering the ladle's double-layer composite corundum brick structure, which has large thermal inertia and relatively low surface thermal conductivity, the system prefers an intermittent incremental heating strategy. Specifically, the heat input is gradually increased to 95 percent of the full-load setting in 15-minute cycles. At the same time, an amplitude limiting algorithm is introduced to control the heat source power response frequency to prevent thermal shock caused by excessive heat input. The surface temperature data collected by the auxiliary temperature monitoring module is input into the proportional-integral-prediction model of the main controller, forming a real-time closed-loop feedback system. If the surface temperature rise rate is detected to be lower than the expected value, it is automatically determined to be excessive thermal resistance, triggering a pulsed high heat flux intervention strategy.

[0065] Near the end of the pure heating phase, the system initiates a residual moisture identification program and invokes the transient negative pressure application module to perform a low-amplitude extraction operation on the exhaust duct for no more than 30 seconds, with the extraction force not exceeding 130% of the conventional exhaust negative pressure. During the disturbance application period, the humidity sensing unit monitors the exhaust duct humidity parameters at twice the sampling frequency. If the system detects a sudden increase in humidity exceeding 3% within 10 seconds of the disturbance initiation, it determines that deep residual moisture still exists within the ladle. The control module automatically extends the current hold phase and plans a second round of disturbance operation. If no significant humidity pulse response is detected during the second disturbance, the system determines that the drying process is complete and enters the programmed cooling phase. Throughout the operation, the target range of the entropy growth rate, the heat flow regulation strategy, and the thresholds for each stage used by the system are all derived from historical sample statistical patterns extracted from a local database using a data-driven modeling algorithm. They are optimized and verified based on data from over 30 actual baking conditions. All parameter settings have clear engineering basis and control boundaries, ensuring the system's good feasibility and adaptability. These are all expandable implementation methods known to those skilled in the art.

[0066] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent control system for ladle baking, characterized in that: The system comprises: The exhaust gas monitoring unit is configured in the exhaust duct during the ladle baking process and is used to collect the humidity, temperature and flow parameters of the exhaust gas in real time; The feature extraction and stage identification unit is connected to the exhaust gas monitoring unit and is configured to: obtain a baking entropy growth rate characteristic index characterizing the baking process inside the ladle based on exhaust gas humidity parameters, exhaust gas temperature parameters, and exhaust gas flow parameters through coupled calculation; and dynamically identify at least one physical and chemical stage of the current baking process among the intense evaporation stage, evaporation slowing stage, pure heating stage, and uniform heat saturation stage by continuously analyzing the temporal changes of the baking entropy growth rate characteristic index; The baking control unit is connected to the feature extraction and stage identification unit and to the heating device of the ladle, and is configured to: set the corresponding target baking entropy growth rate interval based on the current physical and chemical stage identified by the feature extraction and stage identification unit; dynamically adjust the total heat output of the heating device based on the deviation between the baking entropy growth rate characteristic index monitored in real time and the target baking entropy growth rate interval to guide the baking process so that the inside of the ladle achieves homogenized drying and heating.

2. The intelligent control system for ladle baking according to claim 1, characterized in that: The exhaust gas monitoring unit includes a humidity sensor configured to monitor exhaust gas humidity parameters, a temperature sensor configured to monitor exhaust gas temperature parameters, and a flow sensor or a flow estimation module configured to monitor exhaust gas flow parameters.

3. The intelligent control system for ladle baking according to claim 2, characterized in that: The characteristic index of the baking entropy growth rate includes the water evaporation per unit time calculated by the exhaust gas humidity parameters, exhaust gas temperature parameters and exhaust gas flow parameters. Among them, the water evaporation per unit time is higher than the preset water evaporation initial threshold at the initial stage of baking, and shows a downward trend with the removal of free water and part of the bound water in the refractory material and forms a characteristic inflection point.

4. The intelligent control system for ladle baking according to claim 3, characterized in that: The feature extraction and stage identification unit is configured to determine that the baking process transitions from a violent evaporation stage dominated by water evaporation or an evaporation slowdown stage dominated by water evaporation to a pure heating stage dominated by refractory material matrix heating by detecting a decreasing inflection point of the water evaporation amount per unit time in a time series.

5. The intelligent control system for ladle baking according to claim 1, characterized in that: The baking control unit is configured as follows: in the intense evaporation stage, if the baking entropy growth rate characteristic index is lower than the lower limit of the target baking entropy growth rate interval, the heat output of the heating device is increased; if the baking entropy growth rate characteristic index is higher than the upper limit of the target baking entropy growth rate interval, the heat output of the heating device is reduced; in the pure heating stage, the heat output strategy is adjusted according to the changes in the exhaust gas temperature parameters; when the baking entropy growth rate characteristic index tends to the preset stable value, indicating that the inside of the ladle tends to thermal equilibrium and dry state, the baking control unit automatically switches to the insulation stage or the programmed cooling stage.

6. The intelligent control system for ladle baking according to claim 1, characterized in that: The system also includes an auxiliary temperature monitoring unit configured to monitor the surface temperature or near-surface temperature of the ladle lining; when the baking control unit adjusts the total heat output of the heating device, it refers to the feedback of the auxiliary temperature monitoring unit to ensure that the surface temperature or near-surface temperature of the ladle lining does not exceed the preset safety temperature upper limit.

7. The intelligent control system for ladle baking according to claim 1, characterized in that: The baking control unit is configured to select different control strategies according to preset baking target types, including completely drying a newly built ladle, rapidly heating a turnover ladle, or energy-saving baking.

8. The intelligent control system for ladle baking according to claim 1, characterized in that: The feature extraction and stage identification unit is configured to, when determining that the ladle baking process has reached a preset final drying stage or an initial pure heating stage, activate an enhanced discrimination mechanism for residual water evaporation response under transient negative pressure perturbation; the exhaust gas monitoring unit is configured to monitor transient changes in exhaust gas humidity parameters in the exhaust duct at a sampling frequency higher than a conventional sampling frequency during the application of the transient negative pressure perturbation; the feature extraction and stage identification unit is further configured to determine whether the transient changes in the exhaust gas humidity parameters present a specific humidity pulse response indicating that residual water in the ladle has accelerated evaporation under negative pressure, thereby making a final confirmation of whether the ladle has reached a completely dry state; wherein the detection logic of the specific humidity pulse response satisfies: ， in, is the humidity change, is the peak humidity during the negative pressure perturbation, is the baseline humidity value before negative pressure perturbation, It is the preset humidity pulse recognition threshold.

9. The intelligent control system for ladle baking according to claim 8, characterized in that: The system also includes a negative pressure applying unit, which applies a short negative pressure disturbance to the inner cavity of the ladle under the instruction of the feature extraction and stage recognition unit by controlling the suction or bypass valve combination of the exhaust system connected to the ladle exhaust duct.

10. The intelligent control system for ladle baking according to claim 8, characterized in that: If the feature extraction and stage recognition unit detects a specific humidity pulse response, the baking control unit is configured to adjust the subsequent baking strategy, including extending the current low-temperature drying stage or starting the temperature increase at a gentler rate, until there is no specific humidity pulse response when the residual moisture evaporation response enhancement discrimination mechanism under transient negative pressure perturbation is executed again.

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