Control system for oil-free refining device

Abstract

The invention relates to the technical field of intelligent control, and discloses a control system for an oil-free refining device, and the system comprises a compressor control module which is used for adjusting the rotating speed of a compressor according to an optimization control algorithm so as to respond to the load change and optimize the energy efficiency of the compressor; the data acquisition and monitoring module is connected with the compressor control module and is used for acquiring operation data of the control system in real time, the operation data comprises temperature, pressure and flow parameters, and the operation data is transmitted to the optimal control module; and the optimal control module is used for calculating the optimal control input of the compressor and the filter through an optimization algorithm based on the operation data so as to minimize the total energy consumption of the control system. Through an optimal control algorithm and a real-time feedback adjustment mechanism, the energy efficiency and stability of the system are remarkably improved, load changes are dynamically adapted, energy waste and equipment abrasion are reduced, and the overall operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a control system for an oil-free refining unit. Background Technology

[0002] In modern industrial production, especially in the application of oil-free refining units, high efficiency, energy saving, and stable operation have become key requirements. With rising energy costs and increasingly stringent environmental protection standards, enterprises are paying more attention to energy efficiency and the rational use of resources while pursuing production efficiency. Traditional oil-free refining units often face problems such as low operating efficiency and high energy consumption. To improve the overall performance of the system and save energy and costs, developing an intelligent control system capable of dynamically adjusting system operation according to load changes is particularly important.

[0003] Most current control technologies employ fixed-parameter control strategies, typically maintaining system stability by setting operating parameters for the compressor and filter. This type of control system can ensure normal operation of the equipment to a certain extent, especially under relatively stable loads or with minimal environmental changes, maintaining relatively good performance. Furthermore, traditional control schemes are relatively simple in design and easy to operate, offering advantages for initial equipment installation and operation management.

[0004] However, existing technologies still have some shortcomings. First, traditional control systems fail to dynamically adjust equipment operating parameters according to actual load changes. When load fluctuations are large, the system still operates according to preset parameters, causing equipment such as compressors and filters to be in an unsuitable state for extended periods, resulting in energy waste and mechanical wear. Second, these systems generally lack real-time feedback adjustment mechanisms, making it impossible to quickly correct for changes in equipment status. For example, when filters are clogged or pressure differentials are too large, the system cannot adjust airflow or filter operating status in a timely manner, leading to increased energy consumption and a higher risk of equipment failure. Furthermore, existing technologies have poor adaptive capabilities and cannot optimize adjustments based on real-time data, making it impossible to fully utilize system performance under various operating conditions, and efficiency often falls short of optimal levels. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a control system for oil-free refining equipment, which solves the problems of existing systems being unable to dynamically adjust operating parameters according to load changes, lacking a real-time feedback adjustment mechanism, and having insufficient adaptive capabilities.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a control system for an oil-free refining unit, comprising: Compressor control module: Used to adjust the compressor speed according to the optimized control algorithm to respond to load changes and optimize compressor energy efficiency; Data acquisition and monitoring module: connected to the compressor control module, it collects real-time operating data of the control system, including temperature, pressure and flow parameters, and transmits the operating data to the optimization control module; Optimization control module: Based on the operating data, it calculates the optimal control inputs for the compressor and filter using an optimization algorithm to minimize the total energy consumption of the control system; Filter control module: Based on the optimal control input, adjusts the filter's operating state, including adjusting the airflow and filter pressure difference; Feedback adjustment module: Connected to the optimization control module and the filter control module respectively, it is used to dynamically adjust the system operating parameters based on the output of the optimization control module.

[0007] Preferably, the compressor control module includes: Compressor drive unit: used to receive speed commands from the optimization control module and control the actual speed of the compressor; Speed ​​calculation unit: used to calculate the target speed based on load changes, the target speed satisfying the following relationship: In the formula, N is the target speed; N0 is the reference speed; Q is the current load flow rate; and Q0 is the reference flow rate.

[0008] Preferably, the data acquisition and monitoring module includes: Sensor acquisition unit: used to measure compressor outlet temperature, pressure and flow parameters; Data processing unit: Used to convert the acquired analog signals into digital signals and transmit them to the optimization control module via bus.

[0009] Preferably, the data processing unit includes: Signal conversion unit: connected to the sensing and acquisition unit, used to convert analog signals into digital signals; Signal transmission unit: Connected to the signal conversion unit, it is used to send digital signals to the optimization control module via the communication bus.

[0010] Preferably, the optimization control module includes: Optimization calculation unit: used to solve the optimization objective function based on the operating data of the data acquisition and monitoring module, and to calculate the optimal control input for the compressor and filter. The optimization objective function is: In the formula, J represents the total energy consumption; P(t) represents the power consumption at time t; and T is the optimization time period. Instruction generation unit: Used to generate control instructions based on the results output by the optimization calculation unit.

[0011] Preferably, the instruction generation unit includes: Instruction Calculation Unit: Used to perform instruction formatting processing on the data output by the optimization calculation unit; Command sending unit: used to send formatted commands to the compressor control module and the filter control module respectively via the control bus.

[0012] Preferably, the filter control module includes: Flow regulation unit: used to regulate airflow according to the control input of the optimization control module; Differential pressure regulating unit: used to control the differential pressure across the filter and maintain it within a preset range.

[0013] Preferably, the differential pressure regulating unit includes: Differential pressure detection unit: used to acquire the differential pressure parameters across the filter; Differential pressure control unit: used to adjust the parameters fed back by the differential pressure detection unit according to the instructions output by the optimization control module.

[0014] Preferably, the feedback adjustment module includes: Status detection unit: used to detect the deviation between the actual operating status of the system and the output of the optimization control module; Parameter adjustment unit: used to correct system operating parameters and update control inputs based on the deviation.

[0015] Preferably, the parameter adjustment unit includes: Deviation calculation unit: used to calculate the deviation between the actual operating state of the system and the target operating state; Parameter correction unit: used to generate corrected operating parameters and update them to the optimization control module.

[0016] This invention provides a control system for an oil-free refining unit. It has the following advantages: 1. This invention employs a technical solution that combines an optimization control module and a feedback adjustment module, achieving the effect of optimizing the operating status of the compressor and filter and minimizing total energy consumption. Compared to existing technologies that rely on a single control strategy, this invention can dynamically adjust the parameters of each component of the system based on real-time operating data, avoiding energy waste and improving the overall system energy efficiency.

[0017] 2. This invention, through a real-time feedback mechanism combined with an optimal control algorithm and filter adjustment strategy, can quickly respond to load fluctuations and adjust the system's operating state. Unlike traditional technologies that use fixed control parameters, it achieves the ability to automatically adapt to different operating conditions, solving the problem of the system's inefficient adjustment under large load fluctuations.

[0018] 3. In terms of compressor control and filter operation, this invention avoids prolonged high-load operation of the equipment by adjusting the speed and flow rate according to load changes. Compared with existing technologies that lack dynamic adjustment, this invention effectively reduces equipment wear, extends the service life of the compressor and filter, and reduces maintenance and replacement costs.

[0019] 4. This invention, through real-time monitoring and parameter adjustment of the feedback adjustment module, enables the system to quickly self-adjust and maintain stable operation in the face of environmental changes and equipment failures. Compared with existing technologies that rely on manual intervention and preset control parameters, this invention provides stronger adaptive capabilities, ensuring stable and efficient operation of the system under various working conditions and reducing the need for human intervention. Attached Figure Description

[0020] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a framework diagram of the compressor control module of the present invention; Figure 3 This is a framework diagram of the data acquisition and monitoring module of the present invention; Figure 4 This is a framework diagram of the optimal control module of the present invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see the appendix Figure 1 - Appendix Figure 4 This invention provides a control system for an oil-free refining unit, comprising: a compressor control module for adjusting the compressor speed according to an optimized control algorithm to respond to load changes and optimize compressor energy efficiency; The compressor control module establishes signal connections with the data acquisition and monitoring module and the optimization control module. Generally, the compressor control module receives control input signals from the optimization control module and adjusts the compressor speed based on the real-time load status. The optimization goal is to make the compressor's operating state closely match the load demand, avoiding energy efficiency losses and mechanical wear caused by long-term high-load operation. Alternatively, the compressor control module integrates drive control and speed calculation functions to ensure that the module can independently complete the compressor speed adjustment task and respond to load changes. Specifically, during operation, the compressor control module actively listens for instructions from the optimization control module and simultaneously uses pressure, flow, and temperature data from the data acquisition and monitoring module to calculate the target speed in real time.

[0023] In this embodiment, the compressor control module includes a compressor drive unit and a speed calculation unit. The compressor drive unit is connected to a variable frequency drive (VFD) device and is used to receive speed command signals from the optimization control module and drive the compressor to run. The VFD device uses a vector control algorithm to adjust the motor speed, achieving stepless speed regulation of the compressor rotor. In some embodiments, the compressor drive unit is further integrated with overcurrent and overvoltage protection circuits to prevent the compressor from continuing to operate under abnormal conditions.

[0024] In this embodiment, the speed calculation unit calculates the target speed in real time based on load changes. Generally, the speed calculation unit determines the target speed based on the ratio between the current load flow and the reference flow. As one possible implementation, the target speed N is calculated using the following formula: In the formula, N is the target speed; N0 is the reference speed; Q is the current load flow rate; and Q0 is the reference flow rate. In one possible implementation, the speed calculation unit also includes a cache module for storing flow rate data over a recent period and performing a moving average. By filtering out abnormal measurement point data, the interference of instantaneous fluctuations on the target speed is reduced. In other embodiments, the speed calculation unit also has redundant logic. When the current load flow sensor malfunctions or data is abnormal, it automatically reverts to the reference speed N0 to ensure continuous system operation.

[0025] Typically, the compressor drive unit and the speed calculation unit are interconnected via an internal high-speed bus. The speed calculation result is converted into a standardized drive signal, which is then directly executed by the drive unit. In some embodiments, the drive unit also feeds back the actual speed data after execution to the data acquisition and monitoring module, enabling monitoring of the speed control closed loop.

[0026] Alternatively, the control precision of the compressor control module can be set to within 1 rpm, which can be achieved by embedding a high-precision digital-to-analog converter module within the speed calculation unit. Furthermore, the built-in fault diagnosis logic of the compressor control module can detect abnormal states such as motor stall and overheating and generate alarm signals.

[0027] Specifically, the compressor control module disclosed in this embodiment not only enables precise speed adjustment of the compressor under different loads, but also achieves real-time response and stable output through the collaborative work of the computing unit and the drive unit. Its close cooperation with the data acquisition and monitoring module and the optimization control module ensures optimal overall system energy efficiency.

[0028] As an extended implementation, the rotational speed calculation unit can be parameter-tuned according to on-site process requirements during actual deployment. For example, the proportional coefficient, flow rate reference value Q0, and rotational speed reference value N0 can be adjusted to match different specifications of oil-free refining units. This parameter flexibility ensures the applicability of the present invention in different operating conditions and different specifications of units.

[0029] In some embodiments, an isolation circuit can be added to the output of the compressor drive unit to shield against high-frequency noise interference and improve signal transmission stability. Additionally, a status indicator unit can be configured to display the current operating status of the compressor, the target speed, and the current speed, allowing operators to intuitively understand the system's operating status.

[0030] Data acquisition and monitoring module: Connected to the compressor control module, this module collects real-time operating data from the control system, including temperature, pressure, and flow parameters. The data is then transmitted to the optimization control module. The data acquisition and monitoring module is connected to both the compressor control module and the optimization control module via an industrial bus to ensure the real-time performance and accuracy of the data stream. Typically, the data acquisition and monitoring module collects real-time operating data from the control system, including but not limited to key parameters such as temperature, pressure, and flow. The collected real-time data is transmitted to the optimization control module for further optimization calculations to ensure optimal energy efficiency and timely response to load changes in the control system.

[0031] In this embodiment, the data acquisition and monitoring module includes a sensor acquisition unit, a data processing unit, a signal conversion unit, and a signal transmission unit. The sensor acquisition unit is responsible for real-time monitoring and acquiring the physical parameters of the equipment operation. Common sensors include resistance temperature detectors (RTDs), pressure sensors, and vortex flow meters. Specifically, the sensor acquisition unit is directly connected to key components of devices such as compressors and filters, and acquires temperature, pressure, and flow data at the compressor inlet and outlet through sensors.

[0032] For example, temperature sensors are installed at the compressor's inlet and outlet to monitor the compressor's temperature in real time; pressure sensors are placed at the compressor's inlet and outlet to collect pressure data in real time; and flow sensors are installed in the pipeline to monitor changes in the flow rate of the entire system. The analog signals collected by these sensors are digitized by a signal conversion unit. Specifically, the analog signals are amplified and filtered by the signal processing circuit before being converted into digital signals and transmitted to the data processing unit.

[0033] The data processing unit performs basic numerical calculations and processing on the received sensor data to remove noise and interference, ensuring the accuracy and reliability of the data. In one possible implementation, the data processing unit also includes a filtering module to remove error data caused by short-term fluctuations or external interference. This module smooths the data, thereby reducing the impact of transient outliers on system decisions.

[0034] In some embodiments, the data processing unit may also have an alarm function. For example, when a parameter exceeds a preset safety range, the data processing unit generates an alarm signal and transmits it to the control system. Generally, the data processing unit performs real-time analysis on each collected parameter and generates a data report. The processed data is then sent to the optimization control module via a signal transmission unit for further optimization decisions.

[0035] Specifically, the data transmission process of the data acquisition and monitoring module follows the following mathematical relationship: Temperature data acquisition: T = T measured +ΔT; In the formula, T is the current system temperature; T measured ΔT represents the original temperature value measured by the sensor; ΔT is a correction term caused by environmental or sensor errors.

[0036] Pressure data acquisition: P = P measured ·(1+∈); In the formula, P is the actual pressure in the system; measured ∈ represents the pressure value measured by the sensor; ∈ is the correction coefficient of the pressure sensor, used to compensate for sensor errors.

[0037] Traffic data collection: Q = k·V; In the formula, Q is the flow rate; V is the flow velocity; and k is the calibration coefficient of the flow sensor.

[0038] In some embodiments, the data acquisition and monitoring module requires a high transmission rate, therefore a high-speed industrial bus communication protocol is used to transmit data. This data is sent to the optimization control module in real time. Based on this data and in conjunction with optimization algorithms, the optimization control module adjusts the compressor speed and filter status to maintain the entire system in optimal operating condition.

[0039] Typically, the transmitted data includes multi-dimensional data such as temperature, pressure, and flow rate. This data is crucial for system status assessment, operational optimization, and load response.

[0040] In some embodiments, the data acquisition and monitoring module may also include a data storage unit for temporarily storing the acquired data. This data can be retrieved by the control system when historical data analysis is needed, for long-term trend research, or for subsequent fault diagnosis. Alternatively, the data storage unit may use non-volatile memory (such as Flash memory) to store the data to avoid data loss due to power outages or other unforeseen circumstances.

[0041] Optimization control module: Based on the operating data, it calculates the optimal control inputs for the compressor and filter using an optimization algorithm to minimize the total energy consumption of the control system; The optimization control module, based on real-time operating data acquired from the data acquisition and monitoring module, calculates the optimal control inputs for the compressor and filter using optimization algorithms to minimize the total energy consumption of the control system. The optimization control module plays a crucial role in the control system, working closely with the data acquisition and monitoring module, compressor control module, and filter control module to coordinate the operation of each component, ensuring the entire system maintains high efficiency and stability under varying load conditions.

[0042] In this embodiment, the optimization control module performs calculations based on real-time acquired data such as temperature, pressure, and flow rate, and optimizes the operating parameters of the compressor and filter accordingly. Typically, the optimization control module uses a dynamic optimization algorithm to determine the optimal control inputs, which include the compressor speed control signal and the filter's operating status adjustment. Through optimization control, the system can effectively reduce energy consumption and improve overall operating efficiency.

[0043] Specifically, the optimization control module includes an optimization calculation unit and a control command generation unit. The optimization calculation unit performs optimization calculations based on real-time data received from the data acquisition and monitoring module, combined with a preset optimization objective function. The objective function is defined as minimizing the energy consumption of the entire control system. The optimization objective function is: In the formula, J represents the total energy consumption; P(t) represents the power consumption at time t; and T is the optimization time period.

[0044] In optimization calculations, the optimization control module uses optimization algorithms (such as linear programming, dynamic programming, and genetic algorithms) to find the control inputs that minimize the objective function. These control inputs include: Compressor speed: Based on the current load flow, temperature and pressure data, the optimization control module calculates the required target speed.

[0045] Filter status: Based on parameters such as flow rate and differential pressure, the optimization control module adjusts the working status of the filter to minimize the filter's energy consumption.

[0046] For example, suppose the energy consumption of a compressor can be expressed by the following relationship: E compressor =k1·N 3 ·Q; In the formula, E compressor Let represent the compressor's energy consumption; k1 be a constant; N be the compressor's rotational speed; and Q be the flow rate. This formula shows that the compressor's energy consumption is directly proportional to the cube of its rotational speed and also directly proportional to the load flow rate.

[0047] For filters, energy consumption is proportional to the pressure difference ΔP and the flow rate Q: E filter = k2·ΔP·Q; In the formula, E filter The filter's energy consumption is represented by k2, which is a constant; ΔP is the pressure difference across the filter; and Q is the flow rate.

[0048] The optimization calculation unit and the optimization control module derive the compressor speed N and filter control parameters to minimize total energy consumption. After solving all objective functions in the system, the control command generation unit generates control signals from the optimal control input and transmits them to the compressor control module and the filter control module.

[0049] As one possible implementation, the optimization control module also has the function of real-time feedback adjustment. In some cases, real-time operating data may experience sudden changes or sensor measurement errors, leading to a deviation between the calculated optimal control input and the actual requirements. In this case, the optimization control module can adaptively adjust based on the real-time feedback data, correcting the original control input and ensuring that the system always remains in an optimal state.

[0050] In some embodiments, the optimization control module may also be equipped with a self-learning mechanism. When the system accumulates sufficient historical data during long-term operation, the optimization control module can analyze the historical data through machine learning algorithms, optimize the control model, and further improve energy efficiency.

[0051] Specifically, the optimization control module can also handle complex operating condition changes, such as drastic load fluctuations or fault recovery. Through dynamic adjustment of the optimization algorithm, the optimization control module ensures efficient system operation under various conditions and minimizes energy waste.

[0052] As an extended implementation method, the optimization control module can also incorporate external environmental data (such as outdoor temperature and humidity) for comprehensive optimization. By considering changes in environmental factors, the system's adaptability and energy-saving performance can be further improved.

[0053] Filter control module: Based on the optimal control input, adjusts the filter's operating state, including adjusting the airflow and filter pressure difference; The primary task of the filter control module is to adjust the filter's operating state based on the optimal control input calculated by the optimization control module. The filter's operating state mainly involves two aspects: adjusting the airflow rate and regulating the pressure difference across the filter. By adjusting these two key parameters, the filter control module ensures the filter operates under optimal conditions, achieving minimal energy consumption and maintaining stable air quality and flow rate in the system.

[0054] In this embodiment, the filter control module includes a flow rate regulation unit and a differential pressure regulation unit. The flow rate regulation unit adjusts the air flow rate according to the optimal control input to ensure that the flow rate is within the optimal range. Specifically, the flow rate regulation unit changes the air flow rate by adjusting the valve opening, thereby adjusting the filter load. The differential pressure regulation unit adjusts the filter state according to the differential pressure signal across the filter to maintain the system operating in a stable state.

[0055] Generally, a filter's energy consumption is closely related to its operating conditions, particularly airflow and pressure differential. Specifically, there is a direct linear relationship between filter energy consumption and flow rate and pressure differential. In this implementation, the flow control unit optimizes the filter's energy efficiency by adjusting the airflow. For example, when the filter is under heavy load, the system automatically increases the flow rate to improve filtration; conversely, when the filter is under light load, the system reduces the flow rate to avoid energy waste.

[0056] Alternatively, the differential pressure regulating unit adjusts the filter's operating status in real time by monitoring the pressure difference across the filter (i.e., the pressure difference between the inlet and outlet). The filter's differential pressure variation is closely related to the degree of filter contamination and airflow. When significant contaminant buildup occurs, the differential pressure increases, requiring filter adjustments to restore optimal filtration. In this situation, the differential pressure regulating unit adjusts the bypass valve or controls the differential pressure balancing system to maintain the differential pressure within a preset, reasonable range.

[0057] Specifically, in this embodiment, the differential pressure regulating unit adjusts the operating state of the filter in the following manner: Flow rate adjustment: Based on the optimal control input, the flow rate regulation unit precisely controls the airflow by adjusting the fan speed, valve opening, and other methods. Flow rate regulation is achieved through the following methods: Q new =Q original +ΔQ; In the formula, Q new Q represents the adjusted airflow rate. original ΔQ represents the initial airflow rate; ΔQ represents the flow rate adjustment based on the optimized input.

[0058] Differential Pressure Regulation: The differential pressure regulation unit adjusts the differential pressure across the filter to prevent it from operating under excessively high differential pressure, which would lead to excessive energy consumption. When the differential pressure changes, the control system adjusts the valve opening or controls the bypass channel to achieve the target differential pressure. The differential pressure regulation process can be represented as follows: ΔP adjust =ΔP measured -ΔP target ; In the formula, ΔP adjust The adjusted pressure difference; ΔP measured The actual measured pressure difference; ΔP target The target pressure difference for the system.

[0059] In practical applications, differential pressure regulating units and flow regulating units usually need to work simultaneously to ensure the stability and efficiency of the system under different loads and operating conditions. Specifically, when the differential pressure increases, the flow regulating unit will appropriately reduce the airflow to reduce the burden on the filter and thus reduce energy consumption; while when the filter is clean and the differential pressure is low, the flow regulating unit will increase the flow to improve the ventilation efficiency of the system.

[0060] In some embodiments, the filter control module is also equipped with a real-time feedback mechanism. When the filter's state changes (such as increased contamination or decreased filtration efficiency), the feedback signal is immediately transmitted to the optimization control module. The optimization control module recalculates the optimized control input based on the new state data and adjusts the filter's operating state in a timely manner. In this way, the system can achieve real-time optimization and adjustment, ensuring that the filter achieves optimal energy efficiency in all operating states.

[0061] Furthermore, the filter control module is designed with system adaptability in mind. In actual operation, changes in airflow and filter differential pressure are affected by many factors, such as ambient air quality, equipment uptime, and maintenance frequency. Therefore, the filter control module not only adjusts the filter's operating status based on real-time data but also performs self-learning optimization based on historical operating data, further improving the system's energy efficiency.

[0062] Feedback adjustment module: connected to the optimization control module and the filter control module respectively, used to dynamically adjust the system operating parameters according to the output of the optimization control module; The feedback adjustment module, as the core component of the control system, is primarily used to dynamically adjust the system's operating parameters. Specifically, the feedback adjustment module is connected to the optimization control module and the filter control module via signals, ensuring that the system can dynamically adjust the operating status of equipment such as the compressor and filter based on the output of the optimization control module and real-time operating data. This adjustment mechanism effectively prevents system instability during fluctuations in operating conditions or equipment failures, thereby further improving energy efficiency and stability.

[0063] In this embodiment, the feedback adjustment module receives control commands from the optimization control module and feedback signals from the filter control module, and compares this information with real-time acquired data. Based on the comparison results, the feedback adjustment module generates correction commands to appropriately adjust the system operating parameters. This dynamic adjustment mechanism ensures that the system can respond quickly to current actual operating conditions, load changes, and equipment status, thereby always maintaining optimal operating conditions.

[0064] Specifically, the feedback adjustment module includes a status detection unit and a parameter adjustment unit. The status detection unit is responsible for real-time monitoring of various operating data of the system, including key parameters such as compressor speed, filter differential pressure, and flow rate. It monitors the current state of the system by collecting signals from the data acquisition and monitoring module and compares them with the calculation results of the optimization control module. If the system state deviates from the optimization target, the status detection unit will identify the deviation and transmit it to the parameter adjustment unit.

[0065] In one possible implementation, the state detection unit determines the deviation of the current system state using the following formula: ΔX = X measured -X optimal ; In the formula, ΔX represents the system deviation; X measured X represents the measured current system parameters (such as speed, flow rate, differential pressure, etc.); optimal These are the ideal parameter values ​​output by the optimization control module.

[0066] Based on feedback information from the status detection unit, the parameter adjustment unit calculates the system parameters that need to be adjusted. The adjusted parameters are then sent to the optimization control module and the filter control module to achieve dynamic system optimization. For example, if the system detects that the compressor speed is higher than the optimal value, the parameter adjustment unit calculates a command to reduce the speed and feeds it back to the compressor control module; if the filter differential pressure exceeds a preset range, the parameter adjustment unit calculates a command to reduce the differential pressure or adjust the flow rate and feeds it back to the filter control module.

[0067] As an alternative, the feedback adjustment module incorporates an adaptive control strategy. By analyzing historical system data, the module can predict potential system deviations and make pre-adjustments based on load variation trends and system state changes. This adaptive adjustment mechanism is optimized using the following formula: in, The optimal parameters for prediction; ΔX history Here, α represents the historical deviation value; α is the adjustment coefficient. By learning from historical data, the feedback adjustment module can respond in advance when changes in the system are anticipated, reducing the delay in real-time adjustments.

[0068] In some embodiments, the feedback adjustment module also has fault diagnosis and alarm functions. When an abnormal situation occurs in the system (such as equipment failure or sensor malfunction), the status detection unit will automatically detect and generate an alarm signal, and the parameter adjustment unit will generate an adjustment strategy based on the fault situation to ensure that the system continues to operate stably.

[0069] For example, when the feedback adjustment module detects an abnormally large increase in the pressure difference of the filter, it may indicate that the filter is clogged or improperly maintained. In this case, the system will automatically reduce the flow rate or trigger a bypass to reduce the load on the filter; at the same time, the feedback adjustment module will activate the fault diagnosis mode to alert the operator to perform maintenance or inspection.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control system for an oil-free refining plant, characterized in that include: Compressor control module: Used to adjust the compressor speed according to the optimized control algorithm to respond to load changes and optimize compressor energy efficiency; Data acquisition and monitoring module: connected to the compressor control module, it collects real-time operating data of the control system, including temperature, pressure and flow parameters, and transmits the operating data to the optimization control module; Optimization control module: Based on the operating data, it calculates the optimal control inputs for the compressor and filter using an optimization algorithm to minimize the total energy consumption of the control system; Filter control module: Based on the optimal control input, adjusts the filter's operating state, including adjusting the airflow and filter pressure difference; Feedback adjustment module: Connected to the optimization control module and the filter control module respectively, it is used to dynamically adjust the system operating parameters based on the output of the optimization control module.

2. The control system for an oil-free refining unit according to claim 1, characterized in that, The compressor control module includes: Compressor drive unit: used to receive speed commands from the optimization control module and control the actual speed of the compressor; Speed ​​calculation unit: used to calculate the target speed based on load changes, the target speed satisfying the following relationship: In the formula, N is the target speed; N0 is the reference speed; Q is the current load flow rate; and Q0 is the reference flow rate.

3. The control system for an oil-free refining unit according to claim 1, characterized in that, The data acquisition and monitoring module includes: Sensor acquisition unit: used to measure compressor outlet temperature, pressure and flow parameters; Data processing unit: Used to convert the acquired analog signals into digital signals and transmit them to the optimization control module via bus.

4. The control system for an oil-free refining unit according to claim 3, characterized in that, The data processing unit includes: Signal conversion unit: connected to the sensing and acquisition unit, used to convert analog signals into digital signals; Signal transmission unit: Connected to the signal conversion unit, it is used to send digital signals to the optimization control module via the communication bus.

5. The control system for an oil-free refining unit according to claim 1, characterized in that, The optimization control module includes: Optimization calculation unit: used to solve the optimization objective function based on the operating data of the data acquisition and monitoring module, and to calculate the optimal control input for the compressor and filter. The optimization objective function is: In the formula, J represents the total energy consumption; P(t) represents the power consumption at time t; and T is the optimization time period. Instruction generation unit: Used to generate control instructions based on the results output by the optimization calculation unit.

6. The control system for an oil-free refining unit according to claim 5, characterized in that, The instruction generation unit includes: Instruction Calculation Unit: Used to perform instruction formatting processing on the data output by the optimization calculation unit; Command sending unit: used to send formatted commands to the compressor control module and the filter control module respectively via the control bus.

7. The control system for an oil-free refining unit according to claim 1, characterized in that, The filter control module includes: Flow regulation unit: used to regulate airflow according to the control input of the optimization control module; Differential pressure regulating unit: used to control the differential pressure across the filter and maintain it within a preset range.

8. The control system for an oil-free refining unit according to claim 7, characterized in that, The differential pressure regulating unit includes: Differential pressure detection unit: used to acquire the differential pressure parameters across the filter; Differential pressure control unit: used to adjust the parameters fed back by the differential pressure detection unit according to the instructions output by the optimization control module.

9. The control system for an oil-free refining unit according to claim 1, characterized in that, The feedback adjustment module includes: Status detection unit: used to detect the deviation between the actual operating status of the system and the output of the optimization control module; Parameter adjustment unit: used to correct system operating parameters and update control inputs based on the deviation.

10. The control system for an oil-free refining unit according to claim 1, characterized in that, The parameter adjustment unit includes: Deviation calculation unit: used to calculate the deviation between the actual operating state of the system and the target operating state; Parameter correction unit: used to generate corrected operating parameters and update them to the optimization control module.

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