Hybrid gas separation system and method based on sensor fusion

Abstract

The application relates to the technical field of gas separation, in particular to a mixed gas separation system and method based on sensor fusion. The application scheme comprehensively analyzes gas parameters and equipment operation conditions, breaks through the limitation of traditional gas separation systems relying on post-maintenance and offline detection, can real-time fuse multi-dimensional data such as the physicochemical properties of mixed gas, separation difficulty quantitative indexes, equipment material resistance and operation states, and issues accurate early warnings in the early stage of equipment performance degradation or before faults occur.

Description

Technical Field

[0001] This application relates to the field of gas separation technology, and in particular to a mixed gas separation system and method based on sensor fusion. Background Technology

[0002] Gas separation is a crucial process in many industrial sectors, including chemical engineering, energy, environmental protection, and semiconductor manufacturing. Its core objective is to efficiently and with high purity extract the target gas (such as high-purity nitrogen, hydrogen, or specific process gases) from multi-component gas mixtures. Membrane separation technology has become one of the mainstream technologies for gas separation due to its advantages such as ease of operation, relatively low energy consumption, and small footprint. However, in practical industrial applications, gas separation processes face a series of severe technical challenges, and existing technologies have significant limitations in addressing these challenges.

[0003] First, there is a lack of dynamic characterization capabilities for the properties of mixed gases. The gas sources used for separation are highly complex, and their composition (such as the volatility and content of each component) and physicochemical properties (such as molecular diameter, critical temperature, and dynamic viscosity) are not constant but dynamically change with fluctuations in upstream processes or changes in the feed gas source. Existing operation and maintenance technologies can usually only provide basic flow or pressure monitoring and cannot obtain multi-dimensional characteristic parameters of the mixed gas in real time and comprehensively. When gas components undergo abrupt changes (such as a surge in the content of non-target gases) or property deterioration (such as excessively high viscosity), traditional systems cannot detect it in time, resulting in blind operation of the separation process. Second, there is a lack of correlation analysis between gas properties and equipment tolerance. The core component of membrane separation equipment—the separation membrane—has strict tolerance requirements for the working environment, including pH, temperature, pressure, and particulate matter concentration. When the mixed gas contains corrosive components (such as acidic gases), particulate impurities, or is under extreme temperature and pressure conditions, it will directly lead to chemical corrosion, physical wear, or performance degradation of the membrane material. In existing technologies, gas quality monitoring and equipment health management are usually two independent systems that are not interconnected. This data silo phenomenon prevents maintenance personnel from quantitatively assessing the severity of damage to equipment caused by the current mixed gas, and makes it even more difficult to predict the cumulative impact of this damage on subsequent separation efficiency. Furthermore, there is a lack of prediction of separation performance and assessment of equipment compatibility. Existing technologies largely focus on monitoring the equipment's own operating parameters (such as compressor vibration and valve opening), neglecting the inherent coupling relationship between gas input characteristics and equipment output performance. For example, for the same membrane separation equipment, the actual separation purity and efficiency may differ significantly when facing mixed gases with different properties. Due to the lack of a comprehensive quantitative prediction model covering the entire chain from gas properties to operating status to separation performance, operators find it difficult to predict the equipment's true performance under specific gas conditions, and are unable to provide scientific equipment matching recommendations before conditions deteriorate. This often leads to two situations: first, the equipment is subjected to excessive harsh operating conditions, accelerating aging or even damage; second, the optimal time for maintenance or adjustment of process parameters is missed, resulting in substandard product purity and economic losses.

[0004] In summary, existing operation and maintenance technologies in the field of mixed gas separation generally suffer from technical deficiencies such as incomplete sensing, lack of coordinated analysis, and inaccurate prediction when dealing with dynamic and complex gas environments. The market urgently needs an intelligent method that can integrate multi-source sensor data, quantitatively assess separation difficulty and equipment damage, and predict separation results in advance to achieve accurate early warning. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, this application proposes a mixed gas separation method based on sensor fusion.

[0006] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a mixed gas separation method based on sensor fusion, comprising the following specific steps: Step 1: Obtain the composition, properties, equipment operation data, and tolerance data of the mixed gases to be separated using sensors; Step 2: Analyze and predict the difficulty of separating the mixed gas by combining the composition and properties of the mixed gas; Step 3: Analyze the damage caused by the mixed gas to the equipment, taking into account the properties of the mixed gas and the equipment's tolerance. Step 4: Analyze the equipment's separation efficiency by combining data on the damage caused by the mixed gas to the equipment and the equipment's operating status. Based on the equipment's separation efficiency and the difficulty of separating the mixed gas, predict the equipment's separation effect. Step 5: Provide equipment matching early warning based on the predicted equipment separation effect.

[0007] In one implementation of this application, the step of acquiring the composition, properties, equipment operation data, and tolerance data of the mixed gases to be separated via sensors includes the following specific steps: S101. The volatility, content ratio, molecular diameter, critical temperature and pressure of each component of the mixed gas are obtained through a gas sensor; at the same time, the average density and dynamic viscosity of the mixed gas are obtained and stored in the gas information storage component. S102. The compressor vibration, valve opening and sealing chamber temperature of the membrane separation equipment are obtained through the equipment sensors and stored in the equipment information storage component. S103. Obtain the corrosion resistance limit (pH, specific gas concentration), temperature resistance limit, and pressure resistance limit of the stored equipment materials (membranes) through the equipment factory settings.

[0008] In one implementation of this application, the prediction of the separation difficulty of the mixed gas includes the following specific aspects: S201. Obtain the volatility, content percentage, molecular diameter, critical temperature, and pressure of each component in the gas mixture; simultaneously, obtain the dynamic viscosity of the gas mixture. S202. Obtain the volatility of each gas in the mixed gas in the separation environment. Divide the absolute value of the difference between the volatility of the target gas and the non-target gas by the safety difference to obtain the volatility difference value. Obtain the content ratio of the target gas and the non-target gas. Divide the absolute value of the difference between the content ratio of each gas by the safety content ratio difference to obtain the content ratio difference value. Obtain the molecular diameter difference between the target gas and the non-target gas by the safety difference to obtain the molecular diameter difference value. Take the weighted sum of the obtained molecular diameter difference value, content ratio difference value and volatility difference value, and then take the reciprocal to obtain the initial separation difficulty. S203. Obtain the dynamic viscosity of the mixed gas, divide it by the safety gas viscosity to obtain the viscosity influence value, multiply the viscosity influence value by the influence coefficient, add it to the value 1, and then multiply it by the initial separation difficulty to obtain the separation difficulty of the mixed gas.

[0009] In one implementation of this application, the damage to the equipment caused by the mixed gas includes the following specific aspects: S301. Obtain the concentration and acidity / alkalinity of the acidic components in the mixed gas, and simultaneously obtain the safe acidity / alkalinity range of the equipment. Obtain acidity / alkalinity anomaly by the standard deviation between the acidity / alkalinity of the acidic components and the safe acidity / alkalinity range of the equipment. Obtain concentration anomaly by dividing the concentration of the acidic components by the safe concentration. Obtain acid-base damage anomaly by multiplying acidity / alkalinity anomaly by concentration anomaly. S302. Obtain the particulate matter concentration, particle size, and hardness in the mixed gas, and simultaneously obtain the viscosity of the gas. Obtain the ratio of the particulate matter concentration, particle size, and hardness to the corresponding safe values ​​to obtain the anomalies of the corresponding parameters. Multiply the anomalies of the parameters to obtain the particulate matter anomalies. Simultaneously obtain the ratio of the gas viscosity to the safe viscosity to obtain the viscosity anomalies. Multiply the viscosity anomalies by the sum of the viscosity anomalies and the value 1, and then multiply them by the particulate matter anomalies to obtain the particulate matter damage anomalies. S303. Obtain the pressure and temperature of the mixed gas, calculate the standard deviation of the pressure and temperature of the mixed gas from the safe range of the corresponding parameters of the equipment, and then sum them by weight to obtain the environmental damage anomaly. S304. The weighted sum of the obtained acid-base damage anomalies, environmental damage anomalies, and particulate matter damage anomalies is used to obtain the damage situation of the mixed gas to the equipment.

[0010] In one implementation of this application, the separation efficiency of the analytical device includes the following specific steps: S401. Obtain the accuracy of the control commands executed by the equipment, the compressor vibration of the membrane separation equipment, the valve opening degree, and the temperature of the sealing chamber; calculate the standard deviation of the corresponding parameters from the corresponding safety range, and then sum them by weight to obtain the abnormal operation of the equipment; S402. Obtain the purity of the separated gas from the membrane separation equipment in the initial state. Divide the safe gas purity by the purity of the separated gas to obtain the separation anomaly. Obtain the initial separation efficiency anomaly of the equipment by weighted summing the separation anomaly and the equipment operation anomaly. S403. The product of the damage to the equipment caused by the mixed gas and the influence coefficient is summed with the value 1. The summation result is multiplied by the initial separation efficiency anomaly to obtain the separation anomaly of the equipment.

[0011] In one implementation of this application, the device separation effect prediction includes the following specific steps: S404. The difficulty in separating the mixed gas and the separation abnormality of the equipment are weighted and summed to obtain an abnormal separation effect.

[0012] In one implementation of this application, the device matching warning includes the following specific steps: The abnormal separation result obtained from the calculation is divided by the set warning threshold to obtain the warning value, which is then compared and analyzed with the set warning level. If the warning value is greater than or equal to 95%, the current equipment is fully capable of separating the gas, and no intervention is required in the output. It is recommended to continue monitoring. If the warning value is greater than or equal to 80% but less than 95%, it indicates a decrease in matching degree and that the equipment performance is moving towards limitation. It is recommended to take one of the following measures: reduce the current processing volume by 10% to maintain purity; wait for the equipment maintenance window and backwash the membrane in advance. If the warning value is less than 80%, the system will immediately issue a warning, indicating a serious mismatch in output, severe equipment damage, or loss of purification capacity. Emergency operation suggestions: skip this equipment section directly and switch to the backup removal section; reduce the intake air volume to below 50% and adjust to low pressure mode.

[0013] Secondly, this application also provides a mixed gas separation system based on sensor fusion, including the following specific modules: Sensor data acquisition module, separation effect prediction module, and matching early warning module; The sensor data acquisition module acquires data on the composition, properties, equipment operation, and tolerance of the mixed gases to be separated through sensors. The separation effect prediction module analyzes and predicts the separation difficulty of the mixed gas by combining the composition and properties of the mixed gas, analyzes the damage of the mixed gas to the equipment by combining the properties of the mixed gas and the equipment's tolerance, analyzes the separation efficiency of the equipment by combining the damage of the mixed gas to the equipment and the equipment's operating data, and predicts the equipment's separation effect by combining the equipment's separation efficiency and the separation difficulty of the mixed gas. The matching early warning module provides device matching early warning based on the predicted device separation effect.

[0014] Thirdly, this application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a mixed gas separation method based on sensor fusion by calling the computer program stored in the memory.

[0015] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a sensor fusion-based method for separating mixed gases.

[0016] Compared with the prior art, this application has the following advantages: By comprehensively analyzing gas parameters and equipment operation, it breaks through the limitations of traditional gas separation systems that rely on post-maintenance and offline detection. It can integrate multi-dimensional data such as the physicochemical properties of mixed gases, quantitative indicators of separation difficulty, equipment material tolerance, and operating status in real time, and issue accurate early warnings in the early stages of equipment performance degradation or before failure occurs. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure in the method embodiments of this application; Figure 2 This is a schematic diagram of the overall process of step three in the method embodiment of this application; Figure 3 This is a schematic diagram of the structure in the system embodiment of this application. Detailed Implementation

[0018] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0019] Example

[0020] Please see Figures 1 to 2 , Figure 1 This is a schematic diagram of the overall process of the sensor fusion-based mixed gas separation method provided in the embodiments of this application, which specifically includes the following steps: Step 1: Obtain the composition, properties, equipment operation data, and tolerance data of the mixed gases to be separated using sensors; In one specific embodiment, the following specific steps are included: S101, the volatility, content ratio, molecular diameter, critical temperature and pressure of each component of the mixed gas are obtained by a gas sensor; at the same time, the average density and dynamic viscosity of the mixed gas are obtained and stored in the gas information storage component; S102. The compressor vibration, valve opening and sealing chamber temperature of the membrane separation equipment are obtained through the equipment sensors and stored in the equipment information storage component. S103. Obtain the stored corrosion resistance limits (pH, specific gas concentration), temperature resistance limits, and pressure resistance limits of the equipment materials (membranes) through the equipment factory settings. In this embodiment, a high-precision gas chromatograph may only output a set of component data every 10 seconds, while a sensitive electrochemical sensor transmits concentration readings every second, and a vibration sensor continuously records the equipment status at a rate of kilohertz. These data not only have vastly different sampling frequencies, but their physical dimensions are also completely different. For example, gas concentration is measured in ppm, temperature is expressed in degrees Celsius, pressure is measured in kilopascals, and vibration speed is measured in millimeters per second. At the same time, the noise characteristics and response delays carried by each data are also different. Physical layer fusion is specifically implemented. The system first uses a high-precision hardware clock to stamp each message with a nanosecond-level timestamp. Then, it uses nearest neighbor interpolation or linear interpolation algorithms to map the measurement values ​​of the slow sensor to the timeline of the fast sensor, ensuring that the data in each time slice comes from the same physical instant. Subsequently, all physical quantities are converted into dimensionless normalized vectors and superimposed with static tolerance boundary parameters extracted from the device's read-only memory (ROM), such as the material's upper temperature limit, upper pressure limit, or corrosion concentration threshold, as prior knowledge. In this way, each data frame is not just a set of isolated readings, but a complete physical description block containing environmental state, device state, safety boundaries, and timestamps. Step 2: Analyze and predict the difficulty of separating the mixed gas by combining the composition and properties of the mixed gas; In this embodiment, the prediction of the separation difficulty of the mixed gas includes the following specific aspects: S201. Obtain the volatility, content percentage, molecular diameter, critical temperature, and pressure of each component in the gas mixture; simultaneously, obtain the dynamic viscosity of the gas mixture. In this embodiment, to assess the difficulty of the mixed gas in the membrane separation process, it is first necessary to obtain the basic physicochemical properties of the gas components themselves. These properties are not all obtained through real-time measurement, but rather require a combination of database queries, online analyzers, and process parameter calculations. Volatility is usually expressed as the saturated vapor pressure of the gas at the separation temperature, which can be retrieved from authoritative databases. For a given temperature, the database will return an accurate vapor pressure value, which is a measure of the tendency of gas molecules to escape from the liquid or solid phase to the gas phase, and directly affects the adsorption capacity of the gas on the membrane surface in membrane separation. Molecular diameter refers to the kinetic diameter, that is, the equivalent size of the gas molecule during the diffusion process. For example, the diameter of hydrogen molecules is approximately 0.289 nm, carbon dioxide is 0.33 nm, methane is 0.38 nm, and nitrogen is 0.364 nm. These values ​​are crucial for membranes dominated by sieving mechanisms. The content percentage is obtained in real time by an online gas analyzer (such as an infrared gas analyzer or a gas chromatograph), and is usually output as a volume percentage or ppm. S202. Obtain the volatility of each gas in the mixed gas in the separation environment. Divide the absolute value of the difference between the volatility of the target gas and the non-target gas by the safety difference to obtain the volatility difference value. Obtain the content ratio of the target gas and the non-target gas. Divide the absolute value of the difference between the content ratio of each gas by the safety content ratio difference to obtain the content ratio difference value. Obtain the molecular diameter difference between the target gas and the non-target gas by the safety difference to obtain the molecular diameter difference value. Take the weighted sum of the obtained molecular diameter difference value, content ratio difference value and volatility difference value, and then take the reciprocal to obtain the initial separation difficulty. In this embodiment, the essence of separation is to utilize the differences in adsorption, dissolution, and diffusion behavior of different components on the membrane material. Therefore, the greater the difference, the lower the separation difficulty theoretically. In specific calculations, the difference in volatility is taken as an absolute value, and the difference in molecular diameter is also taken as an absolute difference. The difference in content ratio is first normalized to the [0, 1] interval and then taken as an absolute value. Although these difference values ​​are all dimensionless, their numerical ranges and physical meanings are completely different: the difference in volatility can be very large (for example, the vapor pressure difference between light hydrocarbons and heavy hydrocarbons can reach several orders of magnitude), while the difference in molecular diameter is usually only a few tenths of a nanometer. Therefore, different weighting coefficients are assigned to each difference. These weights are derived from experimental data and knowledge of membrane material characteristics. For example, for a polymer membrane mainly based on molecular sieving mechanism, the weight of the difference in molecular diameter can be set to 0.5, the difference in volatility to 0.4, and the difference in content ratio to 0.1. S203. Obtain the dynamic viscosity of the mixed gas, divide it by the safety gas viscosity to obtain the viscosity influence value, multiply the viscosity influence value by the influence coefficient, add it to the value 1, and then multiply it by the initial separation difficulty to obtain the separation difficulty of the mixed gas. In this embodiment, high-viscosity gas will generate greater diffusion resistance on the membrane surface and inside the membrane pores, which will reduce the rate at which molecules pass through the membrane layer during the separation process, thereby reducing the effective separation efficiency. Therefore, the final separation difficulty needs to be corrected based on the sum of differences calculated in S202, and a penalty factor determined by viscosity is introduced.

[0021] Step 3: Analyze the damage caused by the mixed gas to the equipment, taking into account the properties of the mixed gas and the equipment's tolerance. In this embodiment, as Figure 2 As shown, the damage caused to the equipment by the mixed gas includes the following specific details: S301. Obtain the concentration and acidity / alkalinity of the acidic components in the mixed gas, and simultaneously obtain the safe acidity / alkalinity range of the equipment. Obtain acidity / alkalinity anomaly by the standard deviation between the acidity / alkalinity of the acidic components and the safe acidity / alkalinity range of the equipment. Obtain concentration anomaly by dividing the concentration of the acidic components by the safe concentration. Obtain acid-base damage anomaly by multiplying acidity / alkalinity anomaly by concentration anomaly. In this embodiment, the gas in the industrial process often contains acidic components, such as hydrogen sulfide, carbon dioxide, and hydrogen chloride. These acidic gases dissolve in water to form acids, causing electrochemical corrosion of metal equipment. The corrosion rate is directly related to the strength and concentration of the acid. This step requires real-time acquisition of two key parameters: the pH value of the solution and the concentration of the acidic gas. For example, the pH value is measured by an online pH meter installed in the gas processing pipeline. The measurement result is usually output as a value of 0-14. The lower the pH value, the stronger the acidity and the greater the tendency to corrode metals. The gas concentration is measured by an infrared gas analyzer or a gas chromatograph, with units of ppm or volume percentage. S302. Obtain the particulate matter concentration, particle size, and hardness in the mixed gas, and simultaneously obtain the viscosity of the gas. Obtain the ratio of the particulate matter concentration, particle size, and hardness to the corresponding safe values ​​to obtain the anomalies of the corresponding parameters. Multiply the anomalies of the parameters to obtain the particulate matter anomalies. Simultaneously obtain the ratio of the gas viscosity to the safe viscosity to obtain the viscosity anomalies. Multiply the viscosity anomalies by the sum of the viscosity anomalies and the value 1, and then multiply them by the particulate matter anomalies to obtain the particulate matter damage anomalies. In this embodiment, in addition to chemical corrosion, solid particles carried in the gas can cause physical wear and blockage of the equipment. Particulate matter may originate from upstream processes (such as catalyst powder, rust products), environmental dust, or condensation products of the process itself. To assess its damage potential, three core parameters of the particulate matter are required: particle size distribution, concentration, and hardness. Particle size distribution is measured using a laser particle size analyzer or dynamic light scattering (DLS) technology, and the results are usually presented in the form of cumulative curves or histograms, for example, D50 (median particle size) is 10 μm. Concentration is measured by an optical particle counter or gravimetric method, with units of mg / m³ or particles / cm³. Hardness needs to be determined from material handbooks based on the material of the particulate matter. For example, sand has a Mohs hardness of 7, rust is about 5-6, while catalyst powder may be between 4-5. During analysis, a wear risk index is usually calculated, which is expressed as wear index = concentration × (average particle size / reference particle size) × (particle hardness / reference hardness), where the reference particle size is usually taken as 10 μm, and the reference hardness is taken as Mohs 6.0. The larger this index is, the higher the risk of erosion and wear on equipment sealing surfaces, valves, membrane surfaces, and pipelines caused by particulate matter. S303. Obtain the pressure and temperature of the mixed gas, calculate the standard deviation of the pressure and temperature of the mixed gas from the safe range of the corresponding parameters of the equipment, and then sum them by weight to obtain the environmental damage anomaly. In this embodiment, temperature and pressure are the two most critical external parameters that determine the structural integrity and material life of the equipment. Exceeding the design range will lead to excessive thermal and mechanical stress, causing equipment deformation, cracking, fatigue failure, or even explosion. This step is achieved by comparing the real-time measured temperature and pressure values ​​with the design allowable range stored in the equipment's ROM. The allowable temperature range is usually an interval, such as -20°C to 85°C, while the allowable pressure range is usually an upper limit (for pressure vessels) or an interval (for vacuum systems). S304. The weighted sum of the obtained acid-base damage anomalies, environmental damage anomalies, and particulate matter damage anomalies yields the damage situation of the mixed gas to the equipment. Chemical corrosion, physical wear, and thermal over-limit are three different damage mechanisms with different impact mechanisms and failure modes on the equipment, but they all ultimately lead to a decline in equipment performance and a shortened lifespan. In order to comprehensively assess the overall risk of the equipment under a unified framework, the damage indices of these three dimensions need to be merged into a comprehensive index, and different weights need to be assigned to these three damage mechanisms. The determination of the weights needs to take into account the equipment type, material properties, and historical failure data.

[0022] Step 4: Analyze the equipment's separation efficiency by combining data on the damage caused by the mixed gas to the equipment and the equipment's operating status. Based on the equipment's separation efficiency and the difficulty of separating the mixed gas, predict the equipment's separation effect. In this embodiment, the separation efficiency of the analytical device includes the following specific steps: S401. Obtain the accuracy of the control command execution of the equipment, the compressor vibration of the membrane separation equipment, the valve opening degree, and the temperature of the sealing chamber; calculate the standard deviation of the corresponding parameters from the corresponding safety range, and then sum them by weight to obtain the abnormal operation of the equipment; In this embodiment, any performance degradation assessment requires a baseline, which is the ideal separation capacity that the equipment possesses in a brand new state or after a major overhaul; This initial value is not obtained through real-time measurement, but is stored as a basic attribute of the equipment in the equipment ROM or database, and comes from the performance test report before the equipment leaves the factory (e.g., product gas purity ≥99.9%, processing capacity 1000Nm³ / h) or the calibration data at the time of initial commissioning; S402. Obtain the purity of the separated gas from the membrane separation device in the initial state. Divide the safe gas purity by the purity of the separated gas to obtain the separation anomaly. Calculate the initial separation efficiency anomaly by weighted summing of the separation anomaly and the device operation anomaly. In this embodiment, during actual operation, in addition to being affected by external gas conditions, the device's mechanical state and operating parameters also change, such as increased vibration, valve jamming, decreased pump flow, and uneven heating. These abnormal operating conditions directly lead to a decrease in separation capacity. Even under identical gas conditions, the device's separation efficiency may decrease. The task of this step is to convert the initial operation anomaly index obtained from step S104 (which already contains information reflecting changes in the device's own operating state) into a reduction in separation capacity. The specific conversion relationship needs to be obtained through experimental or field data fitting. The trend and amplitude of valve opening changes are themselves important indirect characteristic variables. For example, in a compressor system, if the valve opening is significantly larger than the historical baseline value (e.g., from 60% to 90%) under the same operating conditions (flow rate, pressure, speed), this is direct evidence of decreased internal efficiency, seal wear, or flow channel blockage in the compressor, because the control system needs to compensate for the performance loss by increasing the flow area. S403. The product of the damage to the equipment caused by the mixed gas and the influence coefficient is summed with the value 1. The summation result is multiplied by the initial separation efficiency anomaly to obtain the separation anomaly of the equipment. The final separation effect of the equipment, that is, the actual quality of the separated products and the processing efficiency, depends not only on the current health status of the equipment (the result of S402), but also on how difficult it is to separate the mixed gas it is processing (the result of S203), and how serious the cumulative or immediate damage caused to the equipment by the gas is (the result of S304). Meanwhile, the prediction of equipment separation effect includes the following specific steps: S404. The difficulty in separating the mixed gas and the separation abnormality of the equipment are weighted and summed to obtain an abnormal separation effect.

[0023] Step 5: Develop equipment matching and early warning systems based on the predicted equipment separation results. In this embodiment, the device matching warning includes the following specific steps: The abnormal separation result obtained from the calculation is divided by the set warning threshold to obtain the warning value, which is then compared and analyzed with the set warning level. If the warning value is greater than or equal to 95%, the current equipment is fully capable of separating the gas, and no intervention is required in the output. It is recommended to continue monitoring. If the warning value is greater than or equal to 80% but less than 95%, it indicates a decrease in matching degree and that the equipment performance is moving towards limitation. It is recommended to take one of the following measures: reduce the current processing volume by 10% to maintain purity; wait for the equipment maintenance window and backwash the membrane in advance. If the warning value is less than 80%, the system will immediately issue a warning, indicating a serious mismatch in output, severe equipment damage, or loss of purification capacity. Emergency operation suggestions: skip this equipment section directly and switch to the backup removal section; reduce the intake air volume to below 50% and adjust to low pressure mode.

[0024] In this embodiment, it should be noted that the parameter values ​​are obtained by fitting historical data. Specifically, the fitting example is as follows: the composition, properties, equipment operation, and tolerance data of the mixed gases to be separated in the past are obtained, and the judgment results of whether the separation was successful in the past are also obtained. The historical data is imported into each step of this embodiment to analyze the separation effect of the equipment. The analysis results and judgment results are imported into the fitting software for linear fitting to output the value of the set parameter that meets the judgment accuracy.

[0025] It should be noted in this embodiment that the advantages of this embodiment are: by comprehensively analyzing gas parameters and equipment operation, it breaks through the limitations of traditional gas separation systems that rely on post-maintenance and offline detection, and can integrate multi-dimensional data such as the physicochemical properties of the mixed gas, quantitative indicators of separation difficulty, equipment material tolerance and operating status in real time, and issue accurate early warnings in the early stage of equipment performance degradation or before failure occurs.

[0026] like Figure 3 As shown in the embodiments of this application, a mixed gas separation system based on sensor fusion is also provided, comprising: Sensor data acquisition module, separation effect prediction module, and matching early warning module; The sensor data acquisition module acquires data on the composition, properties, equipment operation, and tolerance of the mixed gases to be separated through sensors. The separation effect prediction module analyzes and predicts the separation difficulty of the mixed gas by combining the composition and properties of the mixed gas, analyzes the damage of the mixed gas to the equipment by combining the properties of the mixed gas and the equipment's tolerance, analyzes the equipment's separation efficiency by combining the damage of the mixed gas to the equipment and the equipment's operating data, and predicts the equipment's separation effect by combining the equipment's separation efficiency and the separation difficulty of the mixed gas. The matching early warning module provides early warnings for device matching based on the predicted device separation effect.

[0027] The parameters and steps for implementing the corresponding functions of each unit module in the sensor fusion-based mixed gas separation system of this application can be referred to the parameters and steps in the embodiments of the sensor fusion-based mixed gas separation method described above, and will not be repeated here.

[0028] Embodiments of this application also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a mixed gas separation method based on sensor fusion, which can be loaded by the processor and executed as provided in the above embodiments.

[0029] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the sensor fusion-based mixed gas separation method provided in the above embodiments, etc. The data storage area may store data involved in the sensor fusion-based mixed gas separation method provided in the above embodiments, etc.

[0030] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit this.

[0031] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0032] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for the sensor fusion-based mixed gas separation method.

[0033] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0034] The term includes, or any other variation thereof, is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A mixed gas separation method based on sensor fusion, characterized in that, Includes the following steps: Step 1: Obtain the composition, properties, equipment operation data, and tolerance data of the mixed gases to be separated using sensors; Step 2: Analyze and predict the difficulty of separating the mixed gas by combining the composition and properties of the mixed gas; Step 3: Analyze the damage caused by the mixed gas to the equipment, taking into account the properties of the mixed gas and the equipment's tolerance. Step 4: Analyze the equipment's separation efficiency by combining data on the damage caused by the mixed gas to the equipment and the equipment's operating status. Based on the equipment's separation efficiency and the difficulty of separating the mixed gas, predict the equipment's separation effect. Step 5: Provide equipment matching early warning based on the predicted equipment separation effect.

2. The mixed gas separation method based on sensor fusion according to claim 1, characterized in that, The predicted difficulty of separating the mixed gas includes the following specific aspects: S201. Obtain the volatility, content percentage, molecular diameter, critical temperature, and pressure of each component in the gas mixture; simultaneously, obtain the dynamic viscosity of the gas mixture. S202. Obtain the volatility of each gas in the mixed gas in the separation environment. Divide the absolute value of the difference between the volatility of the target gas and the non-target gas by the safety difference to obtain the volatility difference value. Obtain the content ratio of the target gas and the non-target gas. Divide the absolute value of the difference between the content ratio of each gas by the safety content ratio difference to obtain the content ratio difference value. Obtain the molecular diameter difference between the target gas and the non-target gas by the safety difference to obtain the molecular diameter difference value. Take the weighted sum of the obtained molecular diameter difference value, content ratio difference value and volatility difference value, and then take the reciprocal to obtain the initial separation difficulty. S203. Obtain the dynamic viscosity of the mixed gas, divide it by the safety gas viscosity to obtain the viscosity influence value, multiply the viscosity influence value by the influence coefficient, add it to the value 1, and then multiply it by the initial separation difficulty to obtain the separation difficulty of the mixed gas.

3. The mixed gas separation method based on sensor fusion according to claim 1, characterized in that, The damage to the equipment caused by the mixed gas includes the following specific details: S301. Obtain the concentration and acidity / alkalinity of the acidic components in the mixed gas, and simultaneously obtain the safe acidity / alkalinity range of the equipment. Obtain acidity / alkalinity anomaly by the standard deviation between the acidity / alkalinity of the acidic components and the safe acidity / alkalinity range of the equipment. Obtain concentration anomaly by dividing the concentration of the acidic components by the safe concentration. Obtain acid-base damage anomaly by multiplying acidity / alkalinity anomaly by concentration anomaly. S302. Obtain the particulate matter concentration, particle size, and hardness in the mixed gas, and simultaneously obtain the viscosity of the gas. Obtain the ratio of the particulate matter concentration, particle size, and hardness to the corresponding safe values ​​to obtain the anomalies of the corresponding parameters. Multiply the anomalies of the parameters to obtain the particulate matter anomalies. Simultaneously obtain the ratio of the gas viscosity to the safe viscosity to obtain the viscosity anomalies. Multiply the viscosity anomalies by the sum of the viscosity anomalies and the value 1, and then multiply them by the particulate matter anomalies to obtain the particulate matter damage anomalies. S303. Obtain the pressure and temperature of the mixed gas, calculate the standard deviation of the pressure and temperature of the mixed gas from the safe range of the corresponding parameters of the equipment, and then sum them by weight to obtain the environmental damage anomaly. S304. The weighted sum of the obtained acid-base damage anomalies, environmental damage anomalies, and particulate matter damage anomalies is used to obtain the damage situation of the mixed gas to the equipment.

4. The mixed gas separation method based on sensor fusion according to claim 1, characterized in that, The separation efficiency of the analytical device includes the following specific steps: S401. Obtain the accuracy of the control commands executed by the equipment, the compressor vibration of the membrane separation equipment, the valve opening degree, and the temperature of the sealing chamber; calculate the standard deviation of the corresponding parameters from the corresponding safety range, and then sum them by weight to obtain the abnormal operation of the equipment; S402. Obtain the purity of the separated gas from the membrane separation device in the initial state, and obtain the separation anomaly by dividing the safe gas purity by the purity of the separated gas. The initial separation performance anomaly of the equipment is obtained by weighted summation of separation anomalies and equipment operation anomalies. S403. The product of the damage to the equipment caused by the mixed gas and the influence coefficient is summed with the value 1. The summation result is multiplied by the initial separation efficiency anomaly to obtain the separation anomaly of the equipment.

5. The mixed gas separation method based on sensor fusion according to claim 4, characterized in that, The prediction of the equipment separation effect includes the following specific steps: S404. The difficulty in separating the mixed gas and the separation abnormality of the equipment are weighted and summed to obtain an abnormal separation effect.

6. The mixed gas separation method based on sensor fusion according to claim 1, characterized in that, The device matching warning includes the following specific steps: The abnormal separation result obtained from the calculation is divided by the set warning threshold to obtain the warning value, which is then compared and analyzed with the set warning level. If the warning value is greater than or equal to 95%, the current equipment is fully capable of separating the gas, and no intervention is required in the output. It is recommended to continue monitoring. If the warning value is greater than or equal to 80% and less than 95%, it indicates a decrease in matching degree and that the device performance is moving in the direction of limitation. We recommend taking one of the following measures: reduce the current throughput by 10% to maintain purity; wait for the equipment maintenance window and backwash the membrane in advance; If the warning value is less than 80%, the system will immediately issue a warning, indicating a serious mismatch in output, severe equipment damage, or loss of purification capacity. Emergency operation suggestion: skip this equipment segment and switch to the backup removal segment.

7. The mixed gas separation method based on sensor fusion according to claim 1, characterized in that, The process of acquiring data on the composition, properties, equipment operation, and tolerance of the mixed gases to be separated via sensors includes the following specific steps: S101. The volatility, content ratio, molecular diameter, critical temperature and pressure of each component of the mixed gas are obtained through a gas sensor; at the same time, the particulate matter concentration and dynamic viscosity of the mixed gas are obtained and stored in the gas information storage component. S102. The compressor vibration, valve opening and sealing chamber temperature of the membrane separation equipment are obtained through the equipment sensors and stored in the equipment information storage component. S103. Obtain the stored corrosion resistance limit, temperature resistance limit, and pressure resistance limit of the equipment materials through the equipment's factory settings.

8. A sensor fusion-based mixed gas separation system, used to implement the sensor fusion-based mixed gas separation method according to any one of claims 1-7, characterized in that, Includes the following specific modules: Sensor data acquisition module, separation effect prediction module, and matching early warning module; The sensor data acquisition module acquires data on the composition, properties, equipment operation, and tolerance of the mixed gases to be separated through sensors. The separation effect prediction module analyzes and predicts the separation difficulty of the mixed gas by combining the composition and properties of the mixed gas, analyzes the damage of the mixed gas to the equipment by combining the properties of the mixed gas and the equipment's tolerance, analyzes the separation efficiency of the equipment by combining the damage of the mixed gas to the equipment and the equipment's operating data, and predicts the equipment's separation effect by combining the equipment's separation efficiency and the separation difficulty of the mixed gas. The matching early warning module provides device matching early warning based on the predicted device separation effect.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the sensor fusion-based mixed gas separation method as described in any one of claims 1-7 by calling the computer program stored in the memory.

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