Lifting type release torch combustion control method and system based on self-adaptive gas supply of incandescent light

Abstract

The invention discloses a lifting type release torch combustion control method and system based on incandescent lamp self-adaptive gas supply, relates to the technical field of combustion control, and aims to construct a gas pipe network-torch combustion virtual mapping through field monitoring equipment and a digital twinning real-time optimization technology, and integrate CFD simulation prediction flame form parameters, so as to achieve the purpose of controlling the combustion of the lifting type release torch. Training a gas supply strategy through a PPO algorithm; when it is detected that the release torch is started or the gas supply state of the incandescent light needs to be adjusted, target gas supply parameter data sent by a sensor are obtained; determining a gas supply parameter set according to the judgment result and the equipment identifier, and combining the characteristics of the bionic gas supply structure; whether the target gas supply parameter data is matched with a certain group of gas supply parameters in the gas supply parameter set or not is judged, and if yes, a gas supply adjustment prompt is sent to a background control server; and after receiving the prompt, the background control server determines a gas supply adjustment scheme in combination with a plasma low-carbon combustion technology, and controls the gas supply adjustment equipment to execute the adjustment scheme.

Description

Technical Field

[0001] This invention relates to the field of combustion control technology, and more specifically, to a combustion control method and system for a lifting flare based on adaptive gas supply from a continuous lamp. Background Technology

[0002] In modern industrial production, such as petrochemicals and natural gas, flare combustion systems are crucial for handling excess fuel gas. Traditional flare combustion control systems have several shortcomings. On the one hand, gas supply regulation relies on fixed parameters or simple feedback mechanisms, making it difficult to cope with complex and changing operating conditions. When fuel composition fluctuates or environmental weather conditions change, the gas supply cannot be precisely adjusted, easily leading to incomplete combustion, excessive black smoke emissions from the flare, and energy waste. On the other hand, the gas supply guarantee for the emergency lights lacks intelligent adaptability. During special operating condition switching or system fluctuations, the emergency lights are prone to extinguishing, causing a significant safety hazard of flare combustion interruption. Moreover, the existing system has poor coordination between various devices, and on-site monitoring data cannot be effectively linked with the back-end control, making it difficult to achieve optimized management and control of the overall combustion process. This fails to meet the current industrial production requirements for efficient, environmentally friendly, and stable operation of flare combustion systems.

[0003] Therefore, the existing flare combustion control system has problems such as inaccurate gas supply regulation, frequent abnormalities in the gas supply to the continuous lamp, and poor system coordination. Summary of the Invention

[0004] In order to overcome the problems of inaccurate gas supply regulation, abnormal gas supply from the continuous lamp, and poor system coordination in the existing flare combustion control, this invention discloses a lifting flare combustion control method and system based on adaptive gas supply from the continuous lamp, which can effectively solve the above-mentioned technical problems.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A combustion control method for a lifting flare based on adaptive gas supply from a continuous lamp is disclosed. The method is applied to a combustion control system, which includes field monitoring equipment, gas supply regulating equipment, and a back-end control server. The method includes: The on-site monitoring equipment is based on digital twin real-time optimization technology to construct a virtual mapping of gas pipeline network-flare combustion. It integrates CFD simulation to predict the flame morphology parameters of the continuous lamps and empty flares in the current time period, including turbulence intensity and temperature gradient. The gas supply strategy is trained through the PPO algorithm to determine whether the flame morphology parameters are greater than or equal to a predetermined threshold, and the judgment result is obtained. When the start-up of the flare or the need to adjust the gas supply status of the night lamp is detected, the field monitoring equipment acquires the target gas supply parameter data sent by the sensors installed at the flare and night lamp. The target gas supply parameter data includes gas flow rate, pressure, composition and temperature. The on-site monitoring equipment determines the gas supply parameter set based on the judgment result and the equipment identification corresponding to the flare and the continuous light. The gas supply parameter set includes a preset standard gas supply parameter set or a target sub-gas supply parameter set corresponding to the equipment identification. The determination of the set is combined with the characteristics of the biomimetic gas supply structure. The biomimetic gas supply structure is based on a biomimetic design of a gas distributor to achieve laminar flow gas supply and uses Ni-Ti alloy micro-valve to dynamically adjust the local flow. The on-site monitoring equipment determines whether the target gas supply parameter data matches a certain set of gas supply parameters in the set based on the determined set of gas supply parameters; if they match, the on-site monitoring equipment sends a gas supply adjustment prompt to the background control server, the gas supply adjustment prompt including the target gas supply parameter data and the equipment identifier; The background control server receives gas supply adjustment prompts, determines a gas supply adjustment plan based on the equipment identification and in conjunction with plasma low-carbon combustion technology; the plasma low-carbon combustion technology integrates a plasma generator in the torch head to ionize the gas and lower the ignition point of methane, and uses the flame radiation heat to generate electricity to power the plasma system; and controls the gas supply adjustment equipment to execute the adjustment plan.

[0006] Preferably, when the gas supply parameter set is a preset standard gas supply parameter set, the on-site monitoring equipment determines the gas supply parameter set based on the judgment result and equipment identification, including: The on-site monitoring equipment collects environmental parameters and initial operating parameters of the continuous lamps and flares, and combines them with the characteristics of the digital twin model and the bionic gas supply structure to generate a set of standard gas supply parameters.

[0007] Preferably, the method further includes: If the target gas supply parameter data does not match any set of data in the gas supply parameter set, the field monitoring equipment sends a matching failure prompt to the gas supply regulating equipment. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating equipment.

[0008] Preferably, the background control server determines the gas supply adjustment plan based on the device identifier in the gas supply adjustment prompt, including: The background control server determines whether the gas supply mode of the flare torch and the ever-burning lamp is a dynamic gas supply mode based on the device identifier. In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

[0009] Preferably, after the background control server receives the gas supply adjustment prompt, the method further includes: The background control server determines whether the flare and the continuous light are in emergency operation state based on the target gas supply parameter data. If not in emergency operation mode, the operation of determining the gas supply adjustment plan based on the equipment identification will be triggered.

[0010] Preferably, a lifting flare combustion control system based on adaptive gas supply from a continuous lamp includes on-site monitoring equipment, gas supply regulation equipment, and a back-end control server. The on-site monitoring equipment includes: The module comprises a detection module, an acquisition module, a first judgment module, a first determination module, and a sending module; The background control server includes a receiving module, a second determining module, and an adjusting module; The first judgment module is used to construct a virtual mapping of gas pipeline network-flare combustion based on digital twin real-time optimization technology, integrate CFD simulation to predict flame morphology parameters, train the gas supply strategy through PPO algorithm, and judge whether the flame morphology parameters are greater than or equal to a predetermined threshold to obtain the judgment result. The acquisition module is used to acquire target gas supply parameter data when the detection module detects that the venting torch has been started or the gas supply status of the continuous lamp needs to be adjusted. The first determining module is used to determine the set of gas supply parameters based on the judgment result and the device identifier; The first judgment module is also used to determine whether the target gas supply parameter data matches based on the gas supply parameter set; The sending module is used to send a gas supply adjustment prompt to the background control server when a match is determined. The receiving module is used to receive gas supply adjustment prompts; The second determining module is used to determine the gas supply adjustment scheme based on the equipment identification and in conjunction with plasma low-carbon combustion technology; The adjustment module is used to control the gas supply adjustment equipment to execute the adjustment plan.

[0011] Preferably, when the gas supply parameter set is a preset standard gas supply parameter set, the first determining module determines the gas supply parameter set based on the judgment result and the equipment identifier in the following specific way: By collecting environmental parameters and initial operating parameters of the continuously lit lamps and flares, and combining them with the characteristics of the biomimetic gas supply structure, a set of standard gas supply parameters is generated.

[0012] The sending module is also used to send a matching failure prompt to the gas supply regulating device when a mismatch is determined. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating device.

[0013] The second determining module determines the specific method of the gas supply adjustment plan based on the equipment identification as follows: Determine whether the gas supply mode is dynamic based on the equipment label; In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

[0014] Preferably, the background control server further includes a second judgment module, wherein: The second judgment module is used to determine whether the venting torch and the continuous light are in an emergency operation state based on the target gas supply parameter data after the receiving module receives the gas supply adjustment prompt. The second determining module is also used to determine a gas supply adjustment plan based on the equipment identifier when it is determined that the system is not in an emergency operation state.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention solves the problems of inaccurate gas supply regulation, frequent gas supply anomalies in the continuous lighting lamp, and poor system coordination in existing flare combustion control systems by coordinating the work of on-site monitoring equipment, gas supply regulation equipment, and a back-end control server. The on-site monitoring equipment uses digital twin real-time optimization technology to construct a virtual mapping of the gas pipeline network and flare combustion, and integrates CFD simulation to predict flame morphology parameters, enabling real-time and accurate understanding of the combustion status of the continuous lighting lamp and flare. Simultaneously, the PPO algorithm is used to train the gas supply strategy and judge the flame morphology parameters, providing a scientific basis for gas supply regulation and solving the problem of inaccurate gas supply regulation. Secondly, the on-site monitoring equipment can adjust the gas supply when it detects the flare starting or the continuous lighting lamp's gas supply status. At the designated time, the system can acquire target gas supply parameter data sent by sensors and determine the set of gas supply parameters based on the judgment results and equipment identification. Combined with the characteristics of the biomimetic gas supply structure, it can effectively control and adjust the gas supply to the flare lamp, reducing the risk of abnormal gas supply to the flare lamp. Finally, the entire combustion control system achieves rapid information transmission and processing through close cooperation between the field monitoring equipment and the back-end control server, improving the system's synergy. The field monitoring equipment sends the target gas supply parameter data and equipment identification to the back-end control server. The server determines the gas supply adjustment plan based on this information and controls the gas supply adjustment equipment to perform the adjustment, thereby achieving control over the combustion process of the flare torch, improving combustion efficiency, reducing pollutant emissions, and enhancing the system's adaptability and stability. Attached Figure Description

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other embodiments can be derived from the provided drawings without creative effort.

[0017] Figure 1 This is a diagram illustrating the steps of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0018] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] CFD, or Computational Fluid Dynamics, is a discipline that uses numerical methods and algorithms to solve fluid dynamics problems. In flare combustion control systems, CFD can simulate airflow, temperature, and chemical reactions during flare combustion, helping to predict flame morphology parameters such as turbulence intensity and temperature gradient. This provides a basis for optimizing gas supply strategies. For example, CFD simulations can simulate flare combustion under different gas supply parameters, analyze flame stability and pollutant emissions, and thus determine the optimal set of gas supply parameters to achieve efficient and environmentally friendly combustion.

[0021] The PPO algorithm, or Proximal Policy Optimization algorithm, is an optimization algorithm used to train reinforcement learning agents. It learns how to take optimal actions in different states to maximize cumulative rewards through interaction with the environment. In flare combustion control systems, the PPO algorithm can be used to train gas supply strategies. The principle of the PPO algorithm is as follows: The policy function and the value function are used to evaluate the quality of the current policy. The policy function gives the probability distribution of each action based on the current state, which determines the agent's tendency to choose different actions in different states. The value function is used to evaluate the quality of the current policy. It estimates the expected value of the long-term cumulative reward that the agent can obtain in a given state. In torch combustion control, the state can include flame morphology parameters, such as turbulence intensity and temperature gradient, as well as gas supply parameters, such as gas flow rate and pressure. The action corresponds to the adjustment of the gas supply parameters.

[0022] The advantage function measures the additional benefit gained from taking a certain action in a specific state compared to the average action of the current policy. The advantage function is usually calculated using generalized advantage estimation (GAE), which combines the policy function and the value function. It can balance bias and variance to a certain extent and provide a more accurate direction for policy updates.

[0023] To limit the difference between the old and new policies, the PPO algorithm introduces the idea of ​​truncating the trust region during policy updates. When updating the policy, a threshold, such as ε=0.2, is set to limit the update magnitude of the policy function. When calculating the loss function, the ratio of the probability of choosing the same action by the old and new policies is compared with the threshold. If this ratio exceeds the threshold range, the update is truncated, thereby avoiding the instability of the learning process caused by excessive policy update magnitude.

[0024] Application of PPO algorithm in flare combustion control In the flare combustion control system, the agent continuously tries different ways of adjusting the gas supply parameters by interacting with the environment (i.e., the flare combustion system) and collects sample data. The PPO algorithm uses this data to train the gas supply strategy. The agent can learn the optimal gas supply adjustment scheme based on the current flame morphology parameters and gas supply parameters to achieve efficient and stable combustion.

[0025] The PPO algorithm can adaptively adjust gas supply parameters. As various factors change during the flare combustion process, such as fluctuations in gas composition and changes in environmental conditions, the PPO algorithm can update the strategy in real time and adaptively adjust the gas supply parameters. For example, when flame morphology parameters, such as turbulence intensity, are detected to exceed a preset threshold, the agent can adjust parameters such as gas flow rate and pressure in a timely manner according to the trained strategy to restore the flame to a stable state and ensure the safety and environmental friendliness of combustion.

[0026] By learning the optimal gas supply strategy, the PPO algorithm can improve combustion efficiency and environmental friendliness, enabling the flare combustion system to maintain good combustion under different operating conditions, thereby improving combustion efficiency and reducing energy waste. At the same time, it can also optimize the combustion process and reduce pollutant emissions, such as reducing the emission of harmful gases such as nitrogen oxides and unburned hydrocarbons, thus achieving environmentally friendly operation of flare combustion.

[0027] Example 1

[0028] A combustion control method for a lifting flare based on adaptive gas supply from a continuous lamp is disclosed. The method is applied to a combustion control system, which includes field monitoring equipment, gas supply regulating equipment, and a back-end control server. The method includes: The on-site monitoring equipment is based on digital twin real-time optimization technology to construct a virtual mapping of gas pipeline network-flare combustion. It integrates CFD simulation to predict the flame morphology parameters of the continuous lamps and empty flares in the current time period, including turbulence intensity and temperature gradient. The gas supply strategy is trained through the PPO algorithm to determine whether the flame morphology parameters are greater than or equal to a predetermined threshold, and the judgment result is obtained. When the start-up of the flare or the need to adjust the gas supply status of the night lamp is detected, the field monitoring equipment acquires the target gas supply parameter data sent by the sensors installed at the flare and night lamp. The target gas supply parameter data includes gas flow rate, pressure, composition and temperature. The on-site monitoring equipment determines the gas supply parameter set based on the judgment result and the equipment identification corresponding to the flare and the continuous light. The gas supply parameter set includes a preset standard gas supply parameter set or a target sub-gas supply parameter set corresponding to the equipment identification. The determination of the set is combined with the characteristics of the biomimetic gas supply structure. The biomimetic gas supply structure is based on a biomimetic design of a gas distributor to achieve laminar flow gas supply and uses Ni-Ti alloy micro-valve to dynamically adjust the local flow. The on-site monitoring equipment determines whether the target gas supply parameter data matches a certain set of gas supply parameters in the set based on the determined set of gas supply parameters; if they match, the on-site monitoring equipment sends a gas supply adjustment prompt to the background control server, the gas supply adjustment prompt including the target gas supply parameter data and the equipment identifier; The background control server receives gas supply adjustment prompts, determines a gas supply adjustment plan based on the equipment identification and in conjunction with plasma low-carbon combustion technology; the plasma low-carbon combustion technology integrates a plasma generator in the torch head to ionize the gas and lower the ignition point of methane, and uses the flame radiation heat to generate electricity to power the plasma system; and controls the gas supply adjustment equipment to execute the adjustment plan.

[0029] When the gas supply parameter set is a preset standard gas supply parameter set, the field monitoring equipment determines the gas supply parameter set based on the judgment result and equipment identification, including: The on-site monitoring equipment collects environmental parameters and initial operating parameters of the continuous lamps and flares, and combines them with the characteristics of the digital twin model and the bionic gas supply structure to generate a set of standard gas supply parameters.

[0030] The method further includes: If the target gas supply parameter data does not match any set of data in the gas supply parameter set, the field monitoring equipment sends a matching failure prompt to the gas supply regulating equipment. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating equipment.

[0031] The background control server determines the gas supply adjustment plan based on the device identifier in the gas supply adjustment prompt, including: The background control server determines whether the gas supply mode of the flare torch and the ever-burning lamp is a dynamic gas supply mode based on the device identifier. In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

[0032] After the background control server receives the gas supply adjustment prompt, the method further includes: The background control server determines whether the flare and the continuous light are in emergency operation state based on the target gas supply parameter data. If not in emergency operation mode, the operation of determining the gas supply adjustment plan based on the equipment identification will be triggered.

[0033] For specific implementation details, please refer to [link / reference]. Figure 1 On-site monitoring equipment, including various high-precision sensors such as gas flow sensors, pressure sensors, component analyzers, and temperature sensors, is installed around the flare and emergency lights of the natural gas processing plant to collect gas supply parameter data in real time. These sensors are connected to the main control unit of the on-site monitoring equipment via wired or wireless communication. At the same time, the on-site monitoring equipment, gas supply regulation equipment, and back-end control server establish a stable and high-speed communication connection through industrial Ethernet or 4G / 5G wireless communication networks.

[0034] Using digital twin real-time optimization technology, a virtual mapping model of gas pipeline network-flare combustion is constructed based on the gas pipeline network design drawings, flare stack and flare lamp technical parameters of the natural gas processing plant. This model simulates the flow of gas in the pipeline network, pressure changes, and various physical and chemical reaction processes during flare combustion.

[0035] Based on historical data and theoretical research on torch combustion, parameters required for CFD simulation are set, such as the physicochemical properties of the fuel gas, burner structure, initial temperature and pressure, etc., to predict the flame morphology parameters of the continuous lamps and empty torches in the current time period, such as turbulence intensity and temperature gradient.

[0036] Historical data on torch combustion is collected, including gas supply parameters, flame morphology parameters, and combustion efficiency under different operating conditions. This data is used to train the PPO algorithm, enabling it to learn the optimal gas supply strategy under different conditions. At the same time, thresholds for the flame morphology parameters of the continuous lamp and the empty torch are determined to judge whether the flame status is normal.

[0037] The detection module of the on-site monitoring equipment operates in real time, constantly monitoring whether the flare flare is started. It makes judgments by analyzing the pressure changes and valve opening / closing signals of the flare flare system. At the same time, it monitors whether the gas supply status of the keep-alive lamp needs adjustment, for example, based on the combustion status of the keep-alive lamp, such as flame color, temperature changes, and the stability of the gas supply.

[0038] When the flare is detected to be activated, the acquisition module immediately acquires the target gas supply parameter data sent by the sensors installed at the flare and the mandrel, including real-time gas flow rate, pressure, composition and temperature. At the same time, the first judgment module, based on the constructed digital twin model, calls the CFD simulation program, inputs the current target gas supply parameter data and other relevant conditions, and predicts the flame morphology parameters of the mandrel and the flare in the current time period, namely turbulence intensity and temperature gradient.

[0039] The predicted flame morphology parameters are compared with a predetermined threshold. The gas supply strategy trained by the PPO algorithm is used to determine whether these parameters are greater than or equal to the threshold, thereby obtaining the judgment result and determining whether the current gas supply status needs to be adjusted.

[0040] Based on the above judgment results and the equipment identification corresponding to the flare and the night lamp (each device has a unique identification code to distinguish different flares and night lamps), the first determination module determines the gas supply parameter set. It is assumed that the gas supply parameter set in this operation is the preset standard gas supply parameter set. The on-site monitoring equipment collects environmental parameters of the night lamp and flare, such as wind speed, humidity, and atmospheric pressure, as well as the initial operating parameters of the equipment, such as initial gas supply flow rate, pressure, and gas composition ratio. Then, combined with the digital twin model and the characteristics of the biomimetic gas supply structure, a standard gas supply parameter set is generated. The biomimetic gas supply structure is a gas distributor designed based on biomimetic principles, mimicking the blood circulation system in a living organism or the transport tissue of a plant, to achieve laminar gas supply and reduce combustion instability caused by turbulence. At the same time, Ni-Ti alloy micro-valve is used to dynamically adjust the local flow rate according to the real-time situation to further optimize the gas supply effect.

[0041] The first judgment module compares and matches the acquired target gas supply parameter data with each group of gas supply parameters in the set according to the determined gas supply parameter set, and determines whether there is a gas supply parameter group that matches the target gas supply parameter data.

[0042] If the target gas supply parameter data matches a set of gas supply parameters in the gas supply parameter set, the sending module sends a gas supply adjustment prompt to the background control server, which includes detailed target gas supply parameter data and corresponding equipment identification information.

[0043] After the receiving module of the background control server receives the gas supply adjustment prompt, the second judgment module determines whether the flare and the emergency light are in an emergency operation state based on the target gas supply parameter data in the prompt, such as gas leakage, excessive pressure or abnormal temperature. If the judgment result is that they are not in an emergency operation state, the second determination module begins to determine the gas supply adjustment plan based on the equipment identification.

[0044] The second determining module, based on the equipment identification, determines whether the gas supply mode of the flare and the night lamp is dynamic. If it is dynamic, it calculates and determines real-time gas supply adjustment parameters, such as adjusting the specific values ​​of gas flow and pressure, based on the gas supply strategy trained by the digital twin model and PPO algorithm, combined with real-time data from plasma low-carbon combustion technology, such as the working status and ionization effect of the plasma generator. If it is not dynamic, it calls the preset fixed gas supply adjustment parameters as the gas supply adjustment scheme. Finally, the adjustment module sends control commands to the gas supply adjustment equipment to execute the adjustment scheme, such as controlling the valve opening on the gas supply pipeline and adjusting the working status of the gas distributor, so as to achieve adaptive gas supply for the night lamp and stable combustion of the flare, so that its combustion efficiency reaches the optimal level, while reducing pollutant emissions.

[0045] If the target gas supply parameter data does not match any set of data in the gas supply parameter set, the sending module of the field monitoring equipment sends a matching failure prompt to the gas supply regulating equipment. After receiving the prompt, the gas supply regulating equipment automatically triggers the manual intervention program, prompting the field operator to perform manual inspection and adjustment, or starts the fault diagnosis program to diagnose the gas supply system, find the problem and resolve it in a timely manner.

[0046] Example 2

[0047] A combustion control system for a lifting flare torch based on adaptive gas supply from a continuous lamp includes on-site monitoring equipment, gas supply regulation equipment, and a back-end control server. The on-site monitoring equipment includes: The module comprises a detection module, an acquisition module, a first judgment module, a first determination module, and a sending module; The background control server includes a receiving module, a second determining module, and an adjusting module; The first judgment module is used to construct a virtual mapping of gas pipeline network-flare combustion based on digital twin real-time optimization technology, integrate CFD simulation to predict flame morphology parameters, train the gas supply strategy through PPO algorithm, and judge whether the flame morphology parameters are greater than or equal to a predetermined threshold to obtain the judgment result. The acquisition module is used to acquire target gas supply parameter data when the detection module detects that the venting torch has been started or the gas supply status of the continuous lamp needs to be adjusted. The first determining module is used to determine the set of gas supply parameters based on the judgment result and the device identifier; The first judgment module is also used to determine whether the target gas supply parameter data matches based on the gas supply parameter set; The sending module is used to send a gas supply adjustment prompt to the background control server when a match is determined. The receiving module is used to receive gas supply adjustment prompts; The second determining module is used to determine the gas supply adjustment scheme based on the equipment identification and in conjunction with plasma low-carbon combustion technology; The adjustment module is used to control the gas supply adjustment equipment to execute the adjustment plan.

[0048] When the gas supply parameter set is a preset standard gas supply parameter set, the first determining module determines the gas supply parameter set based on the judgment result and the equipment identifier in the following manner: By collecting environmental parameters and initial operating parameters of the continuously lit lamps and flares, and combining them with the characteristics of the biomimetic gas supply structure, a set of standard gas supply parameters is generated.

[0049] The sending module is also used to send a matching failure prompt to the gas supply regulating device when a mismatch is determined. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating device.

[0050] The second determining module determines the specific method of the gas supply adjustment plan based on the equipment identification as follows: Determine whether the gas supply mode is dynamic based on the equipment label; In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

[0051] The background control server further includes a second judgment module, wherein: The second judgment module is used to determine whether the venting torch and the continuous light are in an emergency operation state based on the target gas supply parameter data after the receiving module receives the gas supply adjustment prompt. The second determining module is also used to determine a gas supply adjustment plan based on the equipment identifier when it is determined that the system is not in an emergency operation state.

[0052] Please see Figure 2 The detection module monitors in real time whether the venting flare is started. It makes a judgment by monitoring changes in the pressure sensor data of the venting flare system and the opening and closing signals of the venting valve. At the same time, it monitors whether the gas supply status of the keep-alive lamp needs to be adjusted. It makes a judgment based on the image analysis of the flame monitoring camera of the keep-alive lamp, such as changes in flame shape and color, as well as fluctuations in gas supply pressure.

[0053] When the detection module detects that the flare torch is started or the gas supply status of the night light needs to be adjusted, the acquisition module quickly acquires the target gas supply parameter data sent by the sensors installed at the flare torch and night light, including high-precision gas flow, pressure, composition and temperature data. These sensors use advanced measurement technology to ensure the accuracy and real-time nature of the data.

[0054] The first judgment module, on the one hand, uses a virtual mapping model of gas pipeline network-flare combustion built based on digital twin real-time optimization technology, and integrates a CFD simulation program to predict the flame morphology parameters (turbulence intensity, temperature gradient) of the mandatory lamps and flares in the current time period. On the other hand, it compares the predicted flame morphology parameters with a pre-determined threshold through a gas supply strategy trained by the PPO algorithm, and obtains a judgment result to determine whether the gas supply status needs to be adjusted. In addition, this module is also used to determine whether the target gas supply parameter data matches a certain set of gas supply parameters in the set based on the determined set of gas supply parameters.

[0055] The first determining module determines the gas supply parameter set based on the judgment result and the equipment identification corresponding to the flare and the pilot light. When the gas supply parameter set is the preset standard gas supply parameter set, it collects environmental parameters of the pilot light and flare, such as wind direction, wind speed, and ambient temperature, as well as initial operating parameters of the equipment, such as gas flow rate and pressure at initial ignition. Combining the digital twin model and the characteristics of the biomimetic gas supply structure, it generates the standard gas supply parameter set. The biomimetic gas supply structure is based on a gas distributor designed with biomimicry, which imitates the respiratory system of animals or the opening and closing principle of stomata of plants to achieve laminar gas supply and improve combustion stability. At the same time, it utilizes the shape memory effect and high sensitivity of the Ni-Ti alloy micro-valve to dynamically adjust the local flow rate to adapt to different combustion needs.

[0056] When the first judgment module determines that the target gas supply parameter data matches the gas supply parameter set, the sending module sends a detailed gas supply adjustment prompt to the background control server, including the accurate target gas supply parameter data and equipment identification. If the target gas supply parameter data does not match any set of data in the gas supply parameter set, a matching failure prompt is sent to the gas supply adjustment equipment, triggering the manual intervention or fault diagnosis procedure of the gas supply adjustment equipment to ensure the normal operation of the gas supply system.

[0057] The receiving module receives gas supply adjustment prompts from the on-site monitoring equipment in real time, ensuring the integrity and accuracy of data transmission.

[0058] The second judgment module determines whether the flare and the emergency light are in an emergency operation state based on the target gas supply parameter data in the received gas supply adjustment prompt. For example, it determines whether there is an emergency situation such as excessive combustible gas concentration caused by gas leakage, overpressure detected by the pressure sensor, or abnormal high temperature detected by the temperature sensor.

[0059] When the second determining module determines that the system is not in an emergency operation state, it determines the gas supply adjustment plan based on the equipment identification. Specifically, it first determines whether the gas supply mode of the flare and the emergency light is dynamic. If it is dynamic, it calculates and determines the real-time gas supply adjustment parameters, such as the precise gas flow adjustment amount and pressure adjustment range, based on the gas supply strategy trained by the digital twin model and PPO algorithm, combined with real-time data of plasma low-carbon combustion technology, such as the current and voltage parameters of the plasma generator and the changes in the composition of the gas after ionization. If it is not dynamic, it directly calls the preset fixed gas supply adjustment parameters as the gas supply adjustment plan.

[0060] According to the gas supply adjustment plan determined by the second determining module, the adjustment module sends precise control commands to the gas supply adjustment equipment to control the gas supply adjustment equipment to execute the adjustment plan. For example, it controls the opening degree of the electric valve on the gas supply pipeline, adjusts the working state of the gas distributor and the power output of the plasma generator, so as to achieve precise control of the combustion of the lamp and the flare, ensure the stability and efficiency of combustion, and reduce carbon emissions.

[0061] The gas supply regulation equipment mainly consists of high-precision electric valves, gas distributors, plasma generators, and corresponding actuators on the gas supply pipeline. The electric valves precisely regulate the gas flow and pressure according to the instructions of the background control server. The gas distributor is based on a biomimetic gas supply structure design to achieve uniform and stable gas distribution. The plasma generator generates plasma at the flare head, ionizes the gas, lowers the ignition point of methane, and improves combustion efficiency. After receiving the control instructions from the background control server, these devices respond quickly and adjust their working status to meet the combustion requirements of the continuous lamps and flare stacks, ensuring the safe, stable, and environmentally friendly operation of the entire combustion system.

[0062] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A combustion control method for a lifting flare based on adaptive gas supply from a continuous lamp, characterized in that, The method is applied to a combustion control system, which includes field monitoring equipment, gas supply regulation equipment, and a back-end control server. The method includes: The on-site monitoring equipment is based on digital twin real-time optimization technology to construct a virtual mapping of gas pipeline network-flare combustion. It integrates CFD simulation to predict the flame morphology parameters of the continuous lamps and empty flares in the current time period, including turbulence intensity and temperature gradient. The gas supply strategy is trained through the PPO algorithm to determine whether the flame morphology parameters are greater than or equal to a predetermined threshold, and the judgment result is obtained. When the start-up of the flare or the need to adjust the gas supply status of the night lamp is detected, the field monitoring equipment acquires the target gas supply parameter data sent by the sensors installed at the flare and night lamp. The target gas supply parameter data includes gas flow rate, pressure, composition and temperature. The on-site monitoring equipment determines the gas supply parameter set based on the judgment result and the equipment identification corresponding to the flare and the continuous light. The gas supply parameter set includes a preset standard gas supply parameter set or a target sub-gas supply parameter set corresponding to the equipment identification. The determination of the set is combined with the characteristics of the biomimetic gas supply structure. The biomimetic gas supply structure is based on a biomimetic design of a gas distributor to achieve laminar flow gas supply and uses Ni-Ti alloy micro-valve to dynamically adjust the local flow. The on-site monitoring equipment determines whether the target gas supply parameter data matches a certain set of gas supply parameters in the set based on the determined set of gas supply parameters; if they match, the on-site monitoring equipment sends a gas supply adjustment prompt to the background control server, the gas supply adjustment prompt including the target gas supply parameter data and the equipment identifier; The background control server receives gas supply adjustment prompts, determines a gas supply adjustment plan based on the equipment identification and in conjunction with plasma low-carbon combustion technology; the plasma low-carbon combustion technology integrates a plasma generator in the torch head to ionize the gas and lower the ignition point of methane, and uses the flame radiation heat to generate electricity to power the plasma system; and controls the gas supply adjustment equipment to execute the adjustment plan.

2. The combustion control method according to claim 1, characterized in that, When the gas supply parameter set is a preset standard gas supply parameter set, the field monitoring equipment determines the gas supply parameter set based on the judgment result and equipment identification, including: The on-site monitoring equipment collects environmental parameters and initial operating parameters of the continuous lamps and flares, and combines them with the characteristics of the digital twin model and the bionic gas supply structure to generate a set of standard gas supply parameters.

3. The combustion control method according to claim 2, characterized in that, The method further includes: If the target gas supply parameter data does not match any set of data in the gas supply parameter set, the field monitoring equipment sends a matching failure prompt to the gas supply regulating equipment. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating equipment.

4. The combustion control method according to claim 1, characterized in that, The background control server determines the gas supply adjustment plan based on the device identifier in the gas supply adjustment prompt, including: The background control server determines whether the gas supply mode of the flare torch and the ever-burning lamp is a dynamic gas supply mode based on the device identifier. In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

5. The combustion control method according to claim 1, characterized in that, After the background control server receives the gas supply adjustment prompt, the method further includes: The background control server determines whether the flare and the continuous light are in emergency operation state based on the target gas supply parameter data. If not in emergency operation mode, the operation of determining the gas supply adjustment plan based on the equipment identification will be triggered.

6. A combustion control system for a lifting flare torch based on adaptive gas supply from a continuous lamp, used to implement the combustion control method according to any one of claims 1-5, characterized in that, It includes on-site monitoring equipment, gas supply regulation equipment, and a back-end control server. The on-site monitoring equipment includes: The module comprises a detection module, an acquisition module, a first judgment module, a first determination module, and a sending module; The background control server includes a receiving module, a second determining module, and an adjusting module; The first judgment module is used to construct a virtual mapping of gas pipeline network-flare combustion based on digital twin real-time optimization technology, integrate CFD simulation to predict flame morphology parameters, train the gas supply strategy through PPO algorithm, and judge whether the flame morphology parameters are greater than or equal to a predetermined threshold to obtain the judgment result. The acquisition module is used to acquire target gas supply parameter data when the detection module detects that the venting torch has been started or the gas supply status of the continuous lamp needs to be adjusted. The first determining module is used to determine the set of gas supply parameters based on the judgment result and the device identifier; The first judgment module is also used to determine whether the target gas supply parameter data matches based on the gas supply parameter set; The sending module is used to send a gas supply adjustment prompt to the background control server when a match is determined. The receiving module is used to receive gas supply adjustment prompts; The second determining module is used to determine the gas supply adjustment scheme based on the equipment identification and in conjunction with plasma low-carbon combustion technology; The adjustment module is used to control the gas supply adjustment equipment to execute the adjustment plan.

7. The combustion control system according to claim 6, characterized in that, When the gas supply parameter set is a preset standard gas supply parameter set, the first determining module determines the gas supply parameter set based on the judgment result and the equipment identifier in the following manner: By collecting environmental parameters and initial operating parameters of the continuously lit lamps and flares, and combining them with the characteristics of the biomimetic gas supply structure, a set of standard gas supply parameters is generated.

8. The combustion control system according to claim 7, characterized in that, The sending module is also used to send a matching failure prompt to the gas supply regulating device when a mismatch is determined. The matching failure prompt is used to trigger manual intervention or troubleshooting procedures of the gas supply regulating device.

9. The combustion control system according to claim 7, characterized in that, The second determining module determines the specific method of the gas supply adjustment plan based on the equipment identification as follows: Determine whether the gas supply mode is dynamic based on the equipment label; In the case of dynamic gas supply mode, the gas supply strategy trained based on the digital twin model and PPO algorithm is combined with real-time data of plasma low-carbon combustion to determine the real-time gas supply adjustment parameters. If it is not in dynamic gas supply mode, the preset fixed gas supply adjustment parameters will be used as the gas supply adjustment scheme.

10. The combustion control system according to claim 7, characterized in that, The background control server further includes a second judgment module, wherein: The second judgment module is used to determine whether the venting torch and the continuous light are in an emergency operation state based on the target gas supply parameter data after the receiving module receives the gas supply adjustment prompt. The second determining module is also used to determine a gas supply adjustment plan based on the equipment identifier when it is determined that the system is not in an emergency operation state.

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