Automatic rectification control system and method

Abstract

The invention relates to the field of rectification processing, in particular to an automatic rectification control system and method, and the system comprises the following modules: a sensor module which is used for monitoring the temperature, pressure, liquid level and flow parameters in a rectification kettle in real time, converting the physical quantities into electric signals, and transmitting the electric signals to a control system; the sensor module comprises a temperature sensor, a pressure sensor, a liquid level sensor and a flow sensor. The automatic and intelligent level of the rectification process is improved, comprehensive monitoring and control of the rectification process are achieved through the sensor module, the control system module, the self-adaptive control module, the fault early warning and diagnosis module and the intelligent prediction control module, key parameters in the rectification process can be collected in real time, and the rectification efficiency is improved. Accurate data support is provided for a control system, and the real-time state of the rectification process is monitored in real time; the control strategy can be dynamically adjusted according to the real-time state and historical data of the rectification process so as to optimize the rectification effect.

Description

Technical Field

[0001] The present invention relates to the field of rectification processing, and more specifically, to an automated rectification control system and method. Background Art

[0002] In the chemical industry, rectification is a widely used separation technique for separating components from mixtures. Traditional rectification processes mainly rely on manual operation and monitoring, which are not only inefficient but also vulnerable to human factors, resulting in unstable rectification effects and difficult-to-guarantee product quality.

[0003] With the development of industrial automation and intelligent technologies, automated control systems have been widely applied in various industrial production processes to improve production efficiency, reduce costs, and enhance the stability and reliability of the system. However, the automated control system for the rectification process has the following problems: it cannot accurately monitor and control key parameters in the rectification process in real time, it is not convenient to adaptively adjust control strategies to adapt to changes in the rectification process, and it cannot give timely warning prompts to the operator when a fault occurs, reducing the intelligence of the control system.

[0004] Therefore, we make improvements and propose an automated rectification control system and method. Summary of the Invention

[0005] The purpose of the present invention is to address the problems that the existing traditional rectification control system cannot accurately monitor and control key parameters in the rectification process in real time, it is not convenient to adaptively adjust control strategies to adapt to changes in the rectification process, and it cannot give timely warning prompts to the operator when a fault occurs.

[0006] To achieve the above-mentioned invention purposes, the present invention provides an automated rectification control system and method to improve the above problems.

[0007] Specifically, this application is as follows:

[0008] The automated rectification control system includes the following modules:

[0009] Sensor module: used to continuously monitor the temperature, pressure, liquid level, and flow rate parameters in the rectification kettle, and convert these physical quantities into electrical signals for transmission to the control system;

[0010] The sensor module includes a temperature sensor, a pressure sensor, a liquid level sensor, and a flow rate sensor;

[0011] Control system module: receives signals from the sensor module, and after data processing and logical judgment, issues control instructions;

[0012] Adaptive control module: integrated in the intelligent prediction control module, dynamically adjusts the output of the heating and cooling devices according to the prediction results and real-time data;

[0013] Fault warning and diagnosis module: By analyzing sensor data and actuator status, it monitors the operating status of the rectification system in real time. Once abnormalities and potential faults are detected, it immediately issues a warning.

[0014] Intelligent predictive control module: Based on machine learning algorithms, it uses historical operation data and real-time sensor data to predict the changing trends of parameters such as temperature, pressure, liquid level, and flow rate in the rectification kettle, and adjusts the control strategy in advance.

[0015] Heating module: It is wound around the surface of the rectification kettle in the form of heating wires to uniformly heat the rectification kettle and ensure that the materials inside the rectification kettle are evenly heated.

[0016] As a preferred technical solution of this application, the temperature sensors are installed at the top, bottom, and middle of the rectification kettle to monitor the temperature at each point in real time, and the pressure sensor monitors the pressure inside the rectification kettle in real time.

[0017] As a preferred technical solution of this application, the liquid level sensor monitors the height of the liquid level inside the rectification kettle in real time, and the flow sensor monitors the feed rate, reflux rate, and withdrawal rate in real time.

[0018] As a preferred technical solution of this application, the cooling device is wound around the surface of the rectification kettle in the form of cooling pipes to uniformly cool down the rectification kettle.

[0019] As a preferred technical solution of this application, it includes a stirring module: The stirring rod is rotated by a driving motor to stir the liquid in the inner cavity of the rectification kettle. The stirring structure is driven vertically by an electric push rod to adjust the position of the stirring structure and uniformly stir the liquid at different positions.

[0020] As a preferred technical solution of this application, the intelligent predictive control module includes the following steps:

[0021] S1. Real-time data acquisition: Real-time acquisition of temperature, pressure, liquid level, and flow rate parameters of the rectification kettle, and transmission of them to the microcontroller. The microcontroller regularly reads the temperature, pressure, liquid level, and flow rate data and stores them in the storage module.

[0022] S2. Data preprocessing: Filter and denoise the acquired temperature, pressure, liquid level, and flow rate data, and input the processed data into the intelligent predictive control module.

[0023] S3. Model training and prediction: Use machine learning algorithms to learn the historical temperature, pressure, liquid level, and flow rate data and control operations, establish a prediction model, and predict the changing trends of future temperature, pressure, liquid level, and flow rate data according to the real-time data and the prediction model.

[0024] S4. Real-time prediction: Use the trained machine learning model to predict the real-time sensor data, obtain the change trends of the temperature, pressure, liquid level, and flow rate parameters in the rectification still, compare the prediction results with the preset thresholds, and determine whether to adjust the control strategy.

[0025] As a preferred technical solution of this application, the fault warning and diagnosis module includes the following steps:

[0026] S1. Sensor data acquisition: Real-time collect various parameter data of the rectification system through sensors, including but not limited to temperature, pressure, liquid level, and flow rate, and clean and correct abnormal data.

[0027] S2. Establish a warning model: Establish a fault warning model based on historical data, set warning thresholds, and trigger a warning when the monitored data exceeds the thresholds.

[0028] S3. Establish a diagnosis model: Use machine learning algorithms to establish a fault diagnosis model, and identify the fault types and causes by analyzing sensor data and actuator states.

[0029] S4. Real-time monitoring: Use sensors and actuators to monitor the operating status of the rectification system in real time, and compare the real-time monitoring data with the warning model to determine whether there are any abnormalities.

[0030] S5. Warning release: When abnormal data is detected, immediately trigger the warning mechanism, and notify relevant personnel through text messages, emails, and audible and visual alarms.

[0031] S6. Fault diagnosis: Use the fault diagnosis model to deeply analyze the abnormal data, determine the fault types and causes, and provide a detailed fault diagnosis report, including the fault location and the affected scope.

[0032] S7. Fault handling: According to the fault diagnosis results, formulate corresponding fault handling solutions, notify the maintenance personnel to handle the faults in a timely manner, and ensure the stable operation of the rectification system.

[0033] The automatic rectification control method includes the following steps:

[0034] A. Use the sensor module to collect key parameters in the rectification process in real time, including but not limited to temperature, pressure, liquid level, and flow rate.

[0035] B. Transmit the data collected by the sensor module to the control system module, and the control system module processes and analyzes the received data to monitor the real-time status of the rectification process.

[0036] C. According to the analysis results of the control system module, the adaptive control module automatically adjusts the control strategy to adapt to the changes in the rectification process and ensure the stability and efficiency of the rectification process.

[0037] As a preferred technical solution of the present application, the following steps are further included:

[0038] D. The fault warning and diagnosis module monitors the sensor data and the output of the control system module in real time. Once abnormalities and potential faults are detected, the warning mechanism is immediately triggered, and fault diagnosis information is provided to promptly take measures to prevent the occurrence or expansion of faults;

[0039] E. The intelligent predictive control module uses historical data and real-time data to establish a prediction model through machine learning algorithms, predicts the future trend of the rectification process, and adjusts the control strategy in advance according to the prediction results to achieve more accurate and efficient control.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] In the solution of the present application:

[0042] 1. To solve the problems in the prior art that the rectification control system cannot monitor and control the key parameters in the rectification process in real time and accurately, it is not convenient to adaptively adjust the control strategy to adapt to the changes in the rectification process, and it cannot give early warnings to the operator in time when a fault occurs. The present application improves the automation and intelligent level of the rectification process. Through the sensor module, the control system module, the adaptive control module, the fault warning and diagnosis module, and the intelligent predictive control module, it realizes the comprehensive monitoring and control of the rectification process, can collect the key parameters in the rectification process in real time, provides accurate data support for the control system, monitors the real-time state of the rectification process in real time, and can dynamically adjust the control strategy according to the real-time state and historical data of the rectification process to optimize the rectification effect;

[0043] 2. Through the fault warning and diagnosis module and the intelligent predictive control module of the present application, the sensor data and the output of the control system module can be monitored in real time. Once abnormalities and potential faults are detected, the warning mechanism is immediately triggered and fault diagnosis information is provided. Providing fault diagnosis information helps maintenance personnel quickly locate and solve problems, thereby reducing the failure rate and maintenance cost;

[0044] 3. The present application uses historical data and real-time data to establish a prediction model, predicts the future trend of the rectification process, adjusts the control strategy in advance according to the prediction results, and achieves more accurate and efficient control, enhancing the intelligent level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic block diagram of the automated rectification control system provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0047] As described in the background art, the traditional distillation control system cannot monitor and control the key parameters in the distillation process in real time and accurately, is not convenient for adaptively adjusting the control strategy to adapt to the changes in the distillation process, and cannot give an early warning to the operator in time when a fault occurs.

[0048] To solve this technical problem, the present invention provides an automated distillation control system and method, which is applied to the field of distillation processing.

[0049] Specifically, please refer to Figure 1 , the automated distillation control system includes the following modules:

[0050] Sensor module: used to monitor the temperature, pressure, liquid level and flow rate parameters in the distillation kettle in real time, and convert these physical quantities into electrical signals and transmit them to the control system;

[0051] The sensor module includes a temperature sensor, a pressure sensor, a liquid level sensor and a flow rate sensor;

[0052] Control system module: receives the signals from the sensor module, and issues control instructions after data processing and logical judgment;

[0053] Adaptive control module: integrated in the intelligent prediction control module, and dynamically adjusts the output of the heating and cooling devices according to the prediction results and real-time data;

[0054] Fault warning and diagnosis module: monitors the operating state of the distillation system in real time by analyzing the sensor data and the actuator state, and immediately issues a warning once an abnormality and potential fault are found;

[0055] Intelligent prediction control module: based on machine learning algorithms, uses historical operation data and real-time sensor data to predict, and is not limited to, the change trends of the temperature, pressure, liquid level and flow rate parameters in the distillation kettle, and adjusts the control strategy in advance;

[0056] Heating module: is wound around the surface of the distillation kettle in the form of heating wires to uniformly heat the distillation kettle to ensure that the materials inside the distillation kettle are uniformly heated.

[0057] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0058] Example 1, please refer to Figure 1 , an automated rectification control system, including the following modules:

[0059] Sensor module: used to monitor the temperature, pressure, liquid level and flow rate parameters in the rectification kettle in real time, and convert these physical quantities into electrical signals and transmit them to the control system;

[0060] The sensor module includes a temperature sensor, a pressure sensor, a liquid level sensor and a flow sensor;

[0061] By setting a temperature sensor, a pressure sensor, a liquid level sensor and a flow sensor, the temperature, pressure, liquid level and feed flow rate in the inner cavity of the rectification kettle can be detected respectively, and each parameter in the rectification kettle can be monitored in real time to keep each data within the normal range value. Once data anomalies occur, the operator can be notified in time, facilitating the operator to take timely actions.

[0062] Control system module: receives signals from the sensor module, and after data processing and logical judgment, issues control instructions;

[0063] Adaptive control module: integrated in the intelligent prediction control module, dynamically adjusts the output of the heating and cooling devices according to the prediction results and real-time data;

[0064] Precise and stable temperature control is achieved through the adaptive control module, reducing energy waste and ensuring the processing quality of the materials;

[0065] Fault warning and diagnosis module: monitors the operating state of the rectification system in real time by analyzing sensor data and actuator states, and immediately issues a warning once anomalies and potential faults are found;

[0066] After the fault warning and diagnosis system issues a warning, it provides fault diagnosis information and solution suggestions to improve the reliability and safety of the system.

[0067] Intelligent prediction control module: based on machine learning algorithms, uses historical operation data and real-time sensor data to predict the change trends of parameters such as temperature, pressure, liquid level and flow rate in the rectification kettle, and adjusts the control strategy in advance;

[0068] Heating module: uses heating wires to wrap around the surface of the rectification kettle to uniformly heat the rectification kettle and ensure that the materials inside the rectification kettle are evenly heated.

[0069] By setting a heating module, the surface of the distillation kettle is heated evenly, the control sensitivity is high, the distillation kettle is easily heated quickly, the material in the inner cavity of the distillation kettle is heated evenly, and the processing quality of the finished product is improved.

[0070] The present application improves the automation and intelligence level of the distillation process, realizes comprehensive monitoring and control of the distillation process through the sensor module, control system module, adaptive control module, fault warning and diagnosis module and intelligent predictive control module, can collect key parameters in the distillation process in real time, provide accurate data support for the control system, and monitor the real-time status of the distillation process in real time; can dynamically adjust the control strategy according to the real-time status and historical data of the distillation process to optimize the distillation effect.

[0071] Temperature sensors are installed at the top, bottom and middle of the distillation kettle to monitor the temperature of each point in real time, and pressure sensors monitor the pressure in the distillation kettle in real time.

[0072] The liquid level sensor monitors the height of the liquid level in the distillation kettle in real time, and the flow sensor monitors the feed volume, reflux volume and output volume in real time.

[0073] The cooling device uses a cooling pipe wound around the surface of the distillation kettle to evenly cool the distillation kettle and ensure that the material inside the distillation kettle is evenly cooled.

[0074] The cooling pipe is connected to the external cooling equipment. By continuously delivering cooling medium to the inner cavity of the cooling pipe, the surface of the distillation kettle is evenly cooled as needed, thereby achieving a uniform cooling effect on the material, improving the control accuracy of the material temperature, and avoiding uneven heating of the material.

[0075] It includes a stirring module: the stirring rod is driven to rotate by a driving motor to stir the liquid in the distillation kettle; the stirring structure is driven to move vertically by an electric push rod, and the position of the stirring structure is adjusted to evenly stir the liquids at different positions.

[0076] The stirring module is used to evenly stir the materials in the distillation chamber, so that the materials are evenly heated during the distillation process. The electric push rod drives the stirring structure to move vertically, and the position of the stirring structure is adjusted to thoroughly stir the materials at different positions, thereby improving the distillation processing effect.

[0077] The intelligent predictive control module includes the following steps:

[0078] S1, real-time data acquisition: real-time acquisition of the temperature, pressure, liquid level and flow parameters of the distillation kettle, and transmits them to the microcontroller, which regularly reads the temperature, pressure, liquid level and flow data and stores them in the storage module;

[0079] S2. Data preprocessing: Filter and denoise the collected temperature, pressure, liquid level, and flow rate data, and input the processed data into the intelligent predictive control module;

[0080] S3. Model training and prediction: Use machine learning algorithms to learn the historical temperature, pressure, liquid level, and flow rate data and control operations, establish a prediction model, and predict the future change trends of temperature, pressure, liquid level, and flow rate data based on real-time data and the prediction model;

[0081] S4. Real-time prediction: Use the trained machine learning model to predict the real-time sensor data, obtain the change trends of the temperature, pressure, liquid level, and flow rate parameters in the rectification kettle, compare the prediction results with the preset thresholds, and determine whether to adjust the control strategy.

[0082] The fault warning and diagnosis module includes the following steps:

[0083] S1. Sensor data acquisition: Real-time collect various parameter data of the rectification system through sensors, including but not limited to temperature, pressure, liquid level, and flow rate, and clean and correct abnormal data;

[0084] S2. Establish a warning model: Establish a fault warning model based on historical data, set warning thresholds, and trigger a warning when the monitored data exceeds the thresholds;

[0085] S3. Establish a diagnosis model: Use machine learning algorithms to establish a fault diagnosis model, and identify the fault types and causes by analyzing sensor data and actuator states;

[0086] S4. Real-time monitoring: Use sensors and actuators to real-time monitor the operating status of the rectification system, compare the real-time monitoring data with the warning model, and determine whether there are any abnormalities;

[0087] S5. Warning release: When abnormal data is detected, immediately trigger the warning mechanism and notify relevant personnel through text messages, emails, and audible and visual alarms;

[0088] S6. Fault diagnosis: Use the fault diagnosis model to deeply analyze the abnormal data, determine the fault types and causes, and provide a detailed fault diagnosis report, including the fault location and the affected range;

[0089] S7. Fault handling: According to the fault diagnosis results, formulate corresponding fault handling plans, notify the maintenance personnel to handle the faults in a timely manner, and ensure the stable operation of the rectification system.

[0090] Example 2 further optimizes the automated rectification control method provided in Example 1. The automated rectification control system includes the following steps:

[0091] A. Use the sensor module to collect key parameters in the rectification process in real time, including but not limited to temperature, pressure, liquid level, and flow rate;

[0092] B. Transmit the data collected by the sensor module to the control system module, and the control system module processes and analyzes the received data to monitor the real-time state of the rectification process;

[0093] C. According to the analysis results of the control system module, the adaptive control module automatically adjusts the control strategy to adapt to the changes in the rectification process, ensuring the stability and efficiency of the rectification process;

[0094] D. The fault warning and diagnosis module monitors the sensor data and the output of the control system module in real time. Once abnormalities and potential faults are detected, it immediately triggers the warning mechanism and provides fault diagnosis information to take timely measures to prevent the occurrence or expansion of faults;

[0095] E. The intelligent predictive control module uses historical data and real-time data to establish a prediction model through machine learning algorithms, predicts the future trend of the rectification process, and adjusts the control strategy in advance according to the prediction results to achieve more precise and efficient control.

[0096] Obviously, the embodiments described above are only a part of the embodiments of the present invention, rather than all embodiments. The accompanying drawings show the preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present invention in other related technical fields is similarly within the scope of the patent protection of the present invention.

Claims

1. Automated distillation control system, characterized in that: Includes the following modules: Sensor module: used to monitor the temperature, pressure, liquid level and flow parameters in the distillation kettle in real time, and convert these physical quantities into electrical signals and transmit them to the control system; The sensor module includes a temperature sensor, a pressure sensor, a liquid level sensor and a flow sensor; Control system module: receives signals from the sensor module, and issues control instructions after data processing and logical judgment; Adaptive control module: integrated into the intelligent predictive control module, dynamically adjusts the output of heating and cooling devices based on the prediction results and real-time data; Fault warning and diagnosis module: By analyzing sensor data and actuator status, the operating status of the distillation system is monitored in real time. Once an abnormality or potential fault is found, an early warning is issued immediately; Intelligent predictive control module: Based on machine learning algorithms, it uses historical operation data and real-time sensor data to predict the changing trends of temperature, pressure, liquid level and flow parameters in the distillation kettle, and adjust the control strategy in advance; Heating module: The heating wire is wound around the surface of the distillation kettle to evenly heat the distillation kettle and ensure that the materials inside the distillation kettle are evenly heated.

2. The automated distillation control system according to claim 1, characterized in that: The temperature sensors are installed at the top, bottom and middle of the distillation kettle to monitor the temperature of each point in real time, and the pressure sensor monitors the pressure in the distillation kettle in real time.

3. The automated distillation control system according to claim 2, characterized in that: The liquid level sensor monitors the height of the liquid level in the distillation kettle in real time, and the flow sensor monitors the feed volume, reflux volume and production volume in real time.

4. The automated distillation control system according to claim 3, characterized in that: The cooling device adopts a cooling pipe wound around the surface of the distillation kettle to evenly cool the distillation kettle.

5. The automated distillation control system according to claim 4, characterized in that: It includes a stirring module: the stirring rod is driven to rotate by a driving motor to stir the liquid in the distillation kettle; the stirring structure is driven to move vertically by an electric push rod to adjust the position of the stirring structure to evenly stir the liquids at different positions.

6. The automated distillation control system according to claim 5, characterized in that: The intelligent prediction control module comprises the following steps: S1, real-time data acquisition: real-time acquisition of the temperature, pressure, liquid level and flow parameters of the distillation kettle, and transmits them to the microcontroller, which regularly reads the temperature, pressure, liquid level and flow data and stores them in the storage module; S2, data preprocessing: filtering and denoising the collected temperature, pressure, liquid level and flow data, and inputting the processed data into the intelligent prediction control module; S3, model training and prediction: Use machine learning algorithms to learn historical temperature, pressure, liquid level and flow data and control operations, establish a prediction model, and predict future temperature, pressure, liquid level and flow data change trends based on real-time data and prediction models; S4. Real-time prediction: Use the trained machine learning model to predict the real-time sensor data to obtain the changing trend of the temperature, pressure, liquid level and flow parameters in the distillation kettle, compare the predicted results with the preset thresholds, and determine whether the control strategy needs to be adjusted.

7. The automated distillation control system according to claim 6, characterized in that: The fault warning and diagnosis module comprises the following steps: S1. Sensor data acquisition: collect various parameter data of the distillation system in real time through sensors, including but not limited to temperature, pressure, liquid level and flow rate, and clean and correct abnormal data; S2. Establish an early warning model: Establish a fault early warning model based on historical data, set an early warning threshold, and trigger an early warning when the monitored data exceeds the threshold; S3. Establish a diagnostic model: Use machine learning algorithms to establish a fault diagnosis model to identify fault types and causes by analyzing sensor data and actuator status; S4. Real-time monitoring: Use sensors and actuators to monitor the operating status of the distillation system in real time, compare the real-time monitoring data with the early warning model, and determine whether there is an abnormality; S5. Early warning release: When abnormal data is monitored, the early warning mechanism is triggered immediately to notify relevant personnel through SMS, email, sound and light alarm; S6. Fault diagnosis: Use the fault diagnosis model to conduct in-depth analysis of abnormal data, determine the fault type and cause, and provide a detailed fault diagnosis report, including the fault location and impact range; S7. Fault handling: According to the fault diagnosis results, formulate corresponding fault handling plans and notify maintenance personnel to handle the faults in time to ensure the stable operation of the distillation system.

8. An automated distillation control method, using the automated distillation control system according to claim 7, characterized in that: The following steps are involved: A. Use sensor modules to collect key parameters in the distillation process in real time, including but not limited to temperature, pressure, liquid level and flow rate; B. Transmitting the data collected by the sensor module to the control system module, which processes and analyzes the received data to monitor the real-time status of the distillation process; C. Based on the analysis results of the control system module, the adaptive control module automatically adjusts the control strategy to adapt to the changes in the distillation process and ensure the stability and efficiency of the distillation process.

9. The automated distillation control method according to claim 8, characterized in that: The following steps are also included: D. The fault warning and diagnosis module monitors the sensor data and the output of the control system module in real time. Once an abnormality or potential fault is found, the warning mechanism is triggered immediately and fault diagnosis information is provided so that timely measures can be taken to prevent the fault from occurring or expanding; E. The intelligent predictive control module uses historical data and real-time data to establish a predictive model through machine learning algorithms to predict the future trend of the distillation process and adjust the control strategy in advance according to the prediction results to achieve more accurate and efficient control.

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