Method and system for tunnel kiln calcination of lithium carbonate

Abstract

A method and system for preparing lithium carbonate by calcination in a tunnel kiln are disclosed. The method includes: designing and preparing the tunnel kiln; loading raw materials, specifically lithium carbonate raw materials, onto the material car of the tunnel kiln; and calcination heating, heating according to a preset temperature rise curve, controlling the calcination temperature between 870℃ and 920℃. The temperature rise curve is determined using partial differential equations, which describe the temperature changes of the raw materials at different calcination times and in different regions of the tunnel kiln. This application, through an optimized calcination process, enables precise temperature control during calcination, thereby improving the uniformity of calcination temperature, increasing calcination efficiency and thermal energy utilization, reducing energy consumption, improving the product quality of lithium carbonate, and enhancing the reliability and safety of the calcination process.

Description

Technical Field

[0001] This application relates to the field of lithium extraction technology, and in particular to a method and system for preparing lithium carbonate by calcination in a tunnel kiln. Background Technology

[0002] Lepidolite, also known as lepidolite, is an important mineral resource containing abundant rare metals such as lithium, sodium, potassium, rubidium, cesium, and aluminum. Lepidolite is the most common lithium mineral and a crucial source of lithium for lithium extraction. Lithium and its salts are fundamental materials for lithium-ion batteries, earning it the title of "industrial MSG" and "energy star" from scientists. It is the best material for producing lithium-ion batteries and a vital metal for developing new energy sources and materials. Therefore, the comprehensive development and utilization of lepidolite has significant economic and strategic value.

[0003] Traditional methods for extracting lithium typically involve roasting and acid leaching of lepidolite. However, this process suffers from low energy efficiency, environmental pollution, and safety hazards. In particular, during the high-temperature roasting process, a significant amount of heat is lost and not effectively recovered, leading to energy waste. Furthermore, the lack of a proper safety accident early warning mechanism creates potential risks in the production process, necessitating urgent improvement. Summary of the Invention

[0004] In view of this, in order to solve at least one of the above defects, this application provides a method for preparing lithium carbonate by calcination in a tunnel kiln, which can improve energy utilization efficiency, is environmentally friendly and can improve the early warning mechanism for safety accidents.

[0005] Additionally, this application also provides a system for preparing lithium carbonate by calcination in a tunnel kiln.

[0006] This application provides a method for preparing lithium carbonate by calcination in a tunnel kiln, the method comprising:

[0007] Design and preparation of tunnel kilns;

[0008] Loading raw materials, specifically lithium carbonate raw materials, onto the feed cars of the tunnel kiln, the feed cars being used to move between different areas of the tunnel kiln; and

[0009] The roasting process is carried out according to a preset temperature rise curve, and the roasting temperature is controlled between 870℃ and 920℃.

[0010] The heating curve is formulated using partial differential equations, which are used to describe the temperature changes of the raw material at different roasting times and in different areas of the tunnel kiln.

[0011] In some possible embodiments, the method for constructing the partial differential equation includes:

[0012] Based on the dependence of roasting temperature on roasting time and roasting location during the roasting process, the parameters affecting the roasting temperature distribution are determined, wherein the roasting location is the spatial position of the raw material in the tunnel kiln;

[0013] Based on the parameters, establish partial differential equations for the calcination time, calcination location, and calcination temperature.

[0014] Numerical methods are selected to solve the partial differential equations and determine the boundary and initial conditions;

[0015] Simulate the temperature rise curve based on the boundary conditions and the initial conditions; and

[0016] The heating curve is optimized based on the partial differential equation.

[0017] In some possible embodiments, when the heating curve reflects the temperature distribution along a one-dimensional spatial position in the tunnel kiln, the partial differential equation is as follows (I):

[0018]

[0019] Where T(x,t) is the roasting temperature at different roasting positions x and roasting times t;

[0020] It is the rate of change of calcination temperature with respect to the calcination time t;

[0021] α is the thermal diffusivity;

[0022] It is the second spatial derivative of the roasting temperature at different roasting positions x, representing the propagation of heat in the raw material;

[0023] Q(x,t) is a function of an external heat source or heat loss used to heat the tunnel kiln.

[0024] In some possible embodiments, the method further includes the following during the roasting and heating process:

[0025] A safety accident early warning system is set up to monitor the roasting and heating process in real time and to predict and alarm risks.

[0026] In some possible embodiments, the safety incident early warning system includes:

[0027] Monitoring equipment is used to collect process data during the roasting process;

[0028] The intelligent analysis system is used to monitor and analyze the collected process data in real time for risk prediction and alarm purposes; and

[0029] An emergency response procedure is used to activate emergency procedures after the intelligent analysis system issues an alarm.

[0030] In some possible embodiments, the method for constructing the intelligent analysis system includes:

[0031] Data collection and preprocessing;

[0032] Principal component analysis was applied to analyze the preprocessed data to obtain analytical results; and

[0033] The analysis results are used for risk prediction and alerts.

[0034] In some possible embodiments, the method for performing data analysis on the preprocessed data using principal component analysis includes:

[0035] Calculate the covariance matrix, standardize the collected data to ensure that the influence of each variable on the result is consistent, and calculate the covariance matrix of the standardized data.

[0036] Extract principal components, perform eigenvalue decomposition on the covariance matrix, find eigenvalues ​​and corresponding eigenvectors, arrange the eigenvalues ​​in descending order of value, and select the eigenvectors corresponding to the first few eigenvalues ​​as principal components.

[0037] Dimensionality reduction and analysis project the original data onto the selected principal components to reduce the data dimensionality and analyze the actual meaning represented by each principal component to determine the risk components related to risk prediction.

[0038] Establish an early warning model, select a risk model based on the risk results, train the early warning model using historical data, and determine the early warning threshold based on the historical data; and

[0039] Real-time monitoring and analysis: The early warning model is applied to monitor the roasting process in real time and issue risk warnings. An alarm is issued when the monitoring data exceeds the threshold.

[0040] In some possible embodiments, the method further includes, during the roasting heating step, establishing a heat recovery and reuse system to recover waste heat and exhaust gas from the roasting process and to reuse the waste heat and exhaust gas.

[0041] This application also provides a system for implementing the method for preparing lithium carbonate by tunnel kiln roasting as described above. The system is a closed-loop system, comprising:

[0042] A tunnel kiln, wherein a movable feed car is provided inside the tunnel kiln, the feed car being used to hold lithium carbonate raw material and to move the raw material to different areas of the tunnel kiln; and

[0043] The heating module is used to heat the tunnel kiln according to a preset heating curve to roast the raw material at a roasting temperature of 870℃~920℃. The heating curve is formulated using a partial differential equation, which describes the temperature changes of the raw material at different roasting times and in different areas of the tunnel kiln.

[0044] In some possible embodiments, the system further includes: a heat recovery module and a safety accident early warning system. The heat recovery and reuse system is used to recover waste heat and exhaust gas during the roasting process; the safety accident early warning system is used to monitor the roasting heating process in real time and to predict and alarm risks.

[0045] The method and system for preparing lithium carbonate by tunnel kiln roasting provided in this application have the following beneficial effects:

[0046] (1) Through the optimized roasting process, the temperature can be precisely adjusted during the roasting process, thereby improving the uniformity of the roasting temperature, increasing the roasting efficiency and the utilization rate of heat energy, reducing energy consumption, improving the product quality of lithium carbonate, and enhancing the reliability and safety of the roasting process.

[0047] (2) Through heat energy recovery and reuse technologies, energy efficiency can be improved, energy consumption and production costs can be reduced, and environmental impact can be minimized. For example, high-efficiency heat exchangers can be used to collect hot gases emitted from tunnel kilns and preheat raw materials and air entering the kiln. Waste heat can be converted into steam to drive turbines for power generation, achieving further utilization of energy. In addition, waste heat storage systems can be used to collect and store heat energy that is not suitable for immediate use, so that it can be used during peak demand periods.

[0048] (3) By setting up a safety accident early warning module, especially by adopting an intelligent analysis system and utilizing big data and machine learning technologies, it is possible to effectively process and analyze large amounts of real-time data, identify key risk factors, and thus provide a scientific basis for timely risk warnings. This improves the efficiency and accuracy of safety monitoring, helps prevent potential risks, and ensures the safety and stability of the entire production process. In addition, the safety accident early warning module includes an emergency response mechanism. Once an anomaly is detected, the system will automatically activate emergency procedures, including cutting off energy supply and activating the cooling system.

[0049] (4) By using new high-efficiency heat insulation materials inside the tunnel kiln, it is beneficial to reduce heat loss and further reduce energy consumption. Attached Figure Description

[0050] Figure 1This is a flowchart of a method for preparing lithium carbonate by tunnel kiln roasting according to an embodiment of this application.

[0051] Figure 2 This is a framework diagram of a tunnel kiln roasting system for preparing lithium carbonate according to an embodiment of this application.

[0052] Explanation of main component symbols

[0053] A system for preparing lithium carbonate by calcination in a tunnel kiln 100; a tunnel kiln 10; a heating and temperature control module 20; a heat recovery module 30; a safety accident early warning module 40; and a controller 50. Detailed Implementation

[0054] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Although the description of this application is presented in conjunction with embodiments, this does not mean that the features of this application are limited to this embodiment. On the contrary, the purpose of describing the application in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of this application. To provide a thorough understanding of this application, many specific details will be included in the following description. This application may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of this application, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0055] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0056] The embodiments of this application are described below with reference to the accompanying drawings. Unless otherwise specified, the data range values ​​recorded in this application shall include the end values.

[0057] Please see Figure 1 This application provides a method for preparing lithium carbonate by calcination in a tunnel kiln, the method comprising:

[0058] Step S1: Tunnel kiln design and preparation.

[0059] Step S2: Loading raw materials. The processed lithium carbonate raw materials are loaded onto the material carts of the tunnel kiln, which are used to move in different areas of the tunnel kiln.

[0060] Step S3, roasting and heating, heating according to a preset temperature rise curve, and controlling the roasting temperature between 870℃ and 920℃. The temperature rise curve is formulated using a partial differential equation, which is used to describe the temperature changes of the raw material at different roasting times and in different areas of the tunnel kiln.

[0061] In step S1, the tunnel kiln needs to be designed and prepared. The design of the tunnel kiln ensures that the necessary conditions can be provided for the efficient roasting of lithium carbonate. This mainly includes the following aspects: the structural design of the tunnel kiln, the selection of the tunnel kiln lining material, the design of the thermal and power system, the design of the exhaust and heat recovery system, and the design of the safety and monitoring system.

[0062] The following is a detailed explanation of each of the above aspects.

[0063] Firstly, the structural design of the tunnel kiln

[0064] First, the length and width of the tunnel kiln are determined based on the production scale and the internal flow layout. The length is generally determined by production capacity and roasting time; a sufficient tunnel kiln length ensures thorough roasting of the raw materials. The tunnel kiln mainly consists of the kiln body and the material carts. The kiln body's interior includes preheating, drying, roasting, and cooling zones. The material carts can move within different areas of the kiln, facilitating the movement of the raw materials in these areas.

[0065] Secondly, the kiln body should possess sufficient mechanical strength and stability to withstand long-term high-temperature operation and mechanical loading. Furthermore, the selection of refractory materials used in the tunnel kiln must consider their stability at high temperatures to ensure that the refractory materials do not affect lithium carbonate during the roasting process.

[0066] Secondly, the selection of tunnel kiln lining materials.

[0067] Materials that are resistant to high temperatures, wear, and chemical corrosion, such as high-alumina bricks or silica mullite bricks, are selected to ensure the stability and service life of the kiln at high temperatures. At the same time, the lining is ensured to have good thermal insulation properties to reduce heat loss.

[0068] Thirdly, thermal and power system design

[0069] (1) Selection of heating method: Direct or indirect heating can be selected, such as using gas, oil or electric heating. The heating devices need to be evenly distributed to ensure uniform temperature inside the kiln and avoid local overheating or underheating.

[0070] (2) Set up a temperature control system: Configure a high-precision temperature control system to monitor and adjust the temperature inside the kiln in real time. For example, the temperature sensors should be reasonably distributed to comprehensively monitor the temperature distribution inside the kiln.

[0071] (3) Power system: Ensure the material car conveying system is stable and reliable, and can evenly push the raw materials into the kiln. For example, remotely controlled material cars can be used to transport the raw materials to the corresponding positions. In addition, the design of the power system should consider energy saving and ease of maintenance.

[0072] Fourthly, exhaust and heat recovery systems.

[0073] (1) Exhaust system design: The exhaust ports should be arranged reasonably to ensure that the waste gas in the kiln can be effectively discharged and to prevent air backflow. In addition, the exhaust system should comply with environmental protection requirements to reduce the emission of harmful gases.

[0074] (2) Heat recovery device: Design a heat exchanger or other heat recovery equipment to collect the high-temperature waste gas emitted from the kiln and recover its heat energy. In addition, the heat recovery system should be efficient and stable to maximize the utilization of waste heat.

[0075] Fifthly, security and monitoring systems

[0076] (1) Safety protection measures: Design necessary safety valves, pressure relief devices and fire prevention systems. In addition, ensure that the kiln can be shut down quickly and safely in an emergency.

[0077] (2) Monitoring system: Video surveillance and sensors are installed to monitor the kiln's operating status in real time. It is connected to the central control room to achieve remote monitoring and control.

[0078] Through the detailed steps described above, the design and preparation of the tunnel kiln can provide a solid foundation for the effective, safe, and efficient roasting of lithium carbonate.

[0079] Step S2 mainly includes the following steps:

[0080] Step S21, Raw material selection: Select lithium carbonate raw materials of appropriate purity to ensure that there are no impurities or that the impurity content is within a controllable range.

[0081] Step S22, pretreatment of raw materials: lithium carbonate is crushed and sieved to obtain raw materials with uniform particle size, so as to facilitate uniform heating and reaction in the subsequent process.

[0082] In step S3, during the roasting and heating process, it is necessary to maintain a micro-oxygen or inert atmosphere inside the tunnel kiln to prevent the oxidation of lithium carbonate.

[0083] In step S3, the most critical aspect is temperature control, which is constructed in step S2. The design of the heating curve and the roasting heating process will be explained in detail below.

[0084] The heating curve is formulated using a partial differential equation (PDE), which describes the temperature changes of the raw material at different roasting times (t) and different roasting positions (x) within the tunnel kiln. In chemical processes, especially during roasting, accurate temperature control is crucial. This embodiment uses a partial differential equation to describe and optimize the heating curve, providing more precise temperature control, thereby improving product quality, saving energy, and even reducing waste generation.

[0085] In some embodiments, the method for constructing the partial differential equation includes:

[0086] Step 1: Determine the equation parameters

[0087] Based on the dependence of temperature on roasting time and roasting location during the roasting process, parameters affecting temperature distribution are determined, where the roasting location refers to the spatial position of the raw material within the tunnel kiln. The dependence of temperature on time and space during roasting includes factors such as heat conduction, convection, and radiation; key parameters affecting temperature distribution include thermal conductivity, heat capacity, and material properties.

[0088] Step 2, constructing equations

[0089] Based on the parameters described, establish partial differential equations for the reaction roasting time, roasting location, and roasting temperature. These equations should be able to describe the temperature changes of the raw materials at different times and locations.

[0090] For example, when the heating curve reflects the temperature distribution along one-dimensional space in the tunnel kiln, a one-dimensional partial differential equation reflecting the relationship between time, space, and temperature is established. The goal here is to describe the temperature distribution of the material in the tunnel kiln as time and spatial location change. In this case, the partial differential equation is shown in equation (I):

[0091]

[0092] Where T(x,t) is the temperature at different roasting positions x and roasting times t;

[0093] It is the rate of change of temperature with respect to the calcination time t;

[0094] α is the thermal diffusivity;

[0095] It is the second spatial derivative of temperature at different roasting positions x, representing the propagation of heat in the raw material;

[0096] Q(x,t) is a function of external heat source or heat loss.

[0097] For example, in the process of roasting lithium carbonate in a tunnel kiln, to determine the optimal heating curve based on the material characteristics and kiln design, the aforementioned partial differential equations can be used to simulate the temperature distribution of the material under different heating strategies. For instance, by adjusting the power of the heating element (affecting Q(x,t)), it is possible to observe how the temperature distribution changes with time and kiln length, thereby optimizing the heating process to improve energy efficiency and product quality.

[0098] Step 3: Numerical solution of the equation

[0099] The partial differential equations are solved using numerical methods to determine the boundary and initial conditions. Numerical methods may include the finite difference method, the finite element method, etc.

[0100] Specifically, the finite element method (FEM) is an effective approach for solving the one-dimensional partial differential equation of heat conduction. The FEM divides a continuous problem domain (such as the space inside a tunnel kiln) into a series of smaller elements and approximates the solution to the partial differential equation on these smaller elements, thereby obtaining an approximate solution for the entire domain. The following are the steps for solving the one-dimensional partial differential equation of heat conduction using the finite element method:

[0101] Step a: Discretization of the domain

[0102] Define the geometric domain: Describe the one-dimensional spatial geometric domain of the tunnel kiln, such as the length of the kiln.

[0103] Mesh generation: The length of the tunnel kiln is divided into a series of small intervals (elements). Each node in an element represents a specific location. The finer the mesh, the more accurate the result, but the greater the computational cost.

[0104] Step b: Establish element equations

[0105] Shape function selection: Select an appropriate shape function (such as a linear or quadratic function) for each element to approximate the temperature distribution within the element.

[0106] Assembly element equations: Based on the fundamental principles of the finite element method, the original partial differential equations are transformed into local equations for each element. These local equations are related to shape functions and material properties (such as thermal diffusivity).

[0107] Step c: Apply boundary conditions and initial conditions

[0108] Boundary conditions: Apply boundary conditions at both ends of the tunnel kiln, such as fixed temperature or adiabatic conditions.

[0109] Initial conditions: Set the initial temperature distribution, which may be uniform or preset according to the actual situation.

[0110] Step d: Solving and post-processing

[0111] Solving the linear equation system: The obtained linear equation system is solved using numerical methods (such as Gaussian elimination or iterative methods) to obtain the temperature of each node.

[0112] Results Analysis: The calculated temperature distribution was analyzed to understand how the temperature changes with time and location during the roasting process.

[0113] Optimization and Verification: Based on the solution results, optimize the heating curve and verify the accuracy and reliability of the calculation results in actual operation.

[0114] By using steps a to d above, solving the one-dimensional heat conduction partial differential equation using the finite element method can provide accurate guidance for temperature control in tunnel kilns and help optimize the roasting process.

[0115] Step 4: Simulate the heating process

[0116] The heating curve is simulated based on the boundary conditions and the initial conditions. Specifically, computer simulation is used to predict the temperature distribution under different conditions and to examine the impact of different heating curves on the final temperature distribution.

[0117] Step 5, Equation-based optimization

[0118] Optimize the heating curve: Based on the aforementioned partial differential equation, optimize the heating curve. Specifically, analyze the simulation results to find the optimal heating curve, while also considering energy efficiency, product quality, and safety.

[0119] Experimental validation: Test the optimized temperature rise curve in a laboratory or industrial environment. Collect data and compare it with simulation results to verify the effectiveness of the optimization.

[0120] Results and Applications.

[0121] By following the steps above, precise optimization of temperature control during the roasting and heating process can be achieved, thereby improving product quality, reducing energy consumption, and enhancing the reliability and safety of the process.

[0122] Step S3 also includes the recovery and reuse of thermal energy. The construction of the thermal energy recovery and reuse system is also completed in step S2.

[0123] Heat energy recovery and reuse mainly include: (1) recovering high-temperature exhaust gas from tunnel kilns by installing high-efficiency heat exchangers for preheating feed or generating hot water / steam. (2) using recovered heat energy to drive turbines to generate electricity, thereby improving overall energy utilization efficiency.

[0124] Regarding the steps of "heat energy recovery and reuse", the following will describe in detail how to effectively recover and utilize the heat energy generated in the tunnel kiln during the calcination of lithium carbonate in order to improve energy efficiency and reduce production costs.

[0125] Step 1: Design of the heat recovery system

[0126] Selection of heat recovery equipment: Choose appropriate heat recovery equipment, such as heat exchangers or waste heat boilers, based on the type of heat generated during the roasting process (e.g., high-temperature exhaust gas, waste hot water steam, etc.). Furthermore, the equipment materials must be heat-resistant and corrosion-resistant to ensure long-term stable operation.

[0127] Optimization of heat exchange efficiency: Design efficient heat exchange systems to maximize heat energy conversion efficiency.

[0128] Select the appropriate type and size of heat exchanger based on the temperature and flow rate of the kiln exhaust gas.

[0129] Step 2: Thermal Energy Conversion and Utilization

[0130] Thermal energy conversion: The heat energy in waste gas is converted into steam, hot water, or other heat transfer fluids through heat exchangers or waste heat boilers. Variable frequency technology should be considered to adjust the operating efficiency of the heat exchanger according to actual needs.

[0131] Thermal energy reuse: Utilizing recovered thermal energy for kiln preheating, producing hot water or steam for other processes. The generated steam can be used for power generation, further improving energy efficiency.

[0132] Step 3: Maintenance and optimization of the heat recovery system

[0133] Regular maintenance: Regularly inspect the heat exchanger and related piping to ensure there are no leaks, blockages, or other problems. Regularly clean carbon deposits and sediment to maintain heat exchange efficiency.

[0134] System optimization: Based on actual operating data, adjust system configuration and operating parameters to adapt to different production conditions. Employ an intelligent management system to monitor system performance in real time and make timely adjustments.

[0135] Step 4: Safety and Environmental Considerations

[0136] Safety controls: Safety valves and overload protection devices are installed to prevent accidents caused by excessive temperature or pressure. Monitoring equipment, such as temperature sensors and pressure gauges, is installed in critical areas to monitor the system's operating status in real time.

[0137] Environmental compliance: Ensure the heat recovery system complies with local environmental standards to minimize environmental impact. Treat exhaust emissions to ensure they meet environmental requirements.

[0138] By implementing steps 1 through 4 above, the heat energy generated during the calcination of lithium carbonate in the tunnel kiln can be efficiently recovered and utilized, improving energy efficiency while also meeting environmental and safety requirements. The implementation of these steps needs to be adjusted according to specific production conditions and equipment configuration.

[0139] Step S3 also includes: cooling and collecting the product.

[0140] Slow cooling: After calcination, the temperature is lowered slowly to avoid cracks or stress caused by rapid cooling of lithium carbonate crystals.

[0141] Product collection and screening: The roasted lithium carbonate is unloaded from the kiln, cooled, and then screened to obtain a particle size distribution that meets the requirements.

[0142] In the process of calcining lithium carbonate in a tunnel kiln, the cooling and collection steps are crucial to ensure the quality and safety of the final product. The following is a detailed description of these steps:

[0143] Step 1, Cooling Process

[0144] Controlling the cooling rate: The cooling rate needs to be precisely controlled to prevent cracks or structural damage caused by thermal stress. A suitable cooling profile should be established based on the characteristics of lithium carbonate after calcination.

[0145] Uniform cooling: Ensure uniform airflow within the kiln to avoid localized overheating or undercooling. Airflow can be controlled by adjusting the kiln's fans or airflow guide plates.

[0146] Temperature monitoring: The entire cooling process is monitored using temperature sensors to ensure that the temperature is controlled within a preset range.

[0147] Step 2, Product Collection and Processing

[0148] Safe unloading: Once the temperature has dropped to a safe range, begin unloading the processed lithium carbonate. Use specialized mechanical equipment for unloading to minimize the risk of personnel coming into contact with the high-temperature material.

[0149] Preliminary screening: The roasted product undergoes preliminary screening to remove unsuitable particles or impurities. If necessary, a vibrating screen or other grading equipment can be used for separation.

[0150] Product quality testing: The collected lithium carbonate samples are tested for chemical composition and physical properties, such as purity and particle size distribution. Based on the test results, the roasting or cooling parameters are adjusted to optimize product quality.

[0151] Step 3: Exhaust Gas Treatment and Environmental Protection

[0152] Exhaust gas purification: Collecting and purifying the exhaust gas generated during the cooling process to ensure compliance with environmental standards. Filter bags, electrostatic precipitators, or other exhaust gas treatment equipment can be used.

[0153] Waste Management: Waste generated during the cooling and screening processes should be properly handled. Consider recycling or safe disposal methods to reduce environmental pollution.

[0154] Through steps 1 to 3 above, cooling and collection during the calcination of lithium carbonate in a tunnel kiln can be effectively achieved, ensuring the quality and safety of the final product while meeting environmental protection requirements.

[0155] In step S3, the method further includes: setting up a safety accident early warning system to monitor the roasting heating process in real time and to predict and alarm risks.

[0156] In some embodiments, the safety accident early warning system includes: monitoring equipment, an intelligent analysis system, and an emergency response procedure. The monitoring equipment is used to collect process data during the roasting process. Temperature sensors, pressure sensors, gas detection sensors, etc., are installed at key locations in the tunnel kiln. The intelligent analysis system is used to monitor and analyze the collected process data in real time to predict risks and issue alarms. That is, by monitoring data in real time through the intelligent analysis system, potential risks are predicted, and timely early warnings are issued. The emergency response procedure is used to activate emergency procedures after the intelligent analysis system issues an alarm; that is, once an anomaly is detected, emergency procedures are automatically activated, such as cutting off energy supply or activating the cooling system.

[0157] In the process of roasting lithium carbonate in a tunnel kiln, a safety accident early warning system is a key component to ensure production safety. The following is a detailed description of the construction of such a system, mainly including the following steps:

[0158] I. System Design and Layout:

[0159] Monitoring point setup: Install sensors for temperature, pressure, and flow rate at key locations (such as heating zones, cooling zones, and heat recovery equipment) to monitor the kiln's operating status. Install gas detectors in areas where leaks or hazardous material accumulation may occur.

[0160] Intelligent analysis systems are used for data integration and analysis: data collected from monitoring points is integrated into a central control system. Data analysis and pattern recognition technologies are used to predict potential security issues.

[0161] Principal Component Analysis (PCA) is used for data dimensionality reduction and pattern recognition. In intelligent analytics systems, PCA can help identify and monitor important data trends and anomalies, thereby predicting potential risks.

[0162] Specifically, the construction methods for intelligent analysis systems include:

[0163] Step 1: Data collection and preprocessing.

[0164] Data collection: Collect relevant data from the intelligent monitoring system, such as parameters like temperature, pressure, and flow rate.

[0165] Data preprocessing: Cleaning the data and removing erroneous or missing values. Standardizing the data to ensure that each variable has the same influence.

[0166] Step 2: Apply principal component analysis to analyze the preprocessed data and obtain the analysis results.

[0167] When applying Principal Component Analysis (PCA) to an intelligent monitoring system, the goal is to extract the most critical information from a large amount of monitoring data in order to more effectively identify anomalies and predict potential risks. The following are the specific implementation steps:

[0168] Step 2.1: Calculate the covariance matrix

[0169] Data standardization: Standardize the collected data to ensure that each variable has a consistent impact on the results.

[0170] Constructing the covariance matrix: Calculate the covariance matrix of the standardized data. The covariance matrix reflects the correlation between variables.

[0171] Step 2.2: Extract principal components

[0172] Finding eigenvalues ​​and eigenvectors: Perform eigenvalue decomposition on the covariance matrix to find its eigenvalues ​​and corresponding eigenvectors.

[0173] Principal components are selected by choosing the eigenvectors corresponding to the largest eigenvalues ​​as principal components. These principal components capture the main trends in the data.

[0174] Step 2.3, Dimension Reduction and Explanation

[0175] Data dimensionality reduction: Projecting the original data onto selected principal components to achieve data dimensionality reduction.

[0176] Interpreting principal components: Analyzing the practical meaning of each principal component, such as whether it represents a combination of temperature, pressure, or other important operating parameters. Identifying which principal components are most critical for predicting the system's risk.

[0177] Through these steps, PCA can effectively extract the most important information from complex datasets, enabling intelligent monitoring systems to more accurately and efficiently identify anomalies and predict potential risks.

[0178] Step 3: Risk prediction and early warning.

[0179] After performing data analysis using Principal Component Analysis (PCA), the next step is to utilize the results for risk prediction and early warning. The detailed implementation steps are as follows:

[0180] Step 3.1: Establish an early warning model

[0181] Model selection: Based on the results of PCA, select an appropriate statistical or machine learning model for risk prediction, such as linear regression, logistic regression, decision tree, etc.

[0182] Model Training: Train the early warning model using historical data. This data should include normal operating conditions and known cases of anomalies or failures.

[0183] Threshold setting: Based on historical data, determine the thresholds used for early warning, such as setting risk score limits under specific conditions.

[0184] In step 3.1, specifically, a risk warning model will be built using a linear regression model as an example. This model will use data processed by principal component analysis (PCA) to predict potential risks and anomalies.

[0185] Taking linear regression as an example:

[0186] Step a, Model Selection: Select a linear regression model as the tool for risk prediction. Linear regression is a statistical method used to establish the relationship between independent variables (features) and dependent variables (target variables, i.e., risk indicators).

[0187] Step b, Model training

[0188] Data preparation: Collect historical monitoring data and apply PCA to transform this data into principal components. Determine the risk indicator for each sample (e.g., 1 / 0 for failure occurrence / non-occurrence).

[0189] Model Training: A linear regression model is trained using principal components from historical data as independent variables and risk indicators as dependent variables. Techniques such as cross-validation are applied to optimize model parameters and evaluate the model's accuracy and reliability.

[0190] Step c, threshold setting

[0191] Determine the warning threshold: Analyze the model output to determine the risk score threshold used to trigger a warning. This may be based on the incidence rate of risk events in historical data. The threshold setting needs to balance the sensitivity and specificity of the warning, avoiding excessive false alarms and missed alarms.

[0192] By following the steps above, a risk warning model based on linear regression can be established for real-time monitoring and evaluation of the system's operational status, enabling timely detection of potential anomalies or risks. This model helps identify problems early, allowing for preventative measures to be taken and avoiding accidents.

[0193] Step 3.2: Real-time monitoring and analysis

[0194] Data stream processing: Real-time processing of data collected from the monitoring system and application of PCA for dimensionality reduction.

[0195] Application Model: Input the real-time processed data into the early warning model to conduct risk assessment.

[0196] Trend analysis: Continuously analyze data trends to identify abnormal patterns or trends that may lead to risks.

[0197] Step 3.3: Issue an early warning

[0198] Anomaly detection: Continuously monitor and analyze data, and immediately identify any anomalies exceeding preset thresholds as potential risks.

[0199] Notify relevant personnel: When a potential risk is detected, the system will automatically send an early warning notification to relevant personnel, including specific information about the risk, possible causes, and suggested countermeasures.

[0200] Recording and Feedback: Record the details of each alert and provide feedback to optimize the alert model and process.

[0201] Through these steps, intelligent monitoring systems can effectively predict potential risks and issue early warnings at critical moments, thereby improving security and reducing potential losses.

[0202] Results and Applications

[0203] By using PCA technology, intelligent analysis systems can effectively process and analyze large amounts of real-time data, identify key risk factors, and thus provide a scientific basis for timely risk warnings. This not only improves the efficiency and accuracy of safety monitoring but also helps prevent potential risks and ensures the safety and stability of the entire chemical production process.

[0204] II. Real-time monitoring and early warning

[0205] Real-time data monitoring: Monitors data from each monitoring point in real time, and immediately issues an alarm if the data exceeds a preset safety threshold. A multi-level alarm system is configured to respond according to the severity of the hazard.

[0206] Early warning and emergency response: When a potential risk is detected, the system automatically issues an early warning and notifies relevant personnel. It is equipped with automatic emergency response measures, such as automatically cutting off power and activating the emergency cooling system.

[0207] III. Safety Management and Training

[0208] Safety Management System: Establish and improve the safety management system, including safe operating procedures and emergency plans. Regularly test and maintain the safety system to ensure its normal operation.

[0209] Employee Training: Regularly train operators on safety knowledge and emergency response skills. Ensure that every employee understands the meaning of warning signals and knows the correct action in an emergency.

[0210] IV. Accident Investigation and Improvement

[0211] Accident Investigation: In the event of a safety incident, a timely investigation should be conducted to analyze the cause. Accident data should be collected for future prevention of similar incidents.

[0212] Continuous improvement: Based on the results of accident investigations and issues identified during safety inspections, continuously improve the safety system. Regularly update technologies and equipment to enhance safety levels.

[0213] By implementing these steps, an efficient safety accident early warning system can be effectively established and maintained to ensure the safety of the lithium carbonate roasting process in tunnel kilns.

[0214] Step S3 also includes quality control and evaluation. Product testing involves analyzing the composition of the roasted lithium carbonate to ensure product quality meets standards. Process evaluation involves periodically assessing the efficiency and energy consumption of the entire roasting process and continuously optimizing process parameters.

[0215] This application embodiment designs and optimizes the system for preparing lithium carbonate by calcining in a tunnel kiln, which enables precise temperature control during the calcination process, thereby improving the uniformity of the calcination temperature, achieving efficient and environmentally friendly lithium carbonate production, improving the product quality of lithium carbonate, and simultaneously increasing the utilization rate of thermal energy, reducing energy consumption, and enhancing the reliability and safety of the calcination process.

[0216] Please see Figure 2 As shown in the embodiments of this application, a system 100 for implementing the method of preparing lithium carbonate by tunnel kiln roasting as described above is also provided. The system 100 is a closed-loop system, including: a tunnel kiln 10, a heating and temperature control module 20, and a heat recovery module 30. The tunnel kiln 10 is used to contain lithium carbonate raw materials. The heating and temperature control module 20 is used to heat the tunnel kiln according to a preset heating curve and to regulate the temperature of the material inside the tunnel kiln 10 to roast the raw materials at a roasting temperature of 870℃~920℃. The heat recovery module 30 is used to recover waste heat and exhaust gas during the roasting process.

[0217] In some embodiments, the system 100 further includes a safety accident early warning module 40, which can monitor the roasting heating process in real time and perform risk prediction and alarm.

[0218] Understandably, the system 100 also includes a controller 50 for system control of the coordinated operation between the aforementioned components.

[0219] The method and system for preparing lithium carbonate by tunnel kiln roasting provided in this application have the following beneficial effects:

[0220] (1) Through the optimized roasting process, the temperature can be precisely adjusted during the roasting process, thereby improving the uniformity of the roasting temperature, increasing the roasting efficiency and the utilization rate of heat energy, reducing energy consumption, improving the product quality of lithium carbonate, and enhancing the reliability and safety of the roasting process.

[0221] (2) Through heat energy recovery and reuse technologies, energy efficiency can be improved, energy consumption and production costs can be reduced, and environmental impact can be minimized. For example, high-efficiency heat exchangers can be used to collect hot gases emitted from tunnel kilns and preheat raw materials and air entering the kiln. Waste heat can be converted into steam to drive turbines for power generation, achieving further utilization of energy. In addition, waste heat storage systems can be used to collect and store heat energy that is not suitable for immediate use, so that it can be used during peak demand periods.

[0222] (3) By setting up a safety accident early warning module, especially by adopting an intelligent analysis system and utilizing big data and machine learning technologies, it is possible to effectively process and analyze large amounts of real-time data, identify key risk factors, and thus provide a scientific basis for timely risk warnings. This improves the efficiency and accuracy of safety monitoring, helps prevent potential risks, and ensures the safety and stability of the entire production process. In addition, the safety accident early warning module includes an emergency response mechanism. Once an anomaly is detected, the system will automatically activate emergency procedures, including cutting off energy supply and activating the cooling system.

[0223] (4) By using new high-efficiency heat insulation materials inside the tunnel kiln, it is beneficial to reduce heat loss and further reduce energy consumption.

[0224] The technical solutions of the embodiments of this application will be further described below through specific examples.

[0225] Example 1

[0226] Lithium carbonate was prepared using the aforementioned optimized tunnel kiln roasting process.

[0227] Comparative Example 1

[0228] Lithium carbonate was prepared using a traditional tunnel kiln roasting process.

[0229] The experimental data obtained from Example 1 and Comparative Example 1 are shown in Table 1 below.

[0230] Table 1

[0231]

[0232]

[0233] As can be seen from Table 1, compared with the traditional tunnel kiln roasting process (Comparative Example 1), the improved tunnel kiln roasting process of this application (Example 1) has significant improvements in roasting efficiency, energy consumption, product quality, environmental impact, and safety. Specifically, these improvements are manifested in the following aspects:

[0234] Regarding roasting efficiency: Example 1 improved by 7 percentage points compared to Comparative Example 1. The optimized tunnel kiln roasting process in this application shows higher production efficiency.

[0235] In terms of energy consumption: Example 1 reduced energy consumption by 3 kWh / kg compared to Comparative Example 1, indicating that the optimized tunnel kiln roasting process of this application is more energy efficient.

[0236] Regarding the quality of lithium carbonate products: Example 1 showed an increase of 25 kg compared to Comparative Example 1, indicating that the optimized tunnel kiln roasting process of this application can produce more high-quality products.

[0237] Regarding environmental impact: Compared with Comparative Example 1, the exhaust emissions of Example 1 were reduced by 40%. The optimized tunnel kiln roasting process of this application effectively reduced environmental pollution.

[0238] Regarding the accident rate: Example 1 reduced the accident rate by 3 percentage points compared to Comparative Example 1. The optimized tunnel kiln roasting process in this application effectively improves the safety of the production process.

[0239] Emergency response time: Example 1 shortened the time by 50% compared to Comparative Example 1. The optimized tunnel kiln roasting process in this application improves the efficiency of emergency handling through the intelligent early warning system, significantly shortens the accident response time, and greatly improves safety.

[0240] Regarding the improvement of employees' safety awareness: Example 1 improved by 30 percentage points compared to Comparative Example 1. The optimized tunnel kiln roasting process in this application enhanced employees' safety awareness.

[0241] In addition, compared with the traditional roasting process, the optimized roasting scheme of this application improves energy utilization by 30%, product qualification rate by 20%, total heat energy utilization from 65% to 95%, reduces overall operating costs by about 20%, and improves production efficiency.

[0242] In summary, the optimized tunnel kiln roasting process for preparing lithium carbonate in this application not only improves production efficiency and product quality, but also reduces energy consumption and environmental impact, and significantly enhances the safety of the production process.

[0243] It should be noted that the above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Where there is no conflict, the embodiments and features described in the embodiments of this application can be combined with each other. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for preparing lithium carbonate by calcination in a tunnel kiln, characterized in that, include: Tunnel kiln design and preparation; The raw materials are loaded into the material carts of the tunnel kiln, which are used to move in different areas of the tunnel kiln. as well as The roasting process is carried out according to a preset temperature rise curve, and the roasting temperature is controlled between 870℃ and 920℃. The heating curve is formulated using partial differential equations, which are used to describe the temperature changes of the raw material at different roasting times and in different areas of the tunnel kiln. During the roasting and heating process, the method further includes: A safety accident early warning system is set up to monitor the roasting and heating process in real time, and to predict and alarm risks. The safety incident early warning system includes: Monitoring equipment is used to collect process data during the roasting process; The intelligent analysis system is used to monitor and analyze the collected process data in real time for risk prediction and alarm purposes; and An emergency response procedure is used to activate an emergency response procedure after the intelligent analysis system issues an alarm. The method for constructing the intelligent analysis system includes: Data collection and preprocessing; Principal component analysis was applied to analyze the preprocessed data to obtain analytical results; and The analysis results are used for risk prediction and alerts. The method for performing data analysis on the preprocessed data using principal component analysis includes: Calculate the covariance matrix, standardize the collected data to ensure that the influence of each variable on the result is consistent, and calculate the covariance matrix of the standardized data. Extract principal components, perform eigenvalue decomposition on the covariance matrix, find eigenvalues ​​and corresponding eigenvectors, arrange the eigenvalues ​​in descending order of value, and select the eigenvectors corresponding to the first few eigenvalues ​​as principal components. Dimensionality reduction and analysis involve projecting the original data onto the selected principal components to achieve data dimensionality reduction, and analyzing the actual meaning represented by each principal component to determine the risk components related to risk prediction. Establish an early warning model, select a risk model based on the risk components, train the early warning model using historical data, and determine the early warning threshold based on the historical data; and Real-time monitoring and analysis: The early warning model is applied to monitor the roasting process in real time and issue risk warnings. An alarm is issued when the monitoring data exceeds the threshold.

2. The method for preparing lithium carbonate by tunnel kiln roasting according to claim 1, characterized in that, The method for constructing the partial differential equations includes: Based on the dependence of roasting temperature on roasting time and roasting location during the roasting process, the parameters affecting the roasting temperature distribution are determined, wherein the roasting location is the spatial position of the raw material in the tunnel kiln; Based on the parameters, establish partial differential equations for the calcination time, calcination location, and calcination temperature. Numerical methods are selected to solve the partial differential equations and determine the boundary and initial conditions; Simulate the temperature rise curve based on the boundary conditions and the initial conditions; and The heating curve is optimized based on the partial differential equation.

3. The method for preparing lithium carbonate by tunnel kiln roasting according to claim 2, characterized in that, When the heating curve reflects the temperature distribution along a one-dimensional spatial position in the tunnel kiln, the partial differential equation is as follows (I): （I）， Where T(x, t) is the roasting temperature at different roasting positions x and roasting times t; It is the rate of change of calcination temperature with respect to the calcination time t; α is the thermal diffusivity; It is the second spatial derivative of the roasting temperature at different roasting positions x, representing the propagation of heat in the raw material; Q(x, t) is a function of an external heat source or heat loss used to heat the tunnel kiln.

4. The method for preparing lithium carbonate by tunnel kiln roasting according to claim 1, characterized in that, In the roasting and heating step, the method further includes: Establish a heat recovery and reuse system to recover waste heat and exhaust gas during the roasting process and reuse the waste heat and exhaust gas.

5. A system for implementing the method for preparing lithium carbonate by tunnel kiln roasting as described in any one of claims 1-4, characterized in that, The system is a closed-loop system, including: A tunnel kiln, wherein a movable feed car is provided inside the tunnel kiln, the feed car being used to hold lithium carbonate raw material and to move the raw material to different areas of the tunnel kiln; and The heating module is used to heat the tunnel kiln according to a preset heating curve to roast the raw material at a roasting temperature of 870℃~920℃. The heating curve is formulated using a partial differential equation, which describes the temperature changes of the raw material at different roasting times and in different areas of the tunnel kiln. The system also includes: A heat recovery and reuse system is used to recover waste heat and exhaust gas from the roasting process; and A safety accident early warning system is used to monitor the roasting and heating process in real time and to predict and alarm risks.

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