Optimization method of spiral flow channel type hydrate slurry multiphase cyclone separation device

Abstract

The invention relates to a spiral flow channel type hydrate slurry multiphase cyclone separation device optimization method which is applied to the technical field of hydrate slurry multiphase separation and comprises the steps that the device structure and structure parameters of a target spiral flow channel type hydrate slurry multiphase cyclone separation device are obtained; constructing an initial structure geometric model based on the device structure; constructing a data sample space based on the structure parameters and a preset single factor change scheme; performing numerical simulation modeling based on the initial geometric model, the data sample space and a preset model to generate a CFD simulation model; determining significance geometric parameters based on a preset analysis method and the CFD simulation model; performing optimization processing on the significance geometric parameters based on preset optimization design to generate efficiency prediction data; and carrying out optimization design on the efficiency prediction data based on a genetic algorithm so as to adjust the initial structure geometric model. The hydrate slurry multi-phase spiral flow channel type hydrate slurry multi-phase cyclone separation device has the effect of improving the separation efficiency of each phase of the hydrate slurry multi-phase spiral flow channel type hydrate slurry multi-phase cyclone separation device.

Description

Technical Field

[0001] The present application relates to the technical field of multiphase separation of hydrate slurries, and in particular to an optimization method for a spiral flow channel type multiphase cyclone separation device for hydrate slurries. Background Art

[0002] Natural gas hydrates are a clean and pollution-free unconventional strategic energy source. Their large-scale development plays a key role in optimizing the energy consumption structure and promoting the development of a low-carbon economy. In view of the special occurrence status of natural gas hydrate reservoirs, which are shallow, non-diagenetic, weakly cemented, fragile and lack dense layer coverage, a solid-state fluidized bed mining method is adopted. Through mechanical crushing, seabed sand removal, fluidized bed lifting and other processes, the controllable mining of shallow non-diagenetic natural gas hydrates in the South China Sea has been achieved. One of the key technologies is the need to separate and backfill the mud and sand in the natural gas hydrate mixed slurry in real time.

[0003] Most existing mining methods are dedicated to the desanding and purification of slurry in solid-state fluidized mining of natural gas hydrates. For example, casing, power fluid pipes, swirl baffles, recovery mechanisms, and sand discharge mechanisms are set up to separate mud and sand from natural gas hydrates for purification. However, simple post-mining purification treatment cannot fundamentally improve the overall efficiency. Therefore, there is an urgent need for a spiral flow channel hydrate slurry multiphase cyclone separation device that can improve the separation efficiency of each phase of hydrate slurry. Summary of the Invention

[0004] In order to improve the separation efficiency of each phase of a spiral flow channel hydrate slurry multiphase cyclone separation device, the present application provides an optimization method for a spiral flow channel hydrate slurry multiphase cyclone separation device.

[0005] In a first aspect, the present application provides a method for optimizing a spiral flow channel hydrate slurry multiphase cyclone separation device, which adopts the following technical solution:

[0006] A method for optimizing a spiral flow channel hydrate slurry multiphase cyclone separation device, comprising:

[0007] Obtaining the device structure and structural parameters of a target spiral flow channel type hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section, and an underflow pipe;

[0008] constructing an initial structural geometric model based on the device structure;

[0009] Constructing a data sample space based on the structural parameters and a preset single-factor variation scheme;

[0010] Perform numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model;

[0011] Determining significant geometric parameters in the data sample space based on a preset analysis method and the CFD simulation model;

[0012] Optimizing the significant geometric parameters based on a preset optimization design to generate efficiency prediction data;

[0013] Optimizing the efficiency prediction data based on a genetic algorithm to generate an optimal design solution;

[0014] The initial structural geometric model is adjusted based on the optimal design solution to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

[0015] By adopting the above technical solution, the target spiral flow channel type hydrate slurry multiphase cyclone separation device is set to a structure consisting of an overflow pipe, a spiral flow channel, a flow stabilizing cone, a cylindrical section, a conical section and an underflow pipe. The hydrate slurry is separated by the above structure, and simulation experiments are carried out using data sample space and a preset model. The parameters corresponding to the device structure are adjusted based on the experimental data, so that separation can be carried out most efficiently under the parameters of the optimal design scheme, thereby improving the separation efficiency of each phase of the hydrate slurry multiphase spiral flow channel type hydrate slurry multiphase cyclone separation device.

[0016] Optionally, performing numerical simulation modeling based on the initial geometric model, the data sample space, and a preset model to generate a CFD simulation model includes:

[0017] The target hydrate slurry is simplified to generate a multiphase flow model of mud-sand-hydrate-natural gas-seawater;

[0018] Performing transformation adjustment on the initial geometric model based on the data sample space to generate an adjusted geometric model;

[0019] A CFD simulation model reflecting the flow space of the mud-hydrate-natural gas-seawater multiphase flow model within the adjusted geometric model is constructed based on the mixed multiphase flow model and the Reynolds stress turbulence model.

[0020] Optionally, determining the influencing geometric parameters in the data sample space based on a preset analysis method and the CFD simulation model includes:

[0021] Perform separation simulation based on the CFD simulation model to generate separation efficiency of mud, sand, hydrate and natural gas;

[0022] Calculating the quantitative rate of the three-phase separation efficiency of mud, sand, hydrate and natural gas based on a preset analysis method, and generating a quantitative rate result list;

[0023] The saliency geometric parameters are determined based on the quantization rate result list and preset selection rules.

[0024] Optionally, the preset optimization design includes a steepest climb design, a curved surface response design, and an additional experimental design, and the optimizing the significant geometric parameters based on the preset optimization design to generate efficiency prediction data includes:

[0025] Determining the variable center point of the significant geometric parameter based on the steepest climb design;

[0026] Determining the data relationship between the significant geometric parameters and the three-phase separation efficiency of mud, sand, hydrate and natural gas based on the variable center point and the surface response design;

[0027] The data relationship is calibrated and verified based on the additional experimental design to generate efficiency prediction data.

[0028] Optionally, the determining of the data relationship between the significant geometric parameter and the three-phase separation efficiency of mud, sand, hydrate, and natural gas based on the variable center point and the surface response design includes:

[0029] The significant geometric parameters are defined as x1, x2, x3, and x4 respectively;

[0030] The three-phase separation efficiencies of mud, hydrate and natural gas are defined as y1, y2 and y3 respectively;

[0031] Based on the least squares method and multiple linear regression, a multiple quadratic regression equation between x1, x2, x3, x4 and y1, y2, y3 is obtained, and the multiple quadratic regression equation is used as the data relationship.

[0032] Optionally, the optimizing design of the efficiency prediction data based on a genetic algorithm to generate an optimal design solution includes:

[0033] Get the target number of iterations;

[0034] Bringing the efficiency prediction data into the genetic algorithm for iterative calculation, and recording the number of iterations;

[0035] When the number of iterations reaches the target number of iterations, the iterative calculation is stopped to obtain calculation efficiency prediction data;

[0036] Optimization adjustment parameters are determined based on the computational efficiency prediction data, and the optimization adjustment parameters are used as the optimal design solution.

[0037] Optionally, adjusting the initial structural geometric model based on the optimal design solution includes:

[0038] Determining a target adjustment structure and target adjustment data based on the optimal design solution and the structural parameters;

[0039] The initial geometric model is adjusted based on the target adjustment structure and the target adjustment data.

[0040] In a second aspect, the present application provides an optimization system for a spiral flow channel hydrate slurry multiphase cyclone separation device, which adopts the following technical solutions:

[0041] A spiral flow channel hydrate slurry multiphase cyclone separation device optimization system, comprising:

[0042] a device data acquisition module for acquiring the device structure and structural parameters of a target spiral flow channel hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section, and an underflow pipe;

[0043] An initial model building module, used for building an initial structural geometric model based on the device structure;

[0044] A data sample construction module, used for constructing a data sample space based on the structural parameters and a preset single factor change scheme;

[0045] A simulation model generation module is used to perform numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model;

[0046] An influencing parameter determination module, configured to determine significant geometric parameters in the data sample space based on a preset analysis method and the CFD simulation model;

[0047] A prediction data generation module, configured to optimize the influencing geometric parameters based on a preset optimization design to generate efficiency prediction data;

[0048] An optimal solution generation module is used to optimize the efficiency prediction data based on a genetic algorithm to generate an optimal design solution;

[0049] The device optimization and adjustment module is used to adjust the initial structural geometric model based on the optimal design scheme to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

[0050] By adopting the above technical solution, the target spiral flow channel hydrate slurry multiphase cyclone separation device is set to a structure consisting of an overflow pipe, a spiral flow channel, a flow stabilizing cone, a cylindrical section, a conical section and an underflow pipe. The hydrate slurry is separated by the above structure, and simulation experiments are carried out using data sample space and a preset model. The parameters corresponding to the device structure are adjusted based on the experimental data, so that separation can be carried out most efficiently under the parameters of the optimal design scheme, thereby improving the separation efficiency of each phase of the spiral flow channel hydrate slurry multiphase cyclone separation device.

[0051] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0052] An electronic device comprising a processor coupled to a memory;

[0053] The processor is used to execute the computer program stored in the memory, so that the electronic device executes the computer program of the method for optimizing the spiral flow channel hydrate slurry multiphase cyclone separation device as described in any one of the first aspects.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0055] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the method for optimizing the spiral flow channel hydrate slurry multiphase cyclone separation device according to any one of the first aspects.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] The target spiral flow channel hydrate slurry multiphase cyclone separation device is set as a structure consisting of an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section and an underflow pipe. The hydrate slurry is separated by the above structure. Simulation experiments are carried out using data sample space and a preset model. The parameters corresponding to the device structure are adjusted based on the experimental data, so that the separation can be carried out most efficiently under the parameters of the optimal design scheme, thereby improving the separation efficiency of each phase of the spiral flow channel hydrate slurry multiphase cyclone separation device. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of a method for optimizing a spiral flow channel hydrate slurry multiphase cyclone separation device provided in an embodiment of the present application.

[0059] Figure 2 This is an initial structural diagram of a spiral flow channel hydrate slurry multiphase cyclone separation device provided in an embodiment of the present application.

[0060] Figure 3This is a diagram of the initial structural geometry model of a spiral flow channel hydrate slurry multiphase cyclone separation device provided in an embodiment of the present application.

[0061] Figure 4 Schematic diagram of the relationship between the hypervolume fraction HV value and the number of iterations provided in the embodiment of the present application.

[0062] Figure 5 It is a schematic diagram of the relationship between the separation efficiencies of each phase of the Pareto optimal solution provided in the embodiment of the present application.

[0063] Figure 6 It is a cloud diagram of the mud and sand volume distribution of the initial scheme and the optimal design scheme of a spiral flow channel hydrate slurry multiphase cyclone separation device provided in the embodiment of the present application.

[0064] Figure 7 This is a structural block diagram of an optimization system for a spiral flow channel hydrate slurry multiphase cyclone separation device provided in an embodiment of the present application.

[0065] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0066] Explanation of the accompanying symbols: 1. Overflow pipe; 2. Spiral flow channel; 3. Flow stabilization cone; 4. Cylindrical section; 5. Conical section; 6. Underflow pipe. DETAILED DESCRIPTION

[0067] The present application is further described in detail below with reference to the accompanying drawings.

[0068] The present application provides an optimization method for a spiral-channel hydrate slurry multiphase cyclone separation device. The method can be performed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0069] Figure 1 A schematic flow chart of a method for optimizing a spiral flow channel hydrate slurry multiphase cyclone separation device provided in an embodiment of the present application.

[0070] like Figure 1 As shown, the main process of the method is described as follows (steps S101 to S108):

[0071] Step S101, obtaining the device structure and structural parameters of a target spiral flow channel type hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section and an underflow pipe.

[0072] In this embodiment, referring to Figure 2 As shown, the overall structure of the target spiral flow channel hydrate slurry multiphase cyclone separation device includes six parts: an overflow pipe 1, a spiral flow channel 2, a flow stabilizing cone 3, a cylindrical section 4, a cone section 5 and an underflow pipe 6, wherein the spiral flow channel 2 is composed of spiral blades.

[0073] Step S102: constructing an initial structural geometric model based on the device structure.

[0074] In this embodiment, the overflow pipe 1, spiral flow channel 2, flow stabilizing cone 3, cylindrical section 4, cone section 5 and underflow pipe 6 are set with initial structural parameters according to the separation requirements, and then the simulation model is established using the initial structural parameters. Figure 3 The initial structural geometry model is shown.

[0075] Step S103: constructing a data sample space based on the structural parameters and the preset single factor variation scheme.

[0076] In this embodiment, the structural parameters are initial data obtained in the initial stage of the design scheme based on experimental data and existing separation data. The structural parameters include the bottom flow port diameter ds, the overflow pipe diameter d0, the pitch B, the number of spiral blade turns n, the number of spiral blade heads m, the cone section angle α, the overflow pipe insertion depth h, and the cylindrical section length H. Table 1 shows the structural parameters of the initial design scheme of the spiral flow channel hydrate slurry multiphase cyclone separation device, where the initial values ​​are the values ​​of the structural parameters used to create the initial structural geometric model.

[0077]

[0078] Table 1

[0079] After obtaining the structural parameters of the initial design scheme, the single-factor change method is used, that is, controlling one factor as a variable and the other factors as quantitative, to construct the data sample space, thereby obtaining data groups with various combination patterns. When using the single-factor change method to create the data sample space, the data value corresponding to each structural parameter is a range value, thereby generating the data sample space shown in Table 2.

[0080]

[0081] Table 2

[0082] Step S104 , performing numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model.

[0083] For step S104, the target hydrate slurry is simplified to generate a mud-sand-hydrate-natural gas-seawater multiphase flow model; the initial geometric model is transformed and adjusted based on the data sample space to generate an adjusted geometric model; and a CFD simulation model reflecting the flow space of the multiphase flow model within the adjusted geometric model is constructed based on the mixed multiphase flow model and the Reynolds stress turbulence model.

[0084] In this embodiment, before creating the CFD simulation model, the hydrate slurry is simplified into a solid-solid-gas-liquid multiphase flow model containing mud, hydrate, natural gas and seawater. According to the flow field characteristics of the spiral flow channel hydrate slurry multiphase cyclone separation device, after the hydrate slurry enters the spiral flow channel hydrate slurry multiphase cyclone separation device and undergoes the diversion and stabilization of the spiral flow channel, the natural gas phase with the smallest density is squeezed into the innermost layer of the spiral flow channel, swirls downward along the outer wall of the overflow pipe, and is discharged from the overflow port under the action of the internal vortex and the circulating flow; the mud and sand phase and the hydrate phase are pre-separated by the spiral flow channel and enter the cylindrical section and conical section of the separation device. The mud and sand phase swirls downward along the inner wall of the separation device along with the external vortex and is discharged from the bottom flow port, and the hydrate phase swirls upward along the axis along the internal vortex and is discharged from the overflow port. Based on the mud-hydrate-natural gas-seawater multiphase flow in the cyclonic flow field.

[0085] Based on the above flow trends, the initial geometric model is transformed and adjusted according to the data values ​​in the data sample space to generate multiple adjusted geometric models. The mixed multiphase flow model is used to calculate the continuity and momentum of the fluid in the spiral flow channel hydrate slurry multiphase cyclone separation device within a certain time period. The continuity calculation equation and momentum calculation equation are:

[0086] ,and

[0087] ;

[0088] Wherein, ρ is the density of hydrate slurry, in kg / m3; t is the time, in s; u, υ, w are the components of the velocity vector in the x, y and z directions, in m / s; P is the pressure acting on the hydrate slurry microelement, in MPa; τ ij is the stress tensor, with the unit of MPa; Fi is the volume force acting on the hydrate slurry microelement, with the unit of N.

[0089] Afterwards, considering that the Reynolds stress turbulence model has higher calculation accuracy in the simulation of strong vortex and can simulate the flow field more accurately, the turbulence model is the Reynolds stress model for analysis. The Reynolds stress equation is:

[0090] ;

[0091] in, is the time rate of change of Reynolds stress; C ij is the convection term; D T,ij is the turbulent diffusion term; D L,ij is the molecular viscous diffusion term; P ij is the shear stress generating term; G ij is the buoyancy generation term; φ ij is the pressure strain term; ε ij is the pressure strain term; F ij Generates terms for system rotations.

[0092] Based on the calculation results, the numerical simulation control conditions of the spiral flow channel hydrate slurry multiphase cyclone separation device were set, and numerical simulation was carried out to generate a CFD simulation model that reflects the flow space of the mud-hydrate-natural gas-seawater multiphase flow model within the adjusted geometric model.

[0093] Step S105 : determining influencing geometric parameters in the data sample space based on a preset analysis method and a CFD simulation model.

[0094] For step S105, a separation simulation is performed based on the CFD simulation model to generate the separation efficiency of mud, hydrate, and natural gas; the quantization rate of the three-phase separation efficiency is calculated based on the preset analysis method to generate a quantization rate result list; and the significant geometric parameters are determined based on the quantization rate result list and the preset selection rules.

[0095] In this embodiment, the separation efficiency of the hydrate slurry during separation is simulated according to the CFD simulation model. The three-phase separation efficiency includes the mud-sand separation efficiency Es, the hydrate separation efficiency Eh, and the natural gas separation efficiency En. A sensitivity analysis is performed on eight factors of the spiral flow channel hydrate slurry multiphase cyclone separation device, namely, the bottom flow port diameter ds, the overflow pipe diameter d0, the pitch B, the number of spiral blade turns n, the number of spiral blade heads m, the cone section angle α, the overflow pipe insertion depth h, and the cylindrical section length H. The analysis is conducted to determine which of the eight factors will have a greater impact on the separation efficiency in the actual separation process.

[0096] Specifically, a quantitative analysis method is used to quantitatively analyze the influence of various structural parameters on the mud-sand separation efficiency Es, hydrate separation efficiency Eh, and natural gas separation efficiency En of the target spiral flow channel hydrate slurry multiphase cyclone separation device, that is, the quantification rate. The calculation formula of the quantification rate is:

[0097] ;

[0098] Wherein, Q is the quantization rate; Ki is the sensitivity of any structural parameter to the separation efficiency of each phase of the spiral flow channel hydrate slurry multiphase cyclone separation device; K is the sum of the sensitivities of the structural parameters to the separation efficiency of each phase of the spiral flow channel hydrate slurry multiphase cyclone separation device.

[0099] The quantization ratios of the structural parameters to the mud-sand separation efficiency Es, hydrate separation efficiency Eh, and natural gas separation efficiency En were analyzed to determine the magnitude of the structural parameter quantization ratios. The quantization ratios of all parameters were sorted in descending order of numerical value, and the top n influencing geometric parameters were selected according to a preset selection rule. In this embodiment, the underflow port diameter ds, overflow pipe diameter d0, screw pitch B, and cone angle α were selected as the influencing geometric parameters. It should be noted that after the influencing geometric parameters were determined, only these influencing geometric parameters were numerically transformed and calculated in the subsequent verification process, while the other structural parameters remained unchanged.

[0100] Step S106 , optimizing the influencing geometric parameters based on the preset optimization design to generate efficiency prediction data.

[0101] For step S106, the variable center point of the significant geometric parameter is determined based on the steepest climb design; the data relationship between the significant geometric parameter and the three-phase separation efficiency is determined based on the variable center point and the surface response design; the data relationship is calibrated and verified based on the additional experimental design to generate efficiency prediction data.

[0102] Furthermore, the data relationship between the influencing geometric parameters and the four separation efficiencies is determined based on the variable center point and surface response design, including: defining the variable center points as x1, x2, x3, and x4 respectively; defining the separation efficiencies of mud, hydrate, and natural gas as y1, y2, and y3 respectively; and deriving the multivariate quadratic regression equation between x1, x2, x3, and x4 and y1, y2, and y3 based on the least squares method and multivariate linear regression, and using the multivariate quadratic regression equation as the data relationship.

[0103] In this embodiment, the preset optimization design includes steepest climb design, surface response design and additional test design. The steepest climb design, surface response design and additional test design are used to optimize the influencing geometric parameters and generate efficiency prediction data.

[0104] First, the variable center points of the significant geometric parameters are determined through the steepest climbing design. The variable center points of the significant geometric parameters are shown in Table 3.

[0105]

[0106] Table 3

[0107] Based on the calculated variable center points, a BBD experimental design was conducted with the mud-sand separation efficiency Es, hydrate separation efficiency Eh, and natural gas separation efficiency En as response functions. A total of 29 experimental groups were generated. The BBD experimental design results were obtained through CFD simulation. The significant geometric parameters (i.e., underflow diameter ds, overflow pipe diameter d0, screw pitch B, and cone angle α) were defined as x1, x2, x3, and x4, respectively. The three-phase separation efficiencies (i.e., mud-sand separation efficiency Es, hydrate separation efficiency Eh, and natural gas separation efficiency En as response functions) were defined as y1, y2, and y3, respectively. The residual data for the mud-sand separation efficiency, hydrate separation efficiency, and natural gas separation efficiency were analyzed for their normal distributions to verify the accuracy of the design variables and the fitting data for the response targets.

[0108] The least squares method is used for fitting, and the multivariate quadratic regression equations between the design variables x1, x2, x3, x4 and the response functions y1, y2, y3 are obtained through multivariate linear regression. The multivariate quadratic regression equations are:

[0109] ;

[0110] ;

[0111] .

[0112] In the BBD experimental design parameter space, 9 groups of structural parameter combinations were randomly designed for additional experiments. The relative errors between the predicted values ​​of each phase separation efficiency and the actual values ​​of the numerical simulation were analyzed, and the accuracy of the prediction model was evaluated using the average relative error. The formulas for relative error and average relative error are:

[0113] ;

[0114] ;

[0115] Among them, ε is the relative error between the predicted value and the numerical simulation value; Δε is the average relative error between the predicted value and the numerical simulation value; p is the calculated value of the prediction model; s is the numerical simulation value; i is the serial number of the additional test group; n is the number of additional test groups, thereby completing the calibration and verification of the data relationship and generating efficiency prediction data.

[0116] Step S107: Optimize the efficiency prediction data based on a genetic algorithm to generate an optimal design solution.

[0117] For step S107, obtain the target number of iterations; bring the efficiency prediction data into the genetic algorithm for iterative calculation, and record the number of iterations; when the number of iterations reaches the target number of iterations, stop the iterative calculation and obtain the calculation efficiency prediction data; determine the optimization adjustment parameters based on the calculation being less than the prediction data, and use the optimization adjustment parameters as the optimal design solution.

[0118] In this embodiment, in order to comprehensively improve the efficiency of mud-sand separation, hydrate separation efficiency, and natural gas separation efficiency, a multi-objective optimization framework is constructed based on the NSGA-III genetic algorithm and the efficiency prediction data obtained by the above calculations. The formula of the multi-objective optimization framework is:

[0119] ;

[0120] Among them, g1, g2, g3, and g4 are the value ranges of the bottom flow port diameter ds, the overflow pipe diameter d0, the pitch B, and the cone section angle α, respectively.

[0121] The parameters of the NSGA-III genetic algorithm are further set. A real number encoder is used to encode the influencing geometric parameters xi (i=1,2,3,4). The bottom flow port diameter ds is encoded as x1, the overflow pipe diameter d0 is encoded as x2, the pitch B is encoded as x3, and the cone angle α is encoded as x4. The optimization range of the influencing geometric parameters bottom flow port diameter ds (x1), overflow pipe diameter d0 (x2), pitch B (x3), and cone angle α (x4) is set. Generate the initial population. By randomly generating N individual initial populations, the population matrix composed of design variables is:

[0122] ;

[0123] Among them, the four columns of the matrix are the bottom flow port diameter, overflow pipe diameter, screw pitch and cone section angle.

[0124] Then, the quadratic polynomials of the mud-sand phase, hydrate phase, and natural gas phase obtained by the above calculation, namely the efficiency prediction data, are used as the calculation method of the fitness value. The fitness value corresponding to each individual, namely the separation efficiency, is added to the above formula to obtain the following formula:

[0125] ;

[0126] By simulating the binary crossover operator (SBX) and the polynomial mutation operator, the population is subjected to crossover, mutation, and recombination. After selection, crossover, and mutation operations, the resulting offspring population is combined with the parent population for iterative processing. The top N individuals are selected as genetic offspring through non-dominated sorting and reference point sorting, and a new parent population is generated by non-dominated sorting. The individuals in the population are divided into levels according to the dominance relationship. Through predefined reference points, a niche retention operation is performed to retain the better individuals in the same level.

[0127] In this application, the initial population is set to 180 and the number of iterations is set to 2400. In order to test the performance of the algorithm under this condition, the hypervolume index HV is used for testing. Figure 4The relationship between the number of iterations and the HV index shows that when the number of iterations exceeds 1800, the HV value no longer changes and the value is 1, indicating that the convergence, extensiveness and distribution of the solution set of the multi-objective algorithm are good.

[0128] After multi-objective optimization operation, we get Figure 5 The Pareto optimal solution set distribution is shown. The Pareto optimal solution set distribution exhibits a curved surface shape and is relatively uniformly distributed. Using the response function constraints of mud-sand phase separation efficiency Es ≥ 85%, hydrate phase separation efficiency Eh ≥ 95%, and natural gas phase separation efficiency Eg ≥ 75%, 171 solutions meeting these conditions were found. In this example, the solution with the highest mud-sand phase separation efficiency was selected as the final optimal solution.

[0129] Step S108 : adjusting the initial structural geometric model based on the optimal design solution to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

[0130] With respect to step S108 , a target adjustment structure and target adjustment data are determined based on the optimal design solution and the structural parameters; and the initial geometric model is adjusted based on the target adjustment structure and the target adjustment data.

[0131] The structural parameters corresponding to the optimal solution are: ds = 22.4 mm, ds is taken as 22.5 mm; d0 = 32.1 mm, rounded to 32 mm; B = 32.1 mm, rounded to 32 mm; α = 6.1°, rounded to 6°. According to the rounded structural parameters and the values ​​of the remaining structural parameters, the final geometric model of the spiral flow channel hydrate slurry multiphase cyclone separation device was constructed. CFD simulation was carried out to obtain the structural parameters and separation efficiency comparison of the initial scheme and the final optimized scheme of the spiral flow channel hydrate slurry multiphase cyclone separation device. The structural parameters and separation efficiency of each phase of the initial scheme and the optimal design scheme are generated as shown in Table 4.

[0132]

[0133] Table 4

[0134] The separation efficiency of mud-sand phase and natural gas phase of the optimal design of spiral channel hydrate slurry multiphase cyclone separation device is significantly improved by about 7.6% and 29.4% respectively. The volume concentration distribution of mud-sand phase of the initial structure and the optimal design is shown in the figure. Figure 6 As shown in the figure, the volume concentration of the mud and sand phase in the optimal design scheme is higher near the bottom flow pipe of the spiral flow channel hydrate slurry multiphase cyclone separation device.

[0135] Figure 7 A structural block diagram of a spiral flow channel hydrate slurry multiphase cyclone separation device optimization system 200 provided in an embodiment of the application.

[0136] like Figure 7 As shown, the spiral channel hydrate slurry multiphase cyclone separation device optimization device 200 mainly includes:

[0137] The device data acquisition module 201 is used to obtain the device structure and structural parameters of the target spiral flow channel type hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section, and an underflow pipe;

[0138] An initial model building module 202 is used to build an initial structural geometric model based on the device structure;

[0139] A data sample construction module 203 is used to construct a data sample space based on structural parameters and a preset single factor change scheme;

[0140] A simulation model generation module 204 is used to perform numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model;

[0141] An influencing parameter determination module 205 is used to determine significant geometric parameters in the data sample space based on a preset analysis method and a CFD simulation model;

[0142] The prediction data generation module 206 is used to optimize the influencing geometric parameters based on the preset optimization design to generate efficiency prediction data;

[0143] The optimal solution generation module 207 is used to optimize the efficiency prediction data based on the genetic algorithm and generate the optimal design solution;

[0144] The device optimization and adjustment module 208 is used to adjust the initial structural geometric model based on the optimal design solution to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

[0145] As an optional implementation of this embodiment, the simulation model generation module 204 is specifically used to simplify the target hydrate slurry to generate a multiphase flow model; transform and adjust the initial geometric model based on the data sample space to generate an adjusted geometric model; and construct a CFD simulation model that reflects the flow space of the four-phase flow model within the adjusted geometric model based on the mixed multiphase flow model and the Reynolds stress turbulence model.

[0146] As an optional implementation of this embodiment, the influencing parameter determination module 205 is specifically used to perform separation simulation based on the CFD simulation model to generate the three-phase separation efficiency of mud, hydrate and natural gas; calculate the quantization rate of the three-phase separation efficiency based on the preset analysis method to generate a quantization rate result list; determine the significant geometric parameters based on the quantization rate result list and preset selection rules.

[0147] As an optional implementation of this embodiment, the prediction data generation module 206 includes:

[0148] A variable center determination module is used to determine the variable center point of the significant geometric parameters based on the steepest climb design;

[0149] A data relationship determination module is used to determine the data relationship between significant geometric parameters and three-phase separation efficiency based on variable center points and surface response design;

[0150] The efficiency data generation module is used to calibrate and verify the data relationship based on the additional experimental design and generate efficiency prediction data.

[0151] In this optional embodiment, the data relationship determination module is specifically used to define the center points of the variables as x1, x2, x3, and x4 respectively; define the separation efficiency of mud, hydrate, and natural gas as y1, y2, and y3 respectively; and derive the multivariate quadratic regression equation between x1, x2, x3, and x4 and y1, y2, and y3 based on the least squares method and multivariate linear regression, and use the multivariate quadratic regression equation as the data relationship.

[0152] As an optional implementation of this embodiment, the optimal solution generation module 207 is specifically used to obtain the target number of iterations; bring the efficiency prediction data into the genetic algorithm for iterative calculation, and record the number of iterations; stop the iterative calculation when the number of iterations reaches the target number of iterations, and obtain the calculation efficiency prediction data; determine the optimization adjustment parameters based on the calculation being less than the prediction data, and use the optimization adjustment parameters as the optimal design solution.

[0153] As an optional implementation of this embodiment, the device optimization and adjustment module 208 is specifically used to determine the target adjustment structure and target adjustment data based on the optimal design solution and structural parameters; and adjust the initial geometric model based on the target adjustment structure and target adjustment data.

[0154] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0155] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0156] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] Figure 8 This is a structural block diagram of the electronic device 300 provided in an embodiment of the present application.

[0158] like Figure 8 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more communication components 304 , and a communication bus 305 .

[0159] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned method for optimizing the spiral flow channel hydrate slurry multiphase cyclone separation device. The memory 302 is used to store various types of data to support the operation of the electronic device 300. Such data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0160] The I / O interface 303 provides an interface between the processor 301 and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more thereof, therefore, the corresponding communication component 304 may include: Wi-Fi components, Bluetooth components, NFC components.

[0161] The electronic device 300 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the method for optimizing the spiral flow channel hydrate slurry multiphase cyclone separation device given in the above embodiment.

[0162] Communication bus 305 may include a path for transmitting information between the aforementioned components. Communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, for example. Communication bus 305 may be divided into an address bus, a data bus, a control bus, and the like.

[0163] The electronic device 300 may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., as well as fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0164] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the spiral flow channel hydrate slurry multiphase cyclone separation device are implemented.

[0165] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0166] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0167] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A method for optimizing a spiral channel hydrate slurry multiphase cyclone separation device, characterized in that: include: Obtaining the device structure and structural parameters of a target spiral flow channel type hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section, and an underflow pipe; constructing an initial structural geometric model based on the device structure; Constructing a data sample space based on the structural parameters and a preset single-factor variation scheme; Perform numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model; Determining significant geometric parameters in the data sample space based on a preset analysis method and the CFD simulation model; Optimizing the significant geometric parameters based on a preset optimization design to generate efficiency prediction data; Optimizing the efficiency prediction data based on a genetic algorithm to generate an optimal design solution; The initial structural geometric model is adjusted based on the optimal design solution to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

2. The method according to claim 1, characterized in that The performing of numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model includes: The target hydrate slurry is simplified to generate a mud-sand-hydrate-natural gas-seawater multiphase flow model; Performing transformation adjustment on the initial geometric model based on the data sample space to generate an adjusted geometric model; A CFD simulation model reflecting the flow space of the multiphase flow model within the adjusted geometric model is constructed based on the mixed multiphase flow model and the Reynolds stress turbulence model.

3. The method according to claim 1, characterized in that Determining the significant geometric parameters in the data sample space based on the preset analysis method and the CFD simulation model includes: Perform separation simulation based on the CFD simulation model to generate separation efficiency of mud, sand, hydrate and natural gas phases; Calculating the quantitative rate of the three-phase separation efficiency of mud, sand, hydrate and natural gas based on a preset analysis method, and generating a quantitative rate result list; The saliency geometric parameters are determined based on the quantization rate result list and preset selection rules.

4. The method according to claim 3, characterized in that in, The preset optimization design includes a steepest climb design, a surface response design, and an additional experimental design. The optimization processing of the significant geometric parameters based on the preset optimization design to generate efficiency prediction data includes: Determining the variable center point of the significant geometric parameter based on the steepest climb design; Determining the data relationship between the influencing geometric parameters and the three-phase separation efficiency of mud, sand, hydrate and natural gas based on the variable center point and the surface response design; The data relationship is calibrated and verified based on the additional experimental design to generate efficiency prediction data.

5. The method according to claim 4, characterized in that The data relationship between the significant geometric parameters and the four separation efficiencies determined based on the variable center point and the surface response design includes: The significant geometric parameters are defined as x1, x2, x3, and x4 respectively; The separation efficiencies of the three phases of mud, hydrate and natural gas are defined as y1, y2 and y3 respectively; Based on the least squares method and multiple linear regression, a multiple quadratic regression equation between x1, x2, x3, x4 and y1, y2, y3 is obtained, and the multiple quadratic regression equation is used as the data relationship.

6. The method according to claim 3, characterized in that The optimizing design of the efficiency prediction data based on the genetic algorithm to generate the optimal design solution includes: Get the target number of iterations; Bringing the efficiency prediction data into the genetic algorithm for iterative calculation, and recording the number of iterations; When the number of iterations reaches the target number of iterations, the iterative calculation is stopped, and the calculation efficiency and the corresponding significant geometric parameter prediction data are obtained; Optimization adjustment parameters are determined based on the computational efficiency prediction data, and the optimization adjustment parameters are used as the optimal design solution.

7. The method according to claim 6, characterized in that The adjusting the initial structural geometric model based on the optimal design solution includes: Determining a target adjustment structure and target adjustment data based on the optimal design solution and the structural parameters; The initial geometric model is adjusted based on the target adjustment structure and the target adjustment data.

8. An optimization system for a spiral flow channel hydrate slurry multiphase cyclone separation device, characterized in that: include: a device data acquisition module for acquiring the device structure and structural parameters of a target spiral flow channel hydrate slurry multiphase cyclone separation device, wherein the device structure includes an overflow pipe, a spiral flow channel, a flow stabilization cone, a cylindrical section, a cone section, and an underflow pipe; An initial model building module, used for building an initial structural geometric model based on the device structure; A data sample construction module, used to construct a data sample space based on the structural parameters and a preset single factor change scheme; A simulation model generation module is used to perform numerical simulation modeling based on the initial geometric model, the data sample space and the preset model to generate a CFD simulation model; An influencing parameter determination module, configured to determine significant geometric parameters in the data sample space based on a preset analysis method and the CFD simulation model; A prediction data generation module, configured to optimize the significant geometric parameters based on a preset optimization design to generate efficiency prediction data; An optimal solution generation module is used to optimize the efficiency prediction data based on a genetic algorithm to generate an optimal design solution; The device optimization and adjustment module is used to adjust the initial structural geometric model based on the optimal design scheme to generate an optimized spiral flow channel hydrate slurry multiphase cyclone separation device.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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