Method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery material

Abstract

The invention discloses a method for extracting and recovering high-purity lithium carbonate and iron phosphate from a waste lithium battery material, and particularly relates to the technical field of extraction and recovery, and the method comprises the following steps: step 1, in a first extraction stage, obtaining a first extraction control index of an extraction device at a T1 + a moment, and obtaining first characteristic data of a leachate in the extraction device; marking the first extraction control index and the first characteristic data as iron phosphate characteristic data; t1 and a are integers greater than zero; 2, inputting the iron phosphate characteristic data into a pre-constructed iron phosphate quality analysis model to obtain a first quality index at the moment T1 + a; according to the method, the extraction state is predicted in real time through the quality analysis model, and early warning of the abnormal extraction state is generated; when the extraction equipment is in the abnormal extraction state, the system can automatically generate the adjusting instruction and adjust according to the optimal adjusting strategy, it is ensured that the extraction process is recovered to the normal state in time, and uncontrollable risks in operation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of extraction and recovery, and more specifically, to a method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials. Background Art

[0002] In the recycling of waste lithium batteries, lithium carbonate and iron phosphate, as important materials in battery manufacturing, have extremely high recycling value. Through an effective extraction and recovery process, not only can high-purity lithium carbonate be extracted from waste batteries, but also the positive electrode material iron phosphate can be recovered for the preparation of lithium iron phosphate batteries. However, traditional recycling methods have many shortcomings in process control, product purity and resource utilization, such as complex processes, low efficiency, and difficulty in ensuring product purity. Therefore, how to introduce intelligent control in the entire recycling process, accurately manage each link, ensure efficient recycling while improving product quality, has become an important research direction in today's waste lithium battery recycling technology.

[0003] In the prior art, a Chinese patent application with publication number CN117270591A discloses an extraction production line monitoring method and system, which obtains the absorbance of the liquid in each extraction tank to light of the same set wavelength in real time; maps the obtained absorbance information to the concentration of the set element in the liquid in each extraction tank, and determines the balance point position according to the distribution of the concentration of the set element in the liquid in each extraction tank; when the balance point position is not the preset position, generates prompt information for adjusting the process or adjusts the flow rate of the liquid flowing into the extraction tank according to the relationship between the balance point position and the preset position. Although this method can realize real-time monitoring and control of the extraction production line, research and application of the above method and prior art found that the control system has low accuracy, which may lead to over-adjustment or under-adjustment, and cannot maintain a stable process.

[0004] To this end, the present invention provides a method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials, comprising:

[0007] Step 1: In the first extraction stage, obtain the first extraction control index of the extraction equipment at time T1+a, and obtain the first characteristic data of the leaching liquid in the extraction equipment; mark the first extraction control index and the first characteristic data as iron phosphate characteristic data; T1 and a are both integers greater than zero.

[0008] Step 2: Input the iron phosphate characteristic data into the pre-built iron phosphate quality analysis model to obtain the first quality index at time T1+a.

[0009] Step 3: Determine whether the extraction equipment at time T1+a is in an abnormal extraction state based on the first quality index. If an abnormal extraction state occurs, generate an extraction adjustment instruction and jump to step 4; if no abnormal extraction state occurs, generate a second extraction instruction and jump to step 5.

[0010] Step 4: According to the extraction adjustment instruction, the extraction equipment is adjusted based on the first quality index to optimize the extraction process and obtain high-purity iron phosphate.

[0011] Step 5: Receive the second extraction instruction, obtain the second quality index of the extraction equipment at time T2+b in the second extraction stage, and determine whether the extraction equipment at time T2+b is in an abnormal extraction state based on the second quality index; if so, generate a second extraction adjustment instruction and jump to step 6; if not, continue the extraction until the extraction of lithium carbonate is completed.

[0012] Step 6: Receive a second extraction adjustment instruction, and adjust the extraction equipment based on the second quality index to obtain high-purity lithium carbonate.

[0013] Furthermore, the method for obtaining the first extraction control index of the extraction device at time T1+a includes:

[0014] Step a1. Collect first parameter characteristic data at time T1, wherein the first parameter characteristic data includes a first temperature value, a first pH value and a first stirring speed; mark the first temperature value, the first pH value and the first stirring speed as Ws1, Ph1 and Sd1 respectively.

[0015] Step a2. Formulate and calculate the first parameter characteristic data to obtain the first extraction control index at time T1, and the calculation formula is:

[0016] θ1=(Ws1×α1+Ph1×α2+Sd1×α3)+(μ1×Ws1×Ph1);

[0017] Wherein, θ1 represents the first extraction control index at time T1, α1 represents the weight factor of the first temperature value, α2 represents the weight factor of the first pH value, α3 represents the weight factor of the first stirring speed, and μ1 represents the weight coefficient of the interaction between the first temperature value and the first pH value.

[0018] Step a3: Input the first extraction control index at time T1 into a pre-built parameter analysis model to predict the first extraction control index at time T1+a.

[0019] Furthermore, the method for generating the pre-built parameter analysis model includes:

[0020] Obtain the first historical extraction control index, and establish an extraction time series set based on the first historical extraction control index, wherein the extraction time series set includes u first historical extraction control indexes, the time intervals for obtaining the u first historical extraction control indexes are equal, and the u first historical extraction control indexes correspond to a preset time; the unit of the preset time is seconds or minutes.

[0021] Preset the sliding step size and sliding window length; convert the historical first extraction control index in the extraction time series set into multiple training samples using the sliding window method, use the training samples as the input of the recurrent neural network model, predict the first extraction control index after the sliding step size as the output, use the first extraction control index of each training sample as the prediction target, and use the prediction accuracy as the training target to train the recurrent neural network model; generate a parameter analysis model for predicting the first extraction control index in the future time based on the historical first extraction control index in the extraction time series set; wherein the recurrent neural network model is an RNN neural network model.

[0022] Furthermore, the first characteristic data includes iron ion concentration, viscosity of the leachate and impurity percentage of the leachate; the iron ion concentration is directly analyzed by a concentration analysis device; the viscosity of the leachate is measured by a rotational viscometer or a capillary viscometer.

[0023] The method for obtaining the impurity percentage of the leachate includes:

[0024] Step b1. Collecting a leachate sample and analyzing the impurity ion concentration using a concentration analysis device; the impurity ion concentration includes the concentration of copper, zinc and lithium ions.

[0025] Step b2. Calculate the mass of the impurity ions: m i =C i ×V, where m i represents the mass of the i-th impurity ion, C i represents the concentration of the i-th impurity ion; V represents the volume of the leachate sample.

[0026] Step b3. Add up the masses of all impurity ions to get the total mass of impurities: m 总 =∑m i .

[0027] Step b4. Obtain the total mass M of the leachate sample 浸出液 , according to the total mass of impurities m 总 The total mass of the leaching solution sample M 浸出液 , calculate the impurity percentage Zbf:

[0028] Furthermore, the method for generating the pre-built iron phosphate quality analysis model includes:

[0029] Each set of iron phosphate characteristic data is converted into the form of a first characteristic vector, and all elements of the first characteristic vector are used as inputs of the iron phosphate quality analysis model. The iron phosphate quality analysis model uses the first quality index predicted by each set of iron phosphate characteristic data as output, the actual first quality index corresponding to each set of iron phosphate characteristic data as the prediction target, and the sum of the first prediction accuracies of all predicted first quality indexes as the training target; wherein the calculation formula for the first prediction accuracy is: Z1=(a n -y n ) 2 , where n is the number of each set of iron phosphate feature data, Z1 is the first prediction accuracy, a n is the predicted first quality index corresponding to the nth group of iron phosphate characteristic data, y n is the actual first quality index corresponding to the nth group of iron phosphate feature data; the iron phosphate quality analysis model is trained until the sum of the first prediction accuracies reaches convergence and the training is stopped; the iron phosphate quality analysis model is a deep neural network model or a deep belief network model.

[0030] Further, the method for determining whether the extraction device at time T1+a is in an abnormal extraction state includes:

[0031] The first quality index at time T1+a predicted by the iron phosphate quality analysis model is marked as Zls. A first index threshold is preset, and the preset first index threshold includes S1 and S2, S1>S2.

[0032] The first quality index is compared with a preset first index threshold.

[0033] If Zls>S1 or Zls<S2, it is determined that the extraction equipment at time T1+a is in an abnormal extraction state.

[0034] If S1≥Zls≥S2, it is determined that the extraction equipment at time T1+a is in a normal extraction state, and the extraction is continued to be completed with the first parameter characteristic data corresponding to the first extraction control index, and the second extraction stage is entered.

[0035] Further, the method for adjusting the extraction device based on the first quality index includes:

[0036] The first parameter characteristic data corresponding to the first quality index is input into the pre-built digital twin model for simulation to obtain the optimal adjustment strategy.

[0037] Methods for achieving the best tuning strategy include:

[0038] Step c1. Acquire first parameter characteristic data of the extraction equipment, use the first pH value and the first stirring speed in the first parameter characteristic data as fixed quantities, the first temperature value as a variable, and use the current parameter value of the first temperature value as W.

[0039] Step c2: Let W=W+D1, and record the first quality index under the parameter value W, where D1 is a natural number greater than zero.

[0040] Step c3. Repeat step c2, when W is equal to the preset first temperature threshold, obtain G first quality indexes under the first temperature parameter value, and jump to step c4, where G is an integer greater than zero;

[0041] Step c4. taking the first temperature value and the first stirring speed as fixed quantities, the first pH value as a variable, and taking the current parameter value of the first pH value as U.

[0042] Step c5. Reset W, set U=U+D2, and record the first quality index under the parameter value U, where D2 is a natural number greater than zero.

[0043] Step c6. Repeat step c5, when U is equal to the preset first pH threshold, obtain H first quality indexes under the first pH parameter value, where H is an integer greater than zero.

[0044] Step c7. Use the first temperature value and the first pH value as fixed quantities, the first stirring speed as a variable, and the current parameter value of the first stirring speed as F.

[0045] Step c8. Reset U, set F=F+D3, and record the first quality index under the parameter value F, where D3 is a natural number greater than zero.

[0046] Step c9. Repeat step c8, when F is equal to the preset first stirring speed threshold, obtain K first quality indexes under the first stirring speed parameter value, where K is an integer greater than zero.

[0047] Step c10. Mark the G first mass indices under the first temperature parameter value, the H first mass indices under the first pH parameter value, and the K first mass indices under the first stirring speed parameter value as first extraction control data, accumulate and fuse the first extraction control data to obtain L first mass indices, and sort the L first mass indices from large to small according to numerical values.

[0048] Step c11. taking the first temperature value, the first pH value and the first stirring speed of the extraction equipment corresponding to the first quality index with the largest value in the sorting as the optimal adjustment strategy.

[0049] Furthermore, in the first extraction stage, the first temperature value in the first parameter characteristic data has a value range of [40°C, 60°C], the first pH value has a value range of [3,5] and the first stirring speed has a value range of [100rpm, 300rpm]; in the second extraction stage, the second temperature value in the second parameter characteristic data has a value range of [50°C, 70°C], the second pH value has a value range of [8,10] and the second stirring speed has a value range of [50rpm, 150rpm].

[0050] Furthermore, the method for obtaining the second quality index of the extraction device at time T2+b includes:

[0051] The second extraction control index of the extraction device at time T2+b and the second characteristic data of the extraction mixture in the extraction device are obtained and marked as lithium carbonate characteristic data; the second characteristic data includes lithium ion concentration, viscosity of the extraction mixture and impurity percentage.

[0052] The lithium carbonate characteristic data is input into a pre-built lithium carbonate mass analysis model to obtain the second mass index at time T2+b.

[0053] Furthermore, the method for generating the lithium carbonate quality analysis model includes:

[0054] Each set of lithium carbonate characteristic data is converted into the form of a second characteristic vector, and all elements of the second characteristic vector are used as inputs of a lithium carbonate quality analysis model. The lithium carbonate quality analysis model uses the second quality index predicted by each set of lithium carbonate characteristic data as output, uses the actual second quality index corresponding to each set of lithium carbonate characteristic data as a prediction target, and uses minimizing the sum of the second prediction accuracies of all predicted second quality indexes as a training target; wherein the calculation formula for the second prediction accuracy is: Z2=(b m -s m ) 2 , where m is the number of each set of lithium carbonate feature data, Z2 is the second prediction accuracy, and b m is the predicted second quality index corresponding to the mth group of lithium carbonate characteristic data, s m is the actual second quality index corresponding to the mth group of lithium carbonate feature data; the lithium carbonate quality analysis model is trained until the sum of the second prediction accuracies reaches convergence and the training is stopped; the lithium carbonate quality analysis model is a deep neural network model or a deep belief network model.

[0055] Furthermore, the method for obtaining the second extraction control index of the extraction device at time T2+b includes:

[0056] Step d1. Collect the second parameter characteristic data at time T2, wherein the second parameter characteristic data includes a second temperature value, a second pH value and a second stirring speed; mark the second temperature value, the second pH value and the second stirring speed as Ws2, Ph2 and Sd2 respectively.

[0057] Step d2. Based on the first extraction control index, the second parameter characteristic data is calculated by formula to obtain the second extraction control index at time T2, and the calculation formula is:

[0058] θ2=θ1-(Ws2×β1+Ph2×β2+Sd2×β3)+(μ2×Ws2×Ph2).

[0059] Wherein, θ2 represents the second extraction control index, β1 represents the weight factor of the second temperature value, β2 represents the weight factor of the second pH value, β3 represents the weight factor of the second stirring speed, and μ2 represents the weight coefficient of the interaction between the second temperature value and the second pH value.

[0060] Step d3: Input the second extraction control index at time T2 into the pre-constructed parameter analysis model to predict the second extraction control index at time T2+b.

[0061] Further, the method for determining whether the extraction device at time T2+b is in an abnormal extraction state includes:

[0062] The second mass index at time T2+b predicted by the mass analysis model of lithium carbonate is marked as Zlz; and a second index threshold is preset, wherein the preset second index threshold includes Y1 and Y2, and Y1>Y2.

[0063] The second quality index is compared with a preset second index threshold.

[0064] If Zlz>Y1 or Zls<Y2, it is determined that the extraction equipment at time T2+b is in an abnormal extraction state.

[0065] If Y1≥Zlz≥Y2, it is determined that the extraction equipment at time T2+b is in a normal extraction state, and the extraction is continued to be completed with the second parameter characteristic data corresponding to the second extraction control index to obtain high-purity lithium carbonate.

[0066] Technical effects and advantages of the present invention:

[0067] 1. The present invention can predict the separation effect of iron phosphate in the first extraction stage in real time by obtaining the first extraction control index and the first characteristic data of the extraction equipment at time T1+a and inputting them into the iron phosphate quality analysis model, and can predict the separation effect of lithium carbonate in the second extraction stage in real time by obtaining the second extraction control index and the second characteristic data of the extraction equipment at time T2+b and inputting them into the lithium carbonate quality analysis model. Based on different extraction stages and precise extraction control methods, the error in the operation process can be effectively reduced, ensuring that the optimal temperature, pH value and stirring speed are achieved during the extraction process, thereby improving the purity and recovery rate of the product.

[0068] 2. The present invention predicts the extraction state in real time through the quality analysis model and generates an early warning of abnormal extraction state. When the extraction equipment is in an abnormal extraction state, the system will automatically generate an adjustment instruction and adjust it according to the optimal adjustment strategy to ensure that the extraction process returns to a normal state in time, reducing the uncontrollable risks in the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of the method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials in Example 1;

[0070] Figure 2 A flow chart of a method for obtaining the first extraction control index at time T1+a of the extraction equipment of Example 1;

[0071] Figure 3 This is a flow chart of a method for obtaining the impurity percentage of the leachate of Example 1;

[0072] Figure 4 This is a flow chart of the method for obtaining the optimal adjustment strategy in Example 1. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and a similar second unit may be referred to as a first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] Example 1

[0077] See also Figure 1 As shown, this embodiment discloses a method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials, comprising:

[0078] Step 1: In the first extraction stage, obtain the first extraction control index of the extraction equipment at time T1+a, and obtain the first characteristic data of the leaching liquid in the extraction equipment; mark the first extraction control index and the first characteristic data as iron phosphate characteristic data; T1 and a are both integers greater than zero.

[0079] It should be understood that: the extraction equipment is used to prepare a solvent extractor for high-purity lithium carbonate and iron phosphate, and the solvent extractor includes at least a leaching reactor, a pH automatic control system, a temperature control system, a filtering and centrifugal device, and an automatic stirring system; wherein the leaching reactor is used to mix the waste lithium battery material with the solvent for preliminary dissolution and reaction; the pH automatic control system is used to monitor and adjust the pH value of the solution to ensure the separation conditions of iron phosphate and lithium carbonate; the temperature control system is composed of a heating and cooling system, and according to the feedback of the temperature sensor, the temperature in the leaching reactor is dynamically adjusted to ensure the optimal precipitation conditions; the filtering and centrifugal device is used to separate the solid matter after precipitation, such as iron phosphate and lithium carbonate; the automatic stirring system is used to ensure the uniform mixing of the extractant and the waste battery material to prevent precipitation or insufficient local reaction. The leachate contains useful components dissolved in the solid matter (such as iron ions and lithium ions) and undissolved impurities.

[0080] It should be noted that: in the first extraction stage, a preset concentration of the first extractant is added to the leachate, and D2EHPA (di(2-ethylhexyl)phosphoric acid) is selected as the selective extractant for iron. The purpose of setting the first extraction stage is to optimize the precipitation efficiency of iron phosphate under different conditions, avoid the formation of impurities, and improve the purity of the product, while ensuring the stability and high recovery rate of this extraction stage. The conditions of the first extraction stage (such as pH value, temperature and stirring speed) precisely control the leachate, thereby ensuring the purity of the optimal iron phosphate extraction.

[0081] See also Figure 2 As shown, specifically, the method for obtaining the first extraction control index of the extraction equipment at time T1+a includes:

[0082] Step a1. Collect first parameter characteristic data at time T1, wherein the first parameter characteristic data includes a first temperature value, a first pH value and a first stirring speed; mark the first temperature value, the first pH value and the first stirring speed as Ws1, Ph1 and Sd1 respectively.

[0083] It should be noted that: the first temperature value is obtained by collecting the temperature sensor connected to the temperature control system; the first pH value is obtained by collecting the pH meter connected to the pH automatic control system.

[0084] Step a2. Formulate and calculate the first parameter characteristic data to obtain the first extraction control index at time T1, and the calculation formula is:

[0085] θ1=(Ws1×α1+Ph1×α2+Sd1×α3)+(μ1×Ws1×Ph1).

[0086] Wherein, θ1 represents the first extraction control index at time T1, α1 represents the weight factor of the first temperature value, α2 represents the weight factor of the first pH value, α3 represents the weight factor of the first stirring speed, and μ1 represents the weight coefficient of the interaction between the first temperature value and the first pH value.

[0087] It should be noted that: the larger the value of μ1, the more significant the influence of the interaction between the first temperature value and the first pH value on the extraction efficiency; the larger the value of the first parameter characteristic data, the larger the first extraction control index, and conversely, the smaller the value of the first parameter characteristic data, the smaller the first extraction control index. The calculation of the first extraction control index is a dimensionless calculation.

[0088] It is further explained that the weight factor can be set according to actual conditions, and the weight factor reflects the degree of influence of each first parameter characteristic data on the actual extraction purity of the extraction equipment. Technical personnel in this field can preset corresponding weight factors according to the degree of influence of each first parameter characteristic data on the actual extraction purity of the extraction equipment, so as to accurately evaluate the purity of iron phosphate actually extracted by the extraction equipment.

[0089] Step a3: Input the first extraction control index at time T1 into a pre-built parameter analysis model to predict the first extraction control index at time T1+a.

[0090] The method for generating the pre-built parameter analysis model includes:

[0091] Obtain the first historical extraction control index, and establish an extraction time series set based on the first historical extraction control index, wherein the extraction time series set includes u first historical extraction control indices, the time intervals for obtaining the u first historical extraction control indices are equal, and the u first historical extraction control indices correspond to a preset time; the preset time can be in seconds or minutes, which is specifically set by the staff.

[0092] The sliding step size and the sliding window length are preset according to the actual experience of the staff; the historical first extraction control index in the extraction time series set is converted into multiple training samples using the sliding window method, the training samples are used as the input of the recurrent neural network model, and the first extraction control index after the sliding step is predicted as the output. The first extraction control index of each training sample is used as the prediction target, and the prediction accuracy is used as the training target to train the recurrent neural network model; a parameter analysis model is generated for predicting the first extraction control index in the future time based on the historical first extraction control index in the extraction time series set; wherein the recurrent neural network model is an RNN neural network model.

[0093] The first characteristic data include the iron ion concentration, the viscosity of the leachate and the impurity percentage of the leachate.

[0094] It should be noted that: the iron ion concentration is directly analyzed by a concentration analysis device; the higher the iron ion concentration, the more iron content is extracted from the leachate in the first extraction stage, which helps to judge the effect and efficiency of iron phosphate extraction; the viscosity of the leachate is measured by a rotational viscometer or a capillary viscometer; higher viscosity affects fluidity and mass transfer efficiency, and also indicates the presence of higher suspended solids (iron phosphate). The concentration analysis device includes an ICP-OES or AAS device.

[0095] See also Figure 3 As shown, the method for obtaining the impurity percentage of the leachate includes:

[0096] Step b1. Collecting a leachate sample and analyzing the impurity ion concentration using a concentration analysis device; the impurity ion concentration includes the concentration of copper, zinc and lithium ions.

[0097] Step b2. Calculate the mass of the impurity ions: m i =C i ×V, where m i represents the mass of the i-th impurity ion, C i represents the concentration of the i-th impurity ion; V represents the volume of the leaching solution sample;

[0098] Step b3. Add up the masses of all impurity ions to get the total mass of impurities: m 总 =∑m i ;

[0099] Step b4. Obtain the total mass M of the leachate sample 浸出液 , according to the total mass of impurities m 总 The total mass of the leaching solution sample M 浸出液 , calculate the impurity percentage Zbf:

[0100] It should be noted that the total mass of the leachate sample M 浸出液 It is measured in advance by an electronic balance and stored in the system database.

[0101] Step 2: Input the iron phosphate characteristic data into the pre-built iron phosphate quality analysis model to obtain the first quality index at time T1+a.

[0102] In implementation, the method for generating the pre-built iron phosphate quality analysis model includes:

[0103] Each group of iron phosphate characteristic data is converted into the form of a first characteristic vector, and all elements of the first characteristic vector are used as input of an iron phosphate quality analysis model. The iron phosphate quality analysis model uses the first quality index predicted by each group of iron phosphate characteristic data as output, and the actual first quality index corresponding to each group of iron phosphate characteristic data as a prediction target, and minimizing the sum of the first prediction accuracies of all predicted first quality indices as a training target.

[0104] It should be noted that the first quality index specifically refers to the purity of the iron phosphate extracted and recovered in the first extraction stage, which is measured and obtained according to methods such as ICP-OES (inductively coupled plasma emission spectroscopy) and XRF (X-ray fluorescence spectroscopy).

[0105] The calculation formula of the first prediction accuracy is: Z1 = (a n -y n ) 2, where n is the number of each set of iron phosphate feature data, Z1 is the first prediction accuracy, a n is the predicted first quality index corresponding to the nth group of iron phosphate characteristic data, y n is the actual first quality index corresponding to the nth group of iron phosphate characteristic data; the iron phosphate quality analysis model is trained until the sum of the first prediction accuracies reaches convergence and the training is stopped.

[0106] It should be noted that the iron phosphate quality analysis model is a deep neural network model or a deep belief network model.

[0107] Step 3: Determine whether the extraction equipment at time T1+a is in an abnormal extraction state based on the first quality index. If an abnormal extraction state occurs, generate an extraction adjustment instruction and jump to step 4; if no abnormal extraction state occurs, generate a second extraction instruction and jump to step 5.

[0108] In implementation, the method for determining whether the extraction device at time T1+a is in an abnormal extraction state includes:

[0109] The first quality index at time T1+a predicted by the iron phosphate quality analysis model is marked as Zls. A first index threshold is preset, and the preset first index threshold includes S1 and S2, S1>S2.

[0110] The first quality index is compared with a preset first index threshold.

[0111] If Zls>S1 or Zls<S2, it is determined that the extraction equipment at time T1+a is in an abnormal extraction state.

[0112] If S1≥Zls≥S2, it is determined that the extraction equipment at time T1+a is in a normal extraction state, and the extraction is continued to be completed with the first parameter characteristic data corresponding to the first extraction control index, and the second extraction stage is entered.

[0113] It should be noted that: the preset first index threshold is that personnel in this technical field obtain iron phosphate characteristic data multiple times in the first extraction stage, wherein Q groups of iron phosphate characteristic data are obtained each time, the mean of Q groups of iron phosphate characteristic data is calculated, the first quality index corresponding to the mean of the iron phosphate characteristic data is predicted by the iron phosphate quality analysis model, the multiple first quality indices are sorted, the maximum value among the multiple first quality indices is taken as the maximum value of the first index threshold, and the minimum value among the multiple first quality indices is taken as the minimum value of the first index threshold; the range between the minimum value of the first index threshold to the maximum value of the first index threshold is the preset first index threshold.

[0114] Step 4: According to the extraction adjustment instruction, the extraction equipment is adjusted based on the first quality index to optimize the extraction process and obtain high-purity iron phosphate.

[0115] In a specific embodiment, the method for adjusting the extraction device based on the first quality index includes:

[0116] The first parameter characteristic data corresponding to the first quality index is input into the pre-built digital twin model for simulation to obtain the optimal adjustment strategy.

[0117] It should be noted that the pre-built digital twin model is specifically a virtual simulation model of the production workshop, which is generated based on various historical measured data of the production workshop, and performs real-time data updates and model updates based on a number of sensors. The various historical measured data include physical data, equipment operating parameters, and object structure data, etc.; the pre-built digital twin model is implemented based on existing digital twin construction technologies, such as ANSYS, Azure Digital Twins, Siemens Mindsphere, etc. Therefore, the present invention will not go into details about this.

[0118] See also Figure 4 Specifically, the method for obtaining the best adjustment strategy includes:

[0119] Step c1. Acquire first parameter characteristic data of the extraction equipment, use the first pH value and the first stirring speed in the first parameter characteristic data as fixed quantities, the first temperature value as a variable, and use the current parameter value of the first temperature value as W.

[0120] Step c2: Let W=W+D1, and record the first quality index under the parameter value W, where D1 is a natural number greater than zero.

[0121] Step c3. Repeat step c2. When W is equal to the preset first temperature threshold, obtain G first quality indexes under the first temperature parameter value, and jump to step c4, where G is an integer greater than zero.

[0122] Step c4. taking the first temperature value and the first stirring speed as fixed quantities, the first pH value as a variable, and taking the current parameter value of the first pH value as U.

[0123] Step c5. Reset W, set U=U+D2, and record the first quality index under the parameter value U, where D2 is a natural number greater than zero.

[0124] Step c6. Repeat step c5, when U is equal to the preset first pH threshold, obtain H first quality indexes under the first pH parameter value, where H is an integer greater than zero.

[0125] Step c7. Use the first temperature value and the first pH value as fixed quantities, the first stirring speed as a variable, and the current parameter value of the first stirring speed as F.

[0126] Step c8. Reset U, set F=F+D3, and record the first quality index under the parameter value F, where D3 is a natural number greater than zero;

[0127] Step c9. Repeat step c8, when F is equal to the preset first stirring speed threshold, obtain K first quality indexes under the first stirring speed parameter value, where K is an integer greater than zero.

[0128] Step c10. Mark the G first mass indices under the first temperature parameter value, the H first mass indices under the first pH parameter value, and the K first mass indices under the first stirring speed parameter value as first extraction control data, accumulate and fuse the first extraction control data to obtain L first mass indices, and sort the L first mass indices from large to small according to numerical values.

[0129] Step c11. taking the first temperature value, the first pH value and the first stirring speed of the extraction equipment corresponding to the first quality index with the largest value in the sorting as the optimal adjustment strategy.

[0130] It should be noted that: the value range of the first temperature value is [40°C, 60°C], the value range of the first pH value is [3,5] and the value range of the first stirring speed is [100rpm, 300rpm]. The current parameter value of the above steps is simulated from the minimum value of the corresponding parameter value until the optimal adjustment strategy is obtained.

[0131] Step 5: Receive the second extraction instruction, obtain the second quality index of the extraction equipment at time T2+b in the second extraction stage, and determine whether the extraction equipment at time T2+b is in an abnormal extraction state based on the second quality index; if so, generate a second extraction adjustment instruction and jump to step 6; if not, continue the extraction until the extraction of lithium carbonate is completed.

[0132] It should be noted that: in the second extraction stage, a second extractant of a preset concentration is added to the leachate from which the iron phosphate has been extracted, and it is marked as an extraction mixture, wherein the second extractant is a sodium carbonate solution (Na2CO3) as a selective extractant for lithium. The purpose of setting up the second extraction stage is to optimize the precipitation efficiency of lithium carbonate extraction under different chemical conditions, avoid the formation of impurities, and improve the purity of the product, while ensuring the stability and high recovery rate of the extraction stage. The conditions of the second extraction stage (such as pH value, temperature and stirring speed) will be precisely controlled for the extraction mixture to ensure the optimal extraction purity of lithium carbonate.

[0133] In implementation, the method for obtaining the second quality index of the extraction device at time T2+b includes:

[0134] The second extraction control index of the extraction device at time T2+b and the second characteristic data of the extraction mixture in the extraction device are obtained and marked as lithium carbonate characteristic data; the second characteristic data includes lithium ion concentration, viscosity of the extraction mixture and impurity percentage.

[0135] The lithium carbonate characteristic data is input into a pre-built lithium carbonate mass analysis model to obtain the second mass index at time T2+b.

[0136] It should be noted that the method for obtaining the second characteristic data is the same as the method for obtaining the first characteristic data, which will not be repeated here. It is worth noting that the impurity ion concentration of the mixed solution includes the concentration of copper, zinc and iron ions.

[0137] Among them, the method for generating the lithium carbonate quality analysis model includes:

[0138] Each group of lithium carbonate characteristic data is converted into the form of a second characteristic vector, and all elements of the second characteristic vector are used as input of a lithium carbonate quality analysis model. The lithium carbonate quality analysis model uses the second quality index predicted by each group of lithium carbonate characteristic data as output, the actual second quality index corresponding to each group of lithium carbonate characteristic data as a prediction target, and minimizing the sum of the second prediction accuracies of all predicted second quality indices as a training target.

[0139] It should be noted that the second quality index specifically refers to the purity of lithium carbonate extracted and recovered in the second extraction stage, which is measured and obtained according to methods such as ICP-OES (inductively coupled plasma emission spectroscopy) and XRF (X-ray fluorescence spectroscopy).

[0140] The calculation formula of the second prediction accuracy is: Z2 = (b m -s m ) 2 , where m is the number of each set of lithium carbonate feature data, Z2 is the second prediction accuracy, and b m is the predicted second quality index corresponding to the mth group of lithium carbonate characteristic data, s m is the actual second quality index corresponding to the mth group of lithium carbonate feature data; the lithium carbonate quality analysis model is trained until the sum of the second prediction accuracies reaches convergence and the training is stopped; the lithium carbonate quality analysis model is any one of a deep neural network model or a deep belief network model.

[0141] The method for obtaining the second extraction control index of the extraction equipment at time T2+b includes:

[0142] Step d1. Collect the second parameter characteristic data at time T2, wherein the second parameter characteristic data includes a second temperature value, a second pH value and a second stirring speed; mark the second temperature value, the second pH value and the second stirring speed as Ws2, Ph2 and Sd2 respectively.

[0143] It should be noted that: the second temperature value is obtained by collecting the temperature sensor connected to the temperature control system; the second pH value is obtained by collecting the pH meter connected to the pH automatic control system.

[0144] Step d2. Based on the first extraction control index, the second parameter characteristic data is calculated by formula to obtain the second extraction control index at time T2, and the calculation formula is:

[0145] θ2=θ1-(Ws2×β1+Ph2×β2+Sd2×β3)+(μ2×Ws2×Ph2).

[0146] Wherein, θ2 represents the second extraction control index, β1 represents the weight factor of the second temperature value, β2 represents the weight factor of the second pH value, β3 represents the weight factor of the second stirring speed, and μ2 represents the weight coefficient of the interaction between the second temperature value and the second pH value.

[0147] It should be noted that: the larger the value of μ2 is, the more significant the interaction between temperature and pH value is in affecting the extraction efficiency. The calculation of the second extraction control index is a dimensionless calculation.

[0148] It is further explained that the weight factor can be set according to actual conditions, and the weight factor reflects the influence of each second parameter characteristic data on the actual extraction purity of the extraction equipment. Technical personnel in this field can preset the corresponding weight factor according to the influence of each second parameter characteristic data on the actual extraction purity of the extraction equipment, so as to accurately evaluate the purity of lithium carbonate actually extracted by the extraction equipment.

[0149] Step d3: Input the second extraction control index at time T2 into the pre-constructed parameter analysis model to predict the second extraction control index at time T2+b.

[0150] In implementation, the method for determining whether the extraction device at time T2+b is in an abnormal extraction state includes:

[0151] The second mass index at time T2+b predicted by the mass analysis model of lithium carbonate is marked as Zlz. A second index threshold is preset, and the preset second index threshold includes Y1 and Y2, Y1>Y2.

[0152] The second quality index is compared with a preset second index threshold.

[0153] If Zlz>Y1 or Zls<Y2, it is determined that the extraction equipment at time T2+b is in an abnormal extraction state.

[0154] If Y1≥Zlz≥Y2, it is determined that the extraction equipment at time T2+b is in a normal extraction state, and the extraction is continued to be completed with the second parameter characteristic data corresponding to the second extraction control index to obtain high-purity lithium carbonate.

[0155] Step 6: Receive a second extraction adjustment instruction, and adjust the extraction equipment based on the second quality index to obtain high-purity lithium carbonate.

[0156] It should be noted that the method of adjusting the extraction equipment based on the second quality index is the same as the method of adjusting the extraction equipment based on the first quality index in step 4, and will not be elaborated here. It is worth noting that in the second extraction stage, the second temperature value in the second parameter characteristic data has a value range of [50°C, 70°C], a value range of [8,10], and a value range of [50rpm, 150rpm]. As in step 4, the current parameter value is simulated from the minimum value until the optimal adjustment strategy is obtained.

[0157] This embodiment can predict the separation effect of iron phosphate in the first extraction stage in real time by obtaining the first extraction control index and the first characteristic data of the extraction equipment at time T1+a and inputting them into the iron phosphate quality analysis model. By obtaining the second extraction control index and the second characteristic data of the extraction equipment at time T2+b and inputting them into the lithium carbonate quality analysis model, it can predict the separation effect of lithium carbonate in the second extraction stage in real time. Based on different extraction stages and precise extraction control methods, errors in the operation process can be effectively reduced, ensuring that the optimal temperature, pH value and stirring speed are achieved during the extraction process, thereby improving the purity and recovery rate of the product.

[0158] This embodiment predicts the extraction state in real time through the quality analysis model and generates an early warning of abnormal extraction state. When the extraction equipment is in an abnormal extraction state, the system automatically generates an adjustment instruction and adjusts it according to the optimal adjustment strategy to ensure that the extraction process returns to normal in time, reducing uncontrollable risks in operation.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0160] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials, characterized in that: include: Step 1: In the first extraction stage, a first extraction control index of the extraction device at time T1+a is obtained, and first characteristic data of the leaching liquid in the extraction device is obtained; The first extraction control index and the first characteristic data are marked as iron phosphate characteristic data; T1 and a are both integers greater than zero; Step 2: Inputting the iron phosphate characteristic data into a pre-built iron phosphate quality analysis model to obtain the first quality index at time T1+a; Step 3: Determine whether the extraction device at time T1+a is in an abnormal extraction state according to the first quality index. If an abnormal extraction state occurs, generate an extraction adjustment instruction and jump to step 4; If no abnormal extraction state occurs, a second extraction instruction is generated and the process jumps to step 5; Step 4: According to the extraction adjustment instruction, the extraction equipment is adjusted based on the first quality index to optimize the extraction process and obtain high-purity iron phosphate; Step 5: receiving a second extraction instruction, obtaining a second quality index of the extraction device at time T2+b in the second extraction stage, and determining whether the extraction device at time T2+b is in an abnormal extraction state based on the second quality index; if so, generating a second extraction adjustment instruction and jumping to step 6; if not, continuing the extraction until the extraction of lithium carbonate is completed; Step 6: Receive a second extraction adjustment instruction, and adjust the extraction equipment based on the second quality index to obtain high-purity lithium carbonate.

2. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 1, characterized in that: The method for obtaining the first extraction control index of the extraction equipment at time T1+a includes: Step a1. Collecting first parameter characteristic data at time T1, wherein the first parameter characteristic data includes a first temperature value, a first pH value and a first stirring speed; marking the first temperature value, the first pH value and the first stirring speed as Ws1, Ph1 and Sd1 respectively; Step a2. Formulate and calculate the first parameter characteristic data to obtain the first extraction control index at time T1, and the calculation formula is: θ1=(Ws1×α1+Ph1×α2+Sd1×α3)+(μ1×Ws1×Ph1); Wherein, θ1 represents the first extraction control index at time T1, α1 represents the weight factor of the first temperature value, α2 represents the weight factor of the first pH value, α3 represents the weight factor of the first stirring speed, and μ1 represents the weight coefficient of the interaction between the first temperature value and the first pH value; Step a3: Input the first extraction control index at time T1 into a pre-built parameter analysis model to predict the first extraction control index at time T1+a.

3. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 2, characterized in that: The method for generating the pre-built parameter analysis model includes: Obtain the first historical extraction control index, and establish an extraction time series set based on the first historical extraction control index, wherein the extraction time series set includes u first historical extraction control indexes, the time intervals for obtaining the u first historical extraction control indexes are equal, and the u first historical extraction control indexes correspond to a preset time; the unit of the preset time is seconds or minutes; Preset the sliding step size and sliding window length; convert the historical first extraction control index in the extraction time series set into multiple training samples using the sliding window method, use the training samples as the input of the recurrent neural network model, predict the first extraction control index after the sliding step size as the output, use the first extraction control index of each training sample as the prediction target, and use the prediction accuracy as the training target to train the recurrent neural network model; generate a parameter analysis model for predicting the first extraction control index in the future time based on the historical first extraction control index in the extraction time series set; wherein the recurrent neural network model is an RNN neural network model.

4. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 3, characterized in that: The first characteristic data includes the iron ion concentration, the viscosity of the leachate and the impurity percentage of the leachate; the iron ion concentration is directly analyzed by a concentration analysis device; the viscosity of the leachate is measured by a rotational viscometer or a capillary viscometer; The method for obtaining the impurity percentage of the leachate includes: Step b1. Collecting a leachate sample and analyzing the impurity ion concentration by a concentration analysis device; the impurity ion concentration includes the concentration of copper, zinc and lithium ions; Step b2. Calculate the mass of the impurity ions: m i =C i ×V, where m i represents the mass of the i-th impurity ion, C i represents the concentration of the i-th impurity ion; V represents the volume of the leaching solution sample; Step b3. Add up the masses of all impurity ions to get the total mass of impurities: m 总 =∑m i ; Step b4. Obtain the total mass M of the leachate sample 浸出液 , according to the total mass of impurities m 总 The total mass of the leaching solution sample M 浸出液 , calculate the impurity percentage Zbf:

5. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 4, characterized in that: Methods for generating pre-built iron phosphate mass analysis models include: Each set of iron phosphate characteristic data is converted into the form of a first characteristic vector, and all elements of the first characteristic vector are used as inputs of the iron phosphate quality analysis model. The iron phosphate quality analysis model uses the first quality index predicted by each set of iron phosphate characteristic data as output, the actual first quality index corresponding to each set of iron phosphate characteristic data as the prediction target, and the sum of the first prediction accuracies of all predicted first quality indexes as the training target; wherein the calculation formula for the first prediction accuracy is: Z1=(a n -y n ) 2 , where n is the number of each set of iron phosphate feature data, Z1 is the first prediction accuracy, a n is the predicted first quality index corresponding to the nth group of iron phosphate characteristic data, y n is the actual first quality index corresponding to the nth group of iron phosphate feature data; the iron phosphate quality analysis model is trained until the sum of the first prediction accuracies reaches convergence and the training is stopped; the iron phosphate quality analysis model is a deep neural network model or a deep belief network model.

6. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 5, characterized in that: The method for determining whether the extraction device at time T1+a is in an abnormal extraction state includes: The first quality index predicted by the iron phosphate quality analysis model at time T1+a is marked as Zls; and a first index threshold is preset, wherein the preset first index threshold includes S1 and S2, and S1>S2; comparing the first quality index with a preset first index threshold; If Zls>S1 or Zls<S2, it is determined that the extraction equipment at time T1+a is in an abnormal extraction state; If S1≥Zls≥S2, it is determined that the extraction equipment at time T1+a is in a normal extraction state, and the extraction is continued to be completed with the first parameter characteristic data corresponding to the first extraction control index, and the second extraction stage is entered.

7. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 6, characterized in that: The method for adjusting the extraction device based on the first quality index includes: Inputting the first parameter characteristic data corresponding to the first quality index into the pre-built digital twin model for simulation to obtain the best adjustment strategy; Methods for achieving the best tuning strategy include: Step c1. Acquire first parameter characteristic data of the extraction device, use the first pH value and the first stirring speed in the first parameter characteristic data as fixed quantities, the first temperature value as a variable, and use the current parameter value of the first temperature value as W; Step c2. Let W = W + D1, and record the first quality index under the parameter value W, where D1 is a natural number greater than zero; Step c3. Repeat step c2, when W is equal to the preset first temperature threshold, obtain G first quality indexes under the first temperature parameter value, and jump to step c4, where G is an integer greater than zero; Step c4. taking the first temperature value and the first stirring speed as fixed quantities, the first pH value as a variable, and taking the current parameter value of the first pH value as U; Step c5. Reset W, set U=U+D2, and record the first quality index under the parameter value U, where D2 is a natural number greater than zero; Step c6. Repeat step c5, and when U is equal to the preset first pH threshold, obtain H first quality indexes under the first pH parameter value, where H is an integer greater than zero; Step c7. taking the first temperature value and the first pH value as fixed quantities, the first stirring speed as a variable, and taking the current parameter value of the first stirring speed as F; Step c8. Reset U, set F=F+D3, and record the first quality index under the parameter value F, where D3 is a natural number greater than zero; Step c9. Repeat step c8, when F is equal to the preset first stirring speed threshold, obtain K first quality indexes under the first stirring speed parameter value, where K is an integer greater than zero; Step c10. Mark G first quality indices under the first temperature parameter value, H first quality indices under the first pH parameter value, and K first quality indices under the first stirring speed parameter value as first extraction control data, accumulate and fuse the first extraction control data to obtain L first quality indices, and sort the L first quality indices from large to small according to the values; Step c11. taking the first temperature value, the first pH value and the first stirring speed of the extraction equipment corresponding to the first quality index with the largest value in the sorting as the optimal adjustment strategy.

8. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 7, characterized in that: In the first extraction stage, the first temperature value in the first parameter characteristic data is in the range of [40°C, 60°C], the first pH value is in the range of [3,5] and the first stirring speed is in the range of [100rpm, 300rpm]; in the second extraction stage, the second temperature value in the second parameter characteristic data is in the range of [50°C, 70°C], the second pH value is in the range of [8,10] and the second stirring speed is in the range of [50rpm, 150rpm].

9. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 8, characterized in that: The method for obtaining the second quality index of the extraction device at time T2+b includes: Obtaining a second extraction control index of the extraction device at time T2+b and second characteristic data of the extraction mixture in the extraction device, and marking them as lithium carbonate characteristic data; the second characteristic data includes lithium ion concentration, viscosity of the extraction mixture and impurity percentage; The lithium carbonate characteristic data is input into a pre-built lithium carbonate mass analysis model to obtain the second mass index at time T2+b.

10. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 9, characterized in that: The method for generating the lithium carbonate quality analysis model comprises: Each set of lithium carbonate characteristic data is converted into the form of a second characteristic vector, and all elements of the second characteristic vector are used as inputs of a lithium carbonate quality analysis model. The lithium carbonate quality analysis model uses the second quality index predicted by each set of lithium carbonate characteristic data as output, uses the actual second quality index corresponding to each set of lithium carbonate characteristic data as a prediction target, and uses minimizing the sum of the second prediction accuracies of all predicted second quality indexes as a training target; wherein the calculation formula for the second prediction accuracy is: Z2=(b m -s m ) 2 , where m is the number of each set of lithium carbonate feature data, Z2 is the second prediction accuracy, and b m is the predicted second quality index corresponding to the mth group of lithium carbonate characteristic data, s m is the actual second quality index corresponding to the mth group of lithium carbonate feature data; the lithium carbonate quality analysis model is trained until the sum of the second prediction accuracies reaches convergence and the training is stopped; the lithium carbonate quality analysis model is a deep neural network model or a deep belief network model.

11. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 10, characterized in that: The method for obtaining the second extraction control index of the extraction equipment at time T2+b includes: Step d1. Collecting second parameter characteristic data at time T2, wherein the second parameter characteristic data includes a second temperature value, a second pH value, and a second stirring speed; marking the second temperature value, the second pH value, and the second stirring speed as Ws2, Ph2, and Sd2, respectively; Step d2. Based on the first extraction control index, the second parameter characteristic data is calculated by formula to obtain the second extraction control index at time T2, and the calculation formula is: θ2=θ1-(Ws2×β1+Ph2×β2+Sd2×β3)+(μ2×Ws2×Ph2); Wherein, θ2 represents the second extraction control index, β1 represents the weight factor of the second temperature value, β2 represents the weight factor of the second pH value, β3 represents the weight factor of the second stirring speed, and μ2 represents the weight coefficient of the interaction between the second temperature value and the second pH value; Step d3: Input the second extraction control index at time T2 into a pre-constructed parameter analysis model to predict the second extraction control index at time T2+b.

12. The method for extracting and recovering high-purity lithium carbonate and iron phosphate from waste lithium battery materials according to claim 11, characterized in that: The method for determining whether the extraction device at time T2+b is in an abnormal extraction state includes: The second mass index predicted by the mass analysis model of lithium carbonate at time T2+b is marked as Zlz; and a second index threshold is preset, wherein the preset second index threshold includes Y1 and Y2, and Y1>Y2; comparing the second quality index with a preset second index threshold; If Zlz>Y1 or Zls<Y2, it is determined that the extraction equipment at time T2+b is in an abnormal extraction state; If Y1≥Zlz≥Y2, it is determined that the extraction equipment at time T2+b is in a normal extraction state, and the extraction is continued to be completed with the second parameter characteristic data corresponding to the second extraction control index to obtain high-purity lithium carbonate.

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