Papermaking wastewater pollutant removal method based on nanofiltration fractionation

Abstract

The invention relates to the technical field of wastewater treatment, and particularly discloses a papermaking wastewater pollutant removal method based on nanofiltration fractionation, and the method comprises the following steps: collecting real-time conformation parameters of papermaking wastewater lignin in a nanofiltration system, and calculating a conformation fluctuation ratio to judge whether conformation imbalance analysis is needed or not; if necessary, carrying out conformation imbalance analysis by combining the characteristic parameters of the nanofiltration membrane, and judging whether lignin conformation dynamic trend tracking is triggered or not; extracting conformation dynamic trend and distribution offset characteristics, and dividing an unbalance interval of the nanofiltration system; if the lignin enters the unbalance interval, fitting a quantitative relation between the lignin conformation conversion rate and interception efficiency attenuation, constructing an interception response model and a conformation regulation boundary, and when real-time conformation parameters touch the boundary, starting targeted conformation regulation according to the type of the unbalance interval. According to the method, high-efficiency optimization of the adaptability of the lignin in the papermaking wastewater and the nanofiltration membrane is realized, and the pollutant removal efficiency and the stability of a nanofiltration system are favorably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and in particular to a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation. Background Art

[0002] In the field of paper industry wastewater treatment, nanofiltration technology is widely used because it can effectively intercept large molecular pollutants such as lignin and realize water resource recycling. However, the existing nanofiltration system still has the following technical bottlenecks in the treatment process: Existing technologies focus solely on the static physical and chemical properties of lignin (such as total content and average molecular weight), unaware that lignin undergoes conformational transitions under conditions such as wastewater flow and pressure fluctuations. These can include sudden increases or decreases in aggregate size and transitions from a contracted to an extended state. Due to the lack of real-time monitoring of conformational parameters, existing technologies are unable to capture these dynamic changes. Consequently, when problems such as membrane pore blockage and retention rate fluctuations arise, it is difficult to trace the conformational causes, leading to delayed system responses.

[0003] When the nanofiltration membrane becomes clogged or the retention stability decreases, existing technologies find it difficult to distinguish whether it is caused by lignin conformational shift or membrane aging itself. There is a lack of quantitative correlation analysis between the dynamic trend of conformation and the type of membrane performance deterioration. It is impossible to determine whether the conformation is dominant or the membrane's own deterioration is the core cause, resulting in blind regulation direction.

[0004] To this end, the present invention provides a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation. Summary of the Invention

[0005] The object of the present invention is to provide a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for removing pollutants from papermaking wastewater based on nanofiltration fractionation comprises the following steps: Collect the real-time conformational parameters of lignin in papermaking wastewater in the nanofiltration system, perform conformational dynamic analysis on the real-time conformational parameters to obtain the conformational volatility, and determine whether the conformational imbalance analysis of lignin and nanofiltration membrane is needed; If conformational imbalance analysis is required, a conformational characteristic data set is established based on the real-time conformational parameters, and conformational imbalance analysis is performed on the conformational characteristic data set to determine whether lignin conformational dynamic trend tracking is triggered; If the lignin conformational dynamic trend tracking is triggered, the conformational dynamic trend and distribution deviation characteristics are extracted based on the conformational characteristic data set, and the conformational deviation analysis is performed to obtain the imbalance interval of the nanofiltration system; If the nanofiltration system enters the imbalance zone, the quantitative relationship between the lignin conformational transition rate and the attenuation of the interception efficiency is fitted. Based on the quantitative relationship, a retention response model under conformational interference is constructed. The conformational regulation boundary is constructed through the interception response model. If the real-time conformational parameter touches the regulation boundary, the conformational regulation is initiated.

[0007] As a further solution of the present invention: the method of performing the conformational dynamics analysis is: Obtain the average particle size and particle size distribution width of the aggregates in real-time conformational parameters; Calculate the change rate of the average particle size of the agglomerates during the monitoring period to obtain the particle size change rate; Calculate the change rate of the average stretching degree of the molecular chain during the monitoring period to obtain the stretching change rate; The particle size change rate and the extension change rate are weighted and summed to obtain the conformational fluctuation rate; Comparative analysis is performed based on conformational fluctuations to determine whether conformational imbalance analysis is triggered.

[0008] As a further solution of the present invention: the method of performing the conformational imbalance analysis is: Acquire the characteristic parameters of the nanofiltration membrane in real time, and establish a conformational characteristic data set in combination with the real-time conformational parameters; The lignin retention rate within the conformational characteristic data set was extracted and combined with the conformational change rate to perform nonlinear amplification trigger analysis to obtain the nonlinear response ratio; A joint distribution model of conformation and nanofiltration membrane parameters was constructed, and the rate of change of adaptive entropy was obtained by performing ordered imbalance analysis. The distribution kurtosis of the adaptation entropy change rate was extracted, and a three-dimensional joint criterion was constructed based on the nonlinear response ratio, distribution kurtosis and adaptation entropy change rate to determine whether the dynamic trend tracking analysis of lignin conformation was triggered.

[0009] As a further solution of the present invention: the method of performing the ordered imbalance analysis is: Based on the average particle size of aggregates, membrane flux and retention rate in the conformational characteristic data set as joint features, the conformational three-dimensional distribution was obtained by fitting; Based on the three-dimensional distribution of conformations, the conformational adaptation entropy is obtained through the entropy equation; The change rate of the conformational adaptation entropy between the current and adjacent monitoring periods is obtained and calculated to obtain the adaptation entropy change rate.

[0010] As a further solution of the present invention: the method of constructing the three-dimensional joint criterion is: The nonlinear response ratio, distribution kurtosis and adaptation entropy change rate are used as three-dimensional joint features; Construct a normal baseline library of historical joint criteria for nanofiltration systems; Calculate the three-dimensional deviation between the three-dimensional joint feature and the normal baseline library; Based on the three-dimensional deviation, the collaborative deviation analysis is performed to obtain the collaborative deviation index; Trajectory similarity was determined based on the cooperative deviation index, and if the trajectories were similar, the dynamic trend of lignin conformation was tracked.

[0011] As a further solution of the present invention: the method of performing conformational shift analysis is: Extract dynamic trend indicators for quantification of lignin dynamic characteristics; extracting distribution shift indices of conformational shifts; Combining dynamic trend indicators and distribution shift indicators, we can analyze the conformational shift and the type of nanofiltration membrane performance deterioration. If the blockage is conformation-dominated, a stability analysis of the target retention of the nanofiltration membrane is performed to determine whether the conformation-dominated retention is unstable; Based on the results of stability analysis, the imbalance interval is divided by combining trajectory similarity and cooperative deviation index.

[0012] As a further solution of the present invention: the method of analyzing the type of deterioration of the nanofiltration membrane performance is: Obtain and calculate the second-order derivative of the membrane specific flux in the nanofiltration membrane characteristic parameters; By using the partial least squares regression algorithm, the change rate of the average particle size of the agglomerates in the dynamic trend index within the continuous monitoring period and the change rate of the particle size distribution width were used as independent variables, and the second-order derivative of the membrane specific flux was used as the dependent variable. Get the absolute value of the regression coefficient output by the partial least squares regression algorithm and the sum of the absolute values ​​of all coefficients; The absolute value of the regression coefficient is compared with the sum of the absolute values ​​of all coefficients to obtain the contribution weight of the agglomerate size deviation to the blockage. Critical blockage analysis was performed based on the contribution weight and the second-order derivative of the membrane specific flux to determine whether the blockage was conformation-dominated.

[0013] As a further solution of the present invention: the method for determining whether the conformation-dominated interception is unstable is: If the blockage is conformation-dominated, the coefficient of variation of the retention rate within the characteristic Dalton interval is calculated; Based on the random regression forest algorithm, a stretch-entrapment fluctuation correlation model was constructed to calculate the contribution weight of the molecular chain stretch deviation to the interception fluctuation. Based on the coefficient of variation and contribution weight, a comparison analysis was performed through the baseline library to determine whether the conformation-dominated retention was unstable.

[0014] As a further solution of the present invention: the method of constructing the conformational regulation boundary is: Extract the average particle size and molecular chain extension of the aggregates in the conformational characteristic data set and combine them to construct a core conformation group; Constructing a retention response model, inputting the core conformation group into the retention response model, and the retention response model outputs the predicted value of membrane specific flux and retention rate in the future monitoring period; The regulatory boundary conditions were constructed based on conformational fitness entropy, membrane specific flux prediction value, retention rate prediction value, and cooperative deviation index; Taking the molecular chain extension of the average particle size of the aggregates as the coordinate axis, a two-dimensional control boundary is constructed; The core conformation group that satisfies the regulatory boundary is marked as the boundary outside the regulatory boundary; The points outside the boundary are fitted with polynomial functions to construct a continuous control boundary curve.

[0015] As a further solution of the present invention: the method of constructing the interception response model is: The interception response model is constructed through the long short-term memory network algorithm, and the dynamic trend indicator, distribution offset indicator, and imbalance interval identifier are input into the interception response model.

[0016] Beneficial effects of the present invention: (1) The average particle size of lignin aggregates and the stretch of the molecular chain are collected as conformational parameters. The conformational fluctuation rate is calculated by weighted calculation of the particle size change rate and the stretch change rate, which enables real-time capture and quantitative characterization of the dynamic changes in lignin conformation. This is conducive to identifying abnormal conformational fluctuations, providing a trigger basis for whether to initiate subsequent conformational imbalance analysis, reducing the deterioration of membrane performance caused by undetected conformational mutations, and improving the timeliness of system response.

[0017] (2) By constructing a conformational characteristic data set, combining the nonlinear response equation to calculate the nonlinear response ratio, analyzing the adaptive entropy change rate through the entropy value equation, and performing imbalance precursor analysis based on a three-dimensional joint criterion, a quantitative assessment of the correlation between conformation and membrane performance is achieved. By using a normal baseline library and three-dimensional deviation calculation, the accuracy of conformational dynamic trend tracking trigger judgment is improved, and the reliability of imbalance warning is enhanced.

[0018] (3) Dynamic trend indicators such as the aggregate size change rate and the molecular chain stretching deviation slope, as well as distribution deviation characteristics such as the size distribution width change rate, were extracted. Conformation-dominated blockage was analyzed using partial least squares regression. The conformation-dominated interception instability was determined using a random forest algorithm. Finally, the imbalance interval was divided based on trajectory similarity and cooperative deviation index. This approach achieved the attribution of the effect of conformational deviation on membrane performance deterioration, clarified the degree and type of membrane imbalance, and provided a basis for the formulation of targeted regulatory strategies.

[0019] (4) The quantitative relationship between conformational transition rate and retention efficiency attenuation was fitted using the nonlinear least squares method. A retention response model was constructed using a long-short-term memory network to predict future membrane performance. A two-dimensional control boundary was constructed based on the average particle size of the aggregates and the molecular chain extension, and differentiated regulation was initiated based on the type of imbalance interval. This achieved predictability in conformational control and targeted control measures to address conformation-dominated issues through dynamic boundaries and predictive models, which facilitated the timely recovery of membrane performance and improved the stability and pollutant removal efficiency of the nanofiltration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flow chart of a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation of the present invention; Figure 2 It is a flow chart for judging whether the conformational parameters of the present invention reach the regulatory boundary; Figure 3 This is a module diagram of a papermaking wastewater pollutant removal system based on nanofiltration graded separation in the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1 See also Figure 1 As shown, the present invention is a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation, comprising the following steps: S1. Collecting the real-time conformational parameters of lignin in papermaking wastewater in the nanofiltration system, performing conformational dynamic analysis on the real-time conformational parameters to obtain conformational volatility, and determining whether a conformational imbalance analysis of lignin and the nanofiltration membrane is required; Among them, the method of collecting the real-time conformation parameters of papermaking wastewater lignin and the characteristic parameters of the nanofiltration membrane in the nanofiltration system is: Preferably, online monitoring equipment is installed at the water inlet pipe of the nanofiltration system, the inlet of the nanofiltration membrane module and the outlet of the concentrated liquid: It includes a dynamic light scattering (DLS) sensor for real-time measurement of lignin solution in a nanofiltration system used to grade and separate papermaking wastewater. DLS uses laser irradiation on lignin aggregates and utilizes the fluctuations in scattered light intensity generated by the Brownian motion of particles to calculate the particle size distribution through cumulant analysis. The particle size distribution includes: the average particle size of lignin aggregates and the particle size distribution width; UV-visible online spectrometer: absorbance A of the characteristic peak of lignin at 280 nm 280 , by the A of the lignin standard with known elongation 280 Curve, establish the quantitative relationship between absorbance and molecular chain stretching, and obtain the average stretching of the molecular chain; For example, by the formula: Get the average stretching degree Sz of the molecular chain, where 、 is the calibration coefficient; It is understood by those skilled in the art that since the extension of the lignin molecular chain is directly related to the exposure degree of its conjugated structure (benzene ring, double bond, etc.), the more extended the molecular chain segment is, the easier it is for the conjugated group to interact with ultraviolet light, and the higher the characteristic absorption peak intensity at 280nm is; The average particle size of aggregates and the width of particle size distribution are used as real-time conformational parameters; The real-time conformational parameters collected during the monitoring period were extracted, and the change rate of the average particle size of the aggregates between the initial and final values ​​during the monitoring period was calculated to obtain the particle size change rate; Synchronously obtain and calculate the change rate of the average stretching degree of the molecular chain between the initial and final values ​​within the monitoring period to obtain the stretching change rate; The particle size change rate and the extension change rate are weighted and summed to obtain the conformational fluctuation rate; Among the conformational fluctuations, the weight of the particle size change rate is set to 0.6, and the weight of the molecular chain stretch change rate is set to 0.4. This is because particle size changes are directly related to membrane pore blockage and have a more significant impact on the continuity of system operation, while stretch changes mainly affect the retention stability and have a weaker immediate impact. This allocation can accurately quantify the actual risk of conformational fluctuations to the system and provide a basis for determining whether to trigger conformational imbalance analysis. It can be understood that the conformational volatility quantifies the degree of dynamic changes in the lignin conformation. The physical meaning of the conformational volatility is: through weighted fusion, the change rate of the average particle size of the aggregates, which reflects the fluctuation of the aggregation state, and the change rate of the average extension of the molecular chain, which reflects the fluctuation of the spatial morphology of the molecular chain, it characterizes the overall fluctuation amplitude and rate of the conformation of lignin from macro-aggregates to micro-molecular chains during the monitoring period, and intuitively reflects the degree to which the conformation deviates from the stable state. Among them, the function of calculating conformational volatility is: Function 1: As the trigger for conformational imbalance analysis, the conformational volatility is compared with the preset threshold to directly determine whether to initiate subsequent conformational imbalance analysis. When the volatility is higher than the threshold, it indicates that the conformational change has exceeded the normal stable range, and further analysis is required to determine whether the conformation is unbalanced with the nanofiltration membrane, providing a starting criterion for subsequent processes; Function 2: Real-time capture of abnormal conformational fluctuations to reduce membrane performance deterioration. The conformational volatility rate achieves real-time quantification of dynamic changes in lignin conformation, and can promptly identify sudden conformational changes such as a sudden increase in aggregate size and excessive stretching of molecular chains. If abnormalities are not captured in time, they may lead to membrane pore blockage and a sudden drop in retention rate. Therefore, the conformational volatility rate can provide early warning of potential risks and improve the timeliness of system response. Function 3: Providing basic data for subsequent conformational dynamic trend analysis. Conformational volatility is the core basic parameter that characterizes conformational dynamic characteristics. It supports subsequent in-depth analysis of the direction, rate, and impact of conformational deviation, forming a logical closed loop of real-time monitoring-trend analysis-control. The conformational fluctuation rate is compared with a preset conformational fluctuation threshold. If the conformational fluctuation rate is higher than the preset conformational fluctuation threshold, the conformational imbalance analysis is triggered.

[0024] S2. If conformational imbalance analysis is required, a conformational characteristic data set is established based on the real-time conformational parameters, and conformational imbalance analysis is performed on the conformational characteristic data set to determine whether lignin conformational dynamic trend tracking is triggered; The method of obtaining the real-time characteristic parameters and the real-time conformation parameters to establish the conformation characteristic data set is as follows: Obtain the theoretical and real-time characteristic Dalton interval rejection rate, membrane specific flux, and pore size of the nanofiltration membrane as characteristic parameters of the nanofiltration membrane; Preferably, the characteristic Dalton range is 200-1000; It is understandable that the characteristic parameters of the theoretical nanofiltration membrane are based on the nominal parameters of the nominal molecular weight cut-off, pore size distribution, and surface charge density provided by the membrane manufacturer. The actual retention performance in the characteristic Dalton range is verified through offline experiments: simulated solutions are prepared using standard substances (such as PEG200, PEG500, PEG1000), the retention rate of the membrane for substances with different molecular weights is tested, and the molecular weight-retention rate curve is drawn to determine the retention rate range of 200-1000 Daltons and the critical retention point (such as the retention rate for 500 Dalton substances); The characteristic parameters of the nanofiltration membrane in real time are collected through the online gel permeation chromatography (GPC) module, pressure sensor and flow meter to collect the real-time characteristic Dalton interval rejection rate and membrane specific flux; Performing spatiotemporal alignment processing on the real-time conformational parameters and the real-time characteristic parameters of the nanofiltration membrane, and combining the real-time conformational parameters and the characteristic parameters of the nanofiltration membrane after the spatiotemporal alignment processing to obtain a conformational characteristic data set; It can be understood that by matching the time synchronization of the acquisition moments of the same monitoring period and the spatial correlation of the same monitoring position of the corresponding nanofiltration system, the spatiotemporal alignment of the real-time conformational parameters and the characteristic parameters of the nanofiltration membrane is achieved, and then the aligned data are integrated into a conformational characteristic data set; Among them, the method of performing conformational imbalance analysis on the conformational characteristic data set to determine whether to trigger the dynamic trend tracking of lignin conformation is: S201, obtaining the interception change rate and performing nonlinear amplification trigger analysis in combination with the conformational change rate to obtain a nonlinear response ratio; Obtaining a change rate of lignin retention in the conformational characteristic data set during a monitoring period to obtain a retention change rate; Based on the interception change rate and combined with the conformational change rate, a nonlinear amplification trigger analysis is performed to obtain the nonlinear response ratio of the conformation; Preferably, through the nonlinear response equation: Obtaining the nonlinear response ratio of the conformation ; in, is the real-time conformational change rate, is the real-time intercept change rate, is the conformational baseline change rate, is the intercept benchmark change rate; It should be explained that the conformational baseline change rate refers to the normal change rate of the conformational parameters when the system is running stably, which corresponds to the dynamic baseline value of the conformation under the interception stable state. The interception benchmark change rate is the normal fluctuation rate of the lignin interception rate when the system is running stably, which serves as the dynamic benchmark value for the stable interception performance; S202, constructing a joint distribution model of conformation-nanofiltration membrane parameters, and performing ordered imbalance analysis to obtain an adaptive entropy change rate; Preferably, the conformational three-dimensional distribution is obtained by fitting the average particle size D, membrane flux J and retention rate R in the conformational characteristic data set as joint features. ; Based on the three-dimensional distribution of conformations, the entropy equation is used: Obtain conformational adaptation entropy H; It will be understood by those skilled in the art that the first step is to collect the Time series data, and then map the data into the particle size-flux-retention rate dimensional space (each axis defines the physically feasible interval, such as 、 、 Finally, the frequency of occurrence of the combined state in a small interval in the three-dimensional space is calculated by multivariate Gaussian distribution as the conformational three-dimensional distribution The probability value in this interval is used to quantify the probability law of the three relationships; The domain of the three-dimensional integral in the entropy equation is Cartesian product of the physically feasible intervals of the three parameters; For example: , The actual range of agglomerate particle size is: from the initial particle size Critical particle size for membrane pore blocking ; The actual range of membrane flux is: from the initial flux To the acceptable lower limit of attenuation ; The actual range of the retention rate is from the design The retention rate reaches the allowed fluctuation threshold : Obtain and calculate the change rate of the conformational adaptation entropy H between the current and adjacent monitoring periods to obtain the adaptation entropy change rate; Extract the distribution kurtosis of the adaptive entropy change rate based on the nonlinear response ratio , distribution kurtosis and adaptation entropy change rate to construct a three-dimensional joint criterion to determine whether to trigger the dynamic trend tracking analysis of lignin conformation; Among them, by constructing the time series probability distribution of the adaptive entropy change rate, and then calculating the ratio of the fourth-order central moment of the distribution to the square of the second-order central moment, the kurtosis of the distribution is obtained; Among them, the method of constructing the three-dimensional joint criterion is: The nonlinear response ratio , distribution kurtosis and adaptation entropy change rate as three-dimensional joint features; S211. Construct a normal baseline library of historical joint criteria for nanofiltration systems; Preferably, from the stable operation period of the nanofiltration system, the low fluctuation-high adaptation samples are identified by clustering algorithm, and the nonlinear response ratio is extracted. , distribution kurtosis and adaptive entropy change rate baseline distribution model, based on the three types of baseline distribution models, to build a normal baseline library; It can be understood by those skilled in the art that when constructing a normal baseline library of historical joint criteria for the nanofiltration system, the historical data of the system during stable operation is first screened, and samples with low fluctuation (small fluctuation of conformation and membrane performance parameters) and high adaptability (high matching degree between conformation and membrane performance, such as low adaptability entropy) are identified through a clustering algorithm; for these samples, three types of indicators are extracted: nonlinear response ratio (nonlinear degree of conformation-retention dynamic correlation), distribution kurtosis (steepness of the distribution of the adaptation entropy change rate, reflecting the concentration of change), and adaptation entropy change rate (time change rate of adaptation entropy), and their distribution models (such as statistical mean, standard deviation or probability distribution parameters) in the steady state are established respectively; finally, the three types of baseline models are integrated to form a normal baseline library, which provides a reference threshold and distribution characteristics of the steady state for subsequent judgment of conformational imbalance; S212, calculating the three-dimensional deviation between the three-dimensional joint feature and the normal baseline library; Preferably, by formula 1: Obtaining the deviation of nonlinear response ; in, is the cumulative distribution function of the standard normal distribution, which is used to calculate the probability of the value in the stable state. They are the nonlinear response ratio calculated in real time, the normal baseline library, and the stable operation The mean of In the normal baseline library, stable operation The standard deviation of Through formula 2: Obtaining the entropy change deviation of conformational adaptation entropy ; Among them, H is the real-time calculated conformational adaptation entropy change rate, In the stable state, the adaptive entropy change rate is lower than the real-time value probability; in, is the probability density function in the baseline distribution model that adapts the entropy change rate; Through formula three: Get the kurtosis skewness ; in, They are the minimum and maximum values ​​of kurtosis during stable operation in the normal baseline library; Will 、 、 As three-dimensional deviation; It should be noted that 、 、 The value range of is [0,1]. The larger the value, the more significant the deviation from normality. The larger the value, the faster the entropy decreases. It means that deviation occurs only when the kurtosis exceeds the upper limit of the normal interval. The larger the value, the sharper the distribution. S213, performing collaborative deviation analysis based on the three-dimensional deviation to obtain a collaborative deviation index; By formula: Get the collaborative deviation index C; in, is the Pearson correlation coefficient of the nonlinear response ratio, distribution kurtosis, and adaptation entropy change rate in the normal baseline library, In the normal baseline library, the Pearson correlation coefficient between the rate of change of adaptive entropy and the kurtosis of the distribution is Pearson correlation coefficient between nonlinear response ratio and distribution kurtosis in the normal baseline library; Used to reflect the synergistic relationship of the three characteristics under normal conditions; S214, determining the similarity of trajectories based on the cooperative deviation index, and tracking the dynamic trend of lignin conformation if the trajectories are similar; Obtain the collaborative deviation index within M monitoring periods and construct a collaborative deviation sequence; Obtain non-imbalanced sample trajectories from the normal baseline library for dynamic time warping (DTW) analysis and trajectory similarity C; When satisfied: And when the trajectory similarity is lower than the preset trajectory similarity threshold, it triggers the tracking of the dynamic trend of lignin conformation; in, is the trajectory similarity dataset of non-imbalanced samples in the normal baseline library, for the median of is the threshold for outlier judgment.

[0025] Example 2 like Figure 1 As shown, the present invention is a method for removing pollutants from papermaking wastewater based on nanofiltration fractionation, which also includes the following steps: S3. If lignin conformational dynamic trend tracking is triggered, the conformational dynamic trend and distribution shift characteristics are extracted based on the conformational characteristic data set, and conformational shift analysis is performed to obtain the imbalance interval of the nanofiltration system; Among them, the method of extracting the conformational dynamic trend and distribution deviation characteristics based on the conformational characteristic data set and performing conformational deviation analysis to obtain the imbalance interval of the nanofiltration system is as follows: S301, extracting dynamic trend indicators for quantifying dynamic characteristics of lignin; Preferably, by the formula: Obtain the rate of change of the average particle size of the agglomerates within the continuous monitoring period , Among them, the continuous monitoring period is higher than 3, is the change in the average particle size of the aggregates during the continuous monitoring period due to the molecular chain extension. is the length of the continuous monitoring period; Obtain the deviation slope of the average extension of the molecular chain and the acceleration of the conformational fluctuation rate during the continuous monitoring period; The deviation slope of the average stretching degree of the molecular chain, the acceleration of the conformational fluctuation rate, and the rate of change of the average particle size of the aggregates within the continuous monitoring period are used as dynamic trend indicators; S302, extracting a distribution shift index of the conformational shift; Obtain the rate of change of particle size distribution width during the continuous monitoring period; The rate of change of the distribution width is calculated by calculating the difference between the particle size distribution width of the current monitoring period and the distribution width in the baseline library, and then the difference is compared with the distribution width in the baseline library to obtain the distribution deviation index. S303. Combining the dynamic trend index and the distribution shift index, analyzing the conformation shift and the type of nanofiltration membrane performance deterioration; Calculate the second-order derivative of the membrane flux in the characteristic parameters of the nanofiltration membrane, and use the partial least squares regression algorithm to calculate the change rate of the average particle size of the agglomerates in the dynamic trend index within the continuous monitoring period. , with the rate of change of the particle size distribution width as the independent variable and the second derivative of the membrane specific flux as the dependent variable; Get partial least squares regression algorithm output The absolute value of the regression coefficient and the sum of the absolute values ​​of all coefficients; Will The absolute value of the regression coefficient is ratioed to the sum of the absolute values ​​of all coefficients to obtain the contribution weight of the agglomerate size deviation to the blockage; Critical blockage analysis is performed based on contribution weights and the second-order derivative of membrane specific flux to determine whether it is conformation-dominated blockage; It should be noted that the contribution weight of conformational changes (such as aggregate deformation and molecular chain conformational fluctuations) to membrane blockage is first quantified through an attribution model. At the same time, the second-order derivative of the membrane specific flux (a flux index that eliminates the influence of pressure) is calculated to characterize the accelerated characteristics of flux decay. The second-order derivative reflects the changing trend of the flux change rate. When the conformational contribution weight exceeds a critical threshold and the change pattern of the second-order derivative of the membrane specific flux (such as sign mutation, amplitude exceeding the critical value) matches the nonlinear blockage dynamics caused by conformational dynamic imbalance (such as accelerated blockage caused by membrane pore shrinkage or aggregate jamming due to conformational disorder), it is determined to be conformation-dominated blockage. S304, if the blockage is conformation-dominated, then performing stability analysis on the target retention of the nanofiltration membrane to determine whether the conformation-dominated retention is unstable; If the blockage is conformation-dominated, the coefficient of variation of the retention rate within the characteristic Dalton interval is calculated; Among them, the coefficient of variation reflects the degree of dispersion of the retention rate fluctuation; Based on the random regression forest algorithm, a stretch-entrapment fluctuation correlation model was constructed to calculate the contribution weight of the molecular chain stretch deviation to the interception fluctuation. Based on the coefficient of variation and contribution weight, a comparison analysis was performed with the baseline library to determine whether the conformation-dominated retention was unstable. It can be understood that when constructing the stretchability-retention fluctuation correlation model based on the random regression forest algorithm, the real-time monitoring data of the molecular chain stretchability (such as the stretchability values ​​at different times) is used as the input feature, and the fluctuation amplitude of the retention rate in the corresponding retention fluctuation data is used as the output target. The nonlinear correlation between the two is fitted by training the model; after the model training is completed, the feature importance evaluation mechanism (such as the reduction in impurity when the node is split) is used to calculate the weight of the molecular chain stretchability deviation among all factors affecting the retention fluctuation, that is, to obtain its contribution weight to the retention fluctuation; When judging conformation-dominated retention instability based on the coefficient of variation and contribution weight, first calculate the coefficient of variation of the real-time retention fluctuation and combine it with the contribution weight of the molecular chain stretch deviation to the retention fluctuation obtained above; then compare the two with the retention fluctuation coefficient of variation threshold and the conformational factor contribution weight threshold during stable operation in the normal baseline library. If the real-time coefficient of variation exceeds the baseline threshold and the contribution weight of the molecular chain stretch deviation also exceeds the critical weight of conformation dominance in the baseline, it is judged to be conformation-dominated retention instability; S305. Based on the results of the stability analysis, the imbalance interval is divided by combining the trajectory similarity and the collaborative deviation index; For example, the conditions for determining the warning interval are: Coordination deviation index C , trajectory similarity S , when the cooperative deviation index and trajectory similarity are in the corresponding warning range, the conformational deviation begins to affect the membrane performance, but has not yet formed a significant deterioration trend; The one-way imbalance interval can be set as: Cooperative Deviation Index C , trajectory similarity S , and a single dimension (such as the conformational fit entropy deviation) in the nonlinear response, distribution kurtosis, and fit entropy deviation is much higher than other dimensions (for example, the deviation of this dimension accounts for more than 60% of the total deviation), reflecting a unidirectional imbalance in conformational or membrane performance and local deterioration of the system; The global imbalance range is set as follows: the collaborative deviation index is higher than , the trajectory similarity is higher than 0.6, and the nonlinear response, distribution kurtosis, and adaptation entropy deviation simultaneously exceed their respective baseline thresholds, the coupling relationship between conformation and membrane performance is completely broken, multiple characteristic trajectories simultaneously deviate from the stable state, and the system performance is on the verge of being out of control; Numbering different imbalance intervals to obtain imbalance interval identifiers; It is understandable that the purpose of dividing the imbalance interval is: Function 1: Provides a hierarchical basis for tracing the root causes of conformational-membrane performance imbalances. Different imbalance intervals correspond to specific characteristic deviation patterns. For example, the warning interval may originate from a slight fluctuation in a single parameter, while global imbalance involves the coordinated collapse of multiple features. Interval labels can quickly identify the dominant cause of the imbalance (whether it is abnormal conformational dynamics, membrane performance degradation, or a coupling failure between the two), providing direction for subsequent accurate diagnosis and reducing the blindness of traditional diagnosis. Function 2: Supporting the dynamic iteration and effectiveness evaluation of regulatory strategies. The division of imbalance intervals establishes a closed-loop relationship between state, intervention, and effect. By recording the response effects of regulatory measures in different intervals (for example, whether fine-tuning the warning interval prevents the onset of unidirectional imbalance), the adaptability of the strategy can be quantitatively evaluated, and critical thresholds can be iteratively optimized to optimize intervention timing, forming a continuously improving regulatory system.

[0026] S4. If the nanofiltration system enters the imbalance zone, the quantitative relationship between the lignin conformational transition rate and the interception efficiency attenuation is fitted, and an interception response model under conformational interference is constructed based on the quantitative relationship. The conformational regulation boundary is constructed through the interception response model, and conformational regulation is initiated if the real-time conformational parameter touches the regulation boundary; Among them, the quantitative relationship between the lignin conformational transition rate and the retention efficiency attenuation is fitted as follows: The conformational transition rate is obtained by summing the aggregate size change rate, the deviation slope of the average extension, and the conformational fluctuation rate. ; Obtain the attenuation rate of the rejection rate and the attenuation rate of the membrane specific flux in the characteristic Dalton interval, sum the attenuation rate of the rejection rate and the attenuation rate of the membrane specific flux to obtain the attenuation coefficient of the rejection efficiency ; By nonlinear least squares method: Fitting the functional relationship between the retention efficiency and the conformational transition rate; in, 、 、 are the parameters of the nonlinear least squares fitting, is the basic contribution coefficient, Characterize the exponential effect of the conformational transition rate on the retention rate, is the offset correction term, and the three are determined by minimizing the sum of squares of the residuals between the predicted interception rate and the actual value; Among them, the method of constructing the interception response model under conformational interference based on the quantitative relationship is: A retention response model is constructed using the long short-term memory network algorithm, and the dynamic trend indicator, distribution offset indicator, and imbalance interval identifier are input into the retention response model; Those skilled in the art will appreciate that, when constructing a retention response model, the dynamic trend indicator, distribution offset indicator, and imbalance interval identifier are first normalized and preprocessed to unify the data scale. An LSTM network structure is then designed, with the input layer dimension matching the total number of the three types of indicators. Time series dependencies are captured through 1-2 LSTM hidden layers, and the hidden layer outputs are mapped to the output layer via a fully connected layer. The output target is the retention rate or retention fluctuation value at a future moment. The model is then trained using historical operating data (input features and corresponding actual retention responses), with the mean square error used as the loss function. The network weights are iteratively adjusted using the Adam optimizer, and a dropout layer is introduced to suppress overfitting, so that the model can learn the dynamic relationship between the input indicators and the retention response, thereby achieving time series prediction of the retention behavior. It should be noted that the interception response model outputs the predicted values ​​of interception efficiency and membrane specific flux in the future monitoring period; Among them, the way to construct the conformational regulation boundary through the interception response model is: The average particle size D of the aggregates and the molecular chain extension S are combined to construct the core conformation group; The core conformation group is input into the interception response model, and the interception response model outputs the predicted values ​​of membrane specific flux and interception rate in the future monitoring period; The regulatory boundary conditions were constructed based on conformational fitness entropy, membrane specific flux prediction value, retention rate prediction value, and cooperative deviation index; Taking the molecular chain extension of the average particle size of the aggregates as the coordinate axis, a two-dimensional control boundary is constructed; The core conformation group that satisfies the regulatory boundary is marked as the boundary outside the regulatory boundary; By polynomial function: Fit the points outside the boundary and construct a continuous control boundary curve; in, 、 、 The quadratic polynomial fitting coefficients are obtained by regressing the data outside the boundary; Among them, if the conformational parameters touch the regulatory boundary, the way to initiate conformational regulation is: Obtain the real-time core conformation group and bring in the continuous boundary curve to calculate the real-time boundary; Among them, the real-time core conformation group is the real-time conformation parameter; like Figure 2 As shown, if the real-time boundary breaks through the control boundary curve and the predicted value of the membrane specific flux of the interception response model is lower than the preset critical value, it is determined that the conformational parameter has reached the control boundary, otherwise the real-time boundary is continuously monitored; If the conformational parameters reach the regulatory boundary, a corresponding conformational regulation strategy is established based on the imbalance interval type; Exemplarily, the corresponding conformational control strategy is established as follows: Total imbalance: Emergency addition of dispersant (0.3 g / L) and reduction of operating pressure by 10%, with the control target to reduce D by 20% within 10 minutes; One-way imbalance (blockage-dominated): conventionally add dispersant (0.1-0.2 g / L), aiming for D to fall back to within the boundary within 30 minutes; One-way imbalance (dominated by retention instability): adjust the inlet pH to 4.0-4.5, with the goal of reducing S by 15% within 30 minutes; Warning range: Preventively add 0.05g / L dispersant to inhibit further deterioration of conformation.

[0027] Example 3 like Figure 3 As shown, the present invention is a papermaking wastewater pollutant removal system based on nanofiltration fractionation, which also includes the following modules: Conformation acquisition module: used to collect real-time conformational parameters of lignin in papermaking wastewater in the nanofiltration system, perform conformational dynamic analysis on the real-time conformational parameters to obtain conformational volatility, and determine whether a conformational imbalance analysis of lignin and nanofiltration membrane is required; Imbalance analysis module: If conformational imbalance analysis is required, a conformational characteristic data set is established based on the real-time conformational parameters, and conformational imbalance analysis is performed on the conformational characteristic data set to determine whether lignin conformational dynamic trend tracking is triggered; Interval determination module: If the lignin conformation dynamic trend tracking is triggered, the conformation dynamic trend and distribution deviation characteristics are extracted based on the conformation characteristic data set, and the conformation deviation analysis is performed to obtain the imbalance interval of the nanofiltration system; Conformation optimization module: If the nanofiltration system enters the imbalance zone, the quantitative relationship between the lignin conformational transition rate and the attenuation of the interception efficiency is fitted. Based on the quantitative relationship, a interception response model under conformational interference is constructed. The conformational control boundary is constructed through the interception response model. If the real-time conformational parameter touches the control boundary, the conformational regulation is initiated.

[0028] The above is a detailed description of an embodiment of the present invention. However, the content is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for removing pollutants from papermaking wastewater based on nanofiltration fractionation, characterized in that: The steps include: Collect the real-time conformational parameters of lignin in papermaking wastewater in the nanofiltration system, perform conformational dynamic analysis on the real-time conformational parameters to obtain the conformational volatility, and determine whether the conformational imbalance analysis of lignin and nanofiltration membrane is needed; If conformational imbalance analysis is required, a conformational characteristic data set is established based on the real-time conformational parameters, and conformational imbalance analysis is performed on the conformational characteristic data set to determine whether lignin conformational dynamic trend tracking is triggered; If the lignin conformational dynamic trend tracking is triggered, the conformational dynamic trend and distribution deviation characteristics are extracted based on the conformational characteristic data set, and the conformational deviation analysis is performed to obtain the imbalance interval of the nanofiltration system; If the nanofiltration system enters the imbalance zone, the quantitative relationship between the lignin conformational transition rate and the attenuation of the interception efficiency is fitted. Based on the quantitative relationship, a retention response model under conformational interference is constructed. The conformational regulation boundary is constructed through the interception response model. If the real-time conformational parameter touches the regulation boundary, the conformational regulation is initiated.

2. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 1, characterized in that: The conformational dynamics analysis was performed as follows: Obtain the average particle size and particle size distribution width of the aggregates in real-time conformational parameters; Calculate the change rate of the average particle size of the aggregates during the monitoring period to obtain the particle size change rate; Calculate the change rate of the average stretching degree of the molecular chain during the monitoring period to obtain the stretching change rate; The particle size change rate and the extension change rate are weighted and summed to obtain the conformational fluctuation rate; Comparative analysis is performed based on conformational fluctuations to determine whether conformational imbalance analysis is triggered.

3. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 1, characterized in that: The conformational imbalance analysis was performed as follows: Acquire the characteristic parameters of the nanofiltration membrane in real time, and establish a conformational characteristic data set in combination with the real-time conformational parameters; The lignin retention rate within the conformational characteristic data set was extracted and combined with the conformational change rate to perform nonlinear amplification trigger analysis to obtain the nonlinear response ratio; A joint distribution model of conformation and nanofiltration membrane parameters was constructed, and the rate of change of adaptive entropy was obtained by performing ordered imbalance analysis. The distribution kurtosis of the adaptation entropy change rate was extracted, and a three-dimensional joint criterion was constructed based on the nonlinear response ratio, distribution kurtosis and adaptation entropy change rate to determine whether the dynamic trend tracking analysis of lignin conformation was triggered.

4. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 3, characterized in that: The method for performing the ordered imbalance analysis is: Based on the average particle size of aggregates, membrane flux and retention rate in the conformational characteristic data set as joint features, the conformational three-dimensional distribution was obtained by fitting; Based on the three-dimensional distribution of conformations, the conformational adaptation entropy is obtained through the entropy equation; The change rate of the conformational adaptation entropy between the current and adjacent monitoring periods is obtained and calculated to obtain the adaptation entropy change rate.

5. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 3, characterized in that: The method of constructing the three-dimensional joint criterion is: The nonlinear response ratio, distribution kurtosis and adaptation entropy change rate are used as three-dimensional joint features; Construct a normal baseline library of historical joint criteria for nanofiltration systems; Calculate the three-dimensional deviation between the three-dimensional joint feature and the normal baseline library; Based on the three-dimensional deviation, the collaborative deviation analysis is performed to obtain the collaborative deviation index; Trajectory similarity was determined based on the cooperative deviation index, and if the trajectories were similar, the dynamic trend of lignin conformation was tracked.

6. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 1, characterized in that: The conformational shift analysis is performed as follows: Extract dynamic trend indicators for quantification of lignin dynamic characteristics; extracting distribution shift indices of conformational shifts; Combining dynamic trend indicators and distribution shift indicators, we can analyze the conformational shift and the type of nanofiltration membrane performance deterioration. If the blockage is conformation-dominated, a stability analysis of the target retention of the nanofiltration membrane is performed to determine whether the conformation-dominated retention is unstable; Based on the results of stability analysis, the imbalance interval is divided by combining trajectory similarity and cooperative deviation index.

7. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 6, characterized in that: The method for analyzing the type of deterioration of the nanofiltration membrane performance is: Obtain and calculate the second-order derivative of the membrane specific flux in the nanofiltration membrane characteristic parameters; By using the partial least squares regression algorithm, the change rate of the average particle size of the agglomerates in the dynamic trend index within the continuous monitoring period and the change rate of the particle size distribution width were used as independent variables, and the second-order derivative of the membrane specific flux was used as the dependent variable. Get the absolute value of the regression coefficient output by the partial least squares regression algorithm and the sum of the absolute values ​​of all coefficients; The absolute value of the regression coefficient is compared with the sum of the absolute values ​​of all coefficients to obtain the contribution weight of the agglomerate size deviation to the blockage. Critical blockage analysis was performed based on the contribution weight and the second-order derivative of the membrane specific flux to determine whether the blockage was conformation-dominated.

8. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 6, characterized in that: The method to determine whether the conformation-dominated retention is unstable is: If the blockage is conformation-dominated, the coefficient of variation of the retention rate within the characteristic Dalton interval is calculated; Based on the random regression forest algorithm, a stretch-entrapment fluctuation correlation model was constructed to calculate the contribution weight of the molecular chain stretch deviation to the interception fluctuation. Based on the coefficient of variation and contribution weight, a comparison analysis was performed through the baseline library to determine whether the conformation-dominated retention was unstable.

9. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 1, characterized in that: The conformational regulation boundary is constructed as follows: Extract the average particle size and molecular chain extension of the aggregates in the conformational characteristic data set and combine them to construct a core conformation group; Constructing a retention response model, inputting the core conformation group into the retention response model, and the retention response model outputs the predicted value of membrane specific flux and retention rate in the future monitoring period; The regulatory boundary conditions were constructed based on conformational fitness entropy, membrane specific flux prediction value, retention rate prediction value, and cooperative deviation index; Taking the molecular chain extension of the average particle size of the aggregates as the coordinate axis, a two-dimensional control boundary is constructed; The core conformation group that satisfies the regulatory boundary is marked as the boundary outside the regulatory boundary; The points outside the boundary are fitted with polynomial functions to construct a continuous control boundary curve.

10. The method for removing pollutants from papermaking wastewater based on nanofiltration fractionation according to claim 9, characterized in that: The interception response model is constructed as follows: The interception response model is constructed through the long short-term memory network algorithm, and the dynamic trend indicator, distribution offset indicator, and imbalance interval identifier are input into the interception response model.

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