Natural gas filtering system and method

Abstract

The invention discloses a natural gas filtering system and method, relates to the technical field of natural gas filtering, and aims to solve the problems that the natural gas filtering system is manually cleaned regularly according to a preset time table, the cleaning and maintenance cost is high, and unnecessary maintenance cost and system downtime are caused. Along with the use of the filter, the loss performance of the filter can be changed, the filter is inevitable to have problems, the problems cannot be solved by cleaning at the moment, and the filtering effect is influenced, an automatic cleaning system is designed by comprehensively calculating and analyzing the dirt degree of the filter from the pressure direction, the optical direction and the material resistivity direction. Parameters for judging the degree of equipment are obtained from the visual image direction and the vibration direction, the visual damage coefficient and the damage vibration coefficient are integrated to analyze the damage degree of the filter in multiple directions, a manual maintenance scheme is designed, and multiple benefits of early warning in advance, high-precision monitoring, automatic maintenance, maintenance cost reduction and the like are brought to a filtering system. And the performance and maintainability of the filtering system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas filtration, and more particularly to a natural gas filtration system and method. Background Art

[0002] Natural gas is a clean energy widely used in heating, cooking, power generation and other fields. However, the natural gas pipelines and equipment systems from the natural gas source to the end user may be affected by impurities, particles, droplets and other contaminants. These contaminants may cause damage to pipelines and equipment, reduce system efficiency, and even pose potential risks to the environment and human health.

[0003] Deficiencies of existing technologies:

[0004] Currently, natural gas filtration systems typically use a series of filters and separation equipment, such as screens, cyclones and drying towers, to remove pollutants. They are regularly cleaned manually according to a scheduled schedule, which is costly and may lead to unnecessary maintenance costs and system downtime. As the filter is used, the filter wear performance will change, and the filter will inevitably have problems. At this time, cleaning cannot solve the problem, affecting the filtration effect.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a natural gas filtering system and method, which achieves the goal of solving the problems raised in the above-mentioned background technology by filtering natural gas.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A natural gas filtering method comprises the following steps:

[0009] Step S1, by measuring the differential pressure between the inlet and outlet, using a differential pressure sensor, combining the fluid density and velocity, and calculating the standard and actual differential pressures, the degree of dirt in the filter is determined;

[0010] Step S2, using an optical sensor to detect the optical density or color change of the dirt layer on the filter surface, calculating the transmittance by measuring the initial and actual transmitted light intensities, and analyzing to determine the degree of dirt;

[0011] Step S3, using a resistivity sensor to monitor the resistivity change of the filter material, and analyzing and determining the degree of fouling by measuring the initial and actual resistivity;

[0012] Step S4, using pressure, optics and material resistivity to evaluate the degree of fouling, comprehensively considering the fouling optical coefficient, fouling optical coefficient and fouling resistivity coefficient, calculating a fouling degree parameter, judging the degree of fouling of the filter based on the parameter, and triggering an automatic cleaning operation when the parameter exceeds a set cleaning threshold;

[0013] Step S5, using a vibration sensor to monitor the vibration of the filter, by collecting vibration data and establishing a benchmark, using the vibration frequency data to establish a damage model, and calculating a damage index to evaluate the damage degree of the filter;

[0014] Step S6, using a visual sensor to collect a filter image, and through image processing, feature extraction and benchmark data, using a visual damage coefficient to evaluate the damage degree of the filter;

[0015] Step S7, after self-cleaning, if the dirt degree coefficient is still greater than the cleaning threshold, an alarm is triggered. The damage threshold is set by obtaining the parameters of the equipment damage degree through visual images and vibration data to monitor the equipment damage degree parameters in real time. When the parameter is greater than the damage threshold, an alarm is also triggered and manual maintenance inspection is required.

[0016] In a preferred embodiment, it is characterized in that: a turbine flowmeter is used to measure the speed of the fluid passing through a pipe or pipeline and convert it into a flow rate, and the density of the fluid is obtained from a reference data manual, a fluid property database or data provided by a fluid supplier; the standard differential pressure under a clean filter state is determined; a differential pressure sensor is used to measure the differential pressure under actual conditions; the fouling differential pressure is obtained by subtracting the actual differential pressure from the standard differential pressure, which represents the additional resistance caused by the accumulated dirt in the filter; the density of the fluid, flow rate, dirt and pressure difference are integrated to analyze the fouling pressure coefficient.

[0017] In a preferred embodiment, it is characterized in that: a resistivity sensor is used to measure the initial resistivity Bv of the material, the current passing through the filter material is measured in amperes, a voltage sensor is used to measure the voltage passing through the filter material, and Ohm's law is used to calculate the resistance of the filter material based on the current and voltage drop; the actual resistivity can be calculated by dividing the resistance by the length and cross-sectional area of ​​the filter material; the actual resistivity is compared with the initial resistivity to evaluate the resistivity change, a larger resistivity change indicates a dirt resistivity coefficient, and a smaller resistivity change indicates a higher dirt resistivity coefficient.

[0018] In a preferred embodiment, it is characterized in that: an optical sensor is used to measure the transmitted light intensity of the filter in a dirt-free state to obtain the initial transmitted light intensity, which is the intensity of the light after passing through the filter or transparent material; an optical sensor is used to measure the actual transmitted light intensity; the transmittance refers to the ratio of the transmitted light intensity to the initial transmitted light intensity, expressed as a percentage.

[0019] In a preferred embodiment, the parameters for judging the degree of dirt obtained from the pressure direction, optical direction and material resistivity direction are integrated, and the degree of dirt parameters are calculated through a weighted summation formula; a cleaning threshold is set, and the degree of dirt parameters are monitored in real time. When the degree of dirt parameters is greater than the cleaning threshold, the machine is shut down to trigger an automatic cleaning operation.

[0020] The technical effects and advantages of a natural gas filtration system and method of the present invention are as follows:

[0021] 1. Through the pressure difference sensor, optical sensor and resistivity sensor, the information of pressure difference, optical density and resistivity is obtained, the dirt condition of the filter is evaluated from multiple angles to obtain the dirt degree parameters in multiple directions, and finally the dirt degree coefficient is calculated by the weighted summation formula and compared with the cleaning threshold to determine whether it is self-cleaning. The implementation of the automatic cleaning solution further improves the autonomy of the system, and can automatically trigger the cleaning operation according to the dirt degree parameters, reducing the need for manual intervention, thereby reducing maintenance costs, extending equipment life, and ensuring efficient operation of the equipment.

[0022] 2. Vibration sensors and visual sensors are used to obtain damage vibration coefficients and visual damage coefficients, and multi-dimensional parameters are integrated to calculate the degree of filter damage and compare it with the damage threshold to determine the alarm maintenance situation. At the same time, automatic cleaning solutions and manual maintenance solutions are introduced to ensure that the filter can be effectively maintained under different conditions, improve the maintainability and performance of the filtration system, reduce maintenance and replacement costs, and extend the life of the equipment. Most importantly, the comprehensive calculation method combined with multi-sensor data allows the status of the filter to be evaluated based on multiple parameters, thereby improving the accuracy of monitoring.

[0023] The present invention is adopted. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The figure is a schematic flow chart of a natural gas filtering method according to the present invention.

[0025] Figure 2 The figure is a schematic diagram of the structure of a natural gas filtration system of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example 1

[0028] See also Figure 1 As shown, the natural gas filtration system and method described in this embodiment include the following steps:

[0029] 1. Obtaining the Dirt Pressure Coefficient

[0030] The differential pressure sensor is used to measure the differential pressure between the inlet and outlet of the filter. The differential pressure sensor is used to measure the differential pressure between the inlet and outlet of the pipe or filter to determine the resistance change in the fluid and thus estimate the degree of fouling. The calculation of the differential pressure is based on the following parameters and schemes:

[0031] 1. Determine fluid density and fluid velocity: Use a turbine flowmeter to directly measure the velocity of the fluid passing through a pipe or pipeline and convert it into flow velocity Sv z , in cubic meters per second (m 3 / s) represents the density of the fluid ρ y Obtained from reference data books, fluid property databases, or data provided by fluid suppliers, in kg / m3 (kg / m 3 )express.

[0032] 2. Determine the standard differential pressure: Determine the standard differential pressure Fz0 in the clean filter state, which is the differential pressure of the filter in the absence of dirt;

[0033] 3. Measure the actual differential pressure: Use the differential pressure sensor to measure the actual differential pressure Fz1, which is the differential pressure in the current state of the filter;

[0034] 4. Calculate the dirt differential pressure: By subtracting the actual differential pressure from the standard differential pressure, calculate the dirt differential pressure Fz2 = Fz1-Fz0, which represents the additional resistance caused by the accumulated dirt in the filter;

[0035] 5. Fouling calculation: According to the density and flow rate of the fluid, the fouling pressure coefficient can be calculated using the following formula: Where Tz is the dirt pressure coefficient, Fz2 is the dirt differential pressure, ρ y is the density of the fluid, Sv z is the fluid flow rate. The larger the dirt pressure coefficient, the greater the degree of dirt and the more cleaning is needed.

[0036] Pressure sensors provide several important benefits for detecting fouling on the filter surface. First, they provide a real-time monitoring method that can quickly identify the accumulation of fouling by measuring the change in differential pressure across the filter. This facilitates early fouling detection, allowing maintenance teams to take timely action, reducing downtime and maintenance costs. In addition, pressure sensors provide highly accurate measurements that can accurately estimate the degree of fouling, helping to develop precise maintenance plans, thereby improving the maintainability and reliability of the filtration system. Most importantly, the automated integration capabilities of pressure sensors make them part of an intelligent system that can adjust cleaning operations based on actual conditions, reducing the need for manual intervention and increasing the autonomy and automation of the system. In summary, the application of pressure sensors brings multiple benefits to the filtration system, such as early warning, high-precision monitoring, automated maintenance, and reduced maintenance costs, which improves the performance and maintainability of the filtration system.

[0037] 2. Obtaining the Dirt Optical Coefficient

[0038] Optical sensors are used to detect the optical density or color change of the dirt layer on the filter surface. As the dirt layer increases, the light transmission process decreases, so the degree of dirt can be identified. Calculating the optical density or color change usually involves measuring the intensity of the transmitted light. The following is the method used by optical sensors to calculate the degree of dirt:

[0039] 1. Measure the initial transmitted light intensity: In the absence of dirt, use an optical sensor to measure the transmitted light intensity to obtain the initial transmitted light intensity Jk0, which is the intensity of light after passing through the filter or transparent material, usually expressed in photocurrent or light intensity units (such as watts / square meter);

[0040] 2. Measure the actual transmitted light intensity: Use an optical sensor to measure the actual transmitted light intensity Jk1 over time;

[0041] 3. Calculate transmittance: Transmittance refers to the ratio of transmitted light intensity to initial transmitted light intensity, expressed as a percentage. For example, the formula for calculating transmittance is: Where Rd is the transmittance, Jk0 is the initial transmitted light intensity, and Jk1 is the actual transmitted light intensity;

[0042] 4. Calculate the dirt optical coefficient: The degree of dirt is usually inversely proportional to the transmittance. A decrease in transmittance indicates an increase in the degree of dirt. The following formula can be used to calculate the dirt optical coefficient: Ug = 100% - Rd, where Ug is the dirt optical coefficient;

[0043] Optical sensors are a powerful technology for detecting fouling on the filter surface. They can accurately monitor the degree and distribution of fouling in real time, providing the advantage of early fouling detection. Compared with traditional contact detection methods, optical sensors do not need to contact the filter surface, reducing the potential risk of damage, while providing highly accurate measurement results to help develop accurate maintenance plans. In addition, the real-time monitoring capabilities and automated integration of optical sensors make them a powerful tool for improving system performance, reducing maintenance costs, extending equipment life, and reducing downtime.

[0044] 3. Obtaining the Dirt Resistivity Coefficient

[0045] Resistivity sensors are used to monitor changes in the resistivity of the filter material. The dirt layer usually has a higher resistivity, so when the resistivity changes, it can indicate the degree of dirt. Resistivity measurements are often used to monitor changes in the resistivity of the filter material to estimate the degree of dirt. The following is a resistivity measurement scheme for calculating differential pressure (or degree of dirt):

[0046] 1. Measure the initial resistivity: When the filter is in a clean state, use a resistivity sensor to measure the initial resistivity Bv of the material, where the resistivity sensor is used to measure the resistivity of the filter material.

[0047] 2. Measure current: Measure the current through the filter material in amperes. Use a voltage sensor to measure the voltage across the filter material. Use Ohm's law to calculate the resistance of the filter material based on the current and voltage drop: R = U / I, where R is resistance, U is voltage, and I is current.

[0048] 3. Calculate Resistivity: By dividing the resistance by the length and cross-sectional area of ​​the filter material, the actual resistivity can be calculated: Where ρz is the resistivity, R is the resistance, and Ax is the cross-sectional area of ​​the filter material in square meters (m 2 ) as the unit, Lp is the length of the filter material in meters (m), which can be obtained from the data manual or data provided by the filter supplier.

[0049] 4. Calculate the dirt resistivity coefficient: The actual resistivity is compared to the initial resistivity to evaluate the resistivity change. The dirt resistivity coefficient is usually inversely proportional to the resistivity change. A larger resistivity change indicates a dirt resistivity coefficient, and a smaller resistivity change indicates a higher dirt resistivity coefficient. The following formula can be used to calculate the dirt resistivity coefficient: Where Eo is the dirt resistivity coefficient, ρz is the actual resistivity, and Bv is the initial resistivity.

[0050] Using resistivity measurement to monitor fouling on the filter surface has multiple benefits. Firstly, this method is a non-invasive monitoring technique that does not require direct contact with the filter surface, therefore reducing the risk of wear on the filter material. Secondly, resistivity measurement provides highly accurate fouling monitoring, allowing for timely identification of changes in the degree of fouling, allowing for precise maintenance planning. In addition, it is a real-time monitoring method that can respond immediately to fouling accumulation, reducing maintenance costs and minimizing downtime. Most importantly, resistivity measurement is an automated monitoring method that can be easily integrated into smart systems to achieve automatic maintenance and early warnings, improving the autonomy and maintainability of the system. In summary, resistivity measurement is an effective and accurate monitoring technology that provides filter systems with multiple benefits such as early fouling detection, highly accurate maintenance planning, reduced costs, and improved performance and maintainability.

[0051] 4. Design an automatic cleaning solution

[0052] The above three methods obtain the parameters for judging the degree of dirt from the pressure direction, optical direction and material resistivity direction respectively. The dirt degree parameter is calculated by integrating the dirt optical coefficient Tz, the dirt optical coefficient Ug and the dirt resistivity coefficient Eo in multiple directions through the weighted summation formula. The following is a calculation formula: T w =μ1Tz+μ2Eo+μ3Ug, where T w is the dirt degree parameter, μ1, μ2, μ3 are the proportional parameters between the dirt optical coefficient, the dirt optical coefficient and the dirt resistivity coefficient. The larger the dirt degree parameter is, the more dirt the filter has, the greater the need for cleaning, and the greater the cleaning intensity.

[0053] Set the cleaning threshold and monitor the dirt level parameters in real time. When the dirt level parameters are greater than the cleaning threshold, the machine will be shut down to trigger the automatic cleaning operation.

[0054] This embodiment introduces a natural gas filtration system and method, which combines a differential pressure sensor, an optical sensor, and a resistivity sensor to obtain fouling degree parameters in multiple directions. This comprehensive method can achieve multiple technical effects and advantages such as early fouling detection, high-precision maintenance planning, and automatic cleaning. First, it provides multi-dimensional fouling monitoring, combines information on differential pressure, optical density, and resistivity, evaluates the fouling of the filter from multiple angles, and improves the accuracy of monitoring. Secondly, the system realizes real-time monitoring, allowing immediate response to the accumulation of dirt, which helps to reduce downtime and maintenance costs. In addition, the data of multiple sensors are integrated to make maintenance plans more accurately and reduce the complexity of maintenance. Most importantly, the implementation of the automatic cleaning scheme further improves the autonomy of the system, and can automatically trigger cleaning operations based on the fouling degree parameters, reducing the need for manual intervention, thereby reducing maintenance costs, extending the life of the equipment, and ensuring the efficient operation of the equipment. In summary, the technical effects of Example 1 include multi-dimensional fouling monitoring, real-time monitoring, accurate maintenance planning, and automatic cleaning operations, which improve the performance, maintainability, and reliability of the filtration system.

[0055] Example 2

[0056] There are still technical problems in the above embodiment 1. The automatic cleaning solution is difficult to clean automatically. As the filter is used, the filter wear performance will change. The filter will inevitably have problems. At this time, cleaning cannot solve the problem and affect the filtering effect. At this time, an alarm needs to be issued to notify manual inspection to clean stubborn dirt or replace damaged parts. The following is a method for monitoring natural gas filters:

[0057] First, a series of sensors, including vibration sensors and sound sensors, are installed at key locations of five or more natural gas filters.

[0058] 1. Obtaining the damage vibration coefficient

[0059] The vibration of the filter can be monitored using a vibration sensor mounted on the filter. When filter components are loose or damaged, the vibration characteristics will change. The system can detect problems by analyzing the vibration data. Here is an example scenario that shows how to use a vibration sensor to calculate the degree of filter damage:

[0060] 1. Vibration data collection: Use a vibration sensor to monitor the vibration of the filter and collect vibration data in three directions, expressed as a_x, a_y, and a_z. And extract the vibration frequency Wq from the vibration data.

[0061] 2. During the initial installation and use of the filter, record the vibration data and establish a baseline, denoted as Fp0.

[0062] 3. Use vibration frequency data to build a model to monitor damage. Here, a simple exponential model can be used to represent the change of filter vibration frequency over time: F(t) = Fp0*e -kt , where F(t) is the vibration frequency at time t, Fp0 is the reference vibration frequency, and k is a constant reflecting the damage rate.

[0063] 5. Damage degree assessment: By comparing the current vibration frequency F(t) with the reference frequency Fp0, the damage index Dj is calculated to indicate the damage degree of the filter. The damage index can be calculated using the following formula: In the formula, Dj t A measure of the degree of damage, between 0 and 1, where values ​​close to 1 indicate a normal filter and values ​​close to 0 indicate severe damage.

[0064] Vibration sensors have unique advantages in monitoring the degree of filter damage. Through real-time, non-invasive vibration data collection, they can detect potential problems early and issue alarms in time, effectively reducing maintenance costs and production downtime. This technology also allows data analysis and trend monitoring to help predict maintenance needs, optimize maintenance plans, and ensure equipment reliability and production efficiency.

[0065] 2. Obtaining the Visual Damage Coefficient

[0066] Vision sensors are used to monitor the appearance of filters. Image processing techniques are used to analyze filter damage, such as cracks, dirt, or wear. Vision sensors are used to calculate the degree of filter damage, which involves image processing and analysis techniques. The following is an example scenario that describes how to use image data to calculate the degree of damage:

[0067] 1. Image data acquisition: Use an image sensor to take an image of the filter. The image is analyzed for the appearance and damage of the filter.

[0068] 2. Image processing: Preprocess the acquired images to improve the accuracy of the analysis. This can include denoising, image enhancement, and image segmentation operations.

[0069] 3. Extract features from the image to calculate the degree of damage. The features include the color, texture, shape, and cracks of the object. The specific technology has been implemented and will not be elaborated here.

[0070] 4. Establish baseline data: When the filter is installed and used for the first time, record the image data and establish the image baseline coefficient. This will serve as a reference for the normal state.

[0071] 5. Visual damage coefficient calculation: Calculate the damage degree by comparing the current image features with the reference image features. The damage degree can be calculated using the following formula: Where Yt is the visual damage coefficient, n is the number of features, Cz i is the value of the i-th feature of the current image, Cz 0i is the value of the i-th feature of the reference image.

[0072] It should be noted that, image features: select the features to be extracted from the image, for example, color histogram, texture features, shape features, etc., baseline data: image data collected under normal conditions, including the baseline values ​​of each feature, visual damage degree index: use the above formula to calculate the visual damage coefficient to obtain a numerical value representing the degree of filter damage, feature weight: if different features have different effects on the degree of damage, you can assign weights to each feature and consider these weights in the calculation.

[0073] The use of visual inspection systems for filter damage detection brings multiple benefits. First, such systems can provide intuitive visual information, allowing operators or automated systems to quickly identify abnormalities in the filter's appearance, such as cracks, dirt, or physical damage. Secondly, the visual inspection system can monitor in real time, so that problems can be discovered early, thereby reducing unnecessary downtime and production interruptions. In addition, it helps to improve safety because it can identify problems that may cause leaks or contamination. Most importantly, the visual inspection system provides visual evidence to help support maintenance decisions, thereby reducing repair and replacement costs, extending the service life of equipment, while ensuring product quality and environmental health. In short, the visual inspection system provides a highly visual and accurate means for monitoring the degree of filter damage, enhancing the maintainability and efficiency of the production process.

[0074] 3. Designing a manual maintenance plan

[0075] The above two methods obtain the parameters for judging the degree of equipment from the visual image direction and the vibration direction respectively. The equipment damage degree parameter is calculated by integrating the visual damage coefficient and the damage vibration coefficient in multiple directions through the weighted summation formula. The following is a calculation formula: Lk = δ1Yt + δ2Dj t , where Lk is the equipment damage degree parameter, δ1 and δ2 are proportional parameters. The larger the equipment damage degree parameter, the greater the degree of filter damage, and more manual inspection, maintenance and replacement are required.

[0076] After the self-cleaning solution is executed in Example 1, if the fouling degree coefficient is still greater than the cleaning threshold, the machine is shut down to trigger an alarm and a maintenance check is called.

[0077] Set a damage threshold and monitor the equipment damage parameters in real time. When the equipment damage parameters are greater than the damage threshold, the machine will be shut down and trigger an alarm, calling for manual maintenance inspection.

[0078] This embodiment introduces a vibration sensor and a visual sensor to obtain the damage vibration coefficient and the visual damage coefficient, and integrates multi-dimensional parameters to calculate the degree of damage of the filter. The technical effect and advantage of this method is that it integrates the analysis of vibration and visual data and provides more comprehensive filter monitoring. First, the vibration sensor can monitor the vibration of the filter in real time, which is used to detect looseness or damage of components, realize early fault detection, and reduce downtime and maintenance costs. Secondly, the visual sensor can capture abnormalities in the appearance of the filter, such as cracks, dirt or physical damage, and provide visual evidence to help make maintenance decisions, reduce maintenance and replacement costs, and extend the life of the equipment. Most importantly, the comprehensive calculation method combined with multi-sensor data allows the status of the filter to be evaluated based on multiple parameters, thereby improving the accuracy of monitoring. In addition, the introduction of automatic cleaning solutions and manual maintenance solutions ensures that the filter can be effectively maintained in different situations, improving the maintainability and performance of the filter system. In summary, the technical effects of Example 2 include multi-dimensional monitoring, real-time fault detection, automatic cleaning and manual maintenance, which improve the performance, maintainability and reliability of the filter system.

[0079] Example 3

[0080] This embodiment is an introduction to a natural gas filtration system. Figure 2 A structural schematic diagram of a natural gas filtration system of the present invention is given, which includes a monitoring module, a dirt analysis module, a damage analysis module, an automatic cleaning module, and a manual maintenance module, and there are signal connections between the modules;

[0081] Monitoring module: connected to each sensor, used to obtain sensor data, perform preliminary data sorting, and send sensor data to the dirt analysis module and damage analysis module;

[0082] Dirt analysis module: Analyzes the degree of dirt from the pressure direction, optical direction and material resistivity direction, integrates to obtain the degree of dirt parameters, compares the degree of dirt parameters with the set cleaning threshold, and sends the comparison results to the automatic cleaning module;

[0083] Automatic cleaning module: monitors the comparison result of the dirt degree parameter and the set cleaning threshold in real time. When the dirt degree parameter is greater than the cleaning threshold, the machine stops to trigger the automatic cleaning operation, and sends the comparison result of the dirt degree parameter after automatic cleaning and the set cleaning threshold to the manual maintenance module;

[0084] Damage analysis module: Analyze the parameters of equipment damage degree from the visual image direction and vibration direction, integrate the visual damage coefficient and damage vibration coefficient in multiple directions to calculate the equipment damage degree parameters through the weighted summation formula, compare the equipment damage degree parameters with the set damage threshold, and send the comparison results to the manual maintenance module;

[0085] Manual maintenance module: Analyze the comparison results of the dirt degree parameter after automatic cleaning and the set cleaning threshold. If the dirt degree coefficient is still greater than the cleaning threshold, the machine will be shut down to trigger an alarm and call for maintenance inspection; and analyze the comparison results of the equipment damage degree parameter and the set damage threshold. When the equipment damage degree parameter is greater than the damage threshold, the machine will be shut down to trigger an alarm and call for maintenance inspection

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0088] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A natural gas filtering method, characterized in that: The steps include: Step S1, by measuring the differential pressure between the inlet and outlet, using a differential pressure sensor, combining the fluid density and velocity, and calculating the standard and actual differential pressures, the degree of dirt in the filter is determined; Step S2, using an optical sensor to detect the optical density or color change of the dirt layer on the filter surface, calculating the transmittance by measuring the initial and actual transmitted light intensities, and analyzing to determine the degree of dirt; Step S3, using a resistivity sensor to monitor the resistivity change of the filter material, and analyzing and determining the degree of fouling by measuring the initial and actual resistivity; Step S4, using pressure, optics and material resistivity to evaluate the degree of fouling, comprehensively considering the fouling optical coefficient, fouling optical coefficient and fouling resistivity coefficient, calculating a fouling degree parameter, judging the degree of fouling of the filter based on the parameter, and triggering an automatic cleaning operation when the parameter exceeds a set cleaning threshold; Step S5, using a vibration sensor to monitor the vibration of the filter, by collecting vibration data and establishing a benchmark, using the vibration frequency data to establish a damage model, and calculating a damage index to evaluate the damage degree of the filter; Step S6, using a visual sensor to collect a filter image, and through image processing, feature extraction and benchmark data, using a visual damage coefficient to evaluate the damage degree of the filter; Step S7, after self-cleaning, if the dirt degree coefficient is still greater than the cleaning threshold, an alarm is triggered. The damage threshold is set by obtaining the parameters of the equipment damage degree through visual images and vibration data to monitor the equipment damage degree parameters in real time. When the parameter is greater than the damage threshold, an alarm is also triggered and manual maintenance inspection is required.

2. A natural gas filtering method according to claim 1, characterized in that: The speed of the fluid passing through the pipe or pipeline is measured by a turbine flowmeter and converted into flow rate. The density of the fluid is obtained from the reference data manual, fluid property database or data provided by the fluid supplier; the standard differential pressure under the clean filter state is determined; the differential pressure under actual conditions is measured using a differential pressure sensor; the fouling differential pressure is obtained by subtracting the actual differential pressure from the standard differential pressure, which represents the additional resistance caused by the accumulated dirt in the filter; the fouling pressure coefficient is obtained by integrating the fluid density, flow rate, dirt and differential pressure analysis.

3. A natural gas filtering method according to claim 1, characterized in that: Using an optical sensor to measure the intensity of light transmitted through the filter in a dirt-free state, obtaining the initial intensity of light transmitted, which is the intensity of light after passing through the filter or transparent material; Using an optical sensor to measure the actual intensity of light transmitted; Transmittance refers to the ratio of transmitted light intensity to initial transmitted light intensity, expressed as a percentage.

4. A natural gas filtering method according to claim 1, characterized in that: A resistivity sensor is used to measure the initial resistivity Bv of the material. The current through the filter material is measured in amperes. A voltage sensor is used to measure the voltage through the filter material. Using Ohm's law, the resistance of the filter material is calculated from the current and voltage drops. The actual resistivity can be calculated by dividing the resistance by the length and cross-sectional area of ​​the filter material. The actual resistivity is compared to the initial resistivity to evaluate the resistivity change. A larger resistivity change indicates a dirt resistivity factor, and a smaller resistivity change indicates a higher dirt resistivity factor.

5. A natural gas filtering method according to claim 4, characterized in that: The parameters for judging the degree of fouling obtained from the pressure direction, optical direction and material resistivity direction are integrated, and the fouling degree parameters are calculated through a weighted summation formula; Set the cleaning threshold and monitor the dirt level parameters in real time. When the dirt level parameters are greater than the cleaning threshold, the machine will be shut down to trigger the automatic cleaning operation.

6. A natural gas filtering method according to claim 1, characterized in that: Use vibration sensors to monitor the vibration of the filter and collect vibration data in three directions. During the initial installation and use of the filter, record the vibration data and establish a baseline. Use the vibration frequency data to build a model to monitor damage. By comparing the current vibration frequency with the baseline frequency, calculate the damage index to indicate the degree of filter damage.

7. A natural gas filtering method according to claim 1, characterized in that: Use an image sensor to capture an image of the filter, analyze the image for appearance and damage of the filter, pre-process the captured image, including denoising, image enhancement, and image segmentation operations, and extract features from the image for calculating the degree of damage. The features include the color, texture, shape, and cracks of the object; During the initial installation and use of the filter, the image data is recorded and an image reference coefficient is established; the degree of damage is calculated by comparing the current image features with the reference image features.

8. A natural gas filtering method according to claim 7, characterized in that: The parameters for judging the degree of equipment are obtained from the visual image direction and the vibration direction. The visual damage coefficient and the damage vibration coefficient are integrated in multiple directions to calculate the equipment damage degree parameter through a weighted summation formula. The larger the equipment damage degree parameter, the greater the damage degree of the filter, and the more manual inspection, maintenance and replacement are required. After the self-cleaning plan is executed, if the dirt degree coefficient is still greater than the cleaning threshold, the shutdown triggers an alarm and calls for manual maintenance inspection; Set a damage threshold and monitor the equipment damage parameters in real time. When the equipment damage parameters are greater than the damage threshold, the machine will be shut down and trigger an alarm, calling for manual maintenance inspection.

9. A natural gas filtration system for implementing a natural gas filtration method as claimed in any one of claims 1 to 6, characterized in that: It includes monitoring module, dirt analysis module, damage analysis module, automatic cleaning module and manual maintenance module, and there are signal connections between the modules; Monitoring module: connected to each sensor, used to obtain sensor data, perform preliminary data sorting, and send sensor data to the dirt analysis module and damage analysis module; Dirt analysis module: Analyzes the degree of dirt from the pressure direction, optical direction and material resistivity direction, integrates to obtain the degree of dirt parameters, compares the degree of dirt parameters with the set cleaning threshold, and sends the comparison results to the automatic cleaning module; Automatic cleaning module: monitors the comparison result of the dirt degree parameter and the set cleaning threshold in real time. When the dirt degree parameter is greater than the cleaning threshold, the machine stops to trigger the automatic cleaning operation, and sends the comparison result of the dirt degree parameter after automatic cleaning and the set cleaning threshold to the manual maintenance module; Damage analysis module: Analyze the parameters of equipment damage degree from the visual image direction and vibration direction, integrate the visual damage coefficient and damage vibration coefficient in multiple directions to calculate the equipment damage degree parameters through the weighted summation formula, compare the equipment damage degree parameters with the set damage threshold, and send the comparison results to the manual maintenance module; Manual maintenance module: Analyze the comparison results between the dirt degree parameters after automatic cleaning and the set cleaning threshold. If the dirt degree coefficient is still greater than the cleaning threshold, the machine will be shut down to trigger an alarm and call for manual maintenance inspection; and analyze the comparison results between the equipment damage degree parameters and the set damage threshold. When the equipment damage degree parameters are greater than the damage threshold, the machine will be shut down to trigger an alarm and call for manual maintenance inspection.

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