Biomass gas grid-connected conveying self-adaptive adjusting method and system

Abstract

The invention discloses a biomass gas grid-connected conveying self-adaptive adjusting method and system, and relates to the technical field of biomass gas, and the method comprises the steps of generating a quality qualification mark based on scheduling instruction data and biomass gas quality data, and dynamically adjusting the biomass gas flow by applying a self-adaptive control algorithm. Flow data after adjustment are collected; analyzing whether the gas data is abnormal or not, and generating an abnormal mark; detecting and analyzing pressure data corresponding to the pressure regulating component based on the pressure abnormal mark, and performing corresponding safety control; the problem of limitation existing in the current biomass gas grid-connected conveying self-adaptive adjusting process is solved, the adjusting valve is detected based on an abnormal mark, adjusting valve data are obtained, the comprehensive evaluation coefficient of the adjusting valve is obtained through calculation, and meanwhile early warning prompt is conducted; and feasibility, comprehensiveness and objectivity analysis of biomass gas grid-connected conveying self-adaptive adjustment is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomass gas, specifically to a biomass gas grid-connected delivery self-adaptive adjustment method and system. BACKGROUND

[0002] With the development of science and technology, the importance of self-adaptive adjustment in the process of biomass gas grid-connected delivery is increasingly prominent. Traditional delivery methods rely on manual inspection and adjustment, which is inefficient and has a lagging response.

[0003] The prior art, such as the invention patent application with publication number CN119371996A, discloses a biomass gasification gas delivery system, which includes a biomass gasification furnace, and further includes: a cooling device arranged on the gas output pipeline of the biomass gasification furnace; a water-cooled circulating mechanism connected to the cooling device; a guide exhaust cylinder connected to the output pipeline of the biomass gasification furnace; an exhaust cylinder cleaning mechanism arranged on the outer wall of the guide exhaust cylinder; a tar and dust removal mechanism arranged above the guide exhaust cylinder; an ammonia water supplement mechanism connected to the input pipeline of the exhaust cylinder cleaning mechanism through the water-cooled circulating mechanism; and a monitoring and purification mechanism arranged on the output end of the tar and dust removal mechanism

[0004] For the above-mentioned scheme, the present inventors have found that the above-mentioned technology at least has the following technical problems: 1. Currently, when the pipe network issues a scheduling instruction or detects a real-time demand signal, the scheduling instruction data is not pre-acquired, and the quality data of the biomass gas is not acquired at the same time, which results in the inability to generate a quality qualified mark; a parallel threshold comparison is not performed by using an embedded comparator hardware unit, which cannot avoid the response lag caused by software delay, cannot ensure the immediate response of quality detection, lacks a basis for subsequent flow regulation by pre-acquiring scheduling instructions and quality data, and cannot reduce the risk of pipe network corrosion or insufficient heat value caused by unqualified gas.

[0005] 2. Currently, there is a lack of dynamic determination of a gain parameter set based on the quality qualified mark to realize real-time generation of a regulation signal, optimization of flow regulation accuracy by dynamic gain adjustment, analysis of gas quality fluctuations, moisture impurities, and pressure fluctuations, and comparison with preset threshold values to generate an abnormal mark; comprehensive monitoring of gas quality to realize early warning; a pressure evaluation algorithm executed by an embedded processor to perform real-time calculation, generation of operation instructions, automatic execution of safety control, and formation of a closed-loop control.

[0006] 3. Currently, there is a lack of quantitative regulation valve health status, which cannot realize predictive maintenance, and cannot integrate multiple sensor data, response speed, wear condition, pressure difference before and after, valve inner condition, and valve discharge flow information. SUMMARY

[0007] In view of the above technical deficiencies, the purpose of the present application is to provide a biomass gas grid-connected transportation adaptive adjustment method and system.

[0008] To solve the above technical problems, the present application adopts the following technical solutions: the present application provides a biomass gas grid-connected transportation adaptive adjustment method in the first aspect, which comprises the following steps: step 1, when the pipe network issues a scheduling instruction or detects a real-time demand signal, pre-acquire the scheduling instruction data, simultaneously acquire the quality data of the biomass gas, and generate a quality qualified flag.

[0009] Step 2, based on the quality qualified flag, apply an adaptive control algorithm to dynamically adjust the biomass gas flow, and collect the adjusted flow data.

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

[0011] Step 4, based on the pressure abnormal flag, detect and analyze the pressure data corresponding to the pressure regulating component, and perform corresponding safety control.

[0012] Step 5, based on the abnormal flag, detect the regulating valve, acquire the regulating valve data, and calculate the comprehensive evaluation coefficient of the regulating valve.

[0013] Step 6, based on the comprehensive evaluation coefficient of the regulating valve, perform early warning prompt.

[0014] Preferably, the quality data of the biomass gas includes a methane purity value and a total sulfur content value.

[0015] Preferably, the generation of the quality qualified flag comprises: comparing the methane purity value with a methane purity threshold value, and simultaneously comparing the total sulfur content value with a total sulfur content threshold value; when the methane purity value is greater than or equal to the methane purity threshold value, and the total sulfur content value is less than or equal to the total sulfur content threshold value, the quality is qualified, and the quality qualified flag is marked as 1; otherwise, the quality is unqualified, and the quality qualified flag is marked as 0.

[0016] Preferably, the application of the adaptive control algorithm to dynamically adjust the biomass gas flow and the collection of the adjusted flow data comprise: based on the quality qualified flag, determining a gain parameter set of the adaptive control algorithm, wherein the gain parameter set includes a proportional gain, an integral gain and a differential gain; applying the adaptive control algorithm to calculate an adjustment signal of the biomass gas flow, 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, and collecting the adjusted flow 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 an abnormal flag include: A1, based on the pre-acquired gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data, respectively calculating a gas quality fluctuation deviation value, a moisture and impurity deviation value, and a pressure fluctuation deviation value.

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

[0020] A3, when any one of the gas quality abnormal flag, the moisture and impurity abnormal flag, and the pressure abnormal flag is 1, indicating an abnormal state, an abnormal flag is generated as 1; otherwise, indicating a normal state, an abnormal flag is generated as 0.

[0021] Preferably, based on the pressure abnormal flag, the pressure data corresponding to the pressure regulating component is detected and analyzed, and corresponding safety control is performed, including: when the pressure abnormal flag is 1, real-time pressure data of the pressure regulating component is acquired through a pre-deployed pressure sensor, and a pressure evaluation coefficient of the pressure regulating component is calculated, and then the pressure evaluation coefficient of the pressure regulating component is compared with a corresponding preset pressure evaluation threshold value to generate an operation determination result; and then based on the operation determination result, a corresponding operation instruction is generated, so that the operation instruction is executed to perform safety control.

[0022] Preferably, based on the abnormal flag, the regulating valve is detected to acquire regulating valve data, including: when the abnormal flag is 1, the regulating valve is detected to acquire regulating valve data; the regulating valve data includes response speed, wear condition, front and rear pressure difference, valve inner condition, and valve discharge flow; and a regulating valve comprehensive evaluation coefficient is calculated based on the regulating valve data.

[0023] Preferably, the calculation of the regulating valve comprehensive evaluation coefficient based on the regulating valve data includes: weighting and summing the response speed evaluation coefficient, the wear condition evaluation coefficient, the front and rear pressure difference evaluation coefficient, the valve inner condition evaluation coefficient, and the valve discharge flow evaluation coefficient, and taking the calculation result as the regulating valve comprehensive evaluation coefficient.

[0024] The biomass gas grid-connected conveying adaptive adjustment system provided in the second aspect of the application comprises: a quality qualified mark generation module, which is configured to, when a scheduling instruction is issued by a pipe network or a real-time demand signal is detected, pre-acquire scheduling instruction data, simultaneously acquire quality data of the biomass gas, and generate a quality qualified mark;

[0025] A flow data acquisition module is configured to, based on the quality qualified mark, apply an adaptive control algorithm to dynamically adjust the flow of the biomass gas, and acquire adjusted flow data.

[0026] An abnormality mark generation module is configured to, based on the pre-acquired gas data, analyze whether the gas data is abnormal, and generate an abnormality mark.

[0027] A safety control module is configured to, based on the pressure abnormality mark, detect and analyze pressure data corresponding to a pressure regulating component, and perform corresponding safety control.

[0028] An adjusting valve detection module is configured to, based on the abnormality mark, detect an adjusting valve, acquire adjusting valve data, and calculate an adjusting valve comprehensive evaluation coefficient.

[0029] A warning terminal is configured to, based on the adjusting valve comprehensive evaluation coefficient, perform a warning prompt.

[0030] The biomass gas grid-connected conveying adaptive adjustment method and system provided by the application have the following beneficial effects: 1. Based on scheduling instruction data and quality data of the biomass gas, the quality qualified mark is generated, the adaptive control algorithm is applied to dynamically adjust the flow of the biomass gas, and the adjusted flow data is acquired. Whether the gas data is abnormal is analyzed, and the abnormality mark is generated. Based on the pressure abnormality mark, the pressure data corresponding to the pressure regulating component is detected and analyzed, and the corresponding safety control is performed. The limitations in the current biomass gas grid-connected conveying adaptive adjustment process are solved. Based on the abnormality mark, the adjusting valve is detected, the adjusting valve data is acquired, the adjusting valve comprehensive evaluation coefficient is calculated, and the warning prompt is performed. The feasibility, comprehensiveness and objectivity of the biomass gas grid-connected conveying adaptive adjustment are realized.

[0031] 2. When the scheduling instruction is issued by the pipe network or the real-time demand signal is detected, the scheduling instruction data is pre-acquired, the quality data of the biomass gas is acquired, and the quality qualified mark is generated. The embedded comparator hardware unit performs parallel threshold comparison, the methane purity and the total sulfur content are synchronously verified, the binary quality qualified mark is output in a single clock cycle, the response lag caused by software delay is avoided, the data real-time processing is realized, the instant response of the quality detection is ensured, and the reliability is improved. The pre-acquisition of the scheduling instruction and the quality data provides a basis for subsequent flow adjustment, and reduces the risk of pipe network corrosion or insufficient heat value caused by unqualified gas.

[0032] 3, The application dynamically determines the gain parameter set based on the quality qualified mark, realizes the real-time generation of the adjustment signal, the algorithm matches the flow control demand, optimizes the flow adjustment precision through dynamic gain adjustment, for example, the proportional gain quickly responds to the error, and the integral gain eliminates the cumulative error; improve the system stability, ensure that the adjusted data is used for subsequent analysis, and reduce overshoot and energy waste.

[0033] 4, The application analyzes the quality fluctuation, moisture impurity and pressure fluctuation, and compares with the preset threshold to generate an abnormal mark; comprehensively monitor the gas quality, realize early warning; set the abnormal mark, such as the quality abnormal mark is 1, trigger step 4, ensure the data is used continuously, prevent pipeline safety accidents, such as rupture caused by pressure fluctuation. The pressure evaluation algorithm performs real-time calculation through the embedded processor, generates operation instructions such as "increase pressure" or "decrease pressure"; hardware acceleration quantifies pressure deviation, combines control center decision logic to package data, realizes low delay response; automatically execute safety control, such as adjusting the opening of the pressure regulator or activating the relief valve, to form a closed loop control.

[0034] 5, The application quantifies the health status of the regulating valve, realizes predictive maintenance, integrates multiple sensor data, response speed, wear condition, pressure difference before and after, valve condition, valve discharge flow, uses weighted summation formula; collect data through 10Hz sensor array, reduce downtime, prolong equipment life, and evaluation coefficient is used for warning in step 6. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0036] Figure 1 The flow chart of the method embodiment of the present application is shown.

[0037] Figure 2 The connection diagram of the system structure of the present application is shown. DETAILED DESCRIPTION

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

[0039] Please refer to Figure 1As shown, the present application provides a biomass gas grid-connected transmission adaptive adjustment method in the first aspect, comprising: step 1, when the pipeline issues a scheduling instruction or detects a real-time demand signal, pre-acquire scheduling instruction data, at the same time, acquire quality data of the biomass gas, and generate a quality qualified mark.

[0040] In one specific example, the quality data of the biomass gas includes a methane purity value and a total sulfur content value.

[0041] It should be noted that the quality data of the biomass gas is acquired in real time by a pre-deployed gas chromatograph sensor, and the data acquisition frequency is 10 Hz, ensuring real-time data.

[0042] In one specific example, the generation of the quality qualified mark comprises: comparing the methane purity value with a methane purity threshold value, and at the same time, comparing the total sulfur content value with a total sulfur content threshold value; when the methane purity value is greater than or equal to the methane purity threshold value, and the total sulfur content value is less than or equal to the total sulfur content threshold value, the quality is qualified, and the quality qualified mark is marked as 1; otherwise, the quality is unqualified, and the quality qualified mark is marked as 0.

[0043] It should be noted that the methane purity threshold value is a fixed value of 95%, and when the methane purity value is less than 95%, it indicates that the biomass gas has too low a calorific value; the total sulfur content threshold value is a fixed value of 1000 ppm, and when the total sulfur content value is greater than 1000 ppm, it indicates that the biomass gas is corrosive to the pipeline. The quality qualified mark is in binary form.

[0044] Further, the generation of the quality qualified mark is performed by a parallel threshold comparison of an embedded comparator hardware unit: when a data packet of the methane purity value and the total sulfur content value arrives at a register, the comparator synchronously checks the logical condition that the methane purity value is greater than or equal to the methane purity threshold value, and the total sulfur content value is less than or equal to the total sulfur content threshold value, and outputs the quality qualified mark within a single clock cycle, avoiding response lag caused by software delay.

[0045] When the pipeline issues a scheduling instruction or detects a real-time demand signal, the present application pre-acquires scheduling instruction data, at the same time, acquires quality data of the biomass gas, and generates a quality qualified mark; a parallel threshold comparison is performed by an embedded comparator hardware unit, synchronous checking of the methane purity and the total sulfur content is performed, and a binary quality qualified mark is output within a single clock cycle, avoiding response lag caused by software delay, realizing real-time data processing, ensuring immediate response of quality detection, and improving reliability; by pre-acquiring scheduling instruction and quality data, a basis is provided for subsequent flow adjustment, and the risk of pipeline corrosion or insufficient calorific value caused by unqualified gas is reduced.

[0046] ​Step 2, based on the quality pass mark, applying an adaptive control algorithm to dynamically adjust the biomass gas flow, and collecting the adjusted flow data.

[0047] In one specific example, the application of the adaptive control algorithm to dynamically adjust the biomass gas flow, and collecting the adjusted flow data, includes: based on the quality pass mark, determining a gain parameter set of the adaptive control algorithm, wherein the gain parameter set includes a proportional gain, an integral gain and a differential gain; applying the adaptive control algorithm to calculate an adjustment signal of the biomass gas flow, 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, and collecting the adjusted flow data.

[0048] It should be noted that the proportional gain is used to adjust the system output according to the size of the current error; its role is to quickly respond to errors, and 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 integral of the error over time, integral control will affect the current control of past errors, thereby ensuring the steady-state accuracy of the system. The differential gain is used to predict the trend of error change; by calculating the rate of change of error over time, the rate of change corresponds to the differential, and the differential control responds to system changes in advance, reduces overshoot, and improves system stability.

[0049] It should be noted that the formula of the adaptive control algorithm is: And the adjustment signal at the time point t is calculated by the formula of the adaptive control algorithm . Wherein represents the flow error at the time point t, is the proportional gain, represents the integral gain, represents the differential gain, is the time point t, represents the integral of the error from the initial time to the current time, represents the integral variable, represents the error rate, which is the derivative of the error at the time point t.

[0050] It should be noted that the adjustment signal represents the opening control amount of the flow regulating valve, with a unit of percentage (%), which is used to directly drive the regulating valve actuator; the flow error Wherein is the real-time biomass gas flow, is the target flow.

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

[0052] The present application dynamically determines the gain parameter set based on the quality qualified mark, realizes real-time generation of the adjustment signal, and optimizes the flow adjustment precision through dynamic gain adjustment, for example, the proportional gain quickly responds to the error, and the integral gain eliminates the cumulative error; improves the system stability, ensures that the adjusted data is 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 abnormal flag.

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

[0055] In one specific example, the analysis of whether the gas data is abnormal and the generation of the abnormal flag comprises: A1, based on the pre-acquired gas quality fluctuation data, moisture and impurity data, and pressure fluctuation data, respectively calculating the gas quality fluctuation deviation value, the moisture and impurity deviation value, and the pressure fluctuation deviation value.

[0056] A2, compare the gas quality fluctuation deviation value with the preset gas quality fluctuation threshold value, if the gas quality fluctuation deviation value is greater than or equal to the gas quality fluctuation threshold value, generate a gas quality abnormal flag of 1, otherwise generate a gas quality abnormal flag of 0; compare the moisture and impurity deviation value with the preset moisture and impurity threshold value, if the moisture and impurity deviation value is greater than or equal to the moisture and impurity threshold value, generate a moisture and impurity abnormal flag of 1, otherwise generate a moisture and impurity abnormal flag of 0; compare the pressure fluctuation deviation value with the preset pressure fluctuation threshold value, if the pressure fluctuation deviation value is greater than or equal to the pressure fluctuation threshold value, generate a pressure abnormal flag of 1, and trigger step 4, otherwise generate a pressure abnormal flag of 0.

[0057] A3, when any one of the gas quality abnormal flag, the moisture and impurity abnormal flag, and the pressure abnormal flag is 1, indicating an abnormal state, then generate an abnormal flag of 1; otherwise, indicating a normal state, generate an abnormal flag of 0.

[0058] It should be noted that the gas quality fluctuation deviation value, the moisture and impurity deviation value, and the pressure fluctuation deviation value are calculated respectively, and the specific calculation process is as follows: according to the calculation formula The gas quality fluctuation deviation value is calculated as , wherein is the gas quality fluctuation data, The reference value of the gas quality fluctuation data; according to the calculation formula The moisture and impurity deviation value is calculated Wherein The moisture and impurity data is represented as The reference value of the moisture and impurity data represented as gas quality fluctuation data; according to the calculation formula The pressure fluctuation deviation value is calculated Wherein The pressure fluctuation data is represented as The reference value of the pressure fluctuation data.

[0059] It should be noted that the gas quality fluctuation deviation value represents the absolute deviation of the real-time fluctuation amplitude of the gas quality composition from the reference value, in percentage, which is collected by the pre-deployed gas chromatograph sensor at a frequency of 10 Hz; 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, which is obtained in real time by the humidity sensor and the particulate matter detector; the pressure fluctuation deviation value represents the absolute deviation of the amount of change of the pipe network pressure from the reference value, in kilopascals, which is monitored by the pressure sensor.

[0060] It should be noted that the gas quality fluctuation data is the original value collected by the sensor; the reference value of the gas quality fluctuation data is pre-set as the average gas quality composition value under steady state; the moisture and impurity data is the original value collected by the sensor; the reference value of the moisture and impurity data is pre-set as the maximum allowable content under the safety standard; the pressure fluctuation data is the original value collected by the sensor; the reference value of the pressure fluctuation data is pre-set as the pipe network design pressure value.

[0061] It should be noted that the reference value of the gas quality fluctuation data, the reference value of the moisture and impurity data, and the reference value of the pressure fluctuation data are pre-set based on historical data and safety standards and stored in the read-only memory.

[0062] Step 4, based on the pressure anomaly flag, detecting and analyzing the pressure data corresponding to the pressure regulating component, and performing corresponding safety control.

[0063] In one specific example, based on the pressure anomaly flag, detecting and analyzing the pressure data corresponding to the pressure regulating component, and performing corresponding safety control, includes: when the pressure anomaly flag is 1, obtaining the real-time pressure data of the pressure regulating component by the pre-deployed pressure sensor, and 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 pre-set pressure evaluation threshold to generate an operation decision result; and then based on the operation decision result, generating a corresponding operation instruction, so as to execute the operation instruction 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 it is determined that the pressure is too low, a "raise pressure" instruction is generated to control the working pressure regulator to increase the opening degree; if it is determined that the pressure is too high, a "lower pressure" instruction is generated to control the monitoring pressure regulator to reduce the opening degree or trigger the safety cut-off valve; if the pressure anomaly persists, a "bleed operation" instruction is generated to activate the bleed valve to release the pressure.

[0066] Further, the generation of the operation instruction depends on the decision logic of the control center, which is encapsulated as a data packet for transmission through a relevant network protocol, ensuring low-latency response.

[0067] It should be noted that executing the operation instruction includes driving the pressure regulating component through an actuator, such as adjusting the valve opening degree of the working pressure regulator or triggering the closing mechanism of the safety cut-off valve, etc.; the execution result is fed back to the control center through a sensor, forming a closed-loop control.

[0068] It should be noted that the pressure evaluation coefficient of the pressure regulating component is calculated by a pressure evaluation formula; wherein the pressure evaluation formula is , wherein represents the real-time pressure data corresponding to the pressure regulating component, a preset reference pressure value corresponding to the pressure regulating component, represents a pressure correction factor corresponding to the pressure regulating component,

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

[0070] Further, the physical nature of the pressure evaluation algorithm is to convert the analog pressure signal collected by the sensor into a digital evaluation index through real-time calculation by an embedded processor, to quantify the pressure deviation; wherein the value of the pressure correction factor is calibrated based on historical data, and by reducing the pressure correction factor, the algorithm can enhance the response to small fluctuations and avoid misjudgment.

[0071] The application analyzes gas quality fluctuation, moisture impurities and pressure fluctuation, and compares with preset threshold to generate abnormal flag; comprehensively monitors gas quality, realizes early warning; sets abnormal flag, such as gas quality abnormal flag is 1, triggers step 4, ensures data coherence, prevents pipeline safety accidents, such as rupture caused by pressure fluctuation. The pressure evaluation algorithm performs real-time calculation through the embedded processor, generates operation instructions such as 'increase pressure' or 'decrease pressure'; hardware acceleration quantifies pressure deviation, combines control center decision logic to package data, realizes low delay response; automatically executes safety control, such as adjusting pressure regulator opening or activating relief valve, forms closed loop control.

[0072] Step 5, based on the abnormal flag, detecting the regulating valve, obtaining regulating valve data, and calculating the regulating valve comprehensive evaluation coefficient.

[0073] In one specific example, the regulating valve data is obtained by detecting the regulating valve based on the abnormal flag, including: when the abnormal flag is 1, detecting the regulating valve to obtain the regulating valve data; the regulating valve data includes response speed, wear condition, pressure difference before and after, valve inner condition, and valve discharge flow after the valve; and the regulating valve comprehensive evaluation coefficient is calculated based on the regulating valve data.

[0074] It should be noted that the regulating valve data is obtained by the pre-deployed sensor array, and the sensor array includes response speed sensor, wear detection sensor, pressure difference sensor, valve inner visual sensor and flow sensor, and the data acquisition frequency is 10Hz, ensuring real-time performance.

[0075] It should be noted that the response speed represents the time delay of the regulating valve from receiving the instruction to completing the action; the wear condition represents the wear degree of the valve body; the pressure difference before and after is obtained by the pressure difference sensor; the valve inner condition is a score value, ranging from 0 to 1, the image is obtained based on the visual sensor, and the corrosion or blockage degree of the valve inner part is analyzed based on the image; the valve discharge flow after the valve is monitored in real time by the flow meter.

[0076] In one specific example, the regulating valve comprehensive evaluation coefficient is calculated based on the regulating valve data, including: the response speed evaluation coefficient, the wear condition evaluation coefficient, the pressure difference before and after evaluation coefficient, the valve inner condition evaluation coefficient and the valve discharge flow after the valve evaluation coefficient are weighted and summed, and the calculation result is taken as the regulating valve comprehensive evaluation coefficient.

[0077] It should be noted that the response speed evaluation coefficient is calculated by the formula wherein represents the response speed, represents the reference value of the response speed, represents the correction factor corresponding to the response speed; the wear condition evaluation coefficient is calculated by the formula ​obtaining the wear condition evaluation coefficient wherein represents the wear condition, represents the correction factor corresponding to the wear condition; the correction factor is calculated by the formula obtaining the front-rear pressure difference evaluation coefficient wherein represents the front-rear pressure difference, represents the reference value of the front-rear pressure difference, represents the correction factor corresponding to the front-rear pressure difference; the correction factor is calculated by the formula obtaining the valve trim condition evaluation coefficient wherein represents the valve trim condition, represents the correction factor corresponding to the valve trim condition; the correction factor is calculated by the formula obtaining the valve post discharge flow evaluation coefficient wherein represents the valve post discharge flow, represents the reference value of the valve post discharge flow, represents the correction factor corresponding to the valve post discharge flow.

[0078] Further, the comprehensive evaluation coefficient of the regulating valve is calculated by the formula wherein represents the weight factor corresponding to the th regulating valve data, represents the number corresponding to the response speed, the wear condition, the front-rear pressure difference, the valve trim condition and the valve post discharge flow, , and , and . It should be noted that the correction factor corresponding to the response speed, the correction factor corresponding to the wear condition, the correction factor corresponding to the front-rear pressure difference, the correction factor corresponding to the valve trim condition and the correction factor corresponding to the valve post discharge flow have a value range of 0-1.

[0079] It should be noted that the comprehensive evaluation coefficient of the regulating valve quantifies the overall health status of the regulating valve, and the higher the value, the better the status, which is achieved by weighting and comprehensively evaluating each index evaluation coefficient; the design of each evaluation coefficient ensures the normalization of the index and avoids unit incompatibility.

[0080] It should be noted that the weight factor corresponding to the response speed, the wear condition, the front-rear pressure difference, the valve trim condition and the valve post discharge flow in the regulating valve data is obtained by the factor analysis method, which first condenses the data of the response speed, the wear condition, the front-rear pressure difference, the valve trim condition and the valve post discharge flow, then obtains the variance explanation rate after rotation, and obtains the weight by subtracting the cumulative variance explanation rate.

[0081] It should be noted that the weight factor corresponding to the response speed, the wear condition, the front-rear pressure difference, the valve trim condition and the valve post discharge flow in the regulating valve data is obtained by the factor analysis method, which first condenses the data of the response speed, the wear condition, the front-rear pressure difference, the valve trim condition and the valve post discharge flow, then obtains the variance explanation rate after rotation, and obtains the weight by subtracting the cumulative variance explanation rate.

[0082] It should be noted that the factor analysis method is a known technology, which is a multivariate statistical analysis method of reducing a number of variables with complex relationships to a few comprehensive factors from the dependent relationship of the internal correlation of the research variables; information condensation is represented as the calculation of the median; the variance explained rate is the information amount of factor extraction, and the variance explained rate = eigenvalue / total analysis item number; the variance explained rate after rotation represents the variance explained rate of the factor after maximum variance rotation.

[0083] It should be noted that the weight sum of 1 ensures unbiased evaluation, fuses multi-source sensor data, dynamically adapts to pipe network changes through weighted synthesis and correction factors, and calculates the process in an embedded system. Hardware accelerators are used to implement formula calculation to avoid software delay and improve response efficiency.

[0084] The application quantifies the health status of the regulating valve, realizes predictive maintenance, integrates multi-sensor data, and uses a weighted sum formula to respond to speed, wear condition, pressure difference before and after, valve inner condition, and valve discharge flow after the valve. Data is collected by a 10Hz sensor array to reduce downtime and prolong equipment life, and the evaluation coefficient is used for step 6 warning.

[0085] Step 6, based on the regulating valve comprehensive evaluation coefficient, a warning prompt is given.

[0086] It should be noted that the result of subtracting the warning threshold of the regulating valve comprehensive evaluation coefficient from the regulating valve comprehensive evaluation coefficient 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, the warning prompt operation is triggered; the warning prompt operation includes generating a warning signal based on the warning state and transmitting the warning signal to the main console for display or alarm sending.

[0087] It should be noted that the physical meaning of the difference value is the deviation degree of the health status of the regulating valve; when the difference value is less than or equal to zero, it means that the state of the regulating valve is lower than the safety threshold and needs to be warned; when the difference value is greater than zero, it means that the state is normal; the physical meaning of the regulating valve comprehensive evaluation coefficient is a quantitative indicator of the overall health status of the regulating valve, and the higher the value, the better the state; The warning threshold of the regulating valve comprehensive evaluation coefficient is preset based on historical maintenance data and pipe network safety standards and stored in the read-only memory of the control center, and its physical meaning is the critical health value that triggers the warning.

[0088] Further, the essence of the threshold comparison algorithm is to perform real-time numerical subtraction operation through embedded comparator hardware unit, and output the difference value in a single clock cycle to avoid software delay; the algorithm design ensures response efficiency and improves system supervision ability through fast deviation detection.

[0089] It should be noted that the early warning prompt operation includes: generating an early warning signal based on the early warning state, wherein the early warning signal is binary data, the abnormal value is 1, and the normal value is 0; the early warning signal is packaged into a data packet through a network protocol and transmitted to the main console, and the main console displays text early warning information based on the early warning signal, such as displaying that the state of the regulating valve is abnormal, or triggering an audible and light alarm.

[0090] Further, the generation of the early warning signal depends on the decision logic of the control center, and the data transmission adopts a point-to-point communication protocol to ensure low-delay response and support management decisions through real-time alarms.

[0091] Referring to Figure 2 The application provides a biomass gas grid-connected transportation adaptive adjustment system in the second aspect.

[0092] The biomass gas grid-connected transportation adaptive adjustment system 100 can be installed in an electronic device. According to the functions implemented, the biomass gas grid-connected transportation adaptive adjustment system 100 can include a quality qualified mark generation module 101, a flow data acquisition module 102, an abnormality mark generation module 103, a safety control module 104, a regulating valve detection module 105, and a warning terminal 106. The modules of the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

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

[0094] The quality qualified mark generation module is used to pre-acquire scheduling instruction data when the pipe network issues a scheduling instruction or detects a real-time demand signal, simultaneously acquire quality data of the biomass gas, and generate a quality qualified mark.

[0095] The flow data acquisition module applies an adaptive control algorithm to dynamically adjust the flow of biomass gas based on the quality qualified mark, and acquires the flow data after adjustment.

[0096] The abnormality mark generation module analyzes whether the gas data is abnormal based on the pre-acquired gas data, and generates an abnormality mark.

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

[0098] The regulating valve detection module detects the regulating valve based on the abnormality mark, acquires regulating valve data, and calculates a comprehensive evaluation coefficient of the regulating valve.

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

[0100] The biomass gas grid-connected conveying self-adaptive adjustment method and system provided by the application generates a quality qualified mark based on scheduling instruction data and biomass gas quality data, applies a self-adaptive control algorithm to dynamically adjust the biomass gas flow, and collects the adjusted flow data; analyzes whether the gas data is abnormal and generates an abnormal mark; based on the pressure abnormal mark, the pressure data corresponding to the pressure regulating component is detected and analyzed, and corresponding safety control is performed; the limitation problems existing in the current biomass gas grid-connected conveying self-adaptive adjustment process are solved, based on the abnormal mark, the regulating valve is detected, the regulating valve data is obtained, and the comprehensive evaluation coefficient of the regulating valve is calculated, and a warning prompt is performed; the feasibility, comprehensiveness and objectivity of the biomass gas grid-connected conveying self-adaptive adjustment are realized.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0102] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0103] In addition, the function modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

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

[0105] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive environment, acquire knowledge and use knowledge to obtain the best results.

[0106] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for adaptive regulation of grid-connected delivery of biomass gas, characterized in that, The method comprises the following steps: Step 1: When the pipe network issues a scheduling instruction or detects a real-time demand signal, pre-acquire the scheduling instruction data, simultaneously acquire the quality data of the biogas, and generate a quality qualified mark; Step 2: Based on the quality qualified mark, apply an adaptive control algorithm to dynamically adjust the flow of the biogas, and collect the adjusted flow data; Step 3: Based on the pre-acquired gas data, analyze whether the gas data is abnormal, and generate an abnormal mark; Step 4: Based on the pressure abnormal mark, detect and analyze the pressure data corresponding to the pressure regulating component, and perform corresponding safety control; Step 5: Based on the abnormal mark, detect the regulating valve, acquire the regulating valve data, and calculate a comprehensive evaluation coefficient of the regulating valve; Step 6: Based on the comprehensive evaluation coefficient of the regulating valve, perform a pre-warning prompt.

2. The biomass gas grid connected delivery self-adaptive regulation method according to claim 1, characterized in that, The quality data of the biogas comprises a methane purity value and a total sulfur content value.

3. The biomass gas grid connected delivery adaptive conditioning method of claim 1, wherein, The generation of the quality qualified mark comprises: Comparing the methane purity value with a methane purity threshold value, and simultaneously comparing the total sulfur content value with a total sulfur content threshold value; when the methane purity value is greater than or equal to the methane purity threshold value, and the total sulfur content value is less than or equal to the total sulfur content threshold value, the quality is qualified, and the quality qualified mark is marked as 1; otherwise, the quality is unqualified, and the quality qualified mark is marked as 0.

4. The biomass gas grid connected delivery adaptive conditioning method of claim 1, wherein, The application of the adaptive control algorithm to dynamically adjust the flow of the biogas, and the collection of the adjusted flow data, comprises: Based on the quality qualified mark, determine a gain parameter set of the adaptive control algorithm, wherein the gain parameter set comprises a proportional gain, an integral gain and a differential gain; apply the adaptive control algorithm to calculate an adjustment signal of the flow of the biogas, 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 use the adjustment signal to dynamically adjust the flow of the biogas, and collect the adjusted flow data.

5. The biomass gas grid connected delivery adaptive conditioning method of claim 1, wherein, The gas data comprises gas quality fluctuation data, moisture and impurity data and pressure fluctuation data.

6. The biomass gas grid connected delivery adaptive conditioning method of claim 1, wherein, The analysis of whether the gas data is abnormal, and the generation of the abnormal mark, comprises: A1: Based on the pre-acquired gas quality fluctuation data, moisture and impurity data and pressure fluctuation data, respectively calculate a gas quality fluctuation deviation value, a moisture and impurity deviation value and a pressure fluctuation deviation value; A2: Compare the gas quality fluctuation deviation value with a preset gas quality fluctuation threshold value; if the gas quality fluctuation deviation value is greater than or equal to the gas quality fluctuation threshold value, generate a gas quality abnormal mark as 1, otherwise generate the gas quality abnormal mark as 0; compare the moisture and impurity deviation value with a preset moisture and impurity threshold value; if the moisture and impurity deviation value is greater than or equal to the moisture and impurity threshold value, generate a moisture and impurity abnormal mark as 1, otherwise generate the moisture and impurity abnormal mark as 0; compare the pressure fluctuation deviation value with a preset pressure fluctuation threshold value; if the pressure fluctuation deviation value is greater than or equal to the pressure fluctuation threshold value, generate a pressure abnormal mark as 1, and trigger step 4, otherwise generate the pressure abnormal mark as 0. A3、When any one of the temperament abnormality sign, the water impurity abnormality sign, and the pressure abnormality sign is 1, it indicates an abnormal state, and an abnormality sign of 1 is generated; otherwise, it indicates a normal state, and an abnormality sign of 0 is generated.

7. The biomass gas grid connected delivery adaptive conditioning method of claim 1, wherein, The pressure abnormality sign is used to detect and analyze pressure data corresponding to the pressure regulating component, and corresponding safety control is performed. When the pressure abnormality sign is 1, real-time pressure data of the pressure regulating component is acquired by a pre-deployed pressure sensor, a pressure evaluation coefficient of the pressure regulating component is calculated, and the pressure evaluation coefficient of the pressure regulating component is compared with a corresponding preset pressure evaluation threshold to generate an operation determination result; and based on the operation determination result, a corresponding operation instruction is generated, and the operation instruction is executed to perform safety control.

8. The biomass gas grid connected delivery adaptive conditioning method of claim 7, wherein, The abnormality sign is used to detect the regulating valve to acquire regulating valve data, including: When the abnormality sign is 1, the regulating valve is detected to acquire regulating valve data; the regulating valve data includes response speed, wear condition, front and rear pressure difference, valve inner condition, and valve discharge flow; and a regulating valve comprehensive evaluation coefficient is calculated based on the regulating valve data.

9. The biomass gas grid connected delivery adaptive conditioning method of claim 1 wherein, The abnormality sign is used to detect the regulating valve to acquire regulating valve data, including: The response speed evaluation coefficient, the wear condition evaluation coefficient, the front and rear pressure difference evaluation coefficient, the valve inner condition evaluation coefficient, and the valve discharge flow evaluation coefficient are weighted and summed, and the calculation result is used as the regulating valve comprehensive evaluation coefficient.

10. A biomass gas grid connected delivery adaptive conditioning system performing any of claims 1-9, characterized by, Including: A quality eligibility sign generation module is configured to, when a dispatch instruction is issued by the pipe network or a real-time demand signal is detected, pre-acquire dispatch instruction data, acquire quality data of the biofuel gas, and generate a quality eligibility sign; A flow data acquisition module is configured to, based on the quality eligibility sign, apply an adaptive control algorithm to dynamically adjust the flow of the biofuel gas, and acquire adjusted flow data; An abnormality sign generation module is configured to, based on pre-acquired fuel gas data, analyze whether the fuel gas data is abnormal, and generate an abnormality sign; A safety control module is configured to, based on the pressure abnormality sign, detect and analyze pressure data corresponding to the pressure regulating component, and perform corresponding safety control; A regulating valve detection module is configured to, based on the abnormality sign, detect the regulating valve to acquire regulating valve data, and calculate a regulating valve comprehensive evaluation coefficient; A warning terminal is configured to, based on the regulating valve comprehensive evaluation coefficient, perform a warning prompt.

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