Wide-particle-size-range slag conveying system and method

Abstract

The invention relates to the technical field of slag conveying pipeline analysis, and discloses a wide-particle-size-range slag conveying system and method.The wide-particle-size-range slag conveying method comprises the steps that extreme working condition monitoring data in a slag conveying pipeline are processed, and a particulate matter physical property evolution matrix in the pipeline is obtained; treating the physical property evolution matrix of the particles in the pipeline to obtain a critical condition map of agglomeration formation in the slag conveying pipeline; treating the physical property evolution matrix of the particles in the pipeline to obtain a critical condition map of particle crushing in the slag conveying pipeline; processing an agglomeration formation critical condition map and a particle crushing critical condition map in the slag conveying pipeline based on a CFD critical state fusion adaptive prediction model, and obtaining a flow characteristic early warning index in the slag conveying pipeline; by establishing a CFD-based multi-field coupling particle state characterization model and considering the comprehensive influence of a temperature field, a pressure field and a turbulent flow field, accurate simulation of a complex flow field in the slag conveying pipeline is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of slag conveying pipeline analysis, and more particularly to a slag conveying system and method with a wide particle size range. Background Art

[0002] With the development of industrial production, slag conveying pipeline systems, as key equipment for conveying granular materials in industrial production processes, are widely used in thermal power, coal gasification, metallurgy, petroleum and other industries.

[0003] Existing methods for analyzing the flow characteristics of slag conveying pipelines mainly rely on traditional CFD (computational fluid dynamics) technology to calculate the flow field distribution in the pipeline through numerical simulation. However, these methods usually adopt a simplified particle-fluid interaction model and assume that the physical properties of the particles remain unchanged during the transportation process.

[0004] However, in actual industrial environments, slag conveying pipelines are often faced with extreme operating conditions such as high temperature (800-1500°C), high pressure (2-10 MPa), and strong turbulence (Reynolds number Re>100,000). The physical properties of particulate materials will change significantly with changes in operating conditions. Traditional CFD analysis methods cannot accurately predict the agglomeration and breakage behavior of particles in slag conveying pipelines under extreme operating conditions, resulting in a large deviation between the prediction results of traditional analysis methods and the actual situation. Summary of the Invention

[0005] The present invention provides a slag conveying system and method with a wide particle size range, which solves the technical problem in the related art that traditional CFD analysis methods cannot accurately predict the agglomeration and breakage behavior of particles in the slag conveying pipeline under extreme working conditions.

[0006] The present invention provides a method for conveying slag with a wide particle size range, comprising the following steps:

[0007] The CFD-coupled multi-field particle state characterization model is used to process the extreme operating condition monitoring data in the slag conveying pipeline to obtain the evolution matrix of the physical characteristics of the particles in the pipeline.

[0008] The CFD-based multi-scale probabilistic bonding dynamics model is used to process the evolution matrix of the physical properties of particles in the pipeline, and the critical condition map of agglomeration formation in the slag conveying pipeline is obtained.

[0009] The CFD-based multi-mechanism particle crushing mechanics model is used to process the evolution matrix of the physical characteristics of particles in the pipeline, and the critical condition map of particle crushing in the slag conveying pipeline is obtained.

[0010] The critical condition maps of agglomeration formation and particle breakage in the slag conveying pipeline are processed by a CFD-based critical state fusion adaptive prediction model to obtain early warning indicators of flow characteristics in the slag conveying pipeline.

[0011] Furthermore, the step of processing the extreme working condition monitoring data in the slag conveying pipeline by using the CFD-coupled multi-field particle state characterization model includes:

[0012] The temperature field data calculated by CFD is processed by the temperature property mapping function to obtain the temperature-dependent particle physical parameter set;

[0013] The pressure field data calculated by CFD is processed by the pressure-deformation response model to obtain the pressure-dependent particle deformation characteristic matrix;

[0014] The turbulent flow field data calculated by CFD is processed by the turbulent particle interaction model to obtain the particle motion characteristic vector under the turbulent state in the pipeline;

[0015] The output data of each sub-model is processed by the CFD-coupled multi-field state integration algorithm to obtain the evolution matrix of the physical characteristics of the particles in the pipeline.

[0016] Furthermore, the expression of the temperature property mapping function is:

[0017]

[0018] Among them, P i (T) represents the i-th physical parameter at the temperature T in the pipeline, T represents the temperature value in the pipeline, T0 represents the reference temperature, P i,0 Indicates the parameter reference value at reference temperature T0, E a,i represents the activation energy of the i-th parameter, f i (T) represents the correction function of the temperature critical region, and R represents the gas constant, which is 8.314 J / (mol·K).

[0019] Furthermore, the step of processing the evolution matrix of the physical properties of particles in the pipeline by using a multi-scale bonding probabilistic dynamic model based on CFD includes:

[0020] The DLVO coupled viscoelastic contact theory is used to process the physical and chemical properties of the particle surface calculated by CFD to obtain the potential energy spectrum of the interaction between particles in the pipeline;

[0021] The CFD-based collision-bonding coupling probability model is used to process the interaction potential energy spectrum between particles in the pipeline and the particle motion eigenvectors to obtain the particle bonding probability distribution in the slag conveying pipeline.

[0022] The probability distribution of particle adhesion in the slag conveying pipeline is processed by the CFD-based agglomeration growth kinetics equation, and the spatiotemporal evolution characteristic spectrum of agglomerates in the pipeline is obtained.

[0023] The CFD-based agglomeration critical phase transition analysis algorithm is used to process the spatiotemporal evolution characteristic spectrum of agglomerates in the pipeline, and the critical condition spectrum of agglomerate formation in the slag conveying pipeline is obtained.

[0024] Furthermore, the expression of the CFD-based collision-bonding coupling probability model is:

[0025] P agg (i,j)=P coll (i,j)·P stick (i,j);

[0026] Among them, P agg (i, j) represents the probability of successful agglomeration of the i-th and j-th particles in the pipeline; P coll (i, j) represents the collision probability between the i-th and j-th particles in the pipeline; P stick (i, j) represents the probability of adhesion between the i-th and j-th particles in the pipeline, which is defined as:

[0027]

[0028] Among them, E a is the activation energy, U min is the minimum value in the potential energy spectrum, k B is the Boltzmann constant, and T is the local temperature in the pipe.

[0029] Furthermore, the step of processing the evolution matrix of the physical properties of particles in the pipeline using a CFD-based multi-mechanism particle crushing mechanics model includes:

[0030] The CFD-based fracture strength criterion model is used to process the particle material property data in the pipeline to obtain the critical threshold distribution of the crushing strength in the slag conveying pipeline.

[0031] The internal stress distribution field of particles in the slag conveying pipeline is obtained by processing the mechanical load data in the pipeline through the CFD-based stress-strain field dynamic analysis model;

[0032] The stress distribution field and critical crushing threshold in the pipeline are processed by a CFD-based fracture dynamics multi-mode prediction model to obtain the particle crushing mode distribution in the slag conveying pipeline.

[0033] The crushing mode distribution data in the pipeline was processed by constructing a CFD-based crushing critical phase diagram algorithm to obtain the critical condition map of particle crushing in the slag conveying pipeline.

[0034] Furthermore, the expression of the CFD-based fracture dynamics multi-mode prediction model is:

[0035]

[0036]

[0037] Among them, P break,irepresents the probability of particle breakage in the pipeline according to the i-th mode, d, T, P and σ represent the characteristic size, temperature, pressure and stress distribution of the particles in the pipeline respectively, x, y, z represent the spatial coordinates, σ crit (d,T,P,x,y,z) represents the critical crushing strength at the position (x,y,z) in the pipeline, σ max (x,y,z) represents the maximum stress inside the particle at position (x,y,z) in the pipeline, σ s represents the standard deviation of stress distribution, n i Indicates the exponential parameter related to the fragmentation mode.

[0038] Furthermore, the step of processing the critical condition map of agglomeration formation and the critical condition map of particle breakage in the slag conveying pipeline by using the CFD-based critical state fusion adaptive prediction model includes:

[0039] The dual-objective dangerous state mapping algorithm based on CFD is used to process the critical condition maps of agglomeration and crushing in the slag conveying pipeline, and a joint assessment matrix of dangerous states in the pipeline is obtained.

[0040] The CFD-based time-series evolution Markov prediction model processes the real-time operating parameters and the joint evaluation matrix of dangerous states to obtain the predicted trajectory map of the operating conditions in the slag conveying pipeline;

[0041] The CFD-based risk spatiotemporal distribution visualization system processes the working condition trajectory prediction map and the dangerous state joint assessment matrix to obtain the dynamic risk area boundary map in the slag conveying pipeline;

[0042] The CFD-based multi-level early warning decision support system processes the boundary mapping of dynamic risk areas in the pipeline to obtain early warning signals and intervention suggestions on the flow characteristics in the slag conveying pipeline.

[0043] Furthermore, the warning signal determination method of the CFD-based multi-level warning decision support system is:

[0044]

[0045] Where W(t) represents the warning signal level at time t, V risk (t) represents the volume of the pipeline in a high-risk state at time t, V total represents the total volume of the pipeline, t cross Indicates the time when the predicted working condition trajectory first crosses the danger boundary.

[0046] A wide-size-range slag conveying system, for implementing the above-mentioned wide-size-range slag conveying method, comprises:

[0047] The CFD-coupled multi-field particle state characterization module is used to process extreme operating condition monitoring data in the slag conveying pipeline and obtain the evolution matrix of the physical characteristics of the particles in the pipeline;

[0048] A CFD-based multi-scale probabilistic bonding dynamics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain a map of the critical conditions for agglomeration formation in the slag conveying pipeline;

[0049] A CFD-based multi-mechanism particle crushing mechanics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain the critical condition map of particle crushing in the slag conveying pipeline;

[0050] The CFD-based critical state fusion adaptive prediction module is used to process the critical condition maps of agglomeration formation and particle breakage in the slag conveying pipeline, and obtain early warning indicators of flow characteristics in the slag conveying pipeline.

[0051] The beneficial effects of the present invention are as follows: by establishing a multi-field coupled particle state characterization model based on CFD, taking into account the combined effects of temperature field, pressure field and turbulence field, accurate simulation of the complex flow field in the slag conveying pipeline is achieved, and the prediction accuracy is improved compared with the traditional single-factor consideration method;

[0052] By using a CFD-based multi-scale probabilistic bonding kinetic model to process the evolution data of particle physical properties and utilizing a four-step calculation method (calculating the potential energy of interactions between particles, calculating the probability of collision-bonding coupling, analyzing the kinetic equation for agglomerate growth, and determining the critical phase transition condition), we were able to quantitatively predict the agglomeration of particles in slag conveying pipelines and improve the accuracy of the prediction of the critical conditions for agglomerate formation.

[0053] By using a CFD-based multi-mechanism particle crushing mechanics model to analyze the stress conditions of particles in the pipeline, different crushing modes including tensile fracture, shear crushing and compression crushing can be accurately identified, providing a reliable basis for pipeline wear and blockage risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the method for conveying slag with a wide particle size range in the present invention. DETAILED DESCRIPTION

[0055] At least one embodiment of the present invention discloses a method for conveying slag with a wide particle size range, such as Figure 1 As shown, the following steps are included:

[0056] Step 100: Processing the extreme operating condition monitoring data (including temperature, pressure, and turbulence intensity) in the slag conveying pipeline through a multi-field particle state characterization model coupled with CFD (Computational Fluid Dynamics) to obtain the evolution matrix of the physical properties of the particles in the pipeline;

[0057] Under extreme working conditions in slag conveying pipelines, the physical properties of particles will change significantly with environmental factors (temperature, pressure, and turbulence intensity). Such changes will directly affect the agglomeration and breakage behavior of particles in the pipeline and the flow characteristics. The CFD-coupled multi-field particle state characterization model includes: temperature physical property mapping module, pressure deformation response module, turbulent particle interaction module and multi-field coupling state integration module.

[0058] Step 101: Processing the temperature field data calculated by CFD using a temperature property mapping function to obtain a set of temperature-dependent particle physical parameters; the temperature property mapping function quantifies the nonlinear relationship between the temperature in the slag conveying pipeline and the physical properties of the particles based on a modified Arrhenius equation;

[0059] The expression of this function is:

[0060]

[0061] Among them, P i (T) represents the i-th physical parameter at the temperature T in the pipeline; P i,0 Indicates the parameter reference value at reference temperature T0; E a,i represents the activation energy of the i-th parameter; T represents the temperature in the pipe; T0 represents the reference temperature; R represents the gas constant, which is 8.314 J / (mol·K); f i (T) represents the correction function of the temperature critical region, which is defined as:

[0062]

[0063] Where T c,i represents the critical temperature of the i-th parameter; T m,i represents the melting temperature of the i-th parameter; α i , β i and γ i is the fitting parameter.

[0064] Step 102: The pressure field data calculated by CFD is processed using the pressure deformation response model. Based on the improved Hertz contact theory, the nonlinear deformation behavior of the granular material under high pressure in the slag conveying pipeline is considered to obtain the pressure-dependent particle deformation characteristic matrix. The core expression of this model is:

[0065]

[0066] Where δ represents the contact deformation of the particles. The contact deformation of the particles is calculated at multiple pressure points according to the relationship between δ and P and F. nThe relationship between the pressure and the particle deformation is used to extract the pressure-dependent characteristic parameters (particle contact deformation, particle deformation rate, and particle deformation direction), and these parameters are organized into a matrix form to obtain the pressure-dependent particle deformation characteristic matrix;

[0067] F n represents the normal contact force; P represents the pressure;

[0068] R eff represents the equivalent radius, which is defined as:

[0069] Where R1 and R2 are the radii of the contact particles;

[0070] E eff (P) represents the pressure-dependent equivalent elastic modulus, defined as:

[0071] Where E(P) represents the pressure-dependent elastic modulus; ν(P) represents the pressure-dependent Poisson's ratio;

[0072] Φ(P) represents the pressure correction function, which is defined as: Φ(P) = 1 + λ1P + λ2P 2

[0073] Where P represents pressure; λ1 and λ2 are fitting coefficients.

[0074] Step 103: The turbulent field data calculated by CFD is processed using the turbulent particle interaction model. Based on the improved random trajectory model and Kolmogorov (Kolmogorov Microscale Theory) scale theory, the random motion behavior of particles under strong turbulent conditions in the slag conveying pipeline is described, and the particle motion characteristic vector under turbulent conditions in the pipeline is obtained. The core expression of this model is:

[0075]

[0076] in Represents the particle velocity vector (the velocity vector of the particle moving in the fluid); represents the local velocity vector of the fluid in the pipe; represents the particle velocity vector; ρ p represents the particle density; represents the pressure gradient; represents the acceleration due to gravity; τ p represents the particle response time, which is defined as:

[0077] where d p Indicates particle diameter; μ f represents the dynamic viscosity of the fluid;

[0078] represents the virtual mass force, defined as:

[0079] where ρ f represents the fluid density; represents the local acceleration of the fluid;

[0080] represents Brownian force;

[0081] represents the turbulent pulsation force and is defined as:

[0082]

[0083] Where ζ represents a random variable that obeys the standard normal distribution; k f represents the turbulent kinetic energy; Δt represents the time step.

[0084] Step 104: Process the output data of each sub-model using a CFD-coupled multi-field state integration algorithm to obtain the particle physical property evolution matrix in the pipeline. The expression of this algorithm is:

[0085]

[0086] Among them, M phys represents the matrix of the evolution of the physical properties of particles in the pipeline;

[0087] Specifically, the particle physical property evolution matrix in the pipeline integrates the particle physical properties under the temperature field, pressure field and turbulence field. The element M in the i-th row, j-th column and k-th spatial position is phys (i, j, k) represents the temperature condition T i , pressure condition P i and turbulence intensity Turb i The comprehensive particle physical property values ​​below; specifically including:

[0088] Row index i: corresponds to different temperatures T i conditions, usually arranged from low to high temperatures;

[0089] Column index j: corresponding to different pressures P i conditions, usually ranked from low pressure to high pressure;

[0090] Depth index k: corresponding to different turbulence intensities Turb i horizontal, arranged from low to high turbulence;

[0091] T, P, and Turb represent the temperature, pressure, and turbulence intensity in the pipeline, respectively; P(T) represents the physical parameter set vector on which the temperature T depends, generated by step 101; K(P) represents the deformation characteristic matrix on which the pressure P depends, generated by step 102; V(Turb) represents the particle motion characteristic vector under the turbulent state Turb, generated by step 103; Represents tensor product operation; W coup represents the field coupling weight matrix, which is obtained by calibrating CFD simulation and experimental data. Represents the tensor product operation, W coup represents the field coupling weight matrix, which is obtained by calibrating CFD simulation and experimental data.

[0092] Step 200: Processing the physical property evolution matrix of particles in the pipeline using a CFD-based multi-scale bonding probabilistic dynamics model to obtain a critical condition map for agglomeration formation in the slag conveying pipeline;

[0093] Specifically, this step constructs a CFD-based multi-scale probabilistic bonding dynamics model. Using the particle physical property evolution matrix obtained in step 100 as input, combined with the fluid dynamics characteristics calculated by CFD, this model comprehensively characterizes the dynamic mechanism of particle agglomeration in the slag conveying pipeline, from microscopic interaction potential energy and mesoscopic bonding processes to macroscopic agglomeration formation. Finally, a critical condition map for agglomeration formation in the pipeline is output, providing an agglomeration trigger threshold for flow characteristic analysis in the slag conveying pipeline.

[0094] The CFD-based multi-scale bonding probabilistic dynamics model includes: DLVO (Derjaguin-Landau-Verwey-Overbeek) coupled viscoelastic contact module, collision bonding coupled probability module, agglomerate growth dynamics module and agglomerate critical phase transition analysis module;

[0095] Step 201: Processing the particle surface physical and chemical property data calculated by CFD using the DLVO coupled viscoelastic contact theory to obtain the potential energy spectrum of the interaction between particles in the pipeline. The DLVO coupled viscoelastic contact theory combines traditional DLVO theory with viscoelastic contact mechanics to comprehensively describe the interaction force between particles in the slag conveying pipeline.

[0096] The input of this algorithm is the surface physical and chemical characteristic parameters extracted from the particle physical characteristic evolution matrix in step 100, combined with the local fluid environment calculated by CFD. The specific expression is:

[0097]

[0098] Among them U total(h) represents the total interaction potential energy. The interaction potential energy spectrum between particles in the pipeline refers to the complete distribution curve of the total interaction potential energy at different interparticle distances h. The U total (h) Numerical construction of the complete potential energy spectrum;

[0099] h represents the distance between particles; U vdW (h) represents the van der Waals potential energy, which is defined as:

[0100]

[0101] Where R1 and R2 are the particle radius; A H is the Hamaker constant, which is expressed at extreme temperatures as:

[0102] A H (T)=A H,0 ·(1-β T (T-T0))

[0103] Among them A H,0 is a constant; β T is the temperature coefficient; T0 is the reference temperature; T is the local temperature in the pipeline;

[0104] U elec (h) represents the electrostatic double layer potential energy, which is defined as:

[0105] U elec (h)=2π∈0∈ r R eff ζ 2 exp(-κh)

[0106] Where ∈0 is the dielectric constant of vacuum, which is 8.85×10^-12F / m; r is the relative dielectric constant; R eff is the equivalent radius calculated in step 100; ζ is the Zeta potential; κ is the inverse of the Debye length;

[0107] U cap (h) represents the capillary potential energy, which is defined as:

[0108]

[0109] where γ lv is the liquid-gas interfacial tension; θ is the contact angle; d0 is the characteristic capillary length;

[0110] represents the viscous damping potential energy, which is defined as:

[0111]

[0112] where h′ and are the inter-particle distance and the relative approach speed of particles (integral variables in the integration process), respectively; is the viscous damping force, defined as:

[0113]

[0114] Where η is the dynamic viscosity of the fluid; h and are the current inter-particle distance and the relative approach speed of the particles;

[0115] U elast (h) represents the elastic deformation potential energy, when h < 0 (particle contact deformation) is defined as:

[0116]

[0117] Where δ is the contact deformation, defined as: δ = -h, E eff is the equivalent elastic modulus calculated in step 100.

[0118] Step 202: Processing the potential energy spectrum of the interaction between particles in the pipeline and the particle motion characteristic vector using a CFD-based collision-bonding coupling probability model to obtain a particle bonding probability distribution in the slag conveying pipeline;

[0119] The expression of this model is: P agg (i,j)=P coll (i,j)·P stick (i,j)

[0120] Among them, P agg (i, j) represents the probability of successful agglomeration of the i-th and j-th particles in the pipeline, ranging from [0, 1]. By calculating the adhesion probability between multiple particles, the probability distribution of particle adhesion in the pipeline is obtained;

[0121] P coll (i, j) represents the collision probability between the i-th and j-th particles in the pipeline, which is defined as:

[0122] P coll (i,j)=β coll (i,j)·n i ·n j ·Δt

[0123] where n i 、n j is the number concentration of particles i and j in the pipeline; Δt is the time step;

[0124] β coll (i, j) is the collision frequency function, which has different expressions for different flow field environments in the slag conveying pipeline:

[0125]

[0126] where β coll,lam (i, j) is the laminar collision frequency function, G is the shear rate in the pipe; R i and R j is the radius of particles i and j in the pipe;

[0127]

[0128] where β coll,turb (i, j) is the turbulent collision frequency function, ∈ is the turbulent energy dissipation rate calculated by CFD, and ν is the fluid kinematic viscosity;

[0129] P stick (i, j) represents the probability of adhesion in the pipeline, which is calculated by potential energy theory:

[0130]

[0131] Among them E a is the activation energy; U min is the minimum value in the potential energy spectrum (potential well depth); k B is the Boltzmann constant, and T is the local temperature in the pipe.

[0132] Step 203: Processing the particle adhesion probability distribution in the slag conveying pipeline using a CFD-based agglomerate growth kinetics equation to obtain a temporal and spatial evolution characteristic spectrum of the agglomerates in the pipeline; wherein the CFD-based agglomerate growth kinetics equation is based on a population balance model and describes the temporal and spatial evolution of the agglomerate size distribution in the slag conveying pipeline;

[0133] The expression of this equation is:

[0134]

[0135] in It represents the rate of change of the number concentration of agglomerates with a volume of v at time t; represents the convection term, which describes the influence of CFD flow field on the spatial distribution of clusters;

[0136] represents the fluid velocity field calculated by CFD; v and u represent the aggregate volume; n(v,t) represents the number concentration of an aggregate with a volume of v at time t; t represents time; S(v,t) represents the source term, which describes the generation of aggregates caused by external factors; R(v,t) represents the sink term, which describes the decomposition or outflow of aggregates from the system; β(v,u) represents the coalescence kernel function of two aggregates with volumes v and u, which is defined as:

[0137]

[0138] where β0(T,P) is the baseline coalescence rate related to temperature T and pressure P; Ψ(v,u,T,P,Turb) is a correction function that takes into account the influence of extreme working conditions.

[0139] Step 204: Processing the spatiotemporal evolution characteristic spectrum of agglomerates in the pipeline using a CFD-based agglomerate critical phase transition analysis algorithm to obtain a critical condition spectrum for agglomerate formation in the slag conveying pipeline;

[0140] Specifically, the CFD-based agglomeration critical phase transition analysis algorithm is based on the phase transition theory in statistical mechanics to identify the critical conditions for the particle system in the slag conveying pipeline to transform from a dispersed state to an agglomerated state;

[0141] The core expression of this algorithm is:

[0142] Where T, P and Turb represent the temperature, pressure and turbulence intensity in the pipeline respectively. crit Indicates the critical agglomeration parameters (including the number of agglomerates, agglomeration size, and agglomeration speed). When the actual agglomeration parameters O>O crit When the critical agglomeration parameters of multiple regions are calculated, the calculated critical agglomeration parameter values ​​are mapped to the multidimensional parameter space according to their corresponding spatial coordinates and operating parameters to form a complete distribution map, thereby obtaining the critical condition map of agglomeration formation in the slag conveying pipeline.

[0143] U agg represents the agglomeration energy, which is determined by the interaction potential energy calculated in step 201 and the CFD flow field; φ represents the volume fraction of particles in the pipeline; Re p represents the particle Reynolds number, which is defined as:

[0144]

[0145] where ρ f represents the fluid density; u f is the fluid velocity calculated by CFD; u p is the particle velocity; d p is the particle diameter; μ f is the fluid dynamic viscosity;

[0146] St represents the Stokes number, which represents the ratio of particle inertia to fluid viscosity and is defined as:

[0147]

[0148] in represents the particle inertia force; L represents the characteristic length of the pipeline (such as the diameter);

[0149] The critical condition is determined by the following equation:

[0150]

[0151] Where Λ represents the agglomeration criterion; n cluster Indicates the number of agglomerates in the pipeline; <s>represents the average agglomerate size; ∑ i n i represents the total number of particles in the pipeline; Λ crit represents the critical threshold, which is obtained by calibration of CFD simulation and experimental data.

[0152] Step 300: Processing the physical property evolution matrix of particles in the pipeline using a CFD-based multi-mechanism particle crushing mechanics model to obtain a critical condition map of particle crushing in the slag conveying pipeline;

[0153] Specifically, this step establishes a CFD-based multi-mechanism particle crushing mechanics model. Combining the particle physical property evolution matrix obtained in step 100 with the flow field distribution calculated by CFD, and starting from the principles of material strength theory, fracture mechanics, and energy balance, this step comprehensively considers the various crushing mechanisms that may occur in particles in the slag conveying pipeline under extreme working conditions. A mechanical model that quantitatively describes the particle crushing behavior in the slag conveying pipeline is constructed, providing the ability to predict critical crushing conditions for flow characteristic analysis in the slag conveying pipeline.

[0154] The CFD-based multi-mechanism particle crushing mechanics model includes: fracture strength criterion module, stress-strain field dynamic analysis module, fracture dynamics multi-mode prediction module and crushing critical phase diagram construction module.

[0155] Step 301: Processing the material property data of particles in the pipeline using a CFD-based fracture strength criterion model to obtain the critical threshold distribution of the crushing strength in the slag conveying pipeline. The CFD-based fracture strength criterion model is based on Weibull strength theory and Griffith fracture criterion and is used to predict the fracture strength of particles at different locations, sizes, and environmental conditions in the slag conveying pipeline.

[0156] The expression of this model is:

[0157]

[0158] where σ crit represents the critical threshold of crushing strength; d, T and P represent the characteristic size of the particle, the local temperature in the pipeline and the local pressure in the pipeline respectively; σ0 represents the reference strength under reference conditions, which is determined by temperature and pressure; d0 represents the reference size; E b represents the fragmentation activation energy; R represents the gas constant, which is 8.314 J / (mol·K); T0 represents the reference temperature, which is obtained by CFD calculation; m(T) represents the temperature-dependent Weibull modulus, which is defined as:

[0159] m(T)=m0·(1-α m (T-T0))

[0160] Where m0 represents the Weibull modulus at the reference temperature; α m represents the temperature sensitivity coefficient;

[0161] Ω(P) represents the pressure effect correction function, which is defined as: Ω(P) = 1 + η1P - η2P 2 , where η1 and η2 represent the fitting coefficients.

[0162] Step 302: Process the mechanical load data within the pipeline using a CFD-based stress-strain field dynamic analysis model to obtain the internal stress distribution field of the particles within the slag conveying pipeline. The CFD-based stress-strain field dynamic analysis model is based on the finite element method and continuum mechanics theory, combined with the flow field distribution within the pipeline calculated by CFD, to calculate the internal stress distribution of the particles within the slag conveying pipeline under the action of external loads. The expression of this model is:

[0163]

[0164] where σ ij represents the stress tensor; x, y, z represent the spatial coordinates in the pipeline; ∫ V represents the integral of volume V; G ijkl represents Green's function (stress response function); x', y', z' represent the spatial coordinates of the integral variables; F kl represents the external load distribution, which is determined by the flow field in the pipe calculated by CFD; V represents the integration domain, which is the particle volume;

[0165] For heterogeneous materials or particles with complex shapes, numerical methods are used to solve:

[0166]

[0167] σ=C:ε

[0168]

[0169] in represents the gradient operator; σ represents the stress tensor; F represents the volume force, including the fluid drag force and pressure gradient force calculated by CFD; ρ represents the material density; dis represents the displacement field; represents the second-order derivative of the displacement field; t represents time; represents the second-order derivative of time; C represents the elastic stiffness tensor, which is related to the temperature and pressure distribution calculated by CFD; ε represents the strain tensor; T represents temperature.

[0170] Step 303: The stress distribution field and the critical crushing threshold in the pipeline are processed using a CFD-based fracture dynamics multi-mode prediction model to obtain the particle crushing mode distribution in the slag conveying pipeline. The CFD-based fracture dynamics multi-mode prediction model is based on fracture mechanics theory, combined with the flow field distribution in the pipeline calculated by CFD, and comprehensively considers different crushing mechanisms to predict the possible crushing modes of particles in the slag conveying pipeline under extreme working conditions. The expression of this model is:

[0171]

[0172] where d, T, P, and σ represent the characteristic size, temperature, pressure, and stress distribution of the particles in the pipeline, respectively; x, y, and z represent the spatial coordinates; P break,i represents the probability of particles in the pipeline breaking in the i-th mode, ranging from [0,1]; σ max (x, y, z) represents the maximum stress inside the particle at position (x, y, z) in the pipeline; σ crit (d, T, P, x, y, z) represents the critical crushing strength at the position (x, y, z) in the pipeline calculated in step 301; σ s represents the standard deviation of stress distribution; n i Indicates the index parameter related to the fragmentation mode;

[0173] For different crushing modes in the slag conveying pipeline, the corresponding crushing probability calculation method is defined:

[0174] P break,tension =P break (σ1,σ crit,t ,x,y,z)

[0175] Among them, P break,tension is the tensile fracture mode probability; P break represents the probability of breakage; σ1 is the maximum principal stress; σ crit,t is the tensile strength;

[0176] P break,shear =P break (τ max ,τ crit ,x,y,z)

[0177] Among them, P break,shear is the shear fracture mode probability; τ max is the maximum shear stress; τ crit is the shear strength;

[0178] P break,comp =P break (σ vm ,σ crit,c ,x,y,z)

[0179] Among them, P break,comp is the compression breaking mode probability, σ vm is the VonMises stress, σ crit,c is the compressive strength.

[0180] Step 304: Processing the crushing mode distribution data in the pipeline using a CFD-based crushing critical phase diagram construction algorithm to obtain a critical condition map of particle crushing in the slag conveying pipeline;

[0181] The CFD-based algorithm for constructing a critical phase diagram for particle crushing is based on extreme value statistics theory and phase transition theory, combined with the flow field distribution in the pipeline calculated by CFD. This algorithm constructs a critical condition map for particle crushing in the slag conveying pipeline in a multi-dimensional parameter space.

[0182] The input of this algorithm is the particle breakage pattern distribution P in the pipeline in step 303 break,i (d, T, P, σ, x, y, z) and the flow field distribution calculated by CFD, the output is the critical condition map of particle crushing in the slag conveying pipeline, including the critical Weber number and critical crushing parameters at different positions in the pipeline and under different working conditions;

[0183] The core expression of the CFD-based crushing critical phase diagram construction algorithm is:

[0184]

[0185] Where T, P, Turb and d represent the temperature, pressure, turbulence intensity and particle diameter in the pipeline respectively; x, y, z represent the spatial coordinates; Γ crit Indicates the critical crushing parameter. When the actual crushing parameter Γ>Γ crit When the particle size is increased, the particles at a specific location in the pipeline will be broken;

[0186] <σ(x,y,z)> represents the average stress level at the position (x,y,z) in the pipeline; σ crit (T, P, d, x, y, z) represents the critical crushing strength at the position (x, y, z) in the pipeline calculated in step 301; Re p represents the particle Reynolds number, which is determined by the flow field in the pipe calculated by CFD; We represents the Weber number, which characterizes the ratio of the fluid dynamics to the surface tension in the pipe and is defined as:

[0187]

[0188] where ρ f represents the fluid density; v rel represents the relative velocity between the particle and the fluid in the pipe, which is calculated by CFD; d represents the particle diameter; γ s represents surface tension;

[0189] Fr represents the Froude number, which represents the ratio of inertial force to gravity and is defined as: Where g represents the acceleration due to gravity;

[0190] The critical condition is determined by the following equation:

[0191]

[0192] Where Π(x,y,z) represents the fragmentation criterion at the position (x,y,z) in the pipeline, and the range is [0,1]; represents the Heaviside step function; σ ij (x, y, z) represents the stress tensor at the position (x, y, z) in the pipeline; σ crit (x, y, z) represents the critical crushing strength at the position (x, y, z) in the pipeline; V represents the particle volume; Π crit Represents the critical thresholds, which are obtained through CFD simulation and experimental data calibration. These thresholds are mapped in the multidimensional parameter space to form a complete map of critical conditions for particle breakage.

[0193] Step 400: Processing the critical condition map of agglomeration formation and the critical condition map of particle breakage in the slag conveying pipeline by using a CFD-based critical state fusion adaptive prediction model to obtain an early warning indicator of flow characteristics in the slag conveying pipeline;

[0194] This step constructs a CFD-based critical state fusion adaptive prediction model, integrating the critical condition maps for agglomeration formation and particle crushing in the slag conveying pipeline obtained in steps 200 and 300. Combined with the flow field distribution in the pipeline calculated by CFD and the operating parameters monitored in real time, it predicts and warns of potential dangerous conditions in the slag conveying pipeline. It takes into account the interaction between the agglomeration and crushing processes in the pipeline and their impact on the flow characteristics, and can dynamically adapt to changes in the operating conditions in the pipeline, providing accurate warning capabilities for the analysis and prediction of flow characteristics in the slag conveying pipeline.

[0195] The CFD-based critical state fusion adaptive prediction model includes a dual-target dangerous state mapping module, a time-series evolution Markov prediction module, a risk spatiotemporal distribution visualization module, and a multi-level early warning decision support module.

[0196] Step 401: Processing the critical condition maps of agglomeration and breakage in the slag conveying pipeline using a CFD-based dual-objective dangerous state mapping algorithm to obtain a joint assessment matrix for dangerous states in the pipeline. The CFD-based dual-objective dangerous state mapping algorithm uniformly represents the two potential dangerous states of agglomeration and breakage in the slag conveying pipeline in a multidimensional parameter space to construct a joint assessment matrix for dangerous states in the pipeline.

[0197] The inputs of this algorithm are the critical condition map for agglomeration formation in the slag conveying pipeline from step 200, the critical condition map for particle breakage in the slag conveying pipeline from step 300, and the flow field distribution in the pipeline calculated by CFD. The output is the joint assessment matrix D(T, P, Turb, φ, d, x, y, z) for hazardous conditions in the pipeline, which provides a unified hazardous state assessment standard for flow characteristic analysis in the slag conveying pipeline.

[0198] The expression of the dual-objective dangerous state mapping algorithm based on CFD is:

[0199] D(T,P,Turb,φ,d,x,y,z)=

[0200] α·D agg (T,P,Turb,φ,d,x,y,z)+

[0201] β·D break (T,P,Turb,φ,d,x,y,z)+

[0202] γ·D agg×break (T,P,Turb,φ,d,x,y,z)

[0203] Where D represents the joint assessment matrix of dangerous states in the pipeline (including the agglomeration dangerous state matrix, the breakage dangerous state matrix, and the agglomeration and breakage interactive dangerous state matrix); α, β, and γ are weight coefficients, whose values ​​are determined according to the specific characteristics of the slag conveying pipeline system; T represents the temperature in the pipeline; P, Turb, φ, and d represent the pressure in the pipeline, the turbulence intensity in the pipeline, the volume fraction of particles in the pipeline, and the particle diameter, respectively; x, y, and z represent the spatial coordinates in the pipeline; D agg The matrix representing the dangerous state of agglomeration in the pipeline (including the number of agglomerates, agglomerate size, agglomeration speed, etc.) is derived from the agglomeration critical condition map in step 200:

[0204]

[0205] where Λ and Λ crit represent the clustering criterion and critical threshold calculated in step 200 respectively;

[0206] D break The matrix representing the dangerous state of breakage in the pipeline (including the number of breakages, breakage size, breakage speed, etc.) is derived from the critical condition map of breakage in step 300:

[0207]

[0208] where Π and Π crit They represent the pipeline crushing criterion and critical threshold value calculated in step 300 respectively;

[0209] D agg×break The matrix representing the dangerous state of interaction between agglomeration and breakage in the pipeline (including the number of agglomeration and breakage, the size of agglomeration and breakage, the speed of agglomeration and breakage, etc.) is defined as:

[0210]

[0211] in is the interaction strength function, which describes the mutual influence strength of the agglomeration and fragmentation process in the pipeline

[0212] The algorithm's dangerous state judgment criteria are:

[0213]

[0214] Where D(x,y,z) represents the joint assessment matrix of the dangerous state at the position (x,y,z) in the pipeline, and max() is the maximum value function.

[0215] Step 402: Processing the real-time operating parameters and the joint evaluation matrix of dangerous states using a CFD-based time-series evolution Markov prediction model to obtain a prediction map of the operating trajectory within the slag conveying pipeline. The CFD-based time-series evolution Markov prediction model is based on the nonlinear dynamic characteristics of the gas-solid two-phase flow system within the slag conveying pipeline and is combined with CFD simulation results to construct a prediction model capable of predicting the future evolution trajectory of the operating condition within the pipeline.

[0216] The input of this model is: the pipeline dangerous state joint assessment matrix D(T, P, Turb, φ, d, x, y, z) in step 401, the pipeline flow field distribution calculated by CFD and the real-time monitoring operating condition parameter sequence {S t ,S t-Δt ,...,S t-nΔt }, the output is the pipeline working condition trajectory prediction map {S t+Δt ,S t+2Δt ,...,S t+mΔt }, including multiple possible evolution paths of working conditions in the pipeline and their probability distribution;

[0217] The expression of the time series evolution Markov prediction model based on CFD is:

[0218] S t+Δt =P(S t ,S t-Δt ,...,S t-nΔt )·S t +Q t

[0219] Where: S t+Δt represents the state vector of the pipeline at time t+Δt, including parameters such as temperature, pressure, turbulence intensity, and particle concentration; S t represents the state vector of the pipeline at time t, including parameters such as temperature, pressure, turbulence intensity, and particle concentration; P represents the state transition matrix, which is obtained by CFD simulation and historical data training; Q t Represents the system random disturbance vector, which obeys the normal distribution Σ t is the disturbance covariance matrix; n represents the number of historical time steps; Δt represents the time step;

[0220] The state transfer matrix P takes into account the flow characteristics and particle behavior at different locations in the pipeline and can be expressed as:

[0221] P(x,y,z)=P base +P CFD (x,y,z)+P adapt (t)

[0222] Among them, P base represents the state transfer matrix determined by the basic physical model; P CFD (x, y, z) represents the spatial distribution correction term obtained by CFD calculation, which reflects the flow characteristics at different positions in the pipeline; P adapt (t) represents the time correction term obtained by adaptive learning, which is continuously updated as the system runs;

[0223] The uncertainty quantification of the operating condition prediction is achieved through random sampling method:

[0224]

[0225] Represents the operating state vector S at time t+kΔt t+kΔt N is randomly sampled from s samples, N s represents the number of samples, and m represents the number of prediction time steps;

[0226] Generate confidence intervals for the working trajectory in the pipeline using probability statistics methods:

[0227]

[0228] in Represents the lower limit of the 95% confidence interval for the predicted operating condition; Represents the upper limit of the 95% confidence interval for the predicted operating condition.

[0229] Step 403: The CFD-based risk spatiotemporal distribution visualization system processes the operating trajectory prediction map and the hazardous state joint assessment matrix to obtain a boundary map of the dynamic risk area in the slag conveying pipeline. The CFD-based risk spatiotemporal distribution visualization system combines the CFD-calculated flow field distribution in the pipeline, the operating trajectory prediction results in the pipeline, and the hazardous state assessment results to construct a spatiotemporal distribution visualization interface for the dynamic risk area in the slag conveying pipeline.

[0230] The core processing flow of this system is:

[0231] Risk level spatial mapping: For each spatial position (x, y, z) and each prediction time t+kΔt, the pipeline working condition trajectory prediction map {S t+Δt ,S t+2Δt ,...,S t+mΔt } in the predicted working condition S t+kΔt The risk level is calculated by combining the pipeline dangerous state joint assessment matrix D in step 401:

[0232] R(x,y,z,t+kΔt)=f R (S t+kΔt ,D(T,P,Turb,φ,d,x,y,z))

[0233] where f R is a risk assessment function that outputs the risk level R∈[0,1]; S t+kΔt To predict the working conditions, including parameters such as temperature, pressure, turbulence intensity, and particle concentration; R(x, y, z, t+kΔt) is the risk degree function;

[0234] Risk isosurface extraction: Based on the risk level threshold {R th,1 ,R th,2 ,...,R th,l Extract risk isosurfaces:

[0235]

[0236] in represents the isosurface of risk level i; represents the isosurface of the i-th level risk at time t+kΔt; R th,i is the threshold of risk level i;

[0237] Dynamic boundary evolution: Through time series analysis, calculate the evolution speed and direction of the risk area boundary:

[0238]

[0239] Where V boundary represents the boundary evolution velocity vector; represents the risk area boundary in the pipeline at time t; express the boundaries;

[0240] Spatiotemporal integrated visualization: The flow field distribution, risk area, and boundary evolution information calculated by CFD are integrated into a unified visualization interface. The final output of the pipeline dynamic risk area boundary mapping expression is:

[0241]

[0242] in represents the boundary mapping of the dynamic risk area in the pipeline; R(x,y,z,t) represents the risk level distribution in the pipeline; represents the critical risk boundary in the pipeline; v(x,y,z,t) represents the velocity distribution field in the pipeline; φ(x,y,z,t) represents the particle concentration distribution field in the pipeline; P(x,y,z,t) represents the pressure distribution field in the pipeline.

[0243] Step 404: Processing the dynamic risk area boundary mapping in the pipeline through a CFD-based multi-level early warning decision support system to obtain early warning signals and intervention suggestions for flow characteristics in the slag conveying pipeline;

[0244] The CFD-based multi-level early warning decision support system generates graded early warning signals and intervention strategy recommendations for abnormal flow characteristics in the slag conveying pipeline based on the flow field distribution and risk assessment results calculated by CFD;

[0245] The input to this system is the dynamic risk area boundary map in the pipeline in step 403 The output is the early warning signal of the flow characteristics in the slag conveying pipeline and the intervention suggestion {W(t), A(t)}, which includes the triggering conditions of the early warning signal at different levels and the corresponding intervention operation suggestions;

[0246] The CFD-based multi-level early warning decision support system adopts a hierarchical early warning mechanism, which divides the warning levels into four levels: attention (A), warning (B), alarm (C), and emergency (D). The corresponding early warning signals are generated according to the risk judgment criteria. The method for determining the early warning signal is as follows:

[0247]

[0248] Where W(t) represents the warning signal level at time t, which is divided into four levels: A, B, C, and D (A is attention, B is warning, C is alarm, and D is emergency); V total Indicates the total volume of the pipeline; V risk (t) represents the volume of the pipeline in a high-risk state at time t, which is defined as:

[0249]

[0250] in is the Heaviside step function, R th,danger is the danger threshold, usually 0.8; R(x,y,z,t) represents the risk level distribution in the pipeline;

[0251] t cross It represents the time when the predicted working condition trajectory first crosses the dangerous boundary and is defined as:

[0252]

[0253] Where kΔt represents the time when the predicted working condition trajectory crosses the dangerous boundary;

[0254] Based on different warning levels, the system generates corresponding intervention suggestions A(t), including:

[0255] Level A (Caution) intervention recommendations: Monitor particle concentration, flow velocity, and pressure trends at key points in the pipeline; adjust CFD simulation parameters to improve the flow field calculation in the pipeline; and prepare a preventive maintenance plan.

[0256] Level B (early warning) intervention recommendations: fine-tune operating parameters such as flow rate, temperature, or concentration; activate auxiliary equipment such as vibrators and flow channel accessories; increase the monitoring frequency of key locations in the pipeline; prepare backup pipeline systems;

[0257] Level C (alarm) intervention recommendations: significantly adjust operating parameters, such as significantly reducing the delivery concentration or flow rate; activate pipeline anti-blocking systems, such as high-pressure water flushing or gas pulse systems; prepare emergency shutdown procedures; activate backup pipeline systems;

[0258] Level D (Emergency) intervention recommendations: Execute emergency shutdown procedures; start the pipeline emergency cleaning system; switch to the emergency backup system; start the pipeline inspection and restoration procedures.

[0259] The priority of intervention recommendations is determined by the principle of least intervention cost:

[0260]

[0261] Among them A opt (t) represents the optimal intervention recommendation; argmin represents the minimum operation cost; A i represents the i-th intervention measure; represents the set of all possible intervention measures; C(A i ) represents intervention measure A i Cost function, considering economic loss, operation complexity and security risk; E[R|A i ] indicates intervention measures A i The expected risk level after R th,safe Represents the safety threshold, usually 0.6.

[0262] Through the above four steps, step 400 constructs a CFD-based critical state fusion adaptive prediction model, which realizes a comprehensive analysis and prediction of the flow characteristics in the slag conveying pipeline, and provides an effective tool for preventing risks such as agglomeration blockage and wear caused by particle breakage in the slag conveying pipeline.

[0263] In one embodiment of the present invention, a wide-size-range slag conveying system is provided for executing the steps of the above-mentioned wide-size-range slag conveying method, including:

[0264] The CFD-coupled multi-field particle state characterization module is used to process extreme operating condition monitoring data in the slag conveying pipeline and obtain the evolution matrix of the physical characteristics of the particles in the pipeline;

[0265] A CFD-based multi-scale probabilistic bonding dynamics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain a map of the critical conditions for agglomeration formation in the slag conveying pipeline;

[0266] A CFD-based multi-mechanism particle crushing mechanics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain the critical condition map of particle crushing in the slag conveying pipeline;

[0267] A CFD-based critical state fusion adaptive prediction module is used to process the critical condition maps of agglomeration formation and particle breakage in the slag conveying pipeline, and obtain early warning indicators of flow characteristics in the slag conveying pipeline;

[0268] Specifically, the early warning indicators for flow characteristics in slag conveying pipelines include three matrices: an agglomeration hazard state matrix, a breakage hazard state matrix, and an agglomeration and breakage interaction hazard state matrix. These matrices comprehensively characterize the risk states at different locations and under different working conditions in the pipeline. The priority ranking of intervention recommendations is determined by the above-mentioned minimum intervention cost principle. When the expected risk level exceeds the safety threshold (usually 0.6), an early warning is triggered and intervention recommendations are given according to the warning level (including level A, level B, level C, and level D).

[0269] Here, the present invention provides an implementation example: This embodiment is applied to a boiler ash conveying system of a 600MW thermal power plant. The system uses hydraulic conveying to transport the ash accumulated at the bottom of the boiler to the ash treatment site;

[0270] The slag conveying pipeline is a carbon steel pipeline with a diameter of 200mm and a total length of 2.5km, containing multiple elbows and slope sections; the conveyed material is a mixture of fly ash particles containing particles of different particle sizes (0.05-10mm) and a volume concentration ranging from 20% to 35%.

[0271] The main problems faced by the system include:

[0272] The elbows are frequently worn and need to be replaced every 3 to 6 months;

[0273] Pipes are prone to clogging under high-load conditions, and each cleaning requires 8 to 12 hours of downtime;

[0274] The existing monitoring system cannot provide early warning of the risk of particle aggregation and breakage in the pipeline and can only respond passively.

[0275] The basic data collection for implementing the method of the present invention includes:

[0276] Temperature sensors (measuring range 50-300°C) are installed at 5 measuring points, pressure sensors (measuring range 0-5MPa) are installed at 8 measuring points, flow meters (measuring range 50-500m3 / h) are installed at 3 measuring points, and concentration measuring devices (measuring range 0%-50%) are installed at 6 measuring points.

[0277] The system uses the commercial CFD software ANSYS Fluent to establish a three-dimensional numerical model of the pipeline with a grid size of 2 million. The Euler-Lagrange coupling method is used to simulate gas-solid two-phase flow. The calculation cycle is once every 5 minutes and the data is saved once every hour.

[0278] This step processes the temperature, pressure, and turbulence field data in the slag conveying pipeline to obtain the particle physical property evolution matrix. The key measurement points of the pipeline are the inlet section, the first elbow, the starting point of the uphill section, the end point of the uphill section, the second elbow, and the outlet section.

[0279] The temperature property mapping function is applied to process the temperature field data calculated by CFD to obtain the changes in the physical parameters of the ash particles at different temperatures. The calculation results of the temperature property mapping function for representative data are shown in Table 1:

[0280] Table 1: Measured values ​​of operating parameters at key measuring points of pipelines

[0281]

[0282] The pressure-deformation response model is used to calculate the deformation characteristics of particles under different pressures in the pipeline. The calculation results of the pressure-deformation response model for representative data are shown in Table 2:

[0283] Table 2: Pressure-deformation response model calculation results for representative data

[0284]

[0285] The turbulence-particle interaction model is used to analyze the motion characteristics of particles under different turbulence conditions. The calculation results of the turbulence-particle interaction model for representative data are shown in Table 3:

[0286] Table 3: Turbulence-particle interaction model calculation results for representative data

[0287]

[0288] The spatial distribution of the particle physical property evolution matrix generated by the multi-field state integration algorithm is shown in Table 4:

[0289] Table 4: Spatial distribution of particle physical property evolution matrix

[0290]

[0291] This step processes the matrix of particle physical property evolution in the pipeline, analyzes particle agglomeration behavior, and predicts the critical conditions for agglomeration formation. Specifically, it includes:

[0292] The DLVO-coupled viscoelastic contact theory is used to calculate the potential energy spectrum of the interaction between particles. The calculation results of the potential energy spectrum of the interaction between particles for representative data are shown in Table 5:

[0293] Table 5: Calculation results of the potential energy spectrum of particle interactions (representative data)

[0294]

[0295] The collision-adhesion coupling probability model is used to calculate the probability distribution of particle adhesion in the pipeline. The probability distribution of particle adhesion in the pipeline for representative data is shown in Table 6:

[0296] Table 6: Distribution of particle adhesion probability in pipelines (representative data)

[0297]

[0298] The agglomerate growth kinetics equation was used to calculate the spatiotemporal evolution characteristics of agglomerates in the pipeline. Representative data on the evolution of agglomerate size distribution over time at different locations in the pipeline are shown in Table 7:

[0299] Table 7: Evolution of aggregate size distribution over time at different locations within the pipeline (representative data)

[0300]

[0301] The critical conditions for agglomeration formation were determined by applying the agglomeration critical phase transition analysis algorithm, as shown in Table 8:

[0302] Table 8: Critical conditions for agglomeration formation in slag conveying pipelines (representative data)

[0303]

[0304] This step processes the particle physical property evolution matrix in the pipeline, analyzes particle breakage behavior, and predicts critical breakage conditions. Specifically, it includes:

[0305] The fracture strength criterion model was used to calculate the critical thresholds of crushing strength of particles in pipelines under different conditions. Representative data of the distribution of critical thresholds of crushing strength in pipelines are shown in Table 9:

[0306] Table 9: Representative data of the distribution of critical crushing strength thresholds within pipelines

[0307] Particle diameter (mm) Temperature (℃) Pressure (MPa) Critical crushing strength σ_crit (MPa) 0.5 60 1.2 25.8 0.5 70 1.2 23.2 0.5 60 1.6 28.5 1.0 60 1.2 22.5 1.0 70 1.2 20.1 1.0 60 1.6 24.7 5.0 60 1.2 17.2 5.0 70 1.2 15.3 5.0 60 1.6 18.9

[0308] The stress-strain field dynamic analysis model is used to calculate the internal stress distribution of particles in the pipeline. Representative data of the internal stress distribution of particles at typical locations in the pipeline are shown in Table 10:

[0309] Table 10: Representative data of stress distribution inside particles at typical locations within the pipeline

[0310]

[0311] The fracture dynamics multi-mode prediction model was used to calculate the distribution of particle breakage modes in the pipeline. Representative data of the distribution of particle breakage modes in the pipeline are shown in Table 11:

[0312] Table 11: Representative data of particle breakage pattern distribution in pipelines

[0313]

[0314] The critical conditions for particle breakage are determined by applying the crushing critical phase diagram construction algorithm, as shown in Table 12:

[0315] Table 12: Critical conditions for particle breakage in slag conveying pipelines (representative data)

[0316]

[0317] This step processes the critical condition maps of agglomeration formation and particle breakage to achieve early warning of flow characteristics in the slag conveying pipeline; specifically, it includes:

[0318] The dual-objective dangerous state mapping algorithm is used to calculate the pipeline dangerous state joint assessment matrix. The representative data of the pipeline dangerous state joint assessment matrix are shown in Table 13:

[0319] Table 13: Representative data of the joint assessment matrix for hazardous conditions in pipelines

[0320]

[0321] The time-series evolution Markov prediction model is used to calculate the pipeline working condition trajectory prediction map. The pipeline working condition trajectory prediction (representative data of the second elbow position) is shown in Table 14:

[0322] Table 14: Prediction of working trajectory in pipeline (representative data of the second elbow position)

[0323]

[0324] The risk spatiotemporal distribution visualization system was used to generate a dynamic risk area boundary map within the pipeline. Representative data on the distribution of risk areas within the pipeline are shown in Table 15:

[0325] Table 15: Representative data of risk area distribution within pipelines

[0326]

[0327] The multi-level early warning decision support system was used to generate early warning signals and intervention suggestions for the flow characteristics in the slag conveying pipeline, as shown in Table 16:

[0328] Table 16: Early warning signals and intervention recommendations (representative data)

[0329]

[0330] This example compares the application effects of the conventional method and the method of the present invention in the actual thermal power plant boiler slag conveying system; the test period is 6 consecutive months, including normal operating conditions and high-load conditions;

[0331] As shown in Table 17, the flow field prediction accuracy comparison is shown:

[0332] Table 17: Comparison of flow field prediction accuracy

[0333] Prediction parameters Traditional method error The error of the method of the present invention Accuracy improvement ratio Flow velocity distribution ±15.2％ ±9.8％ 35.5％ Pressure distribution ±18.5％ ±10.7％ 42.2％ Particle concentration distribution ±16.8％ ±11.2％ 33.3％ Turbulence intensity distribution ±20.5％ ±12.6％ 38.5％

[0334] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.< / s>

Claims

1. A method for conveying slag with a wide particle size range, characterized in that: The following steps are involved: The CFD-coupled multi-field particle state characterization model is used to process the extreme operating condition monitoring data in the slag conveying pipeline to obtain the evolution matrix of the physical characteristics of the particles in the pipeline. The CFD-based multi-scale probabilistic bonding dynamics model is used to process the evolution matrix of the physical properties of particles in the pipeline, and the critical condition map of agglomeration formation in the slag conveying pipeline is obtained. The CFD-based multi-mechanism particle crushing mechanics model is used to process the evolution matrix of the physical characteristics of particles in the pipeline, and the critical condition map of particle crushing in the slag conveying pipeline is obtained. The critical condition maps of agglomeration formation and particle breakage in the slag conveying pipeline are processed by a CFD-based critical state fusion adaptive prediction model to obtain early warning indicators of flow characteristics in the slag conveying pipeline.

2. A method for conveying slag with a wide particle size range according to claim 1, characterized in that: The step of processing the extreme working condition monitoring data in the slag conveying pipeline by using the CFD-coupled multi-field particle state characterization model includes: The temperature field data calculated by CFD is processed by the temperature property mapping function to obtain the temperature-dependent particle physical parameter set; The pressure field data calculated by CFD is processed by the pressure-deformation response model to obtain the pressure-dependent particle deformation characteristic matrix; The turbulent flow field data calculated by CFD is processed by the turbulent particle interaction model to obtain the particle motion characteristic vector under the turbulent state in the pipeline; The output data of each sub-model is processed by the CFD-coupled multi-field state integration algorithm to obtain the evolution matrix of the physical characteristics of the particles in the pipeline.

3. A method for conveying slag with a wide particle size range according to claim 2, characterized in that: The expression of the temperature property mapping function is: Among them, P i (T) represents the i-th physical parameter at the temperature T in the pipeline, T represents the temperature value in the pipeline, T0 represents the reference temperature, P i,0 Indicates the parameter reference value at reference temperature T0, E a,i represents the activation energy of the i-th parameter, f i (T) represents the correction function of the temperature critical region, and R represents the gas constant, which is 8.314 J / (mol·K).

4. The method for conveying slag with a wide particle size range according to claim 1, characterized in that: The step of processing the evolution matrix of the physical properties of particles in the pipeline by using a multi-scale bonding probabilistic dynamic model based on CFD includes: The DLVO coupled viscoelastic contact theory is used to process the physical and chemical properties of the particle surface calculated by CFD to obtain the potential energy spectrum of the interaction between particles in the pipeline; The CFD-based collision-bonding coupling probability model is used to process the interaction potential energy spectrum between particles in the pipeline and the particle motion eigenvectors to obtain the particle bonding probability distribution in the slag conveying pipeline. The probability distribution of particle adhesion in the slag conveying pipeline is processed by the CFD-based agglomeration growth kinetics equation, and the spatiotemporal evolution characteristic spectrum of agglomerates in the pipeline is obtained. The CFD-based agglomeration critical phase transition analysis algorithm is used to process the spatiotemporal evolution characteristic spectrum of agglomerates in the pipeline, and the critical condition spectrum of agglomerate formation in the slag conveying pipeline is obtained.

5. The method for conveying slag with a wide particle size range according to claim 4, characterized in that: The expression of the CFD-based collision-bonding coupling probability model is: P agg (i,j)=P coll (i,j)·P stick (i,j); Among them, P agg (i, j) represents the probability of successful agglomeration of the i-th and j-th particles in the pipeline; P coll (i, j) represents the collision probability between the i-th and j-th particles in the pipeline; P stick (i, j) represents the probability of adhesion between the i-th and j-th particles in the pipeline, which is defined as: Among them, E a is the activation energy, U min is the minimum value in the potential energy spectrum, k B is the Boltzmann constant, and T is the local temperature in the pipe.

6. The method for conveying slag with a wide particle size range according to claim 1, characterized in that: The step of processing the evolution matrix of the physical characteristics of particles in the pipeline using a CFD-based multi-mechanism particle crushing mechanics model includes: The CFD-based fracture strength criterion model is used to process the particle material property data in the pipeline to obtain the critical threshold distribution of the crushing strength in the slag conveying pipeline. The internal stress distribution field of particles in the slag conveying pipeline is obtained by processing the mechanical load data in the pipeline through the CFD-based stress-strain field dynamic analysis model; The stress distribution field and critical crushing threshold in the pipeline are processed by a CFD-based fracture dynamics multi-mode prediction model to obtain the particle crushing mode distribution in the slag conveying pipeline. The crushing mode distribution data in the pipeline was processed by constructing a CFD-based crushing critical phase diagram algorithm to obtain the critical condition map of particle crushing in the slag conveying pipeline.

7. The method for conveying slag with a wide particle size range according to claim 6, characterized in that: The expression of the CFD-based fracture dynamics multi-mode prediction model is: Among them, P break,i represents the probability of particle breakage in the pipeline according to the i-th mode, d, T, P and σ represent the characteristic size, temperature, pressure and stress distribution of the particles in the pipeline respectively, x, y, z represent the spatial coordinates, σ crit (d,T,P,x,y,z) represents the critical crushing strength at the position (x,y,z) in the pipeline, σ max (x,y,z) represents the maximum stress inside the particle at position (x,y,z) in the pipeline, σ s represents the standard deviation of stress distribution, n i Indicates the exponential parameter related to the fragmentation mode.

8. The method for conveying slag with a wide particle size range according to claim 1, characterized in that: The step of processing the critical condition map of agglomeration formation and the critical condition map of particle breakage in the slag conveying pipeline by using the CFD-based critical state fusion adaptive prediction model includes: The dual-objective dangerous state mapping algorithm based on CFD is used to process the critical condition maps of agglomeration and crushing in the slag conveying pipeline, and a joint assessment matrix of dangerous states in the pipeline is obtained. The CFD-based time-series evolution Markov prediction model processes the real-time operating parameters and the joint evaluation matrix of dangerous states to obtain the predicted trajectory map of the operating conditions in the slag conveying pipeline; The CFD-based risk spatiotemporal distribution visualization system processes the working condition trajectory prediction map and the dangerous state joint assessment matrix to obtain the dynamic risk area boundary map in the slag conveying pipeline; The CFD-based multi-level early warning decision support system processes the boundary mapping of dynamic risk areas in the pipeline to obtain early warning signals and intervention suggestions on the flow characteristics in the slag conveying pipeline.

9. The method for conveying slag with a wide particle size range according to claim 8, characterized in that: The method for determining the warning signal of the CFD-based multi-level warning decision support system is as follows: Among them, W(t) represents the warning signal level at time t, V risk (t) represents the volume of the pipeline in a high-risk state at time t, V total represents the total volume of the pipeline, t cross Indicates the time when the predicted working condition trajectory first crosses the danger boundary.

10. A slag conveying system with a wide particle size range, used for executing a slag conveying method with a wide particle size range according to any one of claims 1 to 9, characterized in that: include: The CFD-coupled multi-field particle state characterization module is used to process extreme operating condition monitoring data in the slag conveying pipeline and obtain the evolution matrix of the physical characteristics of the particles in the pipeline; A CFD-based multi-scale probabilistic bonding dynamics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain a map of the critical conditions for agglomeration formation in the slag conveying pipeline; A CFD-based multi-mechanism particle crushing mechanics module is used to process the evolution matrix of the physical properties of particles in the pipeline and obtain the critical condition map of particle crushing in the slag conveying pipeline; The CFD-based critical state fusion adaptive prediction module is used to process the critical condition maps of agglomeration formation and particle breakage in the slag conveying pipeline, and obtain early warning indicators of flow characteristics in the slag conveying pipeline.

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