Predictive system and method for ion concentration and ph control of hydrophobic interface behavior

Abstract

This invention discloses a prediction system and method for regulating hydrophobic interface behavior by ion concentration and pH value. The method first collects solution chemical parameters, interface physicochemical parameters, and water molecule microstate parameters; it then calculates the bulk ligand distribution using a water molecule energy state model as the first boundary condition; combined with a ligand evolution model, it obtains the evolution of the number of interfacial ligands and calculates energy evolution parameters including hydrogen bond structure energy, chemical potential, and mixing entropy; it uses an ion-induced model to calculate the ion activity gradient induced by ligand evolution, and accordingly corrects the intrinsic potential and dielectric constant to construct a potential model reflecting the uneven ion distribution; subsequently, it couples the energy evolution and potential models to quantitatively analyze the number of nanobubbles, foam stability, and hydrophobicity; finally, it compares the predicted values ​​with the process target values, automatically generating and executing dosing adjustment commands. This invention significantly improves the accuracy of interfacial behavior prediction and industrial control under high-salt environments.

Description

Technical Field

[0001] This invention belongs to the field of industrial process simulation and automatic control technology, specifically relating to an industrial prediction system and method for regulating the behavior of hydrophobic interfaces by ion concentration and pH value. Background Technology

[0002] In modern industrial processes such as flotation separation, water treatment, and foam control, the behavioral characteristics of hydrophobic interfaces (such as the strength of interfacial interactions, bubble formation patterns, and foam stability) are core technical indicators that determine process efficiency and product quality. Especially when using seawater or high-salinity solutions as treatment media, fluctuations in the types and concentrations of ions in the solution, as well as pH values, significantly alter the electrical double-layer structure and hydrophobic force distribution near the interface, thereby affecting the number of bubble nucleations and the macroscopic stability of the foam system.

[0003] In existing technologies, the analysis and production control of hydrophobic interfaces and foam behavior mainly rely on the following two methods:

[0004] 1. Experimental Testing Method: This method involves observing foam height, lifespan, or nanobubble quantity distribution in a laboratory environment by changing conditions such as solution salinity or pH. While intuitive, this method suffers from the extreme complexity of industrial conditions (such as fluctuations in seawater composition), long testing cycles, high costs, and significant time lag, making it difficult to achieve real-time optimization and adjustment of the production process.

[0005] 2. Empirical Model Method: This method establishes simple linear correspondences based on historical data. However, in complex solution environments, the regulation of hydrophobic interfaces by ions exhibits extremely strong nonlinearity and involves the dynamic coupling of the microscopic energy states of water molecules and interface charges. Simple empirical models often lack sufficient prediction accuracy, leading to overdosing of reagents or regulatory failure.

[0006] Furthermore, existing simulation software primarily focuses on building purely theoretical models, lacking the technical means to translate microscopic energy state changes at the interface into industrial control commands. In actual production, operators often find it difficult to make precise control decisions (such as specific dosages or pH adjustment ranges) based on microscopic interface parameters. Therefore, establishing an intelligent control scheme that can accurately simulate ion control mechanisms and directly serve industrial execution systems is a pressing technical problem that needs to be solved in flotation and related industrial fields. Summary of the Invention

[0007] The technical objective of this invention is to address the current lack of technical means to convert changes in the microscopic energy state of an interface into industrial control commands, by providing a predictive system and method for regulating the behavior of hydrophobic interfaces by ion concentration and pH value.

[0008] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution.

[0009] In a first aspect, embodiments of the present invention provide a prediction system for the regulation of hydrophobic interface behavior by ion concentration and pH value, comprising:

[0010] The parameter acquisition module is used to acquire the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated; the solution chemical parameters include ion species, ion concentration, and pH value.

[0011] The energy state distribution simulation module is used to input the microscopic state parameters of the water molecules into a preset water molecule energy state model and calculate the bulk ligand distribution as the first boundary condition.

[0012] The interface dynamic evolution module is used to calculate the evolution of the number of ligand structures at the hydrophobic interface by using a preset hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and the basic physicochemical parameters; and based on the evolution of the number of ligand structures at the hydrophobic interface, according to the pre-calculated hydrophobic interface energy model, including hydrogen bond structure energy, chemical potential, and mixing entropy, to obtain the hydrophobic interface energy evolution as a function of distance.

[0013] The ion-regulated potential coupling module is used to calculate the ion activity change induced by the evolution of interfacial water molecule ligands based on a pre-calculated hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, and according to a pre-constructed ion-induced model. Based on this, it determines the induced charge density and induced potential of the hydrophobic interface. Combined with the collected intrinsic potential of the condensed-state hydrophobic interface, and through nonlinear correction of the induced potential and dielectric constant, it obtains a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions. Based on this, it calculates the specific adsorption of ions in water by the surface potential, and then calculates the effect of adsorbed ions on the hydrophobic effect by changing the interfacial activity distribution.

[0014] The bubble behavior prediction module is used to obtain the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential of the condensed matter interface based on a pre-constructed condensed matter interface model. Based on the influence of adsorbed ions on hydrophobic interactions, it constructs an ion-regulated hydrophobic interaction of the condensed matter interface and qualitatively and quantitatively analyzes the number and stability of nanobubbles. Based on the pre-constructed condensed matter hydrophobic interface model and gas-liquid interface model, and according to the evolution of hydrophobic energy at different hydrophobic interfaces, it uses a pre-constructed interfacial hydrophobic force equation to obtain the corresponding interfacial ion-regulated hydrophobic force. Combined with the modified DLVO interaction, it obtains the modified interaction force, realizing the ion-regulated interaction of the hydrophobic interface.

[0015] The control instruction generation and execution module is used to compare the predicted value of the number of nanobubbles, the foam stability assessment value, the hydrophobic force or the corrected interaction force with the preset process target value, determine the target ion concentration component or target pH value required to make the solution environment reach the target state, automatically calculate the amount of reagent to be added to adjust the target component, and send the dosing instruction to the dosing execution mechanism to adjust the solution composition.

[0016] Secondly, embodiments of the present invention provide a method for predicting the behavior of hydrophobic interfaces controlled by ion concentration and pH value. This method is applied to the automatic control of industrial flotation or water treatment systems and includes the following steps:

[0017] Step 1: Collect the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated; the solution chemical parameters include ion species, ion concentration, and pH value;

[0018] Step 2: Input the microscopic state parameters of the water molecules into the preset water molecule energy state model, calculate the bulk ligand distribution, and use it as the first boundary condition;

[0019] Step 3: Using a pre-defined hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and the basic physicochemical parameters, calculate the evolution of the number of ligand structures at the hydrophobic interface; and based on the evolution of the number of ligand structures at the hydrophobic interface, according to a pre-calculated hydrophobic interface energy model, which includes hydrogen bond structure energy, chemical potential, and mixing entropy, obtain the hydrophobic interface energy evolution as a function of distance.

[0020] Step 4: Based on the pre-calculated hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, and according to the pre-constructed ion-induced model, calculate the ion activity changes induced by the evolution of interfacial water molecule ligands, and determine the induced charge density and induced potential of the hydrophobic interface accordingly; combined with the collected intrinsic potential of the condensed hydrophobic interface, and through nonlinear correction of the induced potential and dielectric constant, obtain a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions, and calculate the specific adsorption of ions in water by the surface potential, and then calculate the effect of adsorbed ions on the hydrophobic effect by changing the interfacial activity distribution.

[0021] Step 5: Based on the pre-constructed condensed matter interface model, the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential are obtained for the condensed matter interface. Based on the influence of adsorbed ions on the hydrophobic interaction by changing the interfacial activity distribution, the ion-regulated hydrophobic interaction of the condensed matter interface is constructed, and the number of nanobubbles and foam stability are qualitatively and quantitatively analyzed. Based on the constructed condensed matter hydrophobic interface model and gas-liquid interface model, according to the evolution of hydrophobic energy of different hydrophobic interfaces, the corresponding ion-regulated hydrophobic force is obtained using the pre-constructed interfacial hydrophobic force equation. Combined with the modified DLVO interaction, the modified interaction force is obtained, realizing the ion-regulated interaction of the hydrophobic interface.

[0022] Step 6: Compare the number of nanobubbles, foam stability, hydrophobicity or modified interaction force with the preset process target value, automatically calculate and generate the dosing adjustment command for the ion concentration or pH value, and output the command to the actuator to adjust the composition of the solution to be treated.

[0023] It should be understood that the summary section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description.

[0024] Compared with existing technologies, the prediction system and method for regulating hydrophobic interface behavior by ion concentration and pH value provided in this invention have the following beneficial technical effects: This invention, by constructing a water molecule ligand energy state model and an ion-induced model, deeply couples the microscopic interface ligand evolution with macroscopic solution chemical parameters (ion concentration and pH value), achieving cross-scale quantitative prediction of hydrophobic interface interactions and bubble behavior. This solves the technical pain points of difficult assessment of interface behavior and ambiguous industrial control logic in complex solution environments. Simultaneously, by converting the prediction results into closed-loop reagent dosing commands in real time, it replaces the traditional control method relying on experience and high-hysteresis experiments. This not only significantly improves the control accuracy of foam stability in flotation and water treatment systems but also greatly reduces reagent consumption and process operating costs. By introducing a water molecule ligand energy state model, this invention achieves cross-scale coupling from microscopic hydrogen bond breaking to macroscopic hydrophobic force fields, significantly improving the prediction accuracy of foam stability in complex solution systems such as seawater flotation.

[0025] To address the problem in existing technologies where it is difficult to accurately determine the strength of hydrophobic interactions under different ion concentrations and pH conditions, this invention introduces a coupled calculation process between the hydrophobic interface state and the ionic environment, using ion concentration and pH as core input parameters, thereby achieving quantitative prediction of changes in hydrophobic interactions. Unlike existing technologies that rely on empirical judgment or single-condition testing, this invention can directly output the trend of hydrophobic interaction changes under given solution conditions, making the determination of the hydrophobic interface state more stable and reliable.

[0026] Secondly, addressing the problem in existing technologies that make it difficult to continuously assess interfacial surface potential under varying environmental conditions, this invention establishes a computational relationship between ion distribution and interfacial state, enabling simultaneous prediction of surface potential changes with ion concentration and pH. This approach avoids the limitations of traditional methods that rely on single-point experiments for surface potential measurement and struggle to compare results under different operating conditions. It allows for rapid assessment of changes in interfacial electrical properties through computation, thereby improving the accuracy of interfacial behavior analysis.

[0027] Furthermore, addressing the issues of unpredictable nanobubble numbers and significant fluctuations in bubble formation under different solution conditions in existing technologies, this invention calculates and evaluates the number of generated bubbles based on the coupling results of hydrophobic interaction strength and surface potential. Since the bubble formation process is simultaneously influenced by hydrophobic interactions and interfacial electrical properties, this invention avoids errors caused by judging a single parameter through integrated calculation, making the predicted nanobubble number more consistent with the actual operating trend.

[0028] Furthermore, addressing the problem that existing technologies primarily rely on empirical adjustments for foam stability and lack effective prediction methods, this invention achieves the prediction of foam stability trends through comprehensive analysis of changes in the number of nanobubbles and the state of interfacial interactions. This method can reflect the influence of ion concentration and pH changes on foam structural stability, transforming the foam control process from empirical operation to a parameter-controllable calculation process, thereby improving the controllability and repeatability of process adjustments.

[0029] This invention effectively solves the problem of inaccurate prediction of hydrophobic interactions, surface potential and bubble behavior in the prior art by introducing a hydrophobic interface coupling calculation method under ion concentration and pH conditions. It achieves reliable evaluation of the number of nanobubbles and foam stability, and has good engineering applicability and promotion value. Attached Figure Description

[0030] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this application in any way. Furthermore, the shapes and scales of the components in the drawings are merely illustrative to aid in understanding this application and do not specifically limit the shapes and scales of the components. Those skilled in the art, guided by the teachings of this application, can select various possible shapes and scales to implement this application according to specific circumstances. In the drawings:

[0031] Figure 1 A schematic diagram of the structure of the prediction system for regulating hydrophobic interface behavior by ion concentration and pH value provided in the example;

[0032] Figure 2 This is an example diagram showing the variation of hydrogen bond vibration frequency at the hydrophobic interface with distance in the embodiment;

[0033] Figure 3 This is an example diagram showing the variation of covalent bond vibration frequency at the hydrophobic interface with distance in the embodiment;

[0034] Figure 4 This is an example diagram showing the variation of dielectric constant with distance in the embodiment;

[0035] Figure 5 This is an example diagram showing the variation of refractive index with distance in the embodiments;

[0036] Figure 6 This is an example diagram showing the variation of induced potential with distance in the embodiment;

[0037] Figure 7 This is an example diagram showing the variation of total potential with distance in the embodiment;

[0038] Figure 8 This is an example graph showing the change in adsorbed cation concentration with distance in the embodiments;

[0039] Figure 9 This is an example diagram showing the change in induced anion concentration with distance in the embodiments;

[0040] Figure 10 This is an example diagram illustrating the variation of ion-controlled hydrophobic energy with distance in the embodiments;

[0041] Figure 11 This is an example diagram illustrating the variation of ion-controlled vapor bridge energy with distance in the embodiments;

[0042] Figure 12 This is an example diagram illustrating the variation of surface potential at the gas-liquid interface with distance in the embodiment;

[0043] Figure 13 This is an example diagram illustrating the variation of hydrophobic energy at the gas-liquid interface with distance in the embodiment;

[0044] Figure 14 This is an example diagram illustrating how the DLVO effect between bubbles varies with distance in the embodiment. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0046] This invention provides a predictive system for the regulation of hydrophobic interface behavior by ion concentration and pH value, such as... Figure 1As shown, it includes a parameter acquisition module, an energy state distribution simulation module, an interface dynamic evolution module, an ion regulation potential coupling module, a bubble behavior prediction module, and a regulation command generation and execution module.

[0047] The parameter acquisition module is used to collect the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated; the solution chemical parameters include ion species, ion concentration, and pH value.

[0048] The energy state distribution simulation module is used to input the microscopic state parameters of water molecules into a preset water molecule energy state model and calculate the bulk ligand distribution as the first boundary condition.

[0049] The interface dynamic evolution module is used to calculate the evolution of the number of ligand structures at the hydrophobic interface by using a preset hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and basic physicochemical parameters. Based on the evolution of the number of ligand structures at the hydrophobic interface, and according to the predetermined hydrophobic interface energy model, which includes hydrogen bond structure energy, chemical potential and mixing entropy, the energy evolution of the hydrophobic interface as a function of distance is obtained.

[0050] The ion-regulated potential coupling module is used to calculate the ion activity changes induced by the evolution of interfacial water molecule ligands based on a pre-determined hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, according to a pre-constructed ion-induced model. Based on this, the induced charge density and induced potential of the hydrophobic interface are determined. Combined with the collected intrinsic potential of the condensed-state hydrophobic interface, a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions is obtained through nonlinear correction of the induced potential and dielectric constant. Based on this, the specific adsorption of ions in water by the surface potential is calculated, and then the influence of adsorbed ions on the hydrophobic effect by changing the interfacial activity distribution is calculated.

[0051] The bubble behavior prediction module is used to obtain the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential of the condensed matter interface based on a pre-constructed condensed matter interface model. Based on the influence of adsorbed ions on the hydrophobic interaction by changing the interfacial activity distribution, it constructs an ion-regulated hydrophobic interaction of the condensed matter interface and qualitatively and quantitatively analyzes the number and stability of nanobubbles. Based on the pre-constructed condensed matter hydrophobic interface model and gas-liquid interface model, and according to the evolution of hydrophobic energy of different hydrophobic interfaces, it uses a pre-constructed interfacial hydrophobic force equation to obtain the corresponding interfacial ion-regulated hydrophobic force. Combined with the modified DLVO interaction, the modified interaction force is obtained, realizing the ion-regulated interaction of the hydrophobic interface.

[0052] The control instruction generation and execution module compares the predicted value of the number of nanobubbles, the foam stability assessment value, the hydrophobic force or the modified interaction force with the preset process target value to determine the target ion concentration or target pH value required to make the solution environment reach the target state. Based on this, it automatically calculates the amount of reagent to be added to adjust the target component and sends a dosing instruction to the dosing execution mechanism to adjust the solution composition.

[0053] In this embodiment, a hydrogen bond structure model and hydrogen bond energy states of water molecules at hydrophobic interfaces are pre-constructed, including: the hydrogen bond structure model classifies the existence forms of water molecule ligands into six types: monomers, dimers, trimers, tetramers, pentamers, and hexamers (ligand morphologies). The hydrogen bond energy states of water molecules include the interfacial hydrogen bond states of the ligands, the dipole-dipole interaction energy, and the gas monomer energy.

[0054] In this embodiment, the basic physicochemical parameters of the hydrophobic interface may include interface type, contact angle, surface tension, ambient temperature, ambient pressure, and surface potential. The microscopic state parameters of water molecules include coordination number, hydrogen bond length, covalent bond length, and bulk average bond length.

[0055] In the embodiments, based on the coordination number of water molecules, hydrogen bond length, and covalent bond length, the energy states of water ligands with different hydrogen bond structures and the average energy state of the bulk phase are calculated using a pre-constructed water molecule energy state model. Based on the Boltzmann distribution of the water molecule ligand energy states and the average energy state, the bulk phase ligand distribution, i.e. the first boundary condition, is obtained (see Equations 1-4 below).

[0056] In this embodiment, the energy state distribution simulation module includes the following equations 1-4. The interface dynamic evolution module calculates the evolution of the number of ligand structures at the hydrophobic interface using the following equations 5-19, and obtains the energy evolution of the hydrophobic interface as a function of distance using the following equations 7-11.

[0057] In the embodiment, the ion-controlled potential coupling module uses Equations 20-27 to calculate the change in interfacial ion activity, the induced charge density at the hydrophobic interface, and the induced potential; and uses Equations 28-35 to calculate the effect of adsorbed ions on the hydrophobic interaction.

[0058] In the embodiment, the bubble behavior prediction module uses Equations 36-43 to analyze the number and stability of nanobubbles, and uses Equations 44-47 to determine the hydrophobic force of the interface and realize the regulation of the interaction between ions and the hydrophobic interface.

[0059] In this embodiment, the pre-constructed water molecule energy state model includes: 1. Calculating the interaction energy based on the hydrogen bond lengths O:H and covalent bond lengths OH of the water molecule, which is the water molecule ligand hydrogen bond energy state, as shown in Equation 1 below:

[0060] (Equation 1);

[0061] In the formula, For the hydrogen bond energy state of ligand j, The microscopic charge of a water molecule. =0.24e=0.4×10 -19 C, water molecule dipole moment =5×10 -30 C·m, For hydrogen bond angle, vacuum permittivity =8.854×10 -12 F / m, dielectric constant , The length of the hydrogen bond is given.

[0062] 2. Based on the dipole moment and covalent bond length of the monomeric water molecule, the dipole interaction is calculated, which is the dipole energy state of the monomeric water molecule. The expression is:

[0063] (Equation 2);

[0064] Among them, the static dielectric constant of water Vacuum static dielectric constant , The incident light frequency, Hz, water refractive index Vacuum refractive index Planck's constant h = 6.626 × 10 -34 J·s, radius of water molecule Intermolecular distance , where k is the Boltzmann constant and T is the temperature.

[0065] 3. Calculate the energy states of a gaseous monomer based on the Boltzmann constant. :

[0066] (Equation 3);

[0067] 4. Combining the energy states of gaseous monomers, the bulk ligand distribution is obtained based on the Boltzmann distribution (as shown in Equation 4 below).

[0068] The bulk water at the hydrophobic interface is set as the first boundary condition, and the innermost part closest to the hydrophobic interface is set as the second boundary condition. Using the Boltzmann distribution, key thermodynamic parameters such as enthalpy, entropy, and chemical potential at the interface are quantitatively calculated. The specific calculation method is as follows:

[0069] The bulk ligand distribution of the first boundary condition is established using the Boltzmann distribution, and the calculation formula is as follows:

[0070] (Equation 4);

[0071] In the formula, The number of ligands in phase j. This represents the total number of water molecule layers in the bulk phase. The average energy of the bulk phase.

[0072] Combining the hydrogen bond structure model and the contact angle θ parameter of the second boundary condition, the energy of the second boundary condition is calculated as follows:

[0073] (Equation 5);

[0074] In the formula, The cohesive energy of bulk water molecules, The second boundary condition is the cohesive energy of water molecules.

[0075] It is the adhesion energy of the interaction between liquid molecules and solid surfaces. The surface tension is the second boundary condition.

[0076] Energy difference between the first boundary condition and the second boundary condition for:

[0077] (Formula 6);

[0078] in, ; for This represents the maximum energy state of a water molecule with the i-th hydrogen-bonded structure. Based on the energy difference... Sure The boundary constant of ligand j .

[0079] Thermodynamic parameters at the interface include, but are not limited to, parameters such as hydrogen bond energy, entropy, chemical potential, density potential, and evaporation. The formula for calculating the interface energy state is as follows:

[0080] (Equation 7);

[0081] In the formula, Let be the energy state of the water molecule with the i-th hydrogen bond structure. The chemical potential of liquid phase molecules, η represents the chemical potential of gas-phase molecules.

[0082] hydrogen bond energy for: (Equation 8);

[0083] In the formula, It is a gaseous single-element energy state.

[0084] Entropy at the interface The calculation formula is as follows:

[0085] (Equation 9);

[0086] In the formula, Let j be the number of ligands in the i-th layer of water molecules. Let be the number of gaseous water molecules transformed from the water molecules in the i-th layer. Number of ligands in bulk water molecule layer j The number of gaseous water molecules that are transformed from bulk water molecules. This represents the entropy change at the interface. This represents the thermodynamic concentration of gaseous monomers in the bulk phase. For the ligand j of the water molecule in the i-th layer of the interface, Let be the thermodynamic concentration of the gaseous monomer in the i-th layer of the interface. denoted as ωj, where ωj is the thermodynamic concentration of

[0087] Chemical potential at the interface The calculation formula is as follows:

[0088] (Equation 10);

[0089] In the formula, is the standard chemical potential for the conversion of j-ligand into a gaseous monomer.

[0090] (Equation 11);

[0091] In the formula, is the standard chemical potential of the gaseous monomer.

[0092] Density potential at the interface The calculation formula is as follows:

[0093] (Equation 12);

[0094] In the formula, Let be the water density of the i-th layer at the hydrophobic interface. This represents the number of water molecules in the interfacial water molecule layer. The mass of a water molecule. and The volumes of water molecules corresponding to the gaseous monomer and the liquid j-ligand, respectively. This refers to the number of gaseous monomers. Let j be the number of ligands.

[0095] In the embodiment, the interface vibration frequency is determined as follows:

[0096] (Equation 13);

[0097] In the formula, Let be the average vibrational frequency of the water molecules in the i-th layer. Let j be the vibrational frequency of ligand j, and N be the total number of water molecules in the i-th layer of the interface.

[0098] Calculate the interfacial dielectric constant of ligand j water molecules based on interfacial vibrational frequencies. :

[0099] (Equation 14);

[0100] In the formula, The vibrational frequency of the O:H moiety of the hydrogen bond in the j-ligand of the water molecule is . Let be the vibrational frequency of the covalent bond in the j-ligand of the water molecule. Let be the spatial density of water molecules, e be the electron charge, and m be the mass of a water molecule. Its vacuum permittivity is 8.854 × 10⁻¹² F / m. The incident light frequency; This is the loss term of the harmonic oscillator. It is the imaginary unit.

[0101] The dielectric constant of the i-th water molecule interface after hydrophobic interface correction is then... for:

[0102] (Equation 15);

[0103] In the formula, Let be the dielectric constant of the i-th layer of water molecules. Let j be the dielectric constant of the ligand. Let be the water density of the i-th layer at the hydrophobic interface.

[0104] Correspondingly, the refractive index of the i-th layer of water molecules for:

[0105] (Equation 16);

[0106] Based on the boundary conditions and the differences in hydrogen bond energy states, the formula for calculating ligand distribution in the condensed matter hydrophobic interface model is as follows:

[0107] (Equation 17);

[0108] In the formula, N is the total number of water molecules in the i-th layer, which is a constant; The constant for gaseous monomers; It is the rate constant for the conversion of gaseous monomers into other ligands.

[0109] The formula for calculating ligand distribution in the gas-liquid interface model is as follows:

[0110] (Equation 18);

[0111] In the formula, N is the total number of water molecules in the i-th layer.

[0112] The evolution equation for the number of ligand structures at hydrophobic interfaces is as follows:

[0113] (Equation 19);

[0114] In the formula, The boundary constant for ligand j determines the number of ligands for ligand j under the second boundary condition (i.e., the cutoff point of hydrophobic energy after entropy compensation). Let be the rate constant for the conversion of ligand j to other ligands. Let be the conversion rate constant for the transformation of other ligands to ligand j, and let x be the distance in the evolution of the number of interfacial ligand structures, in nanometers. This represents the number of other ligands in the evolution of ligand j. The hexamer, representing the highest hydrogen bond energy state, is used as the evolution endpoint to constrain the evolution of the number of interface ligand structures.

[0115] In the embodiments, the number of ions enriched by hydrophobic interaction is determined by using the ion type and the hydration energy of water molecules, combined with the interfacial hydrochemical potential determined by the pH value, and then the hydrophobic interface induced charge density is obtained.

[0116] When dealing with high-salinity environments such as seawater, the ion-regulated potential coupling module not only considers the electrostatic attraction of ions but also quantifies the capture capacity of the low-coordinated water molecule layer at the hydrophobic interface for ions with different hydration energies through an ion-induced model. The calculated ion activity gradient reflects the drastic concentration fluctuations of ions within a range of tens of nanometers near the interface. These fluctuations directly lead to the reconstruction of the induced charge density at the hydrophobic interface, thereby generating an induced potential superimposed on the intrinsic potential, ultimately achieving precise correction of macroscopic hydrophobic forces.

[0117] As an example, in the case of ions present at the hydrophobic interface, it is necessary to consider a series of effects of ion activity on hydrophobic interactions. First, an ion-induced model of the hydrophobic interface is constructed:

[0118] Interfacial ion hydration energy for:

[0119] (Equation 20);

[0120] In the formula, The valence state is ionic, and H is the distance between the ion and the water molecule. The hydrogen bond energy of the water molecules in the ion hydration layer (can be calculated by substituting the hydrogen bond parameters into Equation 1). The angle between the ion and the water molecule dipole bond is denoted as α.

[0121] Hydrophobic interactions disrupt the interfacial hydrogen bond structure, leading to the enrichment of interfacial ions and an increase in the number of hydrated water molecules in these ions. The calculation formula is as follows:

[0122] (Equation 21);

[0123] A model for the induction of ion enrichment by hydrophobic interactions was constructed, and the interfacial water chemical potential was obtained based on the interfacial water molecule ligand structure. The calculation formula is as follows:

[0124] (Equation 22);

[0125] In the formula, The number of water molecules used for hydration formed by the j-th ligand in the i-th layer. The number of hydrated water molecules formed by the i-th layer of gaseous monomers. The standard chemical potential of the gaseous monomer, The standard chemical potential of the liquid ligand;

[0126] Then induce ion activity for:

[0127] (Equation 23);

[0128] In the formula, ; For the interfacial water chemical potential, The nearest neighbor water molecule is the hydration energy of the ion, and M is the coordination number of the water molecule in which the ion undergoes hydration. The quantity of bulk ion A (a preset value).

[0129] Based on the change in interfacial ion activity, the induced charge density at the hydrophobic interface is obtained:

[0130] (Equation 24);

[0131] In the formula, The hydrophobic interface induces charge density for hydrophobic induced ions. To induce the number of anions / cations through hydrophobicity, Hydrophobic induction of anion / cation valence states;

[0132] Among them, due to the difference between the hydration energy state and the hydrogen bond energy state of different ions, the specific adsorption of ions by hydrophobic interactions can be obtained through Boltzmann distribution. The formula for calculating the ion distribution is as follows:

[0133] (Equation 25);

[0134] In the formula, represents the partition function of the hydration energies of different ions. The difference in hydration energy between different ions;

[0135] Based on the influence of hydrophobic interfaces on the dielectric constant and the formation of induced charge density, the induced potential of hydrophobic interfaces is calculated. :

[0136] (Equation 26);

[0137] The eigenpotential of a condensed hydrophobic surface exists. By combining the eigenpotential of the condensed charged hydrophobic interface, the potential change of the hydrophobic interface can be determined (i.e., the hydrophobic interface potential model).

[0138] (Equation 27);

[0139] In the formula, The intrinsic potential of the condensed hydrophobic interface, the Debye length .

[0140] In some embodiments, the calculation formula for determining the activity of adsorbed ions at the hydrophobic interface is also included:

[0141] (Equation 28);

[0142] in, This represents the number of ions in the bulk phase.

[0143] Based on the changes in induced ion activity, adsorbed ion activity, and water molecule quantity at the hydrophobic interface, the energy of the condensed matter hydrophobic interface is derived. In the embodiments, the energy of the condensed hydrophobic interface can be... The value is compared with the preset target value, and then the corresponding dosing instructions are given.

[0144] As an example, condensed matter hydrophobic interface energy The formula is:

[0145] (Equation 29);

[0146] In the formula, It represents the chemical potential.

[0147] Hydration energy of adsorbed ions:

[0148] (Formula 30);

[0149] In the formula, M is the ion hydration number. This represents the number of hydrophobically induced ions. This represents the number of ions in the bulk phase. This represents the number of adsorbed ions; Hydrophobic energy at condensed matter interfaces, including hydrogen bond structure energy. Chemical potential and mixed entropy .

[0150] Among them, hydrogen bond structure energy :

[0151] (Equation 31);

[0152] In the formula, This represents the number of j-ligands in the interface that did not participate in ion hydration. The number of j-ligands in the bulk phase that did not participate in ion hydration;

[0153] Chemical potential :

[0154] (Equation 32);

[0155] In the formula The chemical potential of liquid water molecules, The chemical potential of gaseous water molecules at the condensed-state hydrophobic interface. The chemical potential of ions at the hydrophobic interface of condensed matter and their hydrated water molecules;

[0156] Mixed entropy:

[0157] (Equation 33);

[0158] In the formula, The activity of gaseous monomers at the interface. The activity of liquid water molecules and ions at the interface. This refers to the activity of gaseous monomers in the bulk phase. The activity of bulk liquid water molecules and ions;

[0159] Energy of gaseous monomers :

[0160] (Equation 34);

[0161] In the formula, This represents the number of gaseous monomers at the interface that did not participate in ion hydration. The number of gaseous monomers in the bulk phase that did not participate in ion hydration;

[0162] Based on the changes in ion activity and water structure of charged hydrophobic surfaces, the changes in contact angle at hydrophobic interfaces were obtained:

[0163] (Equation 35);

[0164] In the formula, The structural energy of the innermost water molecule;

[0165] The following describes how to construct a hydrophobic interface to regulate the number of nanobubbles using ions:

[0166] The total amount of gaseous monomers at the charged hydrophobic interface is:

[0167] (Equation 36);

[0168] In the formula, This represents the number of ligands in the gaseous monomer under the second boundary condition (i.e., the cutoff point of hydrophobic energy after entropy compensation). The integral upper limit of the difference between the amount of gaseous monomers and ionized water at the interface is given by the formula. In the formula, x represents the distance away from the hydrophobic interface. The destructive effect of hydrophobic interaction on the hydrogen bond structure weakens, and the hydrogen bond structure at the interface gradually strengthens as it moves away from the hydrophobic interface, generating an energy gradient. The number of water molecule layers i corresponding to the hydrogen bond structure increases, which is the change in interface distance. x and i can be converted to each other. For example, the distance corresponds to the number of water molecule layers multiplied by the layer thickness, which are different manifestations of the same parameter.

[0169] From the innermost part of the interface to the points limit The number of ions at that location is:

[0170] (Equation 37);

[0171] Adsorbed ions preferentially hydrate with low-energy water molecules, and the total amount of remaining gaseous monomers is:

[0172] (Equation 38);

[0173] The effect of ion activity on the number of nanobubbles is as follows:

[0174] (Equation 39);

[0175] R is the average radius of the nanobubble. R can be the radius of the collected particles or the radius of the calculated nanobubble.

[0176] The following model constructs a method to regulate bubble stability through hydrophobic interactions. Unlike condensed-state charged hydrophobic surfaces, gaseous monomers at the gas-liquid interface escape into the gas phase, leading to localized densification of the interface. Furthermore, only the hydrophobic-induced potential exists at the gas-liquid interface; the adsorption of intrinsic surface potential need not be considered. The specific steps are as follows:

[0177] The chemical potential of ionized water induced at the gas-liquid interface is:

[0178] (Formula 40);

[0179] The corresponding number of induced ions is:

[0180] (Equation 41);

[0181] Hydrophobicity of gas-liquid interface for:

[0182]

[0183] (Equation 42);

[0184] The structural energy of the gas-liquid interface. The chemical potential of the ions at the gas-liquid interface is given by the formulas for calculating the chemical potential of liquid water molecules and the mixing entropy at the gas-liquid interface, which are similar to those for condensed hydrophobic interfaces.

[0185] The surface tension at the gas-liquid interface is:

[0186] (Equation 43);

[0187] The escape of gaseous monomers and the enhancement of hydrogen bond structure due to the enrichment of interfacial ions synergistically control the local densification of the hydrophobic interface. An energy barrier exists at the gas-liquid interface, which determines the stability of the bubble.

[0188] The equation for ion-controlled interfacial hydrophobicity is:

[0189] (Equation 44);

[0190] In the formula, Hydrophobicity regulated by ions It is an interfacial hydrophobic energy.

[0191] Condensed matter interface model Employing condensed matter interface hydrophobic energy The gas-liquid interface utilizes the hydrophobic energy of the gas-liquid interface. .

[0192] The dielectric constant and refractive index parameters of the hydrophobic interface were modified to account for the DLVO effect at the hydrophobic interface. The modified interaction force formula is as follows:

[0193] (Formula 45);

[0194] In the formula, A is the corrected Hamark constant:

[0195] (Formula 46);

[0196] The dielectric constant of the hydrophobic surface, Let be the dielectric constant of the i-th layer of the hydrophobic interface. The refractive index of the hydrophobic surface, Let be the refractive index of the i-th layer of the hydrophobic interface, and h be Planck's constant. Z is the incident light frequency; Z is the corrected double-layer interaction constant.

[0197] (Equation 47);

[0198] The embodiments pre-construct a hydrophobic interface ligand evolution model, including:

[0199] Step 1: Calculate the second boundary condition energy of water molecules at the hydrophobic interface based on the surface tension and contact angle parameters of the hydrophobic interface (see Equation 5 above).

[0200] Step 2: Based on the difference in cohesive energy between the first and second boundary conditions, substitute the correspondence between contact angle and energy into the microstructure to construct the relationship between contact angle and the energy of the microscopic energy state of water molecules (see Equation 6).

[0201] Step 3: Based on the relationship between the contact angle and the energy of the microscopic energy state of water molecules, obtain the ligand structure of water molecules at the corresponding contact angle. According to the energy state difference of different water molecules, obtain the evolution equation of the number of ligand structures at the hydrophobic interface (see Equation 19 below).

[0202] In this embodiment, based on the evolution of the number of ligand structures of water molecules at the hydrophobic interface, a hydrophobic interface energy model is constructed to obtain the corresponding hydrophobic interface energy evolution as a function of distance. The hydrophobic interface energy model can be found in Equation 7-11 above.

[0203] In the embodiments, the method for pre-constructing an ion-induced model includes:

[0204] Step 1: Calculate the hydration energy of the ion with water molecules according to the type of ion, and calculate the ion enrichment induced by hydrophobic interaction according to the interfacial hydrochemical potential (see Equations 20-23 above).

[0205] Step 2: Based on the ion hydration energy and the interfacial hydrochemical potential, obtain the number of hydrophobic interface induced ions and calculate the corresponding hydrophobic interface induced charge density and induced potential (Equations 24-26).

[0206] In the embodiments, the pre-constructed condensed hydrophobic interface model and gas-liquid interface model (Equations 17 and 18) are as follows:

[0207] Step 1: Based on the calculated distribution of ligands and ion enrichment at the hydrophobic interface, obtain the corrected interfacial hydrophobic interaction energy (see Equations 29-35 above for the condensed matter hydrophobic interface model).

[0208] Step 2: Calculate the corresponding physical parameter changes based on the changes in ion activity and dielectric constant, including: charge density, induced potential, and number of nanobubbles (see Equations 36-41 above for the gas-liquid interface model).

[0209] Step 3: Based on the changes in these physical parameters, calculate the modified hydrophobic interface DLVO effect and the interface hydrophobic force (see Equations 42-47 above).

[0210] In this embodiment, after obtaining the energy change parameters corresponding to the two types of interfaces, the hydrophobic force can be characterized based on the negative gradient relationship between interface energy and displacement. This hydrophobic force reflects the intrinsic attraction of the hydrophobic interface due to the evolution of water molecule ligands, and its strength is directly determined by the energy change parameters calculated above. In this embodiment, the method for determining the interface hydrophobic force includes: Step 1: Selecting different types of hydrophobic interface energy states based on the actual situation according to the obtained hydrophobic interface energy evolution; Step 2: Calculating the interaction force of the hydrophobic interface through the De Jakin approximation to obtain the hydrophobic attraction model of the hydrophobic interface to the particles (Equations 44-46).

[0211] In some embodiments, the control instruction generation and execution module is connected to a dosing pump actuator via a communication bus, and the dosing pump actuator adjusts the injection flow rate of flotation reagent or pH adjuster according to the received instructions.

[0212] In this embodiment, the parameter acquisition module may include a pH sensor, an ion electrode, and an online contact angle measurement device.

[0213] In some embodiments, the system further includes a result output module for displaying in real time the dynamic evolution trends of hydrophobic interaction strength, surface potential, and foam stability in numerical or curve form.

[0214] Based on the same inventive concept as the prediction system for the regulation of hydrophobic interface behavior by ion concentration and pH value provided in the above embodiments, the present invention also provides a prediction method for the regulation of hydrophobic interface behavior by ion concentration and pH value. This method is applied to the automatic control of industrial flotation or water treatment systems, and includes:

[0215] Step 1: Collect the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated; the solution chemical parameters include ion species, ion concentration, and pH value;

[0216] Step 2: Input the microscopic state parameters of water molecules into the preset water molecule energy state model, calculate the bulk ligand distribution, and use it as the first boundary condition;

[0217] Step 3: Using the pre-defined hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and the basic physicochemical parameters, calculate the evolution of the number of ligand structures at the hydrophobic interface (5-19); based on the evolution of the number of ligand structures at the hydrophobic interface, according to the pre-calculated hydrophobic interface energy model, which includes hydrogen bond structure energy, chemical potential and mixing entropy, obtain the hydrophobic interface energy evolution as a function of distance (Equations 7-11).

[0218] Step 4: Based on the pre-calculated hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, and according to the pre-constructed ion-induced model, calculate the ion activity gradient induced by the evolution of interfacial water molecule ligands, and determine the induced charge density and induced potential of the hydrophobic interface accordingly; combined with the collected intrinsic potential of the condensed-state hydrophobic interface, obtain a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions through nonlinear correction of the induced potential and dielectric constant, and then calculate the effect of adsorbed ions on hydrophobic interaction by changing the interfacial activity distribution; (Equations 28-35);

[0219] Step 5: Based on the constructed gas-liquid interface model, the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential are obtained for the gas-liquid interface. Based on the influence of adsorbed ions on hydrophobic interaction, the ion-regulated hydrophobic interaction of the gas-liquid interface is constructed, and the number of nanobubbles and foam stability are qualitatively and quantitatively analyzed (Equations 36-43). Based on the constructed condensed-state hydrophobic interface model and gas-liquid interface model, and according to the energy evolution of different hydrophobic interfaces, the ion-regulated interfacial hydrophobic force equation is obtained. Combined with the modified DLVO effect, the interaction regulation of ions on the hydrophobic interface is realized (Equations 44-47).

[0220] Step 6: Compare the number of nanobubbles, foam stability, hydrophobicity or regulated interaction force with the preset process target value, automatically calculate and generate dosing adjustment instructions for ion concentration or pH value, and output the instructions to the actuator to adjust the composition of the solution to be treated.

[0221] In some embodiments, step 5 includes:

[0222] 5.1: Substitute the hydrophobic interface energy evolution obtained in step 3 into the preset de Jaggin approximation equation to calculate the hydrophobic attraction component that reflects the strength of the hydrophobic interface attraction.

[0223] 5.2: Using the modified DLVO model, the hydrophobic attraction component and the double-layer repulsion component determined by the hydrophobic interface potential model are vector-superimposed to obtain the ion-controlled total interfacial force model.

[0224] 5.3: Based on the total interfacial force model and combined with the surface tension parameters of the solution, the energy barrier for bubble nucleation at the hydrophobic interface is calculated, thereby predicting the number of nanobubbles and foam stability.

[0225] Example: Predicting the effects of ion concentration and pH on hydrophobic interfaces and foam stability under seawater flotation conditions. In this example, the method is applied to the analysis of hydrophobic interface behavior and foam stability prediction in a high-salinity solution environment, specifically the calculation and control of bubble behavior in a flotation system under seawater conditions. This example focuses on demonstrating the influence of changes in ion concentration and pH on hydrophobic interactions, interfacial surface potential, the number of nanobubbles, and foam stability.

[0226] The system used in this embodiment runs on a computer device, which includes a processor, a memory, and an input / output interface. These components are interconnected via a communication bus. The processor executes computer program instructions stored in the memory, the memory stores program code and data generated during execution, and the input / output interface is used for parameter input and result output.

[0227] In the specific implementation process, basic parameter information is first input into the system through the parameter acquisition module. These basic parameters include interface type parameters, solution chemical parameters, and operating environment parameters. The interface type parameters characterize the hydrophobic properties of the interface to be analyzed; the solution chemical parameters include at least the main ion species, ion concentrations, and pH values ​​in the solution; and the operating environment parameters describe the temperature and pressure conditions under which the system operates. In this embodiment, the solution chemical parameters are selected as the typical ion composition of seawater, and different operating conditions are simulated by changing the ion concentrations and pH values.

[0228] After the parameters are input, the system calls the energy state distribution simulation module and the interface dynamic evolution module to process the interface type parameters and calculate the hydrophobic interaction state parameters under the current ionic environment and operating conditions. These parameters reflect the strength of the hydrophobic interactions exhibited by the interface in the solution environment and serve as the basic input for subsequent calculations.

[0229] Subsequently, based on the hydrophobic interface state parameters and solution chemical parameters, the system invokes the ion-modulated potential coupling module to calculate the influence of ion distribution near the interface on the interfacial electrical properties. This module comprehensively considers the changes in interfacial charge state under different ion concentrations and pH conditions, and outputs the corresponding interfacial surface potential results. This step reflects the effect of changes in the solution environment on the modulation of interfacial electrical properties.

[0230] After obtaining the hydrophobic interaction state parameters and surface potential calculation results, the system further calls the bubble behavior prediction module to calculate and evaluate the number of nanobubbles and foam stability. This module analyzes the generation trend of bubbles near the interface based on the coupling relationship between hydrophobic interactions and surface potential, and outputs the changes in the number of nanobubbles and related foam stability parameters under different ion concentrations and pH conditions. This method can intuitively reflect the impact of changes in the ionic environment on the stability of the foam system. The control command generation and execution module can control the dosage of reagents and send dosing instructions to the dosing execution mechanism to adjust the solution composition.

[0231] Finally, the system's results output module integrates and processes the outputs from each calculation module, displaying them in numerical or graphical form. Users can compare the calculation results under different ion concentrations and pH conditions to analyze the relationships between changes in hydrophobic interactions, surface potential changes, and the number of nanobubbles and foam stability, thus providing a basis for parameter selection in the flotation or foam control process.

[0232] Figures 2-14 The figures show the experimental results of the embodiments. As can be seen from the figures, through the above implementation methods, the prediction of hydrophobic interface behavior and foam stability under different ion concentrations and pH conditions can be achieved without extensive experimental testing. Those skilled in the art can implement the method of this invention based on the steps and system structure of this embodiment, without needing additional exploratory work or creative effort.

[0233] This technical solution addresses the issue of existing technologies relying excessively on numerous experimental tests, having long testing cycles, and having results easily affected by environmental fluctuations in the analysis of hydrophobic interface behavior, specifically focusing on the prediction of the effects of ion concentration and pH on hydrophobic interfaces and foam stability under seawater flotation conditions. In particular, it overcomes the shortcomings of existing simulation methods, which often deal with single scales or single factors and are unable to reflect the coupling relationship between the ion environment, the state of the hydrophobic interface, and bubble behavior. This solution effectively compensates for the technical deficiencies of existing technologies in handling high-salt environments such as seawater, where the calculation of ion effects and hydrophobic effects is performed separately, resulting in poor stability of prediction results and difficulty in accurately determining the number distribution of nanobubbles.

[0234] The beneficial technical effects of this scheme lie in the fact that, by establishing a unified multi-scale coupled simulation system, it enables rapid and accurate prediction of hydrophobic interface behavior and foam stability under different ion concentrations and pH conditions without the need for extensive exploratory experiments. It not only simultaneously considers the interaction between the hydrophobic interface state and the complex ionic environment, intuitively reflecting the regulatory laws governing the influence of environmental parameter changes on bubble generation trends and foam stability, but also greatly enhances its engineering application, providing a scientific and reliable theoretical basis and data support for parameter selection and operational optimization in industrial processes such as seawater flotation, water treatment, and foam control.

[0235] The embodiments can be implemented through a computer program, modularizing and integrating the above calculation process. Users only need to input basic parameters such as ion concentration and pH to obtain results related to hydrophobic interactions, surface potential, nanobubble quantity, and foam stability. This reduces reliance on extensive experimental testing, lowers experimental costs, and improves the efficiency of parameter selection and operating condition optimization. Furthermore, this method is simple to operate and has a clear calculation process, making it suitable for widespread application in industrial processes such as flotation separation, water treatment, and foam control.

[0236] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, or a tablet computer, or any combination of these devices.

[0237] The above provides a detailed description of the prediction system and method for regulating hydrophobic interface behavior by ion concentration and pH value provided in this application. Specific examples are used in this paper to illustrate the principle and implementation of this application. The above description of the embodiments is only for the purpose of helping to understand the concept of this application and should not be construed as a limitation on the scope of protection of this application.

Claims

1. A predictive system for the regulation of the behavior of hydrophobic interfaces by ion concentration and pH, characterized in that, include: The parameter acquisition module is used to collect the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated. The solution chemical parameters include ion species, ion concentration, and pH value; The energy state distribution simulation module is used to input the microscopic state parameters of the water molecules into a preset water molecule energy state model and calculate the bulk ligand distribution as the first boundary condition. The interface dynamic evolution module is used to calculate the evolution of the number of ligand structures at the hydrophobic interface by using a preset hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and the basic physicochemical parameters; based on the evolution of the number of ligand structures at the hydrophobic interface, and according to a predetermined hydrophobic interface energy model, which includes hydrogen bond structure energy, chemical potential and mixing entropy, the evolution of hydrophobic interface energy as a function of distance is obtained. The ion-regulated potential coupling module is used to calculate the ion activity change induced by the evolution of interfacial water molecule ligands based on a pre-determined hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, and according to a pre-constructed ion-induced model. Based on this, it determines the induced charge density and induced potential of the hydrophobic interface. Combined with the collected intrinsic potential of the condensed-state hydrophobic interface, and through nonlinear correction of the induced potential and dielectric constant, it obtains a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions. Based on this, it calculates the specific adsorption of ions in water by the surface potential, and then calculates the effect of adsorbed ions on the hydrophobic effect by changing the interfacial activity distribution. The bubble behavior prediction module is used to obtain the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential of the condensed matter interface based on a pre-constructed condensed matter interface model. It also constructs an ion-regulated hydrophobic interaction mechanism for the condensed matter interface based on the influence of adsorbed ions on the hydrophobic interaction by changing the interfacial activity distribution, and qualitatively and quantitatively analyzes the number and stability of nanobubbles. Furthermore, based on the pre-constructed condensed matter hydrophobic interface model and gas-liquid interface model, and according to the evolution of hydrophobic energy at different hydrophobic interfaces, it uses a pre-constructed interfacial hydrophobic force equation to obtain the corresponding interfacial ion-regulated hydrophobic force. Combined with a modified DLVO interaction, the modified interaction force is obtained, thus realizing the ion-mediated regulation of the hydrophobic interface interaction. The control instruction generation and execution module is used to compare the predicted value of the number of nanobubbles, the foam stability assessment value, and the hydrophobic force or the modified interaction force with the preset process target value to determine the target ion concentration component or target pH value required to make the solution environment reach the target state. Based on this, it automatically calculates the amount of reagent to be added to adjust the target ion concentration component or target pH value, and sends a dosing instruction to the dosing execution mechanism to adjust the solution composition.

2. The prediction system of claim 1, wherein, The parameter acquisition module includes a pH sensor, an ion electrode, and an online contact angle measurement device.

3. The prediction system of claim 1, wherein, The water molecule energy state model classifies the existence forms of water molecule ligands into monomers, dimers, trimers, tetramers, pentamers, and hexamers.

4. The prediction system of claim 1, wherein, The control command generation and execution module is connected to a dosing pump actuator via a communication bus. The dosing pump actuator adjusts the injection flow rate of flotation reagent or pH adjuster according to the received command.

5. The prediction system of claim 1, wherein, The system also includes a result output module, which is used to display the dynamic evolution trend of hydrophobic interaction strength, surface potential and foam stability in real time in numerical or curve form.

6. A method for predicting the behavior of a hydrophobic interface regulated by ionic concentration and pH, characterized in that, The method is applied to the automatic control of industrial flotation or water treatment systems, and includes the following steps: Step 1: Collect the solution chemical parameters, basic physicochemical parameters of the hydrophobic interface, and microscopic state parameters of water molecules of the solution to be treated; the solution chemical parameters include ion species, ion concentration, and pH value; Step 2: Input the microscopic state parameters of the water molecules into the preset water molecule energy state model, calculate the bulk ligand distribution, and use it as the first boundary condition; Step 3: Using a pre-defined hydrophobic interface ligand evolution model, combined with the constraints of the first boundary condition and the basic physicochemical parameters, calculate the evolution of the number of ligand structures at the hydrophobic interface; based on the evolution of the number of ligand structures at the hydrophobic interface, and according to the pre-calculated hydrophobic interface energy model, which includes hydrogen bond structure energy, chemical potential and mixing entropy, obtain the hydrophobic interface energy evolution as a function of distance. Step 4: Based on the pre-calculated hydrophobic interface energy model, combined with the collected ion types, ion concentrations, and pH values, and according to the pre-constructed ion-induced model, calculate the ion activity changes induced by the evolution of interfacial water molecule ligands, and determine the induced charge density and induced potential of the hydrophobic interface accordingly; combined with the collected intrinsic potential of the condensed hydrophobic interface, and through nonlinear correction of the induced potential and dielectric constant, obtain a hydrophobic interface potential model reflecting the uneven distribution of interfacial ions, and calculate the specific adsorption of ions in water by the surface potential, and then calculate the effect of adsorbed ions on the hydrophobic effect by changing the interfacial activity distribution. Step 5: Based on the pre-constructed condensed matter interface model, the interfacial ion activity gradient, hydrophobic interface induced charge density, and induced potential are obtained for the condensed matter interface. Based on the influence of adsorbed ions on the hydrophobic interaction by changing the interfacial activity distribution, the ion-regulated hydrophobic interaction of the condensed matter interface is constructed, and the number of nanobubbles and foam stability are qualitatively and quantitatively analyzed. Based on the constructed condensed matter hydrophobic interface model and gas-liquid interface model, according to the evolution of hydrophobic energy of different hydrophobic interfaces, the corresponding ion-regulated hydrophobic force is obtained using the pre-constructed interfacial hydrophobic force equation. Combined with the modified DLVO interaction, the modified interaction force is obtained, realizing the ion-regulated interaction of the hydrophobic interface. Step 6: Compare the number of nanobubbles, foam stability, hydrophobicity or modified interaction force with the preset process target value, automatically calculate and generate the dosing adjustment command for the ion concentration or pH value, and output the command to the actuator to adjust the composition of the solution to be treated.

7. The prediction method of claim 6, wherein, The water molecule energy state model construction in step 2 includes: Based on the hydrogen bond lengths O:H and covalent bond lengths OH of water molecules, calculate the hydrogen bond energy states of water ligands. Calculate the dipole energy state of a single water molecule based on its dipole moment and covalent bond length. Based on the energy states of the gaseous monomers, the bulk ligand distribution is obtained according to the Boltzmann distribution.

8. The prediction method of claim 6, wherein, In step 3, the energy of the second boundary condition of water molecules at the hydrophobic interface is calculated using the surface tension and contact angle in the basic physicochemical parameters. Based on the energy difference between the first and second boundary conditions, the ligand boundary parameters are determined, thereby determining the evolution of the number of ligand structures at the hydrophobic interface.

9. The prediction method of claim 6, wherein, In step 4, the number of ions enriched by hydrophobic interaction is determined by using the ion types and the hydration energy of water molecules, combined with the interfacial hydrochemical potential determined by the pH value, and then the hydrophobic interface induced charge density is obtained.

10. The prediction method of claim 6, wherein, Step 5 includes: 5.1: Substitute the hydrophobic interface energy evolution obtained in step 3 into the preset de Jaggin approximation equation to calculate the hydrophobic attraction component that reflects the strength of the hydrophobic interface attraction. 5.2: Using the modified DLVO model, the hydrophobic attractive force component and the double-layer repulsive force component determined by the hydrophobic interface potential model are vector-superimposed to obtain the ion-controlled total interface force model. 5.3: Based on the total interfacial force model and combined with the surface tension parameters of the solution, the energy barrier for bubble nucleation at the hydrophobic interface is calculated, thereby predicting the number of nanobubbles and foam stability.

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