Method for dynamically optimizing sandwich extrusion process through multi-physics field coupling model

Abstract

The invention relates to a method for dynamically optimizing a sandwich extrusion process through a multi-physical field coupling model, which comprises the following steps: establishing a multi-field coupling model, and obtaining the influence of each parameter on plant meat through a collaborative experiment; combining a multi-field coupling model and a collaborative experiment to obtain an influence rule of each field parameter on the sandwich structure, and then building the rule into a process library; acquiring data of motor current, die head outlet pressure, screw rotating speed and fluid temperature in the extruder in real time through a sensor, and inputting the data into the multi-field coupling model; the process library selects an optimal process parameter combination according to the data of the plant meat formula and a self-adaptive optimization algorithm; in the extrusion process, the multi-field coupling model monitors technological parameters in real time and dynamically adjusts the technological parameters so as to ensure the optimal states of the temperature, the pressure and the shearing rate. By dynamically optimizing the process parameters, excessive dependence on human experience is avoided, so that the trial and error cost in the production process is reduced.

Description

Technical Field

[0001] This application relates to the field of sandwich production optimization technology, and in particular to a method for dynamically optimizing sandwich extrusion process through a multiphysics coupling model. Background Technology

[0002] Currently, plant-based meat, as a newly emerging and rapidly rising research focus in the food processing field, is leading a new trend in industry development. Numerous scholars both domestically and internationally, using mainstream plant-based proteins such as soy protein and wheat protein as a foundation, are deeply exploring the specific effects of extrusion technology on the physicochemical properties of these raw materials. These studies not only enrich the theoretical basis of plant protein processing technology but also promote the further development and optimization of plant-based meat as a sustainable food solution. However, a consumer survey revealed a significant consumer preference: most consumers prefer to buy meat products rather than their substitutes. The primary factor behind this phenomenon is the relative scarcity of vegetarian products on the market, their limited flavor options, and concerns about the nutritional risks of long-term vegetarianism, such as iron deficiency and amino acid imbalances, which deter many consumers. Despite the numerous challenges facing the plant-based meat market, optimizing key attributes such as flavor, texture, and appearance to more closely resemble real meat can significantly enhance its market appeal. Furthermore, blending plant-based meat with specific ingredients to innovatively develop new hybrid plant-based meat products can effectively attract new consumer groups. Compared to single-ingredient plant-based meat products, this type of mixed meat product exhibits more significant consumer preference in terms of appearance, aroma, and texture, thereby stimulating a stronger desire to purchase among consumers.

[0003] With the rapid development of the plant-based meat industry, extrusion technology has become one of the key technologies in manufacturing plant-based meat products. Traditional plant-based meat extrusion processes usually rely on manual experience to adjust process parameters, resulting in high trial-and-error costs during production. Furthermore, it is difficult to accurately control key factors such as temperature, pressure, and shear force during production, thereby affecting product quality, especially the stability of the sandwich structure.

[0004] Existing extrusion processes have the following problems: they rely on manual experience to adjust parameters such as temperature, pressure, and feeding speed, which are difficult to adjust in real time and can easily lead to fluctuations in product quality; they require a large number of experiments to optimize process parameters, resulting in long production debugging times and high costs; and they consider each physical field independently, lacking comprehensive multi-physics coupling modeling, which leads to insufficient understanding of the multiple factors affecting materials during the extrusion process.

[0005] Therefore, we propose a method for dynamically optimizing the sandwich extrusion process using a multiphysics coupling model.

[0006] Application content To address the shortcomings of existing production technologies, this applicant provides a method for dynamically optimizing sandwich extrusion processes using a multiphysics coupling model. By establishing a multiphysics coupling model combining CFD and finite element methods, the complex physical processes inside the extruder are fully reproduced in virtual space. Furthermore, a process library is established with a collaborative experimental structure, so process optimization is no longer based on intuition but on established data patterns.

[0007] The technical solution adopted in this application is as follows: A method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model includes the following steps: A multi-field coupling model was established, and mathematical models of the temperature field, pressure field, and shear rate field of the raw material in the extrusion process were established based on CFD and finite element analysis. The effects of various parameters on plant-based meat were obtained through collaborative experiments; By combining a multi-field coupling model and collaborative experiments, the influence of each field parameter on the sandwich structure was obtained, and then the law was built into a process library. The system collects real-time data on motor current, die outlet pressure, screw speed, and fluid temperature in the extruder using sensors, and inputs this data into a multi-field coupling model. Based on plant-based meat formulation data, the process library uses an adaptive optimization algorithm to select the optimal combination of process parameters. During the extrusion process, a multi-field coupling model monitors and dynamically adjusts process parameters in real time to ensure optimal temperature, pressure, and shear rate.

[0008] Its further features are: Temperature is collected by a temperature sensor, and real-time temperature acquisition and PID temperature control are achieved through a PLC main controller to maintain temperature stability.

[0009] The pressure of the extruder die head is collected in real time by a pressure transmitter. When the pressure exceeds the preset threshold, the PLC control unit slowly increases the power of the deionized water supply pump to dilute the material until the pressure returns to normal. When the pressure is lower than the preset threshold, the PLC control unit slowly decreases the power of the deionized water supply pump to concentrate the material until the pressure returns to normal.

[0010] By using pressure sensors, temperature sensors, and screw speed data, combined with the rheological properties and flow patterns of plant protein materials, the shear rate of the material at different locations is calculated. When the shear rate exceeds the preset range, the screw speed is adjusted: the screw speed is reduced when the shear rate is too high, and the screw speed is increased when the shear rate is too low.

[0011] A multiphysics coupling model simulates the flow field of plant protein material in an extruder, including: Temperature field: After the material enters the cooling zone, the surface temperature drops rapidly, while the center temperature is higher, exhibiting a V-shaped distribution; Shear rate field: The shear rate is highest in the screw section, decreases in the die section, and is greatest at the cooling section wall, decreasing towards the central axis; Pressure field: The pressure decreases in the cooling channel, starting from the initial position of 3.3 MPa.

[0012] After the plant protein meat was stably extruded, the extruded plant protein meat samples were collected and weighed every 15 seconds. Sample data were collected at 12 time points in each experiment. The effects of cooling temperature and feed rate on extrusion stability were analyzed through multiple sets of experiments.

[0013] Under the same feed amount, the extrusion stability of plant protein meat decreases as the cooling temperature increases; under the same cooling temperature, when the cooling temperature is greater than or equal to 60℃, the experimental group with a smaller feed amount has better extrusion stability.

[0014] The crust of the plant-based meat sandwich is made from a mixture of soy protein isolate, wheat gluten, and deionized water in a specific mass ratio, with the soy protein isolate to wheat gluten ratio being 7:3 and the powder to water ratio being 4:6.

[0015] The composition of soy protein isolate includes: crude protein 87.78±0.08%, crude fat 2.84±0.04%, crude fiber 3.35±0.16%, ash 0.85±0.05%, and moisture 4.71±0.07%; the composition of wheat gluten includes: crude protein 80.26±0.61%, crude fat 2.07±0.12%, crude fiber 2.47±0.29%, ash 0.89±0.03%, and moisture 4.68±0.14%.

[0016] The multiphysics coupling model was simulated using the CFD software Polyflow, based on the physical property parameters. These parameters included the rheological and physical properties of the plant protein raw material, as well as the physical properties of the screw material. The rheological parameters included zero-shear viscosity, limiting viscosity, relaxation time, non-Newtonian index, viscosity-temperature coefficient, and reference temperature. The physical property parameters included density, heat transfer coefficient, and specific heat capacity.

[0017] The beneficial effects of this application are as follows: This application features a compact and rational structure, and is easy to operate. By establishing a multiphysics coupled model combining CFD and finite element methods, it fully reproduces the complex physical processes inside the extruder in virtual space. This makes the previously invisible "black box" process transparent and visible, providing a powerful tool for understanding the process mechanism. Combined with collaborative experiments, parameters such as "cooling temperature" and "feed rate" are quantitatively correlated with "extrusion stability" and "extrusion swell," establishing a reliable process library. Process optimization is no longer based on intuition but on established data patterns.

[0018] In addition, this application also has the following advantages: (1) By adjusting the pressure and shear rate in real time, the interface between the crust and the filling is kept stable during co-extrusion, avoiding problems such as the filling breaking through the crust or uneven distribution, thus perfectly forming a "skin-wrapped filling" sandwich structure. Precise control of the shear rate is the key to forming meat-like fibers. This method ensures that the plant protein material undergoes optimal shearing in the screw section to form a good fibrous structure, while avoiding excessive shearing in the die section that could lead to structural damage.

[0019] (2) By predicting and dynamically controlling the cooling temperature through modeling, the residence time of the material in the viscous flow state is effectively shortened, prompting it to quickly transform into a highly elastic state, thereby significantly suppressing the extrusion swell phenomenon and ensuring the uniformity of product dimensions. A "nervous system" for the extrusion process is constructed using multiple sensors such as temperature, pressure, current, and rotation speed, realizing real-time acquisition of key parameters throughout the process. The PLC main controller acts as the "brain," executing preset PID and control logic.

[0020] (3) When developing new products or changing formulas, extensive virtual simulations can be performed in a multi-field coupling model to quickly screen out feasible process windows, greatly reducing the number of on-site trials and the scrap rate. Under optimal process parameters, the equipment can operate at higher speeds without sacrificing quality, increasing single-machine capacity. At the same time, a stable extrusion process reduces the defect rate and directly improves product yield. Stable pressure and temperature control avoids overload operation of the equipment and reduces energy consumption per unit product. Meanwhile, it reduces the impact of drastic pressure and temperature fluctuations on the equipment and extends the service life of the screw, barrel, and die.

[0021] (4) Not limited to specific soy protein formulations. By updating the physical property parameters in the multi-field coupling model and conducting new synergistic experiments, the system and process library can quickly adapt to different raw material systems such as pea protein and wheat protein, as well as formulations with different moisture contents and different fat contents. By simulating the flow behavior of different materials, the mold structure and process parameters can be pre-designed and optimized, achieving product innovations that are difficult to achieve with traditional methods. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the process of this application.

[0023] Figure 2 This is a flow field velocity field contour map of this application.

[0024] Figure 3 This is a flow field temperature field cloud map of this application.

[0025] Figure 4 This is a flow field shear rate field contour map of this application.

[0026] Figure 5 This is a flow field pressure field contour map of this application.

[0027] Figure 6 This is a collaborative experiment for this application.

[0028] Figure 7 This is a schematic diagram of the CFD finite element simulation of this application. Detailed Implementation

[0029] The specific embodiments of this application are described below with reference to the accompanying drawings.

[0030] A method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model includes the following steps: A multi-field coupling model was established, and mathematical models of the temperature field, pressure field, and shear rate field of the raw material in the extrusion process were established based on CFD and finite element analysis. The effects of various parameters on plant-based meat were obtained through collaborative experiments; By combining a multi-field coupling model and collaborative experiments, the influence of each field parameter on the sandwich structure was obtained, and then the law was built into a process library. The system collects real-time data on motor current, die outlet pressure, screw speed, and fluid temperature in the extruder using sensors, and inputs this data into a multi-field coupling model. Based on plant-based meat formulation data, the process library uses an adaptive optimization algorithm to select the optimal combination of process parameters. During the extrusion process, a multi-field coupling model monitors and dynamically adjusts process parameters in real time to ensure optimal temperature, pressure, and shear rate.

[0031] Temperature is collected by a temperature sensor, and real-time temperature acquisition and PID temperature control are achieved through PLC main control.

[0032] The pressure of the extruder die is collected in real time by a pressure transmitter. When the pressure is too high, the PLC slowly increases the power of the deionized water supply pump to dilute the material until the pressure returns to normal, at which point the increase in pump power stops. When the pressure is too low, the PLC slowly decreases the power of the deionized water supply pump to concentrate the material until the pressure returns to normal, at which point the decrease in pump power stops.

[0033] By using pressure and temperature sensors, combined with the screw speed of the extruder, and based on the known rheological properties and flow patterns of the material, the shear rate at different locations can be calculated. When the shear rate is too high, the screw speed is reduced; when the shear rate is too low, the screw speed is increased.

[0034] like Figure 2As shown in the velocity field cloud diagram, the plant protein material in the screw section of the extruder undergoes eddy current motion under the disturbance of the screw, at which point the plant protein material velocity is at its maximum. After entering the cooling section, the velocity of the plant protein material experiences a sharp decrease at the die head, a brief increase in velocity after entering the cooling die, and then a slowdown again. This is because the cross-sectional area from the die head area to the cooling channel gradually decreases, causing the material velocity to increase. Furthermore, as the temperature decreases and the viscosity increases, the material velocity gradually decreases. The velocity near the wall is lower than the velocity at the central axis due to the velocity difference caused by friction between the cooling channel wall and the fluid, as well as the viscosity gradient caused by the temperature gradient.

[0035] like Figure 3 As shown in the flow field temperature field cloud diagram: After the plant protein material fluid enters the cooling zone, it undergoes a heat conduction effect with the pipe wall, and the surface temperature of the plant protein material drops rapidly. However, due to the low thermal conductivity of the plant protein material, the surface temperature of the material is significantly lower than the center temperature, exhibiting a V-shaped distribution.

[0036] like Figure 4 As shown in the flow field shear rate cloud diagram: In the screw section, the periodic change in the screw thread clearance causes the material to be rapidly squeezed and released, causing the plant protein material fluid to undergo severe shearing action, thus generating a high shear rate; after entering the die head section, the plant protein material gradually changes from vortex motion to laminar flow motion, and the shear rate drops rapidly; in the cooling channel, the shear rate of the fluid is the largest near the wall and gradually decreases towards the central axis. This is because in the pipe channel, the wall surface generates huge flow resistance to the fluid flow, causing the fluid velocity to be distributed parabolically. This velocity distribution characteristic causes the velocity gradient to be the largest at the wall and gradually decrease towards the center.

[0037] like Figure 5 As shown in the flow field pressure cloud diagram: the initial position is 3.3 MPa, which is consistent with the actual production situation, and the pressure shows a decreasing trend in the cooling channel.

[0038] In summary, the established flow field model conforms to the normal flow law of fluid in the cooling channel, and can simulate the changing trends and distribution laws of various physical parameters of plant protein materials during the cooling and molding process, thus serving as a reference.

[0039] A collaborative experimental method was used. After the plant-based meat protein began to be stably extruded, the extruded plant-based meat protein was collected and weighed every 15 seconds. Data was collected at 12 time points for each experiment. Six sets of data were obtained as follows: Figure 6 As shown; A comparative analysis of the experimental results from groups 1, 2, and 3, as well as groups 4, 5, and 6, clearly shows that under the same feed rate, the extrusion stability of plant-based meat protein decreases with increasing cooling temperature. This indicates that within an appropriate temperature range, lowering the cooling temperature is beneficial for optimizing the extrusion stability of the product. Specifically, lowering the cooling temperature allows the plant-based protein material to rapidly transition to a highly elastic state, reducing its time in the viscous flow state, thus helping to maintain a stable extrusion speed. Conversely, increasing the cooling temperature prolongs the time the plant-based protein material remains in the viscous flow state, amplifying the interference from factors such as tube wall friction and pressure changes during extrusion, leading to decreased extrusion stability. Furthermore, excessively high cooling temperatures cause significant extrusion swelling of the plant-based meat protein, further affecting its extrusion stability.

[0040] A longitudinal comparative analysis of the experimental results from groups 1 and 4, groups 2 and 5, and groups 3 and 6 revealed that the feed rate has a smaller impact on extrusion stability compared to the cooling temperature. Specifically, when the cooling temperature is 45℃, the feed rate has a negligible effect on extrusion stability; when the cooling temperature is greater than or equal to 60℃, the experimental groups with smaller feed rates exhibit better stability. Analyzing the reasons for this phenomenon, increasing the feed rate during extrusion leads to increased pressure on the material within the extruder, thus affecting the material flow rate and extrusion stability. Especially at high feed rates, the pressure gradient within the flow channel may be uneven, ultimately resulting in fluctuations in extrusion stability.

[0041] In one embodiment, the plant-based meat sandwich pastry is made from a mixture of soy protein isolate, wheat gluten, and deionized water. The fillings for plant-based meat sandwiches are diced bamboo shoots and red bean paste. The soy protein isolate was purchased from Shandong Linyi Shansong Biological Products Co., Ltd., and its composition was as follows: crude protein 87.78±0.08%, crude fat 2.84±0.04%, crude fiber 3.35±0.16%, ash 0.85±0.05%, and moisture 4.71±0.07%. The gluten powder was purchased from Binzhou Zhongyu Food Co., Ltd. in Shandong Province. Its components are crude protein 80.26±0.61%, crude fat 2.07±0.12%, crude fiber 2.47±0.29%, ash 0.89±0.03%, and moisture 4.68±0.14%.

[0042] The bamboo shoot cubes were purchased from Zhangzhou Mingcheng Food Co., Ltd., and the ingredients are bamboo shoots, edible salt, and citric acid.

[0043] The red bean paste was purchased from Beijing Daoxiangcun Food Co., Ltd., and its ingredients are white sugar, water, red beans (25%), and vegetable oil.

[0044] Quality Score: Soy protein isolate: wheat gluten = 7:3; Powder (mixture of soy protein isolate and wheat gluten): water = 4:6.

[0045] Table 1 (Numerical Simulation Property Parameters)

[0046] Figure 7 The diagram shows a CFD finite element simulation. The CFD software used is Polyflow. Table 1 shows the parameters set in Polyflow. The simulation is performed based on the data in Table 1.

[0047] By establishing a multiphysics coupled model combining CFD and finite element methods, the complex physical processes inside the extruder were fully reproduced in virtual space. This made the previously invisible "black box" process transparent and visible, providing a powerful tool for understanding the process mechanism.

[0048] By combining collaborative experiments, parameters such as "cooling temperature" and "feed rate" were quantitatively correlated with "extrusion stability" and "extrusion swell," establishing a reliable process library. Process optimization is no longer based on intuition but on verifiable data patterns.

[0049] By adjusting the pressure and shear rate in real time, the interface between the crust and the filling is kept stable during the co-extrusion process, avoiding problems such as the filling breaking through the crust or uneven distribution, thus perfectly forming a "skin-wrapped filling" sandwich structure.

[0050] Precise control of the shear rate is key to forming meat-like fibers. This method ensures that plant protein materials undergo optimal shearing in the screw section to form a good fibrous structure, while avoiding excessive shearing that could damage the structure in the die section.

[0051] By predicting and dynamically controlling the cooling temperature using models, the residence time of the material in the viscous flow state is effectively shortened, prompting it to quickly transform into a highly elastic state. This significantly suppresses extrusion swell and ensures product dimensional uniformity. A "nervous system" for the extrusion process is constructed using multiple sensors, including those for temperature, pressure, current, and rotational speed, enabling real-time acquisition of key parameters throughout the process. The PLC controller acts as the "brain," executing preset PID control logic.

[0052] Reduce start-up and commissioning time and material waste: When developing new products or changing formulas, extensive virtual simulations can be performed in a multi-field coupling model to quickly identify feasible process windows, significantly reducing the number of on-site trials and scrap rates. Under optimal process parameters, the equipment can operate at higher speeds without sacrificing quality, increasing single-machine capacity. Simultaneously, a stable extrusion process reduces the defect rate, directly improving product yield. Stable pressure and temperature control prevents equipment overload operation, reducing energy consumption per unit product. Furthermore, it reduces the impact on the equipment caused by drastic pressure and temperature fluctuations, extending the service life of the screw, barrel, and die.

[0053] Not limited to specific soy protein formulations. By updating the physical property parameters in the multi-field coupling model and conducting new synergistic experiments, this system and process library can quickly adapt to different raw material systems such as pea protein and wheat protein, as well as formulations with different moisture contents and fat contents.

[0054] By simulating the flow behavior of different materials, mold structures and process parameters can be pre-designed and optimized, achieving product innovations that are difficult to achieve using traditional methods. Sensors on the extruder collect real-time data such as motor current, die outlet pressure, screw speed, and fluid temperature, and input this data into a multiphysics coupled model. CFD and finite element analysis models are used for real-time prediction and adjustment of parameters such as screw torque, heating coil power, and feeding speed. Based on data from different plant-based meat formulations, an adaptive optimization algorithm selects the optimal combination of process parameters. During extrusion, process parameters are monitored in real-time and dynamically adjusted to ensure optimal temperature, pressure, and shear rate. This enables precise control of process parameters, significantly improving the quality of plant-based meat fillings. Dynamic optimization of process parameters avoids over-reliance on human experience, thereby reducing trial-and-error costs in the production process.

[0055] The above description is an explanation of this application and not a limitation thereof. The scope of this application is defined by the claims. Within the scope of protection of this application, any form of modification may be made.

Claims

1. A method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model, characterized in that, Includes the following steps: A multi-field coupling model was established, and mathematical models of the temperature field, pressure field, and shear rate field of the raw material in the extrusion process were established based on CFD and finite element analysis. The effects of various parameters on plant-based meat were obtained through collaborative experiments; By combining a multi-field coupling model and collaborative experiments, the influence of each field parameter on the sandwich structure was obtained, and then the law was built into a process library. The system collects real-time data on motor current, die outlet pressure, screw speed, and fluid temperature in the extruder using sensors, and inputs this data into a multi-field coupling model. Based on plant-based meat formulation data, the process library uses an adaptive optimization algorithm to select the optimal combination of process parameters. During the extrusion process, a multi-field coupling model monitors and dynamically adjusts process parameters in real time to ensure optimal temperature, pressure, and shear rate.

2. The method for dynamically optimizing sandwich extrusion process using a multiphysics coupling model as described in claim 1, characterized in that: Temperature is collected by a temperature sensor, and real-time temperature acquisition and PID temperature control are achieved through a PLC main controller to maintain temperature stability.

3. The method for dynamically optimizing sandwich extrusion process using a multiphysics coupling model as described in claim 2, characterized in that: The pressure of the extruder die head is collected in real time by a pressure transmitter. When the pressure exceeds the preset threshold, the PLC control unit slowly increases the power of the deionized water supply pump to dilute the material until the pressure returns to normal. When the pressure is lower than the preset threshold, the PLC control unit slowly decreases the power of the deionized water supply pump to concentrate the material until the pressure returns to normal.

4. The method for dynamically optimizing sandwich extrusion process using a multiphysics coupling model as described in claim 3, characterized in that: By using pressure sensors, temperature sensors, and screw speed data, combined with the rheological properties and flow patterns of plant protein materials, the shear rate of the material at different locations is calculated. When the shear rate exceeds the preset range, the screw speed is adjusted: the screw speed is reduced when the shear rate is too high, and the screw speed is increased when the shear rate is too low.

5. The method for dynamically optimizing sandwich extrusion process using a multiphysics coupling model as described in claim 1, characterized in that: A multiphysics coupling model simulates the flow field of plant protein material in an extruder, including: Temperature field: After the material enters the cooling zone, the surface temperature drops rapidly, while the center temperature is higher, exhibiting a V-shaped distribution; Shear rate field: The shear rate is highest in the screw section, decreases in the die section, and is greatest at the cooling section wall, decreasing towards the central axis; Pressure field: The pressure decreases in the cooling channel, starting from the initial position of 3.3 MPa.

6. The method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model as described in claim 1, characterized in that: After the plant protein meat was stably extruded, the extruded plant protein meat samples were collected and weighed every 15 seconds. Sample data were collected at 12 time points in each experiment. The effects of cooling temperature and feed rate on extrusion stability were analyzed through multiple sets of experiments.

7. The method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model as described in claim 6, characterized in that: Under the same feed amount, the extrusion stability of plant protein meat decreases as the cooling temperature increases; under the same cooling temperature, when the cooling temperature is greater than or equal to 60℃, the experimental group with a smaller feed amount has better extrusion stability.

8. The method for dynamically optimizing sandwich extrusion process using a multiphysics coupling model as described in claim 1, characterized in that: The crust of the plant-based meat sandwich is made from a mixture of soy protein isolate, wheat gluten, and deionized water in a specific mass ratio, with the soy protein isolate to wheat gluten ratio being 7:3 and the powder to water ratio being 4:

6.

9. The method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model as described in claim 8, characterized in that: The composition of soy protein isolate includes: crude protein 87.78±0.08%, crude fat 2.84±0.04%, crude fiber 3.35±0.16%, ash 0.85±0.05%, and moisture 4.71±0.07%; the composition of wheat gluten includes: crude protein 80.26±0.61%, crude fat 2.07±0.12%, crude fiber 2.47±0.29%, ash 0.89±0.03%, and moisture 4.68±0.14%.

10. The method for dynamically optimizing a sandwich extrusion process using a multiphysics coupling model as described in claim 1, characterized in that: The multiphysics coupling model was simulated using the CFD software Polyflow, based on the physical property parameters. These parameters included the rheological and physical properties of the plant protein raw material, as well as the physical properties of the screw material. The rheological parameters included zero-shear viscosity, limiting viscosity, relaxation time, non-Newtonian index, viscosity-temperature coefficient, and reference temperature. The physical property parameters included density, heat transfer coefficient, and specific heat capacity.

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