Automated feeding and discharging method and system for sodium ion battery hard carbon preparation kiln

Abstract

The present invention discloses an automated feeding and discharging method and system for a sodium ion battery hard carbon preparation kiln, which relates to the technical field of sodium ion batteries, and comprises the following steps of obtaining index parameters of raw materials to be put in, constructing a data set according to the index parameters, and obtaining feeding influence data; constructing a feeding index coefficient model based on a machine learning algorithm according to the feeding influence data to obtain a feeding index coefficient; obtaining performance indicators of finished products; constructing a discharging index coefficient model based on a machine learning algorithm according to the performance indicators to obtain a discharging index coefficient; obtaining a relationship coefficient between the feeding index coefficient and the discharging index coefficient through a linear regression method according to the feeding index coefficient and the discharging index coefficient; the present application solves the problem of failing to comprehensively consider the influence of multiple performance indicators through the constructed feeding and discharging index coefficient model, which leads to the negative impact of other performance indicators when optimizing a certain indicator.

Description

Technical Field

[0001] The present invention relates to the technical field of sodium ion batteries, and more specifically, to an automated feeding and discharging method and system for a kiln for preparing hard carbon for sodium ion batteries. Background Art

[0002] Sodium-ion batteries have attracted extensive attention in the field of energy storage in recent years due to their abundant raw materials, low cost and environmental friendliness. Hard carbon, as the negative electrode material of sodium-ion batteries, has become a research focus due to its excellent electrochemical properties and good cycle stability. However, the preparation process of hard carbon is complicated and usually requires high-temperature heat treatment and a reasonable feeding and discharging system to ensure the uniformity and stability of the material.

[0003] With the development of industrial automation and intelligent technology, more and more sodium-ion battery manufacturers have begun to introduce advanced control systems, such as programmable logic controllers and monitoring and data acquisition systems, to achieve full-process monitoring and control. This not only improves production efficiency, but also enhances product consistency and reliability.

[0004] The above disclosed technical solutions have at least the following technical problems:

[0005] The current automated feeding and discharging of the sodium ion battery hard carbon preparation kiln does not make full use of real-time data analysis, and fails to monitor the type and particle size of the feed raw materials in real time, resulting in the inability to make timely adjustments according to changes during the preparation process, resulting in the performance indicators of the finished product failing to reach the expected indicators. In addition, the current method focuses on the optimization of a single indicator, but fails to comprehensively consider the influence of multiple performance indicators, which results in other performance indicators being negatively affected when optimizing a certain indicator. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an automated feeding and discharging method and system for a sodium ion battery hard carbon preparation kiln, which constructs a feeding and discharging index coefficient model to solve the problem of failing to comprehensively consider the influence of multiple performance indicators, which leads to negative impacts on other performance indicators when optimizing a certain indicator.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An automated feeding and discharging method for a sodium-ion battery hard carbon preparation kiln includes obtaining index parameters of raw materials to be put in, constructing a data set based on the index parameters, and obtaining feeding influence data; constructing a feeding index coefficient model based on a machine learning algorithm based on the feeding influence data to obtain a feeding index coefficient; obtaining performance indicators of finished products, the performance indicators including capacity loss, conductivity and specific capacitance; constructing a discharging index coefficient model based on a machine learning algorithm based on the performance indicators to obtain a discharging index coefficient; and obtaining a relationship coefficient between the feeding index coefficient and the discharging index coefficient through a linear regression method analysis.

[0009] In a preferred embodiment, the feed influence data includes pyrolysis reaction rate influence data, and the steps for obtaining the pyrolysis reaction rate influence data are as follows: obtaining the reaction rate of the material to be tested at different temperatures and reactant concentrations, and obtaining the slope and intercept by linear regression analysis of the relationship between temperature and reaction rate based on the Arrhenius equation, thereby obtaining the frequency factor; performing linear regression on ln(k) versus 1 / T according to the Arrhenius equation to obtain the slope, thereby calculating the activation energy;

[0010] The reaction rate of the material to be tested at a fixed temperature and different reactant concentrations is obtained, and the power relationship between the reaction rate and the reactant concentration is obtained through regression analysis; the reaction rate and temperature distribution data under different conditions are obtained through numerical simulation methods, and the temperature-related correction function is determined through curve fitting methods to ensure that the model output is consistent with the experimental data; the height or volume change of the material is monitored by a sensor, and the feed rate and the total amount of material in the kiln are calculated in combination with the density of the material; the index data in the kiln are obtained, and the index data include temperature, amount of substance, gas pressure and volume, and the gas constant is obtained according to the index data based on the ideal gas equation; according to the frequency factor, activation energy, concentration of the reactant, the total amount of material in the kiln, the power relationship between the reaction rate and the reactant concentration, the feed rate, temperature, gas constant and correction function, the pyrolysis reaction rate influencing data is calculated based on the preset formula to obtain the pyrolysis reaction rate influencing data.

[0011] In a preferred embodiment, the feed influencing data includes atmosphere fluidity, and the steps for obtaining the atmosphere fluidity are as follows: obtaining the relative humidity and absolute humidity data in the gas flow environment for data analysis to obtain the specific impact of humidity changes on atmosphere fluidity, thereby obtaining the humidity influencing factor; obtaining the atmosphere fluidity data under different humidities, performing data regression analysis, and obtaining the initial atmosphere fluidity; calculating based on the initial atmosphere fluidity and the humidity influencing factor based on a preset atmosphere fluidity formula to obtain the atmosphere fluidity.

[0012] In a preferred embodiment, the steps for obtaining the capacity loss are as follows: according to the chemical composition and electrochemical reaction of the material, the number of electrons that each sodium ion can take away is calculated to obtain the theoretical capacity before preparation; the hard carbon material to be tested is made into the negative electrode of the battery, and the charge and discharge cycle test is carried out with the electrolyte and the positive electrode material, and the actual capacity data of each cycle is collected, and the actual capacity data is subjected to linear regression analysis to obtain characteristic data; the capacity loss is calculated based on the actual capacity data and the theoretical capacity before preparation based on a preset capacity loss formula.

[0013] In a preferred embodiment, the steps for obtaining the conductivity are as follows: the thickness of the test sample at different positions is measured multiple times by a vernier caliper to obtain an average value; the voltage and current are obtained by a four-probe method, in which two probes are used to apply current and the other two probes are used to measure voltage, thereby reducing the influence of contact resistance, and the resistance is calculated based on Ohm's law according to the voltage and current; if the cross-section of the test sample is a regular shape, the cross-sectional area is directly measured; if it is an irregular shape, a more accurate three-dimensional reconstruction is performed by computer tomography, and the cross-sectional area is calculated to ensure the accuracy of the data; the conductivity is calculated based on the thickness, resistance and cross-sectional area of ​​the test sample based on a preset conductivity formula.

[0014] In a preferred embodiment, the specific capacitance acquisition step is as follows: obtaining current data and charging or discharging time of multiple groups of materials to be tested during the charging or discharging process in a set time period, and removing data with excessive current data fluctuations to ensure data accuracy; obtaining the mass of the material to be tested, which must be dried and uniform before measuring the mass to avoid the influence of moisture or other substances on the mass and improve the measurement accuracy; and calculating the specific capacitance based on the preset specific capacitance formula according to the charging or discharging current, charging or discharging time and the mass of the material to be tested.

[0015] In a preferred embodiment, the relationship coefficient between the feed index coefficient and the discharge index coefficient is improved by adjusting the feed index parameter to obtain the best discharge index, as follows: is larger, indicating that each unit change in the output index coefficient will lead to a significant change in the feed index coefficient, which may mean that the feed index coefficient has a stronger influence on the output index coefficient; if the slope It is smaller, indicating that the influence of the feed index coefficient on the discharge index coefficient is weaker; according to the influence strength of the feed index coefficient on the discharge index coefficient, the parameters in the feed influencing data are adjusted to improve the feed index coefficient and achieve the best discharge index.

[0016] An automated feeding and discharging method and system for a sodium ion battery hard carbon preparation kiln, characterized in that it includes a data acquisition module, a feed coefficient model construction module, a finished product performance index extraction module, a discharging coefficient model construction module and an analysis and adjustment module, and there is a connection between the modules; the data acquisition module is used to obtain the index parameters of the raw materials to be put in, and obtain the constructed data set according to the index parameters to obtain the feed impact data; the feed coefficient model construction module is used to construct a feed index coefficient model based on a machine learning algorithm according to the feed impact data to obtain the feed index coefficient; the finished product performance index extraction module is used to obtain the performance indicators of the finished product, and the performance indicators include capacity loss, conductivity and specific capacitance; the discharging coefficient model construction module is used to construct a discharging index coefficient model based on a machine learning algorithm according to the performance indicators to obtain the discharging index coefficient; the analysis and adjustment module is used to analyze the relationship coefficient between the feed index coefficient and the discharging index coefficient by a linear regression method according to the feed index coefficient and the discharging index coefficient, and improve the relationship coefficient between the feed index coefficient and the discharging index coefficient by adjusting the feed index parameters to obtain the best discharging index.

[0017] The technical effects and advantages of the automatic feeding and discharging method of the sodium ion battery hard carbon preparation kiln of the present invention are as follows:

[0018] 1. The present invention can monitor and adjust the feeding and discharging process in real time through the automated feeding and discharging method, reduce manual intervention, and improve the overall efficiency of the production line. This automated control can ensure a stable supply of materials, thereby improving the continuity of production. By reasonably controlling the temperature, concentration and feeding rate factors, the raw materials can be maximized, waste can be reduced, and the utilization rate of resources can be improved. By improving energy efficiency and reducing waste generation, the impact of the production process on the environment can be reduced, which is in line with the development trend of modern green manufacturing.

[0019] 2. The feed and discharge index coefficient model constructed by the present invention based on the machine learning algorithm can accurately adjust the input ratio of raw materials. This data-driven method can optimize the formula according to real-time feedback, improve the consistency and performance of the product, and achieve precise control of key parameters such as kiln temperature and atmosphere fluidity through in-depth analysis of the pyrolysis reaction rate and its influencing factors. This not only helps to improve the efficiency of the reaction, but also improves product quality and reduces the production of defective products. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a structural schematic diagram of an automated feeding and discharging method for a sodium ion battery hard carbon preparation kiln according to the present invention.

[0021] Figure 2 This is a schematic structural diagram of an automated feeding and discharging system for a sodium ion battery hard carbon preparation kiln according to the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] Embodiment 1, Figure 1 The present invention provides an automated feeding and discharging method for a sodium ion battery hard carbon preparation kiln, comprising the following steps:

[0024] S1, obtaining index parameters of the raw materials to be put in, constructing a data set according to the index parameters, and obtaining feed impact data.

[0025] In this embodiment, the index parameters of the raw materials to be put in are obtained, and a data set is constructed according to the index parameters to obtain the feed impact data, specifically:

[0026] The feed impact data include pyrolysis reaction rate impact data and atmosphere fluidity, as follows:

[0027] Pyrolysis reaction rate influence data refers to the amount of reactant reduction or product increase per unit time during the thermal decomposition process of a substance. Specifically, the reaction rate is defined as the amount of reactant reduction or product increase per unit time. It is a physical quantity that describes the speed of a chemical reaction and is usually expressed by the change in the concentration of reactants or products per unit time.

[0028] The steps for obtaining the pyrolysis reaction rate influence data are as follows:

[0029] Obtain the reaction rate of the material to be tested at different temperatures and reactant concentrations, and obtain the slope and intercept of the relationship between temperature and reaction rate through linear regression analysis based on the Arrhenius equation, thereby obtaining the frequency factor;

[0030] According to the Arrhenius equation, ln(k) is linearly regressed against 1 / T to obtain the slope, thereby obtaining the activation energy;

[0031] Obtain the reaction rate of the material to be tested at a fixed temperature and different reactant concentrations, and obtain the power relationship between the reaction rate and the reactant concentration through regression analysis;

[0032] The reaction rate and temperature distribution data under different conditions are obtained through numerical simulation methods, and the temperature-related correction function is determined through curve fitting methods to ensure that the model output is consistent with the experimental data;

[0033] The height or volume change of the material is monitored by sensors, and the feed rate and the total amount of material in the kiln are calculated based on the density of the material;

[0034] Obtaining index data in the kiln, the index data including temperature, amount of substance, gas pressure and volume, and calculating based on the ideal gas equation according to the index data to obtain a gas constant;

[0035] The pyrolysis reaction rate influence data is obtained by calculating based on the preset pyrolysis reaction rate influence data formula according to the frequency factor, activation energy, concentration of reactants, the total amount of materials in the kiln, the power relationship between the reaction rate and the concentration of reactants, the feed rate, temperature, gas constant and correction function.

[0036] The calculation formula of the Arrhenius equation is as follows:

[0037]

[0038] The calculation formula of the ideal gas equation is as follows:

[0039]

[0040] The calculation formula of the pyrolysis reaction rate influence data is as follows:

[0041]

[0042] In the formula, The pyrolysis reaction rate affects the data. is the frequency factor, e is the natural logarithm, E is the activation energy, which represents the energy required for the reaction to proceed, R is the gas constant, and m represents the power relationship between the reaction rate and the concentration of the reactants. is the concentration of the reactant, is the feed rate, is the total amount of material in the kiln, It is a temperature-dependent correction function used to describe the influence of heat conduction and other thermal effects, determined by experiments or numerical simulations. is the temperature, is the reaction rate constant, is the gas pressure, is the volume of gas, It is the amount of substance.

[0043] Further, the advantages of considering the pyrolysis reaction rate by temperature, reactant concentration, feed rate and total amount of material are analyzed:

[0044] Reasonable control of temperature, concentration and feed rate factors, maximize the use of raw materials, reduce waste, improve resource utilization, and reduce the impact of the production process on the environment by improving energy efficiency and reducing waste generation, which is in line with the development trend of modern green manufacturing;

[0045] At higher temperatures, the structural properties of hard carbon can be effectively improved, such as increasing conductivity and compatibility, improving its performance as anode material for batteries, and selectively affecting the reaction path by precisely controlling the temperature, promoting the formation of desired products and reducing the formation of by-products;

[0046] Relatively accurate mathematical formulas are established to describe the pyrolysis reaction rate. These formulas provide a theoretical basis for the design and optimization of automated control systems, making the feeding and discharging processes more efficient and stable.

[0047] It should be noted that an increase in the frequency factor usually means an increase in the effective collision frequency between reactants, thereby increasing the reaction rate. If the frequency factor decreases, the reaction rate will decrease, and vice versa. The higher the activation energy, the more energy is required for the reaction, and the corresponding temperature increase can significantly affect the rate. If the activation energy increases, the reaction rate will decrease, and vice versa. When the temperature is high enough, reducing the activation energy will help increase the reaction rate. Increasing the feed rate will increase the reaction rate, but if it is too high, it will cause the reactor to be overloaded, thereby affecting the reaction efficiency.

[0048] Atmosphere fluidity refers to the flow and distribution characteristics of inert gas or reactive gas in the reactor during the pyrolysis reaction. It reflects the movement of the atmosphere in the reaction space and directly affects gas mass transfer, heat transfer and the uniformity of reactants.

[0049] The steps for obtaining the atmosphere fluidity are as follows:

[0050] Obtain the relative humidity and absolute humidity data in the gas flow environment for data analysis to obtain the specific impact of humidity changes on the flow of the atmosphere, thereby obtaining the humidity influencing factor;

[0051] Obtain the atmospheric fluidity data under different humidity conditions, perform data regression analysis, and obtain the initial atmospheric fluidity;

[0052] The atmosphere fluidity is calculated based on the initial atmosphere fluidity and the humidity influencing factor based on a preset atmosphere fluidity formula to obtain the atmosphere fluidity.

[0053] The calculation formula of the atmosphere fluidity is as follows:

[0054]

[0055] In the formula, It is the fluidity of the atmosphere. is the initial atmosphere fluidity, that is, the atmosphere fluidity value not affected by humidity, e is the natural logarithm, and h is the influence factor of humidity.

[0056] It should be noted that the atmosphere fluidity formula describes the effect of humidity on atmosphere fluidity. The higher the humidity factor, the more significant the decrease in atmosphere fluidity, which leads to a slowdown in heat and mass transfer efficiency during pyrolysis and affects the reaction rate. Therefore, maintaining appropriate humidity helps control atmosphere fluidity and thus optimizes the conditions for hard carbon preparation.

[0057] S2, constructing a feed index coefficient model based on the feed impact data and a machine learning algorithm to obtain a feed index coefficient.

[0058] In this embodiment, a feed index coefficient model is constructed based on the feed impact data based on a machine learning algorithm to obtain a feed index coefficient.

[0059] The specific calculation formula of the material index coefficient model is as follows:

[0060]

[0061] In the formula, is the feed index coefficient, is the pyrolysis reaction rate, It is the fluidity of the atmosphere. is the frequency factor, e is the natural logarithm, E is the activation energy, which represents the energy required for the reaction to proceed, R is the gas constant, and m represents the power relationship between the reaction rate and the concentration of the reactants. is the concentration of the reactant, represents the concentration of reactant A, is the feed rate, is the total amount of material in the kiln, It is a temperature-dependent correction function used to describe the influence of heat conduction and other thermal effects, determined by experiments or numerical simulations. is the temperature, is the initial atmosphere fluidity, that is, the atmosphere fluidity value not affected by humidity, and h is the influence factor of humidity.

[0062] It should be noted that increasing the activation energy will reduce the pyrolysis reaction rate and thus reduce the feed index coefficient. Therefore, adjusting the pyrolysis conditions or catalyst to reduce the activation energy will help improve the feed index coefficient value. In the atmosphere fluidity, an increase in the humidity factor will reduce the atmosphere fluidity value, thereby increasing the feed index coefficient, and vice versa. A high feed index coefficient value indicates that the pyrolysis reaction is faster and the fluidity is moderate, which is conducive to the formation of the target product.

[0063] S3, obtaining performance indicators of the finished product, wherein the performance indicators include capacity loss, conductivity and specific capacitance.

[0064] In this embodiment, the performance indicators of the finished product are obtained, and the performance indicators include capacity loss, conductivity and specific capacitance, as follows:

[0065] Capacity loss refers to the difference between the actual available capacity and the theoretical capacity of the energy storage device during the charging and discharging process. As the number of charge and discharge cycles increases, the chemical properties and structure of the material will change, resulting in a decrease in the effective energy storage capacity.

[0066] The steps for obtaining the capacity loss are specifically as follows:

[0067] According to the chemical composition and electrochemical reaction of the material, the number of electrons that each sodium ion can take away is calculated to obtain the theoretical capacity before preparation;

[0068] The hard carbon material to be tested is made into the negative electrode of the battery, and is matched with the electrolyte and the positive electrode material to carry out the charge and discharge cycle test, and the actual capacity data of each cycle is collected, and the actual capacity data is processed by linear regression analysis to obtain the characteristic data;

[0069] The capacity loss is calculated based on the actual capacity data and the theoretical capacity before preparation based on a preset capacity loss formula to obtain the capacity loss.

[0070] The specific calculation formula of the capacity loss is as follows:

[0071]

[0072] In the formula, is the capacity loss, is the theoretical capacity before preparation, is the actual measured capacity.

[0073] It should be noted that if the capacity loss exceeds the preset value, it means that there is a problem in the material preparation process, and the proportion or type of material needs to be manually adjusted; through multiple charge and discharge tests, the capacity attenuation is monitored. If the hard carbon material produced shows obvious capacity loss, it means that there is an error in the operation during the preparation process.

[0074] Conductivity, conductivity refers to the ability of a material to conduct electric current. Conductivity is closely related to the type, temperature and structure of the material. In batteries and capacitors, conductivity affects their charging and discharging efficiency and response speed. Materials with high conductivity can conduct electrons faster and improve the performance of the device.

[0075] The steps for obtaining the conductivity are as follows:

[0076] The thickness of the test sample at different positions is measured multiple times by a vernier caliper to obtain an average value;

[0077] By using a four-probe method, two probes are used to apply current and the other two probes are used to measure voltage, thereby reducing the influence of contact resistance, obtaining voltage and current, and calculating resistance based on Ohm's law according to the voltage and current;

[0078] If the cross section of the test sample is of regular shape, the cross-sectional area is directly measured; if it is of irregular shape, more accurate three-dimensional reconstruction is performed through computer tomography to calculate the cross-sectional area and ensure the accuracy of the data;

[0079] The conductivity is calculated based on a preset conductivity formula according to the thickness, resistance and cross-sectional area of ​​the test sample.

[0080] The specific calculation formula of the conductivity is as follows:

[0081]

[0082] In the formula, is the conductivity, is the thickness of the sample, is the resistor, is the cross-sectional area of ​​the sample.

[0083] It should be noted that during the measurement process, multiple measurements are required and the average value is calculated to improve the reliability of the results, and environmental conditions such as temperature and humidity are controlled to avoid affecting resistance and conductivity and ensure the accuracy of the data.

[0084] Specific capacitance refers to the charge storage capacity of a material per unit mass during the charging and discharging process. Specific capacitance is an important parameter for measuring the energy density of a capacitor or battery, reflecting how much electrical energy is stored in a certain mass of material. The higher the specific capacitance, the stronger the energy storage capacity under the same mass conditions.

[0085] The steps for obtaining the specific capacitance are as follows:

[0086] Obtain the current data and charging or discharging time of multiple groups of materials to be tested during the charging or discharging process in a set time period, and remove the data with excessive current fluctuations to ensure data accuracy;

[0087] Obtain the mass of the material to be tested. Before measuring the mass, it must be dry and uniform to avoid the influence of moisture or other substances on the mass and improve the accuracy of the measurement;

[0088] The specific capacitance is obtained by calculating based on a preset specific capacitance formula according to the charging or discharging current, the charging or discharging time and the mass of the material to be tested.

[0089] The specific calculation formula of the specific capacitance is as follows:

[0090]

[0091] In the formula, is the specific capacitance, is the charge or discharge current, is the charge or discharge time, is the quality of the sample.

[0092] S4, constructing a discharge index coefficient model based on the performance index and a machine learning algorithm to obtain a discharge index coefficient.

[0093] In this embodiment, the discharging index coefficient model is constructed based on the machine learning algorithm according to the performance index to obtain the discharging index coefficient, which is specifically as follows:

[0094] The specific calculation formula of the discharge index coefficient is as follows:

[0095]

[0096] In the formula, is the discharge index coefficient, is the capacity loss, , and is the weight coefficient, is the conductivity, represents the maximum conductivity, is the specific capacitance, Indicates the maximum specific capacitance.

[0097] S4, analyzing the feed index coefficient and the discharge index coefficient by a linear regression method to obtain the relationship coefficient between the feed index coefficient and the discharge index coefficient, and improving the relationship coefficient between the feed index coefficient and the discharge index coefficient by adjusting the feed index parameters to obtain the optimal discharge index.

[0098] In this embodiment, the relationship coefficient between the feed index coefficient and the discharge index coefficient is obtained by linear regression analysis according to the feed index coefficient and the discharge index coefficient, and the relationship coefficient between the feed index coefficient and the discharge index coefficient is improved by adjusting the feed index parameter to obtain the optimal discharge index, which is as follows:

[0099] The specific calculation formula of the relationship coefficient between the feed index coefficient and the discharge index coefficient is as follows:

[0100]

[0101] The specific calculation formula of the slope is as follows:

[0102]

[0103] In the formula, is the feed index coefficient, is the discharge index coefficient, is the error term, is the number of data points, is the slope.

[0104] It should be noted that the slope indicates the influence of the feed index coefficient on the discharge index coefficient. >0, it indicates that the two are positively correlated; if the slope <0, it indicates that the two are negatively correlated.

[0105] The relationship coefficient between the feed index coefficient and the discharge index coefficient is improved by adjusting the feed index parameter to obtain the best discharge index, which is as follows:

[0106] If the slope Large, indicating that each unit change in the output index coefficient will lead to a significant change in the feed index coefficient, which may mean that the feed index coefficient has a strong influence on the feed index coefficient;

[0107] If the slope It is smaller, indicating that the influence of the feed index coefficient on the discharge index coefficient is weaker;

[0108] According to the influence intensity of the feed index coefficient on the discharge index coefficient, the parameters in the feed influencing data are adjusted to improve the feed index coefficient and achieve the best discharge index.

[0109] Embodiment 2, Figure 2 A system for an automated feeding and discharging method for a sodium ion battery hard carbon preparation kiln, comprising a data acquisition module, a feeding coefficient model building module, a finished product performance index extraction module, a discharging coefficient model building module and an analysis and adjustment module, with connections between the modules;

[0110] A data acquisition module is used to obtain the index parameters of the raw materials to be put in, and to construct a data set based on the index parameters to obtain the feed impact data;

[0111] A feed coefficient model building module is used to build a feed index coefficient model based on the feed impact data based on a machine learning algorithm to obtain a feed index coefficient;

[0112] A finished product performance index extraction module, used to obtain the performance index of the finished product, wherein the performance index includes capacity loss, conductivity and specific capacitance;

[0113] A discharge coefficient model building module is used to build a discharge index coefficient model based on the performance index and a machine learning algorithm to obtain a discharge index coefficient;

[0114] The analysis and adjustment module is used to obtain the relationship coefficient between the feed index coefficient and the discharge index coefficient through a linear regression method according to the feed index coefficient and the discharge index coefficient, and to improve the relationship coefficient between the feed index coefficient and the discharge index coefficient by adjusting the feed index parameters to obtain the optimal discharge index.

[0115] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0116] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0117] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0118] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0119] 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.

[0120] 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. An automated feeding and discharging method for a sodium ion battery hard carbon preparation kiln, characterized in that: include: Obtaining index parameters of the raw materials to be put in, constructing a data set based on the index parameters, and obtaining feed impact data; Constructing a feed index coefficient model based on the feed impact data and a machine learning algorithm to obtain a feed index coefficient; Obtaining performance indicators of the finished product, wherein the performance indicators include capacity loss, conductivity and specific capacitance; Constructing a discharge index coefficient model based on a machine learning algorithm according to the performance index to obtain a discharge index coefficient; According to the feed index coefficient and the discharge index coefficient, a linear regression method is used to analyze the relationship coefficient between the feed index coefficient and the discharge index coefficient, and the relationship coefficient between the feed index coefficient and the discharge index coefficient is improved by adjusting the feed index parameter to obtain the optimal discharge index; The specific calculation formula of the feed index coefficient model is as follows: The specific calculation formula of the discharge index coefficient is as follows: In the formula, is the feed index coefficient, is the pyrolysis reaction rate, It is the fluidity of the atmosphere. is the frequency factor, e is the natural logarithm, E is the activation energy, which represents the energy required for the reaction to proceed, R is the gas constant, and m represents the power relationship between the reaction rate and the concentration of the reactants. is the concentration of the reactant, represents the concentration of reactant A, is the feed rate, is the total amount of material in the kiln, It is a temperature-dependent correction function used to describe the influence of heat conduction and other thermal effects, determined by experiments or numerical simulations. is the temperature, is the initial atmosphere fluidity, that is, the atmosphere fluidity value not affected by humidity, h is the influence factor of humidity, is the discharge index coefficient, is the capacity loss, , and is the weight coefficient, is the conductivity, represents the maximum conductivity, is the specific capacitance, represents the maximum value of specific capacitance; The relationship coefficient between the feed index coefficient and the discharge index coefficient is obtained by analyzing the feed index coefficient and the discharge index coefficient through a linear regression method, which is specifically as follows: The specific calculation formula of the relationship coefficient between the feed index coefficient and the discharge index coefficient is as follows: The specific calculation formula of the slope is as follows: In the formula, is the feed index coefficient, is the discharge index coefficient, is the error term, is the number of data points, is the slope.

2. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 1, characterized in that: The feed influencing data includes pyrolysis reaction rate influencing data, and the steps for obtaining the pyrolysis reaction rate influencing data are as follows: Obtain the reaction rate of the material to be tested at different temperatures and reactant concentrations, and obtain the slope and intercept of the relationship between temperature and reaction rate through linear regression analysis based on the Arrhenius equation, thereby obtaining the frequency factor; According to the Arrhenius equation, ln(k) is linearly regressed against 1 / T to obtain the slope, thereby obtaining the activation energy; Obtain the reaction rate of the material to be tested at a fixed temperature and different reactant concentrations, and obtain the power relationship between the reaction rate and the reactant concentration through regression analysis; The reaction rate and temperature distribution data under different conditions are obtained through numerical simulation methods, and the temperature-related correction function is determined through curve fitting methods to ensure that the model output is consistent with the experimental data; The height or volume change of the material is monitored by sensors, and the feed rate and the total amount of material in the kiln are calculated based on the density of the material; Obtaining index data in the kiln, the index data including temperature, amount of substance, gas pressure and volume, and calculating based on the ideal gas equation according to the index data to obtain a gas constant; The pyrolysis reaction rate influence data is obtained by calculating based on the preset pyrolysis reaction rate influence data formula according to the frequency factor, activation energy, concentration of reactants, the total amount of materials in the kiln, the power relationship between the reaction rate and the concentration of reactants, the feed rate, temperature, gas constant and correction function.

3. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 2 is characterized in that: The feed influencing data includes atmosphere fluidity, and the steps for obtaining the atmosphere fluidity are as follows: Obtain the relative humidity and absolute humidity data in the gas flow environment for data analysis to obtain the specific impact of humidity changes on the flow of the atmosphere, thereby obtaining the humidity influencing factor; Obtain the atmospheric fluidity data under different humidity conditions, perform data regression analysis, and obtain the initial atmospheric fluidity; The atmosphere fluidity is calculated based on the initial atmosphere fluidity and the humidity influencing factor based on a preset atmosphere fluidity formula to obtain the atmosphere fluidity.

4. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 3, characterized in that: The steps for obtaining the capacity loss are specifically as follows: According to the chemical composition and electrochemical reaction of the material, the number of electrons that each sodium ion can take away is calculated to obtain the theoretical capacity before preparation; The hard carbon material to be tested is made into the negative electrode of the battery, and is matched with the electrolyte and the positive electrode material to carry out the charge and discharge cycle test, and the actual capacity data of each cycle is collected, and the actual capacity data is processed by linear regression analysis to obtain the characteristic data; The capacity loss is calculated based on the actual capacity data and the theoretical capacity before preparation based on a preset capacity loss formula to obtain the capacity loss.

5. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 4, characterized in that: The steps for obtaining the conductivity are as follows: The thickness of the test sample at different positions is measured multiple times by a vernier caliper to obtain an average value; By using a four-probe method, two probes are used to apply current and the other two probes are used to measure voltage, thereby reducing the influence of contact resistance, obtaining voltage and current, and calculating resistance based on Ohm's law according to the voltage and current; If the cross section of the test sample is of regular shape, the cross-sectional area is directly measured; if it is of irregular shape, more accurate three-dimensional reconstruction is performed through computer tomography to calculate the cross-sectional area and ensure the accuracy of the data; The conductivity is calculated based on a preset conductivity formula according to the thickness, resistance and cross-sectional area of ​​the test sample.

6. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 5, characterized in that: The steps for obtaining the specific capacitance are as follows: Obtain the current data and charging or discharging time of multiple groups of materials to be tested during the charging or discharging process in a set time period, and remove the data with excessive current fluctuations to ensure data accuracy; Obtain the mass of the material to be tested. Before measuring the mass, it must be dry and uniform to avoid the influence of moisture or other substances on the mass and improve the accuracy of the measurement; The specific capacitance is obtained by calculating based on a preset specific capacitance formula according to the charging or discharging current, the charging or discharging time and the mass of the material to be tested.

7. The automated feeding and discharging method for preparing hard carbon for sodium ion batteries according to claim 6, characterized in that: The relationship coefficient between the feed index coefficient and the discharge index coefficient is improved by adjusting the feed index parameter to obtain the best discharge index, which is as follows: If the slope Large, indicating that each unit change in the output index coefficient will lead to a significant change in the feed index coefficient, which may mean that the feed index coefficient has a strong influence on the feed index coefficient; If the slope It is smaller, indicating that the influence of the feed index coefficient on the discharge index coefficient is weaker; According to the influence intensity of the feed index coefficient on the discharge index coefficient, the parameters in the feed influencing data are adjusted to improve the feed index coefficient and achieve the best discharge index.

8. A system using the automated feeding and discharging method for preparing a kiln for sodium ion battery hard carbon according to any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a feed coefficient model building module, a finished product performance index extraction module, a discharge coefficient model building module and an analysis and adjustment module, and there are connections between the modules; A data acquisition module is used to obtain the index parameters of the raw materials to be put in, and to construct a data set based on the index parameters to obtain the feed impact data; A feed coefficient model building module is used to build a feed index coefficient model based on the feed impact data based on a machine learning algorithm to obtain a feed index coefficient; A finished product performance index extraction module, used to obtain the performance index of the finished product, wherein the performance index includes capacity loss, conductivity and specific capacitance; A discharge coefficient model building module is used to build a discharge index coefficient model based on the performance index and a machine learning algorithm to obtain a discharge index coefficient; The analysis and adjustment module is used to obtain the relationship coefficient between the feed index coefficient and the discharge index coefficient through a linear regression method according to the feed index coefficient and the discharge index coefficient, and to improve the relationship coefficient between the feed index coefficient and the discharge index coefficient by adjusting the feed index parameters to obtain the optimal discharge index.

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