A method, system and device for determining a type of membrane fouling

Abstract

The application discloses a membrane pollution type determination method, system and equipment, relates to the membrane separation technical field, and comprises the following steps: obtaining a time original curve of water production flow of a membrane system; constructing a fitting function of water production flow about time; taking the original curve as a benchmark, and taking the sum of squares of deviations of fitting flow and original flow as a target function; solving unknown numbers in the fitting function to obtain a final fitting function, with the target function minimum as a target; calculating a determination coefficient based on the original curve and the final fitting function; judging whether the determination coefficient is greater than a preset threshold value, if yes, current fitting data can be directly used, and if not, the original curve is subjected to segmented fitting to obtain a segmented fitting function; calculating membrane pollution model parameters based on the current fitting data or the segmented fitting function; and determining a membrane pollution type based on the membrane pollution model parameters. The above method in the application can accurately predict the membrane pollution type.

Description

Technical Field

[0001] This invention relates to the field of membrane separation technology, and in particular to a method, system and equipment for determining the type of membrane fouling. Background Technology

[0002] Membrane separation technology is a commonly used technique in environmental engineering; however, membrane fouling during the filtration process severely limits its large-scale application. By identifying the real-time type of membrane fouling (e.g., Figure 8 This method allows for the identification of membrane fouling conditions using mathematical solutions, without interrupting production or damaging the membrane module. This enables the development of targeted strategies to control and mitigate membrane fouling.

[0003] Due to the actual data situation, the original technology cannot be used directly for the following reasons:

[0004] Issues such as the accuracy of flow meter readings mean that real-time flow rates typically cannot be expressed continuously (discrete points). For example, the flow rate might change directly from 1.00 m³ / d to 0.99 m³ / d. Instead, it cannot display the continuous changes in flow rate over time, ranging from 1.00 to 0.99 (e.g., 1.000-0.999-0.998-...-0.990) or even more digits.

[0005] Flow meter readings are typically collected using a data logger. Due to limitations in data storage space, data is usually collected at fixed time intervals, such as every 3 seconds. This inevitably results in these data points being discrete.

[0006] Directly using the recorded actual data (which is jagged) to solve existing techniques will cause drastic fluctuations in the first derivative, leading to repeated abnormal changes in the type of membrane fouling and making it difficult to determine the true situation. Therefore, in practical situations, existing techniques are difficult to use directly.

[0007] In existing technologies, the determination of membrane fouling modes is based on the fouling mode definitions given by Hermans and Bredee: The pollution level is determined by solving for the value of n. In this formula, t represents time, w represents the weight of the flowing water, k is a coefficient related to the unit water quality, and n is a dimensionless number characterizing the pollution pattern. Treating time as the dependent variable is difficult to understand. Furthermore, the left-hand side of the above formula involves a second derivative with respect to time, making calculation even more challenging. This can be transformed using differential transformations to... Where J represents the operating flux, after this change, J is the measurement result. Only (dj / dt) needs to be calculated to analyze the membrane fouling pattern. However, the difficulty in analyzing the membrane fouling pattern lies in calculating the flux change over time. Currently, flow meters can be used to measure real-time flux, but calculating the flux change over time requires differentiating the flux over time. Common methods for calculating this differential include: 1) Direct analysis of experimental data, using the principle of difference: dj / dt≈Δj / Δt, i.e., analyzing the flux change over intermittent time intervals. This method has the problem of error accumulation and amplification because during the measurement of flux j, the online flow meter or PLC has already used the difference method (calculated as Δv / Δt) to measure the flow rate. 2) Using a high-order polynomial to fit the data, obtaining the flux change over time, and then differentiating the function. This method reveals a problem of huge oscillations in the derivative result, with high fluctuations. This problem still exists even when using a 10th-order polynomial. This is due to the difference between the polynomial fitting data and the actual physical laws.

[0008] To simplify the direct calculation of dj / dt, many scholars have made corresponding attempts. One approach is to use an exponential function to fit the data. This method was initially developed by Zydney and validated using a filtration model substance (bovine serum albumin). This model posits that water permeates the membrane in two ways: 1) Water permeates through the membrane pores, and the covering of these pores leads to increased membrane fouling. 2) Water permeates through the fouling substances covering the membrane pores and becomes permeate. The mathematical model is as follows: Taniguchi attempted to apply this method in practice. He proposed a method that uses an exponential function to fit data and then performs differential analysis on the exponential function to obtain the pollution pattern. Taniguchi also validated the method in natural water bodies, and the model has a clear physical meaning. However, he used a manual calculation table method, which involves a large amount of computation, slow calculation speed, and insufficient fitting accuracy.

[0009] Therefore, how to identify membrane fouling types and assess membrane fouling category transitions across all time periods has become an urgent problem to be solved in this field. Summary of the Invention

[0010] The purpose of this invention is to provide a method, system, and device for determining membrane fouling types, so as to achieve solutions under any time series, obtain stable data, and identify the true and reliable membrane fouling types.

[0011] To achieve the above objectives, the present invention provides the following solution:

[0012] A method for determining membrane fouling type, comprising:

[0013] Obtain the raw time curve of the permeate flow rate of the membrane system;

[0014] Construct a fitting function for the permeate flow rate with respect to time;

[0015] Using the original curve as a reference, and the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function;

[0016] With the objective function as the minimum, the unknowns in the fitting function are solved to obtain the final fitting function;

[0017] The coefficient of determination is calculated based on the original curve and the final fitting function;

[0018] Determine whether the determination coefficient is greater than a preset threshold. If so, the current fitted data can be used directly.

[0019] If not, then the original curve is piecewise fitted to obtain a piecewise fitting function;

[0020] Calculate membrane fouling model parameters based on the current fitted data or piecewise fitted function;

[0021] The membrane fouling type is determined based on the parameters of the membrane fouling model.

[0022] Optionally, the expression for the fitting function of the permeate flow rate with respect to time is as follows:

[0023]

[0024] Where Q* represents the fitted flow rate, Q0 represents the initial flow rate, k1 and k2 represent unknowns, and t represents time.

[0025] Optionally, the expression for the determination coefficient is as follows:

[0026] R 2 =[(SSR) / (SST)]

[0027] Where SSR represents the regression sum of squares and SST represents the total sum of squares.

[0028] Optionally, the preset threshold is 0.95 or 0.98.

[0029] Optionally, the membrane fouling model parameters are calculated based on the current fitted data or piecewise fitted function using the following formula:

[0030]

[0031] Where J represents flux, t represents time, k represents fouling factor, and n represents membrane fouling model parameters.

[0032] Optionally, the types of membrane fouling include:

[0033] Filter cake layer, complete blockage, intermediate blockage, and standard orifice blockage;

[0034] When n=0, it is a filter cake layer; when n=2, it is completely blocked; when n=1, it is partially blocked; and when n=1.5, it is blocked in the standard orifice.

[0035] Based on the above-described method of this invention, this invention further provides a membrane fouling type determination system, comprising:

[0036] The raw curve acquisition module is used to acquire the raw time curve of the permeate flow rate of the membrane system.

[0037] The fitting function construction module is used to construct a fitting function of the permeate flow rate with respect to time.

[0038] The objective function determination module is used to take the original curve as a reference and use the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function.

[0039] The objective function solving module is used to solve for the unknowns in the fitting function with the objective function as the goal, and obtain the final fitting function.

[0040] The coefficient of determination calculation module is used to calculate the coefficient of determination based on the original curve and the final fitting function;

[0041] The judgment module is used to determine whether the determination coefficient is greater than a preset threshold. If so, the current fitted data can be used directly.

[0042] The piecewise fitting function determination module is used to perform piecewise fitting on the original curve to obtain the piecewise fitting function if no;

[0043] The membrane fouling model parameter calculation module is used to calculate membrane fouling model parameters based on the current fitting data or piecewise fitting function.

[0044] The membrane fouling type determination module is used to determine the membrane fouling type based on the membrane fouling model parameters.

[0045] Optionally, the fitting function construction module specifically adopts the following formula:

[0046]

[0047] Where Q* represents the fitted flow rate, Q0 represents the initial flow rate, k1 and k2 represent unknowns, and t represents time.

[0048] Fourthly, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method for determining the type of membrane fouling.

[0049] Fifthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining the type of membrane fouling.

[0050] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0051] The method described in this invention utilizes the idea of ​​fitting existing data (discrete points) into a smooth curve, sets the function type of the fitted curve, performs the fitting first, and then evaluates the goodness of fit R. 2 If the goodness-of-fit value is greater than the required value (e.g., 0.95 or better, 0.98), it can be expressed as a function. Based on the obtained function expression, it is easy to solve the problem under any time series, effectively solving the problem of drastic fluctuations in the first derivative, thereby obtaining stable data and identifying the true and reliable membrane fouling type. This enables the identification of membrane fouling type and the assessment of membrane fouling type transitions across the entire time series. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 Flowchart of the membrane fouling type determination method provided by the present invention;

[0054] Figure 2 This is a schematic diagram of the original curve provided by the present invention;

[0055] Figure 3 A schematic diagram of the fitting function provided by this invention;

[0056] Figure 4 This is a schematic diagram of the actual membrane filtration process provided by the present invention;

[0057] Figure 5 A schematic diagram of different parts of the fitting function provided by this invention;

[0058] Figure 6 A schematic diagram of the fitting function superposition process provided by the present invention;

[0059] Figure 7 A schematic diagram showing the comparison between the fitting function and the actual curve provided by this invention;

[0060] Figure 8 A schematic diagram of membrane fouling types provided by the present invention;

[0061] Figure 9 The fitting effect is illustrated in the embodiments provided by the present invention.

[0062] Figure 10 A graph showing the relationship between membrane fouling type and time in an embodiment of the present invention. Detailed Implementation

[0063] 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.

[0064] The purpose of this invention is to provide a method, system, and device for determining membrane fouling types, so as to achieve solutions under any time series, obtain stable data, and identify the true and reliable membrane fouling types.

[0065] This invention utilizes the idea of ​​fitting existing data (discrete points) into a smooth curve. The function type of the fitted curve is defined, and the fit is first performed, followed by an evaluation of the goodness of fit R. 2 If the goodness-of-fit value exceeds the requirement (e.g., 0.95 or better, 0.98), it is then expressed as a function. Based on the obtained function expression, it is easy to solve the problem under any time series (effectively solving the problem of drastic fluctuations in the first derivative), thereby obtaining stable data and identifying the true and reliable membrane fouling type. This enables the identification of membrane fouling type and the assessment of membrane fouling type transitions across the entire time series.

[0066] We use an exponential function to fit the data, thus ensuring that both the original function and the derivative function are continuous and smooth.

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1

[0069] Figure 1 The flowchart of the membrane fouling type determination method provided by the present invention is as follows: Figure 1 As shown, the method includes:

[0070] Step 1: Obtain the raw time curve of the permeate flow rate of the membrane system.

[0071] Specifically, a flow meter is used to detect the permeate flow rate of the membrane system, and a recorder or other recording method is used to record the real-time data of the permeate flow rate (Q) and the corresponding time (t), obtaining the raw curve of the permeate flow rate as a function of time. See details below. Figure 2 .

[0072] Step 2: Construct a fitting function for the permeate flow rate with respect to time.

[0073] Specifically, two exponential functions are constructed for fitting, and then the two are added together to establish a fitting function formula for the permeate flow rate (Q*) versus time (t). See details below. Figure 3 .

[0074] That is, under the conditions of fixed operating pressure and fixed influent concentration, an exponential function with two unknowns (k1 and k2) was constructed, and an exponential function of the form was established, where Q* is the fitted flow rate, Q0 is the initial flow rate value (the starting point of the original data flow rate), and t is time. The specific formula is as follows:

[0075]

[0076] The first item represents the portion of water that flows directly through the membrane pores, while the second item represents the portion of water that flows through the membrane pores and then permeates through the membrane pores after passing through the blockage.

[0077] like Figure 4 As shown, the actual process of membrane filtration is analyzed. In the water production process, two main aspects occur simultaneously in the early stage: (1) reduction of effective pores, Figure 4 The middle part is marked as "pore"; (2) After filtration through the already formed contamination layer, it undergoes secondary filtration through the membrane. After the effective pores are completely gone, it is all preliminary filtration of the filter cake layer, followed by membrane filtration. Figure 4 The term is written as "penetration".

[0078] Figure 5 The diagram shows the construction of models for the two processes of pores and permeation.

[0079] Summing the Q values ​​at the same time t yields the combined flow Q, as detailed in [reference needed]. Figure 6 , Figure 6 The Chinese character is written as "pore + permeation".

[0080] Step 3: Using the original curve as a reference, and using the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function.

[0081] Step 4: With the objective function as the minimum, solve for the unknowns in the fitting function to obtain the final fitting function.

[0082] Using the original curve obtained in step 1 as the baseline, denoted as "original", and with the sum of the squares of the deviations between the fitted flow rate and the original flow rate (baseline) as the objective function, and minimizing the objective function, flow rate vs. time data fitting is performed. This automatically solves for k1 and k2 in the fitting formula. The resulting flow rate and time fitted curve, denoted as "fitted", yields the final fitting function. (See [link to documentation]). Figure 7 .

[0083] Step 5: Calculate the coefficient of determination based on the original curve and the final fitting function.

[0084] Based on the raw and fitted data regarding permeate flow rate and time obtained in steps 1 and 4, the coefficient of determination R is calculated. 2 To evaluate the degree of fit in the fitting process.

[0085] R 2 =[(SSR) / (SST)]

[0086] Here, SSR represents the regression sum of squares, and SST represents the total sum of squares. The regression sum of squares is the sum of the squares of the differences between the predicted and actual values ​​of all observations in the regression model, while the total sum of squares is the sum of the squares of the differences between all observations and their mean.

[0087] R 2 The value of is between 0 and 1. The closer it is to 1, the better the regression model fits the data. The closer it is to 0, the worse the regression model's ability to interpret the data.

[0088] Step 6: Determine whether the determination coefficient is greater than the preset threshold. If so, the current fitted data can be used directly.

[0089] Step 7: If not, then perform piecewise fitting on the original curve to obtain a piecewise fitting function.

[0090] Based on the coefficient of determination (fit effect) obtained in step 5 above, determine whether the current fitted data meets the standard. If R... 2 A value greater than 0.95, and even better, greater than 0.98, indicates that the fitted data can be directly used for subsequent calculations. If R0.05... 2 If the value is less than 0.95, the original data is segmented, and each segment is fitted separately. The fitted function is expressed as a piecewise function, and the piecewise function is used for subsequent calculations.

[0091] Step 8: Calculate the membrane fouling model parameters based on the current fitted data or piecewise fitted function.

[0092] Step 9: Determine the membrane fouling type based on the membrane fouling model parameters.

[0093] Based on the fitted curves of flow rate and time obtained through step 7, the membrane fouling model parameter n is calculated using the following formula:

[0094]

[0095] Where J is flux, Q is flow rate, and t is time.

[0096]

[0097] Taking the logarithm of both sides, we get:

[0098]

[0099] by Let y be the slope of x, ln(j) be the slope of x, and ln(k) be the slope of x. We can obtain: y = nx + b, where n is the slope of x at time t. Solving for n will give us the slope.

[0100] Based on the relationship between n and t, the formation and transformation results of membrane fouling types during the filtration process can be determined.

[0101] In other words, if you want to know the pollution type at time t, you can directly find the corresponding n at time t. If you want to know the change of n within a time period, you can find the n within that time period.

[0102] Among them, the type conversion refers to the change in the membrane fouling situation. For example, initially, the membrane pores may be blocked first, and after the blockage is complete, it will turn into a filter cake (entirely outside the membrane pores). This type of fouling will change throughout the filtration process.

[0103] When n = 0, it is a filter cake layer; when n = 2, it is completely blocked; when n = 1, it is partially blocked; when n = 1.5, it is blocked in the standard orifice. (See diagram for further explanation.) Figure 8 .

[0104] See Figure 9 In this embodiment, regarding typical membrane contaminants, yeast membrane fouling under different operating conditions (denoted as A4-A12) was measured, and the corresponding raw data were obtained. Fitting data was then obtained using this technique. The fitted data was used for relevant membrane fouling analysis, as follows:

[0105] Evaluation of fitting and measured results, R 2 Greater than 0.95.

[0106] See Figure 10 , Figure 10The diagram shows the transformation of membrane fouling types under different operating conditions and filtration times. Further analysis can be conducted on the rate of change in membrane fouling types, i.e., the time it takes for a negative value to return to above 0. By combining the membrane fouling data under different operating conditions and at different operating times, comparative analysis from multiple perspectives can also be performed.

[0107] Example 2

[0108] In order to execute the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a membrane fouling type determination system is provided below, including:

[0109] The raw curve acquisition module is used to acquire the raw time curve of the permeate flow rate of the membrane system.

[0110] The fitting function construction module is used to construct a fitting function of the permeate flow rate with respect to time.

[0111] The objective function determination module is used to take the original curve as a reference and use the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function.

[0112] The objective function solving module is used to solve for the unknowns in the fitting function with the objective function as the goal, and obtain the final fitting function.

[0113] The coefficient of determination calculation module is used to calculate the coefficient of determination based on the original curve and the final fitting function;

[0114] The judgment module is used to determine whether the determination coefficient is greater than a preset threshold. If so, the current fitted data can be used directly.

[0115] The piecewise fitting function determination module is used to perform piecewise fitting on the original curve to obtain the piecewise fitting function if no;

[0116] The membrane fouling model parameter calculation module is used to calculate membrane fouling model parameters based on the current fitting data or piecewise fitting function.

[0117] The membrane fouling type determination module is used to determine the membrane fouling type based on the membrane fouling model parameters.

[0118] Example 3

[0119] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the membrane fouling type determination method provided in Embodiment 1.

[0120] Example 4

[0121] Based on the description of Embodiment 3, Embodiment 4 of the present invention provides a storage medium on which a computer program is stored, which can be executed by a processor to implement the membrane fouling type determination method of Embodiment 1.

[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0123] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining the type of membrane fouling, characterized in that, include: Obtain the raw time curve of the permeate flow rate of the membrane system; Construct a fitting function for the permeate flow rate with respect to time; Using the original curve as a reference, and the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function; With the objective function as the minimum, the unknowns in the fitting function are solved to obtain the final fitting function; The coefficient of determination is calculated based on the original curve and the final fitting function; Determine whether the determination coefficient is greater than a preset threshold; if so, directly use the current fitted data. If not, then the original curve is piecewise fitted to obtain a piecewise fitting function; Calculate membrane fouling model parameters based on the current fitted data or piecewise fitted function; The membrane fouling type is determined based on the parameters of the membrane fouling model. The expression for the fitting function of the water production flow rate with respect to time is as follows: Among them, Q Q0 represents the fitted flow rate, and Q0 represents the initial flow rate value. k 1 and k 2 represents an unknown number. t Indicates time.

2. The method for determining membrane fouling type according to claim 1, characterized in that, The expression for the coefficient of determination is as follows: R 2 =[(SSR) / (SST)] Where SSR represents the regression sum of squares and SST represents the total sum of squares.

3. The method for determining the type of membrane fouling according to claim 1, characterized in that, The preset threshold is 0.95 or 0.

98.

4. The method for determining membrane fouling type according to claim 1, characterized in that, The membrane fouling model parameters are calculated based on the current fitted data or piecewise fitted function using the following formula: in, J Indicates flux. t Indicates time, k Indicates the dirt coefficient. n This represents the parameters of the membrane fouling model.

5. The method for determining membrane fouling type according to claim 1, characterized in that, The types of membrane fouling include: Filter cake layer, complete blockage, intermediate blockage, and standard orifice blockage; When n=0, it is a filter cake layer; when n=2, it is completely blocked; when n=1, it is partially blocked; and when n=1.5, it is blocked in the standard orifice.

6. A membrane fouling type determination system, characterized in that, include: The raw curve acquisition module is used to acquire the raw time curve of the permeate flow rate of the membrane system. The fitting function construction module is used to construct a fitting function of the permeate flow rate with respect to time. The objective function determination module is used to take the original curve as a reference and use the sum of the squares of the deviations between the fitted flow rate and the original flow rate as the objective function. The objective function solving module is used to solve for the unknowns in the fitting function with the objective function as the goal, and obtain the final fitting function. The coefficient of determination calculation module is used to calculate the coefficient of determination based on the original curve and the final fitting function; The judgment module is used to determine whether the determination coefficient is greater than a preset threshold. If so, the current fitted data is used directly. The piecewise fitting function determination module is used to perform piecewise fitting on the original curve to obtain the piecewise fitting function if no; The membrane fouling model parameter calculation module is used to calculate membrane fouling model parameters based on the current fitting data or piecewise fitting function. A membrane fouling type determination module is used to determine the membrane fouling type based on the membrane fouling model parameters; The fitting function construction module specifically adopts the following formula: ； Among them, Q Q0 represents the fitted flow rate, and Q0 represents the initial flow rate value. k 1 and k 2 represents an unknown number. t Indicates time.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the membrane fouling type determination method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the membrane fouling type determination method as described in any one of claims 1-5.

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