Intelligent carbon capture control system and control method based on MVC framework

Abstract

This invention relates to the field of carbon capture technology, specifically to an intelligent carbon capture control system and method based on the MVC framework. The system includes a perception layer, an MVC core layer, and an execution layer. The perception layer is responsible for collecting operational parameter data of the carbon capture system. The model module in the MVC core layer stores the process data model, control algorithm model, and equipment parameter model of the carbon capture system. The view module provides a human-machine interface. The controller module receives operation commands and threshold trigger signals, calls the algorithm of the model module to generate commands and sends them to the execution layer, and feeds back data to the model module and view module. The execution layer is responsible for executing the control commands issued by the MVC core layer to the actuators and feeding back execution layer data. This system reduces system maintenance costs through a decoupled framework, enhances scalability through modular design, improves operation and maintenance efficiency through full-process visualization, and enhances the intelligent control level of the carbon capture system through data-driven approaches.

Description

Technical Field

[0001] This invention relates to the field of carbon capture technology, and more specifically, to an intelligent carbon capture control system and control method based on the MVC framework. Background Technology

[0002] Carbon capture technology, as one of the key supporting technologies for achieving the "dual carbon" target, is widely used in industries such as thermal power, steel, and chemicals. Its core is to capture carbon dioxide from industrial emissions through absorption, adsorption, and membrane separation, and then store or utilize it as a resource. With the large-scale application of carbon capture systems, the complexity of these systems has increased significantly, involving core requirements such as multi-parameter monitoring, multi-device collaborative control, and dynamic adjustment of operating conditions. Traditional control systems have gradually revealed the following shortcomings:

[0003] High coupling and difficult maintenance: Traditional carbon capture control systems often adopt an integrated "monitoring-control-display" architecture, with data processing, control logic, and interface display code highly coupled. When it is necessary to adjust carbon capture process parameters (such as absorber level and desorption tower temperature) or optimize the control algorithm, the entire system needs to be reconstructed, which is costly and prone to triggering cascading failures.

[0004] Poor scalability and weak adaptability: Carbon capture systems in different industrial scenarios (such as post-combustion capture in the thermal power industry and pre-combustion capture in the chemical industry) differ in monitoring parameters and equipment models. Traditional control systems are custom-developed for fixed scenarios and are difficult to quickly adapt to new process requirements or the addition of monitoring equipment (such as adding carbon dioxide purity sensors or energy consumption monitoring modules).

[0005] Low data utilization efficiency and insufficient intelligence: A large amount of monitoring data generated during carbon capture (such as flue gas flow, absorbent concentration, and energy consumption data) is disconnected from the control logic, making it impossible to optimize control strategies through data-driven approaches. Furthermore, the interface can only provide basic data display, making it difficult to support in-depth operations such as condition analysis and fault diagnosis by maintenance personnel.

[0006] The MVC framework, as a mature software architecture pattern, decouples data, control logic, and the user interface by dividing an application into three independent modules: Model, View, and Controller. It has been widely used in fields such as the Internet and industrial control. However, there is currently no technical solution to deeply integrate the MVC framework with carbon capture systems to address the aforementioned shortcomings of traditional carbon capture control systems. Summary of the Invention

[0007] In view of the above-mentioned problems in the existing technology, the purpose of this invention is to provide an intelligent carbon capture and control system and control method based on the MVC framework.

[0008] To solve the above problems, the technical solution adopted by the present invention is as follows: A smart carbon capture and control system based on the MVC framework, the system includes a sensing layer (1), an MVC core layer (2) and an execution layer (3), the MVC core layer (2) being communicatively connected to the sensing layer (1) and the execution layer (3) respectively; The sensing layer (1) is used to collect the operating parameter data of the carbon capture system. The sensing layer (1) includes a process parameter sensor group (11), an equipment status sensor group (12), and an environmental parameter sensor group (13). The MVC core layer (2) includes a model module (21), a view module (22), and a controller module (23). The execution layer (3) is used to execute the control instructions issued by the MVC core layer (2); The model module (21) is configured to store the process data model, control algorithm model and equipment parameter model of the carbon capture system, receive the operating parameter data collected by the sensing layer (1), preprocess the operating parameter data, and output the data processing results in response to the call command of the controller module (23). The view module (22) is configured to provide a human-computer interaction interface, display running status data and receive operation instructions from maintenance personnel; The controller module (23) is configured to schedule the model module (21), the view module (22) and the execution layer (3), and generate control commands to send to the execution layer (3) and synchronously feed back data.

[0009] Furthermore, the process parameter sensor group (11) includes a flue gas flow sensor, a carbon dioxide concentration sensor, an absorber level sensor, a desorption tower temperature sensor, and an absorbent concentration sensor. The equipment status sensor group (12) includes a pump body vibration sensor, a fan speed sensor, and a valve opening sensor; The environmental parameter sensor group (13) includes an integrated temperature, humidity and pressure sensor.

[0010] Furthermore, the model module (21) includes a data preprocessing unit and an anomaly detection sub-model; The data preprocessing unit is configured to perform filtering, noise reduction, and normalization on the operating parameter data. The anomaly detection sub-model is configured to determine whether there are anomalies in the preprocessed operating parameter data based on thresholding and trend analysis.

[0011] Furthermore, the control algorithm model includes an absorption tower level control model based on PID algorithm, a desorption tower temperature regulation model based on fuzzy control, and a composite control model based on both.

[0012] Furthermore, the control system includes a communication module (4), which adopts dual-mode communication of industrial Ethernet and LoRa. The sensing layer (1) communicates with the model module (21) via LoRa, and the controller module (23) communicates with the execution layer (3) via industrial Ethernet.

[0013] A smart carbon capture control method based on the MVC framework using the above-described control system includes the following steps: Step 1: Data Acquisition and Preprocessing The perception layer (1) collects operating parameter data and transmits the operating parameter data to the model module (21) for preprocessing; Step 2: Data Modeling and Anomaly Detection The model module (21) calls the process data model to analyze the preprocessed operating parameter data, and uses the anomaly detection sub-model to monitor the abnormal data after analysis. Step 3: Control command generation and execution: The controller module (23) receives operation instructions or threshold trigger signals, calls the control algorithm to generate instructions and sends them to the execution layer (3). Step 4: Status Feedback and Dynamic Optimization The execution layer (3) provides feedback on the running status, the model module (21) updates the model, and the view module (22) displays the status; Step 5: Data Storage and Son-in-Law: The model module (21) stores data, and the view module (22) supports historical data query.

[0014] Furthermore, in step 2, the process data model is a carbon capture efficiency prediction model based on a BP neural network. The input parameters include flue gas flow rate, absorbent concentration, and desorption tower temperature, and the output parameter is carbon capture efficiency.

[0015] Furthermore, in step 3, the instructions include instructions for adjusting the frequency of the absorbent delivery pump, adjusting the heating power of the desorption tower, and adjusting the opening of the flue gas regulating valve.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) Decoupled architecture improves maintainability: This invention completely decouples the data flow (model module), control flow (controller module), and interface display (view module) of the carbon capture system through the MVC framework. When it is necessary to optimize the carbon capture process (such as changing the absorbent type), only the process data model of the model module needs to be modified, without adjusting the interface or control logic; when it is necessary to upgrade the monitoring interface, only the view module needs to be iterated, without affecting the core control function, thus reducing maintenance costs.

[0017] (2) Modular design enhances scalability: The sensing layer adopts a standardized sensor interface. When adding a new sensor (such as a carbon dioxide purity online monitoring sensor), it is only necessary to update the data parsing rules in the model module to adapt it. The execution layer supports hot-swapping of the actuator. The controller module is compatible with different models of pumps, valves and other equipment through a unified instruction protocol, and can be quickly adapted to carbon capture systems in different scenarios such as thermal power and chemical industry.

[0018] (3) Data-driven intelligent control: The model module integrates the process data model and the control algorithm model, and uses multi-dimensional data collected by the perception layer to achieve dynamic optimization. For example, when the ambient temperature drops and the activity of the absorbent decreases, the model module can predict the change in carbon capture efficiency through the correlation model, and the controller module can adjust the absorbent delivery in advance to avoid efficiency fluctuations. Compared with traditional fixed parameter control, this can improve carbon capture efficiency and reduce energy consumption.

[0019] (4) Full-process visualization improves operation and maintenance efficiency: The view module provides a multi-dimensional human-machine interaction interface. Operation and maintenance personnel can intuitively grasp the trend of carbon capture efficiency change through the process curve, quickly locate equipment abnormalities (such as excessive pump vibration) through the fault alarm panel, and trace back the cause of the fault by combining the data traceability panel, thus shortening the operation and maintenance response time. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the framework structure of the control system described in this invention; Figure 2 This is a schematic diagram of the data flow interaction in the MVC core layer of the control system described in this invention; Figure 3 This is a schematic diagram of the steps of the control method described in this invention; In the diagram: 1-Perception layer; 11-Process parameter sensor group; 12-Equipment status sensor group; 13-Environmental parameter sensor group; 2-MVC core layer; 21-Model module; 22-View module; 23-Controller module; 3-Execution layer; 31-Actuator group; 32-Data feedback unit; 4-Communication module. Detailed Implementation

[0021] The present invention will be further described below with reference to specific embodiments.

[0022] The intelligent carbon capture and control system based on the MVC framework described in this invention includes a sensing layer 1, an MVC core layer 2, an execution layer 3, and a communication module 4. The MVC core layer 2 is connected to the sensing layer 1 and the execution layer 3 through the communication module 4.

[0023] Sensing layer 1 is used to collect operating parameter data of the carbon capture system, and it includes process parameter sensor group 11, equipment status sensor group 12, and environmental parameter sensor group 13. Process parameter sensor group 11 includes a flue gas flow sensor, a carbon dioxide concentration sensor, an absorber level sensor, a desorption tower temperature sensor, and an absorbent concentration sensor. Specifically, the flue gas flow sensor is an MF5706 mass flow sensor with a measurement range of 0-5000 m³ / h. 3 / h, accuracy ±1%. The carbon dioxide concentration sensor uses an IR-200 infrared sensor, with a measurement range of 0-100%VOL and a response time ≤2s. The absorber level sensor uses an immersion-type hydrostatic level gauge, with a measurement range of 0-10m and an accuracy of ±0.5%.

[0024] The equipment status sensor group 12 includes a pump body vibration sensor, a fan speed sensor, and a valve opening sensor. The pump body vibration sensor uses a VB-Z9500 vibration transmitter with a measurement range of 0-20 mm / s, and the fan speed sensor uses a Hall effect speed sensor with a measurement range of 0-3000 r / min. The environmental parameter sensor group 13 includes an integrated temperature, humidity, and pressure sensor with a temperature measurement range of -40℃ to 85℃, humidity of 0-100%RH, and pressure of 80-120 kPa.

[0025] In the MVC core layer 2, the model module 21 uses an STM32H743 microcontroller as the core processing unit. It stores a carbon capture efficiency prediction model based on a BP neural network. The input parameters of the model include flue gas flow rate, absorbent concentration, and desorption tower temperature. The output parameter is the carbon capture efficiency. It also stores a PID + fuzzy composite control algorithm model.

[0026] The view module 22 uses a 10-inch industrial touchscreen and features a Qt-based human-machine interface, including a parameter monitoring panel, a control command input panel, a fault alarm panel, and a data traceability panel. The parameter monitoring panel displays real-time parameters such as flue gas flow rate and carbon capture efficiency; the control command input panel allows manual setting of parameters such as absorbent flow rate and desorption tower temperature; the fault alarm panel provides assurance through audible and visual alarms and pop-up prompts; and the data traceability panel provides access to historical operating data for the past three years.

[0027] The controller module 23 uses a PLC S7-1200 as the scheduling unit and supports Modbus-TCP and LoRa communication protocols.

[0028] In execution layer 3, the absorbent delivery pump adopts an ISG type vertical centrifugal pump, equipped with a frequency converter, and the flow rate adjustment range is 0-50m³ / h. 3 / h, the desorption tower heating device uses an electric heating tube with a power adjustment range of 0-100kW, the flue gas regulating valve uses an electric butterfly valve with an opening adjustment range of 0-100% and a response time of ≤5s, and the data feedback unit 32 uses an encoder and a flow sensor to collect valve opening and pump flow data in real time.

[0029] The communication module 4 adopts dual-mode communication of industrial Ethernet (between controller module 23 and execution layer 3) and LoRa (between perception layer 1 and model module 21). The LoRa communication distance is ≥1km and the industrial Ethernet transmission rate is 100Mbps.

[0030] The intelligent carbon capture control method based on the MVC framework described in this invention, based on the aforementioned control system, further includes the following steps: Step 1: Data Acquisition and Preprocessing Data is collected every 1 second by each sensor in sensing layer 1. The raw data collected by the flue gas flow sensor is 4200 m³ / s. 3 / h, after filtering and denoising by the preprocessing unit of model module 21, is normalized to 0.84 (corresponding to a measurement range of 0-5000m). 3 / h). The data collected by the carbon dioxide concentration sensor was 15% VOL, which was 0.15 after preprocessing. The data collected by the absorber level sensor was 6.2m, which was 0.62 after preprocessing.

[0031] Step 2: Data Modeling and Anomaly Detection Model module 21 calls the BP neural network prediction model, inputting the pre-processed flue gas flow rate (0.84), absorbent concentration (0.25, preset initial value), and desorption tower temperature (0.6, preset initial value), and outputs a predicted carbon capture efficiency of 88%. The anomaly detection sub-model detects that all data are within the preset threshold range and no abnormal signals are generated. The parameter monitoring panel of view module 22 displays the above data in real time.

[0032] Step 3: Control command generation and execution: Model module 21, based on the process data model, determines that the current carbon capture efficiency of 88% is lower than the target value of 90%, and generates a threshold trigger signal sent to controller module 23. Controller module 23 calls the PID+fuzzy composite control algorithm model to calculate that the absorbent concentration needs to be increased to 0.3 (corresponding to an actual concentration of 30%), and generates the control command "Increase absorbent delivery pump frequency to 50Hz", which is sent to execution layer 3 via industrial Ethernet. The absorbent delivery pump executes the command, adjusting the frequency from 40Hz to 50Hz and the absorbent flow rate from 30m³ / h. 3 / h increased to 45m 3 / h.

[0033] Step 4: Status Feedback and Dynamic Optimization The data feedback unit 32 of execution layer 3 collected data showing that the actual frequency of the absorbent delivery pump was 50Hz and the flow rate was 45m³ / h. 3 The data is fed back to controller module 23 at a rate of / h. Controller module 23 then synchronizes the feedback data to model module 21 and view module 22. Model module 21 updates the process data model, resetting the absorbent concentration parameter to 0.3 and re-predicting the carbon capture efficiency as 91%. The parameter monitoring panel of view module 22 updates the absorbent flow rate to 45m³ / h. 3 / h, the process curve shows that the carbon capture efficiency is on the rise.

[0034] Step 5: Data Storage and Traceability: Model module 21 stores the aforementioned raw data, control commands, and feedback data. Operation and maintenance personnel can query the carbon capture efficiency change curve for that period through the data traceability panel of view module 22 to confirm the adjustment effect.

[0035] When maintenance personnel input the operation command "Raise the desorption tower temperature to 120℃" through the view module 22, the controller module 23 directly calls the control algorithm model to generate the corresponding control command, driving the desorption tower heating device to execute, following the same process as above. When the data collected by the pump body vibration sensor is 18mm / s (0.9 after preprocessing), exceeding the preset threshold of 0.8, the abnormal detection sub-model generates an abnormal signal. The controller module 23 drives the view module 22 to issue an audible and visual alarm, and simultaneously sends a control command "Reduce the fan speed to 2000r / min" to prevent equipment damage.

Claims

1. A smart carbon capture and control system based on the MVC framework, characterized in that, The system includes a perception layer (1), an MVC core layer (2), and an execution layer (3), wherein the MVC core layer (2) is communicatively connected to the perception layer (1) and the execution layer (3), respectively. The sensing layer (1) is used to collect the operating parameter data of the carbon capture system. The sensing layer (1) includes a process parameter sensor group (11), an equipment status sensor group (12), and an environmental parameter sensor group (13). The MVC core layer (2) includes a model module (21), a view module (22), and a controller module (23). The execution layer (3) is used to execute the control instructions issued by the MVC core layer (2); The model module (21) is configured to store the process data model, control algorithm model and equipment parameter model of the carbon capture system, receive the operating parameter data collected by the sensing layer (1), preprocess the operating parameter data, and output the data processing results in response to the call command of the controller module (23). The view module (22) is configured to provide a human-computer interaction interface, display running status data and receive operation instructions from maintenance personnel; The controller module (23) is configured to schedule the model module (21), the view module (22) and the execution layer (3), and generate control commands to send to the execution layer (3) and synchronously feed back data.

2. The intelligent carbon capture and control system based on the MVC framework according to claim 1, characterized in that, The process parameter sensor group (11) includes a flue gas flow sensor, a carbon dioxide concentration sensor, an absorber level sensor, a desorption tower temperature sensor, and an absorbent concentration sensor. The equipment status sensor group (12) includes a pump body vibration sensor, a fan speed sensor, and a valve opening sensor; The environmental parameter sensor group (13) includes an integrated temperature, humidity and pressure sensor.

3. The intelligent carbon capture and control system based on the MVC framework according to claim 1, characterized in that, The model module (21) includes a data preprocessing unit and an anomaly detection sub-model; The data preprocessing unit is configured to perform filtering, noise reduction, and normalization on the operating parameter data. The anomaly detection sub-model is configured to determine whether there are anomalies in the preprocessed operating parameter data based on thresholding and trend analysis.

4. The intelligent carbon capture and control system based on the MVC framework according to claim 1, characterized in that, The control algorithm model includes an absorption tower level control model based on PID algorithm, a desorption tower temperature regulation model based on fuzzy control, and a composite control model based on both.

5. The intelligent carbon capture and control system based on the MVC framework according to claim 1, characterized in that, The control system includes a communication module (4), which uses industrial Ethernet and LoRa dual-mode communication. The sensing layer (1) communicates with the model module (21) via LoRa, and the controller module (23) communicates with the execution layer (3) via industrial Ethernet.

6. A smart carbon capture control method based on the MVC framework using the control system described in any one of claims 1-5, characterized in that, Includes the following steps: Step 1: Data Acquisition and Preprocessing The perception layer (1) collects operating parameter data and transmits the operating parameter data to the model module (21) for preprocessing; Step 2: Data Modeling and Anomaly Detection The model module (21) calls the process data model to analyze the preprocessed operating parameter data, and uses the anomaly detection sub-model to monitor the abnormal data after analysis. Step 3: Control command generation and execution: The controller module (23) receives operation instructions or threshold trigger signals, calls the control algorithm to generate instructions and sends them to the execution layer (3). Step 4: Status Feedback and Dynamic Optimization The execution layer (3) provides feedback on the running status, the model module (21) updates the model, and the view module (22) displays the status; Step 5: Data Storage and Son-in-Law: The model module (21) stores data, and the view module (22) supports historical data query.

7. The intelligent carbon capture and control method based on the MVC framework according to claim 6, characterized in that, In step 2, the process data model is a carbon capture efficiency prediction model based on a BP neural network. The input parameters include flue gas flow rate, absorbent concentration, and desorption tower temperature, and the output parameter is the carbon capture efficiency.

8. The intelligent carbon capture and control method based on the MVC framework according to claim 6, characterized in that, In step 3, the instructions include instructions for adjusting the frequency of the absorbent delivery pump, adjusting the heating power of the desorption tower, and adjusting the opening of the flue gas regulating valve.

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