A method and device for dynamically monitoring the size of particle aggregates in a disturbed flow field

Abstract

The present application discloses a method and device for dynamically monitoring the size of particle agglomerates in a disturbed flow field, and relates to the field of data processing technology. The dimensionless particle diameter is obtained based on the flow field characteristic parameters, the flow field shear rate, and the particle diameter; the cohesion number is obtained based on the flow field characteristic parameters, the particle diameter, and the particle's Hamaker constant; a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter is constructed to obtain the number of particle agglomerates in the equilibrium stage; based on the dimensionless particle diameter and the number of particle agglomerates in the equilibrium stage, the dimensionless agglomerate size in the equilibrium stage is obtained to characterize the relative size of the particle agglomerates; based on the dimensionless agglomerate size in the equilibrium stage, the actual size of the agglomerates in the equilibrium stage is obtained through conversion operations; and a feedback control mechanism is implemented based on the monitoring results. This method solves the problem of how to achieve real-time monitoring of the size distribution of particle agglomerates in a disturbed flow field and improve the accuracy of the monitoring results.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for dynamically monitoring the size of particle agglomerates in a disturbed flow field. Background Art

[0002] Suspended particles will agglomerate in a disturbed flow field due to the attraction between particles. This mechanism plays a dominant role in processes such as sewage treatment, sediment erosion and transportation in rivers and oceans, and chemical reactors. At the same time, the size distribution of aggregates will directly affect the mass transfer efficiency and system stability of the above processes.

[0003] However, current methods for measuring particle aggregate size rely primarily on offline sampling, performed using equipment such as electron microscopes and particle analyzers. However, this approach has significant limitations: First, the sampling process inevitably disrupts the original flow field conditions, causing secondary breakage or reorganization of aggregates, leading to significant deviations between the measured results and the actual operating conditions; second, offline analysis exhibits a time lag, making it impossible to monitor the dynamic changes in particle aggregate size in a disturbed flow field in real time.

[0004] Therefore, how to achieve real-time monitoring of the size distribution of particle agglomerates in a disturbed flow field and improve the accuracy of the monitoring results has become an urgent problem to be solved. Summary of the Invention

[0005] In an embodiment of the present application, a method for dynamically monitoring the size of particle aggregates in a disturbed flow field is provided, which solves the problem of how to achieve real-time monitoring of the size distribution of particle aggregates in a disturbed flow field and improve the accuracy of the monitoring results.

[0006] In the first aspect, an embodiment of the present application provides a method for dynamically monitoring the size of particle agglomerates in a disturbed flow field, the method comprising: real-time collection of flow field characteristic parameters, flow field shear rate, particle physical parameters and particle Hamaker constant; wherein the flow field characteristic parameters include fluid density and fluid dynamic viscosity, and the particle physical parameters include particle diameter and particle density; obtaining dimensionless particle diameter based on flow field characteristic parameters, flow field shear rate and particle diameter; obtaining cohesion number based on flow field characteristic parameters, particle diameter and particle Hamaker constant to quantify the cohesive force characteristics between particles; constructing a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter to obtain the number of particle agglomerates in the equilibrium stage; obtaining the dimensionless agglomerate size in the equilibrium stage based on the dimensionless particle diameter and the number of particle agglomerates in the equilibrium stage to characterize the relative size of the particle agglomerates; obtaining the actual size of the agglomerates in the equilibrium stage through conversion operation based on the dimensionless agglomerate size in the equilibrium stage; monitoring the actual size of the agglomerates in the equilibrium stage obtained, and executing a feedback control mechanism based on the monitoring results.

[0007] In a possible implementation, obtaining the dimensionless particle diameter based on the flow field characteristic parameter, the flow field shear rate, and the particle diameter includes: obtaining the dimensionless particle diameter using the expression: ;in, is the dimensionless particle diameter, is the particle diameter, is the fluid density, is the flow field shear rate, is the dynamic viscosity of the fluid.

[0008] In one possible implementation, before obtaining the cohesion number, the method further includes: using the particle diameter to obtain the cohesion range and the microscopic size of the particle surface roughness; the expression for obtaining the cohesion range is: ;in, is the cohesive force range; the expression for obtaining the microscopic size of the particle surface roughness is: ;in, is the microscopic size of the particle surface roughness.

[0009] In a possible implementation, obtaining the cohesion number based on the flow field characteristic parameters, the particle diameter, and the particle Hamaker constant includes: obtaining the cohesion number using the expression: ;in, is the cohesion number, is the particle's Hamaker constant, is the cohesive force range, is the microscopic size of the particle surface roughness.

[0010] In one possible implementation, before obtaining the number of particle agglomerates in the equilibrium stage, the method further includes: obtaining a first empirical coefficient and a second empirical coefficient using the particle density, the fluid density, and the cohesion number; obtaining a natural logarithm mean using the cohesion number; and obtaining a natural logarithm standard deviation using the particle density and the fluid density. The expression for obtaining the first empirical coefficient is: ;in, is the first empirical coefficient, is the particle density, is the natural logarithm, is an exponential function; the expression for obtaining the second empirical coefficient is: ;in, is the second empirical coefficient; the expression for obtaining the natural logarithm mean is: ;in, is the natural logarithm mean; the expression for obtaining the natural logarithm standard deviation is: ;in, is the natural logarithm standard deviation.

[0011] In a possible implementation, constructing a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter to obtain the number of particle agglomerates in the equilibrium stage includes: an expression of the log-normal probability density function relationship model is: ;in, is the number of particle aggregates in the equilibrium stage.

[0012] In a possible implementation, obtaining the dimensionless aggregate size at the equilibrium stage based on the dimensionless particle diameter and the number of particle agglomerates at the equilibrium stage includes obtaining an expression for the dimensionless aggregate size at the equilibrium stage as follows: ;in, is the dimensionless aggregate size at equilibrium, is the fractal dimension.

[0013] In a possible implementation, the dimensionless aggregate size at the equilibrium stage is converted to obtain the actual aggregate size at the equilibrium stage, including: the conversion operation expression is: ;in, is the actual size of the aggregates at the equilibrium stage.

[0014] In one possible implementation, the actual size of the aggregates obtained in the equilibrium stage is monitored, and a feedback control mechanism is executed based on the monitoring results, including: if the average decrease rate of the actual size of the aggregates in the equilibrium stage obtained within the preset monitoring period is greater than a preset threshold, it is determined to be the stage of rapid aggregate breakage, and the flow field shear rate adaptive adjustment mechanism is triggered at this time, and the current flow field shear rate is reduced according to the preset first reduction ratio; the actual size of the aggregates in the equilibrium stage is continuously monitored, and its average decrease rate is calculated; if the average decrease rate is still greater than the preset threshold within the preset time interval, the flow field shear rate adaptive adjustment mechanism is triggered again, and the current flow field shear rate is reduced according to the preset second reduction ratio; when the actual size of the aggregates in the equilibrium stage monitored is continuously lower than the preset lower limit and exceeds the preset time, it is determined that the current operating condition is abnormal, and the minimum shear protection state is entered to maintain the flow field shear rate not lower than the preset minimum operating limit.

[0015] In the second aspect, an embodiment of the present application provides a dynamic monitoring device for the size of particle agglomerates in a disturbed flow field, the device comprising: an acquisition module for real-time acquisition of flow field characteristic parameters, flow field shear rate, particle physical parameters and particle Hamaker constant; wherein the flow field characteristic parameters include fluid density and fluid dynamic viscosity, and the particle physical parameters include particle diameter and particle density; a module for obtaining dimensionless particle diameter, for obtaining dimensionless particle diameter based on flow field characteristic parameters, flow field shear rate and particle diameter; a module for obtaining cohesion number, for obtaining cohesion number based on flow field characteristic parameters, particle diameter and particle Hamaker constant, so as to quantify the cohesive force characteristics between particles; and a module for obtaining particle size in the equilibrium stage. The module for obtaining the number of aggregates is used to construct a log-normal probability density function relationship model between the number of particle aggregates in the equilibrium stage and the dimensionless particle diameter, so as to obtain the number of particle aggregates in the equilibrium stage; the module for obtaining the dimensionless aggregate size in the equilibrium stage is used to obtain the dimensionless aggregate size in the equilibrium stage based on the dimensionless particle diameter and the number of particle aggregates in the equilibrium stage, so as to characterize the relative size of the particle aggregates; the module for obtaining the actual size of aggregates in the equilibrium stage is used to obtain the actual size of aggregates in the equilibrium stage through conversion operations based on the dimensionless aggregate size in the equilibrium stage; the monitoring module is used to monitor the actual size of aggregates in the equilibrium stage obtained, and to execute a feedback control mechanism based on the monitoring results.

[0016] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects: The embodiments of the present application provide a method for dynamically monitoring the size of particle agglomerates in a disturbed flow field, which can timely reflect the dynamic changes of the flow field and particle state by collecting flow field characteristic parameters, flow field shear rate, particle physical parameters and particle Hamaker constant in real time. Compared with the traditional offline sampling and analysis method, the present application can realize real-time monitoring of the size distribution of particle agglomerates in a disturbed flow field, which provides the possibility for timely adjustment of process parameters and optimization of system operation, and effectively avoids decision-making errors caused by time lag. By obtaining the dimensionless particle diameter based on the flow field characteristic parameters, flow field shear rate and particle diameter, and obtaining the cohesion number based on the flow field characteristic parameters, particle diameter and particle Hamaker constant, the key factors affecting particle agglomeration are accurately quantified. The constructed lognormal probability density function relationship model accurately describes the relationship between the number of particle aggregates and the dimensionless particle diameter at equilibrium, thereby obtaining the dimensionless aggregate size and actual aggregate size at equilibrium. The actual aggregate size obtained at equilibrium is monitored, and a feedback control mechanism is implemented based on the monitoring results. This improves the precision and accuracy of the monitoring results and reduces measurement deviations caused by sampling that disrupts flow field conditions. It also addresses the problem of how to achieve real-time monitoring of particle aggregate size distribution in disturbed flow fields and improve the accuracy of monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments of the present application or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a method for dynamically monitoring the size of particle aggregates in a disturbed flow field provided in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of a sewage treatment process provided in an embodiment of the present application;

[0020] Figure 3 A schematic diagram showing the trend of the number of particle agglomerates changing with the shear rate of the flow field in the equilibrium stage provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of a device for dynamically monitoring the size of particle aggregates in a disturbed flow field provided in an embodiment of the present application;

[0022] Figure 5 A schematic diagram of a server for dynamically monitoring the size of particle aggregates in a disturbed flow field provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0024] The following description of some of the technologies involved in the embodiments of this application is provided to facilitate understanding and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted from the following description.

[0025] The size of particle aggregates has a significant impact on the operation of sewage treatment systems. It is directly related to the sedimentation performance of aggregates and the effectiveness of solid-liquid separation, which in turn affects the effluent water quality and operational stability of the entire sewage treatment system. Specifically, if the aggregate size is too small, it is easy to be lost along with the effluent during the sewage treatment process, increasing the difficulty of mud-water separation and affecting treatment efficiency; when the aggregate size is too large, its structure will become loose, and the diffusion of internal substances will be restricted, which may lead to a decrease in sewage treatment effect. Moreover, the size of particle aggregates is not static, but is affected by a combination of various operating conditions such as stirring intensity, hydraulic shear, sludge age, and organic load, showing a dynamic change in actual operation. Therefore, accurately monitoring the changes in particle aggregate size during sewage treatment is of vital importance to ensure the efficient and stable operation of the sewage treatment process.

[0026] However, current methods for measuring particle aggregate size rely primarily on offline sampling, performed using equipment such as electron microscopes and particle analyzers. However, this approach has significant limitations: First, the sampling process inevitably disrupts the original flow field conditions, causing secondary breakage or reorganization of aggregates, leading to significant deviations between the measured results and the actual operating conditions; second, offline analysis exhibits a time lag, making it impossible to monitor the dynamic changes in particle aggregate size in a disturbed flow field in real time.

[0027] Therefore, the present application provides a dynamic monitoring method for the size of particle aggregates in a disturbed flow field, which can realize real-time monitoring of the size distribution of particle aggregates in a disturbed flow field, and provide a solid basic support for in-depth exploration of the particle aggregation and fragmentation mechanism and optimization of sewage treatment process parameters.

[0028] The present invention provides a method for dynamically monitoring the size of particle aggregates in a disturbed flow field. Figure 1 As shown, the method includes steps S101 to S107. Figure 1 This is only an execution sequence shown in the embodiment of the present application, and does not represent the only execution sequence of a method for dynamically monitoring the size of particle agglomerates in a disturbed flow field. In the case that the final result can be achieved, Figure 1 The steps shown may be performed in parallel or reversed.

[0029] S101: Real-time collection of flow field characteristic parameters, flow field shear rate, particle physical parameters, and particle Hamaker constant. Flow field characteristic parameters include fluid density and fluid dynamic viscosity, and particle physical parameters include particle diameter and particle density.

[0030] Specifically, particles in this application refer to pollutant particles dispersed in a fluid, typically inorganic or organic microscopic solid particles with a particle size generally ranging from micrometers to submicrometers. Particle aggregates in this application refer to sludge flocs formed by the aggregation of microbial flora, organic colloids, and inorganic particles within the wastewater treatment flow field.

[0031] Furthermore, the flow field shear rate is a key physical quantity used to characterize the fluid deformation rate, which can intuitively reflect the intensity of the shear effect exerted by the flow field on the particles. In actual measurements, the parameter value can be obtained by measuring the flow field velocity gradient. The dynamic viscosity of the fluid reflects the internal friction characteristics of the fluid resisting flow, and can be obtained in real time through a viscosity sensor. The fluid density is defined as the mass of a unit volume of fluid and can be monitored online by a densitometer. The particle diameter and particle density describe the geometric size and mass density characteristics of a single particle, respectively. The particle diameter can be measured using a laser particle size analyzer, and the particle density can be measured using a density analyzer. The Hamaker constant of the particle is an important parameter that describes the intensity of the van der Waals force between particles, and its size is closely related to the material properties of the particle. When obtaining this parameter, the corresponding Hamaker constant can be quickly obtained based on the specific material information of the particle by querying the online material database.

[0032] Figure 2 A schematic diagram of a sewage treatment process provided in an embodiment of the present application. Figure 2As shown, the system includes a water inlet, a screen tank, a primary sedimentation tank, a biochemical tank, a secondary sedimentation tank, a disinfection tank, and a water outlet, all connected in sequence. The water inlet serves as the starting point for sewage treatment, receiving raw sewage from urban residential or industrial areas. This sewage is complex and contains various pollutants. After entering the screen tank, the sewage passes through a screen device of varying thicknesses to effectively remove large, easily entangled floating debris, such as plastic bags, paper scraps, and leaves. This step is intended to prevent subsequent equipment from clogging or damaging, ensuring the smooth progress of subsequent treatment processes. After treatment in the screen tank, the sewage enters the primary sedimentation tank. The primary sedimentation tank primarily removes some organic and inorganic suspended matter through static sedimentation, forming a primary sediment. This process helps reduce the burden on subsequent treatment units. The biochemical tank is the core link in sewage treatment. In this embodiment, the biochemical tank is used not only to achieve the biodegradation of organic pollutants and the removal of nutrients such as nitrogen and phosphorus using the activated sludge process, but also to obtain flow field shear rate, fluid dynamic viscosity, fluid density, particle diameter, particle density, and particle Hamaker constant. The biochemical tank is equipped with an aeration system and monitoring devices. The aeration system provides oxygen to the microorganisms, promoting their metabolic activity. The monitoring devices include a multi-point flow field sensor and a linear particle size analyzer. These monitors acquire relevant parameters in real time, providing data support for subsequent analysis and processing. After the biochemical reaction in the biochemical tank, the mixed liquid enters the secondary sedimentation tank, where gravity sedimentation separates the sludge particles from the supernatant. Part of the settled sludge is returned to the biochemical tank to maintain the stability of the system's bacterial flora and ensure the continued effectiveness of the biochemical treatment. The remaining sludge enters the sludge treatment system for further treatment and disposal. The supernatant from the secondary sedimentation tank enters the disinfection tank. Chlorine or ultraviolet light is added to the disinfection tank to kill any remaining pathogens, ensuring that the treated water meets discharge standards and safeguards public health. The water treated in the disinfection tank is ultimately discharged through the outlet to natural water bodies or industrial water systems, achieving water recycling or meeting discharge standards.

[0033] S102: Obtaining a dimensionless particle diameter based on flow field characteristic parameters, flow field shear rate, and particle diameter.

[0034] Obtaining the dimensionless particle diameter based on the flow field characteristic parameters, the flow field shear rate and the particle diameter includes: obtaining the dimensionless particle diameter using the expression: .in, is the dimensionless particle diameter, is the particle diameter, is the fluid density, is the flow field shear rate, is the dynamic viscosity of the fluid.

[0035] Specifically, dimensionless particle diameter is a relative parameter used to describe the motion characteristics of particles in a shear flow field. In actual calculations, it is derived through a specific method using key parameters such as particle diameter, flow field shear rate, fluid density, and fluid dynamic viscosity. It provides a key basis for subsequently determining particle agglomeration trends.

[0036] S103: Obtaining a cohesion number based on flow field characteristic parameters, particle diameters, and Hamaker constants of the particles to quantify the cohesive force characteristics between the particles.

[0037] Before obtaining the cohesion number, it is also necessary to use the particle diameter to obtain the cohesion range and the microscopic size of the particle surface roughness. The expression for obtaining the cohesion range is: .in, is the cohesive force range. The expression for obtaining the microscopic size of the particle surface roughness is: .in, is the microscopic size of the particle surface roughness.

[0038] The cohesion number is obtained based on the flow field characteristic parameters, particle diameter and particle Hamaker constant, including: The expression for obtaining the cohesion number is: .in, is the cohesion number, is the particle's Hamaker constant, is the cohesive force range, is the microscopic size of the particle surface roughness.

[0039] The cohesion number is a dimensionless parameter that characterizes the relative strength of interparticle interactions and external shear forces. Its value determines the tendency of particles to aggregate: when the cohesion number is large, the internal cohesive forces between particles dominate, making agglomeration more likely and forming larger aggregates. When the cohesion number is small, external shear forces dominate, making it difficult for particles to stably bond and tend to disperse. Therefore, the larger the cohesion number, the easier it is for particles to aggregate, while the smaller the cohesion number, the less likely they are to form stable aggregates. It should be noted that the higher the shear rate in the flow field, the greater the external shear force.

[0040] Specifically, the cohesion number profoundly reflects the competitive relationship between the internal cohesive forces between particles and the external flow field disturbance forces. In practical engineering scenarios like sewage treatment, where particles interact with flow fields, the tendency of particles to agglomerate is a crucial issue, and the cohesion number is a key metric describing this phenomenon. It doesn't consider any single factor in isolation, but rather comprehensively considers multiple factors.

[0041] S104: constructing a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter to obtain the number of particle agglomerates in the equilibrium stage.

[0042] The equilibrium stage in this application refers to the stage where, after the particles undergo a dynamic process of collision, agglomeration, and fragmentation under the influence of a disturbed flow field, the number and size distribution of particle agglomerates within the system tend to stabilize and no longer undergo significant macroscopic changes. This stage reflects the dynamic equilibrium between the internal cohesion between particles and the external shear force under specific flow field shear rate and particle physical parameters, and the system is in a stable state. Collecting the number and size characteristics of particle agglomerates during this stage can accurately reflect the impact of flow field conditions on particle agglomeration behavior. Therefore, this application uses data from this stage to calculate and monitor particle agglomerate size.

[0043] Before obtaining the number of particle agglomerates in the equilibrium stage, the method further includes: obtaining a first empirical coefficient and a second empirical coefficient using the particle density, the fluid density and the cohesion number.

[0044] Using the cohesion number, obtain the natural logarithm mean.

[0045] Using the particle density and fluid density, obtain the natural logarithmic standard deviation.

[0046] The expression for obtaining the first empirical coefficient is: .in, is the first empirical coefficient, is the particle density, is the natural logarithm, is an exponential function.

[0047] The expression for obtaining the second empirical coefficient is: .in, is the second empirical coefficient.

[0048] The expression for obtaining the natural logarithm mean is: .in, is the natural logarithm mean.

[0049] The expression for obtaining the natural logarithmic standard deviation is: .in, is the natural logarithm standard deviation.

[0050] Specifically, in a disturbed flow environment, after a certain period of time, particles will gradually tend to form agglomerates with statistically stable characteristics. The distribution of the number of particles in these agglomerates can be described by a lognormal distribution. In order to accurately predict the number of particles contained in a particle agglomerate at equilibrium, that is, the number of particle agglomerates at equilibrium, and to reflect the intrinsic connection between the microscopic dynamic behavior and macroscopic structural characteristics of the particles, it is necessary to establish a lognormal probability density function relationship model between the number of particle agglomerates at equilibrium and the dimensionless particle diameter through empirical regression or numerical simulation to predict the number of particle agglomerates at equilibrium.

[0051] A log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter is constructed to obtain the number of particle agglomerates in the equilibrium stage, including: the expression of the log-normal probability density function relationship model is: .in, is the number of particle aggregates in the equilibrium stage.

[0052] S105: Based on the dimensionless particle diameter and the number of particle agglomerates in the equilibrium stage, obtain the dimensionless agglomerate size in the equilibrium stage to characterize the relative size of the particle agglomerates.

[0053] Based on the dimensionless particle diameter and the number of particle agglomerates in the equilibrium stage, the dimensionless agglomerate size in the equilibrium stage is obtained, including: the expression for obtaining the dimensionless agglomerate size in the equilibrium stage is: .in, is the dimensionless aggregate size at equilibrium, is the fractal dimension.

[0054] Specifically, in the study of particle agglomeration in actual engineering scenarios such as sewage treatment, in order to more accurately describe the characteristics of particle aggregates, it is necessary to quantify their size. However, the direct use of absolute size is often affected by the difference in measurement scale under different working conditions, which is not conducive to comparison and normalization analysis. Therefore, the introduction of dimensionless aggregate size is of great significance. Dimensionless aggregate size can effectively remove the influence of absolute scale, making the aggregate size under different working conditions comparable, which is convenient for comparison and normalization analysis. In practical applications, fractal dimension The value can be directly taken as 2. Through the above expression, combined with the dimensionless particle diameter obtained in the previous step and the number of particle agglomerates in the equilibrium stage, the dimensionless aggregate size in the equilibrium stage can be calculated to more accurately characterize the relative size of particle agglomerates and provide strong support for subsequent research and analysis.

[0055] S106: Based on the dimensionless aggregate size at the equilibrium stage, the actual aggregate size at the equilibrium stage is obtained through a conversion operation.

[0056] Based on the dimensionless aggregate size in the equilibrium stage, the actual aggregate size in the equilibrium stage is obtained through conversion operations, including: the expression of the conversion operation is: .in, is the actual size of the aggregates at the equilibrium stage.

[0057] Specifically, in practical engineering scenarios such as wastewater treatment, simply obtaining the dimensionless aggregate size at equilibrium is insufficient to fully understand the impact of particle aggregates on the system. To further investigate the characteristics of particle aggregates and their impact on system performance, it is necessary to further calculate the actual aggregate size at equilibrium.

[0058] Through conversion, the dimensionless aggregate size at equilibrium can be converted to the actual aggregate size at equilibrium. Calculating the actual aggregate size at equilibrium is important in many ways. First, this actual size can be used to further assess the impact of particle agglomeration on system performance. For example, in sewage treatment systems, the settling velocity of aggregates directly affects the efficiency of solid-liquid separation, and the actual aggregate size is closely related to this settling velocity. Furthermore, larger aggregates may increase the risk of filtration blockage, impacting the normal operation of the system. Second, the actual aggregate size provides a key basis for system status assessment, control optimization, and engineering design. By monitoring and analyzing the actual size, the system's operating status can be promptly understood, system parameters can be optimized, and the system's treatment efficiency and stability can be improved. It also provides important reference data for engineering design and renovation.

[0059] S107: Monitor the actual size of the aggregates in the equilibrium stage, and execute a feedback control mechanism based on the monitoring results.

[0060] The method of monitoring the actual size of the aggregates in the equilibrium stage and executing a feedback control mechanism based on the monitoring results includes the following steps.

[0061] If the average rate of decrease in the actual aggregate size during the equilibrium phase, obtained within a preset monitoring period, exceeds a preset threshold, the system is identified as entering the rapid aggregate breakup phase. This triggers the adaptive shear rate adjustment mechanism, reducing the current shear rate by a preset first reduction ratio. The actual aggregate size during the equilibrium phase is continuously monitored, and its average rate of decrease is calculated.

[0062] Specifically, in actual industrial operation scenarios, this application focuses on dynamic monitoring of the actual size of aggregates in the equilibrium phase of a disturbed flow field and establishes a feedback control mechanism based on the changing trends of aggregate size. This mechanism aims to achieve adaptive adjustment of stirring intensity and thus maintain a dynamic balance of particle stability.

[0063] When the average decrease rate of the actual size of the aggregates in the equilibrium stage is greater than the preset threshold value of 0.5μm / min within the preset monitoring period of 45s, it is determined that this is the stage of rapid fragmentation of the aggregates. At this stage, in order to avoid further fragmentation of the particles by excessive fluid shear force, the shear rate adaptive adjustment mechanism is immediately triggered. According to the preset first reduction ratio of 20%, the current flow field shear rate is reduced. For example, if the current shear rate is 200s -1 , after being adjusted downward, it becomes 160s -1 At the same time, the actual size of the aggregates in the equilibrium stage was continuously monitored, and its average decrease rate was calculated.

[0064] If the average descent rate is still greater than the preset threshold within the preset time interval, the flow field shear rate adaptive adjustment mechanism is triggered again to reduce the current flow field shear rate according to the preset second downward adjustment ratio.

[0065] Specifically, the preset time interval may be 5 minutes, and the preset second downward adjustment ratio may be 10%.

[0066] It should be noted that in the process of reducing the current flow field shear rate according to the preset second downward adjustment ratio, there is a clear preset minimum operating limit, that is, the flow field shear rate shall not be lower than 50s -1 to ensure the basic operational stability of the system.

[0067] When the actual size of the aggregates monitored in the equilibrium stage continues to be lower than the preset lower limit and exceeds the preset time, it is determined that the current operating condition is abnormal and enters the minimum shear protection state to maintain the flow field shear rate not lower than the preset minimum operating limit.

[0068] Specifically, the preset lower limit value may be 2 μm. The preset time may be 1 minute. When an abnormality occurs in the current operating condition, the alarm module is triggered, prompting a manual inspection to determine whether there is excessive dosage of reagent, excessive stirring, or other abnormal operating conditions.

[0069] In sewage treatment, the stirring device is in continuous operation. The initial shear rate is not a fixed value, but can be adjusted between 100 and 300s according to the specific process requirements. -1 This flexibility allows the system to adapt to operational requirements under varying process conditions, laying the foundation for a feedback control mechanism. This mechanism ensures that the actual size changes of aggregates during the equilibrium phase are dynamically linked to the shear rate of the flow field. This not only maintains the structural stability of particle aggregates but also optimizes energy consumption and treatment efficiency, providing timely, low-intrusion decision-making support for sludge floc control in industrial processes such as sewage treatment.

[0070] Furthermore, external shear force is positively correlated with flow field shear rate. Under conditions where the fluid dynamic viscosity and particle physical properties are relatively stable, the greater the flow field shear rate, the stronger the external shear force on the particles, and the more likely the agglomerates are to break up. Therefore, in actual industrial operations, to achieve stable control of agglomerate structure, the external shear force can be weakened by reducing the flow field shear rate. Commonly used control measures include reducing the agitator speed and adopting intermittent stirring strategies. These methods effectively reduce the flow field shear rate while ensuring uniform mixing and mass transfer efficiency, thereby inhibiting excessive agglomerate breakage and improving system stability and energy efficiency.

[0071] This application realizes the real-time collection of multiple parameters, and after a series of calculations, including obtaining dimensionless particle diameter, cohesion number, number of particle agglomerates in the equilibrium stage, dimensionless agglomerate size in the equilibrium stage and actual size of agglomerates in the equilibrium stage, it finally realizes the dynamic monitoring of particle agglomerate size in the disturbed flow field. This method can realize real-time and continuous calculation of particle agglomerate size distribution in the disturbed flow field without relying on manual sampling or complex imaging devices, greatly improving the efficiency and accuracy of monitoring. This method is not only suitable for sewage treatment scenarios, but can also be widely used in various scenarios such as sediment erosion and transportation in rivers and oceans, chemical reactors, etc., showing good universality and engineering feasibility, and providing key data support for subsequent particle transport and process control.

[0072] The following is an example to further illustrate the method for dynamically monitoring the size of particle aggregates in a disturbed flow field of the present application.

[0073] A study was conducted in a biochemical tank at a municipal sewage treatment plant to investigate real-time monitoring of particle aggregate size during the sewage treatment process. The influent flow rate was 5,000 m³ / day, and the measured temperature on that day was 20°C and the ambient atmospheric pressure was 101.3 kPa. During the study, multi-point flow field sensors were used to monitor flow field data at various locations within the tank in real time, and a linear particle size analyzer was used to continuously acquire particle physical properties. The resulting data is shown in Table 1. Table 1 shows the monitoring data for the sewage treatment plant's flow field dynamics parameters and particle physical properties.

[0074] In this application, a multi-point flow sensor and a linear particle size analyzer are used in conjunction to monitor the dynamic changes in particle agglomerate size in a disturbed flow field. The multi-point flow sensor can acquire key parameters such as flow shear rate, fluid density, and fluid dynamic viscosity at multiple locations within the flow field in real time, ensuring comprehensive perception of changes in fluid properties. The linear particle size analyzer is used to measure the size distribution characteristics of particles and particle agglomerates, and to reflect trends in particle aggregation or fragmentation.

[0075] Table 1

[0076]

[0077] Taking position 1 as an example, the monitoring data shows that the flow field shear rate is 50s -1 , the fluid dynamic viscosity is 0.001Pa·s, and the fluid density is 1000kg·m -3 , particle diameter is 5μm, particle density is 2650kg / m -3 , the particle's Hamaker constant is 10 -20 J. Based on these parameters, the following key parameters were calculated sequentially according to the methods of this application. The dimensionless particle diameter is 0.0354. The cohesive force range is 0.25 μm. The microscopic size of the particle surface roughness is 0.00125 μm. The cohesion number is 0.01. The first empirical coefficient is 8.035. The second empirical coefficient is 0.331. The natural logarithm mean is 4.236. The natural logarithm standard deviation is 0.601. The number of particle agglomerates in the equilibrium stage is 9.62. The dimensionless agglomerate size in the equilibrium stage is 0.110. The actual agglomerate size in the equilibrium stage is 15.6 μm.

[0078] Continuing with the method of this application, the data from positions 2 to 6 were processed, and the number of particle aggregates and the actual size of the aggregates in the equilibrium stage at these positions were obtained in turn. The relevant results are summarized in Table 2. Table 2 shows the results of the number of particle aggregates and the actual size of the aggregates in the equilibrium stage at different positions of the sewage treatment.

[0079] Table 2

[0080]

[0081] In actual tests, the dynamic monitoring results of the particle agglomerate size distribution under typical disturbance flow fields using the method proposed in this application, i.e., the actual size of the agglomerates in the equilibrium stage, were compared with the particle agglomerate size data measured by the linear particle size analyzer. The results are shown in Table 3. Under multiple flow field shear rate conditions (e.g., 50s -1 to 300s -1 ), the relative error between the actual size of the aggregates in the equilibrium stage calculated by this application and the measured value is always controlled within 5%, with high consistency and stability.

[0082] Table 3

[0083]

[0084] Figure 3A schematic diagram of the variation trend of particle aggregate count in the equilibrium phase as a function of flow field shear rate is provided for the embodiments of the present application. The figure clearly shows that as the flow field shear rate gradually increases, the number of particle aggregates in the equilibrium phase shows a gradual decrease. Under low shear rate conditions, aggregates can maintain good integrity and quantity. This is because at low shear rates, the shearing effect of the fluid on the aggregates is weak, making them less likely to break, thus allowing them to exist stably. However, as the shear rate increases, the situation changes. A higher shear rate means that the fluid exerts a stronger shearing effect on the aggregates, causing some aggregates to break apart due to the shearing effect. The breakage of aggregates directly leads to a decrease in the overall number of aggregates. This trend has important practical significance. It can dynamically reflect the stability of the microaggregate structure in wastewater treatment. Combined with real-time monitoring data, operators can promptly adjust the stirring intensity during actual industrial operations. For example, if the number of aggregates is found to be decreasing too rapidly, the stirring intensity can be appropriately reduced to reduce the shearing effect of the fluid, thereby maintaining the stability of the aggregates. Furthermore, this trend can help optimize biochemical reaction conditions. During biochemical reactions, aggregate stability significantly impacts reaction efficiency. Adjusting the flow field shear rate optimizes aggregate state, thereby increasing biochemical reaction efficiency. This also positively impacts sludge settling performance and effluent quality. A stable aggregate structure facilitates sludge settling, reducing sludge content in effluent water, thereby improving effluent quality. By studying the trends in particle aggregate counts during the equilibrium phase at different flow field shear rates, combined with real-time monitoring data, it is possible to achieve intelligent and efficient management of wastewater treatment processes, improving overall wastewater treatment effectiveness.

[0085] The embodiment of the present application also provides a dynamic monitoring device 400 for the size of particle agglomerates in a disturbed flow field, such as Figure 4 As shown, the device includes: an acquisition module 401, a module for obtaining dimensionless particle diameter 402, a module for obtaining cohesion number 403, a module for obtaining the number of particle agglomerates in the equilibrium stage 404, a module for obtaining the dimensionless agglomerate size in the equilibrium stage 405, a module for obtaining the actual size of agglomerates in the equilibrium stage 406, and a monitoring module 407.

[0086] The acquisition module 401 is used to collect flow field characteristic parameters, flow field shear rate, particle physical parameters, and particle Hamaker constant in real time. The flow field characteristic parameters include fluid density and fluid dynamic viscosity, and the particle physical parameters include particle diameter and particle density.

[0087] The dimensionless particle diameter obtaining module 402 is used to obtain the dimensionless particle diameter based on the flow field characteristic parameters, the flow field shear rate and the particle diameter.

[0088] The module 403 for obtaining the cohesion number is used to obtain the cohesion number based on the flow field characteristic parameters, the particle diameter and the Hamaker constant of the particle, so as to quantify the cohesion characteristics between the particles.

[0089] The module 404 for obtaining the number of particle agglomerates in the equilibrium stage is used to construct a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter, so as to obtain the number of particle agglomerates in the equilibrium stage.

[0090] The module 405 for obtaining dimensionless aggregate size at equilibrium stage is used to obtain dimensionless aggregate size at equilibrium stage based on dimensionless particle diameter and the number of particle aggregates at equilibrium stage, so as to characterize the relative size of particle aggregates.

[0091] The module 406 for obtaining the actual size of aggregates in the equilibrium stage is used to obtain the actual size of aggregates in the equilibrium stage through conversion based on the dimensionless size of aggregates in the equilibrium stage.

[0092] The monitoring module 407 is used to monitor the actual size of the aggregates in the equilibrium stage and implement a feedback control mechanism based on the monitoring results.

[0093] Some modules in the apparatus described herein may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0094] The devices or modules described in the above application embodiments can be implemented by computer chips or physical devices, or by products with certain functions. For ease of description, the above devices are described separately by function in various modules. When implementing the embodiments of this application, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0095] The methods, devices, or modules described herein can be implemented in the form of computer-readable program code. The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in the form of pure computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the means for implementing various functions may be considered to be both a software module for implementing the method and a structure within a hardware component.

[0096] like Figure 5 As shown, an embodiment of the present application also provides a dynamic monitoring server for the size of particle agglomerates in a disturbed flow field, including a memory 501 and a processor 502; the memory 501 is used to store computer-executable instructions; the processor 502 is used to execute computer-executable instructions to implement a dynamic monitoring method for the size of particle agglomerates in a disturbed flow field described above in an embodiment of the present application.

[0097] An embodiment of the present application also provides a computer-readable storage medium, which stores executable instructions. When a computer executes the executable instructions, it can implement the dynamic monitoring method of the size of particle agglomerates in a disturbed flow field described above in the embodiment of the present application.

[0098] Through the description of the above implementation methods, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, or can be embodied through the implementation process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the method described in the embodiments of the present application.

[0099] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to in detail. Each embodiment focuses on the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations.

[0100] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for dynamically monitoring the size of particle aggregates in a disturbed flow field, characterized in that: include: Real-time collection of flow field characteristic parameters, flow field shear rate, particle physical parameters and particle Hamaker constant; wherein the flow field characteristic parameters include fluid density and fluid dynamic viscosity, and the particle physical parameters include particle diameter and particle density; Obtain dimensionless particle diameter based on flow field characteristic parameters, flow field shear rate and particle diameter; The cohesion number is obtained based on the flow field characteristic parameters, particle diameter and particle Hamaker constant to quantify the cohesive force characteristics between particles; A log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter was constructed to obtain the number of particle agglomerates in the equilibrium stage; Based on the dimensionless particle diameter and the number of particle agglomerates in the equilibrium stage, the dimensionless aggregate size in the equilibrium stage is obtained to characterize the relative size of particle agglomerates. Based on the dimensionless aggregate size in the equilibrium stage, the actual aggregate size in the equilibrium stage is obtained through conversion operations; The actual size of the aggregates in the equilibrium stage is monitored, and a feedback control mechanism is implemented based on the monitoring results.

2. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 1, characterized in that: The step of obtaining the dimensionless particle diameter based on the flow field characteristic parameters, the flow field shear rate, and the particle diameter includes: The expression for obtaining the dimensionless particle diameter is: ;in, is the dimensionless particle diameter, is the particle diameter, is the fluid density, is the flow field shear rate, is the dynamic viscosity of the fluid.

3. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 2, characterized in that: Before obtaining the cohesion number, it also includes: using the particle diameter to obtain the cohesion range and the microscopic size of the particle surface roughness; The expression for obtaining the cohesion range is: ;in, is the range of cohesion; The expression for obtaining the microscopic size of particle surface roughness is: ;in, is the microscopic size of the particle surface roughness.

4. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 3, characterized in that: The method of obtaining the cohesion number based on the flow field characteristic parameters, the particle diameter and the particle Hamaker constant includes: The expression for obtaining the cohesion number is: ;in, is the cohesion number, is the particle's Hamaker constant, is the cohesive force range, is the microscopic size of the particle surface roughness.

5. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 4, characterized in that: Before obtaining the number of particle agglomerates in the equilibrium stage, the method further includes: obtaining a first empirical coefficient and a second empirical coefficient using the particle density, the fluid density and the cohesion number; Using the cohesion number, obtain the natural logarithm mean; Using the particle density and fluid density, obtain the natural logarithmic standard deviation; The expression for obtaining the first empirical coefficient is: ;in, is the first empirical coefficient, is the particle density, is the natural logarithm, is an exponential function; The expression for obtaining the second empirical coefficient is: ;in, is the second empirical coefficient; The expression for obtaining the natural logarithm mean is: ;in, is the natural logarithm mean; The expression for obtaining the natural logarithmic standard deviation is: ;in, is the natural logarithm standard deviation.

6. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 5, characterized in that: The step of constructing a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter to obtain the number of particle agglomerates in the equilibrium stage includes: The expression of the lognormal probability density function relationship model is: ;in, is the number of particle aggregates in the equilibrium stage.

7. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 6, characterized in that: The step of obtaining the dimensionless aggregate size at the equilibrium stage based on the dimensionless particle diameter and the number of particle agglomerates at the equilibrium stage includes: The expression for obtaining the dimensionless aggregate size at the equilibrium stage is: ;in, is the dimensionless aggregate size at equilibrium, is the fractal dimension.

8. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 7, characterized in that: The dimensionless aggregate size in the equilibrium stage is obtained by converting the actual aggregate size in the equilibrium stage, including: The expression of the conversion operation is: ;in, is the actual size of the aggregates at the equilibrium stage.

9. The method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to claim 1, characterized in that: The actual size of the aggregates in the equilibrium stage is monitored, and a feedback control mechanism is implemented based on the monitoring results, including: If the average decrease rate of the actual size of the aggregates in the equilibrium stage obtained within the preset monitoring period is greater than the preset threshold, it is determined to be the stage of rapid aggregate breakup. At this time, the flow field shear rate adaptive adjustment mechanism is triggered to reduce the current flow field shear rate according to the preset first downward adjustment ratio; The actual size of aggregates in the equilibrium stage is continuously monitored and its average decreasing rate is calculated; If the average decreasing rate is still greater than the preset threshold within the preset time interval, the flow field shear rate adaptive adjustment mechanism is triggered again to reduce the current flow field shear rate according to the preset second downward adjustment ratio; When the actual size of the aggregates monitored in the equilibrium stage continues to be lower than the preset lower limit and exceeds the preset time, it is determined that the current operating condition is abnormal and enters the minimum shear protection state to maintain the flow field shear rate not lower than the preset minimum operating limit.

10. A device for dynamically monitoring the size of particle aggregates in a disturbed flow field, used to implement the method for dynamically monitoring the size of particle aggregates in a disturbed flow field according to any one of claims 1 to 9, characterized in that: include: An acquisition module, configured to acquire in real time flow field characteristic parameters, flow field shear rate, particle physical properties, and particle Hamaker constants; wherein the flow field characteristic parameters include fluid density and fluid dynamic viscosity, and the particle physical properties include particle diameter and particle density; A module for obtaining dimensionless particle diameter is used to obtain dimensionless particle diameter based on flow field characteristic parameters, flow field shear rate and particle diameter; The module for obtaining the cohesion number is used to obtain the cohesion number based on the flow field characteristic parameters, particle diameter and particle Hamaker constant to quantify the cohesion characteristics between particles; A module for obtaining the number of particle agglomerates in the equilibrium stage is used to construct a log-normal probability density function relationship model between the number of particle agglomerates in the equilibrium stage and the dimensionless particle diameter, and obtain the number of particle agglomerates in the equilibrium stage; The module for obtaining dimensionless aggregate size in the equilibrium stage is used to obtain dimensionless aggregate size in the equilibrium stage based on dimensionless particle diameter and the number of particle aggregates in the equilibrium stage, so as to characterize the relative size of particle aggregates; A module for obtaining the actual size of aggregates in the equilibrium stage is used to obtain the actual size of aggregates in the equilibrium stage through conversion operations based on the dimensionless aggregate size in the equilibrium stage; The monitoring module is used to monitor the actual size of the aggregates in the equilibrium stage and implement a feedback control mechanism based on the monitoring results.

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