Floccule median particle size prediction method and device considering algae effect

Abstract

The invention relates to the technical field of hydraulics, in particular to a method and device for predicting the median particle size of a flocculating constituent under the action of algae, and the method comprises the steps: obtaining the sediment concentration, sediment density and water turbulent shear strength in a water body of a target area; acquiring the chlorophyll concentration of algae in the water body and acquiring the percentage of organic matters covering the surface of the sediment according to the chlorophyll concentration of algae; and obtaining model correction parameters of the target area, correcting the target prediction model by using the model correction parameters, predicting change information of the median particle size of the flocculating constituent in the water body of the target area along with time based on the corrected target prediction model, the sediment concentration, the sediment density and the turbulent shear strength of the water body, and obtaining a prediction result of the median particle size of the flocculating constituent. According to the method, the median particle sizes of the algae-mud flocculating constituent and the pure mineral flocculating constituent in the water body can be accurately predicted under the condition that only conventional parameters in the water body are determined, the calculation cost is reduced while the accuracy of a prediction result is effectively improved, and the applicability of the method is improved.

Description

Technical Field

[0001] The present application relates to the field of hydraulics technology, and in particular to a method and device for predicting the median particle size of flocs taking into account the action of algae. Background Art

[0002] The transport of cohesive fine-grained sediment in aquatic environments plays an important role in the morphological evolution of rivers, lakes and estuaries and in estuarine channel engineering. Unlike non-cohesive sediments that are transported individually, cohesive sediments are often transported in the form of flocculation, i.e., individual particles aggregate together. More than 90% of fine suspended sediment exists in the form of aggregates. Therefore, studying the flocculation of cohesive sediment is an important research topic for understanding the mechanics of sediment transport.

[0003] Microalgae are commonly found in aquatic environments such as estuaries and coasts. The mechanism of floccules formed by their combination with viscous sediment has been the focus of research in recent years. Existing large-scale landform evolution models mostly consider the physical and chemical properties of the sediments themselves, while algae, which are prominent organisms in estuary environments, will have a significant impact on the flocculation of viscous sediment, thereby affecting landform evolution and simulation accuracy.

[0004] In the relevant technology, numerical models have become the main tool for studying flocculation dynamics. Among them, there are two main methods used to describe the flocculation process: one is the Lagrangian flocculation model, and the other method involves modeling the floc size distribution (FSD) using multiple groups. With the improvement of computing power and analytical methods, the combination of dynamic flocs and modeling methods has gradually become more widely used in a wider range of sediment transport models.

[0005] However, the flocculation model in the related technology has a limited scope of application under complex hydrodynamic and sediment concentration conditions such as the combined effects of viscous silt and microalgae. It is difficult to capture the specific response mechanism of microalgae-mineral aggregation. At the same time, it lacks a simple dynamic floc size modeling form and cannot be coupled with existing large-scale models to effectively predict the size changes caused by the combination of biological factors and clay minerals. This problem needs to be solved urgently. Summary of the invention

[0006] The present application provides a method and device for predicting the median particle size of flocs under the action of algae, so as to solve the problems that the flocculation model in the related art has a limited applicable range under complex hydrodynamic and sediment concentration conditions such as the combined action of viscous silt and microalgae, and is difficult to capture the specific response mechanism of microalgae-mineral aggregation. At the same time, it lacks a simple dynamic floc size modeling form and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals.

[0007] The first aspect of the present application provides a method for predicting the median particle size of flocs under the action of algae, comprising the following steps: obtaining the sediment concentration, sediment density and turbulent shear strength of the water body in the target area; obtaining the algae chlorophyll concentration in the water body, and obtaining the percentage of organic matter covering the sediment surface based on the algae chlorophyll concentration; obtaining the model correction parameters of the target area, and after correcting the target prediction model using the model correction parameters, predicting the change information of the median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body, so as to obtain a prediction result of the median particle size of flocs.

[0008] Optionally, in one embodiment of the present application, the prediction of the temporal change information of the median particle size of flocs in the water body of the target area based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body includes: calculating the volume fraction of mineral clay based on the sediment concentration and the sediment density.

[0009] Optionally, in one embodiment of the present application, the formula for obtaining the change information is:

[0010]

[0011] Among them, D f is the median particle size of the flocs in the water, t is the time, D p is the initial particle size, k A , k B are dimensionless parameters, representing the aggregation and fragmentation efficiency of the floccules, is the volume fraction of the mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c represents the concentration of algae chlorophyll a, and G represents the turbulent shear strength of the water body.

[0012] Optionally, in one embodiment of the present application, the expression of the percentage is:

[0013]

[0014] Among them, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c,max is the average chlorophyll concentration in the basin worldwide, θ c,max is the maximum value of organic matter coverage on marine sediments, C c Indicates the concentration of algae chlorophyll a.

[0015] Optionally, in one embodiment of the present application, obtaining the model correction parameters of the target area includes: obtaining image information or particle size observation data of the target area; obtaining the median particle size of flocs in the target area based on the image information or the particle size observation data to obtain the model correction parameters.

[0016] The second aspect of the present application provides a device for predicting the median particle size of flocs under the action of algae, including: a first acquisition module, used to obtain the sediment concentration, sediment density and turbulent shear strength of the water body in the target area; a second acquisition module, used to obtain the algae chlorophyll concentration in the water body, and obtain the percentage of organic matter covering the sediment surface based on the algae chlorophyll concentration; a prediction module, used to obtain the model correction parameters of the target area, and after correcting the target prediction model using the model correction parameters, predict the change information of the median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body, so as to obtain the prediction result of the median particle size of flocs.

[0017] Optionally, in one embodiment of the present application, the prediction module includes: a calculation unit, configured to calculate a volume fraction of mineral clay according to the sediment concentration and the sediment density.

[0018] Optionally, in one embodiment of the present application, the formula for obtaining the change information is:

[0019]

[0020] Among them, D f is the median particle size of the flocs in the water, t is the time, D p is the initial particle size, k A , k B are dimensionless parameters, representing the aggregation and fragmentation efficiency of the floccules, is the volume fraction of the mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c represents the concentration of algae chlorophyll a, and G represents the turbulent shear strength of the water body.

[0021] Optionally, in one embodiment of the present application, the expression of the percentage is:

[0022]

[0023] Among them, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c,max is the average chlorophyll concentration in the basin worldwide, θ c,max is the maximum value of organic matter coverage on marine sediments, C c Indicates the concentration of algae chlorophyll a.

[0024] Optionally, in one embodiment of the present application, the prediction module includes: a first acquisition unit, used to acquire image information or particle size observation data of the target area; a second acquisition unit, used to acquire the median particle size of the flocs in the target area based on the image information or the particle size observation data to obtain the model correction parameters.

[0025] The third aspect of the present application provides an electronic device, comprising: 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 predicting the median particle size of flocs taking into account the action of algae as described in the above embodiment.

[0026] 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 predicting the median particle size of flocs under the action of algae.

[0027] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for predicting the median particle size of flocs taking into account the action of algae.

[0028] The embodiment of the present application can derive a unified floc median particle size prediction model applicable to algae and pure minerals based on the characteristics of the distribution of sediment, water flow turbulence, and algae concentration in the target area, thereby determining the relationship between the evolution of the floc median particle size over time under algae conditions. Thus, it is possible to predict the median particle size of algae-mud flocs and pure mineral flocs in the water body by only determining the conventional parameters in the water body, effectively improving the accuracy of the prediction results while ensuring the correctness of the prediction results, significantly reducing the calculation cost, and improving the applicability of the present application. Thus, it solves the problem that the flocculation model in the related art has a relatively limited usable range under the combined effects of complex hydrodynamics and sediment concentration conditions such as viscous sediment and microalgae, and it is difficult to capture the specific response mechanism of microalgae-mineral aggregation. At the same time, it lacks a simple dynamic floc size modeling form and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals.

[0029] 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

[0030] 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:

[0031] Figure 1 This is a schematic diagram of a framework of a floc median particle size prediction system considering the action of algae according to an embodiment of the present application;

[0032] Figure 2 A flowchart of a method for predicting the median particle size of flocs considering the effect of algae according to an embodiment of the present application;

[0033] Figure 3 A schematic diagram of an experimental device for one embodiment of the present application;

[0034] Figure 4 This is a schematic diagram of the kaolin particle size distribution results of one embodiment of the present application;

[0035] Figure 5 This is a flow chart of a method for predicting the equilibrium median particle size of flocs according to one embodiment of the present application;

[0036] Figure 6 k in one embodiment of this application A / k B Schematic diagram of the change of average value with chlorophyll a concentration;

[0037] Figure 7 It is a schematic diagram comparing the calculation result and the measurement result of the equilibrium median particle size of flocs in one embodiment of the present application;

[0038] Figure 8 k for one embodiment of this application A / k B Schematic diagram of the change law with turbulent shear;

[0039] Fig. 9 A schematic diagram of simulation result verification of an embodiment of the present application;

[0040] Fig.10 It is a schematic diagram of the structure of a device for predicting the median particle size of flocs under the action of algae according to an embodiment of the present application;

[0041] Fig.11 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0042] Reference numerals:

[0043] 10-Device for predicting median particle size of flocs under the action of algae: 100-first acquisition module, 200-second acquisition module and 300-prediction module; 1101-memory, 1102-processor and 1103-communication interface. DETAILED DESCRIPTION

[0044] 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.

[0045] The following describes the method and device for predicting the median particle size of flocs under the action of algae according to the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the flocculation model in the related art mentioned in the above background technology has a relatively limited scope of application under complex hydrodynamic and sediment concentration conditions such as the combined action of viscous silt and microalgae, and it is difficult to capture the specific response mechanism of microalgae-mineral aggregation. At the same time, it lacks a simple dynamic floc size modeling form and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals. The present application provides a method for predicting the median particle size of flocs under the action of algae. In this method, a unified floc median particle size prediction model suitable for algae and pure minerals can be obtained based on the characteristics of the distribution of water sediment, water flow turbulence and algae concentration in the target area, thereby determining the relationship between the evolution of the median particle size of flocs over time under algae conditions. Thus, it is possible to predict the median particle size of algae-mud flocs and pure mineral flocs in the water body by only determining the conventional parameters in the water body, effectively improving the accuracy of the prediction results while ensuring the correctness of the prediction results, significantly reducing the calculation cost, and improving the applicability of the present application. Thus, it solves the problem that the flocculation model in the related art has a relatively limited applicable range under the combined effects of complex hydrodynamics and sediment concentration conditions such as viscous silt and microalgae, lacks a simple dynamic floc size modeling form, is difficult to capture the specific response mechanism of microalgae-mineral aggregation, and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals.

[0046] Figure 1 This is a schematic diagram of a framework of a floc median particle size prediction system considering the effect of algae according to an embodiment of the present application. Figure 1 As shown, the river channel comprehensive resistance coefficient prediction system in the embodiment of the present application mainly includes but is not limited to: a data acquisition module M1, a data processing module M2 and a prediction result module M3.

[0047] Among them, the data acquisition module M1 is used to obtain the basic information of the water body in the target area and the observation data of water flow, sediment and algae content. The basic information of the water body in the target area includes water temperature, wind speed, observation point, observation time, etc. The water flow monitoring data includes water depth and average flow velocity of the section, etc. The sediment monitoring data includes water sediment concentration, sediment density, etc. The algae content observation data includes chlorophyll concentration observation data, etc.

[0048] The data processing module M2 processes the acquired data and calculates the water flow shear rate, the percentage of organic matter covering the sediment surface, the volume fraction of mineral clay, and the model correction parameters.

[0049] The prediction result module M3 predicts the median particle size of the water flocs in the target area according to the results of the data processing module.

[0050] Specifically, Figure 2 A flow chart of a method for predicting the median particle size of flocs taking into account the action of algae provided in an embodiment of the present application.

[0051] like Figure 2 As shown, the method for predicting the median particle size of flocs under the action of algae includes the following steps:

[0052] In step S201, the sediment concentration, sediment density and water turbulence shear strength in the water body of the target area are obtained.

[0053] It can be understood that the turbulent shear strength of water bodies can be understood here as the magnitude of the shear force generated in the water body due to the turbulence of the water flow (i.e., non-laminar state, parameters such as water flow velocity and direction vary randomly in space and time). This shear force is generated by the interaction between different velocity layers inside the water flow, which reflects the impact of the degree of water flow turbulence on the internal structure of the water body.

[0054] In some embodiments, factors affecting flocculation include, but are not limited to, physical factors such as shear stress, suspended sediment concentration, sediment composition, and chemical factors such as salinity and pH. However, the effect of biological factors on flocculation, i.e., biological flocculation, is rarely taken into account due to its complexity. These biological components enhance particle aggregation by secreting highly viscous extracellular dissolved organic matter, such as extracellular polymers. Algae cells can also cause entanglement and aggregation through their own protrusions, combining with sediment particles and forming larger flocs. These biological processes directly affect the density and structural changes of flocs, thereby increasing their sedimentation rate. In addition, inactivated organic matter also contributes to the flocculation process, because the dead cells of organisms such as microalgae can accumulate as particulate organic matter. This organic matter will be the source of sticky substances, which aggregate with clay minerals and sediment particles to form flocs.

[0055] Based on this, the embodiments of the present application can obtain data such as sediment concentration, sediment density and water turbulence shear strength in the water body of the target area, and greatly consider the different influencing factors that may be included in the prediction of the median particle size of flocs.

[0056] For example, sediment concentrations within a low concentration range can promote the formation of flocs, but excessively high sediment concentrations will gradually weaken the promoting effect on flocs and even turn into an inhibitory effect. Particles with higher sediment density may be more difficult to be dispersed or broken by water flow, which is conducive to the stable existence of flocs to a certain extent. Strong turbulent shear strength will destroy the internal structure of flocs, making them loose or even disintegrated. This is because turbulent shear force will act on the surface of flocs and between internal particles, causing the connecting bonds between particles to break, while weaker turbulent shear strength is conducive to the stable existence and further growth of flocs.

[0057] Specifically, the sediment concentration in the embodiments of the present application can be obtained by, but not limited to, filtration or turbidity meter observation; the sediment density can be obtained by, but not limited to, weighing or drainage; the turbulent shear strength of the water body can be solved by, but not limited to, the following formula:

[0058]

[0059] Among them, G represents the turbulent shear strength of water, ε is the dissipation rate of turbulence; v is the kinematic viscosity of the fluid; λ is the Kolmogorov (Kolmogorov microscale) microscale.

[0060] It should be noted that the target area here can be understood as the area for predicting the median particle size of flocs under the action of algae, which can be selected according to actual needs or specific circumstances, and the embodiments of the present application do not impose specific limitations.

[0061] The embodiments of the present application can obtain the sediment concentration, sediment density and turbulent shear strength of the water body in the target area, thereby helping to improve the accuracy of the prediction results of the median particle size of flocs under the action of algae.

[0062] Step S202, obtain the algae chlorophyll concentration in the water body, and obtain the percentage of organic matter covering the sediment surface according to the algae chlorophyll concentration. The percentage expression can be expressed as follows:

[0063]

[0064] Among them, θ c Indicates the percentage of organic matter covering the sediment surface, C c Indicates the algae chlorophyll a concentration, C c,max is the average chlorophyll concentration in the basin worldwide, θ c,maxIt is the maximum value of organic matter cover on marine sediments.

[0065] It is understood that sediments here can be understood as sticky sediments mediated by microorganisms (such as bacteria, plankton and secretions) in the water column. The flocculation of sticky sediments plays an important role in aquatic environments because it affects the transport, deposition and morphological dynamics of suspended sediments. At the same time, sticky sediments play a key role in regulating the transport of nutrients and organic matter, affecting underwater nutrient cycles and ecosystems.

[0066] In the actual implementation process, in addition to considering the impact of sediment in the water on flocs in the water body, this application also considers that the algae chlorophyll concentration is also an important parameter for measuring the degree of eutrophication of the water body, which can reflect the amount of algae biomass in the water body. Therefore, when predicting the median particle size of flocs under the action of algae, this application obtains the sediment concentration, sediment density and turbulent shear strength of the water body in the target area, and can also obtain the algae chlorophyll concentration in the water body, judge the amount of algae in the water body according to the concentration, and then calculate the percentage of organic matter on the sediment surface covered by the algae chlorophyll concentration.

[0067] Specifically, the percentage of algal chlorophyll concentration covering the organic matter on the sediment surface can be expressed as follows:

[0068]

[0069] Among them, C c,max represents the average chlorophyll concentration in the basin worldwide. In the present embodiment, 13.39 μg / L can be taken. c,max is the maximum value of organic matter coverage on marine sediments, which can be taken as 0.15 in the present embodiment. c Indicates the chlorophyll a concentration of algae, which can be measured by, but is not limited to, an ultraviolet spectrophotometer or a portable chlorophyll fluorescence meter.

[0070] Step S203, obtain the model correction parameters of the target area, and after correcting the target prediction model using the model correction parameters, predict the change information of the median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the sediment concentration, sediment density and turbulent shear strength of the water body, so as to obtain the prediction result of the median particle size of flocs. The formula for obtaining the change information can be expressed as:

[0071]

[0072] Among them, D f is the median particle size of flocs in water, t is time, D p is the initial particle size, k A , k Bare dimensionless parameters, representing the aggregation and fragmentation efficiency of flocs, is the volume fraction of mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the sediment surface, C c represents the concentration of algae chlorophyll a, and G represents the turbulent shear strength of water.

[0073] As a possible implementation method, the embodiment of the present application mainly but not limited to utilizes an improved flocculation model when predicting the median particle size of flocs under the action of algae. Among them, the improved flocculation model can be understood as a correction based on the described three-dimensional flocculation formula. For example, the embodiment of the present application can use the Winterwerp flocculation model (a mathematical model for describing and simulating the flocculation process of viscous sediment in a fluid) as the target prediction model, and use the model correction parameters of the target area to correct and improve the model to obtain a corrected and usable target prediction model.

[0074] Specifically, the Winterwerp flocculation model can take the time change rate of the median particle size of the flocs as the difference between the floc aggregation rate and the fragmentation rate, which can be expressed as follows:

[0075]

[0076] Among them, the first and second terms represent the aggregation and fragmentation efficiency parameters k respectively. A , k B (dimensionless) Scaled floc aggregation and fragmentation terms; represents the volume fraction of mineral clay, which can be expressed as the sediment concentration c s and sediment density ρ s express, D P is the initial particle size; n f represents the fractal dimension (dimensionless), which can be in the range of 1 to 3. It describes the flocculent structure from a linear chain (n f =1) transition to a compact spherical structure (n f =3); n f (=2) is a typical value for natural mineral clay flocs; turbulent stress τ w and floc strength τ f It can be expressed as τ w =μG, τ f =F y / D f 2 ; μ represents the dynamic viscosity of the fluid; F yrepresents the yield strength of the flocs, which is determined by the physical and chemical properties of the sediment and water. y A commonly used estimated value of 10-10N may be used; q represents an empirical coefficient, and a reasonable value of q=0.5 may be used in the embodiment of the present application.

[0077] Will Substituting into formula (3), the equilibrium median particle size D of the floccules is e Can be made by dD f / dt=0 is obtained and expressed as follows:

[0078]

[0079] It should be noted that the formulas and derivation processes in the embodiments of the present application are based on the equilibrium flocculation state. Flocculation is a process involving both aggregation and fragmentation. When the aggregation rate is equal to the fragmentation rate, the flocculation reaches a stable state, that is, a dynamic equilibrium state. The corresponding floc size and time are respectively referred to as the equilibrium floc size and equilibrium floc time. The equilibrium flocculation state can be understood here as indicating a stable state (dynamic equilibrium state) when the floc aggregation rate is equal to the fragmentation rate under specific environmental conditions, such as turbulent shear and salinity, as well as mineral clay and biological components. The median particle size of the flocs at this time is called the equilibrium median particle size D of the flocs. e .

[0080] Next, the embodiment of the present application improves the Winterwerp flocculation model, i.e., formula (3), to be applicable to the algae situation in the embodiment of the present application. The specific process can be expressed as follows:

[0081] The above equations show that the Winterwerp flocculation model can consider the effects of shear rate and sediment concentration on flocculation, but does not include some biological factors in the aggregation and fragmentation terms. Therefore, the present embodiment takes into account the effects of microalgae on floc aggregation and fragmentation efficiency in the flocculation process involving microalgae. By changing the floc aggregation and fragmentation efficiency, that is, k A , k B As a model correction parameter, the target prediction model is corrected to adapt the Winterwerp flocculation model to the microalgae conditions. Based on this, the embodiment of the present application can modify formula (4) as follows:

[0082]

[0083] Among them, k A,m , k B,m It is the parameter of floc aggregation and fragmentation efficiency in the presence of microalgae.

[0084] Further, the relationship between polymer flocculants (such as colloids) and sediment flocculation can be expressed as follows:

[0085]

[0086] where θ is the fraction of polymer material covering the surface of the sediment (dimensionless).

[0087] It should be noted that polymer flocculants can be adsorbed on the surface of mineral clay particles to form a connecting "bridge" between particles. This flocculation form is similar to the flocculation form of microalgae. The embodiment of this application draws on the flocculation effect between polymer flocculants and sediment flocculation and applies it to biological flocculation. Considering that when θ is close to 0, k expressed by formula (6) A / k B tends to 0, which contradicts the formula (4) of the Winterwerp flocculation model. Therefore, the embodiment of the present application can be used to A,m , k B,m Modify it to satisfy the following two conditions to ensure the original physical meaning of the formula: (a) When the algae content tends to 0, k A,m , k B,m Convert to original k A , k B form; (b) k A,m , k B,m It is necessary to maintain the same monotonicity as formula (5). At this time, k A,m , k B,m It can be expressed as:

[0088]

[0089] Among them, θ c Indicates the organic matter coverage. Combined with formulas (7) to (8), formula (3) can be converted into a form suitable for algae action, which can be expressed as follows:

[0090]

[0091] Among them, D f is the median particle size of flocs in water, t is time, D p is the initial particle size, k A , k B are dimensionless parameters, representing the aggregation and fragmentation efficiency of flocs, is the volume fraction of mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the sediment surface, C c represents the concentration of algae chlorophyll a, and G represents the turbulent shear strength of water.

[0092] Formula (4) can also be converted into a prediction form of the equilibrium median particle size of flocs under the action of algae:

[0093]

[0094] When θ c = 0, the biological flocculation model in formula (10) can be transformed into the original Winterwerp flocculation model in formula (4), which shows that the model is a unified model.

[0095] Formula (9) and Formula (10) are the corrected target prediction models proposed in the embodiments of the present application, which can be applied to the prediction of the median particle size of the unified flocs with algae and pure minerals. The prediction model can obtain the sediment concentration c by the above-mentioned s , sediment density ρ s , water turbulence shear strength G and chlorophyll a concentration C c The change information of the median particle size of flocs in the water body of the target area over time is predicted by using parameters such as floccules, so as to obtain the final prediction result of the median particle size of flocs.

[0096] Optionally, in one embodiment of the present application, obtaining model correction parameters of the target area includes: obtaining image information or particle size observation data of the target area; obtaining the median particle size of flocs in the target area according to the image information or particle size observation data to obtain the model correction parameters.

[0097] Based on the relevant descriptions of other embodiments, it can be understood that when the target prediction model is corrected in this application, the flocs are aggregated. k A and crushing efficiency k B As a model correction parameter, to correct the target prediction model.

[0098] In some embodiments, when determining the model correction parameters, the model correction parameters may be obtained by, but not limited to, obtaining image information or particle size observation data of the target area to obtain the median particle size of the flocs in the target area, thereby obtaining the model correction parameters.

[0099] For example, the present application can sample the median particle size D of the flocs in the target area through an image processing program or particle size observation data. f , and is fitted into formula (10). For example, it can be obtained through indoor flocculation tests combined with image processing program calibration; or it can be obtained by sampling the median particle size D of the flocs in the target area through field observation instruments such as LISST. f50 Calibrated and corrected.

[0100] For example, some studies have shown that k A / k BThe value may be a function of turbulent shear. Therefore, the present embodiment can also show k by fitting the curve of the existing experimental data. A / k B and sediment concentration c s and the turbulent shear strength of water body G, thus obtaining floc aggregation k A and crushing efficiency k B Among them, k A and k B The relationship between can be expressed as follows:

[0101] k A / k B =2170-47560G -1.68 -1206.6(c s -0.7) (11)

[0102] Optionally, in one embodiment of the present application, the temporal variation information of the median particle size of flocs in the water body of the target area is predicted based on the corrected target prediction model and the sediment concentration, sediment density and turbulent shear strength of the water body, including: calculating the volume fraction of mineral clay based on the sediment concentration and sediment density.

[0103] In other embodiments, when the present application uses the corrected target prediction model, sediment concentration, sediment density and turbulent shear strength of water body to predict the change information of the median particle size of flocs in the water body of the target area over time, it mainly calculates the mineral clay volume fraction based on the sediment concentration and sediment density, and then predicts the change information of the median particle size of flocs in the water body of the target area over time.

[0104] Specifically, the volume fraction of mineral clay The sediment concentration c s and sediment density ρ s OK, the formula can be expressed as follows:

[0105]

[0106] The present application is described in detail below with reference to a specific embodiment.

[0107] It is clear that microalgae, including their secretions, may be highly involved in the flocculation process of suspended sediments, thereby affecting the geometry, FSD and other properties of the flocs. In order to understand its mechanism and quantitatively analyze these effects, the present embodiment can design a series of experiments based on a self-designed flocculation stirring tank generation system and a flocculation observation system, so as to quantify the key floc properties (median floc size D f ) and establish a prediction model for the median particle size of microalgae-mineral flocs.

[0108] Figure 3 This is a schematic diagram of an experimental device for an embodiment of the present application. Figure 3 As shown, the experimental equipment of the embodiment of the present application can be divided into two parts: a flocculant generation system and a flocculant observation (observation of flocculant particle size and structure) system. Figure 3 a) was carried out in a 2.3 L mixing tank with an inner diameter of 125 mm and a height of 185 mm, equipped with a digital display stirrer having a paddle with a diameter of 75 mm and a height of 25 mm, located 10 mm above the bottom of the tank, for adjusting the mixing rate and, in turn, the turbulent shear rate.

[0109] Floc Observation System ( Figure 3 b) In a 350 mm high sedimentation column, the specific dimensions are shown in Figure 3 The upper side of b. Acrylic baffles are installed inside the sedimentation column to evenly divide the entire column space into nine parts. Floc characteristics such as size and structure are observed using a camera body with 4000x 6000 pixels, an adapter, and a high-resolution fixed-focus lens. In addition, the observation system is equipped with a white LED light source and a black background board to capture clear floc images. In the recorded images, each pixel is equal to 0.5μm, and the capture rate of the camera is set to 0.1s / frame. During the entire floc observation process, an 8mm wide-mouth pipette was mainly used to transfer the floc sample from the floc mixing tank to the sedimentation column.

[0110] Table 1 is a test condition table of an embodiment of the present application, which can be expressed as follows:

[0111] Table 1

[0112]

[0113]

[0114] Next, the present application example can study the flocculation characteristics of purified kaolin minerals (due to their abundance in natural sediments) combined with common microalgae (mid-costal algae) under certain turbulent shear and sedimentation conditions. In the present application example, mainly based on quantitative equilibrium flocculation, the data obtained from the two series of experimental cases Ⅰ and Ⅱ in Table 1 are analyzed to represent 5 turbulent shear, 2 suspended sediment concentrations and 4 algae concentrations of physical mixing flocculation cases:

[0115] Table 2 is a table of the main mineral composition of an embodiment of the present application, which can be shown as follows:

[0116] Table 2

[0117]

[0118]

[0119] Among them, the mineral clay used in the examples of the present application is purified (96.5%) natural kaolin, which is widely present in the natural environment, in order to ensure the versatility of the method for predicting the median particle size of flocs under the action of algae in the examples of the present application. Table 2 summarizes the mineral composition and basic characteristics of the clay samples used in this example. Among them, the mineral composition can be analyzed by X-ray diffusion (XRD) (3°–70° (2θ)), scanning speed: 8° / min). In addition, the particle size distribution of the clay can also be measured using a particle size analyzer in the examples of the present application. Figure 4 This is a schematic diagram of the kaolin particle size distribution results of an embodiment of the present application. Figure 4 As shown, it can be seen that the median size (d50) and average size of the clay sample are 1.72 μm and 2.55 μm, respectively.

[0120] Table 3 is a summary table of average chlorophyll a values ​​of major rivers and estuaries around the world in one embodiment of the present application. It can be expressed as follows:

[0121] Table 3

[0122]

[0123] In order to effectively estimate the range of average chlorophyll a content, the present embodiment includes the average chlorophyll a values ​​of major rivers and estuaries around the world, including but not limited to the Yangtze River Estuary and the Pearl River Estuary in China, the Yeongsan Reservoir in South Korea, the Mississippi River Estuary and Chesapeake Bay in the United States, and three major estuaries in Europe. Thus, the average value of the global maximum chlorophyll a concentration was obtained in the present embodiment: 13.39 μg / L.

[0124] Figure 5 This is a schematic diagram of a method for predicting the equilibrium median particle size of flocs according to an embodiment of the present application. Figure 5 As shown, the floc equilibrium median particle size can be predicted by following the steps below:

[0125] S1: Obtain sediment concentration c according to Table 1 s , sediment density ρ s and other information;

[0126] S2: Determine the turbulent shear strength G of the water body according to the formula given above;

[0127] S3: Determine the algae chlorophyll concentration C by measuring with a UV spectrophotometer or a portable chlorophyll fluorescence meter c ;

[0128] S4: Determine C according to Table 3 c,max =13.39μg / L;

[0129] S5: Determine the percentage of organic matter covering the sediment surface according to the formula (2) given above: c ;

[0130] S6: Obtain model correction parameter k based on the experimental data obtained A and k B ;

[0131] S7: Substitute the previously determined parameters into formula (9) to obtain the relationship between the median particle size of flocs under the action of algae and time;

[0132] S8: According to formula (10), the equilibrium median particle size D of the floccules in the embodiment of the present application is obtained: e Predicted value.

[0133] Figure 6 k in one embodiment of this application A / k B Schematic diagram of the change of average value with chlorophyll a concentration. Figure 6 As shown, based on the calculation results, it can be seen that k A / k B The average value does not vary with algae concentration. This means that the same k can be used at different algae concentrations. A / k B Value, therefore, the present application embodiment can be pure kaolin suspension in k A / k B The value is used as a uniform parameter in the formula.

[0134] Figure 7 is the equilibrium median particle size D of the floccules in one embodiment of the present application e Schematic diagram comparing the calculated values ​​with the measured results obtained by the image processing script. Figure 7 As shown in the results, it can be seen that the calculated value of the formula basically conforms to the 1:1 curve, with an average error of 9.7%. Excluding the measurement error, this result shows that formula (10) has a high prediction accuracy.

[0135] Additionally, the embodiment of the present application may also use the following method to determine the dimensionless coefficient k: A and k B : Studies have shown that k A / k B The value may be a function of turbulent shear. The embodiments of the present application can be verified using experimental data. Figure 8 k for one embodiment of this application A / k B Schematic diagram of the change law of turbulent shear. Figure 8As shown in Figure 2, it can be found that when the suspended sediment concentration is between 150 and 1400 mg / L, the fitting curve shows a good correlation with G, and k is derived accordingly. A / k B The equation for the change of G and suspended sediment concentration is:

[0136] k A / k B =2170-47560G -1.68 -1206.6(c s -0.7) (13)

[0137] By substituting formula (11) into formula (10), this embodiment can obtain a prediction form of the equilibrium size of microalgae-mineral flocs taking into account the suspended sediment concentration. Fig. 9 This is a schematic diagram of the simulation result verification of an embodiment of the present application.

[0138] According to the method for predicting the median particle size of flocs under the action of algae proposed in the embodiment of the present application, a unified floc median particle size prediction model suitable for algae and pure minerals can be obtained according to the characteristics of the distribution of sediment, water flow turbulence and algae concentration in the target area, so as to determine the relationship between the evolution of the median particle size of flocs with time under the condition of algae. Thus, it is achieved that only conventional parameters in the water body need to be determined to predict the median particle size of algae-mud flocs and pure mineral flocs in the water body, which effectively improves the accuracy of the prediction results while ensuring the correctness of the prediction results, significantly reduces the calculation cost, and improves the applicability of the present application. Thus, the problem that the flocculation model in the related art has a relatively limited usable range under the combined action of complex hydrodynamics and sediment concentration conditions such as viscous sediment and microalgae, and it is difficult to capture the specific response mechanism of microalgae-mineral aggregation, lacks a simple dynamic floc size modeling form, and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals is solved.

[0139] Next, a device for predicting the median particle size of flocs taking into account the effect of algae according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0140] Fig.10 It is a schematic diagram of the structure of a device for predicting the median particle size of flocs taking into account the action of algae according to an embodiment of the present application.

[0141] like Fig.10 As shown, the device 10 for predicting the median particle size of flocs under the action of algae includes: a first acquisition module 100 , a second acquisition module 200 and a prediction module 300 .

[0142] The first acquisition module 100 is used to acquire the sediment concentration, sediment density and turbulent shear strength of the water body in the target area.

[0143] The second acquisition module 200 is used to acquire the concentration of algae chlorophyll in the water body, and acquire the percentage of organic matter covering the surface of the sediment according to the concentration of algae chlorophyll.

[0144] The prediction module 300 is used to obtain the model correction parameters of the target area, and after correcting the target prediction model using the model correction parameters, predict the change information of the median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the sediment concentration, sediment density and turbulent shear strength of the water body to obtain the prediction result of the median particle size of flocs.

[0145] Optionally, in one embodiment of the present application, the prediction module 300 includes: a computing unit.

[0146] The calculation unit is used to calculate the volume fraction of mineral clay according to the sediment concentration and sediment density.

[0147] Optionally, in one embodiment of the present application, a formula for obtaining change information can be expressed as:

[0148]

[0149] Among them, D f is the median particle size of flocs in water, t is time, D p is the initial particle size, k A , k B are dimensionless parameters, representing the aggregation and fragmentation efficiency of flocs, is the volume fraction of mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the sediment surface, C c represents the chlorophyll concentration of algae, and G represents the turbulent shear strength of water.

[0150] Optionally, in one embodiment of the present application, the expression of percentage can be expressed as:

[0151]

[0152] Among them, θ c Indicates the percentage of organic matter covering the sediment surface, C c,max is the average chlorophyll concentration in the basin worldwide, θ c,max is the maximum value of organic matter coverage on marine sediments, C c Indicates the algae chlorophyll concentration.

[0153] Optionally, in one embodiment of the present application, the prediction module 300 includes: a first acquisition unit and a second acquisition unit.

[0154] Wherein, the first acquisition unit is used to acquire image information or particle size observation data of the target area.

[0155] The second acquisition unit is used to acquire the median particle size of the flocs in the target area according to the image information or the particle size observation data to obtain the model correction parameters.

[0156] It should be noted that the aforementioned explanation of the embodiment of the method for predicting the median particle size of flocs under the action of algae is also applicable to the device for predicting the median particle size of flocs under the action of algae in this embodiment, and will not be repeated here.

[0157] According to the device for predicting the median particle size of flocs under the action of algae proposed in the embodiment of the present application, a unified floc median particle size prediction model applicable to algae and pure minerals can be obtained according to the characteristics of the distribution of sediment, water flow turbulence and algae concentration in the target area, so as to determine the relationship between the evolution of the median particle size of flocs with time under the condition of algae. Thus, it is achieved that only conventional parameters in the water body need to be determined to predict the median particle size of algae-mud flocs and pure mineral flocs in the water body, which effectively improves the accuracy of the prediction results while ensuring the correctness of the prediction results, significantly reduces the calculation cost, and improves the applicability of the present application. Thus, the problem that the flocculation model in the related art has a relatively limited usable range under the combined action of complex hydrodynamics and sediment concentration conditions such as viscous sediment and microalgae, and it is difficult to capture the specific response mechanism of microalgae-mineral aggregation, lacks a simple dynamic floc size modeling form, and cannot be coupled with the existing large-scale model to effectively predict the size changes caused by the combination of biological factors and clay minerals is solved.

[0158] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0159] A memory 1101 , a processor 1102 , and a computer program stored in the memory 1101 and executable on the processor 1102 .

[0160] When the processor 1102 executes the program, the method for predicting the median particle size of flocs considering the effect of algae provided in the above embodiment is implemented.

[0161] Furthermore, the electronic device further comprises:

[0162] The communication interface 1103 is used for communication between the memory 1101 and the processor 1102 .

[0163] The memory 1101 is used to store computer programs that can be executed on the processor 1102 .

[0164] The memory 1101 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.

[0165] If the memory 1101, the processor 1102 and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101 and the processor 1102 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.11 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.

[0166] Optionally, in a specific implementation, if the memory 1101, the processor 1102 and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102 and the communication interface 1103 can communicate with each other through an internal interface.

[0167] The processor 1102 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.

[0168] The 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 predicting the median particle size of flocs under the action of algae.

[0169] The embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the method for predicting the median particle size of flocs under the action of algae provided in the embodiment of the present application is implemented.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The storage medium mentioned above may be a read-only memory, a 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 predicting the median particle size of flocs under the action of algae, characterized in that: The following steps are involved: Obtain the sediment concentration, sediment density and turbulent shear strength of the water in the target area; Obtaining the algae chlorophyll concentration in the water body, and obtaining the percentage of organic matter covering the surface of the sediment according to the algae chlorophyll concentration; The model correction parameters of the target area are obtained, and after correcting the target prediction model using the model correction parameters, the change information of the median particle size of flocs in the water body of the target area over time is predicted based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body to obtain a prediction result of the median particle size of flocs.

2. The method according to claim 1, characterized in that The information on the change over time of the median particle size of flocs in the water body of the target area is predicted based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body, including: The mineral clay volume fraction is calculated according to the sediment concentration and the sediment density.

3. The method according to claim 2, characterized in that The formula for obtaining the change information is: Among them, D f is the median particle size of the flocs in the water, t is the time, D p is the initial particle size, k A , k B are dimensionless parameters, representing the aggregation and fragmentation efficiency of the floccules, is the volume fraction of the mineral clay, n f is the fractal dimension, F y is the floc strength, μ is the dynamic viscosity of the fluid, q is a dimensionless parameter, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c represents the concentration of algae chlorophyll a, and G represents the turbulent shear strength of the water body.

4. The method according to claim 1, characterized in that: The percentage is expressed as: Among them, θ c Indicates the percentage of organic matter covering the surface of the sediment, C c,max is the average chlorophyll concentration in the basin worldwide, θ c,max is the maximum value of organic matter coverage on marine sediments, C c Indicates the algae chlorophyll a concentration.

5. The method according to claim 1, characterized in that The obtaining of the model correction parameters of the target area includes: Acquiring image information or particle size observation data of the target area; The median particle size of the flocs in the target area is obtained according to the image information or the particle size observation data to obtain the model correction parameter.

6. A device for predicting the median particle size of flocs under the action of algae, characterized in that: The following steps are involved: The first acquisition module is used to obtain the sediment concentration, sediment density and turbulent shear strength of the water body in the target area; A second acquisition module is used to acquire the concentration of algae chlorophyll in the water body, and acquire the percentage of organic matter covering the surface of the sediment according to the concentration of algae chlorophyll; A prediction module is used to obtain the model correction parameters of the target area, and after correcting the target prediction model using the model correction parameters, predict the change information of the median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the sediment concentration, the sediment density and the turbulent shear strength of the water body, so as to obtain the prediction result of the median particle size of flocs.

7. The device according to claim 6, characterized in that The prediction module comprises: A calculation unit is used to calculate the volume fraction of mineral clay according to the sediment concentration and the sediment density.

8. 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 predicting the median particle size of flocs under the action of algae as described in any one of claims 1 to 5.

9. 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 predicting the median particle size of flocs taking into account the action of algae as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, it is used to implement the method for predicting the median particle size of flocs taking into account the action of algae as described in any one of claims 1 to 5.

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