Process condition optimization method and device for silver-based hydrogen absorption molecular sieve

Abstract

The invention provides a process condition optimization method and device of a silver-based hydrogen absorption molecular sieve, and relates to the technical field of molecular sieve preparation, and the method comprises the following steps: obtaining application requirements and performance intervals of a target silver-based hydrogen absorption molecular sieve; acquiring initial raw material information; acquiring a first process parameter group; preparing a first silver-based hydrogen absorption molecular sieve; obtaining a first performance test result, and comparing the first performance test result with the performance interval; when the first performance test result exceeds the performance interval, acquiring a second process parameter group; preparing a second silver-based hydrogen absorption molecular sieve, and judging whether a second performance test result of the second silver-based hydrogen absorption molecular sieve exceeds the performance interval or not; and iteratively executing until the performance test result falls into the performance interval, and obtaining a target process parameter group. Through the method and the device, the technical problem that an optimization result is not ideal enough due to the fact that interaction among multiple parameters cannot be comprehensively considered in the prior art can be solved, and the technical effects of remarkably reducing the production cost and improving the product quality are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of molecular sieve preparation, and in particular to a method and device for optimizing process conditions of a silver-based hydrogen-absorbing molecular sieve. Background Art

[0002] With the widespread application of hydrogen as a clean energy, the efficient storage and release of hydrogen has become an important research direction in the field of energy technology. Silver-based hydrogen absorbing molecular sieves have become a research hotspot for hydrogen storage materials due to their excellent hydrogen absorption performance and stability. The preparation process of silver-based hydrogen absorbing molecular sieves is complex, and their performance is affected by multiple process parameters. Therefore, how to optimize these process conditions to prepare silver-based hydrogen absorbing molecular sieves with excellent performance and stability has become an important topic in research and industrial production.

[0003] In the existing technology, the process parameters are usually optimized by trial and error or single factor analysis, but the limitation of these methods is that they cannot fully consider the interaction between multiple parameters, which often leads to unsatisfactory optimization results. In addition, with the increase of process complexity, the existing methods are difficult to adapt to the demand for efficient and precise process control in modern production.

[0004] In summary, the prior art has a technical problem that the optimization result is not ideal due to the inability to fully consider the interaction between multiple parameters. Summary of the invention

[0005] The purpose of the present application is to provide a method and device for optimizing the process conditions of a silver-based hydrogen-absorbing molecular sieve, so as to solve the technical problem in the prior art that the optimization results are not ideal due to the inability to fully consider the interactions between multiple parameters.

[0006] In view of the above problems, the present application provides a method and device for optimizing the process conditions of a silver-based hydrogen-absorbing molecular sieve.

[0007] In a first aspect, the present application provides a method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve, wherein the method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve is implemented by a device for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve, wherein the method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve comprises: interacting with a production management system to obtain application requirements of a target silver-based hydrogen absorbing molecular sieve, and setting a performance range of the target silver-based hydrogen absorbing molecular sieve based on the application requirements; obtaining initial raw material information, wherein the initial raw material information comprises molecular sieve carrier information and silver salt solution information; configuring ion exchange process parameters according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtaining a first process parameter group, wherein the process parameters comprise exchange exchange time, exchange temperature and stirring speed; under the first process parameter group, immersing the molecular sieve carrier into a silver salt solution for ion exchange to prepare a first silver-based hydrogen absorbing molecular sieve; performing a performance test on the first silver-based hydrogen absorbing molecular sieve, obtaining a first performance test result, and comparing it with the performance range; when the first performance test result exceeds the performance range, optimizing multiple process parameters in the first process parameter group to obtain a second process parameter group; preparing a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and judging whether the second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; iteratively executing until the performance test result falls within the performance range, and obtaining a target process parameter group.

[0008] In a second aspect, the present application further provides a process condition optimization device for a silver-based hydrogen absorbing molecular sieve, which is used to execute a process condition optimization method for a silver-based hydrogen absorbing molecular sieve as described in the first aspect, wherein the process condition optimization device for a silver-based hydrogen absorbing molecular sieve comprises: a performance interval setting module, which is used to interact with the production management system, obtain the application requirements of the target silver-based hydrogen absorbing molecular sieve, and set the performance interval of the target silver-based hydrogen absorbing molecular sieve based on the application requirements; an initial raw material information acquisition module, which is used to obtain initial raw material information, and the initial raw material information includes molecular sieve carrier information and silver salt solution information; a first process parameter group configuration module, which is used to configure the process parameters of ion exchange according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtain a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed; ion an exchange module, used for, under the first process parameter group, immersing the molecular sieve carrier into a silver salt solution for ion exchange to prepare a first silver-based hydrogen absorbing molecular sieve; a first performance test module, used for performing a performance test on the first silver-based hydrogen absorbing molecular sieve, obtaining a first performance test result, and comparing it with the performance range; a second process parameter group acquisition module, used for, when the first performance test result exceeds the performance range, optimizing multiple process parameters in the first process parameter group to obtain a second process parameter group; a second performance test module, used for preparing a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determining whether the second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; a target process parameter group acquisition module, used for iterative execution until the performance test result falls within the performance range to obtain a target process parameter group.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: An interactive production management system is provided, which obtains application requirements of a target silver-based hydrogen absorbing molecular sieve, and sets a performance range of the target silver-based hydrogen absorbing molecular sieve based on the application requirements; obtains initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information; configures ion exchange process parameters according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtains a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed; under the first process parameter group, immerses the molecular sieve carrier into a silver salt solution for ion exchange to prepare a first silver-based hydrogen absorbing molecular sieve; performs a performance test on the first silver-based hydrogen absorbing molecular sieve, obtains a first performance test result, and compares it with the performance range; when the first performance test result exceeds the performance range, optimizes multiple process parameters in the first process parameter group to obtain a second process parameter group; prepares a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determines whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; iteratively executes until the performance test result falls within the performance range, and obtains a target process parameter group. Combining historical production data and process simulation technology, we systematically optimize the key process parameters in the ion exchange process to ensure that the final prepared silver-based hydrogen-absorbing molecular sieve reaches the best performance. Through iterative optimization, we can achieve the technical effect of significantly reducing production costs and improving product quality.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic flow chart of a method for optimizing process conditions for a silver-based hydrogen-absorbing molecular sieve according to the present application; Figure 2 This is a schematic structural diagram of a process condition optimization device for a silver-based hydrogen-absorbing molecular sieve according to the present application.

[0013] Description of reference numerals: Performance interval setting module 11, initial raw material information acquisition module 12, first process parameter group configuration module 13, ion exchange module 14, first performance test module 15, second process parameter group acquisition module 16, second performance test module 17, target process parameter group acquisition module 18. DETAILED DESCRIPTION

[0014] This application solves the technical problem in the prior art that the optimization results are not ideal due to the inability to fully consider the interaction between multiple parameters by providing a method and device for optimizing the process conditions of a silver-based hydrogen-absorbing molecular sieve. Combined with historical production data and process simulation technology, the key process parameters in the ion exchange process are systematically optimized to ensure that the final prepared silver-based hydrogen-absorbing molecular sieve reaches the best performance. Through iterative optimization, the technical effect of significantly reducing production costs and improving product quality is achieved.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0016] For example, please refer to the attached Figure 1 The present application provides a method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve, wherein the method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve is applied to a device for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve, and the method for optimizing process conditions of a silver-based hydrogen absorbing molecular sieve specifically comprises the following steps: Step 1: Interact with the production management system to obtain application requirements of the target silver-based hydrogen absorbing molecular sieve, and set the performance range of the target silver-based hydrogen absorbing molecular sieve based on the application requirements.

[0017] Specifically, the interactive production management system is an existing intelligent management platform that can interact with users or other systems in real time, and can collect and analyze various data in the production process. The interactive production management system obtains the application requirements of the target silver-based hydrogen-absorbing molecular sieve. The target silver-based hydrogen-absorbing molecular sieve refers to the molecular sieve to be optimized for process conditions, and the application requirements refer to the performance standards that the target silver-based hydrogen-absorbing molecular sieve needs to achieve in actual applications. For example, when used in automotive fuel cells, the molecular sieve may need to have efficient hydrogen absorption capacity and excellent thermal stability.

[0018] After obtaining the application requirements of the target silver-based hydrogen-absorbing molecular sieve, set the performance range of the molecular sieve. The performance range refers to the specific performance index range that the molecular sieve needs to achieve. For example, the hydrogen absorption performance index range is between 10 and 15 mmol / g. In the production process, all process parameters will be adjusted around this range to ensure that the performance of the final product meets expectations. By setting the performance range in detail, it provides a basis for the subsequent optimization of process conditions.

[0019] Step 2: Obtaining initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information.

[0020] Specifically, the initial raw material information refers to the basic material data required before producing the target silver-based hydrogen-absorbing molecular sieve, wherein the initial raw material information includes the molecular sieve carrier information and the silver salt solution information, and the molecular sieve carrier information includes the type of molecular sieve, pore size, specific surface area, etc. The silver salt solution information includes the type of silver salt used, the concentration, the pH value of the solution, etc. This information plays a decisive role in the exchange efficiency of silver ions and the uniformity of their distribution on the molecular sieve. Different silver salts (such as silver nitrate, silver acetate, etc.) may show different effects under different process conditions. By accurately obtaining and analyzing the initial raw material information, a reliable basis can be provided for the subsequent ion exchange process parameter setting, ensuring that various parameters can be adjusted more specifically during the process optimization process, and ultimately producing a silver-based hydrogen-absorbing molecular sieve that meets the expected performance.

[0021] Step three: configuring the process parameters of ion exchange according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtaining a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed.

[0022] Specifically, process parameters refer to the key variables that need to be controlled during the ion exchange process, including exchange time, exchange temperature and stirring speed. These parameters have a direct impact on the effect of ion exchange, the uniformity of silver ion distribution in the molecular sieve, and the final hydrogen absorption performance of the molecular sieve. Exchange time refers to the time required for ion exchange between the molecular sieve carrier and the silver salt solution. A longer exchange time may help to increase the exchange amount of silver ions, thereby enhancing the hydrogen absorption capacity of the molecular sieve. However, too long a time may also lead to side reactions or material loss, so it is necessary to find a balance between efficiency and stability. Exchange temperature refers to the temperature at which the ion exchange reaction is carried out. Temperature affects the reaction rate and the diffusion rate of silver ions. Generally speaking, a proper increase in temperature can speed up the reaction rate, but too high a temperature may cause damage to the molecular sieve structure or decomposition of the silver salt. Therefore, it is crucial to determine the appropriate temperature range. Stirring speed refers to the stirring intensity of the solution during the ion exchange process. The stirring speed affects the uniformity of the solution and the adequacy of the ion exchange. Too low a stirring speed may lead to uneven local silver ion concentration, while too high a stirring speed may cause material wear or bubble generation, affecting the stability of the reaction.

[0023] The first process parameter group is a plurality of groups of process parameters retrieved from a historical production database based on the molecular sieve carrier information, the silver salt solution information and the application requirements, and will be used for subsequent experiments and performance tests to provide basic data for further optimization.

[0024] Step 4: Under the first process parameter group, the molecular sieve carrier is immersed in a silver salt solution for ion exchange to prepare a first silver-based hydrogen-absorbing molecular sieve.

[0025] Specifically, the molecular sieve carrier is a material with a highly regular pore structure, and its structure is the same as the molecular sieve carrier information mentioned above, and is generally used for adsorption and separation of gas or liquid molecules. In the present application, the role of the molecular sieve carrier is to provide an effective adsorption interface so that silver ions can effectively enter and occupy the pores of the molecular sieve, thereby giving it the ability to absorb hydrogen. Next, according to the determined first process parameter group, the molecular sieve carrier is immersed in the prepared silver salt solution. The silver salt solution is an aqueous solution containing silver ions, such as silver nitrate or silver acetate, configured based on the aforementioned silver salt solution information. The silver ions in the solution will be exchanged with other cations (such as sodium ions) in the molecular sieve carrier by ion exchange, thereby being fixed in the pores of the molecular sieve. This process determines the hydrogen absorption capacity and stability of the silver-based molecular sieve.

[0026] During the ion exchange process, the exchange time, exchange temperature and stirring speed need to be strictly controlled according to the first process parameter group, so that the first silver-based hydrogen-absorbing molecular sieve can be prepared. It should be noted that this preparation is a small-batch preparation, which can also be understood as sample preparation. The performance of the prepared first silver-based hydrogen-absorbing molecular sieve will be verified in subsequent tests to determine whether it meets the expected performance requirements. If the performance does not meet the target, the process parameters will be further optimized as needed to ensure that the performance of the final product is stable and excellent, and then mass production will be carried out to assist in the production of silver-based hydrogen-absorbing molecular sieves that meet specific application requirements, thereby providing an efficient solution for hydrogen absorption needs in industrial applications.

[0027] Step 5: Perform a performance test on the first silver-based hydrogen absorbing molecular sieve, obtain a first performance test result, and compare it with the performance range.

[0028] Specifically, in the preparation process of the silver-based hydrogen absorbing molecular sieve, after completing the ion exchange, the next key step is to perform a performance test on the first silver-based hydrogen absorbing molecular sieve to evaluate whether it has reached the expected performance standard. Performance testing refers to measuring and evaluating the various performance indicators of the prepared silver-based hydrogen absorbing molecular sieve through a series of experimental means. Common performance tests include but are not limited to hydrogen absorption capacity tests, thermal stability tests, mechanical strength tests, etc. The purpose of these tests is to determine whether the molecular sieve can exhibit the desired performance characteristics in practical applications. For example, in the hydrogen absorption capacity test, the adsorption efficiency can be evaluated by measuring the amount of hydrogen adsorbed by the molecular sieve at a specific temperature and pressure. The specific test can be carried out by professional and technical personnel in this field based on the existing technology, which will not be expanded here.

[0029] The first performance test result refers to the specific data obtained during the performance test. These data include the actual measured values ​​of various performance indicators. For example, the hydrogen absorption capacity indicates the amount of hydrogen that can be adsorbed per gram of material of the molecular sieve; thermal stability may be evaluated by measuring the structural changes of the molecular sieve under high temperature conditions. Then compare the first performance test result with the pre-set performance range. For example, the performance range requires the hydrogen absorption capacity to be between 10 and 15 mmol / g. If the actual hydrogen absorption capacity data obtained in the test is 12 mmol / g, it means that the molecular sieve has reached the performance standard; on the contrary, if the test result is only 8 mmol / g, it means that the hydrogen absorption performance of the molecular sieve does not meet the requirements and the process parameters need to be further adjusted.

[0030] Through this comparison, it is possible to quickly determine whether the silver-based hydrogen-absorbing molecular sieve produced under the current process conditions meets the design requirements. If the test results are within the performance range, it means that the current process parameter settings are effective and batch production can be carried out; if the results exceed the performance range, further analysis and adjustment of the process parameters are required to ensure that each batch of silver-based hydrogen-absorbing molecular sieves produced meets strict quality standards and meets the performance requirements in actual applications.

[0031] Step six: When the first performance test result exceeds the performance range, multiple process parameters in the first process parameter group are optimized to obtain a second process parameter group.

[0032] Specifically, when the first performance test result exceeds the pre-set performance range, it indicates that the current process parameter settings cannot meet the performance requirements of the target silver-based hydrogen absorbing molecular sieve. At this time, it is necessary to optimize the multiple process parameters in the first process parameter group to obtain the second process parameter group, so as to adjust the process conditions to ensure that the performance of the final product can meet the expected standards. Process parameter optimization refers to analyzing the relationship between the current process parameters and the performance results to determine which parameters need to be adjusted and how to adjust them to achieve the best results. In this process, the process parameters involved include exchange time, exchange temperature and stirring speed. These parameters influence each other, and the joint effect of the parameters on the performance of the final product needs to be comprehensively considered during optimization.

[0033] During the optimization process, different combinations of process parameters can be simulated by an equivalent ion exchanger, and these combinations can be tested to find the best parameter settings and generate a new combination of process parameters, namely the second process parameter group. This new parameter group should be closer to actual needs and be able to show better performance in subsequent production and testing. For example, through optimization, the final second process parameter group may include extending the exchange time to 4 hours, increasing the exchange temperature to 60°C, and reducing the stirring speed to 200RPM. These optimized parameters will be used in the next round of silver-based hydrogen-absorbing molecular sieve production to verify its effect.

[0034] Through this iterative optimization process, the setting range of process parameters can be gradually narrowed, so that the silver-based hydrogen-absorbing molecular sieve finally produced can accurately reach the set performance range, thereby ensuring the consistency and high quality of the product. This not only improves the reliability of the process, but also reduces the trial and error cost of production, making the production of silver-based hydrogen-absorbing molecular sieves more efficient and accurate.

[0035] Step seven: preparing a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determining whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range.

[0036] Specifically, after optimizing the production process of the silver-based hydrogen absorbing molecular sieve, the optimal second process parameter group was determined. Next, it is necessary to produce a small batch of samples under the conditions of the second process parameter group to prepare a second silver-based hydrogen absorbing molecular sieve and perform a performance test on it to verify whether it meets the expected performance standards. Similar to the performance test of the first silver-based hydrogen absorbing molecular sieve, the performance test here includes a comprehensive evaluation of the key performance indicators of the second silver-based hydrogen absorbing molecular sieve, which may include hydrogen absorption capacity, thermal stability, mechanical strength, etc. Through these tests, specific second performance test results can be obtained. For example, the test may show that the hydrogen absorption capacity of the second silver-based hydrogen absorbing molecular sieve under specific conditions is 12.5mmol / g, as well as its structural stability under high temperature conditions.

[0037] Compare the second performance test result with the previously set performance range. Only when the test result falls within this range can the product be considered qualified. For example, assuming that the performance range stipulates that the hydrogen absorption capacity must be between 10 and 15mmol / g, then if the second performance test result is 12.5mmol / g, it means that the performance of this molecular sieve meets the requirements; conversely, if the test result is lower than 10mmol / g or higher than 15mmol / g, it means that the performance is not up to standard. From this, it can be judged whether the second silver-based hydrogen absorption molecular sieve has reached the expected quality standard. If the second performance test result falls completely within the performance range, it means that the current second process parameter group is effective, and the produced molecular sieve has reached the expected performance standard and can enter the large-scale production stage. However, if the test result exceeds the performance range, it means that the process parameters need to be further adjusted and optimized, or the material and process design needs to be reviewed to ensure that the performance of the final product meets the requirements.

[0038] Step 8: Iterate until the performance test result falls within the performance range and obtain the target process parameter group.

[0039] Specifically, in the process of process optimization of silver-based hydrogen absorbing molecular sieve, if the second performance test result fails to fall within the preset performance range, it means that the current process parameters still need to be further optimized. At this time, it is necessary to enter the iterative execution stage, that is, repeat the above steps six and seven, and gradually approach the ideal process parameter combination through continuous adjustment and testing until the performance test results meet the requirements, and finally obtain the target process parameter group. Iterative execution is a feedback-based optimization strategy. After each process parameter adjustment, a new silver-based hydrogen absorbing molecular sieve will be prepared and its performance tested. If the test results fail to meet the expected standards (that is, they do not fall into the performance range), it is necessary to analyze the shortcomings of the current process parameters according to the test results, and then adjust the process parameters accordingly. In each round of iteration, the new process parameters will be used to prepare the next batch of silver-based hydrogen absorbing molecular sieves and perform performance tests again. This cycle will continue until the test results stably fall into the predetermined performance range. Through this iterative process of continuous adjustment and optimization, the process parameters will gradually stabilize, and the produced silver-based hydrogen absorbing molecular sieves will be closer and closer to the expected performance standards. As the number of iterations increases, the adjustment range will gradually decrease, and precise control will eventually be achieved.

[0040] When the final performance test results completely fall within and stabilize within the performance range, and all performance indicators meet the expected requirements, the corresponding process parameter combination is the target process parameter group. This parameter group will be used to guide subsequent large-scale production to ensure that each batch of silver-based hydrogen-absorbing molecular sieves can maintain high consistency and excellent performance. Through this iterative optimization process, not only the stability of product quality is ensured, but also a solid foundation is laid for large-scale industrial production, so that the production of silver-based hydrogen-absorbing molecular sieves has reached the best state in terms of technology and economy.

[0041] Further, step three of this application includes: A ternary search vector is constructed according to the molecular sieve carrier information, the silver salt solution information and the application requirements; the ternary search vector is input into the historical production database of the production management system, and based on the existing production records in the historical production database, the similarity between the ternary search vector and the ternary parameter group vector corresponding to each production record is calculated; the target production record with the highest similarity to the ternary search vector is obtained; and the corresponding ion exchange process parameter group in the target production record, including exchange time, exchange temperature and stirring speed, is extracted as the first process parameter group.

[0042] Specifically, a ternary search vector is first constructed and used for similarity search in the historical production database of the production management system. Simply put, it is to use the existing production data to guide the current process parameter settings, thereby improving the efficiency and accuracy of process optimization. The ternary search vector is a three-dimensional vector constructed based on the molecular sieve carrier information, silver salt solution information and application requirements. Each dimension corresponds to a key parameter, such as the pore size of the molecular sieve, the concentration of the silver salt, and the performance requirements of the target product.

[0043] Next, the ternary search vector is input into the historical production database of the production management system for retrieval. The historical production database stores a large number of past production records, including the specific process parameters used in each production process and their corresponding production results. By calculating the similarity between the ternary search vector and the ternary parameter group vector corresponding to each production record in the database, the production record closest to the current demand is identified. The higher the similarity, the more likely the process parameters in the production record are to be applicable to the current production task.

[0044] By searching in the database, multiple groups of target production records with extremely high similarity to the ternary search vector are searched, assuming a similarity of 98%. The target production record may correspond to multiple process schemes for the successful preparation of efficient hydrogen-absorbing silver-based molecular sieves in previous production, and the ion exchange process parameter group is extracted, including the key exchange time, exchange temperature and stirring speed, as the first process parameter group in the current process condition optimization process. By using the intelligent retrieval and similarity calculation methods of historical data, the blindness of process parameter configuration can be effectively reduced, production efficiency can be improved, and the final silver-based hydrogen-absorbing molecular sieve can be ensured to meet the expected performance requirements.

[0045] Furthermore, step six of this application includes: Construct an equivalent ion exchanger, and initialize the configuration of the equivalent ion exchanger based on the molecular sieve carrier information and the silver salt solution information; control the equivalent ion exchanger after initialization configuration to perform ion exchange simulation, optimize the exchange time, exchange temperature and stirring speed in the first process parameter group, and obtain multiple candidate process parameter groups; screen the multiple candidate process parameter groups to obtain a second process parameter group.

[0046] Specifically, an equivalent ion exchanger is a virtual or physical model device used to simulate the actual ion exchange process. The exchange process between silver ions and molecular sieve carriers can be reproduced in a computer environment, helping researchers to quickly evaluate the impact of different process parameter combinations on the performance of molecular sieves without large-scale production. The initialization configuration process includes inputting information such as the pore size, surface area, chemical composition of the molecular sieve, as well as parameters such as the concentration and pH value of the silver salt solution into the equivalent ion exchanger to ensure that the simulation results are highly consistent with the actual production situation. This can be implemented based on existing digital twin technology.

[0047] After completing the initialization configuration, the equivalent ion exchanger is then controlled to simulate the exchange time, exchange temperature and stirring speed in the first process parameter group to observe the effect of these parameter changes on the performance of the silver-based molecular sieve. The purpose of the simulation is to optimize these parameters to obtain multiple potential process parameter groups, referred to as multiple candidate process parameter groups. For example, assume that the exchange time in the current first process parameter group is 3.5 hours, the exchange temperature is 55°C, and the stirring speed is 250RPM. By simulating different combinations of process parameters in the equivalent ion exchanger, it can be found that extending the exchange time to 4 hours or increasing the exchange temperature to 60°C may increase the hydrogen absorption capacity of the silver-based molecular sieve. At the same time, appropriately adjusting the stirring speed can improve the uniform distribution of silver ions, thereby improving the consistency of the product. These different parameter combinations will generate corresponding candidate process parameter groups, for example, exchange time 4 hours, temperature 60°C, stirring speed 200RPM, etc.

[0048] Next, the generated multiple candidate process parameter groups are screened. This process mainly evaluates whether each parameter group can optimally meet the target application requirements without exceeding the performance range based on its performance in the simulation. The screening criteria may include factors such as the improvement in hydrogen absorption capacity, changes in production costs, and process stability, and finally determine the second process parameter group, that is, the optimal process parameter combination.

[0049] By using an equivalent ion exchanger for parameter optimization, not only can the process development be accelerated while reducing costs, but the accuracy of process parameter settings can also be significantly improved, providing reliable guidance for actual production, helping to ensure that the produced silver-based hydrogen-absorbing molecular sieves can stably meet the expected performance requirements, thereby improving the overall quality of the product.

[0050] Furthermore, the present application also includes the following steps: The first process parameter group is traversed to obtain the first process parameter, and the remaining two process parameters are fixed, and the first process parameter is optimized according to the performance range to obtain multiple single optimization parameter groups; the first process parameter group is traversed to obtain the first process parameter and fix it, and the remaining two process parameters are jointly optimized according to the performance range to obtain multiple double optimization parameter groups; all process parameters in the first process parameter group are jointly optimized according to the performance range to obtain a full optimization parameter group; the multiple single optimization parameter groups, multiple double optimization parameter groups and full optimization parameter group are summarized to obtain multiple process parameter groups to be selected.

[0051] Specifically, the specific steps of obtaining multiple process parameter groups to be selected include: first traversing the first process parameter group, and obtaining each first process parameter in turn, such as exchange time, exchange temperature and stirring speed. In this process, only one parameter is adjusted at a time, and the other two parameters are fixed. In this way, for each individual process parameter, its impact on the performance of the molecular sieve under different settings can be analyzed, and optimized according to the performance range. For example, under the condition of fixed exchange temperature and stirring speed, the exchange time is gradually adjusted, which may be increased from 3 hours to 4 hours, and then to 5 hours, to observe the changing trend of hydrogen absorption capacity. Through this single parameter optimization, multiple single optimization parameter groups are obtained, each of which represents the optimal combination of the single parameter under different settings.

[0052] Next, continue to traverse the first process parameter group. This time, while fixing one parameter, jointly optimize the remaining two parameters. This process is more complicated than single parameter optimization because changes in two parameters may produce synergistic effects, which significantly affect the final performance. For example, when the exchange time is fixed, while optimizing the exchange temperature and stirring speed, it may be found that certain combinations of temperature and speed can significantly improve the hydrogen absorption performance of silver-based molecular sieves. Through this joint optimization, multiple dual-optimization parameter groups can be obtained, each of which reflects the best combination after the coordinated optimization of two process parameters.

[0053] Finally, a global joint optimization is performed, that is, all parameters in the first process parameter group are optimized simultaneously. This process takes into account the interaction between all parameters, and finds the optimal parameter combination under the comprehensive consideration of various performance indicators to obtain a fully optimized parameter group, that is, the optimal process parameter combination.

[0054] Finally, the single optimization parameter group, double optimization parameter group and full optimization parameter group are summarized to generate multiple candidate process parameter groups. These candidate parameter groups contain the optimal combinations under different optimization methods, providing a rich selection space for the next step of screening.

[0055] For example, suppose that in single parameter optimization, the best exchange time is 4 hours, the best exchange temperature is 60°C, and the best stirring speed is 250RPM. In dual parameter optimization, it may be found that the combination of an exchange time of 4 hours and a stirring speed of 200RPM performs best at a temperature of 65°C. In global optimization, the best overall parameter combination may be an exchange time of 5 hours, an exchange temperature of 55°C, and a stirring speed of 300RPM. These different parameter combinations will be used as candidate process parameter groups for further screening and experimental verification. Ensuring that the final selected process parameter group can maximize the performance of the silver-based hydrogen-absorbing molecular sieve and adapt to specific application requirements not only improves the efficiency and accuracy of the production process, but also provides strong data support for actual production.

[0056] Furthermore, the present application also includes the following steps: Acquire multiple process parameter adjustment intervals, namely, exchange time interval, exchange temperature interval and stirring speed interval; perform adjustable analysis on multiple candidate process parameter groups based on the exchange time interval, exchange temperature interval and stirring speed interval, and obtain multiple feasible process parameter groups; perform execution cost analysis on the multiple feasible process parameter groups, screen the multiple feasible process parameter groups based on the execution cost analysis results, and obtain a second process parameter group.

[0057] Specifically, multiple process parameter adjustment intervals include exchange time intervals, exchange temperature intervals** and stirring speed intervals. These intervals define the variable range of each parameter during the optimization process. For example, the exchange time interval may be set to 3 to 6 hours, the exchange temperature interval to 50°C to 70°C, and the stirring speed interval to 200RPM to 300RPM. These intervals are determined based on historical experience to ensure that parameter adjustments are made within a reasonable and effective range without causing problems such as excessive consumption or quality degradation.

[0058] Next, based on multiple process parameter adjustment intervals, the previously generated multiple candidate process parameter groups are subjected to an adjustable analysis, and the goal of the adjustable analysis is to adjust each candidate process parameter group within these adjustment intervals. For example, for a candidate process parameter group, if its exchange time is set to 4 hours, then it can be adjusted within the interval of 3 to 6 hours, thereby obtaining multiple feasible process parameter groups.

[0059] Then, an execution cost analysis is performed on multiple feasible process parameter groups. The execution cost analysis aims to evaluate the cost-effectiveness of each parameter group in actual production, including material consumption, energy consumption, equipment operating time and maintenance costs. For example, if a parameter group operates at a lower temperature, although it can save energy costs, it may extend the reaction time and increase the production cycle, thereby increasing the overall cost; conversely, a higher stirring speed may increase equipment wear and lead to higher maintenance costs. By comprehensively considering these factors, the execution cost of each feasible process parameter group can be calculated.

[0060] Finally, based on the results of the cost analysis, multiple feasible process parameter groups are screened, and those combinations with the best cost-effectiveness are selected as the second process parameter group. For example, suppose there are two feasible process parameter groups, one group performs well under the conditions of exchange time 4.5 hours, temperature 60°C, and stirring speed 250RPM, and the cost is low; the other group performs well under the conditions of exchange time 5 hours, temperature 65°C, and stirring speed 240RPM, but the cost is high. Through cost analysis, the first group with lower cost may be preferred as the final second process parameter group. The final second process parameter group can not only meet the performance requirements, but also achieve the best balance in cost-effectiveness, thereby providing reliable parameter settings for large-scale production, ensuring the economy and sustainability of the silver-based hydrogen absorbing molecular sieve production process, and improving the overall efficiency of process optimization.

[0061] Furthermore, the present application also includes the following steps: Interact with the production management system to obtain the batch production volume of the target silver-based hydrogen absorbing molecular sieve; obtain the execution costs of multiple feasible process parameter groups according to the batch production volume, and sort the execution costs from large to small to obtain the execution cost analysis result; extract the feasible process parameter group with the smallest execution cost in the execution cost analysis result as the second process parameter group.

[0062] Specifically, by interacting with the production management system, the batch production volume of the target silver-based hydrogen-absorbing molecular sieve can be accurately obtained. Batch production volume refers to the number of molecular sieves produced in a certain production cycle. This data is crucial because it directly affects the consumption of raw materials, the use time of equipment and the overall production cost during the production process. For example, assuming that the batch production volume provided by the production management system is 1,000 kilograms, all cost calculations will be based on this production volume.

[0063] Next, the execution costs of the multiple feasible process parameter groups previously screened are calculated based on the batch production volume. The execution costs include raw material costs, energy costs, equipment operating costs, labor costs, etc. All costs are weighted to obtain the execution costs of multiple feasible process parameter groups. The execution cost of each parameter group in actual operation may be different, depending on the process settings of each parameter group. For example, one process parameter group may require a longer exchange time, thereby increasing energy consumption and equipment operating time, resulting in a higher execution cost; another parameter group may use a lower temperature, which saves energy, but may require more raw materials to achieve the same performance effect.

[0064] Then, the execution costs of all feasible process parameter groups are sorted from large to small to generate the execution cost analysis results. The execution cost analysis results clearly show the specific cost of each feasible process parameter group under the current batch production volume. For example, if the calculation results show that the execution cost of a certain process parameter group is 100 yuan per kilogram, and the execution cost of another group is 120 yuan per kilogram, then the former is obviously more economical.

[0065] Finally, from the execution cost analysis results, the feasible process parameter group with the lowest execution cost is extracted as the final second process parameter group and used in the subsequent production plan to ensure that the production process can be completed at the lowest cost while meeting the performance requirements, thereby optimizing the production cost and improving the overall production efficiency.

[0066] Furthermore, the present application also includes the following steps: A parameter group monitor is set to monitor in real time the execution of the target process parameter group in the process of producing the target silver-based hydrogen absorbing molecular sieve; deviation thresholds of multiple process parameters are pre-set; when the parameter group monitor detects that the deviation between the actual value of any process parameter and the corresponding set value in the target process parameter group exceeds the preset deviation threshold, an abnormal process condition reminder is issued.

[0067] Specifically, in the production process of silver-based hydrogen-absorbing molecular sieves, in order to ensure that the process conditions are always kept in the optimal state and the quality of each batch of products is consistent, it is necessary to set up a parameter group monitor to monitor the execution of the target process parameter group in the production process in real time. The parameter group monitor is a monitoring tool integrated in the production system, which can collect and analyze the actual operating values ​​of each key process parameter in the production process in real time. The target process parameter group usually includes key parameters such as exchange time, exchange temperature, and stirring speed. By monitoring these parameters, it can be ensured that the production conditions are strictly implemented in accordance with the optimized target process parameter group.

[0068] When setting up the parameter group monitor, the deviation thresholds of multiple process parameters need to be pre-set. The deviation threshold refers to the allowable error range between the actual value of each process parameter and its set value. For example, assuming that the target exchange time is set to 4 hours, a deviation threshold of ±5 minutes can be set; the exchange temperature is set to 60°C, the deviation threshold may be ±1°C; the stirring speed is set to 250RPM, and the deviation threshold is ±10RPM. These thresholds ensure that during the production process, slight fluctuations will not affect product quality, but once the threshold is exceeded, an alarm will be issued in time.

[0069] During the real-time monitoring process, the parameter group monitor will continuously compare the actual process parameter values ​​with the set values ​​in the target process parameter group. Once it is detected that the deviation between the actual value and the set value of any process parameter exceeds the preset deviation threshold, the abnormal process condition reminder is immediately triggered. This reminder can be in many forms, such as displaying an alarm message on the monitoring screen in the control room, sending text messages or email notifications to operators, and even automatically adjusting production equipment to correct deviations. Through this real-time monitoring and abnormal reminder mechanism, any abnormal situation in the production process can be discovered and handled in a timely manner, ensuring that the process conditions are always maintained in the optimal state, improving the safety and reliability of production, and effectively reducing the defective rate, ensuring that each batch of silver-based hydrogen absorbing molecular sieves meets the expected performance standards, thereby improving production efficiency and product quality stability.

[0070] In summary, the process condition optimization method of a silver-based hydrogen-absorbing molecular sieve provided in this application has the following technical effects: An interactive production management system is provided, which obtains application requirements of a target silver-based hydrogen absorbing molecular sieve, and sets a performance range of the target silver-based hydrogen absorbing molecular sieve based on the application requirements; obtains initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information; configures ion exchange process parameters according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtains a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed; under the first process parameter group, immerses the molecular sieve carrier into a silver salt solution for ion exchange to prepare a first silver-based hydrogen absorbing molecular sieve; performs a performance test on the first silver-based hydrogen absorbing molecular sieve, obtains a first performance test result, and compares it with the performance range; when the first performance test result exceeds the performance range, optimizes multiple process parameters in the first process parameter group to obtain a second process parameter group; prepares a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determines whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; iteratively executes until the performance test result falls within the performance range, and obtains a target process parameter group. Combining historical production data and process simulation technology, we systematically optimize the key process parameters in the ion exchange process to ensure that the final prepared silver-based hydrogen-absorbing molecular sieve reaches the best performance. Through iterative optimization, we can achieve the technical effect of significantly reducing production costs and improving product quality.

[0071] Example 2: Based on the process condition optimization method of a silver-based hydrogen absorbing molecular sieve in the above-mentioned Example 1, the present application also provides a process condition optimization device for a silver-based hydrogen absorbing molecular sieve, please refer to the attached Figure 2 The process condition optimization device of the silver-based hydrogen absorbing molecular sieve comprises: The performance interval setting module 11 is used to interact with the production management system, obtain the application requirements of the target silver-based hydrogen absorbing molecular sieve, and set the performance interval of the target silver-based hydrogen absorbing molecular sieve based on the application requirements.

[0072] The initial raw material information acquisition module 12 is used to acquire initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information.

[0073] The first process parameter group configuration module 13 is used to configure the process parameters of ion exchange according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtain the first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed.

[0074] The ion exchange module 14 is used to immerse the molecular sieve carrier into a silver salt solution to perform ion exchange under the first process parameter group to prepare a first silver-based hydrogen-absorbing molecular sieve.

[0075] The first performance testing module 15 is used to perform a performance test on the first silver-based hydrogen absorbing molecular sieve, obtain a first performance test result, and compare it with the performance range.

[0076] The second process parameter group acquisition module 16 is used to optimize the multiple process parameters in the first process parameter group to obtain a second process parameter group when the first performance test result exceeds the performance range.

[0077] The second performance testing module 17 is used to prepare a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determine whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range.

[0078] The target process parameter group acquisition module 18 is used for iterative execution until the performance test result falls within the performance interval, thereby acquiring the target process parameter group.

[0079] Furthermore, the first process parameter group configuration module 13 in the process condition optimization device for the silver-based hydrogen absorbing molecular sieve is also used for: A ternary search vector is constructed according to the molecular sieve carrier information, the silver salt solution information and the application requirements; the ternary search vector is input into the historical production database of the production management system, and based on the existing production records in the historical production database, the similarity between the ternary search vector and the ternary parameter group vector corresponding to each production record is calculated; the target production record with the highest similarity to the ternary search vector is obtained; and the corresponding ion exchange process parameter group in the target production record, including exchange time, exchange temperature and stirring speed, is extracted as the first process parameter group.

[0080] Furthermore, the second process parameter group acquisition module 16 in the process condition optimization device for silver-based hydrogen absorbing molecular sieve is also used for: Construct an equivalent ion exchanger, and initialize the configuration of the equivalent ion exchanger based on the molecular sieve carrier information and the silver salt solution information; control the equivalent ion exchanger after initialization configuration to perform ion exchange simulation, optimize the exchange time, exchange temperature and stirring speed in the first process parameter group, and obtain multiple candidate process parameter groups; screen the multiple candidate process parameter groups to obtain a second process parameter group.

[0081] Furthermore, the second process parameter group acquisition module 16 in the process condition optimization device for silver-based hydrogen absorbing molecular sieve is also used for: The first process parameter group is traversed to obtain the first process parameter, and the remaining two process parameters are fixed, and the first process parameter is optimized according to the performance range to obtain multiple single optimization parameter groups; the first process parameter group is traversed to obtain the first process parameter and fix it, and the remaining two process parameters are jointly optimized according to the performance range to obtain multiple double optimization parameter groups; all process parameters in the first process parameter group are jointly optimized according to the performance range to obtain a full optimization parameter group; the multiple single optimization parameter groups, multiple double optimization parameter groups and full optimization parameter group are summarized to obtain multiple process parameter groups to be selected.

[0082] Furthermore, the second process parameter group acquisition module 16 in the process condition optimization device for silver-based hydrogen absorbing molecular sieve is also used for: Acquire multiple process parameter adjustment intervals, namely, exchange time interval, exchange temperature interval and stirring speed interval; perform adjustable analysis on multiple candidate process parameter groups based on the exchange time interval, exchange temperature interval and stirring speed interval, and obtain multiple feasible process parameter groups; perform execution cost analysis on the multiple feasible process parameter groups, screen the multiple feasible process parameter groups based on the execution cost analysis results, and obtain a second process parameter group.

[0083] Furthermore, the second process parameter group acquisition module 16 in the process condition optimization device for silver-based hydrogen absorbing molecular sieve is also used for: Interact with the production management system to obtain the batch production volume of the target silver-based hydrogen absorbing molecular sieve; obtain the execution costs of multiple feasible process parameter groups according to the batch production volume, and sort the execution costs from large to small to obtain the execution cost analysis result; extract the feasible process parameter group with the smallest execution cost in the execution cost analysis result as the second process parameter group.

[0084] Furthermore, the process condition optimization device for the silver-based hydrogen absorbing molecular sieve further includes a process condition abnormality reminder module, and the process condition abnormality reminder module is used to: A parameter group monitor is set to monitor in real time the execution of the target process parameter group in the process of producing the target silver-based hydrogen absorbing molecular sieve; deviation thresholds of multiple process parameters are pre-set; when the parameter group monitor detects that the deviation between the actual value of any process parameter and the corresponding set value in the target process parameter group exceeds the preset deviation threshold, an abnormal process condition reminder is issued.

[0085] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The process condition optimization method and specific examples of a silver-based hydrogen absorbing molecular sieve in Example 1 are also applicable to a process condition optimization device of a silver-based hydrogen absorbing molecular sieve in this embodiment. Through the detailed description of the process condition optimization method of a silver-based hydrogen absorbing molecular sieve, those skilled in the art can clearly know the process condition optimization device of a silver-based hydrogen absorbing molecular sieve in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0086] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0087] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing process conditions of a silver-based hydrogen-absorbing molecular sieve, characterized in that: include: Interactive production management system, obtain application requirements of target silver-based hydrogen absorbing molecular sieve, and set performance range of target silver-based hydrogen absorbing molecular sieve based on the application requirements; Acquiring initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information; According to the molecular sieve carrier information, the silver salt solution information and the application requirements, the process parameters of the ion exchange are configured to obtain a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed; Under the first process parameter group, the molecular sieve carrier is immersed in a silver salt solution for ion exchange to prepare a first silver-based hydrogen-absorbing molecular sieve; Performing a performance test on the first silver-based hydrogen absorbing molecular sieve, obtaining a first performance test result, and comparing the result with the performance range; When the first performance test result exceeds the performance range, optimizing the multiple process parameters in the first process parameter group to obtain a second process parameter group; preparing a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determining whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; The process is iterated until the performance test result falls within the performance range, and the target process parameter group is obtained.

2. The process condition optimization method of a silver-based hydrogen absorbing molecular sieve according to claim 1, characterized in that: According to the molecular sieve carrier information, the silver salt solution information and the application requirements, the process conditions of the ion exchange are configured to obtain a first process parameter group, including: Constructing a ternary search vector according to the molecular sieve carrier information, the silver salt solution information and the application requirements; Inputting the ternary search vector into a historical production database of a production management system, and calculating the similarity between the ternary search vector and the ternary parameter group vector corresponding to each production record based on the existing production records in the historical production database; Obtaining the target production record with the highest similarity to the ternary search vector; The corresponding ion exchange process parameter group in the target production record is extracted, including exchange time, exchange temperature and stirring speed, as the first process parameter group.

3. The process condition optimization method of a silver-based hydrogen absorbing molecular sieve according to claim 1, characterized in that: Optimizing the first process parameter group to obtain a second process parameter group includes: Constructing an equivalent ion exchanger, and initializing and configuring the equivalent ion exchanger based on the molecular sieve carrier information and the silver salt solution information; Controlling the equivalent ion exchanger after initialization configuration to perform ion exchange simulation, optimizing the exchange time, exchange temperature and stirring speed in the first process parameter group, and obtaining a plurality of process parameter groups to be selected; A plurality of process parameter groups to be selected are screened to obtain a second process parameter group.

4. The process condition optimization method of a silver-based hydrogen absorbing molecular sieve according to claim 3, characterized in that: Obtain multiple candidate process parameter groups, including: Traversing the first process parameter group, obtaining a first process parameter, fixing the remaining two process parameters, optimizing the first process parameter according to the performance range, and obtaining a plurality of single optimization parameter groups; Traversing the first process parameter group, obtaining a first process parameter and fixing it, and jointly optimizing the remaining two process parameters according to the performance range to obtain multiple dual-optimization parameter groups; Jointly optimizing all process parameters in the first process parameter group according to the performance interval to obtain a fully optimized parameter group; The multiple single optimization parameter groups, the multiple double optimization parameter groups and the full optimization parameter group are summarized to obtain multiple process parameter groups to be selected.

5. The process condition optimization method of a silver-based hydrogen absorbing molecular sieve according to claim 3, characterized in that: Screening multiple candidate process parameter groups to obtain a second process parameter group includes: Obtain multiple process parameter adjustment intervals, namely, exchange time interval, exchange temperature interval and stirring speed interval; Based on the exchange time interval, the exchange temperature interval and the stirring speed interval, multiple candidate process parameter groups are respectively analyzed for adjustability to obtain multiple feasible process parameter groups; An execution cost analysis is performed on multiple feasible process parameter groups, and the multiple feasible process parameter groups are screened based on the execution cost analysis result to obtain a second process parameter group.

6. The process condition optimization method of a silver-based hydrogen absorbing molecular sieve according to claim 5, characterized in that: Performing an execution cost analysis on multiple feasible process parameter groups, screening the multiple feasible process parameter groups based on the execution cost analysis result, and obtaining a second process parameter group, including: Interacting with the production management system to obtain the batch production volume of the target silver-based hydrogen absorbing molecular sieve; Obtaining execution costs of a plurality of feasible process parameter groups respectively according to the batch production volume, and sorting the execution costs from large to small to obtain an execution cost analysis result; A feasible process parameter group with the minimum execution cost in the execution cost analysis result is extracted as the second process parameter group.

7. The process condition optimization method for a silver-based hydrogen absorbing molecular sieve according to claim 1, characterized in that: Also includes: Setting a parameter group monitor to monitor in real time the execution of the target process parameter group in the process of producing the target silver-based hydrogen absorbing molecular sieve; Pre-set deviation thresholds for multiple process parameters; When the parameter group monitor detects that the deviation between the actual value of any process parameter and the corresponding set value in the target process parameter group exceeds the preset deviation threshold, an abnormal process condition reminder is issued.

8. A process condition optimization device for a silver-based hydrogen absorbing molecular sieve, characterized in that: The steps for implementing the process condition optimization method of a silver-based hydrogen absorbing molecular sieve as described in any one of claims 1 to 7 include: A performance interval setting module, used for interacting with the production management system, obtaining application requirements of the target silver-based hydrogen absorbing molecular sieve, and setting the performance interval of the target silver-based hydrogen absorbing molecular sieve based on the application requirements; An initial raw material information acquisition module, used to acquire initial raw material information, wherein the initial raw material information includes molecular sieve carrier information and silver salt solution information; A first process parameter group configuration module, used to configure the process parameters of ion exchange according to the molecular sieve carrier information, the silver salt solution information and the application requirements, and obtain a first process parameter group, wherein the process parameters include exchange time, exchange temperature and stirring speed; An ion exchange module, used for immersing the molecular sieve carrier into a silver salt solution for ion exchange under the first process parameter group to prepare a first silver-based hydrogen-absorbing molecular sieve; A first performance testing module, used to perform a performance test on the first silver-based hydrogen absorbing molecular sieve, obtain a first performance test result, and compare it with the performance range; A second process parameter group acquisition module, configured to optimize a plurality of process parameters in the first process parameter group to acquire a second process parameter group when the first performance test result exceeds the performance range; A second performance testing module, used for preparing a second silver-based hydrogen absorbing molecular sieve under the second process parameter group, and determining whether a second performance test result of the second silver-based hydrogen absorbing molecular sieve exceeds the performance range; The target process parameter group acquisition module is used for iterative execution until the performance test result falls within the performance range, thereby acquiring the target process parameter group.

Images