Natural gas pipeline intelligent pressure regulation and flow dynamic optimization system based on PLC optimization system

Abstract

The invention discloses a natural gas pipeline intelligent pressure regulation and flow dynamic optimization system based on a PLC optimization system. The system comprises a data acquisition and monitoring module, a PLC core control module, an intelligent optimization algorithm module, a man-machine interaction module and a safety protection module. According to the method, the defects in the aspects of pressure regulation, flow distribution and safety risk in the prior art are overcome, a deep learning optimization layer is innovatively overlaid on the basis of a traditional PLC control layer through a PLC and AI fusion framework, and organic cooperation of hard real-time control and intelligent decision making is achieved. The PLC is responsible for rapidly and accurately controlling the equipment, the AI is used for analyzing complex data, predicting the trend and optimizing the control strategy, the intelligent level of the system is remarkably improved, and the safety coefficient can be automatically adjusted according to the pipeline aging degree. For example, the safety coefficient alpha of a new pipeline is set to be 1.0, and the safety coefficient alpha of the pipeline running for 10 years is adjusted to be 1.3, so that the safety and the reliability of the system are ensured under different pipeline states.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas pipeline control, and particularly to an intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system. Background Art

[0002] With the continuous growth of the demand for natural gas energy, higher requirements are put forward for the efficiency, stability and safety of natural gas pipeline transportation systems. Traditional natural gas pipeline control systems often rely on relatively single control strategies in terms of pressure regulation and flow distribution, and it is difficult to adapt to complex and changeable working conditions, such as pipeline pressure fluctuations and dynamic changes in flow demand, resulting in low gas transmission efficiency, energy waste and potential safety risks. Therefore, it is of great significance to develop a system that can monitor in real time, intelligently regulate and optimize the operating parameters of natural gas pipelines.

[0003] The existing technologies have the following deficiencies:

[0004] In terms of pressure regulation

[0005] Single control strategy is difficult to cope with complex pressure fluctuations: Traditional systems mostly use PID control strategies with fixed parameters to regulate pipeline pressure. In actual operation, natural gas pipelines are affected by factors such as the stability of gas source supply and uneven gas consumption of users, and the pressure fluctuations are complex and changeable. For example, during the peak period of urban gas use, a large number of residents use gas at the same time, and the pipeline pressure drops sharply. Due to the fixed parameters of PID control, the adjustment response is slow, and it is unable to quickly increase the pressure to the normal range, resulting in insufficient gas supply to some users. When the gas supply pressure at the gas source end suddenly changes, the PID control is prone to overshoot, causing the pipeline pressure to be too high in a short time, threatening pipeline safety.

[0006] Lack of adaptability to dynamic changes in pipeline characteristics: As the service life of the pipeline increases, the inner wall will change in roughness due to factors such as corrosion and scaling, which will in turn affect the gas flow characteristics and pressure distribution. Traditional pressure regulation systems cannot perceive and adapt to these changes in real time. Taking a long-distance pipeline as an example, after several years of operation, the gas flow resistance increases in some areas of the pipeline due to corrosion, and the pressure drop increases under the same flow rate. However, the traditional system still works according to the original fixed adjustment mode, and it is difficult to maintain the pressure stability of the whole pipeline, which may cause local pressure to be too high or too low, affecting the gas transmission efficiency and safety.

[0007] Slow pressure regulation response speed: Traditional systems rely on the connection of simple sensors and controllers, and there is a delay in signal transmission and processing. When the pressure at a certain monitoring point of the pipeline is detected to be abnormal, from the signal being uploaded to the controller, and then to the controller issuing an adjustment instruction and executing it, this process takes a long time. For example, when a pipeline leaks, the pressure drops rapidly, and the traditional system cannot start emergency pressure regulation measures in a short time, resulting in a large amount of natural gas leakage, causing resource waste and safety hazards.

[0008] In terms of flow distribution

[0009] Difficult to accurately match dynamic flow requirements: Users' flow requirements for natural gas change dynamically with factors such as time, season, and production activities. Traditional systems set flow distribution plans based on historical experience and cannot adjust in real time according to actual needs. For example, during the peak production season, industrial users' gas consumption increases significantly. The traditional system may not be able to increase the corresponding regional flow supply in time, affecting the normal production of enterprises; during off-peak hours, it cannot reduce the flow transmission in time, resulting in energy waste.

[0010] Lack of consideration for the differentiated needs of different user types: The gas usage characteristics of industrial users and civil users are significantly different. Industrial users have large and relatively stable gas consumption and require high stability of gas supply pressure and flow; civil users have scattered and volatile gas consumption. The traditional flow distribution system adopts a "one-size-fits-all" model and cannot optimize the flow distribution for different user types. For example, during the winter heating period, the civil gas demand increases significantly. It is difficult for the traditional system to ensure the stable gas supply for industrial users while guaranteeing civil heating, resulting in an impact on industrial production.

[0011] Poor flow distribution coordination ability: In a complex gas supply network with multiple gas sources and multiple pipe networks, each part of the traditional system operates relatively independently and lacks an effective flow distribution coordination mechanism. It is difficult to dynamically allocate the gas supply flow of different gas sources according to the actual needs of each pipe network, easily resulting in a situation where some pipe networks have an excess flow while some have a shortage. For example, when the gas supply of a gas source pipeline in a certain area decreases due to an unexpected accident, the traditional system cannot quickly allocate enough flow from other gas sources to supplement, affecting the stability of the natural gas supply in that area.

[0012] In terms of safety risks

[0013] Limited and lagging leak detection means: Traditional leak detection mostly relies on manual inspections and simple pressure and flow monitoring. Manual inspections are restricted by time and space and cannot cover the entire pipeline in real time, and it is difficult to detect small leak points. For leak detection methods based on pressure and flow, they can only be detected when the leakage amount reaches a certain level, causing obvious pressure and flow changes, with a large delay. For example, when a pipeline has a small crack due to corrosion and slowly leaks natural gas, traditional detection means may not be able to detect it for a long time. When the leakage accumulates to a certain extent, it is extremely likely to trigger safety accidents such as fires and explosions.

[0014] Lack of prediction and early warning capabilities for potential safety risks: Traditional systems are unable to predict potential safety risks based on information such as pipeline operation data and equipment status. For example, in the case of aging and fatigued pipeline equipment, the system cannot issue an early warning before a failure is about to occur. Take compressors as an example. Traditional systems can only perform repairs after a compressor fails and shuts down, and cannot arrange maintenance in advance during the stage of component wear and performance degradation, increasing the risk of pipeline operation interruption.

[0015] Incomplete emergency response mechanism: In the face of sudden pipeline safety incidents such as earthquakes and third-party construction damage, traditional systems have a slow emergency response. On the one hand, information transmission is not timely after an incident occurs, and relevant departments cannot quickly grasp the situation. On the other hand, the emergency handling process is cumbersome and lacks an efficient coordination mechanism. For example, when a pipeline is damaged by third-party construction and natural gas leaks, traditional systems may not be able to quickly organize repair forces, close relevant valves, and control the leak, thus expanding the scope of the accident. Summary of the Invention

[0016] The present invention provides an intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system to solve the above-mentioned existing technical problems.

[0017] The technical solution of the present invention is realized as follows:

[0018] An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system includes: a data acquisition and monitoring module, a PLC core control module, an intelligent optimization algorithm module, a human-machine interaction module, and a safety protection module;

[0019] The data acquisition and monitoring module uses multi-source sensors to comprehensively monitor various characteristic data of natural gas, including key parameters such as pressure, temperature, flow rate, and combustible gas concentration. By real-time analyzing the characteristic data, once an anomaly is detected, the alarm mechanism is immediately triggered, and corresponding safety measures are quickly taken through the PLC core control module. For example, when the detected pressure rises abnormally or the combustible gas concentration exceeds the standard, the safety protection module can automatically close relevant valves and cut off the gas source, effectively preventing the accident from further expanding. At the same time, this module can also be closely linked with the emergency response system to ensure that the emergency plan can be quickly activated in case of an emergency, organize repair forces, and minimize losses to the greatest extent.

[0020] The PLC core control module is responsible for receiving real-time data from the data acquisition and monitoring module, and regulating the operating state of the natural gas pipeline according to preset safety thresholds and optimization strategies provided by the intelligent optimization algorithm module. It can quickly respond to abnormal data, trigger the safety protection mechanism, dynamically adjust the pipeline pressure according to the flow demand, and achieve the optimal distribution of flow rate.

[0021] The intelligent optimization algorithm module, based on big data analysis, machine learning, and artificial intelligence algorithms, deeply mines and intelligently analyzes the operation data of natural gas pipelines, predicts pipeline flow trends, identifies potential operation bottlenecks, and dynamically adjusts control strategies according to historical data and real-time operation conditions to maximize pipeline operation efficiency.

[0022] The human-machine interaction module enables operators to easily monitor the pipeline operation status, receive alarm information, and perform necessary control operations through an intuitive user interface. This module supports multiple data visualization display methods, including charts, curve graphs, and bar graphs.

[0023] The safety protection module, by continuously monitoring various characteristic parameters of the natural gas pipeline, when detecting abnormal or characteristic parameters exceeding the preset safety threshold, this module will activate the emergency response mechanism to quickly contain potential safety risks. At the same time, the safety protection module also has a self-diagnosis function to identify and report faults or abnormal states of the system itself, ensuring the reliability and stability of the entire monitoring system.

[0024] Preferably, the data acquisition and monitoring module further includes a multi-source sensor unit and a data preprocessing unit. The multi-source sensor unit accurately acquires pipeline pressure data in real time through a differential pressure sensor, obtains flow information using an ultrasonic flowmeter, measures temperature with a thermocouple, and analyzes gas component data using a gas chromatograph. These sensors work together to provide comprehensive and real-time basic pipeline operation data for the system. The data preprocessing unit uses the Kalman filter algorithm to filter the original data, effectively removing noise interference, eliminating outliers through specific algorithms to ensure data reliability, and at the same time performing timestamp alignment to integrate the processed data into a standardized data packet for efficient processing by subsequent modules.

[0025] Preferably, the PLC core control module specifically includes a dynamic pressure regulation control unit and a flow optimization unit. The dynamic pressure regulation control unit, based on the pressure set value and the real-time collected pressure data, uses a combination of PID control algorithm and fuzzy algorithm to accurately adjust the opening degrees of pressure regulating valves and pressure regulating equipment to achieve stable control of pipeline pressure. The flow optimization unit: learns historical flow patterns with the help of an LSTM neural network, predicts fluctuations in flow demand. According to the prediction results, dynamically adjusts the compressor speed to meet the flow demand at different times, improving the rationality and accuracy of flow distribution.

[0026] Preferably, the intelligent optimization algorithm module specifically includes a multi-objective optimization unit and an adaptive learning unit. The multi-objective optimization unit adopts the NSGA-II algorithm to maximize the gas transmission efficiency and optimize the overall operation performance of the pipeline on the premise of ensuring stable pressure. The adaptive learning unit uses online reinforcement learning technology to continuously update the pressure regulation strategy library, and can automatically adjust the pressure regulation strategy according to the real-time operation status and feedback information to adapt to the changing pipeline working conditions.

[0027] Preferably, the human-computer interaction module includes a visualization monitoring unit and a parameter configuration unit. The visualization monitoring unit intuitively displays the pipeline operation status in the form of a 3D pipeline pressure, flow, and thermal diagram. When the characteristic parameters of pressure or flow exceed the set threshold, it timely issues an alarm signal through sound, light, and text messages, etc., facilitating the operators to promptly grasp the pipeline abnormal conditions. The parameter configuration unit modifies the algorithm weights according to actual requirements or sets corresponding strategies for special working conditions to enhance the flexibility and operability of the system.

[0028] Preferably, the safety protection module includes an emergency cut-off unit and a leakage detection unit. The emergency cut-off unit quickly triggers an emergency shutdown system to cut off the pipeline path when the pipeline pressure exceeds the safety threshold, preventing safety accidents caused by excessive pressure. The leakage detection unit accurately locates the pipeline leakage point based on the negative pressure wave method and acoustic fingerprint recognition technology, and the positioning accuracy can reach ≤50 meters.

[0029] Preferably, for the intelligent pressure regulation and flow dynamic optimization system of the natural gas pipeline, its intelligent pressure regulation and flow dynamic optimization methods include a hierarchical pressure regulation control method, a flow dynamic optimization method, and an energy efficiency optimization method.

[0030] Preferably, the hierarchical pressure regulation control method includes the following steps:

[0031] SA1: By setting a pressure sensor group every 2 km on the pipeline, the sectional pressure values of the pipeline are obtained.

[0032] SA2: The PLC calculates the obtained pressure data to obtain the pressure gradient. If the pressure difference between adjacent sections > 0.3 MPa, the pressure regulating valve linkage mechanism is started, that is, the opening of the upstream valve increases linearly, and the opening of the downstream valve is non-linearly compensated and reduced.

[0033] SA3: Adopt model predictive control (MPC) technology to perform rolling optimization on the pressure trajectory in the next 5 minutes to ensure the accuracy and stability of pressure control.

[0034] Preferably, the flow dynamic optimization method includes the following steps:

[0035] SC1: Build a traffic demand prediction model. This model uses historical usage, weather data, user types (industrial / civil), etc. as input data, and through a deep learning model based on the Attention mechanism, outputs the 24-hour traffic prediction results.

[0036] SC2: Dynamically adjust the operation strategy of the compressor group according to the traffic prediction results. During the low-demand period, start 1 variable-frequency compressor and keep it running at 40-60% load; during the high-demand period, parallel 3 compressors and keep them running at 80% load, and combine with an air storage tank for peak shaving to meet the traffic demands at different times.

[0037] Preferably, the energy efficiency optimization method includes the following steps:

[0038] Establish a three-dimensional relationship model of pressure-flow-energy consumption, and use the gradient descent method to find the optimal operating point. For example, at a pressure of 0.7 MPa, when the flow rate increases by 12%, the energy consumption only increases by 5%. Through such optimization, the maximum energy efficiency of the system is achieved.

[0039] The present invention solves the deficiencies in the aspects of pressure regulation, flow distribution, and safety risks of the prior art. And based on the traditional PLC control layer through the PLC and AI fusion architecture, the deep learning optimization layer is innovatively superimposed to realize the organic coordination of hard real-time control and intelligent decision-making. The PLC is responsible for quickly and accurately controlling the equipment, while the AI is used to analyze complex data, predict trends, and optimize control strategies, significantly improving the intelligent level of the system. And the present invention can also automatically adjust the safety factor according to the degree of pipeline aging. For example, the safety factor α of a new pipeline is set to 1.0, and the safety factor α of a pipeline in operation for 10 years is adjusted to 1.3, so as to ensure the safety and reliability of the system under different pipeline states. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of a natural gas pipeline intelligent pressure regulation and flow dynamic optimization system based on the PLC optimization system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the exemplary embodiments will be described in detail here, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the appended claims.

[0042] Embodiment 1

[0043] As shown Figure 1 in the figure, an intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system of the present invention includes: a data acquisition and monitoring module, a PLC core control module, an intelligent optimization algorithm module, a human-machine interaction module, and a safety protection module;

[0044] The data acquisition and monitoring module uses multi-source sensors to comprehensively monitor various characteristic data of natural gas, including key parameters such as pressure, temperature, flow rate, and combustible gas concentration. By real-time analyzing the characteristic data, once an abnormality is found, the alarm mechanism is immediately triggered, and corresponding safety measures are quickly taken through the PLC core control module. For example, when detecting an abnormal increase in pressure or an excessive combustible gas concentration, the safety protection module can automatically close the relevant valves and cut off the gas source to effectively prevent the further expansion of the accident. At the same time, this module can also be closely linked with the emergency response system to ensure that the emergency plan can be quickly activated in case of an emergency, organize the repair force, and minimize losses to the greatest extent.

[0045] In the natural gas pipeline system of the present invention, the data acquisition and monitoring module uses various types of sensors to comprehensively collect various key characteristic data of natural gas. These sensors are distributed at each key node of the pipeline network to ensure accurate and real-time data acquisition;

[0046] (1) Pressure sensor: Usually, a high-precision pressure transmitter is used, and its working principle is based on the piezoresistive effect or the capacitive effect. The piezoresistive pressure sensor measures pressure by detecting the resistance change caused by the pressure change, while the capacitive pressure sensor obtains the pressure value by detecting the capacitance change caused by the pressure. These sensors can accurately measure the pressure of natural gas in the pipeline, and the measurement range can be customized according to the actual operating pressure of the pipeline, generally between 0 - 10 MPa, and the measurement accuracy can reach ±0.1%. They are installed at the starting point, ending point, branch point of the pipeline, and the inlet and outlet of key equipment to monitor the pressure change in real time.

[0047] (2) Temperature sensor: Commonly used temperature sensors include thermocouples and thermal resistors. The thermocouple uses the thermoelectric effect to convert the temperature change into the change of thermoelectromotive force; the thermal resistor measures the temperature based on the characteristic that the metal resistance changes with temperature. The temperature sensor is installed on the outer wall or inside of the pipeline and can accurately measure the temperature of natural gas. The measurement range is usually between -40°C - 120°C, and the accuracy can reach ±0.5°C. Temperature data is of great significance for the flow calculation of natural gas, the correction of physical property parameters, and the thermal stress analysis of the pipeline.

[0048] (3) Flow sensor: Ultrasonic flow meter is a commonly used flow measurement device. It uses the difference in propagation time of ultrasonic waves in the fluid, downstream and upstream, to measure the flow rate of the fluid and then calculate the flow rate. This flow meter has the advantages of high measurement accuracy, no obstruction to the fluid, and adaptability to different pipe diameters. The measurement accuracy can generally reach ±0.5%-±1%, and it can be applied to pipes with diameters ranging from tens of millimeters to several meters. In addition, there are other types of flow sensors such as turbine flowmeters and vortex flowmeters, which can be selected according to specific application scenarios and needs.

[0049] (4) Combustible gas concentration sensor: used to detect natural gas leaks. Commonly used combustible gas concentration sensors include catalytic combustion type and semiconductor type. Catalytic combustion type sensors measure gas concentration by detecting the change in heat generated when combustible gas burns on the catalyst surface; semiconductor type sensors measure based on the principle that the electrical properties of semiconductor materials change with gas concentration. These sensors are installed in leak-prone parts of pipelines, near valves, and around equipment. They can monitor the concentration of combustible gas in real time and send out an alarm signal in a timely manner when the concentration exceeds the set safety threshold.

[0050] Through the above sensors, the data collected by each sensor is transmitted to the data acquisition terminal through wired or wireless communication. Wired communication methods include Ethernet, RS-485, etc., which have the advantages of stable transmission and high reliability; wireless communication methods include ZigBee, LoRa, GPRS, etc., which are suitable for some occasions where wiring is not easy. The data acquisition terminal will preliminarily process and store the received sensor data, and then transmit the data to the server of the monitoring center through the network.

[0051] At the monitoring center, after the server receives the sensor data, it first performs data preprocessing. Preprocessing includes data cleaning, filtering, and calibration. Data cleaning is to remove noise, outliers, and missing values ​​from the data; filtering is to use digital filtering algorithms (such as Kalman filtering, sliding average filtering, etc.) to smooth the data and improve the data quality; calibration is to correct the data according to the sensor's calibration curve to ensure the accuracy of the data.

[0052] The preprocessed data is analyzed using real-time data analysis algorithms to detect whether there are any anomalies. Common anomaly detection algorithms include threshold detection, statistical analysis, and machine learning algorithms.

[0053] (1) Threshold detection: Set a normal value range for each monitoring parameter. When the monitoring data exceeds this range, it is judged as abnormal. For example, the normal range of pressure is set to 2-8MPa. When the pressure sensor measurement value is higher than 8MPa or lower than 2MPa, the system determines that the pressure is abnormal.

[0054] (2) Statistical analysis: By statistically analyzing historical data, a statistical model of the data is established, such as mean, standard deviation, probability distribution, etc. Then, based on the deviation between the current data and the statistical model, it is judged whether there is an anomaly. For example, calculate the mean and standard deviation of the pressure data. When the deviation between the current pressure value and the mean exceeds 3 times the standard deviation, the pressure is considered abnormal.

[0055] (3) Machine learning algorithms: Use machine learning algorithms (such as neural networks, support vector machines, etc.) to learn from a large amount of historical data and establish an anomaly detection model. These models can automatically identify abnormal patterns in the data and have high accuracy and adaptability. For example, use a neural network to jointly analyze multiple parameters such as pressure, temperature, and flow rate to judge whether there is an abnormal operating state in the pipeline.

[0056] The PLC core control module is responsible for receiving real-time data from the data acquisition and monitoring module and regulating the operating state of the natural gas pipeline according to the preset safety threshold and the optimization strategy provided by the intelligent optimization algorithm module. It can quickly respond to abnormal data, trigger the safety protection mechanism, dynamically adjust the pipeline pressure according to the flow demand, and achieve the optimal allocation of flow.

[0057] The PLC core control module precisely regulates the pipeline operating state in the intelligent pressure regulation and flow dynamic optimization system of the natural gas pipeline according to the preset rules and optimization strategies to ensure the safety, efficiency, and stability of natural gas transportation.

[0058] (1) The PLC core control module and the data acquisition and monitoring module are connected through a stable and reliable communication interface (such as Ethernet, RS-485, etc.). The data acquisition and monitoring module continuously transmits key parameters such as pressure, temperature, flow rate, and combustible gas concentration to the PLC in real time. These data are received by the PLC in the form of digital signals, providing a basic basis for subsequent decision-making and control.

[0059] (2) The received raw data may have problems such as noise interference and data missing. The PLC will preprocess this data, including filtering, calibration, and data verification. For example, use a moving average filtering algorithm to remove high-frequency noise in the data to make the data smoother and more stable; correct the measurement error of the sensor through a calibration algorithm to ensure the accuracy of the data; verify the data to check whether the data is within a reasonable range, and mark or eliminate abnormal data if it appears.

[0060] (3) Safety thresholds for each monitoring parameter are preset in the PLC. These thresholds are determined based on the design standards, operating experience, and safety requirements of the natural gas pipeline. For example, the upper threshold for pressure may be set at 90% of the pipeline design pressure, and the lower threshold is set at the minimum pressure value that can ensure normal transportation; the safety threshold for combustible gas concentration is set according to relevant national standards and safety codes.

[0061] (4) When the data received by the PLC exceeds the preset safety threshold, it will respond quickly. If an abnormal increase in pressure is detected, the PLC will immediately trigger the safety protection mechanism, close the relevant regulating valves through control instructions, reduce the input of natural gas, prevent the pressure from rising further, and avoid safety accidents such as pipeline rupture due to overpressure; if the combustible gas concentration exceeds the standard is detected, the PLC will start the ventilation equipment, reduce the combustible gas concentration, and send out an alarm signal in a timely manner to notify relevant personnel to take measures.

[0062] (5) The intelligent optimization algorithm module will deeply mine and analyze the operation data of the natural gas pipeline according to big data analysis, machine learning, and artificial intelligence algorithms to generate optimization strategies. The PLC core control module receives these optimization strategies and analyzes and interprets them. For example, the optimization strategy may give the optimal set value of the pipeline pressure and the optimal opening degree of the regulating valve under different flow demands.

[0063] (6) According to the optimization strategy, the PLC will dynamically adjust the pressure and flow of the pipeline to achieve the optimal distribution of flow. When the flow demand increases, the PLC will control the compressor to increase the speed, increase the transportation pressure of natural gas, and at the same time appropriately increase the opening degree of the regulating valve to ensure that sufficient natural gas can be smoothly transported to the destination; when the flow demand decreases, the PLC will reduce the speed of the compressor, reduce the transportation pressure, and correspondingly reduce the opening degree of the regulating valve to avoid waste of energy.

[0064] (7) The PLC will combine historical flow data and real-time monitoring data to predict future flow demands. For example, by analyzing the gas consumption patterns of different time periods, different seasons, and different users, the peak and trough flows can be predicted. At the same time, according to the actual needs and priorities of users, the flow is reasonably allocated. For important industrial users and residential users, their gas consumption needs are given priority; for some non-urgent gas consumption needs, they can be appropriately adjusted when the flow is tight.

[0065] (8) In the process of achieving optimal flow distribution, the PLC needs to coordinate the control of pressure and flow. By precisely adjusting the opening degree of the regulating valve and the rotational speed of the compressor, the pressure and flow in the pipeline are maintained in the best matching state. For example, in a long-distance transmission pipeline, according to the resistance characteristics of the pipeline and the flow demand, the pressure of each pumping station along the way is reasonably adjusted to ensure that natural gas can be efficiently transported with the minimum energy consumption.

[0066] The intelligent optimization algorithm module, based on big data analysis, machine learning, and artificial intelligence algorithms, deeply mines and intelligently analyzes the operation data of the natural gas pipeline, predicts the pipeline flow trend, identifies potential operation bottlenecks, and dynamically adjusts the control strategy according to historical data and real-time operation conditions to maximize the pipeline operation efficiency.

[0067] The intelligent optimization algorithm module, based on big data analysis, machine learning, and artificial intelligence algorithms, deeply mines and intelligently analyzes the operation data of the natural gas pipeline, aiming to maximize the pipeline operation efficiency.

[0068] First, the module collects data from various data sources of the natural gas pipeline system, including but not limited to data such as pressure, temperature, flow, and combustible gas concentration transmitted in real time by the data acquisition and monitoring module, as well as relevant information such as historical operation records, equipment maintenance data, and meteorological data. These data are integrated into a unified data platform through network interfaces or database connections to provide a basis for subsequent analysis and processing.

[0069] Then, the collected data may have problems such as noise, missing values, and outliers, and need to be cleaned and preprocessed. Noise is removed by using data smoothing techniques (such as moving average method, median filtering, etc.), missing values are filled by using interpolation methods or model-based methods, and outliers are detected and processed by using statistical analysis or machine learning algorithms to ensure the quality and integrity of the data.

[0070] Next, feature extraction and selection are performed on the preprocessed data to mine features that have an important impact on the pipeline operation status and flow trend. For example, features such as pressure change rate and pressure peak are extracted from pressure data; statistical features such as flow mean and standard deviation in different time periods are extracted from flow data; features related to seasons and weather conditions are generated by combining meteorological data and time information. Through feature engineering, the prediction accuracy and efficiency of the model can be improved.

[0071] By applying big data analysis techniques such as association rule mining and clustering analysis, explore the internal relationships and patterns among data. For example, through association rule mining, discover the associations between gas usage patterns of different user types and meteorological factors; use clustering analysis to classify the operating states of pipelines into different categories for formulating targeted control strategies. These analysis results provide valuable information for subsequent machine learning and artificial intelligence algorithms.

[0072] And use machine learning algorithms to build a flow prediction model to predict the future flow trends of pipelines. Commonly used algorithms include linear regression, decision tree, random forest, support vector machine, long short-term memory network (LSTM), etc. These algorithms are trained based on historical flow data and related influencing factors (such as time, meteorological conditions, user behavior, etc.) to learn the patterns and regularities in the data. For example, the LSTM network can effectively process time series data, capture the long-term dependencies of flow over time, and thus achieve relatively accurate flow predictions. Through flow trend prediction, the system can adjust control strategies in advance, optimize resource allocation, and ensure that the pipeline can meet the needs of users.

[0073] Use machine learning algorithms to analyze the operation data of pipelines and identify potential operation bottlenecks. For example, through anomaly detection algorithms (such as density-based spatial clustering algorithm DBSCAN, isolation forest, etc.), discover abnormal patterns in the data, and these abnormal patterns may indicate that there is a problem in a certain link of the pipeline, such as pipeline blockage, equipment failure, etc. In addition, classification algorithms (such as logistic regression, neural network, etc.) can also be used to classify the operating states of pipelines, judge whether the current state is in a bottleneck state, and analyze the reasons for the bottleneck. By promptly identifying operation bottlenecks, the system can take corresponding measures for optimization and improvement to improve the overall operating efficiency of the pipeline.

[0074] Adopt reinforcement learning algorithms to dynamically adjust control strategies to maximize the operating efficiency of pipelines. Reinforcement learning is a method in which an agent interacts with the environment, continuously tries different actions, and learns the optimal strategy based on the reward signals feedback by the environment. In the natural gas pipeline system, the agent can be regarded as the executor of the control strategy, and the environment is the actual operating state of the pipeline. The agent selects a control action (such as adjusting the opening of the pressure regulating valve, changing the speed of the compressor, etc.) according to the current pipeline state. After executing this action, the environment will feedback a reward signal, which reflects the impact of the current action on the pipeline operating efficiency. The goal of the agent is to learn the optimal control strategy that can obtain the maximum reward through continuous interaction with the environment. For example, reinforcement learning algorithms such as deep Q-network (DQN) can be used to achieve this goal. DQN approximates the Q-value function by constructing a deep neural network, thereby being able to handle complex state and action spaces and realizing more intelligent control strategy optimization.

[0075] Genetic algorithms can be used to optimize the adjustment parameters of pressure regulating valves, the operating parameters of compressors, etc. The algorithm first randomly generates an initial set of control parameter combinations (i.e., the population), and then evaluates each parameter combination according to a certain fitness function (such as indicators like pipeline operation efficiency, energy consumption, etc.), selects the parameter combinations with higher fitness for genetic operations (such as crossover, mutation), and generates a new generation of parameter combinations. Through continuous iterative evolution, the parameter combinations in the population gradually tend to the optimal solution, thereby achieving the improvement of pipeline operation efficiency.

[0076] The intelligent optimization algorithm module continuously adjusts the control strategy according to the real-time operation status and historical data to adapt to the changes in pipeline operation conditions. When the operating state of the pipeline changes, such as an increase in user demand, a decline in equipment performance, weather changes, etc., the module will collect relevant data in real time and re-analyze and predict. According to the new analysis results, the control strategy is adjusted in a timely manner, such as adjusting the opening of the pressure regulating valve, changing the operating mode of the compressor, etc., to ensure that the pipeline always operates in an optimal state. At the same time, the module will also feedback the adjusted control strategy to the PLC core control module, and the PLC will execute specific control operations. Through this real-time feedback and dynamic adjustment mechanism, the system can continuously optimize the pipeline operation efficiency, improve energy utilization efficiency, reduce operating costs, and ensure the safe and stable operation of the pipeline.

[0077] The human-machine interaction module enables operators to easily monitor the pipeline operation status, receive alarm information, and perform necessary control operations through an intuitive user interface, and this module supports various data visualization display methods, including charts, curve graphs, and bar graphs.

[0078] The safety protection module, by continuously monitoring various characteristic parameters of the natural gas pipeline, when detecting abnormal or characteristic parameters exceeding the preset safety threshold, this module will initiate an emergency response mechanism to quickly contain potential safety risks. At the same time, the safety protection module also has a self-diagnosis function to identify and report faults or abnormal states of the system itself, ensuring the reliability and stability of the entire monitoring system.

[0079] Preferably, the data acquisition and monitoring module further includes a multi-source sensor unit and a data preprocessing unit. The multi-source sensor unit accurately collects pipeline pressure data in real time through a differential pressure sensor, obtains flow information using an ultrasonic flowmeter, measures temperature with a thermocouple, and analyzes gas component data with a gas chromatograph. These sensors work together to provide comprehensive and real-time basic pipeline operation data for the system. The data preprocessing unit filters the original data using the Kalman filter algorithm to effectively remove noise interference, eliminates outliers through a specific algorithm to ensure data reliability, and simultaneously aligns timestamps to integrate the processed data into a standardized data packet for efficient processing by subsequent modules.

[0080] Preferably, the PLC core control module specifically includes a dynamic pressure regulation control unit and a flow optimization unit. The dynamic pressure regulation control unit, based on the pressure set value and the real-time collected pressure data, uses a combination of the PID control algorithm and the fuzzy algorithm to precisely adjust the opening degrees of the pressure regulating valve and the pressure regulating equipment, achieving stable control of the pipeline pressure. The flow optimization unit: learns the historical flow patterns with the help of an LSTM neural network, predicts the fluctuations in flow demand. According to the prediction results, it dynamically adjusts the compressor speed to meet the flow demands at different times, improving the rationality and accuracy of flow distribution.

[0081] Preferably, the intelligent optimization algorithm module specifically includes a multi-objective optimization unit and an adaptive learning unit. The multi-objective optimization unit uses the NSGA-II algorithm to maximize the gas transmission efficiency and optimize the overall operation performance of the pipeline while ensuring pressure stability. The adaptive learning unit uses online reinforcement learning technology to continuously update the pressure regulation strategy library, and can automatically adjust the pressure regulation strategy according to the real-time operation status and feedback information to adapt to the changing pipeline working conditions.

[0082] Preferably, the human-machine interaction module includes a visual monitoring unit and a parameter configuration unit. The visual monitoring unit intuitively displays the pipeline operation status in the form of a 3D pipeline pressure, flow, and heat map. When the characteristic parameters of pressure or flow exceed the set threshold, it promptly sends an alarm signal through sound, light, and text messages, etc., facilitating operators to promptly grasp pipeline anomalies. The parameter configuration unit modifies the algorithm weights according to actual needs or sets corresponding strategies for special working conditions, enhancing the flexibility and operability of the system.

[0083] Preferably, the safety protection module includes an emergency cut-off unit and a leakage detection unit; the emergency cut-off unit can quickly trigger an emergency shutdown system to cut off the pipeline passage when the pipeline pressure exceeds the safety threshold, preventing safety accidents caused by excessive pressure; the leakage detection unit can accurately locate the pipeline leakage point based on the negative pressure wave method and acoustic fingerprint recognition technology, and the positioning accuracy can reach ≤50 meters.

[0084] Preferably, for the intelligent pressure regulation and flow dynamic optimization system of the natural gas pipeline, its intelligent pressure regulation and flow dynamic optimization methods include a hierarchical pressure regulation control method, a flow dynamic optimization method, and an energy efficiency optimization method.

[0085] Preferably, the hierarchical pressure regulation control method includes the following steps:

[0086] SA1, using the multi-source sensor unit in the data acquisition and monitoring module, install a set of pressure sensors every 2 km along the natural gas pipeline. These sensors are high-precision differential pressure sensors that can stably and real-time collect the pipeline pressure data at their locations; the collected data is quickly summarized to the data preprocessing unit through wired or wireless transmission; the data preprocessing unit preliminarily processes the original pressure data, and uses the Kalman filter algorithm to remove the noise generated by factors such as pipeline vibration and electromagnetic interference to ensure the accuracy of the data. At the same time, timestamp marking is performed for subsequent data synchronization and analysis.

[0087] SA2, the PLC core control module obtains the preprocessed pressure data, calculates the pressure gradient by calculating the pressure values collected by adjacent pressure sensors. The specific calculation method is: assume that the pressure values collected by two adjacent pressure sensors are P1 and P2 respectively, and the distance is L = 2 km, then the pressure gradient ΔP = (P2 - P1) / L; and when ΔP I ··1 / 40.3 MPa / km, trigger the pressure regulating valve linkage mechanism of the dynamic pressure regulation control unit. For the upstream pressure regulating valve, the increase in its opening ΔO up is linearly related to the pressure difference, that is, ΔO up = k1×ΔP, where k1 is the linear adjustment coefficient, which is preset according to the specifications of the pressure regulating valve and the actual operating conditions of the pipeline. For the downstream pressure regulating valve, the decrease in its opening ΔO down is calculated using a nonlinear compensation algorithm, ΔO down = k2×ΔP 2 , k2 is the nonlinear compensation coefficient, which is also determined according to the actual working conditions. This nonlinear compensation method can more accurately adapt to the downstream pipeline pressure change and avoid excessive pressure fluctuations.

[0088] SA3, based on historical pressure data and the current pressure change trend, a pipeline pressure prediction model is established. This model takes into account factors such as the physical characteristics of the pipeline, the gas flow state, and the adjustment characteristics of the pressure regulating valve.

[0089] The established model is used to predict the pressure trajectory within the next 5 minutes. During the prediction process, newly collected pressure data is continuously incorporated into the model for updating to achieve rolling optimization. At the same time, the multi-objective optimization unit takes pressure stability and gas transmission efficiency as goals to evaluate and adjust the predicted pressure trajectory. If the predicted pressure trajectory deviates from the set stable range, the opening command of the pressure regulating valve is adjusted to make the pressure return to stability. The adaptive learning unit continuously optimizes the pressure prediction model and the pressure regulation strategy according to the real-time feedback of the pressure control effect to improve the accuracy and stability of pressure control.

[0090] Preferably, the flow dynamic optimization method includes the following steps:

[0091] SC1: The data acquisition and monitoring module continuously collects historical usage data, which covers the natural gas flow usage in different time periods and different seasons. At the same time, real-time weather data is obtained, including information such as temperature, humidity, wind force, and user type (industrial / civilian) data. All data is aggregated to the data preprocessing unit for cleaning and sorting, removing outliers and performing standardization processing;

[0092] The processed data is input into a deep learning model based on the Attention mechanism. This model consists of multiple hidden layers, and the Attention mechanism can automatically assign weights to different input data, highlighting the factors that have a greater impact on flow prediction. For example, during the winter heating season, temperature data has a greater impact on the natural gas flow demand, and the Attention mechanism will assign a higher weight to the temperature data. Through training with a large amount of historical data, the model learns the complex relationships between the data and thus outputs the flow prediction results for the next 24 hours;

[0093] SC2: The flow optimization unit in the PLC core control module receives the flow prediction results. During the low-flow period (judged based on historical data and real-time monitoring, such as the period when residential gas consumption decreases at night), 1 variable-frequency compressor is started. According to the predicted flow demand, by adjusting the power supply frequency of the compressor, its operating load is maintained between 40 - 60%. For example, if the predicted low-flow period flow is Q low , and the rated flow of the compressor is Q rated , then by adjusting the frequency f, the actual output flow Q of the compressor = f × Q rated satisfies and is close to Q low .

[0094] During peak flow periods (such as winter heating periods or peak industrial gas consumption periods), start the 3 parallel compressors. Each compressor operates at 80% load, that is, the output flow of each compressor is 0.8Qrated. At the same time, combine with the gas storage tank for peak shaving. When the actual flow demand exceeds the total output capacity of the 3 compressors, the gas storage tank releases the stored natural gas to supplement the flow; when the flow demand is lower than the total output of the compressors, the excess natural gas is stored in the gas storage tank to achieve a stable supply of flow and meet the flow demands at different times.

[0095] Preferably, the energy efficiency optimization method includes the following steps:

[0096] Establish a three-dimensional relationship model of pressure-flow-energy consumption, and use the gradient descent method to find the optimal operating point. For example, at a pressure of 0.7 MPa, when the flow rate increases by 12%, the energy consumption only increases by 5%. Through such optimization, the maximum energy efficiency of the system is achieved.

[0097] As mentioned above, it is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Although the present invention has been disclosed as above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or equivalent changes by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system, characterized in that, It includes a data acquisition and monitoring module, a PLC core control module, an intelligent optimization algorithm module, a human-machine interaction module, and a safety protection module; The data acquisition and monitoring module uses multi-source sensors to comprehensively monitor various characteristic data of natural gas, including key parameters such as pressure, temperature, flow rate, and combustible gas concentration. By analyzing the characteristic data in real time, once an abnormality is detected, the alarm mechanism is immediately triggered, and corresponding safety measures are quickly taken through the PLC core control module. For example, when the pressure is detected to rise abnormally or the combustible gas concentration exceeds the standard, the safety protection module can automatically close the relevant valves and cut off the gas source to effectively prevent the further expansion of the accident. At the same time, this module can also be closely linked with the emergency response system to ensure that the emergency plan can be quickly activated in case of an emergency, organize the repair force, and minimize losses to the greatest extent; The PLC core control module is responsible for receiving real-time data from the data acquisition and monitoring module, and regulating the operating state of the natural gas pipeline according to the preset safety threshold and the optimization strategy provided by the intelligent optimization algorithm module. It can quickly respond to abnormal data, trigger the safety protection mechanism, dynamically adjust the pipeline pressure according to the flow demand, and achieve the optimal distribution of flow rate; The intelligent optimization algorithm module is based on big data analysis, machine learning, and artificial intelligence algorithms to deeply mine and intelligently analyze the operation data of the natural gas pipeline, predict the pipeline flow trend, identify potential operation bottlenecks, and dynamically adjust the control strategy according to historical data and real-time operation conditions to maximize the pipeline operation efficiency; The human-machine interaction module enables operators to easily monitor the pipeline operating state, receive alarm information, and perform necessary control operations through an intuitive user interface. This module supports a variety of data visualization display methods, including charts, curve graphs, and bar graphs; The safety protection module, by real-time monitoring various characteristic parameters of the natural gas pipeline, when detecting abnormal or characteristic parameters exceeding the preset safety threshold, this module will start the emergency response mechanism to quickly contain potential safety risks. At the same time, the safety protection module also has a self-diagnosis function to identify and report the faults or abnormal states of the system itself to ensure the reliability and stability of the entire monitoring system.

2. The intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on the PLC optimization system according to claim 1, characterized in that, The data acquisition and monitoring module also includes a multi-source sensor unit and a data preprocessing unit. The multi-source sensor unit accurately collects pipeline pressure data in real time through a differential pressure sensor, obtains flow information using an ultrasonic flowmeter, measures temperature with a thermocouple, and analyzes gas component data using a gas chromatograph. The data preprocessing unit uses the Kalman filter algorithm to filter the original data to remove noise interference, eliminates outliers through a specific algorithm to ensure the reliability of the data, aligns the timestamps at the same time, and integrates the processed data into a standardized data packet.

3. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 1, characterized in that, The PLC core control module specifically includes a dynamic voltage regulation control unit and a flow optimization unit. The dynamic voltage regulation control unit adjusts the opening degrees of a pressure regulating valve and pressure regulating equipment by combining the PID control algorithm and the fuzzy algorithm based on the pressure set value and the real-time collected pressure data, so as to achieve stable control of the pipeline pressure. The flow optimization unit uses an LSTM neural network to learn the historical flow patterns and predict the fluctuations of the flow demand; according to the prediction results, the compressor speed is dynamically adjusted to meet the flow demands in different periods.

4. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 1, characterized in that, The intelligent optimization algorithm module specifically includes a multi-objective optimization unit and an adaptive learning unit. The multi-objective optimization unit adopts the NSGA-II algorithm to maximize the gas transmission efficiency and optimize the overall operation performance of the pipeline on the premise of ensuring pressure stability; the adaptive learning unit uses online reinforcement learning technology to continuously update the pressure regulation strategy library, and can automatically adjust the pressure regulation strategy according to the real-time operation state and feedback information to adapt to the changing pipeline working conditions.

5. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 1, characterized in that, The human-machine interaction module includes a visual monitoring unit and a parameter configuration unit. The visual monitoring unit intuitively displays the pipeline operation state in the form of a 3D pipeline pressure, flow, and thermal map. When the characteristic parameters of pressure or flow exceed the set threshold, warning signals are sent in time through sound, light, and text messages, etc., so that the operators can timely grasp the pipeline abnormal conditions; the parameter configuration unit corrects the algorithm weights according to actual requirements.

6. The intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on the PLC optimization system according to claim 1, characterized in that, The safety protection module includes an emergency cut-off unit and a leakage detection unit; the emergency cut-off unit quickly triggers an emergency shutdown system to cut off the pipeline path when the pipeline pressure exceeds the safety threshold, preventing safety accidents caused by excessive pressure; the leakage detection unit accurately locates the pipeline leakage point based on the negative pressure wave method and the acoustic fingerprint recognition technology.

7. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 1, characterized in that The intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines, its intelligent pressure regulation and flow dynamic optimization methods include a hierarchical pressure regulation control method, a flow dynamic optimization method, and an energy efficiency optimization method.

8. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 1, characterized in that, The hierarchical pressure regulation control method includes the following steps: SA1: A pressure sensor group is set every 2 km on the pipeline to obtain the sectional pressure values of the pipeline; SA2: The PLC calculates the obtained pressure data to obtain the pressure gradient; if the pressure difference between adjacent sections > 0.3 MPa, the pressure regulating valve linkage mechanism is started, that is, the opening degree of the upstream valve increases linearly, and the opening degree of the downstream valve is compensated non-linearly and reduced; SA3: The model predictive control (MPC) technology is adopted to perform rolling optimization on the pressure trajectory in the next 5 minutes to ensure the accuracy and stability of pressure control.

9. An intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on a PLC optimization system according to claim 7, characterized in that, The flow dynamic optimization method includes the following steps: SC1: A flow demand prediction model is constructed. This model uses historical usage, weather data, user types (industrial / civil), etc. as input data, and outputs the 24-hour flow prediction results through a deep learning model based on the Attention mechanism. SC2: Dynamically adjust the operation strategy of the compressor group according to the flow prediction results; during the low-demand period, start 1 variable-frequency compressor and keep it operating at a load of 40-60%; during the peak-demand period, parallelly connect 3 compressors and keep them operating at a load of 80%, and combine with the gas storage tank for peak shaving to meet the flow requirements at different times.

10. The intelligent pressure regulation and flow dynamic optimization system for natural gas pipelines based on the PLC optimization system according to claim 1, wherein, The energy efficiency optimization method includes establishing a three-dimensional relationship model of pressure-flow-energy consumption and using the gradient descent method to find the optimal operating point.

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