A remote online tritium monitoring system

Abstract

This invention relates to the field of nuclear radiation monitoring technology, specifically a remote online tritium monitoring system, comprising: a data acquisition module for synchronously acquiring end-point tritium concentration, pipeline environmental parameters, and background radiation data; a coupling model construction module for constructing a coupled model of gas-solid interface adsorption kinetics and fluid transport, and establishing a dynamic transfer function; a source term inversion and reconstruction module for performing deconvolution operations to inversely deduce the true tritium concentration at the source; a state assessment and threshold adjustment module for real-time calculation of pipeline adsorption saturation and dynamic adjustment of alarm thresholds; an early warning generation module for generating early leak warning commands; and an operation and maintenance prediction module for identifying pipeline maintenance needs based on the trend of adsorption performance degradation. This invention overcomes the defects of signal lag and peak clipping, achieving a second-level response to sudden pulse leaks.

Description

Technical Field

[0001] This invention relates to the field of nuclear radiation monitoring technology, specifically a remote online tritium monitoring system. Background Technology

[0002] In the application scenario of radiation safety monitoring of nuclear facilities, remote monitoring systems rely on long-distance sampling pipelines to transmit radioactive gases to safe areas for analysis, in order to ensure the physical isolation of monitoring equipment from high-radiation environments. The monitoring host usually needs to combine terminal detection data and fluid transmission parameters to perceive the radiation status of sampling points in real time.

[0003] For acquiring tritium concentration data, existing solutions generally adopt a direct end-point measurement architecture. This involves pumping gas to a tritium monitor located at the end of the pipeline using a sampling pump, collecting ionization signals using an ionization chamber or proportional counter, and directly using the concentration value measured at the end as the true value at the source to execute alarm logic. Although this solution is feasible to some extent in short-distance sampling or steady-state emission environments, it relies too heavily on the raw data from a single end-point detection point and lacks decoupling analysis of the adsorption kinetics at the gas-solid interface. When encountering long-distance transmission and dynamic environments with drastic temperature and humidity fluctuations, the physical adsorption of tritium on the inner wall of the sampling pipeline will produce a significant memory effect and physical hysteresis, resulting in severe peak clipping and time delay in the end-point signal compared to the true signal at the source.

[0004] Furthermore, solutions relying solely on hardware detection lack the ability to adaptively compensate for changes in environmental background and pipeline adsorption saturation. They are also unable to eliminate false positive readings caused by cosmic ray interference or pipeline desorption, leading to the easy occurrence of missed reports of sudden pulse leaks or false reports of background fluctuations during the monitoring process. This makes it difficult to support the system's highly sensitive, second-level response for accurate source term reconstruction. Therefore, how to establish a monitoring mechanism with physical model inversion capabilities to effectively compensate for pipeline transmission distortion while improving the real-time performance and reliability of remote tritium online monitoring has become an urgent technical problem to be solved. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a remote online tritium monitoring system. Specifically, the technical solution of the present invention includes:

[0006] The data acquisition module is used to simultaneously acquire fluid state and radiation field at the end of the sampling pipeline, and obtain end tritium concentration data, environmental parameters along the pipeline, and background radiation data.

[0007] The coupled model construction module is used to construct a coupled model of gas-solid interface adsorption kinetics and fluid transport based on pipeline environmental parameters, and to establish the dynamic transport transfer function of the sampling pipeline. The source term inversion and reconstruction module is used to take the terminal tritium concentration data as the observation value, input it into the coupled model of gas-solid interface adsorption kinetics and fluid transport to solve the inverse problem, perform deconvolution operation, and reverse-engineer the true tritium concentration at the source end of the sampling pipeline inlet.

[0008] The state assessment and threshold adjustment module is used to calculate the pipeline adsorption saturation in real time based on the gas-solid interface adsorption kinetics and fluid transport coupling model, and to dynamically adjust the alarm threshold based on the pipeline adsorption saturation.

[0009] The early warning generation module is used to compare the actual tritium concentration at the source with the alarm threshold and generate an early leak warning command.

[0010] The operation and maintenance prediction module is used to perform long-term evolution analysis on the lag deviation characteristics between the actual tritium concentration at the source and the tritium concentration at the end, capture the adsorption performance degradation trend of the sampling pipeline, and identify pipeline maintenance needs based on the adsorption performance degradation trend.

[0011] Preferably, the method for obtaining terminal tritium concentration data includes: acquiring the raw pulse signal output by the detector in real time; acquiring ambient background radiation data and constructing a dynamic background subtraction model based on the ambient background radiation data; using the dynamic background subtraction model to perform energy spectrum analysis and background stripping on the raw pulse signal to extract the net tritium count rate; and generating terminal tritium concentration data based on the net tritium count rate and the detector calibration factor.

[0012] Preferably, the method for constructing a coupled model of gas-solid interface adsorption kinetics and fluid transport includes: obtaining pipe wall temperature and sample gas humidity from the environmental parameters along the pipeline; determining the adsorption rate constant and desorption rate constant of the inner wall of the sampling pipeline based on the pipe wall temperature and sample gas humidity; constructing a mass conservation equation including convection, diffusion, and source-sink terms, substituting the adsorption rate constant and desorption rate constant into the source-sink terms to describe the mass exchange process of tritium on the gas and solid phase surfaces; introducing the pipeline adsorption coverage variable and describing the time evolution of pipeline adsorption coverage based on the Langmuir adsorption isotherm theory; and simultaneously solving the mass conservation equation and the time evolution equation of pipeline adsorption coverage to generate a coupled model of gas-solid interface adsorption kinetics and fluid transport.

[0013] Preferably, the method for performing deconvolution includes: discretizing the gas-solid interface adsorption kinetics and fluid transport coupling model into a state-space equation; defining the source-end true tritium concentration as the system state vector and the terminal tritium concentration data as the observation vector; initializing the covariance matrix of the system state vector; performing a time update step to predict the prior state estimate at the current time based on the system state vector at the previous time; performing a measurement update step to calculate the Kalman gain, correcting the prior state estimate using the terminal tritium concentration data at the current time, and obtaining the posterior state estimate; and outputting the posterior state estimate as the source-end true tritium concentration at the current time, thus completing the deconvolution operation.

[0014] Preferably, the method for dynamically adjusting the alarm threshold based on pipeline adsorption saturation includes: setting a preset baseline alarm threshold and an adsorption saturation determination threshold; obtaining the current pipeline adsorption saturation; comparing the pipeline adsorption saturation with the adsorption saturation determination threshold; if the pipeline adsorption saturation is less than the adsorption saturation determination threshold, determining that the sampling pipeline is in a strong adsorption effect state, calling a preset downward correction coefficient to multiply and reduce the baseline alarm threshold, and calculating the alarm threshold; if the pipeline adsorption saturation is greater than or equal to the adsorption saturation determination threshold, determining that the sampling pipeline is in a penetration state, and directly setting the baseline alarm threshold as the alarm threshold.

[0015] Preferably, the method for generating an early leakage warning command includes: obtaining the current actual tritium concentration at the source and an alarm threshold; determining whether the actual tritium concentration at the source is greater than or equal to the alarm threshold; if the actual tritium concentration at the source is greater than or equal to the alarm threshold, generating a first-level leakage warning command and triggering an audible and visual alarm; if the actual tritium concentration at the source is less than the alarm threshold, calculating the rate of change of the actual tritium concentration at the source and comparing the rate of change with a preset abrupt change threshold; if the rate of change is greater than the preset abrupt change threshold, generating a trend warning command; if the rate of change is less than or equal to the preset abrupt change threshold, determining that the system is in a safe state and not generating a warning command.

[0016] Preferably, the method for capturing the adsorption performance degradation trend of the sampling pipeline includes: selecting multiple pulse leakage events that have occurred in history, and obtaining the source-end true tritium concentration curve and the end tritium concentration data curve corresponding to each event; extracting the peak time of the source-end true tritium concentration curve and the peak time of the end tritium concentration data curve, and calculating the time difference between the two as the transport lag time; extracting the falling edge feature of the end tritium concentration data curve and calculating the tail decay constant; constructing a feature vector sequence containing the transport lag time and the tail decay constant; performing sliding window trend analysis on the feature vector sequence, and if the transport lag time shows a monotonically increasing trend and the tail decay constant shows a monotonically decreasing trend, then it is determined that there is an adsorption performance degradation trend; otherwise, it is determined that there is no adsorption performance degradation trend.

[0017] Preferably, the method for identifying pipeline maintenance needs based on the trend of adsorption performance degradation includes: establishing a linear regression model of the trend of adsorption performance degradation to predict the predicted value of the transmission lag time within a preset time window; setting a preset pipeline failure threshold; comparing the predicted value of the transmission lag time with the pipeline failure threshold; if the predicted value of the transmission lag time is greater than or equal to the pipeline failure threshold, generating a pipeline cleaning or replacement recommendation instruction; if the predicted value of the transmission lag time is less than the pipeline failure threshold, generating a pipeline health status confirmation instruction.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. This invention effectively solves the monitoring distortion problem caused by memory effect and physical lag in long-distance sampling pipelines by constructing a coupled model of gas-solid interface adsorption kinetics and fluid transport and a deconvolution operation mechanism. Unlike traditional schemes that rely solely on direct measurement at the end, this scheme uses the extended Kalman filter algorithm to solve the inverse problem. By setting the process noise covariance of source-end relaxation and along-the-path constraint, the filter is forced to project the end observation residuals inversely to the source end. Thus, even when physically far from the source term, the true source-end tritium concentration at the inlet can be inversely deduced from the smoothed and lagging end signal, achieving a second-level response to sudden pulse leakage and accurate restoration of source term intensity.

[0020] 2. This invention introduces a multidimensional dynamic background subtraction and non-negative truncation strategy, which significantly improves the extraction purity of weak tritium signals under complex radiation environments. By constructing a multiple linear regression model that includes external gamma dose rate and high-energy cosmic ray accompanying counts, this method can quantify and remove false positive counts caused by environmental background and electromagnetic interference in real time. Combined with non-negative truncation processing, it eliminates non-physical negative values ​​caused by statistical fluctuations, ensuring the statistical significance of monitoring data in low-level radioactivity measurements and effectively avoiding false alarms caused by environmental radiation fluctuations.

[0021] 3. This invention establishes a dynamic threshold adjustment and humidity correction mechanism based on pipeline adsorption saturation, achieving an adaptive balance between monitoring sensitivity and false alarm rate. The system automatically reduces the alarm threshold to compensate for signal attenuation under strong adsorption effect based on the real-time calculated pipe wall adsorption coverage rate, and maintains the baseline threshold under penetration effect. At the same time, a humidity correction function is introduced to accurately describe the competitive inhibition effect of water molecules on adsorption sites, thereby ensuring that no minor leaks are missed while preventing false alarms caused by threshold jumps due to changes in environmental temperature and humidity or pipeline penetration effect.

[0022] 4. This invention constructs a dual early warning and defense system and a feature evolution-based operation and maintenance prediction mechanism, endowing the system with full life cycle health management capabilities. By combining the absolute value of the source concentration and the threshold of the rate of change mutation, the system can capture abnormal trends in the very early stage of leakage. At the same time, by using a sliding window to analyze the transmission lag time and tail decay constant of historical pulse events, the system identifies the degradation trend of pipeline adsorption performance and establishes a linear regression model to predict the remaining service life, realizing the transformation from passive post-event maintenance to proactive predictive maintenance and avoiding monitoring blind spots caused by pipeline performance failure. Attached Figure Description

[0023] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0024] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0026] Example 1:

[0027] Please see Figure 1 A remote online tritium monitoring system, comprising:

[0028] The data acquisition module is used to simultaneously acquire fluid state and radiation field at the end of the sampling pipeline, and obtain end tritium concentration data, environmental parameters along the pipeline, and background radiation data.

[0029] The coupled model construction module is used to construct a coupled model of gas-solid interface adsorption kinetics and fluid transport based on pipeline-side environmental parameters, and to establish the dynamic transport transfer function of the sampling pipeline.

[0030] The source term inversion and reconstruction module is used to take the terminal tritium concentration data as the observation value, input it into the gas-solid interface adsorption kinetics and fluid transport coupling model to solve the inverse problem, perform deconvolution operation, and reverse the inversion to deduce the true tritium concentration at the source end of the sampling pipeline inlet;

[0031] The state assessment and threshold adjustment module is used to calculate the pipeline adsorption saturation in real time based on the gas-solid interface adsorption kinetics and fluid transport coupling model, and to dynamically adjust the alarm threshold based on the pipeline adsorption saturation.

[0032] The early warning generation module is used to compare the actual tritium concentration at the source with the alarm threshold and generate an early leak warning command.

[0033] The operation and maintenance prediction module is used to perform long-term evolution analysis on the lag deviation characteristics between the actual tritium concentration at the source and the tritium concentration at the end, capture the adsorption performance degradation trend of the sampling pipeline, and identify pipeline maintenance needs based on the adsorption performance degradation trend.

[0034] This embodiment provides a remote online tritium monitoring system designed to address monitoring distortion caused by memory effects and physical hysteresis in long-distance sampling pipelines. The system constructs a digital twin model, treating the pipeline as a dynamic, solvable operator, thus achieving logical zero-distance monitoring even when physically far from the source term. The system activates a data acquisition module to provide full-dimensional physical boundary conditions for subsequent mathematical inversion. This module connects not only to the tritium monitor located at the pipeline end (a general term for macroscopic monitoring devices, with its core signal pickup component corresponding to the detector in this embodiment and subsequent embodiments), but also integrates temperature sensors, humidity sensors, and flow meters installed along the sampling pipeline. The coupled model construction module, based on fluid mechanics and surface chemistry theories, dynamically generates a gas-solid interface adsorption kinetics and fluid transport coupled model describing tritium transport and adsorption behavior within the pipe and on the pipe wall using real-time acquired environmental parameters. This model is essentially a time-varying transfer function that changes with environmental parameters.

[0035] Based on this, the source term inversion and reconstruction module, as the computational core of the system, treats the terminal tritium concentration as a contaminated observation value. Using the aforementioned coupled model, it solves the mathematical inverse problem. By performing deconvolution operations, the system can inversely deduce the true source-end tritium concentration at the sampling pipeline inlet, which is not coated by the pipeline, from the smooth, lagging terminal signal. The state assessment and threshold adjustment module uses the coupled model to calculate the adsorption saturation of the pipeline inner wall in real time and dynamically adjusts the alarm threshold based on this saturation to balance sensitivity and false alarm rate. Simultaneously, the early warning generation module directly compares the inverted true source-end tritium concentration with the dynamically adjusted alarm threshold, rather than using lagging terminal data, thereby generating a forward-looking early leak warning command. The maintenance prediction module tracks the deviation characteristics between the source-end and terminal data over a long period, identifies the degradation trend of pipeline adsorption performance, and issues maintenance requests before the pipeline completely fails.

[0036] The system constructed in this embodiment no longer relies on simple hardware upgrades, but compensates for physical defects through algorithms, overcoming the signal lag and peak clipping defects that are common in traditional remote monitoring. It achieves second-level response to sudden pulse leaks and accurate restoration of source term intensity, significantly improving the safety and reliability of radiation monitoring of nuclear facilities.

[0037] Example 2:

[0038] Methods for obtaining terminal tritium concentration data include:

[0039] Real-time acquisition of the raw pulse signal output by the detector;

[0040] Acquire background radiation data and construct a dynamic background subtraction model based on the background radiation data;

[0041] The original pulse signal was analyzed by energy spectrum analysis and background stripping using a dynamic background subtraction model to extract the net tritium count rate;

[0042] Terminal tritium concentration data are generated based on the net tritium count rate and the detector calibration factor.

[0043] This embodiment details the specific method for acquiring terminal tritium concentration data, aiming to extract high-purity tritium signals from a complex radiation background. The data acquisition module uses a high-speed ADC (analog-to-digital converter) to acquire in real-time the raw pulse signal stream output from the detector, i.e., the radiation-sensitive element integrated inside the aforementioned tritium monitor, such as a proportional counter or ionization chamber. The system acquires environmental background radiation data in real-time, mainly contributed by gamma rays and cosmic rays, and constructs a dynamic background subtraction model based on historical data and the current environmental radiation level. Specifically, the dynamic background subtraction model is constructed using the following linear regression equation:

[0044]

[0045] in, For a moment The predicted background count rate within the region of interest (ROI) for tritium observation is specified in units of GPS counts per second or... ; The ambient dose rate measured by an external independent gamma probe ( ); The cosmic ray count rate measured by the detector at the high-energy end; The gamma dose rate to count rate conversion coefficient, whose physical dimensions are strictly defined as follows: To ensure that the coefficient, after being multiplied by the dose rate, can be converted into the dimension of the count rate, the coefficient was obtained through calibration experiments under different gamma field intensities; The cosmic ray correlation coefficient is designed to correct for high-energy cosmic ray particles, rather than Compton scattering, which is mainly generated by external gamma rays on the detector wall and is already included in the first term. The background contribution is formed by the energy deposited in the low-energy tritium window when passing through the detector sensitive volume. This coefficient is obtained by fitting the long-term stable test under tritium-free conditions. This is the detector's inherent dark count;

[0046] Using this dynamic background subtraction model, multichannel energy spectrum analysis was performed on the original pulse signal. Since tritium's β-ray energy is low, with a maximum of only 18.6 keV and mainly concentrated in the low-energy region, the system calculated the integrated count rate of the total spectrum in the ROI region. And perform background stripping operation. The net tritium count rate, which belongs solely to tritium, is extracted. Considering the non-physical phenomenon that statistical fluctuations in low-level radioactivity measurements may lead to negative net count rates, the system performs non-negative truncation at this point. Based on a pre-calibrated detector calibration factor, i.e., efficiency coefficient ,unit: / ³, using the formula The net tritium count rate is converted into a standard volumetric activity concentration, which generates terminal tritium concentration data.

[0047] This embodiment improves the confidence level of the background prediction to a statistically significant level by introducing a multiple regression model that includes external dose and high-energy co-occurrence counts, effectively eliminating false positive interference in the complex electromagnetic and radiation environment of nuclear facilities.

[0048] Example 3:

[0049] Methods for constructing coupled models of gas-solid interface adsorption kinetics and fluid transport include:

[0050] Obtain pipe wall temperature and sample gas humidity from the environmental parameters along the pipeline;

[0051] Based on the pipe wall temperature and sample gas humidity, the adsorption rate constant and desorption rate constant of the inner wall of the sampling pipeline are determined.

[0052] A mass conservation equation containing convection, diffusion, and source-sink terms is constructed. The adsorption rate constant and desorption rate constant are substituted into the source-sink terms to describe the mass exchange process of tritium on the gas and solid phase surfaces.

[0053] The pipeline adsorption coverage rate variable is introduced, and the temporal evolution of the pipeline adsorption coverage rate is described based on the Langmuir adsorption isotherm theory.

[0054] By combining the mass conservation equation with the time evolution equation of pipeline adsorption coverage, a coupled model of gas-solid interface adsorption kinetics and fluid transport is generated.

[0055] This embodiment details the method for constructing a coupled model of gas-solid interface adsorption kinetics and fluid transport, which is the physical basis for source term inversion. The system acquires environmental parameters along the pipeline, specifically including pipe wall temperature, sample gas humidity, and sample gas flow rate. Based on the pipe wall temperature and sample gas humidity, the adsorption rate constant and desorption rate constant of the inner wall of the sampling pipeline are determined, and the calculation formulas are as follows:

[0056]

[0057]

[0058] in, : Adsorption rate constant, derived from Arrhenius calculations, is physically the mass transfer coefficient of the gas-solid interface. It characterizes the effective deposition linear velocity of tritium molecules diffusing from the bulk gas phase and binding to the tube wall surface. It is clear that it is not a dimensionless probability, but a kinetic parameter with velocity dimensions, with units of meters per second.

[0059] : Desorption rate constant, derived from Arrhenius calculations, physically represents the probability of adsorbed molecules detaching from the tube wall per unit time, measured in seconds;

[0060] Pipe wall temperature, obtained from temperature sensor data, is the thermodynamic temperature of the inner wall of the pipeline, and is measured in Kelvin.

[0061] The relative humidity of the sample gas is obtained from a humidity sensor. It is hereby specified that its unit is a percentage value, with a range of 0-100, rather than a decimal of 0-1, to match the standard output format of engineering sensors.

[0062] Ideal gas constant, standard value is 8.314. ;

[0063] Pre-exponential factor, obtained through standard sample gas pulse experiment calibration;

[0064] Activation energy, determined by temperature-dependent adsorption experiments;

[0065] : Humidity adsorption correction function, characterizing the competitive inhibition effect caused by water molecules occupying adsorption sites, wherein, This is the humidity competitive adsorption coefficient, expressed as a percentage of humidity. The value was obtained by fitting a breakthrough curve in a humidity range of 20% to 80%, with typical values ​​ranging from 0.01 to 0.1; for example, when the humidity is 50% and When the correction factor is This significantly reduces the adsorption rate;

[0066] : Humidity desorption correction function, where, The water molecule replacement effect coefficient is expressed in units of 1000 ppm. The values ​​were obtained by fitting desorption tailing curves under different humidity levels, with typical values ​​ranging from 0.1 to 0.5.

[0067] Based on the one-dimensional convection-diffusion reaction equation, a mass conservation equation describing the transport of tritium in the gas phase is constructed. To ensure dimensional consistency, the saturated adsorption capacity is introduced to normalize the source and sink terms. The specific form of the equation is as follows:

[0068]

[0069] in, Sample flow rate, measured in meters per second, is the volumetric flow rate collected in real time by a flow meter. Conversion yields: ;

[0070] : Axial diffusion coefficient, in square meters per second, calculated based on the Taylor-Aris dispersion mechanism: ,in, Let be the molecular diffusion coefficient of tritium in air, taken as a constant. The unit is ;

[0071] : Inner diameter of the sampling pipeline, in meters; Volume activity concentration of tritium in the gas phase, in units of ;

[0072] The saturated adsorption capacity of the tube wall, expressed in becquerels per square meter, is determined by static adsorption equilibrium experiments on short tubes of the same material under isothermal conditions; in the source and sink terms on the right side of the equation, Indicates adsorption flux , Indicates desorption flux The difference between the two is multiplied by the specific surface area term. That is, it is converted into the rate of change of volume concentration;

[0073] By simultaneously applying the mass conservation equation and the time evolution equation of pipeline adsorption coverage, a coupled model of gas-solid interface adsorption kinetics and fluid transport is generated:

[0074]

[0075] This equation describes the dynamic change in the occupancy ratio of adsorption sites on the tube wall surface over time; here, the adsorption rate constant is taken into account. To demonstrate physical consistency in the two sets of equations despite their different forms: In the mass conservation equation, the source-sink term describes the activity change per unit time and unit volume, therefore the adsorption flux can be used directly. ,unit In the coverage evolution equation, Since it is a dimensionless ratio, its rate of change needs to be determined by the adsorption flux. Divide by the saturated adsorption capacity per unit area ,unit The conclusion is that Thus, export Divide by here The mathematical form of the equations; this treatment ensures a strict closed loop between the change in microscopic surface coverage and the decay of macroscopic fluid concentration in terms of physical picture and dimensions; by combining the above partial differential equations, a complete coupled model of gas-solid interface adsorption kinetics and fluid transport is formed.

[0076] This embodiment clarifies the unit system of the humidity parameter and the specific physical meaning of the correction function, and supplements the coupled derivation of the kinetic constant in different conservation equations, ensuring the robustness of the model under different environmental conditions.

[0077] Example 4:

[0078] Methods for performing deconvolution operations include:

[0079] The coupled model of gas-solid interface adsorption kinetics and fluid transport is discretized into a state-space equation. The actual tritium concentration at the source is defined as the system state vector, and the tritium concentration data at the end is defined as the observation vector.

[0080] Initialize the covariance matrix of the system state vector;

[0081] The execution time update step predicts the prior state estimate for the current time based on the system state vector of the previous time step;

[0082] Perform a measurement update step, calculate the Kalman gain, use the current terminal tritium concentration data to correct the prior state estimate, and obtain the posterior state estimate; output the posterior state estimate as the current source terminal true tritium concentration, and complete the deconvolution operation.

[0083] This embodiment details the specific method for performing deconvolution operations; because the coupled model contains The nonlinear coupling terms are presented in this embodiment, and the inverse problem is solved using the Extended Kalman Filter (EKF) algorithm. The finite difference method is used to discretize the coupling model in space, and the pipeline length is... Divided into Each spatial node, step size Specifically, for any internal node ( The convection term is handled using an upwind difference scheme, and the diffusion term is handled using a central difference scheme. A discretized state transition function is constructed. as follows:

[0084]

[0085] in The adsorption and desorption source and sink terms are defined above; to ensure the numerical stability of the explicit differential iteration, this embodiment strictly limits the time interval from the walk length. The CFL condition is satisfied; the system state vector is defined here. An augmented vector containing the concentrations and adsorption coverage of all nodes. And the actual tritium concentration at the source, i.e., the concentration at the inlet node. As the core state component to be estimated;

[0086] For a system of nonlinear discrete equations, calculate its posterior estimate at the previous time step. The Jacobian matrix at the given location serves as the state transition matrix. Initialization: Set the initial state vector. The zero vector is defined, and the terminal tritium concentration data is defined as the observation vector. Corresponding node Here it is clarified that the observation equation is defined as follows: ,in, for The row vector, only the first The element is 1;

[0087] Initialize the covariance matrix of the system state vector The initial noise covariance matrix is ​​typically set to a large multiple of the identity matrix to characterize the uncertainty of the initial state; the process noise covariance matrix is ​​set accordingly. ; Define the observation noise covariance matrix It is hereby made clear that, It is a scalar or 1×1 matrix, and its value is equal to the measurement noise variance σ_meas² of the terminal tritium concentration monitor. This value is obtained through steady-state testing and statistics of the detector, and a typical value is... to This is a key step in achieving source term inversion: initializing the covariance matrix of the system state vector; setting the process noise covariance matrix. This is a crucial step in achieving source term inversion. To ensure that the filter can correctly attribute the deviation observed at the end to changes in the source input, while also taking into account the unavoidable errors in the physical model, this embodiment constructs a non-uniformly distributed process noise matrix:

[0088]

[0089] in, The process noise variance of the source node concentration is set to a maximum value, such as... This characterizes the source terms as having high uncertainty and the possibility of sudden changes, allowing the filter to make significant state corrections at the source end; The process noise variance of the pipeline transmission node concentration is set as a model tolerance constraint value, for example... to For unmodeled dynamics such as flow field fluctuations, diffusion coefficient changes, and wall adsorption non-uniformity in actual physical systems, in order to prevent the state estimator from diverging or producing serious biases due to over-reliance on the ideal model, this variance value must not be set to zero.

[0090] This embodiment employs a physical anchoring method, based on the measurement noise variance of the flow sensor. and the truncation error of the difference scheme Determine its lower limit, that is This setting macroscopically forces the pipeline to follow the physical transport equation, and microscopically reserves statistical space for absorption model mismatch in the filter, thereby ensuring the robustness of the inversion process. The noise variance of the adsorption process is set to... ;

[0091] By employing this source-end relaxed and along-the-path constrained noise covariance design, the Kalman gain matrix is ​​forced. The observation residuals at the end are inversely projected to the source node. This achieves physical deconvolution reconstruction; its underlying physical mechanism lies in the fact that, within the Bayesian framework of Kalman filtering, the gain matrix... Essentially, it allocates the observation residuals based on the uncertainty weights of each state component; because we are... The matrix artificially introduces significant uncertainty at the source and strong determinism along the transmission path, but it is not absolutely rigid, according to the covariance propagation equation. The source node will occupy the dominant eigenvalue in the prior covariance matrix; therefore, when the filter attempts to minimize the posterior estimation error, it must mathematically choose to correct the least accurate source state component to eliminate the observation bias, rather than correcting the transmission process component that is strongly constrained; this design successfully utilizes the anisotropy of process noise and overcomes the time irreversibility of the diffusion equation, enabling information at the end of the pipeline to flow back to the source.

[0092] Execute the time update step to predict the prior state. And update the covariance Based on this, a measurement update step is performed to calculate the Kalman gain. Using measured data The prior state estimate is corrected to obtain the posterior estimate. Extract the first element from the state vector. The output is the current source tritium concentration.

[0093] Example 5:

[0094] Methods for dynamically adjusting alarm thresholds based on pipeline adsorption saturation include:

[0095] Preset baseline alarm threshold and adsorption saturation determination threshold;

[0096] Obtain the current pipeline adsorption saturation;

[0097] The adsorption saturation of the pipeline is compared with the adsorption saturation judgment threshold;

[0098] If the adsorption saturation of the pipeline is less than the adsorption saturation judgment threshold, the sampling pipeline is determined to be in a strong adsorption effect state. The preset downward correction coefficient is called to multiply and reduce the baseline alarm threshold to calculate the alarm threshold.

[0099] If the adsorption saturation of the pipeline is greater than or equal to the adsorption saturation judgment threshold, the sampling pipeline is determined to be in a penetration state, and the baseline alarm threshold is directly set as the alarm threshold.

[0100] This embodiment details a method for dynamically adjusting alarm thresholds based on pipeline adsorption saturation; the system presets a baseline alarm threshold. and adsorption saturation determination threshold It is hereby clarified that the baseline alarm threshold is... It is set according to the derived air concentration (DAC) limit in the regulations for radiation protection management at nuclear facilities sites, and is based on the principle of early warning classification. Typically, it is set at 10% of the DAC value as the first-level early warning line; the adsorption saturation threshold is also considered. The saturation saturation was obtained by conducting a standard sample gas step response experiment, i.e., a breakthrough experiment, on the sampling pipeline. Specifically, it is defined as the saturation value corresponding to the inflection point in the breakthrough curve where the adsorption rate begins to decrease significantly, with a typical value of 30%. The average adsorption saturation of the pipeline inner wall at the current moment was extracted from the solution process of the coupled model. It is hereby clarified that the average adsorption saturation It is the arithmetic mean of the adsorption coverage of all spatial nodes in the state vector, i.e. Compare the pipeline adsorption saturation with the adsorption saturation determination threshold.

[0101] In response to the pipeline adsorption saturation being less than the adsorption saturation threshold The system determines that the sampling pipeline is in a state of strong adsorption effect. At this time, the memory effect of the pipeline wall manifests as strong signal absorption. To prevent the slight leakage signal from being completely erased, the system calls the preset downward correction coefficient. The baseline alarm threshold is reduced by multiplication; specifically, the correction factor is lowered. It is not a fixed constant, but a quadratic function based on the saturation margin, and the calculation formula is as follows:

[0102]

[0103] in, The minimum reduction limit is preset to 0.4, meaning it can be reduced to at least 40% of the baseline value; this formula ensures that even when adsorption sites are extremely empty... When the saturation level approaches the threshold, the threshold is automatically tightened to improve sensitivity; and as the saturation level approaches the judgment threshold, the correction coefficient smoothly transitions to 1.

[0104] The final alarm threshold is calculated. In response to the adsorption saturation of the pipeline being greater than or equal to the adsorption saturation determination threshold, the system determines that the sampling pipeline is in a penetration state and directly sets the baseline alarm threshold as the alarm threshold. This embodiment constructs a nonlinear adaptive threshold function to ensure that no false alarms are missed under low saturation conditions, while avoiding false alarms caused by threshold jumps due to hard shear.

[0105] Example 6:

[0106] Methods for generating early leak warning instructions include:

[0107] Obtain the current actual tritium concentration at the source and the alarm threshold;

[0108] Determine whether the actual tritium concentration at the source is greater than or equal to the alarm threshold;

[0109] If the actual tritium concentration at the source is greater than or equal to the alarm threshold, a Level 1 leak warning command will be generated, and an audible and visual alarm will be triggered.

[0110] If the actual tritium concentration at the source is less than the alarm threshold, the rate of change of the actual tritium concentration at the source is calculated and compared with the preset mutation threshold.

[0111] If the rate of change is greater than the preset mutation threshold, a trend warning command will be generated.

[0112] If the rate of change is less than or equal to the preset mutation threshold, the system is determined to be in a safe state and no warning command is generated.

[0113] This embodiment details a method for generating an early leak warning command. The system acquires the source-end true tritium concentration reconstructed at the current moment and the dynamically adjusted alarm threshold. It determines whether the source-end true tritium concentration is greater than or equal to the alarm threshold. In response to the source-end true tritium concentration being greater than or equal to the alarm threshold, the system immediately generates a first-level leak warning command and triggers an audible and visual alarm. Since the source-end true tritium concentration is time-compensated, this alarm usually precedes the direct alarm from the end detector by several minutes to tens of minutes. In response to the source-end true tritium concentration being less than the alarm threshold, the system further calculates the rate of change of the source-end true tritium concentration, i.e., the first derivative, and compares this rate of change with a preset abrupt change threshold.

[0114] To be clear, the preset mutation threshold Using formula Calculate, where, The change rate weighting coefficient is obtained by calculating the standard deviation of the source-end inversion concentration change rate based on the background noise statistics of the system during stable operation without leakage. ,in accordance with Criterion setting Make the calculated Greater than This avoids false alarms caused by statistical fluctuations. The typical value range is 0.2 to 0.5. Note the use of the symbol here. To distinguish it from the humidity-competitive adsorption coefficient defined in Example 3 , The baseline concentration is the average value of the actual tritium concentration at the source measured within a preset time window before the current moment, such as the past hour. The time step for calculating the rate of change or a preset trend determination window, in seconds, is used for this division operation to ensure... Possessing and rate of change Consistent physical dimensions;

[0115] In response to a rate of change greater than a preset mutation threshold This indicates that the concentration at the source is rising rapidly. Although it has not yet exceeded the standard, there is a risk of leakage. The system generates a trend warning command to remind the operator to pay attention. If the rate of change is less than or equal to the preset mutation threshold, the system is determined to be in a safe state and no warning command is generated.

[0116] This embodiment combines absolute value alarm and rate of change trend alarm to form a dual defense mechanism. In particular, it is based on the rate of change analysis of the inverted source term and supplements the calculation logic of the mutation threshold and the basic concentration parameters, eliminating potential conflicts in the use of symbols and dimensional mismatch problems. It can detect anomalies in the very early stage of leakage, that is, when the concentration has not yet reached the legal limit but is increasing rapidly, thus buying valuable golden time for emergency response.

[0117] Example 7:

[0118] Methods for capturing the degradation trend of adsorption performance in sampling pipelines include:

[0119] Multiple pulse leakage events that occurred in history were selected, and the source-end true tritium concentration curve and the terminal tritium concentration data curve corresponding to each event were obtained;

[0120] Extract the peak time of the actual tritium concentration curve at the source end and the peak time of the tritium concentration data curve at the end end, calculate the time difference between the two, and use it as the transmission lag time.

[0121] Extract the falling edge characteristics of the terminal tritium concentration data curve and calculate the tail decay constant;

[0122] Construct a sequence of feature vectors containing transmission lag time and tail attenuation constant;

[0123] Sliding window trend analysis is performed on the feature vector sequence. If the transmission lag time shows a monotonically increasing trend and the tail decay constant shows a monotonically decreasing trend, it is determined that there is a trend of adsorption performance degradation; otherwise, it is determined that there is no trend of adsorption performance degradation.

[0124] This embodiment details a method for capturing the degradation trend of adsorption performance in a sampling pipeline; the system automatically selects multiple historical pulse leakage events; to ensure the validity of the analysis data and prevent noise interference in trend calculation, the system performs signal-to-noise ratio (SNR) verification on the selected events: calculating the peak value of the terminal curve. Standard deviation of baseline noise The ratio, only when The event is only included in the trend analysis sequence when it is in the specified condition; otherwise, it is removed.

[0125] For valid events, the source-end reconstructed curve and the terminal measured curve are obtained, and the peak times of the source-end true tritium concentration curve and the terminal tritium concentration data curve are extracted; a local quadratic polynomial fitting is then used. ,in, For the fitting coefficients, the time to analytically calculate the extreme points is... , respectively denoted as and Calculate the time difference between the two. This serves as the transmission lag time; simultaneously, the falling edge data of the terminal curve is extracted, and an exponential decay equation is fitted. Calculate the tail attenuation constant ;in, To reduce the baseline concentration, i.e., the environmental background or steady-state residual concentration;

[0126] Construct a feature vector sequence containing transmission lag time and tail attenuation constant, and perform sliding window trend analysis on this sequence; specifically, construct a feature vector sequence of length... A sliding window is used, with the cumulative system runtime (SCRT) as the independent variable. Using characteristic parameters as dependent variables Perform linear regression analysis and calculate the regression slope. and determination coefficient If the transmission delay time shows a monotonically increasing trend Furthermore, the tail decay constant exhibits a monotonically decreasing trend. If the signal-to-noise ratio is positive, it indicates that the pipeline memory effect is aggravated and desorption is slowed down, and the system determines that there is a trend of adsorption performance degradation; otherwise, it determines that there is no trend of adsorption performance degradation. This embodiment avoids misjudgment caused by low-level radiometric statistical fluctuations by introducing a signal-to-noise ratio threshold verification mechanism.

[0127] Example 8:

[0128] Methods for identifying pipeline maintenance needs based on the trend of adsorption performance degradation include:

[0129] Establish a linear regression model to predict the transport lag time within a preset time window; preset pipeline failure threshold.

[0130] The predicted transmission lag time is compared with the pipeline failure threshold;

[0131] If the predicted transmission lag time is greater than or equal to the pipeline failure threshold, a pipeline cleaning or replacement recommendation instruction is generated.

[0132] If the predicted transmission lag time is less than the pipeline failure threshold, a pipeline health status confirmation command is generated.

[0133] This embodiment details a method for identifying pipeline maintenance needs based on the trend of adsorption performance degradation. Based on the extracted feature sequences, a linear regression model with respect to time is established. This model is used to predict the transmission lag time within a preset time window, such as the predicted value for the next 30 days. The formula is as follows:

[0134]

[0135] in, The transmission lag time prediction is derived from regression model calculations. Its physical meaning is the expected delay of the signal caused by the pipeline at a future time, and the unit is seconds.

[0136] : Regression intercept, representing the transmission lag time under the initial state or the current benchmark;

[0137] The slope coefficient, derived from historical data fitting, physically represents the rate of pipeline aging.

[0138] Predicting time variables;

[0139] A preset pipeline failure threshold; this threshold is hereby specified. It is not arbitrarily set, but is based on the baseline lag time measured during the initial stage of system debugging. Definitely defined This multiple is established based on engineering experience, representing that the physical adsorption effect has exceeded the linear stability region of the algorithm compensation; the predicted transmission lag time is compared with the pipeline failure threshold; in response to the predicted transmission lag time being greater than or equal to the pipeline failure threshold, indicating that the pipeline is about to fail, the system automatically generates a pipeline cleaning or replacement suggestion instruction and pushes it to the operation and maintenance terminal; in response to the predicted transmission lag time being less than the pipeline failure threshold, a pipeline health status confirmation instruction is generated.

[0140] This embodiment realizes the transformation from post-maintenance to predictive maintenance. By scientifically predicting the remaining service life of pipelines, i.e., RUL, maintenance personnel can reasonably arrange pipeline purging, cleaning or replacement plans, which avoids monitoring accidents caused by pipeline performance degradation and resource waste caused by premature replacement.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A remote online tritium monitoring system, characterized in that, include: The data acquisition module is used to simultaneously acquire the fluid state and radiation field at the end of the sampling pipeline, and obtain the tritium concentration data at the end, environmental parameters along the sampling pipeline, and background radiation data. The coupled model construction module is used to construct a coupled model of gas-solid interface adsorption kinetics and fluid transport based on environmental parameters along the sampling pipeline, and to establish the dynamic transport transfer function of the sampling pipeline. The source term inversion and reconstruction module is used to take the terminal tritium concentration data as the observation value, input it into the gas-solid interface adsorption kinetics and fluid transport coupling model to solve the inverse problem, perform deconvolution operation, and reverse the inversion to deduce the true tritium concentration at the source end of the sampling pipeline inlet; The state assessment and threshold adjustment module is used to calculate the adsorption saturation of the sampling pipeline in real time based on the gas-solid interface adsorption kinetics and fluid transport coupling model, and to dynamically adjust the alarm threshold based on the adsorption saturation of the sampling pipeline. The early warning generation module is used to compare the actual tritium concentration at the source with the alarm threshold and generate an early leak warning command. The operation and maintenance prediction module is used to perform long-term evolution analysis on the lag deviation characteristics between the actual tritium concentration at the source and the tritium concentration at the end, capture the adsorption performance degradation trend of the sampling pipeline, and identify the maintenance needs of the sampling pipeline based on the adsorption performance degradation trend. Methods for constructing coupled models of gas-solid interface adsorption kinetics and fluid transport include: Obtain pipe wall temperature and sample gas humidity from environmental parameters along the sampling pipeline; Based on the pipe wall temperature and sample gas humidity, the adsorption rate constant and desorption rate constant of the inner wall of the sampling pipeline are determined. A mass conservation equation containing convection, diffusion, and source-sink terms is constructed. The adsorption rate constant and desorption rate constant are substituted into the source-sink terms to describe the mass exchange process of tritium on the gas and solid phase surfaces. The adsorption coverage rate variable of the sampling pipeline is introduced, and the temporal evolution of the adsorption coverage rate of the sampling pipeline is described based on the Langmuir adsorption isotherm theory. By combining the mass conservation equation with the time evolution equation of the adsorption coverage of the sampling pipeline, a coupled model of gas-solid interface adsorption kinetics and fluid transport is generated. Methods for performing deconvolution operations include: The coupled model of gas-solid interface adsorption kinetics and fluid transport is discretized into a state-space equation. The actual tritium concentration at the source is defined as the system state vector, and the tritium concentration data at the end is defined as the observation vector. Initialize the covariance matrix of the system state vector; The execution time update step predicts the prior state estimate for the current time based on the system state vector of the previous time step; Perform a measurement update step, calculate the Kalman gain, use the current terminal tritium concentration data to correct the prior state estimate, and obtain the posterior state estimate; The posterior state estimate is output as the current source-end true tritium concentration, and the deconvolution operation is completed.

2. The remote tritium online monitoring system according to claim 1, characterized in that, Methods for obtaining terminal tritium concentration data include: Real-time acquisition of the raw pulse signal output by the detector; Acquire background radiation data and construct a dynamic background subtraction model based on the background radiation data; The original pulse signal was analyzed by energy spectrum analysis and background stripping using a dynamic background subtraction model to extract the net tritium count rate; Terminal tritium concentration data are generated based on the net tritium count rate and the detector calibration factor.

3. The remote tritium online monitoring system according to claim 1, characterized in that, Methods for dynamically adjusting alarm thresholds based on the adsorption saturation of sampling pipelines include: Preset baseline alarm threshold and adsorption saturation determination threshold; Obtain the current adsorption saturation of the sampling pipeline; The adsorption saturation of the sampling pipeline is compared with the adsorption saturation judgment threshold; If the adsorption saturation of the sampling pipeline is less than the adsorption saturation judgment threshold, the sampling pipeline is determined to be in a strong adsorption effect state. The preset downward correction coefficient is called to multiply and reduce the baseline alarm threshold to calculate the alarm threshold. If the adsorption saturation of the sampling pipeline is greater than or equal to the adsorption saturation judgment threshold, the sampling pipeline is determined to be in a penetration state, and the baseline alarm threshold is directly set as the alarm threshold.

4. The remote tritium online monitoring system according to claim 3, characterized in that, Methods for generating early leak warning instructions include: Obtain the current actual tritium concentration at the source and the alarm threshold; Determine whether the actual tritium concentration at the source is greater than or equal to the alarm threshold; If the actual tritium concentration at the source is greater than or equal to the alarm threshold, a Level 1 leak warning command will be generated, and an audible and visual alarm will be triggered. If the actual tritium concentration at the source is less than the alarm threshold, the rate of change of the actual tritium concentration at the source is calculated and compared with the preset mutation threshold. If the rate of change is greater than the preset mutation threshold, a trend warning command will be generated. If the rate of change is less than or equal to the preset mutation threshold, the system is determined to be in a safe state and no warning command is generated.

5. The remote tritium online monitoring system according to claim 1, characterized in that, Methods for capturing the degradation trend of adsorption performance in sampling pipelines include: Multiple pulse leakage events that occurred in history were selected, and the source-end true tritium concentration curve and the terminal tritium concentration data curve corresponding to each event were obtained; Extract the peak time of the actual tritium concentration curve at the source end and the peak time of the tritium concentration data curve at the end end, calculate the time difference between the two, and use it as the transmission lag time. Extract the falling edge characteristics of the terminal tritium concentration data curve and calculate the tail decay constant; Construct a sequence of feature vectors containing transmission lag time and tail attenuation constant; Sliding window trend analysis is performed on the feature vector sequence. If the transmission lag time shows a monotonically increasing trend and the tail decay constant shows a monotonically decreasing trend, it is determined that there is a trend of adsorption performance degradation. Otherwise, it is determined that there is no trend of adsorption performance decline.

6. The remote tritium online monitoring system according to claim 5, characterized in that, Methods for identifying sampling pipeline maintenance needs based on the trend of adsorption performance degradation include: A linear regression model of the adsorption performance degradation trend was established to predict the transport lag time within a preset time window. Preset sampling pipeline failure threshold; The predicted transmission lag time is compared with the sampling pipeline failure threshold; If the predicted transmission lag time is greater than or equal to the sampling pipeline failure threshold, a sampling pipeline cleaning or replacement recommendation instruction is generated. If the predicted transmission lag time is less than the sampling pipeline failure threshold, a sampling pipeline health status confirmation command is generated.

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