Lithium oxide preparation method, system and equipment

Abstract

The invention relates to the technical field of lithium oxide preparation, and provides a lithium oxide preparation method, system and equipment, and the method comprises the following steps: obtaining multi-dimensional historical process parameters possibly influencing the quality of a lithium oxide preparation process; obtaining a plurality of target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and constructing a multi-target optimization function according to the plurality of target optimization items; constructing a process parameter optimization model according to the multi-objective optimization function, and obtaining a training data set to train the process parameter optimization model; and according to the trained process parameter optimization model, obtaining an optimal lithium oxide preparation process control parameter group. According to the method, the production cost can be reduced, the production efficiency and the product quality can be improved, the comprehensive production benefit of lithium oxide preparation can be improved, and the problems that the lithium oxide preparation method in the prior art is poor in process parameter coordination and the comprehensive production benefit needs to be further improved due to lack of collaborative optimization on the process parameters are solved.

Description

Technical Field

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

[0002] Lithium oxide is an important inorganic compound widely used in fields such as batteries, nuclear energy, ceramics, etc., and it can be prepared by the thermal decomposition method of anhydrous lithium hydroxide.

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

[0004] Although the above-mentioned lithium oxide preparation production methods can prepare the desired lithium oxide, they do not optimize the process parameters such as pressure and temperature in a coordinated manner. The coordination of their process parameters is poor, and the comprehensive production efficiency needs to be further improved. Summary of the Invention

[0005] Based on this, in order to solve the problems in the prior art that the coordination of process parameters is poor in the lithium oxide preparation method due to the lack of coordinated optimization of process parameters, and the comprehensive production efficiency needs to be further improved, the present invention provides a method, system and equipment for preparing lithium oxide. By obtaining multiple target optimization items and constructing a multi-objective optimization function, and obtaining a preferred process control parameter group for lithium oxide preparation through a process parameter optimization model, it can coordinate and optimize multiple process control parameters, which is beneficial to improving the comprehensive production efficiency of lithium oxide preparation. The specific technical solutions are as follows: A method for preparing lithium oxide, comprising the following steps: Obtain multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; Obtain multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function according to the multiple target optimization items; Construct a process parameter optimization model according to the multi-objective optimization function, and train the process parameter optimization model with a training data set obtained from multi-dimensional historical process parameters; According to the trained process parameter optimization model, obtain a preferred process control parameter group for lithium oxide preparation.

[0006] The method for preparing lithium oxide 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 optimized set of process control parameters for lithium oxide preparation. It can reduce production costs, improve production efficiency and product quality, is conducive to improving the comprehensive production efficiency of lithium oxide preparation, and solves the problems of poor coordination of process parameters and the need to further improve the comprehensive production efficiency in the existing lithium oxide preparation methods due to the lack of collaborative optimization of process parameters.

[0007] Preferably, the specific method for obtaining multiple target optimization items includes the following steps: Obtain the looseness of lithium oxide according to the tapped density, median particle size, and standard deviation of particle size distribution of lithium oxide; Construct a constraint penalty term of the multi-objective optimization function according to the looseness of lithium oxide; Among them, multiple target optimization items include the constraint penalty term.

[0008] Preferably, the specific method for obtaining multiple target optimization items includes the following steps: Obtain the lithium oxide cost item according to the raw material cost, energy consumption cost, and time cost; Obtain the lithium oxide conversion yield item according to the mass of the initial raw material and the mass of the generated lithium oxide; Among them, multiple target optimization items include the lithium oxide cost item and the lithium oxide conversion yield item.

[0009] Preferably, the multi-dimensional historical process parameters include the historical equipment negative pressure value, historical nitrogen content, historical target heating temperature, historical heating rate, historical heat preservation time, and the purity of the thermoplastic material powder. The optimized set of process control parameters for lithium oxide preparation includes the optimized equipment negative pressure value, heating temperature, heating rate, and heat preservation time.

[0010] Preferably, the lithium oxide looseness item ; Among them, represents the actual tapped density value, represents the preset tapped density threshold, represents the actual median particle size, represents the preset median particle size threshold, represents the actual standard deviation value of particle size distribution, represents the preset standard deviation value of particle size distribution.

[0011] Preferably, the multi-objective optimization function ; Among them, represents the optimal looseness, represents the norm, Represents the lithium oxide cost item, represents the lithium oxide conversion yield item, respectively represent the weight coefficients of the lithium oxide looseness item, the lithium oxide cost item, and the lithium oxide conversion yield item.

[0012] A lithium oxide preparation system for implementing the lithium oxide preparation method, comprising: A process parameter acquisition module for acquiring multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; An optimization function construction module for acquiring multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and constructing a multi-objective optimization function based on the multiple target optimization items; An optimization model training module for constructing a process parameter optimization model according to the multi-objective optimization function, and training the process parameter optimization model according to the training data set obtained from the multi-dimensional historical process parameters; A control parameter acquisition module for acquiring a preferred group of lithium oxide preparation process control parameters according to the trained process parameter optimization model.

[0013] Preferably, the optimization function construction module includes: A constraint penalty term acquisition unit for obtaining the lithium oxide looseness according to the tapped density, median particle size, and particle size distribution standard deviation of lithium oxide, and constructing a constraint penalty term of the multi-objective optimization function according to the lithium oxide looseness; A lithium oxide cost item acquisition unit for obtaining the lithium oxide cost item according to the raw material cost, energy consumption cost, and time cost; A lithium oxide conversion yield item acquisition unit for obtaining the lithium oxide conversion yield item according to the initial raw material mass and the mass of the generated lithium oxide; Among them, the multiple target optimization items include the constraint penalty term, the lithium oxide cost item, and the lithium oxide conversion yield item.

[0014] Preferably, the multi-dimensional historical process parameters include the historical equipment negative pressure value, historical nitrogen content, historical target heating temperature, historical heating rate, historical heat preservation time, and the purity of the thermoplastic material powder. The preferred group of lithium oxide preparation process control parameters includes the preferred equipment negative pressure value, heating temperature, heating rate, and heat preservation time. The thermoplastic material powder is PE powder or PP powder.

[0015] A lithium oxide preparation device, comprising: A controller; A memory storing executable instructions; Among them, the executable instructions can run on the controller and implement the lithium oxide preparation method. Description of the Drawings

[0016] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0017] Figure 1 is a schematic overall process diagram of a method for preparing lithium oxide in an embodiment of the present invention; Figure 2 is a schematic process diagram of a specific method for obtaining multiple target optimization items in an embodiment of the present invention Figure 1 ; Figure 3 is a schematic process diagram of a specific method for obtaining multiple target optimization items in another embodiment of the present invention Figure 2 ; Figure 4 is a schematic overall structure diagram of a method for preparing lithium oxide in an embodiment of the present invention. Detailed Embodiments

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be 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 protection scope of the present invention.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration and do not represent the only implementation.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] The "first" and "second" mentioned in the present invention do not represent specific quantities and orders, but are only used for name distinction.

[0022] Before elaborating on the specific embodiments of the present application, a brief introduction to the prior art will be given.

[0023] For the process parameters in the preparation of lithium oxide, if only a single process parameter is optimized, it is often difficult to obtain relatively ideal comprehensive production benefits. For example, if only the high conversion yield is pursued, it may be necessary to sacrifice cost and / or time, and it is difficult to obtain relatively ideal comprehensive production benefits; if only the equipment pressure or the target heating temperature in the preparation process is optimized for parameters, ignoring other process parameters including the heating rate and the holding time, it may also lead to an increase in production cost and a decrease in product quality, and it is difficult to obtain relatively ideal comprehensive production benefits.

[0024] To solve the above problems and co-optimize the process parameters in the preparation of lithium oxide to improve the comprehensive production benefits, an embodiment of the present invention provides a method for preparing lithium oxide, as Figure 1 shown, which includes the following steps: S1, Obtain multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process.

[0025] Specifically, the multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process include but are not limited to the historical equipment negative pressure value, the historical nitrogen content, the historical target heating temperature, the historical heating rate, the historical holding time, and the purity of the thermoplastic material powder.

[0026] The lithium oxide can be prepared by a method of thermally decomposing anhydrous lithium hydroxide by heating.

[0027] Preferably, in this embodiment, the specific method for preparing lithium oxide includes: mixing anhydrous lithium hydroxide powder with a certain amount of thermoplastic material powder such as pe or pp powder, with the powder ratio being about 10%. After evacuating the high-temperature decomposition furnace to a negative pressure, a small amount of nitrogen is filled, and the negative pressure is maintained. Then it is slowly heated to the target heating temperature. After holding for a certain time, the gas generated by the decomposition of pe or pp is used to carry out the water vapor generated by the dehydration of lithium hydroxide, and then lithium oxide is obtained. In this way, the gas decomposed from the thermoplastic material powder can prevent lithium hydroxide from forming a molecular field trap and agglomerating during the dehydration process, so as to prepare loose lithium oxide, solving the problem of easy caking and agglomeration existing in the existing method for preparing lithium oxide by heating and decomposing anhydrous lithium hydroxide due to direct dehydration.

[0028] S2, Obtain multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function according to the multiple target optimization items.

[0029] Preferably, as Figure 2 shown, in step S2, the specific method for obtaining multiple target optimization items includes the following steps: S21, Obtain the looseness of lithium oxide according to the tapped density, median particle size, and standard deviation of particle size distribution of lithium oxide.

[0030] S22. Construct a constraint penalty term for the multi-objective optimization function based on the looseness of the lithium oxide.

[0031] Among them, the multiple objective optimization terms include the constraint penalty term, and the multi-dimensional historical process parameters also include the historical looseness of the lithium oxide.

[0032] By constructing a constraint penalty term for the multi-objective optimization function with the looseness of the lithium oxide, when the looseness of the lithium oxide is unreasonable, the process parameter optimization model can be guided to converge to the optimal state, so that the lithium oxide with a loose state that meets the requirements and is more ideal can be prepared according to the lithium oxide preparation method.

[0033] In addition, by obtaining the looseness of the lithium oxide, it is also possible to more conveniently, intuitively and clearly judge the loose state of the lithium oxide.

[0034] S3. Construct a process parameter optimization model according to the multi-objective optimization function, and obtain a training data set based on the multi-dimensional historical process parameters to train the process parameter optimization model.

[0035] The process parameter optimization model can be understood as a neural network model that can be deeply learned, which includes multiple input layers and multiple output layers. Before training the process parameter optimization model according to the training data set, preprocess the training data set such as cleaning and data augmentation to improve the quality of the training data set.

[0036] S4. According to the trained process parameter optimization model, obtain an optimized set of process control parameters for lithium oxide preparation.

[0037] For the set of process control parameters for lithium oxide preparation, it includes but is not limited to the equipment negative pressure value, heating temperature, heating rate, and heat preservation time. The optimized set of process control parameters for lithium oxide preparation includes the optimized equipment negative pressure value, heating temperature, heating rate, and heat preservation time.

[0038] The lithium oxide preparation method obtains the looseness of the lithium oxide, constructs a constraint penalty term for the multi-objective optimization function based on the looseness of the lithium oxide, and trains the process parameter optimization model to obtain an optimized set of process control parameters for lithium oxide preparation. It can not only prepare lithium oxide with a loose state that meets the requirements and is more ideal, and then conveniently, intuitively and clearly judge the loose state of the lithium oxide, but also co-optimize multiple process control parameters, overcoming the problem of poor coordination of process parameters and the need to further improve the comprehensive production efficiency in the existing lithium oxide preparation method due to the lack of co-optimization of process parameters. It can reduce production costs, improve production efficiency and product quality, and is beneficial to improving the comprehensive production efficiency of lithium oxide preparation.

[0039] As a preferred technical solution, such as Figure 3As shown, in step S2, the specific method for obtaining multiple target optimization items further includes the following steps: S23. Obtain a lithium oxide cost item based on raw material cost, energy consumption cost, and time cost.

[0040] Specifically, the lithium oxide cost item can be understood as the weighted value of the raw material cost , energy consumption cost , and time cost , that is, ; respectively represent the weight coefficients of the raw material cost , energy consumption cost , and time cost , which can be set by technicians.

[0041] For the raw material cost, it can be obtained by obtaining the initial raw material quality and price and calculating based on the initial raw material quality and price. The energy consumption cost includes, but is not limited to, the energy consumption cost of the decomposition furnace such as electricity cost and gas cost. The time cost can be understood as the time value from the start of lithium hydroxide decomposition to the final obtaining of lithium oxide.

[0042] S24. Obtain a lithium oxide conversion yield item based on the initial raw material quality and the generated lithium oxide quality . Specifically, .

[0043] Among them, the multiple target optimization items include a lithium oxide cost item and a lithium oxide conversion yield item.

[0044] The lithium oxide looseness item ; Among them, represents the actual tapped density value, represents the preset tapped density threshold, represents the actual median particle size, represents the preset median particle size threshold, represents the actual particle size distribution standard deviation value, represents the preset particle size distribution standard deviation.

[0045] The tapped density value characterizes the bulk density of particles after vibration compaction (unit: g / cm³), reflecting the degree of tightness between particles. The higher the tapped density, the denser the particle arrangement and the lower the looseness. The preset tapped density threshold is an empirical threshold that can be set by technicians according to experience. The tapped density can reflect the looseness of lithium oxide powder to a certain extent. By setting the preset tapped density threshold, the influence degree 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.

[0046] The median particle size is used to reflect the overall thickness of particles, and the unit can be µm or mm. Preferably, by normalizing the particle diameter of lithium oxide powder, the particle diameter can be correlated with the looseness. The range of the preset median particle size threshold 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 looseness are relatively ideal. In the actual production process, the median particle size of lithium oxide powder can be changed by adjusting parameters such as the nitrogen gas flow rate and the negative pressure value.

[0047] The standard deviation of particle size distribution is used to characterize the degree of dispersion of the particle size distribution of a particle group. The larger the value, the greater the particle size difference, which may lead to the coexistence of locally dense or loose regions. In the formula In The exponential term is used to penalize the non-uniformity brought about by the particle size distribution of the particle group. Preferably, the preset standard deviation of particle size distribution is 0.1. When the actual standard deviation of particle size distribution When, the value of the looseness drops rapidly. Through The exponential term can reflect the importance of uniform particle size for a stable loose structure.

[0048] Specifically, the formula is used to quantify the influence of the standard deviation of particle size distribution on the looseness. 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 perspectives of mathematical meaning and practical application: 1. Mathematical mechanism: is used to convert the standard deviation into variance, amplifying the quantitative impact of the degree of dispersion. The larger the variance, the more significant the difference in the particle size of the particle group, resulting in the coexistence of locally dense or loose regions. The denominator is the preset standard deviation of particle size distribution, which is an empirical parameter used to adjust the decay rate. A smaller denominator will cause the value to increase rapidly, thereby accelerating the decay of the exponential function.

[0049] Specific example: 1.1: When = 0.2, When, , has little impact on the looseness score; 1.2: When = 0.3, at this time, , the looseness score drops significantly; 1.3: When = 0.4, at this time, , the looseness score deteriorates sharply.

[0050] 2. Physical meaning: 2.1. Exponential term penalizes the wide particle size distribution through non-linear attenuation. When > 0.3, the looseness score drops rapidly, reflecting the importance of uniform particle size for a stable loose structure, thus having the effect of suppressing the wide particle size distribution.

[0051] 2.2. Function requires a narrow particle size distribution ( < 0.2), which can avoid local densification or pore non-uniformity caused by excessive particle size differences and has the effect of guiding process optimization.

[0052] 3. Parameter design basis 3.1. Denominator can be obtained through a parameter multi-scale statistical model and experimental data. The purpose is to balance the attenuation rate and the scoring sensitivity within the engineering acceptable dispersion range. Generally speaking, the acceptable range of the standard deviation of the actual particle size distribution is 0.15 - 0.25. Correspondingly, the preset standard deviation of the particle size distribution According to experience, it is set to 0.1 as optimal.

[0053] 3.2. In the function , the exponential term acts together with the actual tapped density value and the actual median particle size, which can ensure that the optimization of the looseness meets the synergistic conditions of low tapped density, moderate particle size, and narrow distribution.

[0054] Generally speaking, by amplifying the dispersion effect and setting the attenuation threshold, the negative impact of the standard deviation of the particle size distribution on the looseness can be quantified exponentially, providing mathematical constraints for material preparation and process optimization.

[0055] The lithium oxide looseness term combines three core parameters: tapped density, median particle size, and standard deviation of particle size distribution, and is used to quantify the looseness of lithium oxide products, which can facilitate users to intuitively and clearly judge the loose state of lithium oxide powder.

[0056] The multi-objective optimization function ; Wherein, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield item, respectively represent the weight coefficients of the lithium oxide looseness item, the lithium oxide cost item, and the lithium oxide conversion yield item, which can be set by technicians according to experience.

[0057] For the training data set, it includes but is not limited to historical equipment negative pressure values, historical nitrogen contents, historical target heating temperatures, historical heating rates, historical heat preservation times, historical thermoplastic material powder purities, historical lithium oxide looseness item information, historical lithium oxide cost item information, and historical lithium oxide conversion yield item information.

[0058] The historical lithium oxide looseness 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 looseness item information includes historical lithium oxide tapped 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 mass and generated lithium oxide mass information.

[0059] Based on the multi-objective optimization function , it can use as the loss function of the process parameter optimization model, and finally obtain at least one preferred set of lithium oxide preparation process control parameters through an iterative optimization method.

[0060] Through the multi-objective optimization function , the collaborative optimization of multiple process control parameters is carried out, overcoming the problem of poor coordination of process parameters and the need for further improvement of the comprehensive production benefit in the existing lithium oxide preparation method due to the lack of collaborative optimization of process parameters. It can reduce production costs, improve production efficiency and product quality, and is beneficial to improving the comprehensive production benefit of lithium oxide preparation.

[0061] An embodiment of the present invention also provides a lithium oxide preparation system for implementing the lithium oxide preparation method as Figure 4 shown, which includes a process parameter acquisition module, an optimization function construction module, an optimization model training module, and a control parameter acquisition module.

[0062] The process parameter acquisition module is used to acquire multi-dimensional historical process parameters that may affect the quality of the lithium oxide preparation process; the optimization function construction module is used to acquire multiple target optimization items for quantifying the optimization quality of the lithium oxide preparation process, and construct a multi-objective optimization function according to the multiple target optimization items; The optimization model training module is used to construct a process parameter optimization model according to the multi-objective optimization function, and train the process parameter optimization model according to the training data set obtained from the multi-dimensional historical process parameters; the control parameter acquisition module is used to acquire a preferred lithium oxide preparation process control parameter set according to the trained process parameter optimization model.

[0063] Preferably, the optimization function construction module includes a constraint penalty term acquisition unit, a lithium oxide cost item acquisition unit, and a lithium oxide conversion yield item acquisition unit.

[0064] The constraint penalty term acquisition unit is used to obtain the lithium oxide looseness according to the tapped density, median particle size, and standard deviation of particle size distribution of lithium oxide, and construct a constraint penalty term of the multi-objective optimization function according to the lithium oxide looseness.

[0065] Constructing a constraint penalty term of the multi-objective optimization function with lithium oxide looseness can guide the process parameter optimization model to converge to the optimal state when the lithium oxide looseness is unreasonable, so that lithium oxide with a loose state that meets the requirements and is more ideal can be prepared according to the lithium oxide preparation method.

[0066] The lithium oxide cost item acquisition unit is used to obtain the lithium oxide cost item according to 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 according to the mass of the initial raw material and the mass of the generated lithium oxide.

[0067] Among them, the multiple target optimization items include a constraint penalty term, a lithium oxide cost item, and a lithium oxide conversion yield item.

[0068] The multi-objective optimization function ; among them, represents the optimal looseness, represents the norm, represents the lithium oxide cost item, represents the lithium oxide conversion yield item, respectively represent the weight coefficients of the lithium oxide looseness item, the lithium oxide cost item, and the lithium oxide conversion yield item, which can be set by technicians according to experience.

[0069] The lithium oxide cost item can be understood as the weighted value of the raw material cost , the energy consumption cost , and the time cost , that is ; respectively represent the raw material cost , the energy consumption cost and the time cost of the weight coefficients, which can be set by technicians. According to the initial raw material quality and the generated lithium oxide quality to obtain the lithium oxide conversion yield term , for example, the initial raw material quality is 10 kg, and the finally generated lithium oxide quality is 7.6 kg, then the lithium oxide conversion yield term .

[0070] The training data set includes but is not limited to historical equipment negative pressure values, historical nitrogen contents, historical target heating temperatures, historical heating rates, historical heat preservation times, historical thermoplastic material powder purities, historical lithium oxide looseness term information, historical lithium oxide cost term information, and historical lithium oxide conversion yield term information. When training the process parameter optimization model through the training data set, input the training data set into the process parameter optimization model, and through continuous iterative optimization, finally obtain at least one preferred lithium oxide preparation process control parameter group, and select one of the preferred lithium oxide preparation process control parameter groups to control the operation of the system.

[0071] The historical lithium oxide looseness term information, historical lithium oxide cost term information, and historical lithium oxide conversion yield term information can be determined according to their specific function formula settings. In this embodiment, the historical lithium oxide looseness term information includes historical lithium oxide tapped density, median particle size, and particle size distribution standard deviation information, the historical lithium oxide cost term information includes historical raw material cost, energy consumption cost, and time cost information, and the historical lithium oxide conversion yield term information includes historical initial raw material quality and generated lithium oxide quality information.

[0072] By using the multi-objective optimization function to co-optimize multiple process control parameters, it overcomes the problem in the existing lithium oxide preparation method that the coordination of process parameters is poor due to the lack of co-optimization of process parameters, and the comprehensive production efficiency needs to be further improved. It can reduce production costs, improve production efficiency and product quality, and is beneficial to improving the comprehensive production efficiency of lithium oxide preparation.

[0073] For the specific preparation process of lithium oxide, it includes: mixing anhydrous lithium hydroxide powder with a certain amount of thermoplastic material powder such as PE or PP powder, with the powder ratio (the ratio of PE or PP powder to anhydrous lithium hydroxide) being about 10%. After evacuating the negative pressure of the high-temperature decomposition furnace, a small amount of nitrogen is filled, and the negative pressure is maintained. Then, it is slowly heated to 400 °C and kept warm for 6 hours. The gas generated by the decomposition of PE (Polyethylene) or PP (Polypropylene) will carry out the water vapor generated by the dehydration of lithium hydroxide, thereby obtaining lithium oxide. In this way, the gas decomposed from the thermoplastic material powder can prevent the lithium hydroxide during the dehydration process from forming molecular field trap agglomeration, thus preparing loose lithium oxide and solving the problem of easy caking and agglomeration existing in the existing method of preparing lithium oxide by directly dehydrating anhydrous lithium hydroxide through heating decomposition.

[0074] When training the process parameter optimization model through the training data set, preferably, the training data set is first subjected to dimensionless processing and normalization processing to improve the convergence and robustness of the model.

[0075] For the weight coefficients of the lithium oxide looseness term, the lithium oxide cost term, and the lithium oxide conversion yield term, they can be dynamically adjusted according to the actual situation. For example, if the market's requirement for the looseness of lithium oxide increases, the weight coefficient of the lithium oxide looseness term can be correspondingly increased. If the enterprise is controlling costs, the weight coefficient of the lithium oxide cost term can be appropriately increased.

[0076] For the process control parameter group of lithium oxide preparation, the powder ratio range is , the heating temperature range is °C, the heat preservation time range is hours, the negative pressure value range is 800 - 1200 Pa, and the heating rate range is °C / min. One preferred process control parameter group for lithium oxide preparation includes a powder ratio of , a heating temperature of °C, a heat preservation time of hours, a negative pressure value of 1000 Pa, and a heating rate range of °C / min.

[0077] 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 cooperatively optimizes multiple process control parameters based on the process parameter optimization model to obtain an optimized set of process control parameters for lithium oxide preparation. It can reduce production costs, improve production efficiency and product quality, is conducive to improving the comprehensive production benefit of lithium oxide preparation, and solves the problems of poor coordination of process parameters and the need for further improvement of the comprehensive production benefit in the existing lithium oxide preparation methods due to the lack of cooperative optimization of process parameters.

[0078] 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 run on the controller and implement the lithium oxide preparation method described above.

[0079] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0080] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to 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 quality of the lithium oxide preparation process optimization, and construct a multi-objective optimization function according to the multiple target optimization items; 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; According to the trained process parameter optimization model, an optimal lithium oxide preparation process control parameter group is obtained.

2. A method for preparing lithium oxide according to claim 1, characterized in that: The specific method for obtaining multiple target optimization items includes the following steps: The looseness of lithium oxide is obtained according to the tap density, median particle size and standard deviation of particle size distribution of lithium oxide; Constructing a constraint penalty term of a multi-objective optimization function according to the lithium oxide looseness; Among them, the multiple objective optimization items include constraint penalty items.

3. A method for preparing lithium oxide according to claim 2, characterized in that: The specific method for obtaining multiple target optimization items 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, multiple target optimization items include lithium oxide cost item and lithium oxide conversion yield item.

4. A method for preparing lithium oxide according to claim 3, characterized in that: The multi-dimensional historical process parameters include historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical insulation 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 insulation time.

5. A method for preparing lithium oxide according to claim 4, characterized in that: The lithium oxide bulkiness term ; in, Indicates the actual tap density value, represents the preset tap density threshold, represents the actual median particle size, represents 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.

6. A method for preparing lithium oxide according to claim 5, 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 respectively represent the weight coefficients of the lithium oxide looseness term, the lithium oxide cost term and the lithium oxide conversion yield term.

7. A lithium oxide preparation system, used to prepare the lithium oxide according to any one of claims 1 to 6, characterized in that: include: A process parameter acquisition module is used to obtain multi-dimensional historical process parameters that may affect the quality of lithium oxide preparation process; An optimization function construction module, 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 according to 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; 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.

8. A lithium oxide preparation system as claimed in claim 7, characterized in that: The optimization function building module includes: A constraint penalty item acquisition unit, used to acquire the looseness 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 a multi-objective optimization function according to the looseness of lithium oxide; A lithium oxide cost item acquisition unit, used for acquiring the lithium oxide cost item according to the raw material cost, energy consumption cost and time cost; A lithium oxide conversion yield item acquisition unit, used to acquire a lithium oxide conversion yield item according to the mass of the initial raw material and the mass of the generated lithium oxide; Among them, multiple objective optimization items include constraint penalty items, lithium oxide cost items, and lithium oxide conversion yield items.

9. A lithium oxide preparation system as claimed in claim 8, characterized in that: The multi-dimensional historical process parameters include historical equipment negative pressure values, historical nitrogen content, historical target heating temperature, historical heating rate, historical insulation 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 insulation time. The thermoplastic material powder is PE powder or PP powder.

10. 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 as described in any one of claims 1 to 6.

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