Saline water concentration process prediction system

Abstract

The embodiment of the invention provides a brine concentration process prediction system. The device comprises a data collection part for collecting data of initial saline water and data of high-concentration saline water; a data preprocessing unit that converts the data collected by the data collection unit into data that can be applied to a back-end data processing unit; a data processing unit for calculating the solubility of each precipitate component using the data converted by the data preprocessing unit; and a data prediction unit for predicting the final precipitation amount of the precipitate and the concentration of each ion component in the final concentrated brine by using the solubility of each precipitate component calculated by the data processing unit.

Description

Technical Field

[0001] This embodiment relates to a system for predicting the types of precipitates, the amount of precipitation, and the component concentrations in brine during the brine concentration process. Background Art

[0002] Lithium (Li) is an element with an atomic number of 3. Except for hydrogen (No. 1) and helium (No. 2) which are gaseous at room temperature, it is the smallest element and also the lightest metallic element, with unique properties that cannot be replaced by other elements. For this reason, lithium plays a core role in high-performance lithium-ion batteries. As the usage of lithium-ion batteries in mobile phones, electric vehicles, etc. increases, the demand for lithium is also continuously growing. Most of the lithium on Earth exists in seawater, but due to the too low concentration of lithium in seawater, commercial extraction is not possible; to produce lithium compounds, it must be extracted from lithium-containing minerals or from brines with high lithium content. The most common method for extracting lithium from brines containing high concentrations of sodium (Na), potassium (K), magnesium (Mg), or calcium (Ca) is to enclose the brine in large-scale ponds and concentrate it by natural evaporation similar to salt pans. After obtaining high-concentration brine, impurities are purified chemically to obtain a lithium-containing solution with a high lithium concentration and low other element content, and then it is extracted in the form of solid lithium compounds.

[0003] During the process of concentrating low-concentration brine into high-concentration brine, the concentrations of various ions in the brine exceed the solubility, resulting in precipitation. The representative precipitate is sodium chloride (NaCl). In the case of the famous Atacama Salt Lake in Chile, which is a lithium-producing brine resource, the lithium concentration is increased by more than 20 to 30 times through ponds. Assuming 20-fold concentration, 95% of the initially present water will evaporate. This means that starting from 20 units of brine, 19 units must be evaporated, leaving only 1 unit to achieve a 20-fold concentration. During this process, the dissolved salts equivalent to about 30% to 40% of the mass of the evaporated water are precipitated.

[0004] Among these salts, if substances containing lithium precipitate, it will cause process losses. Even if the precipitated salts themselves do not contain lithium, since they precipitate in the ponds and the precipitated salts are soaked by the brine, a certain amount of concentrated brine cannot be sent to the subsequent process. In addition, since a part of the lithium will leak or be lost from the bottom of the pond, lithium loss occurs during the brine concentration process. Generally, more lithium is lost during the concentration process than during the impurity purification process of high-concentration brine. Therefore, it is very important to calculate the yield of the brine concentration process.

[0005] To predict the yield of the brine concentration process and the composition entering the impurity purification process after concentration, the most required information is the types and quantities of the precipitates. The evaporation of water does not cause lithium loss, and usually the amount of loss is not large. Therefore, most of the lithium loss occurs in the precipitates and the liquid phase.

[0006] Therefore, during the brine concentration process, in order to predict the content of each element, it is necessary to develop a system that can more reliably predict the type of precipitate, the amount of precipitate, and the concentration of components in the brine when the concentration of each element increases due to evaporation. Summary of the Invention

[0007] Technical Problem

[0008] In one embodiment of the present invention, a system for predicting the composition of precipitates, the amount of precipitates, and the concentration of each component in concentrated brine during the brine concentration process is provided.

[0009] Technical Solution

[0010] A brine concentration process prediction system according to an embodiment of the present invention includes a data collection unit that collects data of initial brine and data of high-concentration brine; a data preprocessing unit that converts the data collected by the data collection unit into data applicable in a backend data processing unit; a data processing unit that calculates the solubility of each precipitate component using the data converted by the data preprocessing unit; and a data prediction unit that predicts the amount of precipitate of the final precipitate and the concentration of each ionic component in the final concentrated brine using the solubility of each precipitate component calculated by the data processing unit.

[0011] In the data preprocessing unit, the concentration change values of each ionic component in the high-concentration brine and the initial brine collected by the data collection unit are calculated.

[0012] In the data processing unit that calculates the solubility of each precipitate component using the data converted by the data preprocessing unit,

[0013] The solubility of each precipitate component is calculated through multiple nonlinear regression analysis or machine learning.

[0014] The carbon dioxide absorption amount is further calculated in the data processing unit,

[0015] The carbon dioxide absorption amount is calculated based on the concentrations of calcium (Ca), sulfate (SO4), boron (B), and pH value in the initial brine and the high-concentration brine.

[0016] The data preprocessing unit, the data processing unit, and the data prediction unit,

[0017] Repeat the calculation according to the change of the high-concentration brine data collected by the data collection unit to calculate the optimal concentration of each ionic component in the final concentrated brine.

[0018] The lithium concentration in the final concentrated brine ranges from 3.5 g / L to 18 g / L.

[0019] The error range of the amount of precipitate of the final precipitate predicted by the prediction unit is within 5%.

[0020] Advantages of the Invention

[0021] According to one embodiment of the present invention, it is possible to predict the type and amount of precipitated salt, as well as the content of components in the concentrated brine, based on the change in the concentration of components in the brine. Thus, by adjusting the operating conditions of the brine concentration process, the advantage of increasing the lithium production can be achieved.

[0022] According to one embodiment of the present invention, the type and amount of precipitated salt, as well as the content of components in the concentrated brine, can be predicted based on the change in the concentration of components in the brine, and thus it can be used for the design of the evaporation concentration process in salt lake areas where quantitative prediction is difficult. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematically shows a prediction system for a brine concentration process according to one embodiment of the present invention.

[0024] Figure 2 Outlines the method for calculating the component concentration during the brine concentration process.

[0025] Figure 3 Shows the activity coefficient and ionic strength according to the existing thermodynamic calculation formula.

[0026] Figure 4 Shows the results of Experimental Example 1 and Comparative Example 1.

[0027] Figure 5 Shows the results according to Example 1 and Experimental Example 1.

[0028] Figure 6 Shows the results of Example 2 and Experimental Example 1.

[0029] Figure 7 Shows the results according to Example 3 and Experimental Example 2. DETAILED DESCRIPTION OF THE INVENTION

[0030] In the description of the present invention, terms such as first, second, and third are used to describe various parts, components, regions, layers, and / or sections, but these parts, components, regions, layers, and / or sections should not be limited by these terms. These terms are only used to distinguish one part, component, region, layer, and / or section from another part, component, region, layer, and / or section. Therefore, without departing from the scope of the present invention, the first part, component, region, layer, and / or section described below can also be described as the second part, component, region, layer, and / or section.

[0031] The terms used in this document are only for describing specific embodiments and are not intended to limit the present invention. Unless the context clearly indicates the contrary, the singular forms used are also intended to include the plural forms. It should also be understood that the term "comprising" used in the specification may specifically refer to a certain property, field, integer, step, action, element, and / or component, but does not exclude the existence or addition of other properties, fields, integers, steps, actions, elements, and / or components.

[0032] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention belongs. For terms defined in a dictionary, they should be interpreted as having a meaning consistent with the relevant technical literature and the content disclosed herein, and should not be interpreted in an idealized or overly formal sense.

[0033] Embodiments of the present invention will be described in detail below. However, this is only provided as an example, and the present invention is not limited thereto, and the present invention is only defined by the scope of the appended claims.

[0034] Figure 1 A prediction system for a brine concentration process according to an embodiment of the present invention is schematically shown.

[0035] Referring to Figure 1 , according to an embodiment of the present invention, the prediction system for the brine concentration process includes a data collection unit 10, a data preprocessing unit 20, a data processing unit 30, and a data prediction unit 40.

[0036] The data collection unit 10 can collect various data such as the types of components, component concentration data, precipitate composition, precipitation amount, meteorological data, and brine pH value in the initial brine and the high-concentration brine. The data collection unit 10 can establish a database to store the collected data.

[0037] For example, in the data collection unit 10, the following measured data can be input.

[0038] - Ion concentration in the brine

[0039]

Table 1

[0040] Li (g / L) <![CDATA[SO4 (g / L)]]> Ca (g / L) Mg (g / L) B (g / L) K (g / L) Na (g / L) 1.702 3.793 2.324 0.031 0.581 17.91 101.88 1.857 3.763 2.383 0.049 0.633 19.35 106.98 2.894 5.126 3.063 0.113 0.824 28.78 101.03 3.412 5.357 3.119 0.133 0.848 33.99 97.80 5.046 4.039 2.526 0.196 0.925 0.05 87.35

[0041] The data collected in the data collection unit 10 can be transferred to the data preprocessing unit 20. In the data preprocessing unit 20, the concentration difference of each ionic component in the high-concentration brine and the initial brine can be calculated, and the calculated concentration difference of each ionic component can be transferred to the data processing unit 30.

[0042] For example, the data preprocessing section 20 can perform the following calculations using data such as the ion concentration and pH in the brine.

[0043] 1) Concentrations of anions (OH - , Cl - ) that cannot be obtained from the collection section

[0044] → Calculate the OH - concentration based on the pH (using the phreeqc thermodynamic program)

[0045] → Calculate the Cl - concentration considering charge balance

[0046] 2) Calculate the B(OH)3:B(OH)4 - ratio of the ions labeled B

[0047] → Since the pH value has been determined, use B(OH)3 + OH - → B(OH)4 - equilibrium constant of the reaction for calculation

[0048] → Since the amounts of all cations and anions have been calculated, recalculate the Cl - ion concentration.

[0049] Through the above series of calculations, the data preprocessing section 20 can obtain the following data.

[0050]

Table 2

[0051] (Concentration unit: mol / L)

[0052] <![CDATA[Li + > <![CDATA[SO4 2- > <![CDATA[Ca 2+ > <![CDATA[Mg 2+ > <![CDATA[B(OH)3]]> <![CDATA[B(OH)4 - > <![CDATA[K + > <![CDATA[Na + > <![CDATA[Cl - > <![CDATA[OH - > 0.245 0.039 0.058 0.001 0.002 0.052 0.458 4.432 5.122 0.000

[0053] The data processing section 30 can calculate the effective solubility of each precipitate component using the received concentration values of each ion component.

[0054] To calculate the effective solubility of the precipitate component, the chemical formulas and solubilities of candidate substances that can precipitate during the concentration process can be pre-input through a thermodynamic database and existing experimental results.

[0055] Then, input the data of the concentration experiment results. Through multiple nonlinear regression analysis, the concentration change trends of each elemental component can be obtained. Then, by correcting the activity coefficient of the input thermodynamic database, the effective solubility of each precipitate component can be calculated. The effective solubility of each precipitate component can be calculated as a function of the ionic strength and the concentration of each ion.

[0056] For example, using the data obtained by the data preprocessing unit 20, the data processing unit 30 can proceed as follows.

[0057] - Ionic strength

[0058] - KSP calculation of the main precipitate

[0059]

[0060] The KSP of NaCl is calculated as follows based on the ionic strength.

[0061]

Table 3

[0062] Li (g / L) Ionic strength [Na] [Cl] <![CDATA[K SP > 1.702 10.706 4.432 5.122 22.70 1.857 11.102 4.596 5.312 24.42 2.894 11.826 4.367 5.536 24.18 3.412 11.983 4.230 5.609 23.73 5.046 12.495 3.805 5.84 22.25 9.471 12.636 3.246 5.898 19.15 11.01 12.604 3.082 5.874 18.10

[0063] Moreover, when considering carbon dioxide in the atmosphere, further calculations can be performed by considering the following reaction.

[0064]

[0065] Ca 2+ + CO3 2- → CaCO3

[0066] The data prediction unit 40 can predict the precipitated component, the precipitation amount, and the ionic component concentration in the final concentrated brine by applying the effective solubility values of the respective precipitate components received from the data processing unit 30.

[0067] As an example, the data prediction unit 40 can proceed as follows.

[0068] Starting from the input Li at 1.7 g / L in the data collection unit 10, the concentration is gradually increased.

[0069] 1) Calculate the concentrations including Cl and OH.

[0070] 2) Calculate the ionic strength.

[0071] 3) Calculate the solubility of each precipitate.

[0072] 4) After removing the same concentration as the precipitate, repeat the method of 1).

[0073] The results can predict the pH value, ionic concentration, precipitate components, etc. in the brine at each concentration stage.

[0074] Figure 2 The method for calculating the component concentration during the brine concentration process is outlined.

[0075] Figure 2 (a) shows the process of calculating the concentration of ionic components in brine according to traditional thermodynamic calculations.

[0076] Generally, in traditional thermodynamic calculations, the solubility of precipitates is calculated using the solubility product. Specifically, the log value of the solubility product, i.e., the pKSP value, is used.

[0077] For example, if the pK of CaSO4 SP = 2.3,

[0078]

[0079] In the formula (1), a is the ion activity of each component (hereinafter referred to as activity), and can be expressed by the following relationship.

[0080]

[0081] Here, a i is the activity of element i, γ i is the activity coefficient of element i, c i is the concentration of element i, and c0 is the standard concentration, usually 1 mol / L.

[0082] After calculating the ionic strength in traditional thermodynamic calculations, the activity coefficient of each element at the corresponding ionic strength is calculated according to a complex formula to calculate the activity (concentration × activity coefficient), and then the activity is compared with the inherent solubility product of each substance to calculate whether precipitation occurs and the amount of precipitation, and the equilibrium concentration is reflected accordingly. This method requires a large amount of calculation.

[0083] In addition, as Figure 3 shown, significant errors are known to exist in the activity coefficients calculated in traditional thermodynamic calculation formulas in solutions with high ionic concentrations.

[0084] Referring to Figure 3 it can be confirmed that even when the ionic strength (ionic strength, z i is the charge of the ion, c i is the concentration of the ion) is only 2 mol / L, there are still large errors between the calculation formulas.

[0085] Especially for brine, the ionic strength exceeds 5 mol / L. Therefore, it is difficult to accurately calculate the activity coefficient using general thermodynamic calculation formulas. Even when using formulas known to be applicable to high-concentration solutions, the error is still large.

[0086] When the activity coefficient is inaccurate, the activity value used for solubility calculation will change. Therefore, the calculated values of solubility and precipitation amount will also be different. Thus, if existing thermodynamic calculations are used for predicting the brine concentration process, there will be a large difference between the calculated precipitation amount and the actual result. Moreover, due to the application of solubility values that are quite different from the actual ones, the types of precipitated substances themselves may also change.

[0087] In the formula (2), the activity coefficient γ represents the value of the effective concentration relative to the actual concentration. In high-concentration solutions, due to the influence of other surrounding ions on the charge amount of ions and the interaction with solvent particles, etc., the effective concentration may be different from the actual concentration. For this reason, it can be calculated based on the solution property - ionic strength, and its calculation formula is as follows.

[0088]

[0089] In the formulas (3) and (4), the subscripts “+” and “-” respectively represent the cation and anion of the substance under consideration, while a and c represent the cations and anions other than the substance under consideration. For example, if the substance considered is NaCl, the subscript “+” represents Na + , and the subscript “-” represents Cl - . In addition, a can be K + , Ca 2+ , Mg 2+ , etc., and c can be SO4 2- , B(OH)4 - , etc. And in formulas (3) and (4), the F in the first term can be expressed as follows.

[0090]

[0091] In the equations (2) to (5), A, B c,a , C c,a , Φ x,y , Ψ xc,a , etc. are already existing values reported by the Pitzer equation, etc.

[0092] Figure 2(b) shows the process of calculating the concentrations of various components in the brine according to an embodiment of the present invention.

[0093] Referring to Figure 2 (b), in traditional thermodynamic calculations, it is calculated whether precipitation occurs by comparing the solubility represented by the solubility product of thermodynamic parameters with the product of the activities of each element. However, in the present invention, the precipitation amount is calculated by comparing the corrected solubility of the corresponding component (instead of changing the activity by calculating the activity coefficient, but by changing the solubility value) with the concentration, thereby reducing the calculation amount and obtaining a concentrated combined result that reflects the actual experimental results.

[0094] In an embodiment of the present invention, the brine composition based on experimental results is input, the activity coefficient γ of each component is calculated and then Ksp is calculated, and then a linear or quadratic function fitting of the ion concentrations of each component is performed on the Ksp obtained for the target component, or the K of each component is recalculated through machine learning sp so that the effective solubility of the precipitate can be obtained.

[0095] The reason why the above calculation can be performed in the present invention is that during the brine concentration process, the composition does not change suddenly in the concentration process, and complex physical quantities such as ionic strength and activity coefficient ultimately depend on the composition. Therefore, when the concentrations of Na, K, and Cl change, various thermodynamic physical quantities also change in a certain relationship. Therefore, if the solubility can be expressed as a function of the easily calculable ionic strength and the concentrations of each ion, there is no need to calculate the complex activity coefficient, and the effective solubility suitable for the actual value can also be calculated. In this way, compared with the existing thermodynamic calculations, the accuracy is improved, and at the same time, due to the reduction of the calculation amount, there is no need to equip a separate system for calculation.

[0096] On the one hand, the aforementioned data collection unit 10 can collect data such as temperature, atmospheric pressure, CO2 concentration, etc., as well as data such as the precipitation amount of calcium carbonate (CaCO3) and pH value. After the high-concentration brine data is newly collected by the data collection unit 10, it can be transmitted to the data preprocessing unit 20. Based on the results calculated by the data preprocessing unit 20, the data processing unit 30 can calculate data such as the effective solubility. At this time, using the calculated and updated precipitation amount of calcium carbonate (CaCO3), the CO2 absorption amount and dissolution amount can be obtained through equilibrium concentration calculation. The absorption amount of the carbon dioxide can be calculated from the calcium (Ca), sulfate (SO4), boron (B) concentrations and pH value in the initial brine and high-concentration brine. Although not explicitly stated in the present invention, the calcium (Ca), sulfate (SO4), boron (B) concentrations obviously refer to the concentrations of calcium ions, sulfate ions, and boron ions.

[0097] Specifically, the changes caused by the continuous absorption of trace CO2 in the atmosphere can be comprehensively considered. This means that the precipitation amount of CaCO3 that was not previously considered will be taken into account when considering the CO2 solubility. However, the precipitation amount of CaCO3 is less than one-tenth of the main precipitation form of calcium, CaSO4, and the time required to reach chemical equilibrium is relatively long, so this is not considered in most existing brine concentration simulation calculations. However, in addition to carbonates, calcium can also be converted into sulfates (CaSO4), boron compounds (CaB x O y amorphous), etc. Therefore, if the concentration of Ca can be accurately predicted, the concentrations of SO4 and B can also be improved.

[0098] Specifically, the reaction of CO2 in the atmosphere to form carbonate (CO3 2- ) is as follows and can change the pH value in the brine.

[0099] CO2(g) + H2O(l) → CO3 2- (aq) + 2H + (aq)

[0100] After CO2 dissolves in the brine, it releases carbonate (CO3 2- ), reacts with calcium ions to precipitate CaCO3 precipitate, thereby consuming carbonate (CO3 2- ). At the same time, CO2 in the atmosphere further dissolves into the brine. During this process, the pH value of the brine decreases, and the change in the pH value causes the solubility of many precipitates to change. In particular, it affects the solubility of pH-sensitive Mg(OH)2 and thus also plays an important role in the prediction of Mg concentration. Therefore, considering the dissolution of CO2 in the atmosphere can not only improve the prediction of Ca concentration, but also enhance the prediction effects of B, SO4, and Mg concentrations.

[0101] Therefore, it has the advantages of more accurately predicting the content of ionic components in concentrated brine, the precipitation amount of precipitates, etc., and can improve the production efficiency and economy of lithium throughout the brine concentration process.

[0102] The value of the CO₂ absorption amount is used in the data processing unit 30 to adjust the Ca concentration and perform iterative calculations until a result consistent with the on-site data is obtained. This result is connected to "prediction of concentrated brine composition", so that the expected composition and the amount of precipitates at the final concentration can be calculated.

[0103] Meanwhile, in the data preprocessing unit 20, the data processing unit 30, and the data prediction unit 40, iterative calculations are performed according to the changes in the high-concentration brine data collected by the data collection unit 10, and the optimal concentration of each component in the final concentrated brine can be obtained. The lithium concentration in the final concentrated brine can be in the range of 3.5 g / L to 18 g / L.

[0104]

Embodiments of the Invention

[0105] The embodiments of the present invention will be described in detail below. However, these are provided only as examples and do not limit the present invention, which is only defined by the following claims.

[0106] (Experimental Example 1)

[0107] A concentration experiment was carried out using brine, and the component concentrations at each concentration stage are shown in Table 4 below.

[0108]

Table 4

[0109]

[0110] (Comparative Example 1)

[0111] Using the brine containing Component C.1 in Table 4 as the raw material, the concentration of each ionic component was calculated using the traditional thermodynamic program OLIFlowsheet11.0.

[0112] Figure 4 The results obtained from Experimental Example 1 and Comparative Example 1 are shown.

[0113] Reference Figure 4 , the difference in the concentration values of the ionic components between Experimental Example 1 and Comparative Example 1 is relatively large, which is considered to be due to the high ionic concentration in the brine, the non-ideal composition, and the calculation of the ideal composition in a wide range due to the characteristics of the thermodynamic program, resulting in errors in the activity and solubility calculations, and further causing differences in the solubility of each ionic component.

[0114] For example, assume there is an error in the calculation of the solubility of KCl, resulting in an underestimation of the amount of precipitate by 5%. When the concentration of Li in the brine is 1 g / L and the concentration of K is 10 g / L, assume the concentration of Li in the brine is concentrated to 25 g / L, and the measured concentration of K in the concentrated brine is 45 g / L.

[0115] In the actual situation, the amount of precipitated K is equivalent to 205 g / L of the standard after concentration. (If there is no precipitation, it should be 10×25 / 1 = 250 g / L, but only 45 g / L remains) If the amount of precipitation is underestimated by 5%, the predicted precipitated K is equivalent to 195 g / L, and the predicted remaining concentration of K is 250 g / L - 195 g / L = 55 g / L. Therefore, a 5% error in the prediction of the precipitate results in a 22% difference in the composition of the concentrated brine, and this concentration difference further affects the prediction of the amount and type of the precipitate, ultimately leading to a relatively large error.

[0116] (Example 1)

[0117] Using brine C1 in Table 4 as the raw material, combined with the experimental results of brine concentration at each stage, and using the brine concentration process prediction system, the concentrations of each component were calculated.

[0118] Figure 5 The results of Example 1 and Experimental Example 1 are shown.

[0119] Reference Figure 5 , it can be confirmed that the difference in the concentration values of each component between Example 1 and Experimental Example 1 is significantly reduced compared to Figure 4 shown in Comparative Example 1. In particular, for components K and SO4 2 -, the difference between Example 1 and Experimental Example 1 is reduced compared to Figure 4 in Comparative Example 1.

[0120] (Example 2)

[0121] Except that the CO2 absorption was corrected in the brine concentration process prediction system, the concentrations of each component were calculated in the same manner as in Example 1.

[0122] Figure 6 The results according to Example 2 and Experimental Example 1 are shown.

[0123] Referring to Figure 6 , it can be confirmed that the difference in the concentration values of each element between Example 2 and Experimental Example 1 is reduced compared to the difference between Example 1 and Experimental Example 1, and the qualitative trend of increase or decrease of each element with concentration is also predicted similarly to the experimental results.

[0124] On the other hand, in Example 2, the error in the amount of precipitate generated during the concentration process was confirmed to be less than 1%. In the present invention, more than 90% of the precipitate is (NaCl + KCl). When the Li concentration in the brine is 1.7 g / L, the Na concentration is 101.88 g / L, and when the Li concentration in the concentrated brine is 28.6 g / L and the Na concentration is 26.21 g / L, calculated according to the standard before concentration (C1 in Table 4), the remaining Na in the concentrated brine is approximately 1.56 g / L (26.21 * 1.7 / 28.6), and the precipitated Na is calculated to be 100.3 g / L. According to the prediction result of Example 2, Na exists in the concentrated brine at a concentration of approximately 1.79 g / L, and 100.1 g / L of the Na before concentration is precipitated as Na precipitate, so the prediction result is approximately 0.2% smaller than the actual experimental result (Experimental Example 1). In addition, if calculated in the same way, the predicted K precipitate is 0.4% more than the actual experimental result, and the amount of the entire precipitate is predicted within an error range of less than 0.2%.

[0125] (Experimental Example 2)

[0126] A concentration experiment was carried out using the brine, and the component concentrations at each concentration stage are shown in Table 5 below.

[0127]

Table 5

[0128]

[0129] (Example 3)

[0130] Using the brine C1 in Table 5 above as the raw material and adopting the same method as in Example 2, the concentrations of each component were calculated.

[0131] Figure 7 The results obtained according to Example 3 and Experimental Example 2 are shown.

[0132] Referring to Figure 7 , it can be confirmed that the difference in the concentration values of each ionic component on which Example 3 and Experimental Example 2 are based is small. In addition, it can be confirmed that the error range of the Na, K, and SO4 components is within 5%.

[0133] The present invention is not limited to the above-described embodiments and can be manufactured in various different forms. Those with ordinary knowledge in the technical field will be able to understand that the present invention can be implemented in other specific forms without changing its technical idea or basic characteristics. Therefore, the above-described embodiments are exemplary in all aspects and should not be regarded as restrictive.

Claims

1. A prediction system for a brine concentration process, comprising: A data collection unit that collects data of initial brine and data of high-concentration brine; A data preprocessing unit that converts the data collected by the data collection unit into data applicable in a backend data processing unit; A data processing unit that calculates the solubility of each precipitate component using the data converted by the data preprocessing unit; A data prediction unit that predicts the precipitation amount of the final precipitate and the concentration of each ionic component in the final concentrated brine using the solubility of each precipitate component calculated by the data processing unit.

2. The brine concentration process prediction system according to claim 1, wherein, In the data preprocessing unit, the change value of the concentration of each ionic component in the high-concentration brine and the initial brine collected by the data collection unit is calculated.

3. The brine concentration process prediction system according to claim 1, wherein, In the data processing unit that calculates the solubility of each precipitate component using the data converted by the data preprocessing unit, The solubility of each precipitate component is calculated through multiple nonlinear regression analysis or machine learning.

4. The brine concentration process prediction system according to claim 1, wherein, In the data processing unit, the carbon dioxide absorption amount is further calculated.

5. The brine concentration process prediction system according to claim 2, wherein, The carbon dioxide absorption amount, Is calculated based on the calcium (Ca), sulfate (SO4), boron (B) concentrations and pH value in the initial brine and the high-concentration brine.

6. The brine concentration process prediction system according to claim 1, wherein, The data preprocessing unit, the data processing unit and the data prediction unit, Repeatedly calculate according to the change of the data of the high-concentration brine collected by the data collection unit to calculate the optimal concentration of each ionic component in the final concentrated brine.

7. The brine concentration process prediction system according to claim 6, wherein, The lithium concentration in the final concentrated brine ranges from 3.5 g / L to 18 g / L.

8. The brine concentration process prediction system according to claim 1, wherein, The error range of the precipitation amount of the final precipitate predicted by the prediction unit is within 5%.

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