Brine concentration process prediction system

Abstract

This embodiment provides a brine concentration process prediction system including: a data collection unit that collects data on initial brine and high-concentration brine; a data pre-processing unit that converts the data collected by the data collection unit so that it can be applied to a subsequent data processing unit; a data processing unit that calculates the solubility of each precipitate component using the data converted by the data pre-processing unit; and a data prediction unit that calculates the final precipitate amount and the concentration of each ion component in the final concentrated brine 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 type of precipitate, the amount of precipitate, and the concentration of components in saltwater during the saltwater concentration process. [Background technology]

[0002] Lithium (Li), element number 3, is the smallest and lightest metallic element, excluding hydrogen (number 1) and helium (number 2), which are gases at room temperature. It possesses unique properties that cannot be substituted by other elements. These characteristics make it a key component of high-performance lithium-ion batteries, and demand for it continues to grow as the use of lithium-ion batteries in mobile phones and electric vehicles increases. While most of the Earth's lithium is found in seawater, its concentration is too low to be commercially extracted. To produce lithium compounds, it must be extracted from lithium-containing minerals or from brine with a high lithium content. The most common method for extracting lithium from brine containing high concentrations of sodium (Na), potassium (K), magnesium (Mg), or calcium (Ca) is to confine the brine in a large pond and concentrate it through natural evaporation, similar to a salt pan, to create a highly concentrated brine. Then, impurities are chemically purified to create a lithium-containing solution with a high lithium concentration and low contents of other elements, which is then extracted in the form of a solid lithium compound.

[0003] During this process, when low-concentration brine is concentrated to a high concentration, various ions in the brine become concentrated beyond their solubility, causing precipitation, with a typical precipitate being sodium chloride (NaCl). In the case of Chile's Atacama Salt Lake, the most famous brine resource for producing lithium, the Li concentration is increased by 20 to 30 times per pond, but assuming a 20-fold concentration, 95% of the water that was initially present will evaporate. This means that starting from 20% brine, 19% will evaporate, leaving only 1, and the concentration will increase 20 times, and various salts equivalent to about 30 to 40% of the mass of the evaporated water will precipitate in this process.

[0004] If a substance containing lithium precipitates from this salt, a process loss occurs. Even if the precipitated salt itself does not contain lithium, it precipitates within the pond, and because the precipitated salt is wet with brine, a certain amount of concentrated brine cannot be sent to the next process. Also, lithium is lost during the brine concentration process because there is a portion that leaks or is lost at the bottom of the pond. However, in general, lithium loss during concentration is greater than the lithium loss during the process of refining impurities from high-concentration brine, so it is important to calculate the yield of the brine concentration process.

[0005] The most important information to predict the yield of the brine concentration process and the composition of the impurity purification process after concentration is the type and amount of precipitates. Water evaporation does not cause lithium loss, and in general, the amount lost is not large, with most lithium loss occurring from precipitates and water.

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

[0007] In one embodiment of the present invention, a system is provided for predicting the composition of precipitates, the amount of precipitates, and the concentration of each component in concentrated brine during the brine concentration process. [Means for solving the problem]

[0008] A brine concentration process prediction system according to one embodiment of the present invention may include a data collection unit that collects data on initial brine and high-concentration brine; a data pre-processing unit that converts data collected by the data collection unit so that the data can be applied to a subsequent data processing unit; a data processing unit that calculates the solubility of each precipitate component using the data converted by the data pre-processing unit; and a data prediction unit that predicts the final precipitate amount and the concentration of each ion component in the final concentrated brine using the solubility of each precipitate component calculated by the data processing unit.

[0009] The data preprocessing unit may calculate a concentration change value for each ion component in the high-concentration salt water and the initial salt water collected by the data collecting unit.

[0010] The data processing unit calculates the effective solubility of each precipitate component using the data converted by the data preprocessing unit, and the solubility of each component can be calculated through multiple nonlinear regression analysis or machine learning.

[0011] In addition, the data processing unit can additionally calculate the amount of carbon dioxide absorption, which can be calculated from the calcium (Ca), sulfate (SO4), boron (B) concentrations and pH values ​​in the initial brine and the high-concentration brine.

[0012] The data pre-processing unit, the data processing unit, and the data prediction unit may calculate optimal concentrations of each component in the final concentrated brine by repeating calculations according to changes in data of the highly concentrated brine collected by the data collecting unit.

[0013] The Li concentration in the final concentrated brine may be in the range of 3.5 g / L to 18 g / L, and the error range of the final deposition amount predicted by the prediction unit may be 5% or less. [Effects of the Invention]

[0014] According to one embodiment of the present invention, the type and amount of precipitated salts and the content of components in the concentrated brine can be predicted from changes in the concentrations of components in the brine, which has the advantage that lithium production can be increased by adjusting the operating conditions of the brine concentration process.

[0015] According to one embodiment of the present invention, the type and amount of precipitated salt, as well as the content of components in concentrated brine, can be predicted from changes in the concentration of components in brine. This can be used to design evaporation concentration processes in salt lake areas, where quantitative prediction is difficult. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a schematic diagram illustrating a brine concentration process prediction system according to an embodiment of the present invention. [Figure 2] 1 shows a schematic diagram of a method for calculating component concentrations during the brine concentration process. [Figure 3] This shows the activity coefficient and ionic strength according to existing thermodynamic calculation formulas. [Figure 4] The results of Experimental Example 1 and Comparative Example 1 are shown. [Figure 5] The results of Example 1 and Experimental Example 1 are shown. [Figure 6] The results of Example 2 and Experimental Example 1 are shown. [Figure 7] The results of Example 3 and Experimental Example 2 are shown. DETAILED DESCRIPTION OF THE INVENTION

[0017] In describing the present invention, terms such as first, second, and third are used to describe various parts, components, regions, layers, and / or sections, but are not limited to these. These terms are used to distinguish one part, component, region, layer, or section from another part, component, region, layer, or section. Therefore, a first part, component, region, layer, or section described below may be referred to as a second part, component, region, layer, or section without departing from the scope of the present invention.

[0018] The terminology used herein is merely for the purpose of referring to particular embodiments and is not intended to limit the present invention. As used herein, the singular form includes the plural form unless the context clearly dictates otherwise. As used in the specification, the meaning of "comprising" embodies certain properties, regions, integers, steps, operations, elements, and / or components, and does not exclude the presence or addition of other properties, regions, integers, steps, operations, elements, and / or components.

[0019] Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention belongs. Terms defined in commonly used dictionaries are additionally interpreted as having a meaning consistent with the relevant technical literature and the presently disclosed content, and are not interpreted in an ideal or very formal sense unless otherwise defined.

[0020] DETAILED DESCRIPTION OF THE INVENTION The following detailed description of the present invention is given by way of example only, and the present invention is not limited thereto, but is defined only by the scope of the claims set forth below.

[0021] FIG. 1 is a schematic diagram of a system for predicting a brine concentration process according to one embodiment of the present invention. Referring to FIG. 1, a system for predicting a brine concentration process according to an embodiment of the present invention may include a data collection unit 10, a data pre-processing unit 20, a data processing unit 30, and a data prediction unit 40.

[0022] The data collection unit 10 can collect various data such as the type of components in the initial saltwater and the high saltwater concentration, component concentration data, precipitate composition, precipitate amount, weather data, saltwater pH value, etc. The data collection unit 10 can build a database to store the collected data.

[0023] As an example, the data collection unit 10 can input the following already measured data.

[0024] -Ion concentration in salt water

[0025] [Table 1]

[0026] The data collected by the data collecting unit 10 may be transmitted to a data pre-processing unit 20. The data pre-processing unit 20 may calculate a difference in concentration of each ion component between the high-concentration salt water and the initial salt water, and transmit the calculated difference in concentration of each ion component to a data processing unit 30.

[0027] For example, the following calculations can be performed in the data pre-processing unit 20 using data such as ion concentration and pH in the salt water. 1) Anions (OH) that cannot be obtained in the collection section - , Cl - )concentration → pH to OH - Calculate the concentration (using the phreeqc thermodynamic program) → Considering charge balance, Cl - Calculate the concentration 2) Ion B(OH)3:B(OH)4 - Calculate the ratio →Since the pH is fixed, B(OH)3 + OH - →B(OH)4 - Calculation using the equilibrium constant of the reaction →Since the amounts of all cations and anions have been calculated, Cl - Recalculate ion concentrations Through the above series of calculations, the data preprocessing unit 20 can obtain the following data:

[0028] [Table 2]

[0029] The data processing unit 30 can calculate the effective solubility of each precipitate component using the transferred concentration values ​​of each ion component.

[0030] To calculate the effective solubility of each precipitate component, the chemical formula and solubility of candidate substances that can be precipitated during the concentration process can be input in advance through a thermodynamic database and existing experimental results.

[0031] Then, by inputting the concentration experiment result data, the trend of the concentration change of each elemental component can be obtained through multiple nonlinear regression analysis. Then, the activity coefficient of the input thermodynamic database can be corrected to calculate the effective solubility of each precipitate component. The effective solubility of each precipitate component can be calculated as a function of ionic strength and the concentration of each ion.

[0032] For example, the data processing unit 30 can perform the following using the data obtained by the data preprocessing unit 20.

[0033]

number

[0034] -KSP calculation of major precipitates

number

number

number

number

number

[0035] The KSP of NaCl based on ionic strength can be calculated as follows:

[0036] KSP of NaCl by I

[0037] [Table 3]

[0038] On the other hand, when considering atmospheric CO2, additional calculations can be performed taking into account the following reaction:

number

number

[0039] The data prediction unit 40 can predict the precipitated precipitate components, the precipitate amount, and the ionic component concentrations in the final concentrated brine by applying the effective solubility values ​​for each precipitate component transmitted from the data processing unit 30.

[0040] As an example, the data prediction unit 40 can perform the following. The concentration was gradually increased from 1.7 g / L of Li input in the data collection unit 10. 1) Calculate the concentration including Cl and OH 2) Calculate ionic strength 3) Calculate the solubility of each precipitate 4) After removing the concentration of precipitates, repeat the calculation method of 1) As a result, it is possible to predict the pH, ion concentration, precipitate components, etc. in brine at each concentration stage.

[0041] Figure 2 shows a schematic diagram of how to calculate the component concentrations during the brine concentration process.

[0042] Figure 2(a) shows the process of calculating the ionic component concentrations in saltwater using existing thermodynamic calculations.

[0043] Generally, in existing thermodynamic calculations, the solubility of a precipitate is calculated using the solubility product, specifically the pKSP value, which is the negative logarithm of the solubility product.

[0044] Example: In the case of CaSO4, pK SP If =2.3,

number

[0045] In the above formula (1), a is the ion activity (hereinafter referred to as activity) of each component, and can be expressed by the following relational expression.

[0046]

number

[0047] Existing thermodynamic calculations require a huge amount of calculations, as they first calculate the ionic strength, then use a complex formula to calculate the activity coefficient (concentration x activity coefficient) for each element at that ionic strength, and then compare the product of the activity with the specific solubility product of each substance to calculate whether or not precipitation will occur and the amount of precipitation, and then calculate the equilibrium concentration based on this.

[0048] In addition, as shown in Figure 3, the activity coefficient calculated using existing thermodynamic formulas is known to have a large error in solutions with high ion concentrations.

[0049] Referring to FIG. 3, the ionic strength

number

[0050] In the above formula (2), the activity coefficient γ is a value that represents the effective concentration relative to the actual concentration, and in high-concentration solutions, the effective concentration may differ from the actual concentration because the charge of the ion, the interaction with the solvent particles, etc. are affected by other ions in the vicinity. This can be calculated from the ionic strength, which is a property of the solution, using the following formula:

[0051]

number

number

[0052] In the formulas (3) and (4), the subscripts + and - represent the cations and anions of the substance under consideration, and a and c represent the cations and anions of the substance under consideration. For example, if the substance under consideration is NaCl, the subscript + represents Na + and the subscript - means Cl - Also, a means K + , Ca 2+ , Mg 2+ etc., and c is SO4 2- , B(OH)4 - In equations (3) and (4), the first term F can be expressed as follows:

[0053]

number

[0054] In the formulas (2) to (5), A and B c,a , C c,a , Φ x,y , Ψ xc,a These are values ​​that have already been reported using the Pitzer equation (equation Pitzer) and other methods.

[0055] FIG. 2(b) illustrates a process for calculating the concentration of each component in saltwater according to an embodiment of the present invention. Referring to FIG. 2(b), conventional thermodynamic calculations calculate whether precipitation occurs by comparing the solubility expressed as the solubility product, which is a thermodynamic value, with the activity product of each element. However, in the present invention, the amount of precipitation is calculated by comparing the corrected solubility of the component (the solubility value is changed instead of calculating the activity coefficient and changing the activity) with the concentration, thereby reducing the amount of calculation and obtaining concentrated composition results that reflect actual experimental results.

[0056] In one embodiment of the present invention, the saltwater composition according to the experimental results is input, and the activity coefficient γ of each component is calculated. SP Calculate the K calculated for the target component. SP is fitted with a linear or quadratic function of the ion concentration of each component, or component-specific K is calculated through machine learning. SP can be recalculated to calculate the effective solubility of the precipitate.

[0057] The reason why the present invention is able to perform the above calculations is that the composition of the brine concentration process does not change suddenly during the concentration process, and complex physical quantities such as ionic strength and activity coefficient ultimately depend on the composition, so when the concentrations of Na, K, and Cl change, various thermodynamic physical quantities also change in a certain relationship. Therefore, if solubility can be expressed as a function of ionic strength, which can be easily calculated, and the concentration of each ion, it is possible to calculate effective solubility that is appropriate for the actual value without having to calculate complex activity coefficients. This has the advantage of being more accurate than existing thermodynamic calculations, requiring less calculation effort, and not requiring a separate system for calculation.

[0058] Meanwhile, the data collection unit 10 can collect data such as temperature, atmospheric pressure, and CO2 concentration, as well as data such as the amount of calcium carbonate (CaCO3) precipitated and pH value. After high-concentration brine data is newly collected by the data collection unit 10, it can be transmitted to the data pre-processing unit 20. Using the results calculated by the data pre-processing unit 20, the data processing unit 30 can calculate data such as effective solubility. At this time, the amount of CO2 absorbed and dissolved can be calculated through equilibrium concentration calculations using the calculated and updated amount of calcium carbonate (CaCO3) precipitated. The amount of carbon dioxide absorbed can be calculated from the calcium (Ca), sulfate (SO4), and boron (B) concentrations and pH value in the initial brine and high-concentration brine. Although not explicitly stated herein, it is obvious that the calcium (Ca), sulfate (SO4), and boron (B) concentrations refer to the concentrations of calcium ions, sulfate groups, and boron ions.

[0059] Specifically, it is possible to take into account all changes due to the continuous absorption of CO2, which is present in trace amounts in the atmosphere. This means that when considering the solubility of CO2, the amount of CaCO3 precipitation, which was not previously taken into account, is taken into account. The amount of CaCO3 precipitation is less than 1 / 10 of the amount of CaSO4 precipitation, which is the main precipitation phase of Ca, and it takes a long time to reach chemical equilibrium, so it is not taken into account in most existing brine concentration simulation calculations. However, calcium exists not only as carbonate but also as sulfate (CaSO4), boron compounds (CaB x O y Since the calcium concentration is converted into amorphous phases, the concentrations of SO4 and B can be improved by accurately predicting the calcium concentration.

[0060] Specifically, CO2 in the atmosphere converts to carbonate groups (CO3 2- ) can change the pH in saltwater as follows: CO2(g) + H2O(l) → CO3 2- (aq)+2H + (aq)

[0061] CO2 is dissolved in saltwater and forms carbonate groups (CO3 2-) and reacts with calcium ions to form CaCO3 precipitates, releasing carbonate groups (CO3 2- ) is consumed. Therefore, additional atmospheric CO2 is dissolved into the brine, lowering the pH of the brine during this process. As the pH changes, the solubility of many precipitated phases changes. In particular, it changes the solubility of Mg(OH)2, which is pH-sensitive, and therefore plays an important role in predicting Mg concentrations. Therefore, considering atmospheric CO2 dissolution has the effect of improving concentration predictions for B, SO4, and Mg in addition to Ca.

[0062] This has the advantage of enabling more accurate prediction of the content of ionic components in the concentrated brine and the amount of precipitates, and has the advantage of improving lithium production efficiency and economy throughout the brine concentration process.

[0063] The CO2 absorption value is used to adjust the Ca concentration in the data processor 30, and calculations are repeated until a result that matches the field data is produced. The result leads to a "Concentrated Brine Composition Prediction" that can calculate the expected composition and amount of precipitate at the final concentration.

[0064] Meanwhile, the data pre-processing unit 20, the data processing unit 30, and the data prediction unit 40 can calculate the optimum concentration of each component in the final concentrated brine by repeatedly calculating according to changes in the data of the high concentration brine collected by the data collecting unit 10, and the Li concentration in the final concentrated brine may be in the range of 3.5 g / L to 18 g / L. [Example]

[0065] DETAILED DESCRIPTION OF THE INVENTION The following detailed description of the present invention is given by way of example only, and the present invention is not limited thereto, but is defined only by the scope of the claims set forth below.

[0066] (Experimental Example 1) Concentration experiments were conducted using saltwater, and the concentrations of components at each concentration stage are shown in Table 4 below.

[0067] [Table 4]

[0068] (Comparative Example 1) Using brine containing the components listed in C.1 of Table 4 as the raw material, the concentrations of each ion component were calculated using the existing thermodynamic program OLI Flowsheet 11.0.

[0069] FIG. 4 shows the results of Experimental Example 1 and Comparative Example 1. Referring to FIG. 4, it can be seen that there is a large difference in the concentration values ​​of each ionic component between Experimental Example 1 and Comparative Example 1. This is thought to be due to the fact that the saltwater has a high ionic concentration, which is a non-ideal composition. However, due to the characteristics of the thermodynamic program, which is designed to calculate an ideal composition over a wide range, errors occur in the calculation of activity and solubility, resulting in differences in the solubility of each ionic component.

[0070] As an example, consider a case where an error in the calculation of KCl solubility results in a 5% undercalculation of the amount of precipitate. Suppose the brine contains 10g / L of potassium when the Li concentration is 1g / L. However, after concentrating the brine to a Li concentration of 25g / L, the K concentration in the concentrated brine is measured as 45g / L. The actual amount of precipitated K is 205g / L, based on the post-concentration value. (If no precipitation had occurred, the amount would be 10*25 / 1 = 250g / L, but only 45g / L remains.) Undercalculating the amount of precipitate by 5%, we would have predicted that 195g / L of K would have precipitated, leading to a predicted residual K concentration of 250g / L - 195g / L = 55g / L. Consequently, a 5% error in the precipitate prediction results in a 22% difference in the concentrated brine composition. Again, this difference in concentration can affect the predicted amount and type of precipitate, resulting in significant errors.

[0071] Example 1 Using the brine C1 in Table 4 as a raw material, the concentrations of each component were calculated using a brine concentration process prediction system that took into account the experimental results of the step-by-step brine concentration.

[0072] FIG. 5 shows the results of Example 1 and Experimental Example 1. Referring to FIG. 5, it can be seen that the difference in the concentration values ​​of each component between Example 1 and Experimental Example 1 is significantly reduced compared to Comparative Example 1 shown in FIG. 4. In particular, the components K and SO4 2- It was confirmed that the difference between Example 1 and Experimental Example 1 was relatively reduced compared to Comparative Example 1 in FIG.

[0073] Example 2 The concentrations of each component were calculated in the same manner as in Example 1, except that CO2 absorption was corrected in the brine concentration process prediction system.

[0074] FIG. 6 shows the results of Example 2 and Experimental Example 1. Referring to FIG. 6, it can be seen that the difference in the concentration values ​​of each element between Example 2 and Experimental Example 1 is smaller than the difference between Example 1 and Experimental Example 1, and it can be seen that the qualitative tendency of each element to increase or decrease due to concentration is also predicted similarly to the experimental results.

[0075] Meanwhile, the error in the amount of precipitate generated during the concentration process in Example 2 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. If the Li concentration in the concentrated brine is 28.6 g / L and the Na concentration is 26.21 g / L, it is calculated that Na will remain in the concentrated brine to a concentration of approximately 1.56 (26.21 * 1.7 / 28.6) g / L, based on the concentration before concentration (C1 in Table 4), and the precipitated Na is calculated to be 100.3 g / L. In Example 2, the predicted Na concentration is approximately 1.79 g / L, and 100.1 g / L of the Na before concentration is calculated to precipitate as Na precipitate, which is approximately 0.2% smaller than the actual experimental result (Experimental Example 1). Furthermore, when K precipitates were calculated using the same method, the predicted amount was 0.4% higher than the actual experimental result, and the total amount of precipitates was predicted within an error range of 0.2% or less.

[0076] (Experimental Example 2) Concentration experiments were conducted using saltwater, and the concentrations of components at each concentration stage are shown in Table 5 below.

[0077] [Table 5]

[0078] Example 3 Using the saltwater C1 in Table 5 as a raw material, the concentrations of each component were calculated in the same manner as in Example 2.

[0079] FIG. 7 shows the results of Example 3 and Experimental Example 2. 7, it can be seen that there is little difference in the concentration values ​​of each ion component between Example 3 and Experimental Example 2. It can also be seen that the error range for Na, K, and SO4 components is within 5%.

[0080] The present invention is not limited to the above-described embodiments, and can be manufactured in various different forms, and a person skilled in the art to which the present invention pertains should understand that the present invention can be embodied in other specific forms without changing the technical concept or essential features of the present invention. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and are not limiting.

Claims

1. A data collection unit for collecting data on the initial saltwater and highly saltwater; a data preprocessing unit that converts the data collected by the data collection unit so that the data can be applied to a subsequent 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 final amount of precipitate and the concentration of each ion component in the final concentrated brine using the solubility of each precipitate component calculated by the data processing unit; Brine concentration process prediction system.

2. The data preprocessing unit calculates a concentration change value of each ion component in the high-concentration salt water and the initial salt water collected by the data collecting unit. The brine concentration process prediction system according to claim 1 .

3. a data processing unit that calculates effective solubilities of each precipitate component using the data converted by the data preprocessing unit; Calculate the solubility of each precipitate component through multiple nonlinear regression analysis or machine learning. The brine concentration process prediction system according to claim 1 .

4. The data processing unit additionally calculates the amount of carbon dioxide absorbed. The brine concentration process prediction system according to claim 1 .

5. The amount of carbon dioxide absorbed is Calcium (Ca), sulfate (SO ) in the initial saltwater and highly saltwater ４ ), calculated from the boron (B) concentration and pH value; The brine concentration process prediction system according to claim 2 .

6. The data preprocessing unit, the data processing unit, and the data prediction unit are Calculating the optimum concentration of each ion component in the final concentrated brine by repeatedly calculating according to changes in the data of the high concentration brine collected by the data collecting unit; The brine concentration process prediction system according to claim 1 .

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

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

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