CFD-based industrial dust removal pipe network design method and system

Abstract

The application discloses a kind of industrial dust removal pipe network design method and system based on CFD.The method comprises the following steps: constructing initial pipe network, arranging dust collector in the vicinity of dust production small, small air volume dust removal point, using parallel symmetric dendritic structure to construct pipe network, make each branch pipe local resistance loss consistent, and optimize elbow arrangement, adjust dust hood connection direction, set up the included angle between pipeline and horizontal plane is not less than preset angle;With minimum inclination branch pipe as model, using discrete phase model according to actual working condition dust parameter simulation obtains the critical flow velocity of no dust accumulation;Based on critical flow velocity and initial pipe network structure, pressure balance adjustment is carried out using CFD simulation, so that the pressure loss deviation of each branch pipe is within the preset range;The air volume of each dust removal point obtained by CFD calculation is compared with the design value, and the branch pipe diameter is adjusted iteratively optimized until the air volume deviation is within the preset range, to obtain the final pipe network structure.The application can reduce the most unfavorable branch pipe pressure loss, reduce the air volume of other branch pipes, further reduce the air volume and resistance of the dust collector, and reduce the energy consumption of the dust removal system through pressure loss balance and accurate air volume distribution.

Description

Technical Field

[0001] This invention relates to the field of industrial ventilation and dust removal technology, specifically to a CFD-based industrial dust removal pipeline design method and system. Background Technology

[0002] In industrial dust removal systems, the rationality of the pipeline design directly affects the dust removal effect and system energy consumption. Traditional dust removal pipeline designs often rely on empirical formulas and static impedance calculations, which are difficult to accurately reflect the airflow distribution and pressure loss under complex operating conditions. This leads to problems such as uneven airflow distribution, local dust accumulation, low airflow at some dust-generating points causing dust dispersion, and high energy consumption in some branch pipes due to valve adjustments to balance the system.

[0003] While existing technologies employ CFD simulations for pipeline network optimization, they primarily focus on local structural adjustments and lack systematic design methods and iterative optimization mechanisms. This is particularly evident in areas such as determining critical flow velocities, pressure balance, and airflow distribution. Therefore, a scientific and systematic dust collection pipeline network design method is urgently needed to achieve efficient and energy-saving dust collection systems. Summary of the Invention

[0004] To address the technical problems existing in the design of dust removal pipeline networks, such as reliance on empirical formulas, difficulty in accurately reflecting complex working conditions, uneven pressure loss distribution, and unreasonable air volume allocation, this invention provides a CFD-based industrial dust removal pipeline network design method and system.

[0005] This invention is achieved through the following technical solution: Firstly, this application provides a CFD-based industrial dust collection pipeline design method, comprising the following steps: Step 1: Construct the initial pipeline network: Based on the dust generation and air volume distribution of each dust removal point, arrange the dust collectors near the dust removal points where the dust generation and air volume are lower than the preset threshold. Use the experienced air volume and pipe diameter of each dust removal point as the initial design value, and construct the pipeline network using a parallel symmetrical tree structure. This ensures that the local resistance loss of the branch pipes connecting each dust removal point is basically consistent. Optimize the bend arrangement of the branch pipes, adjust the connection direction between the branch pipes and the dust collection hood, and set the angle between all pipes and the horizontal plane to be no less than the preset angle. Step 2: Using the branch pipe with the smallest inclination angle as a model, the discrete phase model is used to simulate the dust parameters under actual working conditions to obtain the critical flow velocity that does not accumulate dust. Step 3: Based on the critical flow velocity and the pipeline structure constructed in Step 1, CFD simulation is used to adjust the pressure balance so that the pressure loss deviation of each branch pipe is within the preset range. Step 4: Compare the air volume of each dust removal point obtained by CFD calculation with the design value, and perform iterative optimization by adjusting the diameter of the branch pipe until the air volume deviation of each dust removal point is within the preset range, thus obtaining the final pipeline structure.

[0006] Preferably, the initial pipeline construction in step 1 further includes: setting up multiple dust collection hoods in parallel for dust-generating points with significantly large dust collection surface areas, and ensuring that the bottom corners of each dust collection hood meet the dust collection coverage requirements.

[0007] Preferably, the optimization of the branch pipe bend arrangement in step 1 specifically includes: when the extension line of the bend outlet section points directly to a dust removal point in three-dimensional space, or can point to the dust removal point after spatial deflection and the angle of deflection is not greater than the angle of the bend itself, the branch pipe of the dust removal point is directly connected to the bend along the extension line or deflection line and merges into the main pipeline, so as to save the extra bend and reduce the local resistance loss when the branch pipe of the dust removal point merges into the main pipeline.

[0008] Preferably, in the parallel symmetrical dendritic structure described in step 1, the number of Y-shaped aggregated gradual expansion structures of each branch is the same or the difference does not exceed a threshold.

[0009] Preferably, the preset angle in step 1 is an inclined angle that can prevent dust deposition; the preset range of pressure loss deviation in step 3 is a deviation range that meets the requirements of pipeline pressure balance; and the preset range of air volume deviation in step 4 is a deviation range that meets the requirements of air volume distribution at each dust removal point.

[0010] Preferably, the dust collector in step 1 is arranged near a dust collection point where the dust generation is lower than a preset threshold and the air volume is lower than a preset threshold. The preset threshold is determined based on the dust generation and air volume distribution characteristics of each dust collection point.

[0011] Preferably, step 2 uses a discrete phase model to obtain the critical flow velocity that prevents dust accumulation, specifically including: Using the branch pipe with the smallest inclination angle as the geometric model, a three-dimensional pipe model is established and meshed. Set the boundary conditions for the inlet section as velocity inlet and the boundary conditions for the outlet section as pressure outlet; Set the dust particle concentration, density, and particle size distribution parameters according to the actual working conditions; Gradually adjust the inlet air velocity and observe the movement trajectory of dust particles in the pipe. The inlet air velocity corresponding to the point that there is no obvious particle deposition in the pipe is taken as the critical flow velocity for no dust accumulation.

[0012] Preferably, step 3 uses CFD simulation for pressure balance adjustment, specifically including: A three-dimensional geometric model is established based on the pipeline network structure constructed in step 1, and meshing is performed. Set the boundary conditions of the inlet section as pressure inlet and the boundary conditions of the outlet section as pressure outlet, and adjust the pressure difference between the inlet and outlet so that the wind speed of the branch with the minimum inclination angle reaches the critical flow velocity. Obtain the pressure loss distribution of each branch pipe and calculate the percentage deviation between the pressure loss of each branch pipe and the average pressure loss; If the deviation percentage exceeds the preset range, reduce the number of local resistance elements in the most unfavorable branch until the deviation percentage is within the preset range.

[0013] Preferably, step 4 involves iterative optimization by adjusting the branch pipe diameter, specifically including: The air volume at each dust removal point obtained from the CFD simulation is compared with the design value, and the percentage deviation of the air volume at each point is calculated. If the percentage deviation of the air volume at a certain dust removal point exceeds the preset range and the calculated air volume is greater than the design air volume, then the diameter of the corresponding branch pipe will be reduced. If the percentage deviation of the air volume at a certain dust removal point exceeds the preset range and the calculated air volume is less than the design air volume, then the diameter of the corresponding branch pipe should be increased. The CFD model is updated and resimulated based on the adjusted pipe diameter until the percentage deviation of airflow at all dust removal points is within the preset range.

[0014] Secondly, this application also provides a CFD-based industrial dust collection pipeline design system, including: The pipeline construction module is used to arrange dust collectors near dust collection points with dust generation and air volume below a preset threshold, based on the dust generation and air volume distribution of each dust collection point. The module uses the experienced air volume and pipe diameter of each dust collection point as the initial design value and adopts a parallel symmetrical tree-like structure to construct the pipeline network. This ensures that the local resistance loss of the branch pipes connecting each dust collection point remains consistent, optimizes the bend arrangement of the branch pipes, adjusts the connection direction between the branch pipes and the dust collection hood, and sets that the angle between all pipes and the horizontal plane is not less than a preset angle. The critical velocity determination module is used to perform CFD simulation calculations based on the dust parameters of the actual working conditions, using the branch pipe with the smallest inclination angle as a model and a discrete phase model, to obtain the critical velocity that does not accumulate dust. The pressure balance adjustment module is used to perform pressure balance adjustment based on the critical flow velocity and the pipeline structure constructed by the pipeline construction module, using CFD simulation calculations to ensure that the pressure loss deviation of each branch pipe is within a preset range. The air volume iterative optimization module is used to compare the air volume of each dust removal point obtained by CFD calculation with the design value, and to perform iterative optimization by adjusting the diameter of the branch pipes until the air volume deviation of each dust removal point is within the preset range, thus obtaining the final pipeline structure.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This application proposes an industrial dust collection pipeline design method. First, based on the dust generation and airflow distribution at each dust collection point, dust collectors are arranged in reasonable locations, and a parallel symmetrical dendritic structure is used to construct the pipeline network. By controlling the number of Y-shaped convergent gradually expanding structures, optimizing the arrangement of elbows, and adjusting the connection direction of dust hoods, the local resistance loss of each branch pipe is ensured to be close from the structural source. Then, using the branch pipe with the minimum inclination angle as a model, a discrete phase model combined with actual dust parameters is used to simulate and obtain the critical flow velocity that does not accumulate dust, providing key parameter basis for system design. On this basis, using the critical flow velocity as a control benchmark, the pressure loss distribution of the pipeline network is obtained through CFD simulation, and the structure of the most unfavorable branch pipe is optimized until the pressure loss deviation meets the requirements. Finally, by comparing the airflow at each dust collection point with the design value, the diameter of the branch pipe is dynamically adjusted to achieve precise control of airflow distribution. This method organically integrates the dust collector location, pipeline structure, and critical flow velocity to form a complete design closed loop. It adopts a pressure balance strategy that prioritizes structural optimization and uses valve regulation as a supplement, avoiding the energy waste caused by traditional valve regulation. The critical flow velocity calculation is separated from the pipeline simulation, which ensures both accuracy and efficiency. Through iterative optimization, it achieves precise control of both pressure loss and air volume, making it suitable for industries such as sand and gravel mining and steelmaking, and can significantly reduce the energy consumption of dust removal systems.

[0016] This application also proposes a CFD-based industrial dust collection pipeline design system, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned CFD-based industrial dust collection pipeline design method. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the CFD-based industrial dust collection pipeline design method of the present invention; Figure 2 This is a cloud map showing the pressure and velocity distribution of the pipeline network in Embodiment 1 of the present invention; Figure 3 This is a cloud map showing the pressure and velocity distribution of the pipeline network in Embodiment 2 of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] A CFD-based design method for industrial dust collection pipelines includes the following steps: Step 1: Based on the distribution of dust generation and air volume at multiple dust collection points, the dust collector is placed near the dust collection point with low dust generation and low air volume. This can reduce the pressure loss of the most unfavorable branch pipe and reduce the air volume of other branch pipes.

[0022] Placing the dust collector near dust-generating points with low dust volume and low airflow can effectively reduce the pressure loss of the most unfavorable branch pipe, while also reducing the airflow in other branch pipes. This, in turn, reduces the airflow and resistance flowing through the dust collector, thereby reducing the overall system resistance from three dimensions. This method avoids the increased energy consumption problem caused by placing dust collectors near dust-generating points with high dust volume and high airflow in traditional designs.

[0023] In some embodiments, when the dust collection points are relatively dispersed, multiple dust collectors may be installed or a combination of centralized and decentralized dust collection may be adopted.

[0024] Step 2: Based on the process characteristics, dust generation area and dust properties of each dust-generating point, determine the initial air volume with reference to design specifications or engineering experience, and preliminarily select the corresponding pipe diameter. This can also be understood as using the experience air volume and pipe diameter of each dust removal point as the initial design value.

[0025] Using empirical airflow and pipe diameter as initial design values ​​provides initial boundary conditions for subsequent CFD simulations, reducing the computational burden of iterative optimization. This method combines engineering practice experience with numerical simulation techniques, ensuring the practicality of the design while reserving space for precise optimization.

[0026] In some embodiments, the initial air volume can be determined using a theoretical calculation formula, and the pipe diameter can be initially selected based on the air volume and the recommended flow rate range.

[0027] Step 3: Construct the pipe network using a parallel symmetrical dendritic structure, so that the number of Y-shaped convergence and gradual expansion structures of each branch pipe is as similar as possible, ensuring that the local resistance loss of each branch pipe is kept as consistent as possible, and achieving a balanced distribution of pressure loss.

[0028] For example, with the main pipeline as the trunk line, each branch pipe is connected in parallel to the main pipeline in a Y-shaped gradual expansion manner, and the number of Y-shaped gradual expansion structures of each branch pipe does not exceed 1.

[0029] The approximately parallel symmetrical dendritic structure ensures that the number of local resistance elements in each branch is similar, thereby guaranteeing a basic balance in resistance loss across the branches. The Y-shaped converging and expanding structure has a smaller local resistance coefficient compared to right-angled confluence, effectively reducing system energy consumption. This method addresses the issue of uneven pressure loss from the structural design stage, avoiding additional energy consumption caused by later reliance on valve adjustments.

[0030] In some embodiments, when the dust collection points are unevenly distributed, a tree-like structure with main and branch pipes merging in stages can be adopted, and the local resistance can be further optimized by adjusting the merging angle and the length of the gradually expanding section.

[0031] Step 4: If the extension line of the elbow outlet section can directly point to a dust removal point in three-dimensional space, or can point to a dust removal point after a small deflection in space, then the branch pipe of the dust removal point can be directly connected to this elbow through the extension line or deflection line and merged into the main pipeline, so as to reduce the number of elbows and the turning angle, and reduce local resistance loss by optimizing the elbow layout.

[0032] Elbows are one of the main sources of local resistance in pipelines. By rationally planning the pipeline route so that elbows naturally extend to nearby dust collection points, the number of unnecessary elbows can be reduced. At the same time, using small-angle elbows instead of large-angle elbows as much as possible can effectively reduce local resistance loss. This method optimizes local resistance without increasing the pipeline length.

[0033] In some embodiments, large-radius bends can be used instead of standard bends to further reduce the local drag coefficient; for cases where large-angle bends must be used, guide vanes can be considered to improve airflow distribution.

[0034] Step 5: For dust hoods that need to be connected to bends greater than 90°, the bend angle can be reduced by changing the direction of the dust hood outlet, thereby reducing the local resistance loss caused by excessive bend angle. For example, for dust hoods that need to be connected to bends greater than 90°, top-mounted dust hoods are preferred; if side-mounted dust hoods must be used, adjust the outlet direction of the dust hood so that the bend angle is as small as possible. By adjusting the outlet direction of the dust hood, the bend angle can be reduced, thus reducing local resistance loss.

[0035] The direction of the dust hood's outlet directly affects the bend angle of the connecting pipe. By adjusting the outlet direction to make the pipe straighter, the angle and number of bends can be effectively reduced, thus lowering local resistance. This method optimizes the pipe connection without affecting the suction performance, reflecting a design philosophy of controlling resistance at its source.

[0036] In some embodiments, the dust hood type, such as top suction, side suction, or bottom suction, can be flexibly selected according to the location of the dust-generating surface and the operating space, and the outlet direction can be adjusted accordingly.

[0037] Step 6: For dust-generating points whose dust collection area is twice or more than the dust collection area of ​​other dust-generating points, set up multiple dust collection hoods in parallel to ensure that the bottom corner of each dust collection hood is greater than 30°; for dust-generating points with significantly large dust-generating areas, improve dust collection efficiency by setting up several dust collection hoods in parallel and summing them up.

[0038] Using a single dust hood over a large dust-generating area makes it difficult to achieve effective airflow coverage, easily leading to dust escape. By setting up multiple dust hoods side by side, the large dust-generating surface can be broken down into multiple small dust collection areas, each with independent control, improving dust collection coverage. The design with a bottom angle greater than 30° prevents excessive local flow resistance at the edges of the hood, which would otherwise cause dust to escape.

[0039] In some embodiments, depending on the shape of the dust-generating surface and the process characteristics, a strip-shaped dust hood or a ring-shaped dust hood can be used instead of multiple parallel dust hoods to achieve more uniform airflow coverage.

[0040] Step 7: The angle between all branch pipes and main pipes in the entire pipeline network and the horizontal plane should not be less than 30°, so as to reduce the deposition of dust in the pipeline by using gravity.

[0041] Dust deposition in pipes is primarily influenced by airflow velocity and pipe inclination angle. A larger inclination angle corresponds to a lower critical velocity for dust-free deposition. For pipes with an inclination angle greater than 30°, the probability of dust accumulation is very low when the internal airflow velocity reaches the critical velocity corresponding to 30°. The 30° inclination angle design balances engineering feasibility and dust-prevention effectiveness, providing a benchmark for subsequent determination of the critical velocity.

[0042] In some embodiments, where space constraints prevent the achievement of a 30° tilt angle, a higher design wind speed can be used to compensate for the insufficient tilt angle, or a dust removal device can be installed.

[0043] Step 8: Using the minimum tilt angle branch pipe set in Step 7 as the calculation model, and based on the dust particle concentration, density and particle size distribution under actual working conditions, CFD simulation is performed using velocity inlet and pressure outlet boundary conditions to obtain the critical wind speed at the minimum tilt angle that does not accumulate dust.

[0044] S8.1. Using the branch pipe with the smallest inclination angle (30° angle with the horizontal plane) as the geometric model, establish a three-dimensional pipeline model, and mesh generation to ensure that the boundary layer resolution meets the calculation accuracy requirements.

[0045] S8.2 The inlet adopts a velocity inlet boundary condition, and the initial inlet velocity is set according to the design air volume; the outlet adopts a pressure outlet boundary condition, which is set to atmospheric pressure; the wall adopts a no-slip boundary condition.

[0046] S8.3. Set the particle concentration (e.g., 100g / m³) according to the actual dust characteristics under working conditions. 3 ), density (e.g., 2907 kg / m³) 3 Parameters such as particle size distribution (e.g., median particle size of 75 μm) and collision coefficient between particles and the wall surface.

[0047] S8.4 Gradually adjust the inlet velocity and observe the movement trajectory and deposition of dust particles in the pipe. The inlet velocity corresponding to the point that there is no obvious particle deposition in the pipe is the critical flow velocity for no dust accumulation.

[0048] Traditional design methods often rely on empirical formulas to estimate critical flow velocities, neglecting the differences in dust characteristics and the influence of pipe inclination angles. This application uses a branch pipe with the minimum inclination angle as a model and employs a discrete phase model combined with actual dust parameters for numerical simulation, accurately obtaining the non-dust-accumulating critical flow velocity under specific operating conditions. This method separates the calculation of critical flow velocity from the simulation of the pipe network structure, ensuring both calculation accuracy and improving overall design efficiency. The accurate determination of the critical flow velocity provides a scientific basis for controlling the minimum pipe segment wind speed in subsequent CFD simulations.

[0049] Step 9: Based on the critical velocity determined in Step 8 and the pipe network structure constructed in Step 7, pressure balance simulation and adjustment are performed using the CFD method. Specifically, pressure inlet and pressure outlet boundary conditions are used to adjust the pressure difference between the inlet and outlet boundaries so that the air velocity in the pipe section with the minimum inclination angle is greater than the critical velocity, and the pressure loss distribution is observed. If the pressure loss deviation of each branch is within the allowable range (±10%), the total pressure difference between the inlet and outlet, the total air volume, and the air volume of each dust removal point are output. If the deviation exceeds the limit, optimization is performed by reducing the number of local resistance elements in the most unfavorable branch. If the requirements still cannot be met even after adjustment to the limit, pressure loss balance is achieved by adjusting the valve opening.

[0050] S9.1 Based on the pipeline network structure determined in steps 3 to 7, establish a complete three-dimensional geometric model, perform mesh generation, and ensure that the mesh quality of key parts meets the calculation accuracy requirements.

[0051] S9.2. The inlet adopts a pressure inlet boundary condition, and the inlet pressure is estimated based on the initial air volume in step 2; the outlet adopts a pressure outlet boundary condition, which is set to atmospheric pressure; the inlet and outlet pressure difference is adjusted so that the critical flow velocity determined in step 8 is used as the design air velocity of the minimum inclination pipe section.

[0052] S9.3 Run CFD simulation to obtain the pressure loss distribution of each branch pipe. Calculate the percentage deviation between the pressure loss of each branch pipe and the average pressure loss, and determine whether it meets the allowable range of ±10%.

[0053] S9.4 Pressure Loss Balance Adjustment: If the pressure loss deviation of each branch pipe is within the allowable range, record the total pressure difference between the inlet and outlet, the total air volume, and the air volume of each dust removal point, and proceed to step 10. If the pressure loss of a certain branch is significantly greater than that of other branches (the most unfavorable branch), then reduce the number of Y-shaped convergence and expansion structures or the number of elbows in that branch, and reduce local resistance elements by optimizing the pipeline layout. If adjusting to the limit (such as when the local resistance element cannot be further reduced) still cannot meet the pressure loss balance requirement, then the valve opening on the branch with the smaller pressure loss is reduced to increase its local resistance, so that the pressure loss of each branch tends to be balanced. The calculation is iterated until the pressure loss deviation is within the allowable range.

[0054] This step uses CFD simulation to accurately obtain the pressure loss distribution of the pipeline network. Utilizing a strategy of "optimizing the most unfavorable branch and balancing the resistance of each branch," pressure loss equilibrium is achieved through both structural optimization and valve regulation. Structural optimization (reducing local resistance components) is the preferred solution, generating no additional energy consumption; when structural optimization is limited, valve regulation is used as a supplementary means. This method achieves precise control of pressure loss balance, avoiding the energy waste caused by relying solely on valve regulation in traditional designs.

[0055] In some embodiments, the allowable range of pressure loss deviation can be adjusted to ±5% or ±15% depending on the system size and accuracy requirements; if the number of resistance elements cannot be further reduced, the pressure loss can be reduced by increasing the diameter of the most unfavorable branch pipe.

[0056] Step 10: Compare the air volume of each dust removal point obtained by CFD calculation with the design value, and achieve precise control of air volume distribution by adjusting the branch pipe diameter.

[0057] Specifically, if the percentage deviation between the calculated air volume of each dust removal point and the design value is within the allowable range (±5%), the design is terminated and the fan selection is carried out accordingly; if it exceeds the allowable range, the corresponding branch pipe diameter is adjusted according to the direction of deviation, and the process is iterated until the air volume distribution meets the design requirements.

[0058] S10.1 Air volume comparison analysis: Compare the air volume of each dust removal point obtained by simulation in step 9 with the design air volume set in step 2, and calculate the percentage deviation of air volume at each point.

[0059] S10.2 Deviation Judgment: If the air volume deviation of all dust removal points is within ±5%, the design is complete. The output inlet and outlet total pressure difference, total air volume, and air volume of each dust removal point are used to select the fan. If the airflow deviation at the dust removal point exceeds ±5%, proceed to the next adjustment step.

[0060] S10.3, Adjustment of branch pipe diameter: If the calculated air volume at a certain dust removal point is greater than the design air volume, then the diameter of the corresponding branch pipe is reduced, the resistance of the branch pipe is increased, and the air volume is distributed to other branch pipes. If the calculated air volume at a certain dust removal point is less than the design air volume, then the diameter of the corresponding branch pipe should be increased to reduce the resistance of the branch pipe and increase its air volume.

[0061] S10.4 Update the CFD model according to the adjusted pipe diameter, return to step 9 to re-perform the simulation calculation until the air volume deviation of all dust removal points is within ±5%.

[0062] This step achieves precise control of airflow distribution through dynamic adjustment of the branch pipe diameter. Adjusting the pipe diameter directly changes the resistance characteristics of the branch pipe, thus affecting airflow distribution, and does not incur additional energy loss compared to valve regulation. An iterative optimization mechanism ensures that the final design meets the airflow requirements of each dust collection point, avoiding the energy waste caused by the "oversized engine for a small load" approach of traditional designs. This method combines numerical simulation with engineering optimization, enabling refined design of the dust collection system.

[0063] In some embodiments, the allowable range of airflow deviation can be adjusted to ±3% or ±10% according to process requirements; in addition to pipe diameter adjustment, airflow distribution can also be achieved by adjusting the number of local resistance elements or valve opening.

[0064] This invention combines a systematic design process with CFD simulation and iterative optimization to organically integrate three key elements: dust collector location, pipeline structure, and critical velocity, forming a complete closed-loop pipeline design that overcomes the shortcomings of traditional designs that rely on empirical formulas. It employs a parallel symmetrical dendritic structure and a Y-shaped converging and expanding structure to keep the local resistance losses of each branch pipe close, achieving balanced resistance distribution from the structural source and avoiding the extra energy waste caused by valve regulation in traditional designs. The critical velocity calculation is separated from the pipeline structure simulation, using a minimum inclination angle pipe section combined with a DPM model and actual operating parameters to solve for the critical velocity, ensuring both the accuracy of the critical velocity determination and significantly improving overall computational efficiency. Through an iterative optimization mechanism, the pipeline structure and branch pipe diameters are dynamically adjusted to achieve precise control of pressure loss distribution and airflow allocation, ensuring that each dust collection point receives the airflow required by the process. This invention is applicable to various industrial dust collection systems in industries such as sand and gravel mining and steelmaking, and features accuracy, low cost, and convenience, helping industrial enterprises achieve green and low-carbon transformation.

[0065] Example 1 Taking the dust removal system design of a screening workshop in a sand and gravel plant as an example, the method of this invention is used for pipeline design.

[0066] Step 1: Based on the distribution of each dust collection point, arrange the dust collectors near dust collection points A and B, which have relatively small dust generation and air volume, in order to shorten the length of the main pipeline.

[0067] Step 2: Use the experienced air volume and pipe diameter of each dust removal point as the initial design parameters to provide a basis for subsequent optimization.

[0068] Step 3: Construct a parallel symmetrical tree-like pipe network so that the number of Y-shaped convergence and expansion structures of the three branch pipes is the same, which is 1 for each.

[0069] Step 4: Use side suction for the F outlet of the dust hood, and avoid using bends greater than 90° to reduce local resistance loss.

[0070] Step 5: Ensure that all pipes are at an angle of at least 30° to the horizontal plane to prevent dust accumulation.

[0071] Step 6: Using a 30° inclined branch pipe as a model, and employing a discrete phase model, based on actual dust parameters (dust concentration 100g / m³)... 3 Dust density 2907 kg / m³ 3 The dust particle size distribution is 75 μm. The critical flow velocity for no dust accumulation was obtained through CFD simulation.

[0072] Step 7: Based on the critical velocity determined in Step 6 and the pipe network structure constructed in Step 5, perform CFD simulation. Set the pressure inlet and pressure outlet boundary conditions, adjust the inlet and outlet pressure difference to make the air velocity in the lowest velocity pipe section reach the critical velocity of 12 m / s, and observe the pressure loss distribution. The initial simulation results show that the pressure loss deviation of each branch pipe is within the allowable range (±10%), which meets the requirements. The total inlet and outlet pressure difference is 110 Pa, the total air volume is 12.4 kg / s, and the air volume distribution at each dust removal point is balanced.

[0073] Step 8: Compare the airflow at each dust collection point obtained from CFD calculations with the design requirements. It was found that the airflow at one dust collection point was 5% too low. Based on the direction of the deviation, the diameter of that branch pipe was increased, the CFD model was updated, and the simulation was repeated. The airflow deviation was reduced to 3%, meeting the design requirements (±5%). After iterative optimization, a highly efficient and energy-saving dust collection pipeline system was finally obtained. The pressure and velocity distribution cloud diagrams of the final pipeline design structure are shown below. Figure 2 As shown.

[0074] Example 2 Taking the dust removal system design of a fine crushing workshop in a sand and gravel plant as an example, the method of this invention is used for pipeline design.

[0075] Step 1: Based on the distribution of each dust collection point, arrange the dust collectors on the side of dust collection points B, C, E and F, where the dust generation and air volume are relatively small, in order to shorten the length of the main pipeline.

[0076] Step 2: Use the experienced air volume and pipe diameter of each dust removal point as the initial design parameters to provide a basis for subsequent optimization.

[0077] Step 3: Construct a parallel symmetrical dendritic network so that the number of Y-shaped convergence and expansion structures in each branch is the same, ensuring that the local resistance loss of each branch is similar.

[0078] Step 4: Optimize the arrangement of bends. All bends that can be connected to dust removal points E and F after the straight line extension after the bend are grouped into the same main pipe to reduce the number of bends and the angle of the bend.

[0079] Step 5: Ensure that all pipes are at an angle of at least 30° to the horizontal plane to prevent dust accumulation.

[0080] Step 6: Using a 30° inclined branch pipe as a model, and employing a discrete phase model, based on actual dust parameters (dust concentration 100g / m³)... 3 Dust density 2907 kg / m³ 3 (Dust particle size 75μm), the critical flow velocity for dust-free accumulation was obtained as 12m / s through CFD simulation.

[0081] Step 7: Based on the critical velocity determined in Step 6 and the pipe network structure constructed in Step 5, perform CFD simulation. Set the pressure inlet and pressure outlet boundary conditions, adjust the inlet and outlet pressure difference to make the air velocity in the lowest velocity pipe section reach the critical velocity of 12 m / s, and observe the pressure loss distribution. The initial simulation results show that the pressure loss deviation of each branch pipe is within the allowable range (±10%), which meets the requirements. The total inlet and outlet pressure difference is 166 Pa, the total air volume is 10.2 kg / s, and the air volume distribution at each dust removal point is balanced.

[0082] Step 10: Airflow Adjustment and Iterative Optimization Comparing the airflow at each dust removal point obtained from CFD calculations with the design requirements revealed that the airflow at one dust removal point was 5% lower than expected. Based on the direction of the deviation, the diameter of that branch pipe was increased, the CFD model was updated, and the simulation was repeated. The airflow deviation was reduced to 3%, meeting the design requirements (±5%). The final pressure and velocity distribution cloud map of the pipeline network design structure is shown below. Figure 3 As shown.

[0083] Example 3 A CFD-based industrial dust collection pipeline design system includes: The pipeline construction module is used to arrange dust collectors near dust collection points with dust generation and air volume below a preset threshold, based on the dust generation and air volume distribution of each dust collection point. The module uses the experienced air volume and pipe diameter of each dust collection point as the initial design value and adopts a parallel symmetrical tree-like structure to construct the pipeline network. This ensures that the local resistance loss of the branch pipes connecting each dust collection point remains consistent, optimizes the bend arrangement of the branch pipes, adjusts the connection direction between the branch pipes and the dust collection hood, and sets that the angle between all pipes and the horizontal plane is not less than a preset angle. The critical velocity determination module is used to simulate the dust parameters under actual working conditions using a discrete phase model with the branch pipe with the smallest inclination angle as the model, and obtain the critical velocity for dust-free operation. The pressure balance adjustment module is used to adjust the pressure balance based on the critical flow velocity and the pipeline structure constructed by the pipeline construction module, using CFD simulation to ensure that the pressure loss deviation of each branch pipe is within a preset range. The air volume iterative optimization module is used to compare the air volume of each dust removal point obtained by CFD calculation with the design value, and to perform iterative optimization by adjusting the diameter of the branch pipes until the air volume deviation of each dust removal point is within the preset range, thus obtaining the final pipeline structure.

[0084] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0085] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0086] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the CFD-based industrial dust collection pipeline design method described in any of the above embodiments.

[0087] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanning display, etc.; the communication method adopted by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), and wireless connection (including Wi-Fi, Bluetooth, Bluetooth Low Energy, and IEEE 802.11s-based communication technology).

[0088] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the CFD-based industrial dust collection pipeline design method described in any of the above embodiments.

[0089] For descriptions of relevant parts of the CFD-based industrial dust collection pipeline design system, electronic equipment, and computer-readable storage medium provided in this application's embodiments, please refer to the detailed descriptions of the corresponding parts in the CFD-based industrial dust collection pipeline design method provided in this application's embodiments; they will not be repeated here. Furthermore, parts of the technical solutions provided in this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0090] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A CFD-based design method for industrial dust collection pipeline networks, characterized in that, Includes the following steps: Step 1: Construct the initial pipeline network: Based on the dust generation and air volume distribution of each dust removal point, arrange the dust collectors near the dust removal points where the dust generation and air volume are lower than the preset threshold. Use the experienced air volume and pipe diameter of each dust removal point as the initial design value, and construct the pipeline network using a parallel symmetrical tree structure to ensure that the local resistance loss of the branch pipes connecting each dust removal point is consistent. Optimize the bend arrangement of the branch pipes, adjust the connection direction between the branch pipes and the dust collection hood, and set the angle between all pipes and the horizontal plane to be no less than the preset angle. Step 2: Using the branch pipe with the smallest inclination angle as a model, a discrete phase model is used to simulate the dust parameters under actual working conditions to obtain the critical flow velocity without dust accumulation. Step 3: Based on the critical flow velocity and the pipeline structure constructed in Step 1, CFD simulation is used to adjust the pressure balance so that the pressure loss deviation of each branch pipe is within the preset range. Step 4: Compare the air volume of each dust removal point obtained by CFD calculation with the design value, and perform iterative optimization by adjusting the diameter of the branch pipe until the air volume deviation of each dust removal point is within the preset range, thus obtaining the final pipeline structure.

2. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, Step 1, which involves constructing the initial pipeline network, also includes setting up multiple dust collection hoods in parallel for dust-generating points with significantly large dust collection surface areas, and ensuring that the bottom corners of each dust collection hood meet the dust collection coverage requirements.

3. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, Step 1, which optimizes the arrangement of bends in the branch pipe, specifically includes: when the extension line of the bend outlet section points directly to a dust removal point in three-dimensional space, or can point to the dust removal point after spatial deflection and the angle of deflection is not greater than the angle of the bend itself, the branch pipe of the dust removal point is directly connected to the bend along the extension line or deflection line and merges into the main pipeline, so as to save the extra bend and reduce the local resistance loss when the branch pipe of the dust removal point merges into the main pipeline.

4. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, In the parallel symmetrical dendritic structure described in step 1, the number of Y-shaped aggregated gradual expansion structures of each branch is the same or the difference does not exceed a threshold.

5. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, The preset angle mentioned in step 1 is an inclination angle that can prevent dust deposition; the preset range of pressure loss deviation mentioned in step 3 is a deviation range that meets the requirements of pipeline pressure balance; and the preset range of air volume deviation mentioned in step 4 is a deviation range that meets the requirements of air volume distribution at each dust removal point.

6. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, The dust collector mentioned in step 1 is arranged at a dust collection point where the dust generation is lower than a preset threshold and the air volume is lower than a preset threshold. The preset threshold is determined based on the dust generation and air volume distribution characteristics of each dust collection point.

7. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, Step 2 uses a discrete phase model to obtain the critical flow velocity without dust accumulation, specifically including: Using the branch pipe with the smallest inclination angle as the geometric model, a three-dimensional pipe model is established and meshed. Set the inlet section boundary conditions to velocity inlet and the outlet section boundary conditions to pressure outlet. Set the dust particle concentration, density, and particle size distribution parameters according to the actual working conditions; Gradually adjust the inlet velocity and observe the movement trajectory of dust particles in the pipe. The inlet velocity corresponding to the point that there is no obvious particle deposition in the pipe is taken as the critical flow velocity for no dust accumulation.

8. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, Step 3 uses CFD simulation to adjust the pressure balance, specifically including: A three-dimensional geometric model is established based on the pipeline network structure constructed in step 1, and meshing is performed. Set the inlet to a pressure inlet boundary condition and the outlet to a pressure outlet boundary condition, and adjust the inlet and outlet pressure difference so that the wind speed in the branch with the minimum inclination angle reaches the critical flow velocity. Obtain the pressure loss distribution of each branch pipe and calculate the percentage deviation between the pressure loss of each branch pipe and the average pressure loss; If the deviation percentage exceeds the preset range, reduce the number of local resistance elements in the most unfavorable branch until the deviation percentage is within the preset range.

9. The CFD-based industrial dust collection pipeline design method according to claim 1, characterized in that, Step 4 involves iterative optimization by adjusting the branch pipe diameter, specifically including: The air volume at each dust removal point obtained by CFD simulation is compared with the design value, and the percentage deviation of air volume at each dust removal point is calculated. If the percentage deviation of the air volume at a certain dust removal point exceeds the preset range and the calculated air volume is greater than the design air volume, then the diameter of the corresponding branch pipe will be reduced. If the percentage deviation of the air volume at a certain dust removal point exceeds the preset range and the calculated air volume is less than the design air volume, then the corresponding branch pipe diameter will be increased. The CFD model is updated and resimulated based on the adjusted pipe diameter until the percentage deviation of airflow at all dust removal points is within the preset range.

10. A CFD-based industrial dust collection pipeline design system, characterized in that, include: The pipeline construction module is used to arrange dust collectors near dust collection points with dust generation and air volume below a preset threshold, based on the dust generation and air volume distribution of each dust collection point. The module uses the experienced air volume and pipe diameter of each dust collection point as the initial design value and adopts a parallel symmetrical tree-like structure to construct the pipeline network. This ensures that the local resistance loss of the branch pipes connecting each dust collection point remains consistent, optimizes the bend arrangement of the branch pipes, adjusts the connection direction between the branch pipes and the dust collection hood, and sets that the angle between all pipes and the horizontal plane is not less than a preset angle. The critical velocity determination module is used to simulate the dust parameters under actual working conditions using a discrete phase model with the branch pipe with the smallest inclination angle as the model, and obtain the critical velocity for dust-free operation. The pressure balance adjustment module is used to perform pressure balance adjustment using CFD simulation based on the critical flow velocity and the pipeline structure formed by the pipeline construction module, so that the pressure loss deviation of each branch pipe is within a preset range. The air volume iterative optimization module is used to compare the air volume of each dust removal point obtained by CFD calculation with the design value, and to perform iterative optimization by adjusting the diameter of the branch pipes until the air volume deviation of each dust removal point is within the preset range, thus obtaining the final pipeline structure.

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