Method and system for adaptive regulation of grid-connected delivery of biomass gas

Abstract

The application discloses a biomass gas grid-connected conveying self-adaptive adjustment method and system, relates to the technical field of biomass gas, and comprises the following steps: generating a quality qualified mark based on scheduling instruction data and biomass gas quality data, applying a self-adaptive control algorithm to dynamically adjust the biomass gas flow, and collecting the adjusted flow data; analyzing whether the gas data is abnormal and generating an abnormal mark; based on the pressure abnormal mark, detecting and analyzing the pressure data corresponding to the pressure regulating component and performing corresponding safety control; the limitation problems existing in the current biomass gas grid-connected conveying self-adaptive adjustment process are solved, the adjustment valve is detected based on the abnormal mark, adjustment valve data is acquired, an adjustment valve comprehensive evaluation coefficient is calculated, and a warning prompt is simultaneously performed; and the biomass gas grid-connected conveying self-adaptive adjustment feasibility, comprehensiveness and objectivity are analyzed.

Description

Technical Field

[0001] This application relates to the field of biomass gas technology, specifically to an adaptive regulation method and system for grid-connected transmission of biomass gas. Background Technology

[0002] With the development of technology, the importance of adaptive regulation in the grid-connected transmission of biomass gas is becoming increasingly prominent. Traditional transmission methods rely on manual inspection and adjustment, which is inefficient and has a slow response time.

[0003] The prior art, such as the invention application patent with announcement number CN119371996A, discloses a biomass gasification fuel gas transmission system, including a biomass gasifier, and further comprising: a cooling device disposed on the gas output pipeline of the biomass gasifier; a water-cooled circulation mechanism connected to the cooling device; a guide waste discharge cylinder connected to the output pipeline of the biomass gasifier; a waste discharge cylinder cleaning mechanism disposed on the outer wall of the guide waste discharge cylinder; a tar and dust removal mechanism disposed above the guide waste discharge cylinder; an ammonia water replenishment mechanism connected to the input pipeline of the waste discharge cylinder cleaning mechanism via the water-cooled circulation mechanism; and a monitoring and purification mechanism disposed at the output end of the tar and dust removal mechanism.

[0004] Regarding the above-mentioned solutions, the inventors of this application have found that the above-mentioned technologies have at least the following technical problems: 1. Currently, there is a lack of pre-acquisition of dispatch instruction data and simultaneous acquisition of biogas quality data when the pipeline network issues dispatch instructions or detects real-time demand signals, resulting in the inability to generate quality qualification marks; 2. The absence of an embedded comparator hardware unit to perform parallel threshold comparisons makes it impossible to avoid response lag caused by software delays, and cannot ensure immediate response of quality detection; 3. The lack of pre-acquisition of dispatch instructions and quality data to provide a basis for subsequent flow regulation makes it impossible to reduce the risk of pipeline corrosion or insufficient calorific value caused by unqualified gas.

[0005] 2. Currently, there is a lack of dynamic determination of gain parameter sets based on quality qualification indicators to achieve real-time generation of adjustment signals. There is no optimization of flow regulation accuracy through dynamic gain adjustment, which cannot improve system stability. There is a lack of analysis of gas quality fluctuations, moisture impurities, and pressure fluctuations, and comparison with preset thresholds to generate abnormal indicators. Comprehensive monitoring of gas quality is not possible, and early warning cannot be achieved. There is a lack of pressure assessment algorithms that are executed in real time by embedded processors, and no operation instructions are generated. There is a lack of automatic execution of safety controls, and closed-loop control cannot be formed.

[0006] 3. Currently, there is a lack of quantitative control valve health status, making predictive maintenance impossible. There is no comprehensive multi-sensor data, including information on response speed, wear condition, pressure difference before and after the valve, condition of valve internals, and downstream discharge flow. Summary of the Invention

[0007] To address the aforementioned technical shortcomings, the purpose of this application is to provide an adaptive regulation method and system for biomass gas grid-connected transmission.

[0008] To solve the above-mentioned technical problems, this application adopts the following technical solution: In the first aspect, this application provides an adaptive regulation method for biomass gas grid-connected transmission, which includes the following steps: Step 1: When the pipeline issues a dispatching instruction or detects a real-time demand signal, the dispatching instruction data is pre-acquired, and the quality data of biomass gas is acquired at the same time to generate a quality qualification mark.

[0009] Step 2: Based on the quality qualification mark, apply an adaptive control algorithm to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data.

[0010] Step 3: Based on the pre-acquired gas data, analyze whether the gas data is abnormal and generate an anomaly flag.

[0011] Step 4: Based on the pressure anomaly indicator, detect and analyze the pressure data corresponding to the pressure regulating component, and implement corresponding safety controls.

[0012] Step 5: Based on the anomaly indicators, detect the control valve, obtain the control valve data, and calculate the comprehensive evaluation coefficient of the control valve.

[0013] Step 6: Based on the comprehensive evaluation coefficient of the regulating valve, issue an early warning.

[0014] Preferably, the quality data of the biogas includes methane purity and total sulfur content.

[0015] Preferably, generating the quality qualification mark includes: comparing the methane purity value with the methane purity threshold, and simultaneously comparing the total sulfur content value with the total sulfur content threshold; when the methane purity value is greater than or equal to the methane purity threshold and the total sulfur content value is less than or equal to the total sulfur content threshold, the quality is qualified, and the quality qualification mark is recorded as 1; otherwise, the quality is unqualified, and the quality qualification mark is recorded as 0.

[0016] Preferably, the application of the adaptive control algorithm to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data includes: determining a gain parameter set for the adaptive control algorithm based on the quality qualification indicator, wherein the gain parameter set includes proportional gain, integral gain, and derivative gain; calculating an adjustment signal for the biomass gas flow rate using the adaptive control algorithm, wherein the adaptive control algorithm generates the adjustment signal based on real-time flow data, target flow data, and the gain parameter set; and then using the adjustment signal to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data.

[0017] Preferably, the gas data includes gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data.

[0018] Preferably, the analysis of whether the gas data is abnormal and the generation of anomaly indicators include: A1, calculating the gas fluctuation deviation value, moisture and impurity deviation value and pressure fluctuation deviation value based on the pre-acquired gas quality fluctuation data, moisture and impurity data and pressure fluctuation data, respectively.

[0019] A2. Compare the gas quality fluctuation deviation value with a preset gas quality fluctuation threshold. If the gas quality fluctuation deviation value is greater than or equal to the gas quality fluctuation threshold, a gas quality abnormality flag of 1 is generated; otherwise, a gas quality abnormality flag of 0 is generated. Compare the moisture impurity deviation value with a preset moisture impurity threshold. If the moisture impurity deviation value is greater than or equal to the moisture impurity threshold, a moisture impurity abnormality flag of 1 is generated; otherwise, a moisture impurity abnormality flag of 0 is generated. Compare the pressure fluctuation deviation value with a preset pressure fluctuation threshold. If the pressure fluctuation deviation value is greater than or equal to the pressure fluctuation threshold, a pressure abnormality flag of 1 is generated, and step 4 is triggered; otherwise, a pressure abnormality flag of 0 is generated.

[0020] A3. When any one of the abnormal gas quality indicator, abnormal moisture and impurities indicator, or abnormal pressure indicator is 1, it indicates an abnormal state, and an abnormal indicator of 1 is generated; otherwise, it indicates a normal state, and an abnormal indicator of 0 is generated.

[0021] Preferably, the step of detecting and analyzing the pressure data corresponding to the pressure regulating component based on the pressure anomaly flag, and performing corresponding safety control, includes: when the pressure anomaly flag is 1, acquiring real-time pressure data of the pressure regulating component through a pre-deployed pressure sensor, calculating the pressure evaluation coefficient of the pressure regulating component, and then comparing the pressure evaluation coefficient of the pressure regulating component with the corresponding preset pressure evaluation threshold to generate an operation judgment result; and then generating a corresponding operation command based on the operation judgment result, thereby executing the operation command for safety control.

[0022] Preferably, the step of detecting the control valve based on the anomaly flag and obtaining control valve data includes: detecting the control valve and obtaining control valve data when the anomaly flag is 1; the control valve data includes response speed, wear condition, pressure difference before and after, condition of valve internals, and discharge flow rate after valve; and calculating the comprehensive evaluation coefficient of the control valve based on the control valve data.

[0023] Preferably, the step of calculating the comprehensive evaluation coefficient of the control valve based on the control valve data includes: weighting and summing the response speed evaluation coefficient, wear condition evaluation coefficient, front and rear pressure difference evaluation coefficient, valve internal condition evaluation coefficient, and valve downstream discharge flow evaluation coefficient, and using the calculation result as the comprehensive evaluation coefficient of the control valve.

[0024] In a second aspect, this application provides a biomass gas grid-connected transmission adaptive regulation system, including: a quality qualification mark generation module, used to pre-acquire dispatch instruction data and simultaneously acquire biogas quality data to generate a quality qualification mark when the pipeline issues a dispatch instruction or detects a real-time demand signal;

[0025] The flow data acquisition module, based on the quality qualification mark, applies an adaptive control algorithm to dynamically adjust the biomass gas flow rate and acquires the adjusted flow data.

[0026] The anomaly flag generation module analyzes whether the gas data is abnormal based on the pre-acquired gas data and generates anomaly flags.

[0027] The safety control module detects and analyzes the pressure data corresponding to the pressure regulating component based on the pressure anomaly indicator, and performs corresponding safety controls.

[0028] The control valve detection module detects the control valve based on anomaly indicators, acquires control valve data, and calculates the comprehensive evaluation coefficient of the control valve.

[0029] The early warning terminal provides early warnings based on the comprehensive evaluation coefficient of the regulating valve.

[0030] The beneficial effects of this application are as follows: 1. The adaptive regulation method and system for biomass gas grid connection and transmission provided in this application generates a quality qualification mark based on dispatch command data and biomass gas quality data, applies an adaptive control algorithm to dynamically regulate the biomass gas flow rate, and collects the regulated flow rate data; analyzes whether the gas data is abnormal and generates an abnormality mark; based on the pressure abnormality mark, detects and analyzes the pressure data corresponding to the pressure regulating component, and performs corresponding safety control; it solves the limitations existing in the current adaptive regulation process of biomass gas grid connection and transmission, detects the regulating valve based on the abnormality mark, obtains the regulating valve data, calculates the comprehensive evaluation coefficient of the regulating valve, and provides early warning prompts; it realizes the analysis of the feasibility, comprehensiveness, and objectivity of adaptive regulation of biomass gas grid connection and transmission.

[0031] 2. When the pipeline network issues a dispatch command or detects a real-time demand signal, this application pre-acquires dispatch command data and simultaneously acquires biogas quality data to generate a quality qualification mark. An embedded comparator hardware unit is used to perform parallel threshold comparisons and synchronous verification of methane purity and total sulfur content, outputting a binary quality qualification mark within a single clock cycle. This avoids response lag caused by software delays, achieves real-time data processing, ensures immediate response to quality detection, and improves reliability. By pre-acquiring dispatch commands and quality data, a foundation is provided for subsequent flow regulation, reducing the risk of pipeline corrosion or insufficient calorific value due to substandard biogas.

[0032] 3. This application dynamically determines the gain parameter set based on the quality qualification mark to realize the real-time generation of the adjustment signal. The algorithm fits the flow control requirements and optimizes the flow regulation accuracy through dynamic gain adjustment. For example, proportional gain quickly responds to errors, and integral gain eliminates accumulated errors. It improves system stability, ensures that the regulated data can be used for subsequent analysis, and reduces overshoot and energy waste.

[0033] 4. This application analyzes gas quality fluctuations, moisture and impurities, and pressure fluctuations, and compares them with preset thresholds to generate anomaly indicators; it comprehensively monitors gas quality to achieve early warning; it sets anomaly indicators, such as triggering step 4 when the gas quality anomaly indicator is 1, ensuring data continuity and preventing pipeline safety accidents, such as ruptures caused by pressure fluctuations. The pressure assessment algorithm performs real-time calculations through an embedded processor to generate operation instructions, such as "increase pressure" or "decrease pressure"; hardware accelerates the quantification of pressure deviations, and combines the control center's decision logic with data packet encapsulation to achieve low-latency response; it automatically executes safety controls, such as adjusting the regulator opening or activating the vent valve, forming a closed-loop control.

[0034] 5. This application quantifies the health status of the control valve to achieve predictive maintenance. It integrates data from multiple sensors, including response speed, wear condition, pressure difference before and after the valve, condition of valve internals, and discharge flow after the valve, and uses a weighted summation formula. Data is collected through a 10Hz sensor array to reduce downtime due to faults and extend equipment life. The evaluation coefficient is used for early warning in step 6. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating the implementation steps of the method described in this application.

[0037] Figure 2 This is a schematic diagram of the system structure connection of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] Please see Figure 1As shown, this application provides an adaptive regulation method for biomass gas grid-connected transmission in the first aspect, including: Step 1, when the pipeline issues a scheduling instruction or detects a real-time demand signal, pre-acquiring scheduling instruction data, and simultaneously acquiring biomass gas quality data to generate a quality qualification mark.

[0040] In one specific instance, the quality data of the biogas includes methane purity and total sulfur content.

[0041] It should be noted that the quality data of biogas is acquired in real time through a pre-deployed gas chromatography sensor at a data acquisition frequency of 10Hz to ensure data real-time performance.

[0042] In a specific example, generating a quality compliance mark includes: comparing the methane purity value with a methane purity threshold and comparing the total sulfur content value with a total sulfur content threshold; when the methane purity value is greater than or equal to the methane purity threshold and the total sulfur content value is less than or equal to the total sulfur content threshold, the quality is qualified, and the quality compliance mark is recorded as 1; otherwise, the quality is unqualified, and the quality compliance mark is recorded as 0.

[0043] It should be noted that the methane purity threshold is a fixed value of 95%. When the methane purity value is less than 95%, it indicates that the calorific value of the biogas is too low. The total sulfur content threshold is also a fixed value. When the total sulfur content is greater than When the biogas corrosion occurs, it indicates that the quality qualification mark is in binary form.

[0044] Furthermore, the generation of the quality pass flag is achieved by performing parallel threshold comparison through an embedded comparator hardware unit: when the data packets of methane purity value and total sulfur content value arrive at the register, the comparator synchronously verifies the logical condition that the methane purity value is greater than or equal to the methane purity threshold and the total sulfur content value is less than or equal to the total sulfur content threshold, and outputs the quality pass flag within a single clock cycle, avoiding response lag caused by software delay.

[0045] This application pre-acquires dispatch command data and biogas quality data when the pipeline network issues a dispatch command or detects a real-time demand signal, generating a quality qualification mark. An embedded comparator hardware unit performs parallel threshold comparisons and synchronous verification of methane purity and total sulfur content, outputting a binary quality qualification mark within a single clock cycle. This avoids response lag caused by software delays, achieves real-time data processing, ensures immediate response to quality detection, and improves reliability. By pre-acquiring dispatch commands and quality data, a foundation is provided for subsequent flow regulation, reducing the risk of pipeline corrosion or insufficient calorific value due to substandard biogas.

[0046] Step 2: Based on the quality qualification mark, apply an adaptive control algorithm to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data.

[0047] In a specific example, the application of an adaptive control algorithm to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data includes: determining a gain parameter set for the adaptive control algorithm based on the quality qualification indicator, wherein the gain parameter set includes proportional gain, integral gain, and derivative gain; calculating an adjustment signal for the biomass gas flow rate using the adaptive control algorithm, wherein the adaptive control algorithm generates the adjustment signal based on real-time flow data, target flow data, and the gain parameter set; and then using the adjustment signal to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data.

[0048] It should be noted that the proportional gain is used to adjust the system output according to the magnitude of the current error; its function is to respond quickly to errors. The higher the gain, the faster the response speed, but it may lead to system instability. The integral gain is used to eliminate long-term errors; by calculating the cumulative component of the error over time, integral control incorporates past errors into the current control, thereby ensuring the steady-state accuracy of the system. The derivative gain is used to predict the trend of error changes; by calculating the rate of change of the error over time, the rate of change corresponds to the derivative, and derivative control responds to system changes in advance, reducing overshoot and improving system stability.

[0049] It should be noted that the formula for the adaptive control algorithm is as follows: And the formula of the adaptive control algorithm is used to calculate the result in Adjustment signal at time point ,in Indicated as in Flow error at a given time point For proportional gain, Represented as integral gain, Expressed as differential gain, For a point in time, It is expressed as the integral of the error from the initial time to the current time. Represented as an integral variable, Expressed as the rate of change of error, it is The derivative of the error at a given time point.

[0050] It should be noted that the control signal represents the opening control amount of the flow control valve, expressed as a percentage (%), and is used to directly drive the valve actuator; flow error. ,in For real-time biomass gas flow rate, For target traffic.

[0051] It should be noted that the real-time biomass gas flow rate is collected at a frequency of 10Hz by a pre-deployed flow sensor; the target flow rate is generated in real time based on pipeline scheduling instructions; the proportional gain represents the response strength to the current error; the integral gain represents the adjustment strength to eliminate historical accumulated errors; and the differential gain represents the trend of prediction error changes.

[0052] This application dynamically determines the gain parameter set based on quality qualification marks, realizing the real-time generation of adjustment signals. The algorithm is tailored to the needs of flow control. Through dynamic gain adjustment, the flow regulation accuracy is optimized. For example, proportional gain quickly responds to errors, and integral gain eliminates accumulated errors. It improves system stability, ensures that the regulated data can be used for subsequent analysis, and reduces overshoot and energy waste.

[0053] Step 3: Based on the pre-acquired gas data, analyze whether the gas data is abnormal and generate an anomaly flag.

[0054] In one specific instance, the gas data includes gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data.

[0055] In a specific instance, the analysis of whether the gas data is abnormal and the generation of anomaly indicators include: A1, calculating the gas fluctuation deviation value, moisture and impurity deviation value and pressure fluctuation deviation value based on the pre-acquired gas quality fluctuation data, moisture and impurity data and pressure fluctuation data, respectively.

[0056] A2. Compare the gas quality fluctuation deviation value with a preset gas quality fluctuation threshold. If the gas quality fluctuation deviation value is greater than or equal to the gas quality fluctuation threshold, a gas quality abnormality flag of 1 is generated; otherwise, a gas quality abnormality flag of 0 is generated. Compare the moisture impurity deviation value with a preset moisture impurity threshold. If the moisture impurity deviation value is greater than or equal to the moisture impurity threshold, a moisture impurity abnormality flag of 1 is generated; otherwise, a moisture impurity abnormality flag of 0 is generated. Compare the pressure fluctuation deviation value with a preset pressure fluctuation threshold. If the pressure fluctuation deviation value is greater than or equal to the pressure fluctuation threshold, a pressure abnormality flag of 1 is generated, and step 4 is triggered; otherwise, a pressure abnormality flag of 0 is generated.

[0057] A3. When any one of the abnormal gas quality indicator, abnormal moisture and impurities indicator, or abnormal pressure indicator is 1, it indicates an abnormal state, and an abnormal indicator of 1 is generated; otherwise, it indicates a normal state, and an abnormal indicator of 0 is generated.

[0058] It should be noted that the gas quality fluctuation deviation value, moisture and impurity deviation value, and pressure fluctuation deviation value are calculated separately. The specific calculation process is as follows: According to the calculation formula... The gas quality fluctuation deviation value was calculated. ,in Represented as temperament fluctuation data, This is represented as a reference value for atmospheric pressure fluctuation data; based on the calculation formula... The moisture and impurity deviation values ​​were calculated. ,in Data is presented as moisture and impurities. This is presented as a reference value for moisture and impurities data; based on the calculation formula. The pressure fluctuation deviation value was calculated. ,in Represented as pressure fluctuation data, This serves as a reference value for pressure fluctuation data.

[0059] It should be noted that the gas chromatography-mass spectrometry fluctuation deviation value represents the absolute deviation of the real-time fluctuation range of gas chromatography components from the reference value, in percentage, and is collected at a frequency of 10Hz by a pre-deployed gas chromatography sensor; the moisture and impurity deviation value represents the absolute deviation of the moisture and impurity content from the reference value, in milligrams per cubic meter, and is acquired in real time by a humidity sensor and a particulate matter detector; the pressure fluctuation deviation value represents the absolute deviation of the pipeline pressure change from the reference value, in kilopascals, and is monitored by a pressure sensor.

[0060] It should be noted that the gas composition fluctuation data are the raw values ​​collected by the sensors; the reference value for the gas composition fluctuation data is preset to the average gas composition value under steady state; the moisture and impurity data are the raw values ​​collected by the sensors; the reference value for the moisture and impurity data is preset to the maximum allowable content under safety standards; the pressure fluctuation data are the raw values ​​collected by the sensors; the reference value for the pressure fluctuation data is preset to the pipeline design pressure value.

[0061] It should be noted that the reference values ​​for gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data are based on historical data and safety standard presets, and are stored in read-only memory.

[0062] Step 4: Based on the pressure anomaly indicator, detect and analyze the pressure data corresponding to the pressure regulating component, and implement corresponding safety controls.

[0063] In a specific example, the step of detecting and analyzing the pressure data corresponding to the pressure regulating component based on the pressure anomaly flag, and performing corresponding safety control, includes: when the pressure anomaly flag is 1, acquiring real-time pressure data of the pressure regulating component through a pre-deployed pressure sensor, calculating the pressure evaluation coefficient of the pressure regulating component, and then comparing the pressure evaluation coefficient of the pressure regulating component with the corresponding preset pressure evaluation threshold to generate an operation judgment result; and then generating a corresponding operation command based on the operation judgment result, thereby executing the operation command for safety control.

[0064] It should be noted that if the pressure evaluation coefficient of the pressure regulating component is greater than or equal to the pressure evaluation threshold, it is determined that the pressure is too low; if the pressure evaluation coefficient of the pressure regulating component is less than the pressure evaluation threshold, it is determined that the pressure is too high.

[0065] It should be noted that if the pressure is determined to be too low, an "increase pressure" command is generated to control the working pressure regulator to increase its opening; if the pressure is determined to be too high, an "decrease pressure" command is generated to control the monitoring pressure regulator to decrease its opening or trigger the safety shut-off valve; if the abnormal pressure persists, a "venting operation" command is generated to activate the venting valve to release the pressure.

[0066] Furthermore, the generation of operation instructions depends on the decision-making logic of the control center, and is encapsulated into data packets through relevant network protocols to ensure low-latency response.

[0067] It should be noted that the execution of operation instructions includes: driving pressure regulating components through the actuator, such as adjusting the valve opening of the working pressure regulator or triggering the closing mechanism of the safety shut-off valve; the execution results are fed back to the control center through sensors to form a closed-loop control.

[0068] It should be noted that the pressure assessment coefficient of the pressure regulating component is calculated using a pressure assessment formula; where the pressure assessment formula is: ,in This represents the real-time pressure data corresponding to the pressure regulating component. The preset reference pressure value corresponding to the pressure regulating component. This represents the pressure correction factor corresponding to the pressure regulating component.

[0069] It should be noted that real-time pressure data is acquired by a pressure sensor at a frequency of 10Hz, representing the actual pressure value of the pressure regulating component; the reference pressure value is set based on the pipeline design pressure value and stored in read-only memory; the pressure correction factor is used to adjust the algorithm's sensitivity to pressure fluctuations, and .

[0070] Furthermore, the physical essence of the pressure assessment algorithm is to perform real-time calculations through an embedded processor to convert the analog pressure signal collected by the sensor into a digital assessment index to quantify the pressure deviation; wherein the value of the pressure correction factor is based on historical data calibration, and by reducing the pressure correction factor, the algorithm's response to small fluctuations can be enhanced, avoiding misjudgment.

[0071] This application analyzes gas quality fluctuations, moisture and impurities, and pressure fluctuations, and compares them with preset thresholds to generate anomaly indicators; it comprehensively monitors gas quality to achieve early warning; it sets anomaly indicators, such as triggering step 4 when the gas quality anomaly indicator is 1, ensuring data continuity and preventing pipeline safety accidents, such as ruptures caused by pressure fluctuations. The pressure assessment algorithm performs real-time calculations through an embedded processor to generate operation instructions, such as "increase pressure" or "decrease pressure"; hardware accelerates the quantification of pressure deviations, and combines the control center's decision logic with data packet encapsulation to achieve low-latency response; it automatically executes safety controls, such as adjusting the regulator opening or activating the vent valve, forming a closed-loop control.

[0072] Step 5: Based on the anomaly indicators, detect the control valve, obtain the control valve data, and calculate the comprehensive evaluation coefficient of the control valve.

[0073] In a specific example, the step of detecting the control valve based on the anomaly flag and obtaining control valve data includes: when the anomaly flag is 1, detecting the control valve and obtaining control valve data; the control valve data includes response speed, wear condition, pressure difference before and after, condition of valve internals, and discharge flow rate after valve; and calculating the comprehensive evaluation coefficient of the control valve based on the control valve data.

[0074] It should be noted that the control valve data is acquired through a pre-deployed sensor array, which includes a response speed sensor, a wear detection sensor, a differential pressure sensor, a valve internals vision sensor, and a flow sensor. The data acquisition frequency is 10Hz to ensure real-time performance.

[0075] It should be noted that response speed represents the time delay from receiving a command to completing an action; wear condition represents the degree of wear on the valve body; the pressure difference across the valve is obtained through a differential pressure sensor; the condition of the valve internals is a score value ranging from 0 to 1, based on images acquired by a vision sensor, and the degree of corrosion or blockage of the valve internals is analyzed based on the images; and the downstream discharge flow rate is monitored in real time by a flow meter.

[0076] In a specific example, the process of calculating the comprehensive evaluation coefficient of the control valve based on the control valve data includes: weighting and summing the response speed evaluation coefficient, wear condition evaluation coefficient, front and rear pressure difference evaluation coefficient, valve internal condition evaluation coefficient, and valve downstream discharge flow evaluation coefficient, and using the calculation result as the comprehensive evaluation coefficient of the control valve.

[0077] It should be noted that, through the calculation formula Resulting in the response speed evaluation coefficient ,in This is expressed as response speed. This is represented as a reference value for response speed. This is expressed as a correction factor corresponding to the response speed; calculated using the formula... derive wear condition assessment coefficient ,in This indicates the wear and tear condition. This is expressed as a correction factor corresponding to the wear condition; calculated using the formula... Calculate the pressure difference evaluation coefficient before and after. ,in This is expressed as the pressure difference between the front and rear sides. This is expressed as a reference value for the pressure difference before and after. This is expressed as a correction factor corresponding to the pressure difference before and after; calculated using the formula... Calculate the valve internal condition assessment coefficient ,in This indicates the condition of the valve's internal components. This represents the correction factor corresponding to the condition of the valve internals; based on the calculation formula... Determine the evaluation coefficient of the discharge flow rate after the valve. ,in This is expressed as the discharge flow rate after the valve. This is expressed as a reference value for the discharge flow rate after the valve. This represents the correction factor corresponding to the discharge flow rate after the valve.

[0078] Furthermore, according to the calculation formula The comprehensive evaluation coefficient of the control valve is calculated. ,in Represented as the first The weighting factor corresponding to each control valve data point These are indicated by numbers corresponding to response speed, wear condition, pressure difference before and after the valve, condition of valve internals, and downstream discharge flow rate. ,and and .

[0079] It should be noted that the correction factors for response speed, wear condition, pressure difference, valve internal condition, and downstream discharge flow rate are all within the range of 0-1.

[0080] It should be noted that the comprehensive evaluation coefficient of the control valve quantifies the overall health status of the control valve, and the higher the value, the better the status. This is achieved by weighting and combining the evaluation coefficients of various indicators. The design of each evaluation coefficient ensures that the indicators are normalized and avoids unit incompatibility.

[0081] It should be noted that the weighting factors corresponding to response speed, wear condition, pressure difference before and after, valve internal condition, and downstream discharge flow rate in the control valve data are obtained by factor analysis. First, the data of response speed, wear condition, pressure difference before and after, valve internal condition, and downstream discharge flow rate are condensed. Then, the variance explained after rotation is obtained, and the weights are obtained by dividing the cumulative variance explained.

[0082] It should be noted that factor analysis is a well-known technique. It is a multivariate statistical analysis method that starts by studying the internal dependencies of variables and reduces some variables with complex relationships to a few comprehensive factors. Information condensation is expressed as the calculation of the median. The variance explained rate is the amount of information extracted by the factors. Variance explained rate = eigenvalues ​​ / total number of analysis terms. The rotated variance explained rate is expressed as the variance explained by the factors after maximum variance rotation.

[0083] It should be noted that a total weight of 1 ensures unbiased evaluation. Multi-source sensor data is integrated, and the network changes are dynamically adapted through weighted synthesis and correction factors. The calculation process is executed in an embedded system, and the formula calculation is implemented using a hardware accelerator to avoid software latency and improve response efficiency.

[0084] This application quantifies the health status of control valves to achieve predictive maintenance. It integrates data from multiple sensors, including response speed, wear condition, pressure difference before and after the valve, condition of valve internals, and downstream discharge flow rate, and uses a weighted summation formula. Data is collected through a 10Hz sensor array to reduce downtime due to faults and extend equipment life. The evaluation coefficient is used for early warning in step 6.

[0085] Step 6: Based on the comprehensive evaluation coefficient of the regulating valve, issue an early warning.

[0086] It should be noted that the result of subtracting the warning threshold of the comprehensive evaluation coefficient of the control valve from the comprehensive evaluation coefficient of the control valve is recorded as the difference value; when the difference value is less than or equal to zero, the warning state is abnormal, otherwise the warning state is normal; when the warning state is abnormal, a warning prompt operation is triggered; the execution of the warning prompt operation includes generating a warning signal based on the warning state and transmitting the warning signal to the main control console for display or alarm sending.

[0087] It should be noted that the physical meaning of the difference value is the degree of deviation of the control valve's health status; when the difference value is less than or equal to zero, it indicates that the control valve's status is below the safety threshold and a warning is required; when the difference value is greater than zero, it indicates that the status is normal; the physical meaning of the control valve's comprehensive evaluation coefficient is a quantitative indicator of the overall health status of the control valve, and the higher the value, the better the status; the warning threshold of the control valve's comprehensive evaluation coefficient is based on historical maintenance data and pipeline safety standards, and is stored in the read-only memory of the control center, and its physical meaning is the critical health value that triggers the warning.

[0088] Furthermore, the essence of the threshold comparison algorithm is to perform real-time numerical subtraction operations through an embedded comparator hardware unit, outputting the difference value within a single clock cycle to avoid software latency; the algorithm design ensures response efficiency and improves the system's monitoring capability through rapid deviation detection.

[0089] It should be noted that the early warning prompt operation includes: generating an early warning signal based on the early warning status, wherein the early warning signal is binary data, with a value of 1 when abnormal and a value of 0 when normal; encapsulating the early warning signal into a data packet through a network protocol and transmitting it to the main control console; the main control console displays text early warning information based on the early warning signal, such as indicating that the regulating valve status is abnormal, or triggering an audible and visual alarm.

[0090] Furthermore, the generation of early warning signals relies on the decision-making logic of the control center, and data transmission adopts a point-to-point communication protocol to ensure low-latency response and support management decisions through real-time alarms.

[0091] Please see Figure 2 As shown, this application provides a biomass gas grid-connected adaptive regulation system in its second aspect.

[0092] The biomass gas grid-connected adaptive regulation system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the biomass gas grid-connected adaptive regulation system 100 may include a quality compliance flag generation module 101, a flow data acquisition module 102, an anomaly flag generation module 103, a safety control module 104, a regulating valve detection module 105, and an early warning terminal 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0093] In this embodiment, the functions of each module / unit are as follows:

[0094] The quality qualification mark generation module is used to pre-acquire dispatch instruction data and simultaneously acquire biogas quality data when the pipeline network issues a dispatch instruction or detects a real-time demand signal, and then generate a quality qualification mark.

[0095] The flow data acquisition module, based on the quality qualification mark, applies an adaptive control algorithm to dynamically adjust the biomass gas flow rate and acquires the adjusted flow data;

[0096] The anomaly flag generation module analyzes whether the gas data is abnormal based on the pre-acquired gas data and generates anomaly flags.

[0097] The safety control module detects and analyzes the pressure data corresponding to the pressure regulating component based on the pressure anomaly indicator, and performs corresponding safety controls.

[0098] The control valve detection module detects the control valve based on anomaly indicators, acquires control valve data, and calculates the comprehensive evaluation coefficient of the control valve.

[0099] The early warning terminal provides early warnings based on the comprehensive evaluation coefficient of the regulating valve.

[0100] The adaptive regulation method and system for biomass gas grid-connected transmission provided in this application generates a quality qualification mark based on dispatch command data and biomass gas quality data. It then applies an adaptive control algorithm to dynamically regulate the biomass gas flow rate and collects the regulated flow data. The system analyzes whether the gas data is abnormal and generates anomaly flags. Based on the pressure anomaly flags, it detects and analyzes the pressure data corresponding to the pressure regulating components and performs corresponding safety controls. This method solves the limitations of current adaptive regulation processes for biomass gas grid-connected transmission. Based on the anomaly flags, it detects the regulating valve, acquires the regulating valve data, calculates the comprehensive evaluation coefficient of the regulating valve, and provides early warning prompts. This method achieves a feasibility, comprehensiveness, and objectivity analysis of adaptive regulation for biomass gas grid-connected transmission.

[0101] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0102] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0104] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0105] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for adaptive regulation of biomass gas grid-connected transmission, characterized in that, include: Step 1: When the pipeline network issues a dispatch instruction or detects a real-time demand signal, pre-acquire dispatch instruction data and simultaneously acquire biogas quality data to generate a quality qualification mark. The quality data of the biogas includes methane purity and total sulfur content. The generation of the quality qualification mark includes: The methane purity value is compared with the methane purity threshold, and the total sulfur content value is also compared with the total sulfur content threshold. When the methane purity value is greater than or equal to the methane purity threshold and the total sulfur content value is less than or equal to the total sulfur content threshold, the quality is qualified and the quality qualification mark is recorded as 1; otherwise, the quality is unqualified and the quality qualification mark is recorded as 0. Step 2: Based on the quality qualification mark, apply an adaptive control algorithm to dynamically adjust the biomass gas flow rate and collect the adjusted flow rate data; Step 3: Based on the pre-acquired gas data, analyze whether the gas data is abnormal and generate an anomaly flag; The gas data includes gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data; The analysis of whether the gas data is abnormal and the generation of anomaly flags include: A1. Based on the pre-acquired gas and gas fluctuation data, moisture and impurity data, and pressure fluctuation data, calculate the gas and gas fluctuation deviation value, moisture and impurity deviation value, and pressure fluctuation deviation value, respectively. A2. Compare the gas quality fluctuation deviation value with a preset gas quality fluctuation threshold. If the gas quality fluctuation deviation value is greater than or equal to the gas quality fluctuation threshold, a gas quality abnormality flag of 1 is generated; otherwise, a gas quality abnormality flag of 0 is generated. Compare the moisture impurity deviation value with a preset moisture impurity threshold. If the moisture impurity deviation value is greater than or equal to the moisture impurity threshold, a moisture impurity abnormality flag of 1 is generated; otherwise, a moisture impurity abnormality flag of 0 is generated. Compare the pressure fluctuation deviation value with a preset pressure fluctuation threshold. If the pressure fluctuation deviation value is greater than or equal to the pressure fluctuation threshold, a pressure abnormality flag of 1 is generated, and step 4 is triggered; otherwise, a pressure abnormality flag of 0 is generated. A3. When any one of the abnormal gas quality indicator, abnormal moisture and impurities indicator, or abnormal pressure indicator is 1, it indicates an abnormal state, and an abnormal indicator of 1 is generated; otherwise, it indicates a normal state, and an abnormal indicator of 0 is generated. Step 4: Based on the pressure anomaly indicator, detect and analyze the pressure data corresponding to the pressure regulating component, and implement corresponding safety controls; Step 5: Based on the anomaly indicators, detect the control valve, obtain control valve data, and calculate the comprehensive evaluation coefficient of the control valve; Step 6: Based on the comprehensive evaluation coefficient of the regulating valve, issue an early warning.

2. The adaptive regulation method for biomass gas grid-connected transmission according to claim 1, characterized in that, The application of an adaptive control algorithm dynamically adjusts the biomass gas flow rate and collects the adjusted flow rate data, including: Based on the quality qualification criteria, a set of gain parameters for the adaptive control algorithm is determined, wherein the set of gain parameters includes proportional gain, integral gain, and derivative gain; the adaptive control algorithm is applied to calculate an adjustment signal for the biomass gas flow rate, wherein the adaptive control algorithm generates the adjustment signal based on real-time flow data, target flow data, and the set of gain parameters; then the adjustment signal is used to dynamically adjust the biomass gas flow rate, and the adjusted flow data is collected.

3. The adaptive regulation method for biomass gas grid-connected transmission according to claim 1, characterized in that, The method of detecting and analyzing pressure data corresponding to the pressure regulating component based on pressure anomaly indicators, and performing corresponding safety controls, includes: When the pressure anomaly flag is 1, real-time pressure data of the pressure regulating component is obtained through a pre-deployed pressure sensor, and the pressure evaluation coefficient of the pressure regulating component is calculated. Then, the pressure evaluation coefficient of the pressure regulating component is compared with the corresponding preset pressure evaluation threshold to generate an operation judgment result. Based on the operation judgment result, a corresponding operation command is generated, and the operation command is executed for safety control.

4. The adaptive regulation method for biomass gas grid-connected transmission according to claim 3, characterized in that, The process of detecting the control valve based on anomaly flags and acquiring control valve data includes: When the abnormality flag is 1, the control valve is inspected to obtain control valve data. The control valve data includes response speed, wear condition, pressure difference before and after, condition of valve internals, and discharge flow rate after valve. The comprehensive evaluation coefficient of the control valve is calculated based on the control valve data.

5. The adaptive regulation method for biomass gas grid connection and transmission according to claim 4, characterized in that, The comprehensive evaluation coefficient of the control valve is calculated based on the control valve data, including: The response speed evaluation coefficient, wear condition evaluation coefficient, pressure difference evaluation coefficient, valve internal condition evaluation coefficient, and valve discharge flow evaluation coefficient are weighted and summed, and the calculation result is used as the comprehensive evaluation coefficient of the control valve.

6. A regulating system for implementing the adaptive regulation method for grid-connected biomass gas transmission according to any one of claims 1-5, characterized in that, include: The quality qualification mark generation module is used to pre-acquire dispatch instruction data and simultaneously acquire biogas quality data when the pipeline network issues a dispatch instruction or detects a real-time demand signal, and then generate a quality qualification mark. The flow data acquisition module, based on the quality qualification mark, applies an adaptive control algorithm to dynamically adjust the biomass gas flow rate and acquires the adjusted flow data; The anomaly flag generation module analyzes whether the gas data is abnormal based on the pre-acquired gas data and generates anomaly flags. The safety control module detects and analyzes the pressure data corresponding to the pressure regulating component based on the pressure anomaly indicator, and performs corresponding safety controls. The control valve detection module detects the control valve based on anomaly indicators, acquires control valve data, and calculates the comprehensive evaluation coefficient of the control valve. The early warning terminal provides early warnings based on the comprehensive evaluation coefficient of the regulating valve.

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