Construction method of salt lake and salt pan system model based on CFD simulation

Abstract

The invention relates to a CFD (computational fluid dynamics) simulation-based salt lake and salt pan system model construction method, which comprises the steps of construction of a geometric model conforming to an actual salt pan size, grid division, setting of boundary conditions and UDS variables corresponding to four resource elements such as Li < + >, K < + >, B and Mg < 2 + >. The salt pan system model disclosed by the invention can accurately predict the distribution of the resource elements in the actual brine preparation process of the salt lake salt pan system, fully considers the effect of the brine evaporation rate and diffusion rate on the resource element migration, and can truly reflect the distribution rule of the resource elements in the salt pan system in the actual brine preparation process; a reasonable basis is provided for improving the salt pan system resource element recovery rate.

Description

Technical Field

[0001] The invention relates to the technical field of salt lake resource distribution determination, and in particular to a method for constructing a salt lake salt pan system model based on CFD simulation. Background Art

[0002] Salt lake resources are unique liquid minerals formed under natural geographical conditions. With the advancement of science and technology and the development of emerging industries, the strategic significance of salt lake resources to economic development has become increasingly prominent. Brine is rich in inorganic salt resources. In addition to elements such as potassium, sodium, chlorine, and magnesium, the overall reserves of lithium, boron, rubidium, cesium, iodine, and bromine are also considerable. Lithium and its compounds are important strategic resources for national economic and national defense construction, and have important applications in energy storage batteries, fine chemicals, atomic energy thermonuclear fusion, and other fields. With the transformation of new energy, the demand for lithium products in the lithium battery field continues to increase rapidly, and the world is paying more and more attention to lithium resources. Potassium is mainly used to make potash fertilizers needed for agriculture and is the food among food. Boron and its compounds can be processed into different functional materials due to their special properties such as light weight, flame retardancy, heat resistance, high hardness and high strength, and are widely used in many industrial fields required for the development of the national economy, such as light industry and textiles, metallurgy, glass and ceramics, electromechanical industry, agriculture, daily chemicals, and fine chemicals; magnesium is known as the green engineering material with the most development and application potential in the 21st century. Magnesium alloys are widely used in transportation, aerospace, national defense and military industry, 3C electronics and other fields due to their low density, high specific strength, good damping performance, strong anti-electromagnetic interference ability, and easy recycling. However, the current comprehensive utilization of salt lake resources is low. Many companies have the problem of mining rich and abandoning poor, mining thick and abandoning thin in the exploitation of salt lake resources. Most companies still use the single production method of potash fertilizer for associated and symbiotic minerals, which results in serious waste of resources. The loss of resource elements lithium, potassium, boron and magnesium is serious. In the production process of solar salt pans, the actual recovery rates of elements such as lithium, potassium, boron and magnesium are much lower than their theoretical recovery rates, especially in the Xitaijinaier Salt Lake, where the recovery rate of potassium resources is less than 50%, and the recovery rates of lithium and boron resources are less than 30% and 40% respectively, which poses a challenge to the sustainable development of salt lake resources. In order to improve the recovery rate of resource elements, it is crucial to adjust the concentration distribution and resource utilization process in the salt pan system. Because the spatial structure of the salt pan is very complex, the existing salt field production conditions are not suitable for actual ion concentration measurement. Summary of the invention

[0003] The purpose of the present invention is to provide a salt lake salt pan system model based on CFD simulation and a method for predicting the distribution of resource elements thereof, so as to solve the problem that under the existing salt pan production process conditions, it is difficult to accurately measure the concentration distribution of each element ion in brine on the complex spatial structure of the salt pan by experimental means, resulting in huge losses of salt lake resource elements during the evaporation process of the salt pan.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] Establishment of CFD model of on-site salt pan evaporation process:

[0006] Regional division: According to the area of ​​the salt lake mining area, the actual salt fields are reasonably divided into regions.

[0007] Considering that the flow of salt lake brine is affected by many factors, 2D flow and 3D flow research is carried out. Combined with the actual climate conditions and light conditions, parameters such as the temperature and sunshine intensity of the salt lake brine are set, and the circulation flow and natural mixing process of the brine are considered.

[0008] A geometric model was established with the length and width of the model set at 12m and 4m, and the depth set at 1.05m (where the depth includes the thickness of the brine evaporation layer d=5cm). The geometric models of different brine production stages were established using SolidWorks software.

[0009] The entire model is divided into hexahedral grids to reduce the number of grids. After grids of different sizes are imported into Fluent17.0, single-phase flow field simulation is performed. The velocity field data at the center of the injection port under different grid sizes are analyzed and compared to ensure the accuracy of the calculation results.

[0010] Set boundary conditions, the unit of geometric dimensions is m, turn on the gravity acceleration in the Z direction, and its value is -9.81m / s2. Select the Standradk-ε model as the turbulence model, use the standard wall function to deal with near-wall problems, set a brine inlet and outlet when establishing the model, the inlet adopts a velocity inlet, and the free outlet overflows. The ion concentrations at the inlet and outlet are also set here.

[0011] The control equations are solved using the pressure-velocity coupled SIMPLEC algorithm. The gradient discretization method selects Least Squares Cell Based, the second-order upwind scheme, the pressure selects Second Order, the momentum selects Second Order Upwind, and the relaxation factors are all set to the default value of 0.1.

[0012] Open the UDS equation and set 4 variables corresponding to Li + , K + , B, Mg 2+ For the four resource element ions, the solution area is all fluid domains, the flux function is set to mass flow rate, the diffusion rate of the corresponding ions is set in the brine parameter interface, and the brine evaporation rate calculated by the empirical formula is used as a parameter to input the brine evaporation layer of the model.

[0013] The physical properties of brine, such as density and viscosity, are modified on the basis of liquid water. When the UDS equation is turned on, the diffusion rate of the brine resource element is set here; the geometric model is divided into two fluid zones, namely the brine evaporation layer (Brinemasszone) and the brine layer (Brinezone). A mass source term is added to the brine evaporation layer to represent the evaporation rate of brine during the evaporation process. The value is a constant and negative value, representing mass transfer from liquid water to water vapor; the model is consistent with the proportion of the actual salt lake salt field system.

[0014] Monitor settings, the residual values ​​of various physical quantities in the simulation process are set at 1.0×10-3, the initialization method adopts hybrid initialization (HybridInitialization), the brine evaporation layer and the brine layer area in the patch evaporation pool correspond to the resource element ion concentration value; set the number of iterations to 40000 steps, and then start the calculation. After the calculation converges, save the relevant files and do data post-processing analysis.

[0015] The evaporation coefficient f of brine is given by the empirical formula:

[0016] f=100-(0.1978r-0.00167)·B 1.825 Formula (1)

[0017] Where r is the relative humidity RH (in decimal form) and B is the Baume degree of brine.

[0018] The evaporation of brine is calculated by the empirical formula:

[0019] E b =f·E Formula (2)

[0020] In the formula, E b is the evaporation of brine, f is the conversion coefficient, and E is the evaporation of fresh water. The evaporation rate of brine in Xitai Salt Lake is obtained through relevant empirical formulas. The evaporation rate of brine is shown in the following formula:

[0021] k=E b ·ρ·d -1 Formula (3)

[0022] Where, ρ is the brine density, d is the thickness of the brine evaporation layer;

[0023] Using the statistical error analysis method, the diffusion rate of resource elements was determined based on the overall average relative error MAPE between the indoor simulated evaporation measured data and the CFD simulation value. The relative error δ refers to the difference between the predicted value and the true value, and then the ratio to the true value, which can be expressed as:

[0024]

[0025] Where d is the experimental value of the sampling point; d0 is the simulated value of the sampling point.

[0026] According to the definition, the average relative error is the arithmetic mean of the relative errors, and the relationship between them can be expressed as:

[0027]

[0028] In the formula, n is the number of samples at the sampling point.

[0029] Research on prediction of resource element distribution during brine evaporation in Xitai Salt Lake:

[0030] Distribution law of resource elements in different brine production sections: According to the division of brine production sections, including sodium salt fields, potassium mixed salt fields, carnallite salt fields and tail brine salt fields, CFD simulation of flow evaporation process was carried out, and the concentration distribution of resource element ions in brine was studied by referring to the concentration data of resource element ions in brine.

[0031] Distribution patterns of resource elements at different temperatures (seasons): Study the concentration variation patterns of resource element ions in sodium salt fields, potassium mixed salt fields and tail brine salt fields at different temperatures (seasons), and analyze the impact of reduced evaporation due to lower brine temperature in winter on the distribution of resource element ion concentrations.

[0032] Advantages and beneficial effects of the present invention:

[0033] This invention applies SolidWorks modeling and ANSYS-Fluent simulation methods to the study of the distribution of salt lake resource elements for the first time, completes the simulation of brine in different brine production stages of Xitaijinaier Salt Lake, studies the spatiotemporal distribution of resource elements in salt fields in the sodium salt stage, potassium salt stage, carnallite stage and tail brine stage, and reveals the migration law of resource elements during the evaporation process of salt fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the geometric model of the natural evaporation process of the salt field on site. a) Sodium salt pond; b) Potash salt pond; c) Carnallite pond; d) Old brine pond.

[0035] Figure 2 This is a diagram showing the distribution of lithium ion resource elements at different temperatures (seasons) in the Qinghai Salt Lake sodium pool. a) Li + Concentration changes in July and December; b) is the overall Li + Concentration distribution; c) is the overall Li + Concentration distribution.

[0036] Figure 3 This is the distribution pattern of lithium ion resource elements in Qinghai Salt Lake Potassium Pool in summer. a) Li + Concentration changes in July; b) Li+ Concentration distribution. DETAILED DESCRIPTION

[0037] Example 1

[0038] The experiment was conducted in a sodium pool at the Qinghai Salt Lake in July (summer), when the temperature is high, the evaporation of fresh water is large, and the air humidity is relatively low.

[0039] According to the actual size and geometry of the summer sodium pool, a geometric model is established and meshed. The brine injection rate and brine ion concentration at the inlet and the conditions at the outlet are set.

[0040] According to on-site measurements, the density of sodium pool brine in summer is 1.227-1.291g / cm 3 The specific value selected from the three sampling points in this example is 243g / cm 3 , 1.246g / cm 3 and 1.247 g / cm 3 The brine Baume degrees were 28.300°Be', 28.628°Be' and 28.721°Be', the air humidity was 18.02%, the brine evaporation coefficients were 0.848, 0.845 and 0.844, the brine evaporation amounts were 9.136mm / day, 9.101mm / day and 9.091mm / day, and the brine evaporation rate was 1135.09kg / m 3 day, 1133.97kg / m 3 day and 1133.64kg / m 3 ·day, and the lithium ion diffusion rate is 886.435kg / m·day.

[0041] The brine injection flow rate was set to 5.0×10 -2 m / s.

[0042] The inlet adopts velocity inlet, the outlet is overflow in the form of free outflow, and the inlet mass concentration is set to 0.280g / L.

[0043] Through CFD simulation, the surface velocity distribution of brine and the concentration distribution of lithium ions in the sodium pool in summer were obtained.

[0044] Depend on Figure 2 a)Li + Changes in concentration in July and Figure 2From b), we can see that in the sodium salt stage, as the evaporation process proceeds, the lithium ion concentration in the brine will increase. Along the flow direction of the brine, the lithium ion concentration is "diluted" and reduced on the brine injection side, and the surface lithium ion concentration is higher than the bottom lithium ion concentration. On the X plane, when moving along the +Y direction, the concentration of lithium ions in the sodium salt stage will decrease near the entrance because the Li+ concentration of the injected brine is lower. On the Y plane, when moving along the Z axis, the concentration of lithium ions in the sodium salt stage will increase due to the evaporation of the brine. On the Z plane, when moving along the X axis (i.e., the direction of brine flow), as the evaporation process proceeds, the concentration of lithium ions in the sodium salt stage will gradually increase, and the lithium ion concentration on the surface is higher than that in the bottom layer.

[0045] K + , B 4 O7 2- Mg 2+ The concentration trend and Li + The concentration distribution trend is consistent in all directions within the salt field.

[0046] Example 2

[0047] The on-site sodium pool of Qinghai Salt Lake in December (winter) was selected for the experiment. In winter, the temperature is lower than that in summer, the evaporation of fresh water is reduced, and the air humidity may increase. According to the analysis data of the on-site sodium pool of Qinghai Salt Lake in July (summer), various parameters in winter were adjusted.

[0048] In this example, the density and brine Baume degree of the three sampling points are the same as those of the summer sodium pool, and the air humidity is adjusted to 35.13%. According to the freshwater evaporation and air humidity in winter, the brine evaporation coefficient and evaporation amount are recalculated by formula (1) (2), and then the brine evaporation rate in winter is obtained by formula (3). The brine evaporation coefficients of the three sampling points are 0.697, 0.691 and 0.689, the brine evaporation amounts are 0.457 mm / day, 0.452 mm / day and 0.452 mm / day, and the brine evaporation rates are 56.27 kg / m 3 day, 56.38kg / m 3 day and 56.74kg / m 3 Through CFD optimization and error analysis of the experimental data and CFD simulation values, when the MAPE at the sampling point is the smallest, which is the diffusion rate of the resource element ions, the resource element lithium ion diffusion rate is adjusted to 44.429kg / m·day.

[0049] like Figure 2 a)Li + Changes in concentration in December and Figure 2From c), we can see that, similar to the summer sodium pool experiment, the distribution of brine and resource element ions in the winter sodium pool was obtained. As the brine temperature is lower in winter and the evaporation is reduced, the concentration of resource element ions in the salt pan is lower than that in summer.

[0050] Example 3

[0051] The sampling salt ponds of potassium mixed salt pools include the medium potassium salt field and the medium light salt field. The experimental conditions of summer potassium mixed salt are similar to those of summer sodium ponds, but the brine composition and resource element ion concentration in potassium ponds are different from those in sodium ponds.

[0052] According to the characteristics of the potassium pool, the geometric model and mesh division are established, and the corresponding inlet and outlet conditions, as well as the initial parameters of brine and resource element ions are set.

[0053] The specific value of brine density in the summer potassium mixed salt stage in this example is 1.318 g / cm 3 and 1.310g / cm 3 The brine Baume degree is 34.985°Be' and 34.313°Be', the air humidity is the same as that of the sodium pool in summer, the brine evaporation coefficient is 0.777 and 0.785, the brine evaporation amount is 8.364mm / day and 8.448mm / day, and the brine evaporation rate is 1102.40kg / m 3 day and 1106.66kg / m 3 ·day, the lithium ion diffusion rate is the same as that of the sodium pool in summer.

[0054] The brine injection flow rate was set to 5.0×10 -2 m / s.

[0055] The inlet adopts velocity inlet, the outlet is overflow in the form of free outflow, and the inlet mass concentration is set to 0.280g / L.

[0056] Through CFD simulation, the distribution of brine and resource element lithium ions in the potassium pool in summer is obtained. Figure 3 It can be seen that in the potassium mixed salt stage, as the evaporation process proceeds, the lithium ion concentration in the brine will increase. Since lithium exists in the brine in the form of ions, it will be concentrated as the water evaporates during the evaporation process. Therefore, as the evaporation proceeds, the lithium ion concentration in the brine will gradually increase. On the X plane, when moving along the +Y direction, due to the Li +The concentration is lower, and the concentration of lithium ions in the potassium mixed salt stage will decrease near the entrance. On the Y plane, when moving along the Z axis, the concentration of lithium ions in the potassium mixed salt stage will gradually increase due to the evaporation of the brine. This is because as the water evaporates, the solutes in the brine will gradually concentrate, including lithium ions. On the Z plane, when moving along the X axis (i.e., the direction of brine flow), the concentration of lithium ions in the sodium salt stage will gradually increase as the evaporation process proceeds. Since the evaporation process starts from the surface of the brine, the lithium ion concentration on the surface is higher than that on the bottom layer.

[0057] B 4 O7 2- The concentration trend of Li + The concentration distribution trends are consistent.

[0058] The above description is only a preferred embodiment of the present invention. For those skilled in the art, several modifications and improvements may be made without departing from the inventive concept, which all belong to the protection scope of the present invention.

Claims

1. A method for constructing a salt lake and salt pan system model based on CFD simulation, characterized in that: The following steps are involved: Step 1: Use SolidWorks to establish the geometric model of the salt pan system. The salt pan system includes sodium salt pool, potassium salt pool, carnallite pool and old brine pool. Take brine as the liquid phase and set the density and viscosity of the brine. Step 2: Use hexahedral grids to divide the entire model, import grids of different sizes into ANSYS-Fluent to perform single-phase flow field simulation, and analyze and compare the velocity field data at the center of the injection port under different grid sizes; Step 3: Set the boundary conditions of the actual salt field evaporation process CFD, turn on the gravity acceleration in the Z direction, select the Standradk-ε model as the turbulence model, use the standard wall function to deal with the near-wall problem, set a brine inlet and outlet when establishing the model, the inlet adopts the velocity inlet, the free outlet overflows, and the ion concentration of the inlet and outlet is also set here; Step 4: The control equations are solved using the pressure-velocity coupled SIMPLEC algorithm. The gradient discretization method is LeastSquares Cell Based, the second-order upwind format, the pressure is Second Order, the momentum is Second OrderUpwind, and the relaxation factors are all set to the default value of 0.

1. Step 5: Set UDS variables corresponding to Li + , K + , B, Mg 2+ Resource element, the solution area is all fluid domains, the flux function is set to mass flow rate, the diffusion rate of the corresponding ions is set in the brine parameter interface, and the result of the brine evaporation rate is used as a parameter to input the brine evaporation layer of the model; Step 6: Monitor settings. The residual values ​​of each physical quantity during the simulation are set at 1.0×10 -3 ,The initialization method adopts mixed initialization, and the brine evaporation layer and brine layer area in the Patch evaporation pool correspond to the ion concentration values ​​of resource elements; Set the number of iterations to 30,000-50,000 steps, then start the calculation. After the calculation converges, save the relevant files and do data post-processing analysis.

2. The method according to claim 1, characterized in that: In step 2, grids of different sizes are imported into ANSYS-Fluent to perform single-phase flow field simulation. Under the condition of a brine injection velocity of 0.01 m / s, after waiting for the flow field calculation to converge, the velocity field data at the center of the brine injection port under different grid sizes are analyzed and compared.

3. The method according to claim 1, characterized in that In step 5, the evaporation coefficient f of brine is given by the empirical formula: f=100-(0.1978r-0.00167)·B 1.825 Formula (1) Where r is the relative humidity RH (in decimals); B is the brine Baume degree, The evaporation of brine is calculated by the empirical formula: E b =f·E Formula (2) In the formula, E b is the evaporation of brine, f is the conversion coefficient, E is the evaporation of fresh water, and the evaporation rate of Xitai Salt Lake brine is obtained through relevant empirical formulas. The evaporation rate of brine is shown in the following formula: k=E b ·ρ·d -1 Formula (3) Where ρ is the brine density and d is the thickness of the brine evaporation layer.

4. The method according to claim 1, characterized in that: The step 5 uses the statistical error analysis method to determine the diffusion rate of resource elements based on the overall average relative error MAPE of the indoor simulated evaporation measured data and the CFD simulation value. The relative error δ refers to the difference between the predicted value and the true value, and then the ratio to the true value, which is expressed as: In the formula, d is the experimental value of the sampling point; d0 is the simulated value of the sampling point, The mean relative error is the arithmetic mean of the relative errors, expressed as: In the formula, n is the number of samples at the sampling point.

Images