Method and device for constructing a model of microplastic removal process in sewage treatment plants

Abstract

The present application relates to the technical field of sewage treatment, and in particular to a method and device for constructing a microplastic removal process model of a sewage treatment plant, wherein the method comprises: collecting monitoring data of microplastics in the inlet and outlet of the sewage treatment plant; dividing the different particle size intervals of microplastics based on a pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, and obtaining the parameters to be identified of the removal process model based on the particle size intervals; dividing the monitoring data into a training set and a validation set, identifying the parameters to be identified based on the training set, and using the validation set to test the removal process model after the identification of the parameters to be identified, and obtaining the simulated value of the microplastics in the effluent of the sewage treatment plant. Thus, the technical problems in the related art, such as the lack of analysis of the main migration and transformation effects of microplastics of different particle sizes in the sewage treatment plant, the inability to simulate the amount of microplastics and their particle size distribution discharged into the water environment after the sewage is treated, and the poor universality, are solved.

Description

Technical Field

[0001] The present application relates to the technical field of sewage treatment, and in particular to a method and device for constructing a model of microplastic removal process in a sewage treatment plant. Background Art

[0002] The potential risks that microplastics bring to the ecosystem are increasingly attracting attention. Among them, the removal of microplastics by sewage treatment plants is a key link in the collection, treatment and discharge process of microplastics. Analyzing the emission reduction characteristics of microplastics in sewage treatment plants is of great significance for environmental risk assessment and control.

[0003] In the related technology, microplastics can be removed by adding microplastic precipitation and attachment components; flocculants can also be used to promote the flocculation and sedimentation removal of microplastics; and related magnetic removal devices can also be used to remove microplastics from sewage.

[0004] However, the relevant technology does not analyze the main migration and transformation effects of microplastics of different particle sizes in sewage treatment plants, nor can it simulate the amount of microplastics and their particle size distribution discharged into the water environment after sewage treatment. It has poor universality and needs to be solved urgently. Summary of the invention

[0005] The present application provides a method and device for constructing a model of the microplastic removal process in a sewage treatment plant, in order to solve the problems in the related technology of not analyzing the main migration and transformation effects of microplastics of different particle sizes in sewage treatment plants, not being able to simulate the amount of microplastics and their particle size distribution discharged into the water environment after sewage treatment, and poor universality.

[0006] The first aspect of the present application provides a method for constructing a microplastic removal process model for a sewage treatment plant, comprising the following steps: collecting monitoring data on microplastics in the inlet and outlet of the sewage treatment plant; dividing the microplastics into different particle size intervals based on a pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, and obtaining parameters to be identified for the removal process model based on the particle size intervals; dividing the monitoring data into a training set and a validation set, identifying the parameters to be identified based on the training set, and using the validation set to verify the removal process model after the parameters to be identified, to obtain simulated values ​​of the microplastics in the effluent of the sewage treatment plant.

[0007] Optionally, in one embodiment of the present application, before dividing the microplastics into different particle size intervals, it also includes: establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size, wherein the removal process model includes a material balance model of the microplastics according to particle size.

[0008] Optionally, in one embodiment of the present application, the material balance model may be expressed as:

[0009]

[0010]

[0011] Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

[0012] Optionally, in one embodiment of the present application, the removal process model based on the pre-established migration and transformation mechanism of microplastics by particle size divides the microplastics into different particle size intervals, and obtains the parameters to be identified of the removal process model based on the particle size intervals, including: removing the microplastics in the mud phase based on the adsorption of the microplastics by the microplastics; agglomerating and cracking the microplastics of adjacent particle sizes to obtain the different particle size intervals, and using a preset kinetic model to obtain the parameters to be identified of the removal process model.

[0013] Optionally, in one embodiment of the present application, the expression of the preset dynamic model may be:

[0014]

[0015]

[0016]

[0017] Among them, N 1 、N 2 、N 3 Respectively represent R 1 , R 2 , R 3 Abundance of microplastics in three particle size ranges, k 1 , k 2 , k 3 Respectively represent R 1 , R 2 , R 3 Microplastic adsorption and precipitation rates in three particle size ranges, p represents R 1 and R 2 The particle agglomeration in the interval is R 3 Probability of interval particle size, β 1,2 , β 2,3 represents the effective collision frequency between microplastic particles with particle size 1 and particle size 2, and particle size 2 and particle size 3, respectively, 1 and B 2Represent the decomposition rates of microplastic particles with particle size 1 and particle size 2 respectively.

[0018] Optionally, in one embodiment of the present application, the monitoring data of the microplastics may include, but is not limited to, at least one of: microplastic abundance, water treatment process, particle size distribution, shape, and component characteristics.

[0019] The second aspect of the present application provides a device for constructing a microplastic removal process model for a sewage treatment plant, comprising: a collection module for collecting monitoring data on microplastics in the inlet and outlet of the sewage treatment plant; an acquisition module for dividing the microplastics into different particle size intervals based on a pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, and obtaining parameters to be identified of the removal process model based on the particle size intervals; a verification module for dividing the monitoring data into a training set and a verification set, identifying the parameters to be identified based on the training set, and using the verification set to verify the removal process model after the parameters to be identified, so as to obtain simulated values ​​of the microplastics in the effluent of the sewage treatment plant.

[0020] Optionally, in one embodiment of the present application, it also includes: establishing a module for establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size before dividing the microplastics into different particle size intervals, wherein the removal process model includes a material balance model of the microplastics according to particle size.

[0021] Optionally, in one embodiment of the present application, the material balance model may be expressed as:

[0022]

[0023]

[0024] Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

[0025] Optionally, in one embodiment of the present application, the acquisition module includes: a removal unit, used to remove microplastics in the mud phase based on the adsorption of microplastics by the microplastics; an acquisition unit, used to agglomerate and crack the microplastics of adjacent particle sizes to obtain the different particle size ranges, and use a preset kinetic model to obtain the parameters to be identified of the removal process model.

[0026] Optionally, in one embodiment of the present application, the expression of the preset dynamic model may be:

[0027]

[0028]

[0029]

[0030] Among them, N 1 、N 2 、N 3 Respectively represent R 1 , R 2 , R 3 Abundance of microplastics in three particle size ranges, k 1 , k 2 , k 3 Respectively represent R 1 , R 2 , R 3 Microplastic adsorption and precipitation rates in three particle size ranges, p represents R 1 and R 2 The particle agglomeration in the interval is R 3 Probability of interval particle size, β 1,2 , β 2,3 represents the effective collision frequency between microplastic particles with particle size 1 and particle size 2, and particle size 2 and particle size 3, respectively, 1 and B 2 Represent the decomposition rates of microplastic particles with particle size 1 and particle size 2 respectively.

[0031] Optionally, in one embodiment of the present application, the monitoring data of the microplastics may include, but is not limited to, at least one of: microplastic abundance, water treatment process, particle size distribution, shape, and component characteristics.

[0032] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a microplastic removal process model for a sewage treatment plant as described in the above embodiment.

[0033] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for constructing a model of microplastic removal process in a sewage treatment plant.

[0034] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, implements the above-mentioned method for constructing a model of microplastic removal process in a sewage treatment plant.

[0035] The embodiment of the present application can collect monitoring data of microplastics in the inlet and outlet of the sewage plant, and obtain the parameters to be identified by using the removal process model of the migration and transformation mechanism of microplastics by particle size, and then obtain the simulated value of the microplastics in the effluent of the sewage plant through identification and inspection of the monitoring data. The present application can simulate the removal process of microplastics in the sewage plant by particle size by analyzing the main migration and transformation effects of microplastics of different particle sizes in the sewage plant, quantitatively predict the amount of microplastics in the effluent of the sewage plant and their particle size distribution, and provide a basis for the ecological risk assessment of microplastics by particle size. Thus, the problems in the related art that the main migration and transformation effects of microplastics of different particle sizes in the sewage plant are not analyzed, and the amount of microplastics and their particle size distribution discharged into the water environment after treatment of sewage cannot be simulated, and the universality is poor.

[0036] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0038] Figure 1 A flowchart of a method for constructing a microplastic removal process model for a sewage plant provided according to an embodiment of the present application;

[0039] Figure 2 A box-shaped schematic diagram of the abundance of microplastics in the inlet and outlet water of a sewage treatment plant provided according to an embodiment of the present application;

[0040] Figure 3 A schematic diagram of the proportion of microplastic particle size distribution and length distribution in the inlet and outlet water of a sewage treatment plant provided according to an embodiment of the present application;

[0041] Figure 4 A schematic diagram of the distribution of microplastic shape characteristics of the influent and effluent of a sewage treatment plant according to an embodiment of the present application;

[0042] Figure 5 A schematic diagram of the distribution characteristics of microplastic components in the influent and effluent of a sewage treatment plant according to an embodiment of the present application;

[0043] Figure 6 A schematic diagram of the aggregation and pyrolysis of microplastics in different particle size ranges provided in accordance with an embodiment of the present application;

[0044] Figure 7 A schematic diagram of a block diagram of the removal process of microplastic migration, adsorption precipitation, aggregation, cracking, etc. provided in accordance with an embodiment of the present application;

[0045] Figures 8 to 12A schematic diagram of the result of the probability distribution of the parameters to be identified obtained by the HSY (Hornberge Spear Young) algorithm according to an embodiment of the present application;

[0046] Fig.13 A schematic diagram of the results of the model verification of the removal process in different particle size intervals provided in the embodiment of the present application;

[0047] Fig.14 A flowchart of the working principle of a method for constructing a model of microplastic removal process in a sewage plant provided according to one embodiment of the present application;

[0048] Fig.15 A schematic block diagram of a device for constructing a model of microplastic removal process in a sewage treatment plant according to an embodiment of the present application;

[0049] Fig.16 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0051] The following describes the method and device for constructing a model of the microplastic removal process in a sewage plant according to an embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the main migration and transformation effects of microplastics of different particle sizes in the sewage plant are not analyzed, and the amount of microplastics and their particle size distribution discharged into the water environment after sewage treatment cannot be simulated, and the universality is poor, the present application provides a method for constructing a model of the microplastic removal process in a sewage plant, in which the monitoring data of the microplastics in the inlet and outlet of the sewage plant can be collected, and the removal process model of the migration and transformation mechanism of microplastics by particle size can be used to obtain the parameters to be identified, and then the simulated value of the microplastics in the effluent of the sewage plant can be obtained through the identification and verification of the monitoring data. The present application can simulate the microplastic removal process of the sewage plant by particle size by analyzing the main migration and transformation effects of microplastics of different particle sizes in the sewage plant, quantitatively predict the amount of microplastics and their particle size distribution in the effluent of the sewage plant, and provide a basis for the ecological risk assessment of microplastics by particle size. This solves the problems in related technologies, such as the lack of analysis on the main migration and transformation of microplastics of different particle sizes in sewage treatment plants, the inability to simulate the amount of microplastics and their particle size distribution discharged into the water environment after sewage treatment, and poor universality.

[0052] Specifically, Figure 1This is a flow chart of a method for constructing a microplastic removal process model for a sewage treatment plant according to an embodiment of the present application.

[0053] like Figure 1 As shown, the method for constructing the microplastic removal process model of the sewage treatment plant includes the following steps:

[0054] In step S101, monitoring data on microplastics in the inlet and outlet water of a sewage treatment plant is collected.

[0055] It is understandable that the monitoring data of microplastics in the sewage treatment plant's influent can be regarded as the monitoring data of microplastics in the sewage treatment plant's sewage, and the monitoring data of microplastics in the sewage treatment plant's effluent can be regarded as the monitoring data of microplastics after treatment by the sewage treatment plant. The embodiments of the present application can evaluate the amount of microplastics and their particle size distribution discharged into the water environment after sewage treatment by the sewage treatment plant by comparing the monitoring data of microplastics in the influent and effluent.

[0056] In some embodiments, the embodiments of the present application can obtain monitoring data on microplastics in the inlet and outlet water of a sewage treatment plant by collecting information on the inlet and outlet water of the sewage treatment plant.

[0057] It will be understood by those skilled in the art that in order to analyze the differences in microplastic emissions in different regions and at different times, the sewage plant data selected in the embodiments of the present application should have a larger geographical and time span. In addition, in order to ensure that there are enough samples in the future, the embodiments of the present application should collect as much monitoring data as possible.

[0058] For example, the embodiment of the present application can select 11 sewage treatment plants distributed in different regions of the country, collect various characteristics of microplastics in the inlet and outlet water of the sewage treatment plants in each season, and thereby obtain as much monitoring data on microplastics in the inlet and outlet water as possible.

[0059] Optionally, in one embodiment of the present application, the monitoring data of microplastics may include, but is not limited to, at least one of: microplastic abundance, water treatment process, particle size distribution, shape, and component characteristics.

[0060] As a possible implementation method, the monitoring data of microplastics collected in the embodiments of the present application may include but is not limited to: microplastic abundance, water treatment process, particle size distribution, shape and component characteristics, etc., and the present application does not impose specific restrictions.

[0061] The abundance of microplastics can be the number or concentration of microplastics, which directly reflects the existence and distribution of microplastics. For example, the monitoring data of microplastic abundance collected in the embodiment of the present application can be as follows: Figure 2As shown in the data, the abundance of microplastics in the influent of 11 sewage treatment plants ranged from 160n / L to 2940n / L, with an average abundance of 848n / L, and there were regional and seasonal differences in the abundance of microplastics. The abundance of microplastics in the effluent of 11 sewage treatment plants ranged from 25n / L to 463n / L, with an average abundance of 135n / L.

[0062] The water treatment process may be a technology and method used in the sewage treatment process, which may include but is not limited to: modified SBR (Sequencing Batch Reactor Activated Sludge Process), AO (Anaerobic-Oxic), A2 / O (Anaerobic-Anoxic-Oxic), AB (Adsorption Biodegradation), MSBR (Modified Sequencing Batch Reactor), MUCT process (Modified University of Cape Town), MBR (Membrane Bioreactor), etc., and this application is not specifically limited. For example, the implementation results of different water treatment processes in the embodiments of the present application can be shown in Table 1. It can be seen from Table 1 that in the embodiments of the present application, different water treatment processes have similar removal effects on microplastics, and there is no significant difference overall. Among them, Table 1 is a statistical table of average removal rates of different water treatment processes provided according to an embodiment of the present application.

[0063] Table 1

[0064] Water treatment process Wastewater treatment plant number Removal rate (%) Improved SBR 1 81.9 AO 2 82.2 A2 / O 3、4、9、11 78.8 AB Method 10 78.6 MSBR 7 83.0 MUCT Process 8 83.9 MBR 5、6 89.3

[0065] The particle size distribution can be the particle size of microplastics, and the particle size distribution describes the proportion and distribution of microplastic particles of different sizes. For example, the particle size distribution and length distribution of microplastics in the sewage treatment plant of the present application embodiment can be as follows: Figure 3 As shown. Figure 3 (a) It can be seen that in the present embodiment, the particle size distribution of microplastics in the inlet and outlet water is mainly less than 40μm and 40μm-80μm, of which less than 40μm accounts for 58.0% and 67.0% respectively, and 40μm-80μm accounts for 27.6% and 20.3% respectively. Figure 3 (b) It can be seen that in terms of length distribution, there is no obvious difference in the length range and distribution of the inlet and outlet water in the embodiments of the present application. Microplastics with a length range of 40μm-80μm account for the largest proportion in the inlet and outlet water, both at 43%.

[0066] The shape can be spherical, fibrous, or other shapes, which are not specifically limited in this application. For example, the shape characteristics of the inlet and outlet water in the embodiment of this application can be as follows: Figure 4 As shown, Figure 4 (a) is the shape characteristic of the inlet water, Figure 4 (b) is the shape characteristic of the water outlet, Figure 4 It can be seen that the proportion of fiber microplastics in the outlet water of the embodiment of the present application is higher than the proportion of fiber microplastics in the inlet water.

[0067] The composition characteristics of the microplastics in the influent and effluent of the sewage treatment plant in the present application are as follows: Figure 5 shown.

[0068] In step S102, based on a pre-established removal process model of the migration and transformation mechanism of microplastics according to particle size, the microplastics are divided into different particle size intervals, and the parameters to be identified of the removal process model are obtained based on the particle size intervals.

[0069] In the actual implementation process, the embodiment of the present application can be based on the pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, and can obtain different particle size intervals by dividing the microplastics into particle size intervals, and then obtain the parameters to be identified from the removal process model based on the particle size intervals. Among them, the particle size interval can be set by a person skilled in the art according to the actual situation, and this application does not make any specific restrictions.

[0070] In some embodiments, the embodiments of the present application can be based on a pre-established removal process model of the migration and transformation mechanism of microplastics according to particle size, and use an interval method to divide the particle size of microplastics into three different particle size intervals, such as the small particle size interval, which can be expressed as R1, and its particle size distribution is less than 40μm; the medium particle size interval, which can be expressed as R2, and its particle size distribution is 40μm-80μm; the large particle size interval, which can be expressed as R3, and its particle size distribution is greater than 80μm.

[0071] In some other embodiments, the embodiments of the present application may use other methods to divide the particle size of microplastics to obtain multiple different particle size ranges, and the present application does not make specific limitations.

[0072] Optionally, in one embodiment of the present application, before dividing the microplastics into different particle size intervals, it also includes: establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size, wherein the removal process model includes a material balance model of microplastics according to particle size. The expression of the material balance model can be:

[0073]

[0074]

[0075] Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

[0076] It can be understood by those skilled in the art that the removal process model established in the embodiment of the present application includes a material balance model, wherein the expression of the material balance model can be:

[0077]

[0078]

[0079] Where V represents the volume of the sewage plant reactor, in m 3 , N i represents the abundance of microplastics of the i-th particle size group, in n / L, N i0 represents the influent abundance of microplastics of the i-th particle size, in n / L, and Q represents the influent flow rate, in m 3 / s, r represents the retention coefficient, recorded as Q / V, which is related to the hydraulic retention time of the sewage plant, f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

[0080] Optionally, in one embodiment of the present application, based on a pre-established removal process model of the migration and transformation mechanism of microplastics according to particle size, different particle size intervals of microplastics are divided, and parameters to be identified of the removal process model are obtained based on the particle size intervals, including: removing microplastics in the mud phase based on the adsorption of microplastics; agglomerating and cracking microplastics of adjacent particle sizes to obtain different particle size intervals, and using a preset kinetic model to obtain parameters to be identified of the removal process model.

[0081] As a possible implementation method, when establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size, some basic assumptions can be made. For example, the entire sewage treatment plant can be regarded as a completely mixed reactor in the present embodiment of the application. Microplastics of a specific particle size will undergo adsorption, precipitation, agglomeration, and pyrolysis in the reactor, thereby obtaining different particle size intervals, and then using a preset kinetic model based on different particle size intervals to obtain the parameters to be identified. Among them, the adsorption, agglomeration, and pyrolysis kinetic equations can be:

[0082]

[0083] Among them, i, j, and k represent the different particle sizes of microplastics, N k represents the number of microplastics with particle size k, β i,j , β j,k represents the effective collision frequency between microplastic particles with particle sizes i and j, and j and k, γ j,k represents the probability that a particle with a particle size of j will be broken down to produce a particle with a particle size of k, B j and B k Represent the decomposition rates of microplastic particles with particle sizes j and k, respectively.

[0084] Furthermore, the embodiments of the present application can regard the microplastics in the collected microplastic adsorption sedimentation mud phase as removed microplastics, that is, the microplastics in the mud phase are no longer considered; in addition, the adsorption and precipitation between the microplastics and other particles or activated sludge in the embodiments of the present application obey the first-order reaction kinetics. Agglomeration and pyrolysis generally occur between microplastic particles of adjacent particle sizes, and the agglomeration between microplastic particles of the same particle size is included in the adsorption, and no additional microplastic source is introduced during the entire treatment process.

[0085] For example, in the embodiment of the present application, small-sized particles in the R1 and R2 intervals may agglomerate to form medium-sized particles in the R2 interval, or may form large-sized particles in the R3 interval, and the large-sized particles may be cleaved into particles in adjacent particle size intervals. The schematic diagram of agglomeration and cleavage can be shown as follows: Figure 6 Further, the adsorption, agglomeration and pyrolysis of microplastics in the removal process of the present invention can be as follows: Figure 7 Furthermore, the embodiment of the present application can obtain the parameters to be identified based on the preset dynamic model.

[0086] Optionally, in one embodiment of the present application, the expression of the preset dynamic model may be:

[0087]

[0088]

[0089]

[0090] Among them, N 1 、N 2 、N 3 Respectively represent R 1 , R 2 , R 3 Abundance of microplastics in three particle size ranges, k 1 , k 2 , k3 Respectively represent R 1 , R 2 , R 3 Microplastic adsorption and precipitation rates in three particle size ranges, p represents R 1 and R 2 The particle agglomeration in the interval is R 3 Probability of interval particle size, β 1,2 , β 2,3 represents the effective collision frequency between microplastic particles with particle size 1 and particle size 2, and particle size 2 and particle size 3, respectively, 1 and B 2 Represent the decomposition rates of microplastic particles with particle size 1 and particle size 2 respectively.

[0091] In the actual implementation process, the expression of the preset dynamic model in the embodiment of the present application can be:

[0092]

[0093]

[0094]

[0095] Among them, N 1 、N 2 、N 3 Respectively represent R 1 , R 2 , R 3 Abundance of microplastics in three particle size ranges, k 1 , k 2 , k 3 Respectively represent R 1 , R 2 , R 3 Microplastic adsorption and precipitation rates in three particle size ranges, p represents R 1 and R 2 The particle agglomeration in the interval is R 3 Probability of interval particle size, β 1,2 , β 2,3 represents the effective collision frequency between microplastic particles with particle size 1 and particle size 2, and particle size 2 and particle size 3, respectively, 1 , B 2 Represent the decomposition rates of microplastic particles with particle size 1 and particle size 2 respectively.

[0096] Furthermore, the embodiment of the present application may use the fourth-order Runge-Kutta method to solve the expression of the preset dynamic model, or may use other methods to solve it, and the present application does not make any specific limitations.

[0097] From the above analysis, it can be seen that the embodiment of the present application can obtain k based on the preset kinetic model.1 , k 2 , k 3 , β 1,2 , β 2,3 , B 1 , B 2 , r and p, a total of 9 parameters to be identified. In addition, the initial values ​​of the 9 parameters to be identified in the embodiment of the present application refer to the relevant values ​​in the suspended particulate matter, nanoparticle removal process model research and microplastic hydrodynamic water quality model research, and a reasonable value range is given through model trial calculation. The initial values ​​of the parameters can be shown in Table 2, and it is assumed that each parameter obeys a uniform distribution. Among them, Table 2 is a priori setting table of preset kinetic model parameters provided according to an embodiment of the present application.

[0098] Table 2

[0099] parameter Parameter meaning Value range Parameter distribution <![CDATA[k 1 ]]> R1 adsorption precipitation rate (3.0,6.0) Even distribution <![CDATA[k 2 ]]> R2 adsorption precipitation rate (3.0,6.0) Even distribution <![CDATA[k 3 ]]> R3 adsorption precipitation rate (33.0,43.0) Even distribution <![CDATA[β 1,2 ]]> R1 and R2 effective collision frequency <![CDATA[(0,2×10 -3 )]]> Even distribution <![CDATA[β 2,3 ]]> R2 and R3 effective collision frequencies <![CDATA[(0,2×10 -3 )]]> Even distribution <![CDATA[B 1 ]]> R2 cleavage rate (0,1.5) Even distribution <![CDATA[B 2 ]]> R3 cleavage rate (0,1.5) Even distribution r Stay coefficient (0.5,0.7) Even distribution p The probability of agglomeration into the R3 interval particle size (0,1) Even distribution

[0100] In step S103, the monitoring data is divided into a training set and a validation set, the parameters to be identified are identified based on the training set, and the removal process model after the parameters to be identified is tested using the validation set to obtain the simulated value of microplastics in the effluent of the sewage treatment plant.

[0101] It is understandable that the embodiments of the present application may use the HSY algorithm to divide the collected monitoring data into a training set and a validation set of a certain proportion, or may use other methods, which are not specifically limited in the present application.

[0102] In addition, the embodiments of the present application can use the KS test (Kolmogorov-Smirnov test) to evaluate the identifiability of the parameters to be identified and the accuracy of the parameters to be identified in the training set on the verification set. Other methods can also be used for evaluation, and the present application does not make specific limitations.

[0103] During the actual implementation process, the embodiment of the present application can divide the collected monitoring data into a training set and a validation set according to a certain ratio, and determine the prior distribution of the parameters to be identified based on existing research, and then identify the parameters to be identified to obtain the simulated value of microplastics in the effluent of the sewage treatment plant.

[0104] For example, the embodiment of the present application may adopt the HSY algorithm, randomly select two-thirds of the monitoring data as the training set, and one-third of the monitoring data as the validation set, and the sampling number is set to 100,000 times. For each set of training sets of random parameter sampling, the embodiment of the present application may adopt the fourth-order Runge-Kutta method to solve the preset dynamic model and then identify the parameters to be identified, and use the KS test to evaluate the identifiability of the parameters to be identified and the accuracy of the parameters to be identified in the training set on the validation set. In addition, the embodiment of the present application also defines the acceptable standard for simulation results as the proportion of simulation data with a relative error of less than or equal to 40% is greater than or equal to 75%. Furthermore, in the embodiment of the present application, the identification results of the parameters to be identified by removing the process model can be as follows: Figures 8 to 12 As shown in Table 3, a total of 1382 feasible solutions are obtained. Table 3 is a schematic table of identification results of parameters to be identified in the removal process model provided according to an embodiment of the present application.

[0105] Table 3

[0106] parameter Parameter Value variance KS test statistic <![CDATA[k 1 ]]> 4.80 0.62 0.22 <![CDATA[k 2 ]]> 4.17 0.61 0.25 <![CDATA[k 3 ]]> 38.27 2.78 0.06 <![CDATA[β 1,2 ]]> 0.001 <![CDATA[5.1×10 -4 ]]> 0.12 <![CDATA[β 2,3 ]]> 0.001 <![CDATA[5.7×10 -4 ]]> 0.09 <![CDATA[B 1 ]]> 0.86 0.37 0.17 <![CDATA[B 2 ]]> 0.39 0.27 0.15 r 0.61 0.06 0.07 p 0.43 0.24 0.16

[0107] Furthermore, for microplastics with small particle size (less than 40 μm), there is an obvious peak at the adsorption and precipitation rate k 1 In the probability distribution of , the two cumulative distribution curves of acceptable parameters and unacceptable parameters are clearly separated, so the parameters are more identifiable. 1,2 No obvious peak appeared, but it still passed the KS test (p<0.01). That is to say, the small-particle microplastics in the embodiment of the present application will undergo adsorption, precipitation and agglomeration in the sewage treatment plant, k 1 The sensitivity is higher than β 1,2 , which also means that the adsorption and sedimentation effect of small-particle microplastics is stronger than the agglomeration effect.

[0108] For microplastics with medium particle size (40 μm-80 μm), the adsorption and precipitation rate k 2 and the cleavage rate B 1 The probability distributions of the two also show obvious peaks, although the effective collision frequency β 1,2 , β 2,3 The probability distribution of is relatively uniform within the range of values, but all parameters pass the KS test (p<0.01), indicating that the four parameters are identifiable. 2 and B 1 The identifiability is strong, β 1,2 and β 2,3 The identifiability is weak. 1,2 and β 2,3The values ​​of are very close, indicating that the agglomeration strength between microplastics of different particle sizes is similar. Therefore, it can be inferred that the medium-sized microplastics in the embodiment of the present application are mainly subjected to adsorption, precipitation and pyrolysis in the sewage treatment plant, and similar agglomeration occurs with microplastics of small and large particle sizes.

[0109] For microplastics with large particle size (greater than 80 μm), the adsorption and precipitation rate k 3 In comparison, the cleavage rate B 2 The probability distribution of B has a more significant peak, and the two cumulative distribution curves of acceptable parameters and unacceptable parameters are clearly separated. 2 The identifiability is stronger than k 3 According to the parameter sensitivity results, the large-particle microplastics in the present embodiment are mainly pyrolyzed in the sewage treatment plant, and adsorption, precipitation and agglomeration also occur.

[0110] In addition, in the examples of this application, the parameter r has a weak identifiability, indicating that the hydraulic retention time has an impact on the removal of microplastics, but the actual design range of the hydraulic retention time of the sewage plant has little effect on the removal of microplastics. The parameter p has a strong identifiability, and the average value of the identification result is 0.43, indicating that the small particle size R 1 and median particle size R 3 The microplastics aggregated to form large particles R 3 The probability of generating a medium particle size R is about 0.43. 2 The probability is about 0.57.

[0111] Furthermore, the embodiment of the present application takes the mean as the final value of the parameter to be identified and verifies it on the verification set. The verification result on the verification set can be as follows: Fig.13 As shown. Among them, Fig.13 (a) is the verification result of small particle size (less than 40 μm). Fig.13 (b) is the verification result of medium particle size (40μm-80μm), Fig.13 (c) is the verification result of large particle size (greater than 80μm). Fig.13 It can be seen that in the embodiment of the present application, except for the large errors of individual monitoring points, the simulated values ​​and monitored values ​​of the abundance of microplastics in each particle size range of the sewage treatment plant effluent can be well matched, and the simulated data with an error of less than or equal to 40% accounted for 73.2%, and the average error was 45.2%. The model simulation results are acceptable, which reflects the reliability of the method for constructing a model of microplastic removal process in sewage treatment plants in the embodiment of the present application.

[0112] Combine the following Figure 2-14 As shown, the working principle of the method for constructing a model of microplastic removal process in a sewage treatment plant proposed in this embodiment is introduced in detail using a specific embodiment.

[0113] in, Fig.14 This is a flow chart of the working principle of a method for constructing a model of microplastic removal process in a sewage treatment plant provided according to one embodiment of the present application.

[0114] Step S1404: Collect monitoring data of microplastics in the sewage treatment plant’s influent and effluent. That is, the embodiment of the present application can select 11 sewage treatment plants distributed in different regions of the country, collect various characteristics of microplastics in the sewage treatment plant’s influent and effluent in different seasons, and then obtain as much monitoring data of microplastics in the influent and effluent as possible. Among them, the monitoring data of microplastic abundance collected by the embodiment of the present application can be as follows: Figure 2 The results of different water treatment processes can be shown in Table 1; the particle size distribution and length distribution of microplastics can be shown in Figure 3 As shown; the shape characteristics of the water inlet and outlet can be as follows Figure 4 As shown; the changes in the characteristics of microplastic components in inlet and outlet water can be shown as Figure 5 shown.

[0115] Step S1402: Generalize the microplastic removal process in the sewage treatment plant. That is to say, when the embodiment of the present application establishes a removal process model of the migration and transformation mechanism of microplastics according to particle size, some basic assumptions can be made. For example, the embodiment of the present application can regard the entire sewage treatment plant as a completely mixed reactor, and microplastics of a specific particle size will undergo adsorption precipitation, agglomeration, and cracking in the reactor, thereby obtaining different particle size intervals. Furthermore, the embodiment of the present application can use an interval method to divide the particle size of microplastics into three different particle size intervals, such as a small particle size interval, which can be expressed as R1, and its particle size distribution is less than 40μm; a medium particle size interval, which can be expressed as R2, and its particle size distribution is 40μm-80μm; a large particle size interval, which can be expressed as R3, and its particle size distribution is greater than 80μm. Then, the embodiment itself uses a preset kinetic model to obtain the parameters to be identified based on different particle size intervals.

[0116] Step S1403: Establish a preset dynamic model. That is, the embodiment of the present application can obtain k based on the preset dynamic model. 1 , k 2 , k 3 , β 1,2 , β 2,3 , B 1 , B 2 , r and p, a total of 9 parameters to be identified. In addition, the initial values ​​of the 9 parameters to be identified in the embodiment of the present application refer to the relevant values ​​in the suspended particulate matter, nanoparticle removal process model research and microplastic hydrodynamic water quality model research, and a reasonable value range is given through model trial calculation, and the initial values ​​of the parameters can be shown in Table 2.

[0117] Step S1404: Identify the parameters to be identified. That is, the embodiment of the present application can adopt the HSY algorithm, randomly select two-thirds of the monitoring data as the training set, and one-third of the monitoring data as the verification set, and the sampling number is set to 100,000 times. For each group of training sets of random parameter sampling, the embodiment of the present application can use the fourth-order Runge-Kutta method to solve the preset dynamic model and then identify the parameters to be identified, and use the KS test to evaluate the identifiability of the parameters to be identified and the accuracy of the parameters to be identified in the training set on the verification set. Furthermore, in the embodiment of the present application, the identification result of the parameters to be identified by removing the process model can be as follows Figures 8 to 12 As shown in Table 3, a total of 1382 feasible solutions are obtained.

[0118] Among them, for microplastics with small particle size (less than 40 μm), there is an obvious peak in the adsorption and precipitation rate k 1 In the probability distribution of , the two cumulative distribution curves of acceptable parameters and unacceptable parameters are clearly separated, so the parameters are more identifiable. 1,2 No obvious peak appeared, but it still passed the KS test (p<0.01). That is to say, the small-particle microplastics in the embodiment of the present application will undergo adsorption, precipitation and agglomeration in the sewage treatment plant, k 1 The sensitivity is higher than β 1,2 , which also means that the adsorption and sedimentation effect of small-particle microplastics is stronger than the agglomeration effect.

[0119] For microplastics with medium particle size (40 μm-80 μm), the adsorption and precipitation rate k 2 and the cleavage rate B 1 The probability distributions of the two also show obvious peaks, although the effective collision frequency β 1,2 , β 2,3 The probability distribution of is relatively uniform within the range of values, but all parameters pass the KS test (p<0.01), indicating that the four parameters are identifiable. 2 and B 1 The identifiability is strong, β 1,2 and β 2,3 The identifiability is weak. 1,2 and β 2,3 The values ​​of are very close, indicating that the agglomeration strength between microplastics of different particle sizes is similar. Therefore, it can be inferred that the medium-sized microplastics in the embodiment of the present application are mainly subjected to adsorption, precipitation and pyrolysis in the sewage treatment plant, and similar agglomeration occurs with microplastics of small and large particle sizes.

[0120] For microplastics with large particle size (greater than 80 μm), the adsorption and precipitation rate k 3In comparison, the cleavage rate B 2 The probability distribution of B has a more significant peak, and the two cumulative distribution curves of acceptable parameters and unacceptable parameters are clearly separated. 2 The identifiability is stronger than k 3 According to the parameter sensitivity results, the large-particle microplastics in the present embodiment are mainly pyrolyzed in the sewage treatment plant, and adsorption, precipitation and agglomeration also occur.

[0121] In addition, in the examples of this application, the parameter r has a weak identifiability, indicating that the hydraulic retention time has an impact on the removal of microplastics, but the actual design range of the hydraulic retention time of the sewage plant has little effect on the removal of microplastics. The parameter p has a strong identifiability, and the average value of the identification result is 0.43, indicating that the small particle size R 1 and median particle size R 2 The microplastics aggregated to form large particles R 3 The probability of generating a medium particle size R is about 0.43. 2 The probability is about 0.57.

[0122] Step S1405: Remove process model verification. That is, the embodiment of the present application can take the mean as the final value of the parameter to be identified and verify it on the verification set. The verification result on the verification set can be as follows: Fig.13 As shown. Fig.13 It can be seen that in the embodiment of the present application, except for the large errors of individual monitoring points, the simulated values ​​and monitored values ​​of the abundance of microplastics in each particle size range of the sewage treatment plant effluent can be well matched, and the simulated data with an error of less than or equal to 40% accounted for 73.2%, and the average error was 45.2%. The model simulation results are acceptable, which reflects the reliability of the method for constructing a model of microplastic removal process in sewage treatment plants in the embodiment of the present application.

[0123] According to the method for constructing a model of the microplastic removal process in a sewage plant proposed in the embodiment of the present application, the monitoring data of the microplastics in the inlet and outlet of the sewage plant can be collected, and the parameters to be identified can be obtained by using the removal process model of the microplastic migration and transformation mechanism of the particle size, so that the simulated value of the microplastics in the sewage plant effluent can be obtained through the identification and inspection of the monitoring data. The present application can simulate the microplastic removal process of the sewage plant by particle size by analyzing the main migration and transformation effects of microplastics of different particle sizes in the sewage plant, quantitatively predict the amount of microplastics and their particle size distribution in the effluent of the sewage plant, and provide a basis for the ecological risk assessment of microplastics by particle size. Thus, the problems in the related art that the main migration and transformation effects of microplastics of different particle sizes in the sewage plant are not analyzed, and the amount of microplastics and their particle size distribution discharged into the water environment after the sewage is treated cannot be simulated, and the universality is poor.

[0124] Next, a device for constructing a model of microplastic removal process in a sewage treatment plant proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0125] Fig.15 This is a block diagram of a device for constructing a model of microplastic removal process in a sewage treatment plant provided in an embodiment of the present application.

[0126] like Fig.15 As shown, the device 10 for constructing a model of microplastic removal process in a sewage treatment plant includes: a collection module 100, an acquisition module 200 and a verification module 300.

[0127] Among them, the collection module 100 is used to collect monitoring data on microplastics in the inlet and outlet water of the sewage treatment plant.

[0128] The acquisition module 200 is used to divide the microplastics into different particle size intervals based on a pre-established removal process model of the migration and transformation mechanism of microplastics according to particle size, and to obtain the parameters to be identified of the removal process model based on the particle size intervals.

[0129] The inspection module 300 is used to divide the monitoring data into a training set and a verification set, identify the parameters to be identified based on the training set, and use the verification set to inspect the removal process model after the parameters to be identified, so as to obtain the simulated value of microplastics in the effluent of the sewage treatment plant.

[0130] Optionally, in one embodiment of the present application, it also includes: establishing a module.

[0131] Among them, a module is established to establish a removal process model of the migration and transformation mechanism of microplastics according to particle size before dividing the microplastics into different particle size intervals, wherein the removal process model includes a material balance model of microplastics according to particle size.

[0132] Optionally, in one embodiment of the present application, the expression of the material balance model may be:

[0133]

[0134]

[0135] Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

[0136] Optionally, in one embodiment of the present application, the acquisition module 200 includes: a removal unit and an acquisition unit.

[0137] Among them, the removal unit is used to remove microplastics in the mud phase based on microplastic adsorption.

[0138] The acquisition unit is used to agglomerate and crack microplastics of adjacent particle sizes to obtain different particle size ranges, and use a preset kinetic model to obtain the parameters to be identified in the removal process model.

[0139] Optionally, in one embodiment of the present application, the expression of the preset dynamic model may be:

[0140]

[0141]

[0142]

[0143] Among them, N 1 、N 2 、N 3 Respectively represent R 1 , R 2 , R 3 Abundance of microplastics in three particle size ranges, k 1 , k 2 , k 3 Respectively represent R 1 , R 2 , R 3 Microplastic adsorption and precipitation rates in three particle size ranges, p represents R 1 and R 2 The particle agglomeration in the interval is R 3 Probability of interval particle size, β 1,2 , β 2,3 represents the effective collision frequency between microplastic particles with particle size 1 and particle size 2, and particle size 2 and particle size 3, respectively, 1 and B 2 Represent the decomposition rates of microplastic particles with particle size 1 and particle size 2 respectively.

[0144] Optionally, in one embodiment of the present application, the monitoring data of microplastics may include, but is not limited to, at least one of: microplastic abundance, water treatment process, particle size distribution, shape, and component characteristics.

[0145] It should be noted that the aforementioned explanation of the embodiment of the method for constructing a model of the microplastic removal process in a sewage treatment plant is also applicable to the device for constructing a model of the microplastic removal process in a sewage treatment plant of this embodiment, and will not be repeated here.

[0146] According to the device for constructing a model of the microplastic removal process in a sewage plant proposed in the embodiment of the present application, the monitoring data of the microplastics in the inlet and outlet of the sewage plant can be collected, and the parameters to be identified can be obtained by using the removal process model of the migration and transformation mechanism of microplastics by particle size, so as to obtain the simulated value of the microplastics in the effluent of the sewage plant through identification and inspection of the monitoring data. The present application can simulate the removal process of microplastics in sewage plants by particle size by analyzing the main migration and transformation effects of microplastics of different particle sizes in sewage plants, quantitatively predict the amount of microplastics in the effluent of sewage plants and their particle size distribution, and provide a basis for the ecological risk assessment of microplastics by particle size. Thus, the problems in the related art that the main migration and transformation effects of microplastics of different particle sizes in sewage plants are not analyzed, the amount of microplastics discharged into the water environment after treatment and their particle size distribution cannot be simulated, and the problems of poor universality are solved.

[0147] Fig.16 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0148] A memory 1601 , a processor 1602 , and a computer program stored in the memory 1601 and executable on the processor 1602 .

[0149] When processor 1602 executes the program, it implements the method for constructing a model of microplastic removal process in a sewage treatment plant provided in the above-mentioned embodiment.

[0150] Furthermore, the electronic device further comprises:

[0151] The communication interface 1603 is used for communication between the memory 1601 and the processor 1602 .

[0152] The memory 1601 is used to store computer programs that can be executed on the processor 1602 .

[0153] The memory 1601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0154] If the memory 1601, the processor 1602 and the communication interface 1603 are implemented independently, the communication interface 1603, the memory 1601 and the processor 1602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.16 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0155] Optionally, in a specific implementation, if the memory 1601, the processor 1602 and the communication interface 1603 are integrated on a chip, the memory 1601, the processor 1602 and the communication interface 1603 can communicate with each other through an internal interface.

[0156] The processor 1602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0157] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a model of microplastic removal process in a sewage treatment plant.

[0158] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements the above-mentioned method for constructing a model of microplastic removal process in a sewage treatment plant.

[0159] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0160] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0161] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0162] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0163] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0164] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0165] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0166] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for constructing a model of microplastic removal process in a sewage treatment plant, characterized in that: The following steps are involved: Collect monitoring data on microplastics in the influent and effluent of sewage treatment plants; Based on a pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, the microplastics are divided into different particle size intervals, and the parameters to be identified of the removal process model are obtained based on the particle size intervals; The monitoring data is divided into a training set and a validation set, the parameters to be identified are identified based on the training set, and the removal process model after the parameters to be identified are tested using the validation set to obtain a simulated value of microplastics in the effluent of the sewage treatment plant; Before dividing the microplastics into different particle size intervals, the method further includes: Establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size, wherein the removal process model includes a material balance model of microplastics according to particle size; Wherein, the expression of the material balance model is: Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

2. The method according to claim 1, characterized in that The removal process model based on the pre-established migration and transformation mechanism of microplastics by particle size divides the microplastics into different particle size intervals, and obtains the parameters to be identified of the removal process model based on the particle size intervals, including: Based on the microplastics adsorbing the microplastics in the sediment mud phase, the microplastics in the mud phase are removed; The microplastics of adjacent particle sizes are agglomerated and cracked to obtain the different particle size intervals, and the parameters to be identified of the removal process model are obtained using a preset kinetic model.

3. The method according to claim 2, characterized in that The expression of the preset kinetic model is: Among them, N1, N2, and N3 represent the abundance of microplastics in the three particle size intervals of R1, R2, and R3, respectively; k1, k2, and k3 represent the adsorption and precipitation rates of microplastics in the three particle size intervals of R1, R2, and R3, respectively; p represents the probability that particles in the R1 and R2 intervals agglomerate into the particle size of the R3 interval; β 1,2 , β 2,3 They represent the effective collision frequencies between microplastic particles of size 1 and size 2, and size 2 and size 3 respectively; B1 and B2 represent the pyrolysis rates of microplastic particles of size 1 and size 2 respectively.

4. The method according to claim 1, characterized in that The monitoring data of microplastics include: at least one of microplastic abundance, water treatment process, particle size distribution, shape and component characteristics.

5. A device for constructing a model of microplastic removal process in a sewage treatment plant, characterized in that: include: The collection module is used to collect monitoring data on microplastics in the inlet and outlet of sewage treatment plants; An acquisition module is used to divide the microplastics into different particle size intervals based on a pre-established removal process model of the migration and transformation mechanism of microplastics by particle size, and to acquire the parameters to be identified of the removal process model based on the particle size intervals; A verification module, used to divide the monitoring data into a training set and a verification set, identify the parameters to be identified based on the training set, and use the verification set to verify the removal process model after the parameters to be identified, so as to obtain the simulated value of microplastics in the effluent of the sewage treatment plant; The method further includes: establishing a module for establishing a removal process model of the migration and transformation mechanism of microplastics according to particle size before dividing the microplastics into different particle size intervals, wherein the removal process model includes a material balance model of microplastics according to particle size; The material balance model can be expressed as: Where V represents the volume of the sewage plant reactor, N i represents the abundance of microplastics of the i-th particle size group, N i0 represents the influent abundance of microplastics of the i-th particle size group, Q represents the influent flow rate, r represents the retention coefficient, which is related to the hydraulic retention time of the sewage plant, and f(N i ) indicates the rate at which microplastics are removed due to adsorption, agglomeration and cracking.

6. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for constructing a microplastic removal process model for a sewage treatment plant as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for constructing a microplastic removal process model for a sewage treatment plant as described in any one of claims 1 to 4.

8. A computer program product, characterized in that It includes a computer program, which, when executed, is used to implement the method for constructing a microplastic removal process model for a sewage treatment plant as described in any one of claims 1 to 4.

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