A stable transportation regulation system based on deep-sea ore conveying relay bin

Abstract

The application provides a stable transportation regulation system based on a deep-sea ore conveying relay bin, which comprises a relay bin, a flow distribution regulation device, a state monitor, a mining ship, an optimization control unit, an actuator, a flow disturbance and guide unit and a coupling simulation unit. The relay bin comprises a body, a right feed inlet, a left feed inlet, a lifting outlet, a lifting pipe and a suction inlet. Through the collaborative design of feed regulation, in-bin flow guide and disturbance control, state monitoring and multi-target optimization control based on coupling simulation, the closed-loop regulation of the particle transportation process of the relay bin is realized, the particle deposition and abnormal disturbance are reduced while the lifting transportation efficiency is improved, the transportation process under the deep-sea environment has good adaptability, the unstable factors in the system operation process are reduced, and the overall engineering application reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining, and particularly to a stable transportation regulation system based on a deep-sea ore transportation relay bin. Background Art

[0002] In the process of deep-sea mineral resource development, the relay bin, as an important intermediate unit connecting the seabed mining equipment and the riser lifting system, plays a key role in temporarily storing, redistributing, and stably feeding mineral particles. Its structural form and operating state directly affect the stability and efficiency of the mineral transportation system. In the prior art, in view of problems such as uneven particle transportation, easy accumulation, and blockage inside the relay bin, researchers have proposed various structural optimization and control schemes. For example, the application document with the patent application number CN202510226519.X proposed a mobile telescopic vector adsorption relay bin. By setting vector telescopic adsorption pipes and multi-degree-of-freedom rotation mechanisms, the adsorption pipes can move in multiple directions in space, thereby realizing multi-directional adsorption and lifting of particles in the bin, improving the problem of uneven particle distribution and local accumulation to a certain extent. At the same time, the application document with the patent application number CN202512051713.1 proposed a relay bin device with intelligent detection and adjustment of mineral height. By setting a height adjustment structure driven by an airbag inside the relay bin and combining a high-pressure waterproof radar and an ultrasonic sensor, real-time detection and adjustment of the accumulation height of mineral particles are achieved, improving the stability and safety of the system operation. In addition, in the field of mineral transportation and separation systems, the application document with the patent application number CN202610189968.6 proposed a mobile gravity separation system. By introducing pulp concentration sensors, flow sensors, and a central control unit, data interaction and linkage control between multiple devices are achieved, forming a certain degree of closed-loop adjustment mechanism, and improving the automation level of the system operation.

[0003] From the above existing technologies, it can be seen that the existing solutions mostly focus on single-structure optimization or local function improvement. Although they improve the particle transportation behavior or the detection and control ability to a certain extent, there is still a problem of insufficient system integration as a whole. The feeding adjustment, in-bin flow field control, particle state detection, and actuators have not been designed in a unified and coordinated manner, making it difficult to achieve stable transportation under complex working conditions.

[0004] Existing technologies for particle transport states are mostly at the qualitative analysis level, lacking quantitative evaluation indicators that can be used for engineering control. This makes it difficult to accurately characterize particle aggregation, retention, and flow stability. Furthermore, existing control methods are mostly based on simple threshold or local feedback regulation, lacking multi-objective optimization decision-making mechanisms. This makes it difficult to achieve globally optimal control under different operating conditions. Moreover, existing solutions generally lack the design concept of combining numerical simulation methods with actual control strategies, failing to construct an integrated "simulation-evaluation-control" regulation system, resulting in insufficient adaptability and generalization ability of the control strategy.

[0005] Therefore, existing technologies have not yet formed a complete closed-loop control mechanism for particle transport stability during deep-sea ore transportation, making it difficult to meet the engineering requirements for efficient, stable, and low-disturbance transport in complex marine environments. Summary of the Invention

[0006] In view of this, the present invention proposes a stable transport control system based on a deep-sea ore transport relay warehouse, which solves the problems of particle agglomeration, local stagnation and unstable transport in the particle transport process of the relay warehouse in the prior art, and lack of system-level collaborative control and optimization decision-making mechanism, thereby realizing efficient, stable and low-disturbance operation of the ore transport process.

[0007] The technical solution of this invention is implemented as follows: A stable transport and control system based on a deep-sea ore transport relay bin includes a relay bin, a flow distribution and adjustment device, a status monitor, an optimization control unit, an actuator, a flow disturbance and guidance unit, and a coupling simulation unit. The relay bin includes a main body, a feed inlet, a lifting outlet, a lifting pipe, and a suction inlet. The flow disturbance and guidance unit includes an adjustable flow guide shroud and flow disturbance nozzles. The feed inlet is symmetrically arranged on both sides of the top of the main body. The lifting pipe is located inside the main body, with its top end extending outside the main body and connecting to the lifting outlet. The suction inlet is located at the bottom of the lifting pipe. The adjustable flow guide shroud is located on the outer wall of the lifting pipe and above the suction inlet. The flow disturbance nozzles are symmetrically arranged on both sides of the bottom of the relay bin. The flow distribution and adjustment device is connected to the feed inlet. The optimization control unit is data-connected to the status monitor, the actuator, and the coupling simulation unit. The actuator is data-connected to the flow distribution and adjustment device, the adjustable flow guide shroud, and the flow disturbance nozzles.

[0008] Preferably, the adjustable flow guide includes a stator, a rotor, fan blades, a support base, and a driven connector. The stator is disposed on the outer wall of the lifting tube, the rotor is disposed on the stator, the support base is spaced apart on the outer wall of the rotor, one end of the driven connector is hinged to the support base, and the other end is hinged to the fan blades. The fan blades are distributed circumferentially along the outer side of the rotor, and their outer surface is provided with flow guide ribs. The actuator is data-connected to the rotor.

[0009] Preferably, it also includes a control method, the control method comprising the following steps: Step S1: The condition monitor collects the particle volume fraction, local flow velocity and pipeline pressure drop in the body, and calculates the particle agglomeration coefficient, low-velocity retention coefficient, transport pulsation coefficient and blockage risk index respectively. Step S2: Based on the comparison between the congestion risk index and the preset state classification threshold, the operation status of the relay warehouse is classified into stable operation status, mild clustering status, early warning status and high congestion risk status. Step S3: Define the control variable vector and the state vector. The coupled simulation unit performs CFD-DEM coupled simulation on the relay warehouse and constructs a representative model describing the mapping relationship between the control variable vector and the state vector. Step S4: When the operating status is a warning state or a high congestion risk state, the optimization control unit constructs a multi-objective optimization model based on the representative model and outputs the optimal control variable vector; Step S5: Obtain the target state vector based on the optimal control variable vector and the representative model, update the control variable vector based on the target state, and obtain the updated control variable vector. Step S6: The actuator drives and controls the flow distribution adjustment device, the adjustable guide shroud, and the turbulence nozzle according to the updated control variable vector.

[0010] Preferably, the specific steps of step S1 are as follows: The condition monitor collects particle volume fraction. Local flow velocity and the pressure drop of the riser pipe ; Calculate the particle segregation coefficient :

[0011] in The standard deviation of particle volume fraction. This represents the average particle volume fraction. Calculate the low-speed retention coefficient :

[0012] in For flow rate threshold, This represents the number of particles with local flow velocities below a flow velocity threshold. This represents the total number of particles; Calculate the transport pulsation coefficient :

[0013] in The standard deviation of pipeline pressure drop. This represents the average pressure drop across the pipeline. Calculate the congestion risk index:

[0014] in For the weighting coefficients, satisfying .

[0015] Preferably, the expression for classifying the relay warehouse's operating status in step S2 is as follows:

[0016] in To block the risk index, This indicates a stable operating state. This indicates a state of mild aggregation. This indicates an early warning status. This indicates a high risk of congestion. This is the threshold for state classification.

[0017] Preferably, when the relay bin is in a stable operating state, the current operating parameters are maintained; when the relay bin is in a slightly segregated state, the feed distribution ratio and disturbance intensity are slightly adjusted through the flow distribution adjustment device and the turbulence nozzle.

[0018] Preferably, the specific steps of step S3 are as follows: Define the control variable vector and the state vector as follows:

[0019]

[0020] in To control the variable vector, For the feed distribution ratio, For the adjustable air deflector angle, For the adjustable flow deflector opening ratio, For the disturbance intensity, For state vectors, The particle segregation coefficient, For low-speed retention coefficient, This is the transport pulsation coefficient. Congestion risk index; The coupled simulation unit performs CFD-DEM coupled simulation on the relay warehouse, and after obtaining sample data of control variable vectors and state vectors, constructs a sample dataset. ,in For the i-th control variable vector sample data, This is the sample data for the i-th state vector; Construct a representative model describing the mapping relationship between the control variable vector and the state vector:

[0021] The representative model is represented by a parametric model as follows:

[0022] Where S is the state vector and X is the control variable vector. This is a representative model built based on regression methods. This is a representative model built based on machine learning methods; Establish a dynamic prediction model based on a parametric model:

[0023] in Let be the state vector at time t+1. Let be the state vector at time t. Let be the vector of control variables at time t.

[0024] Preferably, the specific steps of step S4 are as follows: A multi-objective optimization model is established based on the state vector mapped from the control variable vector output by the representative model:

[0025] in To control the particle clustering coefficient under the variable vector mapping, To control the low-speed retention coefficient under the vector mapping of variables, To control the transport pulsation coefficient under the vector mapping of variables, The target vector; The Pareto optimal solution set of the control variable vector is calculated based on the NSGA-II algorithm combined with the objective vector:

[0026] in , which is the Pareto optimal solution set; Select the optimal control variable vector from the Pareto optimal solution set. .

[0027] Preferably, the specific steps of step S5 are as follows: Substitute the optimal control variable vector into the parameterized model to calculate the target state vector:

[0028] in Let be the target state vector. This is the optimal control variable vector.

[0029] The control variable vector is updated by combining the target state vector to obtain the updated control variable vector. The formula for calculating the updated control variable vector is as follows:

[0030] in For the updated control variable vector, To control the variable vector, For the adjustment coefficient matrix, Let be the target state vector. This is the state vector.

[0031] Preferably, the specific steps of step S6 are as follows: Extract the updated values ​​of feed ratio, guide angle, guide opening ratio, and disturbance intensity from the updated control variable vector: The formula for calculating the updated feed ratio value is:

[0032] in Let be the feed flow rate of the j-th feed inlet at time t. Total feed flow rate

[0033] The flow rate allocation ratio for the j-th feed inlet; The formula for calculating the updated guide angle value is:

[0034] in Update the value for the guide angle. The guide angle at the previous moment, To adjust the gain coefficient for the guide angle, The low-speed dwell coefficient at the current moment. The threshold for the low-speed dwell coefficient; The formula for calculating the updated value of the flow guide opening ratio is:

[0035] in This is the updated value for the guide opening ratio. The initial guide opening ratio, The gain coefficient is adjusted to accommodate the aperture ratio. The particle segregation coefficient at the current moment. This is the threshold value for the particle segregation coefficient; The formula for calculating the updated disturbance intensity value is as follows:

[0036] in This is the updated value for the disturbance intensity. As the reference disturbance strength, This represents the perturbation gain corresponding to the particle segregation coefficient. This represents the disturbance gain corresponding to the low-speed dwell coefficient. The actuator drives and controls the flow distribution adjustment device according to the updated feed ratio value, drives and controls the adjustable guide shroud according to the updated guide angle value and the updated guide opening ratio value, and drives and controls the turbulence nozzle according to the updated disturbance intensity value.

[0037] Compared with the prior art, the beneficial effects of the present invention are: ① By setting a flow distribution and adjustment device at the feed inlet, the ore particles entering the relay bin are diverted and proportionally adjusted, so that the particles form a relatively uniform initial distribution state before entering the relay bin, thereby suppressing the occurrence of particle agglomeration from the source. This effectively solves the problem of unbalanced particle distribution in the bin caused by uneven feeding in the existing technology and improves the stability of particle transportation.

[0038] ② By setting up turbulence and flow guiding units inside the relay chamber, the adjustable flow guide hood guides particles to converge towards the inlet by adjusting the opening and closing angle, changing the particle flow path, while the turbulence nozzle disturbs the local low-speed retention area, improving the particle resuspension ability. This weakens the tendency of particle deposition and local accumulation from a mechanistic perspective, thereby significantly reducing the risk of blockage in the relay chamber and improving the continuity of particle transportation.

[0039] ③ By setting up a state monitor to collect key parameters such as particle concentration, flow rate and pressure drop in real time, and combining data processing and evaluation mechanisms to extract characteristic quantities such as particle agglomeration degree, retention characteristics and transport stability, the system can be transformed from traditional experience-based judgment to quantitative analysis, thereby improving the system's ability to identify complex working conditions and its response accuracy.

[0040] ④ By introducing a coupled simulation unit, this invention establishes a mapping relationship between operating parameters and particle transport characteristics using the CFD-DEM coupled simulation method, enabling the control system to predict the particle motion patterns under different operating conditions. This overcomes the problems of existing control strategies relying on experience and having poor adaptability, and improves the foresight and reliability of the control strategy.

[0041] ⑤ By optimizing the control unit, parameters such as feed flow distribution, flow guide structure adjustment and disturbance intensity are optimized in a coordinated manner, and dynamic adjustment is achieved through the actuator. This enables global optimization control under multi-variable coupling conditions, avoids the local optimum problem caused by single parameter adjustment, and improves the overall operating efficiency of the system.

[0042] ⑥ This invention constructs a closed-loop control system that combines status monitoring, data processing, optimization decision-making, and execution control, enabling the particle transport process in the relay warehouse to be adjusted in real time according to the operating status, thereby achieving stable control of the transport process, improving ore transport efficiency, and reducing energy consumption.

[0043] ⑦ Through the coordinated design of structural regulation and control strategies, the transport efficiency is improved while particle deposition and abnormal disturbances are reduced. It has good adaptability to the transport process in the deep-sea environment, which helps to reduce the unstable factors in the system operation process and improve the overall reliability of engineering applications. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of a stable transportation and control system based on a deep-sea ore transportation relay warehouse according to the present invention. Figure 2 This is a schematic diagram of the structure of a relay warehouse in a stable transportation and control system based on a deep-sea ore transportation relay warehouse according to the present invention. Figure 3 This is an exploded view of an adjustable flow guide shroud for a stable transport control system based on a deep-sea ore transport relay warehouse according to the present invention. Figure 4 This is a schematic diagram of the opening and closing structure of an adjustable guide hood for a stable transport control system based on a deep-sea ore transport relay warehouse according to the present invention. in Figure 4 (a) is a schematic diagram of the opening structure of the adjustable fairing. Figure 4 (b) is a schematic diagram of the closed structure of the adjustable fairing; Figure 5 This is a comparison diagram of particle concentration distribution under different feeding methods in a stable transportation control system based on a deep-sea ore transportation relay warehouse according to the present invention. Figure 6 This is a comparison diagram of the flow field streamlines under different feeding methods in a stable transportation control system based on a deep-sea ore transportation relay warehouse according to the present invention. Figure 7 This is a flowchart of a control method for a stable transport control system based on a deep-sea ore transport relay warehouse according to the present invention; Figure 8This is a schematic diagram of the CFD-DEM coupled simulation of the relay warehouse by the coupled simulation unit in the control method of the stable transportation control system based on the deep-sea ore transportation relay warehouse of the present invention. In the diagram, 1. Relay compartment; 10. Main body; 11. Right feed inlet; 12. Left feed inlet; 13. Lifting outlet; 14. Lifting pipe; 15. Adjustable guide shroud; 151. Stator; 152. Rotor; 153. Fan blade; 154. Support base; 155. Driven connector; 156. Guide rib; 16. Turbulence nozzle; 17. Suction inlet; 2. Flow distribution and adjustment device; 3. Condition monitor; 4. Mining vessel; 5. Optimization control unit; 6. Actuator; 7. Turbulence and guide unit; 8. Coupling simulation unit. Detailed Implementation

[0046] To better understand the technical content of this invention, a specific embodiment is provided below, and the invention will be further described in conjunction with the accompanying drawings.

[0047] See Figures 1 to 4 This invention provides a stable transportation and control system based on a deep-sea ore conveying relay bin, comprising a relay bin 1, a flow distribution and adjustment device 2, a status monitor 3, a mining vessel 4, an optimization control unit 5, an actuator 6, a flow disturbance and guidance unit 7, and a coupling simulation unit 8. The relay bin 1 includes a body 10, a feed inlet, a lifting outlet 13, a lifting pipe 14, and a suction inlet 17. The feed inlet includes a right feed inlet 11 and a left feed inlet 12. The flow disturbance and guidance unit 7 includes an adjustable flow guide shroud 15 and a flow disturbance nozzle 16. The right feed inlet 11 and the left feed inlet 12 are symmetrically arranged on both sides of the top of the body 10. The lifting outlet 13 is located at the top of the body 10. The lifting pipe 14 is located inside the body 10, with its top end extending outside the body 10. The lifting outlet 13 is connected to the lifting pipe 14. The suction port 17 is located at the bottom of the lifting pipe 14. The adjustable guide shroud 15 is located on the outer wall of the lifting pipe 14 and above the suction port 17. The turbulence nozzles 16 are symmetrically arranged on both sides of the bottom of the relay bin 1. The discharge port of the mining vessel 4 is connected to the right feed port 11 and the left feed port 12 through the flow distribution adjustment device 2. The state monitor 3 monitors the particle transport status in the body 10. The coupling simulation unit 8 performs CFD-DEM coupling simulation on the relay bin 1. The optimization control unit 5 is connected to the state monitor 3, the actuator 6 and the coupling simulation unit 8 respectively. The actuator 6 is connected to the flow distribution adjustment device 2, the adjustable guide shroud 15 and the turbulence nozzles 16 respectively.

[0048] The present invention discloses a stable transport and control system based on a deep-sea ore transport relay bin. The mining vessel 4 can collect seabed ore and transport the ore particles to the flow distribution and regulation device 2. The flow distribution and regulation device 2 is set at the right feed port 11 and the left feed port 12 of the main body 10. It is used to distribute the flow of ore particles entering the relay bin 1 and regulate the feed. By adjusting the flow ratio of different feed channels, the initial distribution state of the particles can be controlled, thereby reducing the occurrence of particle agglomeration from the source.

[0049] The main body 10 is used to temporarily store and redistribute ore particles. It has an intake port 17 at its bottom, which is connected to the riser pipe 14 to transport the particles upward. Inside the main body 10, there is a flow disturbance and guide unit 7, which includes an adjustable guide hood 15 and a flow disturbance nozzle 16. The adjustable guide hood 15 is located above the intake port 17 and its opening angle can be adjusted. By adjusting its opening angle and axial position, the particles are guided to converge towards the intake port 17, changing the particle flow path and distribution state. The flow disturbance nozzle 16 is located around the intake port 17 at the bottom of the main body 10 or near the adjustable guide hood 15. It is arranged at an angle to disturb the local particle retention area, break up the particle aggregation area, improve the particle resuspension capacity, and thus inhibit the occurrence of particle deposition and blockage. The synergistic effect of the adjustable guide hood 15 and the flow disturbance nozzle 16 can realize the joint control of the flow field structure and particle transport behavior in the key area near the intake port 17.

[0050] The status monitor 3 is installed in the main body 10 and related pipelines to monitor the transport characteristics during the particle transport process in real time, including information such as particle concentration, flow rate and pressure drop. The optimization control unit 5 is used to receive the operating data collected by the status monitor 3 and, in combination with the representative model provided by the coupled simulation unit 8, to analyze the system operating status and solve multi-objective optimization problems.

[0051] The coupled simulation unit 8 is used to construct the mapping relationship between operating parameters and particle transport characteristics. Through the CFD-DEM coupled simulation method, the interaction between fluid and particles under different operating conditions is numerically simulated to obtain the flow field distribution and particle motion law in the relay chamber 1, and a simulation driving model that can be used for control decision-making is formed.

[0052] The optimization control unit 5 generates the optimal control strategy based on the output results of the state monitor 3 and the coupled simulation unit 8. The control strategy includes the feed flow distribution ratio, the adjustment parameters of the adjustable guide shroud 15, and the disturbance intensity. The actuator 6 is used to receive the control command and adjust the flow distribution adjustment device 2 and the disturbance and guide unit 7 respectively, thereby realizing the active control of the particle transport process in the relay bin 1.

[0053] The specific working principle is as follows: mineral particles enter the body 10 through the right feed inlet 11 and the left feed inlet 12 on both sides. Under the action of gravity and fluid, they move towards the suction inlet 17 and are conveyed upward through the riser pipe 14. During the particle transport process, the adjustable guide hood 15 guides the convergence path of the particles near the suction inlet 17 by changing its opening angle or spatial position, thereby adjusting the way the particles enter the suction inlet. At the same time, the turbulence nozzle 16 disturbs the local low-speed stagnation area or particle accumulation area by spraying fluid to prevent particle deposition or blockage. The status monitor 3 monitors the particle transport status in the relay bin 1 in real time and transmits the detection signal to the optimization control unit 5. The optimization control unit 5 analyzes and evaluates the operating status in combination with the representative model provided by the coupled simulation unit 8 and generates control commands. The control commands are transmitted to the actuator 6, which coordinates the flow distribution adjustment device 2, the adjustable guide hood 15 and the turbulence nozzle 16 to achieve closed-loop control of the particle transport process in the relay bin 1.

[0054] When increased particle agglomeration or blockage risk is detected, the adjustable guide shroud 15 is retracted or lowered to enhance the converging effect, while the spray intensity of the turbulence nozzle 16 is increased. When the transport state tends to be stable, the opening of the adjustable guide shroud 15 is appropriately expanded or the disturbance intensity is reduced to maintain stable system operation. Through the above structure and control method, dynamic regulation of the mineral particle transport process in the relay bin 1 is realized, improving transport stability and reducing blockage risk. This invention constructs a closed-loop control system that combines state monitoring, optimization decision-making, execution control, and feedback updates, realizing system-level stable control of the particle transport process in the relay bin 1, effectively improving ore transport efficiency, reducing particle accumulation and blockage risk, and enhancing the system's adaptability in complex deep-sea environments.

[0055] Preferably, the adjustable flow guide shroud 15 includes a stator 151, a rotor 152, a fan blade 153, a support base 154, and a driven connector 155. The stator 151 is disposed on the outer wall of the lifting pipe 14, the rotor 152 is disposed on the stator 151, the support base 154 is spaced apart on the outer wall of the rotor 152, one end of the driven connector 155 is hinged to the support base 154, and the other end is hinged to the fan blade 153. The fan blade 153 is circumferentially distributed along the outer side of the rotor 152, and its outer surface is provided with flow guide ribs 156. The actuator 6 is connected to the rotor 152.

[0056] The rotor 152 is the driving mechanism, mounted on the outer wall of the stator 151 installed on the lifting pipe 14. Upon receiving a control signal, it drives the support base 154 to rotate. The fan blade 153 is hinged to the support base 154 via a driven connector 155. During rotation, the fan blade 153 can open synchronously, adjusting the opening size, opening angle, and guiding range of the adjustable guide shroud 15. This alters the convergence path and local flow field distribution of the particle flow near the inlet 17, achieving varying degrees of flow control over the mineral particles. Figure 4 (a) The above is a schematic diagram of the adjustable flow guide 15 in its open state. Figure 4 (b) is a schematic diagram of the adjustable flow guide 15 in the closed state.

[0057] Reference Figure 5 Under unilateral feeding conditions, the particle distribution in relay bin 1 is significantly uneven, with particles mainly moving downwards along one side wall and forming a localized high-concentration aggregation area near the inlet 17, which easily leads to increased particle retention and blockage risks. Under symmetrical feeding conditions, the particle distribution tends to be uniform, the particle concentration distribution in the inlet area is more symmetrical, and the local aggregation phenomenon is significantly reduced. This indicates that uneven feeding is one of the main reasons for particle agglomeration and local accumulation, and the present invention can improve the particle distribution state from the source through bilateral feeding distribution.

[0058] like Figure 6 As shown, under unilateral feeding conditions, the flow field inside the relay bin 1 exhibits significant asymmetry, with strong backflow regions forming locally, causing particles to circulate and stagnate in these areas. Under symmetrical feeding conditions, the flow field structure is more uniform, with a relatively symmetrical circulating flow structure on both sides. The backflow intensity is weakened, which facilitates the uniform transport of particles to the inlet 17 region, resulting in a more even distribution of mineral particles. By installing a flow distribution regulating device 2 at the inlet of the relay bin 1, uniform particle feeding can be achieved, thereby improving the particle transport state within the bin. Simultaneously, for the localized backflow and low-speed stagnation regions in the flow field, the adjustable guide hood 15 and the turbulence nozzle 16 within the main body 10 further regulate the particle flow path and local flow field to achieve comprehensive optimization of the particle transport state.

[0059] Reference Figure 7-8 As shown, the present invention also includes a control method, the control method comprising the following steps: Step S1: The state monitor 3 collects the particle volume fraction within the body 10. Local flow velocity and the pipeline pressure drop of riser pipe 14 The particle agglomeration coefficient, low-velocity retention coefficient, transport pulsation coefficient, and blockage risk index were calculated respectively. Particle segregation coefficient :

[0060] in The standard deviation of particle volume fraction. This represents the average particle volume fraction. Low-speed retention coefficient :

[0061] in For flow rate threshold, This represents the number of particles with local flow velocities below a flow velocity threshold. This represents the total number of particles; Transport pulsation coefficient :

[0062] in The standard deviation of pipeline pressure drop. This represents the average pressure drop across the pipeline. Congestion Risk Index:

[0063] in For the weighting coefficients, satisfying Each weighting coefficient satisfies the normalization constraint and is used to comprehensively reflect the impact of particle agglomeration, low-speed retention and pressure drop fluctuations on blockage risk.

[0064] To achieve a quantitative description of the particle transport process, three sets of operating parameters are obtained through the state monitor 3. Then, based on the operating parameters, the particle agglomeration coefficient, low-speed retention coefficient, and transport pulsation coefficient can be calculated. The particle agglomeration coefficient is used to characterize the uniformity of particle distribution, the low-speed retention coefficient is used to represent the degree of retention, and the transport pulsation coefficient can be used to represent the system volatility. After calculating the three coefficients, the blockage risk index can be calculated by weighted summation. The blockage risk index can be used to determine whether there is a blockage risk in the riser pipe 14.

[0065] Step S2: Based on the comparison between the congestion risk index and the preset state classification threshold, the operating state of relay warehouse 1 is classified. The operating states include stable operating state, slight aggregation state, early warning state, and high congestion risk state. The expression for classifying the operating state of relay warehouse 1 is as follows:

[0066] in To block the risk index, This indicates a stable operating state. This indicates a state of mild aggregation. This indicates an early warning status. This indicates a high risk of congestion. This is the threshold for state classification.

[0067] Determine whether to enter the optimization control phase based on the current status level, specifically: When relay warehouse 1 is in stable operation At this time, maintain the current running parameters and do not trigger optimization calculations; When relay warehouse 1 is in a state of slight aggregation At the same time, the feed distribution ratio and disturbance intensity are slightly adjusted by the flow distribution regulating device 2 and the turbulence nozzle 16; When relay warehouse 1 is in a state of alert and high congestion risk status At this time, it triggers subsequent representative model prediction and multi-objective optimization processes to obtain a new control variable vector.

[0068] Step S3: Define the control variable vector and the state vector. The coupled simulation unit 8 performs CFD-DEM coupled simulation on the relay warehouse 1 and constructs a representative model describing the mapping relationship between the control variable vector and the state vector. The specific steps are as follows: Define the control variable vector and the state vector as follows:

[0069]

[0070] in To control the variable vector, For the feed distribution ratio, For the adjustable air guide angle of the fairing 15, The adjustable flow guide opening ratio of the flow guide shroud 15 For the disturbance intensity, For state vectors, The particle segregation coefficient, For low-speed retention coefficient, This is the transport pulsation coefficient. Congestion risk index; Coupled simulation unit 8 performs CFD-DEM coupled simulation on relay warehouse 1, and after obtaining sample data of control variable vector and state vector, constructs a sample dataset. ,in For the i-th control variable vector sample data, This is the sample data for the i-th state vector; Construct a representative model describing the mapping relationship between the control variable vector and the state vector:

[0071] The representative model is represented by a parametric model as follows:

[0072] Where S is the state vector and X is the control variable vector. This is a representative model built based on regression methods. This is a representative model built based on machine learning methods; Establish a dynamic prediction model based on a parametric model:

[0073] in Let be the state vector at time t+1. Let be the state vector at time t. Let be the vector of control variables at time t.

[0074] To achieve rapid prediction of the control variable vector to the state vector, the coupled simulation unit 8 constructs a representative model after acquiring sample data based on CFD-DEM simulation. The representative model is used to describe the mapping relationship between the control variable vector and the state vector. At the same time, the representative model can be represented by a parametric model. On this basis, a dynamic prediction model is also established to replace the high computational cost simulation, realize the rapid prediction of the control variable vector to the state vector, and provide the objective function evaluation basis for subsequent multi-objective optimization solution.

[0075] Step S4: When the operating state is a warning state or a high congestion risk state, the optimization control unit 5 constructs a multi-objective optimization model based on the representative model and outputs the optimal control variable vector. The specific steps are as follows: A multi-objective optimization model is established based on the state vector mapped from the control variable vector output by the representative model:

[0076] in To control the particle clustering coefficient under the variable vector mapping, To control the low-speed retention coefficient under the vector mapping of variables, To control the transport pulsation coefficient under the vector mapping of variables, The target vector; The Pareto optimal solution set of the control variable vector is calculated based on the NSGA-II algorithm combined with the objective vector:

[0077] in , which is the Pareto optimal solution set; Select the optimal control variable vector from the Pareto optimal solution set. .

[0078] When the relay chamber 1 is in a warning state or a high-congestion-risk state, a multi-objective optimization calculation is initiated. By performing numerical simulations on different parameter combinations, the flow field and particle distribution characteristics inside the relay chamber 1 are obtained and used for subsequent multi-objective optimization calculations. The state vector under the control variable vector mapping is calculated through a representative model, and this is used as the fitness function of the NSGA-II algorithm for optimization. The output of the representative model is used as the input of the fitness function of the multi-objective optimization algorithm to drive the optimization calculation process.

[0079] Step S5: Obtain the target state vector based on the optimal control variable vector and the representative model. Update the control variable vector based on the target state to obtain the updated control variable vector. The specific steps are as follows: Substitute the optimal control variable vector into the parameterized model to calculate the target state vector:

[0080] in Let be the target state vector. This is the optimal control variable vector.

[0081] The control variable vector is updated by combining the target state vector to obtain the updated control variable vector. The formula for calculating the updated control variable vector is as follows:

[0082] in For the updated control variable vector, To control the variable vector, For the adjustment coefficient matrix, Let be the target state vector. This is the state vector.

[0083] After selecting the optimal control variable vector from the Pareto optimal solution set, the state vector under the optimal control variable vector, i.e. the target state vector, can be calculated based on the parameterized model. Then, the difference between the target state vector and the state vector is calculated as the state deviation. The state deviation is used as a feedback signal to drive the control variables to be smoothly iterated and updated from the current value, thereby obtaining the updated control variable vector and realizing a stable transition to the optimal control objective.

[0084] Step S6: The actuator 6 drives and controls the flow distribution adjustment device 2, the adjustable guide shroud 15, and the turbulence nozzle 16 according to the updated control variable vector. The specific steps are as follows: Extract the updated values ​​of feed ratio, guide angle, guide opening ratio, and disturbance intensity from the updated control variable vector: The formula for calculating the updated feed ratio value is:

[0085] in Let be the feed flow rate of the j-th feed inlet at time t. Total feed flow rate

[0086] The flow rate allocation ratio for the j-th feed inlet; The formula for calculating the updated guide angle value is:

[0087] in Update the value for the guide angle. The guide angle at the previous moment, To adjust the gain coefficient for the guide angle, The low-speed dwell coefficient at the current moment. The threshold for the low-speed dwell coefficient; The formula for calculating the updated value of the flow guide opening ratio is:

[0088] in This is the updated value for the guide opening ratio. The initial guide opening ratio, The gain coefficient is adjusted to accommodate the aperture ratio. The particle segregation coefficient at the current moment. This is the threshold value for the particle segregation coefficient; The formula for calculating the updated disturbance intensity value is as follows:

[0089] in This is the updated value for the disturbance intensity. As the reference disturbance strength, This represents the perturbation gain corresponding to the particle segregation coefficient. This represents the disturbance gain corresponding to the low-speed dwell coefficient. The actuator 6 drives the flow distribution adjustment device 2 according to the updated feed ratio value, drives the adjustable guide shroud 15 according to the updated guide angle value and the updated guide opening ratio value, and drives the turbulence nozzle 16 according to the updated disturbance intensity value.

[0090] The guiding angle is adjusted according to the low-velocity retention coefficient. When the low-velocity retention coefficient increases, the guiding angle decreases to enhance particle aggregation. When the low-velocity retention coefficient decreases, the guiding angle is appropriately increased to maintain uniform transport. The guiding opening ratio is continuously adjusted according to the transport state to reduce low-velocity retention and particle agglomeration. The disturbance intensity is dynamically calculated based on the system transport characteristics and is determined by both the degree of particle agglomeration and the degree of low-velocity retention. The larger the two indicators are, the greater the disturbance intensity, thereby achieving the function of dispersing sediment and improving agglomeration. By changing the distribution ratio of each feed, the symmetry of the flow field in the bin can be adjusted to reduce agglomeration and retention.

[0091] After adjusting the feed flow distribution, the opening and closing of the adjustable guide shroud 15, and the intensity of the turbulence nozzle 16 according to the updated control variable vector, the operating status of particle distribution, local flow velocity, and pressure drop in the relay chamber 1 changes accordingly, and is re-collected by the state monitor 3 as input for the next round of state calculation and optimization control.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A stable transportation and control system based on a deep-sea ore transportation relay warehouse, characterized in that, The system includes a relay chamber, a flow distribution and adjustment device, a status monitor, an optimization control unit, an actuator, a flow turbulence and guidance unit, and a coupling simulation unit. The relay chamber includes a main body, a feed inlet, a lifting outlet, a lifting pipe, and a suction inlet. The flow turbulence and guidance unit includes an adjustable flow guide shroud and flow turbulence nozzles. The feed inlets are symmetrically arranged on both sides of the top of the main body. The lifting pipe is located inside the main body, with its top end extending outside the main body and connecting to the lifting outlet. The suction inlet is located at the bottom of the lifting pipe. The adjustable flow guide shroud is located on the outer wall of the lifting pipe and above the suction inlet. The flow turbulence nozzles are symmetrically arranged on both sides of the bottom of the relay chamber. The flow distribution and adjustment device is connected to the feed inlet. The optimization control unit is connected to the status monitor, the actuator, and the coupling simulation unit. The actuator is connected to the flow distribution and adjustment device, the adjustable flow guide shroud, and the flow turbulence nozzles.

2. The stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 1, characterized in that, The adjustable flow guide includes a stator, a rotor, fan blades, a support base, and a driven connector. The stator is disposed on the outer wall of the riser tube, the rotor is disposed on the stator, the support base is spaced apart on the outer wall of the rotor, one end of the driven connector is hinged to the support base, and the other end is hinged to the fan blades. The fan blades are distributed circumferentially along the outer side of the rotor, and their outer surface is provided with flow guide ribs. The actuator is connected to the rotor.

3. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 1, characterized in that, It also includes a control method, which comprises the following steps: Step S1: The condition monitor collects the particle volume fraction, local flow velocity and pipeline pressure drop in the body, and calculates the particle agglomeration coefficient, low-velocity retention coefficient, transport pulsation coefficient and blockage risk index respectively. Step S2: Based on the comparison between the congestion risk index and the preset state classification threshold, the operation status of the relay warehouse is classified into stable operation status, mild clustering status, early warning status and high congestion risk status. Step S3: Define the control variable vector and the state vector. The coupled simulation unit performs CFD-DEM coupled simulation on the relay warehouse and constructs a representative model describing the mapping relationship between the control variable vector and the state vector. Step S4: When the operating status is a warning state or a high congestion risk state, the optimization control unit constructs a multi-objective optimization model based on the representative model and outputs the optimal control variable vector; Step S5: Obtain the target state vector based on the optimal control variable vector and the representative model, update the control variable vector based on the target state, and obtain the updated control variable vector. Step S6: The actuator drives and controls the flow distribution adjustment device, the adjustable guide shroud, and the turbulence nozzle according to the updated control variable vector.

4. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, The specific steps of step S1 are as follows: The condition monitor collects particle volume fraction. Local flow velocity and the pressure drop of the riser pipe ; Calculate the particle segregation coefficient : in The standard deviation of particle volume fraction. This represents the average particle volume fraction. Calculate the low-speed retention coefficient : in For flow rate threshold, This represents the number of particles with local flow velocities below a flow velocity threshold. This represents the total number of particles; Calculate the transport pulsation coefficient : in The standard deviation of pipeline pressure drop. This represents the average pressure drop across the pipeline. Calculate the congestion risk index: in For the weighting coefficients, satisfying .

5. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, The expression for classifying the operational status of the relay warehouse in step S2 is as follows: in To block the risk index, This indicates a stable operating state. This indicates a state of mild aggregation. This indicates an early warning status. This indicates a high risk of congestion. This is the threshold for state classification.

6. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, When the relay bin is in a stable operating state, the current operating parameters are maintained; when the relay bin is in a slightly segregated state, the feed distribution ratio and disturbance intensity are slightly adjusted through the flow distribution adjustment device and the turbulence nozzle.

7. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, The specific steps of step S3 are as follows: Define the control variable vector and the state vector as follows: in To control the variable vector, For the feed distribution ratio, For the adjustable air deflector angle, For the adjustable flow deflector opening ratio, For the disturbance intensity, For state vectors, The particle segregation coefficient, For low-speed retention coefficient, This is the transport pulsation coefficient. Congestion risk index; The coupled simulation unit performs CFD-DEM coupled simulation on the relay warehouse, and after obtaining sample data of control variable vectors and state vectors, constructs a sample dataset. ,in For the i-th control variable vector sample data, This is the sample data for the i-th state vector; Construct a representative model describing the mapping relationship between the control variable vector and the state vector: The representative model is represented by a parametric model as follows: Where S is the state vector and X is the control variable vector. This is a representative model built based on regression methods. This is a representative model built based on machine learning methods; Establish a dynamic prediction model based on a parametric model: in Let be the state vector at time t+1. Let be the state vector at time t. Let be the vector of control variables at time t.

8. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, The specific steps of step S4 are as follows: A multi-objective optimization model is established based on the state vector mapped from the control variable vector output by the representative model: in To control the particle clustering coefficient under the variable vector mapping, To control the low-speed retention coefficient under the vector mapping of variables, To control the transport pulsation coefficient under the vector mapping of variables, The target vector; The Pareto optimal solution set of the control variable vector is calculated based on the NSGA-II algorithm combined with the objective vector: in , which is the Pareto optimal solution set; Select the optimal control variable vector from the Pareto optimal solution set. .

9. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 7, characterized in that, The specific steps of step S5 are as follows: Substitute the optimal control variable vector into the parameterized model to calculate the target state vector: in Let be the target state vector. The optimal control variable vector; The control variable vector is updated by combining the target state vector to obtain the updated control variable vector. The formula for calculating the updated control variable vector is as follows: in For the updated control variable vector, To control the variable vector, For the adjustment coefficient matrix, Let be the target state vector. This is the state vector.

10. A stable transportation and control system based on a deep-sea ore transportation relay warehouse according to claim 3, characterized in that, The specific steps of step S6 are as follows: Extract the updated values ​​of feed ratio, guide angle, guide opening ratio, and disturbance intensity from the updated control variable vector: The formula for calculating the updated feed ratio value is: in Let be the feed flow rate of the j-th feed inlet at time t. Total feed flow rate The flow rate allocation ratio for the j-th feed inlet; The formula for calculating the updated guide angle value is: in Update the value for the guide angle. The guide angle at the previous moment, To adjust the gain coefficient for the guide angle, The low-speed dwell coefficient at the current moment. The threshold for the low-speed dwell coefficient; The formula for calculating the updated value of the flow guide opening ratio is: in This is the updated value for the guide opening ratio. The initial guide opening ratio, The gain coefficient is adjusted to accommodate the aperture ratio. The particle segregation coefficient at the current moment. This is the threshold value for the particle segregation coefficient; The formula for calculating the updated disturbance intensity value is as follows: in This is the updated value for the disturbance intensity. As the reference disturbance strength, This represents the perturbation gain corresponding to the particle segregation coefficient. This represents the disturbance gain corresponding to the low-speed dwell coefficient. The actuator drives and controls the flow distribution adjustment device according to the updated feed ratio value, drives and controls the adjustable guide shroud according to the updated guide angle value and the updated guide opening ratio value, and drives and controls the turbulence nozzle according to the updated disturbance intensity value.

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