A coal-based hard carbon ash content prediction and parameter optimization method and related device

Abstract

This invention belongs to the field of sodium-ion batteries and discloses a method and related device for predicting and optimizing the ash content of coal-based hard carbon. Based on the Arrhenius formula, it introduces multi-field coupling effective activation energy, synergistic intensity factor, and ash embedding characteristic correction coefficient to construct a multi-field composite deashing synergistic reaction model. Then, it uses a Markov stochastic process to construct a probability model for ash occurrence form transformation, distinguishing the differentiated removal laws of free state, embedded state, and inclusions. Next, based on the law of mass conservation, it establishes a mass conservation model including corrections for secondary pollution and carbon loss to predict the final ash content. Finally, it uses the Lagrange multiplier method to construct a multi-objective optimization function, simultaneously optimizing ash content, energy consumption, and acid consumption. This method can significantly improve the accuracy of ash prediction and the universality of the model, avoid local optima, and reduce experimental workload and optimization cycle, providing reliable technical support for the precise, intelligent, and efficient production of coal-based hard carbon deashing processes.

Description

Technical Field

[0001] This invention belongs to the field of sodium-ion battery technology, specifically to the field of coal-based hard carbon; in particular, it relates to a method and related apparatus for predicting the ash content and optimizing parameters of coal-based hard carbon. Background Technology

[0002] Coal-based hard carbon, with its advantages of wide availability of raw materials, low production cost, and excellent cycle electrochemical performance, has become the mainstream choice for anode materials in sodium-ion batteries. In the industrial-scale production of coal-based hard carbon, ash content is a core key indicator directly determining the material's electrochemical performance and product quality grade. The industry has stringent standards for the ash content of battery-grade coal-based hard carbon; conventional battery-grade products require an ash content below 0.5%, while products for high-end battery applications require an ash content below 0.1%. Traditional acid leaching deashing processes, due to their inherent technological limitations, cannot meet the demands of large-scale production of high-quality, ultra-low ash coal-based hard carbon. Therefore, the industry is gradually promoting multi-field composite deashing technologies, such as the thermo-chemical-ultrasonic three-field coupled deashing process. This technology relies on the ultrasonic cavitation effect to enhance the material mass transfer process, effectively compensating for the shortcomings of traditional acid leaching processes, such as long reaction times, high acid consumption, and low deashing efficiency. It significantly improves the deashing efficiency of coal-based hard carbon and shortens the production reaction cycle, making it the mainstream improved technology for high-quality coal-based hard carbon deashing.

[0003] Current multi-field composite deashing processes still suffer from numerous core technological deficiencies in practical applications, severely hindering the precise, intelligent, and efficient production of high-quality coal-based hard carbon deashing processes. Firstly, existing ash prediction models have poor accuracy, mostly relying on empirical or semi-empirical formulas based on experimental data fitting. This disconnects the nonlinear coupling between the thermal, chemical, and ultrasonic fields, failing to accurately characterize the deashing kinetics under the synergistic effect of multiple fields. In engineering applications, model prediction biases generally exceed 15%, indicating insufficient predictive reliability. Secondly, existing technologies cannot quantify the removal impact of different ash occurrence forms in coal. Coal ash exists in three states: free, embedded, and inclusions. The removal difficulty varies significantly among these forms, but existing models treat ash as a homogeneous whole for analysis and prediction, failing to distinguish the differentiated removal patterns of different ash forms, resulting in extremely poor model universality. Secondly, the optimization methods for process parameters are outdated. Currently, the industry mainly relies on single-factor experiments or response surface methodology to complete parameter tuning. This not only involves a large amount of experimental work and a lengthy optimization cycle, but also easily gets stuck in local optima, making it impossible to simultaneously achieve coordinated optimization of multiple core production indicators such as ash content, production energy consumption, and acid consumption. Finally, research on deashing mechanisms is lacking. Existing prediction and optimization models are all black-box models, failing to deeply characterize the microscopic physicochemical mechanisms of multi-stage composite deashing processes. This results in weak extrapolation performance of the models, making it difficult to adapt to the process adjustment needs of different raw coal materials and different production scales.

[0004] It is evident that existing multi-field composite deashing technologies suffer from core problems such as low accuracy and poor universality of ash prediction models, inability to quantify the ash occurrence morphology and the influence of multi-field synergistic effects, and reliance on experience-based trial and error for process parameter optimization without the support of physical mechanisms. These issues make it difficult to meet the industrial production needs of precise deashing of high-quality coal-based hard carbon. Summary of the Invention

[0005] This invention provides a method and related apparatus for predicting and optimizing the ash content of coal-based hard carbon. This method can effectively solve the problems of low accuracy and poor universality of existing multi-field composite deashing technology, such as low ash prediction model accuracy, poor universality, inability to quantify the ash occurrence form and the influence of multi-field synergistic effect, and the reliance on experience trial and error for process parameter optimization without the support of physical mechanisms. It can meet the industrial production needs of high-quality coal-based hard carbon precise deashing.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for predicting and optimizing the ash content of coal-based hard carbon, comprising: Based on the Arrhenius chemical reaction kinetics formula, the effective activation energy of multi-field coupling, the multi-field synergistic strength factor and the correction coefficient of coal-based hard carbon ash embedding characteristics are introduced to construct a multi-field composite deashing synergistic reaction model, and the rate constant of the multi-field composite deashing synergistic reaction is obtained by solving the model. The ash in coal-based hard carbon is divided into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form of the removed ash. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the problem. Based on the law of conservation of mass, the total ash removal rate is determined by the initial mass fraction and removal probability of the three types of ash. A secondary pollution ash correction term and a carbon loss correction term are introduced to construct a mass conservation model and predict the final ash content of coal-based hard carbon after multi-stage composite deashing. Using the final ash content as the primary optimization objective, and unit product energy consumption and unit product acid consumption as secondary optimization objectives, and using the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model, and the mass conservation model as equality constraints, a multi-objective optimization function is constructed and solved to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0007] Furthermore, the multi-field composite deashing synergistic reaction model is specifically expressed as follows:

[0008] In the formula, kTCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 ;K m k is the correction coefficient for the ash dispersive characteristics of coal-based hard carbon; k0 is the pre-exponential factor, min -1 ;a H+ is the activity of hydrogen ions in the acid leaching solution, mol / L; n is the hydrogen ion reaction order; E a0 α is the intrinsic activation energy of the reaction under a single thermal field, J / mol; α is the ultrasonic activation energy reduction coefficient, J·mol. -1 ·(W / mL) 1 / 2 ;P U U is the ultrasonic power density, W / mL; R is the ideal gas constant, taken as 8.314 J·mol⁻¹. -1 ·K -1 T is the reaction temperature, °C; β is the synergistic effect amplification factor; S TCU It is a multi-field cooperative strength factor; and α and P U The value of satisfies < This is to ensure that the effective activation energy is positive.

[0009] Furthermore, the multi-field cooperative strength factor The specific calculation formula is as follows:

[0010] In the formula, T0, a0, and P0 are the threshold intensities of the thermal field, chemical field, and ultrasonic field, respectively, which are determined through single-factor experiments: keeping the intensities of the other two fields constant, changing the field intensity of a single field and measuring the deashing reaction rate, and taking the field intensity corresponding to the inflection point of the curve of the deashing reaction rate versus the field intensity as the threshold intensity; when T0, a0, and P0 are constant, the threshold intensities are determined by the following method: H+ P U When any one of them is lower than its corresponding threshold intensity, S TCU The value is 0; the exponents 0.3, 0.4, and 0.3 in the above formula are constants obtained by fitting through multi-field synergistic deashing experiments.

[0011] Furthermore, the ash content morphology transformation probability model is a continuous-time Markov chain model: free ash, embedded ash, and encapsulated ash are denoted as states S1, S2, and S3, respectively, and the removed ash is denoted as the absorption state S0; the state transition path is S3→S2→S1→S0, where the transition rate k1→S0 is k TCU The transfer rate from S2 to S1 is k2 = ε·k TCU The transfer rate from S3 to S2 is k3 = γ·(P U 2 / d ave 3 )·ε·kTCU ε is the influence coefficient of pore structure in coal-based hard carbon, γ is the ultrasonic inclusion fragmentation efficiency coefficient, and μm 3 ·(W / mL) -2 d ave The average particle size of the ash inclusions is in μm; Let the column vector be p(t) = [p1(t), p2(t), p3(t), p0(t)]. Let Q represent the probability distribution of each state at reaction time t, which satisfies the differential equation dp / dt=Q·p, and the generating matrix Q is:

[0012] Solving for p, we get p(t) = e Qt ·p(0); Initially, all ash particles are in state S i At that time, the probability of being in the absorption state S0 after reaction time t is the removal probability P of the ash in that occurrence form. i (t); When k1, k2, and k3 are not equal, P1(t), P2(t), and P3(t) are calculated according to the following formulas:

[0013]

[0014]

[0015] In the formula, the coefficients A1, A2, and A3 are respectively:

[0016] When there are equal cases among k1, k2, and k3, take the limit form of the corresponding formula, or directly apply the formula to e. Qt Numerical calculations were performed to obtain the removal probabilities.

[0017] Furthermore, the mass conservation model is specifically expressed as follows:

[0018] In the formula, A f denoted as the final ash mass fraction of coal-based hard carbon after multi-stage composite deashing; A0 is the initial ash mass fraction of coal-based hard carbon raw material; P1(t), P2(t), and P3(t) are the removal probabilities of free ash, embedded ash, and inclusion ash after reaction time t, respectively. , , These represent the initial mass fractions of free ash, embedded ash, and inclusion ash in the raw material, respectively, and w 10 +w 20 +w 30 =1; kimp C is the residual impurity coefficient of the acid leaching solution. acid V represents the mass concentration of acid in the leaching solution, in kg / L. acid m0 is the volume of the acid leaching solution, in L; m0 is the initial mass of the coal-based hard carbon raw material, in kg; k0 is the volume of the acid leaching solution, in L; c k is the proportionality coefficient for the oxidation rate of the carbon matrix. TCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 t represents the deashing reaction time, in minutes.

[0019] Furthermore, the multi-objective optimization function adopts a normalized weighted summation form, and the calculation formula is as follows:

[0020] In the formula, To optimize the comprehensive evaluation function for multiple objectives; , , These are the weighting coefficients for ash content, energy consumption, and acid consumption, respectively; and they satisfy... + + =1; This represents the final ash content by mass. The target ash content is the mass fraction. Energy consumption per unit of product; The unit product benchmark energy consumption for existing single-field acid leaching processes; Acid consumption per unit of product; The benchmark acid consumption per unit product for existing single-stage acid leaching processes; T represents the ultrasonic power density; T represents the reaction temperature. The constraints of the multi-objective optimization function also include upper and lower limit inequality constraints on reaction temperature, acid leaching solution concentration, ultrasonic power density, deashing reaction time, and acid leaching solution volume.

[0021] Furthermore, the calculation formulas for the unit product energy consumption, unit product acid consumption, and final product quality are as follows:

[0022]

[0023]

[0024] In the formula, The density of the acid leaching solution; The ambient temperature; For the quality of the final product; Acid recovery rate; This is the proportionality coefficient for the oxidation rate of the carbon matrix.

[0025] A coal-based hard carbon ash content prediction and parameter optimization system includes: The first building module is used to construct a multi-field composite deashing synergistic reaction model based on the Arrhenius chemical reaction kinetics formula, by introducing the multi-field coupling effective activation energy, multi-field synergistic strength factor and coal-based hard carbon ash embedding characteristic correction coefficient, and solving for the multi-field composite deashing synergistic reaction rate constant. The second construction module is used to classify the ash in coal-based hard carbon into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form transformation probability model. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the model. The third construction module is used to determine the total ash removal rate based on the law of conservation of mass, using the initial mass fraction and removal probability of the three types of ash in existence, introducing secondary pollution ash correction terms and carbon loss correction terms, constructing a mass conservation model, and predicting the final ash content of coal-based hard carbon after multi-stage composite deashing. The function solving module is used to construct and solve a multi-objective optimization function with the final ash content as the primary optimization objective, energy consumption per unit product and acid consumption per unit product as secondary optimization objectives, and the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model and the mass conservation model as equality constraints, so as to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0026] A coal-based hard carbon ash content prediction and parameter optimization device, comprising: Memory, used to store computer programs; A processor is used to implement the above-described method for predicting and optimizing the ash content of coal-based hard carbon when executing the computer program.

[0027] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the above-described method for predicting and optimizing the ash content of coal-based hard carbon.

[0028] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for predicting and optimizing the ash content of coal-based hard carbon. Based on the Arrhenius formula, it introduces multi-field coupling effective activation energy, synergistic intensity factor, and ash embedding characteristic correction coefficient to construct a multi-field composite deashing synergistic reaction model. Then, it utilizes Markov stochastic processes to construct a probability model for ash occurrence form transformation, distinguishing the differentiated removal laws of free state, embedded state, and inclusions. Next, based on the law of mass conservation, it establishes a mass conservation model including corrections for secondary pollution and carbon loss to predict the final ash content. Finally, it employs the Lagrange multiplier method to construct a multi-objective optimization function, simultaneously optimizing ash content, energy consumption, and acid consumption. Through the above modeling and derivation, this method overcomes the limitations of traditional empirical formulas that fragment multi-field coupling, accurately characterizing the transformation dynamics of ash in different occurrence forms, and achieving multi-objective synergistic optimization. Using this method can significantly improve the accuracy of ash prediction and the universality of the model, avoid local optima, and reduce experimental workload and optimization cycle, providing reliable technical support for the precise, intelligent, and efficient production of coal-based hard carbon deashing processes. Attached Figure Description

[0029] Figure 1 The following is a flowchart illustrating the implementation of a coal-based hard carbon ash content prediction and parameter optimization method provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a coal-based hard carbon ash content prediction and parameter optimization system provided in an embodiment of the present invention; Figure 3 This is a main flowchart of a coal-based hard carbon ash content prediction and parameter optimization method provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the main structure of a coal-based hard carbon ash content prediction and parameter optimization system provided in an embodiment of the present invention. Detailed Implementation

[0030] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0031] This embodiment provides a method for predicting the ash content and optimizing parameters of coal-based hard carbon. This method can accurately predict the ash content of multi-stage composite deashing processes and achieve multi-objective process parameter optimization, which is of great significance for promoting the advancement of coal-based hard carbon preparation technology.

[0032] like Figure 3 As shown in the figure, this embodiment provides a method for predicting and optimizing the ash content of coal-based hard carbon, and the specific steps are as follows: Based on the Arrhenius chemical reaction kinetics formula, the effective activation energy of multi-field coupling, the multi-field synergistic strength factor and the correction coefficient of coal-based hard carbon ash embedding characteristics are introduced to construct a multi-field composite deashing synergistic reaction model, and the rate constant of the multi-field composite deashing synergistic reaction is obtained by solving the model. The ash in coal-based hard carbon is divided into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form of the removed ash. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the problem. Based on the law of conservation of mass, the total ash removal rate is determined by the initial mass fraction and removal probability of the three types of ash. A secondary pollution ash correction term and a carbon loss correction term are introduced to construct a mass conservation model and predict the final ash content of coal-based hard carbon after multi-stage composite deashing. Using the final ash content as the primary optimization objective, and unit product energy consumption and unit product acid consumption as secondary optimization objectives, and using the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model, and the mass conservation model as equality constraints, a multi-objective optimization function is constructed and solved to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0033] The scheduling method provided in this embodiment will be further explained below: For example, such as Figure 1 As shown in the figure, this embodiment provides a method for predicting and optimizing the ash content of coal-based hard carbon, specifically through the following steps: S1. Raw material property determination and model parameter calibration: Determine the initial ash mass fraction, the initial mass fraction of ash in three occurrence forms, microstructure parameters and various kinetic parameters of the coal-based hard carbon raw material to be treated. S2. Construct a multi-field composite deashing synergistic reaction model and calculate the rate constant k of the multi-field composite deashing synergistic reaction. TCU ; S3. Construct a probability model for the transformation of ash occurrence forms and calculate the removal probabilities P1(t), P2(t), and P3(t) of the three occurrence forms of ash within the reaction time t. S4. Construct a mass conservation model to predict the final ash content of coal-based hard carbon after multi-stage composite deashing. f ; S5. Construct and solve the multi-objective optimization function to obtain the optimal combination of process parameters that meet the target ash content requirements; S6. Process Validation and Parameter Correction: Conduct multiple composite deashing experiments using the optimal combination of process parameters, and measure the ash content A. 实测 If |A实测 A f | / A 实测 If the value is ≤5%, then the optimal combination of process parameters will be used for production; otherwise, return to step S1 to recalibrate the relevant parameters, correct the model, and optimize again.

[0034] Step S1: Calibration of raw material properties and model parameters: In step S1, the property parameters of each raw material and the model parameters were determined as follows: (1) Initial ash mass fraction A0: determined according to the industrial analysis method specified in national standard GB / T 212; (2) Initial mass fraction w of ash in three occurrence forms 10 w 20 w 30 Free ash was separated by flotation and sinking experiments. The remaining part was subjected to crushing, dissociation, acid washing and classification treatment, and the mass fraction of embedded ash and inclusion ash was determined by chemical analysis. The sum of the three was 1. (3) Ash content embedding characteristic correction coefficient K m The particle size distribution of ash particles was statistically analyzed using scanning electron microscopy (SEM) images, and the median particle size d was taken. 50 Calculate using the following formula:

[0035] In the formula, d ref For reference particle size, 20 μm is used; K m The value of K is limited to the range of 0.3 to 1.2. Fine-grained ash particles are tightly bound to the carbon matrix, resulting in a slower reaction rate. m The value is relatively small, and the ash content K of the coarse-grained intercalation is relatively small. m The value is relatively large; (4) Pore structure influence coefficient ε: The BET specific surface area S of the raw material was determined by nitrogen adsorption method. BET According to ε=min(1,S) BET Calculate S using / 200). BET The unit is m 2 / g; (5) Average particle size d of ash inclusions ave The particle size of the ash content of inclusions in multiple fields of view was statistically analyzed using scanning electron microscopy (SEM) combined with image analysis software, and the average value was taken. (6) Pre-exponential factor k0, hydrogen ion reaction order n, and intrinsic reaction activation energy E a0 Acid leaching and deliming kinetics experiments were conducted at different temperatures under a single thermal field. The reaction rate constant k was determined at each temperature. An Arrhenius plot was constructed using lnk against 1 / T. The intercept yielded lnk0. E was obtained by multiplying the negative value of the slope of the straight line by the ideal gas constant R.a0 Keeping other process parameters constant, change the concentration of the acid leaching solution to adjust the ratio of lnk to lna. H+ Perform a linear regression, and the slope is n; where the hydrogen ion activity is a. H+ After measuring the pH value of the acid leaching solution with a pH meter, proceed according to a H+ =10 -pH calculate; (7) Ultrasonic activation energy reduction coefficient α: Keeping other process parameters constant, the apparent activation energy E under different ultrasonic power densities was measured by changing the ultrasonic power density. a , with E a right When plotted, the negative value of the slope of the straight line is α; (8) Synergistic effect amplification factor β: The deashing reaction rate constant k was determined under a single thermal field. h The rate constant k of the deashing reaction under thermo-chemical-ultrasonic three-field coupling TCU According to β=(k TCU k h ) / (k h ·S TCU Calculate and take the average of multiple experimental results; (9) Threshold intensities T0, a0, and P0 of thermal field, chemical field, and ultrasonic field: These were determined by single-factor experiments. The intensities of the other two fields were kept constant. The field intensity of a single field was changed and the deashing reaction rate was measured. The field intensity corresponding to the inflection point of the curve of the deashing reaction rate versus the field intensity was taken as the threshold intensity. The exponents 0.3, 0.4, and 0.3 in equation (3) are constants obtained by fitting through multi-field synergistic deashing experiments. (10) Ultrasonic inclusion fragmentation efficiency coefficient γ: Keeping other process parameters constant, the ultrasonic power density was changed to conduct an inclusion ash removal experiment. The change in inclusion ash removal rate with reaction time was measured. Substituted into equation (8) to back-calculate the state transition rate k3, and then k3 = γ·(P U 2 / d ave 3 )·ε·k TCU γ was obtained through fitting; (11) Residual impurity coefficient k of acid leaching solution imp The ratio of the mass of the residue obtained by evaporating the acid leaching solution to the mass of the solute in the acid leaching solution is determined by a blank experiment, i.e., without adding coal sample. (12) Carbon matrix oxidation rate proportionality coefficient k c The oxidation weight loss rate of the carbon matrix under deashing conditions was determined by thermogravimetric analysis (TG), and the carbon loss mass fraction was calculated as k. c ·k TCU ·t·(1 A0) is obtained by reverse deduction.

[0036] Step S2: Construct a multi-field composite deashing synergistic reaction model: The original Arrhenius formula only applies to chemical reactions driven by a single thermal field and cannot describe the deashing process involving the coupling of a thermal field (T), a chemical field (C), and an ultrasonic field (U). This embodiment makes the following three improvements: First, the activation energy of a single thermal field is modified to an effective activation energy of multi-field coupling. The chemical field influences the reaction rate through hydrogen ion activity, while the ultrasonic field reduces the apparent activation energy of the reaction through localized high temperature and pressure generated by cavitation effects. Second, a multi-field synergistic intensity factor S is added. TCU The nonlinear synergistic effect among the three fields is quantified. A significant synergistic effect occurs if and only if the intensity of each field simultaneously exceeds its respective threshold. This factor uses a power function to describe the contribution weight of each field intensity to the synergistic effect. Third, a gray embedding feature correction coefficient K is introduced. m The effect of the initial microstructure of the raw materials on the deashing reaction rate was characterized.

[0037] The multi-field composite deashing synergistic reaction model is specifically expressed as follows:

[0038] In the formula, k TCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 ;K m k is the correction coefficient for the ash dispersive characteristics of coal-based hard carbon; k0 is the pre-exponential factor, min -1 ;a H+ is the activity of hydrogen ions in the acid leaching solution, mol / L; n is the hydrogen ion reaction order; E a0 α is the intrinsic activation energy of the reaction under a single thermal field, J / mol; α is the ultrasonic activation energy reduction coefficient, J·mol. -1 ·(W / mL) 1 / 2 ;P U U is the ultrasonic power density, W / mL; R is the ideal gas constant, taken as 8.314 J·mol⁻¹. -1 ·K -1 T is the reaction temperature, °C; β is the synergistic effect amplification factor; where α and P U The value of satisfies < To ensure that the effective activation energy is positive. The multi-field synergistic intensity factor S TCU The calculation formula is:

[0039] When T, a H+ P U When any one of the values ​​is lower than its corresponding threshold intensity T0, a0, P0, S TCUA value of 0 indicates that no significant synergistic effect occurs when none of the three fields simultaneously cross the threshold.

[0040] Step S3: Construct a probability model for the transformation of ash content morphology: Coal ash exists in three forms: free, embedded, and inclusions. The removal difficulty of these three forms differs significantly: free ash can be removed by direct contact and reaction with acid leaching solution; embedded ash requires acid leaching solution to penetrate and dissolve along pore channels, transforming it into a free state before removal; inclusion ash is completely encapsulated by a carbon matrix and requires ultrasonic cavitation to break up the inclusions, transforming it into an embedded state before being removed through a step-by-step transformation process. To characterize these differentiated removal patterns, this embodiment abstracts the ash occurrence form transformation process as a continuous-time Markov chain: free ash, embedded ash, and inclusion ash are denoted as states S1, S2, and S3, respectively, and the removed ash is denoted as the absorbed state S0; the state transition path is S3→S2→S1→S0, where the transition rate k1→S0 is k TCU The transfer rate from S2 to S1 is k2 = ε·k TCU The transfer rate from S3 to S2 is k3 = γ·(P U 2 / d ave 3 )·ε·k TCU The probability of inclusion fragmentation is directly proportional to the square of the ultrasonic power density and inversely proportional to the cube of the average particle size of the inclusions, which conforms to the physical laws of ultrasonic cavitation fragmentation.

[0041] Let the column vector be p(t) = [p1(t), p2(t), p3(t), p0(t)]. Let Q represent the probability distribution of each state at reaction time t, which satisfies the differential equation dp / dt=Q·p, and the generating matrix Q is:

[0042] Solving the above differential equation yields:

[0043] Initially, all ash particles are in state S. i At that time, the probability of being in the absorption state S0 after reaction time t is the removal probability P of the ash in that occurrence form. i (t). Matrix exponent e Qt Conventional numerical algorithms such as Padé approximation can be used for calculation, for example, the `expm` function in Matlab or the `expm` function in the Python `scipy.linalg` library. When k1, k2, and k3 are all unequal, P1(t), P2(t), and P3(t) can also be calculated using the following analytical formulas:

[0044]

[0045] as well as:

[0046] The coefficients A1, A2, and A3 in equation (8) are calculated according to equation (9):

[0047] When k1, k2, and k3 are equal, the limit form of the above formula is taken or numerical calculation is performed directly according to formula (5). This establishes a quantitative bridge between the evolution of microscopic ash occurrence morphology and macroscopic process parameters, solving the problem that existing technologies can only predict the total ash content and cannot distinguish the difficulty of removing different ash morphologies.

[0048] Step S4: Construct the mass conservation model: Based on the law of conservation of mass, the total ash removal rate is decomposed into a weighted sum of the removal probabilities of three ash forms according to their initial mass fractions. A secondary pollution ash correction term is introduced to consider the impact of residual acid impurities during acid leaching on the final ash content. A carbon loss correction term is also introduced to consider the relative enrichment of ash caused by the oxidation of a small amount of carbon matrix during the reaction. The carbon loss mass is proportional to the multi-field concerted reaction rate constant, reaction time, and initial carbon mass. The specific expression of the mass conservation model is as follows:

[0049] In the formula, A f denoted as the final ash mass fraction of coal-based hard carbon after multi-stage composite deashing; A0 is the initial ash mass fraction of coal-based hard carbon raw material; P1(t), P2(t), and P3(t) are the removal probabilities of free ash, embedded ash, and inclusion ash after reaction time t, respectively. , , These represent the initial mass fractions of free ash, embedded ash, and inclusion ash in the raw material, respectively, and w 10 +w 20 +w 30 =1; k imp C is the residual impurity coefficient of the acid leaching solution. acid V represents the mass concentration of acid in the leaching solution, in kg / L. acid m0 is the volume of the acid leaching solution, in L; m0 is the initial mass of the coal-based hard carbon raw material, in kg; k0 is the volume of the acid leaching solution, in L; c k is the proportionality coefficient for the oxidation rate of the carbon matrix. TCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 t represents the deashing reaction time, in minutes.

[0050] Step S5: Constructing and solving the multi-objective optimization function: Based on the predicted final ash content A f As the primary optimization objective, unit product energy consumption E and unit product acid consumption C are introduced as secondary optimization objectives. All objectives are normalized, converting target values ​​with different dimensions into dimensionless relative values. The aforementioned physical model is used as an equality constraint to ensure the physical rationality of the optimization results. Upper and lower limits of process parameters and target ash content constraints are introduced to ensure the engineering feasibility of the optimization results. The multi-objective optimization function adopts a normalized weighted summation form:

[0051] In the formula, F is the multi-objective optimization comprehensive evaluation function; ω1, ω2, and ω3 are the weight coefficients of ash content, energy consumption, and acid consumption, respectively, and + + =1; A f The final ash content is the mass fraction; A f,target The target ash content is defined as follows: E is the unit product energy consumption (kWh / kg); E0 is the unit product baseline energy consumption for the existing single-stage acid leaching process (kWh / kg); C is the unit product acid consumption (kg / kg); C0 is the unit product baseline acid consumption for the existing single-stage acid leaching process (kg / kg); the calculation formulas for the unit product energy consumption, unit product acid consumption, and final product quality are as follows:

[0052]

[0053]

[0054] In the formula, P U The ultrasonic power density is expressed in kW / L, and its value is the same as when expressed in W / mL; V acid ρ is the volume of the acid leaching solution, in L; t is the deashing reaction time, in min; ρ is the density of the acid leaching solution, in kg / L; T amb Ambient temperature, °C; m f For final product quality, kg; η acid For acid recovery rate; C acid The mass concentration of acid in the leaching solution is expressed in kg / L; k imp k is the residual impurity coefficient of the acid leaching solution. c This is the proportionality coefficient for the oxidation rate of the carbon matrix.

[0055] The constraints of the multi-objective optimization function are:

[0056] The aforementioned multi-objective optimization problem is a nonlinear constrained optimization problem, which is numerically solved using the Sequential Quadratic Programming (SQP) algorithm: Iterating through multiple feasible points within the constraint range as initial points, and when the change in the objective function F between adjacent iterations is less than 1 × 10⁻⁶, the algorithm continues to solve the problem. -6 Convergence is determined by selecting the solution with the smallest F among the convergent solutions as the optimal combination of process parameters (T, C). acid P U , t, V acid ).

[0057] For example, the optimization method provided in this embodiment has been implemented in practice, and the implementation process is as follows: Example 1: Take 1.0 kg of a certain coal-based hard carbon raw material, and perform raw material property determination and model parameter calibration according to the method described in step S1. The calibration results are shown in Table 1.

[0058] Table 1 shows the results of raw material properties and model parameter calibration.

[0059] The process conditions were set as follows: reaction temperature T = 80℃, hydrochloric acid leaching solution concentration 2.0 mol / L (corresponding to mass concentration C). acid =0.073 kg / L, hydrogen ion activity a H+ =2.0 mol / L), ultrasonic power density P U =0.5 W / mL, deashing reaction time t=60 min, liquid-to-solid ratio 5 mL / g (acid leaching liquid volume V) acid =5.0 L).

[0060] Substitution formula (3): Calculate the multi-field synergistic strength factor: S TCU =(80 / 40) 0.3 ×(2.0 / 0.5) 0.4 ×(0.5 / 0.1) 0.3 =3.474; Effective activation energy =45.0 8.0× =39.34 kJ / mol; Substitution formula (2): k TCU =0.75×4.0×10 4 ×2.0 0.8 ×e 39340 / (8.314×353.15) ×(1+0.15×3.474)=0.1204min -1 .

[0061] The state transition rates are then obtained as follows: k1 = 0.1204 min -1 k2 = 0.6 × 0.1204 = 0.0722 min -1 k3 = 300 × (0.5) 2 / 3.0 3 ) × 0.6 × 0.1204 = 0.2007 min -1 Substituting t=60 min into equations (6) to (8), we obtain P1=0.9993, P2=0.9683, P3=0.9515; the total ash removal rate is 0.45×0.9993+0.35×0.9683+0.20×0.9515=0.9789.

[0062] Substitution formula (10): A f =[0.060×(1 0.9789)+0.001×0.073×5.0 / 1.0] / [1 0.060 × 0.9789 0.0015×0.1204×60×(1 [0.060)]=0.175%.

[0063] A thermo-chemical-ultrasonic three-field composite deashing experiment was conducted according to the above process parameters. The ash content of the product was determined to be 0.183% according to GB / T 212, with a predicted relative error of 4.2%, which is less than 5%.

[0064] Example 2: Using the same raw materials as in Example 1 (i.e., the calibration parameters shown in Table 1), the process conditions were set as follows: reaction temperature T = 70℃, hydrochloric acid leaching solution concentration 3.0 mol / L (corresponding to mass concentration C). acid =0.110 kg / L), ultrasonic power density P U =0.3 W / mL, deashing reaction time t=90 min, liquid-to-solid ratio 6 mL / g (acid leaching liquid volume V) acid =6.0 L).

[0065] Calculation yields: S TCU =3.368, k TCU =0.0713 min -1 P1=0.9984, P2=0.9493, P3=0.8183; Substitute into equation (10) to predict the final ash content A. f =0.422%. A deashing experiment was conducted using the above process parameters, and the measured ash content was 0.408%, with a predicted relative error of 3.5%, which is less than 5%.

[0066] Example 3: (Process Parameter Optimization): Using the same raw materials as in Example 1, with a target ash content A for battery-grade products f,target The target value is set at 0.5%, with weighting coefficients ω1=0.6, ω2=0.25, and ω3=0.15. The process parameter constraints are: 50℃≤T≤90℃, 1.0mol / L≤C. acid ≤4.0 mol / L, 0.1 W / mL≤P U ≤0.5 W / mL, 30 min≤t≤120 min, 3 mL / g≤liquid-solid ratio≤8 mL / g. Based on the existing single-stage acid leaching process (80℃, 3.0 mol / L, 240 min, liquid-solid ratio 8 mL / g), the unit product baseline energy consumption E0=0.563 kWh / kg, and the unit product baseline acid consumption C0=0.138 kg / kg.

[0067] The multi-objective optimization problem shown in equation (11) is solved using a sequential quadratic programming algorithm. The algorithm iteratively solves the problem using multiple feasible points within the constraints as initial points, with a convergence tolerance of 1×10⁻⁶. -6 Taking the convergent solution with the minimum F, the optimal combination of process parameters is obtained as: T=90℃, C acid =4.0 mol / L (0.146 kg / L), P U =0.45 W / mL, t=30 min, liquid-to-solid ratio 3 mL / g, at this time F=0.627.

[0068] The model predicts that the final ash content under the optimal combination of process parameters is 0.093%; the deashing experiment conducted with this parameter combination showed an actual ash content of 0.096%, with a relative error of 3.2%, which meets the target ash content requirement. A comparison of process indicators before and after optimization is shown in Table 3.

[0069] Comparative example: The comparative example uses an existing homogeneous ash content prediction model: it does not distinguish between ash occurrence forms, treats all ash as directly removable free ash, and sets the removal probability at P=1. e kTCU·t The calculations were performed, and the remaining correction terms were the same as those in the mass conservation model of this invention. Under the process conditions of Example 1, the comparative example predicted a final ash content of 0.044%, which deviated from the measured value of 0.183% by 76%; under the process conditions of Example 2, the comparative example predicted a final ash content of 0.081%, which deviated from the measured value of 0.408% by 80%.

[0070] The comparative examples show that the removal of embedded ash and inclusion ash in coal lags significantly behind that of free ash. Homogeneous models that do not distinguish between ash occurrence forms will severely overestimate the deashing effect and underestimate the final ash content. This invention uses an ash occurrence form conversion probability model to characterize the differentiated removal rules of the three ash forms, controlling the relative prediction error to within 5%, significantly improving prediction accuracy and model reliability. The process conditions and results of Examples 1 to 3 and the comparative examples are summarized in Table 2, wherein: Comparative Examples 1 and 2 use homogeneous prediction models that do not distinguish between ash occurrence forms.

[0071] Table 2 compares the process conditions and prediction results of the examples and comparative examples.

[0072] Table 3 shows the comparison of process parameters before and after optimization.

[0073] As shown in Table 3, compared with the conventional multi-field process before optimization, the optimized deashing reaction time was shortened from 60 min to 30 min, and the energy consumption per unit product decreased from 3.04 kWh / kg to 0.99 kWh / kg, a reduction of approximately 67%. Compared with the existing single-field acid leaching benchmark process, the processing cycle was shortened from 240 min to 30 min, and the acid consumption per unit product decreased from 0.138 kg / kg to 0.071 kg / kg, a reduction of approximately 49%. Furthermore, the final ash content decreased from 1.90% (which cannot meet battery-grade requirements) to below 0.1%. It should be noted that the final ash content of the single-field acid leaching process cannot meet battery-grade requirements, and its energy consumption and acid consumption data are only used as a normalization benchmark.

[0074] like Figure 2As shown, this embodiment also provides a coal-based hard carbon ash content prediction and parameter optimization system, including: a first construction module 101, used to construct a multi-field composite deashing synergistic reaction model based on the Arrhenius chemical reaction kinetic formula, introducing multi-field coupling effective activation energy, multi-field synergistic intensity factor and coal-based hard carbon ash embedding characteristic correction coefficient, and solving for the multi-field composite deashing synergistic reaction rate constant; a second construction module 102, used to divide the ash in coal-based hard carbon into three occurrence forms: free ash, embedded ash and inclusion ash, and determine the state transition rate between each occurrence form using the multi-field composite deashing synergistic reaction rate constant, and construct the ash occurrence form... The first module is a state transformation probability model, which solves for the removal probability of ash in each of the three ash forms. The second module is a construction module 103, which is used to determine the total ash removal rate based on the law of conservation of mass, using the initial mass fraction and removal probability of ash in each of the three ash forms. It introduces secondary pollution ash correction terms and carbon loss correction terms to construct a mass conservation model and predict the final ash content. The third module is a function solving module 104, which uses the final ash content as the main optimization objective, the unit product energy consumption and unit product acid consumption as secondary optimization objectives, and the above three models as equality constraints to construct and solve a multi-objective optimization function to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0075] like Figure 4 As shown, this embodiment also provides a coal-based hard carbon ash content prediction and parameter optimization system, including: a first construction module, which is used to construct a multi-field composite deashing synergistic reaction model based on the Arrhenius chemical reaction kinetic formula, by introducing multi-field coupling effective activation energy, multi-field synergistic intensity factor and coal-based hard carbon ash embedding characteristic correction coefficient, and solve for the multi-field composite deashing synergistic reaction rate constant; The second construction module is used to classify the ash in coal-based hard carbon into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form transformation probability model. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the model. The third construction module is used to determine the total ash removal rate based on the law of conservation of mass, using the initial mass fraction and removal probability of the three types of ash in existence, introducing secondary pollution ash correction terms and carbon loss correction terms, constructing a mass conservation model, and predicting the final ash content of coal-based hard carbon after multi-stage composite deashing. The function solving module is used to construct and solve a multi-objective optimization function with the final ash content as the primary optimization objective, energy consumption per unit product and acid consumption per unit product as secondary optimization objectives, and the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model and the mass conservation model as equality constraints, so as to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0076] The present invention also provides a device for predicting and optimizing the ash content of coal-based hard carbon, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the method for predicting and optimizing the ash content of coal-based hard carbon.

[0077] The present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the coal-based hard carbon ash content prediction and parameter optimization method.

[0078] When the processor executes the computer program, it implements the aforementioned steps of predicting and optimizing the ash content of coal-based hard carbon. For example, based on the Arrhenius chemical reaction kinetics formula, it introduces multi-field coupling effective activation energy, multi-field synergistic strength factor, and coal-based hard carbon ash embedding characteristic correction coefficient to construct a multi-field composite deashing synergistic reaction model and solves for the multi-field composite deashing synergistic reaction rate constant. The ash in coal-based hard carbon is divided into three storage forms: free ash, embedded ash, and inclusion ash. The state transition rate between each storage form is determined using the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed sequentially as inclusion ash, embedded ash, free ash, and the probability model of ash storage form transformation, and the result is calculated. The removal probabilities of the three ash forms within the reaction time t are obtained. Based on the law of conservation of mass, the total ash removal rate is determined by the initial mass fraction and removal probability of the three ash forms. A secondary pollution ash correction term and a carbon loss correction term are introduced to construct a mass conservation model and predict the final ash content of coal-based hard carbon after multi-stage composite deashing. The final ash content is used as the main optimization objective, and the unit product energy consumption and unit product acid consumption are used as secondary optimization objectives. The multi-stage composite deashing synergistic reaction model, the ash form conversion probability model, and the mass conservation model are used as equality constraints to construct and solve a multi-objective optimization function to obtain the optimal combination of process parameters that meets the target ash content requirements.

[0079] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, the instruction segments describing the execution process of the computer program in the coal-based hard carbon ash content prediction and parameter optimization equipment. For example, the computer program can be divided into a first building module, a second building module, a third building module, and a function solving module, with the following specific functions: The first building module is used to construct a multi-field composite deashing synergistic reaction model based on the Arrhenius chemical reaction kinetics formula, introducing multi-field coupling effective activation energy, multi-field synergistic intensity factor, and coal-based hard carbon ash embedding characteristic correction coefficient, and solve for the multi-field composite deashing synergistic reaction rate constant; The second building module is used to divide the ash in coal-based hard carbon into three occurrence forms: free ash, embedded ash, and inclusion ash, and determine the state transition rate between each occurrence form using the multi-field composite deashing synergistic reaction rate constant, constructing a state transition path that sequentially transforms into inclusion ash, embedded ash, free ash, and the removed ash occurrence form. The system employs a probability model to calculate the removal probability of each of the three ash occurrence forms within a reaction time t. A third construction module, based on the law of mass conservation, determines the total ash removal rate using the initial mass fraction and removal probability of each of the three ash occurrence forms. It introduces secondary pollution ash correction terms and carbon loss correction terms to construct a mass conservation model, predicting the final ash content of coal-based hard carbon after multi-stage composite deashing. A function solution module, using the final ash content as the primary optimization objective, unit product energy consumption and unit product acid consumption as secondary optimization objectives, and the multi-stage composite deashing synergistic reaction model, the ash occurrence form conversion probability model, and the mass conservation model as equality constraints, constructs and solves a multi-objective optimization function to obtain the optimal combination of process parameters that meets the target ash content requirement.

[0080] The coal-based hard carbon ash content prediction and parameter optimization device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above are examples of coal-based hard carbon ash content prediction and parameter optimization devices and do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the coal-based hard carbon ash content prediction and parameter optimization device may also include input / output devices, network access devices, buses, etc.

[0081] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the coal-based hard carbon ash content prediction and parameter optimization equipment, connecting various parts of the equipment via various interfaces and lines.

[0082] The memory can be used to store the computer program and / or modules. The processor realizes various functions of the coal-based hard carbon ash content prediction and parameter optimization equipment by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0083] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0084] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for predicting and optimizing the ash content of coal-based hard carbon.

[0085] If the modules / units of the coal-based hard carbon ash content prediction and parameter optimization system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0086] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method for predicting and optimizing the ash content of coal-based hard carbon, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method for predicting and optimizing the ash content of coal-based hard carbon. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0087] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0088] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0089] In summary, this embodiment provides a method for predicting and optimizing the ash content of coal-based hard carbon, which has the following significant advantages compared to existing optimization methods: First, high prediction accuracy: The ash content prediction model constructed in this invention is based on multi-field coupling dynamics and the ash content morphology evolution mechanism. The relative errors between the predicted and measured ash content values ​​in Examples 1 to 3 are 4.2%, 3.5%, and 3.2%, respectively, all within 5%. In contrast, the homogeneous model in the comparative example, which does not distinguish between ash content morphologies, has a prediction deviation of over 76% under the same conditions. This indicates that multi-field coupling modeling and ash content morphology differentiation significantly improve prediction accuracy and model reliability.

[0090] Second, it has strong universality: the model explicitly includes raw material property parameters (ash embedding characteristics, pore structure, inclusion particle size, initial morphology distribution). When changing coal types or production scales, only the above parameters need to be recalibrated to make it applicable, without the need to rebuild the model. It is suitable for multi-field composite deashing processes of different coal types and different scales.

[0091] Third, it has high optimization efficiency and good economic benefits: multi-objective optimization with physical model as equality constraint can directly solve the global optimal process parameter combination numerically, avoiding empirical trial and error and local optima; compared with Example 1, Example 3 shortens the deashing reaction time from 60 min to 30 min, and the energy consumption per unit product is reduced from 3.04 kWh / kg to 0.99 kWh / kg, a reduction of about 67%; compared with the existing single-field acid leaching benchmark process, the acid consumption per unit product is reduced from 0.138 kg / kg to 0.071 kg / kg, a reduction of about 49%, the processing cycle is shortened from 240 min to 30 min, and the final ash content stably meets the battery-grade requirements of less than 0.5%.

[0092] Fourth, the mechanism is clear: each link of the model has a clear physicochemical significance, which can quantify the removal law of ash in different occurrence forms and the contribution of multi-field synergistic effects, providing an interpretable basis for process adjustment and reliable technical support for the precise, intelligent and efficient production of coal-based hard carbon deashing process.

[0093] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting and optimizing the ash content of coal-based hard carbon, characterized in that, include: Based on the Arrhenius chemical reaction kinetics formula, the effective activation energy of multi-field coupling, the multi-field synergistic strength factor and the correction coefficient of coal-based hard carbon ash embedding characteristics are introduced to construct a multi-field composite deashing synergistic reaction model, and the rate constant of the multi-field composite deashing synergistic reaction is obtained by solving the model. The ash in coal-based hard carbon is divided into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form of the removed ash. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the problem. Based on the law of conservation of mass, the total ash removal rate is determined by the initial mass fraction and removal probability of the three types of ash. A secondary pollution ash correction term and a carbon loss correction term are introduced to construct a mass conservation model and predict the final ash content of coal-based hard carbon after multi-stage composite deashing. Using the final ash content as the primary optimization objective, and unit product energy consumption and unit product acid consumption as secondary optimization objectives, and using the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model, and the mass conservation model as equality constraints, a multi-objective optimization function is constructed and solved to obtain the optimal combination of process parameters that meets the target ash content requirements.

2. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 1, characterized in that, The multi-field composite deashing synergistic reaction model is specifically expressed as follows: In the formula, k TCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 ;K m This is a correction coefficient for the ash dispersive characteristics of coal-based hard carbon. k0 is the pre-exponential factor, min -1 ;a H+ is the activity of hydrogen ions in the acid leaching solution, mol / L; n is the hydrogen ion reaction order; E a0 α is the intrinsic activation energy of the reaction under a single thermal field, J / mol; α is the ultrasonic activation energy reduction coefficient, J·mol. -1 ·(W / mL) 1 / 2 ;P U U is the ultrasonic power density, W / mL; R is the ideal gas constant, taken as 8.314 J·mol⁻¹. -1 ·K -1 T is the reaction temperature, °C; β is the synergistic effect amplification factor; S TCU It is a multi-field cooperative strength factor; and α and P U The value of satisfies < This is to ensure that the effective activation energy is positive.

3. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 2, characterized in that, The multi-field collaborative strength factor The specific calculation formula is as follows: In the formula, T0, a0, and P0 are the threshold intensities of the thermal field, chemical field, and ultrasonic field, respectively, which are determined through single-factor experiments: keeping the intensities of the other two fields constant, changing the field intensity of a single field and measuring the deashing reaction rate, and taking the field intensity corresponding to the inflection point of the curve of the deashing reaction rate versus the field intensity as the threshold intensity; when T0, a0, and P0 are constant, the threshold intensities are determined by the following method: H+ P U When any one of them is lower than its corresponding threshold intensity, S TCU The value is 0; the exponents 0.3, 0.4, and 0.3 in the above formula are constants obtained by fitting through multi-field synergistic deashing experiments.

4. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 1, characterized in that, The ash content morphology transformation probability model is a continuous-time Markov chain model: free ash, embedded ash, and inclusion ash are denoted as states S1, S2, and S3, respectively, and the removed ash is denoted as the absorption state S0; the state transition path is S3→S2→S1→S0, where the transition rate k1→S0 is k TCU The transfer rate from S2 to S1 is k2 = ε·k TCU The transfer rate from S3 to S2 is k3 = γ·(P U 2 / d ave 3 )·ε·k TCU ε is the influence coefficient of pore structure in coal-based hard carbon, γ is the ultrasonic inclusion fragmentation efficiency coefficient, and μm 3 ·(W / mL) -2 d ave The average particle size of the ash inclusions is in μm; Let the column vector be p(t) = [p1(t), p2(t), p3(t), p0(t)]. Let Q represent the probability distribution of each state at reaction time t, which satisfies the differential equation dp / dt=Q·p, and the generating matrix Q is: Solving for p, we get p(t) = e Qt ·p(0); Initially, all ash particles are in state S i At that time, the probability of being in the absorption state S0 after reaction time t is the removal probability P of the ash in that occurrence form. i (t); When k1, k2, and k3 are not equal, P1(t), P2(t), and P3(t) are calculated according to the following formulas: In the formula, the coefficients A1, A2, and A3 are respectively: When there are equal cases among k1, k2, and k3, take the limit form of the corresponding formula, or directly apply the formula to e. Qt Numerical calculations were performed to obtain the removal probabilities.

5. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 1, characterized in that, The mass conservation model is specifically expressed as follows: In the formula, A f denoted as the final ash mass fraction of coal-based hard carbon after multi-stage composite deashing; A0 is the initial ash mass fraction of coal-based hard carbon raw material; P1(t), P2(t), and P3(t) are the removal probabilities of free ash, embedded ash, and inclusion ash after reaction time t, respectively. , , These represent the initial mass fractions of free ash, embedded ash, and inclusion ash in the raw material, respectively, and w 10 +w 20 +w 30 =1; k imp C is the residual impurity coefficient of the acid leaching solution. acid V represents the mass concentration of acid in the leaching solution, in kg / L. acid m0 is the volume of the acid leaching solution, in L; m0 is the initial mass of the coal-based hard carbon feedstock, in kg; k0 is the volume of the acid leaching solution. c k is the proportionality coefficient for the oxidation rate of the carbon matrix. TCU The rate constant for the multi-field composite deashing synergistic reaction is min. -1 t represents the deashing reaction time, in minutes.

6. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 1, characterized in that, The multi-objective optimization function adopts a normalized weighted summation form, and the calculation formula is as follows: In the formula, To optimize the comprehensive evaluation function for multiple objectives; , , These are the weighting coefficients for ash content, energy consumption, and acid consumption, respectively; and they satisfy... + + =1; This represents the final ash content by mass. The target ash content is the mass fraction. Energy consumption per unit of product; The unit product benchmark energy consumption for existing single-field acid leaching processes; Acid consumption per unit of product; The benchmark acid consumption per unit product for existing single-stage acid leaching processes; T represents the ultrasonic power density; T represents the reaction temperature. The constraints of the multi-objective optimization function also include upper and lower limit inequality constraints on reaction temperature, acid leaching solution concentration, ultrasonic power density, deashing reaction time, and acid leaching solution volume.

7. The method for predicting and optimizing the ash content of coal-based hard carbon according to claim 6, characterized in that, The formulas for calculating unit product energy consumption, unit product acid consumption, and final product quality are as follows: In the formula, The density of the acid leaching solution; The ambient temperature; For the quality of the final product; Acid recovery rate; This is the proportionality coefficient for the oxidation rate of the carbon matrix.

8. A coal-based hard carbon ash content prediction and parameter optimization system, characterized in that, include: The first building module is used to construct a multi-field composite deashing synergistic reaction model based on the Arrhenius chemical reaction kinetics formula, by introducing the multi-field coupling effective activation energy, multi-field synergistic strength factor and coal-based hard carbon ash embedding characteristic correction coefficient, and solving for the multi-field composite deashing synergistic reaction rate constant. The second construction module is used to classify the ash in coal-based hard carbon into three occurrence forms: free ash, embedded ash, and inclusion ash. The state transition rate between each occurrence form is determined by the multi-field composite deashing synergistic reaction rate constant. A state transition path is constructed in the order of inclusion ash, embedded ash, free ash, and the ash occurrence form transformation probability model. The removal probability of each of the three occurrence forms of ash within the reaction time t is obtained by solving the model. The third construction module is used to determine the total ash removal rate based on the law of conservation of mass, using the initial mass fraction and removal probability of the three types of ash in existence, introducing secondary pollution ash correction terms and carbon loss correction terms, constructing a mass conservation model, and predicting the final ash content of coal-based hard carbon after multi-stage composite deashing. The function solving module is used to construct and solve a multi-objective optimization function with the final ash content as the primary optimization objective, energy consumption per unit product and acid consumption per unit product as secondary optimization objectives, and the multi-field composite deashing synergistic reaction model, the ash occurrence form conversion probability model and the mass conservation model as equality constraints, so as to obtain the optimal combination of process parameters that meets the target ash content requirements.

9. A coal-based hard carbon ash content prediction and parameter optimization device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the coal-based hard carbon ash content prediction and parameter optimization method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the coal-based hard carbon ash content prediction and parameter optimization method according to any one of claims 1-7.

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