An online reconstruction method for chaotic attractors in a stirred reactor

Abstract

The present invention discloses an on-line reconstruction method for chaotic attractors in a stirred reactor, comprising the following steps: 1) performing computational fluid dynamics coupled discrete particle simulation on the stirred reactor filled with materials, and collecting the motion data of streakline particles in the stirred reactor; 2) processing the motion data of streakline particles in the stirred reactor by using the phase space reconstruction technique to obtain the chaotic attractors in the stirred reactor; 3) processing the chaotic attractors by using a chaotic attractor prediction model to obtain the prediction results of the chaotic attractors in the stirred reactor in the future t time period. The present invention realizes the real-time prediction of chaotic attractors, and provides new technical means and theoretical support for the optimization and intelligent development of the chemical fluid mixing process.

Description

Technical Field

[0001] The present invention relates to the fields of chaotic mixing of stirred fluids and machine learning, and in particular to an online reconstruction method for a chaotic attractor in a stirred reactor. Background Art

[0002] During chemical fluid mixing, the complexity of the flow field structure directly impacts the efficiency and uniformity of material transfer within the reactor. Traditional methods typically rely on numerical simulations or experimental measurements to understand and predict fluid dynamic behavior, but these methods are limited in real-time performance and accuracy. Summary of the Invention

[0003] The object of the present invention is to provide a method for online reconstruction of a chaotic attractor in a stirred reactor, comprising the following steps:

[0004] 1) Perform computational fluid dynamics coupled discrete particle simulation on a stirred reactor filled with material and collect pulsed particle motion data within the stirred reactor;

[0005] 2) Using phase space reconstruction to process the pulse particle motion data in the stirred reactor, the chaotic attractor in the stirred reactor is obtained;

[0006] 3) The chaotic attractor prediction model is used to process the chaotic attractor and obtain the prediction results of the chaotic attractor in the stirred reactor in the future t period.

[0007] Furthermore, the flow characteristics of the material inside the stirred reactor are laminar flow or transition flow.

[0008] Furthermore, the stirring speed of the stirred reactor is 0 to 600 rpm.

[0009] Furthermore, the computational fluid dynamics coupled discrete particle simulation refers to using Fluent software to simulate and obtain the change of the position of the tracer particles in the stirred reactor over time.

[0010] Furthermore, the cross-sectional shape of the stirred reactor includes but is not limited to square, rectangular, circular, and elliptical.

[0011] Furthermore, the materials contained in the stirred reactor include high molecular polymers, soft materials, high-end grease, and glycerin.

[0012] Furthermore, the pulse line particle motion data refers to the spatial position of particles at different times at the same injection position in the stirred reactor.

[0013] For a cylindrical stirred reactor with a diameter of T=200 mm, the initial injection position includes but is not limited to (X, Y, Z)=(-60 mm, 0 mm, 60 mm).

[0014] Furthermore, the phase space reconstruction technique is used to observe the trajectory distribution of streakline particles moving in the X and Y directions.

[0015] Furthermore, the chaotic attractor prediction model is a trained long short-term memory network model. Model parameters: the number of layers of the input layer input_size = 1, the number of layers of the hidden layer hidden_size = 50, the number of layers of the hidden layer num_layers = 2, the number of layers of the output layer output_size = 1, the learning rate lr = 0.01, and the number of iterations num_epochs = 200.

[0016] The technical effect of the present invention is beyond doubt. The present invention proposes an online reconstruction method based on chaotic attractors. By calculating the data collected by computational fluid dynamics coupled with discrete particle simulation, the phase space reconstruction technique is used to generate chaotic attractors in the stirred reactor. Combining with the deep learning prediction algorithm, this method realizes the real-time prediction of chaotic attractors, providing a new technical means and theoretical support for the optimization and intelligent development of the chemical fluid mixing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the prediction of training data and the loss function.

[0018] Figure 2 is the prediction of test data.

[0019] Figure 3 is the chaotic attractor prediction diagram. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The present invention will be further described below in conjunction with embodiments, but it should not be understood that the above-mentioned subject scope of the present invention is limited to the following embodiments. Without departing from the above-mentioned technical idea of the present invention, various substitutions and changes made according to the common general knowledge and customary means in the art should be included within the protection scope of the present invention.

[0021] Embodiment 1:

[0022] See Figures 1 to 3 , an online reconstruction method for chaotic attractors in a stirred reactor, comprising the following steps:

[0023] 1) Perform computational fluid dynamics coupled with discrete particle simulation on the stirred reactor containing the material, and collect the motion data of streakline particles in the stirred reactor;

[0024] 2) Use phase space reconstruction to process the motion data of streakline particles in the stirred reactor to obtain chaotic attractors in the stirred reactor;

[0025] 3) Process the chaotic attractor using the chaotic attractor prediction model to obtain the prediction result of the chaotic attractor in the stirred reactor in the future t period.

[0026] The flow characteristics of the material inside the stirred reactor belong to laminar flow or transitional flow.

[0027] The stirring speed of the stirred reactor is 0 - 600 revolutions per minute.

[0028] The computational fluid dynamics coupled with discrete particle simulation refers to using Fluent software to simulate and obtain the change of the position of tracer particles in the stirred reactor over time. The cross-sectional shape of the stirred reactor includes but is not limited to square, rectangle, circle, and ellipse.

[0029] The materials contained in the stirred reactor include polymer, soft matter, high-end grease, and glycerol.

[0030] The streamline particle motion data refers to the spatial positions of particles at the same injection position in the stirred reactor at different times.

[0031] For a cylindrical stirred reactor with a diameter of T = 200 mm, the initial injection positions include but are not limited to (X, Y, Z) = (-60 mm, 0 mm, 60 mm).

[0032] The phase space reconstruction technique is used to observe the trajectory distribution of streamline particles in the X and Y directions.

[0033] The chaotic attractor prediction model is a trained long short-term memory network model. Model parameters: the number of layers of the input layer input_size = 1, the number of layers of the hidden layer hidden_size = 50, the number of layers of the hidden layer num_layers = 2, the number of layers of the output layer output_size = 1, the learning rate lr = 0.01, and the number of iterations num_epochs = 200.

[0034] Example 2:

[0035] An on-line reconstruction method for chaotic attractors in a stirred reactor, comprising the following steps:

[0036] 1) Perform computational fluid dynamics coupled with discrete particle simulation on the stirred reactor filled with materials, and collect the streamline particle motion data in the stirred reactor;

[0037] 2) Process the streamline particle motion data in the stirred reactor using the phase space reconstruction technique to obtain the chaotic attractor in the stirred reactor;

[0038] 3) Process the chaotic attractor using the chaotic attractor prediction model to obtain the prediction result of the chaotic attractor in the stirred reactor in the future t period.

[0039] Example 3:

[0040] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of Example 2. Further, the computational fluid dynamics coupled with discrete particle simulation means using Fluent software to simulate and obtain the change of the position of tracer particles in the stirred reactor over time. For a cylindrical stirred reactor with a diameter of T = 200 mm, the initial injection positions include, but are not limited to, (X, Y, Z) = (-60 mm, 0 mm, 60 mm).

[0041] Example 4:

[0042] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of any one of Examples 2-3. Further, the flow characteristics of the internal materials in the stirred reactor belong to laminar flow or transitional flow.

[0043] Example 5:

[0044] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of any one of Examples 2-4. Further, when the stirred reactor is stirring, the stirring speed is 0 to 600 revolutions per minute.

[0045] Example 6:

[0046] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of any one of Examples 2-5. Further, the cross-sectional shape of the stirred reactor includes, but is not limited to, square, rectangle, circle, and ellipse.

[0047] Example 7:

[0048] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of any one of Examples 2-6. Further, the materials contained in the stirred reactor include polymer, soft matter, high-end lubricating grease, and glycerol.

[0049] Example 8:

[0050] A method for on-line reconstruction of chaotic attractors in a stirred reactor, with the technical content being the same as that of any one of Examples 2-7. Further, the streakline particle motion data refers to the spatial positions of particles at different times at the same injection position in the stirred reactor. For a cylindrical stirred reactor with a diameter of T = 200 mm, the same injection positions include, but are not limited to, (X, Y, Z) = (-60 mm, 0 mm, 60 mm).

[0051] Example 9:

[0052] An online reconstruction method for chaotic attractors in a stirred reactor, the technical content being the same as any one of Embodiments 2-8. Further, the phase space reconstruction technology mainly refers to examining the trajectory distribution of the streakline particles in the X and Y directions.

[0053] Embodiment 10:

[0054] An online reconstruction method for chaotic attractors in a stirred reactor, the technical content being the same as any one of Embodiments 2-9. Further, the chaotic attractor prediction model is a trained long short-term memory network model. The input data is the spatial distribution data of the streakline particles from time 0 to t, and the output is the spatial distribution data of the streakline particles from time t to t + m.

[0055] Embodiment 11:

[0056] An online reconstruction method for chaotic attractors in a stirred reactor, the technical content being the same as any one of Embodiments 2-10. Further, the parameters of the long short-term memory network model are as follows: the number of layers in the input layer input_size = 1, the number of layers in the hidden layer hidden_size = 50, the number of layers in the hidden layer num_layers = 2, the number of layers in the output layer output_size = 1, the learning rate lr = 0.01, and the number of iterations num_epochs = 200.

[0057] Embodiment 12:

[0058] An online reconstruction method for chaotic attractors in a stirred reactor, the technical content being the same as any one of Embodiments 1-11. Further, an example of the particle motion data is shown in the following table:

[0059]

[0060]

[0061] [Note] The more data in the flow pattern data table, the better. In actual operation, at least 120,000 pieces are required.

[0062] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0063] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0064] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0066] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0067] 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 fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. An online reconstruction method for chaotic attractors in a stirred reactor, characterized in that It includes the following steps: 1) Conduct computational fluid dynamics coupled discrete particle simulation on the stirred reactor containing materials, and collect the motion data of streakline particles in the stirred reactor; 2) Process the motion data of streakline particles in the stirred reactor by using phase space reconstruction to obtain the chaotic attractor in the stirred reactor; 3) Process the chaotic attractor by using the chaotic attractor prediction model to obtain the prediction result of the chaotic attractor in the stirred reactor in the future t time period; The chaotic attractor prediction model is a trained long short-term memory network model; The streakline particle motion data refers to the spatial positions of particles at the same injection position in the stirred reactor at different times; The phase space reconstruction technology is used to observe the trajectory distribution of streakline particles moving in the X and Y directions.

2. The online reconstruction method of chaotic attractor in a stirring reactor according to claim 1, characterized in that The flow characteristics of the internal materials of the stirred reactor belong to laminar flow or transitional flow.

3. A method for online reconstruction of chaotic attractors in a stirred reactor according to claim 2, characterized in that, The stirring speed of the stirred reactor is 0 - 600 revolutions per minute.

4. A method for online reconstruction of chaotic attractors in a stirring reactor according to claim 1, characterized in that, The computational fluid dynamics coupled discrete particle simulation refers to using Fluent software to simulate and obtain the change of the position of tracer particles in the stirred reactor over time.

5. A method for online reconstruction of chaotic attractors in a stirred reactor according to claim 1, characterized in that, The cross-sectional shape of the stirred reactor includes square, rectangle, circle, and ellipse.

6. The online reconstruction method of chaotic attractors in a stirring reactor according to claim 1, characterized in that The materials contained in the stirred reactor include high molecular polymers, soft matter, high-end lubricating greases, and glycerol.

7. A method for online reconstruction of chaotic attractors in a stirring reactor according to claim 1, characterized in that, Model parameters: the number of layers of the input layer input_size = 1, the number of layers of the hidden layer hidden_size = 50, the number of layers of the hidden layer num_layers = 2, the number of layers of the output layer output_size = 1, the learning rate lr = 0.01, the number of iterations num_epochs = 200.

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