Process and system for the production of lithium hydroxide

Abstract

This specification provides a method and system for producing lithium hydroxide. The method includes: acquiring production data related to the catalyst reaction during the lithium hydroxide production process; performing a weighted average processing on the production data to obtain a weighted average value; performing a random approximation processing on the production data to obtain reference data with a time scale; adjusting a catalyst model based on the weighted average value and the reference data, wherein the catalyst model is established based on the production data related to the catalyst reaction during the lithium hydroxide production process; and executing the lithium hydroxide production process based on the updated catalyst model. Using the above technical solution can improve the production efficiency of lithium hydroxide.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of lithium hydroxide production technology, and in particular to methods and systems for producing lithium hydroxide. Background Technology

[0002] Lithium hydroxide has a wide range of applications. It is one of the main raw materials for producing high-grade lithium-based greases and is primarily used in chemical raw materials, chemical reagents, battery industry, petroleum, metallurgy, glass, ceramics, and other industries. It is also an important raw material for the defense industry, nuclear energy industry, and aerospace industry. For example, in the battery industry, lithium hydroxide is used as an additive in alkaline batteries and nickel-metal hydride batteries to extend battery life and increase storage capacity.

[0003] In actual production, the production process of lithium hydroxide faces numerous challenges due to various factors, including high energy consumption, unstable production efficiency, and fluctuating product quality. Currently, it mainly relies on traditional experience-based methods, lacking precise process control and optimization techniques. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide a method and system for producing lithium hydroxide, which can improve the production efficiency of lithium hydroxide.

[0005] First, this specification provides a method for producing lithium hydroxide, comprising:

[0006] Obtain production data related to catalyst reactions during the lithium hydroxide production process;

[0007] The production data is processed by weighted averaging to obtain the weighted average value corresponding to the production data;

[0008] The production data is subjected to random approximation processing to obtain reference data with a time scale;

[0009] The catalyst model is adjusted based on the weighted average value corresponding to the production data and the reference data. The catalyst model is established based on the production data related to the catalyst reaction in the lithium hydroxide production process.

[0010] The lithium hydroxide production process is carried out based on the updated catalyst model.

[0011] Optionally, the step of performing a weighted average processing on the production data to obtain the weighted average value corresponding to the production data includes:

[0012] Determine the weight of each data point in the production data;

[0013] The weighted average value is obtained based on the weight of each data point and the corresponding data point.

[0014] Optionally, determining the weight of each data point in the production data includes:

[0015] The weight of each type of data point is determined based on the acquisition time of each data point in the production data, and the later the acquisition time of a data point in the production data, the greater its corresponding weight.

[0016] Optionally, obtaining the weighted average based on the weight of each data point and the corresponding data point includes:

[0017] Calculate the product of each data point and its weight, and sum the product values ​​of each data point to obtain the total product value of all data points.

[0018] The weighted average is the ratio between the total product of all data points and the sum of the weights of all data points.

[0019] Optionally, before performing the weighted average processing on the production data, the method further includes:

[0020] The production data is preprocessed to obtain data that meets the format requirements.

[0021] Optionally, the random approximation process includes a dual-timescale random approximation;

[0022] The random approximation processing of the production data to obtain time-scaled reference data includes:

[0023] According to the time scale, the data points in the production data are divided to obtain data with a first time scale and a second time scale respectively.

[0024] On a fast timescale, data points with the first timescale are processed to obtain reference data for reflecting short-term changes in the lithium hydroxide production process;

[0025] On a slow time scale, data points with a second time scale are processed to obtain reference data for reflecting medium- and long-term changes in the lithium hydroxide production process.

[0026] The reference data for short-term changes and the reference data for medium- and long-term changes are referred to as the reference data.

[0027] Optionally, adjusting the catalyst model based on the weighted average of the production data and the reference data includes:

[0028] The parameter estimates in the catalyst model are updated based on the weighted average value, and the key parameters of the catalyst model are adjusted based on the reference data, the key parameters including at least one of weight allocation, reaction rate constant, and temperature dependence.

[0029] Optionally, the lithium hydroxide production process based on the updated catalyst model includes:

[0030] Based on the current production parameters and the catalyst model, determine the production indicators required for the lithium hydroxide production process, and execute the lithium hydroxide production process, wherein the production indicators include at least one of reaction rate and yield.

[0031] Alternatively, the method for producing lithium hydroxide also includes:

[0032] Obtain the actual production volume of lithium hydroxide corresponding to the current production index, and compare the actual production volume of lithium hydroxide with the expected production volume. When it is determined that the difference between the actual production volume and the expected production volume of lithium hydroxide is greater than the preset production difference, adjust the parameters of the catalyst model.

[0033] Accordingly, embodiments of this specification also provide a lithium hydroxide production system, comprising:

[0034] The data acquisition unit is configured to acquire production data related to the catalyst reaction during the lithium hydroxide production process.

[0035] The processing unit is configured to perform weighted averaging on the production data to obtain a weighted average value corresponding to the production data, and to perform random approximation on the production data to obtain reference data with a time scale, and to adjust the catalyst model based on the weighted average value corresponding to the production data and the reference data, wherein the catalyst model is established based on production data related to the catalyst reaction in the lithium hydroxide production process.

[0036] The execution unit is configured to perform the lithium hydroxide production process based on the updated catalyst model.

[0037] The lithium hydroxide production scheme described in this specification involves weighted averaging and random approximation of production data related to the catalyst reaction during the lithium hydroxide production process. This yields a weighted average of the production data and time-scaled reference data. Based on these weighted averages and reference data, the catalyst model can be adjusted, allowing for the execution of the lithium hydroxide production process based on the updated model. Since weighted averaging reflects the actual production data and random approximation identifies and responds to short-term changes and long-term trends in the production process, the updated catalyst model can optimize catalytic reaction conditions, improve yield, and reduce costs by analyzing and simulating the catalyst's behavior during production. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this specification, the drawings used in the description of the embodiments of this specification or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart of a method for producing lithium hydroxide, as illustrated in the embodiments of this specification, is shown.

[0040] Figure 2 This specification illustrates a flowchart of an embodiment for obtaining a weighted average value corresponding to production data;

[0041] Figure 3 A flowchart illustrating an embodiment of this specification for acquiring time-scaled reference data is shown.

[0042] Figure 4 A schematic diagram of a lithium hydroxide production system according to an embodiment of this specification is shown. Detailed Implementation

[0043] As mentioned earlier, currently, we mainly rely on traditional experience-based methods to solve problems such as high energy consumption, unstable production efficiency, and product quality fluctuations, lacking precise process control and optimization methods.

[0044] To address the aforementioned technical problems, this specification provides a lithium hydroxide production scheme. By applying weighted averaging and random approximation to production data related to the catalyst reaction during the lithium hydroxide production process, a weighted average of the production data and time-scaled reference data can be obtained. Based on these weighted averages and reference data, the catalyst model can be adjusted, allowing the lithium hydroxide production process to be executed using the updated catalyst model. Since weighted averaging reflects the actual production data and random approximation identifies and responds to short-term changes and long-term trends in the production process, the updated catalyst model can optimize catalytic reaction conditions, improve yield, and reduce costs by analyzing and simulating the catalyst's behavior during production.

[0045] To enable those skilled in the art to better understand and implement the embodiments of this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0046] Reference Figure 1 The flowchart shown illustrates a method for producing lithium hydroxide. In some examples, the following steps can be performed:

[0047] S11, Obtain production data related to catalyst reactions during the lithium hydroxide production process.

[0048] Specifically, the production process of lithium hydroxide varies depending on the reaction parameters of the catalyst, resulting in differences in the production data. Therefore, it is possible to obtain the actual production data of lithium hydroxide during the production process to provide data support for adjusting the catalyst model.

[0049] In some examples, production data may include a range of parameters that could affect the lithium hydroxide production process, such as reaction conditions, feedstock characteristics, product yield, and quality.

[0050] It is understood that the embodiments in this specification do not impose specific limitations on the type of production data. For example, production data may also include the production date.

[0051] In some embodiments, sensors or other known devices or equipment can be used to acquire production data. For example, a temperature sensor can be used to measure temperature parameters in the reaction conditions.

[0052] S12, perform weighted average processing on the production data to obtain the weighted average value corresponding to the production data.

[0053] Specifically, by applying a weighted average to production data, it is possible to smooth and stabilize data fluctuations, thereby improving the accuracy of the obtained weighted average.

[0054] S13, perform random approximation processing on the production data to obtain reference data with a time scale.

[0055] Specifically, by performing random approximation on production data, the resulting time-scaled reference data can reflect the response speed of production data on fast time scales, as well as the data trends and stability on slow time scales.

[0056] S14, Adjust the catalyst model based on the weighted average value corresponding to the production data and the reference data.

[0057] Specifically, since the weighted average and reference data corresponding to the production data can reflect the changes in production data related to the catalyst reaction during the lithium hydroxide production process, and based on these two, the catalyst model used to determine the parameter information of the catalyst during the production of lithium hydroxide can be adjusted, so that the catalyst model can fully analyze and simulate the behavior of the catalyst in the production process, in order to guide the actual production of lithium hydroxide.

[0058] In some examples, the catalyst model can be built based on production data related to the catalyst reaction during the lithium hydroxide production process.

[0059] As an alternative example, the acquired production data can be fed into at least one preset reaction rate relationship to obtain the variable values ​​in the at least one reaction rate relationship in order to determine the catalyst model.

[0060] As an example, the catalyst model describes the role of the catalyst in the lithium hydroxide production process by including one or more reaction rate equations, where the reaction rate equations can be:

[0061]

[0062] Where r represents the reaction rate, k is the reaction rate constant, and C A and C B is the concentration of the reactant, and m and n are the reaction orders of the reactants.

[0063] Considering the deactivation of the catalyst over time, a model is introduced to describe the catalyst activity:

[0064] a(t) = a0·e -kt (2)

[0065] Where a(t) represents the activity at time t, a0 represents the initial activity, and k is the rate constant of the catalyst.

[0066] In some examples, k can be obtained in the following way:

[0067]

[0068] Where A represents the pre-exponential factor, E a R represents activation energy, R represents gas constant, and T represents temperature.

[0069] By applying data processed by Ruppert-Polyak averaging and dual-timescale stochastic approximation techniques to catalyst models, catalyst models can be established. These models can then optimize catalytic reaction conditions, increase yield, reduce costs, and ensure product quality by analyzing and simulating the behavior of catalysts during the production process.

[0070] In other words, catalyst models can be used to conduct simulation and optimization experiments, predict the performance of catalysts under different operating conditions, and guide the optimization of actual production processes.

[0071] It should be noted that the catalyst models listed in the examples above are for illustrative purposes only. The specific model forms and parameters may vary depending on the specific chemical reaction and production process, and need to be adjusted and customized according to the specific circumstances.

[0072] Furthermore, when applying these models, experimental verification and parameter tuning are usually required to ensure the accuracy and practicality of the models.

[0073] S15, based on the updated catalyst model, execute the lithium hydroxide production process.

[0074] Specifically, by using steps S11 to S14, weighted averaging and random approximation are performed on the production data to obtain data for characterizing the lithium hydroxide production process. This allows for further optimization of the catalyst model, which in turn provides better guidance for the production process.

[0075] For example, based on the reaction rate and yield predicted by the catalyst model, the optimal catalyst dosage and usage cycle can be determined to reduce energy consumption and improve yield and quality.

[0076] It should be noted that there is no necessary order between some steps in the above embodiments. They can be executed simultaneously or sequentially without causing contradictions, and the order can be changed. For example, when actually implementing the steps of the lithium hydroxide production method provided in this specification, it is only necessary to set step S14 to be executed after steps S12 and S13. Steps S12 and S13 can be executed simultaneously or separately. This specification does not impose specific restrictions on the order of steps in the embodiments.

[0077] The lithium hydroxide production method described in the embodiments of this specification can optimize the catalytic reaction conditions, improve the yield, and reduce costs by analyzing and simulating the behavior of the catalyst in the production process, since the weighted average processing can reflect the actual situation of the production data and the random approximation processing can identify and respond to short-term changes and long-term trends in the production process.

[0078] To enable those skilled in the art to better understand and implement the lithium hydroxide production method in the embodiments of this specification, the following description is provided through specific examples and in conjunction with specific application scenarios.

[0079] In practice, when performing weighted averaging on production data, considering that the lithium hydroxide production process is dynamic, the Ruppert-Polyak averaging technique can be used in some examples to improve the accuracy of the subsequently established catalyst model.

[0080] The Ruppert-Polyak averaging technique is an algorithm used for optimization and estimation. It utilizes a weighted average of historical data to improve the current estimate, thus achieving more accurate results. By applying this technique to the lithium hydroxide production process, key information can be extracted from the collected dynamic data of the catalyst reaction, enabling real-time estimation and optimization of catalyst efficiency.

[0081] Furthermore, by applying the Ruppert-Polyak averaging technique to the catalyst model conditioning process, the accuracy and reliability of the updated catalyst model can be improved. This helps us to better understand the working mechanism of the catalyst, thereby making more effective adjustments in the production process to improve production efficiency and the quality of the final product.

[0082] In some embodiments of this specification, reference is made to Figure 2 The flowchart shown in this specification illustrates one embodiment of obtaining a weighted average value corresponding to production data. In some embodiments of this specification, such as... Figure 2 As shown, the following steps can be performed:

[0083] S21, determine the weight of each data point in the production data.

[0084] Specifically, for production data within the same production process, the data points within it have different impacts on the lithium hydroxide production process; that is, the weights of each data point can be considered different. In some examples, the weights of different types of data points can be determined based on the acquisition time of each data point in the production data, with later acquisition times for data points resulting in greater weights.

[0085] Specifically, the later the data points are acquired, the more accurately they can characterize the impact of catalyst reaction-related production data on the lithium hydroxide production process. Consequently, their corresponding weight values ​​are larger, which in turn improves the accuracy of the acquired catalyst model.

[0086] In some other examples, the weight values ​​of data points can also be determined based on other factors.

[0087] As an example, an evaluation system can be established to assign appropriate weight values ​​when certain data points are identified as anomalies.

[0088] For example, negative points are given for temperatures that are too high or too low, and positive points are given for the optimal temperature.

[0089] S22, the weighted average value is obtained based on the weight of each data point and the corresponding data point.

[0090] Specifically, the production process of lithium hydroxide is the result of the interaction of multiple data points. By determining the weighted average value based on the parameters corresponding to each data point, the impact of the current production data on the lithium hydroxide production process can be reflected.

[0091] In some examples in this specification, the weighted average can be obtained in the following manner.

[0092] S221, calculate the product of each data point and its weight, and sum the product values ​​of each data point to obtain the total product value of all data points.

[0093] Specifically, qualitative data such as data points in the embodiments of this specification can be converted into digital form.

[0094] As an example, a mapping relationship between data points and numbers can be established.

[0095] For example, variables such as reaction conditions and raw material characteristics can be mapped to numerical dependent variables that affect the reaction process. For instance, temperature can be mapped to a temperature value, and raw material concentration can be mapped to a specific concentration value.

[0096] S222, the ratio between the total product of all data points and the sum of the weights of all data points is taken as the weighted average.

[0097] Specifically, by mapping data points to numerical data, when determining their corresponding weights, the product value corresponding to a single data point can be obtained. By superimposing the product values ​​of all data points, the total product value can be obtained. Then, based on the sum of the total product value and the weight values ​​of all data points, a weighted average value can be obtained.

[0098] By using the above method to determine the weighted average value corresponding to the production data, the weight of each data point is determined, and based on the weight of each data point and the corresponding data point, the smoothing of short-term fluctuations caused by random fluctuations or outliers can be improved, ensuring that the obtained weighted average value is both accurate and stable.

[0099] In some examples, to further improve the accuracy of the obtained weighted average, the production data can be preprocessed before weighted averaging to obtain data that meets the format requirements.

[0100] Specifically, data cleaning and preprocessing before weighted averaging can remove outliers, fill in missing data, and normalize the data, thereby ensuring the accuracy of production data.

[0101] As a concrete example, when performing weighted averaging using the Ruppert-Polyak averaging technique, the following steps can be performed:

[0102] A1) Data Preprocessing

[0103] Before applying the Ruppert-Polyak averaging technique, data cleaning and preprocessing are performed, including outlier removal, missing data imputation, and data normalization.

[0104] A2) Determine the weights of the data points

[0105] The Ruppert-Polyak averaging technique is applied to the preprocessed data. Specifically, each data point is assigned a weight that decreases over time, allowing the most recent observations to have a greater impact on the estimation results.

[0106] Specifically, in the Ruppert-Polyak averaging technique, the first step is to determine the weight of each data point. The key to choosing the weights is to assign higher weights to the most recent observations while also considering the influence of earlier data. The weights typically decrease as the data point becomes more distant from the current time.

[0107] A3) Calculate the weighted average

[0108] For each data point, its weight is applied, and a weighted average is calculated. This involves multiplying each data point by its corresponding weight, then summing these products, and finally dividing by the sum of the weights.

[0109] In short, analyzing data processed by Ruppert-Polyak averaging can identify key trends and patterns, which are crucial for building and optimizing catalyst models.

[0110] In some examples, lithium hydroxide production is a long-term, dynamic process. To ensure that the catalyst model obtained later can accurately predict the long-term production of lithium hydroxide, a dual-timescale stochastic approximation can be used to stochastically approximate the production data.

[0111] The dual-timescale stochastic approximation technique involves two different timescales: fast and slow. This method allows for simultaneous fast local optimization and slow global optimization, improving the efficiency and accuracy of the overall optimization process. When applied to catalyst modeling, it enables the simulation of catalytic reaction processes, and by running the model on two different timescales, the use of the catalyst can be more precisely adjusted and optimized, thereby improving yield and quality.

[0112] As an example, refer to Figure 3 The flowchart shown in this specification illustrates an embodiment of acquiring time-scaled reference data. Figure 3 As shown, it includes the following steps:

[0113] S31, according to the time scale, divide the data points in the production data into data with a first time scale and a second time scale respectively.

[0114] Specifically, data points can be divided according to the acquisition time of each data point in the production data to obtain data with different time scales.

[0115] In some examples, random approximations can be applied only to data points in the production data that have undergone weighted averaging to improve the accuracy of the obtained reference data.

[0116] S32, on a fast time scale, processes data points with a first time scale to obtain reference data for reflecting short-term changes in the lithium hydroxide production process.

[0117] Specifically, on a rapid timescale, by analyzing and processing data points with the first timescale, short-term changes in the production process can be quickly identified and responded to, thereby helping to adjust the catalyst model in a timely manner to adapt to immediate changes in production conditions.

[0118] In some examples, on fast timescales, catalyst models need to be able to respond quickly to the latest data changes, which often involves focused analysis of recent data on fast timescales in order to adjust key parameters in the production process in a timely manner.

[0119] S33, on a slow time scale, processes data points with a second time scale to obtain reference data for reflecting medium- and long-term changes in the lithium hydroxide production process.

[0120] Specifically, on a slower timescale, by analyzing and processing data points with a second timescale, greater emphasis can be placed on analyzing and understanding long-term data trends. This helps to identify and adapt to long-term changes in the production process, thereby improving the stability and accuracy of the catalyst model and ensuring its reliability and efficiency in long-term operation.

[0121] In some examples, reference data for short-term changes and reference data for medium- to long-term changes are used as reference data to form the data basis for constructing the catalyst model.

[0122] In some examples, statistical methods and machine learning techniques can be used to conduct in-depth analysis of long-term data in order to better understand the performance of catalysts and possible wear patterns during long-term operation.

[0123] By using the method described in the example above, and processing data points simultaneously on both fast and slow timescales, both short-term and medium-to-long-term changes in the lithium hydroxide production process can be taken into account. In other words, by integrating the results obtained from the analysis on both fast and slow timescales, a comprehensive perspective on model performance can be obtained. This can improve the stability and accuracy of the catalyst model and ensure its reliability and efficiency in long-term operation.

[0124] In some implementations, the catalyst model is adjusted based on a weighted average of the production data and reference data.

[0125] In some examples in this specification, the parameter estimates in the catalyst model can be updated based on the weighted average, and the key parameters of the catalyst model can be adjusted based on reference data. The key parameters include at least one of weighting, reaction rate constant, and temperature dependence.

[0126] As a specific example, the catalyst model can be adjusted in the following way.

[0127] Based on the acquired production data and its corresponding dynamic estimates, the basic structure of the catalyst model is determined. This includes identifying key variables and parameters of the model, such as catalyst activity, selectivity, and stability.

[0128] Next, the data processed using the Ruppert-Polyak averaging technique and the dual-timescale stochastic approximation technique are integrated into the catalyst model. This step transforms the data into a format usable by the model and ensures that all key factors are taken into account.

[0129] Next, the initially constructed model needs to be validated using experimental data and historical production data. Based on the validation results, the model should be adjusted as necessary to improve its predictive accuracy and reliability.

[0130] Finally, based on the model's output, strategies for optimizing catalyst use are developed. This may include adjusting reaction conditions, optimizing feedstock ratios, or improving production processes.

[0131] In some examples, to ensure the stability of the long-term production process of lithium hydroxide, the acquisition of catalyst models may also include updating and optimizing the catalyst models as new production data accumulates and production conditions change, so as to adapt to the new changes. In other words, the construction and optimization of catalyst models is a continuous process.

[0132] Furthermore, to determine the usability of the catalyst model, it can be validated and adjusted. That is, based on feedback and results from practical applications, the catalyst model can be adjusted and optimized as necessary to ensure its accuracy and reliability in actual production.

[0133] As an optional example, based on the current production parameters and the catalyst model, the required production indicators for the lithium hydroxide production process are determined, and the lithium hydroxide production process is executed, wherein the production indicators include at least one of reaction rate and yield.

[0134] Specifically, through the above process, an updated catalyst model can be determined. Based on the current production process parameters and the updated catalyst model, the required production indicators can be identified. Furthermore, based on factors such as reaction rate and yield, optimal strategies such as catalyst dosage and usage cycle can be determined to reduce energy consumption and improve yield and quality. This involves developing corresponding optimization schemes for catalyst reaction conditions based on the model results.

[0135] Furthermore, the actual production volume of lithium hydroxide corresponding to the current production indicators is obtained, and the actual production volume of lithium hydroxide is compared with the expected production volume. When it is determined that the difference between the actual production volume and the expected production volume of lithium hydroxide is greater than the preset production difference, the parameters of the catalyst model are adjusted to achieve long-term stable production.

[0136] As an optional example, the parameter tuning process for a catalyst model may include:

[0137] First, continuous monitoring and data collection.

[0138] Specifically, this includes continuously monitoring key parameters during the production process, such as catalyst reaction conditions, yield, and quality, and collecting relevant data to facilitate real-time updates and adjustments to the model.

[0139] Second, dynamic model updates.

[0140] Specifically, this involves dynamically updating the catalyst model using the latest collected data, combined with the Ruppert-Polyak averaging technique and a dual-timescale stochastic approximation technique. This ensures that the model always reflects current production conditions and performance.

[0141] For example, results from analyses on both fast and slow timescales can be synthesized to adjust the parameters of the catalyst model, thereby improving its overall performance. This includes adjusting key parameters such as weighting, reaction rate constants, and temperature dependence within the model.

[0142] For example, a weighted average can be used to update the parameter estimates in a catalyst model, and the weighting strategy can be adjusted as needed to better reflect the actual situation of the data.

[0143] Third, real-time optimization and adjustment.

[0144] Specifically, this includes: optimizing and adjusting the production process in real time based on the latest predictions from catalyst models and actual production conditions to improve efficiency and product quality.

[0145] In some optional examples, execution can continue:

[0146] Fourth, problem identification and resolution.

[0147] Specifically, this includes: if any deviation or problem is found during the production process, immediately use the model to identify the cause and take swift corrective measures to ensure the continuity and stability of production.

[0148] Fifth, continuous improvement and innovation.

[0149] Feeding experience and data from production practices back into the model allows for continuous improvement and innovation. This step is crucial for ensuring the technology's continued development and adaptability to new challenges.

[0150] This specification also provides a lithium hydroxide production system corresponding to the above-described lithium hydroxide production method. The following detailed description is provided with reference to the accompanying drawings and specific embodiments.

[0151] It should be noted that the lithium hydroxide production system described below can be considered as a functional module required to implement the lithium hydroxide production method provided in this specification; the content of the lithium hydroxide production system described below can be referred to in correspondence with the content of the lithium hydroxide production method described above.

[0152] Reference Figure 4 The lithium hydroxide production system shown in this specification embodiment may include, in some embodiments of this specification, the lithium hydroxide production 100, which may include:

[0153] Data acquisition unit 110 is configured to acquire production data related to catalyst reaction during the lithium hydroxide production process;

[0154] The processing unit 120 is configured to perform weighted average processing on the production data to obtain a weighted average value corresponding to the production data, and to perform random approximation processing on the production data to obtain reference data with a time scale, and to adjust the catalyst model based on the weighted average value corresponding to the production data and the reference data, wherein the catalyst model is established based on production data related to the catalyst reaction in the lithium hydroxide production process;

[0155] Execution unit 130 is configured to perform the lithium hydroxide production process based on the updated catalyst model.

[0156] The specific working processes of the data acquisition unit 110, processing unit 120 and execution unit 130 can be found in the aforementioned example, and will not be described in detail here.

[0157] Since weighted average processing can reflect the actual situation of production data, and random approximation processing can identify and respond to short-term changes and long-term trends in the production process, the obtained catalyst model can optimize the conditions of catalytic reaction, improve yield, and reduce costs by analyzing and simulating the behavior of the catalyst in the production process.

[0158] While the embodiments disclosed in this specification are as described above, the present invention is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for producing lithium hydroxide, characterized in that, include: Obtain production data related to catalyst reactions during the lithium hydroxide production process; The production data is processed by weighted averaging to obtain the weighted average value corresponding to the production data; The production data is subjected to random approximation processing to obtain reference data with a time scale; The catalyst model is adjusted based on the weighted average value corresponding to the production data and the reference data. The catalyst model is established based on the production data related to the catalyst reaction in the lithium hydroxide production process. Based on the updated catalyst model, the lithium hydroxide production process was executed. The random approximation process includes dual-timescale random approximation; The random approximation processing of the production data to obtain time-scaled reference data includes: According to the time scale, the data points in the production data are divided to obtain data with a first time scale and a second time scale respectively. On a fast timescale, data points with the first timescale are processed to obtain reference data for reflecting short-term changes in the lithium hydroxide production process; On a slow time scale, data points with a second time scale are processed to obtain reference data for reflecting medium- and long-term changes in the lithium hydroxide production process. The reference data for short-term changes and the reference data for medium- and long-term changes are referred to as the reference data.

2. The method for producing lithium hydroxide according to claim 1, characterized in that, The step of performing a weighted average processing on the production data to obtain the weighted average value corresponding to the production data includes: Determine the weight of each data point in the production data; The weighted average value is obtained based on the weight of each data point and the corresponding data point.

3. The method for producing lithium hydroxide according to claim 2, characterized in that, Determining the weight of each data point in the production data includes: The weight of each type of data point is determined based on the acquisition time of each data point in the production data, and the later the acquisition time of a data point in the production data, the greater its corresponding weight.

4. The method for producing lithium hydroxide according to claim 2, characterized in that, The step of obtaining the weighted average value based on the weight of each data point and the corresponding data point includes: Calculate the product of each data point and its weight, and sum the product values ​​of each data point to obtain the total product value of all data points. The weighted average is the ratio between the total product of all data points and the sum of the weights of all data points.

5. The method for producing lithium hydroxide according to any one of claims 1 to 4, characterized in that, Before performing the weighted average processing on the production data, the method further includes: The production data is preprocessed to obtain data that meets the format requirements.

6. The method for producing lithium hydroxide according to claim 1, characterized in that, The step of adjusting the catalyst model based on the weighted average of the production data and the reference data includes: The parameter estimates in the catalyst model are updated based on the weighted average value, and the key parameters of the catalyst model are adjusted based on the reference data, the key parameters including at least one of weight allocation, reaction rate constant, and temperature dependence.

7. The method for producing lithium hydroxide according to claim 1, characterized in that, The lithium hydroxide production process based on the updated catalyst model includes: Based on the current production parameters and the catalyst model, determine the production indicators required for the lithium hydroxide production process, and execute the lithium hydroxide production process, wherein the production indicators include at least one of reaction rate and yield.

8. The method for producing lithium hydroxide according to claim 7, characterized in that, Also includes: Obtain the actual production volume of lithium hydroxide corresponding to the current production index, and compare the actual production volume of lithium hydroxide with the expected production volume. When it is determined that the difference between the actual production volume and the expected production volume of lithium hydroxide is greater than the preset production difference, adjust the parameters of the catalyst model.

9. A lithium hydroxide production system, characterized in that, include: The data acquisition unit is configured to acquire production data related to the catalyst reaction during the lithium hydroxide production process. The processing unit is configured to perform weighted average processing on the production data to obtain a weighted average value corresponding to the production data, and to perform random approximation processing on the production data to obtain reference data with a time scale, and to adjust the catalyst model based on the weighted average value corresponding to the production data and the reference data, wherein the catalyst model is established based on production data related to the catalyst reaction in the lithium hydroxide production process; The execution unit is configured to perform the lithium hydroxide production process based on the updated catalyst model. The random approximation process includes dual-timescale random approximation; The random approximation processing of the production data to obtain time-scaled reference data includes: According to the time scale, the data points in the production data are divided to obtain data with a first time scale and a second time scale respectively. On a fast timescale, data points with the first timescale are processed to obtain reference data for reflecting short-term changes in the lithium hydroxide production process; On a slow time scale, data points with a second time scale are processed to obtain reference data for reflecting medium- and long-term changes in the lithium hydroxide production process. The reference data for short-term changes and the reference data for medium- and long-term changes are referred to as the reference data.

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