Design method and system of intelligent chlorine dioxide monitoring system and medium

Abstract

The invention relates to the technical field of water treatment disinfection monitoring, and discloses a chlorine dioxide intelligent monitoring system design method, system and medium, and the method comprises the steps: building a turbulence numerical model used for simulating the water flow movement of a disinfection tank and a chlorine attenuation model used for simulating the change of chlorine dioxide in the disinfection tank based on a CFD method; solving the turbulence numerical model and the chlorine attenuation model to obtain the attenuation change speed of chlorine dioxide in each area of the disinfection tank in the water flow flowing process; according to the distribution characteristics of the attenuation change speed, an area with the attenuation change speed larger than a set threshold value is set as a monitoring key area, a chlorine dioxide monitoring sensor is arranged in the monitoring key area, and the set threshold value is determined based on the allowable fluctuation range of the standard value of the residual chlorine at the outlet of the disinfection tank. The Realizable k-epsilon model and the limited rate chemical reaction model are utilized to accurately simulate attenuation of water flow and chlorine dioxide in the disinfection tank, concentration abnormity can be captured in time, and the regulation and control timeliness of the disinfection effect is improved.

Description

Technical Field

[0001] This invention relates to the field of water treatment disinfection monitoring technology, specifically to a design method, system, and medium for a smart chlorine dioxide monitoring system. Background Technology

[0002] In water treatment processes, disinfection is a core step in ensuring water quality safety. Chlorine dioxide is widely used in this stage due to its high disinfection efficiency and low byproduct rate. To ensure disinfection effectiveness, precise monitoring of chlorine dioxide concentration in the disinfection tank is necessary to achieve intelligent dosing and abnormal early warning.

[0003] Traditional online chlorine monitoring devices for disinfection tanks are typically installed at the outlet of the disinfection tank. They rely on detecting the residual chlorine concentration in the effluent to determine whether the disinfectant dosage is up to standard, and then adjust the disinfectant dosage in real time. However, this monitoring method has significant drawbacks: Firstly, when there are sudden changes in the raw water quality or quantity, or when the chlorination equipment malfunctions, at least one disinfection tank's contact time (30-45 minutes) is wasted between discovering the disinfection system malfunction and implementing dosage adjustments. Secondly, due to the dead zone effect, the hydraulic residence time of the disinfectant in the contact tank may be even longer. This results in a significant time lag in judging the chlorination system's operation solely based on the effluent chlorine concentration, making it difficult to detect problems and make timely adjustments, thus affecting the disinfection effect.

[0004] Meanwhile, the sensor placement in traditional monitoring systems often relies on experience or uniform distribution, which has the following shortcomings: First, the representativeness of monitoring points is lacking. The water flow state in the disinfection tank is complex, and local areas such as near the chlorination point and at the corner of the guide wall are prone to sudden changes in concentration. Experience-based placement is difficult to capture these key changes. Second, it leads to a waste of resources. Redundant sensors placed in non-critical areas increase costs and may interfere with decision-making due to invalid data in areas with stable flow fields. Third, the response is lagging, and it is impossible to identify areas with abnormal concentrations in advance, which has an adverse effect on the timeliness of disinfection effect control.

[0005] Computational Fluid Dynamics (CFD) technology can accurately reconstruct the flow and concentration field distribution within a disinfection tank through numerical simulation, providing a theoretical basis for the optimized placement of monitoring points. Therefore, there is an urgent need for a CFD-based design method for a smart chlorine dioxide monitoring system to achieve the scientific placement of sensors. Summary of the Invention

[0006] This invention provides a design method, system, and medium for a smart chlorine dioxide monitoring system to solve the above-mentioned problems.

[0007] This invention is achieved through the following technical solution:

[0008] A design method for a smart chlorine dioxide monitoring system includes:

[0009] A turbulent numerical model for simulating water flow in a disinfection tank and a chlorine decay model for simulating changes in chlorine dioxide in the disinfection tank were established based on CFD methods.

[0010] By solving the turbulence numerical model and the chlorine decay model, the decay rate of chlorine dioxide in each region of the disinfection tank during the water flow process is obtained.

[0011] Based on the distribution characteristics of the decay rate, the area where the decay rate is greater than a set threshold is set as the key monitoring area, and chlorine dioxide monitoring sensors are deployed in the key monitoring area. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

[0012] As an optimization, the specific process of establishing and solving the turbulence numerical model and the chlorine decay model is as follows:

[0013] A three-dimensional geometric model of the disinfection tank is established. The three-dimensional geometric model includes the following characteristic parameters: the length of the main body of the disinfection tank, the width of the corridor, the depth of the tank, the length, number and spacing of the guide walls, the size, depth and flow direction of the inlet, the diameter, depth and arrangement of the chlorination pipe, and the height and shape of the outlet.

[0014] The three-dimensional geometric model is meshed to obtain a computational mesh containing both dense and sparse regions.

[0015] Set the boundary conditions for the three-dimensional geometric model: the inlet adopts the velocity inlet type, and sets the inlet velocity, turbulence intensity, and hydraulic diameter; the outlet adopts the pressure outlet type, and sets the outlet pressure; the wall adopts the no-slip boundary condition; the liquid surface adopts the symmetric boundary condition; the chlorination pipe outlet is set as the mass flow inlet, and the initial mass fraction of chlorine dioxide is defined;

[0016] The physical quantities of the mesh are solved based on the turbulence numerical model, including velocity, pressure, turbulent kinetic energy, turbulent dissipation rate, and turbulent viscosity. At the same time, the component transport-chemical reaction coupled mode is used to simulate solute transport, and the chlorine decay model is activated to calculate the spatial concentration distribution and decay law of chlorine dioxide and free chlorine.

[0017] Based on the set convergence conditions, determine whether the turbulence numerical model and the chlorine decay model have reached a steady state. If so, end the solution process; otherwise, adjust the solution parameters and return to the previous step to recalculate.

[0018] As an optimization, the meshing of the three-dimensional geometric model is specifically performed as follows:

[0019] Determine the grid division regions: The area within a first distance threshold of the chlorination pipe and its surroundings is designated as the first densification zone, where the first distance threshold is 3-5 times the diameter of the chlorination pipe; the area within a second distance threshold of the inlet and its surroundings is designated as the second densification zone, where the second distance threshold is 2-3 times the width of the inlet; the area within a third distance threshold of the outlet and its surroundings is designated as the third densification zone, where the third distance threshold is 2-3 times the width of the outlet; the area within a fourth distance threshold of the main boundary and its surroundings is designated as the fourth densification zone, where the fourth distance threshold is 1 / 5-1 / 4 of the pool depth; the first to fourth distance thresholds are determined based on the flow field gradient distribution.

[0020] The first to fourth encryption zones are divided into tetrahedral unstructured encryption meshes with a mesh size of 0.05-0.1m.

[0021] The three-dimensional geometric model, excluding the encrypted area, is divided into sparse meshes with a mesh size of 0.2-0.5m.

[0022] Verify the mesh quality, ensuring that the mesh orthogonality is ≥0.7, the distortion rate is ≤0.3, and the total number of meshes is 1-1.5 million.

[0023] As an optimization, the convergence condition includes:

[0024] The residuals of each variable are lower than the preset values, with the pressure residual ≤ Momentum, turbulent kinetic energy, turbulent dissipation rate, and component concentration residuals ≤ ;

[0025] The mass flow rate deviation between imports and exports is ≤5%, which conforms to the law of conservation of mass.

[0026] The change in the physical quantity at the monitoring point is ≤1% over 50 consecutive iterations.

[0027] As an optimization, the turbulence numerical model adopts the Realizable k-ε model, and the turbulent kinetic energy equation and dissipation rate equation of the Realizable k-ε model take into account the relationship between the average strain rate and vortex stretching.

[0028] As an optimization, when solving the Realizable k-ε model, the SIMPLE algorithm is used for pressure-velocity coupled solution. The pressure term is discretized using a second-order upwind scheme, while the momentum, turbulent kinetic energy, and turbulent dissipation rate terms are discretized using a second-order central difference scheme.

[0029] As an optimization, the chlorine decay model adopts a finite-rate chemical reaction model, which describes the decay process of chlorine dioxide based on the first-order reaction kinetic equation. The reaction rate constant is corrected according to the water temperature and pH value.

[0030] As an optimization, when solving the finite-rate chemical reaction model, the Coupled algorithm is used to couple the solution of component transport and chemical reaction, and the component concentration equation is discretized using a second-order upwind scheme.

[0031] This invention also discloses a design system for a chlorine dioxide intelligent monitoring system, used to execute the aforementioned design method for a chlorine dioxide intelligent monitoring system, comprising:

[0032] The modeling module is used to establish a turbulent numerical model for simulating the water flow in the disinfection tank based on the CFD method, as well as a chlorine decay model for simulating the changes in chlorine dioxide in the disinfection tank.

[0033] The solution module is used to obtain the rate of decay of chlorine dioxide in each region of the disinfection tank during the water flow process by solving the turbulence numerical model and the chlorine decay model.

[0034] The setting module is used to set areas where the decay rate is greater than a set threshold as key monitoring areas based on the distribution characteristics of the decay rate, and to deploy chlorine dioxide monitoring sensors in the key monitoring areas. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

[0035] The present invention also discloses a storage medium storing a computer program, which, when executed by a processor, implements the aforementioned design method for a smart chlorine dioxide monitoring system.

[0036] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0037] Improved monitoring accuracy: By utilizing the Realizable k-ε model and the finite-rate chemical reaction model, the system accurately simulates the water flow and chlorine dioxide decay in the disinfection tank. Combined with genetic algorithms to optimize monitoring points, it can promptly detect concentration anomalies. Compared with traditional outlet monitoring, it provides early warning 30-45 minutes in advance, improving the timeliness of disinfection effect control.

[0038] Resource utilization optimization: By using algorithms to select monitoring points, the number of sensors in non-critical areas is reduced, thereby lowering costs and avoiding interference from invalid data, thus improving system operating efficiency.

[0039] Model reliability assurance: After verification of similarity between the flow field and concentration field, the CFD model can accurately reproduce the actual flow pattern and concentration distribution in the pool, providing a reliable theoretical basis for the selection of monitoring points and the control of dosage, and helping to achieve intelligent disinfection management. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. The illustrative embodiments and descriptions of this invention are only used to explain this invention and are not intended to limit this invention.

[0041] This embodiment 1 provides a design method for a smart chlorine dioxide monitoring system, including:

[0042] S1. Based on the CFD method, a turbulent numerical model for simulating the water flow in the disinfection pool and a chlorine decay model for simulating the changes in chlorine dioxide in the disinfection pool are established.

[0043] This step involves establishing a CFD numerical model, specifically a turbulence numerical model and a chlorine decay model based on CFD methods.

[0044] The turbulence numerical model adopts the Realizable k-ε model to simulate the motion state of water flow in the disinfection tank. The turbulence kinetic energy equation and dissipation rate equation of the Realizable k-ε model take into account the relationship between the average strain rate and vortex stretching.

[0045] The chlorine decay model employs a finite-rate chemical reaction model to simulate the decay changes of chlorine dioxide in water flow. It describes the decay process of chlorine dioxide based on the first-order reaction kinetic equation, and the reaction rate constant is corrected according to water temperature and pH value.

[0046] This step aims to construct two core models using CFD (Computational Fluid Dynamics) methods: a turbulence numerical model and a chlorine decay model, to simulate the water flow characteristics and chlorine dioxide concentration variation patterns within the disinfection tank, respectively, providing a theoretical basis for the subsequent optimization of monitoring point layout.

[0047] The specific process is as follows:

[0048] 1. Establishment of the numerical model for turbulence (Realizable k-ε model):

[0049] (1) Basis for model selection:

[0050] The water flow in the disinfection tank is a typical turbulent flow (Reynolds number Re is usually greater than 100). This involves complex vortex motion and velocity fluctuations. Compared to the traditional k-ε model, the Realizable k-ε model is more suitable for the scenario of this invention in the following aspects:

[0051] Physical accuracy: The turbulent kinetic energy equation and dissipation rate equation introduce the coupling relationship between the average strain rate and vortex stretching, which can more accurately predict the turbulent characteristics under streamline bending, rotating flow and strong adverse pressure gradient (such as the backflow zone at the corner of the guide wall).

[0052] Computational stability: Constraints are introduced for the calculation of turbulent viscosity, avoiding distortion of traditional models in the high strain rate region, which is especially suitable for simulating jet mixing processes near chlorination pipes;

[0053] Engineering applicability: It has been proven to have high accuracy in flow field simulation of water treatment structures (such as disinfection tanks and sedimentation tanks), and the calculation cost is moderate, making it suitable for engineering applications.

[0054] (2) Model governing equations:

[0055] The Realizable k-ε model describes turbulent characteristics by solving the transport equations for turbulent kinetic energy (k) and turbulent dissipation rate (ε). The core equations are as follows:

[0056] Turbulent kinetic energy equation (k equation):

[0057] ;

[0058] in, The density of a fluid is typically expressed in units of 1. , used to describe the mass properties of fluids; t represents time, in seconds, characterizing the time dimension change of the equation; k: turbulent kinetic energy, in units of This represents the kinetic energy per unit mass of fluid in turbulent motion, reflecting the intensity of turbulence; Indicates speed at The components of the direction (i = 1, 2, 3, corresponding to the x, y, z directions respectively) are expressed in m / s and describe the velocity of the fluid in each direction. , Represents spatial coordinates, in meters (m), used to determine the position of a fluid in space; Molecular dynamic viscosity, measured in Pa·s, reflects the resistance of molecular-level viscosity to fluid motion. This represents the turbulent dynamic viscosity, measured in Pa·s. It is a parameter introduced into the turbulence model to describe the viscous effects of turbulent motion. The Prandtl number (or Schmidt number, depending on the type of equation) represents the turbulent kinetic energy. It is dimensionless, an empirical constant used to determine the diffusion characteristics of turbulent kinetic energy. The turbulent kinetic energy generation term is derived from the average velocity gradient, with units of . , which represents the rate of conversion of kinetic energy into turbulent kinetic energy in the mean flow field; This represents the turbulent kinetic energy generated by buoyancy, in units of... When a fluid has a density gradient (such as due to temperature or concentration differences) and is in a gravitational field, buoyancy affects turbulent kinetic energy. This term describes the energy conversion in this process. Represents turbulent dissipation rate, in units of This represents the rate at which turbulent kinetic energy is dissipated into heat energy; The dissipation of turbulent kinetic energy by fluctuating expansion in compressible turbulent flow, in units of... In the turbulent motion of compressible fluids (such as gases), the pulsating expansion of the fluid consumes turbulent kinetic energy. This term describes this dissipation effect. For incompressible fluids (such as liquids, water flow in water treatment can usually be considered incompressible), this term can be ignored.

[0059] Turbulent dissipation rate equation (ε equation):

[0060] ;

[0061] The Prandtl number (or Schmidt number) represents the turbulent dissipation rate. It is dimensionless, an empirical constant used to determine the diffusion characteristics of the turbulent dissipation rate. , is an empirical coefficient, dimensionless, a constant determined through extensive experiments and engineering applications. It is used to adjust the source and sink terms of the turbulent dissipation rate equation, enabling the model to more accurately simulate the turbulent dissipation process; S represents the modulus of the average strain rate, in units of . , This reflects the deformation rate of the average flow field. For strain rate tensor; v is kinematic viscosity, in units of... , ν is the ratio of molecular dynamic viscosity to fluid density, describing the viscous flow characteristics of a fluid.

[0062] (3) Core role of the model

[0063] By solving the above equations, the results of each monitoring point within the disinfection pool can be obtained:

[0064] Velocity vector distribution (Including axial and radial flow velocities);

[0065] The pressure field distribution p is obtained by solving the continuity equation and the momentum equation. Continuity equation: In incompressible fluids (such as water in water treatment, where the flow is often assumed to be incompressible with a density of...), In the case of (where is a constant), the equation simplifies to It embodies the principle of mass conservation and describes the continuity of fluid in space. Momentum equation: Where p is the pressure, For gravitational acceleration in directional components, Other external forces (such as electromagnetic forces, which are often neglected in general water treatment disinfection tank problems). This equation describes the relationship between the change in fluid momentum and pressure, viscous forces, gravity, etc.

[0066] Turbulent kinetic energy k and turbulent dissipation rate (Reflects the intensity of turbulent mixing);

[0067] Turbulent viscosity (Characterizing momentum transfer capability) to provide flow field boundary conditions for subsequent chlorine decay models.

[0068] 2. Establishment of the chlorine decay model (finite-rate chemical reaction model):

[0069] (1) Basis for model selection:

[0070] The decay process of chlorine dioxide in water follows these characteristics:

[0071] It is a first-order reaction (the reaction rate is directly proportional to the concentration).

[0072] The reaction rate is significantly affected by environmental factors such as water temperature and pH value;

[0073] The chemical reactions and diffusion mass transfer coupling with organic matter and microorganisms in the water need to be considered.

[0074] Finite-rate chemical reaction models can directly couple component transport with chemical reaction kinetics without assuming a timescale relationship between the reaction and turbulent mixing. They are suitable for describing the decay process of chlorine dioxide in this invention, and their computational cost is lower than that of more complex eddy dissipation conceptual models.

[0075] (2) Reaction kinetic equation:

[0076] The first-order reaction kinetic equation for the decay of chlorine dioxide is:

[0077] ;

[0078] Where C is the concentration of chlorine dioxide, in mg / L; t is the reaction time, in min; and q is the reaction rate constant, in m / s². Its value was fitted experimentally and corrected as follows:

[0079] Water temperature correction: ,in, It is a temperature coefficient (with a value of 1.02-1.05, and 1.03 is used in this invention). The rate constant at 25℃ (typical value 0.015-0.03 min⁻¹, determined based on raw water quality);

[0080] pH correction: When the pH value is in the range of 6.5-8.5 (a common range for drinking water), q increases with increasing pH. The correction formula is as follows: , This is the rate constant when pH=7.

[0081] (3) Component transport equations:

[0082] In conjunction with water flow, the concentration distribution of chlorine dioxide must satisfy the convection-diffusion-reaction equation:

[0083] ;

[0084] in, Fluid density, usually measured in units of The mass of a fluid is used to describe its mass characteristics, reflecting the mass of a unit volume of fluid. t represents time, measured in seconds, characterizing the change in the time dimension of the equation and describing the evolution of chlorine dioxide concentration over time; C represents chlorine dioxide concentration, measured in mg / L (or other concentration units, such as...). (etc.), which are the core variables of the equation, reflecting the content of chlorine dioxide in the fluid; It is a velocity vector with units of m / s, describing the speed of fluid movement in space. Its components correspond to the velocity in different directions and determine the convective transport of chlorine dioxide with the fluid. The Hamiltonian operator is a vector differential operator, represented in Cartesian coordinates as follows: , used to describe the rate of change of physical quantities such as spatial gradient and divergence; This is the diffusion coefficient, in units of... It combines the effects of molecular diffusion and turbulent diffusion. , Molecular viscosity For turbulent kinematic viscosity, The Prandtl number or Schmidt number (valued at 0.7) reflects the diffusion capacity of chlorine dioxide in the fluid. The term represents the reaction source, where q is the reaction rate constant. The negative sign indicates that the concentration of chlorine dioxide decreases due to the reaction. This term describes the effect of the chemical reaction on the concentration of chlorine dioxide.

[0085] (4) Core function of the model:

[0086] By solving the above equations in a coupled manner, we can obtain:

[0087] The spatial concentration distribution C of chlorine dioxide in the disinfection tank (the attenuation law from the chlorination point to the outlet); by solving the equation, the chlorine dioxide concentration values ​​at different spatial locations (including various areas from the chlorination point to the outlet) in the disinfection tank at different times can be obtained. The set of these concentration values ​​constitutes the spatial concentration distribution, and then the attenuation law from the chlorination point to the outlet can be analyzed.

[0088] The rate of concentration change (i.e., the rate of decay) in each region Taking the partial derivative of concentration C with respect to time t, we get the change in chlorine dioxide concentration per unit time, which is the decay rate. It reflects how quickly the chlorine dioxide concentration in each region decays over time.

[0089] Location of regions with severe concentration gradients (e.g., the mixing zone downstream of the chlorination pipe, the recirculation zone of the guide wall). The corresponding parameter is the concentration gradient. The concentration gradient is the spatial rate of change of concentration C (given by the Hamiltonian operator). Acting on C, that is Its modulus This reflects the degree of drastic spatial variation in concentration. By analyzing the distribution of concentration gradients, regions with severe concentration gradients, such as the mixing zone downstream of the chlorination pipe and the reflux zone of the guide wall, can be located.

[0090] 3. Model coupling relationship:

[0091] The turbulence numerical model and the chlorine decay model are coupled in the following way:

[0092] The velocity field and turbulent viscosity obtained from the turbulence model are used as the flow field inputs for the chlorine decay model;

[0093] The diffusion coefficient calculation in the chlorine decay model depends on the turbulent viscosity of the turbulence model;

[0094] Both use the same computational grid and time step, and update the flow field and concentration field synchronously during the iteration process until steady-state convergence is achieved.

[0095] By establishing the above model, the coupled process of "water flow - material transport - chemical reaction" in the disinfection pool can be accurately reproduced, providing a quantitative basis for subsequent determination of key monitoring areas.

[0096] S2. By solving the turbulence numerical model and the chlorine decay model, the decay rate of chlorine dioxide in each region of the disinfection tank during the water flow process is obtained.

[0097] By solving the turbulence numerical model and the chlorine decay model, the decay rate of chlorine dioxide in each area of ​​the disinfection tank is obtained, that is, the concentration change rate per unit time.

[0098] In some embodiments, the specific process of solving the turbulence numerical model and the chlorine decay model is as follows:

[0099] S2.1 Establish a three-dimensional geometric model of the disinfection tank. The three-dimensional geometric model includes the following characteristic parameters: the length of the main body of the disinfection tank, the width of the corridor, the depth of the tank, the length, number and spacing of the guide walls, the size, depth and flow direction of the inlet, the diameter, depth and arrangement of the chlorination pipe, and the height and shape of the outlet.

[0100] S2.2. The three-dimensional geometric model is meshed to obtain a computational mesh containing dense and sparse regions.

[0101] In some embodiments, the specific process of meshing the three-dimensional geometric model is as follows:

[0102] S2.2.1 Determining the grid division regions: The area within a first distance threshold of the chlorination pipe and its surroundings is designated as the first denser zone, where the first distance threshold is 3-5 times the diameter of the chlorination pipe; the area within a second distance threshold of the inlet and its surroundings is designated as the second denser zone, where the second distance threshold is 2-3 times the width of the inlet; the area within a third distance threshold of the outlet and its surroundings is designated as the third denser zone, where the third distance threshold is 2-3 times the width of the outlet; the area within a fourth distance threshold of the main boundary and its surroundings is designated as the fourth denser zone, where the fourth distance threshold is 1 / 5-1 / 4 of the pool depth; the first to fourth distance thresholds are determined based on the flow field gradient distribution.

[0103] S2.2.2 The first to fourth encryption zones are divided into tetrahedral unstructured encryption meshes with a mesh size of 0.05-0.1m.

[0104] S2.2.3. The three-dimensional geometric model, excluding the encrypted area, is divided into sparse meshes with a mesh size of 0.2-0.5m.

[0105] S2.2.4 Verify the mesh quality, ensuring that the mesh orthogonality is ≥0.7, the distortion rate is ≤0.3, and the total number of meshes is 1-1.5 million.

[0106] S2.3. Set the boundary conditions of the three-dimensional geometric model: the inlet adopts the velocity inlet type, and sets the inlet velocity, turbulence intensity and hydraulic diameter; the outlet adopts the pressure outlet type, and sets the outlet pressure; the wall adopts the no-slip boundary condition; the liquid surface adopts the symmetric boundary condition; the chlorination pipe outlet is set as the mass flow inlet, and the initial mass fraction of chlorine dioxide is defined;

[0107] S2.4 Solve the physical quantities of the grid based on the turbulence numerical model. The physical quantities include velocity, pressure, turbulent kinetic energy, turbulent dissipation rate and turbulent viscosity. At the same time, the component transport-chemical reaction coupling mode is used to simulate solute transport, and the chlorine decay model is activated to calculate the spatial concentration distribution and decay law of chlorine dioxide and free chlorine.

[0108] In some embodiments, when solving the Realizable k-ε model, the SIMPLE algorithm is used for pressure-velocity coupled solution. The pressure term is discretized using a second-order upwind scheme, and the momentum, turbulent kinetic energy, and turbulent dissipation rate terms are discretized using a second-order central difference scheme.

[0109] In some embodiments, when solving the finite-rate chemical reaction model, the Coupled algorithm is used to solve the coupled solution of component transport and chemical reaction, and the component concentration equation is discretized using a second-order upwind scheme.

[0110] S2.5. Determine whether the turbulence numerical model and the chlorine decay model have reached steady state according to the set convergence conditions. If they have, end the solution; otherwise, adjust the solution parameters and return to the previous step to recalculate.

[0111] In some embodiments, the convergence condition includes:

[0112] The residuals of all variables are lower than the preset values, including the pressure residuals. Momentum, turbulent kinetic energy, turbulent dissipation rate, and component concentration residuals ≤ 1 × 10⁻ 5 ;

[0113] The mass flow rate deviation between imports and exports is ≤5%, which conforms to the law of conservation of mass.

[0114] The change in the physical quantity at the monitoring point is ≤1% over 50 consecutive iterations.

[0115] In other words, the logic of the specific implementation process of S2 is as follows:

[0116] Three-dimensional geometric model establishment (S2.1): Modeling is based on the actual structural parameters of the disinfection pool (pool length, corridor, guide wall, inlet and outlet, chlorination pipe, etc.) to ensure that the model can reflect the geometric characteristics of the real disinfection pool and provide a geometric carrier for subsequent flow field and concentration field simulation.

[0117] Mesh generation (S2.2): An unstructured meshing strategy of "dense region + sparse region" is adopted. Critical areas with large flow and concentration field gradients, such as chlorination pipes, inlets / outlets, and walls, are densely meshed, while other areas are sparsely meshed. This meshing method ensures simulation accuracy in critical areas while controlling computational load, conforming to the "accuracy-efficiency balance" principle of CFD mesh generation.

[0118] Boundary condition setting (S2.3): For different regions such as inlet (velocity inlet), outlet (pressure outlet), wall (no slippage), liquid surface (symmetric), chlorination pipe (mass flow inlet), set boundary conditions that conform to the actual physical process, provide input conditions for model solution, and are a necessary step in CFD simulation.

[0119] Model solution (S2.4): The SIMPLE algorithm (numerical model of turbulence, pressure-velocity coupling) and the Coupled algorithm (chlorine decay model, component-reaction coupling) are used, combined with a second-order discretization scheme (to ensure accuracy), to solve the turbulence and chlorine decay coupling model.

[0120] Convergence criterion (S2.5): Convergence conditions are set from three dimensions: residuals (degree of convergence of each variable), mass conservation (deviation of inlet and outlet flow), and monitoring point stability (iterative change of physical quantities) to ensure the stability and reliability of the solution results.

[0121] In S2.4, the process of solving the coupled turbulence and chlorine decay model using the SIMPLE and Coupled algorithms is based on achieving a coordinated solution of the flow field and concentration field through "iteration-coupling-correction". The specific steps are as follows:

[0122] S2.4.1 Initialization and Parameter Setting: Input the geometric model of the disinfection tank, mesh data, and initial parameters (such as inlet flow rate, chlorine concentration, water temperature, etc.); set the constants of the turbulence model (Realizable k-ε) and the chlorine decay model (finite rate reaction) (such as...). , (e.g., reaction rate q), and initialize velocity, pressure, turbulent kinetic energy k, and dissipation rate. And the overall distribution of chlorine dioxide concentration C;

[0123] S2.4.2 Solving the turbulence model (SIMPLE algorithm):

[0124] Solving the momentum equation: Based on the current pressure field, solve for the coupled turbulent viscosity. (From turbulent kinetic energy k, dissipation rate) The momentum equation (obtained through calculation) yields the temporary velocity field ( , , );

[0125] Pressure correction: The pressure correction equation is derived from the continuity equation, the pressure correction value is calculated, and the pressure field is updated;

[0126] Velocity correction: The temporary velocity field is adjusted using the pressure correction value to obtain a corrected velocity field that satisfies continuity;

[0127] Solving the k and ε equations: Based on the corrected velocity field, solve the transport equations for turbulent kinetic energy k and dissipation rate ε, and update... This completes one iteration of the turbulent field.

[0128] S2.4.3 Solving the chlorine decay model (Coupled algorithm):

[0129] Coupled component transport and reaction: Using the velocity field output from the turbulence model and the turbulent diffusion coefficient (Γ) as inputs, the convection-diffusion-reaction equations of chlorine dioxide are solved simultaneously;

[0130] Synchronous update of concentration field: Directly couple the velocity, diffusion and reaction source terms (R=-ρkC), use a second-order upwind scheme to discretize the spatial terms, and obtain the distribution of chlorine dioxide concentration C in the whole field through iterative solution;

[0131] S2.4.4 Coupled Iteration and Convergence Determination:

[0132] Alternately execute steps S2.4.2 and S2.4.3, feeding back the updated velocity field to the chlorine decay model after each iteration, and feeding back the effect of the concentration field on the fluid density (if considered) to the turbulence model; check the convergence index: whether the residuals of each variable (velocity, pressure, k, ε, C) are below the threshold (e.g., ...). ), whether the deviation of inlet and outlet mass flow rate is ≤5%, and whether the physical quantity at the monitoring point (such as outlet concentration) is stable (change ≤1% over 50 consecutive iterations);

[0133] S2.4.5 Output Results: When the convergence condition is met, the iteration stops, and the velocity field, pressure field, turbulent kinetic energy distribution, and chlorine dioxide concentration field in the disinfection tank are output. Then, the decay rate of each region is calculated. ).

[0134] S3. Based on the distribution characteristics of the decay rate, the area where the decay rate is greater than a set threshold is set as the key monitoring area, and chlorine dioxide monitoring sensors are deployed in the key monitoring area. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

[0135] Areas where the rate of decay change exceeds a set threshold are designated as key monitoring areas. Chlorine dioxide monitoring sensors are deployed in these areas. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection tank outlet (e.g., standard value ±10%).

[0136] After setting the key monitoring areas, the final placement of chlorine dioxide monitoring sensors can be determined using a genetic algorithm. Under the premise of meeting monitoring accuracy and cost constraints, the optimal sensor distribution scheme can be optimized from the set of potential monitoring points in the disinfection pool.

[0137] The specific process is as follows:

[0138] S3.1. Construct a set of potential monitoring points:

[0139] Based on the chlorine dioxide decay rate distribution obtained from CFD simulation, N candidate points are pre-selected in areas where the decay rate is greater than a set threshold (key monitoring areas). (For example, points are evenly distributed in areas with large concentration gradients, or key locations are selected based on flow field characteristics, such as downstream of the chlorination pipe, near the guide wall, etc.).

[0140] S3.2: Define the individual and fitness functions for the genetic algorithm:

[0141] Individual encoding: Uses binary encoding with a length of N, where each bit corresponds to a candidate point. , , This represents the concentration sensitivity coefficient, ranging from 0.1 to 1.0, which is the ratio of the rate of decay of chlorine dioxide at point i to the maximum decay rate in the key area; Encoding format: , This indicates the deployment of chlorine dioxide concentration monitoring sensors. This indicates the deployment of chlorine dioxide concentration monitoring sensors. Only This is valid for a limited time. In subsequent fitness calculations, priority will be given to retaining [this value / item]. High concentrations (rapid decay and greater sensitivity to concentration anomalies) improve monitoring response speed.

[0142] Fitness function: Pareto multi-objective optimization is used, defining three objective functions, and finally outputting the Pareto optimal solution set (there is no absolute optimum, only non-dominated solutions):

[0143] The coverage radius is determined by the sensor accuracy;

[0144] ;

[0145] ;

[0146] To monitor coverage, For sensor cost, For response speed.

[0147] Definition of non-dominated solution: If solution A is in , , If at least one objective is superior to solution B and no objective is inferior to B, then A dominates B; finally, all non-dominated solutions are retained for engineering selection.

[0148] S3.3, Adaptive Crossover Probability :

[0149] ;

[0150] This represents the average fitness of the two individuals to be crossed (in multi-objective scenarios, the "non-dominant level" is used, with lower levels indicating higher fitness).

[0151] Indicates the average non-dominant rank of a population; , .

[0152] Logic: Individuals with low fitness A higher crossover probability is needed to accelerate gene recombination; individuals with high fitness retain a high crossover probability to avoid the loss of superior genes.

[0153] Adaptive mutation probability :

[0154] ;

[0155] f represents the non-dominant rank of the individual to be mutated; Indicates the highest non-dominant rank in a population; , .

[0156] Logic: Individuals with low fitness Higher mutation probabilities are needed to break through local optima; individuals with high fitness retain only those with low mutation probabilities to avoid the destruction of superior genes.

[0157] S3.4 Robust constraint embedding (sensor failure tolerance).

[0158] During the iteration process, a "fault tolerance verification" is added for each candidate solution (individual):

[0159] For each location in the plan Simulate "failure at this point" (temporarily set to...) );

[0160] Calculate monitoring coverage after failure ,like If the solution is not feasible, then the solution is directly eliminated (considered an "infeasible solution").

[0161] Only the solutions that have passed the fault tolerance verification will be retained for the next generation to ensure that the final optimization results can still meet the monitoring requirements when the sensor fails;

[0162] S3.5 Optimized selection process: Elite retention + crowding sorting:

[0163] Elite preservation: The top 10% of non-dominated solutions (elite individuals) in the population are directly copied to the next generation to avoid losing excellent solutions during iteration;

[0164] Crowding ranking: For the remaining 90% of individuals, calculate the crowding degree (the distance between individuals on the Pareto front; the larger the distance, the better the diversity of solutions). Prioritize individuals with high crowding degree to ensure the diversity of the solution set and avoid convergence to a local optimum in the later stages.

[0165] S3.6, Iteration Termination and Optimal Solution Selection:

[0166] Termination condition: Stop iteration when no new individuals are added to the Pareto optimal solution set for 5 consecutive generations (i.e., the solution set is stable), or when the number of iterations reaches 200 generations (which can be adjusted according to requirements);

[0167] Solution selection: From the final Pareto optimal solution set, combined with actual engineering needs (such as budget limits and response speed requirements), a fuzzy comprehensive evaluation is used to select one optimal solution (e.g., when the budget is sufficient). and Choose the best option when the budget is limited. low and (The plan).

[0168] Genetic algorithms can ensure monitoring accuracy while taking into account cost, response speed, and engineering fault tolerance, making them more suitable for the actual application scenario of chlorine dioxide monitoring in disinfection pools.

[0169] Example 2 discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute a design method for a chlorine dioxide intelligent monitoring system as described in Example 1.

[0170] Example 3 discloses a design system for a chlorine dioxide intelligent monitoring system, used to execute the design method for a chlorine dioxide intelligent monitoring system described in Example 1, including:

[0171] The modeling module is used to establish a turbulent numerical model for simulating the water flow in the disinfection tank based on the CFD method, as well as a chlorine decay model for simulating the changes in chlorine dioxide in the disinfection tank.

[0172] The solution module is used to obtain the rate of decay of chlorine dioxide in each region of the disinfection tank during the water flow process by solving the turbulence numerical model and the chlorine decay model.

[0173] The setting module is used to set areas where the decay rate is greater than a set threshold as key monitoring areas based on the distribution characteristics of the decay rate, and to deploy chlorine dioxide monitoring sensors in the key monitoring areas. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

[0174] Example 4 discloses a storage medium storing a computer program, which, when executed by a processor, implements the design method of a chlorine dioxide intelligent monitoring system described in Example 1.

[0175] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for a smart chlorine dioxide monitoring system, characterized in that, include: A turbulent numerical model for simulating water flow in a disinfection tank and a chlorine decay model for simulating changes in chlorine dioxide in the disinfection tank were established based on CFD methods. By solving the turbulence numerical model and the chlorine decay model, the decay rate of chlorine dioxide in each region of the disinfection tank during the water flow process is obtained. Based on the distribution characteristics of the decay rate, the area where the decay rate is greater than a set threshold is set as the key monitoring area, and chlorine dioxide monitoring sensors are deployed in the key monitoring area. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

2. The design method of a chlorine dioxide intelligent monitoring system according to claim 1, characterized in that, The specific process of establishing and solving the turbulence numerical model and the chlorine decay model is as follows: A three-dimensional geometric model of the disinfection tank is established. The three-dimensional geometric model includes the following characteristic parameters: the length of the main body of the disinfection tank, the width of the corridor, the depth of the tank, the length, number and spacing of the guide walls, the size, depth and flow direction of the inlet, the diameter, depth and arrangement of the chlorination pipe, and the height and shape of the outlet. The three-dimensional geometric model is meshed to obtain a computational mesh containing both dense and sparse regions. Set the boundary conditions for the three-dimensional geometric model: the inlet adopts the velocity inlet type, and sets the inlet velocity, turbulence intensity, and hydraulic diameter; the outlet adopts the pressure outlet type, and sets the outlet pressure; the wall adopts the no-slip boundary condition; the liquid surface adopts the symmetric boundary condition; the chlorination pipe outlet is set as the mass flow inlet, and the initial mass fraction of chlorine dioxide is defined; The physical quantities of the mesh are solved based on the turbulence numerical model, including velocity, pressure, turbulent kinetic energy, turbulent dissipation rate, and turbulent viscosity. At the same time, the component transport-chemical reaction coupled mode is used to simulate solute transport, and the chlorine decay model is activated to calculate the spatial concentration distribution and decay law of chlorine dioxide and free chlorine. Based on the set convergence conditions, determine whether the turbulence numerical model and the chlorine decay model have reached a steady state. If so, end the solution process; otherwise, adjust the solution parameters and return to the previous step to recalculate.

3. The design method of a chlorine dioxide intelligent monitoring system according to claim 2, characterized in that, The meshing of the three-dimensional geometric model is specifically performed as follows: The grid division regions are determined as follows: the area within a first distance threshold of the chlorination pipe and its surroundings is designated as the first denser zone, where the first distance threshold is 3-5 times the diameter of the chlorination pipe; the area within a second distance threshold of the inlet and its surroundings is designated as the second denser zone, where the second distance threshold is 2-3 times the width of the inlet; the area within a third distance threshold of the outlet and its surroundings is designated as the third denser zone, where the third distance threshold is 2-3 times the width of the outlet; and the area within a fourth distance threshold of the main boundary and its surroundings is designated as the fourth denser zone, where the fourth distance threshold is 1 / 5-1 / 4 of the pool depth. The first to fourth distance thresholds are determined based on the flow field gradient distribution. The first to fourth encryption zones are divided into tetrahedral unstructured encryption meshes with a mesh size of 0.05-0.1m. The three-dimensional geometric model, excluding the encrypted area, is divided into sparse meshes with a mesh size of 0.2-0.5m. Verify the mesh quality, ensuring that the mesh orthogonality is ≥0.7, the distortion rate is ≤0.3, and the total number of meshes is 1-1.5 million.

4. The design method of a chlorine dioxide intelligent monitoring system according to claim 2, characterized in that, The convergence conditions include: The residuals of all variables are lower than the preset values, including the pressure residual. Momentum, turbulent kinetic energy, turbulent dissipation rate and component concentration residuals ; The mass flow rate deviation between imports and exports is ≤5%, which conforms to the law of conservation of mass. The change in the physical quantity at the monitoring point is ≤1% over 50 consecutive iterations.

5. The design method of a chlorine dioxide intelligent monitoring system according to claim 1, characterized in that, The turbulence numerical model adopts the Realizable k-ε model, and the turbulent kinetic energy equation and dissipation rate equation of the Realizable k-ε model take into account the relationship between the average strain rate and vortex stretching.

6. The design method of a chlorine dioxide intelligent monitoring system according to claim 5, characterized in that, When solving the Realizable k-ε model, the SIMPLE algorithm is used for pressure-velocity coupled solution. The pressure term is discretized using a second-order upwind scheme, while the momentum, turbulent kinetic energy, and turbulent dissipation rate terms are discretized using a second-order central difference scheme.

7. The design method of a chlorine dioxide intelligent monitoring system according to claim 1, characterized in that, The chlorine decay model adopts a finite-rate chemical reaction model, which describes the decay process of chlorine dioxide based on the first-order reaction kinetic equation. The reaction rate constant is corrected according to water temperature and pH value.

8. The design method of a chlorine dioxide intelligent monitoring system according to claim 7, characterized in that, When solving the finite-rate chemical reaction model, the Coupled algorithm is used to solve the coupled component transport and chemical reaction, and the component concentration equation is discretized using a second-order upwind scheme.

9. A design system for a chlorine dioxide intelligent monitoring system, used to execute the design method of a chlorine dioxide intelligent monitoring system according to any one of claims 1-8, characterized in that, include: The modeling module is used to establish a turbulent numerical model for simulating the water flow in the disinfection tank based on the CFD method, as well as a chlorine decay model for simulating the changes in chlorine dioxide in the disinfection tank. The solution module is used to obtain the rate of decay of chlorine dioxide in each region of the disinfection tank during the water flow process by solving the turbulence numerical model and the chlorine decay model. The setting module is used to set areas where the decay rate is greater than a set threshold as key monitoring areas based on the distribution characteristics of the decay rate, and to deploy chlorine dioxide monitoring sensors in the key monitoring areas. The set threshold is determined based on the allowable fluctuation range of the residual chlorine standard value at the disinfection pool outlet.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the design method of the intelligent chlorine dioxide monitoring system according to any one of claims 1 to 8.