Method for detecting catalytic performance of catalytic fiber for methane

Abstract

The application discloses a catalytic fiber methane catalytic performance detection method, relates to the technical field of catalytic performance detection, and is used for solving the problem of methane catalytic performance detection of catalytic fibers under different regions and long-term operation conditions; firstly, the catalytic fiber is placed into a controllable atmosphere furnace before detection, and programmed temperature rising and constant temperature pretreatment are carried out according to a preset temperature rising curve. After the pretreatment, the catalytic fiber is mechanically cut, flatness is checked and is shaped, so that the catalytic fiber meets size and installation requirements. Then, the gas flow of methane and air is independently controlled and proportionally set in the reaction stage, a stable atmosphere is formed through a mixing section and a homogenizing section, and the reaction gas of different temperature sections is sampled and detected. A microchannel array and an adjustable microvalve are arranged at the detection outlet, time and space data are collected through a multi-point sensor, the microvalve is adjusted, finally, the gas response data of the partition are obtained, and long-term operation monitoring is completed.

Description

Technical Field

[0001] This invention relates to the field of catalytic performance testing technology, and more specifically, to a method for testing the methane catalytic performance of catalytic fibers. Background Technology

[0002] Current methane catalytic detection technologies typically only perform single-point gas sampling and average response analysis on the entire catalytic unit, failing to identify local differences in the catalytic reaction caused by factors such as thickness deviations, surface roughness, and carbon buildup in different regions within the catalytic fiber after long-term operation. Existing technologies mostly employ uniform proportional regulation for gas supply, lacking fine-grained control of independent flow rates for methane and air, leading to fluctuations in the composition of the reactant gases and affecting detection stability. Furthermore, gas sampling at the reaction channel outlet largely relies on single-channel analysis of the confluence outlet, ignoring the differences in fluid characteristics between micro-scale channels and failing to provide response data reflecting the true state of local areas. Verification of temperature rise curves and gas mixing effects during detection also largely depends on manual experience, lacking repeatable closed-loop verification methods. Tracking and analyzing changes in catalytic fiber performance during long-term operation remains at the macroscopic level, unable to dynamically adjust micro-valve channels and correct local responses using time-series data. Therefore, existing technologies have shortcomings in atmosphere control, independent detection of micro-scale flow channels, long-term performance tracking, and multi-channel synchronous adjustment.

[0003] To address the above problems, this invention proposes a solution. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method for detecting the methane catalytic performance of catalytic fiber, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a preferred embodiment, it includes:

[0007] In a controlled atmosphere furnace, oxygen concentration of the catalytic fiber is monitored, atmosphere is controlled, and the temperature is increased and held at a constant temperature according to the heating curve to complete the pretreatment.

[0008] The catalytic fibers are sized, CNC cut, and flatness checked, and their shape and size are adjusted in a shaping fixture.

[0009] The flow rates of methane and air are set using a mass flow controller to complete gas mixing and gas chromatography detection according to the temperature rise curve.

[0010] Microchannel arrays are fabricated in the export area and adjustable microvalves are assembled to collect local data and perform adjustment and sampling analysis.

[0011] In a preferred embodiment, the catalytic fiber is pretreated in a controlled environment before testing. After heating and isothermal treatment according to a set heating curve, the fiber is subjected to size acquisition, CNC cutting, flatness and thickness verification, and its shape and size are adjusted in a shaping fixture to meet the installation requirements.

[0012] In a preferred embodiment, the flow rates of methane and air are set and controlled separately, and the two gases are introduced into the mixing section and the homogenization section for mixing. Component detection is performed at the outlet detection point. The starting temperature, target temperature, holding time, and heating rate are set and a heating curve file is generated. The simulation is executed and the temperature curves are compared. A standard gas is selected to establish a chromatographic response relationship. The reaction outlet gas is sampled and detected at each temperature range, and the methane conversion rate is calculated.

[0013] In a preferred embodiment, an interlaced microchannel array is formed in the outlet area, and an installation slot is processed at the beginning of each microchannel and an adjustable microvalve is assembled. Multiple pressure and temperature sensing units are arranged to collect data at time intervals, generate a spatiotemporal parameter matrix, and calculate the microvalve adjustment coefficient.

[0014] In a preferred embodiment, the short-term flow rate regulation trend is extracted, abnormal time ranges are screened, short-term disturbance adjustment intervals are marked and a microvalve adjustment mask is generated, the local adjustment direction vector is calculated and the microvalve is driven to perform adjustment, so that the gas is split into multiple independent sub-channels.

[0015] In a preferred embodiment, a sampling probe is configured at the outlet of each sub-channel to collect gas flow rate, temperature and concentration data, and gas response curves for each sub-region are formed within a preset sampling interval.

[0016] In a preferred embodiment, after normalizing the collected data, the conversion rate offset of each sub-region is calculated, a multi-dimensional feature vector is generated, and clustering is performed to obtain performance level labels. The performance level labels are associated with time series data and microvalve adjustment coefficients to form a comprehensive data matrix. The data is analyzed to screen out sub-regions with abnormal performance and trace the adjustment trajectory back. The expected performance recovery target curve is calculated and the microvalve adjustment correction amount is obtained. The correction amount is transmitted to the corresponding microvalve control unit to perform adjustment. The data of all sub-regions are integrated to calculate the cluster-level adjustment coefficient and synchronously drive the microvalve adjustment within the cluster.

[0017] In a preferred embodiment, water vapor is introduced through a constant-temperature liquid container and the gas is uniformly mixed in a mixing section. At the outlet, a humidity monitoring probe is used to detect and provide feedback correction on the humidity.

[0018] In a preferred embodiment, the furnace body is heated according to the heating curve, and the reaction gas is sent to a gas chromatograph for sampling and analysis. The mass flow controller is called to adjust the inlet flow rates of methane and air. The reaction channel is kept under kinetic control and the reaction data at different temperature ranges are repeatedly detected and recorded. The furnace body is switched to a constant temperature operation mode, and samples are automatically taken at time intervals and the detection results are recorded. The conversion rate change curve is plotted and the activity retention rate and stability index under long-term operation conditions are calculated.

[0019] The technical effects and advantages of the method for detecting the methane catalytic performance of catalytic fiber according to the present invention are as follows:

[0020] This invention achieves uniformity in sample state before detection by pre-treating and sizing the catalytic fiber under controlled atmosphere. During the reaction detection process, the flow rates of methane and air are set and independently controlled, forming a stable detection atmosphere through mixing and homogenization sections. Gas chromatography response calibration ensures the comparability of reaction data across different temperature ranges. By arranging a microchannel array and adjustable microvalve in the outlet area, independent sampling of local gas channels is possible. Closed-loop adjustment of the microvalve opening based on thresholds is performed in each sampling cycle, establishing a dynamic correlation between local fluid state and response data. Further integration with time-series sampling and cluster-level integration allows for correction of performance-discrepancy regions, enabling continuous monitoring and dynamic adjustment under long-term operating conditions, resulting in higher detection accuracy and zone management capabilities. Attached Figure Description

[0021] Figure 1 This is a timing diagram of a method for detecting the methane catalytic performance of catalytic fiber according to the present invention.

[0022] Figure 2 This is a flowchart illustrating the implementation of a method for detecting the methane catalytic performance of catalytic fibers according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example

[0025] This invention discloses a method for detecting the methane catalytic performance of catalytic fibers, such as... Figure 1 As shown, it includes:

[0026] In a controlled atmosphere furnace, oxygen concentration of the catalytic fiber is monitored, atmosphere is controlled, and the temperature is increased and held at a constant temperature according to the heating curve to complete the pretreatment.

[0027] The catalytic fibers are sized, CNC cut, and flatness checked, and their shape and size are adjusted in a shaping fixture.

[0028] The flow rates of methane and air are set using a mass flow controller to complete gas mixing and gas chromatography detection according to the temperature rise curve.

[0029] Microchannel arrays are fabricated in the export area and adjustable microvalves are assembled to collect local data and perform adjustment and sampling analysis.

[0030] like Figure 2 As shown, before the catalytic fiber enters the detection, the catalytic fiber to be tested is first placed in a controlled environment with an air atmosphere. The controlled environment furnace is pre-installed with an air circulation system and an online oxygen concentration monitoring probe. The oxygen concentration value is collected in real time by the probe and fed back to the furnace control program to ensure the stability of the atmosphere composition.

[0031] Based on this, the electric heating unit in the furnace is driven to perform programmed heating operation by the first heating curve file preset in the furnace control program. The furnace temperature value is collected in real time by the distributed thermocouple array and transmitted back to the furnace control program. The furnace control program performs closed-loop adjustment based on the temperature data, so that the furnace temperature gradually increases according to the heating curve and remains constant after reaching the target temperature. This further promotes a uniform and controllable oxidation reaction on the surface of the catalytic fiber, so that the active metal oxides on the fiber surface are uniformly formed and stably solidified, thereby realizing the full generation of active sites on the surface of the catalytic fiber and the structural stabilization of the catalytic layer.

[0032] Subsequently, the pretreated catalytic fibers are fed into a mechanical cutting device equipped with a high-precision cutting table. Before cutting begins, the cutting device uses a laser ranging unit and a displacement encoder to collect the fiber's external dimensions and initial position data. During the cutting process, the CNC-driven cutting tool cuts according to the preset dimensions, while the catalytic fibers are fixed in position by a clamping device set on the cutting table to ensure no displacement deviation in the thickness direction throughout the entire cutting process.

[0033] After cutting, the built-in optical flatness detector is used to check the flatness and thickness consistency of the cut sheet structure to ensure that the processing meets the design requirements.

[0034] Meanwhile, during the shaping process after cutting, the sheet-like catalytic fibers are placed into a shaping fixture with temperature control and pressure regulation. Real-time temperature and pressure values ​​are collected by embedded sensors and fed back to the shaping control program. The shaping control program adjusts the working status of the heating element and the pressing unit according to the parameters, and gradually adjusts the shape and size of the sheet-like structure while ensuring the integrity of the fiber structure, so that its shape and channel installation dimensions are strictly matched.

[0035] Furthermore, to ensure a constant gas composition entering the reaction zone, mass flow rate regulation technology is used to set the flow rates of methane and air separately. Specifically, methane and air are connected to independent gas paths equipped with mass flow control units. The flow control units collect the flow rates of methane and air in real time through internal flow sensors and feed the collected data back to the flow control program. After presetting the methane to air ratio in the control program, the electronically controlled valves are driven to adjust to a stable output state, enabling constant flow control of the two gases within their independent gas paths.

[0036] It should be noted that the preset ratio of methane to air and the stable output state of the electronically controlled valve need to be adjusted according to the actual situation. In this embodiment, the default situation is used as an example.

[0037] Subsequently, the two independently controlled gas streams are introduced into the gas mixing section. The mixing section is designed with a fixed turbulence structure, which causes the two incoming gas streams to cross and split between the turbulence blades, and achieve initial homogenization under the action of turbulent diffusion.

[0038] The mixed gas then enters the homogenization section, where the gas components are further thoroughly mixed spatially by extending the gas residence time and relying on the uniform distribution structure within the section. Simultaneously, to verify the mixing effect, a gas composition detection point is installed at the outlet of the mixing section. This point uses an online gas composition sensor to continuously collect gas composition data. When the sensor output signal remains stable and without fluctuations within a fixed period after real-time analysis by the control program, uniform mixing is confirmed.

[0039] After the gas environment stabilized, based on the characteristics of the catalytic fiber after the aforementioned pretreatment, which makes it suitable for methane catalytic combustion, the initial heating temperature, target temperature, holding time for each temperature segment, and heating rate were determined. Next, the initial heating temperature, target temperature, holding time for each temperature segment, and heating rate were written into the furnace control system to generate a second heating curve file for controlling the heating unit. Subsequently, a simulated operation test was performed without a sample. Temperature data was collected in real time by a thermocouple array arranged in the furnace cavity, and the real-time temperature curve was compared with the second heating curve file. By adjusting the power output of the heating unit, the actual heating process was gradually made to approximate the second heating curve file. When the temperature deviation in multiple consecutive simulated runs was within the allowable range, the second heating curve file was confirmed to be valid.

[0040] The allowable range of temperature deviation needs to be set according to the specific actual scenario, which will not be elaborated in this embodiment.

[0041] During the product detection stage, to establish a traceable conversion relationship for the chromatographic response, a standard gas with known composition and concentration, certified by a third-party traceability mechanism, is first selected. This standard gas is introduced into the sampling inlet of the gas chromatograph via a calibrated mass flow control unit, ensuring the flow rate matches the subsequent actual detection conditions. After the gas chromatograph reaches a stable operating state, multiple injections are performed at fixed time intervals. The chromatographic system records the peak area data of methane and carbon dioxide corresponding to the standard gas. Then, a linear fit is performed between the peak area and the molar concentration of the standard gas to establish a curve corresponding to the peak area and molar concentration. The response factor of the chromatographic process to methane and carbon dioxide is calculated and stored, ensuring a traceable conversion of subsequent detection data from chromatographic signals to actual concentrations.

[0042] After calibration, temperature ranges are set, and within each temperature range, the aforementioned calibrated gas chromatography analysis technique is used to continuously sample and detect the reaction outlet gas. The sampling probe introduces the outlet gas in real time, and the gas chromatography process collects the peak areas of methane and carbon dioxide. Then, the peak areas are substituted into the established response factor calculation formula to output the methane conversion rate for that temperature range.

[0043] It should be noted that, considering the subtle differences in thickness, localized processing roughness, or carbon buildup within a single catalytic fiber, the reaction states in different areas of the same fiber can vary. Furthermore, slight deviations in the inlet gas distribution and localized temperature gradients can also cause inconsistent catalytic effects when the gas flows through the fiber. Existing detection methods typically perform single-point testing at the confluence of the entire fiber's outlet gas. While this simplifies the analysis process, it fails to identify performance differences across different areas of the same catalytic fiber, resulting in average values ​​that fail to reveal local anomalies. Therefore, in this embodiment, high-precision microfluidic control machining technology is employed for sampling and testing. Driven by a CNC micromachining platform, the platform's embedded laser rangefinder and displacement sensors collect real-time data on tool position, channel depth, and channel width. This data is then fed back to the machining control program in real-time. The program performs closed-loop correction of the tool path based on the 3D layout parameters in the design input file, ultimately forming a precisely staggered microchannel array in the outlet region.

[0044] At the beginning of each microchannel, a spiral mounting slot is further processed using micro-nano fabrication technology. The geometric dimensions of the slot are collected by displacement sensors and a vision recognition module and fed back to the micro-nano fabrication controller. The micro-nano fabrication controller makes real-time corrections based on the slot geometric parameters from the early CAD model in the design drawings.

[0045] Subsequently, the adjustable microvalve is fixed to the slot by an embedded locking method. The locking process is executed by a torque-controlled micro actuator, and the locking depth and position are collected by the displacement detection unit and fed back to the locking control program for dynamic adjustment. This ensures that the spatial index position of the adjustable microvalve body in the channel array corresponds one-to-one with the design parameters, realizing the equidistant distribution of the adjustable microvalve in space, so that the outlet structure has an independently controllable multi-microvalve channel.

[0046] After the adjustable microvalve is assembled, multiple micro-fluid pressure sensing units and micro-temperature sensing units are arranged in a ring to obtain the local fluid state of each microchannel. Each sensing unit senses the gas pressure and temperature flowing through that location through a built-in sensitive element;

[0047] Next, the process setting document is checked, and the sampling time parameter N1 is selected based on the experimental requirements and equipment response time. All sensing units are synchronously triggered to sample at time intervals N1. The raw data collected is marked in the control program with the time coordinate t automatically generated by the sampling timestamp and the spatial index x written into the control program when the adjustable microvalve is assembled, forming a spatiotemporal fluid parameter matrix.

[0048] Subsequently, the spatiotemporal fluid parameter matrix is ​​subjected to interpolation to calculate the rate of change of pressure and temperature at each location within the N1 time window. The weighted summation over time then generates the spatiotemporal composite characteristic curve for each microchannel.

[0049] The mathematical model for the microvalve adjustment coefficient K(t,x) is as follows:

[0050] K(t,x)=α×(ΔP(t,x) / Δt)+β×(ΔT(t,x) / Δt);

[0051] ΔP(t,x) represents the pressure change at the spatial index position within the time window N1, ΔT(t,x) represents the temperature change at the spatial index position within the time window N1, Δt represents the sampling interval, and α and β are derived from the fitting and optimization process of previous experimental data. The specific values ​​are recorded in the algorithm configuration file.

[0052] For example, at a certain temperature range T1, the pressure change at the spatial index location within the time window N1 is ΔP = 0.6 kPa, the temperature change is ΔT = 0.8 degrees Celsius, the sampling interval Δt is one second, and the weighting coefficients α and β are determined by experimental fitting to be 0.55 and 0.45, respectively. Substituting these values ​​into the above formula, the microvalve adjustment coefficient K(t,x) at the current time point for this spatial index location is 0.69.

[0053] The fitting method for α and β employs an orthogonal experimental design, specifically including a combination of three factors and four levels: temperature, flow rate, and thickness difference variables. Representative catalytic fiber samples were selected to conduct joint detection experiments on microchannel flow response and temperature changes under multiple control conditions. Each experiment collected no fewer than 100 time window sample points, accumulating over two thousand sample sets. After outlier removal and normalization preprocessing, the experimental data were used to regress the response model of the microvalve adjustment coefficient K(t,x) using a least squares fitting function, yielding the α and β fitting value ranges with minimum error. The range of α is [0.45, 0.60], and the range of β is [0.35, 0.50]. A priority weighting function f_disturbance(t,x) was selected based on the type of fluid disturbance to adjust the dynamic weight ratio. All experimental data and fitting results were stored in the algorithm configuration file and loaded into the control program.

[0054] The algorithm configuration file is further invoked to perform calculations on the spatiotemporal composite feature curve using a dynamic weight function that includes weight ratios obtained from historical experimental fitting and real-time adjustment factors. This results in the microvalve adjustment coefficient K(t,x), where K is the adjustment coefficient, t is the sampling time point, and x is the spatial position of the adjustable microvalve in the array.

[0055] Considering that local microchannels may experience instantaneous high-amplitude fluctuations due to short-term thermal shock, sudden changes in gas density, or the mechanical inertia of adjustable microvalve, after calculating the microvalve adjustment coefficient K(t,x), K(t,x) over a continuous time period is expanded into a spatiotemporal characteristic plane using a sliding time window N2.

[0056] The window length of N2 is determined by the control program reading the analysis window parameters from the experimental configuration file, which were optimized through multiple previous experiments.

[0057] Subsequently, a local curvature analysis algorithm was used to calculate the feature plane and extract the instantaneous flow fluctuation spectrum for each adjustable microvalve position. Spatial difference and time-weighted operations were performed, with the time-weighting coefficients derived from historical empirical values ​​in the configuration file and the spatial difference interval calculated based on the adjustable microvalve spacing in the assembly design, to obtain the short-term flow regulation trend for each position.

[0058] Furthermore, by comparing the short-term flow regulation trend with the average of historical steady-state periods, time ranges with abnormal changes are identified.

[0059] Within the abnormal time range, the Fast Fourier Decomposition algorithm is invoked to decompose the microvalve adjustment coefficient K(t,x) into high-frequency disturbance components and low-frequency reference components, and the energy ratio of the high-frequency components is calculated. The energy ratio calculation parameters are derived from the built-in formulas of signal processing and the acquired data.

[0060] When the proportion of high-frequency energy in two consecutive N2 windows exceeds the preset threshold, the microchannel is marked as a short-term disturbance adjustment interval, and a microvalve adjustment mask M is generated. The mask M is a matrix data structure, with each element corresponding to a channel position and marked as whether it is adjusted first. The mask data is stored in the adjustment queue in real time.

[0061] The preset threshold and adjustment threshold are both determined through orthogonal experimental design. Specifically, during the experimental phase, multiple sets of different threshold combinations are preset, and repeated tests are performed for each combination. The corresponding conversion rate shift, response fluctuation amplitude, and adjustment stability index are recorded. The optimal parameter range is determined through multi-factor orthogonal analysis, and the finally selected parameters are written into the configuration file called by the control program as the preset threshold and adjustment threshold.

[0062] When performing priority adjustment, the local adjustment direction vector is calculated by calling vector operations, combining the instantaneous rate of change of K(t,x) within the current N2 window with the gradient difference at adjacent spatial indices. The gradient difference is calculated from the difference in the microvalve adjustment coefficient K(t,x) between adjacent microvalves, and the difference data comes from real-time sampling data within the same time window.

[0063] The adjustment direction vector is then transmitted to the microvalve actuator. The actuator performs micron-level rotational step adjustment of the valve disc angle based on the direction vector. The adjustment range is collected in real time by the angle feedback module and fed back to the control program to form a closed-loop control.

[0064] Then, the opening of the corresponding micro-valve is adjusted in real time according to the updated micro-valve adjustment coefficient K(t,x); so that the reaction gas about to be discharged is diverted into multiple sub-channels with uniform flow and isolated from each other, thereby dividing the effective detection area of ​​the catalytic fiber into multiple independent sub-regions at the physical level.

[0065] It should be noted that an adjustment threshold is set within each sampling period. When the real-time calculated K(t,x) deviates from the reference value by more than the adjustment threshold, the execution unit is immediately driven to adjust the valve angle. After the adjustment is completed, data is re-acquired through the sensing unit in the next sampling period, and the new data is substituted into the K(t,x) calculation process again to form continuous feedback correction.

[0066] The adjustment threshold value is derived from the adjustment strategy configuration table and was determined by previous experiments.

[0067] Subsequently, to further improve local detection accuracy and adjustment capabilities, a miniature sampling probe was installed at the exit of each physically divided sub-channel. This sampling probe integrates a miniature heat flow sensing unit and a local gas composition sensing unit. Through these sensing elements, it synchronously senses the gas flow rate, temperature, and concentrations of methane and carbon dioxide at the moment of sampling. The sensor output is then transmitted to the synchronous acquisition unit via analog-to-digital conversion. The synchronous acquisition unit performs time-series sampling of multidimensional data according to the sampling interval preset by the control program and buffers and stores it, thereby forming an independent gas response curve for each sub-region.

[0068] The sampling interval value is derived from the system detection configuration file and is set in conjunction with the sensor response time.

[0069] Furthermore, the gas concentration signals acquired at the same time point in each sub-region are normalized along with the corresponding local temperature and flow rate signals.

[0070] The normalization parameters are derived from the standard operating condition data stored during the calibration phase, ensuring that the calculation results are comparable on the same scale.

[0071] The standard operating condition data were obtained by calibration using standard gases before the experiment. During the calibration process, standard gases were introduced into the detection unit through a sampling pipeline at a fixed flow rate and known concentration. Peak area data of methane and carbon dioxide were collected multiple times consecutively. The correspondence between peak area and molar concentration was established through linear fitting, thereby obtaining the gas concentration baseline value under standard operating conditions. This value was then used as the standard operating condition data in the normalization calculation.

[0072] By comparing the normalized data, the additional conversion rate offset caused by thickness deviation, surface roughness, and carbon accumulation in each sub-region is calculated. Subsequently, the statistical mean of historical steady-state operating condition sampling data is used as the overall benchmark value. The offset of each sub-region is compared with the overall benchmark value, and a multi-dimensional feature vector is generated by combining the offset amplitude, data stability index, and response fluctuation amplitude.

[0073] The multidimensional feature vector is specifically defined as follows:

[0074] V(x)=[Rc(x),Fv(x),Tp(x)];

[0075] Rc(x) represents the normalized conversion rate offset of the sub-region, which is derived from the gas concentration of the sampling probe; Fv(x) represents the normalized value of the local gas velocity, which is derived from the velocity of the micro heat flow sensing unit; and Tp(x) represents the normalized value of the local temperature, which is derived from the temperature data of the micro temperature sensing unit.

[0076] Furthermore, dynamic clustering is employed to cluster the multidimensional feature vectors and generate independent performance level labels for each sub-region;

[0077] It should be noted that this embodiment uses the K-means clustering algorithm because it has a fast convergence speed and stable results when processing continuous physical feature data. Specifically, the number of cluster centers k is determined by silhouette coefficient analysis of the distribution characteristics of historical experimental data. The initial center points are automatically initialized using the k-means++ method. During the iteration process, the goal is to minimize the intra-cluster squared error. When the change in cluster centers is less than the convergence threshold in the configuration file for two consecutive iterations, the final performance level label is output.

[0078] Meanwhile, the historical sample data used for K-means clustering training comes from a multi-dimensional feature vector library accumulated during long-term detection, with a total of more than 200 sets of samples. Each set of data corresponds to the conversion rate offset, flow rate and temperature data of different sub-regions in multiple sampling windows. The performance level labels are manually labeled through historical records and standard performance test results.

[0079] To ensure the stability and representativeness of the k-value selection, a silhouette coefficient threshold of 0.5 was set. The clustering effect of k values ​​in the [2, 10] interval was tested sequentially, and the k value corresponding to the maximum silhouette coefficient was selected as the final number of cluster centers. The historical sample database contained over two hundred groups, covering sub-regional data from different temperature ranges and regulation conditions. The feature vector dimension was uniformly set to V(x) = [Rc(x), Fv(x), Tp(x)]. During the iteration process, the k-means++ method was used to initialize the cluster centers, and a cluster center change convergence threshold of 0.01 was set. The clustering process stopped after two consecutive changes were less than this value. All the above clustering hyperparameter settings were recorded in the algorithm control file and dynamically loaded during runtime.

[0080] Furthermore, a comprehensive data matrix is ​​constructed by multidimensionally associating the performance level label of each sub-region with its corresponding time-series gas response curve, the real-time calculated microvalve adjustment coefficient K(t,x), and historical temperature and flow rate feature vectors. Analysis is then performed on this matrix to identify sub-regions where the performance level remains consistently low or fluctuates frequently across multiple sampling windows. The microvalve adjustment trajectory data for these sub-regions, stored in the historical data module, is then retrieved. Combined with real-time flow regulation feedback, a local time-series backtracking analysis is performed. Specifically, the changes in the adjustment coefficient K(t,x) and the performance level label are correlated and compared across multiple sampling windows, extracting the coupling relationship between historical adjustment data and current state data.

[0081] After obtaining the coupling change relationship, the local trend fitting algorithm is called. Based on the fitting model parameters stored in the algorithm configuration file, the dominant factors affecting the performance level change are extracted, and the expected performance recovery target curve of the sub-region is calculated.

[0082] The target curve is calculated by difference with the current microvalve adjustment coefficient K(t,x) to obtain a new microvalve adjustment correction amount Kk(t,x), which is then transmitted to the corresponding microvalve control unit. This drives the control unit to prioritize the gas flow regulation of this sub-region in the next sampling cycle, thereby shortening the performance recovery time.

[0083] Furthermore, cluster-level integration is performed on the data from all sub-regions. Specifically, by analyzing the spatial adjacency of each sub-region and the similarity of performance level labels, multiple sub-regions are grouped into several performance clusters. Within each performance cluster, the microvalve adjustment correction amount Kk(t,x) is weighted and averaged to calculate the cluster-level adjustment coefficient Kc(t), where the weight parameters are derived from the historical response stability data and current fluctuation amplitude indicators of each sub-region within the cluster.

[0084] During the subsequent sampling period, the opening degree of each microvalve in the cluster is adjusted synchronously by the cluster-level adjustment coefficient Kc(t), so that the flow rate and reaction state of all sub-regions in the cluster gradually become consistent, thereby further reducing local performance differences and achieving overall reaction balance and performance improvement of the catalytic fiber.

[0085] During the water vapor resistance test, a constant temperature liquid container was used as the carrier. The container was filled with deionized water output from a pure water preparation device, and the built-in constant temperature control unit performed closed-loop regulation according to the set temperature value pre-written in the control program. When the reaction gas entered the container, it passed through the porous bubble tube and formed stable bubbles below the liquid surface. During the gas's ascent, it carried water vapor through the liquid.

[0086] The temperature values ​​are derived from the experimental configuration file.

[0087] After leaving the liquid, the gas enters the mixing section, where baffles and homogenizing grids are arranged. The fluid homogenization structure utilizes multi-directional, staggered flow splitting and remixing of the water vapor-carrying gas to achieve a uniform distribution of water vapor within the gas. An online humidity monitoring probe is installed at the mixing section outlet. This probe incorporates a capacitive humidity sensor to collect humidity signals in real time. The collected humidity signal is compared with the humidity range set in the control parameter file. If the signal deviates from the range, feedback correction is achieved by adjusting the heating power of the bubbling device and the air intake flow regulating valve until the humidity value stabilizes.

[0088] The humidity feedback control formula is as follows:

[0089] Uctrl = Kh × (Hset - Hmeas);

[0090] Here, Hset represents the target humidity set in the control parameter file, Hmeas represents the real-time measured humidity value, and Kh represents the humidity adjustment gain coefficient, which is derived from the humidity calibration experiment record.

[0091] Then, the electric heating unit inside the furnace is driven according to the heating curve parameters in the experimental configuration file to achieve programmed heating of the starting temperature, heating rate and target temperature.

[0092] The reaction gas at the outlet is introduced into the gas chromatography analysis unit through a sampling pipeline. The chromatographic system separates the methane and carbon dioxide using a capillary column and collects the peak area data using a flame ionization detector. The detection data is then processed into actual concentration values ​​by the response factor conversion module.

[0093] The response factor is derived from the calibration steps completed with standard gas before the experiment. The control program compares the calculated conversion rate with the historical benchmark value under anhydrous conditions and outputs the quantitative evaluation results of the water vapor resistance performance.

[0094] Furthermore, a dual-channel mass flow controller is invoked to adjust the intake flow rates of methane and air respectively. The internal thermal flow sensor of the controller collects the flow rate in real time and compares it with the set value of the kinetic range. The gas supply in the low conversion rate range is achieved by adjusting the proportional valve.

[0095] After the reaction channel is kept under kinetic control, the above-mentioned water atmosphere detection procedure is repeated, and gas chromatography data are continuously collected at each temperature range. The control program calculates and records the reaction rate at different temperature ranges.

[0096] Next, to assess the stability of the catalytic fiber under long-term operating conditions, the furnace was switched to a constant-temperature operation mode, with the temperature set according to the long-term operation configuration file. Automatic sampling was periodically triggered at time intervals N1, and the outlet gas entered the gas chromatography analysis unit via the sampling pipeline. The detection results were converted into concentration data according to the response factor.

[0097] Finally, a conversion rate change curve was plotted with time as the horizontal axis, and the slope and fluctuation amplitude of the curve were calculated to determine the retention of catalytic fiber activity and the trend of performance degradation. Activity retention rate and stability indicators under long-term operating conditions were obtained.

[0098] The formula for calculating the activity retention rate A-rate is as follows:

[0099] A-rate=(Rend / Rinit)×100%;

[0100] Here, Rend represents the average conversion rate measured after long-term operation, and Rinit represents the average conversion rate measured during the initial operation phase. Both are derived from the aforementioned data after gas chromatography detection and normalization.

[0101] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0102] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0103] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0106] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for detecting the methane catalytic performance of catalytic fiber, Its characteristics include: In a controlled atmosphere furnace, oxygen concentration of the catalytic fiber is monitored, atmosphere is controlled, and the temperature is increased and held at a constant temperature according to the heating curve to complete the pretreatment. The catalytic fibers are sized, CNC cut, and flatness checked, and their shape and size are adjusted in a shaping fixture. The process involves setting the flow rates of methane and air using a mass flow controller, mixing the gases, and performing gas chromatography detection according to the heating curve. This includes setting and controlling the flow rates of methane and air separately, introducing the two gases into the mixing section and homogenization section for mixing, detecting the components at the outlet detection point, setting the initial heating temperature, target temperature, holding time, and heating rate, generating a heating curve file, executing a simulation and comparing the temperature curves, establishing a chromatographic response relationship using a standard gas, sampling and detecting the reaction outlet gas at each temperature range, and calculating the methane conversion rate. A microchannel array is fabricated and adjustable microvalve is assembled in the outlet area. Local data is collected and adjusted, and sampled for analysis. The process of collecting local data and performing adjustment and sampling analysis includes configuring sampling probes at the outlet of each sub-channel to collect gas flow rate, temperature, and concentration data, forming gas response curves for each sub-region within a preset sampling interval. After normalizing the collected data, the conversion rate offset of each sub-region is calculated, multi-dimensional feature vectors are generated, and clustering is performed to obtain performance level labels. The performance level labels are correlated with time series data and microvalve adjustment coefficients to form a comprehensive data matrix. The data is analyzed to screen out sub-regions with abnormal performance and trace the adjustment trajectory back. The expected performance recovery target curve is calculated, and the microvalve adjustment correction amount is obtained. The correction amount is transmitted to the corresponding microvalve control unit to execute the adjustment. The data of all sub-regions are integrated to calculate the cluster-level adjustment coefficient and synchronously drive the microvalve adjustment within the cluster.

2. The method for detecting the methane catalytic performance of catalytic fiber according to claim 1, characterized in that: Before testing, the catalytic fiber is pretreated in a controlled environment. After heating and isothermal treatment according to the set heating curve, its size is collected, CNC cut, flatness and thickness are checked, and its shape and size are adjusted in the shaping fixture to meet the installation requirements.

3. The method for detecting the methane catalytic performance of catalytic fiber according to claim 2, characterized in that; An interlaced microchannel array is formed in the export area. At the beginning of each microchannel, a slot is machined and an adjustable microvalve is assembled. Multiple pressure and temperature sensing units are arranged to collect data at time intervals, generate a spatiotemporal parameter matrix, and calculate the microvalve adjustment coefficient.

4. The method for detecting the methane catalytic performance of catalytic fiber according to claim 3, characterized in that: Extract short-term flow regulation trends, filter abnormal time ranges, mark short-term disturbance adjustment intervals and generate microvalve regulation masks, calculate local regulation direction vectors and drive microvalve to perform adjustments, so that the gas is split into multiple independent sub-channels.

5. The method for detecting the methane catalytic performance of catalytic fiber according to claim 4, characterized in that; Water vapor is introduced into a constant-temperature liquid container and the gas is uniformly mixed in a mixing section. At the outlet, a humidity monitoring probe is used to detect and provide feedback correction on the humidity.

6. The method for detecting the methane catalytic performance of catalytic fiber according to claim 5, characterized in that: The furnace body is heated according to the heating curve, and the reaction gas is sent to the gas chromatograph for sampling and analysis. The mass flow controller is called to adjust the inlet flow rate of methane and air. The reaction channel is kept under kinetic control. The reaction data of different temperature ranges are repeatedly detected and recorded. The furnace body is switched to constant temperature operation mode, and samples are automatically taken at time intervals and the detection results are recorded. The conversion rate change curve is plotted and the activity retention rate and stability index under long-term operation conditions are calculated.

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