Control system and method for preparing dimethyl carbonate from propylene carbonate and methanol

Abstract

The invention discloses a control system and method for preparing dimethyl carbonate from propylene carbonate and methanol, and relates to the technical field of dimethyl carbonate preparation, and the method specifically comprises the following steps: obtaining scaling influence information of a scaling layer appearing on the inner wall of a reactor in real time, analyzing after obtaining, and determining the scaling influence information of a scaling layer appearing on the inner wall of the reactor; respectively generating a scaling influence coefficient and a heat exchange efficiency sensitivity index; a scaling influence evaluation model is constructed for the generated scaling influence coefficient and the heat exchange efficiency sensitivity index, an influence evaluation coefficient is generated, analysis is carried out after generation, and the influence degree of a scaling layer appearing on the inner wall of the reactor on the heat exchange efficiency of the reactor is evaluated; and taking corresponding adjustment measures according to the evaluation result of the influence degree of the scaling layer on the inner wall of the reactor on the heat exchange efficiency. Through real-time monitoring and intelligent evaluation of the influence of scaling on the heat exchange efficiency, the problem of inaccurate temperature control caused by scaling is solved, and the heat exchange efficiency and production stability of the reactor are improved.

Description

Technical Field

[0001] The invention relates to the technical field of dimethyl carbonate preparation, and in particular to a control system and a method for preparing dimethyl carbonate by utilizing propylene carbonate and methanol. Background Art

[0002] Dimethyl carbonate (DMC) is an important chemical intermediate, widely used in organic synthesis, coatings, plastics, pesticides, solvents and other fields. It is not only an environmentally friendly solvent, but also has a wide range of applications in the synthesis of polymer materials, medicines and agricultural chemicals. Traditionally, the synthesis of dimethyl carbonate relies on methyl chloride and oxidation reactions, but these methods have problems such as harsh reaction conditions, more by-products and environmental pollution. The preparation of dimethyl carbonate by the reaction of propylene carbonate (CPC) and methanol is a more environmentally friendly and efficient synthesis route, because propylene carbonate as a reactant itself is a relatively green chemical substance, and its reaction with methanol can efficiently generate dimethyl carbonate under mild conditions, with few by-products and little impact on the environment. In order to further improve the production efficiency of the reaction, reduce energy consumption and increase the yield of dimethyl carbonate, precise control of the reaction process is particularly important. Since factors such as temperature, pressure, and raw material flow rate have a significant impact on the reaction rate and yield during the reaction, it is necessary to use intelligent control methods to monitor and adjust these key parameters in real time to ensure that the reaction is carried out under the optimal state, avoid excessive energy consumption or the generation of by-products, and improve the purity of the product and the stability of production. Therefore, precise control of the reaction process can not only optimize production efficiency, but also improve economy and environmental friendliness.

[0003] The existing control technology for preparing dimethyl carbonate from propylene carbonate and methanol usually monitors and adjusts the key parameters in the reaction process in real time through advanced automatic control systems and optimization algorithms. Specifically, the control system integrates multiple data such as temperature, pressure, raw material flow, reactant concentration, etc., collects data of the reaction environment in real time through sensors and actuators, and automatically adjusts the reaction conditions through control algorithms (such as PID control, fuzzy control or machine learning optimization algorithms) to ensure that the reaction is carried out in the optimal state. For example, the temperature control system can automatically adjust the heating or cooling device according to the change of the reaction temperature to keep the reaction temperature within the optimal range to avoid the formation of by-products due to excessively high temperature or the reaction rate due to too low temperature; the pressure control system ensures that the reaction rate and product selectivity reach the optimal state by adjusting the pressure in the reactor; and the raw material flow control automatically adjusts the feeding amount of methanol and propylene carbonate according to the reaction process to achieve efficient reaction. The control system can also adaptively adjust the changes in the reaction process through real-time feedback and prediction models to ensure that high efficiency and stability are always maintained during the production process, thereby improving the yield of dimethyl carbonate, reducing the formation of by-products, reducing energy consumption, and improving product purity. The integration and optimization of these control processes not only makes the reaction process more precise and controllable, but also greatly improves the automation and safety of the production process.

[0004] The prior art has the following deficiencies: In the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, some by-products, impurities or incompletely reacted substances may gradually accumulate on the inner wall of the reactor. These substances will form a tiny scaling layer when flowing through the heat exchange system during the reaction. As the reaction continues, these scaling layers will gradually increase, especially under high temperature and high pressure conditions, the scaling phenomenon is more obvious. In the early stage, the scaling layer may be very thin and will not have a significant impact on the heat exchange efficiency, so the temperature control system cannot detect it in time. However, as the scaling layer thickens, the heat exchange efficiency begins to decline slightly, causing the temperature regulation response of the heat exchanger to become slow. Since the existing control system usually only regulates based on the readings of the temperature sensor, when the temperature fluctuates, it is difficult for the control system to accurately identify whether the temperature fluctuation is caused by scaling, and it is also impossible to compensate for the temperature difference change through real-time calculation. Therefore, in the early and tiny stages of scaling, the control system cannot effectively sense and calibrate the heat exchange efficiency in real time, resulting in deviations in the temperature control of the reactor. This deviation will affect the reaction rate and the uniformity of the reaction, ultimately reducing the yield and quality of dimethyl carbonate, and may even cause instability in the reaction process, thereby affecting the overall production efficiency and product quality.

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

[0006] The object of the present invention is to provide a control system and method for preparing dimethyl carbonate using propylene carbonate and methanol, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a control method for preparing dimethyl carbonate by using propylene carbonate and methanol, which specifically comprises the following steps: 1. In the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate is detected in real time, and when a scaling layer is detected, a mathematical model based on scaling layer detection is constructed; Second, the scaling influence information of the scaling layer appearing on the inner wall of the reactor is obtained in real time, and after acquisition, it is analyzed to generate the scaling influence coefficient and the heat exchange efficiency sensitivity index respectively; The specific steps for generating the scaling influence coefficient and the heat exchange efficiency sensitivity index are as follows: Obtain the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time and perform pretreatment after obtaining; Extracting scaling layer characteristic information and heat exchange performance sensitivity information from scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor; The extracted scaling layer characteristic information and heat exchange performance sensitivity information are analyzed to generate scaling influence coefficient and heat exchange efficiency sensitivity index respectively; 3. Construct a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generate the impact assessment coefficient, and analyze it after generation to assess the impact of the scaling layer appearing on the inner wall of the reactor on the heat exchange efficiency of the reactor; 4. Take corresponding adjustment measures based on the evaluation results of the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; 5. Monitor the heat exchange efficiency of the reactor after the adjustment measures are implemented in real time, continuously obtain the change data of the heat exchange efficiency of the reactor, and dynamically adjust the evaluation model and adjustment measures according to the real-time monitoring results; 6. Store and analyze historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, and improve heat exchange efficiency.

[0008] Preferably, the acquisition logic of the scaling influence coefficient is as follows: Extract the scaling layer characteristic information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, specifically including the average thickness of the scaling layer appearing on the inner wall of the reactor at different times over a period of time, the average temperature of the fluid in the reactor, and the average viscosity of the fluid in the reactor, and calibrate them as DS m TF m and NF m , DS m It represents the average thickness of the fouling layer on the inner wall of the reactor at time m within a period of time, TF m represents the average temperature of the fluid in the reactor at time m over a period of time, NF m represents the average viscosity of the fluid in the reactor at time m within a period of time, m=1, 2, 3, ..., g, g is a positive integer; Calculate the scaling influence coefficient. The specific calculation formula is as follows: , Where SIC is the scaling influence coefficient.

[0009] Preferably, the acquisition logic of the heat exchange efficiency sensitivity index is as follows: Extract the heat exchange performance sensitivity information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, including the heat value transferred inside the reactor at different times over a period of time, the temperature difference between the inner wall of the reactor and the coolant, and the thermal conductivity of the fluid inside the reactor, and calibrate them as QF n , ΔTW n and KF n , QF n Indicates the heat value transferred inside the reactor at time n within a period of time, ΔTW n KF represents the temperature difference between the reactor inner wall and the coolant at time n in a period of time. n represents the thermal conductivity of the fluid inside the reactor at time n in a period of time, n=1, 2, 3, ..., k, k is a positive integer; Calculate the heat exchange efficiency sensitivity index. The specific calculation formula is as follows: , Where HSEC is the heat exchange efficiency sensitivity index.

[0010] Preferably, a scaling impact assessment model is constructed based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index to generate the impact assessment coefficient, which specifically includes the following steps: Collect several scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, and calibrate them as SIC x 、HSEC x and IACx , x represents the number of scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set; The multivariate regression model is selected as the scaling impact assessment model, and the historical data set is used for training to determine the value of the regression coefficient according to the formula: , In the formula, β 0 , β 1 and β 2 is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the regression coefficient β is finally determined. 0 , β 1 and β 2 The value of The final determined regression coefficient is used to construct the scaling impact assessment model, and the scaling impact coefficient SIC and the heat exchange efficiency sensitivity index HSEC generated in real time are input to generate the impact assessment coefficient IAC in real time.

[0011] Preferably, the generated impact assessment coefficient IAC is compared with a preset impact assessment coefficient threshold interval [IAC min , IAC max ] to compare, and evaluate the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor according to the comparison results. The specific comparison analysis is as follows: If IAC<IAC min , the scale layer appearing on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor; If IAC min ≤IAC≤IAC max The scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor; If IAC>IAC max The scaling layer appearing on the inner wall of the reactor has a high impact on the heat exchange efficiency of the reactor.

[0012] Preferably, according to the evaluation result of the influence of the fouling layer on the inner wall of the reactor on the heat exchange efficiency, corresponding adjustment measures are taken, specifically: If the evaluation result shows that the scaling layer on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: maintain the existing operating conditions and regularly monitor the scaling on the inner wall of the reactor; If the evaluation result shows that the scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: optimize the operating parameters, enhance the heat exchange efficiency of the cooling system, reduce the negative impact of scale on heat exchange, and increase the reactor cleaning cycle; If the evaluation result shows that the scaling layer on the inner wall of the reactor has a high impact on the reactor heat exchange efficiency, the adjustment measures to be taken are: immediately carry out scaling removal operations, including comprehensive cleaning of the reactor and heat exchanger, and adjust the operating conditions of the reactor to restore the working efficiency of the heat exchange system and upgrade the temperature control system.

[0013] Preferably, the control system for preparing dimethyl carbonate from propylene carbonate and methanol comprises a scaling detection module, a scaling impact analysis module, an impact assessment model module, an adjustment measure module, a dynamic monitoring and adjustment module, and a data storage and optimization module; A scaling detection module, in the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, performs real-time detection on whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate, and constructs a mathematical model based on scaling layer detection when a scaling layer is detected; The scaling impact analysis module acquires the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time, and analyzes it after acquisition to generate the scaling impact coefficient and heat exchange efficiency sensitivity index respectively; The impact assessment model module constructs a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generates the impact assessment coefficient, and analyzes it after generation to assess the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor; The adjustment measure module takes corresponding adjustment measures according to the evaluation results of the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; The dynamic monitoring and adjustment module monitors the heat exchange efficiency of the reactor in real time after the adjustment measures are implemented, continuously obtains the change data of the heat exchange efficiency of the reactor, and dynamically adjusts the evaluation model and adjustment measures according to the real-time monitoring results; The data storage and optimization module stores and analyzes historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, and improve heat exchange efficiency.

[0014] In the above technical solution, the beneficial effects of the present invention are: 1. The present invention can obtain and analyze the impact of scaling in real time by detecting the scaling condition of the inner wall of the reactor in real time and constructing a mathematical model based on the detection of the scaling layer. This process realizes dynamic monitoring of the scaling layer, which not only effectively avoids the delayed response of the temperature control system due to scaling, but also accurately identifies the potential impact of scaling on the heat exchange efficiency. Unlike traditional control systems that rely on a single temperature sensor, the present invention establishes a mathematical model of the scaling impact coefficient and the heat exchange efficiency sensitivity index, so that the system can evaluate the specific impact of scaling on the heat exchange efficiency in real time, thereby improving the response speed and accuracy of the temperature control system. Through this dynamic monitoring and real-time feedback mechanism, the system can take effective measures at the early stage of scaling to avoid the decrease in heat exchange efficiency caused by scaling accumulation and the resulting production fluctuations, ensure the stability and uniformity of the reaction process, and thereby improve the yield and quality of dimethyl carbonate; 2. The scaling influence coefficient and heat exchange efficiency sensitivity index in the present invention are used to construct a scaling impact assessment model. The generated impact assessment coefficient can be compared with the preset threshold interval to evaluate the impact of scaling on the heat exchange efficiency in real time. The assessment model can accurately determine the specific impact of scaling on the inner wall of the reactor and flexibly adjust the operating parameters based on the evaluation results. Through this mechanism, the system can take different adjustment measures according to actual conditions, such as optimizing operating parameters, enhancing the efficiency of the cooling system, or directly performing scaling removal operations. This flexible adjustment method can not only achieve timely intervention in the early stages of scaling, but also immediately perform effective cleaning and optimization when scaling is more serious, thereby ensuring the heat exchange efficiency and the long-term stability of the reactor. By adjusting and optimizing the control strategy in real time, the heat exchange efficiency of the overall system is improved, reducing potential production losses caused by scaling; 3. The present invention can continuously evaluate the effects of adjustment measures and optimize model parameters by storing and analyzing historical monitoring data. This historical data accumulation and analysis function enables the system to continuously self-learn and optimize during long-term operation, which not only improves the accuracy of scaling monitoring, but also provides accurate predictions for scaling problems that may arise in the future. Through this data-driven optimization mechanism, the system can adapt to different production conditions and continuously improve the intelligence level of the control strategy based on historical data, further improving the heat exchange efficiency and the operational stability of the reactor. In addition, the system can make optimal adjustments according to the real-time status of the reactor by dynamically adjusting the evaluation model and adjustment measures, avoiding the problem of reduced efficiency caused by the lack of continuous optimization and learning mechanisms in traditional systems, and significantly improving the production efficiency and product quality of the entire reaction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a schematic flow chart of a control method for preparing dimethyl carbonate by using propylene carbonate and methanol.

[0016] Figure 2 The module schematic diagram of the control system for preparing dimethyl carbonate by using propylene carbonate and methanol in the present invention. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0018] The present invention provides Figure 1 A control method for preparing dimethyl carbonate by using propylene carbonate and methanol is shown, which specifically comprises the following steps: 1. In the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate is detected in real time, and when a scaling layer is detected, a mathematical model based on scaling layer detection is constructed; In order to achieve real-time detection of the scaling layer on the inner wall of the reactor, it can be achieved by combining a variety of sensor technologies with software algorithms. First, temperature sensors and pressure sensors are used to collect real-time data in the reactor, because the formation of the scaling layer usually leads to a decrease in heat exchange efficiency, which in turn affects the temperature distribution in the reactor. By real-time monitoring of temperature fluctuations and pressure changes in the reactor, the system can perform preliminary detection of scaling formation. In specific implementation, multiple temperature sensors can be set at different positions on the inner wall of the reactor, and the collected temperature data can be compared with the reference data under normal working conditions to identify abnormal temperature changes. If the temperature fluctuation is large or below the normal range in some areas, this may be a sign of scaling layer formation. Secondly, the presence of the scaling layer can be detected by ultrasonic sensors or electromagnetic sensors. Ultrasonic sensors identify the thickness of the scaling layer by measuring the change in the speed of sound waves propagating on the reactor wall, while electromagnetic sensors can determine the presence of scaling by detecting changes in conductivity or magnetic field. The data of these sensors can be collected in real time, and by comparing with the known normal operating state, the presence of the scaling layer can be detected and its degree can be evaluated. Finally, the sensor data is transmitted to the control system through the data acquisition system for real-time monitoring and early warning.

[0019] When the signal of fouling layer formation is detected, the next step is to accurately describe the impact of the fouling layer by constructing a mathematical model. This process usually involves using data analysis and machine learning techniques, combined with information such as temperature, pressure, and sound wave propagation speed collected by the sensor, to construct a relationship model between the fouling layer and the heat exchange efficiency. Specifically, regression analysis or multivariate statistical methods can be used to analyze the correlation between the thickness of the fouling layer, temperature fluctuations, and other heat exchange related parameters to obtain a mathematical description of the fouling layer on the heat exchange efficiency. Based on these real-time data, dynamic model update methods (such as Kalman filters or adaptive filtering algorithms) are used to adjust model parameters in real time to accurately estimate the impact of the fouling layer. These mathematical models can transform the multidimensional data of the sensor into a predictive fouling impact assessment tool. For example, the model may combine the fouling impact coefficient (indicating the impact of the fouling layer on the heat exchange efficiency) with the heat exchange efficiency sensitivity index (describing the sensitivity of the heat exchange efficiency to fouling), and use these coefficients to evaluate the actual impact of fouling on the heat exchange. Through the training and optimization of the model, its adaptability to complex reactor conditions can be enhanced, so that it can accurately identify and provide real-time adjustment suggestions in the early stages of fouling.

[0020] By real-time detection of the formation of the scaling layer and construction of a mathematical model based on scaling layer detection, the aim is to solve the technical problem that the traditional temperature control system cannot detect the impact of the scaling layer on the heat exchange efficiency in time. In the process of preparing dimethyl carbonate by the reaction of propylene carbonate and methanol, the scaling layer is usually very thin at the beginning of the reaction. The traditional temperature-based control system is difficult to detect its impact on the heat exchange efficiency of the reactor, resulting in the system being unable to adjust in time, ultimately affecting the reaction temperature and reaction rate, and reducing product quality and reaction efficiency. By introducing real-time detection of the scaling layer and dynamic evaluation based on a mathematical model, the appearance of the scaling layer can be accurately identified and its impact can be evaluated in time, thereby achieving precise control of the heat exchange system. In this way, not only can the technical bottleneck of the scaling layer not being identified in time be effectively solved, but also the control strategy can be dynamically adjusted to ensure that the reactor is always operating in the best heat exchange state, avoid deviations in the reaction rate, improve the yield and quality of dimethyl carbonate, and ensure the stability and efficiency of the production process.

[0021] Second, the scaling influence information of the scaling layer appearing on the inner wall of the reactor is obtained in real time, and after acquisition, it is analyzed to generate the scaling influence coefficient and the heat exchange efficiency sensitivity index respectively; The specific steps for generating the scaling influence coefficient and the heat exchange efficiency sensitivity index are as follows: Obtain the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time and perform pretreatment after obtaining; Real-time acquisition of scaling influence information of the scaling layer on the inner wall of the reactor can be achieved through a combination of multiple sensor technologies and data acquisition systems. First, temperature sensors, heat flow sensors, surface resistance sensors and other equipment can be used to monitor the temperature changes, heat flow distribution and changes in the surface resistance of the reactor inner wall in real time. Specifically, temperature sensors can be installed at multiple points on the inner wall of the reactor to detect temperature differences at different positions, reflecting the impact of the scaling layer on heat exchange. Heat flow sensors can be used to monitor changes in heat flow and indirectly evaluate the impact of scaling on thermal conductivity. In addition, surface resistance sensors can reflect changes in the thickness of the scaling layer by detecting the increase in the surface resistance of the inner wall of the reactor. These data will reflect the formation and development process of scaling. All these sensor data can be transmitted in real time through the data acquisition system and uploaded to the analysis system. The data are analyzed by software to calculate the impact of the scaling layer in real time.

[0022] After obtaining the scaling impact information in real time, preprocessing is required to ensure data quality and accuracy and avoid noise and erroneous data from interfering with subsequent analysis. First, data preprocessing includes removing noise from sensor signals, such as filtering out high-frequency noise through a low-pass filter to ensure the smoothness of sensor data. Secondly, since the sensor may cause signal fluctuations or distortion due to environmental factors, it is necessary to fill in missing data. The commonly used method is to use interpolation or nearest neighbor method to supplement missing key data such as temperature and heat flow. In addition, since different types of sensors may have different dimensions and units, these data need to be standardized and unified to a standard quantitative scale, which is convenient for subsequent multivariate analysis and model calculation. The standardization method can use the maximum and minimum normalization method or the z-score normalization method to ensure that each data item has the same importance. After these preprocessing steps, the data will be ready for the next step of scaling impact analysis to ensure that the analysis results are reliable and valid.

[0023] Extracting scaling layer characteristic information and heat exchange performance sensitivity information from scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor; The extracted scaling layer characteristic information and heat exchange performance sensitivity information are analyzed to generate scaling influence coefficient and heat exchange efficiency sensitivity index respectively.

[0024] In this embodiment, the logic for obtaining the scaling influence coefficient is as follows: Extract the scaling layer characteristic information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, specifically including the average thickness of the scaling layer appearing on the inner wall of the reactor at different times over a period of time, the average temperature of the fluid in the reactor, and the average viscosity of the fluid in the reactor, and calibrate them as DS m TFm and NF m , DS m It represents the average thickness of the fouling layer on the inner wall of the reactor at time m within a period of time, TF m represents the average temperature of the fluid in the reactor at time m over a period of time, NF m represents the average viscosity of the fluid in the reactor at time m within a period of time, m=1, 2, 3, ..., g, g is a positive integer; To extract the scaling influence information of the scaling layer appearing on the inner wall of the reactor, specifically, real-time data collection can be achieved through sensor networks and data acquisition systems. First, non-contact sensing devices such as ultrasonic sensors or laser scanners can be arranged on the inner wall of the reactor to monitor the changes in the scaling layer in real time. Ultrasonic sensors measure the thickness of the scaling layer by the principle of sound wave reflection, and laser scanners can provide high-precision surface topography scanning to indirectly infer the thickness of the scaling layer. Secondly, the average temperature and viscosity of the fluid in the reactor can be obtained by arranging temperature sensors and viscosity sensors. The temperature sensors should be distributed at different positions in the reactor to record the temperature data of the fluid in real time; while the viscosity sensors can measure the viscosity of the fluid based on the change in resistance when the fluid flows. After collecting these raw data, the data is integrated through the data processing and analysis system to extract the average thickness of the scaling layer on the inner wall of the reactor, the average temperature and viscosity of the fluid.

[0025] Specifically, the average thickness of the scaling layer DS m It can be calculated by periodically obtaining the time delay of the reflected echo signal. The delay of the signal is proportional to the thickness of the scaling layer and can be obtained based on the average value obtained from multiple measurements. The average temperature TF of the fluid m The temperature data at different locations in the reactor are obtained through multi-point temperature sensors, and then the time-weighted average value is calculated. The average viscosity NF of the fluid m It can be determined by fluid mechanics model calculation, combined with the fluid flow state and the measured flow pressure difference and flow velocity data, using the direct measurement value obtained by the viscosity sensor and the calculation model. Through these methods, it can be ensured that the acquired data has high precision in quantification and meets the real-time monitoring requirements, ensuring the accuracy and timeliness of the data.

[0026] Calculate the scaling influence coefficient. The specific calculation formula is as follows: , Where SIC is the scaling influence coefficient.

[0027] This calculation formula is used to evaluate the impact of the fouling layer on the heat exchange efficiency of the reactor, which is called the fouling impact coefficient (SIC). In the formula, DS m(The average thickness of the scaling layer) directly affects the heat exchange efficiency. A thicker scaling layer will significantly reduce the heat exchange effect. Therefore, the logarithmic function ln(1+DS m ) to control the nonlinear growth of the effect of scaling thickness on heat exchange and avoid the extreme effect caused by excessive scaling. m The (average temperature of the fluid) is squared because the effect of temperature on heat exchange is nonlinear. The higher the temperature, the greater the change in heat exchange efficiency. Therefore, the square of the temperature is used to strengthen its weight in the calculation. m (Average viscosity of the fluid) reflects the effect of fluid fluidity on heat exchange. Higher viscosity will weaken fluid fluidity, thereby reducing heat exchange efficiency. Therefore, viscosity affects the scaling influence coefficient through a multiplication relationship with other parameters. Finally, by weighted averaging the data at different times, the formula integrates the time factor, making the scaling influence coefficient more stable and accurately reflecting the actual effect of the scaling layer on the inner wall of the reactor on the heat exchange efficiency, thereby providing a basis for dynamic adjustment.

[0028] The size of the scaling influence coefficient SIC directly reflects the degree of influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency. The larger the SIC value, the more significant the negative impact of factors such as the thickness, temperature and viscosity of the scaling layer on the heat exchange efficiency, which means that the heat exchange efficiency is low; conversely, the smaller the SIC value, the smaller the impact of the scaling layer on the heat exchange, and the heat exchange efficiency is relatively high. Specifically, a thicker scaling layer will significantly increase the thermal resistance, reduce the efficiency of heat conduction, and cause the temperature control of the heat exchange system to be unstable; at the same time, higher fluid temperature and viscosity will aggravate the resistance of the fluid and the decrease in heat exchange efficiency. Therefore, this coefficient can quantify the combined impact of these factors and provide a basis for timely adjustment measures.

[0029] In this embodiment, the logic for obtaining the heat exchange efficiency sensitivity index is as follows: Extract the heat exchange performance sensitivity information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, including the heat value transferred inside the reactor at different times over a period of time, the temperature difference between the inner wall of the reactor and the coolant, and the thermal conductivity of the fluid inside the reactor, and calibrate them as QF n , ΔTW n and KF n , QF n Indicates the heat value transferred inside the reactor at time n within a period of time, ΔTW n KF represents the temperature difference between the reactor inner wall and the coolant at time n in a period of time. n represents the thermal conductivity of the fluid inside the reactor at time n in a period of time, n=1, 2, 3, ..., k, k is a positive integer; In order to extract the heat exchange performance sensitivity information of the scaling layer on the inner wall of the reactor, real-time data acquisition can be performed through sensors and monitoring systems installed at different positions in the reactor. First, the temperature difference between the inner wall of the reactor and the coolant can be monitored in real time by the temperature sensor. The temperature value of the inner wall of the reactor and the coolant fluid can be used to calculate the temperature difference between the two. Secondly, the heat value transferred inside the reactor can be directly obtained by a heat flow sensor (such as a thermocouple, a heat flow meter, etc.), and the heat transfer can be calculated by combining the geometric parameters of the reactor and the flow characteristics of the fluid. Finally, the thermal conductivity of the fluid inside the reactor can be monitored by the fluid temperature and pressure sensor. The state of the fluid in the reactor is then calculated by the physical property model of the fluid (such as calculating the thermal conductivity of the fluid based on the relationship between temperature and pressure). These data can be extracted and recorded in real time by the data acquisition and analysis system, and enter the further analysis process after preprocessing.

[0030] Specifically, these three types of quantitative data are obtained based on the real-time monitoring results of different sensors and instruments. n The temperature difference ΔTW between the inner wall of the reactor and the coolant can be calculated by real-time monitoring of the heat flow in the reactor and combining the reactor geometry and flow rate data. n The temperature difference between the inner wall of the reactor and the coolant fluid is measured by a temperature sensor; the thermal conductivity KF of the fluid inside the reactor n The temperature and pressure data of the fluid need to be extrapolated according to the state equation of the fluid or the existing thermal conductivity table. After the data is collected by the sensor, it will be processed and stored by the data acquisition system, and will be normalized and preprocessed regularly to ensure the accuracy and timeliness of the data, providing a reliable data basis for the subsequent heat exchange efficiency evaluation and scaling impact analysis.

[0031] Calculate the heat exchange efficiency sensitivity index. The specific calculation formula is as follows: , Where HSEC is the heat exchange efficiency sensitivity index.

[0032] This calculation formula is used to evaluate the effect of the fouling layer on the heat exchange efficiency of the reactor. The reason for using this calculation method is to comprehensively consider the effects of heat transfer, temperature difference and fluid thermal conductivity on the heat exchange efficiency. First, QF n (Heat transfer) is a key factor in heat exchange, which directly affects the heat flow inside the reactor. Therefore, putting it in the numerator can highlight the important role of heat transfer in heat exchange efficiency. Secondly, ΔTW n(Temperature difference) reflects the temperature difference between the inner wall of the reactor and the coolant, which is the driving force of heat exchange, so it is weighted and the effect of temperature difference on heat exchange efficiency is enhanced by an exponent of 1.5, because the effect of temperature difference on heat exchange efficiency is nonlinear. The larger the temperature difference, the higher the heat exchange efficiency, so this nonlinear relationship can be simulated more accurately by exponential weighting. Then, KF n (Thermal conductivity of the fluid) As the thermal conductivity of the fluid inside the reactor, it affects the speed at which heat propagates in the fluid. Introduce KF in the denominator n The square root of can balance the effects of heat transfer and temperature difference on heat exchange efficiency, while avoiding over-enhancing the effect of fluid thermal conductivity on efficiency. Finally, by summing and averaging the calculated values ​​at each moment, a comprehensive evaluation of the reactor heat exchange performance during the entire cycle is ensured. This weighted and averaged method can more comprehensively reflect the sensitivity of the fouling layer to the heat exchange efficiency, and provide a more accurate heat exchange efficiency sensitivity index (HSEC) for evaluating the operating efficiency of the reactor.

[0033] There is a direct positive correlation between the size of the heat exchange efficiency sensitivity index (HSEC) and the degree of influence of the scaling layer appearing on the inner wall of the reactor on the heat exchange efficiency of the reactor. Specifically, the larger the HSEC value, the more significant the negative impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency, and vice versa, it means that the scaling layer has little effect on the heat exchange efficiency. This is because the heat exchange efficiency sensitivity index combines the changes in heat transfer, temperature difference and fluid thermal conductivity. The presence of the scaling layer usually increases the thermal resistance of the inner wall of the reactor, making heat transfer more difficult, thereby increasing the temperature difference and further reducing the heat exchange efficiency. As the thickness of the scaling layer increases, the efficiency of heat transfer will decrease significantly, resulting in an increase in the HSEC index. Therefore, the higher the HSEC value, the greater the impact of the scaling layer on the heat exchange and the lower the heat exchange efficiency of the reactor. This trend of change can be used as a reference for the timing of reactor maintenance and cleaning.

[0034] 3. Construct a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generate the impact assessment coefficient, and analyze it after generation to assess the impact of the scaling layer appearing on the inner wall of the reactor on the heat exchange efficiency of the reactor; In this embodiment, a scaling impact assessment model is constructed based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index to generate an impact assessment coefficient, which specifically includes the following steps: Collect several scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, and calibrate them as SIC x 、HSEC x and IAC x, x represents the number of scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set; The collection of scaling influence coefficients, heat exchange efficiency sensitivity indexes, and corresponding impact assessment coefficients generated over a period of time can be achieved through an automated data recording and monitoring system. Specifically, the reactor system can regularly measure and record various data (such as scaling influence coefficients, heat exchange efficiency sensitivity indexes, etc.) through embedded sensors and real-time monitoring software. These sensors include temperature sensors, flow meters, pressure sensors, and other related sensors, which are used to collect scaling conditions, fluid properties, and heat exchange parameters on the inner wall of the reactor in real time. The monitoring system then stores the data of each measurement together with a timestamp in the database. In order to ensure the accuracy and reliability of the data, the system can set up an automated verification mechanism to automatically identify outliers or data errors by comparing the deviations between the preset normal range and the real-time data. Once the data is confirmed to be valid, the system will automatically update the historical data set, which will be used in the subsequent regression model training process to ensure the accuracy and real-time performance of the scaling impact assessment model.

[0035] When the regression coefficient is not known, the impact assessment coefficient can be calculated by making a preliminary estimate based on the scaling impact coefficient and heat exchange efficiency sensitivity index in the historical data set. First, real-time data can be collected over a period of time, including multiple measurements of the scaling impact coefficient and the heat exchange efficiency sensitivity index. Using these historical data, multivariate analysis can be performed through data analysis software to preliminarily build an estimation model for the impact assessment coefficient. Through statistical methods, such as weighted average or model-based speculation, preliminary values ​​of the impact assessment coefficient can be estimated without known regression coefficients. This process relies on the software's ability to process and model historical data, and obtains relatively reliable estimation results through simple weighted or regression models, laying the foundation for subsequent optimization calculations.

[0036] The collection of impact assessment coefficients can be achieved through intelligent monitoring systems and data acquisition software. First, multiple sensors are installed in the reactor to monitor the changes in the scaling impact coefficient and the heat exchange efficiency sensitivity index in real time. Through the data acquisition system, the temperature, flow, pressure, thermal conductivity and other data collected by the sensors are transmitted to the central data analysis platform. The software will automatically process these sensor data, analyze and store the scaling impact coefficient and the heat exchange efficiency sensitivity index. In order to ensure the accuracy and real-time nature of the data, the data acquisition system should regularly clean and verify the data to avoid erroneous data affecting the evaluation results. The system can automatically collect these data at set time intervals and form a historical data set for subsequent model training and optimization. In addition, the data storage module can regularly archive historical data to provide a basis for continuous optimization of the model for impact assessment coefficients.

[0037] The range of x is limited to positive integers greater than or equal to 3 to ensure the calculation stability and accuracy of the regression model. According to the basic principle of regression analysis, there is only one target equation, but the number of regression coefficients to be calculated is three (i.e., β 0 , β 1 and β 2 ). In order to accurately solve these three regression coefficients, there must be at least three independent equations to form a system of equations. Each equation comes from a generated scaling influence coefficient, heat exchange efficiency sensitivity index and corresponding impact assessment coefficient. Therefore, it is necessary to collect data at least three different time points to ensure the integrity and solvability of the system of equations. If the value of x is less than 3, the system of equations cannot be formed, resulting in the inability to correctly calculate the regression coefficients. In addition, adding data points (i.e., larger x values) can improve the model fit and prediction accuracy, thereby enhancing the robustness and practicality of the evaluation model.

[0038] The multivariate regression model is selected as the scaling impact assessment model, and the historical data set is used for training to determine the value of the regression coefficient according to the formula: , In the formula, β 0 , β 1 and β 2 is the regression coefficient; The multiple regression model is a statistical method used to analyze the relationship between multiple independent variables (independent variables) and a dependent variable (dependent variable). In multiple regression, the dependent variable is a linear combination of multiple independent variables, and each independent variable reflects its degree of influence on the dependent variable through the regression coefficient. Through training with historical data sets, the model can identify the relationship between independent and dependent variables, and minimize the error between the predicted value and the actual value by optimizing the regression coefficient, thereby improving the prediction accuracy. The reason for choosing the multiple regression model as the fouling impact assessment model is that the fouling impact coefficient and the heat exchange efficiency sensitivity index may have independent and complex effects on the impact assessment coefficient, and multiple regression can handle multiple influencing factors at the same time and quantify the contribution of each factor to the impact assessment coefficient. Through training with historical data sets, the regression coefficient can be accurately solved, thus forming a model that can evaluate the impact of fouling on the heat exchange efficiency of the reactor in real time. Regression coefficient β 0 , β 1 and β 2 They represent the linear contribution of the constant term and each independent variable to the dependent variable. Specifically, β 0 is a constant term, representing the SIC x and HSEC x When both are zero, it affects the baseline value of the assessment coefficient; 1 Scaling influence coefficient SIC x The contribution to the impact assessment coefficient reflects the impact of the scaling layer on the heat exchange efficiency; β 2 Indicates the heat exchange efficiency sensitivity index HSEC x The contribution to the impact assessment coefficient reflects the effect of the interaction between heat exchange performance and fouling layer on the reactor efficiency.

[0039] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and the regression coefficient β is finally determined. 0 , β 1 and β 2 The value of The purpose of optimizing the regression coefficient by minimizing the error between the predicted value and the actual value is to ensure that the multivariate regression model can accurately reflect the actual relationship between the scaling impact coefficient and the heat exchange efficiency sensitivity index on the impact assessment coefficient in the historical data. The core of this process is to adjust the regression coefficient so that the impact assessment coefficient predicted by the model is as close as possible to the actual observed value, thereby improving the prediction accuracy and reliability of the model. The specific approach is to use the least squares method, that is, to calculate the difference (error) between the predicted value and the actual value of all data points, and minimize the sum of the squares of these errors. In this way, the regression coefficient β 0 , β 1 and β 2It will be adjusted to the optimal value to ensure that the model can achieve the best fit on the given historical data set, thereby ensuring a more accurate assessment of the impact of scaling and heat exchange efficiency in actual applications.

[0040] The final determined regression coefficient is used to construct the scaling impact assessment model, and the scaling impact coefficient SIC and the heat exchange efficiency sensitivity index HSEC generated in real time are input to generate the impact assessment coefficient IAC in real time.

[0041] In this embodiment, the generated impact assessment coefficient IAC is compared with the preset impact assessment coefficient threshold interval [IAC min , IAC max ] to compare, and evaluate the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor according to the comparison results. The specific comparison analysis is as follows: If IAC<IAC min , the scale layer appearing on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor; This situation means that the scaling layer on the inner wall of the reactor has a low impact on the heat exchange efficiency. This shows that the formation of the scaling layer has not yet reached the level of hindering heat transfer, and the heat exchange process of the reactor remains in a relatively efficient state. In this case, the operating temperature of the heat exchanger and the thermal conductivity of the fluid are not significantly disturbed, the performance of the reactor remains stable, and the operator can maintain the current operating mode without immediate cleaning or other maintenance measures. Therefore, under the low impact level, the normal operation of the reactor can continue, and the maintenance work has a lower priority, but it is still necessary to regularly monitor the changes in the scaling layer to prevent the situation from further deteriorating.

[0042] If IAC min ≤IAC≤IAC max The scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor; This situation means that the impact of the scaling layer on the heat exchange efficiency is at a moderate level. This situation usually means that there is a certain amount of scaling on the inner wall of the reactor, but the degree of scaling has not yet completely affected the heat exchange efficiency of the reactor. At this time, although the heat transfer and thermal conductivity are slightly affected, it may cause a slight increase in the energy consumption of the reactor or a slight decrease in the heat exchange efficiency, but it is still within an acceptable range. Operators should pay attention to changes in reactor performance and consider taking appropriate maintenance measures, such as regular cleaning or optimizing the operating conditions of the reactor, to prevent further deterioration of scaling and a significant decrease in efficiency.

[0043] If IAC>IAC max The scaling layer appearing on the inner wall of the reactor has a high impact on the heat exchange efficiency of the reactor.

[0044] This situation means that the scaling layer has seriously affected the heat exchange efficiency of the reactor, which may lead to significant heat loss and energy waste. At this time, the scaling layer on the inner wall of the reactor not only reduces the effective transfer of heat, but also may cause increased temperature fluctuations of the fluid inside the reactor, reduced system energy efficiency, and may even cause overheating or failure of the equipment. If it is not handled for a long time, it may cause equipment damage or production stagnation, resulting in serious economic losses. Therefore, under high impact levels, operators need to immediately take measures such as cleaning, repairing or replacing reactor parts to ensure that the reactor can be restored to the optimal heat exchange state to maintain production efficiency and avoid further failure risks.

[0045] The "pre-set impact assessment coefficient threshold interval" can be determined by a variety of data analysis methods. First, based on the historical data set, the scaling impact coefficient and heat exchange efficiency sensitivity index of the reactor under different operating conditions can be statistically analyzed to calculate the distribution of each evaluation coefficient, such as the mean, standard deviation and other statistics. Then, data mining techniques, such as cluster analysis or quantile analysis, can be used to identify the natural distribution range of the impact assessment coefficient, and then determine a reasonable threshold interval. For example, different impact assessment coefficient data are divided into multiple groups through K-means cluster analysis, and then the group boundaries corresponding to low, medium and high impact levels are set as threshold intervals. Another method is to model the relationship between the impact assessment coefficient and other related variables based on regression analysis, so as to deduce the optimal threshold interval. Finally, according to the set business needs and operating experience, the interval can be appropriately adjusted and optimized in combination with the judgment of engineers. This process can be completed through specialized data analysis software and machine learning tools to ensure that the threshold interval of the evaluation coefficient is both in line with the actual situation and has sufficient scientific basis and accuracy.

[0046] 4. Take corresponding adjustment measures based on the evaluation results of the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; In this embodiment, according to the evaluation results of the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency, corresponding adjustment measures are taken, specifically: If the evaluation result shows that the scaling layer on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: maintain the existing operating conditions and regularly monitor the scaling on the inner wall of the reactor; In the case where the fouling layer has a low impact on the heat exchange efficiency, the adjustment measure is to maintain the existing operating conditions and regularly monitor the fouling of the reactor wall. This adjustment measure can be achieved through the software system, specifically through an integrated monitoring system and data analysis platform, using sensors to collect key parameters such as reactor temperature, flow rate, pressure, etc. in real time and compare them with historical data. Through the data analysis software, the trend of fouling can be tracked to determine whether the fouling layer really has no significant impact on the heat exchange. If the monitoring results show that the fouling increases and affects the heat exchange efficiency, the system will issue an alarm to indicate that cleaning or adjustment is required. The software system will continuously monitor multiple sensors in the reactor (such as temperature, pressure, flow rate, heat exchanger efficiency, etc.). The system can determine whether the changes in temperature and flow exceed the normal range through the set thresholds, and predict the fouling situation based on the trend analysis function. If the fouling has a small impact, the system maintains the existing conditions and automatically triggers the fouling monitoring program during the regular inspection time. This can effectively avoid unnecessary operational adjustments while ensuring stable system efficiency. The purpose of this practice is to maintain the stability of the production process and track the progress of fouling in real time through the monitoring system. If the impact is small, excessive adjustments may result in unnecessary costs and energy waste, so maintaining existing operating conditions can reduce unnecessary interventions without affecting overall production efficiency.

[0047] If the evaluation result shows that the scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: optimize the operating parameters, enhance the heat exchange efficiency of the cooling system, reduce the negative impact of scale on heat exchange, and increase the reactor cleaning cycle; When the impact of the scaling layer on the heat exchange efficiency reaches a moderate level, the system needs to enhance the heat exchange efficiency of the cooling system by optimizing the operating parameters, reduce the negative impact of scaling on the heat exchange, and increase the cleaning cycle of the reactor. This adjustment can be achieved through software automation management. The system can automatically adjust the parameters such as the flow rate, temperature or pressure of the cooling system based on real-time monitoring data. In addition, through the data analysis module, the system can calculate the optimal cleaning cycle and automatically trigger the cleaning operation when the scaling layer accumulates to a certain extent. The software system can optimize the heat exchange efficiency by adjusting the control algorithm of the cooling system. For example, when the impact of scaling reaches a moderate level, the system can automatically calculate and adjust the flow rate and temperature of the cooling medium to improve the efficiency of the heat exchange system. In addition, the system will dynamically evaluate the impact of scaling based on real-time monitoring data, and automatically calculate a new cleaning cycle based on the prediction algorithm. This cycle can be adjusted according to the scaling rate of the reactor to avoid cleaning too early or too late. This adjustment measure can help maintain the efficiency of the heat exchange system and prevent scaling from continuing to have a greater impact on the heat exchanger. By optimizing the operating parameters, not only can the negative impact of scaling on system efficiency be reduced, but the cleaning cycle can also be adjusted according to actual conditions to ensure that the temperature control and heat exchange efficiency of the reactor are fully guaranteed, while improving the stability of the reaction process.

[0048] If the evaluation result shows that the scaling layer on the inner wall of the reactor has a high degree of impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: immediately carry out scaling removal operations, including comprehensive cleaning of the reactor and heat exchanger, and adjust the operating conditions of the reactor to restore the working efficiency of the heat exchange system, and upgrade the temperature control system, using more advanced real-time monitoring and control technology to ensure that when the scaling layer seriously affects the heat exchange efficiency, the control system can accurately identify and compensate in time, thereby avoiding a decrease in reaction rate and product quality, and ensuring a stable and efficient production process.

[0049] When the fouling layer has a serious impact on the heat exchange efficiency, the fouling removal operation must be carried out immediately and the reactor and heat exchanger must be fully cleaned. In addition, the system needs to adjust the operating conditions of the reactor (such as flow rate, temperature, etc.), restore the working efficiency of the heat exchange system, and upgrade the temperature control system to monitor the fouling in real time. This measure needs to be implemented through the automated control of the software system. Through the upgraded temperature control system and real-time monitoring, it is ensured that the system can accurately identify and take compensatory measures when the fouling layer affects the efficiency. The software system can integrate intelligent cleaning scheduling functions, and automatically trigger the cleaning process when it is detected in real time that the fouling layer has affected the heat exchange efficiency. The cleaning operation can be automatically managed by the system controlling the valves and fluid flows of the reactor and heat exchanger. The system will re-evaluate the operating conditions of the reactor and adjust the operating parameters such as flow rate, pressure, cooling temperature, etc. according to the real-time data to ensure that the heat exchange system resumes normal operation. In addition, the temperature control system is upgraded to a more intelligent real-time monitoring system that can accurately identify temperature fluctuations caused by fouling and compensate for temperature differences in real time to maintain the stability of reaction rate and product quality. When the scaling layer has a significant impact on the heat exchange efficiency, if it is not handled in time, it may lead to a decrease in reaction rate, reduced product quality, or even production interruption. Therefore, immediate cleaning operations are necessary to restore the efficiency of the heat exchange system. At the same time, the upgraded temperature control system can compensate for the temperature difference caused by scaling in time, ensuring the temperature control accuracy of the reactor and the stability of the production process, avoiding the long-term negative impact of scaling.

[0050] 5. Monitor the heat exchange efficiency of the reactor after the adjustment measures are implemented in real time, continuously obtain the change data of the heat exchange efficiency of the reactor, and dynamically adjust the evaluation model and adjustment measures according to the real-time monitoring results; In order to achieve real-time monitoring of the heat exchange efficiency of the reactor after the adjustment measures are implemented, it can be achieved through an integrated sensor system, data acquisition system and intelligent control system. First, the key parameters such as temperature, flow, pressure, heat exchange efficiency in the reactor are collected in real time by sensors and transmitted to the central control platform through the data acquisition system. Based on these real-time data, the software system continuously analyzes the changes in the heat exchange efficiency of the reactor and compares them with the preset standards. If the heat exchange efficiency fluctuates significantly, the system will automatically identify and analyze the reasons, such as the growth of the scaling layer, the deviation of the operating parameters, etc. According to the monitoring results, the software system will dynamically adjust the evaluation model, optimize the regression coefficient and adjustment measures to ensure that the reactor can maintain the best heat exchange efficiency under different conditions. In addition, the system will adjust the operating parameters according to the feedback data, such as adjusting the cooling flow, reactor temperature or cleaning cycle, so as to take appropriate control measures at different stages of scaling. This real-time monitoring and dynamic adjustment mechanism can ensure that the reactor is continuously in the best operating state, reduce the impact of scaling on the heat exchange efficiency, and ensure the stability and efficiency of the production process.

[0051] The following table shows the change in heat exchange efficiency with and without adjustment measures at different scaling layer thicknesses. By comparing the data, we can clearly see the negative impact of scaling on heat exchange efficiency and the improvement effect of heat exchange efficiency after the proposed adjustment measures.

[0052]

[0053] Scaling layer thickness (mm): This column reflects the thickness of the scaling layer on the inner wall of the reactor, in millimeters. Scaling is a common phenomenon in heat exchange equipment, and the thickness of the scaling layer is closely related to the degree of scaling accumulation. The thicker the scaling layer, the worse the heat exchange effect, because the scaling layer will form a thermal resistance and hinder the transfer of heat. As the thickness of the scaling layer increases, the heat exchange efficiency of the reactor will drop significantly, so controlling the thickness of the scaling layer is the key to improving the heat exchange efficiency.

[0054] Heat exchange efficiency without adjustment measures (%): This column shows the change in heat exchange efficiency under different scaling layer thickness without any adjustment measures. As the thickness of the scaling layer increases, the heat exchange efficiency decreases significantly. Scaling not only hinders heat transfer, but also may cause uneven flow of fluid in the reactor, further reducing the overall efficiency. For example, when the scaling layer thickness is 1.5mm, the heat exchange efficiency drops to 65.3%, which is nearly 30% lower than when the scaling is less (such as 0.1mm). This data shows that scaling has a significant negative impact on heat exchange efficiency.

[0055] Scaling influence coefficient and heat exchange efficiency sensitivity index before adjustment measures: Scaling influence coefficient: This indicator is used to quantify the impact of scaling on heat exchange efficiency. The larger the value, the more significant the negative impact of scaling on system performance. When no adjustment measures are taken, the scaling influence coefficient gradually increases with the increase of scaling layer thickness. For example, when the scaling layer thickness is 0.1mm, the scaling influence coefficient is only 0.05, and when the scaling layer thickness increases to 1.5mm, the scaling influence coefficient has increased to 0.45, indicating that the impact of scaling is gradually increasing.

[0056] Heat exchange efficiency sensitivity index: This index measures the sensitivity of heat exchange efficiency to scaling. The higher the index, the more sensitive the system heat exchange efficiency is to changes in scaling thickness. As the thickness of the scaling layer increases, the sensitivity index rises, indicating that scaling has a more significant impact on heat exchange efficiency. For example, when the scaling layer is 0.5 mm thick, the sensitivity index is 2.1, and when it is 1.5 mm, the sensitivity index increases to 3.0.

[0057] Heat exchange efficiency after adjustment measures (%): This column shows the heat exchange efficiency after adjustment measures were taken. The heat exchange efficiency of the reactor was significantly improved by optimizing operating conditions, regularly cleaning the reactor, replacing heat exchange pipes, or adopting other anti-fouling technologies. For example, when the thickness of the fouling layer was 0.5 mm, the heat exchange efficiency without taking measures was 85.4%, but after taking adjustment measures, the heat exchange efficiency increased to 88.2%, an increase of nearly 3%. Similarly, when the thickness of the fouling layer was 1.5 mm, the efficiency without taking measures was 65.3%, but after the adjustment measures were implemented, the efficiency returned to 70.5%, an increase of more than 5%. These data demonstrate the effectiveness of adjustment measures in mitigating the effects of fouling.

[0058] Scaling influence coefficient and heat exchange efficiency sensitivity index after adjustment measures: Scaling influence coefficient: After the adjustment measures were implemented, the negative impact of scaling on heat exchange efficiency was significantly reduced. After the adjustment measures, the scaling influence coefficient dropped significantly. For example, when the thickness of the scaling layer was 1.5 mm, the scaling influence coefficient was 0.45 before the adjustment measures and dropped to 0.32 after the adjustment measures were implemented. This shows that after taking effective measures, the thermal resistance of the scaling layer was reduced and the reactor performance was significantly improved.

[0059] Heat exchange efficiency sensitivity index: After the adjustment measures were implemented, the sensitivity of heat exchange efficiency to scaling thickness decreased. For example, when the scaling layer thickness was 1.5 mm, the sensitivity index dropped from 3.0 to 2.4 after the adjustment measures were implemented, indicating that under scaling control, the system's response to changes in scaling thickness slowed down, further confirming the effectiveness of the adjustment measures.

[0060] Evaluation results: It can be clearly seen from the table that the effect of scaling on heat exchange efficiency is very significant, especially when the thickness of the scaling layer exceeds 1.0 mm, the heat exchange efficiency drops significantly, and even reaches an obvious system performance bottleneck. However, taking appropriate adjustment measures, such as regular cleaning and optimizing reaction conditions, can effectively reduce the impact of scaling and improve heat exchange efficiency. This data not only proves the significant impact of scaling on system performance, but also emphasizes the necessity and effectiveness of improving heat exchange efficiency by optimizing technical measures. Especially in more severe scaling conditions, the effect of the adjustment measures is more significant, indicating that the technical solution of the present invention has the feasibility and superiority of practical application.

[0061] 6. Store and analyze historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, improve heat exchange efficiency, and ensure that the reactor maintains a stable heat exchange effect under optimal operating conditions.

[0062] In order to store and analyze historical monitoring data to evaluate the adjustment effect and optimize the model parameters, this can be done by building an integrated data warehouse and machine learning analysis platform. First, the system will store various key parameter data (such as temperature, pressure, heat exchange efficiency, fouling layer accumulation, etc.) collected during the operation of the reactor in the historical data warehouse to ensure long-term preservation and easy retrieval of the data. Through the analysis of these historical data, the software system can identify the specific impact of the adjustment measures on the heat exchange efficiency and the relationship between various operating conditions (such as cooling flow, reactor temperature, cleaning cycle, etc.) and the degree of fouling. Through machine learning algorithms, the system can continuously optimize the parameters of the heat exchange efficiency evaluation model, such as adjusting the regression coefficient of the regression model to improve the accuracy of the prediction. The software platform can also use these data to automatically generate control strategies, and automatically identify which control parameters have the greatest impact on the heat exchange efficiency under different working conditions by continuously comparing the effects before and after the adjustment. In this way, the system can dynamically optimize the control strategy based on the feedback of real-time and historical data, so as to continuously optimize the heat exchange efficiency of the reactor, ensure that the reactor maintains a stable heat exchange effect under the best operating state, maximize production efficiency and improve product quality.

[0063] like Figure 2 The control system for preparing dimethyl carbonate from propylene carbonate and methanol shown includes a scaling detection module, a scaling impact analysis module, an impact assessment model module, an adjustment measure module, a dynamic monitoring and adjustment module, and a data storage and optimization module; A scaling detection module, in the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, performs real-time detection on whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate, and constructs a mathematical model based on scaling layer detection when a scaling layer is detected; The scaling impact analysis module acquires the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time, and analyzes it after acquisition to generate the scaling impact coefficient and heat exchange efficiency sensitivity index respectively; The impact assessment model module constructs a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generates the impact assessment coefficient, and analyzes it after generation to assess the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor; The adjustment measure module takes corresponding adjustment measures according to the evaluation results of the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; The dynamic monitoring and adjustment module monitors the heat exchange efficiency of the reactor in real time after the adjustment measures are implemented, continuously obtains the change data of the heat exchange efficiency of the reactor, and dynamically adjusts the evaluation model and adjustment measures according to the real-time monitoring results; The data storage and optimization module stores and analyzes historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, and improve heat exchange efficiency.

[0064] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0065] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0066] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0067] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0071] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control method for preparing dimethyl carbonate using propylene carbonate and methanol, characterized in that: The specific steps include:

1. In the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate is detected in real time, and when a scaling layer is detected, a mathematical model based on scaling layer detection is constructed; Second, the scaling influence information of the scaling layer appearing on the inner wall of the reactor is obtained in real time, and after acquisition, it is analyzed to generate the scaling influence coefficient and the heat exchange efficiency sensitivity index respectively; The specific steps for generating the scaling influence coefficient and the heat exchange efficiency sensitivity index are as follows: Obtain the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time and perform pretreatment after obtaining; Extracting scaling layer characteristic information and heat exchange performance sensitivity information from scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor; The extracted scaling layer characteristic information and heat exchange performance sensitivity information are analyzed to generate scaling influence coefficient and heat exchange efficiency sensitivity index respectively; 3. Construct a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generate the impact assessment coefficient, and analyze it after generation to assess the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor; 4. Take corresponding adjustment measures based on the evaluation results of the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; 5. Monitor the heat exchange efficiency of the reactor after the adjustment measures are implemented in real time, continuously obtain the change data of the heat exchange efficiency of the reactor, and dynamically adjust the evaluation model and adjustment measures according to the real-time monitoring results; 6. Store and analyze historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, and improve heat exchange efficiency.

2. The control method for preparing dimethyl carbonate by using propylene carbonate and methanol according to claim 1, characterized in that: The acquisition logic of the scaling influence coefficient is as follows: Extract the scaling layer characteristic information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, specifically including the average thickness of the scaling layer appearing on the inner wall of the reactor at different times over a period of time, the average temperature of the fluid in the reactor, and the average viscosity of the fluid in the reactor, and calibrate them as DS m TF m and NF m , DS m It represents the average thickness of the fouling layer on the inner wall of the reactor at time m within a period of time, TF m represents the average temperature of the fluid in the reactor at time m over a period of time, NF m represents the average viscosity of the fluid in the reactor at time m within a period of time, m=1, 2, 3, ..., g, g is a positive integer; Calculate the scaling influence coefficient. The specific calculation formula is as follows: ， Where SIC is the scaling influence coefficient.

3. The control method for preparing dimethyl carbonate by using propylene carbonate and methanol according to claim 2, characterized in that: The acquisition logic of the heat exchange efficiency sensitivity index is as follows: Extract the heat exchange performance sensitivity information from the scaling influence information of the scaling layer appearing on the inner wall of the pretreated reactor, including the heat value transferred inside the reactor at different times over a period of time, the temperature difference between the inner wall of the reactor and the coolant, and the thermal conductivity of the fluid inside the reactor, and calibrate them as QF n , ΔTW n and KF n , QF n Indicates the heat value transferred inside the reactor at time n within a period of time, ΔTW n KF represents the temperature difference between the reactor inner wall and the coolant at time n in a period of time. n represents the thermal conductivity of the fluid inside the reactor at time n in a period of time, n=1, 2, 3, ..., k, k is a positive integer; Calculate the heat exchange efficiency sensitivity index. The specific calculation formula is as follows: ， Where HSEC is the heat exchange efficiency sensitivity index.

4. The control method for preparing dimethyl carbonate by using propylene carbonate and methanol according to claim 3, characterized in that: A scaling impact assessment model is constructed based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index to generate an impact assessment coefficient, specifically including the following steps: Collect several scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, and calibrate them as SIC x 、HSEC x and IAC x , x represents the number of scaling influence coefficients, heat exchange efficiency sensitivity indexes and corresponding impact assessment coefficients generated in the past period of time, x=3, 4, 5, ..., d, d is a positive integer, and the data collected in the past period of time are formed into a historical data set; The multivariate regression model is selected as the scaling impact assessment model, and the historical data set is used for training to determine the value of the regression coefficient according to the formula: ， Where β0, β1 and β2 are regression coefficients; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized, and finally the values ​​of the regression coefficients β0, β1 and β2 are determined; The final determined regression coefficient is used to construct the scaling impact assessment model, and the scaling impact coefficient SIC and the heat exchange efficiency sensitivity index HSEC generated in real time are input to generate the impact assessment coefficient IAC in real time.

5. The control method for preparing dimethyl carbonate by using propylene carbonate and methanol according to claim 4, characterized in that: The generated impact assessment coefficient IAC is compared with the pre-set impact assessment coefficient threshold interval [IAC min , IAC max ] to compare, and evaluate the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor according to the comparison results. The specific comparison analysis is as follows: If IAC<IAC min , the scale layer appearing on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor; If IAC min ≤IAC≤IAC max The scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor; If IAC>IAC max The scaling layer appearing on the inner wall of the reactor has a high impact on the heat exchange efficiency of the reactor.

6. The control method for preparing dimethyl carbonate by using propylene carbonate and methanol according to claim 5, characterized in that: According to the evaluation results of the influence of the scale layer on the inner wall of the reactor on the heat exchange efficiency, corresponding adjustment measures are taken, specifically: If the evaluation result shows that the scaling layer on the inner wall of the reactor has a low impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: maintain the existing operating conditions and regularly monitor the scaling on the inner wall of the reactor; If the evaluation result shows that the scale layer on the inner wall of the reactor has a moderate impact on the heat exchange efficiency of the reactor, the adjustment measures to be taken are: optimize the operating parameters, enhance the heat exchange efficiency of the cooling system, reduce the negative impact of scale on heat exchange, and increase the reactor cleaning cycle; If the evaluation result shows that the scaling layer on the inner wall of the reactor has a high impact on the reactor heat exchange efficiency, the adjustment measures to be taken are: immediately carry out scaling removal operations, including comprehensive cleaning of the reactor and heat exchanger, and adjust the operating conditions of the reactor to restore the working efficiency of the heat exchange system and upgrade the temperature control system.

7. A control system for preparing dimethyl carbonate from propylene carbonate and methanol, which is used to implement the control method for preparing dimethyl carbonate from propylene carbonate and methanol as claimed in any one of claims 1 to 6, characterized in that: It includes scaling detection module, scaling impact analysis module, impact assessment model module, adjustment measures module, dynamic monitoring and adjustment module and data storage and optimization module; A scaling detection module, in the process of preparing dimethyl carbonate by reacting propylene carbonate with methanol, performs real-time detection on whether a scaling layer appears on the inner wall of the reactor used to prepare dimethyl carbonate, and constructs a mathematical model based on scaling layer detection when a scaling layer is detected; The scaling impact analysis module acquires the scaling impact information of the scaling layer appearing on the inner wall of the reactor in real time, and analyzes it after acquisition to generate the scaling impact coefficient and heat exchange efficiency sensitivity index respectively; The impact assessment model module constructs a scaling impact assessment model based on the generated scaling impact coefficient and heat exchange efficiency sensitivity index, generates the impact assessment coefficient, and analyzes it after generation to assess the impact of the scaling layer on the inner wall of the reactor on the heat exchange efficiency of the reactor; The adjustment measure module takes corresponding adjustment measures according to the evaluation results of the influence of the scaling layer on the inner wall of the reactor on the heat exchange efficiency; The dynamic monitoring and adjustment module monitors the heat exchange efficiency of the reactor in real time after the adjustment measures are implemented, continuously obtains the change data of the heat exchange efficiency of the reactor, and dynamically adjusts the evaluation model and adjustment measures according to the real-time monitoring results; The data storage and optimization module stores and analyzes historical monitoring data to evaluate adjustment effects and optimize model parameters, continuously optimize control strategies, and improve heat exchange efficiency.

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