Method and system for dynamic evaluation of imidazolium resin column efficiency for at-211 online separation

Abstract

This invention relates to the field of column efficiency evaluation technology, and discloses a method and system for dynamic evaluation of the column efficiency of imidazolyl resin for online separation of At-211. The method includes: during the continuous separation of At-211, real-time acquisition of the bed height of the imidazolyl resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent; by real-time acquisition of the bed height, effluent pH value, and eluent radioactivity, the swelling disturbance index and peak broadening coefficient are calculated sequentially, and then the column efficiency fluctuation factor and passivation influence factor are obtained through synergistic analysis, finally generating a health index. This scheme can accurately reflect the risk of abnormal resin swelling and separation efficiency degradation, clearly distinguish between reversible fluctuations and irreversible decay of column efficiency, reflect the self-recovery ability and the degree of irreversible degradation accumulation within the column, and improve the accuracy and comprehensiveness of dynamic column efficiency evaluation.

Description

Technical Field

[0001] This invention relates to the field of column efficiency evaluation technology, and specifically to a method and system for dynamic evaluation of the column efficiency of imidazolium resin used for online separation of At-211. Background Technology

[0002] Currently, in the evaluation of the column efficiency of imidazolium resin for online separation of At-211, the main methods are to monitor the adsorption capacity and elution recovery rate of imidazolium resin for At-211, and combine this with column pressure change data to construct a column efficiency change curve. At the same time, an online radioactive detector is used to monitor the radioactivity of At-211 in the eluent. By observing the changes in the activity peak and the time of peak occurrence, the column efficiency decay is judged, thus achieving a dynamic evaluation of column efficiency.

[0003] However, the above evaluation method still has the following technical defects: In long-term continuous analysis of At-211, imidazolium resin will undergo slight swelling and passivation of surface active sites due to repeated adsorption-elution, and the pH value of the eluent will fluctuate slightly with the increase of separation batches. In the existing evaluation method, the column efficiency is judged only by adsorption capacity and elution recovery rate, without considering the degree of resin swelling and the passivation rate of active sites. This makes it impossible to analyze the column efficiency fluctuation caused by reversible swelling and the column efficiency decline caused by irreversible loss of active sites, thus affecting the dynamic evaluation effect of column efficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for dynamic evaluation of the column efficiency of imidazolium resin for online separation of At-211, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] Dynamic evaluation methods for imidazolium resin column efficiency used in the online separation of At-211 include:

[0007] Step S1: During the continuous separation of At-211, the bed height of the imidazolium resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent are collected in real time.

[0008] Step S2: Calculate the bed height and pH value of the effluent after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state; analyze the radioactivity after pretreatment to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation.

[0009] Step S3: Perform a synergistic analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively.

[0010] Step S4: Analyze the trends of column efficiency fluctuation factor and passivation influence factor to obtain health indicators for evaluating the column efficiency of imidazolium resin.

[0011] Furthermore, the pretreated bed height and effluent pH were calculated to obtain a swelling disturbance index, representing the risk of abnormal resin swelling, including:

[0012] Distortion analysis was performed on the pretreated bed height and effluent pH to generate a swelling distortion coefficient that represents the degree of deviation between the resin bed and the chemical environment.

[0013] The pH value of the effluent was integrated and corrected for by combining it with the bed height value to obtain the ion penetration factor, which represents the cumulative chemical penetration effect of the eluent on the resin bed.

[0014] Furthermore, the pretreated bed height and effluent pH were calculated to obtain a swelling disturbance index, which represents the risk of abnormal resin swelling. This also includes:

[0015] By combining the swelling distortion coefficient and the ion penetration factor, a swelling disturbance index representing the risk of abnormal swelling state of the resin was obtained.

[0016] Furthermore, the radioactivity after pretreatment was analyzed to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation, including:

[0017] The time-series profile of radioactivity is analyzed to generate peak distortion coefficients representing the deviation of elution peak symmetry.

[0018] Cumulative analysis of the peak distortion coefficient yields the peak broadening coefficient, which represents the risk of separation performance degradation.

[0019] Furthermore, by conducting a synergistic analysis of the swelling disturbance index and the peak broadening coefficient, we obtained the column effect fluctuation factor, representing the reversible fluctuation of column effect, and the passivation influence factor, representing the irreversible decay of column effect, respectively, including:

[0020] The dynamic correlation between the swelling disturbance index and the peak broadening coefficient during the separation process was analyzed, and a response coefficient representing the degree of synergistic change between the swelling state and the separation efficiency was generated.

[0021] The response coefficients are decomposed into trend-residual values ​​to calculate the instantaneous response strength representing rapid fluctuations and the baseline migration representing slow drifts.

[0022] Furthermore, by conducting a synergistic analysis of the swelling disturbance index and the peak broadening coefficient, the column effect fluctuation factor, representing reversible fluctuations in column effect, and the passivation influence factor, representing irreversible decay of column effect, were obtained, respectively. This also includes:

[0023] Peak values ​​of the instantaneous response intensity were statistically analyzed and corrected for by combining the rate of change of pH value of the effluent, resulting in a dynamic equilibrium coefficient representing the self-recovery capability of in-column disturbance.

[0024] The baseline migration is corrected based on the bed height value to obtain an aging index that represents the degree of irreversible degradation accumulation within the column.

[0025] Furthermore, by conducting a synergistic analysis of the swelling disturbance index and the peak broadening coefficient, the column effect fluctuation factor, representing reversible fluctuations in column effect, and the passivation influence factor, representing irreversible decay of column effect, were obtained, respectively. This also includes:

[0026] Deviation analysis is performed on the dynamic equilibrium coefficient to generate a column effect fluctuation factor representing the reversible fluctuation of column effect;

[0027] The aging index was analyzed to generate a passivation influence factor representing the irreversible decline in column efficiency.

[0028] Furthermore, the trends of column efficiency fluctuation factors and passivation influence factors were analyzed to obtain health indicators for evaluating the column efficiency of imidazolium resin, including:

[0029] A composite analysis of the column efficiency fluctuation factor and the passivation influence factor is performed to generate a health deviation vector representing the degree of deviation from the overall column efficiency status;

[0030] The consistency between the changing trends of the column efficiency fluctuation factor and the passivation influence factor is analyzed to generate a degradation trend index that represents the direction of column efficiency state evolution.

[0031] Furthermore, the trends of column efficiency fluctuation factors and passivation influence factors were analyzed to obtain health indicators for evaluating the column efficiency of imidazolium resin, including:

[0032] A health index for evaluating the efficacy of imidazolium resin columns was generated by jointly assessing the health deviation vector and the degradation trend index.

[0033] Furthermore, the dynamic evaluation system for the column efficiency of imidazolium resin used for the online separation of At-211, applied in the above evaluation method, includes:

[0034] The data acquisition unit is used to collect the bed height of the imidazolyl resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent in real time during the continuous separation process of At-211.

[0035] The column efficiency analysis unit is used to calculate the bed height and effluent pH value after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state, and to analyze the radioactivity after pretreatment to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation.

[0036] The collaborative calculation unit is used to perform collaborative analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively.

[0037] The column efficiency evaluation unit is used to analyze the trends of column efficiency fluctuation factors and passivation influence factors, and obtain health indicators for evaluating the column efficiency of imidazolium resin.

[0038] In summary, the present invention has the following main beneficial effects:

[0039] By real-time acquisition of bed height, effluent pH, and At-211 radioactivity in the eluent, a swelling disturbance index is calculated based on the bed height and effluent pH. A peak broadening coefficient is obtained based on radioactivity analysis, accurately representing the risks of abnormal resin swelling and separation efficiency degradation, respectively. Furthermore, through synergistic analysis of the swelling disturbance index and peak broadening coefficient, a response coefficient, dynamic equilibrium coefficient, and aging index are generated, leading to column efficiency fluctuation factors and passivation influence factors. This allows for precise differentiation between reversible fluctuations and irreversible degradation of column efficiency, reflecting the column's self-recovery capability and the degree of irreversible deterioration accumulation. Finally, by synthesizing and analyzing the column efficiency fluctuation factor and passivation influence factor, a health deviation vector and degradation trend index are constructed, forming a health index that reflects the overall deviation degree, dominant deviation dimension, and evolution trend of column efficiency. This approach can accurately identify column efficiency changes caused by resin swelling and active site passivation, improving the accuracy of dynamic column efficiency assessment during long-term continuous A-211 separation. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the dynamic evaluation method for the online separation of At-211 using imidazolium resin columns according to the present invention.

[0041] Figure 2 This is a schematic diagram of the dynamic evaluation system for the column efficiency of imidazolium resin used for online separation of At-211 according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] refer to Figure 1 and Figure 2 A method for dynamic evaluation of the column efficiency of imidazolium resin for online separation of At-211 includes:

[0044] Step S1: During the continuous separation of At-211, the bed height of the imidazolium resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent are collected in real time.

[0045] Step S2: Calculate the bed height and pH value of the effluent after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state; analyze the radioactivity after pretreatment to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation.

[0046] Step S3: Perform a synergistic analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively.

[0047] Step S4: Analyze the trends of column efficiency fluctuation factor and passivation influence factor to obtain health indicators for evaluating the column efficiency of imidazolium resin.

[0048] In one embodiment, the pretreated bed height and effluent pH are calculated to obtain a swelling disturbance index representing the risk of abnormal resin swelling, including:

[0049] Distortion analysis was performed on the pretreated bed height and effluent pH values ​​to generate a swelling distortion coefficient representing the degree of deviation between the resin bed and the chemical environment. Specifically, the pretreated bed height values ​​were arranged in the order of collection time to form a bed height sequence, and the pretreated effluent pH values ​​were arranged in the same time order to form a pH sequence.

[0050] The window length was set to 10 consecutive collection points. The standard deviation within the window was calculated point by point for the bed height sequence to obtain the height fluctuation sequence representing the degree of local fluctuation in the bed. At the same time, the average rate of change within the window was calculated point by point for the pH time series data to obtain the pH smoothing sequence representing the local stability trend of the chemical environment.

[0051] The values ​​of the high-fluctuation sequence and the pH smoothing sequence are normalized to the 0-1 interval to obtain the normalized fluctuation sequence and the normalized rate of change sequence. For each time point, the Euclidean distance between the normalized fluctuation sequence value and the normalized rate of change sequence value is calculated. The Euclidean distance is the instantaneous swelling distortion value at that time point.

[0052] From the separation time at At-211 to the current time, the instantaneous swelling distortion values ​​at all time points within this time period are summed and then divided by the total number of time points within the time period to obtain the swelling distortion coefficient, which represents the degree of deviation between the resin bed and the chemical environment within this time period.

[0053] The pH value of the effluent is integrated and corrected by the bed height value to obtain the ion penetration factor, which represents the cumulative chemical penetration effect of the eluent on the resin bed. Specifically, the time when At-211 begins to separate is taken as the starting time point, and the pH value of the effluent is continuously acquired at a fixed acquisition frequency. Each acquisition point corresponds to a timestamp and a pH measurement value. By plotting time as the horizontal axis and pH value as the vertical axis, a dynamic curve of pH value changing with time can be drawn.

[0054] The continuous time is divided into several time intervals, with each interval having the time points of two adjacent sampling points as the endpoints. The interval length is the time interval between the two sampling points. For each time interval, the arithmetic mean of the pH values ​​at the two endpoints of the interval is taken as the representative pH value within that interval. The representative pH value is multiplied by the interval length to obtain the area corresponding to that interval. Starting from the area of ​​the first time interval at the beginning, the area is accumulated sequentially up to the area of ​​the last interval at the current time. The accumulated result is the total area under the pH curve from the beginning to the current time. The total area under the pH curve is the cumulative pH effect, which is mainly used to reflect the sustained intensity of the chemical properties of the eluent on the resin.

[0055] Calculate the relative change rate of the current bed height relative to the initial bed height, and use the absolute value of the relative change rate as the bed deformation coefficient. The bed deformation coefficient mainly reflects the degree of physical structural response of the resin due to changes in the chemical environment. Multiply the cumulative effect of pH by the bed deformation coefficient to obtain the cumulative penetration effect value. The cumulative penetration effect value mainly reflects the comprehensive impact of the eluent penetrating the resin bed.

[0056] Divide the cumulative penetration value by the total time from the separation time to the current time to obtain the average penetration intensity per unit time. Normalize the average penetration intensity to the 0-1 range, which is the ion penetration factor representing the cumulative chemical penetration effect of the eluent on the resin bed.

[0057] In one embodiment, the swelling disturbance index, representing the risk of abnormal resin swelling state, is calculated by measuring the pretreated bed height and the pH value of the effluent. The calculation also includes:

[0058] A fusion analysis of the swelling distortion coefficient and the ion penetration factor yields a swelling disturbance index representing the risk of abnormal resin swelling state. Specifically, this includes: calculating the instantaneous rate of change of the swelling distortion coefficient, i.e., the difference between the swelling distortion coefficient at the current moment and the swelling distortion coefficient at the previous moment, and using the absolute value of the instantaneous rate of change as the fluctuation acceleration factor, which reflects the severity of the sudden change in the swelling deviation state; and simultaneously calculating the cumulative slope of the ion penetration factor, i.e., the linear regression slope from the initial moment to the current moment, and using the linear regression slope as the penetration trend factor, which represents the trend of the eluent penetration effect.

[0059] Multiply the swelling distortion coefficient by the fluctuation acceleration factor and normalize the result to the 0-1 interval to obtain the instantaneous risk base value. Then multiply the ion penetration factor by the penetration trend factor and normalize the result to the 0-1 interval to obtain the cumulative risk base value. Calculate the Euclidean norm of the instantaneous risk base value and the cumulative risk base value and normalize the result to the 0-1 interval to obtain the swelling disturbance index, which represents the abnormal risk of resin swelling state.

[0060] By calculating the distortion analysis of bed height and pH sequences, calculating the cumulative effect of pH, and performing multi-factor fusion analysis, the system can accurately reflect the local fluctuations of the resin bed, the stability trend of the chemical environment, and the cumulative chemical penetration effect of the eluent. It can effectively distinguish between column efficiency fluctuations caused by reversible swelling and column efficiency decline caused by irreversible loss of active sites, thereby improving the accuracy and comprehensiveness of dynamic evaluation of imidazolium resin column efficiency.

[0061] In one embodiment, the pretreated radioactivity is analyzed to obtain a peak broadening coefficient representing the risk of separation performance degradation, including:

[0062] The radioactivity is analyzed in time series profile to generate peak distortion coefficients that represent the deviation of the symmetry of the elution peak. Specifically, the process includes: taking the moment when At-211 begins separation as the starting point, continuously acquiring the radioactivity value in the eluent at a fixed acquisition frequency, taking the radioactivity values ​​of the 50 consecutive acquisition points before the current moment, removing the maximum and minimum values, calculating the arithmetic mean of the remaining radioactivity values, using the arithmetic mean as the background reference value at the current moment, and automatically updating the background reference value once for each new acquisition point, forming a dynamic baseline sequence that changes smoothly over time.

[0063] The radioactivity value at the current sampling point is compared with the background reference value at the current time. If the activity value is greater than the background reference value, the sampling point is marked as a potential peak start candidate point. When five consecutive sampling points are marked as potential peak start candidate points, the first marked point is determined as the official start point of the elution peak. Starting from the official start point of the elution peak, the trend of radioactivity value is monitored in real time. When the radioactivity value rises to the maximum value and then begins to decline, and falls back to within 1.5 times the background reference value for the first time, the point is marked as a potential peak end candidate point. When three consecutive sampling points meet the decline pattern, the first point that meets the condition is determined as the official end point of the elution peak. All sampling points within the time interval from the official start point to the official end point constitute the contour data of the current elution peak.

[0064] In the current elution peak profile data, identify the sampling point corresponding to the maximum radioactivity value, take the time position of this point as the peak position, and divide the peak profile into a rising segment before the peak and a falling segment after the peak using the peak position as the dividing point. Calculate the sum of radioactivity values ​​of all sampling points in the rising segment before the peak and the sum of radioactivity values ​​of all sampling points in the falling segment after the peak. These two sums represent the total amount of radioactivity contained in the first half and the second half of the elution peak, respectively.

[0065] The peak distortion coefficient, which represents the deviation of the elution peak symmetry, can be obtained by calculating the ratio of the pre-peak cumulative sum to the post-peak cumulative sum, and by calculating the absolute value of the difference between the ratio and the reference value 1.

[0066] Cumulative analysis of the peak distortion coefficients yields the peak broadening coefficient, which represents the risk of separation performance degradation. Specifically, starting from the first elution peak detected after separation from At-211, the peak distortion coefficients of each elution peak are recorded sequentially in chronological order until the current moment, constructing a peak distortion coefficient sequence. The arithmetic mean of the first ten peak distortion coefficients in the peak distortion coefficient sequence is calculated to obtain the baseline distortion level. The baseline distortion level represents the typical peak symmetry state when the resin column performance is good in the initial stage of separation.

[0067] Starting from the eleventh elution peak, whenever a new peak distortion coefficient is obtained, the arithmetic mean of all peak distortion coefficients from the eleventh peak to the current peak (including the current peak) is calculated to obtain the current average distortion level.

[0068] Calculate the ratio of the current average distortion level to the baseline distortion level, subtract 1 from the ratio and take the absolute value. Normalize the absolute value to the 0-1 range to obtain the peak broadening coefficient, which represents the risk of separation performance degradation. The closer the peak broadening coefficient is to 1, the further the current average peak distortion deviates from the initial baseline, and the higher the risk of separation performance degradation.

[0069] By constructing a dynamic baseline sequence through time-series profile analysis of radioactivity, the profiles of elution peaks are accurately identified and the peak distortion coefficient is calculated. Then, based on the cumulative analysis of multiple rounds of elution peak data, the peak broadening coefficient is obtained, which can accurately represent the symmetry deviation of elution peaks and reflect the degree of peak distortion caused by the passivation of active sites of imidazolium resin and bed deformation. By comparing the baseline distortion level with the current average distortion level, the risk of separation efficiency degradation can be quantified, and the column efficiency fluctuations caused by reversible swelling and the column efficiency decline caused by irreversible loss of active sites can be accurately distinguished, thereby improving the accuracy of dynamic column efficiency assessment.

[0070] In one embodiment, the swelling disturbance index and peak broadening coefficient are analyzed synergistically to obtain a column effect fluctuation factor representing reversible fluctuations in column effect and a passivation influence factor representing irreversible decay of column effect, including:

[0071] The dynamic correlation between the swelling perturbation index and the peak broadening coefficient during the separation process was analyzed to generate a response coefficient representing the degree of synergistic change between the swelling state and the separation efficiency. Specifically, starting from the first acquisition time after the At-211 separation begins, the swelling perturbation index and the peak broadening coefficient are continuously recorded to form two parallel time series. A sliding window with a length of five consecutive acquisition points is set, and the change amplitude of the swelling perturbation index within each sliding window is calculated, i.e., the difference between the maximum and minimum values ​​within the window. At the same time, the change amplitude of the peak broadening coefficient within the same window is calculated, i.e., the difference between the maximum and minimum values ​​within the window.

[0072] Within each sliding window, the direction of change of the swelling disturbance index and the peak broadening coefficient relative to the starting point of the window is calculated. If the end value within the window is greater than the starting value, the direction is marked as positive; if the end value is less than the starting value, the direction is marked as negative; if the end value is equal to the starting value, the direction is marked as zero. The two change amplitudes within the same window are multiplied to obtain the basic value of the coupling strength. At the same time, the two direction marks are compared. If the direction marks are the same, the coupling direction factor is set to 1; if the direction marks are opposite, the coupling direction factor is set to negative 1.

[0073] Multiplying the basic value of coupling strength by the coupling direction factor yields a response coefficient representing the degree of synergistic change between the swelling state and the separation efficiency.

[0074] The response coefficients are decomposed into trend-residual values ​​to calculate the instantaneous response intensity representing rapid fluctuations and the baseline migration representing slow drifts. Specifically, this includes setting a sliding window with a length of one hundred continuous acquisition points, calculating the median value of all response coefficients within the window, using the median value as the dynamic reference value at the center of the window, and generating a dynamic reference sequence that changes with the current time point by point for each new acquisition point.

[0075] Subtract the dynamic baseline value at the same time from the response coefficient value at each time step to obtain the fluctuation residual at that time step.

[0076] Take the absolute values ​​of the fifty consecutive fluctuation residuals before the current moment, calculate the arithmetic mean of these absolute values ​​to obtain the average fluctuation amplitude at that moment, normalize the average fluctuation amplitude to the 0-1 interval, which is the instantaneous response intensity representing the instantaneous disturbance. The greater the instantaneous response intensity, the more severe the high-frequency disturbance on the synergistic relationship between the swelling state and the separation efficiency at the current stage. The dynamic reference value at the current moment is used as the baseline migration amount representing the slow drift.

[0077] In one embodiment, the swelling disturbance index and peak broadening coefficient are analyzed synergistically to obtain a column effect fluctuation factor representing reversible fluctuations in column effect and a passivation influence factor representing irreversible decay of column effect, respectively. The method also includes:

[0078] Peak values ​​of the instantaneous response intensity are statistically analyzed and corrected by the rate of change of the pH value of the effluent to obtain a dynamic equilibrium coefficient representing the self-recovery capability of the column disturbance. Specifically, this includes setting a sliding window with a length of twenty consecutive sampling points, calculating the average value of the instantaneous response intensity within the window, comparing the instantaneous response intensity at the current moment with the average value, and marking the current moment as a potential disturbance peak point if the current instantaneous response intensity exceeds twice the average value.

[0079] For each potential disturbance peak point, starting from the peak point, extend 30 consecutive sampling points backward, record the maximum and minimum values ​​of the instantaneous response intensity within this time period, calculate the difference between the maximum and minimum values ​​as the impact amplitude of the disturbance; at the same time, calculate the number of time points that elapse from the peak point to the minimum point as the recovery time.

[0080] Divide the impact amplitude by the recovery time to obtain the decay rate. The larger the decay rate, the faster the instantaneous response intensity decreases after the disturbance, and the stronger the self-recovery ability of the resin column. Calculate the average rate of change of all pH values ​​within the sliding window. The average rate of change mainly reflects the intensity of the chemical environment fluctuation during the disturbance.

[0081] Each decay rate is divided by the average pH change rate and normalized to the 0-1 range to obtain the corrected recovery rate under chemical perturbation. The arithmetic mean of all recovery rates is calculated from the start of separation to the current time and used as the dynamic equilibrium coefficient representing the self-recovery capability of in-column perturbation.

[0082] The baseline migration is corrected based on the bed height value to obtain an aging index that represents the degree of irreversible degradation accumulation within the column. Specifically, this includes: calculating the range and arithmetic mean of the bed height values ​​of the fifty consecutive sampling points before the current moment, dividing the range by the arithmetic mean to obtain the relative undulation of the bed, which reflects the degree of physical structural instability of the resin bed in the recent period.

[0083] The arithmetic mean of the bed deformation coefficients of the fifty consecutive sampling points before the current moment is calculated to obtain the average cumulative deformation, which represents the continuous change in the bed structure as the separation process proceeds.

[0084] Multiply the relative undulation of the bed layer by the average cumulative deformation to obtain the deformation weighting factor, and multiply the baseline migration by the deformation weighting factor to obtain the aging baseline value;

[0085] From the start of separation to the current moment, the aging baseline values ​​at all moments are recorded. The maximum value of the 100 consecutive aging baseline values ​​before the current moment is taken as the upper limit of the reference. The aging baseline value at the current moment is divided by the upper limit of the reference to obtain the relative aging degree. The relative aging degree is then normalized to the 0-1 range, which is the aging index representing the degree of irreversible degradation accumulation within the column.

[0086] In one embodiment, the swelling disturbance index and peak broadening coefficient are analyzed synergistically to obtain a column effect fluctuation factor representing reversible fluctuations in column effect and a passivation influence factor representing irreversible decay of column effect, respectively. The method also includes:

[0087] The deviation analysis of the dynamic equilibrium coefficient is performed to generate the column effect fluctuation factor representing the reversible fluctuation of column effect. Specifically, it includes: setting a sliding window with a length of 15 consecutive collection points, calculating the maximum and minimum values ​​of the dynamic equilibrium coefficient within the sliding window, and recording the time position corresponding to the maximum value and the time position corresponding to the minimum value. If the maximum value appears before the minimum value, the window is determined to be a disturbance event. The number of time points from the time of the maximum value to the time of the minimum value is calculated as the recovery lag length of the disturbance event.

[0088] The reciprocal of the recovery hysteresis length is used as the recovery rate coefficient. The larger the recovery rate coefficient, the faster the resin column recovers from the disturbed state to the steady state, and the more significant the reversible fluctuation characteristics.

[0089] Within a sliding window of the same length, the dynamic balance coefficients are plotted as a curve that fluctuates over time. The arithmetic mean of all dynamic balance coefficients within the window is calculated and used as the dynamic reference line for the window. The number of times the dynamic balance coefficient curve crosses the dynamic reference line from bottom to top and from top to bottom within the window is counted. Two crosses are counted as one complete fluctuation cycle. The total number of fluctuation cycles is divided by the window length to obtain the fluctuation frequency per unit time. The fluctuation frequency is used to reflect the degree of activity of rapid fluctuations in column efficiency.

[0090] Multiplying the recovery rate coefficient by the fluctuation frequency yields the fluctuation coupling strength. Normalizing the fluctuation coupling strength to the 0-1 range serves as the column effect fluctuation factor representing the reversible fluctuation of the column effect. The closer the column effect fluctuation factor is to 1, the more frequent the column effect fluctuations are and the faster the recovery speed is in the current stage, and the more obvious the reversible fluctuation characteristics are. Otherwise, it indicates that the column effect fluctuations tend to be static or recover slowly, and the reversible fluctuation characteristics are weakened.

[0091] The aging index is analyzed to generate a passivation influencing factor representing the irreversible decline of column efficiency. Specifically, this includes recording the aging index value at each acquisition moment from the start of At-211 separation to the current moment and constructing a historical set in chronological order.

[0092] The current aging index is compared with all aging index values ​​in the historical set. Each historical value is determined to be less than or equal to the current aging index value. The number of data points that meet this condition is counted. The count is divided by the total number of data points in the historical set to obtain the cumulative frequency. The cumulative frequency is normalized to the 0-1 interval and used as the passivation influence factor representing the irreversible decay of column efficiency.

[0093] By synergistically analyzing the swelling disturbance index and peak broadening coefficient, combined with response coefficient trend-residual decomposition, dynamic equilibrium coefficient deviation analysis, and aging index assessment, the column efficiency fluctuation factor representing reversible column efficiency fluctuation and the passivation influence factor representing irreversible column efficiency decay are accurately obtained. This accurately represents the column efficiency fluctuation characteristics caused by reversible swelling of imidazolium resin and the degree of column efficiency decay caused by irreversible loss of active sites. It also reflects the self-recovery ability of the resin column and the accumulation of irreversible degradation, clearly distinguishing the two types of column efficiency changes and improving the accuracy of dynamic evaluation of the column efficiency of At-211 online separation of imidazolium resin.

[0094] In one embodiment, the trends of column efficiency fluctuation factor and passivation influence factor are analyzed to obtain health indicators for evaluating the column efficiency of imidazolium resin, including:

[0095] A composite analysis of the column efficiency fluctuation factor and the passivation influence factor is performed to generate a health deviation vector representing the degree of deviation from the overall column efficiency status. Specifically, this includes: constructing a two-dimensional health status space with the column efficiency fluctuation factor as the horizontal axis and the passivation influence factor as the vertical axis; the column efficiency fluctuation factor and the passivation influence factor together constitute the coordinate position of the health status point in the two-dimensional space at each acquisition time; and recording the coordinates of the health status points at all times from the separation at At-211 to the current time to form a health status trajectory.

[0096] Calculate the median of all column effect fluctuation factors from the separation of At-211 to the current time, and the median of all passivation influence factors. Use the coordinate point formed by these two medians as the health benchmark point.

[0097] Starting from the current health status point and ending at the health benchmark point, construct a directional line segment and calculate the Euclidean distance between the current health status point and the health benchmark point. This Euclidean distance is the magnitude of the health deviation vector.

[0098] Calculate the angle between the line connecting the current health status point and the health benchmark point and the positive direction of the horizontal axis. The angle is the direction angle of the health deviation vector, which reflects the dominant dimension of the current column effect deviation. The direction angle is close to zero degrees, indicating that the deviation is mainly contributed by the column effect fluctuation factor. The direction angle is close to ninety degrees, indicating that the deviation is mainly contributed by the passivation influence factor. The direction angle is close to forty-five degrees, indicating that the two factors work together.

[0099] Divide the magnitude of the health deviation vector by the maximum value among all health deviation vector magnitudes from the start of At-211 separation to the current time to obtain the normalized relative deviation degree. Use the relative deviation degree as the health deviation vector representing the deviation degree of the column effect integrated state.

[0100] The consistency of the changing trends of the column effect fluctuation factor and the passivation influence factor is analyzed to generate a degradation trend index representing the direction of column effect state evolution. Specifically, this includes: constructing time series of column effect fluctuation factor and passivation influence factor from the separation of At-211 to the current time; setting a sliding window with a length of 20 consecutive collection points; calculating the linear regression slope of the two series in each window; obtaining the short-term trend slope of column effect fluctuation factor and passivation influence factor; where a positive slope value indicates an upward trend, a negative slope value indicates a downward trend, and the absolute value of the slope indicates the strength of the trend.

[0101] Treat the two short-term trend slopes as two-dimensional vectors, with the trend slope of the column effect volatility factor as the horizontal axis component and the trend slope of the passivation influence factor as the vertical axis component, to construct the trend vector of the current window;

[0102] For a historical window from the start of At-211 separation to the current time, all passivation influence factors with positive trend slopes and column effect fluctuation factors with negative trend slopes are calculated. The average value of the passivation influence factor trend slope and the average value of the column effect fluctuation factor trend slope within the historical window are calculated. The coordinate point formed by these two average values ​​is regarded as a point on a two-dimensional plane. The vector pointing from the origin to this point is the ideal degradation direction vector. If there is no historical window that meets the conditions, the ideal degradation direction vector is set to the unit vector (-1, 1) direction.

[0103] Calculate the cosine of the angle between the current window trend vector and the ideal degradation direction vector. The closer the cosine of the angle is to 1, the more consistent the current trend direction is with the ideal degradation direction, and the more obvious the trend of the column effect evolving in the direction of irreversible degradation. The closer the cosine of the angle is to -1, the more opposite the current trend direction is to the ideal degradation direction, and the column effect may evolve in the direction of improvement. The closer the cosine of the angle is to 0, the more orthogonal or unrelated the two trend directions are. If the magnitude of the current window trend vector or the ideal degradation direction vector is zero, the cosine of the angle is directly 0.

[0104] Normalizing the cosine of the included angle to the 0-1 range yields a degradation trend index representing the direction of column effect evolution. The closer the degradation trend index is to 1, the closer the column effect evolution direction is to the ideal degradation direction of irreversible passivation and the clearer the trend of deterioration of health status. Otherwise, the opposite is true.

[0105] In one embodiment, analyzing the trends of column efficiency fluctuation factor and passivation influence factor to obtain a health index for evaluating the column efficiency of imidazolium resin further includes:

[0106] A joint evaluation of the health deviation vector and the degradation trend index is performed to generate a health index for evaluating the column efficiency of imidazolium resin. Specifically, the magnitude of the health deviation vector at the current moment is used as the current deviation degree; and the degradation trend index at the current moment is used as the current trend degree.

[0107] After normalizing the current deviation to the 0-1 range, multiply it by the current trend to obtain the comprehensive risk value; calculate the arithmetic mean of all comprehensive risk values ​​from the separation at At-211 to the current time to obtain the average risk level; divide the current comprehensive risk value by the average risk level to obtain the relative risk multiple; normalize the relative risk multiple to the 0-1 range, which is the health index for evaluating the column efficiency of imidazolium resin. The closer the health index is to 1, the higher the column efficiency health risk and the worse the condition, and vice versa.

[0108] By analyzing the trends of column efficiency fluctuation factors and passivation influencing factors, a two-dimensional health state space is constructed to generate a health deviation vector. The degradation trend index is calculated, and the two are combined to obtain a health index. This accurately quantifies the degree of deviation of the overall column efficiency state, clarifies the dominant dimension of column efficiency deviation, clearly judges the direction of column efficiency evolution and the level of health risk, and accurately distinguishes between column efficiency fluctuations caused by reversible swelling and column efficiency decay caused by irreversible loss of active sites, thereby improving the accuracy of At-211 online separation of imidazolium resin column efficiency assessment.

[0109] In one embodiment, the imidazolium resin column efficiency dynamic evaluation system for online separation of At-211 is applied in the above-described evaluation method, including:

[0110] The data acquisition unit is used to collect the bed height of the imidazolyl resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent in real time during the continuous separation process of At-211.

[0111] The column efficiency analysis unit is used to calculate the bed height and effluent pH value after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state, and to analyze the radioactivity after pretreatment to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation.

[0112] The collaborative calculation unit is used to perform collaborative analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively.

[0113] The column efficiency evaluation unit is used to analyze the trends of column efficiency fluctuation factors and passivation influence factors, and obtain health indicators for evaluating the column efficiency of imidazolium resin.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for dynamic evaluation of column efficiency of imidazolium resins for At-211 online separation, characterized by, include: Step S1: During the continuous separation of At-211, the bed height of the imidazolium resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent are collected in real time. Step S2: Calculate the bed height value and the pH value of the effluent after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state. This includes: performing distortion analysis on the bed height value and the pH value of the effluent after pretreatment to generate a swelling distortion coefficient that represents the degree of deviation between the resin bed and the chemical environment. The pH value of the effluent was integrated and corrected by combining it with the bed height value to obtain the ion penetration factor, which represents the cumulative chemical penetration effect of the eluent on the resin bed. By combining the swelling distortion coefficient and the ion penetration factor, a swelling disturbance index representing the risk of abnormal resin swelling state is obtained. The pretreated radioactivity was analyzed to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation. This included: performing time-series profile analysis on the radioactivity to generate the peak distortion coefficient, which represents the deviation of the symmetry of the elution peak. Cumulative analysis of the peak distortion coefficient yields the peak broadening coefficient, which represents the risk of separation performance degradation. Step S3: Perform a synergistic analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively. Step S4: Analyze the trends of column efficiency fluctuation factor and passivation influence factor to obtain health indicators for evaluating the column efficiency of imidazolium resin.

2. The method for dynamic evaluation of imidazolium resin column efficiency for At-211 online separation according to claim 1, characterized in that, By jointly analyzing the swelling disturbance index and the peak broadening coefficient, the column effect fluctuation factor, representing the reversible fluctuation of column effect, and the passivation influence factor, representing the irreversible decay of column effect, were obtained, including: The dynamic correlation between the swelling disturbance index and the peak broadening coefficient during the separation process was analyzed, and a response coefficient representing the degree of synergistic change between the swelling state and the separation efficiency was generated. The response coefficients are decomposed into trend-residual values ​​to calculate the instantaneous response strength representing rapid fluctuations and the baseline migration representing slow drifts.

3. The method for dynamic evaluation of imidazolium resin column efficiency for online separation of At-211 according to claim 2, characterized in that, By combining the swelling disturbance index and the peak broadening coefficient, a column effect fluctuation factor representing reversible fluctuations in column effect and a passivation influence factor representing irreversible decay of column effect were obtained, respectively. Other results include: Peak values ​​of the instantaneous response intensity were statistically analyzed and corrected for by combining the rate of change of pH value of the effluent, resulting in a dynamic equilibrium coefficient representing the self-recovery capability of in-column disturbance. The baseline migration is corrected based on the bed height value to obtain an aging index that represents the degree of irreversible degradation accumulation within the column.

4. The method for dynamic evaluation of column efficiency of imidazolium resin for At-211 online separation according to claim 3, characterized in that, By combining the swelling disturbance index and the peak broadening coefficient, a column effect fluctuation factor representing reversible fluctuations in column effect and a passivation influence factor representing irreversible decay of column effect were obtained, respectively. Other results include: Deviation analysis is performed on the dynamic equilibrium coefficient to generate a column effect fluctuation factor representing the reversible fluctuation of column effect; The aging index was analyzed to generate a passivation influence factor representing the irreversible decline in column efficiency.

5. The method for dynamic evaluation of imidazolium resin column efficiency for At-211 online separation according to claim 4, characterized in that, By analyzing the trends of column efficiency fluctuation factors and passivation influence factors, health indicators for evaluating the column efficiency of imidazolium resin were obtained, including: A composite analysis of the column efficiency fluctuation factor and the passivation influence factor is performed to generate a health deviation vector representing the degree of deviation from the overall column efficiency status; The consistency between the changing trends of the column efficiency fluctuation factor and the passivation influence factor is analyzed to generate a degradation trend index that represents the direction of column efficiency state evolution.

6. The method for dynamic evaluation of column efficiency of imidazolium resin for At-211 online separation according to claim 5, characterized in that, Analyzing the trends of column efficiency fluctuation factors and passivation influence factors yields health indicators for evaluating the column efficiency of imidazolium resin, including: A health index for evaluating the efficacy of imidazolium resin columns was generated by jointly assessing the health deviation vector and the degradation trend index.

7. A dynamic evaluation system for the column efficiency of imidazolium resin for online separation of At-211, applied in the evaluation method as described in any one of claims 2-6, characterized in that, include: The data acquisition unit is used to collect the bed height of the imidazolyl resin column, the pH value of the effluent, and the radioactivity of At-211 in the eluent in real time during the continuous separation process of At-211. The column efficiency analysis unit is used to calculate the bed height and effluent pH value after pretreatment to obtain the swelling disturbance index, which represents the risk of abnormal resin swelling state, and to analyze the radioactivity after pretreatment to obtain the peak broadening coefficient, which represents the risk of separation efficiency degradation. The collaborative calculation unit is used to perform collaborative analysis of the swelling disturbance index and the peak broadening coefficient to obtain the column effect fluctuation factor representing the reversible fluctuation of column effect and the passivation influence factor representing the irreversible decay of column effect, respectively. The column efficiency evaluation unit is used to analyze the trends of column efficiency fluctuation factors and passivation influence factors, and obtain health indicators for evaluating the column efficiency of imidazolium resin.

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