Amino-graphene preparation system and method for automatically improving separation efficiency thereof

Abstract

The application discloses a method for automatically improving separation efficiency of an amino graphene preparation system, and relates to the technical field of graphene preparation.The preparation system comprises a graphene preparation detection module, a primary reaction detection module, a middle reaction detection module and a reminding adjustment display module; the graphene preparation detection module, the primary reaction detection module and the middle reaction detection module are used for detecting and controlling reaction temperature, time, reactant quality and other parameters in the process of preparing amino graphene, so as to obtain a first coefficient DYxs1, a second coefficient DYxs2 and a third coefficient DYxs3; the first coefficient DYxs1, the second coefficient DYxs2 and the third coefficient DYxs3 are compared and analyzed with threshold values after being corrected by the reminding adjustment template, an adjustment scheme is obtained, a control command is generated according to the adjustment scheme, accurate control and optimization are performed, and then efficient preparation and separation of amino graphene are realized, and the preparation and separation effect of the amino graphene is improved.

Description

Technical Field

[0001] This invention relates to the field of graphene preparation technology, specifically to a method for automatically improving the separation efficiency of an aminated graphene preparation system. Background Technology

[0002] Aminated graphene possesses many excellent physical and chemical properties, and has broad application prospects in energy storage, electrochemical sensing, and biomedicine. However, its production and preparation face some challenges, such as low yield, long preparation cycle, and low separation efficiency. These problems restrict the application and further research of aminated graphene. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for automatically improving the separation efficiency of an aminated graphene preparation system. This method utilizes automated equipment to achieve precise control and optimization of the aminated graphene preparation process. By controlling parameters such as reaction temperature, time, and reactant mass, highly efficient preparation and separation of aminated graphene are achieved. This method not only improves the production efficiency and quality of aminated graphene but also holds promise for providing a more stable and reliable material foundation for applications and research in related fields.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for automatically improving the separation efficiency of an aminated graphene preparation system, wherein the aminated graphene preparation system includes a graphene preparation detection module, an initial reaction detection module, a mid-reaction detection module, and a reminder / adjustment display module; the method includes:

[0005] Step S1: The graphene preparation detection module creates a three-step thread for the experimental equipment in the preparation system to detect the voltage DY, duration SC1, and temperature WD1 of the graphene rod in the ionic liquid functionalization preparation process, and analyzes to obtain the first coefficient DYxs.

[0006] The display module is prompted to compare the first coefficient DYxs1 with the threshold, generate the first control command, and make real-time adjustments.

[0007] In step S2, the initial reaction detection module creates three threads to detect the mass ZL1 of the olefin compound reactant, the initial reaction temperature WD2, and the reaction time SC2 during the initiation of the graphene and olefin compound reaction, and analyzes and obtains the second coefficient DYxs2.

[0008] The display module is reminded to compare the second coefficient DYxs2 with the threshold, generate a second control command, and make real-time adjustments.

[0009] Step S3 involves monitoring the process of obtaining intermediate products by using the intermediate reaction monitoring module to detect the high chemical activity of maleic anhydride attached to the graphene sheet, which readily reacts with diamine. This process is completed by creating a 3-step thread, which detects the mass of the reactants (ZL2), the intermediate reaction (WD3), and the duration (SC3) data respectively, and analyzes to obtain the third coefficient (DYxs3).

[0010] The display module is adjusted by comparing the third coefficient DYxs3 with the threshold, generating a third control command and making real-time adjustments.

[0011] Step S4: The first control instruction, the second control instruction, and the third control instruction stored in S1-S3 are stored for analysis by reminding and adjusting the display module.

[0012] Preferably, the generation of the first control command in step S1 specifically includes: after obtaining the first coefficient DYxs1, correcting the voltage DY, duration SC1 and temperature data WD1, comparing the corrected voltage DY, duration SC1 and temperature data WD1 with the standard threshold to obtain the first difference, and specifically generating the first control command.

[0013] The generation of the second control command in step S2 specifically includes: after obtaining the second coefficient DYxs2, correcting the mass ZL1, the initial reaction temperature WD2, and the duration SC2 data, comparing the corrected mass ZL1, the initial reaction temperature WD2, and the duration SC2 data with the standard threshold to obtain the second difference, and specifically generating the second control command.

[0014] The generation of the third control command in step S3 specifically includes: after obtaining the third coefficient DYxs3, correcting the ZL2, WD3 of the intermediate reaction and the duration SC3 data, comparing the corrected ZL2, WD3 of the intermediate reaction and the duration SC3 data with the standard threshold to obtain the first difference, and specifically generating the third control command.

[0015] Preferably, step S1 specifically includes:

[0016] Two high-purity graphite rods are inserted parallel to each other into an aqueous solution containing ionic liquid. A voltage sensor is activated to obtain the voltage DY of the aqueous solution containing ionic liquid. After half an hour of voltage reaction, the anode graphite rod is corroded, while the cations in the ionic liquid are reduced at the cathode and form free radicals. These free radicals combine with electrons present in the graphene sheet, thus forming an ionic liquid-functionalized graphene sheet.

[0017] Set the voltage threshold to 10V-20V, and set the preset voltage range threshold to 90%-109%.

[0018] The voltage DY data is analyzed in the form of weighted percentages to obtain the voltage DY. When the voltage DY weight 1 < 90%, 100% ≥ voltage DY weight 2 ≤ 109%, and DY weight 3 > 109%, the voltage DY value is adjusted according to the weight values ​​1, 2, and 3. The method of adjusting the DY value is as follows: the device voltage regulator is used to increase the voltage. When the weight after adjustment is 2, it indicates that the adjustment is complete and the control command is 1. When the weight value is 3, the first control command is generated, the voltage output device is connected to the device voltage regulator to decrease the voltage, and when the weight after adjustment is 2 again, it indicates that the adjustment is complete and the voltage DY adjustment command is 1.

[0019] S12. After the ionic liquid-functionalized graphene sheet is formed, the black precipitate in the electrolytic cell needs to be washed with anhydrous ethanol. During the drying process after washing, a timer is used to measure the time to obtain the duration SC1.

[0020] Connect the DVI interface to the cloud server to obtain the drying time SC1 of the black precipitate, and compare it with the drying time threshold to obtain the weight value of the time SC1.

[0021] It can be obtained through the following formula:

[0022]

[0023] In the formula, Gz_sj(x1) represents the current drying time, Zc_sj(x2) represents the normal drying time threshold of the server, and Gz_yz represents the drying time factor. When the duration SC1 weight 1 < 89%, 89% ≥ SC1 weight 2 ≤ 123%, and SC1 weight 3 > 120%, the duration SC1 value is adjusted according to the weight values ​​1, 2, and 3. When the SC1 weight is 3, an SC1 adjustment command is generated and a 10-second alarm is issued.

[0024] S13. When the WD1 value in the temperature control system is higher than the standard threshold, a WD1 adjustment command is generated. The graphene refrigerant temperature control is connected to open the refrigerant valve, increase the refrigerant flow to lower the temperature, or the graphene auxiliary heater is connected to start the heating mode. The temperature adjustment is completed and the WD1 adjustment command is stored in the register.

[0025] Preferably, step S2 specifically includes the following steps:

[0026] S21. During the initial reaction adjustment process, a miniature weighing sensor acquires the mass data of the olefin compound reactant tray, connects to the DVI interface to acquire the mass ratio of graphene to olefin compound from the cloud server, compares the acquired mass data with the standard threshold mass ratio to obtain the ZL1 difference, and generates a mass ZL1 adjustment command based on the ZL1 difference.

[0027] S22. Obtain the Diels-Alder reaction of graphene and olefin compounds under inert gas protection, start the fiber optic sensor to obtain the reaction temperature of graphene and olefin compounds, compare the reaction temperature WD2 with the standard temperature threshold to obtain the WD2 difference, and generate the WD2 adjustment command based on the WD2 difference.

[0028] Preferably, step S3 specifically includes the following steps:

[0029] S31. Obtain the reaction adjustment method in the intermediate product, and obtain the mass ZL2, WD3 and duration SC3 of the maleic anhydride attached to the graphene sheet of the intermediate product which has high chemical activity and is easy to react with diamine during the process.

[0030] S32. The collection device collects the intermediate product into the tray, and the micro weighing sensor is activated to obtain the mass of the intermediate product in the tray and obtain the ZL2 value. The obtained mass ZL2 value is compared with the standard threshold mass ratio to obtain the ZL2 difference, and a mass ZL2 adjustment command is generated based on the ZL2 difference.

[0031] The fiber optic sensor is activated to acquire the reaction temperature WD3 between the amino compound and the intermediate product. The reaction temperature WD3 is compared with the standard temperature threshold to obtain the WD3 difference, and a WD3 adjustment command is generated based on the WD3 difference.

[0032] The reaction counter for amino compounds and intermediate products is started to obtain the duration SC3; the duration SC3 is compared with the standard temperature threshold to obtain the duration SC3 difference, and an SC3 adjustment command is generated based on the SC3 difference.

[0033] Preferably, in step S4, the first control command, the second control command, and the third control command are collected and sorted from high to low frequency to obtain the frequency values ​​of the first control command, the second control command, and the third control command. These frequency values ​​are then stored for later prediction.

[0034] Preferably, a big data collection and twin model construction step is performed before step S1 to collect specific parameters of the graphene preparation method in steps S1 to S3, including voltage, mass, duration and temperature values ​​in the graphene separation process. Based on the specific parameters collected by the big data module, corresponding standard thresholds are set.

[0035] Construct a twin model, set up several model branches, and input the data from the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 obtained in steps S1 to S3 into the corresponding model branches. Based on the corresponding model branches, use the Keras library to construct the twin model.

[0036] An aminated graphene preparation system, comprising a graphene preparation detection module, a preliminary reaction detection module, a intermediate reaction detection module, and a reminder and adjustment display module;

[0037] The graphene preparation and detection module is used to create a three-step thread for the experimental equipment in the graphene preparation system, and to detect the voltage DY, duration SC1 and temperature WD1 of the graphite rod in the ionic liquid functionalization preparation process, and to analyze and obtain the first coefficient DYxs.

[0038] The initial reaction detection module is used to create three threads during the initiation of the graphene and olefin compound reaction process, respectively detecting the mass ZL1 of the olefin compound reactant, the initial reaction temperature WD2, and the duration SC2, and analyzing to obtain the second coefficient DYxs2.

[0039] The intermediate reaction monitoring module is used to monitor the process from the completion of the process where maleic anhydride attached to the graphene sheet to obtain the intermediate product is highly chemically active and readily reacts with diamine. It creates a 3-step thread to detect the mass of the reactants ZL2, the intermediate reaction WD3, and the duration SC3 data respectively, and analyzes to obtain the third coefficient DYxs3.

[0040] The reminder adjustment display module is used to generate a first control command by comparing a first coefficient DYxs1 with a threshold, generate a second control command by comparing a second coefficient DYxs2 with a threshold, generate a third control command by comparing a third coefficient DYxs3 with a threshold, and perform real-time adjustments; and store the first control command, the second control command, and the third control command for analysis.

[0041] Preferably, the aminated graphene preparation system further includes a twin model construction module, a big data module, a computing module, and an alarm module;

[0042] The big data module is used to acquire big data on online graphene preparation and to obtain voltage, mass, duration and temperature values ​​during the graphene separation process as a reference.

[0043] The twin model is constructed by setting up several model branches, and the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3 are constructed according to the corresponding branches to build the twin digital model;

[0044] The calculation module is used to calculate the corresponding standard threshold, and to perform Pearson correlation coefficient analysis on the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 to analyze the correlation between each coefficient and the graphene preparation method, generate correlation coefficients, determine the data weight ratio within the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3, and control the adjustment command based on the data weight ratio;

[0045] The alarm module is used to issue a real-time alarm via an audible and visual alarm or a buzzer when the data of the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 exceed the standard threshold.

[0046] This invention provides an aminated graphene preparation system and a method for automatically improving separation efficiency. It offers the following advantages:

[0047] (1) The aminated graphene preparation system and its automatic separation efficiency improvement method promotes precise control and optimization, thereby achieving efficient preparation and separation of aminated graphene and improving the preparation and separation effect of aminated graphene.

[0048] (2) The amino graphene preparation system and its method for automatically improving separation efficiency, the workflow in step S1 includes voltage adjustment of graphene sheets, generation of graphene sheets functionalized with ionic liquid, drying of black precipitate and temperature control, these processes have applications in chemical synthesis, materials science or engineering.

[0049] In this context, using automated and digital methods to control and optimize these processes, by setting voltage thresholds and weights, can save energy while ensuring production quality and improve preparation and separation efficiency.

[0050] (3) The aminated graphene preparation system and its method for automatically improving separation efficiency introduce more monitoring and control steps in the S2 step. These steps involve real-time adjustment of the mass of the compound and the reaction temperature. The use of micro weighing sensors and fiber optic sensors allows for precise measurement and adjustment of mass and temperature, which can improve the accuracy of the chemical reaction and further improve the quality of the final product. The Diels-Alder reaction is carried out at a specific temperature. By monitoring and adjusting the reaction temperature, the reaction conditions can be optimized and the reaction efficiency can be improved. By connecting to the DVI interface to obtain data from the cloud server, remote operation can be conveniently carried out and the degree of automation of the production process can be further improved. By optimizing the mass ratio of graphene to olefin compounds, the utilization efficiency of raw materials can be improved and resource waste can be reduced.

[0051] (4) The amino graphene preparation system and its method for automatically improving separation efficiency can optimize the production process and improve production efficiency by monitoring the reaction time of the amino compound and the intermediate product in step S3 and adjusting the reaction based on the time difference; by optimizing the control of quality, temperature and time, the waste of raw materials and energy can be reduced, thereby saving production costs; real-time monitoring and adjustment of the production process can reduce errors and delays in the production process, thereby improving production efficiency; by storing the frequency values, a data model can be established to predict the trend of the future production process, thereby adjusting the production strategy in advance when necessary. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of the amino-based graphene preparation system of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] Aminated graphene possesses many excellent physical and chemical properties, and has broad application prospects in energy storage, electrochemical sensing, and biomedicine. However, its production and preparation face some challenges, such as low yield, long preparation cycle, and low separation efficiency. These problems restrict the application and further research of aminated graphene.

[0056] Please see Figure 1 This invention provides a method for automatically improving the separation efficiency of an aminated graphene preparation system. The aminated graphene preparation system includes a graphene preparation detection module, a preliminary reaction detection module, a mid-reaction detection module, and a reminder / adjustment display module. The method includes:

[0057] Step S1: The graphene preparation detection module creates a three-step thread for the experimental equipment in the preparation system to detect the voltage DY, duration SC1, and temperature WD1 of the graphene rod in the ionic liquid functionalization preparation process, and analyzes to obtain the first coefficient DYxs.

[0058] The display module is prompted to compare the first coefficient DYxs1 with the threshold, generate the first control command, and make real-time adjustments.

[0059] In step S2, the initial reaction detection module creates three threads to detect the mass ZL1 of the olefin compound reactant, the initial reaction temperature WD2, and the reaction time SC2 during the initiation of the graphene and olefin compound reaction, and analyzes and obtains the second coefficient DYxs2.

[0060] The display module is reminded to compare the second coefficient DYxs2 with the threshold, generate a second control command, and make real-time adjustments.

[0061] Step S3 involves monitoring the process of obtaining intermediate products by using the intermediate reaction monitoring module to detect the high chemical activity of maleic anhydride attached to the graphene sheet, which readily reacts with diamine. This process is completed by creating a 3-step thread, which detects the mass of the reactants (ZL2), the intermediate reaction (WD3), and the duration (SC3) data respectively, and analyzes to obtain the third coefficient (DYxs3).

[0062] The display module is adjusted by comparing the third coefficient DYxs3 with the threshold, generating a third control command and making real-time adjustments.

[0063] Step S4: The first control instruction, the second control instruction, and the third control instruction stored in S1-S3 are stored for analysis by reminding and adjusting the display module.

[0064] The generation of the first control command in step S1 specifically includes: after obtaining the first coefficient DYxs1, correcting the voltage DY, duration SC1 and temperature data WD1, comparing the corrected voltage DY, duration SC1 and temperature data WD1 with the standard threshold to obtain the first difference, and specifically generating the first control command.

[0065] The generation of the second control command in step S2 specifically includes: after obtaining the second coefficient DYxs2, correcting the mass ZL1, the initial reaction temperature WD2, and the duration SC2 data, comparing the corrected mass ZL1, the initial reaction temperature WD2, and the duration SC2 data with the standard threshold to obtain the second difference, and specifically generating the second control command.

[0066] The generation of the third control command in step S3 specifically includes: after obtaining the third coefficient DYxs3, correcting the ZL2, WD3 of the intermediate reaction and the duration SC3 data, comparing the corrected ZL2, WD3 of the intermediate reaction and the duration SC3 data with the standard threshold to obtain the first difference, and specifically generating the third control command.

[0067] In this embodiment, the graphene preparation detection module, the initial reaction detection module, and the intermediate reaction detection module detect and control parameters such as reaction temperature, time, and reactant mass during the preparation of aminated graphene, thereby obtaining the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3. After correcting the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3 by reminding adjustment templates, they are compared and analyzed with thresholds to obtain adjustment schemes. Based on the adjustment schemes, control commands are generated for precise control and optimization, thereby achieving efficient preparation and separation of aminated graphene and improving the preparation and separation effect of aminated graphene.

[0068] Example 2

[0069] This embodiment is an explanation of embodiment 1. Specifically, the generation of the first control command in step S1 includes: after obtaining the first coefficient DYxs1, correcting the voltage DY, duration SC1 and temperature data WD1, comparing the corrected voltage DY, duration SC1 and temperature data WD1 with the standard threshold to obtain the first difference, and specifically generating the first control command.

[0070] The generation of the second control command in step S2 specifically includes: after obtaining the second coefficient DYxs2, correcting the mass ZL1, the initial reaction temperature WD2, and the duration SC2 data, comparing the corrected mass ZL1, the initial reaction temperature WD2, and the duration SC2 data with the standard threshold to obtain the second difference, and specifically generating the second control command.

[0071] The generation of the third control command in step S3 specifically includes: after obtaining the third coefficient DYxs3, correcting the ZL2, WD3 of the intermediate reaction, and SC3 duration data; comparing the corrected ZL2, WD3 of the intermediate reaction, and SC3 duration data with the standard threshold to obtain the first difference; and specifically generating the third control command.

[0072] Preferably, step S1 specifically includes:

[0073] Two high-purity graphite rods are inserted parallel to each other into an aqueous solution containing ionic liquid. A voltage sensor is activated to obtain the voltage DY of the aqueous solution containing ionic liquid. After half an hour of voltage reaction, the anode graphite rod is corroded, while the cations in the ionic liquid are reduced at the cathode and form free radicals. These free radicals combine with electrons present in the graphene sheet, thus forming an ionic liquid-functionalized graphene sheet.

[0074] Set the voltage threshold to 10V-20V, and set the preset voltage range threshold to 90%-109%.

[0075] The voltage DY data is analyzed in the form of weighted percentages to obtain the voltage DY. When the voltage DY weight 1 < 90%, 100% ≥ voltage DY weight 2 ≤ 109%, and DY weight 3 > 109%, the voltage DY value is adjusted according to the weight values ​​1, 2, and 3. The method of adjusting the DY value is as follows: the device voltage regulator is used to increase the voltage. When the weight after adjustment is 2, it indicates that the adjustment is complete and the control command is 1. When the weight value is 3, the first control command is generated, the voltage output device is connected to the device voltage regulator to decrease the voltage, and when the weight after adjustment is 2 again, it indicates that the adjustment is complete and the voltage DY adjustment command is 1.

[0076] S12. After the ionic liquid-functionalized graphene sheet is formed, the black precipitate in the electrolytic cell needs to be washed with anhydrous ethanol. During the drying process after washing, a timer is used to measure the time to obtain the duration SC1.

[0077] Connect the DVI interface to the cloud server to obtain the drying time SC1 of the black precipitate, and compare it with the drying time threshold to obtain the weight value of the time SC1.

[0078] It can be obtained through the following formula:

[0079]

[0080] In the formula, Gz_sj(x1) represents the current drying time, Zc_sj(x2) represents the normal drying time threshold of the server, and Gz_yz represents the drying time factor. When the duration SC1 weight 1 < 89%, 89% ≥ SC1 weight 2 ≤ 123%, and SC1 weight 3 > 120%, the duration SC1 value is adjusted according to the weight values ​​1, 2, and 3. When the SC1 weight is 3, an SC1 adjustment command is generated and a 10-second alarm is issued.

[0081] S13. When the WD1 value in the temperature control system is higher than the standard threshold, a WD1 adjustment command is generated. The graphene refrigerant temperature control is connected to open the refrigerant valve, increase the refrigerant flow to lower the temperature, or the graphene auxiliary heater is connected to start the heating mode. The temperature adjustment is completed and the WD1 adjustment command is stored in the register.

[0082] In this embodiment, the workflow in step S1 includes voltage adjustment of graphene sheets, generation of ionic liquid-functionalized graphene sheets, drying of the black precipitate, and temperature control. These processes have applications in chemical synthesis, materials science, and engineering. In this case, automated and digital methods are used to control and optimize these processes. By setting voltage thresholds and weights, and by real-time monitoring using voltage sensors, the voltage can be precisely controlled, thereby optimizing the graphene sheet functionalization process. Real-time monitoring and adjustment of the ionic liquid-functionalized graphene sheet generation process can improve the quality of the generated graphene sheets. Automated washing and drying of the black precipitate, along with timer-based acquisition of drying duration, optimizes the workflow and saves manpower. An automatic alarm can be issued when the SC1 weight exceeds the threshold, improving the safety of the working environment. Real-time temperature monitoring and adjustment can save energy and improve preparation and separation efficiency while ensuring production quality.

[0083] Example 3

[0084] This embodiment is an explanation of embodiment 2, specifically,

[0085] Step S2 specifically includes the following steps:

[0086] S21. During the initial reaction adjustment process, a miniature weighing sensor acquires the mass data of the olefin compound reactant tray, connects to the DVI interface to acquire the mass ratio of graphene to olefin compound from the cloud server, compares the acquired mass data with the standard threshold mass ratio to obtain the ZL1 difference, and generates a mass ZL1 adjustment command based on the ZL1 difference.

[0087] S22. Obtain the Diels-Alder reaction of graphene and olefin compounds under inert gas protection, start the fiber optic sensor to obtain the reaction temperature of graphene and olefin compounds, compare the reaction temperature WD2 with the standard temperature threshold to obtain the WD2 difference, and generate the WD2 adjustment command based on the WD2 difference.

[0088] The ZL1 quality adjustment command is controlled and adjusted via a quality control device, with a graphene to olefin compound mass ratio of 1:15 and an intermediate product to amino compound mass ratio of 1:12. The following examples illustrate this:

[0089] The current mass ratio of graphene to olefin compounds is 1:20.

[0090] The calculation will be performed here.

[0091] Sx_zl(x1) = 1:15 / mass ratio of graphene to olefin compound

[0092] Hq_zl(x2)=1∶20 / obtained the current mass ratio of graphene to olefin compounds

[0093] Zl_yz(y1) = 3.4% / mass factor of graphene and olefin compounds

[0094] Zl_yz(y1)=[Sx_zl(x1)-Hq_zl(x2)]*100%

[0095] S22. Obtain the Diels-Alder reaction of graphene and olefin compounds under inert gas protection, start the fiber optic sensor to obtain the reaction temperature of graphene and olefin compounds, compare the reaction temperature WD2 with the standard temperature threshold to obtain the WD2 difference, and generate the WD2 adjustment command based on the WD2 difference.

[0096] In this embodiment, more monitoring and control steps are introduced in step S2. These steps involve real-time adjustment of the compound's mass and reaction temperature. The use of miniature weighing sensors and fiber optic sensors allows for precise measurement and adjustment of mass and temperature, which can improve the accuracy of the chemical reaction and further improve the quality of the final product. The Diels-Alder reaction is carried out at a specific temperature. By monitoring and adjusting the reaction temperature, the reaction conditions can be optimized and the reaction efficiency improved. Data from the cloud server can be easily obtained through a DVI interface, enabling convenient remote operation and further improving the automation of the production process. By optimizing the mass ratio of graphene to olefin compounds, the utilization efficiency of raw materials can be improved and resource waste reduced.

[0097] Example 4

[0098] This embodiment is an explanation of embodiment 2, specifically...

[0099] Step S3 specifically includes the following steps:

[0100] S31. Obtain the reaction adjustment method in the intermediate product, and obtain the mass ZL2, WD3 and duration SC3 of the maleic anhydride attached to the graphene sheet of the intermediate product which has high chemical activity and is easy to react with diamine during the process.

[0101] S32. The collection device collects the intermediate product into the tray, and the micro weighing sensor is activated to obtain the mass of the intermediate product in the tray and obtain the ZL2 value. The obtained mass ZL2 value is compared with the standard threshold mass ratio to obtain the ZL2 difference, and a mass ZL2 adjustment command is generated based on the ZL2 difference.

[0102] The fiber optic sensor is activated to acquire the reaction temperature WD3 between the amino compound and the intermediate product. The reaction temperature WD3 is compared with the standard temperature threshold to obtain the WD3 difference, and a WD3 adjustment command is generated based on the WD3 difference.

[0103] The reaction counter for amino compounds and intermediate products is started to obtain the duration SC3; the duration SC3 is compared with the standard temperature threshold to obtain the duration SC3 difference, and an SC3 adjustment command is generated based on the SC3 difference.

[0104] In step S4, the first control command, the second control command, and the third control command are collected and sorted from high frequency to low frequency to obtain the frequency values ​​of the first control command, the second control command, and the third control command. The frequency values ​​are then stored for later prediction.

[0105] In this embodiment, by monitoring the reaction time between the amino compound and the intermediate product in step S3 and adjusting the reaction based on the time difference, the production process can be optimized and production efficiency improved. By optimizing the control of quality, temperature, and time, the waste of raw materials and energy can be reduced, thereby saving production costs. Real-time monitoring and adjustment of the production process can reduce errors and delays in the production process, thereby improving production efficiency. By storing the frequency values, a data model can be established to predict future production process trends, thereby adjusting the production strategy in advance when necessary.

[0106] Example 5

[0107] This embodiment is an explanation of Embodiment 1. Specifically, before step S1, a big data collection and twin model construction step is performed to collect specific parameters in the graphene preparation method of steps S1 to S3, including voltage, mass, duration and temperature values ​​in the graphene separation process. Based on the specific parameters collected by the big data module, corresponding standard thresholds are set.

[0108] Construct a twin model, set up several model branches, and input the data from the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 obtained in steps S1 to S3 into the corresponding model branches. Based on the corresponding model branches, use the Keras library to construct the twin model.

[0109] An aminated graphene preparation system, please refer to Figure 1 The aminated graphene preparation system includes a graphene preparation detection module, a preliminary reaction detection module, a intermediate reaction detection module, and a reminder and adjustment display module.

[0110] The graphene preparation and detection module is used to create a three-step thread for the experimental equipment in the graphene preparation system, and to detect the voltage DY, duration SC1 and temperature WD1 of the graphite rod in the ionic liquid functionalization preparation process, and to analyze and obtain the first coefficient DYxs.

[0111] The initial reaction detection module is used to create three threads during the initiation of the graphene and olefin compound reaction process, respectively detecting the mass ZL1 of the olefin compound reactant, the initial reaction temperature WD2, and the duration SC2, and analyzing to obtain the second coefficient DYxs2.

[0112] The intermediate reaction monitoring module is used to monitor the process from the completion of the process where maleic anhydride attached to the graphene sheet to obtain the intermediate product is highly chemically active and readily reacts with diamine. It creates a 3-step thread to detect the mass of the reactants ZL2, the intermediate reaction WD3, and the duration SC3 data respectively, and analyzes to obtain the third coefficient DYxs3.

[0113] The reminder adjustment display module is used to generate a first control command by comparing a first coefficient DYxs1 with a threshold, generate a second control command by comparing a second coefficient DYxs2 with a threshold, generate a third control command by comparing a third coefficient DYxs3 with a threshold, and perform real-time adjustments; and store the first control command, the second control command, and the third control command for analysis.

[0114] Preferably, the aminated graphene preparation system further includes a twin model construction module, a big data module, a computing module, and an alarm module;

[0115] The big data module is used to acquire big data on online graphene preparation and to obtain voltage, mass, duration and temperature values ​​during the graphene separation process as a reference.

[0116] The twin model is constructed by setting up several model branches, and the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3 are constructed according to the corresponding branches to build the twin digital model;

[0117] The calculation module is used to calculate the corresponding standard threshold, and to perform Pearson correlation coefficient analysis on the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 to analyze the correlation between each coefficient and the graphene preparation method, generate correlation coefficients, determine the data weight ratio within the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3, and control the adjustment command based on the data weight ratio;

[0118] The alarm module is used to issue a real-time alarm via an audible and visual alarm or a buzzer when the data of the first coefficient DYxs, the second coefficient DYxs2, and the third coefficient DYxs3 exceed the standard threshold.

[0119] In this embodiment, the calculation module analyzes the correlation between each coefficient and the graphene preparation method using the Pearson correlation coefficient. This will help identify the key factors affecting graphene production, thereby making the decision-making process clearer and more accurate.

[0120] The data weight ratios calculated by the computing module can enable better decisions in resource allocation and production scheduling, thereby improving production efficiency and reducing production costs.

[0121] The alarm module provides real-time alerts, ensuring that staff can promptly address any potential production issues, thereby preventing accidents and improving the safety of the production process.

[0122] The twin model module uses the Keras library to build models that can help predict future production trends and allow production strategies to be adjusted as needed, thereby improving production efficiency and product quality.

[0123] The analytical results from the computational and twin model modules can help provide more insights into the production process, thereby enhancing its transparency and enabling decision-makers to better understand the production process and the factors affecting production efficiency.

[0124] When the detection data exceeds the standard threshold, the alarm module can quickly issue an alarm, enabling staff to respond promptly and take necessary measures to avoid production interruption or serious losses.

[0125] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for automatically improving the separation efficiency of an amino-based graphene preparation system, characterized in that: The aminated graphene preparation system includes a graphene preparation detection module, an initial reaction detection module, a mid-reaction detection module, and an alert / adjustment display module. The system also includes a twin model construction module, a calculation module, and an alarm module. The twin model construction module is used to set several model branches and construct twin digital models according to the corresponding branches for the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3. The calculation module is used to calculate the corresponding standard thresholds and to perform Pearson correlation coefficient analysis on the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3 to determine the correlation between each coefficient and the graphene preparation method, generating correlation coefficients, judging the data weight ratios within the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3, and controlling adjustment commands based on the data weight ratios. The method includes: Step S1: The graphene preparation detection module creates a three-step thread for the experimental equipment in the preparation system to detect the voltage DY, duration SC1, and temperature WD1 of the graphene rod in the ionic liquid functionalization preparation process, and analyzes to obtain the first coefficient DYxs1. The prompt adjustment module compares the first coefficient DYxs1 with the threshold to generate the first control command. After obtaining the first coefficient DYxs1, the voltage DY, duration SC1 and temperature data WD1 are corrected. The corrected voltage DY, duration SC1 and temperature data WD1 are compared with the standard threshold to obtain the first difference. Specifically, the first control command is generated and real-time adjustment is performed. In step S2, the initial reaction detection module creates three threads to detect the mass ZL1 of the olefin compound reactant, the initial reaction temperature WD2, and the reaction time SC2 during the initiation of the graphene and olefin compound reaction, and analyzes and obtains the second coefficient DYxs2. The reminder adjustment module compares the second coefficient DYxs2 with the threshold to generate a second control command. After obtaining the second coefficient DYxs2, the mass ZL1, the initial reaction temperature WD2, and the duration SC2 data are corrected. The corrected mass ZL1, initial reaction temperature WD2, and duration SC2 data are compared with the standard threshold to obtain a second difference. Specifically, a second control command is generated and real-time adjustments are made. Step S3 involves monitoring the process of obtaining the intermediate product, graphene sheet, using a mid-reaction monitoring module. This process involves highly reactive maleic anhydride attached to the graphene sheet, readily reacting with diamine. A three-step thread is created to detect the reactant mass (ZL2), mid-reaction WD3, and reaction duration (SC3) data, analyzing and obtaining the third coefficient DYxs3. The adjustment and display module compares this third coefficient DYxs3 with a threshold value, generating a third control command. After obtaining the third coefficient DYxs3, the ZL2, mid-reaction WD3, and reaction duration (SC3) data are corrected. The corrected ZL2... The WD3 and SC3 data of the reaction are compared with the standard threshold to obtain the first difference, and a third control command is generated and adjusted in real time. Step S4: The first control instruction, the second control instruction, and the third control instruction stored in S1-S3 are stored for analysis by reminding and adjusting the display module.

2. The method for automatically improving the separation efficiency of an aminated graphene preparation system according to claim 1, characterized in that: In step S4, the first control command, the second control command, and the third control command are collected and sorted from high frequency to low frequency to obtain the frequency values ​​of the first control command, the second control command, and the third control command. The frequency values ​​are then stored for later prediction.

3. The method for automatically improving the separation efficiency of an aminated graphene preparation system according to claim 1, characterized in that: The alarm module in the aminated graphene preparation system is used to issue a real-time alarm via an audible and visual alarm or a buzzer when the data of the first coefficient DYxs1, the second coefficient DYxs2, and the third coefficient DYxs3 exceed the standard threshold.

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