Deep-sea mining multiphase flow particle conveying prediction and regulation method based on flow pattern recognition

Abstract

The invention discloses a deep-sea mining multiphase flow particle conveying prediction and regulation method based on flow pattern recognition. The method comprises the steps that pressure, flow, bubble or liquid drop volume fractions and solid particle volume fractions at different positions in a pipeline are periodically collected through collection equipment; according to the pipe diameter and the gas-liquid ratio and the mixing flow speed obtained through calculation, a pre-built-in flow pattern division algorithm is called, and bubble flow, slug flow, vortex and annular flow are distinguished; solving the volume fractions of gas-liquid discrete units of any diameter in different flow patterns; the conveying speed of solid particles in the multiphase flow is obtained according to the gas-liquid dispersed phase volume fraction; and whether the obtained solid particle conveying speed reaches a preset optimal interval or not is judged, and the flow pattern or the gas-liquid ratio of the multiphase flow in the pipeline is controlled according to the result so as to adjust the solid particle conveying speed. The mining efficiency of submarine minerals can be improved, a dangerous flow pattern formed by an unreasonable gas-liquid distribution ratio can be avoided, and efficiency and safety are both considered.

Description

Technical Field

[0001] The present invention relates to the technical field of particle prediction in deep sea mining, and in particular to a method for predicting and regulating multiphase flow particle transport in deep sea mining based on flow pattern identification. Background Art

[0002] Deep-sea mining requires the long-distance transportation of solid minerals through vertical pipelines. For this demand, air-lift pumping has become a particularly promising method because it does not require the use of submersible pumps. During this transportation process, various flow patterns may be formed, including bubbling flow, slug flow, vortex flow and annular flow, each of which plays a key role in the efficiency and safety of mineral particle transportation. However, the different flow patterns that may appear also bring potential challenges. Under severe slug flow conditions, over-pressurization in the pipeline may pose a significant safety risk. Large liquid slugs and air pockets may cause sudden pressure fluctuations and increase the stress of 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 gas-liquid discrete phases in the pipeline. Therefore, a deep understanding of the distribution of gas-liquid discrete phases in multiphase flow is crucial to optimizing the solid transportation mechanism, which is also the basis for deep-sea mineral mining using air-lift pumping. Most studies only conduct experiments or theoretical calculations on pressure loss and superficial velocity, and it is difficult to accurately obtain the true transportation velocity of large particles. At the same time, different flow patterns are converted into each other in practical applications, and each poses different challenges to the prediction of the transport velocity of large particles.

[0003] Although significant progress has been made in the study of multiphase flow dynamics, it is still quite difficult to accurately predict the transport velocity of large particles in bubbly, slug, eddy and annular flows in application scenarios such as gas lift pumping in deep-sea mining. Existing prediction models often find it difficult to simultaneously consider complex factors such as bubble interactions, turbulence modulation and particle collisions, which limits their applicability and accuracy in different flow patterns. In particular, for larger mineral particles, it is not appropriate to assume that their velocity is the same as the continuous phase velocity because these particles exhibit significant slip effects, which have been well studied in solid-liquid two-phase flows. Inaccurate velocity predictions for large-size particles will result in the inability to accurately control the movement of large particles in large-diameter vertical pipes.

[0004] Therefore, it is an urgent problem for those skilled in the art to propose a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification to solve the difficulties existing in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting and controlling multiphase flow particle transport 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, vortex flow and annular flow.

[0006] To achieve the above object, the present invention provides the following solutions:

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

[0008] S1. Periodically collect the pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different positions in the pipeline through a collection device, and store them in a data buffer;

[0009] S2. Based on the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity, the pre-built-in flow type classification algorithm is called to distinguish between bubbly flow, slug flow, vortex flow, and annular flow;

[0010] S3, according to the different gas-liquid flow patterns identified, respectively according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow, solving the volume fraction of the gas-liquid discrete unit of any diameter in the different flow patterns;

[0011] S4, obtaining the transport velocity of the solid particles in the multiphase flow according to the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles;

[0012] S5. Determine whether the obtained solid particle conveying speed reaches a preset optimal range, and control the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the solid particle conveying speed;

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

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

[0015] Preferably, in S3, solving 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 respectively specifically includes:

[0016] When the flow type is bubbly flow, the kinetic energy ratio of bubbly flow to turbulent flow without bubbles, turbulent energy dissipation, and bubble effective collision frequency coefficient are solved, and finally the volume fraction of gas-liquid discrete units in the bubbly flow is obtained;

[0017] When the flow type is slug flow or vortex flow, the liquid phase void fraction is solved, and the kinetic energy ratio of the slug flow to the turbulent flow without bubbles, the turbulent energy dissipation, and the bubble effective collision frequency coefficient are obtained in turn, and finally the volume fraction of the gas-liquid discrete unit in the slug flow or vortex flow is obtained;

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

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

[0020]

[0021] Among them, d D is the discrete unit diameter, K is the kinetic energy ratio of bubbly flow to turbulent flow without bubbles, D is the pipe diameter, v m is the mixing speed, v * is the friction flow velocity in the pipeline, f is the effective bubble collision frequency coefficient;

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

[0023]

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

[0025]

[0026] Among them, v g is the gas phase velocity, δ is the solution film thickness, ρ g 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 according to the volume fraction of the gas-liquid discrete phase specifically includes:

[0028] Solve the probability density of gas-liquid discrete units, the average volume of gas-liquid discrete units, the minimum vortex size in the pipeline, the maximum vortex size, the average vortex size, and the average vortex life;

[0029] Through the collision between gas-liquid discrete units and solid particles, the kinetic energy of the gas-liquid discrete units is solved according to the probability density of the gas-liquid discrete units, the average volume of the gas-liquid discrete units, the minimum vortex size in the pipeline, the maximum vortex size, the average vortex size, the average vortex life and the volume fraction of the gas-liquid discrete phase, and converted into the kinetic energy of the solid particles, and finally the transport velocity of the solid particles in the multiphase flow is obtained.

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

[0031]

[0032] Among them, v C is the continuous phase velocity, v p0 is the terminal velocity of the particle without considering the collision, v D is the velocity of the discrete unit, e p is the collision restitution coefficient, T 1 、T 2 、T 3 are the vortex acceleration parameter, collision deceleration parameter, and gas-liquid discrete phase acceleration parameter, respectively.

[0033] Preferably, in S5, controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the conveying speed of the solid particles specifically includes:

[0034] When the obtained solid particle conveying speed does not reach the preset optimal range, a control instruction is sent through the control device to adjust the valve opening or liquid injection flow rate, change the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline, and adjust the particle conveying speed; when the obtained solid particle conveying speed reaches the preset optimal range, the current operating conditions are maintained without additional operations.

[0035] The present invention also provides a deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern identification, and a deep-sea mining multiphase flow particle transport prediction and control method based on flow pattern identification using any of the above items comprises:

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

[0037] The flow pattern recognition module is used to call the pre-built-in flow pattern classification algorithm to distinguish bubbly flow, slug flow, vortex flow, and annular flow according to the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity;

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

[0039] A solid particle velocity prediction module is used to obtain the transport velocity of solid particles in the multiphase flow according to 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 judge whether the solid particle conveying speed reaches the preset optimal range, and to control the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the particle conveying speed;

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

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification as described above.

[0043] According to the 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 major 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 with numerical simulation results;

[0045] (2) The present invention combines real-time feedback of the gas phase, liquid phase and solid phase, and can use a computer to perform high-frequency data acquisition and velocity prediction based on a collision acceleration model, thereby effectively improving the accuracy of large particle solid velocity prediction; the present invention can not only improve the mining efficiency of seabed minerals, but also avoid the dangerous flow pattern formed by an unreasonable gas-liquid distribution ratio, taking into account both efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A schematic flow chart of a method for predicting and regulating multiphase flow particle transport in deep-sea mining based on flow pattern identification provided by the present invention;

[0048] Figure 2 It is a comparison chart of the Weber and Dedgeil experimental results in the examples and the predicted results of this application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is 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 multiphase flow particle transport in deep-sea mining based on flow pattern identification, comprising the following steps:

[0052] S1. Periodically collect the pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different positions in the pipeline through a collection device, and store them in a data buffer;

[0053] S2. Based on the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity, the pre-built-in flow type classification algorithm is called to distinguish between bubbly flow, slug flow, vortex flow, and annular flow;

[0054] S3, according to the different gas-liquid flow patterns identified, respectively according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow, solving the volume fraction of the gas-liquid discrete unit of any diameter in the different flow patterns;

[0055] S4, obtaining the transport velocity of the solid particles in the multiphase flow according to the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles;

[0056] S5. Determine whether the obtained solid particle conveying speed reaches a preset optimal range, and control the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the solid particle conveying speed;

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

[0058] The method of the present invention specifically comprises:

[0059] Step S1: Data collection

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

[0061] Step S2: Flow pattern identification

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

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

[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 as follows:

[0065]

[0066] Where p is the pressure required for delivery; n is the Manning roughness coefficient, which is set to 0.012; L is the delivery length; D is the pipe diameter; ρ m is the slurry mixing density; v m is the mixing speed; g is the acceleration due to gravity, which is 9.81 m / s 2 .

[0067] Solve for the density of compressed air:

[0068]

[0069] Among them, p 0 is standard atmospheric pressure, 101.325 kPa; ρ 0 is the air density under standard atmospheric pressure, 1.225kg / m 3 .

[0070] Then, solve for the pipe friction velocity:

[0071]

[0072] Among them, Δ is the absolute roughness, which depends on the pipe material and can be taken as 0.05 for steel pipe.

[0073] Next, according to the different gas-liquid flow patterns identified, the volume fraction of the gas-liquid discrete phase is solved in the following three ways:

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

[0075] Solve for the ratio of kinetic energy between bubbly flow and turbulent flow without bubbles:

[0076]

[0077] Among them, v l is the liquid phase velocity; ρ l , g is the density of liquid and gas phase; μ l is the liquid viscosity coefficient; σ is the surface tension coefficient; d D is the discrete unit diameter; α t is the total void ratio.

[0078] Solve for turbulent energy dissipation:

[0079]

[0080] Where Re is the Reynolds number.

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

[0082]

[0083] Among them, α max is the maximum void ratio, which is taken as 0.8.

[0084] The diameter of 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 vortex flow, the discrete phase exists in the form of dispersed bubbles and the continuous phase that transports the particles exists in the form of liquid.

[0087] Solve for the liquid phase void fraction:

[0088] α l =0.523α t (8)

[0089] Replacing the total void fraction in equations (4)-(6) with the liquid void fraction, we can obtain the kinetic energy ratio of the slug flow to the turbulent flow without bubbles, the turbulent energy dissipation, and the bubble effective collision frequency coefficient in turn. Then, the diameter of the slug flow or vortex is d 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 the particles consists of gas.

[0092] Solve for the film thickness:

[0093]

[0094] 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 is the gas phase velocity.

[0097] Step S4: Large particle velocity prediction

[0098] The velocity of gas-liquid discrete units (bubbles or droplets) is faster than that of particles, which causes them to collide with particles. First, solve the probability density of discrete units:

[0099]

[0100] Where α is the void ratio. When the flow pattern is bubbly flow or annular flow, the total void ratio is taken. When the flow pattern is slug flow or vortex flow, the liquid phase void ratio is taken. D ) is the volume fraction of the gas-liquid discrete unit at any diameter in different flow patterns. The volume fraction of the gas-liquid discrete unit at any diameter in different flow patterns is also different. Specifically, it can be α(d D ) 1 or α(d D ) 2 or α(d D ) 3 .

[0101] Solve for the average volume of discrete elements:

[0102]

[0103] Among them, d Dmax is the maximum size of the gas-liquid discrete phase.

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

[0105]

[0106] Among them, v D is the velocity of the discrete unit; ρ D is the density of discrete units; ρ p is the density of solid particles; V p is the volume of the solid particles.

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

[0108]

[0109] Solve for the maximum vortex size that exchanges energy with solid particles in the pipe:

[0110]

[0111] Among them, d p is the diameter of the solid particles.

[0112] Solve for the mean vortex size:

[0113]

[0114] Where λ is the vortex size; is the vortex probability density, which can be expressed as:

[0115]

[0116] Solve for the mean vortex lifetime:

[0117]

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

[0119]

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

[0121] In addition to the particle acceleration caused by the gas-liquid discrete phase, the particle motion is also affected by the acceleration caused by the vortex drag and the deceleration caused by the collision between particles. Since the particles reach the terminal velocity in the fully developed turbulence, these three rates will reach a dynamic equilibrium, solving the intermediate variable:

[0122]

[0123]

[0124] Among them, T 1 , T 2 , T 3 are the vortex acceleration parameter, collision deceleration parameter, and gas-liquid discrete phase acceleration parameter, respectively. v is the particle concentration, d s is the average diameter of discrete units, e p is the collision recovery coefficient, which can be taken as 0.9 for regularly shaped spherical particles.

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

[0126]

[0127] Summary: First, the distribution law of the gas-liquid discrete phase in the flow field is analyzed, and the average volume of the discrete phase can be obtained. Then, the energy exchange between the gas-liquid discrete phase and a single particle is considered, and the acceleration effect on a single particle is calculated. Finally, the dynamic balance of particle motion is established by comprehensively considering the acceleration and deceleration effects on particles in the flow field, thereby obtaining the particle transport velocity.

[0128] Step S5: Analysis and judgment

[0129] When the calculated particle transport velocity v p When it does not meet expectations, the data processing module (which can be a single-chip microcomputer, DSP (digital signal processor) or industrial computer, etc., with the above-mentioned calculation model built in) automatically sends control instructions to control mechanisms such as valves, flow control valves or frequency conversion speed regulation equipment; the control mechanism adjusts the valve opening degree or liquid injection flow of the gas supply module (such as an air compressor or a high-pressure gas source), thereby changing the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline, and continuing to optimize the particle conveying efficiency; if the monitoring data shows that the particle velocity or flow pattern has reached the optimal range, the current operating conditions are maintained and no additional operations are performed.

[0130] Step S6: Loop execution and optimization control

[0131] The system continuously loops from step S1 to step S6, so that it can grasp 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 the database or uploaded to the monitoring platform for subsequent analysis and recording.

[0132] In a specific embodiment, as shown in Table 1, in order 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 is 300 mm. The flow patterns in these conditions were not clearly recorded in the experiment. They were classified according to the flow pattern classification method for large pipe design in S2; the flow patterns of these conditions are listed in the experimental parameters of Table 1, C a is the vortex, S a It is a slug flow.

[0133] Table 1

[0134]

[0135]

[0136] like Figure 2As shown in the figure, the solid line represents the ideal case where the prediction results are completely consistent with the experimental data, while the dotted line represents the boundary where the relative error does not exceed 20%. According to the calculation, the average relative error between the prediction of the particle transport velocity and the experimental data is 6.2%, and the standard deviation is 0.61m / s. Among all 26 working conditions, the relative error of 25 working conditions does not exceed 20%. Among these working conditions, 9 belong to slug flow and 17 belong to vortex flow. The average relative error of the solid particle transport velocity in the slug flow is 6.3%, while the average relative error under the vortex flow is 6.1%, indicating that the difference between the results under the two flow patterns is not significant. In contrast, the average relative error of the prediction results of Weber and Dedgei is 6.8%, and the proposed model shows higher prediction accuracy, with an average relative error reduction of 8.8%. The reason is that the specific flow pattern of multiphase flow (step S2) and the influence of different flow patterns on particles (step S3) are considered, rather than simply ignoring the influence of different flow patterns. In fact, as mentioned above, there are significant differences in the flow field properties of different flow patterns, and the influence on the particle transport velocity is also very large. It is worth noting that the above results are based only on experimental data of slug flow and vortex flow. If the data of bubbly flow or annular flow are included, the advantage of the model in calculation accuracy may be more significant. Existing prediction models often find it difficult to simultaneously consider complex factors such as gas-liquid discrete phase interaction, turbulence modulation and particle collision, which limits their applicability and accuracy under different flow patterns. The model takes these factors into consideration, so the prediction accuracy has been significantly improved.

[0137] For the prediction of 5 mm particles, the average relative error of the prediction is 8.2%, which is slightly better than the error of Weber and Dedgeil (8.4%); while for 50 mm particles, the average relative error of the prediction is only 1.7%, which is significantly lower than the error of Weber and Dedgeil (3.2%), a decrease of 48%. This shows that the model has advantages in predicting larger particles. The reason is that the characteristics of large particles are taken into account (step S4), instead of using the movement law of small particles in vertical pipes like other models, and when the particle size is large and the density is high, the flow mechanism and resistance characteristics tend to become more complicated. Although traditional empirical or semi-empirical models can characterize the movement of large particles to a certain extent, they usually require the use of a large amount of experimental data to correct the resistance coefficient, and when the working conditions change or the mineral particles are diverse in morphology, the prediction accuracy will drop significantly. The present invention aims to construct a theoretical model that can reflect the interaction mechanism between large particles and turbulence from the perspective of probability analysis, and provide a more robust tool for predicting the transport velocity of mineral particles in vertical pipes. The present invention takes into account the random events that large particles may experience in complex flow fields, such as inter-particle collisions, vortex drag, and instantaneous velocity fluctuations caused by turbulent pulsation. 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, thereby maintaining reliability over a wider range of operating conditions.

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

[0139] The present invention also provides a deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern identification, and a deep-sea mining multiphase flow particle transport prediction and control method based on flow pattern identification using any of the above items comprises:

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

[0141] The flow pattern recognition module is used to call the pre-built-in flow pattern classification algorithm to distinguish bubbly flow, slug flow, vortex flow, and annular flow according to the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity;

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

[0143] A solid particle velocity prediction module is used to obtain the transport velocity of solid particles in the multiphase flow according to 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 judge whether the solid particle conveying speed reaches the preset optimal range, and to control the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the particle conveying speed;

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

[0146] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements a method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification as described above.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0148] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification, characterized in that: The following steps are involved: S1. Periodically collect the pressure, flow rate, flow velocity, bubble or droplet volume fraction, and solid particle volume fraction at different positions in the pipeline through a collection device, and store them in a data buffer; S2. Based on the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity, the pre-built-in flow pattern classification algorithm is called to identify the bubbly flow, slug flow, vortex flow, and annular flow; S3, according to the identified four different gas-liquid flow patterns of bubbly flow, slug flow, vortex flow and annular flow and the characteristics of each gas-liquid flow pattern, the volume fraction of the gas-liquid discrete unit of any diameter in different flow patterns is solved; S4, obtaining the transport velocity of the solid particles in the multiphase flow according to the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles; S5, judging whether the solid particle conveying speed obtained has reached a preset optimal range, and controlling the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the solid particle conveying speed; S6. Continuously cycle through S1-S5 to obtain and adjust the conveying speed of solid particles in real time, and store the conveying speed data and the data collected by the collection equipment in a database.

2. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 1 is characterized in that: The gas-liquid ratio and mixed flow rate in S2 are data obtained by calculating the collected flow rate and flow rate data; the flow rate data includes gas flow rate and liquid flow rate, and the flow rate data includes gas flow rate and liquid flow rate.

3. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 1 is characterized in that: In S3, respectively according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow, solving the volume fraction of the gas-liquid discrete unit of any diameter in different flow patterns specifically includes: When the flow type is bubbly flow, the kinetic energy ratio of bubbly flow to turbulent flow without bubbles, turbulent energy dissipation, and bubble effective collision frequency coefficient are solved, and finally the volume fraction of gas-liquid discrete units in the bubbly flow is obtained; When the flow type is slug flow or vortex flow, the liquid phase void fraction is solved, and the kinetic energy ratio of the slug flow to the turbulent flow without bubbles, the turbulent energy dissipation, and the bubble effective collision frequency coefficient are obtained in turn, and finally the volume fraction of the gas-liquid discrete unit in the slug flow or vortex flow is obtained; When the flow pattern is annular flow, the liquid film thickness is solved, and finally the volume fraction of the gas-liquid discrete unit in the annular flow is obtained.

4. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 3 is characterized in that: The volume fraction of the gas-liquid discrete unit in the bubbly flow is expressed as follows: Among them, d D is the discrete unit diameter, K is the kinetic energy ratio of bubbly flow to turbulent flow without bubbles, D is the pipe diameter, v m is the mixing speed, v * is the pipeline friction velocity, f is the effective bubble collision frequency coefficient; The volume fraction of the gas-liquid discrete unit in the slug flow or vortex flow is expressed as follows: The volume fraction of the gas-liquid discrete unit in the annular flow is expressed as follows: Among them, v g is the gas phase velocity, δ is the solution film thickness, ρ g is the gas phase density, and σ is the surface tension coefficient.

5. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 1 is characterized in that: In S4, obtaining the transport velocity of solid particles in the multiphase flow according to the volume fraction of the gas-liquid discrete phase specifically includes: Solve the probability density of gas-liquid discrete units, the average volume of gas-liquid discrete units, the minimum vortex size in the pipeline, the maximum vortex size, the average vortex size, and the average vortex life; Through the collision between gas-liquid discrete units and solid particles, the kinetic energy of the gas-liquid discrete units is solved according to the probability density of the gas-liquid discrete units, the average volume of the gas-liquid discrete units, the minimum vortex size in the pipeline, the maximum vortex size, the average vortex size, the average vortex life and the volume fraction of the gas-liquid discrete phase, and converted into the kinetic energy of the solid particles, and finally the transport velocity of the solid particles in the multiphase flow is obtained.

6. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 4 is characterized in that: The transport velocity of solid particles in the multiphase flow is expressed as follows: Among them, v C is the continuous phase velocity, v p0 is the terminal velocity of the particle without considering the collision, v D is the velocity of the discrete unit, e p is the collision recovery coefficient, T1, T2, and T3 are the vortex acceleration parameter, collision deceleration parameter, and gas-liquid discrete phase acceleration parameter, respectively.

7. The method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification according to claim 1 is characterized in that: In S5, the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline is controlled according to the result to adjust the conveying speed of the solid particles, which specifically includes: When the obtained solid particle conveying speed does not reach the preset optimal range, a control instruction is sent through the control device to adjust the valve opening or liquid injection flow rate, change the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline, and adjust the particle conveying speed; when the obtained solid particle conveying speed reaches the preset optimal range, the current operating conditions are maintained without additional operations.

8. A deep-sea mining multiphase flow particle transport prediction and control system based on flow pattern identification, using a deep-sea mining multiphase flow particle transport prediction and control method based on flow pattern identification according to any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to periodically collect pressure, flow rate, bubble or droplet volume fraction, and solid particle volume fraction at different positions in the pipeline through a collection device, and store them in a data buffer; The flow pattern recognition module is used to call the pre-built-in flow pattern classification algorithm to distinguish bubbly flow, slug flow, vortex flow, and annular flow according to the pipe diameter, the calculated gas-liquid ratio, and the mixed flow velocity; A gas-liquid discrete unit size distribution prediction module is used to solve the volume fraction of gas-liquid discrete units of any diameter in different flow patterns according to the characteristics of the bubbly flow, slug flow, vortex flow and annular flow respectively according to the different gas-liquid flow patterns identified; A solid particle velocity prediction module is used to obtain the transport velocity of solid particles in the multiphase flow according to the volume fraction of the gas-liquid discrete phase through the collision between the gas-liquid discrete unit and the solid particles; The speed analysis and judgment module is used to judge whether the solid particle conveying speed reaches the preset optimal range, and to control the flow pattern or gas-liquid ratio of the multiphase flow in the pipeline according to the result to adjust the particle conveying speed; The loop execution and optimization control module is used to continuously loop from the data acquisition module to the speed analysis and judgment module, grasp and adjust the particle conveying speed in real time, and store the conveying speed data and acquisition equipment data in the database.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting and controlling multiphase flow particle transport in deep-sea mining based on flow pattern identification as described in any one of claims 1 to 7 is implemented.

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