A method for determining an operation plan to optimize reverse osmosis pretreatment effects

Abstract

The present invention discloses a method for determining an operation scheme for optimizing the effect of reverse osmosis pretreatment, comprising the following steps: determining an optimal pretreatment process and an optimal reverse osmosis operation time as an optimal reverse osmosis scheme using a standardized difference model and a standardized total water output model, based on whether the pretreatment process and the operation time are known; determining two parameters Jpss and k using an intermediate blockage model, and proposing a standardized total water output model V for the practical goal of producing a larger volume of clean water through reverse osmosis pretreatment. N The standardized total water output model makes the pretreatment effects comparable, can optimize the operation plan of the pretreatment process, and provides a new perspective for the selection of pretreatment.

Description

Technical Field

[0001] The present invention relates to the technical field of reverse osmosis pretreatment, and in particular to a method for determining an operation scheme for optimizing reverse osmosis pretreatment effects. Background Art

[0002] With population growth and increasing water pollution, the demand for fresh water is also increasing. Reverse osmosis (RO) is a promising technology that has been widely used in water reuse and high-quality water production, such as wastewater treatment, seawater desalination, and drinking water production. However, membrane fouling is an unavoidable problem, which causes the flux of RO membranes to gradually decay during the filtration process, resulting in high energy consumption and shortened membrane life. Pretreatment technologies can effectively mitigate membrane fouling. Because pollutants have different physical and chemical properties, they physically or chemically interact with the membrane surface during the RO filtration process, causing membrane fouling. Clarifying the mechanism of membrane fouling is key to research; mathematical models have made a significant contribution to understanding the fouling process. Existing studies have confirmed that RO membranes are porous with pore sizes less than 1 nm. Therefore, filtration models used for porous membrane filtration, such as ultrafiltration and microfiltration, can also be used to describe the RO filtration process.

[0003] Existing techniques have modified the intermediate blockage model into a two-parameter model. This model has been shown to effectively fit reverse osmosis filtration processes with pure organic matter, real surface water, seawater, and secondary effluent as influent. The two parameters are Jpss (Jpss) and k (k). Jpss reflects the final permeate flux after a relatively long run, while k reflects the fouling rate. Both can serve as indicators of membrane fouling and vary with water quality. However, to date, research on the model parameters has primarily focused on the differences between different feedwater types; less research has been conducted on the effects of the model parameters on reverse osmosis pretreatment, worthy of further systematic investigation.

[0004] Furthermore, the effectiveness of pretreatment is often evaluated solely by improvements in effluent flux, which fails to explain the full effect and lacks sufficient persuasiveness. Improvements in fouling rate are also a crucial factor that cannot be ignored. More importantly, the goal of pretreatment is to reduce reverse osmosis membrane fouling and produce more clean water than would be achieved without pretreatment. While improvements in final effluent flux or fouling rate do reduce membrane fouling, they do not necessarily produce a greater volume of clean water. It is related to final permeate flux, fouling rate, and filtration time. Therefore, there is currently a lack of comprehensive, quantitative methods for evaluating and optimizing pretreatment effectiveness. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method for determining an operation plan for optimizing the reverse osmosis pretreatment effect.

[0006] The technical solution of the present invention is: a method for determining an operation scheme for optimizing the reverse osmosis pretreatment effect, comprising the following steps:

[0007] S1. If the pretreatment process is known and the operation time t is unknown, the corresponding optimal reverse osmosis operation time t1 is calculated based on the standardized total water output model;

[0008] S2. If the running time t is known and the pre-treatment process is unknown, the standardized total water output model is used to calculate V N value, find V N The pre-treatment process corresponding to the maximum value of is the optimal pre-treatment process;

[0009] S3. If the pre-treatment process and the running time t are both unknown, calculate the ΔV of the corresponding pre-treatment according to the standardized difference model for any pre-treatment process. N value, select the largest ΔV N The pre-treatment process corresponding to the value is the best pre-treatment process; the largest ΔV N The time corresponding to the value is the optimal reverse osmosis operation time t1;

[0010] The optimal pretreatment process and optimal reverse osmosis operation time t1 obtained through the above steps are the optimal reverse osmosis solution;

[0011] The pre-treatment process includes ultrafiltration, ozone oxidation, microfiltration, activated carbon adsorption, and coagulation and sedimentation;

[0012] The standardized total water output model is shown in formula (1):

[0013]

[0014] Where V N is the standardized total water output, The numerator is the actual total water output, and the denominator is the ideal state, that is, the water output without membrane fouling; J N is the normalized water flux and J N = J / J0, J0 is the water flux at the start time, J is the water flux at time t, and t is the operating time; Jpss is the standardized water flux at steady state, reflecting the degree of pollution after a long period of operation, that is, the final permeation flux; k is the pollution constant, which represents the membrane pollution rate in the early stage;

[0015] The standardized difference model is shown in formula (2):

[0016] ΔV N =V N-W -V N-W / O (2)

[0017] Where ΔV N is the standardized difference; V N-Wis the standardized total effluent volume for pretreatment, V N-W / O is the standardized total water output without pre-treatment; V N-W 、V N-W / O The values ​​are obtained according to formula (1).

[0018] Description: By using the two parameters Jpss and k to establish a new standardized total water output model V N , and then with the goal of producing more effluent and correspondingly reducing membrane fouling, the optimal pretreatment model setting is obtained, making the pretreatment efficiency comparable, which can be used to optimize the application of pretreatment, and based on V N The model can effectively improve the pretreatment effect by simply adjusting the reverse osmosis filtration time. In addition, maximizing the pretreatment effect can provide valuable guidance for selecting an appropriate pretreatment process. It also expands the application scope of the improved intermediate blockage model and provides a new perspective for the application of pretreatment.

[0019] Furthermore, the parameters Jpss and k in the formula (1) are determined by an intermediate blocking model.

[0020] Explanation: The intermediate blocking model provides a good fit for reverse osmosis filtration processes with organic matter, real surface water, seawater, and secondary effluent as influent.

[0021] Furthermore, the formula of the intermediate blocking model is:

[0022]

[0023] Among them, J N is the normalized water flux and J N =J / J0, J0 is the water flux at the starting moment, J is the water flux at time t, and t is the operating time.

[0024] Note: Through the above intermediate blockage model calculation, the two parameters Jpss and k and their changes before and after reverse osmosis pretreatment are obtained to facilitate subsequent simulation calculations.

[0025] Furthermore, before step S1, the feasibility of the intermediate blockage model is determined, and the intermediate blockage model is used to fit multiple sets of RO flux data and the determination coefficient R is used. 2 To evaluate the fitting performance, R 2 It is obtained by fitting with the existing software Origin. When R 2 If ≥0.85, the intermediate blocking model is considered to have a good fit, that is, the intermediate blocking model is feasible.

[0026] Furthermore, the RO flux data includes J N and t, and the RO flux data are obtained through experiments or existing literature.

[0027] Note: Through the above judgment method, it is possible to effectively determine the coefficient of determination R 2 The fitting effect can be evaluated by the numerical value, and the feasibility of the intermediate blocking model can be scientifically judged based on the fitting effect.

[0028] Furthermore, the method further includes step S4: establishing an evaluation model to evaluate the effect of the pre-treatment process;

[0029] S4-1. Calculate the parameters Jpss and k obtained with and without pre-processing using formula (3), and express their changes using formulas (4) to (5):

[0030] ΔJpss=Jpss -w -Jpss -w / o (4)

[0031] Δk=kw / o-kw (5)

[0032] Among them, Jpss -w is the standardized water flux and Jpss obtained in the pre-treatment in step S1 -w / o is the standardized water flux obtained without pretreatment; kw is the pollution constant obtained after pretreatment, and kw / o is the pollution constant obtained without pretreatment; ΔJpss is the change in standardized water flux after pretreatment, and Δk is the change in pollution rate after pretreatment;

[0033] S4-2. According to step S4-1, formulas (6) to (7) are established to express the key parameters Jpss and k change rate, and to evaluate the pre-treatment effect:

[0034] R Jpss =ΔJpss (6)

[0035]

[0036] Among them, R Jpss is the rate of change of the final permeation flux; R k The rate of change of pollution velocity; R Jpss and R k The larger the value, the better the effect of the pre-treatment process.

[0037] Note: By determining the change rate of the above key parameters, it is possible to Jpss and R k The value of is used to further evaluate and quantify the pre-treatment effect.

[0038] Furthermore, step S1 is used to simulate and determine the k and Jpss values ​​before and after pretreatment, and then the Δk and ΔJpss values ​​are obtained by formulas (4) to (5). According to the positive and negative values ​​of Δk and ΔJpss, the effect of the current pretreatment process on mitigation of membrane fouling is judged and optimized; the judgment and optimization method is as follows:

[0039] When Δk and ΔJpss are both positive, it means that the pretreatment can increase the final permeation flux and reduce the fouling rate; it is considered to have the effect of slowing down membrane fouling; the corresponding optimization is the maintenance parameter;

[0040] When Δk is positive and ΔJpss is negative, it means that the pretreatment can reduce the fouling rate, but reduces the final permeation flux, which is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t2, keep the pre-processing running time ≤ t2;

[0041] When Δk is negative and ΔJpss is positive, it means that the pretreatment increases the fouling rate but increases the final permeation flux; it is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t3, so that the pre-processing running time is greater than t3.

[0042] Note: Through the above judgment and optimization method, the standardized total water output V N The model optimizes the three situations of mitigating membrane fouling. By determining the corresponding time points, it can ensure the efficiency of pre-treatment water output while reducing the occurrence of membrane fouling to a certain extent, making the pre-treatment process more optimized.

[0043] Furthermore, according to formulas (6) to (7), we can get R Jpss Value, R k According to the size of the two values, compare the influence of the pre-treatment process on Jpss and k. If R Jpss The larger the value, the more obvious the improvement effect of pretreatment on the final permeation flux. On the contrary, if R k The larger the value, the more obvious the improvement effect of the pretreatment process on the membrane fouling rate.

[0044] Note: By setting the above comparison method, you can Jpss 、R k The size of the pre-treatment effect is determined to clarify the targeted improvement of the pre-treatment effect, and based on the improvement rate, further quantitatively evaluate the effects of various aspects of the pre-treatment.

[0045] The beneficial effects of the present invention are:

[0046] (1) The present invention can obtain the two parameters Jpss and k and their changes before and after reverse osmosis pretreatment through the intermediate blocking model; by using the two parameters to establish a new standardized total water output model V N , and then with the goal of producing more water output and correspondingly reducing membrane fouling, the optimized pretreatment model setting is obtained, making the pretreatment efficiency comparable, which can be used to optimize the application of pretreatment, expanding the application scope of the improved intermediate blockage model, and providing a new perspective for the optimization of pretreatment.

[0047] (2) The present invention uses the difference between Jpss and k and the rate of change value to determine whether the pretreatment has a promoting or counter-active effect on the two, and further evaluate and quantify the pretreatment effect; at the same time, it can clarify the way to slow down membrane fouling, clarify the targeted improvement of the pretreatment effect, and further quantitatively evaluate the various aspects of the pretreatment effect based on the improvement rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention R 2 Fitting distribution curve of , n is the amount of data;

[0049] Figure 2 It is a flux curve diagram of the present invention;

[0050] Figure 3 It is a normalized total water output curve of the present invention;

[0051] Figure 4 (a) is the distribution of ΔJpss and Δk, with ΔJpss on the x-axis and ΔJpss on the y-axis. (b) is a local enlarged view of (a) with Δk: -0.12-1 and ΔJpss-06-06; (c) and (e) are curves of models with and without pretreatment.

[0052] Figure 5 It is the V of the ab group N and ΔV N curve chart;

[0053] Figure 6 It is V of ce group N and ΔV N curve chart. DETAILED DESCRIPTION

[0054] The present invention will be further described in detail below in conjunction with specific implementation methods to better demonstrate the advantages of the present invention.

[0055] Example 1:

[0056] A method for determining an operation scheme for optimizing reverse osmosis pretreatment effects comprises the following steps:

[0057] To determine the feasibility of the intermediate blockage model, 43 sets of RO flux data were fitted using the intermediate blockage model and the determination coefficient R was used. 2 To evaluate the fitting performance, when R 2 If ≥0.85, the intermediate blocking model is considered to have a good fit, that is, the intermediate blocking model is feasible;

[0058] like Figure 1 As shown in Table 1, R 2 The fitted curve and distribution of n is the number of data sets; among them, more than 29% of R 2 More than 0.98, half of R 2 R is greater than 0.96, 75% 2 Higher than 0.92, greater than 86% of R 2 greater than 0.90, and all greater than 0.85, indicating that the intermediate blocking model fits well, that is, the intermediate blocking model is feasible;

[0059] S1. Assume groups a and d; the pretreatment process is known, but the operation time t is unknown. Then calculate the corresponding optimal reverse osmosis operation time t1 based on the standardized total water output model;

[0060] The optimal pretreatment process and optimal reverse osmosis operation time t1 obtained through the above steps are the optimal reverse osmosis operation plan;

[0061] The pretreatment process represents any one of all pretreatment processes, including but not limited to: ultrafiltration, ozone oxidation, microfiltration, activated carbon adsorption, coagulation and precipitation;

[0062] The standardized total water output model is shown in formula (1):

[0063]

[0064] Where V N is the standardized total water output, The numerator is the actual total water output, and the denominator is the ideal state, that is, the water output without membrane fouling; J N is the normalized water flux and J N = J / J0, J0 is the water flux at the start time, J is the water flux at time t, and t is the operating time; Jpss is the standardized water flux at steady state, reflecting the degree of pollution after a long period of operation, that is, the final permeation flux; k is the pollution constant, which represents the membrane pollution rate in the early stage;

[0065] The parameters Jpss and k in formula (1) are determined by the intermediate blocking model; the formula of the intermediate blocking model is:

[0066]

[0067] Among them, J N is the normalized water flux and J N =J / J0, J0 is the water flux at the start time, J is the water flux at time t, and t is the operating time;

[0068] The standardized difference model is shown in formula (2):

[0069] ΔV N =V N-W -V N-W / O (2)

[0070] Where ΔV N is the standardized difference; V N-W is the standardized total effluent volume for pretreatment, V N-W / O is the standardized total water output without pre-treatment; V N-W 、V N-W / O The values ​​are obtained according to formula (1);

[0071] The step S4 is also included: establishing an evaluation model to evaluate the effect of the pre-treatment process;

[0072] S4-1. Calculate the parameters Jpss and k obtained with and without pre-processing using formula (3), as shown in formulas (4) to (7). The changes between the two are expressed by formulas (4) to (5):

[0073] ΔJpss=Jpss -w -Jpss -w / o (4)

[0074] Δk=kw / o-kw (5)

[0075] Among them, Jpss -w is the standardized water flux and Jpss obtained in the pre-treatment in step S1 -w / o is the standardized water flux obtained without pretreatment; kw is the pollution constant obtained after pretreatment, and kw / o is the pollution constant obtained without pretreatment; ΔJpss is the change in standardized water flux after pretreatment, and Δk is the change in pollution rate after pretreatment;

[0076] S4-2. According to step S4-1, formulas (6) to (7) are established to express the key parameters Jpss and k change rate, and to evaluate the pre-treatment effect:

[0077] R Jpss =ΔJpss (6)

[0078]

[0079] Among them, RJpss is the rate of change of the final permeation flux; R k The rate of change of pollution velocity; R Jpss and R k The larger the value, the better the pre-processing effect;

[0080] According to formulas (6) to (7), we can get R Jpss Value, R k According to the size of the two values, compare the influence of the pre-treatment process on Jpss and k. If R Jpss The larger the value, the more obvious the improvement effect of pretreatment on the final permeation flux. On the contrary, if R k The larger the value, the more obvious the improvement effect of the pretreatment process on the membrane fouling rate;

[0081] The results show that: by using ozone and ultrafiltration to simulate the calculation, the R k The variation range is greater than R Jpss ; This shows that the effect of pretreatment on reducing the fouling rate is greater than its effect on increasing the final permeation flux; that is, the pretreatment effect has a significant effect on improving the membrane fouling rate;

[0082] The intermediate blocking model is used to simulate and determine the k and Jpss values ​​before and after pretreatment. Then, the Δk and ΔJpss values ​​are obtained by using formulas (4) to (5). Based on the positive and negative values ​​of Δk and ΔJpss, the effect of the current pretreatment process on mitigating membrane fouling is judged and optimized accordingly. The judgment and optimization methods are as follows:

[0083] When Δk and ΔJpss are both positive, it means that the pretreatment can increase the final permeation flux and reduce the fouling rate; it is considered to have the effect of slowing down membrane fouling; the corresponding optimization is the maintenance parameter;

[0084] When Δk is positive and ΔJpss is negative, it means that the pretreatment can reduce the fouling rate, but reduces the final permeation flux, which is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t2, keep the pre-processing running time ≤ t2;

[0085] When Δk is negative and ΔJpss is positive, it means that the pretreatment increases the fouling rate but increases the final permeation flux; it is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t3, so that the pre-processing running time> t3;

[0086] The simulation results of the above three cases are as follows Figures 2-4 As shown, Figure 2 、 3 The intersection of the model curves with and without pre-processing in 4 is ΔV N= 0, the corresponding times are t2 and t3 respectively.

[0087] Example 2

[0088] The difference between this embodiment and embodiment 1 is that, assuming group b, S2, if the running time t is known and the pre-treatment process is unknown, the standardized total water output model is used to calculate V N value, find V N The pre-treatment process corresponding to the maximum value of is the optimal pre-treatment process.

[0089] Example 3

[0090] The difference between this embodiment and embodiment 2 is that, assuming group c and group e, S3, if the pre-treatment process and the running time t are both unknown, then for any pre-treatment process, the ΔV corresponding to the pre-treatment is calculated according to the standardized difference model. N value, select the largest ΔV N The pre-treatment process corresponding to the value is the best pre-treatment process; the largest ΔV N The time corresponding to the value is the optimal reverse osmosis operation time t1.

[0091] Experimental example

[0092] By calculating V N To select the optimal pre-processing operation scheme, evaluate it by calculating the change rate of Jpss and k; Figures 5-6 , are the simulation curves of group ae respectively;

[0093] The optimization and evaluation results are shown in the following table:

[0094] Table 1 Results and evaluation table of the best optimization scheme for pretreatment process

[0095]

[0096]

[0097] like Figures 5-6 As shown in Table 1, the pretreatment effect was evaluated by calculating the change rate of Jpss and k. Different pretreatment processes or different conditions for the same technology may have a great impact on membrane fouling. In group b, UF increased the final permeation flux Jpss by 9.81% and reduced the fouling rate k by 90.38%. Ozonation reduced the final permeation flux by 0.94% and the fouling rate by 69.38%. In group c, 0.32mgO3 / mgDOC reduced the fouling rate by 88.93% and the final permeation flux by 56.65%. 0.93mgO3 / mgDOC reduced the fouling rate by 67.79% and reduced the final permeation flux by 3.16%. Overall, the pretreatment process corresponding to the maximum value of the two was the optimal.

[0098] V of ae group N and ΔV N Curves such as Figures 5-6 As shown in the figure; in group a and group d, each group has only one pretreatment process; the optimal reverse osmosis operation time or membrane cleaning cycle of the two groups is determined by V N The maximum value is determined by the time. They are 52.50h and 1.86h respectively.

[0099] Group b, although there are multiple pre-treatment processes in the group, it can be seen that the V N The curve is always higher than that of raw water and ozone oxidation. Therefore, UF is the most suitable pretreatment process. N The maximum value of , we know that the optimal reverse osmosis operation time or membrane cleaning cycle is 188.62h.

[0100] In groups c and e, each group has multiple pre-treatment processes, and V N The curves are intertwined; therefore, ΔV should be calculated for each pre-treatment N The maximum value; in group c, the pretreatment process of 0.32mgO3 / mgDOC has the largest ΔV N The optimal filtration time or membrane cleaning cycle is 57.11 h. Similarly, UF-2 is considered to be the most suitable pretreatment process for group e, and the optimal reverse osmosis operation time or membrane cleaning cycle is 2.80 h.

Claims

1. A method for determining an operation scheme for optimizing reverse osmosis pretreatment effects, characterized in that: The following steps are involved: S1. If the pretreatment process is known and the operation time t is unknown, the corresponding optimal reverse osmosis operation time t1 is calculated based on the standardized total water output model; S2. If the running time t is known and the pre-treatment process is unknown, the standardized total water output model is used to calculate V N value, find V N The pre-treatment process corresponding to the maximum value of is the optimal pre-treatment process; S3. If the pre-treatment process and the running time t are both unknown, calculate the ΔV of the corresponding pre-treatment according to the standardized difference model for any pre-treatment process. N value, select the largest ΔV N The pre-treatment process corresponding to the value is the best pre-treatment process; the largest ΔV N The time corresponding to the value is the optimal reverse osmosis operation time t1; The optimal pretreatment process and optimal reverse osmosis operation time t1 obtained through the above steps are the optimal reverse osmosis operation plan; The pretreatment process includes ultrafiltration, ozone oxidation, microfiltration, activated carbon adsorption, and coagulation sedimentation. The standardized total water output model is shown in formula (1): Where V N is the standardized total water output, The numerator is the actual total water output, and the denominator is the ideal state, that is, the water output without membrane fouling; J N is the normalized water flux and J N = J / J0, J0 is the water flux at the start time, J is the water flux at time t, and t is the operating time; Jpss is the standardized water flux at steady state, reflecting the degree of pollution after a long period of operation, that is, the final permeation flux; k is the pollution constant, which represents the membrane pollution rate in the early stage; The standardized difference model is shown in formula (2): ΔV N =V N-W -V N-W / O (2) Where ΔV N is the standardized difference; V N-W is the standardized total effluent volume for pretreatment, V N-W / O is the standardized total water output without pre-treatment; V N-W 、V N-W / O The values ​​are obtained according to formula (1); The parameters Jpss and k in formula (1) are determined by the intermediate blocking model; the formula of the intermediate blocking model is: Among them, J N is the normalized water flux and J N =J / J0, J0 is the water flux at the start time, J is the water flux at time t, and t is the operating time; Determine the feasibility of the intermediate blockage model; the method for determining the feasibility of the intermediate blockage model is: fit the RO flux data using the intermediate blockage model and use the determination coefficient R 2 To evaluate the fitting performance, when R 2 ≥0.85, the intermediate blockage model is considered to have a good fit, that is, the intermediate blockage model is feasible; the RO flux data include J N with t; S4: Establish an evaluation model to evaluate the effect of the pretreatment process; S4-1. Calculate the parameters Jpss and k obtained with and without pre-processing using formula (3), and then express their changes using formulas (4) to (5): ΔJpss=Jpss -w -Jpss -w / o (4) Δk=kw / o-kw (5) Among them, Jpss -w The standardized water flux and Jpss obtained by pretreatment -w / o is the standardized water flux obtained without pretreatment; kw is the pollution constant obtained after pretreatment, kw / o is the pollution constant obtained without pretreatment; ΔJpss is the change in standardized water flux after pretreatment, and Δk is the change in pollution rate after pretreatment; S4-2. According to step S4-1, formulas (6) to (7) are established to express the key parameters Jpss and k change rate, and to evaluate the pre-treatment effect: R Jpss =ΔJpss (6) Among them, R Jpss is the rate of change of the final permeation flux; R k The rate of change of pollution velocity; R Jpss and R k The larger the value, the better the effect of the pre-treatment process.

2. The method for determining an operation plan for optimizing reverse osmosis pretreatment effect according to claim 1, wherein: According to formulas (6) to (7), we can get R Jpss value, R k According to the size of the two values, compare the influence of the pre-treatment process on Jpss and k. If R Jpss The larger the value, the more obvious the improvement effect of pretreatment on the final permeation flux. On the contrary, if R k The larger the value, the more obvious the improvement effect of the pretreatment process on the membrane fouling rate.

3. The method for determining an operation plan for optimizing reverse osmosis pretreatment effect according to claim 1, wherein: The intermediate blocking model is used to simulate and determine the k and Jpss values ​​before and after pretreatment. Then, the Δk and ΔJpss values ​​are obtained by using formulas (4) to (5). Based on the positive and negative values ​​of Δk and ΔJpss, the effect of the current pretreatment process on mitigating membrane fouling is judged and optimized accordingly. The judgment and optimization methods are as follows: When Δk and ΔJpss are both positive, it means that the pretreatment can increase the final permeation flux and reduce the fouling rate; it is considered to have the effect of slowing down membrane fouling; the corresponding optimization is the maintenance parameter; When Δk is positive and ΔJpss is negative, it means that the pretreatment can reduce the fouling rate, but reduces the final permeation flux, which is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t2, keep the pre-processing running time ≤ t2; When Δk is negative and ΔJpss is positive, it means that the pretreatment increases the fouling rate but increases the final permeation flux; it is considered to have the effect of slowing down membrane fouling. The corresponding optimization is to find ΔV N =0 time parameter t3, so that the pre-processing running time is greater than t3.

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