Emulsion matrix vertical conveying pressure prediction and adjustment method and device

Abstract

The invention discloses a method and a device for predicting and adjusting vertical conveying pressure of an emulsion matrix, which realize gradient release of kinetic energy of fluid and suppression of impact pressure through dynamic pipeline structure design and collaborative optimization of conveying parameters, combined design of a snakelike pipeline and a sectional horizontal extension section, and cooperation with a tail end U-shaped buffer structure. A multistage speed control device and a variable damping assembly are constructed, and a flow velocity fine regulation and working condition self-adaptive matching mechanism is formed in combination with a pressure prediction model based on machine learning. A fluid-solid-heat multi-physics field coupling analysis technology is introduced, a non-Newtonian fluid constitutive model is optimized, and a pressure distribution rule under a complex working condition is accurately predicted. Through rheological property research and formula improvement, an emulsion matrix system with high thixotropy and suitable yield stress is developed, and the system adaptability is improved from the two dimensions of fluid intrinsic characteristics and a conveying process. According to the technical system, the pressure fluctuation amplitude of the pipeline is remarkably reduced while the conveying efficiency is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of emulsion matrix transportation of mixed emulsion explosive semi-finished products, and in particular to a method and device for predicting and adjusting the vertical transportation pressure of a latex matrix. Background Art

[0002] In underground mine blasting operations, the production and transportation of on-site mixed emulsion explosives is crucial for ensuring blasting effectiveness and safety. As the core component of emulsion explosives, the delivery efficiency and quality of the emulsion matrix directly impact blasting effectiveness. As mining depths increase, the need to transport emulsion matrix to depths exceeding 1,000 meters underground is becoming increasingly urgent. Currently, emulsion matrix transportation is primarily achieved through shaft hoists, trackless vehicles, and pipelines. Pipeline transportation, however, is becoming increasingly popular due to its high efficiency and continuity.

[0003] Traditional technologies for vertical transportation of emulsion matrices face multiple technical bottlenecks. First, fluid pressure increases nonlinearly during vertical lifting, and conventional straight-pipe transportation systems lack a dynamic pressure regulation mechanism, making sudden pressure changes prone to pipeline vibration or seal failure. Second, the non-Newtonian properties of fluids lead to significant prediction errors in traditional Newtonian fluid models. Especially under complex rheological conditions, transportation control based on a single viscosity parameter cannot adapt to dynamic changes in shear rate. Third, existing monitoring systems often rely on offline sampling or single-point detection, which makes it difficult to capture the spatiotemporal evolution of pressure gradients in deep shafts, causing parameter adjustments to lag behind actual operating conditions. Furthermore, traditional pipeline design relies on empirical formulas and fails to fully consider the combined effects of fluid-structure interaction and thermal effects, making local stress concentrations common during long-distance vertical transportation. Finally, the fixed pipe diameter and flow rate matching model cannot balance transportation efficiency and pressure safety, lacking adaptive adjustment capabilities when operating conditions fluctuate. These technical shortcomings hinder the process stability and economic efficiency of vertical transportation in scenarios such as deep mineral development. Accurate control of pressure evolution is urgently needed through innovative pipeline structures, multimodal parameter coupling control, and intelligent prediction technologies. Summary of the Invention

[0004] The present invention provides the following technical solution: a method for predicting and adjusting the vertical conveying pressure of a latex matrix, comprising the following steps: S1. Dynamic control of conveying parameters: By adding serpentine pipelines, ring loops and special structure resistance-increasing devices, and adjusting the diameter of the delivery pipeline, the flow rate is controlled to match the delivery speed and pipe diameter, ensuring that the outlet speed and pressure are controllable. A 10m-20m horizontal pipeline is added every 100m of vertical distance, and a terminal U-shaped pipeline is installed at the end of the delivery pipeline to control the delivery speed and pressure of the fluid; S2. Real-time monitoring and feedback adjustment: Use pressure sensors to monitor the fluid pressure in the pipeline in real time, record pressure data through data collectors, and dynamically adjust transportation parameters based on monitoring results; S3, multi-level speed control: Add a multi-stage speed control device to the conveying pipeline. Each stage of the device contains pipe sections of different diameters and resistance-increasing components to control the conveying speed in stages. S4. Numerical simulation and model optimization: An industrial-grade transportation model was constructed, and numerical simulation software was used to determine the movement patterns of the latex matrix in deep vertical shaft pipelines. The maximum vertical distance was determined based on the local maximum shear strength, and the spacing of the pipeline deceleration devices was optimized. S5. Rheological properties research and formulation optimization: Analyze the rheological properties of latex matrix under different shear strengths, study the effects of flow rate, pipe diameter, viscosity, temperature and other parameters on the delivery process, and optimize the latex matrix formula; S6, Intelligent Prediction and Adaptive Control: A pressure prediction model is built based on machine learning algorithms. By combining real-time monitoring data with historical delivery records, pipeline pressure trends are dynamically predicted and delivery parameters are automatically adjusted to maintain stable pressure. S7. Multi-physics coupling analysis: In the numerical simulation process, multi-physics coupling models such as fluid-solid coupling and thermal coupling are introduced to predict the pressure distribution and variation of the latex matrix under complex working conditions; S8. Optimization of non-Newtonian fluid constitutive model: According to the non-Newtonian fluid properties of latex matrix, a modified power-law fluid model or Bingham fluid model is developed.

[0005] Preferably, in step S1, the inner wall of the serpentine pipeline is coated with a wear-resistant coating to reduce fluid friction loss; The wear-resistant coating adopts ceramic polymer composite coating, which is reinforced with aluminum oxide and has polytetrafluoroethylene as the base material. The coating has both high hardness and low friction coefficient, which can effectively reduce fluid friction loss. An eddy current suppression structure is provided inside the annular loop to reduce energy loss; Spiral guide ribs are installed on the inner wall of the annular loop. The rib height is 5%-8% of the pipe diameter, and the pitch is 2-3 times the pipe diameter. The rib cross-section adopts a streamlined design to reduce interference with the main flow. The outlet diameter of the terminal U-shaped pipeline is larger than that of the delivery pipeline, and a pressure balancing device is provided at each U-shaped elbow; The pressure balancing device is a variable-section regulating valve, whose core components are an elastic diaphragm and a spring mechanism. The diaphragm material is corrosion-resistant Hastelloy. The spring design is customized according to the delivery pressure range to ensure that the diaphragm deformation and pressure difference are linearly related. The control logic is that when the pressure difference before and after the elbow exceeds the set threshold, the diaphragm drives the valve core to move, automatically adjusting the flow area to maintain pressure balance. The resistance increasing device is installed at the end of the pipeline in a grid resistance manner, and the grid is made of shape memory alloy material to adapt to temperature changes; The resistance-increasing device adopts a diamond grid structure with a grid side length of 2%-5% of the pipe diameter and a grid thickness of 1%-2% of the pipe diameter to balance resistance and anti-blocking performance.

[0006] Preferably, step S4 includes optimizing the structure of the pipeline descent device, wherein the descent device adopts an improved Tesla valve structure, including a spiral channel design, a multi-stage series design, and optimized bifurcation number and level, and a drag-reducing coating is provided on the inner wall of the channel to reduce flow resistance; The Tesla valve is a fixed one-way valve based on the principles of fluid dynamics. Its core is to achieve unidirectional flow control of the fluid through an asymmetric geometric structure. Through CFD simulation, it was found that the spiral channel can induce the fluid to generate Dean vortices, enhance radial mixing, and thus reduce local shear stress. In the latex matrix delivery, the spiral channel reduces the average shear rate of the fluid by 20%-30%, which helps to reduce energy loss. The multi-stage series design adopts a staggered series layout: the outlet of each Tesla valve and the inlet of the next stage are staggered 45° in the axial direction to avoid direct impact of the fluid and promote energy dissipation. The number of forks and the level optimization are as follows: the angle between the main channel and the fork channel is designed to be 45° to balance the flow distribution and pressure loss. The fork ratio is defined as the ratio of the cross-sectional area of ​​the fork channel to the cross-sectional area of ​​the main channel. The fluid distribution uniformity is best when BR=0.5. Hierarchical progressive design First level: single bifurcation structure, used to initially reduce flow velocity; Second level: double-bifurcation symmetrical structure, further reducing turbulence through fluid redistribution; The third level: a three-pronged asymmetric structure, combined with a spiral channel to achieve the final speed regulation.

[0007] The drag-reducing coating adopts a jellyfish-like bionic coating with polydimethylsiloxane as the matrix.

[0008] Preferably, in step S1, the pressure sensor statistically analyzes the pressure data of key nodes in the underground mine pipeline, studies the correlation between the latex matrix flow rate, pipeline pressure, flow rate and resistance loss, and ensures intelligent adjustment of the pipeline transportation speed at different depths; By arranging high-precision pressure sensors at key nodes of underground pipelines in mines, real-time pressure data is collected and pre-processed with wavelet denoising and outlier elimination. Combined with flow and velocity monitoring data, a nonlinear correlation model of flow-pressure-velocity-resistance loss is constructed based on the modified Bingham fluid model and machine learning algorithm. The pump speed is dynamically adjusted according to the model prediction results to achieve intelligent optimization of the conveying speed of pipelines at different depths.

[0009] Preferably, step S5 includes research on the chemical composition, addition ratio, and design and selection of key oil phase materials of emulsion explosive raw materials to develop a new latex matrix formula suitable for pipeline transportation; By systematically studying the chemical composition of emulsion explosive raw materials and the influence of addition ratio on the rheological properties of the latex matrix, combined with the molecular structure design of key oil phase materials and the selection of surfactants, a new latex matrix formula with low viscosity, high shear resistance and good stability was developed. The flow resistance reduction rate was verified through pipeline transportation simulation experiments, while meeting the explosion performance indicators.

[0010] Preferably, in step S5, nano-scale reinforcing particles are added to the latex matrix formula to improve the shear resistance and stability of the latex matrix; By adding 0.5%-2% by mass of nano-scale reinforcing particles to the latex matrix formula and using a high-speed homogenizer to achieve uniform dispersion of the particles in the matrix, combined with rheometer testing and verification, the shear strength of the latex matrix is ​​increased by more than 40%. At the same time, long-term stability experiments confirm that its viscosity growth rate is reduced to less than 5%, significantly improving the structural stability during pipeline transportation.

[0011] Preferably, in step S1, the terminal U-shaped pipe is made of the same material as the delivery pipe, and the outlet diameter of the terminal U-shaped pipe is doubled compared to the delivery pipe to reduce the kinetic energy of the fluid; When setting up an end U-shaped pipe at the end of the conveying pipeline, the same material as the conveying pipeline is used, and the outlet diameter is designed to be twice the diameter of the conveying pipeline. By using the continuity equation and Bernoulli equation, the fluid flow rate is reduced through the sudden change of cross-sectional area. At the same time, the U-shaped structure induces the fluid to generate local vortex and energy dissipation, significantly reducing the impact pressure and energy consumption.

[0012] Preferably, in step S1, a dynamic pressure regulating valve is provided at the inlet of the terminal U-shaped pipeline to automatically adjust the fluid passing area according to the real-time monitoring pressure to further reduce the terminal pressure; When a dynamic pressure regulating valve is installed at the inlet of the U-shaped pipeline at the end, a high-precision pressure sensor is used to monitor the pressure in the pipeline in real time and transmit the signal to the PID control system. The system automatically calculates the valve opening adjustment amount based on the pressure deviation, drives the electric actuator to drive the valve disc to rotate or translate, thereby changing the cross-sectional area of ​​the fluid channel.

[0013] Preferably, in step S2, the pressure sensor integrates vibration monitoring and leakage detection functions, and realizes online assessment and early warning of pipeline health status through multi-parameter fusion analysis; By integrating a high-precision three-axis accelerometer and a piezoelectric vibration monitoring module in the pressure sensor, the pipeline pressure fluctuation signal and vibration acceleration data are synchronously collected, and the pressure spectrum characteristics and vibration time-frequency characteristics are extracted using wavelet packet transform. Combined with the support vector machine multi-classification algorithm to fuse multi-dimensional parameters, a pipeline health status assessment model is constructed to identify typical failure modes such as cavitation, wear, and leakage in real time, and achieve 10ms-level response warning through edge computing nodes.

[0014] A device for predicting and adjusting the pressure of a latex matrix vertical conveying adopts the above-mentioned method for predicting and adjusting the pressure of a latex matrix vertical conveying, comprising: a conveying pipeline, a connecting end of the conveying pipeline being provided with an annular loop, a connecting end of the annular loop being provided with a serpentine pipeline; The annular loop and serpentine pipeline use the gradual change of the flow channel curvature radius to induce the fluid to produce controllable vortex dissipation, and combine with the dynamic pressure regulating valve to achieve graded regulation of the terminal pressure.

[0015] In summary, compared with the prior art, the present invention provides a method and device for predicting and adjusting the vertical conveying pressure of a latex matrix, which has the following beneficial effects: This solution significantly improves the flow pattern of fluids in complex pipelines by integrating serpentine pipelines, loops, and resistance-enhancing devices into the conveying system, along with a layout design that features horizontally extended pipelines following vertical sections. Changes in pipeline geometry effectively disperse pressure peaks generated by vertical conveying. The U-shaped pipe at the end creates a pressure buffer, reducing the impact of fluid shock on terminal equipment. This solution combines adaptive adjustment of pipe diameter with flow matching control to establish a dynamic equilibrium between fluid velocity and pipe diameter, avoiding turbulent or laminar instability caused by sudden changes in flow velocity, thereby reducing the pressure fluctuation amplitude at the root. This solution is based on the linkage mechanism of the real-time monitoring system of the pressure sensing network and the data collector, which realizes the second-level response to the pressure change in the pipeline. By dynamically adjusting the transportation parameters to form a closed-loop control circuit, it ensures that abnormal pressure conditions can be suppressed in the budding stage. The multi-stage speed control device decomposes the overall pressure gradient into controllable local pressure change units through the segmented pipe diameter gradient and the stepped arrangement of the resistance components, which not only ensures the transportation efficiency but also avoids the risk of pressure overload in a single pipe section. The method of combining numerical simulation with industrial model optimization enables the pipeline layout design to shift from experience-driven to data-driven. By simulating the movement law of the latex matrix under deep vertical shaft conditions, the optimal layout position of the descent device can be accurately located, reducing unnecessary structural redundancy while ensuring safe vertical distance; The coordinated advancement of rheological properties research and formulation optimization in this solution allows the viscosity characteristics of the latex matrix at a specific shear rate to be optimally matched with the delivery parameters. By reducing temperature sensitivity, the accumulation of thermal stress in the fluid during long-distance delivery is effectively controlled. This solution's intelligent prediction model uses a machine learning algorithm to integrate and analyze historical data with real-time monitoring data, establishing the ability to proactively predict pressure trends. This allows parameter adjustments to shift from passive response to active intervention, significantly improving the system's adaptability. The introduction of a multi-physics coupling analysis model breaks through the limitations of traditional single-factor analysis. Through the combined effects of fluid-solid coupling and thermal coupling, it can accurately characterize the combined effects of complex operating conditions on pressure distribution, providing theoretical support for pressure control under extreme conditions. At the device level of this scheme, the series design of the annular loop and the serpentine pipeline forms a unique pressure regulation network. When the fluid passes through continuous bends, the synergistic effect of the centrifugal effect and the resistance component enables the pressure to be released in an orderly manner. Combined with the energy recovery effect of the U-shaped structure at the end, a complete pressure regulation chain is constructed, enabling the entire conveying system to achieve precise constraints on the pressure fluctuation range while ensuring vertical lifting capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention.

[0017] Figure 2 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figure 1 The present invention provides the following technical solution: a method for predicting and adjusting the vertical conveying pressure of a latex matrix, comprising the following steps: S1. Dynamic control of conveying parameters: By adding serpentine pipelines, ring loops and special structure resistance-increasing devices, and adjusting the diameter of the delivery pipeline, the flow rate is controlled to match the delivery speed and pipe diameter, ensuring that the outlet speed and pressure are controllable. A 10m-20m horizontal pipeline is added every 100m of vertical distance, and a terminal U-shaped pipeline is installed at the end of the delivery pipeline to control the delivery speed and pressure of the fluid; The resistance-increasing device is installed at the end of the pipe in a grid-like resistance mode. The grid is made of shape memory alloy to adapt to temperature changes. The resistance-increasing device adopts a diamond-shaped grid structure with a grid side length of 2%-5% of the pipe diameter and a grid thickness of 1%-2% of the pipe diameter to balance resistance and anti-blocking performance. The optimized curvature radius of the serpentine pipe and the eddy current dissipation design of the annular loop achieve efficient conversion of fluid kinetic energy and pressure buffering, ensuring the stability of the conveying process. The U-shaped pipe at the end further reduces fluid kinetic energy and avoids sudden pressure changes by balancing gravity and fluid inertia. S2. Real-time monitoring and feedback adjustment: Use pressure sensors to monitor the fluid pressure in the pipeline in real time, record pressure data through data collectors, and dynamically adjust transportation parameters based on monitoring results; The pressure sensor converts pressure into an electrical signal through the synergistic effect of sensitive elements and conversion elements. Its core structure consists of an elastic diaphragm and a piezoresistor or capacitor component: when external pressure acts on the diaphragm, the diaphragm produces micron-level deformation, causing the resistance of the piezoresistor to change or the spacing between the capacitor plates to change, and then outputs a voltage signal that is linearly related to the pressure through a Wheatstone bridge or capacitor detection circuit. In the signal processing stage, the sensor has a built-in low-noise amplifier and a high-precision ADC. It uses chopper stabilization technology to eliminate low-frequency noise, and combines digital filtering algorithms to achieve signal purification, and finally outputs digital pressure data through the I2C or SPI interface; The data acquisition system utilizes a modular architecture to achieve high-precision capture and real-time processing of multi-channel signals. Its core components include analog front-end conditioning circuits, a high-resolution analog-to-digital converter, an embedded microprocessor, and a communication interface. The signal input utilizes a differential amplifier circuit and an anti-aliasing filter to suppress common-mode interference and high-frequency noise, ensuring signal integrity. The ADC chip utilizes a 24-bit Σ-Δ converter, combined with oversampling and noise shaping techniques, to achieve signal conversion with a 160dB dynamic range. The pressure sensor uses high-precision piezoelectric crystal elements, combined with temperature compensation circuits, to achieve a measurement accuracy of ±0.1% FS. The data collector has a built-in FPGA chip that supports multi-channel synchronous sampling and real-time data compression, ensuring the integrity and timeliness of monitoring data. S3, multi-level speed control: Add a multi-stage speed control device to the conveying pipeline. Each stage of the device contains pipe sections of different diameters and resistance-increasing components to control the conveying speed in stages. The multi-stage speed control device achieves fine control of flow rate through the combination of variable diameter pipes and adjustable resistance modules. The resistance module adopts a honeycomb structure and adjusts the local resistance coefficient by changing the porosity to meet the speed control requirements under different working conditions. S4. Numerical simulation and model optimization: An industrial-grade transportation model was constructed, and numerical simulation software was used to determine the movement patterns of the latex matrix in deep vertical shaft pipelines. The maximum vertical distance was determined based on the local maximum shear strength, and the spacing of the pipeline deceleration devices was optimized. The numerical simulation software is based on the finite volume method, combining the k-ε turbulence model and the particle trajectory model to achieve multi-field coupled calculations of the flow field, concentration field, and stress field. The optimal layout spacing and structural parameters of the descent device are determined through parametric scanning and genetic algorithm optimization. Numerical simulation software achieves accurate solutions to complex physical fields through a layered architecture and high-performance computing technology. Its core lies in the geometric modeling, meshing, and physical model configuration of the pre-processing module, the numerical method based on the finite volume method in the solver, the MPI+OpenMP hybrid parallel computing framework, and the data field reconstruction and result analysis tools of the post-processing module, combined with performance optimization and a plug-in architecture. S5. Rheological properties research and formulation optimization: Analyze the rheological properties of latex matrix under different shear strengths, study the effects of flow rate, pipe diameter, viscosity, temperature and other parameters on the delivery process, and optimize the latex matrix formula; The full rheological curve of the latex matrix was obtained through combined testing with a rotational rheometer and a capillary rheometer. Response surface analysis was used to establish a mathematical model between rheological parameters and conveying efficiency and energy consumption, guiding the optimization design of the formulation. S6, Intelligent Prediction and Adaptive Control: A pressure prediction model is built based on machine learning algorithms. By combining real-time monitoring data with historical delivery records, pipeline pressure trends are dynamically predicted and delivery parameters are automatically adjusted to maintain stable pressure. The pressure prediction model uses a long-short-term memory network and introduces an attention mechanism to strengthen the influence of key historical data on the prediction results. The adaptive control algorithm is based on the model predictive control framework to achieve real-time optimization and adjustment of conveying parameters. The pressure prediction model is implemented by building a deep learning framework based on a long short-term memory network. Its structure includes multiple layers of LSTM units to extract the long-term dependency features of time-series pressure data. A self-attention mechanism is embedded to weightedly focus on fluid parameters at key historical moments. The output layer uses a fully connected network to generate multi-step predictions. Combined with the model predictive control framework, the prediction results are fed back to the conveying system to dynamically adjust the pump speed and valve opening, forming a closed-loop optimization structure that integrates data-driven and physical constraints. S7. Multi-physics coupling analysis: In the numerical simulation process, multi-physics coupling models such as fluid-solid coupling and thermal coupling are introduced to predict the pressure distribution and variation of the latex matrix under complex working conditions; The fluid-structure coupling model uses the arbitrary Lagrangian-Euler method to realize the dynamic interaction between the fluid and the pipe wall. The thermal coupling model predicts the influence of the temperature field on the rheological properties and pressure distribution by solving the energy equation and the convection heat transfer equation simultaneously. Multi-physics coupling models such as fluid-structure coupling and thermal coupling are implemented through a partitioned solver architecture. The finite volume method is used to solve the Navier-Stokes equations in the fluid domain, and the dynamic mesh deformation is handled based on the arbitrary Lagrangian-Euler method. The finite element method is used to calculate the structural stress-strain in the solid domain. The thermal field is bidirectionally coupled through simultaneous energy equations and convective heat transfer terms. Interface data is exchanged between the various physical fields through an implicit iterative algorithm, and Robin-type boundary conditions or relaxation factors are introduced to ensure the convergence stability of the nonlinear coupling process. S8. Optimization of non-Newtonian fluid constitutive model: Develop a modified power-law fluid model or Bingham fluid model based on the non-Newtonian fluid properties of latex matrix; Develop modified power-law or Bingham fluid models by introducing zero-shear viscosity and infinite-shear viscosity parameters into the constitutive equation to expand the applicability of the power-law model. Alternatively, optimize the yield stress and plastic viscosity parameters of the Bingham model using nonlinear least-squares methods. Develop parameter-rheological property mapping relationships by combining rheometer data with response surface analysis. Ultimately, implement the integrated application of the modified models in multi-physics coupled simulations through numerical solvers. The modified power-law fluid model expands the application range of the model by introducing zero shear viscosity and infinite shear viscosity parameters; the Bingham fluid model improves the prediction accuracy of the flow behavior of the latex matrix by optimizing the calculation method of yield stress and plastic viscosity.

[0020] In step S1, the inner wall of the serpentine pipeline is coated with a wear-resistant coating to reduce fluid friction loss; The wear-resistant coating adopts ceramic polymer composite coating, which is reinforced with aluminum oxide and has polytetrafluoroethylene as the base material. The coating has both high hardness and low friction coefficient, which can effectively reduce fluid friction loss. An eddy current suppression structure is provided inside the annular loop to reduce energy loss; Spiral guide ribs are installed on the inner wall of the annular loop. The rib height is 5%-8% of the pipe diameter, and the pitch is 2-3 times the pipe diameter. The rib cross-section adopts a streamlined design to reduce interference with the main flow. The outlet diameter of the U-shaped pipeline at the end is larger than that of the delivery pipeline, and a pressure balancing device is provided at each U-shaped elbow; The pressure balancing device is a variable-section regulating valve, whose core components are an elastic diaphragm and a spring mechanism. The diaphragm material is corrosion-resistant Hastelloy. The spring design is customized according to the delivery pressure range to ensure that the diaphragm deformation and pressure difference are linearly related. The control logic is that when the pressure difference before and after the elbow exceeds the set threshold, the diaphragm drives the valve core to move, automatically adjusting the flow area to maintain pressure balance. The resistance-increasing device is installed at the end of the pipe in a grid-type resistance mode, and the grid is made of shape memory alloy material to adapt to temperature changes; A diamond grid structure is adopted, with a grid side length of 2%-5% of the pipe diameter and a grid thickness of 1%-2% of the pipe diameter to balance resistance and anti-blocking performance.

[0021] Step S4 involves optimizing the structure of the pipeline descent device. The descent device uses an improved Tesla valve structure, including a spiral channel design, a multi-stage series design, and optimized bifurcation number and level. The inner wall of the channel is provided with a drag-reducing coating to reduce flow resistance. The Tesla valve is a fixed one-way valve based on the principles of fluid dynamics. Its core is to achieve unidirectional flow control of the fluid through an asymmetric geometric structure. Through CFD simulation, it was found that the spiral channel can induce the fluid to generate Dean vortices, enhance radial mixing, and thus reduce local shear stress. In the latex matrix delivery, the spiral channel reduces the average shear rate of the fluid by 20%-30%, which helps to reduce energy loss. The multi-stage series design adopts a staggered series layout: the outlet of each Tesla valve and the inlet of the next stage are staggered 45° in the axial direction to avoid direct impact of the fluid and promote energy dissipation. The number of forks and the level optimization are as follows: the angle between the main channel and the fork channel is designed to be 45° to balance the flow distribution and pressure loss. The fork ratio is defined as the ratio of the cross-sectional area of ​​the fork channel to the cross-sectional area of ​​the main channel. The fluid distribution uniformity is best when BR=0.5. Hierarchical progressive design First level: single bifurcation structure, used to initially reduce flow velocity; Second level: double-bifurcation symmetrical structure, further reducing turbulence through fluid redistribution; The third level: a three-pronged asymmetric structure, combined with a spiral channel to achieve the final speed regulation.

[0022] The drag-reducing coating adopts a jellyfish-like bionic coating with polydimethylsiloxane (PDMS) as the matrix.

[0023] In step S1, the pressure sensor statistically analyzes the pressure data of key nodes in the underground mine pipeline, studies the correlation between the latex matrix flow rate, pipeline pressure, flow rate and resistance loss, and ensures intelligent adjustment of the pipeline transportation speed at different depths; By arranging high-precision pressure sensors at key nodes of underground pipelines in mines, real-time pressure data is collected and pre-processed with wavelet denoising and outlier elimination. Combined with flow and velocity monitoring data, a nonlinear correlation model of flow-pressure-velocity-resistance loss is constructed based on the modified Bingham fluid model and machine learning algorithm. The pump speed is dynamically adjusted according to the model prediction results to achieve intelligent optimization of the conveying speed of pipelines at different depths.

[0024] Step S5 includes research on the chemical composition, addition ratio, and design and selection of key oil-phase materials of emulsion explosive raw materials to develop a new latex matrix formula suitable for pipeline transportation; By systematically studying the chemical composition of emulsion explosive raw materials and the influence of addition ratio on the rheological properties of the latex matrix, combined with the molecular structure design of key oil phase materials and the selection of surfactants, a new latex matrix formula with low viscosity, high shear resistance and good stability was developed. The flow resistance reduction rate was verified through pipeline transportation simulation experiments, while meeting the explosion performance indicators.

[0025] In step S5, nano-scale reinforcing particles are added to the latex matrix formula to improve the shear resistance and stability of the latex matrix; By adding 0.5%-2% by mass of nano-scale reinforcing particles to the latex matrix formula and using a high-speed homogenizer to achieve uniform dispersion of the particles in the matrix, combined with rheometer testing and verification, the shear strength of the latex matrix is ​​increased by more than 40%. At the same time, long-term stability experiments confirm that its viscosity growth rate is reduced to less than 5%, significantly improving the structural stability during pipeline transportation.

[0026] In step S1, the end U-shaped pipe is made of the same material as the delivery pipe, and the outlet diameter of the end U-shaped pipe is doubled compared to the delivery pipe to reduce the kinetic energy of the fluid; When setting up an end U-shaped pipe at the end of the conveying pipeline, the same material as the conveying pipeline is used, and the outlet diameter is designed to be twice the diameter of the conveying pipeline. By using the continuity equation and Bernoulli equation, the fluid flow rate is reduced through the sudden change of cross-sectional area. At the same time, the U-shaped structure induces the fluid to generate local vortex and energy dissipation, significantly reducing the impact pressure and energy consumption.

[0027] In step S1, a dynamic pressure regulating valve is provided at the inlet of the terminal U-shaped pipeline to automatically adjust the fluid passage area according to the real-time monitoring pressure to further reduce the terminal pressure; When a dynamic pressure regulating valve is installed at the inlet of the U-shaped pipeline at the end, a high-precision pressure sensor is used to monitor the pressure in the pipeline in real time and transmit the signal to the PID control system. The system automatically calculates the valve opening adjustment amount based on the pressure deviation, drives the electric actuator to drive the valve disc to rotate or translate, thereby changing the cross-sectional area of ​​the fluid channel.

[0028] In step S2, the pressure sensor integrates vibration monitoring and leakage detection functions, and realizes online assessment and early warning of pipeline health status through multi-parameter fusion analysis; By integrating a high-precision three-axis accelerometer and a piezoelectric vibration monitoring module in the pressure sensor, the pipeline pressure fluctuation signal and vibration acceleration data are synchronously collected, and the pressure spectrum characteristics and vibration time-frequency characteristics are extracted using wavelet packet transform. Combined with the support vector machine multi-classification algorithm to fuse multi-dimensional parameters, a pipeline health status assessment model is constructed to identify typical failure modes such as cavitation, wear, and leakage in real time, and achieve 10ms-level response warning through edge computing nodes.

[0029] See also Figure 2 A device for predicting and adjusting the pressure of a latex matrix vertical conveying adopts the above-mentioned method for predicting and adjusting the pressure of a latex matrix vertical conveying, comprising: a conveying pipeline 1, a ring loop 2 is installed at the connecting end of the conveying pipeline 1, and a serpentine pipeline 3 is installed at the connecting end of the ring loop 2; The annular loop 2 and the serpentine pipeline 3 utilize a gradual change in the curvature radius of the flow channel to induce the fluid to produce controllable vortex dissipation, and combine with a dynamic pressure regulating valve to achieve graded regulation of the terminal pressure.

[0030] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0031] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting and adjusting the vertical conveying pressure of a latex matrix, characterized in that: The following steps are involved: S1. Dynamic control of conveying parameters: By adding serpentine pipelines, ring loops and resistance-increasing devices, 10m-20m of horizontal pipelines are added every 100m of vertical distance, and a terminal U-shaped pipeline is set at the end of the delivery pipeline. The diameter of the delivery pipeline is adjusted to control the flow rate to match the delivery speed and pipe diameter, so as to control the delivery speed and pressure of the fluid; S2. Real-time monitoring and feedback adjustment: Use pressure sensors to monitor the fluid pressure in the pipeline in real time, record pressure data through data collectors, and dynamically adjust transportation parameters based on monitoring results; S3, multi-level speed control: Add a multi-stage speed control device to the conveying pipeline. Each stage of the device contains pipe sections of different diameters and resistance-increasing components to control the conveying speed in stages. S4. Numerical simulation and model optimization: An industrial-grade transportation model was constructed, and numerical simulation software was used to determine the movement patterns of the latex matrix in deep vertical shaft pipelines. The maximum vertical distance was determined based on the local maximum shear strength, and the spacing of the pipeline deceleration devices was optimized. S5. Rheological properties research and formulation optimization: Analyze the rheological properties of latex matrix under different shear strengths, study the effects of flow rate, pipe diameter, viscosity and temperature parameters on the transportation process, and optimize the latex matrix formula; S6, Intelligent Prediction and Adaptive Control: A pressure prediction model is built based on machine learning algorithms. By combining real-time monitoring data with historical delivery records, pipeline pressure trends are dynamically predicted and delivery parameters are automatically adjusted to maintain stable pressure. S7. Multi-physics coupling analysis: In the numerical simulation process, a multi-physics coupling model of fluid-solid coupling and thermal coupling is introduced to predict the pressure distribution and variation of the latex matrix under complex working conditions; S8. Optimization of non-Newtonian fluid constitutive model: According to the non-Newtonian fluid properties of latex matrix, a modified power-law fluid model or Bingham fluid model is developed.

2. A method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In step S1, the inner wall of the serpentine pipeline is coated with a wear-resistant coating to reduce fluid friction loss; An eddy current suppression structure is provided inside the annular loop to reduce energy loss; The outlet diameter of the terminal U-shaped pipeline is larger than that of the delivery pipeline, and a pressure balancing device is provided at each U-shaped elbow; The resistance increasing device is installed at the end of the pipeline in a grid resistance manner, and the grid is made of shape memory alloy material to adapt to temperature changes.

3. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: The step S4 includes optimizing the structure of the pipeline descent device. The descent device adopts an improved Tesla valve structure, including a spiral channel design, a multi-stage series design, and optimization of the number and level of forks. The inner wall of the channel is provided with a drag-reducing coating to reduce flow resistance.

4. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In step S1, the pressure sensor statistically analyzes the pressure data of key nodes in the underground pipeline of the mine, studies the correlation between the latex matrix flow, pipeline pressure, flow rate and resistance loss, and ensures intelligent adjustment of the pipeline transportation speed at different depths.

5. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: The step S5 includes research on the chemical composition, addition ratio, and design and selection of key oil phase materials of the emulsion explosive raw materials, so as to develop a new latex matrix formula suitable for pipeline transportation.

6. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In step S5, nano-scale reinforcing particles are added to the latex matrix formula to improve the shear resistance and stability of the latex matrix.

7. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In step S1, the terminal U-shaped pipeline is made of the same material as the delivery pipeline, and the outlet diameter of the terminal U-shaped pipeline is doubled compared to the delivery pipeline to reduce the kinetic energy of the fluid.

8. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In the step S1, a dynamic pressure regulating valve is provided at the inlet of the terminal U-shaped pipeline, which automatically adjusts the fluid passing area according to the real-time monitoring pressure to further reduce the terminal pressure.

9. The method for predicting and adjusting the vertical conveying pressure of a latex matrix according to claim 1, characterized in that: In step S2, the pressure sensor integrates vibration monitoring and leakage detection functions, and realizes online assessment and early warning of pipeline health status through multi-parameter fusion analysis.

10. A device for predicting and adjusting the vertical conveying pressure of a latex matrix, using the method for predicting and adjusting the vertical conveying pressure of a latex matrix according to any one of claims 1 to 9, characterized in that: include: A delivery pipeline (1), wherein a ring loop (2) is installed at a connection end of the delivery pipeline (1), and a serpentine pipeline (3) is installed at a connection end of the ring loop (2).

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