Method and device for testing coal adsorption characteristics based on x-ray photon correlation spectroscopy

Abstract

The embodiment of the application relates to the field of coal resource exploration, and provides a coal adsorption characteristic test method and device based on an X-ray photon correlation spectroscopy station, which comprises the following steps: loading a coal sample in a sample area of an experimental cavity, vacuum degassing, collecting a reference speckle pattern and a reference scattering intensity, calibrating the cavity in terms of temperature and pressure, introducing a to-be-tested adsorption gas, enabling the coal sample to adsorb under constant temperature and pressure or graded constant temperature and pressure, continuously collecting a speckle pattern sequence, calculating time correlation from the speckle pattern sequence and fitting a multi-scale speckle dynamic behavior, combining the reference scattering intensity to obtain an average scattering intensity change trend, and combining the intensity trend and the multi-scale dynamic behavior to extract an adsorption characteristic parameter. The application can characterize microscopic responses in an adsorption process under controlled temperature and pressure conditions, and improves the accuracy and reliability of parameter extraction.

Description

Technical Field

[0001] This application relates to the field of coal resource exploration, and more specifically to a method and apparatus for testing coal adsorption characteristics based on an X-ray photon correlation spectral line station. Background Technology

[0002] Coal, as a typical porous carbonaceous material, possesses a multi-scale pore structure, including micropores, mesopores, and fissures. Coal exhibits a significant adsorption capacity for gases such as methane and carbon dioxide, and its adsorption characteristics are fundamental parameters in research on coalbed methane development, gas disaster prevention and control, geological carbon dioxide sequestration, and coal chemical processes. The adsorption behavior of coal is not only related to structural parameters such as pore volume and specific surface area, but also closely correlated with temperature, pressure, pore wall interface state, and the local structural response during adsorption.

[0003] Existing methods for testing coal adsorption characteristics mainly include volumetric and gravimetric methods. Volumetric methods calculate the amount of gas adsorbed by measuring changes in system pressure, volume, and state parameters before and after gasification. Gravimetric methods obtain the adsorption amount by monitoring sample mass changes using a high-precision balance. These methods have been widely used in coal adsorption research, providing adsorption isotherms and some adsorption model parameters. However, volumetric and gravimetric methods are essentially macroscopic statistical measurement methods, primarily reflecting the overall adsorption results of the sample. They are difficult to reveal in situ the local structural changes and kinetic responses corresponding to the adsorption process within coal nanopores, especially in characterizing the microscopic fluctuations in the coal matrix or pore interface during adsorption. Furthermore, existing methods for testing coal adsorption characteristics often involve multiple processes simultaneously in the initial adsorption stage, such as gas inlet, diffusion, pore redistribution, and adsorption. Without segmented analysis of time-related signals, diffusion hysteresis can easily be misinterpreted as an adsorption kinetic characteristic, leading to unclear parameter interpretation.

[0004] Therefore, there is an urgent need to propose a completely new technical solution to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0005] This application provides a method and apparatus for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station, which can solve what technical problems?

[0006] In a first aspect, embodiments of this application provide a method for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station, the method comprising: The coal sample is loaded into the sample area of ​​the experimental chamber for coherent X-ray transmission or scattering testing. The coal sample is then subjected to vacuum degassing pretreatment, and a reference speckle pattern and reference scattering intensity are collected after loading. Temperature and pressure calibrations were performed on the experimental chamber. The target adsorption gas is introduced into the experimental chamber at a preset temperature and pressure, so that the coal sample undergoes adsorption under constant temperature and pressure or staged constant temperature and pressure conditions; during the adsorption process, the speckle pattern sequence of the coal sample is continuously acquired by an X-ray photon correlation spectral line station. The correlation function of speckle intensity changing with time is calculated based on the speckle pattern sequence; the corresponding speckle dynamic behavior is fitted based on the attenuation characteristics of the correlation function at different scattering scales. Based on the speckle pattern sequence, the average scattering intensity of the preset scattering region during the adsorption process is statistically obtained, and the average scattering intensity is compared with the reference scattering intensity to obtain the trend of the average scattering intensity. Based on the trend of average scattering intensity variation and speckle dynamic behavior at different scattering scales, the microstructural response and adsorption-induced dynamic changes of coal samples during the adsorption process are analyzed to obtain the adsorption characteristic parameters of coal samples under corresponding temperature and pressure conditions.

[0007] Secondly, embodiments of this application provide a coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station, which has the function of implementing the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station provided in the first aspect above. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above function, and the modules can be software and / or hardware.

[0008] In one embodiment, the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station includes: An experimental chamber, wherein a sample area is provided for containing coal samples; A gas path assembly, which is connected to the experimental chamber, is used to introduce the adsorbed gas to be tested into the experimental chamber and to adjust the gas flow rate and pressure. A vacuum assembly, which is connected to the experimental chamber, is used to perform vacuuming on the experimental chamber. A temperature monitoring component, which is used to collect experimental chamber wall temperature data and temperature data of the sample's vicinity; A pressure monitoring component, which is used to collect pressure data at the intake control end and pressure data in the sample area near the cavity; The data processing module is connected to the gas path assembly, the vacuum assembly, the temperature monitoring assembly and the pressure monitoring assembly respectively, and is adapted to communicate with the detector of the X-ray photon correlation spectral line station. The data processing module is configured to perform the coal adsorption characteristic testing method based on X-ray photon correlation spectral line station as provided in the first aspect.

[0009] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station as described in the first aspect.

[0010] Fourthly, embodiments of this application provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station as described in the first aspect.

[0011] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on a computer, cause the computer to execute the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station provided in the first aspect.

[0012] Compared to existing technologies, this application combines the dynamic speckle testing capability of an X-ray photon correlation spectral line station with in-situ temperature and pressure control during the coal sample adsorption process. This not only allows for the acquisition of the average scattering intensity variation trend during coal sample adsorption but also enables the extraction of speckle dynamic behavior at different scattering scales and analysis of the microstructural response and adsorption-induced dynamic changes in the coal sample during adsorption. Therefore, it effectively solves the problems of existing volumetric and gravimetric methods, which struggle to reveal local structural changes in coal nanopores in situ, characterize the microscopic fluctuations of the coal matrix or pore interface, and distinguish between gas inlet, diffusion, and adsorption processes. This improves the accuracy, reliability, and application value of coal adsorption characteristic testing results. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station in the embodiments of this application. Figure 2 This is a schematic diagram of the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station according to an embodiment of this application. Detailed Implementation

[0014] This application's embodiments can be applied to scenarios such as coal resource exploration, coalbed methane development, gas disaster prevention and control, carbon dioxide geological storage, and coal chemical research. It is particularly suitable for in-situ adsorption characteristic testing and microscopic dynamic response analysis of coal samples under high temperature, high pressure, or variable temperature and pressure conditions. Unlike existing testing methods that primarily rely on volumetric or gravimetric methods to obtain macroscopic adsorption amounts, this application's embodiments can not only obtain information on the average scattering intensity change of coal samples during adsorption but also extract speckle dynamic behavior at different scattering scales using speckle pattern sequences continuously acquired by X-ray photon correlation spectral lines. This enables in-situ characterization of the microstructural response and adsorption-induced dynamic changes during coal sample adsorption.

[0015] The aforementioned system may include an experimental chamber, a gas path assembly, a vacuum assembly, a temperature monitoring assembly, a pressure monitoring assembly, and a data processing module. The experimental chamber contains the coal sample and forms a sample area suitable for coherent X-ray transmission or scattering testing. The gas path assembly introduces the target adsorbed gas into the experimental chamber and regulates the gas flow rate and pressure. The vacuum assembly performs vacuum degassing pretreatment on the coal sample before testing. The temperature monitoring assembly collects the temperature of the experimental chamber walls and the temperature near the sample. The pressure monitoring assembly collects the pressure at the inlet control terminal and the pressure near the sample area within the chamber. The data processing module receives speckle pattern data output from the X-ray photon correlation spectral line station detector and, in conjunction with the temperature and pressure monitoring data, performs adsorption testing process control and analyzes the test results. These components can be integrated into the same testing platform or deployed in a distributed manner and interact with each other via a communication interface.

[0016] The solutions provided in this application mainly involve in-situ adsorption testing and control technology, coherent X-ray speckle timing analysis technology, non-stationary process segmentation identification technology, and adsorption-induced microstructure response characterization technology.

[0017] Specifically, this embodiment of the application establishes an initial scattering reference state for the coal sample before the experiment by performing vacuum degassing pretreatment on the coal sample and acquiring a reference speckle pattern and reference scattering intensity after loading the sample. Temperature and pressure calibrations are performed on the experimental chamber to establish correction relationships between the set temperature and the actual sample temperature, as well as between the inlet control pressure, the pressure near the sample area in the chamber, and the actual pressure in the sample area, thereby improving the consistency between the test conditions and the actual loading state of the coal sample. During the adsorption test, speckle pattern sequences of the coal sample are continuously acquired using an X-ray photon correlation spectral station, and the correlation function of speckle intensity changing with time is calculated based on the speckle pattern sequences to fit the speckle dynamic behavior at different scattering scales. Simultaneously, the average scattering intensity of the preset scattering region during the adsorption process is statistically analyzed and compared with the reference scattering intensity to obtain the trend of average scattering intensity change. Finally, the trend of average scattering intensity change and speckle dynamic behavior are jointly analyzed to obtain the adsorption characteristic parameters of the coal sample under corresponding temperature and pressure conditions.

[0018] X-ray photon correlation spectroscopy is primarily used to obtain dynamic information on the evolution of speckle signals over time during the adsorption process of coal samples. Compared to conventional scattering analysis, which only reflects static structural information, this technique can characterize the local fluctuations, relaxation processes, and dynamic differences of the coal sample's microstructure at different scattering scales through the temporal correlation of speckle pattern sequences. Therefore, the embodiments of this application do not merely statistically analyze the macroscopic adsorption results of coal samples before and after adsorption, but rather identify the microscopic dynamic responses of the coal sample's pore interfaces, coal matrix, and local adsorption regions during adsorption by continuously recording speckle pattern sequences of the coal sample during the adsorption process, thereby improving the analytical capability for adsorption kinetics and adsorption-induced structural changes.

[0019] Furthermore, this embodiment of the application distinguishes different stages of the adsorption process by segmenting the speckle pattern sequence and fitting its dynamic behavior. Specifically, in the initial stage of adsorption testing, multiple coupled processes such as inlet disturbance, in-pore diffusion redistribution, and the actual adsorption response often coexist. If the complete time series data is analyzed as a whole, diffusion lag or inlet disturbance may be misjudged as adsorption kinetic characteristics. To address this, this embodiment of the application segments the speckle pattern sequence based on the speckle intensity time series, the actual temperature change of the sample, and the actual pressure change of the sample area. Within each time segment, the correlation function is calculated, the speckle dynamic behavior is fitted, and the statistical average scattering intensity change trend is calculated. This distinguishes the inlet disturbance stage, the diffusion redistribution stage, the adsorption response stage, and the adsorption equilibrium stage, improving the accuracy and physical interpretability of the extracted adsorption characteristic parameters.

[0020] Compared to existing technologies, this application combines the dynamic speckle testing capability of an X-ray photon correlation spectral line station with in-situ temperature and pressure control during the coal sample adsorption process. This not only allows for the acquisition of the average scattering intensity variation trend during coal sample adsorption but also enables the extraction of speckle dynamic behavior at different scattering scales and analysis of the microstructural response and adsorption-induced dynamic changes in the coal sample during adsorption. Therefore, it effectively solves the problems of existing volumetric and gravimetric methods, which struggle to reveal local structural changes in coal nanopores in situ, characterize the microscopic fluctuations of the coal matrix or pore interface, and distinguish between gas inlet, diffusion, and adsorption processes. This improves the accuracy, reliability, and application value of coal adsorption characteristic testing results.

[0021] In some implementations, the data processing module can be deployed in a data acquisition and processing server. Temperature monitoring components, pressure monitoring components, gas path components, and vacuum components are connected to the data acquisition and processing server. Speckle pattern data output from the X-ray photon correlation spectral line station detector is sent to the data acquisition and processing server. This data acquisition and processing server can perform abnormal frame removal, time sorting, correlation function calculation, speckle dynamic behavior fitting, average scattering intensity change trend extraction, and adsorption characteristic parameter calculation on the speckle pattern sequence, and output the adsorption behavior characterization results of the coal sample under the corresponding test conditions.

[0022] In actual operation, the data acquisition and processing server receives the speckle pattern sequence of coal samples during the adsorption process, along with corresponding temperature and pressure monitoring data. After converting or correcting the actual temperature and pressure of the sample area, it performs time correlation analysis and dynamic behavior fitting on the speckle pattern sequence to extract the dynamic behavior of speckles at different scattering scales. Simultaneously, it statistically analyzes the average scattering intensity of the preset scattering region during the adsorption process to obtain the trend of average scattering intensity variation. Then, it performs joint analysis with the speckle dynamic behavior to obtain one or more of the following: adsorption equilibrium time, adsorption-induced relaxation time, adsorption structure fluctuation index, adsorption stable speckle contrast, equivalent adsorption characteristic value, adsorption pressure response coefficient, and temperature sensitivity coefficient. Finally, it outputs the characterization results of the adsorption behavior of the coal sample under corresponding temperature and pressure conditions.

[0023] It is worth noting that the X-ray photon correlation spectral line station involved in this application can be understood as a synchrotron radiation testing platform used to acquire dynamic information about the microstructure of a sample. Its core function is not simply to obtain a static structural image of the sample at a certain moment, but to irradiate the sample with coherent X-rays and continuously record the changes in the sample's speckle signal over time, thereby analyzing the dynamic evolution process of the sample's internal microstructure.

[0024] In this application, the X-ray photon correlation spectral station is mainly used for in-situ dynamic testing of the coal sample adsorption process. Specifically, when coherent X-rays irradiate the coal sample within the sample area of ​​the experimental chamber, the internal pore interfaces, coal matrix, and distribution of adsorbed gases scatter the X-rays, forming a speckle pattern on the detector. As the adsorption process proceeds, the local structure and interface state within the coal sample change, and the resulting speckle pattern evolves over time. By continuously acquiring and performing time-correlation analysis on this speckle pattern sequence, dynamic response information of the coal sample at different scattering scales during the adsorption process can be obtained.

[0025] In terms of composition, the X-ray photon correlation spectral line station involved in this application may include a coherent X-ray source, optical components for defining and transmitting the X-ray beam, a sample testing area, and a two-dimensional detector. The coherent X-ray source provides a coherent X-ray beam that meets the requirements of photon correlation spectral testing. The sample testing area is used to house the experimental chamber described in this application, allowing the coal sample to be irradiated with X-rays under controlled temperature and pressure conditions. The two-dimensional detector continuously acquires speckle pattern data in a time-resolved manner. If necessary, the line station can also be used in conjunction with a shutter control unit, a beam current monitoring unit, and a data acquisition interface to achieve synchronous control and data recording of the testing process.

[0026] Compared to conventional static small-angle scattering (SAS) tests, X-ray photon correlation (XPRC) spectral lines place greater emphasis on time resolution and dynamic characterization. In other words, this application does not merely utilize the line to obtain the difference in scattering intensity before and after coal sample adsorption; rather, it uses the line to continuously acquire speckle pattern sequences of coal samples during adsorption and further calculates the correlation function of speckle intensity over time to fit the dynamic behavior of speckle at different scattering scales, thereby extracting adsorption characteristic parameters such as adsorption equilibrium time, adsorption-induced relaxation time, and adsorption structure fluctuation index. Therefore, in this application, the line serves as a dynamic data acquisition platform, forming the basis for subsequent adsorption characteristic analysis and parameter calculation.

[0027] In the application scenario described in this application, the X-ray photon correlation spectral line station typically works in conjunction with an experimental chamber. The experimental chamber provides the vacuum, temperature, and pressure control environment required for coal sample adsorption, while the X-ray photon correlation spectral line station performs in-situ irradiation and speckle pattern acquisition of the coal sample without disrupting these environmental conditions. In other words, this application does not emphasize modifying the structure of the X-ray photon correlation spectral line station itself, but rather adapting the experimental chamber, temperature and pressure control process, and X-ray photon correlation spectral testing process to achieve in-situ dynamic characterization of the coal adsorption process.

[0028] It should be noted that the computing devices involved in the embodiments of this application can be servers and / or terminal devices. The servers involved in the embodiments of this application can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem.

[0029] Reference Figure 1 , Figure 1 This is a schematic flowchart illustrating a method for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station, provided in an embodiment of this application. The method can be executed by a coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station. The method includes the following steps 101 to 108: Step 101: Load the coal sample into the sample area of ​​the experimental chamber for coherent X-ray transmission or scattering test, perform vacuum degassing pretreatment on the coal sample, and collect the reference speckle pattern and reference scattering intensity after loading.

[0030] In this embodiment, the coal sample can be a blocky coal sample, a sheet-like coal sample, or a regular coal sample formed by cutting and pressing. Further, optionally, a representative coal sample that meets the sample loading size requirements of the experimental chamber is selected as the test object.

[0031] In this step, the coal sample to be tested can first be screened, cut, or shaped. Then, the coal sample is placed in a vacuum environment for degassing to remove free gas and adsorbed water. The degassed coal sample is then dried at a set temperature until its quality stabilizes. Afterward, the dried coal sample is loaded into the sample area of ​​the experimental chamber and positioned and fixed to reduce the impact of mechanical disturbance on the stability of the speckle signal during subsequent testing. Without introducing the adsorbed gas, an initial speckle pattern is acquired from the loaded coal sample using an X-ray photon correlation spectroscopy station. The intensity of the acquired initial speckle pattern is statistically analyzed to obtain the baseline speckle pattern and baseline scattering intensity after loading. The baseline speckle pattern and baseline scattering intensity can serve as a reference baseline for subsequent dynamic analysis of the adsorption process and calculation of the average scattering intensity change trend.

[0032] As an optional embodiment, in step 101, the coal sample to be tested is screened, cut, or shaped to obtain a coal sample that meets the requirements for loading into the experimental chamber; the coal sample is placed in a vacuum environment for degassing to remove free gas and adsorbed water; the degassed coal sample is dried under a set temperature until the coal sample quality is stable; the dried coal sample is loaded into the sample area of ​​the experimental chamber, and the coal sample is positioned and fixed to reduce the influence of mechanical disturbance on the stability of speckle signal during subsequent testing; under conditions where the adsorbed gas to be tested is not introduced, an initial speckle pattern is acquired from the loaded coal sample using an X-ray photon correlation spectral line station; the intensity of the acquired initial speckle pattern is statistically analyzed to obtain the reference speckle pattern and reference scattering intensity after loading.

[0033] Specifically, in the above embodiments, it is assumed that a raw coal sample collected from a coal mine is selected as the test object. To ensure the representativeness of the test results, a coal sample with a relatively intact structure, no obvious macroscopic cracks, and not severely weathered can be selected. After preliminary crushing, the coal sample is processed into a regular sheet-like sample according to the size requirements of the sample area of ​​the experimental chamber. For example, it can be processed into a thin sheet-like coal sample with a diameter of 3 mm to 10 mm and a thickness of 0.5 mm to 3 mm, or into a block-like coal sample with a side length of 2 mm to 8 mm. It should be understood that the above dimensions are only examples, and in actual applications, they can be adjusted according to the window size of the experimental chamber, X-ray transmission conditions, and test resolution requirements.

[0034] After screening and shaping the coal samples, they are pretreated in a vacuum degassing environment. For example, the coal sample can be placed in a pre-degassing container connected to the experimental chamber, and the ambient pressure can be reduced to a preset vacuum level under the action of a vacuum pump. The vacuum is then maintained for a preset time to remove free gas and some adsorbed water from the pores of the coal sample. Furthermore, the coal sample can be simultaneously heated at a low temperature during the vacuuming process, for example, maintained within the range of 40°C to 120°C, to improve degassing efficiency and avoid significant damage to the microstructure of the coal at high temperatures. When the quality of the coal sample tends to stabilize, the degassing and drying process can be considered basically completed.

[0035] In a more specific embodiment, the processed flake coal sample can be placed in a vacuum drying apparatus and kept at 80 degrees Celsius under vacuum for 8 to 24 hours until the mass change between two consecutive weighings is lower than a preset threshold, for example, lower than 0.1% or lower than the instrument weighing error range. After drying, the coal sample is quickly transferred to the sample area of ​​the experimental chamber to reduce the influence of ambient air and moisture re-adsorption on the test results. During sample loading, sample holders, positioning clamps, or low-background fixing structures can be used to position and fix the coal sample, ensuring that the coal sample is within the coherent X-ray beam irradiation path and that the coal sample does not undergo significant displacement or rotation during subsequent heating, pressurization, and ventilation processes, thereby reducing the impact of mechanical disturbance on the stability of the speckle signal.

[0036] After sample loading but before the target adsorbed gas is introduced, an initial speckle pattern is acquired on the coal sample using an X-ray photon correlation spectroscopy station. For example, with the experimental chamber maintained at baseline vacuum or inert atmosphere, coherent X-rays are controlled to irradiate the sample area, and a preset number of initial speckle patterns are continuously acquired using a detector. During acquisition, parameters such as exposure time, sampling time interval, experimental chamber temperature, and baseline pressure are recorded simultaneously. After intensity statistical processing of the obtained initial speckle patterns, a baseline speckle pattern and baseline scattering intensity are obtained. The baseline speckle pattern characterizes the initial speckle distribution of the coal sample before the target gas adsorption occurs, and the baseline scattering intensity characterizes the average scattering level of the coal sample under baseline conditions.

[0037] Furthermore, in subsequent adsorption tests, the reference speckle pattern and reference scattering intensity can be used as reference baselines. On the one hand, the speckle patterns acquired at subsequent time points can be compared with the reference speckle pattern to analyze the changing characteristics of the speckle dynamic behavior during coal sample adsorption. On the other hand, the average scattering intensity during adsorption can be compared with the reference scattering intensity to extract the trend of average scattering intensity change, thereby providing basic data support for subsequent calculation of adsorption characteristic parameters.

[0038] For example, if the gas to be adsorbed is methane, the baseline speckle pattern and baseline scattering intensity obtained after step 101 reflect the initial structural scattering state of the coal sample before methane adsorption. Subsequently, as methane is introduced and the pressure is gradually increased, the pore interface and local adsorption region of the coal sample change, and the temporal changes in the speckle pattern and the changes in the average scattering intensity can be analyzed relative to the baseline results obtained in step 101. If the gas to be adsorbed is carbon dioxide, step 101 is also used to establish a reference baseline for the coal sample before carbon dioxide adsorption, so as to compare the differences in the microstructural response and adsorption-induced dynamic changes of the coal sample under different adsorbed gas conditions.

[0039] Step 102: Perform temperature and pressure calibration on the experimental chamber.

[0040] In this embodiment, the purpose of temperature calibration is to establish a correspondence between the set temperature and the actual temperature of the sample, avoiding the use of external measurements or set values ​​of the experimental chamber to represent the true heating state of the coal sample. The purpose of pressure calibration is to establish a correspondence between the pressure at the inlet control end and the actual pressure in the sample area, avoiding deviations in the actual pressure in the sample area caused by factors such as pipeline volume, valve volume, and empty space in the chamber.

[0041] As an optional embodiment, in step 102, the experimental chamber wall temperature data and the temperature data of the sample's vicinity are collected; based on the collected experimental chamber wall temperature data and the sample's vicinity temperature data, a correction relationship between the set temperature and the actual sample temperature is established; according to the correction relationship, the actual sample temperature during the experiment is converted or corrected; the inlet control terminal pressure data and the pressure data of the chamber near the sample area are collected; based on the collected inlet control terminal pressure data and the pressure data of the chamber near the sample area, a correction relationship for the actual sample area pressure is established; according to the actual sample area pressure correction relationship, the actual sample area pressure during the experiment is converted or corrected.

[0042] In a specific example, a first temperature acquisition point is set on the outer wall of the experimental chamber, and a second temperature acquisition point is set near the coal sample area. The first temperature acquisition point is used to collect the experimental chamber wall temperature data, and the second temperature acquisition point is used to collect the temperature data of the sample's vicinity. To avoid the second temperature acquisition point obstructing the coherent X-ray path, it can be set near the sample area but away from the main optical path. Before the test begins, a temperature calibration procedure can be performed in the empty chamber state, for example, by gradually increasing the temperature according to multiple set temperature points, and recording the experimental chamber wall temperature data and the sample's vicinity temperature data at each set temperature point. Subsequently, the above calibration process can be repeated in the sample-loaded state to obtain temperature response data under sample-loaded conditions. By comparing the correspondence between the experimental chamber wall temperature, the sample's vicinity temperature, and the sample's actual heating state at different set temperatures, a correction relationship model between the set temperature and the sample's actual temperature can be established.

[0043] For example, in one embodiment, when the temperature control system is set to 80 degrees Celsius, the experimental chamber wall temperature can be measured at 78 degrees Celsius, and the temperature near the sample can be measured at 74 degrees Celsius. After conversion using a pre-calibrated correction relationship, the actual temperature of the coal sample can be determined to be 72 degrees Celsius. When the temperature control system is set to 120 degrees Celsius, the experimental chamber wall temperature can be measured at 117 degrees Celsius, and the temperature near the sample can be measured at 111 degrees Celsius. The corresponding converted actual temperature of the coal sample is 108 degrees Celsius. This shows that if only the set temperature or the outer wall temperature of the chamber is used as the test temperature, there will be a deviation from the actual heating state of the coal sample. Therefore, it is necessary to convert or correct the actual temperature of the sample during the experiment based on the correction relationship. This improves the temperature control accuracy and makes the subsequent adsorption characteristic parameters more consistent with the actual heating conditions of the coal sample.

[0044] During temperature calibration, the sample area and the gas inlet path can be controlled in a coordinated manner. For example, while the experimental chamber is heated, the pipeline section near the gas inlet is heated to prevent the gas to be tested from condensing or adsorbing due to local low temperatures before entering the sample area, thus affecting the true adsorption state of the sample area. In some embodiments, the actual temperature fluctuation of the sample can be monitored continuously for a preset time period, such as 5 to 30 minutes. The gas to be tested and subsequent speckle pattern sequence acquisition are only initiated when the actual temperature fluctuation of the sample does not exceed the preset temperature stability threshold.

[0045] For pressure calibration, a first pressure acquisition point can be set at the gas inlet control end of the gas path, and a second pressure acquisition point can be set at the end of the pipeline near the sample area of ​​the experimental chamber or near the chamber inlet. The first pressure acquisition point is used to collect pressure data at the gas inlet control end, and the second pressure acquisition point is used to collect pressure data near the sample area of ​​the chamber. Before testing, the volume of the pipeline, valve body, connecting sections, and empty space in the experimental chamber can be measured or calibrated to obtain the corresponding volume parameters, and a dead volume correction relationship can be established accordingly. Combining the test temperature, gas inlet control end pressure data, and pressure data near the sample area of ​​the chamber, a correction relationship for the actual pressure in the sample area can be further established.

[0046] For example, in one embodiment, when the set pressure displayed at the gas path control terminal is 1.50 MPa, the actual measured pressure near the sample area may only be 1.42 MPa. After correction based on the gas path dead volume parameters, experimental temperature, and calibration results, the actual pressure in the sample area can be determined to be 1.38 MPa. When the set pressure displayed at the gas path control terminal increases to 3.00 MPa, the pressure near the sample area may be measured as 2.86 MPa. After correction, the actual pressure in the sample area can be determined to be 2.79 MPa. It is evident that due to pipeline pressure drop, dead volume effects, and gas compression effects, there is often a difference between the pressure at the inlet control terminal and the actual pressure state of the coal sample. Therefore, it is necessary to convert or correct the actual pressure in the sample area during the experiment based on the established correction relationship for the actual pressure in the sample area.

[0047] Furthermore, during the adsorption test, the actual pressure in the sample area can be continuously monitored, and compensatory gas intake can be performed based on the deviation between the actual pressure and the preset pressure. For example, when the system target pressure is 2.00 MPa, and the actual pressure in the sample area drops to 1.93 MPa due to continuous adsorption of the coal sample, the gas path components can be controlled to perform supplementary gas intake, restoring the actual pressure in the sample area to within the preset pressure tolerance range. In some embodiments, the fluctuation of the actual pressure in the sample area can also be monitored for a continuous preset time period. Only when the fluctuation amplitude of the actual pressure in the sample area does not exceed the preset pressure stability threshold will the effective speckle pattern sequence under the corresponding conditions be acquired, so as to ensure that the subsequent correlation function calculation and speckle dynamic behavior analysis are based on stable pressure conditions.

[0048] In some implementations, the sample area and the gas inlet path can be controlled in a coordinated manner during the heating process, and compensation gas inlet can be performed according to the deviation between the actual pressure in the sample area and the preset pressure during the adsorption test, so as to improve the consistency between the actual loading state of the sample and the test conditions.

[0049] Through step 102 above, this embodiment of the application can establish a dual correction mechanism for temperature and pressure, making the experimentally recorded values ​​closer to the actual heating and pressure state of the coal sample in the sample area of ​​the experimental chamber. This reduces errors caused by directly substituting the outer wall temperature and inlet pressure values ​​for the actual sample state. Furthermore, it improves the accuracy and reliability of subsequent calculations of the average scattering intensity trend, speckle dynamic behavior, and adsorption characteristic parameters.

[0050] Based on the above embodiment of step 102, a temperature calibration step and a pressure calibration step can be further described. The temperature calibration step is used to obtain the actual heating state of the coal sample during the experiment, and the pressure calibration step is used to obtain the actual pressure state of the sample area where the coal sample is located, thereby improving the consistency between the subsequent adsorption test conditions and the actual loading conditions of the coal sample.

[0051] Further optionally, in the above embodiments, establishing a correction relationship between the set temperature and the actual sample temperature includes: obtaining cavity wall temperature data, sample proximity temperature data, and corresponding data of the actual sample temperature under different set temperature conditions through at least one of cavity calibration, standard sample calibration, or sample loading state calibration; establishing a correspondence model between the set temperature and the actual sample temperature based on the obtained cavity wall temperature data, sample proximity temperature data, and corresponding data of the actual sample temperature; collecting the cavity wall temperature and sample proximity temperature during subsequent adsorption testing, and converting or correcting the actual sample temperature according to the correspondence model; coordinating temperature control of the sample area and the gas inlet path during the heating process to reduce the temperature gradient in the sample area and avoid cold spots; monitoring the fluctuation of the actual sample temperature within a continuously preset time period, and executing the introduction of the adsorbed gas to be tested and speckle pattern sequence acquisition when the fluctuation amplitude of the actual sample temperature is not higher than the preset temperature stability threshold.

[0052] Specifically, initial temperature calibration can be performed in the empty cavity state without coal samples. This can be achieved by controlling the experimental cavity to gradually increase the temperature at multiple preset temperature points. For example, several temperature points can be set at intervals, and each preset temperature point can be held for a predetermined duration. The temperature values ​​of the first temperature acquisition point on the outer wall of the experimental cavity and the temperature values ​​of the second temperature acquisition point near the sample area can be recorded. Through this cavity calibration process, the basic heat transfer response characteristics of the experimental cavity under sample-free conditions can be obtained.

[0053] Furthermore, standard sample calibration is introduced. The standard sample can be a thermally stable sample with known thermophysical parameters that does not significantly interfere with the X-ray path. After the standard sample is loaded into the sample area, heating and holding are performed at multiple set temperature points, and the cavity wall temperature data, sample adjacent temperature data, and the actual temperature data of the standard sample are recorded simultaneously. By comparing the differences between the set temperature, cavity wall temperature, sample adjacent temperature, and the actual temperature of the standard sample, heat transfer deviations under empty cavity conditions can be further corrected, making the subsequently established temperature correspondence model closer to the actual sample loading state.

[0054] In another specific example, calibration can be directly performed using the sample loading state. After loading the coal sample into the sample area, without introducing the target adsorbed gas, the temperature is increased according to the set temperature program, and the cavity wall temperature data and the temperature data of the adjacent locations of the sample are recorded simultaneously. Combining the aforementioned cavity calibration results and standard sample calibration results, a correspondence model between the set temperature applicable to the current coal sample and the actual sample temperature can be established. This correspondence model can be a lookup table, a piecewise fitting relationship, an empirical correction curve, or other model forms that can be used to convert the actual sample temperature.

[0055] For example, in one embodiment, when the set temperature is 60 degrees Celsius, the measured cavity wall temperature is 58 degrees Celsius, and the temperature near the sample is 54 degrees Celsius. After conversion using the corresponding relationship model, the actual sample temperature can be calculated to be 52 degrees Celsius. When the set temperature is 100 degrees Celsius, the measured cavity wall temperature is 97 degrees Celsius, and the temperature near the sample is 91 degrees Celsius. The corresponding actual sample temperature can be calculated to be 89 degrees Celsius. This shows that directly using the set temperature or cavity wall temperature as the actual temperature of the coal sample will introduce a significant deviation. Therefore, it is necessary to convert or correct the actual sample temperature according to the aforementioned corresponding relationship model.

[0056] Furthermore, during the heating process, the sample area and the gas inlet path can be controlled in a coordinated manner. Specifically, on the one hand, the experimental chamber can be heated by the main heating zone to bring the entire sample area to the target temperature. On the other hand, heating units can be installed on key sections of the gas inlet path to ensure that the gas to be adsorbed is kept at a temperature close to that of the sample area before entering it. This reduces the phenomenon of local cold spots caused by low temperatures in the gas inlet path, and avoids condensation, local adsorption, or sudden temperature changes of the gas to be adsorbed before entering the sample area, thereby reducing the temperature gradient in the sample area and improving the stability of the test.

[0057] After reaching the target temperature, the actual temperature fluctuation of the sample can be monitored for a continuously preset duration. For example, monitoring can be performed continuously for several minutes to tens of minutes, during which temperature data from the vicinity of the sample can be continuously collected, and the actual temperature of the sample can be calculated in real time using the corresponding relationship model. When the fluctuation range of the actual sample temperature is not higher than the preset temperature stability threshold, it indicates that the sample's heating state has entered a stable stage, and only then is the introduction of the adsorbed gas to be tested and the subsequent acquisition of speckle pattern sequences initiated. In this way, adsorption testing can be avoided when the temperature has not yet stabilized, reducing the interference of temperature drift on the dynamic behavior of speckle patterns and the analysis of the trend of average scattering intensity changes.

[0058] Further optionally, in the above embodiments, establishing the actual pressure correction relationship of the sample area includes: collecting pressure data at the inlet control end and pressure data near the sample area of ​​the chamber respectively; statistically analyzing the volume parameters corresponding to the empty space of the pipeline, valve body, connecting section, and experimental chamber; establishing a dead volume correction relationship based on the volume parameters; converting or correcting the actual pressure of the sample area by combining the test temperature, the pressure data at the inlet control end, and the pressure data near the sample area of ​​the chamber; continuously monitoring the actual pressure of the sample area during the adsorption test, and performing compensated inlet according to the deviation between the actual pressure of the sample area and the preset pressure; keeping the actual pressure of the sample area within the preset pressure tolerance range, and monitoring the fluctuation of the actual pressure of the sample area within a continuous preset time period; when the fluctuation amplitude of the actual pressure of the sample area is not higher than the preset pressure stability threshold, collecting an effective speckle pattern sequence under the corresponding conditions.

[0059] In the specific implementation of the above embodiments, a first pressure acquisition point can be set near the pressure reduction and control unit in the gas path to obtain pressure data at the inlet control end. Simultaneously, a second pressure acquisition point can be set near the inlet of the sample area in the experimental chamber to obtain pressure data near the sample area. Because the gas flow from the inlet control end to the sample area is affected by factors such as pipe length, valve structure, connection section volume, chamber free space, and test temperature changes, the pressure at the inlet control end is not equivalent to the actual pressure state of the sample area where the coal sample is located.

[0060] To improve pressure correction accuracy, the gas path system can be calibrated using dead volume. Specifically, the effective volume parameters of pipelines, valves, connecting sections, and the empty space of the experimental chamber can be statistically analyzed, and a dead volume correction relationship can be established accordingly. In one embodiment, calibration gas can be introduced into the gas path system without a sample, and the equivalent volume of each part can be inverted using the known volume and pressure change relationship; then, this process can be repeated with a sample loaded to obtain correction parameters that more closely approximate the actual test conditions. Subsequently, by combining the test temperature, inlet control pressure data, and pressure data of the chamber near the sample area, the actual pressure in the sample area can be converted or corrected.

[0061] For example, in a specific case, when the set pressure output by the gas path control system is 1.0 MPa, the pressure at the inlet control end measured at the first pressure acquisition point is approximately 1.02 MPa, while the pressure near the sample area in the cavity measured at the second pressure acquisition point is approximately 0.95 MPa. After considering dead volume correction and test temperature correction, the actual pressure in the sample area where the coal sample is located can be converted to 0.92 MPa. When the set pressure is further increased to 2.5 MPa, the pressure at the inlet control end may reach 2.56 MPa, and the pressure near the sample area in the cavity is 2.41 MPa. After correction, the actual pressure in the sample area can be 2.35 MPa. Therefore, directly using the inlet control end pressure as the pressure condition for the coal sample can easily overestimate the true adsorption pressure of the coal sample. Thus, it is necessary to establish a correction relationship for the actual pressure in the sample area to convert or correct the actual pressure in the sample area during the experiment.

[0062] During the adsorption test, the actual pressure in the sample area may decrease over time due to the continuous adsorption of the analyte gas by the coal sample. To ensure that the adsorption test is conducted under preset pressure conditions, this embodiment continuously monitors the actual pressure in the sample area and performs compensatory gas injection based on the deviation between the actual pressure and the preset pressure. For example, when the system target pressure is 1.50 MPa, and the actual pressure in the sample area drops to 1.43 MPa due to adsorption, the gas path components can be controlled to inject a small amount of gas to restore the actual pressure in the sample area to within the preset pressure tolerance range. Through this compensatory gas injection method, the coal sample can complete the adsorption process under a relatively stable pressure state, avoiding inaccurate judgment of the adsorption stage due to continuous pressure decay.

[0063] Furthermore, before effectively acquiring the speckle pattern sequence, the actual pressure fluctuations in the sample area can be monitored for a continuously preset duration. When the amplitude of the actual pressure fluctuation in the sample area is not higher than the preset pressure stability threshold, it indicates that the current pressure conditions have reached a stable state. At this time, the acquired speckle pattern sequence can more accurately reflect the adsorption dynamics of the coal sample under this pressure condition. Conversely, if the pressure is still fluctuating violently, it may indicate that the system is still in the inlet disturbance stage or the pressure compensation transition stage. At this time, the acquired speckle signal is prone to being mixed with non-target disturbance information, which is not conducive to subsequent correlation function calculation and adsorption characteristic parameter extraction.

[0064] Through the above steps, this embodiment establishes correction and stabilization control mechanisms in both temperature and pressure dimensions, creating a convertible, correctable, and verifiable correspondence between experimental setpoints, monitored values, and the actual loading state of the coal sample. This improves environmental stability during the speckle pattern sequence acquisition phase and enhances the accuracy and reliability of subsequent analyses of the average scattering intensity trend, speckle dynamic behavior, and adsorption characteristic parameters.

[0065] Step 103: The gas to be adsorbed is introduced into the experimental chamber at a preset temperature and pressure, so that the coal sample undergoes adsorption under constant temperature and pressure or staged constant temperature and pressure conditions.

[0066] Step 104: During the adsorption process, the speckle pattern sequence of the coal sample is continuously acquired by an X-ray photon correlation spectral line station.

[0067] In the above steps, steps 103 and 104 can be performed in combination. That is, while controlling the introduction of the gas to be tested and maintaining the stability of the actual temperature and actual pressure of the sample area, the speckle pattern data of the coal sample during the adsorption process is continuously collected by the X-ray photon correlation spectral line station to obtain the time-series speckle information that can reflect the dynamic evolution of the microstructure of the coal sample.

[0068] The preset temperature refers to the target temperature condition set in advance according to the experimental purpose before the adsorption test begins. The preset temperature is not simply equivalent to the value displayed by the temperature control device, but rather a target parameter used to constrain the thermal environment of the coal sample during the adsorption process. Since there may be deviations between the experimental chamber wall temperature, the temperature of the sample's adjacent location, and the actual temperature of the coal sample, it is usually necessary to combine the aforementioned temperature calibration results to correlate the preset temperature with the actual sample temperature.

[0069] The preset pressure refers to the target pressure condition set in advance in the adsorption test, which is used to control the loading state of the gas to be adsorbed in the experimental chamber. The preset pressure is not simply equivalent to the pressure at the inlet control end, but needs to be combined with the pressure correction relationship to determine whether the actual pressure in the sample area reaches the target condition.

[0070] The target gas to be tested refers to the target gas used to test the adsorption characteristics of coal samples. It can be methane, carbon dioxide, or other gases related to coalbed methane development, gas control, or storage and utilization.

[0071] The experimental chamber is used to contain coal samples and provide a controlled temperature, controlled pressure and controlled atmosphere environment. It also needs to meet the requirements of coherent X-ray transmission or scattering tests so that X-rays can effectively irradiate the coal sample and output signals that can be used for speckle analysis.

[0072] Isothermal and isobaric conditions refer to maintaining the environment in which the coal sample is located at a basically constant temperature and pressure during the adsorption test. Isothermal conditions emphasize that the actual temperature of the sample remains stable within the target range, while isobaric conditions emphasize that the actual pressure in the sample area remains stable within the target range. Furthermore, staged isothermal and isobaric conditions involve dividing the adsorption test into multiple pressure steps, with the coal sample maintained at a constant temperature and constant pressure at each pressure step, in order to study the response of the adsorption process to pressure changes.

[0073] Optionally, the graded isothermal and isobaric conditions can be achieved by dividing the preset total pressure range into multiple continuous pressure steps. These pressure steps can be set at equal intervals or non-equal intervals. Preferably, a smaller pressure step size is used in the low-pressure region and a larger pressure step size is used in the high-pressure region to improve the characterization accuracy of the initial adsorption stage and the rapid response stage. Under each pressure step, the actual pressure of the sample area is first adjusted to the corresponding preset pressure, and speckle pattern sequence acquisition is performed after the actual sample temperature and actual sample area pressure meet the stability conditions. After completing the holding and acquisition under the current pressure step, the process switches to the next pressure step.

[0074] An X-ray photon correlation spectral line station is a testing platform capable of providing coherent X-ray beams and time-resolved acquisition of speckle signals from samples. It is used to continuously observe the dynamic response of the microstructure of coal samples during the adsorption process. After coherent X-rays irradiate the coal sample, the internal pore interfaces, coal matrix, and local structural state scatter the X-rays, forming a speckle pattern on the detector. This speckle pattern can be considered a dynamic characterization of the coal sample's microstructure. When the internal structural state of the coal sample changes during adsorption, the speckle pattern also changes accordingly.

[0075] A speckle pattern sequence refers to a set of speckle patterns acquired sequentially over time during adsorption testing. A single speckle pattern reflects the scattering state at a certain moment, while a speckle pattern sequence reflects the dynamic evolution of the coal sample over a period of time. It serves as the foundational data for subsequent correlation function calculations, speckle dynamic behavior fitting, and adsorption characteristic parameter extraction.

[0076] It is worth noting that continuous acquisition in this application refers to continuously recording the speckle pattern of the coal sample according to the preset sampling time interval and exposure conditions during the adsorption process. The purpose is to preserve the trajectory of signal change over time as completely as possible during the adsorption process.

[0077] Time-resolved testing emphasizes repeatedly observing the same sample at different time points to obtain dynamic information about the entire adsorption process from inlet disturbance to adsorption equilibrium. The scattering scale can be understood as the structural scale range corresponding to the X-ray scattering signal. Different scattering scales reflect the response differences of different scale pore interfaces, structural inhomogeneities, or local dynamic processes within the coal sample.

[0078] In-situ testing refers to real-time observation of a coal sample while it is undergoing the actual adsorption process, rather than offline analysis after adsorption is complete. Therefore, it can simultaneously preserve the temperature, pressure, and atmospheric conditions of the coal sample.

[0079] Step 103 allows the target adsorbed gas to enter the coal sample's pore system under controlled temperature and pressure conditions, where it adheres to and redistributes on the pore walls, coal matrix interface, and local active sites, thereby stably triggering and maintaining the adsorption process. Step 104 allows for the continuous recording of the coal sample's speckle pattern sequence using an X-ray photon correlation spectroscopy station during adsorption, thus acquiring dynamic data on the evolution of the coal sample's microstructure over time, providing a foundation for subsequent adsorption characteristic analysis.

[0080] As an optional embodiment, in step 103, a single-stage isothermal-barrier adsorption mode or a multi-pressure-step graded isothermal-barrier adsorption mode can be established according to a preset test plan. After completing temperature and pressure calibration, the temperature and pressure in the experimental chamber are adjusted to the initial test conditions. Subsequently, the gas to be adsorbed is introduced into the experimental chamber, and the flow rate and rate of the gas are controlled to gradually bring the actual pressure in the sample area to the corresponding preset pressure. After the actual pressure in the sample area reaches the corresponding preset pressure, the actual temperature and actual pressure of the sample area are kept stable, allowing the coal sample to undergo adsorption under the corresponding temperature and pressure conditions.

[0081] Furthermore, in step 104, during the stabilization phase corresponding to each preset pressure, the speckle pattern sequence of the coal sample is continuously acquired using an X-ray photon correlation spectral station. When using a graded isothermal and isobaric adsorption mode, after acquiring the speckle pattern sequence under the current pressure step, the actual pressure of the sample area is adjusted to the next preset pressure, and the steps of maintaining the actual temperature and actual pressure of the sample area stable, allowing the coal sample to adsorb, and continuously acquiring the speckle pattern sequence of the coal sample using an X-ray photon correlation spectral station are repeated until the adsorption test under all preset pressure conditions is completed, obtaining the speckle pattern sequence of the coal sample under different temperature and pressure conditions.

[0082] In this step, the gas to be adsorbed can be methane, carbon dioxide, or other target gases used for coal adsorption characteristic testing.

[0083] In the above optional embodiments, if the test objective is to study the adsorption kinetics characteristics of coal samples under single temperature and single pressure conditions, a single-stage isothermal and isobaric adsorption mode can be adopted. Specifically, after completing temperature and pressure calibration, the experimental chamber is first adjusted to the initial test conditions, such as adjusting the actual sample temperature to the target temperature range, and ensuring the experimental chamber is in a baseline vacuum or inert atmosphere state. Subsequently, the gas path assembly is turned on, and the gas to be adsorbed is introduced into the experimental chamber. The flow rate and rate of the gas to be adsorbed are controlled by the flow control unit and pressure regulation unit, so that the actual pressure in the sample area gradually rises to the preset pressure. After the actual pressure in the sample area reaches the target value, the actual sample temperature and actual pressure in the sample area are monitored and corrected by the temperature monitoring component and the pressure monitoring component. The actual pressure in the sample area is kept basically stable by compensating for gas intake, so that the coal sample continues to adsorb under the corresponding temperature and pressure conditions.

[0084] For example, methane can be selected as the target adsorbed gas. The actual temperature of the coal sample is stabilized at a preset temperature, and the actual pressure in the sample area is gradually increased to a target pressure. After reaching the target pressure, the actual pressure in the sample area is maintained within a preset pressure tolerance range, while the actual temperature of the sample is kept within a preset temperature stability threshold range. Under these conditions, methane molecules begin to continuously adsorb at the pore interfaces, coal matrix surface, and local adsorption sites within the coal sample, forming the controlled adsorption state required for subsequent dynamic X-ray photon correlation spectroscopy testing.

[0085] In another optional embodiment, if the purpose of the test is to study the response law of coal sample adsorption characteristics to pressure changes, a multi-pressure-step graded isothermal and isobaric adsorption mode can be adopted. Specifically, multiple continuous pressure steps can be established according to a preset test plan, and these pressure steps can be arranged in ascending order. After temperature and pressure calibration, the temperature and pressure of the experimental chamber are adjusted to the initial test conditions, and then the gas to be adsorbed is introduced into the experimental chamber. After the actual pressure in the sample area reaches the first preset pressure, the stabilization and holding phase corresponding to the first pressure step is entered; after the stabilization and holding phase and data acquisition under the first pressure step are completed, the actual pressure in the sample area is adjusted to the next preset pressure, and the corresponding process is repeated until the adsorption test under all preset pressure conditions is completed.

[0086] For example, carbon dioxide can be selected as the adsorbed gas to be tested, and multiple pressure steps from low to high can be set according to the experimental purpose. In the low-pressure region, a smaller pressure increment can be used to improve the resolution of the initial adsorption response; in the medium-high pressure region, a relatively larger pressure increment can be used to balance test efficiency and adsorption equilibrium characterization. Whenever the actual pressure of the sample area reaches the target value corresponding to a certain pressure step, the actual temperature and actual pressure of the sample area are kept stable, so that the coal sample adsorbs under the temperature and pressure conditions, thereby providing stable test conditions for subsequent speckle pattern sequence acquisition. In one embodiment, the test pressure range can be divided into zones according to the target maximum test pressure, with the lower pressure segment designated as the low-pressure zone, the middle pressure segment as the medium-pressure zone, and the higher pressure segment as the high-pressure zone. Further optionally, the low-pressure zone is used to characterize the initial adsorption response and can use a smaller pressure increment, the medium-pressure zone is used to characterize the continuous enhancement process of adsorption and can use a medium pressure increment, and the high-pressure zone is used to characterize the adsorption response under near-equilibrium or high-load conditions and can use a larger pressure increment. For example, when the target test pressure range is 0 to 10 MPa, 0 to 2 MPa can be divided into a low-pressure zone, 2 to 6 MPa into a medium-pressure zone, and 6 to 10 MPa into a high-pressure zone; a pressure increment of 0.1 to 0.25 MPa is used in the low-pressure zone, a pressure increment of 0.5 to 1.0 MPa is used in the medium-pressure zone, and a pressure increment of 1.0 to 2.0 MPa is used in the high-pressure zone.

[0087] In this step, the gas to be tested can be methane, carbon dioxide, or other target gases used for testing the adsorption characteristics of coal. Different gases have different molecular sizes, adsorption capacities, and diffusion characteristics, resulting in variations in their adsorption processes and microstructural responses after entering the pores of the coal sample. Therefore, in some embodiments, different gases to be tested can be introduced into the same coal sample at the same temperature to compare the differences in the adsorption-induced dynamic changes of the coal sample under the influence of different gases.

[0088] Step 104 is performed based on the isothermal and isobaric or staged isothermal and isobaric adsorption conditions established in step 103. Specifically, during the stable holding phase corresponding to each preset pressure, a speckle pattern sequence of the coal sample is continuously acquired using an X-ray photon correlation spectral station. Specifically, when the actual pressure in the sample area reaches the corresponding preset pressure, and both the actual temperature and actual pressure in the sample area meet the preset stability conditions, the data acquisition program of the X-ray photon correlation spectral station is initiated. Subsequently, speckle patterns of the coal sample are continuously acquired using a detector at preset sampling time intervals and preset exposure times, and the acquired speckle patterns are recorded and stored in chronological order to construct a speckle pattern sequence under the current pressure conditions.

[0089] For example, once a coal sample enters a stable holding phase under the first pressure step, the actual sample temperature, actual pressure in the sample area, sample position, and incident X-ray intensity can be continuously monitored. When these parameters meet the preset acquisition conditions, continuous acquisition of speckle patterns under the current pressure conditions is initiated. During acquisition, the detector acquires one frame of speckle pattern in each sampling cycle, while simultaneously recording auxiliary information such as the acquisition time corresponding to that frame, the current actual sample temperature, and the current actual pressure in the sample area. After completing continuous acquisition within the time period corresponding to the pressure step, the acquired speckle patterns can be organized into a speckle pattern sequence under the current preset pressure for subsequent correlation function calculation and speckle dynamic behavior analysis.

[0090] When using a staged isothermal and isobaric adsorption mode, step 104 needs to be repeated at multiple pressure levels. Specifically, after completing the speckle pattern sequence acquisition at the current pressure level, the actual pressure of the sample area can be adjusted to the next preset pressure. Once the actual pressure of the sample area reaches a stable condition again, the speckle pattern sequence of the coal sample under the new pressure conditions can be continuously acquired using an X-ray photon correlation spectroscopy station. Through this step-by-step loading and acquisition method, time-series speckle data of the coal sample under multiple pressure conditions can be obtained, thereby analyzing the microstructural response law of the coal sample adsorption process with pressure changes.

[0091] For example, a set of speckle pattern sequences of coal samples is first collected at a lower pressure step, and then the pressure is gradually increased to the next pressure step. During the new stable holding phase, the corresponding speckle pattern sequences are collected again. This process is repeated until all adsorption tests under the preset pressure conditions are completed. The resulting multiple sets of speckle pattern sequences contain both dynamic information about the evolution of the coal sample over time within the same pressure step and information about the changes in the microstructure response of the coal sample between different pressure steps.

[0092] In this step, to ensure that the speckle pattern sequence accurately reflects the dynamic changes during the coal sample adsorption process, the speckle contrast, actual sample temperature, actual pressure in the sample area, sample position, and incident X-ray intensity can be monitored in real time during acquisition. When the speckle contrast falls below a preset lower threshold, or any monitored parameter exceeds its corresponding preset range, the current speckle pattern sequence acquisition can be paused or stopped, and acquisition can resume once the parameters return to the preset range. This method reduces the impact of environmental fluctuations, sample drift, and light intensity fluctuations on subsequent correlation analysis.

[0093] Through the coordinated execution of steps 103 and 104 described above, this embodiment of the application can stably trigger and maintain the coal sample adsorption process under controlled temperature and pressure conditions. Furthermore, it can continuously acquire speckle pattern sequences of the coal sample while adsorption occurs, thereby obtaining raw time-series data on the dynamic changes in the microstructure of the coal sample during adsorption. Based on this data, the correlation function of speckle intensity changing with time can be further calculated, the dynamic behavior of speckle at different scattering scales can be fitted, and the adsorption characteristic parameters of the coal sample can be extracted by combining the trend of average scattering intensity changes. In this way, not only can the adsorption behavior of the coal sample under specific temperature and pressure conditions be analyzed, but the response law of the coal sample adsorption characteristics to pressure changes can also be studied, improving the refinement of the test results and the ability to interpret them physically.

[0094] As an optional embodiment, in step 104, during the stabilization phase corresponding to each preset pressure, a speckle pattern sequence of the coal sample is continuously acquired using an X-ray photon correlation spectral line station, including: During the stabilization phase corresponding to each preset pressure, the actual sample temperature, actual sample area pressure, sample position status, and incident X-ray intensity status are continuously monitored. When preset acquisition conditions are met, continuous acquisition of the speckle pattern sequence under the current preset pressure conditions is initiated. These preset acquisition conditions may include at least two of the following: the actual sample temperature fluctuation amplitude is not higher than a preset temperature stabilization threshold; the actual sample area pressure fluctuation amplitude is not higher than a preset pressure stabilization threshold; the sample position drift is not higher than a preset displacement threshold; and the incident X-ray intensity fluctuation is not higher than a preset light intensity fluctuation threshold. During continuous acquisition, speckle patterns are continuously acquired from the coal sample according to preset sampling time intervals and preset exposure times, and the acquisition time, current sample temperature, and current sample area pressure corresponding to each speckle pattern are recorded. Simultaneously, the speckle contrast is calculated and updated in real time. When the speckle contrast falls below a preset lower limit threshold or any monitored item exceeds its corresponding preset range, continuous acquisition of the current speckle pattern sequence is stopped. After completing the continuous acquisition within the stabilization phase corresponding to the current preset pressure, the acquired speckle patterns can be constructed into a speckle pattern sequence under the current preset pressure conditions in chronological order.

[0095] In a specific example, after the actual pressure in the sample area gradually rises and reaches a preset pressure in step 103, the system does not immediately initiate speckle pattern acquisition but first enters a stabilization phase. During this phase, data is continuously received from the temperature monitoring component, pressure monitoring component, sample position monitoring unit, and incident X-ray intensity monitoring unit. Specifically, the temperature monitoring component is used to acquire the actual sample temperature in real time, the pressure monitoring component is used to acquire the actual pressure in the sample area in real time, the sample position monitoring unit is used to determine whether the coal sample has undergone significant displacement or attitude change, and the incident X-ray intensity monitoring unit is used to determine whether the beam is in a stable state. By continuously monitoring these state variables, it is possible to confirm whether the current testing environment has reached a stable state suitable for X-ray photon correlation spectrum testing before the formal acquisition of the speckle pattern.

[0096] Furthermore, in this specific example, the monitoring results can be determined in real time. When at least two of the following conditions are met: the actual temperature fluctuation of the sample is not higher than a preset temperature stabilization threshold, the actual pressure fluctuation of the sample area is not higher than a preset pressure stabilization threshold, the sample position drift is not higher than a preset displacement threshold, and the incident X-ray intensity fluctuation is not higher than a preset light intensity fluctuation threshold, it can be considered that the basic conditions for initiating speckle pattern acquisition are met under the current preset pressure conditions. For example, in one embodiment, the actual sample temperature has remained stable for a period of time, and the actual pressure of the sample area has also remained within the preset pressure tolerance range. Even if there are slight fluctuations in the sample position and incident X-ray intensity, as long as the overall preset acquisition conditions are met, continuous acquisition of the speckle pattern sequence under the current preset pressure conditions can be initiated.

[0097] In another specific example, to improve the quality of the speckle pattern sequence, a short pre-acquisition can be performed before starting continuous acquisition. That is, after meeting the preset acquisition conditions, several frames of test speckle patterns are acquired continuously with a short exposure time, and their speckle contrast and total intensity stability are quickly calculated. If the test results show that the speckle contrast meets the preset requirements, and no obvious saturation, stripe interference, or abnormal dark fields appear in the image, then the continuous acquisition program under the current preset pressure conditions is officially started. In this way, invalid data caused by short-term beam fluctuations or transient sample disturbances can be further filtered out before the formal acquisition.

[0098] During continuous acquisition, speckle patterns of the coal sample can be continuously acquired according to preset sampling and exposure time intervals. Specifically, the detector acquires one frame of speckle pattern in each sampling cycle and simultaneously records the acquisition time, current sample temperature, current sample area pressure, and, if necessary, beam intensity information corresponding to that frame. In a specific example, if the current test objective is to focus on the rapid dynamic changes in the initial adsorption stage, a smaller sampling time interval can be used to improve temporal resolution. If the current test objective is to focus on the slow evolution near equilibrium, the sampling time interval can be appropriately extended to reduce redundant data and improve acquisition efficiency. Similarly, the exposure time can also be adjusted according to the coal sample scattering intensity, detector sensitivity, and speckle quality requirements to ensure that the acquired speckle pattern meets contrast requirements while avoiding overexposure or weak signal.

[0099] In this embodiment, continuous acquisition is not unconditionally continuous, but rather involves real-time feedback control of acquisition quality during the acquisition process. Specifically, after acquiring each frame or several frames of speckle patterns, the speckle contrast is calculated and updated in real time to determine whether the current speckle pattern is still suitable for subsequent time-correlation analysis. If the speckle contrast falls below a preset lower threshold, it may indicate deteriorating coherence conditions, unexpected sample displacement, or image quality degradation. In this case, the system can stop the continuous acquisition of the current speckle pattern sequence. Simultaneously, the actual sample temperature, actual sample pressure, sample position, and incident X-ray intensity can continue to be monitored. If any of these monitored items exceeds the corresponding preset range, the current speckle pattern sequence acquisition can also be stopped to prevent invalid or interfered data from entering subsequent analysis processes.

[0100] For example, a coal sample at the current pressure step has entered a stable adsorption state and a series of speckle patterns has begun to be continuously acquired. Initially, the actual temperature and pressure of the sample area remain stable, and the speckle contrast meets the requirements, thus a set of high-quality speckle patterns is continuously recorded. Subsequently, if the actual pressure in the sample area fluctuates due to gas supply, or if external micro-vibrations cause the sample position to drift beyond a threshold, and the corresponding monitoring item exceeds the preset range, the continuous acquisition is paused. Once the actual pressure in the sample area returns to the preset pressure tolerance range, the sample position stabilizes again, and the preset acquisition conditions are met once more, the continuous acquisition of speckle patterns under the current pressure conditions can be restarted. This ensures that the final retained speckle pattern data reflects the true adsorption dynamics as closely as possible, rather than non-target changes caused by external disturbances.

[0101] After completing continuous data acquisition during the stable holding phase corresponding to the current preset pressure, all acquired valid speckle patterns can be organized and numbered chronologically to construct a speckle pattern sequence under the current preset pressure condition. In a specific example, each frame of speckle pattern can be associated and stored with the corresponding acquisition time, actual sample temperature, actual sample area pressure, beam state, and acquisition status label for subsequent steps such as correlation function calculation, speckle dynamic behavior fitting, and average scattering intensity change trend extraction. Speckle patterns deemed invalid due to insufficient speckle contrast or out-of-range monitoring parameters can also be individually marked and stored for later use in data cleaning and anomaly analysis.

[0102] Furthermore, when using a staged isothermal and isobaric adsorption mode, the above-described acquisition process can be repeated during the stabilization phase corresponding to each preset pressure. That is, the coal sample forms a first set of speckle pattern sequences at the first pressure level, a second set at the second pressure level, and so on, until speckle pattern acquisition is completed for all pressure levels. In this way, dynamic information on the evolution of the coal sample over time within the same pressure level, as well as the differences in dynamic response under different pressure conditions, can be obtained simultaneously. This provides a reliable data foundation for subsequent analysis of the coal sample's adsorption equilibrium time, adsorption-induced relaxation time, adsorption structure fluctuation index, and pressure response characteristics.

[0103] Through the above steps 104, this embodiment can continuously acquire high-quality speckle pattern sequences of coal samples under controlled adsorption conditions, provided that the sample temperature, pressure, position and beam state meet the test requirements. This improves the accuracy of subsequent correlation function calculation and speckle dynamic behavior fitting, reduces the impact of environmental disturbances, sample drift and beam fluctuations on the analysis results of adsorption characteristic parameters, and thus improves the reliability and repeatability of the test method of this application.

[0104] Step 105: Calculate the correlation function of speckle intensity changing with time based on the speckle pattern sequence.

[0105] Since the adsorption process of coal samples is usually not a strictly stationary process, it often includes multiple stages such as gas inlet disturbance, diffusion within pores, local redistribution, and the actual adsorption response. Therefore, before calculating the correlation function, it is necessary to sort, clean, segment, and perform multi-scale processing on the original speckle pattern sequence to improve the accuracy and stability of subsequent correlation analysis.

[0106] As an optional embodiment, in step 105, the continuously acquired speckle pattern sequence is sorted according to the acquisition time, and abnormal frames, invalid frames, or defective frames in the speckle pattern sequence are identified and removed; a target pixel region for time correlation analysis is selected within a preset scattering region, and the speckle intensity of each speckle pattern within the target pixel region is read; based on the speckle intensity time series and the time-varying sequences of the actual sample temperature and actual sample area pressure, a change-point detection method is used to identify signal abrupt change intervals during the adsorption test, and the speckle pattern sequence is divided into multiple time segments; within each time segment, speckle patterns acquired at different times are paired according to a preset multi-resolution time delay sequence, and the correlation degree of speckle intensity of each paired speckle pattern within the target pixel region is calculated; In this process, the shorter the time delay interval within each time segment, the higher the sampling density is used. The correlation of speckle intensity in each paired speckle map within the corresponding target pixel region is calculated, and a weighted statistical method based on quality indicators is used to statistically average the correlation of speckle intensity corresponding to each paired speckle map under the same time delay condition to obtain the correlation result under the corresponding time delay. The process then proceeds to the step of pairing speckle maps acquired at different times according to a preset multi-resolution time delay sequence within each time segment. The process iterates cyclically according to multiple preset time delays in the multi-resolution time delay sequence to obtain the segmental correlation function of speckle intensity changing with time. The segmental correlation functions under different scattering scales are summarized to obtain the corresponding correlation function under different scattering scales.

[0107] In a specific example, after step 104, a set of speckle patterns under a preset pressure condition can be obtained. These speckle patterns are accompanied by auxiliary recording information such as the acquisition time, actual sample temperature, actual pressure in the sample area, and incident X-ray intensity. All speckle patterns can be sorted according to the acquisition time to form a time-ordered sequence of original speckle patterns. This method ensures that subsequent correlation analysis strictly follows the actual time sequence of the coal sample adsorption process, avoiding errors in time correspondence caused by disordered data transmission order, buffer delays, or missing frame insertions.

[0108] After sorting, abnormal, invalid, or defective frames in the speckle pattern sequence can be identified and removed. An abnormal frame can be an image where the total speckle intensity suddenly increases or decreases abnormally, or an image distortion caused by beam fluctuations, detector malfunctions, sudden sample displacement, or instantaneous pressure disturbances. An invalid frame can be an image with exposure failure, data truncation, severe image saturation, or almost no signal. Defective frames can include images containing obvious clusters of bad pixels, stripe artifacts, or locally missing areas. In a specific example, if the incident X-ray intensity of a certain frame fluctuates abnormally at the corresponding moment, and the total image intensity deviates significantly from adjacent frames, it can be identified as an abnormal frame. If a frame has a large area of ​​pixel overflow or severe overall brightness distortion, it can be identified as an invalid frame. If a frame only has obvious bad pixel clusters in a local area while other areas are still usable, it can be identified as a defective frame and removed or partially masked according to set rules.

[0109] Alternatively, by combining the total speckle intensity, the actual temperature of the sample, the actual pressure of the sample area, and the incident X-ray intensity, an anomaly identification method based on median absolute deviation and Hampel filtering can be used to identify and remove abnormal frames, invalid frames, or defective frames in the speckle pattern sequence.

[0110] For example, sequences of total speckle intensity versus time, actual sample temperature versus time, actual sample pressure versus time, and incident X-ray intensity versus time can be constructed separately. Then, a sliding window approach is used to perform local median statistics on each of these time sequences, and the deviation of each moment from the median level within the window is calculated. If the deviation of a frame in total speckle intensity exceeds a preset threshold, and the incident X-ray intensity or actual sample pressure at the corresponding moment also shows a synchronous anomaly, Hampel filtering can be used to determine that the frame is an anomalous frame. For example, during adsorption testing, if the total intensity corresponding to the speckle pattern in frame 150 suddenly increases, while several adjacent frames remain stable, and the beam current monitoring value also shows a sudden increase at that moment, then this frame can be identified as an anomalous frame caused by beam current anomaly and removed. Similarly, if the actual sample pressure at the corresponding moment of a frame experiences a momentary drop, and the image speckle contrast decreases significantly, then it can be determined that this frame may be affected by gas path disturbance and should be excluded from subsequent correlation analysis. This type of method, based on robust statistics and local anomaly detection, is more adaptable to the non-stationary temporal characteristics of the coal adsorption process than the simple fixed threshold method.

[0111] After completing the identification and removal of abnormal frames, a target pixel region for time correlation analysis can be selected within a preset scattering area, and the speckle intensity of each speckle pattern within the target pixel region can be read.

[0112] In a specific example, based on the spatial distribution of the speckle pattern on the detector, a predefined scattering region can be divided into multiple target pixel regions, such as multiple annular regions, fan-shaped regions, or regular sub-regions, to characterize the response at different scattering scales. Then, the pixel intensity of each frame of the speckle pattern within each target pixel region is read and stored, thereby forming a time series of speckle intensity at different scattering scales. In this way, the dynamic response of coal samples at different structural scales can be analyzed separately.

[0113] Next, based on the time series of speckle intensity and the time series of changes in actual sample temperature and actual sample area pressure, the change point detection method can be used to identify the signal abrupt change intervals during the adsorption test, and the speckle pattern sequence can be divided into multiple time segments.

[0114] In a specific example, when the gas to be tested first enters the experimental chamber, the actual pressure in the sample area will rise significantly, and the total intensity and local intensity of the speckle pattern may also change rapidly. As the gas inlet process ends and the diffusion redistribution stage begins, the pressure change gradually decreases, but the speckle pattern continues to evolve slowly. Furthermore, as the adsorption of the coal sample gradually stabilizes, the rate of change of speckle intensity continues to weaken. To avoid mixing these different physical stages in the same correlation function for unified processing, joint change point detection can be performed on the speckle intensity time series, the actual temperature series of the sample, and the actual pressure series of the sample area to identify the key time points of stage transition. For example, the moment of sudden gas inlet change can be regarded as the first type of change point, the moment when the pressure stabilizes but the speckle continues to change can be regarded as the second type of change point, and the moment when the overall speckle change tends to level off can be regarded as the third type of change point. Thus, the complete speckle pattern sequence can be divided into multiple time segments, such as the gas inlet disturbance segment, the diffusion redistribution segment, and the adsorption response segment.

[0115] After time segmentation is completed, speckle images acquired at different times can be paired within each time segment according to a preset multi-resolution time delay sequence, and the correlation of speckle intensity of each paired speckle image within the target pixel area can be calculated.

[0116] For example, if a certain time segment corresponds to the initial rapid change stage of adsorption, a shorter time delay and a higher sampling density are preferred to more sensitively characterize the rapid dynamic process. If another time segment corresponds to the later slow evolution stage of adsorption, the time delay range can be appropriately increased to improve the ability to identify slow-changing processes. In other words, in this application, the shorter the time delay interval, the higher the sampling density, thereby achieving multi-resolution time delay analysis. In specific implementation, a first set of shorter time delays can be selected within a certain time segment to pair speckle patterns from several different sampling times, and then a second set of longer time delays can be selected for further pairing, gradually covering the dynamic range from fast to slow within that time segment.

[0117] To determine the correlation of speckle intensity within the corresponding target pixel region for each paired speckle pattern, a weighted statistical method based on quality indices can be used to statistically average the correlation of speckle intensity for each paired speckle pattern under the same time delay, thereby obtaining the correlation results under the corresponding time delay.

[0118] In a specific example, for the same time delay, there may be multiple available speckle pattern pairs, but the quality of these pairs is not entirely consistent. Some pairs occur at moments when the beam is relatively stable and temperature and pressure fluctuations are small, while others correspond to moments close to the threshold. Therefore, different weights can be assigned to each pair based on quality indicators such as beam stability, actual sample temperature deviation, actual sample region pressure deviation, speckle contrast, and image integrity. Higher-quality pairs account for a larger proportion in the statistical averaging; lower-quality but still acceptable pairs account for a smaller proportion. This approach reduces the interference of low-quality data on correlation results.

[0119] Then, the steps of pairing, calculating the correlation degree, and performing weighted statistical averaging of speckle images collected at different times according to a preset multi-resolution time delay sequence can be repeated in each time segment until all calculations of multiple preset time delays in the multi-resolution time delay sequence are completed, thereby obtaining the segmental correlation function of speckle intensity changing with time.

[0120] For example, for a specific time segment at a given scattering scale, a set of correlation results can be obtained first within a short time delay interval, and then gradually extended to medium and long time delay intervals to ultimately form a complete correlation function curve for that time segment. Subsequently, the same process can be repeated for other time segments at the same scattering scale to obtain piecewise correlation functions for multiple time segments.

[0121] Finally, the piecewise correlation functions at different scattering scales can be summarized to obtain the corresponding correlation functions at different scattering scales. In a specific example, the above steps can be performed on the target pixel regions corresponding to low, medium, and high scattering scales, respectively, and their respective piecewise correlation functions can be obtained. Then, the correlation functions of different time segments at the same scattering scale can be organized into a set of time-series correlation results, and further, a set of correlation functions at different scattering scales can be constructed. This summarization method not only reflects the overall dynamic changes of the coal sample during adsorption but also reveals the differences in local structural responses at different scattering scales.

[0122] Through the above steps 105, the embodiments of this application can perform quality control, stage division, and multi-scale correlation analysis on the original speckle pattern sequence, targeting the characteristics of non-stationarity, multi-stage, and susceptibility to external disturbances in the coal sample adsorption process. This reduces the impact of abnormal frames, defect frames, beam fluctuations, and temperature and pressure disturbances on the correlation function calculation results. On the other hand, it can also improve the ability to distinguish different stages such as inlet disturbance, diffusion redistribution, and adsorption response, thereby providing a more reliable data foundation for speckle dynamic behavior fitting and adsorption characteristic parameter extraction.

[0123] Step 106: Based on the decay characteristics of the correlation function at different scattering scales, fit the corresponding speckle dynamic behavior. The speckle dynamic behavior includes one or more of the following: the rate of speckle correlation decay, decay morphology, relaxation differences at different scattering scales, strength of local structure fluctuations, and time evolution characteristics of the microstructure response during adsorption.

[0124] In this embodiment, for a given scattering scale, a rapid decay of its correlation function indicates that the local structure or interface state corresponding to that scale is changing more actively. A slow decay of its correlation function indicates that the structural evolution at that scale is relatively slow, or that the adsorption process is approaching a stable state. When comparing different scattering scales, if the correlation function decays faster in smaller scale regions than in larger scale regions, it can be considered that the dynamic changes in the initial stage of adsorption are mainly concentrated in finer pore interfaces or local adsorption regions. Conversely, if significant decay is also observed at larger scales, it indicates that a larger range of structural responses within the coal sample also participate in the adsorption process.

[0125] Furthermore, the speckle dynamic behavior includes not only the rate of change but also the mode of change. For example, in some embodiments, the correlation function may exhibit a single-stage decay, in which case the dynamic process at the corresponding scattering scale can be considered relatively simple. In other embodiments, the correlation function may exhibit a fast-then-slow or multi-stage decay characteristic, in which case multiple coupled dynamic processes can be considered to exist simultaneously at that scattering scale, such as a rapid response caused by initial air intake and diffusion redistribution, and a slow response caused by subsequent actual adsorption or local matrix adjustment. Therefore, by fitting the decay characteristics of the correlation function, the dynamic differences in the adsorption process of coal samples at different scattering scales can be distinguished.

[0126] In this embodiment, the characteristic relaxation time is used to characterize the characteristic time corresponding to the decay of speckle correlation. In other words, the characteristic relaxation time reflects the typical time scale during which a coal sample evolves from one structural state to another at a corresponding scattering scale. In a specific example, if the characteristic relaxation time obtained at a certain scattering scale is short, it can be considered that the local adsorption response at that scale is fast; if the characteristic relaxation time is long, it can be considered that the structural adjustment process at that scale is slow, or that the dynamic evolution of the coal sample at that scale lasts longer.

[0127] In this embodiment, speckle contrast is used to characterize the resolvability of speckle patterns. Higher speckle contrast generally indicates better image quality, more stable coherence conditions, and clearer speckle features; decreased speckle contrast may indicate deteriorating image quality, increased sample micro-displacement, reduced beam stability, or localized changes in the coal sample that weaken the resolvability of the speckle pattern. Therefore, speckle contrast can serve as both a parameter for evaluating speckle pattern quality and an important basis for subsequently identifying the adsorption stabilization stage and determining the speckle contrast for adsorption stability.

[0128] In this embodiment, the kinetic index is used to characterize the kinetic changes in the speckle correlation decay process. Specifically, the kinetic index reflects whether the decay of the correlation function is closer to a single uniform relaxation process or exhibits obvious non-uniform, multi-stage, or distributed dynamic behavior. In a specific example, if the kinetic index changes significantly at different time segments or different scattering scales, it indicates that the microstructure response mode during coal sample adsorption has changed, for example, from an initial rapid perturbation stage to a diffusion redistribution stage, or from a continuous adsorption response stage to an adsorption equilibrium stage.

[0129] The main purpose of step 106 is to identify the microscopic dynamic response of the coal sample at different scattering scales during adsorption, based on the correlation function obtained in step 105. Since the adsorption process of coal samples typically involves multiple coupled phenomena such as gas diffusion within pores, local rearrangement of the coal matrix, changes in the pore wall interface state, and the gradual approach to equilibrium during adsorption, the correlation function at different scattering scales often exhibits different decay rates and decay patterns. By fitting these decay characteristics, the speckle dynamic behavior, which reflects the fluctuations and dynamic evolution of the local structure of the coal sample, can be obtained.

[0130] As an optional embodiment, in step 106, within each time segment, attenuation curves of the correlation function changing with time delay at different scattering scales are extracted; for each attenuation curve, at least one candidate dynamic behavior model among single relaxation behavior, distributed relaxation behavior, and multi-stage coupled relaxation behavior is established; a Bayesian variational inference algorithm is used to adaptively fit each candidate dynamic behavior model, and based on the fitting residual, parameter continuity, and consistency of dynamic evolution between adjacent time segments, a target dynamic behavior model matching the current time segment and the current scattering scale is determined; based on the target dynamic behavior model, speckle dynamic feature parameters describing the speckle dynamic behavior of the current time segment are extracted; wherein, the speckle dynamic feature parameters include characteristic relaxation time, fractional relaxation exponent, relaxation distribution width, speckle dynamic scaling exponent, and fractal scattering dimension; multi-scale mutual information correlation is performed on the speckle dynamic feature parameters at different scattering scales to obtain the speckle dynamic behavior of the coal sample in the current time segment.

[0131] In this embodiment, the core purpose of step 106 is to further transform the correlation function obtained in step 105 into a dynamic behavior characterization result with clear physical meaning. Since the microscopic dynamics of coal sample adsorption are not always determined by a single mechanism, but may be simultaneously affected by factors such as inlet perturbation, pore diffusion, interfacial adsorption, local swelling of the coal matrix, or structural rearrangement, the decay pattern of the correlation function at the same scattering scale may be relatively simple or may exhibit obvious multi-stage characteristics. The decay patterns between different scattering scales may also differ. Therefore, this embodiment does not use a single fixed model to uniformly fit all data, but first establishes multiple candidate dynamic behavior models, and then adaptively selects a more suitable target dynamic behavior model based on the data characteristics.

[0132] In a specific example, after step 105, correlation functions for multiple time segments under a preset pressure condition can be obtained, and each time segment also contains correlation functions corresponding to multiple different scattering scales. The data acquisition and processing module can first extract the decay curves of the correlation functions at different scattering scales over time delay within each time segment. For example, in the initial stage of adsorption, the decay curve corresponding to a small scattering scale may decrease rapidly within a short time delay, while the decay curve corresponding to a larger scattering scale decreases more slowly. In the later stage of adsorption, the decay curves at multiple scattering scales may generally tend to flatten out. By extracting these decay curves separately, input can be provided for subsequent model selection and parameter fitting.

[0133] When establishing candidate dynamic behavior models, single relaxation behavior models, distributed relaxation behavior models, and multi-stage coupled relaxation behavior models can be constructed for different physical meanings.

[0134] In a specific example, if the decay curve at a certain scattering scale is smooth, monotonic, and has only one dominant decay process, it can be preferentially classified into a single relaxation behavior model. This model is suitable for describing the local dynamic process of a coal sample controlled by a single dominant mechanism at a certain scale.

[0135] If a decay curve exhibits obvious wide relaxation characteristics, that is, there are significant changes in different time delay ranges, and it is difficult to describe it with a single time scale, then a distributed relaxation behavior model can be used; this model is more suitable for describing the situation where the pore structure of coal samples is non-uniform and multiple time scales coexist.

[0136] If a decay curve exhibits characteristics of rapid initial decay followed by slow decay, segmented transitions, or composite decay, a multi-stage coupled relaxation behavior model can be further employed to characterize the dynamic behavior formed by the superposition of multiple sub-processes, such as initial gas diffusion, local interface rearrangement, and subsequent adsorption stabilization.

[0137] In a further example, a Bayesian variational inference algorithm can be used to adaptively fit the aforementioned candidate dynamic behavior models. Specifically, the parameters to be estimated in each candidate dynamic behavior model can be used as the objects to be optimized, and the possible value ranges of each parameter can be probabilistically estimated based on the data characteristics of the current decay curve. Unlike traditional single least squares fitting, the Bayesian variational inference algorithm can consider parameter uncertainty and model complexity simultaneously during the fitting process, making it more suitable for handling data with high noise, distinct stages, and non-stationarity during coal sample adsorption. In one embodiment, for the decay curve at a certain time segment and a certain scattering scale, a single relaxation behavior model, a distributed relaxation behavior model, and a multi-stage coupled relaxation behavior model can be fitted respectively, and their respective fitting residuals can be calculated. Subsequently, by combining whether the parameter changes between the current time segment and the adjacent time segments are smooth and whether the dynamic evolution trend is continuous, it can be determined which model is more suitable as the target dynamic behavior model for the current time segment and the current scattering scale.

[0138] For example, in a specific test scenario, during the initial adsorption time segment of the coal sample at the first pressure step, the decay curve corresponding to a small scattering scale exhibits a rapid decline accompanied by a slight secondary bend. At this point, the fitting residual of a single relaxation behavior model is large, while a multi-stage coupled relaxation behavior model can better describe the superposition process of the initial rapid diffusion and subsequent local adsorption response. Therefore, the multi-stage coupled relaxation behavior model can be determined as the target dynamic behavior model at this scattering scale. Meanwhile, within the same time segment, the decay curve corresponding to a larger scattering scale may be relatively flat and without a significant secondary bend. In this case, the system can determine a single relaxation behavior model as the target dynamic behavior model at this scattering scale. Thus, this embodiment can adaptively select different models based on the actual data characteristics at different scattering scales, rather than forcibly adopting a single model.

[0139] In a specific example, the implementation process of the above Bayesian variational inference algorithm is as follows: for the correlation function decay curve at a certain time segment and a certain scattering scale, a single relaxation behavior model, a distributed relaxation behavior model, and a multi-stage coupled relaxation behavior model can be established respectively. Subsequently, using the current decay curve data as input, and using the feature relaxation time, dynamic exponent, relaxation distribution width, and mode transition probability as parameters to be estimated, Bayesian variational inference is performed on each candidate model.

[0140] In the inference process, initial parameter values ​​are first set based on the initial downward trend of the current decay curve, and then prior constraints are applied to each parameter to be estimated. Subsequently, the variational posterior distribution of each parameter is iteratively updated, and the corresponding fitting error and parameter uncertainty are calculated. After the iteration converges, the fitting residuals, parameter continuity, and consistency of dynamic evolution of adjacent time segments of each candidate model are compared to determine the target dynamic behavior model. If the fitting residual of the multi-stage coupled relaxation behavior model is the smallest, and the parameter changes remain continuous with the preceding and following time segments, then it is taken as the target dynamic behavior model under the current time segment and the current scattering scale, and the characteristic relaxation time, fractional relaxation exponent, relaxation distribution width, speckle dynamic scaling exponent, and fractal scattering dimension corresponding to the model are output.

[0141] After determining the target dynamic behavior model, speckle dynamic characteristic parameters can be further extracted based on the model. Among these, the characteristic relaxation time characterizes the main time scale corresponding to the correlation decay within the current time segment, reflecting the rate of local dynamic change of the coal sample at that scattering scale. The fractional relaxation index characterizes the degree to which the correlation decay process deviates from the ideal single relaxation behavior, reflecting the non-uniformity of the coal sample's pore structure and the complexity of the dynamic process. The relaxation distribution width describes the dispersion of different relaxation time scales in the system; a larger value usually indicates more pronounced multi-time-scale coupling behavior of the coal sample at the current scattering scale. The speckle dynamic scaling index characterizes the overall scaling relationship of the dynamic response with time delay, helping to identify the dynamic evolution type at the current time segment. The fractal scattering dimension reflects the complexity and self-similarity characteristics of the coal sample's microstructure at the current scattering scale, thus aiding in determining the organizational state of the pore interface and local structure.

[0142] In a specific example, if the characteristic relaxation time extracted at a small scattering scale within a certain time segment is short, the fractional relaxation index deviates significantly from the conventional single relaxation state, and the relaxation distribution width is large, then the adsorption process at this scale can be considered to have strong non-uniformity and multi-process coupling characteristics. Conversely, if the characteristic relaxation time extracted at a larger scattering scale is long and the relaxation distribution width is small, then the dynamic behavior at this scale can be considered relatively simple, mainly manifested as slow overall structural adjustment. This parameter extraction method can transform the abstract decay curve into speckle dynamic characteristic parameters with clear physical meaning.

[0143] Furthermore, multi-scale mutual information correlation can be performed on the speckle dynamic characteristic parameters at different scattering scales to obtain the speckle dynamic behavior of the coal sample in the current time segment.

[0144] In a specific example, the characteristic relaxation time, fractional relaxation exponent, relaxation distribution width, speckle dynamics scaling exponent, and fractal scattering dimension extracted from multiple scattering scales can be used as multidimensional feature vectors. The correlation and information coupling relationships of these feature parameters across different scattering scales can then be analyzed. If there is a high level of mutual information between different scattering scales, it indicates that the dynamic responses of the coal sample at these scales are not independent but rather exhibit significant coupling. If the mutual information level between a certain scattering scale and other scales is low, it indicates that the local structural changes corresponding to that scale are more independent. Through this multi-scale mutual information correlation analysis, the overall speckle dynamic behavior characterization of the coal sample within the current time segment can be obtained, rather than merely the local fitting results at a few individual scales.

[0145] For example, in the initial stage of adsorption, multi-scale mutual information correlation analysis may reveal a high degree of coupling between dynamic characteristic parameters at small and medium scattering scales, and a low degree of coupling with those at large scattering scales. This indicates that the dynamic changes in the initial stage of adsorption are mainly concentrated in smaller pores and local interface regions. However, around adsorption equilibrium, the mutual information between different scattering scales may decrease overall, indicating that the dynamic changes at each scale of the coal sample have gradually weakened and tended to stabilize. In this way, this embodiment can not only obtain the dynamic characteristic parameters at each scattering scale, but also further obtain a more complete multi-scale dynamic behavior description of the coal sample within the current time segment.

[0146] In a specific example, characteristic relaxation times, fractional relaxation exponents, relaxation distribution widths, speckle dynamics scaling exponents, and fractal scattering dimensions can be extracted at small, medium, and large scattering scales, respectively. Within the current time segment, parameter sample sequences corresponding to each scale are constructed according to multiple analysis windows. Subsequently, mutual information values ​​between small and medium scattering scales, small and large scattering scales, and medium and large scattering scales are calculated and normalized. If the normalized mutual information value between small and medium scattering scales is significantly higher than that between small and large scattering scales, it can be considered that the dynamic changes in the initial adsorption stage are mainly concentrated in smaller pores and local interface regions, and that there is strong coupling between small and medium scales. If the normalized mutual information values ​​between different scattering scales decrease overall before and after adsorption equilibrium, it can be considered that the dynamic changes of the coal sample at each scale gradually weaken and tend to stabilize.

[0147] In other words, the current time segment can be further divided into multiple analysis windows, and within each window, one or more of the following can be extracted: characteristic relaxation time, fractional relaxation exponent, relaxation distribution width, speckle dynamics scaling exponent, and fractal scattering dimension at different scattering scales, to form parameter sample sequences corresponding to each scattering scale. Subsequently, mutual information is calculated for the parameter sample sequences corresponding to any two scattering scales, and a multi-scale mutual information matrix is ​​constructed. The mutual information values ​​can be obtained using methods based on discrete binning, kernel density estimation, or nearest neighbor search, and are preferably normalized. Furthermore, the normalized mutual information values ​​can be compared with a reference mutual information baseline or a preset quantile threshold to determine the mutual information level between different scattering scales. A high normalized mutual information value indicates strong dynamic coupling between corresponding scattering scales. A low normalized mutual information value indicates relatively independent dynamic responses at corresponding scattering scales. By summarizing the mutual information results between all scattering scales, a multi-scale speckle dynamic behavior description of the coal sample as a whole within the current time segment can be obtained.

[0148] Regarding the calculation of mutual information values, different methods can be selected based on the actual data volume and computational resources. In a specific example, when the number of analysis windows is large and the parameter distribution is complex, a mutual information estimation method based on nearest neighbor search can be used to improve adaptability to continuous parameter sequences. In other implementations, a mutual information calculation method based on kernel density estimation can also be used to obtain a smoother approximation of the probability distribution. If the data volume is small and the parameter distribution is relatively concentrated, a method based on discrete binning can also be used for approximate calculation. To facilitate comparison between different scattering scales, the calculated mutual information values ​​can be normalized to obtain normalized mutual information results.

[0149] In an example test scenario, a reference mutual information baseline can be obtained beforehand through cavity baseline testing, inert gas testing, or post-adsorption equilibrium stabilization testing. Alternatively, a mutual information quantile threshold range can be constructed based on historical test data. Subsequently, the normalized mutual information values ​​between each pair of scattering scales in the current time segment are compared with the reference mutual information baseline or the preset quantile threshold to determine the mutual information level. If the normalized mutual information value between the small and medium scattering scales is significantly higher than the reference baseline and also higher than the normalized mutual information value between the small and large scattering scales, then it can be considered that there is a strong dynamic coupling between the small and medium scales, while the coupling between the small and large scales is relatively weak.

[0150] For example, in the initial stage of methane adsorption, the analysis results may show that the normalized mutual information value between the small and medium scattering scales is highly coupled, while the normalized mutual information values ​​between the small and large scattering scales, and between the medium and large scattering scales, are relatively low. This indicates that the dynamic changes in the initial stage of adsorption are mainly concentrated in smaller pores and local interface regions, and the responses between these smaller-scale structures are highly synergistic, while larger-scale structures have not yet participated significantly or have a weak response. Conversely, as the adsorption process approaches equilibrium, the normalized mutual information values ​​between multiple scattering scales may decrease overall and tend to approach the reference baseline. At this point, it can be considered that the dynamic changes of the coal sample at different scales are gradually weakening, the coupling degree between the responses at each scale is decreasing, and the system is approaching a stable state.

[0151] In another specific scenario, if the gas to be adsorbed is carbon dioxide, the adsorption of carbon dioxide on the coal sample is more sensitive, and the local structural response caused by adsorption is more obvious. The analysis results may show that in the initial stage of low pressure, the normalized mutual information value between the small and medium scattering scales is not only higher than in the methane scenario, but may also be accompanied by a synchronous increase in the mutual information value between the medium and large scattering scales. This can be explained as follows: under carbon dioxide adsorption conditions, not only do local pores and interface regions respond rapidly, but medium-scale and even large-scale structures also begin to participate in dynamic evolution earlier. Therefore, multi-scale mutual information correlation analysis can not only be used to identify the dynamic coupling changes of the same coal sample at different adsorption stages, but also to compare the differences in multi-scale response characteristics of coal samples under the influence of different adsorbed gases.

[0152] Through step 106 above, this embodiment can transform the correlation function decay characteristics at different time segments and scattering scales into a well-structured and physically meaningful speckle dynamic behavior characterization result. This allows for the identification of differences in local structural responses at different stages and scales during coal sample adsorption. Furthermore, it provides a more reliable input basis for subsequent extraction of adsorption characteristic parameters based on the combined trend of average scattering intensity variation and speckle dynamic behavior.

[0153] Further optionally, for each of the decay curves, establishing at least one candidate dynamic behavior model among single relaxation behavior, distributed relaxation behavior, and multi-stage coupled relaxation behavior includes: For single relaxation behavior, a fractional-order Cauchy relaxation model is constructed to describe the slow relaxation characteristics within the heterogeneous pores of the coal matrix using fractional-order differential operators. For distributed relaxation behavior, a log-normal distribution is used to construct the distributed relaxation probability density function, which is adapted to the non-equilibrium, long-range correlated scattering dynamics characteristics during coal adsorption. For multi-stage coupled relaxation behavior, the relaxation process is divided into three sub-modes: gas diffusion relaxation, matrix swelling relaxation, and adsorption equilibrium relaxation. A hidden Markov mode coupling model is established, and the temporal switching law of different relaxation behaviors is described by the mode transition probability matrix. The single relaxation, distributed relaxation, and multi-stage coupled relaxation models are uniformly embedded into a multi-scale scattering kernel function to form a set of candidate dynamic behavior models covering the entire scattering scale.

[0154] In this embodiment, the establishment of the aforementioned candidate dynamic behavior model is primarily to adapt to various attenuation characteristics that may occur at different scattering scales and time segments during the coal sample adsorption process. Because the pore structure of coal samples exhibits significant multi-scale, heterogeneous, and non-equilibrium characteristics, the attenuation curve of the correlation function sometimes shows a single dominant process, sometimes a wide-distribution relaxation, and in other cases, multiple dynamic sub-processes coupled or alternately dominant. Based on this characteristic, this embodiment does not pre-define that a single model is applicable to all data, but rather prepares multiple candidate dynamic behavior models for different attenuation curves to improve the adaptability of subsequent adaptive fitting.

[0155] In a specific example, for the correlation function decay curve at a certain time segment and a certain scattering scale, if the curve is smooth, continuous, and monotonically decreasing as a whole, and does not show obvious double bending or multi-plateau characteristics, then a single relaxation behavior model can be established first.

[0156] In this embodiment, the single relaxation behavior model is preferably constructed as a fractional-order Cauchy relaxation model. The pores within the coal matrix are not completely homogeneous, and there are complex hysteresis and slow-release effects between local adsorption sites and pore channels. Therefore, the correlation decay process often does not completely conform to the traditional integer-order exponential relaxation law. To address this, a fractional-order differential operator can be introduced to describe slow relaxation and memory effects, enabling the model to reflect the continuous and slow evolution characteristics within the heterogeneous pores of the coal matrix.

[0157] In one specific implementation, the initial parameter range of the single relaxation behavior model can be set based on the initial descent rate and tail decay trend of the current decay curve. For example, the initial value range of the characteristic relaxation time and the initial value range of the fractional relaxation exponent can be set. Then, the current decay curve is input into the model for fitting. If the model residual is found to be small after fitting, and the fractional relaxation exponent changes stably within a reasonable range, then it can be considered that the curve is mainly controlled by a single dominant relaxation process.

[0158] For example, in a specific test scenario, when the coal sample is at a high pressure level and close to adsorption equilibrium, the correlation function at a large scattering scale decays slowly and has no obvious segmentation characteristics. In this case, the fractional Cauchy relaxation model can often better characterize the overall slow adjustment process of the coal sample at a large scale.

[0159] In another specific example, if the correlation function decay curve at a certain scattering scale does not exhibit a smooth decay controlled by a single time scale, but rather shows characteristics of broadening, tailing, or coexistence of different fast and slow processes over the entire time delay range, then a distributed relaxation behavior model can be further established.

[0160] In this embodiment, the distributed relaxation behavior model preferably uses a log-normal distribution to construct the distributed relaxation probability density function. During the adsorption process of the coal sample, different pore sizes, different connectivity, and different local interface states correspond to different relaxation time scales. These relaxation time scales are often not uniformly distributed, but rather closer to skewed or wide distributions. The log-normal distribution can better describe the overall wide relaxation characteristics formed by the superposition of a large number of local relaxation processes.

[0161] In another specific implementation, the initial range of the relaxation time center position and the relaxation distribution width can be set based on the current width of the decay curve. Then, the current decay curve is fitted using a distributed relaxation model. If the fitting results show that the relaxation distribution width is significantly greater than the corresponding level in a single relaxation behavior model, and the fitting residual is lower than that in a single relaxation behavior model, then the dynamic behavior at the current scattering scale can be considered more suitable to be described by a distributed relaxation behavior model.

[0162] For example, in a specific test scenario, when the coal sample is in the middle of adsorption, the diffusion process in the pores and the adsorption process at the local interface occur simultaneously. The decay curve of the correlation function at a small scattering scale may show that the decay is faster in the first part and the tail is obvious in the second part. At this time, the distributed relaxation model based on the log-normal distribution can better characterize the scattering dynamics of the coal sample under non-equilibrium, long-range correlation conditions.

[0163] In another specific example, if a decay curve exhibits obvious segmented characteristics, such as rapid initial decay, slow change in the middle stage, and stabilization again in the later stage, or if the fitting results show that neither a single relaxation model nor a distributed relaxation model can fully explain the current curve, then a multi-stage coupled relaxation behavior model can be established.

[0164] In this embodiment, the multi-stage coupled relaxation behavior model divides the relaxation process into three sub-modes: gas diffusion relaxation, matrix swelling relaxation, and adsorption equilibrium relaxation. Gas diffusion relaxation mainly corresponds to the rapid migration and redistribution of the adsorbed gas in a localized region after entering the coal sample's pore system; matrix swelling relaxation mainly corresponds to the local structural adjustment, interface changes, or weak swelling response of the coal matrix due to adsorption; and adsorption equilibrium relaxation mainly corresponds to the slow decay process as the system gradually approaches a stable state. To describe the transition relationships between these three sub-modes, this embodiment preferably employs a hidden Markov mode coupling model. This model describes the temporal switching patterns of different relaxation sub-modes by setting a mode transition probability matrix.

[0165] In one specific implementation, the weights and mode transition probabilities of the three sub-modes can be initialized first based on the inflection point of the decay curve within the current time segment, the trend of decay rate changes, and the dynamic evolution state of adjacent time segments. Subsequently, a fitting algorithm is used to optimize the hidden Markov mode coupling model so that it can simultaneously explain the fast, medium, and slow processes in the curve.

[0166] For example, in a specific test scenario, when a coal sample is in the initial adsorption stage under low pressure, the decay curve at a small scattering scale may initially show a significant and rapid decay, followed by a slower, continuous change, and then gradually stabilize. At this point, the process can be considered to have sequentially undergone three sub-modes: gas diffusion relaxation, matrix swelling relaxation, and adsorption equilibrium relaxation. Using a hidden Markov mode coupling model, the temporal switching patterns between different relaxation behaviors can be described more clearly, rather than treating them as a single, mixed process.

[0167] In a specific example, considering the correlation function decay curve at a predetermined pressure step and a medium scattering scale, it can be observed that the decay curve exhibits a rapid decline in the initial stage, a slow transition in the middle stage, and a gradual stabilization in the later stage along the time delay axis. Based on this, the speckle dynamic process within the current time segment can be divided into three sub-modes: the gas diffusion relaxation sub-mode, the matrix swelling relaxation sub-mode, and the adsorption equilibrium relaxation sub-mode. The gas diffusion relaxation sub-mode characterizes the fast process corresponding to the rapid migration and redistribution of the adsorbed gas in a local region after entering the coal sample pore system; the matrix swelling relaxation sub-mode characterizes the medium process corresponding to the local structural adjustment, interface change, or weak swelling response of the coal matrix under adsorption; and the adsorption equilibrium relaxation sub-mode characterizes the slow process corresponding to the gradual stabilization of the system.

[0168] In this specific example, the three sub-modes can be initialized based on the morphological characteristics of the decay curve within the current time segment. For instance, if the decay curve decreases the fastest within a shorter time delay, it can be preliminarily considered that this interval is mainly dominated by the gas diffusion relaxor sub-mode; if the decay rate slows down significantly but continues to change within a medium time delay, it can be preliminarily considered that this interval is mainly dominated by the matrix swelling relaxor sub-mode; if the curve gradually enters tailing and tends to stabilize within a longer time delay, it can be preliminarily considered that this interval is mainly dominated by the adsorption equilibrium relaxor sub-mode. Based on the above judgments, initial weights for the three sub-modes can be given. For example, the initial weights for the gas diffusion relaxor sub-mode, the matrix swelling relaxor sub-mode, and the adsorption equilibrium relaxor sub-mode can be set to relatively large, medium, and small, respectively, or set to initial proportions that match the proportions of the fast, medium, and slow segments of the decay curve.

[0169] Furthermore, the mode transition probability matrix can be initialized based on the inflection point of the current decay curve and the dynamic evolution state of adjacent time segments. For example, in the initial stage of adsorption, the probability of "gas diffusion relaxor mode maintaining itself" can be preferentially set to a higher level, while the probability of "gas diffusion relaxor mode transitioning to matrix swelling relaxor mode" can be set to the second highest level. The probability of "matrix swelling relaxor mode transitioning to adsorption equilibrium relaxor mode" can also be set to a moderately high level, while the probability of "adsorption equilibrium relaxor mode returning to gas diffusion relaxor mode" can be set to a lower level. The reason for this setting is that in the actual adsorption process, the dynamic behavior usually evolves along the direction of fast diffusion, moderate structural response, and slow equilibrium, and complete reverse switching rarely occurs.

[0170] For example, in a more specific implementation, the three sub-modes can be denoted as the first sub-mode, the second sub-mode, and the third sub-mode, respectively. The initial mode weights are set as follows: the first sub-mode has a higher weight, the second sub-mode has a lower weight, and the third sub-mode has a lower weight. Simultaneously, the mode transition probability matrix is ​​initialized as follows: the first sub-mode has the highest retention probability, the probability of transitioning from the first sub-mode to the second sub-mode is the second highest, the probability of transitioning from the second sub-mode to the third sub-mode is relatively high, and the third sub-mode has a high retention probability but a low probability of jumping back to the first sub-mode. It should be understood that the above is only an exemplary initialization method; the specific values ​​can be determined jointly by the inflection point of the decay curve within the current time segment, the degree of slope change, and the fitting results of the preceding and following time segments.

[0171] After initial parameter settings are completed, a fitting algorithm can be used to optimize the hidden Markov mode coupling model. Specifically, the correlation function decay curve in the current time segment can be used as the observation sequence, and the three sub-modes can be regarded as hidden states. The following parameters are updated simultaneously through iterative optimization: the weights of each sub-mode, the corresponding relaxation characteristic parameters within each sub-mode, and the mode transition probability matrix. During the fitting process, the algorithm optimizes the model to be able to explain the fast, medium, and slow processes in the decay curve, while ensuring reasonable continuity of mode switching in time. In other words, the fitting result not only requires a small overall residual, but also requires that the temporal evolution of each sub-mode conforms to the physical laws of the coal sample adsorption process.

[0172] In a specific test scenario, the decay curve corresponding to a certain time segment initially drops rapidly, followed by a distinct slope easing zone, and finally enters a tailing equilibrium zone. After fitting with a Hidden Markov Mode Coupling Model (HMM), the results show that: in the early stage of this time segment, the first sub-mode dominates, indicating that this stage mainly corresponds to rapid gas diffusion and local redistribution; in the middle stage, the weight of the second sub-mode gradually increases and becomes dominant, indicating that the local structural adjustment and weak swelling response of the coal matrix are more pronounced; in the later stage, the third sub-mode gradually becomes dominant, indicating that the system has gradually entered the adsorption equilibrium control stage. Meanwhile, the mode transition probabilities obtained from the fitting also show that the probability of transitioning from the first sub-mode to the second sub-mode is higher than the direct probability of transitioning to the third sub-mode, with the highest probability of transitioning from the second sub-mode to the third sub-mode. This is consistent with the actual physical process of adsorption, which gradually transitions from rapid diffusion to structural response, and then to equilibrium decay.

[0173] Furthermore, in another specific example, the initialization of the current time segment can be modified by combining the fitting results of adjacent time segments. For instance, if the previous time segment already shows that the second sub-mode is dominant, and the initial part of the decay curve of the current time segment still exhibits moderate decay characteristics, then the initial weight of the second sub-mode in the current time segment can be appropriately increased, and the initial transition probability of the second sub-mode maintaining itself can be increased. Similarly, if the fitting results of the subsequent time segment show that the third sub-mode is gradually becoming dominant, then during the optimization process of the current time segment, the prior constraint on the transition from the second sub-mode to the third sub-mode can be appropriately strengthened. In this way, the dynamic evolution state between adjacent time segments can be made smoother and more continuous, reducing the problem of unreasonable mode jumps caused by independent fitting of a single segment.

[0174] Through the above implementation methods, the Hidden Markov Modal Coupling Model (HMM) can not only fit the current decay curve but also output results with clear physical meaning. For example, it can output the dominant region of each sub-mode within the current time segment, the relaxation intensity of each sub-mode, the transition relationship between sub-modes, and whether the current time segment as a whole is more biased towards diffusion control, swelling control, or equilibrium control. In this way, speckle correlation data, which originally only represents a single decay curve, can be further interpreted as a complex dynamic behavior resulting from the coupling of multiple dynamic sub-processes with clear physical meaning.

[0175] In this embodiment, to enable the three types of candidate dynamic behavior models to be used uniformly across different scattering scales, single relaxation, distributed relaxation, and multi-stage coupled relaxation models can be uniformly embedded into a multi-scale scattering kernel function, forming a candidate dynamic behavior model set covering the entire scattering scale. In a specific example, low, medium, and high scattering scales can be used as different input ranges of the multi-scale scattering kernel function, and a single relaxation behavior model, a distributed relaxation behavior model, and a multi-stage coupled relaxation behavior model can be loaded at each scattering scale to form a candidate dynamic behavior model set. Subsequently, for each time segment and each scattering scale, the candidate models are fitted and evaluated, and the target model at the current scattering scale is selected based on the fitting residuals, parameter continuity, and consistency of dynamic evolution between adjacent time segments. In this way, model parameters and attenuation behavior at different scattering scales can be compared and selected within a unified framework. Thus, for different scattering scales, instead of establishing completely independent empirical models, the scattering scale factor is introduced into the candidate dynamic behavior model through the multi-scale scattering kernel function, allowing model parameters and attenuation behavior at different scattering scales to be compared and selected within a unified framework.

[0176] For example, a coal sample from a mining area is selected as the test object, and methane gas is introduced into the experimental chamber under constant temperature conditions, causing the coal sample to adsorb at multiple preset pressure steps. During the adsorption response phase of one of the intermediate pressure steps, after processing in step 105, the correlation function decay curves corresponding to the low scattering scale, medium scattering scale, and high scattering scale at the current time segment can be obtained.

[0177] Among them, the decay curves corresponding to low scattering scales show a relatively gentle and smooth decline, the decay curves corresponding to medium scattering scales exhibit a broad distribution, and the decay curves corresponding to high scattering scales show a clear two-stage decay pattern of rapid initial decay followed by slower decay. Based on this, low, medium, and high scattering scales can be used as different input ranges for the multi-scale scattering kernel function, and a single relaxation behavior model, a distributed relaxation behavior model, and a multi-stage coupled relaxation behavior model can be loaded at each scattering scale to form a set of candidate dynamic behavior models.

[0178] Subsequently, within the current time segment, the three candidate models at the low scattering scale were fitted and evaluated. The results showed that the single relaxation behavior model had the smallest fitting residual, and its parameters changed smoothly with the adjacent time segments. Therefore, the single relaxation behavior model was identified as the target model at the low scattering scale. After fitting and evaluating the three candidate models at the medium scattering scale, it was found that the distributed relaxation behavior model could better characterize the wide-distribution decay characteristics. Therefore, it was identified as the target model at the medium scattering scale. After fitting and evaluating the three candidate models at the high scattering scale, it was found that the multi-stage coupled relaxation behavior model performed better in fitting the superposition characteristics of fast and slow processes, and was more consistent with the dynamic evolution trend of the adjacent time segments. Therefore, it was identified as the target model at the high scattering scale.

[0179] In this way, different scattering scales within the same time segment can correspond to different target model types. However, since these candidate models are all embedded in the same multi-scale scattering kernel function framework, the obtained model parameters and attenuation behavior results can still be compared and correlated within a unified framework. This shows that in this specific scenario, the dynamic changes of the coal sample at larger scales are closer to a slow overall adjustment process, at medium scales they better conform to the wide-distribution relaxation characteristics of multi-timescale superposition, and at smaller scales they more clearly exhibit a coupled dynamic process composed of rapid diffusion and subsequent adsorption response.

[0180] Step 107: Based on the speckle pattern sequence, the average scattering intensity of the preset scattering region during the adsorption process is statistically obtained, and the average scattering intensity is compared with the reference scattering intensity to obtain the trend of the average scattering intensity change.

[0181] This step extracts average scattering intensity information from the continuously acquired speckle pattern sequence, reflecting the overall scattering response changes during coal sample adsorption. Unlike steps 105 and 106, which focus more on the correlation and dynamic behavior of speckle patterns over time, step 107 emphasizes characterizing the overall scattering level change of the coal sample during adsorption from an intensity statistical perspective, and further compares this change with the baseline scattering state of the coal sample before adsorption, thus providing another set of key inputs for subsequent extraction of adsorption characteristic parameters.

[0182] As an optional embodiment, in step 107, the speckle intensity within each time window of the speckle pattern sequence is subjected to background subtraction, incident X-ray intensity normalization, and outlier removal. The processed speckle intensity data is averaged using a weighted robust statistical method to obtain the average scattering intensity corresponding to each time window. The average scattering intensity corresponding to each time window is compared with the baseline scattering intensity after sample loading to obtain the trend of average scattering intensity change for each time window. During the comparison, the trend of average scattering intensity change during the inlet disturbance stage is removed or reduced in weight, and corresponding average scattering intensity change trend sequences are established for the diffusion redistribution stage, adsorption response stage, and adsorption equilibrium stage, respectively. Based on the average scattering intensity change trend sequences corresponding to each stage, the average scattering intensity change trend value representing the net adsorption response is obtained.

[0183] Specifically, after obtaining the speckle pattern sequence under the current pressure conditions in step 104, the speckle pattern sequence can be divided according to a preset time window, for example, several consecutive frames of speckle patterns can be considered as a time window. For the speckle pattern within each time window, the background signal caused by the experimental cavity window, environmental scattering, detector dark current, or cavity baseline can be subtracted first to reduce the influence of non-sample factors on the scattering intensity statistics. Subsequently, the intensity of each frame of speckle pattern can be normalized according to the incident X-ray intensity monitoring results at the corresponding time, so that the intensity changes caused by beam fluctuations at different acquisition times are corrected. Afterwards, combined with the aforementioned abnormal frame identification results, images with obvious abnormal fluctuations can be removed or masked to improve the stability of the average scattering intensity statistics.

[0184] In a more specific embodiment, if a time window contains multiple speckle patterns, and the beam intensity of some frames suddenly decreases at the corresponding moment, or the actual pressure in the sample area fluctuates significantly instantaneously, these frames may still exhibit significantly different intensity levels from other frames after background subtraction and incident X-ray intensity normalization. In this case, these frames with large deviations can be regarded as outliers and removed or have their weight reduced in subsequent average intensity calculations to avoid a single abnormal sampling point having an excessive impact on the average scattering intensity of the time window. In a specific embodiment, the speckle pattern sequence obtained under a preset pressure step is divided into fixed time windows. For each frame of speckle pattern within each time window, background subtraction and incident X-ray intensity normalization are first performed to obtain the effective scattering intensity of a single frame; then, the speckle contrast, image defect ratio, actual sample temperature deviation, and actual sample area pressure deviation of each frame are extracted as quality indicators. Subsequently, severely abnormal frames are identified and removed using a method based on median absolute deviation and Hampel filtering, and frames that are not removed but deviate from the ideal state are assigned lower comprehensive weights. Subsequently, a weighted truncated mean was calculated for the effective scattering intensities of a single frame retained within the same time window to obtain the average scattering intensity corresponding to that time window. The average scattering intensity of each time window was then compared with the baseline scattering intensity after sample loading to obtain the average scattering intensity variation sequence. Finally, based on the adsorption process stage segmentation results, the variation sequence of the inlet disturbance stage was removed or weighted down, and average scattering intensity variation trend sequences were established for the diffusion redistribution stage, adsorption response stage, and adsorption equilibrium stage, respectively, thus obtaining the average scattering intensity variation trend value used to represent the net adsorption response.

[0185] After preprocessing, the processed speckle intensity data can be averaged using a weighted robust statistical method to obtain the average scattering intensity corresponding to each time window.

[0186] In a specific example, for speckle intensity data of a preset scattering region within a certain time window, the average can be calculated using weighted median, truncated mean, robust mean, or other statistical methods suitable for suppressing the influence of outliers. Simultaneously, each frame of speckle image can be assigned different weights based on its image quality, speckle contrast, incident X-ray intensity stability, actual sample temperature deviation, and actual pressure deviation in the sample area. Speckle images with high image quality and stable environmental conditions can have higher weights; speckle images with relatively poor image quality but not yet meeting the rejection criteria can have lower weights. This weighted robust statistical method allows the obtained average scattering intensity to more accurately reflect the true scattering level of the coal sample within the current time window. In practical applications, the weighted robust statistical method includes, but is not limited to: determining the comprehensive weight of each frame based on image quality, speckle contrast, incident X-ray intensity stability, actual sample temperature deviation, and actual pressure deviation in the sample area, and performing a weighted average calculation on the remaining scattering intensity after removing outlier tail data.

[0187] Subsequently, the average scattering intensity corresponding to each time window can be compared with the reference scattering intensity after sample loading to obtain the trend of the average scattering intensity change corresponding to each time window.

[0188] In this embodiment, the reference scattering intensity after sample loading is preferably the reference scattering intensity obtained in step 101 under conditions where the target adsorbed gas is not introduced. In this way, the average scattering intensity in each subsequent time window can be compared with this reference state.

[0189] For example, in a specific test scenario, the baseline scattering intensity of a coal sample after loading corresponds to the initial scattering level of the coal sample before the target gas is adsorbed. When methane or carbon dioxide enters the porous system of the coal sample and undergoes adsorption, the state of the pore interface and the local structural distribution of the coal sample change, causing the average scattering intensity to gradually deviate from the initial baseline value. By continuously comparing the current average scattering intensity with the baseline scattering intensity over a time window, a trend curve of the average scattering intensity evolution with the adsorption process can be formed.

[0190] When making the above comparisons, the average scattering intensity variation trend during the intake disturbance stage can be eliminated or reduced in weight, and corresponding average scattering intensity variation trend sequences can be established for the diffusion redistribution stage, the adsorption response stage, and the adsorption equilibrium stage, respectively.

[0191] In a specific example, transient disturbances typically occur at the beginning of the adsorption test when gas first enters the experimental chamber and sample area. At this time, the change in average scattering intensity may reflect more the inlet shock, local flow disturbances, or short-term system instability than the true adsorption response. Therefore, when constructing the trend of average scattering intensity change, the time window of the inlet disturbance stage can be removed from the overall trend based on the time segmentation results identified in step 105, or the weight of this stage's time window in subsequent analysis can be reduced.

[0192] Subsequently, the changes in average scattering intensity corresponding to each time window within the diffusion redistribution stage, adsorption response stage, and adsorption equilibrium stage can be arranged in chronological order to construct their respective average scattering intensity change trend sequences. This allows for the differentiation of scattering intensity change characteristics under different physical stages and facilitates the subsequent joint analysis of the average scattering intensity change trend with the speckle dynamic behavior obtained in step 106.

[0193] For example, in one specific embodiment, when the gas to be adsorbed first enters the sample area, the average scattering intensity changes significantly and fluctuates noticeably in the first few time windows, which can be identified as the inlet disturbance stage and eliminated. In the subsequent period, the average scattering intensity continues to change, but the rate of change gradually decreases, which can be classified as the diffusion redistribution stage. Further on, the change in average scattering intensity exhibits a relatively stable monotonic trend, which can be considered the adsorption response stage. When the change in average scattering intensity further tends to level off and becomes basically stable, it can be considered the adsorption equilibrium stage. Thus, the corresponding average scattering intensity change trend sequence for each stage can be obtained.

[0194] Finally, the average scattering intensity change trend value representing the net adsorption response can be obtained based on the average scattering intensity change trend sequence corresponding to each stage. Further, optionally, the average scattering intensity change trend sequences of the diffusion redistribution stage, adsorption response stage, and adsorption equilibrium stage can be statistically characterized separately, for example, by calculating the stage average change level, stage change rate, stage cumulative change, or equilibrium plateau value, and further extracting the average scattering intensity change trend value that most stably represents the true adsorption effect through weighted combination or stage screening.

[0195] For example, at a certain pressure level, the changes in average scattering intensity during the adsorption response and adsorption equilibrium stages can be used as the primary basis for the net adsorption response, while excluding the inlet disturbance stage and using the results of the diffusion redistribution stage as a secondary reference. The resulting trend value of average scattering intensity change can more effectively reflect the scattering changes caused by the actual adsorption process of the coal sample, rather than being dominated by short-term non-target disturbances.

[0196] In another specific test scenario, if the target adsorbed gas is methane, the change trend of the average scattering intensity may be relatively gradual, reflecting more the gradual adjustment process of the coal sample's pore interface and local adsorption state. If the target adsorbed gas is carbon dioxide, due to its strong adsorption capacity, the change trend of the average scattering intensity may be more pronounced, and the change is faster in the low-pressure region and the initial stage of adsorption. By constructing the average scattering intensity change trend sequence for different stages, not only can the accuracy of net adsorption response identification be improved, but the differences in the overall scattering response of coal samples under different adsorbed gas conditions can also be further compared.

[0197] Through step 107 above, this embodiment of the application can transform the intensity information in the original speckle pattern sequence into an average scattering intensity change result that reflects the overall trend of the coal sample adsorption process. Furthermore, through background subtraction, normalization, outlier removal, robust statistics, and stage separation, the stability and physical meaning of the average scattering intensity change trend are improved. This provides a more reliable data foundation for subsequent joint extraction of adsorption characteristic parameters based on the average scattering intensity change trend and speckle dynamic behavior.

[0198] Step 108: Based on the trend of the average scattering intensity change and the speckle dynamic behavior at different scattering scales, analyze the microstructure response and adsorption-induced dynamic changes of the coal sample during the adsorption process to obtain the adsorption characteristic parameters of the coal sample under corresponding temperature and pressure conditions.

[0199] The adsorption characteristic parameters include one or more of the following: adsorption equilibrium time, adsorption-induced relaxation time, adsorption structure fluctuation index, adsorption stability speckle contrast, equivalent adsorption characteristic value, adsorption pressure response coefficient, and temperature sensitivity coefficient. Specifically, the adsorption equilibrium time characterizes the time it takes for a coal sample to reach a near-steady state from the start of adsorption under given temperature and pressure conditions; the adsorption-induced relaxation time characterizes the characteristic timescale corresponding to the dynamic adjustment of the microstructure of the coal sample during the adsorption response stage; the adsorption structure fluctuation index characterizes the strength of the dynamic fluctuations and local fluctuations of the microstructure of the coal sample during adsorption; the adsorption stability speckle contrast characterizes the resolvability and stability of the speckle pattern corresponding to the adsorption equilibrium stage of the coal sample; the equivalent adsorption characteristic value characterizes the adsorption response intensity mapped from the trend of average scattering intensity change; the adsorption pressure response coefficient characterizes the sensitivity of the coal sample's adsorption behavior to pressure changes; and the temperature sensitivity coefficient characterizes the responsiveness of the coal sample's adsorption behavior to temperature changes.

[0200] As an optional embodiment, in step 108, the dynamic behavior of speckle under different scattering scales and the trend of average scattering intensity change are time-aligned, and a corresponding relationship is established according to the time segmentation in the adsorption test process. The joint features corresponding to the trend of average scattering intensity change and speckle dynamic behavior in the diffusion redistribution stage, adsorption response stage and adsorption equilibrium stage are extracted respectively. Based on the joint features, a coupled characterization relationship between the trend of average scattering intensity change and speckle dynamic behavior is established. According to the coupled characterization relationship, adsorption characteristic parameters for characterizing the adsorption process of coal samples are extracted.

[0201] In a specific example, the speckle dynamic behavior results obtained in step 106 correspond to multiple scattering scales, multiple time segments, and multiple analysis windows, while the average scattering intensity variation trend obtained in step 107 is also statistically analyzed according to time windows or stages. Since the two sets of data may have used different sampling densities, different window lengths, or different time starting points in their original processing, it is necessary to unify the two types of data onto the same time axis before joint analysis. Specifically, the center time of the time window in the average scattering intensity variation trend can be matched with the center time of the analysis window corresponding to the speckle dynamic behavior, or the two can be mapped to the same time segment label, thereby establishing a one-to-one correspondence. This time alignment method ensures that the subsequently extracted joint features truly reflect the structural response under the same adsorption stage and the same time interval.

[0202] Furthermore, after time alignment, the joint features corresponding to the average scattering intensity change trend and speckle dynamic behavior in the diffusion redistribution stage, adsorption response stage, and adsorption equilibrium stage can be extracted respectively.

[0203] For the diffusion and redistribution stage, the key features to be extracted include the rate of change of average scattering intensity, the trend of characteristic relaxation time, and the degree of dynamic coupling at small and medium scattering scales. These features can be used to characterize the migration and redistribution process of gas in local regions after entering the porous system.

[0204] For the adsorption response stage, the following combined features can be extracted: the amplitude of the average scattering intensity change, the fractional relaxation index change, the relaxation distribution width, the multi-scale mutual information results, and the speckle kinetic scaling index. These features can be used to characterize the degree of local structural adjustment and interfacial state change of the coal sample during the actual adsorption process.

[0205] For the adsorption equilibrium stage, the key features to be extracted are the plateau value of the average scattering intensity change, the stable value of the speckle contrast, the steady state of the characteristic relaxation time, and the degree of smoothness of the change in the fractal scattering dimension. These features are used to characterize the magnitude of the remaining dynamic changes and the degree of adsorption equilibrium after the system gradually stabilizes.

[0206] In this embodiment, based on the joint features, a coupled characterization relationship between the trend of average scattering intensity variation and speckle dynamic behavior can be established.

[0207] The trend of average scattering intensity variation can be regarded as a macroscopic characterization of the overall scattering response of the coal sample, while the speckle dynamic behavior at different scattering scales can be regarded as a microscopic characterization of the dynamic changes in the local structure of the coal sample. By jointly analyzing the variation patterns of the two within the same time segment, a coupling relationship between macroscopic scattering changes and microscopic dynamic responses can be established. For example, when the average scattering intensity changes rapidly, the characteristic relaxation time at small scattering scales shortens significantly, and the mutual information level at multiple scales increases, it can be considered that the coal sample has undergone a significant local rapid adsorption response at this stage; while when the change of average scattering intensity gradually tends to plateau, and the speckle dynamic behavior at each scattering scale also slows down and tends to stabilize, it can be considered that the coal sample is gradually approaching an adsorption equilibrium state.

[0208] In other embodiments, the aforementioned joint features can be input into a preset coupling characterization model, such as an empirical mapping relationship, a statistical regression relationship, or a rule-based decision relationship, to extract more stable adsorption characteristic parameters from the joint features. Based on the coupling characterization relationship, adsorption characteristic parameters for characterizing the adsorption process of coal samples can be further extracted.

[0209] In a specific example, the adsorption equilibrium time can be determined by judging whether at least some parameters in the trend of average scattering intensity change and speckle dynamic behavior simultaneously tend to stabilize within multiple consecutive time windows. In other words, if the change in average scattering intensity decreases significantly after a certain moment, and the characteristic relaxation time, speckle contrast, or kinetic parameters at multiple scattering scales also simultaneously enter the stable range, then that moment can be considered the adsorption equilibrium time. The adsorption-induced relaxation time can preferably be determined based on the characteristic relaxation time extracted from the target dynamic behavior model during the adsorption response stage, or based on the time scale where the coupling change between the trend of average scattering intensity change and speckle dynamic behavior is most significant. The adsorption structure fluctuation index can be comprehensively determined based on the fluctuation amplitude of speckle dynamic behavior during adsorption, the coupling strength between multiple scattering scales, and the degree of fluctuation of average scattering intensity change, and is used to characterize the strength of microstructure fluctuations in the coal sample during adsorption. The adsorption stable speckle contrast can be determined based on the stable speckle contrast value corresponding to the adsorption equilibrium stage, and is used to characterize the resolvability and stability of the speckle pattern in the stable adsorption state. The equivalent adsorption characteristic value can be obtained by combining the trend of average scattering intensity variation with baseline correction results and preset calibration relationships, thus mapping the scattering variation information into an adsorption characterization quantity that can be used to compare different coal samples or different test conditions. The adsorption pressure response coefficient can be determined based on the variation relationship of adsorption characteristic parameters corresponding to different pressure steps under the same temperature conditions, and is used to characterize the sensitivity of coal sample adsorption behavior to pressure changes. The temperature sensitivity coefficient can be determined based on the variation relationship of adsorption characteristic parameters under different temperature conditions, and is used to characterize the degree of response of coal sample adsorption behavior to temperature changes.

[0210] In a specific application scenario, a set of test data from a coal sample under methane adsorption conditions can be selected for illustration. For example, in the initial stage of adsorption, the average scattering intensity shows a rapid increase, while the characteristic relaxation times at small and medium scattering scales are significantly shortened. The speckle dynamic behavior indicates that the local pores and interface regions of the coal sample are responding rapidly. At this point, joint characteristic analysis can determine that the current stage is mainly driven by diffusion redistribution and rapid adsorption.

[0211] As the adsorption process continues, the trend of the average scattering intensity change gradually slows down. However, at medium scattering scales and some high scattering scales, obvious changes in relaxation distribution width and enhanced multi-scale mutual information coupling can still be observed. At this point, it can be considered that the coal sample is undergoing a strong adsorption-induced structural adjustment process, and the adsorption-induced relaxation time and adsorption structure fluctuation index can be extracted accordingly.

[0212] Furthermore, when the trend of average scattering intensity change gradually enters a plateau, the dynamic parameters at multiple scattering scales also tend to stabilize, and the speckle contrast remains at a relatively stable level, it can be determined that the coal sample is close to the adsorption equilibrium state, and the corresponding adsorption equilibrium time, adsorption stable speckle contrast, and equivalent adsorption characteristic value can be extracted.

[0213] If the above analysis is repeated on the coal sample under multiple pressure levels and multiple temperature conditions, the adsorption pressure response coefficient and temperature sensitivity coefficient can be further extracted to compare the adsorption characteristics of the coal sample under different environmental conditions.

[0214] Through step 108 described above, this embodiment of the application can integrate the trend of average scattering intensity variation and the dynamic behavior of multi-scale speckle patterns into a unified analytical framework, thereby obtaining more comprehensive and physically interpretable adsorption characteristic parameters than analytical methods that rely solely on a single scattering parameter or a single kinetic parameter. This not only improves the accuracy of extracting parameters such as adsorption equilibrium time, adsorption-induced relaxation time, and equivalent adsorption characteristic values, but also enhances the ability to identify the microstructural response and adsorption-induced dynamic changes during the adsorption process of coal samples.

[0215] Further optionally, the step of extracting adsorption characteristic parameters for characterizing the coal sample adsorption process based on the coupling characterization relationship includes: determining the adsorption equilibrium time based on the moment when at least two of the following parameters—characteristic relaxation time, average scattering intensity variation trend, and speckle dynamic behavior—reach a stable condition within multiple consecutive time windows; determining the adsorption-induced relaxation time based on the speckle dynamic behavior characteristic parameters corresponding to the adsorption response stage; determining the adsorption structure fluctuation index based on the fluctuation amplitude, evolution rate, and multi-scattering scale coupling variation characteristics of the speckle dynamic behavior during adsorption; determining the adsorption stable speckle contrast based on the speckle contrast corresponding to the adsorption equilibrium stage; determining the equivalent adsorption characteristic value based on the average scattering intensity variation trend combined with baseline correction results and a preset calibration relationship; determining the adsorption pressure response coefficient based on the correspondence between the adsorption characteristic parameters and pressure; and determining the temperature sensitivity coefficient based on the correspondence between the adsorption characteristic parameters and temperature.

[0216] Specifically, for test data of a coal sample under methane adsorption conditions, the characteristic relaxation time, speckle contrast, speckle dynamic behavior parameters, and average scattering intensity variation trends within multiple time windows can be analyzed first. Subsequently, these parameters are jointly analyzed in chronological order to extract adsorption characteristic parameters.

[0217] In extracting the adsorption equilibrium time, a multi-parameter synchronous stability determination method is preferred. Specifically, within multiple consecutive time windows, it can be determined whether at least two of the following parameters are simultaneously below their respective preset stability thresholds: the amplitude of change in characteristic relaxation time, the amplitude of change in the trend of change in average scattering intensity, and the speckle dynamic behavior characterization value. If the stability condition is met for multiple consecutive time windows, the moment when the stability condition is first met can be determined as the adsorption equilibrium time.

[0218] For example, in a specific test scenario, during the initial adsorption phase of a coal sample at a certain pressure level, the average scattering intensity shows a continuously increasing trend, and the characteristic relaxation time at small scattering scales is significantly shortened. Subsequently, after a period of time, the average scattering intensity gradually plateaus, and the changes in characteristic relaxation time and speckle dynamic behavior parameters also decrease significantly. When the system detects that the changes in both average scattering intensity and characteristic relaxation time are less than the preset stability thresholds within several consecutive time windows, it can be considered that the coal sample has reached adsorption equilibrium under the given temperature and pressure conditions, and the corresponding time is determined as the adsorption equilibrium time.

[0219] In the extraction of adsorption-induced relaxation time, it can be mainly determined using the characteristic parameters of speckle dynamic behavior during the adsorption response phase. Specifically, within the defined adsorption response phase, the characteristic relaxation time under the target dynamic behavior model can be selected as the main characterization value of adsorption-induced relaxation time.

[0220] For example, in a specific case, if the adsorption response stage at the medium scattering scale best reflects the local porosity and interfacial adsorption behavior of the coal sample, then the characteristic relaxation time obtained by fitting the target dynamic behavior model at this scattering scale can be selected as the adsorption-induced relaxation time of the coal sample under this condition. This parameter can be used to characterize the typical timescale experienced by the coal sample from the start of adsorption to the significant unfolding of the local structural response.

[0221] In extracting the adsorption structure fluctuation index, it can be determined by combining the fluctuation amplitude, evolution rate, and multi-scattering scale coupling characteristics of the speckle dynamic behavior. Specifically, it can be determined by comprehensively analyzing the fluctuation degree, rate of change, and mutual information level between different scattering scales of the speckle dynamic characteristic parameters within different time windows. If the multi-scale dynamic parameters fluctuate significantly and the coupling is enhanced within a certain time period, the internal structure fluctuation of the coal sample can be considered strong; if the parameter changes are gradual and the multi-scale coupling level decreases, the structural fluctuation can be considered weak.

[0222] For example, in the initial stage of adsorption, if the mutual information between the small and medium scattering scales increases significantly and the characteristic relaxation time and relaxation distribution width change drastically, a high adsorption structure fluctuation index can be determined, indicating that a strong dynamic response has occurred in the local pore and interface regions of the coal sample.

[0223] In the extraction of adsorption-stable speckle contrast, it can be determined based on the speckle contrast corresponding to the adsorption equilibrium stage. Specifically, the speckle contrast of multiple time windows can be statistically analyzed within the adsorption equilibrium stage, for example, by taking the steady-state average, steady-state median, or steady-state weighted average as the adsorption-stable speckle contrast.

[0224] For example, at a certain pressure level, if the speckle contrast remains within a relatively stable range over multiple consecutive time windows after the coal sample reaches adsorption equilibrium, this stable value can be defined as the adsorption-stable speckle contrast. This parameter reflects both the stability of the speckle pattern quality and, indirectly, the degree to which the dynamic changes in the microstructure of the coal sample are weakened under adsorption equilibrium conditions.

[0225] In the extraction of equivalent adsorption characteristic values, the values ​​can be determined based on the trend of average scattering intensity change, the baseline correction results, and the preset calibration relationship. Specifically, the trend of average scattering intensity change corresponding to the net adsorption response obtained in step 107 can be used first, and then corrected by combining the inert gas test results, the non-adsorption condition test results, or the sample loading baseline results. After that, the values ​​are substituted into the pre-established calibration relationship to obtain the corresponding equivalent adsorption characteristic values.

[0226] For example, in a specific case, the average scattering intensity change plateau value at a certain pressure level can be obtained through methane adsorption testing. Then, a reference change value under inert gas conditions is subtracted to eliminate the influence of non-adsorption factors. Finally, the corrected change value is converted into an equivalent adsorption characteristic value according to a preset calibration relationship. This characteristic value can be used to compare the differences in adsorption capacity between different coal samples or under different test conditions.

[0227] In the extraction of the adsorption pressure response coefficient, it can be determined based on the corresponding relationship between adsorption characteristic parameters and pressure changes. Specifically, under the same temperature conditions, the adsorption equilibrium time, adsorption-induced relaxation time, equivalent adsorption characteristic value, or adsorption structure fluctuation index corresponding to different pressure steps can be compared, and their response to pressure changes can be calculated.

[0228] For example, in a specific case, if the equivalent adsorption characteristic value increases continuously and significantly with increasing pressure, a higher adsorption pressure response coefficient can be determined accordingly; if a coal sample is particularly sensitive to pressure changes in the low-pressure region, while the increase slows down in the high-pressure region, the adsorption pressure response coefficient can also be given separately using a zoned calculation method.

[0229] In the extraction of temperature sensitivity coefficients, the correlation between adsorption characteristic parameters and temperature changes can be used for determination. Specifically, adsorption tests at the same pressure step can be repeated under different temperature conditions, and the changing trends of parameters such as equivalent adsorption characteristic values, adsorption equilibrium time, or adsorption-induced relaxation time can be compared.

[0230] For example, in a specific case, if the equivalent adsorption characteristic value of a coal sample decreases and the adsorption-induced relaxation time shortens at higher temperatures, then the coal sample can be considered to be more sensitive to temperature changes, and the corresponding temperature sensitivity coefficient can be determined accordingly.

[0231] Through the above methods, this embodiment can extract a variety of adsorption characteristic parameters with clear physical meaning from the speckle dynamic behavior and the trend of average scattering intensity variation, which can be used to characterize the adsorption process of coal samples from different dimensions.

[0232] Optionally, after obtaining the adsorption characteristic parameters of the coal sample under corresponding temperature and pressure conditions, the method further includes: normalizing one or more of the obtained adsorption equilibrium time, adsorption-induced relaxation time, adsorption structure fluctuation index, adsorption stability speckle contrast, equivalent adsorption characteristic value, adsorption pressure response coefficient, and temperature sensitivity coefficient; constructing a set of coal sample adsorption behavior characterization parameters based on the normalized adsorption characteristic parameters; comprehensively determining the adsorption state, adsorption rate, structural response intensity, and environmental sensitivity of the coal sample under corresponding temperature and pressure conditions based on the set of coal sample adsorption behavior characterization parameters; determining whether the coal sample has reached adsorption equilibrium based on the adsorption equilibrium time; determining the speed characteristics of the coal sample adsorption process based on the adsorption-induced relaxation time; determining the strength characteristics of the adsorption-induced microstructure response of the coal sample based on the adsorption structure fluctuation index and adsorption stability speckle contrast; determining the comprehensive adsorption capacity of the coal sample under corresponding temperature and pressure conditions based on the equivalent adsorption characteristic value; determining the response characteristics of the coal sample to pressure and temperature changes based on the adsorption pressure response coefficient and temperature sensitivity coefficient; and outputting the adsorption behavior characterization results of the coal sample.

[0233] Specifically, after the above adsorption characteristic parameters are extracted, these parameters can be normalized to reduce the impact of differences in the dimensions and numerical ranges of different parameters.

[0234] For example, the dimension of adsorption equilibrium time is time, while the dimension or range of equivalent adsorption characteristic values ​​may differ significantly from those of speckle contrast and temperature sensitivity coefficient. Direct comparison might lead to a dominant numerical value for one type of parameter. Therefore, methods such as min-maximum normalization, standard score normalization, or normalization based on a reference sample range can be used to map different parameters to a unified range. This makes the subsequently constructed set of adsorption behavior characterization parameters more effective in reflecting the relative contributions of different parameters.

[0235] After normalization, a set of parameters characterizing the adsorption behavior of coal samples can be constructed based on the normalized adsorption characteristic parameters. In a specific example, this parameter set may include at least one of the following: normalized adsorption equilibrium time, normalized adsorption-induced relaxation time, normalized adsorption structure fluctuation index, normalized adsorption stable speckle contrast, normalized equivalent adsorption characteristic value, normalized adsorption pressure response coefficient, and normalized temperature sensitivity coefficient.

[0236] Subsequently, based on this parameter set, the adsorption state, adsorption rate, structural response intensity, and environmental sensitivity of the coal sample under corresponding temperature and pressure conditions can be comprehensively determined. This comprehensive determination can employ rule-based determination methods, weighted scoring methods, classification mapping methods, or other analytical methods suitable for characterizing the adsorption behavior of coal samples.

[0237] For example, in one specific embodiment, the adsorption equilibrium time can be used to determine whether the coal sample has reached adsorption equilibrium: if the adsorption equilibrium time has occurred within the current test duration and meets the stability condition, it indicates that the coal sample has reached or is close to adsorption equilibrium under that condition; if no stability window has been detected in the current test stage, it indicates that the coal sample is still in a state of continuous adsorption or slow evolution. In an optional embodiment, the stability window can be set as the time interval corresponding to several consecutive speckle patterns. The stability condition can be set as follows: within the stability window, the rate of change of at least two parameters in the trend of characteristic relaxation time and average scattering intensity is lower than the corresponding threshold, and the actual temperature of the sample and the actual pressure of the sample area meet the stability threshold requirements. When multiple consecutive stability windows meet the above stability conditions, it is determined that the coal sample has reached adsorption equilibrium, and the moment when the stability condition is first met is determined as the adsorption equilibrium time.

[0238] The speed of the adsorption process in a coal sample can be determined by the adsorption-induced relaxation time: if the adsorption-induced relaxation time is short, it indicates that the local response of the coal sample is fast; if it is long, it indicates that the duration of the adsorption-induced dynamic change is long.

[0239] The strength of the adsorption-induced microstructural response of coal samples can be judged based on the adsorption structure fluctuation index and the contrast of adsorption stable speckle: if the fluctuation index is high and the speckle contrast changes significantly during the equilibrium stage, it indicates that the structural response of the coal sample is strong during adsorption; otherwise, it indicates that the structural change is relatively weak.

[0240] The comprehensive adsorption capacity of a coal sample under corresponding temperature and pressure conditions can be determined based on the equivalent adsorption characteristic value: if the characteristic value is high, it indicates that the coal sample exhibits a strong adsorption capacity under the current test conditions.

[0241] The response characteristics of a coal sample to pressure and temperature changes can be determined based on the adsorption pressure response coefficient and temperature sensitivity coefficient. For example, if the adsorption pressure response coefficient is high, it indicates that the adsorption behavior of the coal sample changes significantly between different pressure levels; if the temperature sensitivity coefficient is high, it indicates that the coal sample is more sensitive to temperature changes.

[0242] In a specific example, multiple adsorption characteristic parameters of a coal sample under methane adsorption conditions can be normalized to construct a corresponding set of adsorption behavior characterization parameters. Analysis results show that the coal sample exhibits a shorter adsorption-induced relaxation time and a higher adsorption structure fluctuation index in the low-pressure region, indicating a rapid and significant local structure response in the initial stage of adsorption. In the medium- and high-pressure region, its equivalent adsorption characteristic value continuously increases while the adsorption equilibrium time prolongs, indicating enhanced adsorption capacity but an increased time required to establish equilibrium. Simultaneously, the coal sample has a high adsorption pressure response coefficient and a moderate temperature sensitivity coefficient, indicating that its adsorption behavior is more sensitive to pressure changes. Therefore, the system can output the adsorption behavior characterization results of the coal sample under the current temperature and pressure conditions, such as descriptive results like "fast adsorption rate, significant local structure response, strong overall adsorption capacity, sensitive to pressure changes, and moderately sensitive to temperature changes," or output corresponding score values, grade results, or classification labels.

[0243] Through the above-described embodiments, this application can not only extract physically meaningful adsorption characteristic parameters from in-situ speckle pattern sequences, but also further process and comprehensively evaluate these parameters, ultimately outputting coal sample adsorption behavior characterization results for engineering applications. This complete analytical path from raw speckle dynamic data to adsorption behavior evaluation results helps improve the practicality, interpretability, and comparability of coal adsorption characteristic test results.

[0244] In this embodiment, by combining dynamic X-ray photon correlation spectroscopy testing with in-situ temperature and pressure control during coal sample adsorption, not only can the average scattering intensity change information during coal sample adsorption be obtained, but also the speckle dynamic behavior at different scattering scales can be extracted. This enables in-situ characterization of the microstructural response and adsorption-induced dynamic changes during coal adsorption. Simultaneously, by calibrating and correcting the actual sample temperature and actual pressure in the sample area, the consistency between the test conditions and the actual loading state of the coal sample can be improved, enhancing the accuracy of adsorption characteristic parameter calculations. Furthermore, by performing time correlation analysis and dynamic behavior fitting on the speckle pattern sequence, different stages such as inlet disturbance, diffusion redistribution, and adsorption response can be effectively distinguished, reducing the interference of diffusion hysteresis on adsorption kinetics determination, thereby improving the reliability and accuracy of coal adsorption characteristic test results.

[0245] The above describes a method for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station in the embodiments of this application. The following describes the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station that performs the above-described method for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station.

[0246] See Figure 2 ,like Figure 2 The diagram shows a structural schematic of a coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station. The coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station in this embodiment can achieve the above-mentioned... Figure 1 The steps of the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station are executed in the corresponding embodiments. The functions of the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and the modules can be software and / or hardware. The functional implementation of the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station can be referred to Figure 1 The operations performed in the corresponding embodiments are not described in detail here. The coal adsorption characteristic testing device based on X-ray photon correlation spectral line station includes at least the following structure: An experimental chamber, wherein a sample area is provided for containing coal samples; A gas path assembly, which is connected to the experimental chamber, is used to introduce the adsorbed gas to be tested into the experimental chamber and to adjust the gas flow rate and pressure. A vacuum assembly, which is connected to the experimental chamber, is used to perform vacuuming on the experimental chamber. A temperature monitoring component, which is used to collect experimental chamber wall temperature data and temperature data of the sample's vicinity; A pressure monitoring component, which is used to collect pressure data at the intake control end and pressure data in the sample area near the cavity; A data processing module is connected to the gas path assembly, the vacuum assembly, the temperature monitoring assembly, and the pressure monitoring assembly, and is adapted for communication with the detector of the X-ray photon correlation spectral line station; the data processing module is configured to perform... Figure 1 The method for testing coal adsorption characteristics based on X-ray photon correlation spectral line stations described in the corresponding embodiment.

[0247] The data processing module can be implemented by software programs or hardware modules installed in various devices, and this application is not limited to this. The data processing module is connected to the gas path component, the vacuum component, the temperature monitoring component, and the pressure monitoring component, respectively, and the connection method can be wired, wireless, or a combination of wired and wireless. This application does not limit the specific connection method, as long as it can realize data interaction and control command transmission.

[0248] In an optional embodiment, the data processing module can be connected to the gas path assembly and the vacuum assembly via a control interface. This control interface is used to output gas flow control commands, gas pressure control commands, valve opening and closing control commands, and compensation gas intake control commands to the gas path assembly, and to output vacuum start / stop control commands and vacuum intensity control commands to the vacuum assembly. The control interface can be one or more of a serial communication interface, an industrial fieldbus interface, or an Ethernet communication interface.

[0249] In an optional embodiment, the data processing module can be connected to the temperature monitoring component and the pressure monitoring component via a data acquisition interface to receive experimental chamber wall temperature data, sample proximity temperature data, inlet control pressure data, and pressure data in the sample-proximity region of the chamber. The data acquisition interface can be an analog or digital interface. The analog interface can receive analog signals from thermocouples, resistance temperature detectors (RTDs), and pressure sensors, while the digital interface can receive temperature and pressure data with communication protocols. The data processing module can timestamp the received temperature and pressure data and synchronize it with the acquisition time of the speckle pattern sequence.

[0250] In an optional embodiment, the data processing module is adapted to communicate with the detector of an X-ray photon correlation spectral line station. It receives speckle pattern data streams output by the detector and associates and stores the speckle pattern data with the actual sample temperature, actual sample area pressure, and incident X-ray intensity at the corresponding acquisition time. The connection between the data processing module and the detector can be an Ethernet connection, a fiber optic connection, or other high-speed data transmission connection to meet the data throughput requirements for continuous acquisition of speckle pattern sequences.

[0251] In an optional embodiment, the data processing module can also be connected to a sample position monitoring unit, an incident X-ray intensity monitoring unit, or other auxiliary monitoring units to receive sample position drift data and incident X-ray intensity fluctuation data, and to perform start / stop control or quality control on the speckle pattern sequence acquisition process based on preset acquisition conditions. For example, when the sample position drift exceeds a preset displacement threshold, the incident X-ray intensity fluctuation exceeds a preset light intensity fluctuation threshold, or the actual sample temperature or actual pressure in the sample area exceeds the corresponding preset range, the data processing module can control the pause of the current speckle pattern sequence acquisition, and resume or restart acquisition after the data returns to the preset range, in order to avoid invalid or interfered data from entering the subsequent analysis process.

[0252] In an optional embodiment, the data processing module may include a processor, a memory, and a communication interface. The processor executes control logic and data analysis logic, the memory stores speckle pattern sequences, temperature and pressure monitoring data, and intermediate calculation results, and the communication interface establishes communication connections with the gas path assembly, vacuum assembly, temperature monitoring assembly, pressure monitoring assembly, and detector. Through these connections, the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station can achieve communication with… Figure 1 The same functions and steps as in the corresponding embodiments are used to complete the coal adsorption characteristic test and the extraction of adsorption characteristic parameters.

[0253] In this embodiment, the coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station is used to provide controlled temperature, controlled pressure, and controlled atmosphere conditions for the coal sample, and to cooperate with the X-ray photon correlation spectral line station to complete speckle pattern sequence acquisition and subsequent adsorption characteristic parameter analysis during the coal sample adsorption process. The experimental chamber is used to contain the coal sample and form a sample area suitable for coherent X-ray transmission or scattering tests, so as to ensure that the coal sample remains stable in position during vacuum degassing, gas introduction, and temperature and pressure loading, and to meet the requirements of in-situ X-ray irradiation and speckle signal acquisition. The gas path component is used to introduce methane, carbon dioxide, or other adsorbable gases to be tested into the experimental chamber, and to adjust the gas flow rate and pressure during the adsorption test to achieve isothermal and isobaric or staged isothermal and isobaric adsorption conditions. The vacuum component is used to evacuate the experimental chamber before the test, thereby performing vacuum degassing pretreatment on the coal sample and establishing the baseline state after sample loading.

[0254] In this embodiment, the temperature monitoring component is used to collect experimental chamber wall temperature data and sample proximity temperature data, and works with the data processing module to establish a correction relationship between the set temperature and the actual sample temperature, thereby improving the consistency between the experimentally recorded temperature and the actual heating state of the coal sample. The pressure monitoring component is used to collect inlet control pressure data and pressure data near the sample area in the chamber, and works with the data processing module to establish a correction relationship for the actual pressure in the sample area, thereby reducing the influence of factors such as gas path pressure drop, dead volume, and pressure decay during adsorption on the actual pressure state of the sample. In some embodiments, the data processing module can further perform compensated inlet control based on the pressure monitoring results to maintain the actual pressure in the sample area within a preset pressure tolerance range, and initiate effective acquisition of speckle pattern sequences after the temperature and pressure meet the stability criteria.

[0255] In this embodiment, the data processing module is adapted to communicate with the detector of an X-ray photon correlation spectral line station. It receives and stores speckle pattern data of coal samples during adsorption, constructs a speckle pattern sequence, and performs abnormal frame identification and removal, time segmentation, correlation function calculation, speckle dynamic behavior fitting, extraction of average scattering intensity change trends, and calculation of adsorption characteristic parameters. These adsorption characteristic parameters may include one or more of the following: adsorption equilibrium time, adsorption-induced relaxation time, adsorption structure fluctuation index, adsorption stable speckle contrast, equivalent adsorption characteristic value, adsorption pressure response coefficient, and temperature sensitivity coefficient. Through the above structural and functional configuration, this embodiment can acquire dynamic speckle information of the coal sample adsorption process under in-situ controlled temperature and pressure conditions, and realize the analysis of the microstructure response and adsorption-induced dynamic changes during coal sample adsorption, thereby improving the accuracy, reliability, and interpretability of coal adsorption characteristic test results.

[0256] The coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station in this application embodiment has been described above from the perspective of modular functional entities. The coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station in this application embodiment will be described below from the perspective of hardware processing.

[0257] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0258] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0259] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0260] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0261] Furthermore, 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. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0262] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0263] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0264] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for testing the adsorption characteristics of coal based on an X-ray photon correlation spectral line station, characterized in that, The method includes: The coal sample is loaded into the sample area of ​​the experimental chamber for coherent X-ray transmission or scattering testing. The coal sample is then subjected to vacuum degassing pretreatment, and a reference speckle pattern and reference scattering intensity are collected after loading. Temperature and pressure calibrations were performed on the experimental chamber. The target adsorption gas is introduced into the experimental chamber at a preset temperature and pressure, so that the coal sample undergoes adsorption under constant temperature and pressure or staged constant temperature and pressure conditions; during the adsorption process, the speckle pattern sequence of the coal sample is continuously acquired by an X-ray photon correlation spectral line station. The correlation function of speckle intensity changing with time is calculated based on the speckle pattern sequence; the corresponding speckle dynamic behavior is fitted based on the attenuation characteristics of the correlation function at different scattering scales. Based on the speckle pattern sequence, the average scattering intensity of the preset scattering region during the adsorption process is statistically obtained, and the average scattering intensity is compared with the reference scattering intensity to obtain the trend of the average scattering intensity. Based on the trend of average scattering intensity variation and speckle dynamic behavior at different scattering scales, the microstructural response and adsorption-induced dynamic changes of coal samples during the adsorption process are analyzed to obtain the adsorption characteristic parameters of coal samples under corresponding temperature and pressure conditions.

2. The method for testing coal adsorption characteristics based on an X-ray photon correlation spectral line station according to claim 1, characterized in that, The temperature and pressure calibration performed on the experimental chamber includes: Collect experimental chamber wall temperature data and temperature data of locations near the sample; Based on the collected experimental chamber wall temperature data and sample adjacent temperature data, a correction relationship between the set temperature and the actual sample temperature is established. Based on the aforementioned correction relationship, the actual temperature of the sample during the experiment is converted or corrected. Collect pressure data at the intake control end and pressure data in the sample area near the chamber; Based on the collected pressure data at the intake control end and the pressure data near the sample area in the cavity, a correction relationship for the actual pressure in the sample area is established. Based on the actual pressure correction relationship of the sample area, the actual pressure of the sample area during the experiment is converted or corrected.

3. The method for testing coal adsorption characteristics based on X-ray photon correlation spectral lines according to claim 2, characterized in that, The establishment of the correction relationship between the set temperature and the actual temperature of the sample includes: By using at least one of cavity calibration, standard sample calibration, or sample loading state calibration, cavity wall temperature data, sample adjacent location temperature data, and sample actual temperature corresponding data under different set temperature conditions are obtained respectively. Based on the obtained cavity wall temperature data, sample adjacent temperature data, and sample actual temperature data, a model is established to establish the correspondence between the set temperature and the sample actual temperature. During subsequent adsorption tests, the temperature of the chamber wall and the temperature of the sample's adjacent location are collected, and the actual temperature of the sample is converted or corrected according to the corresponding relationship model. During the heating process, the sample area and the air intake path are controlled in a coordinated manner to reduce the temperature gradient in the sample area and avoid the generation of cold spots. The actual temperature fluctuation of the sample is monitored within a continuously preset time period. When the actual temperature fluctuation of the sample is not higher than the preset temperature stability threshold, the gas to be tested is introduced and the speckle pattern sequence is acquired.

4. The method for testing coal adsorption characteristics based on an X-ray photon correlation spectral line station according to claim 2, characterized in that, The establishment of the actual pressure correction relationship in the sample area includes: Pressure data at the intake control end and pressure data in the sample area of ​​the chamber were collected separately. Statistical analysis of the volume parameters corresponding to the empty space of pipelines, valve bodies, connecting sections, and experimental chambers; Establish a dead volume correction relationship based on the volume parameters; By combining the test temperature, the pressure data at the air intake control end, and the pressure data near the sample area in the cavity, the actual pressure in the sample area is converted or corrected. During the adsorption test, the actual pressure in the sample area is continuously monitored, and compensating air intake is performed based on the deviation between the actual pressure in the sample area and the preset pressure. The actual pressure in the sample area is kept within the preset pressure tolerance range, and the fluctuation of the actual pressure in the sample area is monitored for a continuous preset time. When the fluctuation amplitude of the actual pressure in the sample area is not higher than the preset pressure stability threshold, the effective speckle pattern sequence under the corresponding conditions is collected.

5. The method for testing coal adsorption characteristics based on X-ray photon correlation spectral lines according to claim 1, characterized in that, The step of introducing the target adsorption gas into the experimental chamber at a preset temperature and pressure, so that the coal sample undergoes adsorption under constant temperature and pressure or staged constant temperature and pressure conditions, includes: Establish a single-stage isothermal and isothermal adsorption mode or a multi-pressure step-level isothermal and isothermal adsorption mode according to the preset test plan. After completing temperature and pressure calibration, adjust the temperature and pressure inside the experimental chamber to the initial test conditions. The single-stage isothermal and isobaric adsorption mode or the multi-pressure step-level isothermal and isobaric adsorption mode is adopted. The gas to be adsorbed is introduced into the experimental chamber, and the flow rate and rate of the gas to be adsorbed are controlled so that the actual pressure in the sample area gradually reaches the corresponding preset pressure. After the actual pressure in the sample area reaches the corresponding preset pressure, the actual temperature and actual pressure of the sample area are kept stable, so that the coal sample can be adsorbed under the corresponding temperature and pressure conditions. During the adsorption process, the speckle pattern sequence of the coal sample is continuously acquired by an X-ray photon correlation spectral line station, including: During the stable holding phase corresponding to each preset pressure, the speckle pattern sequence of the coal sample was continuously acquired by the X-ray photon correlation spectral line station. When using the graded isothermal and isobaric adsorption mode, after completing the acquisition of the speckle pattern sequence under the current pressure step, the actual pressure of the sample area is adjusted to the next preset pressure, and the steps of maintaining the actual temperature and actual pressure of the sample area stable, allowing the coal sample to adsorb, and continuously acquiring the speckle pattern sequence of the coal sample through the X-ray photon correlation spectral line station are repeated. Until all the adsorption tests under the preset pressure conditions are completed, the speckle pattern sequence of coal samples under different temperature and pressure conditions is obtained.

6. The method for testing coal adsorption characteristics based on an X-ray photon correlation spectral line station according to claim 5, characterized in that, The step of continuously acquiring speckle pattern sequences of coal samples through an X-ray photon correlation spectral line station during the stable holding phase corresponding to each preset pressure includes: After entering the stable holding phase corresponding to the current preset pressure, the actual temperature of the sample, the actual pressure of the sample area, the sample position status, and the incident X-ray intensity status are continuously monitored. If the preset acquisition conditions are met, the speckle pattern sequence will be continuously acquired under the current preset pressure conditions. The preset acquisition conditions include at least two of the following: the actual temperature fluctuation of the sample is not higher than the preset temperature stability threshold, the actual pressure fluctuation of the sample area is not higher than the preset pressure stability threshold, the sample position drift is not higher than the preset displacement threshold, and the incident X-ray intensity fluctuation is not higher than the preset light intensity fluctuation threshold. During continuous acquisition, the coal sample is continuously sampled according to the preset sampling time interval and preset exposure time, and the acquisition time, current sample actual temperature and current sample area actual pressure corresponding to each speckle pattern are recorded. The speckle contrast is calculated and updated in real time to determine whether the speckle contrast has reached a preset lower threshold. If the speckle contrast is lower than the preset lower threshold, the continuous acquisition of the speckle pattern sequence is stopped. Monitor the actual temperature of the sample, the actual pressure of the sample area, the sample position and the intensity of the incident X-rays. If any of the monitored items exceeds the corresponding preset range, the continuous acquisition of the speckle pattern sequence will be stopped. After completing continuous data acquisition during the stable holding phase corresponding to the current preset pressure, the acquired speckle patterns are constructed into a speckle pattern sequence under the current preset pressure in chronological order.

7. The method for testing coal adsorption characteristics based on X-ray photon correlation spectral lines according to claim 1, characterized in that, The step of calculating the correlation function of speckle intensity changing with time based on the speckle pattern sequence includes: The continuously acquired speckle pattern sequence is sorted according to the acquisition time, and abnormal frames, invalid frames or defective frames in the speckle pattern sequence are identified and removed. Select a target pixel region for time correlation analysis within a preset scattering region, and read the speckle intensity of each speckle pattern within the target pixel region; Based on the time series of speckle intensity and the time series of changes in actual sample temperature and actual sample area pressure, a change point detection method is used to identify the signal abrupt intervals during the adsorption test, and the speckle pattern sequence is divided into multiple time segments. Within each time segment, speckle images acquired at different times are paired according to a preset multi-resolution time delay sequence, and the correlation of speckle intensity of each paired speckle image within the target pixel region is calculated; wherein, the shorter the time delay interval within each time segment, the higher the sampling density is used. The correlation of speckle intensity in each paired speckle pattern within the corresponding target pixel region is calculated. Then, a weighted statistical method based on quality index is used to statistically average the correlation of speckle intensity in each paired speckle pattern under the same time delay, and the correlation results under the corresponding time delay are obtained. Jump to the step of pairing speckle images collected at different times according to a preset multi-resolution time delay sequence within each time segment, and iterate cyclically according to multiple preset time delays in the multi-resolution time delay sequence to obtain the segment correlation function of speckle intensity changing with time. By summarizing the piecewise correlation functions at different scattering scales, the corresponding correlation functions at different scattering scales are obtained.

8. The method for testing coal adsorption characteristics based on X-ray photon correlation spectral lines according to claim 1, characterized in that, The step of fitting the corresponding speckle dynamic behavior based on the attenuation characteristics of the correlation function at different scattering scales includes: Within each of the aforementioned time segments, the decay curves of the correlation function as a function of time delay under different scattering scales are extracted respectively; For each of the aforementioned decay curves, at least one candidate dynamic behavior model is established, which includes single relaxation behavior, distributed relaxation behavior, and multi-stage coupled relaxation behavior. A Bayesian variational inference algorithm is used to adaptively fit each candidate dynamic behavior model, and the target dynamic behavior model that matches the current time segment and the current scattering scale is determined based on the fitting residual, parameter continuity and the consistency of dynamic evolution between adjacent time segments. Based on the target dynamic behavior model, speckle dynamic feature parameters are extracted to describe the current time segmented speckle dynamic behavior; wherein, the speckle dynamic feature parameters include feature relaxation time, fractional relaxation exponent, relaxation distribution width, speckle dynamic scaling exponent, and fractal scattering dimension. Multi-scale mutual information correlation is performed on the speckle dynamic characteristic parameters at different scattering scales to obtain the speckle dynamic behavior of coal samples in the current time segment.

9. The method for testing coal adsorption characteristics based on an X-ray photon correlation spectral line station according to claim 8, characterized in that, For each of the aforementioned decay curves, at least one candidate dynamic behavior model is established, including single relaxation behavior, distributed relaxation behavior, and multi-stage coupled relaxation behavior, comprising: For a single relaxation behavior, a fractional-order Cauchy relaxation model is constructed to describe the slow relaxation characteristics in the heterogeneous pores of the coal matrix using fractional-order differential operators. For distributed relaxation behavior, a log-normal distribution is used to construct the distributed relaxation probability density function to adapt to the non-equilibrium, long-range correlated scattering dynamics characteristics in the coal adsorption process. For multi-stage coupled relaxation behavior, the relaxation process is divided into three sub-modes: gas diffusion relaxation, matrix swelling relaxation, and adsorption equilibrium relaxation. A hidden Markov mode coupling model is established, and the temporal switching law of different relaxation behaviors is described by the mode transition probability matrix. By embedding single relaxation, distributed relaxation, and multi-stage coupled relaxation models into a unified multi-scale scattering kernel function, a set of candidate dynamic behavior models covering the entire scattering scale is formed.

10. A coal adsorption characteristic testing device based on an X-ray photon correlation spectral line station, characterized in that, The device includes: An experimental chamber, wherein a sample area is provided for containing coal samples; A gas path assembly, which is connected to the experimental chamber, is used to introduce the adsorbed gas to be tested into the experimental chamber and to adjust the gas flow rate and pressure. A vacuum assembly, which is connected to the experimental chamber, is used to perform vacuuming on the experimental chamber. A temperature monitoring component, which is used to collect experimental chamber wall temperature data and temperature data of the sample's vicinity; A pressure monitoring component, which is used to collect pressure data at the intake control end and pressure data in the sample area near the cavity; The data processing module is connected to the gas path assembly, the vacuum assembly, the temperature monitoring assembly and the pressure monitoring assembly respectively, and is adapted to communicate with the detector of the X-ray photon correlation spectral line station. The data processing module is configured to perform the coal adsorption characteristic testing method based on an X-ray photon correlation spectral line station as described in any one of claims 1 to 9.

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