Method and system for adjusting production process of lithium carbonate, electronic device and product

Abstract

The embodiment of the present specification provides a lithium carbonate production process adjustment method and adjustment system, electronic equipment and product, wherein the production process adjustment method comprises: obtaining component information of waste gas, and determining a production process corresponding to the component information of the waste gas; determining an optimization target and a constraint condition corresponding to the production process of the lithium carbonate; establishing a target function according to the component information of the waste gas, the production process corresponding to the component information of the waste gas, and the optimization target of the production process of the lithium carbonate; when the constraint condition is satisfied, obtaining a plurality of optimization target values corresponding to the target function; and adjusting the production process parameters of the lithium carbonate according to the plurality of optimization target values. By using the above technical solution, the production process of the lithium carbonate can be flexibly adjusted, and the production efficiency of the lithium carbonate is improved.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of lithium carbonate production technology, and in particular to a method and system for adjusting the lithium carbonate production process, electronic equipment, and products. Background Technology

[0002] Lithium carbonate is an inorganic compound with wide applications in various fields. For example, in the battery industry, especially in lithium batteries, lithium carbonate is one of the key raw materials.

[0003] Currently, lithium carbonate is typically prepared by reacting lithium ore (such as spodumene, lepidolite, etc.) with carbonates (such as sodium carbonate or potassium carbonate). For example, lithium ore is crushed and ground, then reacted with carbonates at high temperatures to produce lithium carbonate.

[0004] However, there are some problems in the production process of lithium carbonate, such as high energy consumption, low raw material utilization, and great environmental pressure.

[0005] Against this backdrop, how to intelligently adjust the lithium carbonate production process to improve its production efficiency has become particularly important and remains to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the embodiments of this specification provide a method and system for adjusting the production process of lithium carbonate, electronic equipment, and product, which can flexibly adjust the production process of lithium carbonate and improve the production efficiency of lithium carbonate.

[0007] First, this specification provides a method for adjusting the production process of lithium carbonate, including:

[0008] Obtain the composition information of the waste gas and determine the production process corresponding to the composition information of the waste gas;

[0009] Determine the optimization objectives and constraints corresponding to the lithium carbonate production process;

[0010] Based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objective of the lithium carbonate production process, an objective function is established.

[0011] When the constraints are satisfied, obtain multiple optimization target values ​​corresponding to the objective function;

[0012] The production process parameters of the lithium carbonate are adjusted based on multiple optimization target values.

[0013] Optionally, obtaining the composition information of the exhaust gas includes:

[0014] Obtain the waste gas generated during the lithium carbonate production process;

[0015] The waste gas is analyzed and processed using a pre-set data analysis device to obtain its composition information.

[0016] Optionally, establishing an objective function based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objective of the lithium carbonate production process includes:

[0017] Determine the type of optimization objective;

[0018] Determine the parameter information corresponding to each type of optimization objective. The parameter information includes the composition information of the waste gas and one of the production processes corresponding to the composition information of the waste gas.

[0019] Using the optimization objective as the dependent variable and the parameter information as the independent variable, establish the functional relationship between various types of optimization objectives and parameter information.

[0020] Optionally, when it is determined that the constraint condition is satisfied, obtaining the optimization target value corresponding to the objective function includes:

[0021] Under the condition that the constraints are met, a multi-objective optimization algorithm is used to obtain the optimization target value corresponding to each objective function based on the functional relationship between each type of optimization objective and parameter information.

[0022] Optionally, adjusting the lithium carbonate production process parameters according to multiple optimization target values ​​includes:

[0023] Using a pre-set test model, the optimization target value corresponding to each objective function is predicted, and the expected score improvement value corresponding to each optimization target value is determined.

[0024] When the expected score improvement value is determined to be greater than the preset quantile, the optimization target value corresponding to the largest expected score improvement value is selected as the optimal target value;

[0025] The production process parameters of lithium carbonate are adjusted according to the optimal target value.

[0026] Optionally, the optimization objective includes at least one of maximizing output, minimizing energy consumption, and minimizing exhaust emissions.

[0027] Optionally, the constraints include at least one of raw material supply, equipment production capacity, and environmental protection requirements.

[0028] Accordingly, embodiments of this specification also provide a lithium carbonate production process adjustment system, comprising:

[0029] The parameter acquisition unit is suitable for acquiring the composition information of exhaust gas;

[0030] The objective function establishment unit is adapted to determine the optimization objectives and constraints corresponding to the production process and the lithium carbonate production process, which are based on the composition information of the waste gas; and to establish an objective function based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objectives of the lithium carbonate production process.

[0031] The processing unit is adapted to, when the constraints are satisfied, acquire multiple optimized target values ​​corresponding to the objective function, and adjust the production process parameters of the lithium carbonate according to the multiple optimized target values.

[0032] This specification also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program, it implements the steps of the lithium carbonate production process adjustment method as described in any of the preceding embodiments.

[0033] This specification also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the steps of the lithium carbonate production process adjustment method as described in any of the preceding embodiments.

[0034] By employing the lithium carbonate production process adjustment scheme provided in the embodiments of this specification, the composition information of the waste gas can be obtained to determine the production process corresponding to the composition information of the waste gas. Furthermore, when determining the optimization objectives and constraints for the lithium carbonate production process, an objective function can be established based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objectives of the lithium carbonate production process. Multiple optimization objective values ​​corresponding to the objective function under constraints can be obtained, thereby adjusting the lithium carbonate production process parameters based on these multiple optimization objective values. Since the composition information of the waste gas reflects the raw material conversion efficiency of the current lithium carbonate production process, and the optimization objectives and constraints reflect the production objectives of the lithium carbonate production process, the optimization objective values ​​obtained by combining the above factors are more closely aligned with the actual lithium carbonate production process. Therefore, the lithium carbonate production process parameters obtained based on the optimization objective values ​​can improve the production efficiency of lithium carbonate.

[0035] Furthermore, by using pre-set data analysis equipment to analyze and treat the waste gas generated during the lithium carbonate production process, more accurate information on the composition of the waste gas can be obtained, thereby improving the accuracy of the subsequent lithium carbonate production process parameters.

[0036] Furthermore, by establishing the composition information of the waste gas, the corresponding production process, and the optimization objective of the lithium carbonate production process, an objective function for adjusting the lithium carbonate production process can be established. Moreover, the above three factors can more accurately reflect the actual lithium carbonate production process, thus improving the accuracy of the obtained lithium carbonate production process parameters.

[0037] Furthermore, under the constraints, by employing a multi-objective optimization algorithm, the optimized target values ​​corresponding to each objective function can be obtained, which can take into account multiple objectives in the lithium carbonate production process and achieve the best production strategy.

[0038] Furthermore, on the one hand, by using a preset test model to predict the optimization target value corresponding to each objective function, errors caused by human calculation can be avoided, and the stability and accuracy of the expected score improvement value corresponding to each optimization target value can be improved. On the other hand, by selecting the optimization target value corresponding to the largest expected score improvement value as the optimal target value when the expected score improvement value is greater than the preset quantile, the production process parameters of lithium carbonate can be adjusted based on the optimal target value, and the production process parameters that are more matched to the actual production of lithium carbonate can be obtained, which can better balance the optimization target and the constraints. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this specification, the drawings used in the description of the embodiments of this specification or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart of a lithium carbonate production process adjustment method is shown in an embodiment of this specification;

[0041] Figure 2 A flowchart illustrating one method for obtaining composition information of exhaust gas in an embodiment of this specification is shown;

[0042] Figure 3 A flowchart illustrating the establishment of an objective function in an embodiment of this specification is shown;

[0043] Figure 4 A flowchart illustrating an embodiment of this specification for adjusting lithium carbonate production process parameters is shown.

[0044] Figure 5 A schematic diagram of a lithium carbonate production process adjustment system according to an embodiment of this specification is shown. Detailed Implementation

[0045] As mentioned earlier, there are some problems in the current production process of lithium carbonate, such as high energy consumption, low raw material utilization, and great environmental pressure.

[0046] To address the aforementioned technical problems, embodiments of this specification provide a lithium carbonate production process adjustment scheme. By acquiring the composition information of waste gas, the production process corresponding to the waste gas composition information can be determined. Furthermore, when determining the optimization objectives and constraints for the lithium carbonate production process, an objective function can be established based on the waste gas composition information, the corresponding production process, and the optimization objectives of the lithium carbonate production process. Multiple optimization objective values ​​corresponding to the objective function under constraints can be obtained, thereby adjusting the lithium carbonate production process parameters based on these multiple optimization objective values. Since the waste gas composition information reflects the raw material conversion efficiency of the current lithium carbonate production process, and the optimization objectives and constraints reflect the production objectives of the lithium carbonate production process, the optimization objective values ​​obtained by combining these factors are more closely aligned with the actual lithium carbonate production process. Therefore, the lithium carbonate production process parameters obtained based on the optimization objective values ​​can improve the production efficiency of lithium carbonate.

[0047] To enable those skilled in the art to better understand and implement the embodiments of this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0048] Reference Figure 1 The flowchart shown is a method for adjusting the production process of lithium carbonate, as follows: Figure 1 As shown, it may include the following steps:

[0049] S11, Obtain the composition information of the waste gas and determine the production process corresponding to the composition information of the waste gas.

[0050] Specifically, the production process of lithium carbonate usually involves multiple different production processes. Therefore, the waste gas generated by each production process can be obtained. By analyzing and treating the waste gas, the composition information of the waste gas can be obtained, thereby establishing the correspondence between the composition information of the waste gas and the production process.

[0051] For example, the correspondence between waste gas composition and raw material consumption, and the correspondence between waste gas composition and the operating status of production equipment.

[0052] In some examples, the composition information of the exhaust gas may include the exhaust gas emission volume, exhaust gas composition, and exhaust gas generation time.

[0053] S12, determine the optimization objectives and constraints corresponding to the lithium carbonate production process.

[0054] Specifically, optimization objectives and constraints can be determined based on the current production capacity of lithium carbonate. In some examples, the optimization objective may include at least one of maximizing output, minimizing energy consumption, and minimizing emissions, where: maximizing output refers to the output of lithium carbonate that can be produced by executing the current production process parameters; minimizing energy consumption refers to minimizing the energy consumed (e.g., electrical energy, thermal energy, etc.) when producing the same output of lithium carbonate; and minimizing emissions refers to maximizing raw material utilization and minimizing the amount of emissions when producing the same output of lithium carbonate.

[0055] In some alternative examples, the optimization objective can include at least two of the following: maximizing output, minimizing energy consumption, and minimizing exhaust emissions, thereby improving the production efficiency of lithium carbonate while taking into account multiple optimization objectives.

[0056] In some examples, constraints may include at least one of raw material supply, equipment production capacity, and environmental requirements. Raw material supply refers to the supply capacity of raw materials sufficient to produce lithium carbonate; equipment production capacity refers to the quantity of lithium carbonate that the equipment can produce when production requirements are met; and environmental requirements refer to the requirement that waste gas, wastewater, and waste emitted during the production of lithium carbonate must meet environmental protection requirements.

[0057] In some alternative examples, constraints may include at least one of raw material supply, equipment production capacity, and environmental requirements, thereby allowing for the determination of an optimized target value that more closely reflects the actual lithium carbonate production process while taking into account multiple constraints.

[0058] S13. Based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objective of the lithium carbonate production process, an objective function is established.

[0059] Specifically, the composition information of the exhaust gas can reflect the raw material conversion efficiency of the current lithium carbonate production process, the production process can reflect the lithium carbonate production process, and the optimization target can reflect the production target of the lithium carbonate production process. Therefore, based on the above three factors, an objective function for adjusting the lithium carbonate production process can be established.

[0060] S14, when it is determined that the constraint conditions are met, obtain multiple optimization target values ​​corresponding to the objective function.

[0061] Specifically, by using step S13, an objective function for adjusting the lithium carbonate production process can be obtained. However, considering the relevant constraints in the lithium carbonate production process, the lithium carbonate production process cannot be adjusted indefinitely. Therefore, an optimized objective value that satisfies the constraints can be obtained to more closely approximate the actual lithium carbonate production process.

[0062] S15, adjust the production process parameters of the lithium carbonate according to multiple optimization target values.

[0063] Specifically, the optimized target value can meet the constraints of the lithium carbonate production process, so the production process parameters of lithium carbonate can be adjusted based on the optimized target value.

[0064] The lithium carbonate production process adjustment method described in this specification can be used because the composition information of the waste gas can reflect the raw material conversion efficiency of the current lithium carbonate production process, and the optimization target and constraints can reflect the production target of the lithium carbonate production process. Therefore, the optimization target value obtained by combining the above factors can be more in line with the actual lithium carbonate production process. Thus, the lithium carbonate production process parameters obtained based on the optimization target value can improve the lithium carbonate production efficiency.

[0065] To enable those skilled in the art to better understand and implement the lithium carbonate production process adjustment methods in the embodiments of this specification, the following examples are provided in conjunction with application scenarios.

[0066] In some implementations, the accuracy of the composition information of the exhaust gas directly affects the accuracy of the established objective function, and thus affects the final determined lithium carbonate production process parameters. Therefore, it is necessary to obtain accurate composition information.

[0067] As an example, see Figure 2 The flowchart shown in this specification is one example of a process for obtaining composition information of exhaust gas. In some examples of this specification, such as... Figure 2 As shown, the following steps can be performed:

[0068] S21, Obtain the waste gas generated during the lithium carbonate production process.

[0069] In some examples, the exhaust gas may be generated from the current lithium carbonate production process. In such cases, high-precision gas analysis instruments can be used for real-time sampling to improve the continuity of the obtained exhaust gas.

[0070] In some examples, the exhaust gas may be exhaust gas that was previously obtained and stored during the production of lithium carbonate.

[0071] S22, using a preset data analysis device, the exhaust gas is analyzed and processed to obtain the composition information of the exhaust gas.

[0072] As an example, data analysis equipment with gas chromatography-mass spectrometry (GC-MS) and data analysis and processing capabilities can be used to analyze and process waste gas to obtain more accurate composition information of the waste gas.

[0073] Understandably, in some other examples, other devices or equipment with exhaust gas analysis and treatment functions can be used to analyze and treat the exhaust gas.

[0074] By using pre-set data analysis equipment to analyze and treat the waste gas generated during the lithium carbonate production process, more accurate information on the composition of the waste gas can be obtained, thereby improving the accuracy of the subsequent lithium carbonate production process parameters.

[0075] In some examples, establishing an effective optimization model (objective function) in the lithium carbonate production process is key to achieving the dual goals of production efficiency and environmental friendliness. This is achieved by identifying and incorporating key variables affecting the production process into the optimization model for multi-objective optimization.

[0076] In some implementations, before establishing the objective function, on the one hand, it is necessary to determine the optimization objective and constraints used to establish the objective function, and then integrate the exhaust gas composition detection data, especially focusing on the components with environmental impact and production efficiency; on the other hand, the correlation between exhaust gas composition detection data and production process parameters can be established, and through the above two aspects, production parameters can be adjusted and the production process optimized.

[0077] As an optional example, see [reference] Figure 3 The flowchart shown in this embodiment of the specification illustrates the establishment of an objective function, such as... Figure 3 As shown, the following steps can be performed:

[0078] S31, Determine the type of optimization objective.

[0079] Specifically, in the process of producing lithium carbonate, it is usually necessary to take into account multiple objectives. For different objectives, different optimization objectives are required. Therefore, it is necessary to determine the type of optimization objective used to adjust the production process parameters of lithium carbonate.

[0080] S32, determine the parameter information corresponding to each type of optimization target, wherein the parameter information includes the composition information of the waste gas and one of the production processes corresponding to the composition information of the waste gas.

[0081] Specifically, different types of optimization objectives have different influencing factors. After determining the type of each optimization objective, the parameter information corresponding to each optimization objective can be determined.

[0082] For example, if the optimization objective is to maximize output, then the corresponding parameter information is the production parameters of each process in the production process.

[0083] For example, if the optimization objective is to minimize energy consumption, then the corresponding parameter information is the production parameters of each process in the production process.

[0084] S33, using the optimization objective as the dependent variable and the parameter information as the independent variable, establish the functional relationship between various types of optimization objectives and parameter information.

[0085] As an example, if the optimization objective is to maximize output, then the corresponding objective function is: Max P = f1(x1, x2, ..., x...). n ), where P represents the production of lithium carbonate, and f1 represents the production of lithium carbonate and the production parameters x1, x2, ..., x3. n The relational function.

[0086] As an example, if the optimization objective is to minimize energy consumption, then the corresponding objective function is: Min E = f2(x1, x2, ..., x...). n Where E represents the energy consumption for producing lithium carbonate, and f2 represents the output of lithium carbonate and the production parameters x1, x2, ..., x3. n The relational function.

[0087] As an example, if the optimization objective is to minimize exhaust emissions, then the corresponding objective function is: Min G = f3(x1, x2, ..., x...). n Where P represents the waste gas emissions during the lithium carbonate production process, and f3 represents the lithium carbonate production output and the various production parameters x1, x2, ..., x3. n The relational function.

[0088] It should be noted that for the three objective functions in the above example, the production parameters x1, x2, ..., x... n The types and numbers can be the same or different, and the embodiments in this specification do not limit this.

[0089] By establishing the composition information of the waste gas, the corresponding production process, and the optimization objective of the lithium carbonate production process, an objective function for adjusting the lithium carbonate production process can be established. Furthermore, the above three factors can more accurately reflect the actual lithium carbonate production process, thus improving the accuracy of the obtained lithium carbonate production process parameters.

[0090] In some implementations, once the objective function is established, the optimized objective values ​​corresponding to each functional relationship can be obtained when the constraints are satisfied.

[0091] In some examples, the lithium carbonate production process needs to take multiple objectives into account. Therefore, when obtaining the optimization target value corresponding to the objective function, under the condition of satisfying the constraints, a multi-objective optimization algorithm can be used to obtain the optimization target value corresponding to each objective function based on the functional relationship between each type of optimization objective and parameter information.

[0092] In the optimization model of lithium carbonate production process, multi-objective optimization algorithms are key to finding the optimal solution. Genetic Algorithms (GA), as a powerful optimization tool, are well-suited for solving the objective functions established in the lithium carbonate production process.

[0093] Genetic algorithms are search algorithms that simulate biological evolution, iteratively solving problems through mechanisms such as natural selection, heredity, and mutation. Their basic steps include: generating the initial population, evaluating the fitness function, selection, crossover, and mutation. When applying genetic algorithms to lithium carbonate production processes, the following steps are included:

[0094] Initial population generation: This involves generating a set of random solutions as the initial population, with each solution representing a set of production parameter settings.

[0095] Definition of fitness function: Define a fitness function to evaluate the quality of each solution. For example, it can be a comprehensive evaluation of output, energy consumption, and emissions.

[0096] Selection process: The selection process determines which solutions will be retained for further computation. For example, solutions of higher quality are usually retained.

[0097] Crossover and mutation: Crossover (or pairing) refers to mixing some features of two solutions to produce a new solution, and randomly changing some features of some solutions to introduce a new solution.

[0098] Iterative process: The process of repeatedly selecting, crossing over, and mutating; each iteration is called a generation. As the number of iterations increases, the solutions in the population should gradually approach the optimal solution (i.e., the target value).

[0099] Termination condition: The iteration stops when the preset number of iterations is reached or the quality of the solution meets a specific condition.

[0100] Solution analysis: Select the optimal solution from the final population and analyze the optimal solution to set the parameters for lithium carbonate production.

[0101] Understandably, other types of multi-objective optimization algorithms can also be used to determine the production process parameters of lithium carbonate, such as simulated annealing algorithms.

[0102] By following the steps above, a lithium carbonate production optimization model can be constructed that considers production efficiency, cost control, and environmental impact. This model can provide a scientific basis for production decisions, helping companies achieve environmental sustainability while ensuring production efficiency.

[0103] To facilitate understanding of the process of obtaining the optimization target value in the embodiments of this specification, an example is provided below.

[0104] Assume the objective function is the function listed in step S33 of the example above, and the constraints include:

[0105] Raw material supply constraints: x1≤M, where M is the maximum supply of raw materials.

[0106] Production equipment capacity: x2≤C, where C is the maximum operating capacity of the equipment.

[0107] Environmental standard: G≤S, where S is the maximum permissible emission standard.

[0108] By employing a multi-objective optimization algorithm, we can find the optimal set of solutions that satisfy the constraints based on the objective function, which can then be used as the optimization objective value.

[0109] Using the method described in the example above, a multi-objective optimization algorithm can be used to obtain the optimization target values ​​corresponding to each objective function. However, in practical applications, factors such as cost, efficiency, and environmental standards may conflict. For example, while meeting cost requirements, efficiency and environmental standards may not be guaranteed. Therefore, the obtained optimization target values ​​need to meet the above objective requirements as much as possible in order to find the best production strategy.

[0110] As an example, see Figure 4 The flowchart shown in this specification illustrates an embodiment of adjusting lithium carbonate production process parameters, such as... Figure 4 As shown, the following steps can be performed:

[0111] S41 uses a preset test model to predict the optimization target value corresponding to each objective function and determine the expected score improvement value corresponding to each optimization target value.

[0112] Specifically, the optimization target value can correspond to a set of production process parameters. By inputting the optimization target value into a preset test model, the expected score improvement value corresponding to each optimization target value can be obtained.

[0113] In some examples, the test model can refer to a model capable of implementing Expected Quantile Improvement (EQI), which is a way to guide the search process by predicting improvements in scores. By analyzing the objective functions and their correlations, and combining EQI with the expected quantile improvement, it is possible to find equilibrium solutions in multi-objective optimization problems.

[0114] In some implementations, the basic principle of the expected quantile improvement algorithm is as follows:

[0115] Determine the optimization objectives and constraints, that is, clarify the objectives and constraints that need to be optimized. For example, optimization objectives may include maximizing output, minimizing energy consumption, and controlling waste gas emissions; constraints may include production capacity, raw material limitations, etc.

[0116] Initialize the parameters by selecting an appropriate quantile, such as the median (50th quantile) or a higher quantile.

[0117] In some other examples, other algorithm parameters, such as the number of iterations and the population size, can also be determined.

[0118] Develop a predictive model, such as using a Gaussian process regression model, based on the objective function.

[0119] Calculate the expected quantile improvement, which involves using the prediction model to calculate the expected quantile improvement for each optimization objective value. Optimization objective values ​​with higher expected quantile improvements are likely to result in greater improvements.

[0120] In some optional examples, the above steps can be repeated iteratively until the iteration stopping condition is met (such as reaching the maximum number of iterations or the improvement level is below a threshold). After stopping the iteration, the optimization target value corresponding to the highest expected score improvement value is selected as the optimal target value.

[0121] Applying the expected quantile improvement algorithm to the optimization of lithium carbonate production processes allows for the simultaneous optimization of multiple key indicators, such as increasing yield, reducing energy consumption, and minimizing environmental pollution. Through continuous iteration, the optimal production parameter settings can be found to optimize each objective while satisfying all preset conditions.

[0122] S42, when the expected score improvement value is determined to be greater than the preset quantile, the optimization target value corresponding to the largest expected score improvement value is selected as the optimal target value.

[0123] Specifically, when the expected score improvement value is greater than the preset quantile, it means that the optimization target value obtained in the above example is usable, and by taking the optimization target value corresponding to the largest expected score improvement value as the optimal target value, the balance between optimization target and constraint conditions can be better taken into account.

[0124] Understandably, if the expected score improvement values ​​are all less than the preset quantiles, it is necessary to readjust the optimization objectives and constraints to obtain production process parameters that match the actual production of lithium carbonate.

[0125] S43, Adjust the production process parameters of lithium carbonate according to the optimal target value.

[0126] Specifically, the optimal target value can correspond to a set of process parameters that are more closely related to the actual production of lithium carbonate. Then, the production process parameters of lithium carbonate can be adjusted by comparing the production process parameters corresponding to the optimal target value with the current production process parameters.

[0127] By selecting the optimization target value corresponding to the largest expected score improvement value as the optimal target value, we can better balance the optimization target and the constraints. Therefore, when adjusting the production process parameters of lithium carbonate based on the optimal target value, we can obtain production process parameters that are more compatible with the actual production of lithium carbonate.

[0128] By adopting the lithium carbonate production process adjustment method in the above example, the production process parameters of lithium carbonate can be adjusted to improve production efficiency while taking into account the balance between cost, environmental protection and efficiency.

[0129] In practical applications, the adjusted lithium carbonate production process parameters can be applied to the lithium carbonate production process.

[0130] As an example, the following steps can be performed:

[0131] Review the results provided by the optimization algorithm, including recommended values ​​for production parameters, expected output and costs, and assess the feasibility of these results in actual production, including technical feasibility and economic rationality.

[0132] Once the above production parameters are determined to be available, communicate with the production team to ensure that they understand the specific meaning of the optimization results and their impact on the production process.

[0133] Next, preparations will be made to implement the optimization results. This includes: developing specific operational plans and clarifying the application steps of production parameters in the production process; adjusting production equipment and control systems to adapt to the new production parameters; and, if necessary, providing necessary training to operators to ensure they understand and can execute the new operating procedures.

[0134] Implement and monitor the optimization results, including: implementing production parameters during the production process and adaptively adjusting relevant production parameters; monitoring the production process in real time to ensure the effectiveness and stability of the new production parameters; collecting and analyzing production data to evaluate the actual effects of the optimization implementation.

[0135] Since the production parameters are derived through multi-objective optimization algorithms and expected quantile improvements, the optimal solution can be transformed into actual production operations. Through the above steps, the goals of improving production efficiency, reducing costs, and minimizing environmental impact can be achieved, thus realizing continuous improvement and optimization of the production process.

[0136] As an example, in lithium carbonate production companies, the application of this technology has reduced the emission of harmful components in exhaust gas by 30% while increasing production efficiency by 15%.

[0137] As another example: In lithium carbonate production companies, after adjusting the algorithm, the utilization rate of raw materials increased from 80% to 85%, significantly reducing costs.

[0138] As another example, in regions with stricter environmental requirements, the application of this technology can enable lithium carbonate emissions to meet environmental standards.

[0139] The specification also provides a lithium carbonate production process adjustment system corresponding to the above-described lithium carbonate production process adjustment method. The following detailed description is provided with reference to the accompanying drawings and specific embodiments.

[0140] It should be noted that the lithium carbonate production process adjustment system described below can be considered as a functional module required to implement the lithium carbonate production process adjustment method provided in this specification; the content of the lithium carbonate production process adjustment system described below can be referred to in correspondence with the content of the lithium carbonate production process adjustment method described above.

[0141] Reference Figure 5 The lithium carbonate production process adjustment system shown in this specification embodiment may include, in some embodiments of this specification, the lithium carbonate production process adjustment system 100, which may include:

[0142] The parameter acquisition unit 110 is suitable for acquiring the composition information of the exhaust gas;

[0143] The objective function establishment unit 120 is adapted to determine the optimization objectives and constraints corresponding to the production process and the lithium carbonate production process, which are based on the composition information of the waste gas; and to establish an objective function based on the composition information of the waste gas, the production process and the optimization objectives of the lithium carbonate production process.

[0144] The processing unit 130 is adapted to, when it is determined that the constraints are met, acquire multiple optimized target values ​​corresponding to the objective function, and adjust the production process parameters of the lithium carbonate according to the multiple optimized target values.

[0145] The specific processes of each unit in the lithium carbonate production process adjustment system can be found in the aforementioned content and will not be described in detail here.

[0146] Since the composition information of the exhaust gas can reflect the raw material conversion efficiency of the current lithium carbonate production process, and the optimization target and constraints can reflect the production target of the lithium carbonate production process, the optimization target value obtained by combining the above factors can be more in line with the actual lithium carbonate production process. Therefore, the lithium carbonate production process parameters obtained based on the optimization target value can improve the production efficiency of lithium carbonate.

[0147] This specification also provides an electronic device for adjusting lithium carbonate production process parameters. The electronic device may include a memory and a processor. The memory is adapted to store one or more computer instructions. When the processor executes the computer instructions, it performs the steps of the lithium carbonate production process adjustment method described in any of the foregoing embodiments.

[0148] In practice, electronic devices may also include expansion interfaces suitable for connecting with other devices to enable data interaction.

[0149] Specifically, electronic devices can be general-purpose or special-purpose computer equipment, or more specifically, servers or computer terminals, such as personal computer equipment, portable terminal equipment, etc.

[0150] In practice, the memory, processor, and expansion interface can be connected via a bus.

[0151] In specific implementations, the processor can be implemented by a processing chip such as a central processing unit (CPU) or a field programmable gate array (FPGA), or by an application specific integrated circuit (ASIC) or one or more integrated circuits configured to implement the embodiments of this specification.

[0152] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0153] This specification also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the steps of the lithium carbonate production process adjustment method described in any of the foregoing embodiments.

[0154] This specification also provides a computer-readable storage medium storing computer instructions that, when executed, can perform the steps of the lithium carbonate production process adjustment method described in any of the foregoing embodiments. The computer-readable storage medium can be any suitable readable storage medium, such as an optical disc, a hard disk drive, or a solid-state drive. The instructions stored on the computer-readable storage medium execute the steps of the resonator production process adjustment method described in any of the foregoing embodiments, which will not be described further here.

[0155] The computer-readable storage medium may include, for example, any suitable type of memory cell, memory device, memory article, memory medium, storage device, storage article, storage medium and / or storage cell, such as memory, removable or non-removable medium, erasable or non-erasable medium, writable or rewritable medium, digital or analog medium, hard disk, floppy disk, optical disc read-only memory (CD-ROM), recordable optical disc (CD-R), rewritable optical disc (CD-RW), optical disc, magnetic medium, magneto-optical medium, removable memory card or disk, various types of digital universal optical disc (DVD), magnetic tape, cassette tape, etc.

[0156] Computer instructions may include any suitable type of code implemented using any appropriate high-level, low-level, object-oriented, visual, compiled, and / or interpreted programming language, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, etc.

[0157] While the embodiments disclosed in this specification are as described above, the present invention is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for adjusting the production process of lithium carbonate, characterized in that, include: Obtain the composition information of the waste gas and determine the production process corresponding to the composition information of the waste gas; Determine the optimization objectives and constraints corresponding to the lithium carbonate production process; Based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objective of the lithium carbonate production process, an objective function is established. When the constraints are satisfied, obtain multiple optimization target values ​​corresponding to the objective function; The production process parameters of the lithium carbonate are adjusted based on multiple optimization target values; The objective function is established based on the composition information of the waste gas, the production process corresponding to the composition information of the waste gas, and the optimization objective of the lithium carbonate production process, including: Determine the type of optimization objective; Determine the parameter information corresponding to each type of optimization objective. The parameter information includes the composition information of the waste gas and one of the production processes corresponding to the composition information of the waste gas. Using the optimization objective as the dependent variable and the parameter information as the independent variable, establish functional relationships between various types of optimization objectives and parameter information; When it is determined that the constraint condition is satisfied, obtaining the optimization target value corresponding to the objective function includes: Under the condition that the constraints are met, a multi-objective optimization algorithm is used to obtain the optimization target value corresponding to each objective function based on the functional relationship between each type of optimization objective and parameter information. The step of adjusting the lithium carbonate production process parameters based on multiple optimization target values ​​includes: Using a pre-set test model, the optimization target value corresponding to each objective function is predicted, and the expected score improvement value corresponding to each optimization target value is determined. When the expected score improvement value is determined to be greater than the preset quantile, the optimization target value corresponding to the largest expected score improvement value is selected as the optimal target value; The production process parameters of lithium carbonate are adjusted according to the optimal target value.

2. The method for adjusting the production process of lithium carbonate according to claim 1, characterized in that, The acquisition of the composition information of the exhaust gas includes: Obtain the waste gas generated during the lithium carbonate production process; The waste gas is analyzed and processed using a pre-set data analysis device to obtain its composition information.

3. The method for adjusting the production process of lithium carbonate according to any one of claims 1 to 2, characterized in that, The optimization objectives include at least one of maximizing output, minimizing energy consumption, and minimizing exhaust emissions.

4. The method for adjusting the production process of lithium carbonate according to any one of claims 1 to 2, characterized in that, The constraints include at least one of the following: raw material supply, equipment production capacity, and environmental protection requirements.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the lithium carbonate production process adjustment method as described in any one of claims 1 to 4.

6. A computer program product, comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the processor, they implement the steps of the lithium carbonate production process adjustment method as described in any one of claims 1 to 4.

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