Deep-sea mining multiphase flow particle transport prediction and regulation method based on flow type identification

Abstract

The application discloses a kind of based on flow pattern recognition's deep sea mining multiphase flow particle delivery prediction and regulation method, the method includes: by acquisition equipment periodic acquisition pressure in pipeline, flow, bubble or droplet volume fraction, solid particle volume fraction in different positions;According to pipe diameter and the gas-liquid ratio obtained by calculation, mixed flow velocity, call pre-built flow pattern division algorithm, distinguish bubble flow, slug flow, vortex, annular flow;Solve the gas-liquid discrete unit volume fraction of any diameter in different flow patterns;According to the gas-liquid discrete phase volume fraction, obtain the delivery speed of solid particles in multiphase flow;Whether the solid particle delivery speed obtained is judged to reach the best interval, according to the result control multiphase flow in pipeline flow pattern or gas-liquid ratio, to adjust the delivery speed of solid particles.The present application can improve the efficiency of seabed mineral exploitation, and avoid the dangerous flow pattern formed by unreasonable gas-liquid distribution ratio, efficiency and safety are taken into account.

Description

Technical Field

[0001] This invention relates to the field of particle prediction technology in deep-sea mining, and in particular to a method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition. Background Technology

[0002] Deep-sea mining requires the long-distance transport of solid minerals via vertical pipelines, and air-lift pumping is a particularly promising method for this purpose due to its elimination of the need for submersible pumps. During this transport process, various flow patterns may emerge, including bubbly flow, slug flow, vortex flow, and annular flow, each playing a crucial role in the efficiency and safety of mineral particle transport. However, the different flow patterns that may arise also present potential challenges. Under severe slug flow conditions, excessive pressurization within the pipeline can lead to significant safety risks. Large liquid slugs and air pockets can cause sudden pressure fluctuations and increase stress on the pipeline structure. Pressure changes, especially in high-pressure environments such as deep-sea mining, increase the risk of pipeline rupture or failure. These challenges are closely related to the distribution of the gas-liquid discrete phase within the pipeline. Therefore, a deep understanding of the distribution of the gas-liquid discrete phase in multiphase flow is crucial for optimizing solid transport mechanisms, which is also the foundation for utilizing air-lift pumping in deep-sea mineral mining. Most studies only conduct experimental or theoretical calculations on pressure loss and apparent velocity, making it difficult to accurately obtain the true transport velocity of large particles. Meanwhile, different flow patterns can be converted into each other in practical applications, and each presents different challenges to the prediction of the transport velocity of large particles.

[0003] Despite significant progress in multiphase flow dynamics, accurately predicting the transport velocities of large particles in bubbly, slug, vortex, and annular flows remains a considerable challenge in deep-sea mining applications such as gas lift pumping. Existing prediction models often struggle to simultaneously account for complex factors such as bubble interactions, turbulence modulation, and particle collisions, limiting their applicability and accuracy across different flow patterns. Particularly for larger mineral particles, assuming their velocities are the same as the continuous phase velocities is inapplicable because these particles exhibit significant slip effects, a phenomenon well-studied in solid-liquid two-phase flows. Furthermore, inaccurate velocity predictions for large-diameter particles prevent precise control of their motion in large-diameter vertical pipes.

[0004] Therefore, proposing a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern recognition to solve the difficulties existing in the prior art is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern identification, which can accurately predict the transport velocity of large particles under four main flow patterns: bubbly flow, slug flow, eddy flow, and annular flow.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern identification includes the following steps:

[0008] S1. Periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline using the data acquisition device, and store them in the data buffer.

[0009] S2. Based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity, the pre-built-in flow pattern classification algorithm is invoked to distinguish between bubble flow, slug flow, vortex flow, and annular flow.

[0010] S3. Based on the identified different gas-liquid flow patterns, and according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow, solve for the volume fraction of the gas-liquid discrete unit of any diameter in the different flow patterns.

[0011] S4. By colliding the gas-liquid discrete unit with the solid particles, the conveying velocity of the solid particles in the multiphase flow is obtained according to the volume fraction of the gas-liquid discrete phase.

[0012] S5. Determine whether the obtained solid particle conveying speed has reached the preset optimal range, and adjust the solid particle conveying speed by controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline based on the result.

[0013] S6. Continuously cycle through S1-S5 to monitor and adjust the conveying speed of solid particles in real time, and store the conveying speed data and data collected from the equipment in the database.

[0014] Preferably, the gas-liquid ratio and mixing velocity in S2 are obtained by calculating the collected flow rate and velocity data; the flow rate data includes gas flow rate and liquid flow rate, and the velocity data includes gas velocity and liquid velocity.

[0015] Preferably, in S3, solving for the volume fraction of the gas-liquid discrete unit of any diameter in different flow patterns according to the characteristics of the bubbly flow, slug flow, vortex flow, and annular flow specifically includes:

[0016] When the flow pattern is bubbly flow, the kinetic energy ratio of bubbly flow and turbulent flow without bubbles, the turbulent energy dissipation, and the effective collision frequency coefficient of bubbles are solved to finally obtain the volume fraction of the gas-liquid discrete element in the bubbly flow.

[0017] When the flow pattern is slug flow or eddy flow, the liquid phase porosity is solved, and the kinetic energy ratio of slug flow and bubble-free turbulent flow, turbulent energy dissipation, and effective collision frequency coefficient of bubbles are obtained in sequence. Finally, the volume fraction of gas-liquid discrete units in slug flow or eddy flow is obtained.

[0018] When the flow pattern is annular, the liquid film thickness is solved to obtain the volume fraction of the gas-liquid discrete unit in the annular flow.

[0019] Preferably, the volume fraction of the gas-liquid discrete unit in the bubbly flow is expressed as follows:

[0020]

[0021] Where, d D Where is the diameter of the discrete element, K is the kinetic energy ratio between the bubble flow and the turbulent flow without bubbles, D is the pipe diameter, and v m For the mixing speed, v * denoted as the pipe friction velocity, and f is the effective collision frequency coefficient of the bubble;

[0022] The volume fraction of a gas-liquid discrete element in a slug flow or vortex is expressed as follows:

[0023]

[0024] The volume fraction of a gas-liquid discrete unit in an annular flow is expressed as follows:

[0025]

[0026] Among them, v g ρ is the gas phase velocity, δ is the liquid film thickness, and ρ is the liquid phase velocity. g Where is the gas phase density, and σ is the surface tension coefficient.

[0027] Preferably, in S4, obtaining the transport velocity of solid particles in the multiphase flow based on the volume fraction of the gas-liquid discrete phase specifically includes:

[0028] Solve for the probability density of the gas-liquid discrete element, the average volume of the gas-liquid discrete element, the minimum vortex size, the maximum vortex size, the average vortex size, and the average vortex lifetime in the pipe.

[0029] By colliding with solid particles through gas-liquid discrete units, the kinetic energy of the gas-liquid discrete units is calculated based on the probability density of the gas-liquid discrete units, the average volume of the gas-liquid discrete units, the minimum vortex size, the maximum vortex size, the average vortex size, the average vortex lifetime, and the volume fraction of the gas-liquid discrete phase. This energy is then converted into the kinetic energy of the solid particles, ultimately obtaining the transport velocity of the solid particles in the multiphase flow.

[0030] Preferably, the conveying velocity of solid particles in a multiphase flow is expressed as follows:

[0031]

[0032] Among them, v C For continuous phase velocity, v p0 To disregard the terminal velocity of the particles during collision, v D e represents the velocity of the discrete unit. p T1 represents the collision recovery coefficient, and T2 and T3 represent the vortex acceleration parameter, collision deceleration parameter, and gas-liquid discrete phase acceleration parameter, respectively.

[0033] Preferably, in S5, adjusting the conveying speed of solid particles by controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline based on the results specifically includes:

[0034] When the obtained solid particle conveying speed does not reach the preset optimal range, the control device sends a control command to adjust the valve opening degree or liquid injection flow rate, and changes the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline to adjust the particle conveying speed; when the obtained solid particle conveying speed reaches the preset optimal range, the current operating condition is maintained and no additional operation is performed.

[0035] This invention also provides a deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern recognition, and an application of any of the above-mentioned deep-sea mining multiphase flow particle transport prediction and control methods based on flow pattern recognition, comprising:

[0036] The data acquisition module is used to periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline through the acquisition device, and store them in the data buffer.

[0037] The flow pattern recognition module is used to distinguish between bubbly flow, slug flow, vortex flow, and annular flow by calling a pre-built flow pattern classification algorithm based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity.

[0038] The gas-liquid discrete unit size distribution prediction module is used to calculate the volume fraction of gas-liquid discrete units of any diameter in different flow patterns according to the characteristics of the identified gas-liquid flow, slug flow, vortex flow and annular flow, respectively.

[0039] The solid particle velocity prediction module is used to obtain the transport velocity of solid particles in multiphase flow based on the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles.

[0040] The speed analysis and judgment module is used to determine whether the conveying speed of the solid particles has reached the preset optimal range. Based on the result, the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline is controlled to adjust the conveying speed of the particles.

[0041] The cyclic execution and optimization control module is used to continuously cycle from the data acquisition module to the speed analysis and judgment module, monitor and adjust the particle conveying speed in real time, and store the conveying speed data and data from the acquisition equipment in the database.

[0042] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern recognition as described above.

[0043] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0044] (1) The present invention aims to develop a unified prediction model that can accurately predict the transport velocity of large particles under four main flow patterns: bubbly flow, slug flow, vortex flow and annular flow; optimize the design and operation of multiphase transport systems by providing a systematic particle dynamics analysis framework; and verify the effectiveness and applicability of the proposed model by comparing experimental data and numerical simulation results.

[0045] (2) This invention combines real-time feedback of gas, liquid and solid phases, and can use computers to collect high-frequency data and predict velocity based on collision acceleration model, thereby effectively improving the accuracy of velocity prediction for large solid particles; this invention can improve the mining efficiency of seabed minerals and avoid dangerous flow patterns formed by unreasonable gas-liquid distribution ratio, thus taking into account both efficiency and safety. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A schematic diagram of the process for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern recognition, provided by the present invention;

[0048] Figure 2 This is a comparison chart of the experimental results of Weber and Dedgeil in the embodiments with the prediction results of this application. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] like Figure 1 As shown, the present invention provides a method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition, comprising the following steps:

[0052] S1. Periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline using the data acquisition device, and store them in the data buffer.

[0053] S2. Based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity, the pre-built-in flow pattern classification algorithm is invoked to distinguish between bubble flow, slug flow, vortex flow, and annular flow.

[0054] S3. Based on the identified different gas-liquid flow patterns, and according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow, solve for the volume fraction of the gas-liquid discrete unit of any diameter in the different flow patterns.

[0055] S4. By colliding the gas-liquid discrete unit with the solid particles, the conveying velocity of the solid particles in the multiphase flow is obtained according to the volume fraction of the gas-liquid discrete phase.

[0056] S5. Determine whether the obtained solid particle conveying speed has reached the preset optimal range, and adjust the solid particle conveying speed by controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline based on the result.

[0057] S6. Continuously cycle through S1-S5 to monitor and adjust the conveying speed of solid particles in real time, and store the conveying speed data and data collected from the equipment in the database.

[0058] The method of this invention specifically includes:

[0059] Step S1: Data Acquisition

[0060] The system uses a computer or industrial control computer to control sensors (including multiple sensors such as pressure sensors, flow sensors, density sensors, etc.) to monitor pressure, liquid flow, gas flow and solid particle volume fraction in the riser. It periodically collects data such as pressure, flow, bubble (or droplet) volume fraction and solid particle volume fraction at different locations in the pipeline and stores them in a data buffer.

[0061] Step S2: Manifold Identification

[0062] Based on the collected information such as gas-liquid ratio, pipe diameter, and mixing velocity, a pre-built-in flow pattern classification algorithm is invoked to distinguish between bubbly flow, slug flow, vortex flow, and annular flow. This classification can refer to the flow pattern classification method for large-diameter multiphase flow proposed by Schlegel et al. (J. Schlegel, T. Hibiki, M. Ishii, Development of a comprehensible set of drift-fluxconstitutive models for pipes of various hydraulic diameters, Progress in Nuclear Energy 52(2010)666-677.).

[0063] Step S3: Prediction of the size distribution of the gas-liquid discrete phase

[0064] First, the density of the compressed gas depends on the output pressure of the air compressor, which is equal to the pressure required for delivery. The pressure required for delivery can be approximately calculated using the following formula:

[0065]

[0066] Where p is the pressure required for conveying; n is the Manning roughness coefficient, set to 0.012; L is the conveying length; D is the pipe diameter; ρ m The density of the slurry mixture; v m Let g be the velocity of the mixture; g is the acceleration due to gravity, taken as 9.81 m / s². 2 .

[0067] Solve for the density of compressed air:

[0068]

[0069] Where p0 is standard atmospheric pressure, 101.325 kPa; ρ0 is the air density at standard atmospheric pressure, 1.225 kg / m³. 3 .

[0070] Then, solve for the frictional velocity in the pipe:

[0071]

[0072] Where Δ is the absolute roughness, which depends on the pipe material; for steel pipes, it can be taken as 0.05.

[0073] Next, based on the identified different gas-liquid flow patterns, the volume fraction of the gas-liquid discrete phase is calculated using the following three methods:

[0074] (1) When the flow pattern is identified as bubbly flow: the discrete phase exists in the form of bubbles, while the continuous phase consists of liquid.

[0075] Find the kinetic energy ratio between bubbly flow and turbulent flow without bubbles:

[0076]

[0077] Among them, v l ρ is the liquid phase velocity. l ρ g For liquid and gas phase densities; μ l σ is the liquid viscosity coefficient; σ is the surface tension coefficient; d D α is the diameter of the discrete element; t This represents the total porosity.

[0078] Solve for turbulent energy dissipation:

[0079]

[0080] Where Re is the Reynolds number.

[0081] Solve for the effective collision frequency coefficient of the bubble:

[0082]

[0083] Where, α max The maximum porosity is set to 0.8.

[0084] The diameter in the bubbly flow is d D The volume fraction of the gas-liquid discrete unit (bubble) can be expressed as:

[0085]

[0086] (2) When the flow pattern is identified as slug flow or eddy flow, the discrete phase exists in the form of dispersed bubbles, and the continuous phase of the transported particles exists in the form of liquid.

[0087] Solve for the liquid phase porosity:

[0088] α l =0.523α t (8)

[0089] By replacing the total porosity in equations (4)-(6) with the liquid phase porosity, the kinetic energy ratio of the slug flow and the turbulent flow without bubbles, the turbulent energy dissipation, and the effective collision frequency coefficient of the bubbles are obtained in sequence. Then, the diameter d in the slug flow or vortex is... D The volume fraction of the gas-liquid discrete unit (bubble) can be expressed as:

[0090]

[0091] (3) When the flow pattern is identified as annular flow, the discrete phase exists in the form of droplets, while the continuous phase that transports particles is composed of gas.

[0092] Solve for the liquid film thickness:

[0093]

[0094] Then the diameter of the annular flow is d D The volume fraction of the gas-liquid discrete unit (droplet) can be expressed as:

[0095]

[0096] Among them, v g The velocity is the gas phase velocity.

[0097] Step S4: Large Particle Velocity Prediction

[0098] The discrete gas-liquid elements (bubbles or droplets) have a faster velocity than the particles, enabling them to collide with the particles. First, solve for the probability density of the discrete elements:

[0099]

[0100] Where α is the porosity, the total porosity is used when the flow pattern is bubbly or annular, and the liquid phase porosity is used when the flow pattern is slug or vortex; α(d D ) represents the volume fraction of a gas-liquid discrete unit with any diameter in different flow regimes. The volume fraction of a gas-liquid discrete unit with any diameter also differs in different flow regimes, and can be specifically represented as α(d D )1 or α(d D )2 or α(d D 3.

[0101] Solving for the average volume of the discrete element:

[0102]

[0103] Where, d Dmax This represents the maximum size of the gas-liquid discrete phase.

[0104] The collision between the gas-liquid discrete unit and the solid particle converts the kinetic energy of the discrete unit into the kinetic energy of the particle. Therefore, the axial increase in velocity caused by the discrete unit can be expressed as:

[0105]

[0106] Among them, v D The velocity of the discrete element; ρ D ρ represents the density of the discrete element; p V is the density of the solid particles; p Let be the volume of the solid particle.

[0107] Solve for the minimum vortex size inside the pipe:

[0108]

[0109] Solve for the maximum vortex size that causes energy exchange with solid particles inside the pipe:

[0110]

[0111] Where, d p The diameter is the solid particle diameter.

[0112] Solving for the mean vortex size:

[0113]

[0114] Where λ is the vortex scale; The vortex probability density can be expressed as:

[0115]

[0116] Solve for the average vortex lifetime:

[0117]

[0118] Solve for the terminal velocity of the particle without considering collisions:

[0119]

[0120] Where, ρ C The density is the density of the continuous phase. When the flow pattern is bubbly, slug, or vortex, it is the liquid density; when the flow pattern is annular, it is the gas density. C It is the velocity of the continuous phase. When the flow pattern is bubbly flow, slug flow, or vortex flow, it is the velocity of the liquid. When the flow pattern is annular flow, it is the velocity of the gas.

[0121] Besides the particle acceleration caused by the gas-liquid discrete phase, the particle motion is also affected by the acceleration caused by vortex traction and the deceleration caused by inter-particle collisions. Since the particles reach their terminal velocity in fully developed turbulence, these three velocities will reach dynamic equilibrium. The intermediate variables need to be solved:

[0122]

[0123]

[0124] Where T1, T2, and T3 are the vortex acceleration parameter, collision deceleration parameter, and gas-liquid discrete phase acceleration parameter, respectively, and C v For particle concentration, d s e is the average diameter of the discrete element. p The collision recovery coefficient can be set to 0.9 for regularly shaped spherical particles.

[0125] The transport velocity of solid particles in a multiphase flow can then be expressed as:

[0126]

[0127] In summary, firstly, the distribution pattern of the gas-liquid discrete phase in the flow field is analyzed, and the average volume of the discrete phase can be obtained. Next, considering the energy exchange between the gas-liquid discrete phase and individual particles, the acceleration effect on individual particles is calculated. Finally, by comprehensively considering the acceleration and deceleration effects on particles in the flow field, a dynamic equilibrium of particle motion is established, thereby obtaining the particle transport velocity.

[0128] Step S5: Analysis and Judgment

[0129] When the calculated particle conveying speed v p If the expected results are not met, the data processing module (which can be a microcontroller, DSP (digital signal processor), or industrial computer, with the aforementioned calculation model built-in) automatically sends control commands to the control mechanisms such as valves, flow control valves, or variable frequency speed control equipment. The control mechanism then adjusts the valve opening degree or liquid injection flow rate of the gas supply module (such as an air compressor or high-pressure gas source), thereby changing the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline and further optimizing the particle conveying efficiency. If the monitoring data indicates that the particle velocity or flow pattern has reached the optimal range, the current operating condition is maintained, and no additional operations are performed.

[0130] Step S6: Iterative Execution and Optimization Control

[0131] The process of continuously cycling from step S1 to step S6 enables the system to monitor and adjust the particle conveying speed in real time in deep-sea mining or other complex industrial scenarios. The final output conveying speed parameters and various sensor data can be stored in a database or uploaded to a monitoring platform for subsequent analysis and recording.

[0132] In specific embodiments, as shown in Table 1, to verify the proposed model, a total of 26 experimental conditions were collected from Weber and Dedgeil (M. Weber, Y. Dedgeil, Transport of solids according to the air-lift principle, Fourth Int. Conf. on the Hydraulic Transport of Solids in Pipes (1976) 1-24.). The pipe diameter was 300 mm. The flow patterns in these conditions were not explicitly recorded in the experiments. They were classified according to the flow pattern classification method for large pipe designs in S2; the flow patterns of these conditions are listed in the experimental parameters in Table 1, C a For eddies, S a It is a slug flow.

[0133] Table 1

[0134]

[0135]

[0136] like Figure 2 As shown, the solid line represents the ideal scenario where the predicted results perfectly match the experimental data, while the dashed line represents the boundary where the relative error does not exceed 20%. According to calculations, the average relative error between the predicted particle transport velocity and the experimental data is 6.2%, with a standard deviation of 0.61 m / s. Of the 26 operating conditions, 25 had a relative error not exceeding 20%. Among these conditions, 9 were slug flows and 17 were eddies. The average relative error for solid particle transport velocity in slug flows was 6.3%, while the average relative error in eddies was 6.1%, indicating that the difference in results between the two flow types was not significant. In contrast, Weber and Dedgei's prediction results had an average relative error of 6.8%, and the proposed model demonstrated higher prediction accuracy, reducing the average relative error by 8.8%. This is because the specific flow type of the multiphase flow (step S2) and the influence of different flow types on particles (step S3) were considered, rather than simply ignoring the influence of different flow types. In fact, as mentioned earlier, the flow field properties of different flow types differ significantly, and their influence on particle transport velocity is also very large. It is worth noting that the above results are based solely on experimental data for slug and vortex flows. If data from bubbly or annular flows were included, the model's advantage in computational accuracy would likely be even more significant. Existing prediction models often struggle to simultaneously consider complex factors such as gas-liquid discrete phase interactions, turbulence modulation, and particle collisions, limiting their applicability and accuracy across different flow patterns. This model, however, comprehensively considers these factors, thus significantly improving prediction accuracy.

[0137] For 5 mm particles, the average relative error was 8.2%, slightly better than Weber and Dedgeil's error (8.4%). For 50 mm particles, the average relative error was only 1.7%, significantly lower than Weber and Dedgeil's error (3.2%), a reduction of 48%. This indicates that the model has an advantage in predicting larger particles. This is because it considers the characteristics of large particles (step S4), unlike other models that use the motion of small particles in vertical pipes. When the particle size is large and the density is high, the flow mechanism and drag characteristics often become more complex. Although traditional empirical or semi-empirical models can characterize the motion of large particles to some extent, they usually require a large amount of experimental data for drag coefficient correction, and the prediction accuracy will decrease significantly when the operating conditions change or the mineral particle morphology is diverse. This invention aims to construct a theoretical model that reflects the interaction mechanism between large particles and turbulence from a probabilistic analysis perspective, providing a more robust tool for predicting the transport velocity of mineral particles in vertical pipes. This invention considers the random events that large particles may experience in complex flow fields, such as particle collisions, vortex traction, and instantaneous velocity fluctuations caused by turbulent pulsations. By characterizing the distribution functions of these random processes at the theoretical level, the model no longer relies on empirical corrections to the drag coefficient of large particles, thus maintaining reliability over a wider range of operating conditions.

[0138] In addition, by adjusting the actual working conditions according to the design scheme (steps S5 and S6), the following advantages can be achieved: (1) By predicting and correcting the velocity of large particles in real time, the overall transport efficiency of the pipeline can be improved. (2) Monitoring and adjusting the multiphase flow pattern can effectively reduce problems such as pipe blockage and impact, making the working conditions of deep-sea mining and other applications more stable. (3) Compared with the traditional deep-sea mineral gas lift method, since this device can automatically optimize the gas-liquid ratio according to real-time feedback, unnecessary excessive gas injection or excessive pressurization can be reduced, thus reducing energy consumption.

[0139] This invention also provides a deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern recognition, and an application of any of the above-mentioned deep-sea mining multiphase flow particle transport prediction and control methods based on flow pattern recognition, comprising:

[0140] The data acquisition module is used to periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline through the acquisition device, and store them in the data buffer.

[0141] The flow pattern recognition module is used to distinguish between bubbly flow, slug flow, vortex flow, and annular flow by calling a pre-built flow pattern classification algorithm based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity.

[0142] The gas-liquid discrete unit size distribution prediction module is used to calculate the volume fraction of gas-liquid discrete units of any diameter in different flow patterns according to the characteristics of the identified gas-liquid flow, slug flow, vortex flow and annular flow, respectively.

[0143] The solid particle velocity prediction module is used to obtain the transport velocity of solid particles in multiphase flow based on the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles.

[0144] The speed analysis and judgment module is used to determine whether the conveying speed of the obtained solid particles has reached the preset optimal range. Based on the result, the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline is controlled to adjust the conveying speed of the particles.

[0145] The cyclic execution and optimization control module is used to continuously cycle from the data acquisition module to the speed analysis and judgment module, monitor and adjust the particle conveying speed in real time, and store the conveying speed data and data from the acquisition equipment in the database.

[0146] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern recognition as described above.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0148] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition, characterized in that, Includes the following steps: S1. Periodically collect pressure, flow rate, flow velocity, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline using the data acquisition device, and store them in the data buffer. S2. Based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity, the pre-built-in flow pattern classification algorithm is invoked to identify bubbly flow, slug flow, vortex flow, and annular flow. S3. Based on the four different gas-liquid flow patterns identified—bubble flow, slug flow, vortex flow, and annular flow—and the characteristics of each gas-liquid flow pattern, solve for the volume fraction of the gas-liquid discrete unit of any diameter in each flow pattern. S4. By the collision between the gas-liquid dispersion unit and the solid particles, the conveying speed of the solid particles in the multiphase flow is obtained according to the volume fraction of the gas-liquid dispersion unit. S5. Determine whether the obtained solid particle conveying speed has reached the preset optimal range, and adjust the solid particle conveying speed by controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result. S6. Continuously cycle through S1-S5, acquire and adjust the conveying speed of solid particles in real time, and store the conveying speed data and the data collected by the acquisition device in the database. In step S4, obtaining the transport velocity of solid particles in the multiphase flow based on the volume fraction of the gas-liquid discrete unit specifically includes: Solve for the probability density of the gas-liquid discrete element, the average volume of the gas-liquid discrete element, the minimum vortex size, the maximum vortex size, the average vortex size, and the average vortex lifetime in the pipe. By colliding with solid particles through gas-liquid discrete units, the kinetic energy of the gas-liquid discrete units is calculated based on the probability density of the gas-liquid discrete units, the average volume of the gas-liquid discrete units, the minimum vortex size, the maximum vortex size, the average vortex size, the average vortex lifetime, and the volume fraction of the gas-liquid discrete units. This kinetic energy is then converted into the kinetic energy of the solid particles, ultimately obtaining the transport velocity of the solid particles in the multiphase flow.

2. The method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition as described in claim 1, characterized in that, The gas-liquid ratio and mixing velocity in S2 are obtained by calculating the collected flow rate and velocity data; the flow rate data includes gas flow rate and liquid flow rate, and the velocity data includes gas velocity and liquid velocity.

3. The method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition as described in claim 1, characterized in that, In step S3, the specific steps for determining the volume fraction of the gas-liquid discrete unit of any diameter in different flow patterns, based on the characteristics of bubbly flow, slug flow, vortex flow, and annular flow, include: When the flow pattern is bubbly flow, the kinetic energy ratio of bubbly flow and turbulent flow without bubbles, the turbulent energy dissipation, and the effective collision frequency coefficient of bubbles are solved to finally obtain the volume fraction of the gas-liquid discrete element in the bubbly flow. When the flow pattern is slug flow or eddy flow, the liquid phase porosity is solved, and the kinetic energy ratio of slug flow and bubble-free turbulent flow, turbulent energy dissipation, and effective collision frequency coefficient of bubbles are obtained in sequence. Finally, the volume fraction of gas-liquid discrete units in slug flow or eddy flow is obtained. When the flow pattern is annular, the liquid film thickness is solved to obtain the volume fraction of the gas-liquid discrete unit in the annular flow.

4. The method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition, as described in claim 3, is characterized in that... The volume fraction of the gas-liquid discrete unit in the bubbly flow is expressed as follows: （7） in, d D The diameter of the discrete element. K The ratio of the kinetic energy of bubbly flow to that of turbulent flow without bubbles. D For pipe diameter, v m For mixing speed, The velocity is the frictional velocity in the pipe. f The effective collision frequency coefficient of the bubble; The volume fraction of the gas-liquid discrete unit in the slug flow or vortex is expressed as follows: （9） The volume fraction of the gas-liquid discrete unit in the annular flow is expressed as follows: （11） in, v g For gas phase velocity, To determine the thickness of the liquid film, ρ g The density is the gas phase density. σ is the surface tension coefficient.

5. The method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition, as described in claim 4, is characterized in that... The transport velocity of solid particles in the multiphase flow is expressed as follows: （24） in, v C For continuous phase velocity, v p0 To disregard the terminal velocity of particles during collisions, v D For the velocity of the discrete unit, e p The collision recovery coefficient is... , , These are the vortex acceleration parameters, collision deceleration parameters, and gas-liquid discrete unit acceleration parameters, respectively.

6. The method for predicting and controlling particle transport in multiphase flow in deep-sea mining based on flow pattern recognition as described in claim 1, characterized in that, In step S5, the conveying speed of solid particles is adjusted by controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline based on the results. Specifically, this includes: When the obtained solid particle conveying speed does not reach the preset optimal range, the control device sends a control command to adjust the valve opening degree or liquid injection flow rate, and changes the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline to adjust the particle conveying speed; when the obtained solid particle conveying speed reaches the preset optimal range, the current operating condition is maintained and no additional operation is performed.

7. A deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern recognition, applying the deep-sea mining multiphase flow particle transport prediction and control method based on flow pattern recognition as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different locations in the pipeline through the acquisition device, and store them in the data buffer. The flow pattern recognition module is used to distinguish between bubbly flow, slug flow, vortex flow, and annular flow by calling a pre-built flow pattern classification algorithm based on the pipe diameter and the calculated gas-liquid ratio and mixing velocity. The gas-liquid discrete unit size distribution prediction module is used to calculate the volume fraction of gas-liquid discrete units of any diameter in different flow patterns according to the characteristics of the identified gas-liquid flow, slug flow, vortex flow and annular flow, respectively. The solid particle velocity prediction module is used to obtain the transport velocity of solid particles in multiphase flow based on the volume fraction of the gas-liquid discrete unit by means of the collision between the solid particles and the gas-liquid discrete unit. The speed analysis and judgment module is used to determine whether the conveying speed of the obtained solid particles has reached the preset optimal range. Based on the result, the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline is controlled to adjust the conveying speed of the particles. The cyclic execution and optimization control module is used to continuously cycle from the data acquisition module to the speed analysis and judgment module, monitor and adjust the particle conveying speed in real time, and store the conveying speed data and data from the acquisition equipment in the database.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern recognition, as described in any one of claims 1 to 6.

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