A method, system and equipment for preparing lithium oxide

Abstract

The present invention relates to the technical field of lithium oxide preparation, and provides a method, system, and equipment for preparing lithium oxide. The method comprises: obtaining multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; obtaining multiple target optimization items for quantifying the quality of the lithium oxide preparation process optimization, and constructing a multi-objective optimization function based on the multiple target optimization items; constructing a process parameter optimization model based on the multi-objective optimization function, obtaining a training data set to train the process parameter optimization model; and obtaining a preferred lithium oxide preparation process control parameter group based on the trained process parameter optimization model. The present invention can reduce production costs, improve production efficiency and product quality, and is conducive to improving the comprehensive production benefits of lithium oxide preparation. It solves the problem that the lithium oxide preparation method in the prior art has poor process parameter coordination due to the lack of coordinated optimization of process parameters, and the comprehensive production benefits need to be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium oxide preparation, and in particular to a method, system and equipment for preparing lithium oxide. Background Art

[0002] Lithium oxide is an important inorganic compound that is widely used in batteries, nuclear energy, ceramics and other fields. It can be prepared by thermal decomposition of anhydrous lithium hydroxide.

[0003] After searching, some typical existing technologies were found, such as a method for preparing high-purity lithium oxide with application number "CN201110399220.2", a method for producing lithium oxide with application number "CN201780077059.2", a method for preparing battery-grade lithium oxide with application number "CN202211381833.8", and a method for preparing lithium oxide materials with application number "CN202311513395.0". All lithium oxide materials involve the preparation and production methods of lithium oxide.

[0004] Although the above-mentioned lithium oxide production method can produce the desired lithium oxide, it does not coordinately optimize process parameters such as pressure and temperature. Its process parameters are poorly coordinated, and the overall production efficiency needs to be further improved. Summary of the Invention

[0005] Based on this, in order to solve the problem that the lithium oxide preparation method in the prior art has poor process parameter coordination due to the lack of collaborative optimization of process parameters, and the comprehensive production efficiency needs to be further improved, the present invention provides a lithium oxide preparation method, system and equipment, which obtains multiple target optimization items and constructs a multi-objective optimization function, obtains an optimal lithium oxide preparation process control parameter group through a process parameter optimization model, and can collaboratively optimize multiple process control parameters, which is conducive to improving the comprehensive production efficiency of lithium oxide preparation. The specific technical solution is as follows:

[0006] A method for preparing lithium oxide comprises the following steps:

[0007] Obtain multi-dimensional historical process parameters that may affect the quality of lithium oxide preparation process;

[0008] Acquire multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function based on the multiple target optimization items;

[0009] Constructing a process parameter optimization model according to the multi-objective optimization function, and obtaining a training data set based on multi-dimensional historical process parameters to train the process parameter optimization model;

[0010] According to the trained process parameter optimization model, an optimal lithium oxide preparation process control parameter group is obtained.

[0011] The lithium oxide preparation method obtains multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process and constructs a multi-objective optimization function. Based on the process parameter optimization model, multiple process control parameters are collaboratively optimized to obtain an optimal lithium oxide preparation process control parameter group. The method can reduce production costs, improve production efficiency and product quality, and is conducive to improving the comprehensive production benefits of lithium oxide preparation. It solves the problem in the prior art of lithium oxide preparation methods that the process parameters are poorly coordinated due to the lack of collaborative optimization of the process parameters, and the comprehensive production benefits need to be further improved.

[0012] Preferably, the specific method for obtaining multiple target optimization items includes the following steps:

[0013] The bulkiness of lithium oxide is obtained based on the tap density, median particle size, and particle size distribution standard deviation of lithium oxide;

[0014] Constructing a constraint penalty term of a multi-objective optimization function according to the lithium oxide looseness;

[0015] Among them, multiple objective optimization items include constraint penalty items.

[0016] Preferably, the specific method for obtaining multiple target optimization items includes the following steps:

[0017] Obtain lithium oxide cost items based on raw material cost, energy cost, and time cost;

[0018] Obtaining a lithium oxide conversion yield term based on the mass of the initial raw material and the mass of the generated lithium oxide;

[0019] Among them, multiple target optimization items include lithium oxide cost item and lithium oxide conversion yield item.

[0020] Preferably, the multi-dimensional historical process parameters include historical equipment negative pressure value, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time and thermoplastic material powder purity, and the preferred lithium oxide preparation process control parameter group includes preferred equipment negative pressure value, heating temperature, heating rate and holding time.

[0021] Preferably, the lithium oxide bulkiness item ;

[0022] in, Indicates the actual tap density value, Indicates the preset tap density threshold, represents the actual median particle size, Indicates the preset median particle size threshold, Indicates the standard deviation of the actual particle size distribution. Indicates the preset particle size distribution standard deviation value.

[0023] Preferably, the multi-objective optimization function ;

[0024] in, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield term, They respectively represent the weight coefficients of the lithium oxide looseness term, the lithium oxide cost term and the lithium oxide conversion yield term.

[0025] A lithium oxide preparation system, used to implement the lithium oxide preparation method, comprising:

[0026] A process parameter acquisition module is used to obtain multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process;

[0027] An optimization function construction module is used to obtain multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and to construct a multi-objective optimization function based on the multiple target optimization items;

[0028] An optimization model training module is used to construct a process parameter optimization model according to the multi-objective optimization function, and to obtain a training data set based on multi-dimensional historical process parameters to train the process parameter optimization model;

[0029] The control parameter acquisition module is used to obtain an optimal lithium oxide preparation process control parameter group based on the trained process parameter optimization model.

[0030] Preferably, the optimization function building module includes:

[0031] A constraint penalty item acquisition unit is used to obtain the bulkiness of lithium oxide according to the tap density, median particle size, and particle size distribution standard deviation of lithium oxide, and to construct a constraint penalty item of a multi-objective optimization function according to the bulkiness of lithium oxide;

[0032] A lithium oxide cost item acquisition unit is used to acquire the lithium oxide cost item based on raw material cost, energy consumption cost and time cost;

[0033] a lithium oxide conversion yield item acquisition unit, configured to acquire a lithium oxide conversion yield item based on the mass of the initial raw material and the mass of the generated lithium oxide;

[0034] Among them, multiple objective optimization items include constraint penalty items, lithium oxide cost items, and lithium oxide conversion yield items.

[0035] Preferably, the multi-dimensional historical process parameters include historical equipment negative pressure value, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time and thermoplastic material powder purity, the preferred lithium oxide preparation process control parameter group includes preferred equipment negative pressure value, heating temperature, heating rate and holding time, and the thermoplastic material powder is PE powder or PP powder.

[0036] A lithium oxide preparation device, comprising:

[0037] Controller;

[0038] a memory storing executable instructions;

[0039] The executable instructions can be run on the controller and implement the lithium oxide preparation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but rather the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0041] Figure 1 This is a schematic diagram of the overall process of a method for preparing lithium oxide in one embodiment of the present invention;

[0042] Figure 2 This is a flow chart of a specific method for obtaining multiple target optimization items in one embodiment of the present invention. Figure 1 ;

[0043] Figure 3 This is a flow chart of a specific method for obtaining multiple target optimization items in another embodiment of the present invention. Figure 2 ;

[0044] Figure 4 It is a schematic diagram of the overall structure of a method for preparing lithium oxide in one embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with its embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0046] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly attached to the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation methods.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0048] The "first" and "second" in the present invention do not represent specific quantities and orders, but are only used to distinguish names.

[0049] Before describing the specific embodiments of the present application, a brief introduction to the prior art is given.

[0050] Optimizing only a single process parameter during the lithium oxide production process often makes it difficult to achieve ideal overall production benefits. For example, pursuing only high conversion yields may require sacrificing costs and / or time, making it difficult to achieve ideal overall production benefits. Optimizing only the equipment pressure or target heating temperature during the production process, while ignoring other process parameters such as heating rate and holding time, can also lead to increased production costs and reduced product quality, making it difficult to achieve ideal overall production benefits.

[0051] In order to solve the above problems, the process parameters in the lithium oxide preparation process are optimized in a coordinated manner to improve the overall production efficiency. An embodiment of the present invention provides a method for preparing lithium oxide, such as Figure 1 As shown, the following steps are included:

[0052] S1, obtain multi-dimensional historical process parameters that may affect the quality of lithium oxide preparation process.

[0053] Specifically, the multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process include but are not limited to historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time, and thermoplastic material powder purity.

[0054] The lithium oxide can be prepared by heating anhydrous lithium hydroxide for thermal decomposition.

[0055] Preferably, in this embodiment, the specific method for preparing lithium oxide includes: mixing anhydrous lithium hydroxide powder with a certain thermoplastic material powder, such as polyethylene or polypropylene powder, at a powder ratio of approximately 10%. After evacuating negative pressure from a pyrolysis furnace, a small amount of nitrogen is introduced, the negative pressure is maintained, and the temperature is slowly raised to a target temperature. After holding the temperature for a predetermined period, the gases generated by the decomposition of the polyethylene or polypropylene are removed, thereby removing the water vapor generated by the dehydration of the lithium hydroxide, thereby obtaining lithium oxide. In this manner, the gases decomposed by the thermoplastic material powder prevent the lithium hydroxide from forming a molecular field and clumping during the dehydration process, thereby producing loose lithium oxide. This solves the problem of caking and clumping that exists in existing methods for preparing lithium oxide by thermally decomposing anhydrous lithium hydroxide due to direct dehydration.

[0056] S2, obtaining a plurality of target optimization items for quantifying the quality of the lithium oxide preparation process optimization, and constructing a multi-objective optimization function based on the plurality of target optimization items.

[0057] Preferably, if Figure 2 As shown, in step S2, the specific method for obtaining multiple target optimization items includes the following steps:

[0058] S21, obtaining the bulkiness of lithium oxide according to the tap density, median particle size, and particle size distribution standard deviation of the lithium oxide.

[0059] S22, constructing a constraint penalty term of a multi-objective optimization function according to the lithium oxide looseness.

[0060] Among them, multiple target optimization items include constraint penalty items, and multi-dimensional historical process parameters also include historical lithium oxide looseness.

[0061] By constructing a constraint penalty term of a multi-objective optimization function based on the looseness of lithium oxide, the process parameter optimization model can be guided to converge toward the optimal state when the looseness of lithium oxide is unreasonable, so that lithium oxide in a relatively ideal loose state that meets the requirements can be prepared according to the lithium oxide preparation method.

[0062] In addition, by obtaining the looseness of lithium oxide, the loose state of lithium oxide can be judged more conveniently, intuitively and clearly.

[0063] S3, constructing a process parameter optimization model according to the multi-objective optimization function, and obtaining a training data set according to multi-dimensional historical process parameters to train the process parameter optimization model.

[0064] The process parameter optimization model can be understood as a deep learning neural network model, which includes multiple input layers and multiple output layers. Before training the process parameter optimization model based on the training dataset, the training dataset is first preprocessed by cleaning and data enhancement to improve the quality of the training dataset.

[0065] S4, obtaining an optimal lithium oxide preparation process control parameter group based on the trained process parameter optimization model.

[0066] The lithium oxide preparation process control parameter group includes but is not limited to equipment negative pressure value, heating temperature, heating rate and holding time. The preferred lithium oxide preparation process control parameter group includes preferred equipment negative pressure value, heating temperature, heating rate and holding time.

[0067] The lithium oxide preparation method obtains the looseness of lithium oxide, constructs a constraint penalty term of a multi-objective optimization function based on the looseness of lithium oxide, and trains the process parameter optimization model to obtain an optimal lithium oxide preparation process control parameter group. The method can not only prepare lithium oxide in a loose state that meets the requirements and is relatively ideal, and thus facilitates intuitive and clear judgment of the loose state of lithium oxide, but also can collaboratively optimize multiple process control parameters, thereby overcoming the problems in the prior art of lithium oxide preparation methods of poor process parameter coordination due to the lack of collaborative optimization of process parameters, and the need to further improve the overall production efficiency. The method can reduce production costs, improve production efficiency and product quality, and is conducive to improving the overall production efficiency of lithium oxide preparation.

[0068] As a preferred technical solution, Figure 3 As shown, in step S2, the specific method for obtaining multiple target optimization items also includes the following steps:

[0069] S23, obtaining a lithium oxide cost item based on the raw material cost, energy consumption cost, and time cost.

[0070] Specifically, the lithium oxide cost item can be understood as the raw material cost , energy consumption cost and time cost The weighted value of the three, namely ; Represents raw material costs , energy consumption cost and time cost The weight coefficient can be set by technicians.

[0071] Raw material costs can be calculated by obtaining the initial raw material quality and price. Energy costs include, but are not limited to, the energy costs of the decomposition furnace, such as electricity and gas costs. Time costs can be understood as the time it takes from the start of lithium hydroxide decomposition to the final production of lithium oxide.

[0072] S24, according to the initial raw material quality And the mass of lithium oxide produced Get the lithium oxide conversion yield term. Specifically, .

[0073] Among them, multiple target optimization items include lithium oxide cost item and lithium oxide conversion yield item.

[0074] The lithium oxide bulkiness term ;

[0075] in, Indicates the actual tap density value, Indicates the preset tap density threshold, represents the actual median particle size, Indicates the preset median particle size threshold, Indicates the standard deviation of the actual particle size distribution. Indicates the preset particle size distribution standard deviation value.

[0076] The tap density value characterizes the bulk density of the particles after vibration compaction (unit: g / cm:), reflecting the degree of compaction between the particles. The higher the tap density, the denser the particle arrangement and the lower the looseness. The preset tap density threshold is an empirical threshold and can be set by technical personnel based on experience. The tap density can reflect the looseness of lithium oxide powder to a certain extent. By setting the preset tap density threshold, the degree of influence of bulk density on the looseness of lithium oxide can be quantified. In the actual production process, the looseness of lithium oxide powder can be adjusted by adjusting parameters such as the heating rate and the holding time.

[0077] The median particle size is used to reflect the overall coarseness of the particles and can be expressed in μm or mm. Preferably, the particle diameter of the lithium oxide powder can be normalized to correlate the particle diameter with the bulkiness. The preset median particle size threshold range is 50-80 μm. When the actual median particle size is greater than 50, it can be understood that the proportion of coarse particles is relatively large, and the porosity and bulkiness are relatively ideal. In actual production, the median particle size of the lithium oxide powder can be changed by adjusting parameters such as the nitrogen flow rate and the negative pressure value.

[0078] The standard deviation of particle size distribution is used to characterize the degree of dispersion of particle size distribution. A larger value indicates a greater difference in particle size, which may lead to the coexistence of local dense or loose areas. middle, The exponential term is used to penalize the unevenness of the particle size distribution. is 0.1, when the actual particle size distribution standard deviation is When , the looseness value drops rapidly. The index term can reflect the importance of uniform particle size to stabilize the loose structure.

[0079] Specifically, the formula It is used to quantify the effect of the standard deviation of the particle size distribution on the bulkiness. Its core function is to penalize the discreteness of the particle size distribution through an exponential decay mechanism. The following is a detailed explanation from the perspective of mathematical significance and practical application:

[0080] 1. Mathematical mechanism of action: Used to convert standard deviation into variance and amplify the quantitative impact of the degree of dispersion. The larger the variance, the more significant the difference in particle size, resulting in the coexistence of local dense or loose areas. The standard deviation of the particle size distribution is preset and is an empirical parameter used to adjust the decay rate. A smaller denominator will The value of increases rapidly, thereby accelerating the decay of the exponential function.

[0081] Specific examples:

[0082] 1.1: When =0.2, hour, , The item has little influence on the looseness score;

[0083] 1.2: When =0.3, hour, , The scores on looseness decreased significantly;

[0084] 1.3: When =0.4, hour, , Scores for looseness deteriorated dramatically.

[0085] 2. Physical meaning:

[0086] 2.1. Index Term Penalizes broad particle size distributions by nonlinear decay when When it is >0.3, the looseness score decreases rapidly, which reflects the importance of uniform particle size to stabilize the loose structure, and thus has the effect of suppressing the wide distribution of particle size.

[0087] Functions Requires narrow particle size distribution ( <0.2), which can avoid local density or uneven porosity caused by large differences in particle size and play a role in guiding process optimization.

[0088] 3. Parameter design basis

[0089] 3.1. Denominator The selection can be obtained through parameter multi-scale statistical models and experimental data, with the goal of balancing the attenuation rate and scoring sensitivity within the acceptable discreteness range of the project. Generally speaking, the acceptable range of the actual particle size distribution standard deviation is 0.15-0.25, and correspondingly, the preset particle size distribution standard deviation is According to experience, it is better to set it to 0.1.

[0090] 3.2. In the function In the index term Together with the actual tap density value and the actual median particle size, it ensures that the optimization of bulk meets the synergistic conditions of low tap density, moderate particle size and narrow distribution.

[0091] In general, By amplifying the discrete effect and setting the attenuation threshold, the negative impact of the particle size distribution standard deviation on the looseness can be quantified in an exponential form, providing mathematical constraints for material preparation and process optimization.

[0092] The lithium oxide bulkiness term The three core parameters of tap density, median particle size and standard deviation of particle size distribution are integrated to quantify the looseness of lithium oxide products, which allows users to intuitively and clearly judge the loose state of lithium oxide powder.

[0093] The multi-objective optimization function ;

[0094] in, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield term, The weight coefficients representing the lithium oxide bulkiness term, the lithium oxide cost term, and the lithium oxide conversion yield term, respectively, can be set by technical personnel based on experience.

[0095] The training data set includes but is not limited to historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time, historical thermoplastic material powder purity, historical lithium oxide looseness item information, historical lithium oxide cost item information, and historical lithium oxide conversion yield item information.

[0096] The historical lithium oxide bulkiness item information, historical lithium oxide cost item information, and historical lithium oxide conversion yield item information can be determined according to their specific function formula settings. In this embodiment, the historical lithium oxide bulkiness item information includes historical lithium oxide tap density, median particle size, and particle size distribution standard deviation information; the historical lithium oxide cost item information includes historical raw material cost, energy consumption cost, and time cost information; and the historical lithium oxide conversion yield item information includes historical initial raw material quality and generated lithium oxide quality information.

[0097] Based on the multi-objective optimization function , you can As the loss function of the process parameter optimization model, at least one preferred lithium oxide preparation process control parameter group is finally obtained through iterative optimization.

[0098] Through the multi-objective optimization function , and collaboratively optimize multiple process control parameters, overcoming the problems in the existing lithium oxide preparation method of poor process parameter coordination due to the lack of collaborative optimization of process parameters, and the need to further improve the comprehensive production efficiency. It can reduce production costs, improve production efficiency and product quality, and is conducive to improving the comprehensive production efficiency of lithium oxide preparation.

[0099] One embodiment of the present invention further provides a lithium oxide preparation system for implementing the lithium oxide preparation method, such as Figure 4 As shown, it includes a process parameter acquisition module, an optimization function construction module, an optimization model training module and a control parameter acquisition module.

[0100] The process parameter acquisition module is used to obtain multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; the optimization function construction module is used to obtain multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function based on the multiple target optimization items;

[0101] The optimization model training module is used to construct a process parameter optimization model based on the multi-objective optimization function, and to obtain a training data set based on multi-dimensional historical process parameters to train the process parameter optimization model; the control parameter acquisition module is used to obtain an optimal lithium oxide preparation process control parameter group based on the trained process parameter optimization model.

[0102] Preferably, the optimization function building module includes a constraint penalty item acquisition unit, a lithium oxide cost item acquisition unit and a lithium ion conversion yield item acquisition unit.

[0103] The constraint penalty item acquisition unit is used to obtain the bulkiness of lithium oxide according to the tap density, median particle size and particle size distribution standard deviation of lithium oxide, and construct the constraint penalty item of the multi-objective optimization function according to the bulkiness of lithium oxide.

[0104] The constraint penalty term of the multi-objective optimization function is constructed based on the looseness of lithium oxide. When the looseness of lithium oxide is unreasonable, the process parameter optimization model can be guided to converge toward the optimal state, so that lithium oxide with a relatively ideal loose state that meets the requirements can be prepared according to the lithium oxide preparation method.

[0105] The lithium oxide cost item acquisition unit is used to obtain the lithium oxide cost item based on the raw material cost, energy consumption cost and time cost; the lithium oxide conversion yield item acquisition unit is used to obtain the lithium oxide conversion yield item based on the initial raw material quality and the generated lithium oxide quality.

[0106] Among them, multiple objective optimization items include constraint penalty items, lithium oxide cost items, and lithium oxide conversion yield items.

[0107] The multi-objective optimization function ;in, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield term, The weight coefficients representing the lithium oxide bulkiness term, the lithium oxide cost term, and the lithium oxide conversion yield term, respectively, can be set by technical personnel based on experience.

[0108] The lithium oxide cost item can be understood as the raw material cost , energy consumption cost and time cost The weighted value of the three, namely ; Represents raw material costs , energy consumption cost and time cost The weight coefficient can be set by technicians. And the mass of lithium oxide produced Get the lithium oxide conversion yield , such as the initial raw material quality The final mass of lithium oxide is 10kg. is 7.6 kg, then the lithium oxide conversion yield is .

[0109] The training data set includes, but is not limited to, historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time, historical thermoplastic material powder purity, historical lithium oxide bulkiness information, historical lithium oxide cost information, and historical lithium oxide conversion yield information. When training a process parameter optimization model using the training data set, the training data set is input into the process parameter optimization model, and through continuous iterative optimization, at least one preferred lithium oxide preparation process control parameter group is ultimately obtained, and one of the preferred lithium oxide preparation process control parameter groups is selected to control the operation of the system.

[0110] The historical lithium oxide bulkiness item information, historical lithium oxide cost item information, and historical lithium oxide conversion yield item information can be determined according to their specific function formula settings. In this embodiment, the historical lithium oxide bulkiness item information includes historical lithium oxide tap density, median particle size, and particle size distribution standard deviation information; the historical lithium oxide cost item information includes historical raw material cost, energy consumption cost, and time cost information; and the historical lithium oxide conversion yield item information includes historical initial raw material quality and generated lithium oxide quality information.

[0111] The multi-objective optimization function is used to collaboratively optimize multiple process control parameters, thereby overcoming the problems in the prior art of lithium oxide preparation methods such as poor coordination of process parameters due to the lack of collaborative optimization of process parameters, and the need to further improve the overall production efficiency. This can reduce production costs, improve production efficiency and product quality, and is conducive to improving the overall production efficiency of lithium oxide preparation.

[0112] The specific lithium oxide production process involves mixing anhydrous lithium hydroxide powder with a specific thermoplastic material powder, such as polyethylene (PE) or polypropylene (PP) powder, at a ratio of approximately 10% (PE or PP powder to anhydrous lithium hydroxide). After evacuating the pyrolysis furnace to negative pressure, a small amount of nitrogen is introduced to maintain the negative pressure. The temperature is slowly raised to 400°C and held at this temperature for 6 hours. The gases produced by the decomposition of the polyethylene (PE) or propylene (PP) remove the water vapor produced by the dehydration of the lithium hydroxide, thereby producing lithium oxide. This decomposition of the thermoplastic material prevents the lithium hydroxide from forming molecular traps and clumping during dehydration, resulting in a loose lithium oxide. This solves the problem of agglomeration and clumping associated with direct dehydration by thermal decomposition of anhydrous lithium hydroxide in the existing lithium oxide production method.

[0113] When training the process parameter optimization model using a training data set, preferably, the training data set is first dimensionally non-dimensionalized and normalized to improve the convergence and robustness of the model.

[0114] The weight coefficients of the lithium oxide looseness item, the lithium oxide cost item and the lithium oxide conversion yield item can be dynamically adjusted according to actual conditions. For example, if the market demand for lithium oxide looseness increases, the weight coefficient of the lithium oxide looseness item can be increased accordingly. If the company is controlling costs, the weight coefficient of the lithium oxide cost item can be appropriately increased.

[0115] For the lithium oxide preparation process control parameter group, the powder ratio range is , the heating temperature range is ℃, the holding time range is hours, the negative pressure range is 800-1200Pa, and the heating rate range is ℃ / min. One of the preferred control parameter groups for lithium oxide preparation process includes powder ratio , heating temperature ℃, holding time hours, the negative pressure value is 1000Pa, and the heating rate range is ℃ / min.

[0116] In summary, the lithium oxide preparation system obtains multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process and constructs a multi-objective optimization function, and collaboratively optimizes multiple process control parameters based on the process parameter optimization model to obtain an optimal lithium oxide preparation process control parameter group, which can reduce production costs, improve production efficiency and product quality, and is conducive to improving the comprehensive production benefits of lithium oxide preparation. It solves the problem that the lithium oxide preparation method in the existing technology has poor process parameter coordination due to the lack of collaborative optimization of process parameters, and the comprehensive production benefits need to be further improved.

[0117] An embodiment of the present invention further provides a lithium oxide preparation device, which includes: a controller; a memory storing executable instructions; wherein the executable instructions can be run on the controller and implement the lithium oxide preparation method.

[0118] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for preparing lithium oxide, characterized in that: The steps include: Obtain multi-dimensional historical process parameters that may affect the quality of lithium oxide preparation process; Acquire multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function based on the multiple target optimization items; Constructing a process parameter optimization model according to the multi-objective optimization function, and obtaining a training data set based on multi-dimensional historical process parameters to train the process parameter optimization model; Obtaining an optimal lithium oxide preparation process control parameter group according to the trained process parameter optimization model; The specific method for obtaining multiple target optimization items includes the following steps: The bulkiness of lithium oxide is obtained based on the tap density, median particle size, and particle size distribution standard deviation of lithium oxide; Constructing a constraint penalty term of a multi-objective optimization function according to the lithium oxide looseness; Among them, multiple objective optimization items include constraint penalty items; The lithium oxide bulkiness term ; in, Indicates the actual tap density value, Indicates the preset tap density threshold, represents the actual median particle size, Indicates the preset median particle size threshold, Indicates the standard deviation of the actual particle size distribution. Indicates the preset particle size distribution standard deviation value; The multi-dimensional historical process parameters include historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time, and thermoplastic material powder purity. The preferred lithium oxide preparation process control parameter group includes preferred equipment negative pressure values, heating temperature, heating rate, and holding time. The thermoplastic material powder is PE or PP powder, and the powder ratio range is , the heating temperature range is ℃, the holding time range is hours, the negative pressure range is 800-1200Pa, and the heating rate range is ℃ / min; The specific lithium oxide preparation process includes: mixing anhydrous lithium hydroxide powder with PE or PP powder, pumping negative pressure into the high-temperature decomposition furnace, filling it with nitrogen, maintaining negative pressure, slowly heating and keeping it warm, allowing the gas produced by the decomposition of PE or PP to carry out the water vapor produced by the dehydration of lithium hydroxide, thereby obtaining lithium oxide.

2. A method for preparing lithium oxide according to claim 1, characterized in that: The specific method for obtaining multiple target optimization items also includes the following steps: Obtain lithium oxide cost items based on raw material cost, energy cost, and time cost; Obtaining a lithium oxide conversion yield term based on the mass of the initial raw material and the mass of the generated lithium oxide; Among them, the multiple target optimization items also include lithium oxide cost items and lithium oxide conversion yield items.

3. A method for preparing lithium oxide according to claim 2, characterized in that: The multi-objective optimization function ; in, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield term, They represent the weight coefficients of lithium oxide looseness term, lithium oxide cost term and lithium oxide conversion yield term respectively.

4. A lithium oxide preparation system for preparing lithium oxide according to any one of claims 1 to 3, characterized in that: include: A process parameter acquisition module is used to obtain multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; An optimization function construction module is used to obtain multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and to construct a multi-objective optimization function based on the multiple target optimization items; An optimization model training module is used to construct a process parameter optimization model according to the multi-objective optimization function, and to obtain a training data set based on multi-dimensional historical process parameters to train the process parameter optimization model; A control parameter acquisition module, configured to obtain an optimal lithium oxide preparation process control parameter group based on the trained process parameter optimization model; The optimization function construction module includes: a constraint penalty item acquisition unit, which is used to obtain the bulkiness of lithium oxide according to the tap density, median particle size and particle size distribution standard deviation of lithium oxide, and construct a constraint penalty item of the multi-objective optimization function according to the bulkiness of lithium oxide; Among them, multiple objective optimization items include constraint penalty items; The lithium oxide bulkiness term ; in, Indicates the actual tap density value, Indicates the preset tap density threshold, represents the actual median particle size, Indicates the preset median particle size threshold, Indicates the standard deviation of the actual particle size distribution. Indicates the preset particle size distribution standard deviation value; The multi-dimensional historical process parameters include historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical holding time, and thermoplastic material powder purity. The preferred lithium oxide preparation process control parameter group includes preferred equipment negative pressure values, heating temperature, heating rate, and holding time. The thermoplastic material powder is PE or PP powder, and the powder ratio range is , the heating temperature range is ℃, the holding time range is hours, the negative pressure range is 800-1200Pa, and the heating rate range is ℃ / min; The specific lithium oxide preparation process includes: mixing anhydrous lithium hydroxide powder with PE or PP powder, pumping negative pressure into the high-temperature decomposition furnace, filling it with nitrogen, maintaining negative pressure, slowly heating and keeping it warm, allowing the gas produced by the decomposition of PE or PP to carry out the water vapor produced by the dehydration of lithium hydroxide, thereby obtaining lithium oxide.

5. A lithium oxide preparation system according to claim 4, characterized in that: The optimization function building module also includes: A lithium oxide cost item acquisition unit is used to acquire the lithium oxide cost item based on raw material cost, energy consumption cost and time cost; a lithium oxide conversion yield item acquisition unit, configured to acquire a lithium oxide conversion yield item based on the mass of the initial raw material and the mass of the generated lithium oxide; Among them, the multiple target optimization items also include lithium oxide cost items and lithium oxide conversion yield items.

6. A lithium oxide preparation device, characterized in that: The lithium oxide preparation equipment comprises: Controller; a memory storing executable instructions; The executable instructions can be run on the controller and implement the lithium oxide preparation method according to any one of claims 1 to 3.

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