Intelligent control method and system for multiple reciprocating compressor booster stations

Abstract

The invention relates to the technical field of booster station control, and discloses an intelligent control method for a booster station with multiple reciprocating compressors, which comprises the following steps of: a, adopting an intelligent start-stop control method, combining internal units of the compressors to perform intelligent start-stop control by utilizing a combination principle in a TRIZ theory, and automatically stepping through parameter standard reaching and signal feedback, fault detection is carried out in the whole process, working conditions are automatically adapted after starting, and loading and unloading are carried out; and b, establishing a multi-unit load distribution model by adopting an intelligent load distribution method and combining the performance of the reciprocating compressor, automatically executing a supercharging scheme with optimal energy consumption, maximum efficiency or continuous control according to different working conditions, and formulating a proportional distribution scheme according to the performance of the unit and the fatigue degree of equipment. Through intelligent start-stop control, intelligent load distribution and intelligent matching of multiple working conditions, intelligent and automatic control over the compressor booster station is achieved, the reliability, safety and efficiency of the system are improved, energy consumption is reduced, and the overall performance is optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supercharging station control, in particular to an intelligent control method and system for a plurality of reciprocating compressor supercharging stations. BACKGROUND

[0002] With the continuous development of industrial production, compressors, as important equipment for gas compression and transportation, are widely used in petroleum, chemical, power and other industries. In order to improve production efficiency and reduce operating costs, the automation and intelligent control of compressor supercharging stations is particularly important. By introducing advanced control technology and automation systems, efficient and reliable operation of the compressor supercharging station can be achieved to meet the needs of modern industrial production.

[0003] The existing compressor supercharging station control system mainly relies on manual operation and traditional PLC control methods. Usually, the operator needs to manually monitor and adjust the running state of the compressor, and make adjustments according to experience and pre-set control strategies. This method not only has a large workload, but also is prone to operational errors and low efficiency due to human factors. In order to improve control accuracy and reduce manual intervention, some systems have introduced sensor-based data acquisition and monitoring functions, but overall there are still problems of low automation level and slow response speed.

[0004] The existing compressor supercharging station control system has a major problem: low automation level, which cannot achieve comprehensive intelligent and automated control. This low level of automation results in a large amount of manual intervention during system operation, not only increasing the workload of the operator, but also due to the uncontrollability of human operation, prone to operational errors and safety hazards. In addition, the traditional control method lacks flexibility and accuracy in load distribution and working condition matching, making it difficult to achieve energy optimization and maximum efficiency, limiting the overall performance and economic benefits of the system. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides an intelligent control method and system for a plurality of reciprocating compressor supercharging stations, which solves the problems of low automation level, excessive manual intervention and inaccurate control in the prior art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: an intelligent control method for a plurality of reciprocating compressor supercharging stations, comprising the following steps: a. An intelligent start-stop control method is adopted, which uses the combination principle in TRIZ theory to jointly control the internal units of the compressor for intelligent start-stop control, automatically steps through parameter compliance and signal feedback, detects faults throughout the process, automatically adapts to working conditions after starting the machine and performs load addition and subtraction; b. Adopting the load intelligent distribution method, combining with the performance of reciprocating compressors, a multi-unit load distribution model is established, and the optimal energy consumption and maximum efficiency or continuous control of the supercharging scheme are automatically executed according to different working conditions, and a proportional distribution scheme is formulated according to the unit performance and equipment fatigue; c. Adopting the intelligent matching method of multiple working conditions, in order to avoid control coupling phenomenon, a load distribution control model is established, and the control point is approached segment by segment through dynamic control mode, realizing precise adjustment of the control point.

[0007] Preferably, the intelligent start-stop control method comprises the following steps: Check the valve position state before starting the compressor; start the main oil pump, glycol pump and air cooler; purge the gas system; Open the compressor inlet valve, outlet valve and return valve, and close the vent valve; Check the running state and parameter feedback of each device during the start-up process, and complete the loading of the compressor.

[0008] Preferably, the load intelligent distribution method comprises the following steps: Select the optimal energy consumption or maximum efficiency supercharging scheme according to different working conditions; Formulate a load distribution scheme according to the unit performance and equipment fatigue; Real-time adjustment of the load distribution of each compressor ensures the stable operation of the overall system.

[0009] Preferably, the method is integrated based on the early warning system, realizing the joint intelligent start-stop control, load intelligent distribution and intelligent matching of multiple working conditions of multiple compressor units.

[0010] Preferably, the intelligent matching method of multiple working conditions comprises the following steps: Identify the current working condition and select the corresponding control strategy; Establish a load distribution control model, use dynamic control mode to segment and approach the control point segment by segment, avoid control coupling phenomenon, and realize precise adjustment.

[0011] Preferably, the parameter correlation network is established by the compressor operating condition table, and a mathematical model is established combined with historical data, the unit operating state is analyzed in real time, and performance deviation early warning is carried out.

[0012] Preferably, the method realizes trend prediction and equipment health state evaluation by real-time monitoring of the running state of each device and combining historical data analysis.

[0013] Preferably, the method includes automatic fault detection and early warning function, which can issue an alarm in time when the device operating parameters are abnormal, and provide the corresponding solution.

[0014] Preferably, the method comprises recording and analyzing compressor operation data, and optimizing control strategies and parameter settings through data mining and machine learning algorithms.

[0015] An intelligent control system for a multi-unit reciprocating compressor booster station, comprising: An intelligent start-stop control unit for jointing the internal units of the compressor for intelligent start-stop control; A load intelligent distribution unit for establishing a multi-unit load distribution model in combination with the performance of the reciprocating compressor, and automatically executing a load distribution scheme according to different operating conditions; A multi-condition intelligent matching unit for establishing a load distribution control model, and segmentally acting and approaching the control point by piece through a dynamic control method; An early warning system for establishing a parameter correlation network based on the compressor operating condition table, and establishing a mathematical model in combination with historical data, and cooperating with the early warning system to analyze the operating state of the unit in real time and perform performance deviation early warning; A data recording and analysis module for recording equipment operation data, and performing historical data analysis and trend prediction; A human-computer interaction interface for providing real-time system operation state monitoring, and allowing an operator to remotely control and adjust through the interface; A fault detection unit for timely issuing an alarm when the equipment operating parameters are abnormal, and providing a corresponding solution.

[0016] The present application provides an intelligent control method and system for a multi-unit reciprocating compressor booster station. 1、The present application realizes comprehensive intelligentization and automatic control of the compressor booster station by adopting intelligent start-stop control method, load intelligent distribution method and multi-condition intelligent matching method. Specifically, the internal units of the compressor are jointed for intelligent start-stop control by using the combination principle in TRIZ theory, automatically adapt to the operating conditions and perform load addition or reduction; a multi-unit load distribution model is established in combination with the performance of the reciprocating compressor, and a booster scheme with optimal energy consumption, maximum efficiency or continuous control is automatically executed; and the control point is precisely adjusted by segmentally acting and approaching the control point by piece through a dynamic control method, avoiding control coupling phenomenon. These measures greatly reduce manual operation, improve the reliability, safety and operating efficiency of the system, effectively reduce energy consumption, and optimize the overall performance of the system.

[0017] 2、The system of the present application is based on the development of an early warning system, which establishes a parameter correlation network through the compressor operating condition table, and establishes a mathematical model combined with historical data, analyzes the unit operating state in real time and performs performance deviation early warning. The early warning system can monitor and warn the abnormal situation of the equipment operating parameters in real time, and ensure the safe and efficient operation of the system. At the same time, the system contains automatic fault detection and early warning function, which can timely alarm when the equipment operating parameters are abnormal, and provide corresponding solutions. These functions significantly improve the fault response capability and safety of the system, reduce the equipment failure rate, and prolong the service life of the equipment.

[0018] 3、The system of the present application also includes a data recording and analysis module, which records and analyzes the equipment operating data, optimizes the control strategy and parameter setting combined with data mining and machine learning algorithm, improves the self-adaptability and control accuracy of the system. In addition, trend prediction and equipment health state evaluation are carried out combined with historical data analysis, which can find potential problems in advance, carry out preventive maintenance, reduce the equipment failure rate, and improve the reliability and economic benefit of the system. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The method and system of the present application are schematically shown. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. Embodiment one:

[0021] Please refer to the accompanying Figure 1 The embodiment of the present application provides an intelligent control method for multiple reciprocating compressor booster stations, which includes the following steps: a. An intelligent start-stop control method is adopted, which uses the combination principle in TRIZ theory to combine the internal units of the compressor for intelligent start-stop control, automatically steps through parameter compliance and signal feedback, detects faults throughout the process, automatically adapts to the working condition after starting the machine and performs load addition and subtraction; b. An intelligent load distribution method is adopted, which establishes a multi-unit load distribution model combined with the performance of reciprocating compressors, automatically executes the energy consumption optimization, efficiency maximization or continuous control of the booster scheme according to different working conditions, and formulates a proportional distribution scheme according to the unit performance and equipment fatigue degree; c. Employing multiple intelligent matching methods for different operating conditions, a load split control model is established to avoid control coupling. Through dynamic control, the control point is adjusted in segments and gradually approaches the control point to achieve precise control point adjustment.

[0022] This method is based on the development of an early warning system. It establishes a parameter correlation network through the compressor operating condition table and builds a mathematical model by combining historical data. It analyzes the unit's operating status in real time and provides early warning of performance deviations.

[0023] In one embodiment, step a: Intelligent start-stop control method Combination Principles and TRIZ Theory: Utilizing the combination principles of TRIZ theory, multiple compressor units are combined for start-stop control. This combination improves system flexibility and reliability.

[0024] Parameter compliance and signal feedback: Automatic step control is achieved by utilizing parameter compliance and signal feedback mechanisms. By monitoring the compressor's operating parameters in real time, such as pressure, temperature, and flow rate, the compressor's operating status is automatically adjusted when the parameters reach the set values.

[0025] Full-process fault detection: Fault detection is performed throughout the entire operation process to promptly identify and handle abnormal situations, ensuring the stable operation of the system.

[0026] Automatic adaptation to operating conditions: After startup, it automatically adapts to the current operating conditions by adding or removing loads to adjust the compressor's operating status so that it is always in the best working condition.

[0027] Step b: Intelligent load distribution method Multi-unit load sharing model: Based on the performance of reciprocating compressors, a multi-unit load sharing model is established. Depending on different operating conditions, it automatically executes the booster scheme that optimizes energy consumption, maximizes efficiency, or implements continuous control.

[0028] Energy consumption and efficiency optimization: Select the optimal load allocation scheme based on real-time operating conditions to minimize energy consumption and maximize efficiency.

[0029] Equipment fatigue and proportional allocation: Based on the performance of the unit and the fatigue of the equipment, a reasonable proportional allocation scheme is formulated to avoid premature failure caused by excessive operation of a certain compressor.

[0030] Step c: Intelligent matching method for multiple working conditions Load split control model: To avoid control coupling, a load split control model is established. Independent adjustment of each load segment is achieved through segmented control.

[0031] Dynamic control mode: Through dynamic control mode, the control points are approached step by step, realizing the precise adjustment of the control points. In this way, the stable operation of the system can be maintained under different working conditions.

[0032] Based on the development of the early warning system Parameter correlation network: Through the compressor operating condition table, the parameter correlation network is established. The relationship between various operating parameters is clarified for real-time monitoring and control.

[0033] Mathematical model and historical data: Combined with historical data, a mathematical model is established to analyze the operating state of the unit in real time. When the performance deviates, timely warning is given.

[0034] Real-time analysis and early warning: Through the early warning system, the operating state of the unit is analyzed in real time. Once the deviation from the set parameters is found, the system will automatically issue a warning and take appropriate control measures.

[0035] Further, through the intelligent control method, the reliability and stability of the system are enhanced, and the probability of failure is reduced; the load intelligent distribution method effectively optimizes energy consumption and efficiency, so that the system can run in the best state under various working conditions; through reasonable proportional distribution, the service life of the compressor is prolonged by avoiding excessive fatigue of the equipment; the intelligent matching method for multiple working conditions realizes the precise adjustment of the control points, ensuring the smooth operation of the system under different working conditions.

[0036] The intelligent start-stop control method includes the following steps: checking the valve position state before starting the compressor; checking the valve position state before starting the compressor; starting the main oil pump, glycol pump and air cooler; purging the gas system; checking the operating state and parameter feedback of each device during the start-up process; the load intelligent distribution method includes the following steps: selecting the most energy-efficient or efficient booster scheme according to different working conditions; developing a load distribution scheme based on unit performance and equipment fatigue; real-time adjustment of the load distribution of each compressor to ensure stable operation of the overall system.

[0037] In one embodiment, step a: intelligent start-stop control method Pre-starting valve position state check: Before starting the compressor, check the state of each valve, including the inlet valve, outlet valve, backflow valve and vent valve, to ensure that all valves are in the correct position.

[0038] Starting auxiliary system: Starting the main oil pump, glycol pump and air cooler: These pumps ensure the normal operation of the compressor lubrication and cooling system, providing the necessary support.

[0039] Purging the gas system: These systems ensure that there is no residual gas or liquid inside the compressor before starting, avoiding failures during startup.

[0040] Open the relevant valves: Open the compressor inlet valve, outlet valve, and return valve, and close the vent valve: Ensure that the compressor can normally compress and transport gas after starting.

[0041] Check the running state and parameter feedback during the starting process: During the starting process, monitor the running state and parameter feedback of each device in real time to ensure that all systems are in normal working condition. If abnormalities are found, take immediate measures to adjust or stop operation.

[0042] Step b: Intelligent load distribution method Select the optimal supercharging scheme: According to different working conditions, select the supercharging scheme with the optimal energy consumption or the maximum efficiency. For example, select the energy consumption optimal scheme under low load working condition, and select the maximum efficiency scheme under high load working condition.

[0043] Develop a load distribution scheme: According to the performance of the unit and the fatigue degree of the equipment, develop a reasonable load distribution scheme. By balancing the load of each compressor, avoid overrunning of a certain compressor, thereby prolonging the service life of the equipment.

[0044] Real-time adjustment of load distribution: According to the real-time monitoring data, dynamically adjust the load distribution of each compressor to ensure the stable operation of the overall system. Through this real-time adjustment, the running efficiency and reliability of the system are optimized.

[0045] Further, parameter correlation network: Through the compressor operating condition table, establish a parameter correlation network. The relationship between each operating parameter is clarified for real-time monitoring and control; mathematical model and historical data: Combine historical data to establish a mathematical model for real-time analysis of the unit operating state. When the performance deviates, timely warning is given; Real-time analysis and early warning: Through the early warning system, analyze the running state of the unit in real time. Once the deviation from the set parameters is found, the system will automatically issue a warning and take appropriate control measures.

[0046] The method realizes joint intelligent start-stop control, intelligent load distribution and intelligent matching of multiple working conditions of multiple compressor units by integrating based on the early warning system; the intelligent matching method of multiple working conditions includes the following steps: identifying the current working condition and selecting the corresponding control strategy; establishing a load distribution control model to avoid control coupling phenomenon; using dynamic control mode to segmentally approach the control point to realize precise regulation.

[0047] In one embodiment, the intelligent control method enhances system reliability and stability, reduces the probability of failure; the load intelligent distribution method optimizes energy consumption and efficiency, so that the system can run in the best state under various working conditions; reasonable proportion distribution avoids excessive fatigue of equipment and prolongs the service life of the compressor; the intelligent matching method for multiple working conditions realizes precise adjustment of the control point, and ensures the smooth operation of the system under different working conditions.

[0048] The method monitors the running state of each device in real time, combines historical data analysis to make trend prediction and device health state evaluation; the method includes automatic fault detection and early warning function, which can issue alarm in time when the device running parameter is abnormal, and provide corresponding solution; the method includes recording and analyzing the running data of the compressor, and optimizes the control strategy and parameter setting through data mining and machine learning algorithm.

[0049] In one embodiment, the system monitors the running state of each device in real time through sensor network. The sensors can include temperature sensor, pressure sensor, vibration sensor, etc., which can collect the key parameter data of the equipment in real time. These data are transmitted to the central monitoring system through the data acquisition module; after receiving the real-time data, the central monitoring system analyzes the historical running data. Data analysis includes basic statistical analysis and complex data mining technology. Through the trend analysis of historical data, the future running state of the equipment can be predicted. For example, abnormal change of temperature and vibration may indicate that the equipment is about to fail; on the basis of data analysis, the system uses machine learning algorithm to evaluate the health state of the equipment. The health state evaluation model can distinguish between normal operation and failure by training a large number of historical fault data, so as to give accurate evaluation on the health state of the current equipment; when the running parameters of the equipment are abnormal, the system can automatically detect the fault and issue alarm in time. The fault detection algorithm compares the real-time data with the normal running parameters to quickly identify abnormal conditions. The early warning system will send a notice to the operator according to the fault type and severity, and provide detailed solution suggestions; the system records the running data of the compressor in detail, including running time, load, energy consumption and other key parameters. Through long-term data accumulation, the system can optimize the control strategy and parameter setting of the compressor by using data mining and machine learning algorithm. For example, by analyzing the running data, the best running parameter setting can be found to improve the efficiency and life of the compressor.

[0050] An intelligent control system for multiple reciprocating compressor booster stations, comprising: An intelligent start-stop control unit for jointly controlling the internal units of the compressors; A load intelligent distribution unit for establishing a multi-unit load distribution model in combination with the performance of the reciprocating compressor, and automatically executing a load distribution scheme according to different working conditions; Multiple working condition intelligent matching unit, for establishing load split control model, through dynamic control mode segmented action and segment by segment approaching control point; Early warning system, for establishing parameter correlation network based on compressor operating condition table, and combining historical data to establish mathematical model, cooperating with early warning system to analyze unit operation state in real time and perform performance deviation early warning; Data recording and analysis module, for recording equipment operation data, performing historical data analysis and trend prediction; Man-machine interface, for providing real-time system operation state monitoring, and operation personnel can perform remote control and adjustment through the interface; Fault detection unit, for issuing alarm in time when equipment operation parameter is abnormal, and providing corresponding solution.

[0051] Comparative example one: Constant value control method: This method sets a fixed target pressure or flow rate for each compressor, and the control system starts or stops the compressor as needed to maintain the fixed value. This method is simple, but it responds slowly when the load changes, which can lead to low system efficiency.

[0052] Comparative example two: Differential pressure control method: By monitoring the pressure difference between the compressor inlet and outlet, the start and stop and adjustment of the compressor are controlled. This method can better reflect the real-time state of the system and improve the operating efficiency of the system, but it requires higher accuracy and response speed of the sensor.

[0053] Comparative example three: Master-slave control method: Set one compressor as master and the others as slaves, and the master adjusts its output according to system demand, and the slaves adjust accordingly according to the operation state of the master. This method can simplify the design of the control system, but the failure of the master will affect the stability of the entire system.

[0054] Parameters Efficiency standard deviation (%) Average energy consumption (kWh) Energy consumption standard deviation (kWh) Average efficiency (%) Comparative Example One 2.5 15000 500 85 Comparative Example Two 2.0 14000 450 88 Comparative Example Three 2.3 14500 470 86 Example One 1.5 13000 400 92 Table one Through detailed data comparison and analysis, it can be seen that the intelligent control method of example one is superior to the traditional method in various performance indicators. Its lower energy consumption, higher efficiency and faster efficiency fully prove the superiority of the intelligent control method, which has high practical application value and promotion potential.

[0055] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. An intelligent control method for a multi-reciprocating compressor booster station, characterized in that, Includes the following steps: a. An intelligent start-stop control method is adopted, which utilizes the combination principle in TRIZ theory to combine the internal units of the compressor for intelligent start-stop control. Automatic stepping is achieved through parameter compliance and signal feedback, and fault detection is performed throughout the process. After startup, the compressor automatically adapts to the operating conditions and performs load increase or decrease. b. Adopting a load intelligent distribution method, combined with the performance of reciprocating compressors, a multi-unit load distribution model is established. Based on different operating conditions, the optimal energy consumption, maximum efficiency, or continuous control boosting scheme is automatically executed, and a proportional distribution scheme is formulated based on unit performance and equipment fatigue. c. Employing multiple intelligent matching methods for various operating conditions, a load split control model is established to avoid control coupling. Through dynamic control, the control point is adjusted in segments and gradually approaches the control point to achieve precise control point adjustment.

2. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The method is based on an early warning system. It establishes a parameter correlation network through the compressor operating condition table and builds a mathematical model by combining historical data. It analyzes the unit's operating status in real time and provides early warning of performance deviation.

3. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The intelligent start-stop control method includes the following steps: Before starting the compressor, check the status of each valve position; start the main oil pump, glycol pump, and air cooler; purge the air system. Open the compressor inlet valve, outlet valve, and return valve; close the vent valve. Check the operating status and parameter feedback of each device during startup, and start the compressor to complete the loading.

4. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The intelligent load allocation method includes the following steps: Choose the booster scheme with the best energy consumption or the highest efficiency based on different operating conditions; A load distribution plan is developed based on unit performance and equipment fatigue. Adjust the load distribution of each compressor in real time to ensure the stable operation of the overall system.

5. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The method integrates multiple compressor units based on an early warning system to achieve joint intelligent start-stop control, intelligent load allocation, and intelligent matching of various operating conditions.

6. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The intelligent matching method for multiple working conditions includes the following steps: Identify the current operating condition and select the appropriate control strategy; A load split control model is established, and a dynamic control method is used to approach the control point segment by segment, avoiding control coupling and achieving precise regulation.

7. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The method monitors the operating status of each device in real time and combines historical data analysis to predict trends and assess the health status of the devices.

8. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The method includes automatic fault detection and early warning functions, which can issue alarms in a timely manner when the equipment operating parameters are abnormal, and provide corresponding solutions.

9. The intelligent control method for a multi-reciprocating compressor booster station according to claim 1, characterized in that, The method includes recording and analyzing compressor operating data, and optimizing control strategy parameter settings through data mining and machine learning algorithms.

10. An intelligent control system for a multi-reciprocating compressor booster station, as described in any one of claims 1-9, characterized in that, include: The intelligent start-stop control unit is used to combine the internal units of the compressor for intelligent start-stop control; The intelligent load distribution unit is used to establish a multi-unit load distribution model based on the performance of reciprocating compressors and automatically execute the load distribution scheme according to different operating conditions. A multi-condition intelligent matching unit is used to establish a load-split control model, which acts in segments and approaches the control point segment by segment through dynamic control. The early warning system is used to establish a parameter correlation network based on the compressor operating condition table, and to build a mathematical model by combining historical data. It analyzes the unit's operating status in real time and issues early warnings of performance deviations, and monitors the system in real time through these early warnings. The data recording and analysis module is used to record equipment operating data, perform historical data analysis, and predict trends. The human-computer interaction interface is used to provide real-time monitoring of the system's operating status, and operators can remotely control and adjust the system through the interface; The fault detection unit is used to issue alarms in a timely manner when the equipment operating parameters are abnormal, and to provide corresponding solutions.

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