A water treatment dosing method and dosing system based on alum flower detection

Abstract

The present invention discloses a water treatment dosing method and dosing system based on alum flower detection, which relates to the technical field of water treatment. The method monitors the equivalent parameters of the alum flower morphology in real time, equates the alum flower morphology to spherical particles, and ignores the influence of Brownian motion and fluid shear on the alum flower mechanics. The van der Waals force between the alum flower particles is derived based on the equivalent parameters of the alum flower morphology, and then the Zeta potential is derived. The current metal particle concentration is determined based on the Zeta potential, and the steady-state dosing amount is adjusted based on the current metal particle concentration. The present invention integrates the DLVO mechanical field, electrochemical potential distribution and control theory, constructs a multi-physical field coupling model, realizes real-time updating of force field parameters driven by spatiotemporal data, forms a closed-loop control chain of perception-decision-execution, and significantly improves the system response speed.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental protection, and particularly relates to a water treatment dosing method and a dosing system based on alum floc detection. Background Art

[0002] During the coagulation and sedimentation process, the key to water purification lies in the destabilization of colloids and the formation of flocs. Colloidal particles repel each other due to their negative surface charge, forming a stable suspension. The addition of coagulants (such as Al⁺ and Fe⁺) neutralizes the surface charge, destabilizing the colloids and causing them to aggregate. Coagulation and sedimentation involve three stages: coagulation, flocculation, and sedimentation. During the coagulation stage, the coagulant neutralizes the surface charge of the colloids, lowering their Zeta potential and destabilizing them. During the flocculation stage, the destabilized colloid particles combine through collisions to form visible flocs. This process is influenced by factors such as hydraulic conditions, bridging, and sweep flocculation. The sedimentation stage allows the flocs to settle by gravity and achieve solid-liquid separation.

[0003] Alum flocs, composed of destabilized colloids and coagulant hydrolysis products, possess a porous network structure that further adsorbs pollutants. This structure must be optimized by adjusting it to an optimal range or selecting an appropriate combination of reagents. Furthermore, monitoring and assessment methods such as zeta potential, turbidity analysis, floc imaging analysis, and sedimentation rate testing are essential for real-time optimization of operational plans to achieve efficient and cost-effective solid-liquid separation. In summary, colloid destabilization and alum floc formation are the core of coagulation and sedimentation, requiring comprehensive control of water quality, reagent selection, and hydraulic parameters.

[0004] The coagulation effect has a decisive influence on subsequent process control. Effective coagulation dosing control can minimize chemical consumption and improve effluent quality while ensuring optimal dosing. However, as a core component of water treatment, coagulation dosing has always been a major challenge in water treatment due to its inherent large hysteresis and nonlinear characteristics. Traditional control methods, such as mathematical models, streaming current methods, and simulated filter methods, have not been widely adopted due to their respective shortcomings and lack of reliability. For example, most water plants still use a control strategy based on influent flow rate, combined with manual observation of alum floc morphology and sedimentation tank outlet turbidity to assess dosing effect. They primarily rely on flow ratio to control coagulant dosage, but this method cannot achieve precise control and real-time monitoring of water quality. Fixed-programmed flow ratio automatic control systems only provide test results that reflect water quality at the moment of sampling. The determined coagulant dosage exhibits discontinuity and hysteresis, making optimization and precise control difficult.

[0005] With the rapid development of artificial intelligence technology, especially the widespread application of deep learning, underwater particle imaging technology and computer artificial intelligence control have provided new ideas for the intelligent control of coagulation dosing. The application of deep convolutional neural network models in image recognition, object monitoring and other fields has become quite mature. They can extract the texture features of alum flowers images for identification, and through steps such as threshold segmentation and morphological processing, screen out the main image features for standardization. Then, they are combined with machine learning algorithms such as SVM, BP neural network, and GRNN to determine the amount of alum to be dosed. However, this method is highly dependent on high-quality images and is easily interfered by external factors such as lighting and noise. In addition, feature extraction may not be comprehensive, the generalization ability of the machine learning model is also limited, and the response speed of real-time monitoring is relatively slow. Summary of the Invention

[0006] The present invention aims to provide a water treatment dosing method and system based on alum floc detection, aiming to address the aforementioned issues. This invention integrates the DLVO mechanical field, electrochemical potential distribution, and control theory to construct a multi-physics field coupling model. This allows for real-time updating of force field parameters driven by spatiotemporal data, forming a closed-loop control chain of perception, decision-making, and execution, significantly improving system response speed.

[0007] The present invention is mainly achieved through the following technical solutions:

[0008] A water treatment dosing method based on alum flower detection comprises the following steps:

[0009] Step S1: real-time monitoring of equivalent parameters of alum flower morphology;

[0010] Step S2: The morphology of the alum flower is equivalent to spherical particles, and the effects of Brownian motion and fluid shear on the mechanics of the alum flower are ignored. The van der Waals force between the alum flower particles is derived based on the equivalent parameters of the alum flower morphology. F vdW ;

[0011] Step S3: Derivation of Zeta potential based on van der Waals forces ;

[0012] Step S4: Determine the current metal particle concentration based on the Zeta potential C Al ;

[0013] Step S5: adjusting the steady-state dosage according to the current metal particle concentration.

[0014] In order to better implement the present invention, further, step S1 includes the following steps:

[0015] Step S11: constructing a monochromatic light source alum flower absorbance detection model;

[0016] Step S12: constructing a model of alum flower spatial concentration field based on three-dimensional spatial scanning;

[0017] Step S13: determining the wavelength of the light source for multi-band compensation measurement;

[0018] Step S14: Based on steps S12 and S13, a multi-wavelength compensated alum equivalent concentration field model is constructed:

[0019] ,

[0020] in: C floc ( x , y , z ) is the equivalent concentration of alum flowers for multi-wavelength compensation;

[0021] l is the wavelength of the light source;

[0022] C λ ( x , y , z ) is the wavelength of the light source l The equivalent concentration of alum flowers determined based on the alum flower spatial concentration field model under the following conditions;

[0023] x λ is the wavelength of the light source l The corresponding calculation weight of alum equivalent concentration;

[0024] Step S15: Extract the geometric boundary of the alum flower based on the concentration threshold τ to obtain the equivalent parameters of the alum flower morphology.

[0025] In order to better implement the present invention, further, in the step S11, the alum flower absorbance detection model of the monochromatic light source is:

[0026] ,

[0027] in: A sum is the total absorbance;

[0028] C org is the concentration of organic matter in the sample;

[0029] e org is the absorption coefficient of the sample organic matter;

[0030] C floc is the equivalent concentration of alum flowers;

[0031] e floc is the equivalent molar absorptivity of alum flowers;

[0032] L floc is the equivalent optical path of the alum flower;

[0033] L is the total optical path of the sample cell of the device.

[0034] In order to better implement the present invention, further, step S12 includes the following steps:

[0035] Step A1: Data acquisition: Scan the xoz and yoz planes respectively to measure the absorbance matrix in the x, y, and z directions;

[0036] Step A2: Projection alignment: unify the spatial reference system of the projection data through coordinate transformation;

[0037] Step A3: Fourier reconstruction: Perform a three-dimensional Fourier transform on the projection data, fill the frequency domain space, and then perform an inverse transform to obtain the alum flower spatial concentration field model:

[0038] ,

[0039] in: C ( x , y , z ) is the equivalent concentration of alum flowers in spatial distribution;

[0040] e floc is the equivalent molar absorptivity of alum flowers;

[0041] k x , k y , k z are the frequency domain variables of process data Fourier transform in x, y and z directions respectively;

[0042] C f ( k x , k y , k z ) is the frequency domain of the three-dimensional Fourier transform of the three-dimensional concentration distribution;

[0043] i is an imaginary unit;

[0044] e is the natural logarithm.

[0045] To better implement the present invention, further, in step S13, the alum floc structure includes, from the inside out, a core layer, a hydrophilic colloid coating layer, a microbial aggregation layer, a suspended matter adsorption layer, and an epitaxial expansion layer. Specifically, in step S13, the wavelengths of the light source for multi-band compensation measurement are determined to include 254 nm, 290 nm, 400 nm, 485 nm, and 890 nm.

[0046] In order to better implement the present invention, further, step S15 includes the following steps:

[0047] Step B1: Define the set of alum flower geometry boundaries by the concentration threshold τ:

[0048] ,

[0049] in: is the three-dimensional space to be scanned;

[0050] ∣ is the filtering condition in the collection;

[0051] Step B2: Use the 3D connected component labeling method to binarize the equipotential surface S ( x , y , z ) to segment and mark each connected area as an independent alum flower individual;

[0052] Step B3: The data set of alum flowers is:

[0053] ,

[0054] in: p q ( Am s ) is the spatial position information of each geometric body;

[0055] Am s For collection S ( x , y , z )’s 26-connected geometry;

[0056] q The number of alum flowers counted;

[0057] Step B4: Obtain equivalent parameters of alum flower morphology.

[0058] In order to better implement the present invention, further, in step S15, if the alum flower is a spherical model, the equivalent diameter of the alum flower is:

[0059] ,

[0060] If the alum flower is an ellipsoid model, the equivalent diameter of the alum flower is:

[0061] ,

[0062] in: Lx ( i ) is the individual length of the alum flower in the x direction;

[0063] Ly ( i ) is the individual length of the alum flower in the y direction;

[0064] Lz ( i ) is the individual length of the alum flower in the z direction.

[0065] In order to better implement the present invention, further, in step S5, the steady-state dosage is:

[0066] ,

[0067] ,

[0068] in: k in Enter the rate constant for drug addition;

[0069] k out is the consumption rate constant;

[0070] k 0 is the reaction activity coefficient;

[0071] k ads is the metal ion adsorption equilibrium constant;

[0072] r ( t ) is the number density of alum flowers;

[0073] D b ( t ) is the equivalent diameter of the alum flower;

[0074] K H⁺ is the H⁺ competitive adsorption constant;

[0075] C H⁺ is the H⁺ concentration;

[0076] For intermediate variables:

[0077] ,

[0078] in: e is the dielectric constant of vacuum;

[0079] k D The length of the Debye reciprocal;

[0080] F is the Faraday constant;

[0081] C max is the maximum adsorption capacity.

[0082] Preferably, in step S2, van der Waals forces F vdW for:

[0083] ,

[0084] ,

[0085] in: r i and r j Alum flakes i and alum flakes j The equivalent radius of

[0086] h is the equivalent distance between the surfaces of adjacent alum particles;

[0087] A is the Hamaker constant;

[0088] A 0 is the Hamaker constant in pure solvent;

[0089] α is the ion shielding coefficient;

[0090] I ( t ) is the ionic strength;

[0091] In step S3, the zeta potential for:

[0092] ,

[0093] in: e is the dielectric constant of vacuum;

[0094] k D The length of the Debye reciprocal.

[0095] The present invention is mainly achieved through the following technical solutions:

[0096] A water treatment dosing system based on alum floc detection is used to implement the above-mentioned water treatment dosing method based on alum floc detection, including an alum floc morphology monitoring module, a van der Waals force and Zeta potential calculation module, and a dosing control module;

[0097] The alum flower morphology monitoring module is used to monitor the equivalent parameters of alum flower morphology in water;

[0098] The van der Waals force and zeta potential calculation module is used to sequentially calculate the van der Waals force and zeta potential between alum flower particles based on equivalent parameters of alum flower morphology;

[0099] The dosing control module is used to calculate the current metal particle concentration according to the Zeta potential and adjust the steady-state dosing amount according to the current metal ion concentration.

[0100] In order to better realize the present invention, further, the alum flower morphology monitoring module includes an absorbance scanning module, an alum flower space concentration processing module, a light source wavelength determination module, an alum flower equivalent concentration processing module and an alum flower morphology parameter extraction module;

[0101] The absorbance scanning module is used for three-dimensional scanning to obtain the absorbance matrix of the alum flower;

[0102] The alum flower spatial concentration processing module is used to perform Fourier transform processing based on the absorbance matrix to obtain an alum flower spatial concentration field model;

[0103] The light source wavelength determination module is used to determine the light source wavelength for multi-band compensation measurement;

[0104] The alum floc equivalent concentration processing module is used to obtain the alum floc equivalent concentration based on the light source wavelength and the alum floc spatial concentration field model;

[0105] The alum flower morphological parameter extraction module is used to extract the alum flower geometric body boundary according to the alum flower equivalent concentration and obtain the equivalent parameters of the alum flower morphology.

[0106] The beneficial effects of the present invention are as follows:

[0107] (1) The present invention uses light sources of different wavelengths to accurately monitor alum flocs in three-dimensional space. It not only breaks away from the constraints of image quality, but also realizes comprehensive monitoring of alum floc size, dimensions, density, turbidity, UVCOD and other multi-dimensional water quality parameters. It has the characteristics of high precision, strong real-time monitoring capability, strong adaptability, intelligence and automation, and shows significant advantages in accurate monitoring, online monitoring and refined analysis, providing more comprehensive, accurate and scientific water quality management information support for the accurate and online monitoring of alum flocs in water plant water treatment processes.

[0108] (2) This invention has achieved a disruptive breakthrough in modeling and control technology in the field of water treatment, demonstrating innovation in three dimensions:

[0109] ① It pioneered the integration of optical multi-wavelength sensing, DLVO mechanical field, electrochemical potential distribution and control theory to build a multi-physics field coupling model, completely breaking the traditional single-dimensional analysis paradigm;

[0110] ② A dynamic inference algorithm enables real-time updates of force field parameters driven by spatiotemporal data, forming a closed-loop control chain of perception-decision-execution, which increases the system response speed by 10 times compared to traditional offline optimization.

[0111] ③ In terms of morphological parameter analysis, a fractal dimension intelligent matching wavelength selection mechanism is introduced, and an adaptive optical detection model is established in combination with scattering characteristics, which significantly improves morphological resolution. In the field of process control, the limitations of PID control are further broken through, and nonlinear model predictive control (NMPC) technology is innovatively introduced to precisely control the complex nonlinear dynamics of the dosing process, locking the dosing accuracy at an ultra-high level of ±5%, and achieving millisecond-level response time control.

[0112] This series of innovations forms a full-chain technology upgrade from micro-mechanism analysis to macro-process control, and constitutes the key technical support for the intelligent revolution in water treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 This is the principle block diagram of the absorbance measurement of alum flower multi-band compensation;

[0114] Figure 2 This is a flow chart of the water treatment dosing method based on alum flower detection of the present invention;

[0115] Figure 3 Flow chart of equivalent parameters for monitoring alum flower morphology;

[0116] Figure 4 A flowchart for constructing a model of alum flower spatial concentration field based on three-dimensional spatial scanning;

[0117] Figure 5 This is a flow chart for extracting the boundary of alum flower geometry based on the concentration threshold τ;

[0118] Figure 6 This is a functional block diagram of the water treatment dosing system based on alum flower detection according to the present invention;

[0119] Figure 7 This is the architecture diagram of the water treatment dosing system based on alum floc detection of the present invention. DETAILED DESCRIPTION

[0120] Example 1:

[0121] A water treatment dosing method based on alum flower detection, such as Figure 2 As shown, the following steps are included:

[0122] (1) Real-time monitoring of equivalent parameters of alum flower morphology;

[0123] (2) Derivation of the van der Waals forces between alum floc particles based on the equivalent parameters of alum floc morphology;

[0124] (3) Derivation of Zeta potential based on van der Waals force;

[0125] (4) Determine the current relative concentration of metal ions based on the Zeta potential;

[0126] (5) Optimize the dosage according to the current relative concentration of metal ions.

[0127] Preferably, if Figure 3 As shown in FIG, the equivalent parameters of real-time monitoring of alum flower morphology include the following steps:

[0128] 1) Construct a basic alum flower measurement model - a monochromatic light source alum flower absorbance detection model;

[0129] 2) Obtain three-dimensional spatial scanning data and construct a model of the alum flower spatial concentration field;

[0130] 3) Construct an alum flower structure and adapt the wavelength of the light source for multi-band measurement based on the alum flower structure;

[0131] 4) Construct an equivalent concentration field model of alum flowers based on the equivalent concentrations of alum flowers corresponding to different light source wavelengths;

[0132] 5) The geometric boundary of the alum flower is extracted based on the concentration threshold τ, and the equivalent parameters of the alum flower morphology are determined.

[0133] Preferably, deriving the van der Waals forces between alum floc particles specifically comprises the following steps:

[0134] Basic theory: The lyophobic colloid stability theory is commonly known as the DLVO theory. Colloidal particles attract each other due to the van der Waals force. When they approach each other, the double layers overlap and repel each other. The stability of the colloid depends on the relative size of the two. This theory calculates the variation of attractive and repulsive energies between particles of different shapes with distance. If the repulsive energy is greater than the attractive energy at a certain distance, the colloid is stable; if the attraction is always greater than the repulsion, the colloid will aggregate. As the ion concentration or the valence of the counterions in the solution increases, the van der Waals force remains basically unchanged, the double layer repulsive energy decreases, and the colloid stability decreases until it aggregates. According to the dynamic DLVO model theory, the colloidal force field is derived from the dynamic morphological data. Therefore, it is set as follows:

[0135] 1. Equivalent the shape of alum flowers to spherical particles;

[0136] 2. The derivation model ignores the effects of Brownian motion and fluid shear on the mechanics of alum flowers;

[0137] 3. The Hamaker constant is affected by the ionic strength of the solution;

[0138] The van der Waals force is deduced based on the equivalent parameters of the alum flower morphology:

[0139]

[0140] in: F vdW : Van der Waals force between alum flake particles;

[0141] r i and r j Alum flakes i and alum flakes j The equivalent radius of

[0142] h : The equivalent distance between the surfaces of adjacent alum floc particles is obtained by the spatial position of adjacent alum flocs;

[0143] A :Hamaker constant:

[0144]

[0145] in: A 0: Hamaker constant in pure solvent obtained experimentally;

[0146] α : Ion shielding coefficient, which can be empirically determined, looked up in a table, or calibrated experimentally;

[0147] I ( t) is the ionic strength (unit: mol / m³), which is obtained by online ion selective electrode acquisition and conversion; it can also be obtained by accumulation.

[0148] Specifically, the online ion selective electrode acquisition conversion method is:

[0149]

[0150] in: β : Dosage-ion strength conversion coefficient, look up table or experimental calibration;

[0151] Q ( t ): Real-time dosage;

[0152] I 0: The previous moment, or the initial ionic strength.

[0153] Preferably, deriving the zeta potential specifically comprises the following steps:

[0154] 1) Assumption: The double layer interaction can be described by the Debye-Hückel approximation (low surface potential condition). Based on the DLVO theory, the double layer force formula is:

[0155]

[0156]

[0157] in: F electric : Double layer potential force;

[0158] h : the equivalent distance between adjacent alum flower particle surfaces;

[0159] k : Equivalent conversion empirical constant;

[0160] e : vacuum dielectric constant;

[0161] k D : the length of the Debye reciprocal (unit: m⁻¹);

[0162] : zeta potential;

[0163] e v : elementary charge;

[0164] N A : Avogadro's constant;

[0165] k B : Boltzmann constant;

[0166] T : Absolute temperature (unit: K).

[0167] Specifically, the conversion method for ionic strength is:

[0168]

[0169] in: C i :Electrode detection of cations i molar concentration;

[0170] Z i :cation i The corresponding charge number.

[0171] 2) Derivation of surface potential through force balance relationship:

[0172]

[0173] 3) Substituting into the double layer potential formula, we get:

[0174]

[0175] The solution is:

[0176] .

[0177] Preferably, the zeta potential is established and metal ion (such as aluminum salt, iron salt, etc.) concentration The quantitative relationship includes the following steps:

[0178] Basic model: Stern double layer model: The double layer is divided into a compact Stern layer and a diffuse layer. Metal ions affect the surface charge through adsorption. Adsorption equilibrium: The adsorption of metal ions in the Stern layer obeys the Langmuir isotherm.

[0179] Step 1) According to the Stern layer charge balance equation, the surface charge density (Stern layer) can be obtained:

[0180]

[0181] in: s Stern : The surface charge density can be obtained from the Stern layer charge balance equation;

[0182] F: Faraday constant;

[0183] C max : Maximum adsorption capacity (mol / m²), obtained from experimental data, empirical data or table lookup;

[0184] k ads : Metal ion adsorption equilibrium constant (m³ / mol), obtained from experimental data, empirical data or table lookup;

[0185] : metal ion concentration (mol / m³);

[0186] K H⁺ : H⁺ competitive adsorption constant (m³ / mol), obtained from experimental data, empirical data or lookup table;

[0187] C H⁺ : H⁺ concentration (mol / m³), obtained by online pH sensor; the conversion relationship is:

[0188] ;

[0189] Where: PH is the detection value of the online pH sensor;

[0190] Step 2), the relationship between Zeta potential and surface charge, potential approximate formula:

[0191]

[0192] Step 3): Substitute the Stern layer charge density:

[0193]

[0194] Let the intermediate variable for:

[0195]

[0196] but:

[0197]

[0198] Deformation to obtain metal particle concentration:

[0199]

[0200] Specifically, all calculated zeta potentials are traversed to calculate the average metal particle concentration :

[0201]

[0202]

[0203] in: q : The number of alum flowers counted;

[0204] : The zeta potential of two adjacent alum flower individuals traversed.

[0205] Preferably, optimizing the dosage of the drug according to the current relative concentration of metal ions specifically comprises the following steps:

[0206] Step 1): Assuming that the metal ion concentration is determined by the dosing input and flocculation consumption, the particle mass balance equation is obtained:

[0207]

[0208] in: k in : dosing input rate constant (m³ / (mol·s));

[0209] Q ( t ): Adjust the dosage;

[0210] k out : Consumption rate constant (s -1 ), which is related to the formation rate of alum flowers;

[0211]

[0212] k 0: Reactivity coefficient (m⁻²·s⁻¹);

[0213] r ( t ): alum flower number density (pieces / m³), ,in q ( t ) is the number of blooms in the periodic scanning result at time t;

[0214] Dt x : total length of x-axis;

[0215] Dt y : total length of y axis;

[0216] Dt z : total length of z axis;

[0217] D b ( t ): equivalent diameter.

[0218] Step 2), steady-state dosage calculation when the system is stable When

[0219]

[0220] Combined with the zeta potential, the deformation is obtained:

[0221]

[0222] in: Q steady : Steady-state dosage.

[0223] This invention achieves a multidimensional coupling of optical full-spectrum sensing, DLVO micromechanical fields, electrochemical interface properties, and cybernetics to construct a cross-scale, multi-physics digital twin model, completely breaking the single-dimensional constraints of traditional analytical methods. Secondly, a dynamic inference algorithm is used to construct an intelligent decision-making engine driven by spatiotemporal data, forming a real-time closed-loop "perception-computation-control" system. This system significantly increases response speed by orders of magnitude, achieving a tenfold increase compared to traditional offline optimization. In the morphological analysis dimension, an innovative fractal dimension-driven wavelength adaptive selection mechanism is introduced, combined with dynamic scattering characteristics to construct an intelligent optical detection model, significantly improving morphological parameter resolution. Furthermore, a paradigm shift is achieved in process control, replacing traditional PID control with nonlinear model predictive control (NMPC) technology. This allows for precise control of the complex nonlinear dynamics of the dosing process, maintaining an ultra-high dosing accuracy of ±5% and achieving millisecond-level response time. This systematic innovation has built a full-chain intelligent upgrade system from micro-mechanism cognition to macro-process regulation, providing a key technical paradigm for the intelligent transformation of the water treatment industry, and marking that water treatment technology has officially entered a new era of multi-field coupling and intelligent decision-making.

[0224] Example 2:

[0225] This embodiment is optimized based on the embodiment 1. In the step (1), the specific contents of constructing the alum flower absorbance detection model of the monochromatic light source are as follows:

[0226] 1. Monochromatic light measurement principle

[0227] The interaction between light and matter manifests primarily in three forms: absorption, reflection, and transmission. When the frequency of the incident light wave matches the natural vibrational frequency of the material's atoms, energy absorption occurs. When the frequencies do not match, the unabsorbed light wave acts through reflection (re-radiation of the electron vibration energy at the surface) or transmission (radiation after energy transfer through the material). This selective interaction mechanism determines the color properties of the material. In the field of alum floc detection, turbidity reflects the light reflectivity of particulate matter, while changes in transmitted light intensity form a functional relationship with the morphological characteristics of the alum floc (such as floc density, particle size distribution, and three-dimensional structure). By establishing a mathematical model linking transmission spectral parameters with floc physical parameters, non-invasive dynamic monitoring of the alum floc growth process can be achieved. This photophysical mechanism provides an important online analytical foundation for optimizing water treatment processes.

[0228] 2. Equivalent measurement method

[0229] During the flocculation and sedimentation process, alum flocs, as aggregates of multi-component colloidal particles, undergo a dynamic formation process accompanied by complex optical effects. When incident light passes through a suspension system containing alum flocs, the change in transmitted light intensity is composed of two contributions: (1) the selective absorption of specific wavelengths by dissolved organic matter; and (2) the scattering-absorption coupling effect of alum floc particles on light. Based on the equivalent absorbance theoretical framework of the Lambert-Beer law, the attenuation effect of alum floc aggregates on transmitted light can be equated to absorption behavior, thereby establishing a synergistic model of the absorbance of dissolved organic matter (Aorg) and the equivalent absorbance of alum floc transmission (Afloc). This model needs to consider the nonlinear effects of multi-scale parameters such as alum floc particle size distribution, porosity, and refractive index differences on the equivalent absorbance.

[0230] Additive absorbance: When a medium contains multiple absorbing components, if there is no interaction between the components, the total absorbance of the system is equal to the algebraic sum of the absorbances of the components at the same wavelength. Figure 1 As shown, when measuring absorbance, light can be emitted through the light source array, and then passes through the focusing lens, the flocculation tank and the focusing lens in sequence before reaching the detector array.

[0231] 3. Absorbance additivity and equivalent absorbance model

[0232] 3.1 Establishment of total absorbance of monochromatic light

[0233] The expression for total absorbance is:

[0234]

[0235]

[0236] The calculation formula of total absorbance is simplified and transformed to obtain:

[0237]

[0238]

[0239] in: A sum : total absorbance;

[0240] C org : Sample organic matter concentration, obtained by three-dimensional spatial scanning measurement method;

[0241] e org : Absorption coefficient of sample organic matter;

[0242] C floc : Alum equivalent concentration;

[0243] e floc : equivalent molar absorptivity of alum flowers;

[0244] L org : Equivalent optical path length of organic matter;

[0245] L floc : Alum flower equivalent optical path.

[0246] L : Total optical path.

[0247] 3.2 Equivalent molar absorptivity of alum flowers

[0248] According to the Mie scattering law, the equivalent molar absorption coefficient of alum flowers is obtained:

[0249]

[0250] in: Q ext : Mie astigmatism coefficient;

[0251] k : Equivalent conversion empirical constant;

[0252] r : Average equivalent particle length of water body.

[0253] The rest of this embodiment is the same as that of embodiment 1, so it will not be described again.

[0254] Example 3:

[0255] This embodiment is optimized based on embodiment 1 or 2. In step (1), the specific contents of constructing the alum flower spatial concentration field model are as follows:

[0256] 1. Three-dimensional measurement method by scanning Cartesian coordinate system

[0257] 1.1 Scan the data to obtain the total absorbance of the monochromatic light source

[0258] Alum flower scanning uses a step-by-step planar scanning process. The planar scanning process first performs the x-axis motion scanning process, then the y-axis motion scanning process, and then moves the z-axis, and then repeats the planar scanning process. Of course, if conditions permit, a surface light source can be used for a single transmission to obtain all three-dimensional transmission absorbance at the same time.

[0259] The xoz coordinate plane scanning data is obtained through spatial scanning:

[0260]

[0261] Obtain YOZ coordinate plane scanning data through spatial scanning:

[0262]

[0263] in: l is the number of subdivision steps on the x-axis;

[0264] n for z The number of subdivision steps on the axis;

[0265] m for y The number of subdivision steps along the axis.

[0266] 1.2 Orthogonal Plane Projection Constraints

[0267] When scanning in the xoz and yoz planes, the absorbance distribution can be decomposed into:

[0268]

[0269] .

[0270] 2. Construct a model of alum flower spatial concentration field through spatial field

[0271] like Figure 4 As shown, data acquisition is performed in sequence: measuring the absorbance matrix in the x, y, and z directions; projection alignment: unifying the spatial reference system of the projection data through coordinate transformation; Fourier reconstruction: performing a three-dimensional Fourier transform on the projection data, filling the frequency domain space, and then inverse transforming to obtain the alum flower spatial concentration field model. Specifically, the following steps are included:

[0272] 2.1 Fourier transform of single-wavelength projection data

[0273] 2.1.1 Scan the xoz plane (integrate along the y direction), where the absorbanceA x ( z , x ) is:

[0274]

[0275] right A x ( z , x ) performs a two-dimensional Fourier transform (variables are x, z):

[0276]

[0277] Will A x ( z , x ) into the formula to obtain:

[0278]

[0279] Observe that the integral kernel does not contain y, and the integral can be decomposed into:

[0280]

[0281] Since the integral of y is the impulse function d ( k y ) and finally get:

[0282]

[0283] in: F zx ( k z , k x )for A x ( z , x )’s two-bit Fourier transform;

[0284] k x , k y , k z :Fourier transform of process data is frequency domain variable in x, y, z directions, where k y =0;

[0285] e floc : Equivalent molar absorption coefficient of alum flowers;

[0286] C ( x , y , z ): Equivalent concentration of alum flowers in spatial distribution;

[0287] C f ( k x , k y , k z ): Three-dimensional concentration distribution C ( x , y , z ) in the frequency domain of the three-dimensional Fourier transform;

[0288] ;

[0289] C f ( k x ,0, k z ): xoz plane two-dimensional Fourier transform frequency domain mapping three-dimensional Fourier transform frequency domain;

[0290] 2.1.2 Scan the yoz plane (integrate along the x direction), where the absorbance A y ( z , y ) is:

[0291]

[0292] right A y ( z , y ) performs a two-dimensional Fourier transform (variables are y, z):

[0293]

[0294] Will A y ( z , y ) into the formula to obtain:

[0295]

[0296] Observe that the integral kernel does not contain y, and the integral can be decomposed into:

[0297]

[0298] Since the integral of x is the impulse function d ( k x ) and finally get:

[0299]

[0300] in: F zy ( k z , k y ): A y ( z , y )’s two-bit Fourier transform;

[0301] C f (0, k y , k z ): yoz plane two-dimensional Fourier transform frequency domain mapping three-dimensional Fourier transform frequency domain;

[0302] 2.1.3 Filling rules for three-dimensional Fourier space

[0303] By scanning the orthogonal planes, the three-dimensional Fourier space C f ( k x , k y , k z ) are filled:

[0304] ①From xoz plane scan:

[0305]

[0306] ②From yoz plane scan:

[0307]

[0308] ③Frequency components in the remaining areas C f ( k x , k y , k z ) is not directly measured. Assuming that the unmeasured region component is zero (i.e., ignoring high-frequency information), then:

[0309]

[0310] 2.1.4 Three-dimensional inverse Fourier transform

[0311] Perform an inverse transform on the filled three-dimensional Fourier space to obtain the concentration distribution:

[0312]

[0313] The rest of this embodiment is the same as that of Embodiment 1 or 2, and therefore will not be described in detail.

[0314] Example 4:

[0315] This embodiment is optimized based on any one of Embodiments 1 to 3. In step (1), the specific contents of constructing the equivalent concentration field model of alum flowers and determining the equivalent parameters of alum flower morphology are as follows:

[0316] 1. Determine the final alum flower morphology and quantity based on the alum flower data set corresponding to different wavelengths

[0317] 1.1 Alum flower count (concentration field)

[0318] Step a1: Obtain the equivalent concentration field of different wavelengths through the spatial concentration field model. Of course, different engineers can adapt the concentration field of different bands to establish the equivalent concentration field model of alum flowers:

[0319]

[0320] C floc ( x , y , z ): Equivalent concentration of alum flowers with multi-wavelength compensation;

[0321] l : wavelength of light source;

[0322] C λ ( x , y , z ): wavelength of light source l The equivalent concentration of alum flowers determined based on the alum flower spatial concentration field model under the following conditions;

[0323] Preferably, the wavelengths for determining the composition of substances in alum flowers are 254nm, 290nm, 400nm, 485nm and 890nm. Of course, engineers can adapt it according to different water bodies. Specifically, the equivalent concentrations of 254nm, 290nm, 400nm, 485nm and 890nm wavelengths are obtained: C 254 ( x , y , z )、C 290 ( x , y , z )、 C 400 ( x , y , z )、 C 485 ( x , y , z )、 C 890 ( x , y , z ).

[0324] x λ : wavelength is l Contribution to the equivalent alum floc concentration, , obtained through experience or experimental methods:

[0325] ① Take a certain volume (usually 1L) of alum solution and filter it with ultrafine filter paper (0.015 micron - 3 micron); weigh the mass M;

[0326] ② Take m mass of alum flocs and dissolve them in excess hydrochloric acid, then add excess caustic soda solution, filter and weigh the mass m0 of aluminum hydroxide, then the contribution of flocculant hydrolysis products is: ,in Experimental error coefficient, the default value is 1;

[0327] ③ Estimate the mass of different layers in the alum flower structure with mass m through different experimental methods , its contribution is: .

[0328] Step a2: If Figure 5 As shown, the boundary extraction of the alum flower geometry (isosurface condition):

[0329] ① Define the set of geometric boundaries by the concentration threshold τ:

[0330]

[0331] in: S ( x , y , z ): A collection of alum flower geometric surfaces;

[0332] : The three-dimensional space scanned by the instrument;

[0333] ∣: Filter conditions in the collection.

[0334] ②Use the "3D connected component labeling algorithm": for the binary equipotential surface S ( x , y , z ) for segmentation, marking each connected region as an independent individual voxel. Connectivity criterion: 26-connectivity (voxels are considered connected if they are diagonally adjacent in space). Statistical set S ( x , y , z ) of the 26-connected geometries.

[0335] ③ Let the function p ( Am s ) is the spatial position information of each geometric body, and the function of the position information is S ( x , y , z )but:

[0336] p ( Am s ) = S ( x , y , z )

[0337] in: p ( Am s ): A spatial geometry that satisfies the equipotential surface, p ( Am s )∈ S ( x , y , z );

[0338] S ( x , y , z ): spatial position function of the spatial geometry that satisfies the equipotential surface;

[0339] Am s For collection S ( x , y , z )’s 26-connected geometry;

[0340] ④ The statistical data set of alum flowers is:

[0341]

[0342] in: p q ( Am s ) is the spatial position information of each geometric body;

[0343] q : The number of alum flowers counted.

[0344] 1.2 Calculation of equivalent parameters of alum flower morphology

[0345] 1.2.1 Unit size calculation

[0346] The step size of the x-axis unit subdivision step: ;

[0347] The step size of the y-axis unit subdivision step: ;

[0348] The step size of the z-axis unit subdivision step: .

[0349] Where: Δx: x-axis unit subdivision step;

[0350] Δy: y-axis unit subdivision step;

[0351] Δz: z-axis unit subdivision step;

[0352] Dt x : total length of x-axis;

[0353] Dt y : total length of y axis;

[0354] Dt z : total length of z axis;

[0355] l : Total number of subdivision steps on the x-axis;

[0356] m : Total number of subdivision steps on the y-axis;

[0357] n : Total number of subdivision steps on the z-axis.

[0358] 1.2.2 Parameter calculation

[0359] (1) The number of alum flowers at a certain moment is: q . Then the size is calculated as:

[0360] Individual length of alum flower in x direction: ;

[0361] Length of individual alum flowers in the y direction: ;

[0362] Individual length of alum flower in z direction: ;

[0363] in: x right for x The right critical value of the direction alum flower;

[0364] x left for x The left critical value of the direction alum flower;

[0365] y right for y The right critical value of the direction alum flower;

[0366] y left for y The left critical value of the direction alum flower;

[0367] z right for z The right critical value of the direction alum flower;

[0368] z left for z The left critical value of the direction alum flower.

[0369] (2) The equivalent diameter is:

[0370] 1) The alum flower is a spherical model, and its equivalent diameter is:

[0371]

[0372] 2) The alum flower is an ellipsoid model, and its equivalent diameter is:

[0373]

[0374] (3) The density of alum flowers is:

[0375] 1) The volume of a single voxel: for the i-th voxel, its volume is the number of voxels in the region multiplied by the voxel volume:

[0376]

[0377] in: i : Process data is meaningless;

[0378] N voxel,i : No. i The position information function of the alum flower S (x , y , z ) The number of voxels contained. 2) Volume density:

[0379] .

[0380] The rest of this embodiment is the same as any one of Embodiments 1 to 3, and therefore will not be described in detail.

[0381] Example 5:

[0382] A water treatment dosing system based on alum flower detection, such as Figure 7 As shown, it includes a main control unit, a light detector, a light source, a signal acquisition unit, a signal processing unit, and a light source driving unit. The light detector is connected to the signal acquisition unit, and the signal acquisition unit is connected to the isolation unit and the active unit through the signal processing unit. The main control unit is connected to the light source through the light source driving unit.

[0383] like Figure 6 As shown, the main control unit includes an alum flower morphology monitoring module, a van der Waals force and Zeta potential calculation module and a drug dosing control module. The alum flower morphology monitoring module includes an absorbance scanning module, an alum flower spatial concentration processing module, a light source wavelength determination module, an alum flower equivalent concentration processing module and an alum flower morphology parameter extraction module.

[0384] The van der Waals force and zeta potential calculation module is used to sequentially calculate the van der Waals force and zeta potential between alum flower particles based on equivalent parameters of alum flower morphology;

[0385] The dosing control module is used to calculate the current metal particle concentration according to the Zeta potential and adjust the steady-state dosing amount according to the current metal ion concentration.

[0386] The absorbance scanning module is used for three-dimensional scanning to obtain the absorbance matrix of the alum flower;

[0387] The alum flower spatial concentration processing module is used to perform Fourier transform processing based on the absorbance matrix to obtain an alum flower spatial concentration field model;

[0388] The light source wavelength determination module is used to determine the light source wavelength for multi-band compensation measurement;

[0389] The alum floc equivalent concentration processing module is used to obtain the alum floc equivalent concentration based on the light source wavelength and the alum floc spatial concentration field model;

[0390] The alum flower morphological parameter extraction module is used to extract the alum flower geometric body boundary according to the alum flower equivalent concentration and obtain the equivalent parameters of the alum flower morphology.

[0391] Preferably, the system further comprises a communication unit, a data storage unit, a motor drive unit and a motor respectively connected to the main control unit, wherein the motor drive unit is used to drive the motor to drive the light detector to scan and measure in three-dimensional space. The main control unit is connected to the communication device via the communication unit.

[0392] The present invention integrates DLVO mechanical field, electrochemical potential distribution and control theory to construct a multi-physical field coupling model, realizes real-time updating of force field parameters driven by spatiotemporal data, forms a closed-loop control chain of perception-decision-execution, and significantly improves the system response speed.

[0393] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A water treatment dosing method based on alum flower detection, characterized in that: The following steps are involved: Step S1: real-time monitoring of equivalent parameters of alum flower morphology; Step S11: constructing a monochromatic light source alum flower absorbance detection model; Step S12: constructing a model of alum flower spatial concentration field based on three-dimensional spatial scanning; Step S13: determining the wavelength of the light source for multi-band compensation measurement; Step S14: Based on steps S12 and S13, a multi-wavelength compensated alum equivalent concentration field model is constructed: C floc (x,y,z)=∑ξ λ C λ (x,y,z), Where: C floc (x, y, z) is the equivalent concentration of alum flowers with multi-wavelength compensation; λ is the wavelength of the light source; C λ (x, y, z) is the equivalent concentration of alum flowers determined based on the alum flower spatial concentration field model at the light source wavelength λ; ξ λ Calculate the weight for the equivalent concentration of alum flowers corresponding to the wavelength λ of the light source; Step S15: extracting the geometric boundary of the alum flower based on the concentration threshold τ, and obtaining equivalent parameters of the alum flower morphology; Step S2: The morphology of the alum flower is equivalent to spherical particles, and the effects of Brownian motion and fluid shear on the mechanics of the alum flower are ignored. The van der Waals force F between the alum flower particles is derived based on the equivalent parameters of the alum flower morphology. vdW ; Step S3: Derivation of Zeta potential based on van der Waals forces Step S4: determining the current metal particle concentration according to the Zeta potential; Step S5: adjusting the steady-state dosage according to the current metal particle concentration; The steady-state dosage is: k out =k0ρ(t)(D b (t)) 2 , Where: k in Enter the rate constant for drug addition; k out is the consumption rate constant; k0 is the reaction activity coefficient; k ads is the metal ion adsorption equilibrium constant; ρ(t) is the number density of alum flowers; D b (t) is the equivalent diameter of the alum flower; K H+ H + competitive adsorption constant; C H+ H + concentration; For intermediate variables: Where: ε is the dielectric constant of vacuum; k D The length of the Debye reciprocal; F is the Faraday constant; Γ max is the maximum adsorption capacity.

2. A water treatment dosing method based on alum flower detection according to claim 1, characterized in that, In step S11, the alum flower absorbance detection model of the monochromatic light source is: A sum =C org e org L+(C floc e floc -C org e org )L floc , Among them: A sum is the total absorbance; C org is the concentration of organic matter in the sample; ε org is the absorption coefficient of the sample organic matter; C floc is the equivalent concentration of alum flowers; ε floc is the equivalent molar absorptivity of alum flowers; L floc is the equivalent optical path of the alum flower; L is the total optical path length of the sample cell of the device.

3. A water treatment dosing method based on alum flower detection according to claim 2, characterized in that, The step S12 includes the following steps: Step A1: Data acquisition: Scan the xoz and yoz planes respectively to measure the absorbance matrix in the x, y, and z directions; Step A2: Projection alignment: unify the spatial reference system of the projection data through coordinate transformation; Step A3: Fourier reconstruction: Perform a three-dimensional Fourier transform on the projection data, fill the frequency domain space, and then perform an inverse transform to obtain the alum flower spatial concentration field model: Where: C(x, y, z) is the equivalent concentration of alum flowers in spatial distribution; ε floc is the equivalent molar absorptivity of alum flowers; k x , k y , k z are the frequency domain variables of process data Fourier transform in x, y and z directions respectively; C f (k x , k y , k z ) is the frequency domain of the three-dimensional Fourier transform of the three-dimensional concentration distribution; i is the imaginary unit.

4. A water treatment dosing method based on alum flower detection according to claim 1, characterized in that, In step S13, it is determined that the wavelengths of the light source for the multi-band compensation measurement include 254 nm, 290 nm, 400 nm, 485 nm and 890 nm.

5. A water treatment dosing method based on alum flower detection according to claim 1, characterized in that, The step S15 includes the following steps: Step B1: Define the set of alum flower geometry boundaries by the concentration threshold τ: S(x,y,z)={(x,y,z)∈R 3 |C floc (x,y,z)=τ}, Where: R 3 is the three-dimensional space to be scanned; ∣ is the filtering condition in the collection; Step B2: Use the three-dimensional connected component labeling method to segment the binary equipotential surface S (x, y, z), marking each connected area as an independent alum flower individual; Step B3: The data set of alum flowers is: S floc (x,y,z)={p1(Am s ) p2(Am s )…p q (Am s )}, Where: p q (Am s ) is the spatial position information of each geometric body; Am s is the number of 26-connected geometric entities in the set S(x, y, z); q is the number of alum flowers counted; Step B4: Obtain equivalent parameters of alum flower morphology.

6. A water treatment dosing method based on alum floc detection according to claim 5, characterized in that, In step S15, if the alum flower is a spherical model, the equivalent diameter of the alum flower is: If the alum flower is an ellipsoid model, the equivalent diameter of the alum flower is: Where: Lx(i) is the individual length of the alum flower in the x direction; Ly(i) is the individual length of the alum flower in the y direction; Lz(i) is the individual length of the alum flower in the z direction.

7. A water treatment dosing system based on alum floc detection, used to implement the water treatment dosing method based on alum floc detection according to any one of claims 1 to 6, characterized in that: It includes alum flower morphology monitoring module, van der Waals force and Zeta potential calculation module and dosing control module; The alum flower morphology monitoring module is used to monitor the equivalent parameters of alum flower morphology in water; The van der Waals force and zeta potential calculation module is used to sequentially calculate the van der Waals force and zeta potential between alum flower particles based on equivalent parameters of alum flower morphology; The dosing control module is used to calculate the current metal particle concentration according to the Zeta potential and adjust the steady-state dosing amount according to the current metal ion concentration.

8. A water treatment dosing system based on alum bloom detection according to claim 7, characterized in that: The alum flower morphology monitoring module includes an absorbance scanning module, an alum flower space concentration processing module, a light source wavelength determination module, an alum flower equivalent concentration processing module and an alum flower morphology parameter extraction module; The absorbance scanning module is used for three-dimensional scanning to obtain the absorbance matrix of the alum flower; The alum flower spatial concentration processing module is used to perform Fourier transform processing based on the absorbance matrix to obtain an alum flower spatial concentration field model; The light source wavelength determination module is used to determine the light source wavelength for multi-band compensation measurement; The alum floc equivalent concentration processing module is used to obtain the alum floc equivalent concentration based on the light source wavelength and the alum floc spatial concentration field model; The alum flower morphological parameter extraction module is used to extract the alum flower geometric body boundary according to the alum flower equivalent concentration and obtain the equivalent parameters of the alum flower morphology.

Images