CN117753219A - High-performance filter ultrafiltration membrane and preparation method thereof - Google Patents
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Abstract
The invention relates to the technical field of ultrafiltration membrane preparation, and provides a high-performance filter ultrafiltration membrane and a preparation method thereof, wherein the preparation method comprises the following steps: placing polyvinyl chloride, dimethylacetamide, polyethylene glycol 400, polyethylene glycol 1000, polysulfone and polyvinylpyrrolidone into a stirring kettle for stirring, adaptively adjusting the stirring temperature in the stirring process based on analysis of the stirring state of the casting solution, and standing and defoaming to obtain the casting solution; casting the casting solution on a cleaned substrate, scraping a film on the cast substrate by using a flat plate type film scraping machine in the casting process to obtain a substrate containing the casting solution, and volatilizing, soaking and cleaning to obtain the ultrafiltration membrane. The invention improves the quality of preparing the ultrafiltration membrane by improving the accuracy of the stirring temperature.
Description
Technical Field
The invention relates to the technical field of ultrafiltration membrane preparation, in particular to a high-performance filter ultrafiltration membrane and a preparation method thereof.
Background
With the rapid development of economy, water resources become more and more important factors influencing the development of economy, so that the reduction of sewage discharge and the full recycling of water resources become more and more important. The traditional water regeneration mode has the defects of poor water quality, high energy consumption, large occupied area and the like, and has difficulty in meeting the increasing water resource recycling and regenerating demands. Under the background, membrane water treatment method has been developed, and membrane separation technology is widely used in water resource recycling of various industries due to the advantages of low energy consumption, simple process, high separation efficiency and the like, wherein ultrafiltration is a membrane separation process taking pressure as reasoning, and particles and macromolecular organic substances are separated from fluid based on selective barrier action so as to realize filtration process. Typically ultrafiltration membranes are placed in high performance filters for ultrafiltration.
The ultrafiltration membrane is taken as one of important influencing factors of ultrafiltration operation effect, the quality of ultrafiltration is determined to a great extent, in the preparation process of the ultrafiltration membrane, as a plurality of different materials are required to be added for mixing, the uniformity degree after stirring determines the quality of the ultrafiltration membrane after the ultrafiltration membrane is prepared, and in the current stirring process of the ultrafiltration membrane, the stirring operation is usually carried out by setting relevant fixed parameters, so that the quality of the stirred ultrafiltration membrane is difficult to ensure, and the preparation of the ultrafiltration membrane is further influenced.
The traditional method for detecting the stirring state of the casting solution generally adopts machine vision or a method for monitoring data of a reaction kettle, however, the casting solution has more raw materials and smaller visual difference, and is difficult to stir uniformly, the reaction kettle also has various states, external factors have certain influence on state data in the reaction kettle, and finally, the judgment of the stirring uniformity of the casting solution is easy to be poor, so that the accuracy of adjusting the stirring process parameters of the casting solution is poor.
Disclosure of Invention
The invention provides a high-performance filter ultrafiltration membrane and a preparation method thereof, which aim to solve the problem of poor accuracy of adjustment of casting solution stirring process parameters, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for preparing a high performance ultrafiltration membrane for a filter, the method comprising the steps of:
placing polyvinyl chloride, dimethylacetamide, polyethylene glycol 400, polyethylene glycol 1000, polysulfone and polyvinylpyrrolidone into a stirring kettle for stirring, adaptively adjusting the stirring temperature in the stirring process based on analysis of the stirring state of the casting solution, and standing and defoaming to obtain the casting solution;
casting the casting solution on a cleaned substrate, scraping a film on the cast substrate by using a flat plate type film scraping machine in the casting process to obtain a substrate containing the casting solution, and volatilizing, soaking and cleaning to obtain the ultrafiltration membrane.
Preferably, 30 parts of polyvinyl chloride, 70 parts of dimethylacetamide, 3 parts of polyethylene glycol 400, 5 parts of polyethylene glycol 1000, 5 parts of polysulfone and 4 parts of polyvinylpyrrolidone are placed in a stirring kettle for stirring for 10-12 hours.
Preferably, the static defoaming is specifically defoaming for 2.5-3.5 hours in a vacuum drying oven at the temperature of 30-35 ℃.
Preferably, the thickness of the casting solution on the substrate is 150-200 μm.
Preferably, the soaking is specifically soaking in a dimethylacetamide solution with the mass concentration of 20-25% and the temperature of 35-40 ℃ for 22-24 hours.
Preferably, the volatilization time is 10-15 s; the washing is specifically carried out for 3-5 times by using distilled water.
Preferably, the method for adaptively adjusting the stirring temperature in the stirring process based on the analysis of the stirring state of the casting solution comprises the following steps:
obtaining hyperspectral reflectivity images at each acquisition time based on a hyperspectral camera;
for each pixel in the hyperspectral reflectance image at each acquisition time, taking all hyperspectral reflectance data of the pixel as input of a polynomial curve fitting algorithm, and obtaining a hyperspectral reflectance curve of the pixel by using the polynomial curve fitting algorithm, wherein the transverse axis of the hyperspectral reflectance curve is the length of a wave band, and the longitudinal axis is the reflectance; taking the average value of the reflectivities of all wave bands in the hyperspectral reflectivity curve of the pixel as the representative reflectivity of the pixel;
obtaining a casting solution mixing uniformity coefficient at each acquisition time according to a hyperspectral reflectivity curve and a representative reflectivity of each pixel in the hyperspectral reflectivity image at each acquisition time;
calculating the difference value of the casting solution mixing uniformity coefficient at each acquisition time and the casting solution mixing uniformity coefficient at the last acquisition time, and taking a sequence formed by all the difference values according to the time ascending sequence as a gradient data sequence;
acquiring a stable index of the stirring state of the casting solution at each acquisition time according to the transition rate data sequence;
taking a sequence formed by the stable indexes of the stirring state of the casting solution at all the acquisition moments according to the sequence of the ascending order of time as a stable index data sequence of the stirring state of the casting solution; taking the stable index data sequence of the stirring state of the casting solution as the input of the BP neural network, and taking the output of the BP neural network as the stirring temperature regulation parameter of the casting solution;
and taking the stirring temperature adjusting parameter of the casting solution as the input of the PID controller, and adjusting the stirring temperature in the mixing and stirring process based on the stirring temperature adjusting parameter of the casting solution by using the PID controller.
Preferably, the method for obtaining the film casting solution mixing uniformity coefficient at each collection time according to the hyperspectral reflectance curve and the representative reflectance of each pixel in the hyperspectral reflectance image at each collection time comprises the following steps:
for each pixel in the hyperspectral reflectance image at each acquisition time, calculating the absolute value of the difference between the wave band length of each wave peak point and the wave band length of the last wave peak point in the hyperspectral reflectance curve of the pixel, and calculating the accumulated sum of the absolute values on the hyperspectral reflectance curve; calculating the product of the total number of peak points in a hyperspectral reflectivity curve of the pixel and the representative reflectivity of the pixel, and taking the product of the reciprocal of the product and the accumulated sum as a first composition factor; taking a negative mapping result taking a natural constant as a base and taking a first composition factor as an index as a hyperspectral characteristic index of the pixel;
for any two pixels in the hyperspectral reflectance image at each acquisition time, calculating a similarity measurement result between hyperspectral reflectance curves of the two pixels, calculating the reciprocal of the sum of the absolute value of the difference between hyperspectral characteristic indexes of the two pixels and a first preset parameter, and taking the product of the reciprocal of the sum and the similarity measurement result as a hyperspectral approximation coefficient between the two pixels;
for a hyperspectral reflectivity image at each acquisition time, taking each pixel in the hyperspectral reflectivity image as each image node, taking a hyperspectral approximation coefficient between any two pixels in the hyperspectral reflectivity image as the weight of the edge between any two image nodes, and taking a weighted undirected image determined by all image nodes and the weight of the edge as a casting solution pixel undirected image; taking the undirected image of the casting film liquid pixels as input of a Markov image clustering algorithm, and obtaining clustering results of all pixels in the hyperspectral reflectivity image by using the Markov image clustering algorithm, wherein the clustering results comprise each pixel clustering cluster;
taking each pixel in each pixel cluster of the hyperspectral reflectivity image as a target pixel, calculating a measurement distance between the target pixel and each pixel in the pixel cluster, and taking the average value of the summation of the measurement distances on the pixel cluster as a second composition factor; taking a negative mapping result taking a natural constant as a base and taking a second composition factor as an index as an adjacent approximation coefficient of a target pixel;
for each pixel cluster of the hyperspectral reflectivity image at each acquisition moment, calculating the variation coefficient of the proximity approximation coefficient of all pixels in the pixel cluster, and taking the product of the variation coefficient and the davison bauer index of the pixel cluster as the in-cluster chaotic index of the pixel cluster;
and calculating the intra-cluster chaotic index mean value of all pixel clustering clusters of the hyperspectral reflectivity image at each acquisition time, taking a natural constant as a base, taking the negative mapping result of the sum of the hyperspectral approximation coefficients on the hyperspectral reflectivity image as an index as a third composition factor, and taking the product of the intra-cluster chaotic index mean value and the third composition factor as a casting solution mixing uniformity coefficient at each acquisition time.
Preferably, the method for obtaining the stable index of the stirring state of the casting solution at each collection time according to the transition rate data sequence comprises the following steps:
using the gradient data sequence as the input of a region growing algorithm, and obtaining all the growing data subsequences in the gradient data sequence by using the region growing algorithm;
taking each growth data subsequence in the gradient data sequence as a target growth data subsequence, and taking the difference value between the data average value of the target growth data subsequence and the data average value of the last growth data subsequence as a molecule; calculating a similarity measurement result between the target growth data subsequence and the last growth data subsequence, and taking the sum of the similarity measurement result and a first preset parameter as a denominator; calculating the absolute value of the ratio of the numerator to the denominator;
taking the product of the sequence length of the target growth data subsequence and the absolute value as a stirring temperature anomaly compensation coefficient of the target growth data subsequence;
taking the stirring temperature abnormality compensation coefficient of the target growth data subsequence as a casting solution temperature abnormality correction coefficient at each acquisition time in the target growth data subsequence;
and calculating a negative mapping result taking a natural constant as a base number and the abnormal correction coefficient of the casting solution temperature as an index, and taking the product of the negative mapping result and the casting solution mixing uniformity coefficient at each collection time as a casting solution stirring state stability index at each collection time.
In a second aspect, the embodiment of the invention also provides a high-performance filter ultrafiltration membrane, which is prepared by the preparation method.
The beneficial effects of the invention are as follows: according to the invention, the hyperspectral camera is utilized to collect data of the casting solution reaction kettle, the difference characteristic of hyperspectral reflectivity of each raw material is utilized to identify various raw materials, the stirring uniformity degree at the moment is further judged according to the distribution condition of the raw materials in the casting solution, the temperature abnormality correction coefficient is constructed by judging the characteristic that the stirring temperature influences the mixing uniformity condition of the casting solution, the stable index of the stirring state of the casting solution is finally constructed, and the stirring temperature is adjusted by utilizing the BP neural network and the PID controller. When traditional casting solution stirring condition is detected, the problems of small visual difference of casting solution raw materials and more external influence factors, and inaccurate detection are solved, the detection accuracy of quality and uniformity of the casting solution after stirring is improved, and further parameter adjustment is better carried out on the casting solution stirring process, and finally the quality of preparing the ultrafiltration membrane is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for preparing a high-performance ultrafiltration membrane of a filter according to an embodiment of the present invention;
fig. 2 is a flow chart of an implementation of a method for preparing a high-performance ultrafiltration membrane for a filter according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for preparing a high-performance ultrafiltration membrane for a filter according to an embodiment of the invention is shown, the method comprises the following steps:
and S001, placing the raw materials into a stirring kettle for stirring, and collecting hyperspectral images in the stirring process.
Stirring: 30 parts of polyvinyl chloride, 70 parts of dimethylacetamide, 3 parts of polyethylene glycol 400, 5 parts of polyethylene glycol 1000, 5 parts of polysulfone and 4 parts of polyvinylpyrrolidone are placed into a stirring kettle to be stirred for 10 hours, and the average polymerization degree of the polyvinyl chloride is 1000-3000.
In the stirring process, every interval time T, a hyperspectral camera is utilized to acquire hyperspectral image data once for casting film liquid in a reaction kettle under the condition of keeping surrounding environment conditions, a hyperspectral meter can record spectrum information of a plurality of wave bands at the same time to generate hyperspectral reflectivity image data of the casting film liquid, and the hyperspectral reflectivity image data of n acquisition moments are acquired through the hyperspectral camera. In the invention, t=0.5s and n=300, and an implementer can take values according to actual conditions.
Thus, hyperspectral reflectance image data at the acquisition time are obtained.
Step S002, obtaining hyperspectral approximation coefficients according to hyperspectral image data; acquiring a pixel cluster according to the hyperspectral approximation coefficient; acquiring a chaotic index in a cluster according to the pixel cluster; and obtaining a film casting solution mixing uniformity coefficient according to the in-cluster chaotic index and the hyperspectral approximation coefficient.
In the initial stage of the process of preparing the casting solution, since various raw materials are just put into a stirring kettle, the raw materials are concentrated in proportion in the stirring kettle after being mixed, and the coexistence of various raw materials is presented. As stirring proceeds, the various raw materials begin to mix, gradually dissolve in the solvent in the stirred tank, and finally disperse uniformly in the casting solution. In the process, the hyperspectral data in the stirring kettle can be distributed from the spectrum data of similar substances at first to discrete distribution, and whether uniform casting solution is formed or not is judged, and firstly, the distribution condition of raw materials in the casting solution is required to be judged.
Specifically, each raw material of the casting solution has different reflectivities in each waveband of the hyperspectrum, the relation between the reflectivities and the waveband lengths of each pixel in the hyperspectral reflectivity image at each acquisition time is fitted into a polynomial curve by adopting a polynomial curve fitting method, the input of the polynomial curve fitting algorithm is the data of the reflectivities and the waveband lengths of each pixel, the output of the polynomial curve fitting algorithm is the hyperspectral reflectivity curve of each pixel, the transverse axis of the hyperspectral reflectivity curve is the waveband length, and the longitudinal axis is the reflectivity. The polynomial curve fitting is a well-known technique, and the process is not repeated in the present invention.
Further, the average value of the reflectivities of all the bands in the hyperspectral reflectivities curve of each pixel is taken as the representative reflectivity of each pixel, and the representative reflectivity of each pixel is recorded as f.
For a hyperspectral reflectance image for each acquisition instant, a hyperspectral approximation coefficient (W) from pixel to pixel is constructed:
wherein B is i For the hyperspectral characteristic index of the ith pixel, exp () is an exponential function based on a natural constant, f i S is the total number of peak points in the hyperspectral reflectivity curve of the ith pixel, beta j ,β j-1 The band lengths corresponding to the jth peak point and the jth-1 peak point in the hyperspectral reflectivity curve of the ith pixel are respectively;
w (i, y) is the hyperspectral approximation coefficient between the ith and the yh pixels, B y For the hyperspectral characteristic index of the y-th pixel, E is an error parameter, the denominator is avoided to be 0, the empirical value of the error parameter is 0.1, q i ,q y Hyperspectral reflectance curves for the ith and the y-th pixels, respectively, cd () is a cosine similarity function, cd (q i ,q y ) Is the cosine similarity between the hyperspectral reflectivity curves of the i-th pixel and the y-th pixel.
When the representative reflectivity of the ith pixel is larger and the difference of the wave band length between the wave peaks is smaller, thenThe smaller the value of (1) the first composition factor +.>The smaller, i.e. B i The larger the value of (C) is, the larger the hyperspectral reflectivity of the pixel is, the denser the peak distribution is, and the more obvious the hyperspectral characteristic is. When the hyperspectral characteristic index difference between the ith pixel and the y pixel is smaller, the ++>The larger the value of (c) and the higher the cosine similarity of the hyperspectral reflectance curves of the two pixels, cd (q) i ,q y ) The larger the value of (i) W (i, y), the greater the degree of similarity of the hyperspectral data between the two pixels, the more likely the pixels are to be of the same casting solution raw material.
Further, for the hyperspectral reflectivity image at each acquisition time, taking all pixels as nodes of an undirected image, taking hyperspectral approximation coefficients between the pixels as weights of the undirected image nodes and edges between the nodes, and taking a weighted undirected image constructed by the weights of all the nodes and the edges as a undirected image of the casting solution pixels. And then clustering the casting solution pixel undirected graph by using a Markov graph clustering algorithm, wherein the input of the Markov graph clustering algorithm is the casting solution pixel undirected graph, and the output of the Markov graph clustering algorithm is a plurality of pixel clustering clusters of all pixels in the hyperspectral reflectivity image. Each cluster of picture elements represents to some extent a casting solution raw material. The Markov graph clustering algorithm is a known technology, and the process of the method is not repeated.
Further, for each acquisition time, a casting solution mixing uniformity coefficient of the hyperspectral reflectivity image is constructed:
CR r ＝DBI(C r )×CV r
wherein K is i For the approximation coefficient of the i-th pixel, i, q is the coordinate of the spatial position of the i-th pixel and the q-th pixel, dist () is the Euclidean distance function, dist (i, q) is the Euclidean distance between the i-th pixel and the j-th pixel, G i The total number of pixels in the pixel cluster where the ith pixel is located;
CR r c is the intra-cluster chaotic index of the r-th pixel cluster r For the r-th cluster of pels, DBI () is the davison burg Ding Zhishu (Davies-Bouldin index) function, DBI (C r ) Davison burg Ding Zhishu, CV for the r-th pixel cluster r The variation coefficient of the adjacent approximation coefficients of all pixels in the r-th pixel cluster;
e is the uniform mixing coefficient of casting solution of the hyperspectral reflectivity image, M is the total number of pixels in the hyperspectral reflectivity image, W (i, i-1) is the hyperspectral approximation coefficient of the ith and the ith-1 pixels, and T is the total number of pixel clusters in the hyperspectral reflectivity image.
When the distance between the ith pixel and the pixels in the rest of the same pixel cluster is closer, a second composition factorSmaller (less)>The greater the value of (a), i.e. K i The larger the pixel, the more pixels the pixel is located in and approximated to, and the denser the pixel clusters may be. When the davison bauer index of the r-th pixel cluster is larger, DBI (C r ) The larger the value of (c), the larger the coefficient of variation of the adjacent approximation coefficients of all pixels in the nth pixel cluster, i.e., CR r The larger the cluster, the worse the clustering effect of the cluster and the more scattered the pixel distribution in the cluster, the more scattered the casting film liquid raw material represented by the pixel in the cluster, and the higher the degree of disorder. When the approximation degree between each pixel in the casting solution hyperspectral reflectivity image is smaller, the third composition factor is +>The larger the value of (2) and the larger the confusion index of each cluster +.>The larger the value of (a), i.e., the larger the E, the higher the degree of mixing of the casting solution raw materials at the present moment, each raw material being stirred, the more uniformly the casting solution raw materials are mixed.
So far, the film casting liquid mixing uniformity coefficient of the hyperspectral reflectivity image at each moment is obtained.
Step S003, obtaining an abnormal compensation coefficient of the stirring temperature according to the uniform mixing coefficient of the casting solution; and obtaining the stable index of the stirring state of the casting solution according to the abnormal compensation coefficient of the stirring temperature.
The mixing process is gradually progressive, but the continuous increment of the mixing uniformity coefficient of the casting solution does not necessarily fully indicate that the current stirring temperature is the most suitable, and when the stirring temperature is not suitable, the mixing uniformity coefficient of the casting solution is still increasing, but the increasing speed is decreasing, so that the suitability of the current stirring temperature on the surface cannot be fully achieved by only depending on the mixing uniformity coefficient of the casting solution, and further analysis is needed.
Under the condition of proper stirring temperature, the raw materials of the casting solution start to be mixed and gradually disperse, the occasional increase speed of the casting solution mixing uniformity coefficient is reduced for a very short time, and when the stirring temperature is not suitable, the degree of mixing uniformity change is reduced and the duration is long.
Specifically, the casting solution mixing uniformity coefficient at each moment is subtracted from the casting solution mixing uniformity coefficient at the previous moment to obtain casting solution mixing uniformity gradient, the casting solution mixing uniformity gradient is recorded as delta E, the casting solution mixing uniformity gradient at all moments is ordered according to the moment sequence, a gradient data sequence is constructed, and the gradient data sequence is recorded as H. In the method, a gradient rate data sequence is used as input of a region growing algorithm, each data point is used as a starting point, the output of the region growing algorithm is a plurality of growing regions, the data points in each growing region are arranged according to a time sequence to construct each growing data subsequence, and the region growing algorithm is a known technology and the process of the method is not repeated. Constructing a stirring temperature anomaly compensation coefficient of each growth data sequence:
wherein t is j Stirring temperature anomaly compensation coefficient for jth growth data sequence, Z j ,Z j-1 The j-th and j-1-th growth data sequences, respectively, len (Z j ) For the total number of data points of the jth growth data sequence,the data means of the j-th and j-1-th growth data sequences, pearson () is pearson correlation coefficient function, pearson (Z j ,Z j-1 ) Is the pearson correlation coefficient between the j-th and j-1-th growth data sequences, and e is the error parameter.
When the difference between the mean value of the j-th growth data sequence and the mean value of the previous growth data sequence is larger, the correlation is lower,the larger the value of (C) indicates that the difference of the casting solution gradient change is larger and the change is less relevant in the time period of the growth data sequence compared with the previous time period, the more likely the change is caused by abnormal stirring temperature, and the more the length of the growth data sequence is longer, the more the data volume is, len (Z) j ) The larger the value of (c) is, the longer the duration of the change in the period of time the growth data sequence is, the less likely the accidental resulting change in the rate of change will be.
The stirring temperature anomaly compensation coefficient of each growth data sequence is given to the data points at all the moments in the sequence, the casting solution temperature anomaly correction coefficient at each acquisition moment is obtained, and the casting solution temperature anomaly correction coefficient at the mth acquisition moment is recorded as P m Constructing a stable index of the stirring state of the casting solution at each acquisition time:
in FE m Is the stable index of the stirring state of the casting solution at the mth moment, E m Is the uniform mixing coefficient of the casting solution at the mth moment, P m The temperature abnormality correction coefficient of the casting solution at the mth time.
E, when the mixing uniformity degree of the casting solution is larger at the mth acquisition time m The larger the value of (c) is, the more normal the stirring temperature of the casting solution at the mth moment is,the larger the value of (2), i.e. FE m The larger the film casting liquid is, the better the whole stirring state of the film casting liquid is, and the temperature condition is adapted.
So far, the stable index of the stirring state of the casting solution at each collection time is obtained.
Step S004, obtaining a membrane casting solution stirring temperature adjustment parameter based on a membrane casting solution stirring state stability index by utilizing a BP neural network; and (3) carrying out self-adaptive adjustment on the stirring temperature based on the stirring temperature adjustment parameter of the membrane casting liquid by utilizing a PID controller, so as to finish the preparation of the ultrafiltration membrane.
Calculating the stable index of the stirring state of the casting solution at each moment, and sequencing according to the sequence of the moments to obtain a stable index data sequence FE of the stirring state of the casting solution 1 ,FE 2 ,…,FE n Wherein FE is n The sum of (2) means the stable index of the stirring state of the casting solution at the nth time. And taking the stable index data sequence of the stirring state of the casting solution as the input of the BP neural network, wherein the optimization algorithm of the BP neural network is an SGD algorithm, and the output of the BP neural network is the stirring temperature regulation parameter of the casting solution. The BP neural network is a known technology, and the process of the invention is not repeated. A flow chart of an implementation of the present invention is shown in fig. 2.
And taking the stirring temperature regulation parameters of the casting solution as the input of a PID controller, and sending a temperature regulation signal to an intelligent stirring temperature regulation system by using the PID controller, wherein the temperature regulation signal represents the temperature to be regulated, and regulating the stirring temperature, namely regulating the stirring temperature to a temperature range represented by the temperature regulation signal, so as to improve the uniform mixing efficiency in the process of re-stirring the casting solution, and finally obtaining the uniform and high-quality casting solution. The PID controller is a known technology, and the process of the invention is not repeated.
Defoaming: and (3) defoaming treatment is carried out in a vacuum drying oven at 35 ℃ for 3 hours, so that uniform and high-quality casting film liquid is obtained. Thus, the casting solution with uniform and high quality is obtained.
And preparing the ultrafiltration membrane by using the uniform high-quality casting solution obtained by the steps.
Scraping a film: casting the casting solution on the cleaned glass substrate, and scraping a film on the cast glass substrate by using a flat plate type film scraping machine in the casting process to obtain the glass substrate containing the casting solution with the thickness of 150 mu m.
And (3) precipitation film forming: the glass substrate was volatilized in air for 10s and then immersed in a dimethylacetamide solution having a mass concentration of 25% and a temperature of 40 ℃ for 22 hours.
Cleaning: the ultrafiltration membrane was obtained by washing with distilled water 5 times.
Based on the same inventive concept as the method, the embodiment of the invention also provides a high-performance filter ultrafiltration membrane, which is manufactured by the preparation method of any one of the filter ultrafiltration membranes.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The preparation method of the high-performance filter ultrafiltration membrane is characterized by comprising the following steps of:
placing polyvinyl chloride, dimethylacetamide, polyethylene glycol 400, polyethylene glycol 1000, polysulfone and polyvinylpyrrolidone into a stirring kettle for stirring, adaptively adjusting the stirring temperature in the stirring process based on analysis of the stirring state of the casting solution, and standing and defoaming to obtain the casting solution;
casting the casting solution on a cleaned substrate, scraping a film on the cast substrate by using a flat plate type film scraping machine in the casting process to obtain a substrate containing the casting solution, and volatilizing, soaking and cleaning to obtain the ultrafiltration membrane.
2. The method for preparing a high-performance ultrafiltration membrane for a filter according to claim 1, wherein the ultrafiltration membrane comprises, by weight, 30 parts of polyvinyl chloride, 70 parts of dimethylacetamide, 3 parts of polyethylene glycol 400, 5 parts of polyethylene glycol 1000,
5 parts of polysulfone and 4 parts of polyvinylpyrrolidone are put into a stirring kettle to be stirred for 10 to 12 hours.
3. The method for preparing a high-performance filter ultrafiltration membrane according to claim 1, wherein the static deaeration is carried out in a vacuum drying oven at 30-35 ℃ for 2.5-3.5 h.
4. The method for preparing a high-performance ultrafiltration membrane for a filter according to claim 1, wherein the thickness of the casting solution on the substrate is 150-200 μm.
5. The method for preparing a high-performance filter ultrafiltration membrane according to claim 1, wherein the soaking is specifically performed in a dimethylacetamide solution with a mass concentration of 20-25% and a temperature of 35-40 ℃ for 22-24 hours.
6. The method for preparing a high-performance ultrafiltration membrane for a filter according to claim 1, wherein the volatilization time is 10 to 15s; the washing is specifically carried out for 3-5 times by using distilled water.
7. The method for preparing the high-performance filter ultrafiltration membrane according to claim 1, wherein the method for adaptively adjusting the stirring temperature in the stirring process based on the analysis of the stirring state of the membrane casting solution is as follows:
obtaining hyperspectral reflectivity images at each acquisition time based on a hyperspectral camera;
for each pixel in the hyperspectral reflectance image at each acquisition time, taking all hyperspectral reflectance data of the pixel as input of a polynomial curve fitting algorithm, and obtaining a hyperspectral reflectance curve of the pixel by using the polynomial curve fitting algorithm, wherein the transverse axis of the hyperspectral reflectance curve is the length of a wave band, and the longitudinal axis is the reflectance; taking the average value of the reflectivities of all wave bands in the hyperspectral reflectivity curve of the pixel as the representative reflectivity of the pixel;
obtaining a mixing uniformity coefficient of the mixed solution at each acquisition time according to a hyperspectral reflectivity curve and a representative reflectivity of each pixel in the hyperspectral reflectivity image at each acquisition time;
calculating the difference value of the mixing uniformity coefficient of the mixed solution at each acquisition time and the mixing uniformity coefficient of the mixed solution at the last acquisition time, and taking a sequence formed by all the difference values according to the time ascending sequence as a gradient rate data sequence;
acquiring a mixed solution stirring state stability index at each acquisition time according to the transition rate data sequence;
taking a sequence formed by mixing the mixed solution stirring state stability indexes at all the acquisition moments according to the sequence of the time ascending sequence as a mixed solution stirring state stability index data sequence; taking the mixed solution stirring state stable index data sequence as the input of the BP neural network, and taking the output of the BP neural network as the mixed solution stirring temperature regulation parameter;
and taking the mixed solution stirring temperature adjusting parameter as the input of the PID controller, and adjusting the stirring temperature in the stirring process based on the mixed solution stirring temperature adjusting parameter by using the PID controller.
8. The method for preparing the high-performance ultrafiltration membrane of the filter of claim 7, wherein the method for obtaining the mixing uniformity coefficient of the mixed solution at each acquisition time according to the hyperspectral reflectance curve and the representative reflectance of each pixel in the hyperspectral reflectance image at each acquisition time comprises the following steps:
for each pixel in the hyperspectral reflectance image at each acquisition time, calculating the absolute value of the difference between the wave band length of each wave peak point and the wave band length of the last wave peak point in the hyperspectral reflectance curve of the pixel, and calculating the accumulated sum of the absolute values on the hyperspectral reflectance curve; calculating the product of the total number of peak points in a hyperspectral reflectivity curve of the pixel and the representative reflectivity of the pixel, and taking the product of the reciprocal of the product and the accumulated sum as a first composition factor; taking a negative mapping result taking a natural constant as a base and taking a first composition factor as an index as a hyperspectral characteristic index of the pixel;
for any two pixels in the hyperspectral reflectance image at each acquisition time, calculating a similarity measurement result between hyperspectral reflectance curves of the two pixels, calculating the reciprocal of the sum of the absolute value of the difference between hyperspectral characteristic indexes of the two pixels and a first preset parameter, and taking the product of the reciprocal of the sum and the similarity measurement result as a hyperspectral approximation coefficient between the two pixels;
for a hyperspectral reflectivity image at each acquisition time, taking each pixel in the hyperspectral reflectivity image as each image node, taking a hyperspectral approximation coefficient between any two pixels in the hyperspectral reflectivity image as the weight of the edge between any two image nodes, and taking a weighted undirected image determined by all image nodes and the weight of the edge as a mixed solution pixel undirected image; taking the mixed solution pixel undirected graph as input of a Markov graph clustering algorithm, and obtaining clustering results of all pixels in the hyperspectral reflectivity image by using the Markov graph clustering algorithm, wherein the clustering results comprise each pixel clustering cluster;
taking each pixel in each pixel cluster of the hyperspectral reflectivity image as a target pixel, calculating a measurement distance between the target pixel and each pixel in the pixel cluster, and taking the average value of the summation of the measurement distances on the pixel cluster as a second composition factor; taking a negative mapping result taking a natural constant as a base and taking a second composition factor as an index as an adjacent approximation coefficient of a target pixel;
for each pixel cluster of the hyperspectral reflectivity image at each acquisition moment, calculating the variation coefficient of the proximity approximation coefficient of all pixels in the pixel cluster, and taking the product of the variation coefficient and the davison bauer index of the pixel cluster as the in-cluster chaotic index of the pixel cluster;
and calculating the intra-cluster chaotic index mean value of all pixel clustering clusters of the hyperspectral reflectivity image at each acquisition time, taking a natural constant as a base, taking the negative mapping result of the sum of the hyperspectral approximation coefficients on the hyperspectral reflectivity image as an index as a third composition factor, and taking the product of the intra-cluster chaotic index mean value and the third composition factor as a casting solution mixing uniformity coefficient at each acquisition time.
9. The method for preparing a high-performance ultrafiltration membrane for a filter according to claim 7, wherein the method for obtaining the stability index of the stirring state of the mixed solution at each acquisition time according to the transition rate data sequence comprises the following steps:
using the gradient data sequence as the input of a region growing algorithm, and obtaining all the growing data subsequences in the gradient data sequence by using the region growing algorithm;
taking each growth data subsequence in the gradient data sequence as a target growth data subsequence, and taking the difference value between the data average value of the target growth data subsequence and the data average value of the last growth data subsequence as a molecule; calculating a similarity measurement result between the target growth data subsequence and the last growth data subsequence, and taking the sum of the similarity measurement result and a first preset parameter as a denominator; calculating the absolute value of the ratio of the numerator to the denominator;
taking the product of the sequence length of the target growth data subsequence and the absolute value as a stirring temperature anomaly compensation coefficient of the target growth data subsequence;
taking the stirring temperature abnormality compensation coefficient of the target growth data subsequence as a casting solution temperature abnormality correction coefficient at each acquisition time in the target growth data subsequence;
and calculating a negative mapping result taking a natural constant as a base and the temperature abnormality correction coefficient of the mixed solution as an index, and taking the product of the negative mapping result and the casting solution mixing uniformity coefficient at each collection time as a mixed solution stirring state stability index at each collection time.
10. A high-performance filter ultrafiltration membrane, characterized in that the high-performance filter ultrafiltration membrane is prepared by the method of claim 1
The high-performance filter ultrafiltration membrane according to any one of the above 9.
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