Method and apparatus for predicting median particle size of flocs under algal action

Abstract

This application relates to the field of hydraulics technology, and particularly to a method and apparatus for predicting the median particle size of flocs considering the action of algae. The method includes: obtaining the sediment concentration, sediment density, and turbulent shear strength of the water body in the target area; obtaining the chlorophyll concentration of algae in the water body and determining the percentage of organic matter covering the sediment surface based on the chlorophyll concentration; obtaining model correction parameters for the target area and correcting the target prediction model using these parameters; and predicting the change in the median particle size of flocs in 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, thus obtaining the predicted median particle size of the flocs. This application can accurately predict the median particle size of algae-mud flocs and pure mineral flocs in water bodies when only conventional parameters within the water body are determined, effectively improving the accuracy of the prediction results while reducing computational costs, thus enhancing the applicability of this application.

Description

Technical Field

[0001] This application relates to the field of hydraulics technology, and in particular to a method and apparatus for predicting the median particle size of flocs considering the effects of algae. Background Technology

[0002] The transport of cohesive fine-grained sediments in aquatic environments plays a crucial role in the morphological evolution of rivers, lakes, and estuaries, as well as in estuarine navigation engineering. Unlike non-cohesive sediments that are transported individually, cohesive sediments are typically transported in the form of flocculation, where individual particles aggregate together. Over 90% of fine suspended sediments exist as aggregates. Therefore, studying the flocculation of cohesive sediments is an important area of ​​research for understanding the mechanics of sediment transport.

[0003] Microalgae, widely found in aquatic environments such as estuaries and coastlines, have had their flocculent formation mechanism with viscous sediments a focus of recent research. Existing large-scale geomorphic evolution models mostly consider the physicochemical properties of sediments themselves, but algae, which are prominently present in estuarine environments, will have a significant impact on the flocculation of viscous sediments, thereby affecting geomorphic evolution and simulation accuracy.

[0004] In related technologies, numerical models have become the main tool for studying flocculation kinetics. Two main methods are used to describe the flocculation process: one is the Lagrange flocculation model, and the other involves modeling the floc size distribution (FSD) using multiple groups. With the improvement of computational power and analytical methods, the combination of dynamic flocs and modeling methods is increasingly being applied in a wider range of sediment transport models.

[0005] However, the flocculation models in related technologies have limited applicability under complex hydrodynamic and sediment concentration conditions, such as the combined effects of viscous silt and microalgae. They are difficult to capture the specific response mechanism of microalgae-mineral aggregation, and lack a simple dynamic floc size modeling form. They cannot be coupled with existing large-scale models to effectively predict the size changes caused by the combination of biological factors and clay minerals, which urgently needs to be addressed. Summary of the Invention

[0006] This application provides a method and apparatus for predicting the median particle size of flocs considering the action of algae, in order to solve the problems of the limited applicability of flocculation models in related technologies under complex hydrodynamic and sediment concentration conditions such as the combined action of viscous silt and microalgae, the difficulty in capturing the specific response mechanism of microalgae-mineral aggregation, the lack of a simple dynamic floc size modeling form, and the inability to couple with existing large-scale models to effectively predict the size changes caused by the combination of biological factors and clay minerals.

[0007] The first aspect of this application provides a method for predicting the median particle size of flocs considering 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 chlorophyll concentration of algae in the water body, and obtaining the percentage of organic matter covering the sediment surface based on the 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, sediment density, and turbulent shear strength of the water body, so as to obtain the predicted result of the median particle size of flocs.

[0008] Optionally, in one embodiment of this application, the step of predicting the change in 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 water turbulent shear strength includes: calculating the mineral clay volume fraction based on the sediment concentration and sediment density.

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

[0010]

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

[0012] Optionally, in one embodiment of this application, the percentage is expressed as:

[0013]

[0014] Where, θ c C represents the percentage of organic matter on the surface of the covered sediment. c,max θ represents the average chlorophyll concentration in the watershed worldwide. c,max C represents the maximum organic matter cover on marine sediments. c This indicates the concentration of chlorophyll a in algae.

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

[0016] A second aspect of this application provides a device for predicting the median particle size of flocs considering algal action, comprising: a first acquisition module for acquiring the sediment concentration, sediment density, and turbulent shear strength of the water in a target area; a second acquisition module for acquiring the chlorophyll concentration of algae in the water and acquiring the percentage of organic matter covering the sediment surface based on the chlorophyll concentration; and a prediction module for acquiring model correction parameters for the target area, and after correcting the target prediction model using the model correction parameters, predicting the change in the median particle size of flocs in the water 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, to obtain the predicted result of the median particle size of flocs.

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

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

[0019]

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

[0021] Optionally, in one embodiment of this application, the percentage is expressed as:

[0022]

[0023] Where, θ c C represents the percentage of organic matter on the surface of the covered sediment. c,max θ represents the average chlorophyll concentration in the watershed worldwide. c,max C represents the maximum organic matter cover on marine sediments. c This indicates the concentration of chlorophyll a in algae.

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

[0025] A third aspect of this application provides an electronic device, including: 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 considering algal action as described in the above embodiments.

[0026] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the median particle size of flocs under algal influence.

[0027] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described method for predicting the median particle size of flocs considering algal activity.

[0028] This application's embodiments, based on the characteristics of sediment, water flow turbulence, and algae concentration distribution in the target area, derive a unified floc median particle size prediction model applicable to both algae-containing and pure mineral flocs, thereby determining the relationship between the median particle size of flocs and time under algae-containing conditions. Thus, it achieves the prediction of the median particle size of algae-mud flocs and pure mineral flocs in water bodies by only determining conventional parameters within the water body. This effectively improves the accuracy of prediction results while ensuring correctness, significantly reduces computational costs, and enhances the applicability of this application. Therefore, it solves the problems of related technologies where flocculent models have limited applicability under complex hydrodynamic and sediment concentration conditions, such as the combined effects of viscous sediment and microalgae, making it difficult to capture the specific response mechanism of microalgae-mineral aggregation. Furthermore, these models lack simple dynamic floc size modeling methods and cannot be coupled with existing large-scale models to effectively predict size changes caused by the combination of biological factors and clay minerals.

[0029] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0030] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

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

[0032] Figure 2 This is a flowchart of a method for predicting the median particle size of flocs considering the effects of algae, according to an embodiment of this application.

[0033] Figure 3 This is a schematic diagram of an experimental device according to an embodiment of this application;

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

[0035] Figure 5 This is a flowchart of a method for predicting the equilibrium median particle size of flocs according to an embodiment of this application;

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

[0037] Figure 7 This is a schematic diagram comparing the calculated and measured results of the equilibrium median particle size of flocs in one embodiment of this application;

[0038] Figure 8 k is an embodiment of this application A / k B Schematic diagram illustrating the variation of turbulent shear;

[0039] Figure 9 This is a schematic diagram illustrating the simulation results verification of one embodiment of this application;

[0040] Figure 10 This is a schematic diagram of the structure of the floc median particle size prediction device considering the action of algae provided in the embodiments of this application;

[0041] Figure 11 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0042] Figure label:

[0043] 10-A device for predicting the median particle size of flocs under algal action: 100-First acquisition module, 200-Second acquisition module and 300-Prediction module; 1101-Memory, 1102-Processor and 1103-Communication interface. Detailed Implementation

[0044] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0045] The following describes, with reference to the accompanying drawings, a method and apparatus for predicting the median particle size of flocs considering algal activity, according to embodiments of this application. Addressing the limitations of existing flocculation models in the background art, which are limited in their applicability under complex hydrodynamic and sediment concentration conditions such as the combined effects of viscous silt and microalgae, making it difficult to capture the specific response mechanism of microalgae-mineral aggregation, lacking a simple dynamic floc size modeling approach, and unable to couple with existing large-scale models to effectively predict size changes caused by the combination of biological factors and clay minerals, this application provides a method for predicting the median particle size of flocs considering algal activity. In this method, based on the characteristics of water sediment, water flow turbulence, and algal concentration distribution in the target area, a unified median particle size prediction model applicable to both algal and pure mineral flocs can be derived, thereby determining the relationship between the median particle size of flocs and time under algal conditions. Therefore, this method enables the prediction of the median particle size of algal-mud flocs and pure mineral flocs in water bodies by only determining conventional parameters within the water body. This effectively improves the accuracy of the prediction results while ensuring correctness, significantly reduces computational costs, and enhances the applicability of this application. This solves the problems of related technologies, such as the limited applicability of flocculation models under complex hydrodynamic and sediment concentration conditions (e.g., the combined effects of viscous silt and microalgae), the lack of simple dynamic floc size modeling, the difficulty in capturing the specific response mechanism of microalgae-mineral aggregation, and the inability to couple with existing large-scale models to effectively predict size changes caused by the combination of biological factors and clay minerals.

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

[0047] The data acquisition module M1 is used to acquire basic information about the target area's water body, as well as observation data on water flow, sediment, and algae content. The basic information about the target area's water body includes water temperature, wind speed, observation point, and observation time. The water flow monitoring data includes water depth and average cross-sectional flow velocity. The sediment monitoring data includes water sediment concentration and sediment density. The algae content observation data includes chlorophyll concentration observation data.

[0048] The data processing module M2 processes the acquired data, calculating 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 water flocs in the target area based on the results of the data processing module.

[0050] Specifically, Figure 2 This is a flowchart illustrating a method for predicting the median particle size of flocs considering the effects of algae, provided in an embodiment of this application.

[0051] like Figure 2 As shown, the method for predicting the median particle size of flocs considering algal activity includes the following steps:

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

[0053] It is understandable that the turbulent shear strength of water can be interpreted here as the magnitude of the shear force generated in a water body due to the turbulence of the water flow (i.e., a non-laminar flow state, where 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 within the water flow, and it reflects the influence of the degree of water flow turbulence on the internal structure of the water body.

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

[0055] Based on this, the embodiments of this application can obtain data such as sediment concentration, sediment density and turbulent shear strength in the water body of the target area, taking into account to a great extent the different influencing factors that may be included in the prediction of the median particle size of flocs.

[0056] For example, low sediment concentrations can promote floc formation, but excessively high sediment concentrations will gradually weaken the promoting effect or even turn into an inhibiting effect. Dense particles with higher density may be more difficult for water flow to disperse or break up, which to some extent is beneficial to the stable existence of flocs. Stronger turbulent shear strength will damage the internal structure of flocs, making them loose or even disintegrating. This is because turbulent shear force acts on the floc surface and between internal particles, causing the bonds between particles to break. Weaker turbulent shear strength, on the other hand, is beneficial to the stable existence and further growth of flocs.

[0057] Specifically, the sediment concentration in the embodiments of this application can be obtained by, but is not limited to, methods such as vacuum filtration or observation using a turbidimeter; the sediment density can be obtained by, but is not limited to, weighing or drainage; and the turbulent shear strength of the water body can be calculated using, but is not limited to, the following formula:

[0058]

[0059] Where G represents the turbulent shear strength of the water body, ε is the dissipation rate of the turbulence; v is the kinematic viscosity of the fluid; and 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. It can be selected according to actual needs or specific circumstances. This application embodiment does not impose specific limitations.

[0061] The embodiments of this 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 predicted median particle size of flocs under algal action.

[0062] Step S202: Obtain the chlorophyll concentration of algae in the water body, and determine the percentage of organic matter covering the sediment surface based on the chlorophyll concentration. The percentage can be expressed as follows:

[0063]

[0064] Where, θ c C represents the percentage of organic matter covering the surface of sediments. c C represents the concentration of chlorophyll a in algae. c,max θ represents the average chlorophyll concentration in the watershed worldwide. c,maxThis represents the maximum organic matter coverage on marine sediments.

[0065] Understandably, sediments here can be understood as viscous sediments mediated by microorganisms (such as bacteria, plankton, and secretions) in the water. The flocculation of viscous sediments plays a crucial role in aquatic environments because it influences the transport, deposition, and morphological dynamics of suspended sediments. Simultaneously, viscous sediments play a key role in regulating the transport of nutrients and organic matter, affecting underwater nutrient cycling and ecosystems.

[0066] In practical implementation, this application considers not only the impact of sediment on flocs in the water, but also the chlorophyll concentration of algae as an important parameter for measuring the eutrophication level of water bodies, which can reflect the amount of algal biomass in the water. Therefore, when predicting the median particle size of flocs under the action of algae, this application can obtain the sediment concentration, sediment density and turbulent shear strength of the target area water body, as well as the chlorophyll concentration of algae in the water body. Based on the concentration, it can determine whether there are many or few algae in the water body, and then calculate the percentage of organic matter on the sediment surface covered by the chlorophyll concentration of algae.

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

[0068]

[0069] Among them, C c,max This represents the average chlorophyll concentration in the watershed worldwide; in this embodiment, it can be taken as 13.39 μg / L. θ c,max The maximum organic matter cover on marine sediments can be 0.15 in this embodiment. c The concentration of chlorophyll a in algae can be measured, but is not limited to, by instruments such as ultraviolet spectrophotometers or portable chlorophyll fluorometers.

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

[0071]

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

[0073] As one possible approach, embodiments of this application, when predicting the median particle size of flocs under algal action, primarily utilize, but are not limited to, an improved flocculation model. This improved flocculation model can be understood as a modification based on the described three-dimensional flocculation formula. For example, embodiments of this application can use the Winterwerp flocculation model (a mathematical model for describing and simulating the flocculation process of viscous sediment in fluids) as the target prediction model, and use the model correction parameters of the target region to correct and improve the model, obtaining a corrected and usable target prediction model.

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

[0075]

[0076] The first and second terms represent the aggregation and fragmentation efficiency parameters k, respectively. A k B (Dimensionless) Scaling of floc aggregation and fragmentation terms; The volume fraction representing mineral clay can be expressed as sediment concentration c. s and the density ρ of silt s express, D P n is the initial particle size; f Representing the fractal dimension (dimensionless), which can take values ​​in the range of 1 to 3, it describes the flocculent structure from linear chains (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 represented as τ w =μG,τ f =F y / D f 2 μ represents the dynamic viscosity of the fluid; F yThe yield strength of the flocculent is determined by the physicochemical properties of the sediment and water. In the embodiments of this application, F... y A commonly used estimated value of 10-10N can be used; q represents an empirical coefficient, and in this embodiment, a reasonable value of q = 0.5 can be used.

[0077] Will Substituting into formula (3), the equilibrium median particle size D of the flocs is... e Can be generated by dD f When / dt=0, the result is represented as follows:

[0078]

[0079] It should be noted that the formulas and derivations in the embodiments of this application are based on the equilibrium flocculation state. Flocculation is a process involving both aggregation and breakup. When the aggregation rate equals the breakup rate, flocculation reaches a steady state, i.e., a dynamic equilibrium state. The corresponding floc size and time are respectively called the equilibrium floc size and equilibrium floc time. The equilibrium flocculation state here can be understood as the steady state (dynamic equilibrium state) where the floc aggregation rate equals the breakup 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 floc at this time is called the equilibrium median particle size D of the floc. e .

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

[0081] The equations above show that the Winterwerp flocculation model can consider the effects of shear rate and sediment concentration on flocculation; however, it does not incorporate some biological factors into the aggregation and breakage terms. Therefore, the embodiments of this application take into account the influence of microalgae on floc aggregation and breakage efficiency in flocculation processes involving microalgae. This can be achieved by changing the floc aggregation and breakage efficiency, i.e., k... A k B As model correction parameters, the target prediction model is corrected to adapt the Winterwerp flocculation model to microalgae conditions. Based on this, the embodiment of this application can modify formula (4) as follows:

[0082]

[0083] Where, k A,m k B,m This refers to the parameters for floc aggregation and breakup efficiency in the presence of microalgae.

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

[0085]

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

[0087] It should be noted that polymer flocculants can adsorb onto the surface of mineral clay particles, forming "bridges" connecting the particles. This flocculation form is similar to that of microalgae. The embodiments of this application draw upon the flocculation effect between polymer flocculants and sediment flocculation, and apply it to bioflocculation. Considering that when θ is close to 0, k expressed by formula (6) A / k B The value tends towards 0, which contradicts formula (4) of the Winterwerp flocculation model. Therefore, the embodiments of this application can adjust the value of k. A,m k B,m Modify the formula to satisfy the following two conditions to ensure its original physical meaning: (a) when the algae content approaches 0, k A,m k B,m Convert to raw k A k B Form; (b)k A,m k B,m It needs to maintain the same monotonicity as formula (5). At this point, k A,m k B,m This can be expressed as:

[0088]

[0089] Where, θ c This represents the organic matter coverage rate. Combining formulas (7) to (8), formula (3) can be transformed into a form adapted to algal activity, as follows:

[0090]

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

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

[0093]

[0094] When θ c When =0, the bioflocculation 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] Formulas (9) and (10) are the corrected target prediction models proposed in the embodiments of this application, which can be applied to the prediction of the median particle size of uniform flocs containing algae and pure minerals. This prediction model can obtain the sediment concentration c as described above. s ρ, density of sediment s The turbulent shear strength G of the water body and the concentration of chlorophyll a C c The parameters are used to predict the change of median floc size in the target area of ​​the water body over time, so as to obtain the final prediction result of median floc size.

[0096] Optionally, in one embodiment of this application, obtaining model correction parameters for the target region includes: obtaining image information or particle size observation data of the target region; and obtaining the median particle size of the flocs in the target region based on the image information or particle size observation data to obtain model correction parameters.

[0097] Based on the descriptions of other embodiments, it will be understood that when correcting the target prediction model, this application mainly involves aggregating flocs. k A and crushing efficiency k B As a model correction parameter, it is used to correct the target prediction model.

[0098] In some embodiments, when determining the model correction parameters, the median particle size of flocs in the target area can be obtained by acquiring image information or particle size observation data of the target area, thereby obtaining the model correction parameters.

[0099] For example, this application can sample the median particle size D of flocs in the target region using image processing programs or particle size observation data. f And substitute it into formula (10) to obtain the result. For example, it can be obtained by indoor flocculation test and combined with image processing program calibration; or by sampling the median particle size D of the flocs in the target area using field observation instruments such as LISST. f50 The calibration and correction were obtained.

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

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

[0102] Optionally, in one embodiment of this application, the prediction of the change in median particle size of flocs in the water body of the target area over time based on the corrected target prediction model and the information on sediment concentration, sediment density and water turbulent shear strength includes: calculating the mineral clay volume fraction based on sediment concentration and sediment density.

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

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

[0105]

[0106] The present application will be 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, free surface area (FSD), and other properties of the flocs. To understand the mechanism and quantitatively analyze these effects, embodiments of this application can design a series of experiments based on a self-designed flocculation mixing tank generation system and flocculation observation system. Based on the operating conditions and observed experimental results, the key floc properties (median floc size D) during the flocculation process under equilibrium conditions can be quantified. f And establish a predictive model for the median particle size of microalgae-mineral flocs.

[0108] Figure 3 This is a schematic diagram of an experimental setup according to one embodiment of this application. Figure 3 As shown, the experimental equipment in this embodiment can be divided into two parts: a floc formation system and a floc observation (observing the particle size and structure of flocs) system. The floc formation system ( Figure 3 a) The mixing was carried out in a 2.3L mixing tank. The mixing tank, with an inner diameter of 125mm and a height of 185mm, was equipped with a digital display agitator with blades of 75mm in diameter and 25mm in height, located 10mm above the bottom of the tank, for adjusting the mixing rate, and thus the turbulent shear rate.

[0109] Flocculation Observation System Figure 3 b) Conducted in a settlement column 350mm high; specific dimensions are shown in [the diagram]. Figure 3 The upper side of b. An acrylic baffle is installed inside the settling column, uniformly dividing the entire column space into nine sections. Flocculation characteristics, such as size and structure, are observed using a camera body with 4000 x 6000 pixels, an adapter, and a high-resolution fixed-focus lens. Furthermore, the observation system is equipped with a white LED light source and a black background to capture clear images of the flocs. In the recorded images, each pixel is equal to 0.5 μm, and the camera's capture rate is set to 0.1 s / frame. Throughout the floc observation process, flocculation samples are primarily transferred from the floc mixing tank to the settling column using 8 mm wide-mouth pipettes.

[0110] Table 1 is a test condition table for one embodiment of this application, which can be represented as follows:

[0111] Table 1

[0112]

[0113]

[0114] Next, this application embodiment can study the flocculation characteristics of purified kaolinite minerals (due to their abundance in natural sediments) combined with common microalgae (Skeletonema costatum) under certain turbulent shear and sedimentation conditions. In this application embodiment, the data obtained from the two series of experimental cases I and II in Table 1 are mainly based on quantitative equilibrium flocculation to represent five physical mixing flocculation cases with turbulent shear, two suspended sediment concentrations, and four algal concentrations:

[0115] Table 2 is a table of the main mineral composition of one embodiment of this application, as shown below:

[0116] Table 2

[0117]

[0118]

[0119] The mineral clay used in this embodiment is purified (96.5%) natural kaolin, which is widely found in the natural environment, to ensure the versatility of the median particle size prediction method for flocs considering algal activity in this embodiment. Table 2 summarizes the mineral composition and basic characteristics of the clay samples used in this embodiment. The mineral composition can be analyzed by X-ray diffusion (XRD) (3°–70° (2θ)), scan rate: 8° / min). Furthermore, the particle size distribution of the clay can also be measured using a particle size analyzer in this embodiment. Figure 4 This is a schematic diagram showing the particle size distribution of kaolinite according to one embodiment of this application. Figure 4 As shown, the median size (d50) and average size of the clay samples are 1.72 μm and 2.55 μm, respectively.

[0120] Table 3 is a summary table of average chlorophyll a values ​​for major rivers and estuaries worldwide according to one embodiment of this application. It can be represented as follows:

[0121] Table 3

[0122]

[0123] To effectively estimate the range of average chlorophyll a content, this application's embodiments include average chlorophyll a values ​​from major rivers and estuaries worldwide, including but not limited to the Yangtze River Estuary and Pearl River Estuary in China, Yeongsan Reservoir in South Korea, the Mississippi River Estuary and Chesapeake Bay in the United States, and three major river estuaries in Europe. Thus, this application's embodiments obtained the average global maximum chlorophyll a concentration of 13.39 μg / L.

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

[0125] S1: The sediment concentration c is obtained from Table 1. s ρ, density of sediment s Information such as;

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

[0127] S3: Determine the algal chlorophyll concentration C by measuring with an ultraviolet spectrophotometer or a portable chlorophyll fluorometer. c ;

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

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

[0130] S6: Obtain the model correction parameter k based on the obtained experimental data. 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 algal action and time.

[0132] S8: Obtain the equilibrium median particle size D of the flocs in the embodiments of this application according to equation (10). e Predicted value.

[0133] Figure 6 k in one embodiment of this application A / k B A schematic diagram showing the variation of average values ​​with chlorophyll a concentration. (See diagram below.) Figure 6 As shown, based on the calculation results, it can be seen that k A / k B The average value does not change with algae concentration. This means that the same k can be used at different algae concentrations. A / k B Therefore, the embodiments of this application can reduce the k value in pure kaolin suspension. A / k B The value is used as a uniform parameter in the formula.

[0134] Figure 7 The equilibrium median particle size D of the flocs in one embodiment of this application is e A diagram comparing the calculated values ​​with the measurement results obtained through an image processing script. (Example) Figure 7 As shown in the results, the calculated value of the formula basically conforms to the 1:1 curve, with an average error of 9.7%. Excluding measurement errors, this result shows that formula (10) has high prediction accuracy.

[0135] Additionally, embodiments of this application may also use the following method to determine the dimensionless coefficient k. A and k B Existing research indicates that k A / k B The value may be a function of turbulent shear. The embodiments of this application can be verified using experimental data. Figure 8 k is an embodiment of this application A / k B A schematic diagram illustrating the variation of shear flow with turbulent flow. (See diagram for example.) Figure 8As shown, when the suspended sediment concentration is between 150 and 1400 mg / L, the fitted curve shows a good correlation with G, and based on this, k is derived. A / k B Equations for the variation of G and suspended sediment concentration:

[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 predicted form of the equilibrium size of microalgae-mineral flocs that takes into account the concentration of suspended sediment. Figure 9 This is a schematic diagram illustrating the simulation results verification of one embodiment of this application.

[0138] The method for predicting the median particle size of flocs considering algal activity, as proposed in this application, can derive a unified median particle size prediction model applicable to both algal and pure mineral flocs based on the characteristics of sediment, water flow turbulence, and algal concentration distribution in the target area. This model determines the relationship between the median particle size of flocs and time under algal conditions. Therefore, it enables the prediction of the median particle size of algal-mud flocs and pure mineral flocs in water bodies by only determining conventional parameters within the water body. This effectively improves the accuracy of prediction results while ensuring correctness, significantly reduces computational costs, and enhances the applicability of this application. This solves the problems of limited applicability of related floc models under complex hydrodynamic and sediment concentration conditions, such as the combined effects of viscous sediment and microalgae, difficulty in capturing the specific response mechanism of microalgae-mineral aggregation, lack of simple dynamic floc size modeling, and inability to couple with existing large-scale models to effectively predict size changes caused by the combination of biological factors and clay minerals.

[0139] Next, referring to the accompanying drawings, a device for predicting the median particle size of flocs considering the effects of algae, according to an embodiment of this application, is described.

[0140] Figure 10 This is a schematic diagram of the structure of the floc median particle size prediction device considering the action of algae in an embodiment of this application.

[0141] like Figure 10 As shown, the floc median particle size prediction device 10 considering 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 water turbulent shear strength in the water body of the target area.

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

[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, it predicts the change information of the median particle size of flocs in the water of the target area over time based on the corrected target prediction model and the sediment concentration, sediment density and water turbulent shear strength, so as to obtain the prediction result of the median particle size of flocs.

[0145] Optionally, in one embodiment of this application, the prediction module 300 includes a calculation unit.

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

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

[0148]

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

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

[0151]

[0152] Where, θ c C represents the percentage of organic matter covering the surface of sediments. c,max θ represents the average chlorophyll concentration in the watershed worldwide. c,max C represents the maximum organic matter cover on marine sediments. c This indicates the concentration of chlorophyll in algae.

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

[0154] The first acquisition unit is used to acquire image information or granular observation data of the target area.

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

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

[0157] The median particle size prediction device for flocs considering algal activity proposed in this application can derive a unified median particle size prediction model for both algae-containing and pure mineral flocs based on the characteristics of sediment, water flow turbulence, and algal concentration distribution in the target area. This model determines the relationship between the median particle size of flocs and time under algal conditions. Therefore, it enables the prediction of the median particle size of algae-mud flocs and pure mineral flocs in water bodies by only determining conventional parameters within the water body. This effectively improves the accuracy of prediction results while ensuring correctness, significantly reduces computational costs, and enhances the applicability of this application. This solves the problems of limited applicability of related floc models under complex hydrodynamic and sediment concentration conditions, such as the combined effects of viscous sediment and microalgae, difficulty in capturing the specific response mechanism of microalgae-mineral aggregation, lack of simple dynamic floc size modeling, and inability to couple with existing large-scale models to effectively predict size changes caused by the combination of biological factors and clay minerals.

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

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

[0160] When the processor 1102 executes the program, it implements the method for predicting the median particle size of flocs considering the effects of algae provided in the above embodiments.

[0161] Furthermore, electronic devices also include:

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

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

[0164] The memory 1101 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0165] If the memory 1101, processor 1102, and communication interface 1103 are implemented independently, then the communication interface 1103, memory 1101, and processor 1102 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

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

[0167] The processor 1102 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0168] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the median particle size of flocs considering the effects of algae.

[0169] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the method for predicting the median particle size of flocs considering the effects of algae provided in this application.

[0170] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0171] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0172] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0173] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0174] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0175] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0176] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0177] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting the median particle size of flocs considering the effects of algae, characterized in that, Includes the following steps: Obtain the sediment concentration, sediment density, and turbulent shear strength of the water body in the target area; The concentration of algal chlorophyll in the water body is obtained, and the percentage of organic matter covering the sediment surface is obtained based on the concentration of algal chlorophyll. The model correction parameters of the target area are obtained, and after the target prediction model is corrected using the model correction parameters, the change information of the median particle size of flocs in the water of the target area over time is predicted based on the corrected target prediction model and the sediment concentration, sediment density and water turbulent shear strength, so as to obtain the median particle size prediction result of flocs. The method of predicting the change of 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 water turbulent shear strength includes: calculating the mineral clay volume fraction based on the sediment concentration and sediment density. The formula for obtaining the change information is as follows: ， in, The median particle size of the flocs in the water body. For time, The initial particle size, , These are dimensionless parameters, representing the aggregation and breakup efficiency of the flocs, respectively. The volume fraction of the mineral clay. For fractal dimension, For floc strength, The dynamic viscosity of the fluid. For dimensionless parameters, This indicates the percentage of organic matter on the surface of the covering sediment. This indicates the concentration of chlorophyll a in algae. This indicates the turbulent shear strength of the water body; The model correction parameters are the aggregation and breakup efficiency of the flocculants.

2. The method according to claim 1, characterized in that, The expression for the percentage is: ， in, This indicates the percentage of organic matter on the surface of the covering sediment. This represents the average chlorophyll concentration in the watershed worldwide. This represents the maximum organic matter cover on marine sediments. This indicates the concentration of chlorophyll a in algae.

3. The method according to claim 1, characterized in that, The process of obtaining the model correction parameters for the target region includes: Acquire image information or granular observation data of the target area; The median particle size of the flocs in the target region is obtained based on the image information or the particle size observation data, so as to obtain the model correction parameters.

4. A device for predicting the median particle size of flocs considering the action of algae, characterized in that, Includes the following steps: The first acquisition module is used to acquire the sediment concentration, sediment density and turbulent shear strength of the water body in the target area. The second acquisition module is used to acquire the concentration of algal chlorophyll in the water body and to acquire the percentage of organic matter covering the surface of sediments based on the concentration of algal chlorophyll. The 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 of the target area over time based on the corrected target prediction model and the sediment concentration, sediment density and water turbulent shear strength, so as to obtain the median particle size prediction result of flocs. The prediction module includes a calculation unit for calculating the mineral clay volume fraction based on the sediment concentration and the sediment density. The formula for obtaining the change information is as follows: ， in, The median particle size of the flocs in the water body. For time, The initial particle size, , These are dimensionless parameters, representing the aggregation and breakup efficiency of the flocs, respectively. The volume fraction of the mineral clay. For fractal dimension, For floc strength, The dynamic viscosity of the fluid. For dimensionless parameters, This indicates the percentage of organic matter on the surface of the covering sediment. This indicates the concentration of chlorophyll a in algae. This indicates the turbulent shear strength of the water body; The model correction parameters are the aggregation and breakup efficiency of the flocculants.

5. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for predicting the median particle size of flocs considering the action of algae as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for predicting the median particle size of flocs considering the action of algae as described in any one of claims 1-3.

7. 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 considering the effects of algae as described in any one of claims 1-3.

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