A reactor temperature control method and system

Abstract

This application provides a method and system for controlling the temperature of a reactor, relating to the field of reactor temperature control. By acquiring material temperature data, heat transfer parameters, and the motion data of the cooling medium, and generating heat information and temperature difference information of the cooling medium based on this data, the system calculates heat transfer indices. Based on these heat transfer indices and heat information, the system can dynamically adjust the operating parameters of the cooling medium to achieve precise control of the reactor temperature. The system also generates maintenance information through the heat transfer indices, providing a scientific basis for preventative maintenance of the equipment. It can accurately identify changes in heat transfer efficiency and adjust control strategies in a timely manner, avoiding temperature fluctuations and energy waste caused by scaling. This significantly improves the accuracy, stability, and energy efficiency of reactor temperature control and extends the service life of the equipment.

Description

Technical Field

[0001] This application relates to the field of reactor temperature control technology, and more specifically, to a reactor temperature control method and system. Background Technology

[0002] In chemical production, especially in the preparation of polymer materials, temperature control of the reactor is crucial to ensuring product quality and production safety. Traditional temperature control methods typically rely on preset control parameters and a single temperature measurement point, maintaining the reactor temperature by adjusting the heating or cooling medium.

[0003] In actual production, the situation is much more complex. As the reaction proceeds, the viscosity of the material inside the reactor increases dramatically, becoming extremely thick. In areas difficult for the stirring blades to reach, such as corners near the reactor wall or some dead zones, the material's flowability is very poor. These deteriorated products adhere firmly to the inner wall of the reactor, gradually accumulating into a hard scale layer with each production batch. This scale layer is not only difficult to remove, but more importantly, it has extremely poor thermal conductivity, effectively adding an insulating layer between the material inside the reactor and the jacket cooling medium.

[0004] The formation of this insulating scale completely altered the heat transfer characteristics of the entire reactor. Initially, when the scale was relatively thin, the control system could barely manage. To maintain the reactor temperature at the set value, the controller would instruct the cooling system to provide a lower-temperature or higher-flow-rate cooling medium to overcome the additional thermal resistance introduced by the scale. This led to two serious consequences: First, the system response became extremely sluggish. When the heat load of the reaction inside the reactor changed suddenly, temperature regulation lagged significantly, easily resulting in large overshoots and fluctuations, making it difficult to guarantee consistent product quality. Second, energy consumption increased dramatically because the cooling system needed to operate at a load far exceeding normal operating conditions. Summary of the Invention

[0005] This application discloses a method and system for controlling the temperature of a reactor, aiming to solve the technical problem that existing reactor temperature control methods are difficult to accurately and stably control the temperature under complex operating conditions, especially when scale buildup on the reactor wall leads to a decrease in heat transfer efficiency, thereby affecting product quality and increasing energy consumption.

[0006] The technical solution of this application is as follows: In a first aspect, this application discloses a method for controlling the temperature of a reaction vessel, comprising: Acquire material temperature data, heat transfer parameters, and motion data of the cooling medium; The heat information of the cooling medium is generated based on the motion data of the cooling medium; Temperature difference information is generated based on material temperature data, heat transfer parameters, and motion data. The heat transfer parameters are calculated based on the heat information and temperature difference information. Adjust the operating parameters of the cooling medium based on heat transfer indices and heat information; and generate maintenance information based on heat transfer indices.

[0007] Secondly, this application also discloses a reactor temperature control system, which includes: The data acquisition module is used to acquire material temperature data, heat transfer parameters, and motion data of the cooling medium. The heat determination module is used to generate heat information of the cooling medium based on the motion data of the cooling medium; The temperature difference information acquisition module is used to generate temperature difference information based on material temperature data, heat transfer parameters and motion data; The heat transfer index module is used to calculate heat transfer indexes based on heat information and temperature difference information. The parameter adjustment module is used to adjust the operating parameters of the cooling medium based on heat transfer indicators and heat information; and to generate maintenance information through heat transfer indicators.

[0008] Beneficial Effects: The reactor temperature control method disclosed in this application acquires material temperature data, heat transfer parameters, and cooling medium motion data, and generates heat and temperature difference information of the cooling medium based on this data, thereby calculating heat transfer indices. Based on these heat transfer indices and heat information, the system can dynamically adjust the operating parameters of the cooling medium to achieve precise control of the reactor temperature. Simultaneously, maintenance information is generated through the heat transfer indices, providing a scientific basis for preventative maintenance of the equipment. This method effectively solves the problem in existing technologies where scale buildup on the reactor wall leads to decreased heat transfer efficiency, making it difficult for traditional control systems to accurately and stably control the temperature, thus affecting product quality and increasing energy consumption. By real-time monitoring and comprehensive analysis of multi-dimensional data, this application can accurately identify changes in heat transfer efficiency and adjust control strategies in a timely manner, avoiding temperature fluctuations and energy waste caused by scale buildup, significantly improving the accuracy, stability, and energy efficiency of reactor temperature control, and extending the service life of the equipment. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of a reaction vessel temperature control method provided in this application.

[0010] Figure 2 This is a schematic diagram of a reactor temperature control system provided in this application. Detailed Implementation

[0011] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0012] Reference Figure 1 The diagram illustrates a method for controlling the temperature of a reaction vessel according to an embodiment of the present invention, which may specifically include the following steps: S101, acquire material temperature data, heat transfer parameters and motion data of cooling medium; S102, generates heat information of the cooling medium based on the motion data of the cooling medium; S103 generates temperature difference information based on material temperature data, heat transfer parameters, and motion data; S104, calculates heat transfer parameters based on heat information and temperature difference information; S105 adjusts the operating parameters of the cooling medium based on heat transfer indicators and heat information; and generates maintenance information through heat transfer indicators.

[0013] The reactor temperature control method proposed in this application aims to achieve real-time assessment and prediction of the reactor's heat transfer status by comprehensively analyzing multi-source data, thereby more accurately adjusting the operating parameters of the cooling medium and providing timely maintenance information to address the decrease in heat transfer efficiency caused by problems such as scale buildup on the reactor wall.

[0014] Specifically, "material temperature data" refers to the real-time temperature measurement of the material inside the reactor, which can be obtained through temperature sensors installed inside the reactor. These sensors can be thermocouples, resistance temperature detectors (RTDs), or other types of temperature detectors used to monitor the overall or local temperature of the material. "Heat transfer parameters" refer to physical quantities related to the heat transfer process in the reactor, such as the thermal conductivity of the reactor wall, the heat transfer area, and the specific heat capacity of the cooling medium. These parameters can be preset constants or dynamically estimated based on actual operating conditions. "Cooling medium motion data" refers to the flow state information of the cooling medium in the reactor jacket or cooling coils, such as the flow rate, velocity, and inlet / outlet temperatures of the cooling medium. This data can be obtained through flow meters, temperature sensors, and other equipment. "Heat information" refers to the heat carried away or transferred by the cooling medium per unit time, a key indicator for evaluating the cooling effect. "Temperature difference information" refers to the temperature difference between the material inside the reactor and the cooling medium, the direct driving force behind the heat transfer process. "Heat transfer indexes" are quantitative indicators that comprehensively reflect the overall heat transfer efficiency of the reactor, such as the overall heat transfer coefficient, whose value directly reflects the quality of heat transfer performance. "Operating parameters" refer to adjustable control variables such as the flow rate, temperature, and pressure of the cooling medium. "Maintenance information" refers to predictions or recommendations regarding the timing of cleaning, repair, or other maintenance operations that the reactor may require based on the changing trends of heat transfer parameters.

[0015] In one implementation, it is first necessary to acquire material temperature data, heat transfer parameters, and cooling medium motion data. Material temperature data can be obtained by deploying multiple temperature sensors within the reactor; for example, thermocouple arrays can be installed at different depths and radial positions within the reactor to obtain more comprehensive temperature distribution information. Heat transfer parameters can be preset based on the reactor's design drawings and material properties, or estimated using online identification algorithms based on historical operating data. Cooling medium motion data can be acquired using flow meters and temperature sensors installed at the inlet and outlet of the cooling medium; for example, the instantaneous flow rate and inlet / outlet temperature difference of the cooling medium can be measured.

[0016] Next, the heat information of the cooling medium is generated based on the acquired motion data of the cooling medium. For example, the heat carried away by the cooling medium per unit time can be calculated using the law of conservation of energy, based on the flow rate, specific heat capacity, and inlet / outlet temperature difference of the cooling medium. This can be achieved through a dedicated calculation module that receives data from the flow meter and temperature sensor and executes the corresponding heat calculation formula.

[0017] Subsequently, temperature difference information is generated based on material temperature data, heat transfer parameters, and the movement data of the cooling medium. For example, the logarithmic mean temperature difference between the average temperature of the material inside the vessel and the average temperature of the cooling medium can be calculated. This requires comprehensive consideration of the material temperature distribution and the temperature change of the cooling medium within the jacket.

[0018] Then, the heat transfer parameters are calculated based on the generated heat and temperature difference information. For example, the overall heat transfer coefficient of the reactor can be calculated by combining the ratio of the total heat transfer rate (i.e., heat information) to the logarithmic mean temperature difference with the heat transfer area. This heat transfer coefficient is a dynamically changing indicator that can reflect the heat transfer efficiency of the reactor in real time.

[0019] Furthermore, the operating parameters of the cooling medium are adjusted based on the calculated heat transfer parameters and heat information. For example, when the heat transfer parameters indicate a decrease in heat transfer efficiency, the control system can instruct an increase in the flow rate of the cooling medium or a decrease in the inlet temperature of the cooling medium to compensate for the loss of heat transfer efficiency, thereby maintaining the stability of the material temperature inside the vessel. This adjustment can be based on a PID control algorithm, or it can be based on more advanced predictive control or adaptive control algorithms.

[0020] Finally, maintenance information is generated based on heat transfer metrics. For example, when the heat transfer metrics continuously decline and fall below a preset threshold, the system can automatically generate a maintenance alert, prompting operators that the reactor may have scaling issues and requires cleaning. Maintenance information can be presented as text messages, audible and visual alarms, or work orders integrated into the production management system.

[0021] The reactor temperature control method proposed in this application acquires material temperature data, heat transfer parameters, and cooling medium movement data in real time. Based on this data, it generates heat information and temperature difference information for the cooling medium, and then calculates the heat transfer index. This heat transfer index dynamically reflects the heat transfer efficiency of the reactor, thus providing a precise basis for adjusting the operating parameters of the cooling medium. When the heat transfer index shows a decrease in heat transfer efficiency, the system can promptly adjust the flow rate or temperature of the cooling medium to compensate for heat transfer losses and ensure stable temperature inside the reactor. Furthermore, through continuous monitoring of the heat transfer index, the system can predict potential scaling problems in the reactor and generate maintenance information, prompting operators to perform cleaning or repairs, thereby avoiding product quality problems and increased energy consumption due to decreased heat transfer efficiency.

[0022] Compared to traditional temperature control methods, the innovation of this application lies in its approach: it goes beyond relying solely on a single material temperature measurement for feedback control, introducing real-time calculation and analysis of heat transfer indices. Traditional methods, when scale buildup on the reactor wall leads to a decrease in heat transfer efficiency, often only manage to maintain the temperature by increasing cooling intensity, failing to identify the root cause of the problem or predict maintenance needs. This application, by comprehensively considering various information such as material temperature, heat transfer parameters, and cooling medium movement data, calculates heat transfer indices that directly reflect heat transfer efficiency. The introduction of these indices allows the control system to detect the trend of declining heat transfer efficiency earlier and more accurately, enabling more intelligent control adjustments and maintenance warnings. For example, when the heat transfer indices begin to decline, even if the material temperature has not yet significantly deviated from the set value, the system can identify potential scaling problems and issue early maintenance warnings, thus avoiding production losses and safety hazards caused by delayed problem detection in traditional methods. This control and maintenance strategy based on heat transfer indices significantly improves the accuracy, stability, and foresight of reactor temperature control, effectively overcoming the limitations of traditional methods in dealing with complex operating conditions such as reactor wall scaling.

[0023] Furthermore, this application proposes a more refined method for controlling the temperature of a reactor. By introducing the monitoring and analysis of motor power data and local temperature difference data inside the reactor, the heat transfer index is corrected to improve the accuracy and robustness of temperature control.

[0024] In some embodiments of this application, the above method further includes: Acquire motor power data and local temperature difference data inside the reactor; Trend analysis was performed on the motor power data, local temperature difference data inside the vessel, and heat transfer indicators. When the heat transfer index is on a downward trend and the motor power data and the local temperature difference data inside the vessel are fluctuating within a preset range, the correction coefficient of the cooling medium is calculated. The motion data of the cooling medium is updated by updating the correction coefficient of the cooling medium to obtain the updated motion data; The updated motion data is used to generate heat information for the cooling medium, resulting in new heat information. The corrected heat transfer parameters are calculated based on the new heat and temperature difference information.

[0025] Specifically, acquiring motor power data and local temperature difference data within the reactor refers to using appropriate sensors or instruments to collect real-time power consumption data of the stirring motor during operation and temperature difference data at different locations inside the reactor. Motor power data reflects the resistance of the stirrer during operation within the material. When scale forms on the reactor wall or stirrer surface, the viscosity or flow characteristics of the material may change, thus affecting the stirring power. Local temperature difference data reveals the uniformity of temperature distribution in different areas within the reactor; a decrease or unevenness in local heat transfer efficiency may lead to an increase in local temperature differences. These data, as supplementary information, can more comprehensively reflect the operating status and heat transfer efficiency inside the reactor.

[0026] Furthermore, trend analysis of the aforementioned motor power data, local temperature difference data within the vessel, and heat transfer indicators refers to monitoring and evaluating the patterns of these data changes over time. By conducting long-term or short-term trend analysis on the motor power data, local temperature difference data within the vessel, and heat transfer indicators calculated using the above methods, potential abnormal patterns or signs of performance degradation can be identified. For example, continuous small changes or fluctuations may indicate a slow decline in heat transfer efficiency.

[0027] Specifically, when the aforementioned heat transfer indicators are declining, and the aforementioned motor power data and local temperature difference data within the vessel fluctuate within a preset range, a correction factor for the cooling medium will be calculated. This combination of conditions indicates that when the overall heat transfer efficiency (heat transfer indicators) begins to decline, but the motor power and local temperature difference have not yet changed drastically and instead fluctuate within a relatively stable preset range, this is typically a manifestation of initial scale formation or slight contamination of the heat transfer surface. In this case, a correction factor needs to be calculated to quantify the impact of this subtle change on the operating parameters of the cooling medium. This correction factor aims to compensate for insufficient cooling capacity caused by the decline in heat transfer efficiency.

[0028] In practical applications, the motion data of the cooling medium is updated by using a correction factor, resulting in updated motion data. The motion data of the cooling medium typically includes parameters such as flow rate and velocity. The correction factor is applied to this motion data, for example, by adjusting the flow rate or velocity of the cooling medium, in order to maintain the required cooling effect even when heat transfer efficiency decreases.

[0029] Therefore, based on the updated motion data, the heat information of the cooling medium is generated, resulting in new heat information. Based on the adjusted motion data of the cooling medium, the heat carried away by the cooling medium is recalculated. Because the motion data has been corrected, the generated heat information will more accurately reflect the actual cooling capacity.

[0030] Finally, the corrected heat transfer index was calculated based on the new heat and temperature difference information. Using the updated cooling medium heat information and the original temperature difference information, a new corrected heat transfer index was calculated. This corrected heat transfer index more accurately reflects the actual heat transfer efficiency of the reactor, thus providing a more reliable basis for subsequent adjustments to operating parameters.

[0031] Through the above technical solution, this application can significantly improve the accuracy and robustness of reactor temperature control. Specifically, by introducing and analyzing motor power data and local temperature difference data within the reactor, early warning and accurate identification of subtle changes in heat transfer efficiency inside the reactor can be achieved, especially when scale is initially formed or equipment performance is slightly degraded. Therefore, by calculating and applying the correction coefficient of the cooling medium, the operating parameters of the cooling medium can be adjusted more promptly and accurately, making the calculation of heat transfer indicators closer to actual operating conditions. This ensures that the reactor maintains stable temperature control under various operating conditions, effectively avoiding temperature fluctuations caused by decreased heat transfer efficiency, extending equipment operating cycles, and reducing energy consumption.

[0032] In some preferred embodiments, it is assumed that a thin layer of scale gradually forms on the inner wall of a reactor during long-term operation. Under conventional control methods, the initial impact of the scale layer on the overall heat transfer performance may be insignificant, making timely detection and adjustment impossible. However, according to the solution of this application, when scale begins to form, the power of the stirring motor may slightly increase or fluctuate within a certain range in order to maintain the preset stirring effect. Simultaneously, a decrease in heat transfer efficiency in localized areas within the reactor may cause localized temperature differences to fluctuate within a preset range.

[0033] At this time, the system will continuously monitor the material temperature data, heat transfer parameters, and cooling medium movement data, and calculate the heat transfer index. When the heat transfer index begins to show a slight downward trend, and at the same time the motor power data and the local temperature difference data inside the vessel are detected to fluctuate within the preset range, the system will identify this specific pattern and determine that there may be an initial scale layer.

[0034] Based on this assessment, the system will calculate a correction factor for the cooling medium. This correction factor might be a percentage value used to fine-tune the flow rate or velocity of the cooling medium. Assuming the correction factor is calculated to be 1.02, this means the cooling medium flow rate needs to be increased by 2% to compensate for the slight decrease in heat transfer efficiency.

[0035] Subsequently, the motion data of the cooling medium (such as flow rate) will be updated according to this correction factor. Based on the updated motion data, the system will regenerate the heat information of the cooling medium, obtaining new heat information. Finally, using this new heat information and the original temperature difference information, the system will calculate a corrected heat transfer index. This corrected heat transfer index will more accurately reflect the actual heat transfer capacity of the reactor under the condition of initial scale buildup, thereby guiding subsequent adjustments to the operating parameters of the cooling medium and ensuring precise control of the reactor temperature.

[0036] This application further proposes a method for controlling the temperature of a reaction vessel, the method further comprising: Acquire the vibration signal of the vessel wall; Trend analysis was performed on the motor power data, local temperature difference data inside the vessel, vessel wall vibration signal, and heat transfer indicators. When the heat transfer index is on an upward trend or fluctuates within a preset range, the motor power data is on a downward trend or fluctuates within a preset range, the local temperature difference data inside the vessel is on a downward trend or fluctuates within a preset range, and the vessel wall vibration signal is in a state of instantaneous impact or high-frequency change, it is determined that a local peeling event of scale has occurred. Adjust the decay rate parameter of the heat transfer index according to the local spalling event; The cleaning time is calculated based on the decay rate parameter and the current heat transfer index, and the cleaning time is determined as maintenance information.

[0037] Specifically, acquiring reactor wall vibration signals refers to collecting vibration data of the reactor wall in real time by installing vibration sensors on the outside or inside of the reactor wall. These vibration signals can reflect the physical state of the reactor wall. In particular, when scale peels off inside the reactor, it will produce specific impact or high-frequency vibration characteristics. The purpose is to provide additional, direct physical evidence to help determine scale peeling events.

[0038] The trend analysis of the motor power data, local temperature difference data inside the reactor, reactor wall vibration signal, and heat transfer indicators can be understood as continuously monitoring and identifying patterns in the historical data of these key operating parameters. By applying time series analysis, statistical methods, or machine learning algorithms, specific patterns of these data changes over time are identified, such as increases, decreases, fluctuations, or instantaneous changes. The purpose is to comprehensively evaluate the operating status of the reactor and provide multi-dimensional evidence for subsequent event judgments.

[0039] In practical applications, a localized scale peeling event is identified when the heat transfer index is on an upward trend or fluctuates within a preset range, the motor power data is on a downward trend or fluctuates within a preset range, the local temperature difference data inside the vessel is on a downward trend or fluctuates within a preset range, and the vessel wall vibration signal is in a state of instantaneous impact or high-frequency change. Specifically, the increase or fluctuation of the heat transfer index may reflect a temporary recovery or fluctuation in heat transfer efficiency after localized scale peeling; the decrease or fluctuation of the motor power may be related to a reduction in stirring resistance, as the peeled scale reduces the roughness of the vessel wall; the decrease or fluctuation of the localized temperature difference inside the vessel directly indicates an improvement in localized heat transfer performance; and the instantaneous impact or high-frequency change of the vessel wall vibration signal is a direct manifestation of the physical impact during scale peeling. These conditions combined can identify the occurrence of localized scale peeling with high confidence, aiming to accurately capture specific physical events that cause fluctuations in heat transfer performance.

[0040] Furthermore, adjusting the decay rate parameter of the heat transfer index based on the localized scaling event refers to dynamically correcting the model parameters used to predict the future decay trend of the heat transfer index after detecting a localized scaling event. Scalding may cause the heat transfer index to recover in the short term or its decay pattern to change; therefore, it is necessary to adjust the decay rate parameter to more accurately reflect the current changes in heat transfer performance. The aim is to improve the accuracy of heat transfer index prediction and provide a more reliable basis for maintenance decisions.

[0041] Therefore, the cleaning time is calculated based on the decay rate parameter and the current heat transfer index, and this cleaning time is determined as maintenance information. This means that after adjusting the decay rate parameter, the system will use the updated decay model and the current heat transfer index value to predict when the heat transfer index will drop to a preset critical value, thereby determining when the reactor needs to be cleaned. This cleaning time is output as maintenance information, with the aim of providing a more accurate and timely maintenance plan based on actual operating conditions and specific events.

[0042] Through the above technical solution, this application can accurately identify localized scaling events inside the reactor, significantly improving the precision of reactor internal condition diagnosis. Compared to relying solely on macroscopic changes in heat transfer parameters, introducing reactor wall vibration signals and combining them with multi-parameter trend analysis allows the system to distinguish between performance degradation caused by general scaling and instantaneous changes caused by localized scaling, thus avoiding misjudgments and unnecessary interventions. Therefore, by dynamically adjusting the decay rate parameter of heat transfer parameters, this solution can more accurately predict the reactor cleaning cycle, avoiding resource waste or production risks caused by premature or delayed cleaning. This maintenance information generation mechanism based on specific events not only optimizes maintenance strategies and extends equipment lifespan but also improves production efficiency and safety.

[0043] In some preferred embodiments, it is assumed that during long-term operation, the heat transfer index of a reactor exhibits a slow downward trend, indicating the presence of scaling. However, on a certain day, the system detects a sudden series of high-frequency impact signals recorded by the reactor wall vibration sensor. Simultaneously, the motor power data shows a slight decrease followed by stabilization within a short period, and the local temperature difference data within the reactor also shows a reduction in the temperature difference in localized areas. Meanwhile, the overall heat transfer index fluctuates briefly before slightly increasing. Through the trend analysis module of this application, these combined signals are identified as a localized scaling event. Based on this event, the system immediately adjusts the attenuation rate parameter of the heat transfer index. For example, if the previously predicted attenuation rate was 0.5% per day, after the scaling event, the system may adjust it to 0.4% per day or make more precise adjustments based on the degree of scaling. Subsequently, using the updated attenuation rate parameter and the current heat transfer index, the system recalculates the estimated cleaning time of the reactor and sends it as maintenance information to the operator, guiding them to implement a more precise maintenance plan. For example, the cleaning was originally scheduled for 30 days later, but due to localized peeling leading to a short-term improvement in heat transfer performance, the new cleaning time may be postponed to 35 days, thus optimizing the maintenance cycle.

[0044] This application further proposes a method for controlling the temperature of a reaction vessel, the method further comprising: Obtain the current operating status information of the reactor; When a change in the operating status information is detected, monitor the rate of change of motor power data, local temperature difference data inside the vessel, vessel wall vibration signal and heat transfer index. When the rate of change matches the first preset mode, the warning process for local peeling events is canceled; the first preset mode refers to the benchmark fluctuation mode determined based on the typical fluctuation range and duration of each signal under the corresponding operational state change in historical data. When the rate of change exceeds the range of the first preset mode, the early warning process for localized peeling events continues to be executed.

[0045] Specifically, acquiring the current operating status information of the reactor refers to obtaining information such as the reactor's operating mode and process parameters at a specific point in time or within a specific time period. This operating status information may include, but is not limited to, stirring speed, reactant feed rate, heating or cooling set temperature, pressure, and material type. The purpose is to identify whether the reactor is in a normal process switching or adjustment phase, in order to distinguish between signal fluctuations caused by changes in operating status and signal fluctuations caused by scale shedding.

[0046] When a change in operating status information is detected, the system initiates monitoring of the rate of change of key process parameters. These parameters include motor power data, local temperature difference data within the vessel, vessel wall vibration signals, and heat transfer parameters. The purpose of monitoring the rate of change is to capture the dynamic response of these signals during changes in operating status, so as to compare them with a preset benchmark pattern.

[0047] The first preset mode can be understood as a benchmark fluctuation mode, established through analysis and learning of data from changes in the reactor's historical operating conditions. This mode defines the typical fluctuation range and duration of various monitoring signals (such as motor power data, local temperature difference data within the reactor, reactor wall vibration signal, and heat transfer parameters) when specific operating conditions change. For example, when the stirring speed switches from low to high, the motor power data typically has a predicted rate and amplitude of increase, the local temperature difference data within the reactor may experience brief fluctuations, and the reactor wall vibration signal may also show expected changes within a specific frequency range. The establishment of this first preset mode aims to provide a benchmark for signal behavior under "normal" operating condition changes.

[0048] When the rate of change of each monitored signal matches the first preset mode, it indicates that the current signal fluctuation is caused by normal operating state changes, rather than a scale peeling event. In this case, the system will cancel the early warning process for local peeling events to avoid issuing unnecessary alarms or triggering incorrect maintenance instructions.

[0049] However, when the monitored rate of change exceeds the range of the first preset mode, it may indicate other abnormalities besides normal operational status changes, such as scale peeling. In this case, the system will continue to execute the early warning process for localized peeling events to ensure that potential equipment problems can be identified and addressed in a timely manner.

[0050] This application's solution effectively addresses the problem of false alarms that may occur in the aforementioned scale peeling detection method when the reactor's operating status changes frequently. Specifically, when the reactor's operating status changes, such as with process parameter adjustments or production load switching, relevant sensor data (e.g., motor power data, local temperature difference data within the reactor, reactor wall vibration signals, and heat transfer indicators) will fluctuate. If these changes in operating status are not identified and differentiated, these normal fluctuations may be misjudged as scale peeling events, triggering unnecessary warnings or maintenance operations.

[0051] By establishing and utilizing a first preset pattern, this scheme can compare the currently monitored signal change rate with typical fluctuation patterns in historical data under the same operating conditions. If the current signal change closely matches the preset pattern, it can be reasonably inferred that these changes are due to normal operational adjustments rather than actual scale peeling. Therefore, the system can intelligently cancel the warning process for localized peeling events, avoiding false alarms caused by normal process fluctuations. Conversely, if the signal change rate exceeds the range of the first preset pattern, it indicates a possible anomaly, and the warning process will continue to execute, ensuring that genuine scale peeling events are not missed. This mechanism makes scale peeling detection and warning more accurate and reliable.

[0052] In some preferred embodiments, a specific example is given below. Suppose that during normal operation of a reactor, the stirring speed needs to be increased from 50 RPM to 100 RPM. Without the solution of this application, such an increase in stirring speed may cause a momentary increase in motor power data, a brief high-frequency change in the reactor wall vibration signal, and even slight fluctuations in heat transfer parameters due to changes in fluid dynamics. These signal characteristics may, in some cases, resemble the characteristics of a localized scale peeling event, thereby triggering false alarms.

[0053] However, according to the scheme of this application, when the operator issues a command to increase the stirring speed, the system first obtains information about the change in the operating status of the reactor (stirring speed changes from 50 RPM to 100 RPM). Subsequently, the system monitors the rate of change of motor power data, local temperature difference data inside the reactor, reactor wall vibration signal, and heat transfer indicators. At the same time, the system retrieves a pre-established first preset mode corresponding to "stirring speed increased from 50 RPM to 100 RPM". This first preset mode is derived from historical data analysis and clarifies the typical fluctuation range and duration of each signal under this change in operating status.

[0054] If the rate of increase in motor power data, the changing pattern of vessel wall vibration signal, and the fluctuation amplitude of heat transfer indicators all closely match the first preset mode, the system will determine that these signal changes reflect normal operational adjustments rather than scale peeling. Therefore, the system will cancel the warning process for localized peeling events to avoid issuing unnecessary alarms.

[0055] Conversely, if, during the increase in stirring speed, the motor power data rises significantly beyond the upper limit of the first preset mode, or if the vessel wall vibration signal exhibits continuous abnormal high-frequency vibration, or if the decrease in heat transfer parameters significantly exceeds expectations, the system will determine that these changes exceed the range of normal operating conditions. In this case, the system will continue to execute the early warning process for localized scaling events, reminding operators or maintenance teams to conduct further inspections to confirm whether a genuine scale scaling problem exists. In this way, the proposed solution can effectively distinguish between normal operating fluctuations and actual equipment malfunctions, improving the intelligence level of the early warning system.

[0056] In some embodiments described above, this application proposes adjusting the decay rate parameter of the heat transfer index based on a localized spalling event, and calculating the cleaning time based on the decay rate parameter and the current heat transfer index, thus defining the cleaning time as maintenance information. However, in its implementation, relying solely on a single localized spalling event to adjust the decay rate parameter and predict the cleaning time may not adequately capture the complex dynamic process of scale formation and spalling inside the reactor. A single event may be accidental or localized, and its impact on overall heat transfer efficiency may not accurately reflect long-term maintenance needs. This can lead to deviations in cleaning time prediction, thereby affecting the optimization of production planning and maintenance efficiency.

[0057] To address this, this application further proposes a method for controlling the temperature of a reactor. The method involves calculating the cleaning time based on the decay rate parameter and current heat transfer indicators, and then defining the cleaning time as maintenance information. This includes: monitoring heat transfer indicators, motor power data, local temperature difference data within the reactor, reactor wall vibration signals, and short-term fluctuation parameters of the heat transfer indicators; detecting multiple local spalling events based on the short-term fluctuation parameters; cumulatively calculating the heat transfer indicators when multiple local spalling events are detected within the evaluation time to obtain the change in heat transfer indicators; smoothing the change in heat transfer indicators to obtain smoothed heat transfer indicators; updating the decay rate parameter of the heat transfer indicators based on the multiple local spalling events to obtain updated decay rate parameters; and calculating the cleaning time based on the updated decay rate parameter and the smoothed heat transfer indicators, defining the cleaning time as maintenance information.

[0058] Specifically, monitoring short-term fluctuations in heat transfer parameters, motor power data, local temperature difference data within the vessel, vessel wall vibration signals, and heat transfer parameters involves continuously acquiring real-time data streams of these key operating parameters by deploying appropriate sensors and data acquisition systems. Subsequently, fluctuation analysis is performed on these data within a short time window; for example, the instantaneous rate of change, standard deviation, peak value, or energy of specific frequency components can be calculated to identify minute, rapid changes related to localized scale flaking. These short-term fluctuation parameters can more sensitively reflect subtle changes in the scale state within the vessel, providing a basis for subsequent detection of localized flaking events.

[0059] Among them, detecting multiple local spalling events based on short-term fluctuation parameters refers to analyzing the aforementioned monitored short-term fluctuation parameters using a preset pattern recognition algorithm or threshold judgment logic. For example, when the vessel wall vibration signal experiences a transient impact or a significant increase in high-frequency energy within a very short period of time, accompanied by a specific pattern of transient fluctuations or trend reversals in heat transfer indicators, motor power data, or local temperature difference data within the vessel, it can be identified as a local spalling event. Through continuous monitoring and identification, multiple such events can be detected within a preset evaluation period. The preset pattern recognition algorithm may include a short-time Fourier transform algorithm, a wavelet transform multi-scale decomposition algorithm, and a dynamic time warping algorithm. Specifically, the short-time Fourier transform can calculate the spectrum of the vibration signal within a sliding time window and monitor the instantaneous energy jump in a specific frequency band (such as the high-frequency band). The wavelet transform performs discrete wavelet decomposition on the vibration signal to extract amplitude spikes. The dynamic time warping algorithm can perform similarity matching between the real-time observed "vibration-temperature-power" joint short-time waveform and a pre-calibrated typical peeling event template. This embodiment of the invention does not impose too many restrictions on the preset pattern recognition algorithm.

[0060] In practical applications, when multiple localized spalling events are detected within the evaluation period, the heat transfer index is cumulatively calculated to obtain the change in the heat transfer index. The evaluation period can be a flexibly set cycle, such as a production batch, several hours, or several days. The cumulative calculation of the heat transfer index aims to comprehensively quantify the cumulative impact of multiple localized spalling events on the overall heat transfer efficiency of the reactor. For example, the instantaneous increase or decrease in the heat transfer index caused by each spalling event can be weighted and summed, or the degree of recovery of the heat transfer index after each spalling event can be recorded and combined with the downward trend before spalling to more comprehensively reflect the comprehensive impact of scale on heat transfer performance.

[0061] Furthermore, the changes in the heat transfer index are smoothed to obtain a smoothed heat transfer index. Smoothing can employ various signal processing techniques, such as moving average filtering, exponential smoothing, or Kalman filtering. The purpose is to eliminate potential random noise, measurement errors, or instantaneous abnormal fluctuations in the original data, making the long-term trend of the heat transfer index clearer, more stable, and more reliable, thereby providing a more accurate input for subsequent cleaning time calculations.

[0062] Furthermore, the decay rate parameter of the heat transfer index is updated based on multiple localized spalling events, resulting in an updated decay rate parameter. This means that the decay rate parameter of the heat transfer index is no longer a static adjustment based on a single event, but rather dynamically adjusted according to the cumulative effect of multiple events, their frequency, spalling amplitude, and the recovery of heat transfer performance after spalling. For example, if multiple spalling events occur frequently and the recovery amplitude of the heat transfer index after each spalling is small, it may indicate a faster scale regeneration rate, and the decay rate parameter should be increased accordingly; conversely, if spalling events are sparse and the recovery amplitude is large each time, the decay rate parameter may be decreased. This dynamic adjustment mechanism allows the decay rate parameter to more accurately reflect the actual law of scale growth inside the reactor.

[0063] Therefore, the cleaning time is calculated based on the updated decay rate parameters and the heat transfer index of the smoothing process, and this cleaning time is determined as maintenance information. By comprehensively considering multiple spalling events, cumulative effects, smoothing processes, and dynamic decay rate adjustments, the calculation results of the cleaning time will be more accurate and reliable, providing a more guiding basis for preventive maintenance of the reactor.

[0064] Through the above technical solution, this application can significantly improve the accuracy and reliability of reactor cleaning time prediction. By monitoring multiple short-term fluctuation parameters and detecting multiple local peeling events, the dynamic process of scale formation and peeling can be captured more comprehensively, avoiding the one-sidedness of judging from a single event. Accumulated calculation and smoothing of changes in heat transfer parameters effectively reduce the impact of measurement noise and instantaneous fluctuations on the prediction results, making the decay trend of heat transfer parameters clearer and more stable. More importantly, dynamically updating the decay rate parameters of heat transfer parameters based on multiple local peeling events allows the cleaning time prediction model to adapt to the actual situation of scale growth inside the reactor in real time, thereby providing more accurate maintenance recommendations. This not only helps optimize production scheduling and reduce unplanned downtime, but also extends equipment life, reduces operating costs, and improves overall production efficiency.

[0065] In some preferred embodiments, it is assumed that during the operation of a reactor, its heat transfer parameters, motor power data, local temperature difference data within the reactor, and reactor wall vibration signals are continuously monitored. Within a certain evaluation period, the system analyzes the short-term fluctuation parameters of these signals. For example, it detects three transient impacts in the reactor wall vibration signal within a short period, each followed by a slight transient rebound in the heat transfer parameters, which then continue to decline. These are identified as three independent local spalling events.

[0066] Specifically, the system records the changes in heat transfer parameters before and after each spalling event and accumulates them to obtain a change in heat transfer parameters. For example, the first spalling event causes an instantaneous increase in heat transfer parameters of 0.5%, the second by 0.3%, and the third by 0.4%. These changes are accumulated and combined with the downward trend of heat transfer parameters before the spalling event to calculate a comprehensive change in heat transfer parameters. Subsequently, this accumulated change in heat transfer parameters is smoothed using a moving average algorithm to eliminate possible measurement errors or instantaneous interference, resulting in a more stable and smoothed heat transfer parameter.

[0067] Simultaneously, the system dynamically adjusts the attenuation rate parameter of the heat transfer index based on the frequency of these three localized peeling events, the magnitude of each peeling, and the recovery of heat transfer indicators after peeling. For example, if peeling events are frequent and the recovery magnitude is small each time, the system may determine that the scale regeneration rate is fast, thereby increasing the attenuation rate parameter. Conversely, if peeling events are sparse and the recovery magnitude is large each time, the attenuation rate parameter may be decreased.

[0068] Ultimately, the system uses this updated decay rate parameter and smoothed heat transfer parameters, combined with a preset heat transfer parameter threshold (i.e., the critical point requiring cleaning), to calculate the estimated cleaning time. For example, if the updated decay rate parameter indicates that the heat transfer parameters will decrease at a faster rate, the calculated cleaning time will be earlier. This calculated cleaning time is then used as maintenance information to guide production operators in their maintenance plans. In this way, the prediction of cleaning time is more accurate and can better adapt to the actual dynamic changes in scale buildup inside the reactor.

[0069] This application further proposes the steps for adjusting the attenuation rate parameter of the heat transfer index based on the local spalling event, specifically including: Acquire the first indicator data before the occurrence of the localized spalling event and the second indicator data after the occurrence of the localized spalling event; the first indicator data and the second indicator data refer to the heat transfer capacity indicator data; Based on the first and second indicator data, calculate the improvement of the heat transfer index by the local spalling event; Based on the first and second index data, the rate difference of local spalling events on heat transfer index is analyzed. The amount of attenuation rate adjustment is determined based on the increase magnitude and rate difference; The attenuation rate adjustment is applied to the attenuation rate parameter to obtain the adjusted attenuation rate parameter.

[0070] Specifically, the first indicator data refers to the heat transfer capacity of the reactor before the localized spalling event, reflecting the heat transfer efficiency under scale accumulation conditions. The second indicator data refers to the heat transfer capacity of the reactor after the localized spalling event, reflecting the improvement in heat transfer efficiency after partial scale removal. These heat transfer capacity indicators can be obtained in various ways, such as by real-time monitoring of parameters like material temperature, cooling medium temperature, and flow rate, and calculated using a heat transfer model.

[0071] The calculation of the improvement in heat transfer parameters due to localized spalling events refers to quantifying the instantaneous improvement in heat transfer efficiency by comparing heat transfer capacity data before and after the event. For example, this can be represented by calculating the difference or ratio between the second and first parameter data. The purpose is to intuitively assess the positive impact of localized spalling events on heat transfer performance.

[0072] In practical applications, analyzing the rate difference of heat transfer parameters caused by localized spalling events refers to comparing the trend or slope of the heat transfer parameters over time before and after the occurrence of the event. For example, the decay rate of the heat transfer parameters in the period before and after the event can be calculated separately, and then the difference between the two can be calculated. The purpose is to reveal the impact of localized spalling events on subsequent scale growth or heat transfer attenuation modes.

[0073] Furthermore, the decay rate adjustment amount is determined based on the magnitude and rate difference of the increase. This adjustment amount is derived by comprehensively considering the instantaneous increase in heat transfer capacity brought about by the spalling event and its impact on the subsequent decay trend. For example, if the increase is large and the decay rate slows down significantly after spalling, the adjustment amount may be large to reflect the significant improvement in heat transfer performance. This adjustment amount can be an absolute value or a percentage, used to correct the current decay rate parameter. Thus, the decay rate adjustment amount is applied to the decay rate parameter to obtain the adjusted decay rate parameter. This means that the original decay rate parameter will be corrected according to the actual impact of the local spalling event, making it more accurately reflect the current decay trend of the reactor's heat transfer performance.

[0074] The above technical solutions improve the accuracy of cleaning time prediction, avoid resource waste or reduced production efficiency caused by cleaning too early or too late, and help to understand the dynamic process of scale peeling more deeply, providing more reliable data support for intelligent maintenance and optimized operation of reactors.

[0075] In some preferred embodiments, a specific example is given below. Assume that during the operation of a reactor, by monitoring the reactor wall vibration signal, motor power data, and local temperature difference data within the reactor, the system determines at time T1 that a localized scale peeling event has occurred. Before the event, the system continuously acquires heat transfer capacity index data; for example, the heat transfer capacity index measured at time T0 (before the peeling event) is K1 (i.e., the first index data). After the peeling event, the system continues monitoring and measures the heat transfer capacity index at time T2 (after the peeling event, and after the heat transfer performance has stabilized) as K2 (i.e., the second index data).

[0076] First, the system calculates the improvement in heat transfer performance caused by the local spalling event based on K1 and K2. For example, the improvement can be calculated as (K2 - K1) / K1 * 100%. If K2 is significantly higher than K1, it indicates that the spalling event has brought about a significant improvement in heat transfer performance.

[0077] Secondly, the system analyzes the rate difference in heat transfer performance caused by localized spalling events. This can be done by comparing the average rate of decay of heat transfer performance, R1, before the spalling event (e.g., from T0 to T1) with the average rate of decay, R2, after the spalling event (e.g., from T2 to T3). If R2 is less than R1, it indicates that the spalling event slowed down the rate of decay of heat transfer performance.

[0078] Finally, based on the calculated improvement (e.g., a 10% increase in heat transfer efficiency) and rate difference (e.g., a decrease in decay rate from 0.5% / day to 0.3% / day), the system determines the adjustment amount for the decay rate using a preset algorithm or model. For example, if the improvement is large and the decay rate decreases significantly, the system may lower the current decay rate parameter by a substantial value. This adjustment is applied to the current decay rate parameter to obtain a more accurate and realistic adjusted decay rate parameter. This adjusted parameter will be used for subsequent cleaning time calculations to ensure the accuracy of the maintenance plan.

[0079] In some embodiments described above, the decay rate parameter of the heat transfer index is updated based on multiple local spalling events. However, in actual implementation, if the influence of the current operating state of the reactor on the decay of the heat transfer index is not fully considered, the adjustment of the decay rate parameter may not be accurate enough, thus affecting the accuracy of the cleaning time prediction. To address this, this application further proposes a more refined decay rate parameter update method. By combining the current operating state information of the reactor, the method identifies the downward trend of the heat transfer index caused by local spalling events, thereby more accurately determining the adjustment value of the decay rate parameter.

[0080] The updated decay rate parameters, obtained by updating the heat transfer index based on multiple local spalling events, include: Obtain the current operating status information of the reactor, including stirring speed, reactant feed rate, and heating set temperature; The decreasing trend of heat transfer parameters caused by the multiple local spalling events is identified, the attenuation magnitude corresponding to the decreasing trend is obtained, and the adjustment value of the attenuation rate parameter is determined based on the attenuation magnitude. The adjustment value is applied to the decay rate parameter of the heat transfer index to obtain the updated decay rate parameter.

[0081] Specifically, acquiring the current operating status information of the reactor refers to the system's real-time acquisition or reception of key parameters during the reactor's operation. This operating status information includes: stirring speed, which reflects the uniformity of mixing and fluid dynamics of the materials within the reactor, directly affecting heat transfer efficiency and the mechanisms of scale formation and shedding; reactant feed rate, which reflects changes in reaction intensity and heat load, thus influencing the attenuation of heat transfer parameters; and the heating set temperature, which reflects the reactor's energy input and target temperature conditions, directly impacting the heat transfer process. Acquiring this operating status information provides crucial background data for subsequent analysis of heat transfer parameter attenuation.

[0082] Furthermore, identifying the downward trend of heat transfer indicators caused by the aforementioned multiple local spalling events means that after detecting multiple local spalling events, the system analyzes historical data of heat transfer indicators to distinguish between changes in heat transfer indicators caused by normal operational fluctuations and the actual decrease in heat transfer efficiency caused by scale accumulation and local spalling. Through data filtering, trend analysis, and other techniques, a persistent downward trend in heat transfer indicators related to local spalling events can be accurately identified. This yields the attenuation magnitude corresponding to the downward trend, which quantifies the degree of decrease in heat transfer capacity due to scale accumulation and local spalling under specific operating conditions. Determining the adjustment value of the attenuation rate parameter based on the attenuation magnitude means calculating the necessary correction to the current attenuation rate parameter based on the quantified attenuation magnitude. For example, if the attenuation magnitude is large, the adjustment value may be positive, indicating an accelerated attenuation rate; conversely, if the attenuation magnitude is small, the adjustment value may be negative or close to zero. Finally, the adjustment value is applied to the attenuation rate parameter of the heat transfer indicators to obtain an updated attenuation rate parameter, thereby enabling the attenuation rate parameter to more accurately reflect the current actual heat transfer performance degradation of the reactor.

[0083] This application's solution identifies the declining trend of heat transfer parameters by acquiring the current operating status information of the reactor and combining it with multiple localized spalling events. This allows for a more accurate quantification of the heat transfer efficiency degradation caused by scale accumulation and spalling. Traditional methods may rely solely on the number of localized spalling events or the overall decline in heat transfer parameters, neglecting the modulating effect of operating conditions on the degradation rate. This application, by introducing operating status information such as stirring speed, reactant feed rate, and heating set temperature, enables the system to more accurately distinguish between heat transfer fluctuations caused by changes in operating conditions and actual degradation caused by scale problems, thus avoiding misjudgment. Therefore, based on the identified declining trend and its degradation magnitude, a more precise degradation rate parameter adjustment value can be calculated, ensuring that the degradation rate parameter dynamically and accurately reflects the actual operating status of the reactor and the rate of heat transfer performance degradation.

[0084] The above technical solutions enable a more accurate assessment of the heat transfer performance degradation of the reactor, allowing for more refined and intelligent updates to the degradation rate parameters of the heat transfer index. This not only improves the accuracy of cleaning time prediction, avoiding premature or delayed cleaning and maintenance, thereby reducing production costs and extending equipment lifespan, but also helps optimize production scheduling, improving the overall operating efficiency and economic benefits of the reactor.

[0085] In some preferred embodiments, assuming a reactor is operating continuously, the system detects multiple local spalling events. Simultaneously, the system continuously acquires the reactor's current operating status information, such as a stirring speed maintained at 120 RPM, a reactant feed rate of 150 kg / hr, and a heating setpoint of 110°C. Based on this operating status information and historical heat transfer index data, the system analysis reveals that under these specific operating conditions, the heat transfer index exhibits a significant downward trend after multiple local spalling events. By quantifying this downward trend, a specific attenuation rate is calculated; for example, the heat transfer index has decreased by 3% in the past 24 hours. Based on this attenuation rate, the system determines an adjustment value for the attenuation rate parameter, for example, increasing the current attenuation rate parameter by 0.001 / hour. This adjustment value is then applied to the current heat transfer index attenuation rate parameter, resulting in an updated attenuation rate parameter that better reflects the current actual operating conditions. This updated attenuation rate parameter will be used for subsequent cleaning time calculations, making the maintenance plan more accurate.

[0086] In some embodiments described above, the adjustment value of the decay rate parameter is determined based on the decreasing trend of heat transfer indicators caused by multiple localized spalling events. However, in actual operation, the operating parameters of the reactor (e.g., viscosity parameters of the material inside the reactor, reaction heat load parameters, and set temperature parameters of the cooling medium) may change, and these changes themselves can cause fluctuations or decay in the heat transfer indicators. If the decay caused by changes in operating parameters is not effectively distinguished from the decay caused by scale formation or spalling, the adjustment of the decay rate parameter may not be accurate enough, thus affecting the accuracy of the cleaning time prediction. To address this, this application further proposes a more refined method for adjusting the decay rate parameter. By considering the influence of operating parameters, it can more accurately determine the independent component of heat transfer indicator decay, thereby improving the reliability of maintenance information generation.

[0087] In this embodiment of the invention, the method further includes: Obtain the current operating parameters; Calculate the expected attenuation of heat transfer parameters caused by changes in the operating parameters based on the operating parameters. The observed actual attenuation magnitude is compared with the expected attenuation magnitude to separate the independent component of the heat transfer index attenuation. Based on the independent components, determine the adjustment value of the decay rate parameter.

[0088] Specifically, acquiring current operating parameters refers to obtaining data on various operating conditions that affect the heat transfer performance of the reactor. These operating parameters may include, but are not limited to, the viscosity parameters of the materials inside the reactor, the reaction heat load parameters, and the set temperature parameters of the cooling medium. Real-time or periodic acquisition of these parameters provides basic data for subsequent analysis of the causes of heat transfer index decay.

[0089] The calculation of the expected attenuation of heat transfer indicators caused by changes in operating parameters, based on the operating parameters, can be understood as using a pre-established mathematical model, empirical formula, or historical data analysis results to predict the theoretical attenuation of heat transfer indicators under the current operating parameters due to changes in operating conditions. For example, a heat transfer model based on material viscosity, reaction heat load, and cooling medium set temperature can be established. When these parameters change, the model can output the expected change value of the heat transfer indicators. The pre-established mathematical model can include a model based on the convective heat transfer coefficient or a dynamic energy balance model. The empirical formula can be a simplified mathematical relationship fitted from experimental data of a specific reactor or similar equipment, which is a power function or polynomial. The historical data analysis results can be the results of the baseline model or pattern extracted from historical data during the healthy operation period of the equipment (no scaling, no peeling, no severe fouling) using statistical or machine learning methods.

[0090] In practical applications, comparing the observed actual attenuation with the expected attenuation means quantitatively comparing the actual decrease in heat transfer parameters obtained through real-time monitoring with the expected attenuation calculated above. This comparison aims to identify the portion of the actual attenuation that exceeds or falls short of the expected attenuation.

[0091] Therefore, separating the independent component of heat transfer index decay means subtracting the expected decay caused by changes in operating parameters from the actual observed decay, thus obtaining a decay component that is not directly affected by changes in operating parameters. This independent component is considered to be the main cause of heat transfer performance degradation due to fouling, peeling, or other factors not related to changes in operating parameters.

[0092] Finally, based on the independent component, the adjustment value of the decay rate parameter is determined. The purpose is to ensure that the update of the decay rate parameter more accurately reflects the actual development trend of the scale layer inside the reactor. By using this purer independent component, misjudgments caused by fluctuations in operating parameters can be avoided, making the adjustment of the decay rate parameter more precise.

[0093] Through the above technical solution, this application can more accurately assess the decay of heat transfer parameters in reactors, especially in complex environments with frequent changes in operating parameters. By separating the independent component of heat transfer parameter decay, interference caused by fluctuations in operating parameters on the decay rate parameter adjustment can be effectively avoided, allowing the determined decay rate parameter to more accurately reflect the formation and peeling process of scale. Therefore, the generation of maintenance information, especially the prediction of cleaning time, will be more accurate and reliable, helping to optimize reactor operation management, reduce unnecessary downtime maintenance, and improve production efficiency and equipment utilization.

[0094] In some preferred embodiments, it is assumed that during the operation of the reactor, the set temperature of the cooling medium slightly increases due to changes in the external environment. This typically leads to a slight decrease in heat transfer efficiency, manifested as a decline in heat transfer parameters. Adjusting the decay rate parameter solely based on the observed total decay might incorrectly attribute some of the decay caused by the increase in the set temperature of the cooling medium to scale growth, resulting in an overestimation of the decay rate parameter. The solution in this application first obtains the operating condition parameter of the set temperature of the cooling medium and calculates the expected decay of the heat transfer parameters under this specific operating condition change of an increased set temperature of the cooling medium using a preset heat transfer model. For example, through historical data or simulation models, it can be determined that for every 1°C increase in the set temperature of the cooling medium, the heat transfer parameters are expected to decrease by 0.5%. When a 1.2% decay in the heat transfer parameters is actually observed, by comparing 1.2% (actual decay) with 0.5% (expected decay), an independent decay component of 0.7% can be separated. This 0.7% independent component is considered to be the decay truly caused by scale formation or peeling, and the decay rate parameter of the heat transfer parameters is precisely adjusted accordingly. This ensures more precise adjustment of the decay rate parameter, avoids misjudgments caused by changes in operating parameters, and thus improves the accuracy of cleaning time prediction.

[0095] Furthermore, this application further proposes that the aforementioned operating parameters include the viscosity parameters of the material inside the reactor, the reaction heat load parameters, and the set temperature parameters of the cooling medium; the acquisition of the current operating parameters includes: Acquire viscosity data from multiple viscosity measuring devices and stirring motor power data; When an abnormality is detected in the data of the viscosity measuring device, the abnormal data is corrected or reconstructed based on the data of the remaining normal viscosity measuring devices and the power data of the stirring motor, and based on the preset material rheological characteristic curve, and the viscosity parameters of the material in the reactor are calculated. Acquire data from multiple temperature and flow measurement devices, as well as reactant feed rate and concentration information; When an abnormality is detected in the data of the temperature measuring device or the flow measuring device, the abnormal data is corrected or reconstructed based on the data of the remaining normal viscosity measuring device and the reactant feed rate and concentration information, and based on the preset reaction kinetic law, and the reaction heat load parameters are calculated. Obtain the setpoints of the cooling medium control system; When a deviation is detected between the set value and the actual cooling effect, the set value is calibrated based on the physical properties of the cooling medium by analyzing the historical operation records of the cooling medium control system and obtaining the set temperature parameter of the cooling medium.

[0096] Specifically, operating parameters refer to the key operating conditions and material properties that affect the heat transfer performance and scale formation rate of the reactor. In this application, these parameters specifically include the viscosity parameters of the materials inside the reactor, the reaction heat load parameters, and the set temperature parameters of the cooling medium. Accurate acquisition of these parameters is crucial for subsequent analysis of heat transfer index decay.

[0097] When acquiring the viscosity parameters of the material inside the vessel, the system first obtains viscosity data from multiple viscosity measuring devices and agitator motor power data. Viscosity measuring devices directly measure the material viscosity, while agitator motor power data indirectly reflects viscosity changes, because at a constant agitation speed, higher viscosity requires greater agitation power. When any viscosity measuring device data is detected to be abnormal—for example, data exceeding a reasonable range, remaining unchanged for an extended period, or fluctuating drastically—the system does not simply discard the abnormal data. Instead, based on the remaining normal viscosity measuring device data and agitator motor power data, combined with a preset material rheological characteristic curve, the abnormal data is corrected or reconstructed. The material rheological characteristic curve describes the relationship between material viscosity and agitation power. This curve allows reliable agitator motor power data to be used to infer or correct viscosity data, thereby calculating more accurate viscosity parameters of the material inside the vessel.

[0098] In practical applications, obtaining reaction heat load parameters involves data from multiple temperature and flow measurement devices, as well as reactant feed rate and concentration information. Temperature measurement devices monitor the temperature distribution inside and outside the reactor, while flow measurement devices monitor the flow rates of reactants and cooling media. Reactant feed rate and concentration information directly affect the exothermic or endothermic rate of the reaction. When abnormal data is detected from temperature or flow measurement devices, the system corrects or reconstructs the abnormal data based on the data from the remaining normal viscosity measurement devices, reactant feed rate, and concentration information, combined with preset reaction kinetic laws. Reaction kinetic laws describe the relationship between reaction rate and factors such as temperature and concentration. This information allows for more accurate estimation of the reaction heat load, ensuring data reliability even if some sensor data is abnormal.

[0099] Furthermore, obtaining the set temperature parameter of the cooling medium first involves acquiring the set value of the cooling medium control system. However, the set value is not always perfectly consistent with the actual cooling effect and may contain deviations. When a deviation is detected between the set value and the actual cooling effect, the system analyzes the historical operation records of the cooling medium control system and, in conjunction with the physical properties of the cooling medium (such as specific heat capacity and density), calibrates the set value. This calibration mechanism ensures that the acquired set temperature parameter of the cooling medium more accurately reflects the actual cooling capacity, thereby improving the accuracy of heat transfer analysis.

[0100] Through the above technical solution, this application can significantly improve the accuracy and robustness of acquiring operating parameters in the reactor temperature control method. Traditional methods of acquiring operating parameters often rely on a single sensor or simple data filtering, which are easily affected by factors such as sensor failure and environmental interference, leading to data distortion and affecting subsequent heat transfer index decay analysis and maintenance information generation. This application introduces mechanisms such as multi-source data fusion, abnormal data correction and reconstruction, and setpoint calibration, so that even when some sensor data is abnormal or the control system has deviations, highly reliable viscosity parameters, reaction heat load parameters, and cooling medium setpoint temperature parameters can be obtained. This accurate acquisition of operating parameters makes the calculation of the expected decay of heat transfer index caused by changes in operating parameters more accurate, thereby more effectively separating the heat transfer index decay component caused by independent factors such as scale peeling, ultimately improving the accuracy of decay rate parameter adjustment and the reliability of maintenance information generation, providing more solid data support for predictive maintenance of the reactor.

[0101] In some preferred embodiments, a specific example is given below. Suppose that during the operation of a reactor, one of the three viscosity measuring devices used to measure the viscosity of the material inside the reactor suddenly exhibits an abnormal reading, such as the reading remaining stagnant at a fixed value for an extended period or experiencing a drastic jump. In this case, the system will not directly discard the abnormal data. Instead, it will utilize the data from the remaining two normally functioning viscosity measuring devices, combined with real-time agitator power data. For example, if the agitator power data shows that the material viscosity is slowly increasing, while the abnormal viscosity measuring device reading remains unchanged, the system will, based on a preset material rheological characteristic curve (which describes the empirical relationship between agitator power and viscosity), use the normal viscosity data and agitator power data to correct or reconstruct the abnormal data, thereby calculating a viscosity parameter for the material inside the reactor that more accurately reflects the actual situation.

[0102] For example, when acquiring reaction heat load parameters, if the data from a temperature or flow measurement device is abnormal, the system will utilize other normal temperature and flow data, combined with reactant feed rate and concentration information, and a pre-established reaction kinetic model. For instance, if the reaction kinetic model predicts that the reaction heat load should be a specific value at the current feed rate and concentration, but abnormal sensor data causes a significant deviation in the calculated heat load, the system will correct the abnormal data based on the model prediction and normal sensor data to obtain more accurate reaction heat load parameters. The pre-established reaction kinetic model may include elementary reaction rate equations, parallel reaction network models, artificial neural network models, support vector regression models, etc. This embodiment of the invention does not impose excessive restrictions on the type of reaction kinetic model.

[0103] For example, if the set temperature of the cooling medium control system is 20℃, but analysis of actual cooling effect data such as the inlet and outlet temperatures and flow rate of the cooling medium reveals a persistent deviation between the actual cooling capacity and the 20℃ set value, the system will automatically retrieve the historical operation records of the cooling medium control system, analyze the deviation patterns under different set values ​​and actual cooling effects, and, in conjunction with the physical properties of the cooling medium such as specific heat capacity and density, calibrate the 20℃ set value, for example, to 19.5℃ or 20.5℃, to more accurately reflect the actual cooling effect, thereby providing more reliable input for subsequent heat transfer index analysis.

[0104] Secondly, referring to Figure 2 This application further proposes a reactor temperature control system, which includes: Data acquisition module 201 is used to acquire material temperature data, heat transfer parameters and motion data of cooling medium; The heat determination module 202 is used to generate heat information of the cooling medium based on the motion data of the cooling medium; The temperature difference information acquisition module 203 is used to generate temperature difference information based on the material temperature data, heat transfer parameters and motion data; The heat transfer index module 204 is used to calculate the heat transfer index based on the heat information and temperature difference information. The parameter adjustment module 205 is used to adjust the operating parameters of the cooling medium based on the heat transfer index and heat information; and to generate maintenance information through the heat transfer index.

[0105] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling the temperature of a reaction vessel, characterized in that, include: Acquire material temperature data, heat transfer parameters, and motion data of the cooling medium; Heat information of the cooling medium is generated based on the motion data of the cooling medium; Temperature difference information is generated based on the material temperature data, heat transfer parameters, and motion data. The heat transfer index is calculated based on the heat information and temperature difference information. Adjust the operating parameters of the cooling medium based on the heat transfer index and heat information; and generate maintenance information through the heat transfer index.

2. The method for controlling the temperature of a reaction vessel according to claim 1, characterized in that, The method further includes: Acquire motor power data and local temperature difference data inside the reactor; Trend analysis was performed on the motor power data, local temperature difference data inside the vessel, and heat transfer indicators. When the heat transfer index is on a downward trend and the motor power data and the local temperature difference data inside the vessel are fluctuating within a preset range, the correction coefficient of the cooling medium is calculated. The motion data of the cooling medium is updated by updating the correction coefficient of the cooling medium to obtain the updated motion data; The updated motion data is used to generate heat information for the cooling medium, resulting in new heat information. The corrected heat transfer parameters are calculated based on the new heat and temperature difference information.

3. The method for controlling the temperature of a reaction vessel according to claim 2, characterized in that, The method further includes: Acquire the vibration signal of the vessel wall; Trend analysis was performed on the motor power data, local temperature difference data inside the vessel, vessel wall vibration signal, and heat transfer indicators. When the heat transfer index is on an upward trend or fluctuates within a preset range, the motor power data is on a downward trend or fluctuates within a preset range, the local temperature difference data inside the vessel is on a downward trend or fluctuates within a preset range, and the vessel wall vibration signal is in a state of instantaneous impact or high-frequency change, it is determined that a local peeling event of scale has occurred. Adjust the decay rate parameter of the heat transfer index according to the local spalling event; The cleaning time is calculated based on the decay rate parameter and the current heat transfer index, and the cleaning time is determined as maintenance information.

4. The method for controlling the temperature of a reaction vessel according to claim 3, characterized in that, The method further includes: Obtain the current operating status information of the reactor: When a change in the operating status information is detected, monitor the rate of change of motor power data, local temperature difference data inside the vessel, vessel wall vibration signal and heat transfer index. When the rate of change matches the first preset mode, the warning process for local peeling events is canceled; the first preset mode refers to the benchmark fluctuation mode determined based on the typical fluctuation range and duration of each signal under the corresponding operational state change in historical data. When the rate of change exceeds the range of the first preset mode, the early warning process for localized peeling events continues to be executed.

5. The method for controlling the temperature of a reaction vessel according to claim 3, characterized in that, The step of calculating the cleaning time based on the decay rate parameter and the current heat transfer index, and determining the cleaning time as maintenance information, includes: Monitor heat transfer parameters, motor power data, local temperature difference data inside the vessel, vessel wall vibration signal and short-term fluctuation parameters of heat transfer parameters, and detect multiple local spalling events based on short-term fluctuation parameters; When multiple localized spalling events are detected within the evaluation period, the heat transfer index is cumulatively calculated to obtain the change in the heat transfer index; The change in the heat transfer index is smoothed to obtain the smoothed heat transfer index. The decay rate parameters of the heat transfer index are updated based on multiple local spalling events to obtain the updated decay rate parameters. The cleaning time is calculated based on the updated decay rate parameters and the heat transfer index of the smoothing process, and the cleaning time is determined as maintenance information.

6. The method for controlling the temperature of a reaction vessel according to claim 3, characterized in that, The adjustment of the attenuation rate parameter of the heat transfer index based on the local spalling event includes: Acquire the first indicator data before the occurrence of the localized spalling event and the second indicator data after the occurrence of the localized spalling event; the first indicator data and the second indicator data refer to the heat transfer capacity indicator data; Based on the first and second indicator data, calculate the improvement of the heat transfer index by the local spalling event; Based on the first and second index data, the rate difference of local spalling events on heat transfer index is analyzed. The amount of attenuation rate adjustment is determined based on the increase magnitude and rate difference; The attenuation rate adjustment is applied to the attenuation rate parameter to obtain the adjusted attenuation rate parameter.

7. The method for controlling the temperature of a reaction vessel according to claim 5, characterized in that, The process of updating the attenuation rate parameter of the heat transfer index based on multiple local spalling events to obtain the updated attenuation rate parameter includes: Obtain the current operating status information of the reactor, including stirring speed, reactant feed rate, and heating set temperature; The decreasing trend of heat transfer parameters caused by the multiple local spalling events is identified, the attenuation magnitude corresponding to the decreasing trend is obtained, and the adjustment value of the attenuation rate parameter is determined based on the attenuation magnitude. The adjustment value is applied to the decay rate parameter of the heat transfer index to obtain the updated decay rate parameter.

8. The method for controlling the temperature of a reaction vessel according to claim 7, characterized in that, The method further includes: Obtain the current operating parameters; Calculate the expected attenuation of heat transfer parameters caused by changes in the operating parameters based on the operating parameters. The observed actual attenuation magnitude is compared with the expected attenuation magnitude to separate the independent component of the heat transfer index attenuation. Based on the independent components, determine the adjustment value of the decay rate parameter.

9. The method for controlling the temperature of a reaction vessel according to claim 8, characterized in that, The operating parameters include the viscosity parameters of the material inside the reactor, the reaction heat load parameters, and the set temperature parameters of the cooling medium. The process of obtaining the current operating parameters includes: Acquire viscosity data from multiple viscosity measuring devices and stirring motor power data; When an abnormality is detected in the data of the viscosity measuring device, the abnormal data is corrected or reconstructed based on the data of the remaining normal viscosity measuring devices and the power data of the stirring motor, and based on the preset material rheological characteristic curve, and the viscosity parameters of the material in the reactor are calculated. Acquire data from multiple temperature and flow measurement devices, as well as reactant feed rate and concentration information; When an anomaly is detected in the data of the temperature measuring device or the flow measuring device, the abnormal data is corrected or reconstructed based on the data of the remaining normal viscosity measuring device and the reactant feed rate and concentration information, and based on the preset reaction kinetic law, and the reaction heat load parameters are calculated. Obtain the setpoints of the cooling medium control system; When a deviation is detected between the set value and the actual cooling effect, the set value is calibrated based on the physical properties of the cooling medium by analyzing the historical operation records of the cooling medium control system and obtaining the set temperature parameter of the cooling medium.

10. A temperature control system for a reaction vessel, characterized in that, The system includes: The data acquisition module is used to acquire material temperature data, heat transfer parameters, and motion data of the cooling medium. A heat determination module is used to generate heat information of the cooling medium based on the motion data of the cooling medium; The temperature difference information acquisition module is used to generate temperature difference information based on the material temperature data, heat transfer parameters and motion data. The heat transfer index module is used to calculate the heat transfer index based on the heat information and temperature difference information. The parameter adjustment module is used to adjust the operating parameters of the cooling medium based on the heat transfer index and heat information; and to generate maintenance information through the heat transfer index.

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