Slurry extrusion method and system, electronic equipment and storage medium

Abstract

The invention provides a slurry extrusion method and system, electronic equipment and a storage medium. The method comprises the steps that working data, geometric parameters and slurry viscosity of slurry mixing and extrusion equipment are obtained; according to the working data and the geometric parameters, a velocity field model is established in a cylindrical coordinate system, and the velocity field model comprises a radial velocity, a circumferential velocity and an axial velocity; according to the velocity field model, determining a velocity gradient tensor; obtaining a stretching efficiency index according to the velocity gradient tensor; adjusting parameters of a PID control algorithm according to the viscosity of the slurry, and determining a specified PID control algorithm; and according to the stretching efficiency index and a specified PID control algorithm, generating the screw rotating speed of the slurry mixing and extruding equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of polymer material processing, and particularly relates to a slurry extrusion method and system, an electronic device and a storage medium. BACKGROUND

[0002] In modern industrial production, a slurry mixing extruder is widely used in preparation of high-viscosity slurry such as lithium battery electrode material, solid propellant and modified plastic. The quality of these materials directly affects the performance of the final product.

[0003] The existing device mainly relies on shearing action to achieve mixing, and ignores the important role of tensile deformation in dispersion mixing. The existing device only considers shearing action and simplifies the mixing process as one-dimensional shearing flow. This simplification ignores the three-dimensional characteristics of the actual flow field and does not consider the key role of tensile deformation on mixing effect. Only relying on shearing action for mixing leads to low efficiency of extrusion processing. SUMMARY

[0004] The present application provides a slurry extrusion method, system, electronic device and storage medium to solve the problems in related technologies. The technical solutions are as follows: In a first aspect, the present application provides a slurry extrusion method, comprising: obtaining working data, geometric parameters and slurry viscosity of a slurry mixing extrusion device; establishing a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including radial velocity, circumferential velocity and axial velocity; determining a velocity gradient tensor according to the velocity field model; obtaining a stretching efficiency index according to the velocity gradient tensor; adjusting parameters of a PID control algorithm according to the slurry viscosity to determine a specified PID control algorithm; generating a screw rotation speed of the slurry mixing extrusion device according to the stretching efficiency index and the specified PID control algorithm.

[0005] In an embodiment, the working data includes temperature data, and the method further comprises: dividing a temperature zone of a barrel of the slurry mixing extrusion device according to the temperature data to determine a plurality of temperature zones; obtaining a slurry state; determining a temperature of each temperature zone according to the slurry state; determining an actual shearing heat generation power according to the screw rotation speed; obtaining a reference shearing heat generation power; determining a heating supplementary power according to the actual shearing heat generation power and the reference shearing heat generation power; According to the heating compensation power, the temperature difference constraint of adjacent temperature zones, and the temperature of each temperature zone, a target temperature of each temperature zone and a heating efficiency of each temperature zone are determined.

[0006] In an embodiment, the working data further includes mass flow data, slurry concentration coefficient of variation, total production data, yield data, pressure data, and torque data; the geometric parameter includes motor power; and the method further includes: According to the mass flow data, the motor power, and the heating efficiency of each temperature zone, a minimum unit energy consumption model is determined; According to the slurry concentration coefficient of variation, a maximum mixing uniformity model is determined; According to the total production data and the yield data, a maximum effective productivity is determined; A temperature safety factor, a pressure safety factor, and a torque safety factor are obtained; According to the temperature safety factor, the pressure safety factor, and the torque safety factor, a maximum safety index model is determined; An optimal parameter combination is determined by multi-objective optimization algorithm for multi-objective optimization of the minimum unit energy consumption model, the maximum mixing uniformity model, the maximum effective productivity, and the maximum safety index model, including optimal values of all decision variables.

[0007] In an embodiment, the method further includes: Historical production data are obtained; The original quality control model is trained by the historical production data to generate a trained quality control model; The optimal parameter combination is input into the trained quality control model to generate a predicted quality control result, and the quality prediction value includes density, viscosity, and uniformity.

[0008] In an embodiment, the method further includes: It is judged whether the quality prediction value deviates from a target range; When the deviation of the density exceeds a first specified range, a first specified strategy is generated; According to the first specified strategy, the screw rotation speed, the target temperature of each temperature zone, and the vacuum degree are adjusted; When the deviation of the viscosity exceeds a second specified range, a second specified strategy is generated; According to the second specified strategy, the screw rotation speed, the target temperature of each temperature zone, and the slurry ratio are adjusted; When the deviation of the viscosity exceeds a second specified range, a second specified strategy is generated; According to the third specified strategy, the stretching efficiency target, the target temperature of each temperature zone, and the mixing time are adjusted.

[0009] In an embodiment, the working data further include screw angular velocity, additional angular velocity generated by the interaction of the twin screws, and volume flow rate; the geometric parameters further include barrel eccentricity, inner radius of the barrel, and outer radius of the barrel; a velocity field model is established in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including radial velocity, circumferential velocity, and axial velocity; A cylindrical coordinate system is established with the axis of the slurry mixing and extruding device as the z-axis; A radial velocity component is determined according to the cylindrical coordinate system, the barrel eccentricity, the inner radius of the barrel, the outer radius of the barrel, and the screw angular velocity; A circumferential velocity component is determined according to the cylindrical coordinate system and the additional angular velocity; An axial velocity component is determined according to the cylindrical coordinate system, the volume flow rate, and a velocity distribution function.

[0010] In an embodiment, the stretching efficiency index is obtained according to the velocity gradient tensor, including: The velocity gradient tensor is decomposed to obtain a stretching rate tensor; First, second, and third eigenvalues are determined according to the stretching rate tensor; A principal stretching strength is determined according to the first and second eigenvalues; A total deformation strength is determined according to the first, second, and third eigenvalues; The stretching efficiency index is determined according to the principal stretching strength and the total deformation strength.

[0011] In a second aspect, an embodiment of the present application provides a system, including: A first obtaining module is configured to obtain working data, geometric parameters, and slurry viscosity of a slurry mixing and extruding device; A first establishing module is configured to establish a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including radial velocity, circumferential velocity, and axial velocity; A first determining module is configured to determine a velocity gradient tensor according to the velocity field model; A first obtaining module is configured to obtain a stretching efficiency index according to the velocity gradient tensor; A second determining module is configured to determine a specified PID control algorithm by adjusting parameters of a PID control algorithm according to the slurry viscosity; A first generating module is configured to generate screw rotating speed of the slurry mixing and extruding device according to the stretching efficiency index and the specified PID control algorithm.

[0012] In a third aspect, an electronic device is provided. The electronic device includes at least one processor, and a memory connected with the at least one processor in communication. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the slurry extrusion method described above.

[0013] In a fourth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the method in any one of the aspects described above is performed.

[0014] The advantages or beneficial effects of the technical solutions described above include at least: In this embodiment, the slurry extrusion method includes: obtaining working data, geometric parameters and slurry viscosity of a slurry mixing extrusion device; establishing a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including a radial velocity, a circumferential velocity and an axial velocity; determining a velocity gradient tensor according to the velocity field model; obtaining a stretching efficiency index according to the velocity gradient tensor; adjusting parameters of a PID control algorithm according to the slurry viscosity to determine a specified PID control algorithm; and generating a screw rotation speed of the slurry mixing extrusion device according to the stretching efficiency index and the specified PID control algorithm. Through the slurry extrusion method of this embodiment, the energy utilization efficiency is improved by fully considering the stretching deformation to replace part of the shear deformation, the stretching efficiency index of the three-dimensional stretching deformation of the slurry is calculated in real time, the screw rotation speed is adjusted according to the stretching efficiency index and the specified PID algorithm optimized according to the slurry viscosity, the stretching efficiency is maintained in an optimal range, the working efficiency of the slurry mixing extrusion device is improved, the energy consumption is reduced, and the technical problem that the existing device mainly relies on shear action to achieve mixing and ignores the important role of stretching deformation in dispersion mixing is effectively solved. The existing device only considers the shear action and simplifies the mixing process to one-dimensional shear flow. This simplification ignores the three-dimensional characteristics of the actual flow field and does not consider the key role of stretching deformation in mixing. Only relying on shear action mixing leads to the technical problem of low efficiency of extrusion processing.

[0015] The above summary is merely intended to illustrate the present description and is not intended to limit in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features will become apparent to those skilled in the art upon consideration of the drawings and the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0016] In the drawings, like reference numerals refer to same or similar functionalities throughout the several views. The drawings are not necessarily to scale. It is to be understood that the drawings only depict several embodiments in accordance with the present disclosure and should not be considered to be limiting in any way.

[0017] Figure 1 A schematic diagram of a slurry extrusion method according to an embodiment of the present application.

[0018] Figure 2 A block diagram of an electronic device for implementing a slurry extrusion method according to an embodiment of the present application.

[0019] Figure 3 A three-dimensional model diagram of a topological spiral double-rotor high-efficiency pulping device used to implement a slurry extrusion method according to an embodiment of the present application.

[0020] Figure 4 A structural schematic diagram of a topological spiral double-rotor extruder used to implement a slurry extrusion method according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.

[0022] In the related art, an operator manually adjusts parameters such as speed and temperature by judging the working condition through observing the slurry state, listening to the device running sound, touching the barrel temperature, and the like according to years of accumulated experience. This approach has the following serious problems: 1. The product quality is heavily dependent on the personal experience of the operator, and the product quality produced by different operators can differ by 15% to 20%. 2. The operator needs to be monitored continuously for 24 hours, and the labor cost is high (the annual labor cost exceeds 400,000 yuan per machine). 3. Quality fluctuations caused by human factors cannot be avoided, and the batch qualification rate is only 90% to 94%. 4. The key role of tensile deformation on mixing is not considered, and only shear mixing is relied on, resulting in low efficiency. 5. The mixing quality cannot be quantitatively evaluated, and can only be judged through post-detection, and a large amount of waste products has been produced when the problem is found. 6. The energy dissipation mechanism is not understood, a large amount of energy is wasted in the form of heat energy, and the energy utilization rate is only 20% to 30%. 7. The rheological behavior of high-viscosity slurry is extremely complex, involving non-Newtonian fluid mechanics, heat and mass transfer, chemical reactions, and multiple disciplines, and it is difficult to establish an accurate mathematical model by using traditional engineering methods.

[0023] As shown in Figure 1 , in a first aspect, embodiments of the present application provide a slurry extrusion method, comprising: S110: obtaining working data, geometric parameters, and slurry viscosity of a slurry mixing and extruding device; S120: establishing a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including radial velocity, circumferential velocity, and axial velocity; S130: determining a velocity gradient tensor according to the velocity field model; S140: obtaining a stretching efficiency index according to the velocity gradient tensor; S150: determining a specified PID control algorithm according to the adjustment of the parameters of the PID control algorithm based on the slurry viscosity; S160: generating the screw rotation speed of the slurry mixing and extruding device according to the stretching efficiency index and the specified PID control algorithm.

[0024] Through the slurry extruding method of the embodiment, the energy utilization efficiency is improved by fully considering the replacement of part of the shear deformation by the stretching deformation, the stretching efficiency index of the three-dimensional stretching deformation of the slurry is calculated in real time, the screw rotation speed is adjusted according to the stretching efficiency index and the specified PID algorithm corresponding to the slurry viscosity, the stretching efficiency is maintained in the optimal range, the working efficiency of the slurry mixing and extruding device is improved, and the energy loss is reduced. The existing device mainly relies on shear to achieve mixing, and ignores the important role of stretching deformation in dispersion mixing. The existing device only considers the shear effect, simplifies the mixing process to one-dimensional shear flow. This simplification ignores the three-dimensional characteristics of the actual flow field and does not consider the key role of stretching deformation in mixing. Only relying on shear mixing leads to the technical problem of low efficiency of extrusion processing.

[0025] As shown in Figures 3-4 , the application adopts a topological helical double-rotor extruder, as shown in Figure 3 , the topological helical double-rotor high-efficiency slurry preparation device includes a double-screw extruder, a topological helical double-rotor extruder, and a quantitative continuous feeding device. The double-screw extruder is connected with one quantitative continuous feeding device, and the topological helical double-rotor extruder is connected with three quantitative continuous feeding devices. The electrode slurry raw material system is first premixed in the double-screw extruder, and then extruded into the topological helical double-rotor extruder for further intensive mixing and dispersion. The slurry viscosity is adjusted by the three quantitative continuous feeding devices during the mixing and dispersion process, and finally vacuum defoaming and cooling are performed. The main innovation is in the topological helical double-rotor extruder, as shown in Figure 4 , the volume stretching transport of the topological helical double-rotor extruder is to arrange two eccentric rotors that mesh with each other in parallel in the "8" shaped stator inner cavity. The rotors rotate in the same / different directions and mesh with the stator, so that the volume of the cavity composed of the rotors and the stator changes periodically during rotation to realize volume stretching mixing and conveying.

[0026] The outer surface of the double-rotor is composed of a plurality of eccentric spiral structures and eccentric cylindrical structures with varying lengths connected alternately; the eccentric spiral structure and the eccentric cylindrical structure of the eccentric rotor are meshed with each other, the axis of the eccentric spiral structure of the two rotors is the same as the rotation axis of the rotor, the axis of the eccentric cylindrical structure is eccentric to the rotation axis of the rotor, and the eccentric cylindrical structures at different positions on the same rotor are eccentric in the same direction. The trajectory of the center of the cross section of the rotor is a circle with a radius e, i.e., the eccentric circle of the rotor.

[0027] In step S110, the working data, geometric parameters and slurry viscosity of the slurry mixing and extruding equipment are acquired; In this embodiment, the data in six dimensions of temperature, pressure, speed, torque, flow and viscosity are collected in real time on the slurry mixing and extruding equipment by corresponding sensors, and standardized processing is performed, and a 34-dimensional real-time data vector is output. Specifically: Temperature sensor: 24 PT100 platinum resistors are arranged along the axial direction of the cylinder, 3 in each temperature zone, and distributed in a 120° circle; the sampling frequency of the temperature sensor is 10 Hz (slow response); Pressure sensor: one piezoelectric sensor is installed in each of the feeding section, compression section, melting section and metering section; the sampling frequency of the pressure sensor is 100 Hz (dynamic change) Torque sensor: one strain sensor is installed on the drive shaft of each screw; the sampling frequency of the pressure sensor is 1000 Hz (transient fluctuation); Speed encoder: one 2048-line photoelectric encoder is configured for each screw; the sampling frequency of the speed encoder is 1000 Hz (high-precision control); Flow meter: one Coriolis mass flow meter is installed at the discharge port; Online viscometer: one rotary viscometer is installed before the die head.

[0028] The data in six dimensions of temperature, pressure, speed, torque, flow and viscosity are subjected to outlier processing, for example, 3σ criterion is used to identify outliers.

[0029] The mean μ and standard deviation σ of the data in six dimensions of temperature, pressure, speed, torque, flow and viscosity are identified, and the data points exceeding the range of [μ-3σ, μ+3σ] are replaced by the average of the two normal values before and after the abnormal value.

[0030] The data in six dimensions of temperature, pressure, speed, torque, flow and viscosity are subjected to normalization processing, and all data are mapped to the interval [0, 1]. A unified time stamp benchmark is established, and the data in six dimensions of temperature, pressure, speed, torque, flow and viscosity with different sampling rates are time-aligned by interpolation algorithm.

[0031] A standardized real-time data matrix D(t) is finally obtained D(t) = [T1(t), T2(t),..., T24(t), 24 temperature values P1(t), P2(t), P3(t), P4(t), 4 pressure values M1(t), M2(t), 2 torque values ω1(t), ω2(t), 2 rotational speed values Q(t), 1 flow rate value η(t)] 1 viscosity value The real-time data matrix D(t) is a 34-dimensional real-time data vector.

[0032] In step S120, a velocity field model is established in a cylindrical coordinate system according to the working data and the geometric parameters, and the velocity field model includes a radial velocity, a circumferential velocity and an axial velocity. In this embodiment, an accurate three-dimensional velocity field model is established to calculate the stretching deformation state of the slurry at each position. The three-dimensional velocity field model defines the three-dimensional flow characteristics in the double-screw extruder and reveals the decisive role of the stretching deformation on the mixing effect, thereby providing a basis for optimization control.

[0033] The key parameters in the real-time data matrix D(t) are used for calculation, correction and verification of the velocity field model, for example, the rotational speed data ω1(t), ω2(t) are used to calculate the velocity field, the temperature data T(t) are used to correct the viscosity of the slurry, and the pressure data P(t) are used to verify the accuracy of the flow field calculation.

[0034] A cylindrical coordinate system (r, θ, z) is established with the axis of the extruder as the z-axis.

[0035] Velocity field equation derivation: considering the eccentric rotation of the double screw, the three components of the velocity field are: Radial velocity component vr:

[0036] wherein: e is the eccentricity (determined by geometric design, typical value 5mm); ω is the screw angular velocity; sinθ indicates that the velocity changes with the angle to form a periodic extrusion; (1-(r-ri) / h(θ)) indicates that the velocity gradually decreases from the rotor surface to the stator surface; the radial velocity component vr describes the extrusion and expansion motion of the slurry in the radial direction.

[0037] Circumferential velocity component vθ: vθ = ω·r + Δω(r,θ) wherein Δω is the additional angular velocity generated due to the interaction of the double screw; the circumferential velocity component vθ describes the rotational motion and circumferential conveying of the slurry.

[0038] Axial velocity vz: vz = Q / (π(ro 2 -ri 2 ))·f(r) Where: Q is the volume flow rate; f(r) is the velocity distribution function (determined according to rheology); Physical meaning: The axial velocity vz describes the transport of the slurry in the extrusion direction.

[0039] According to the cylindrical coordinate system (r, θ, z), the radial velocity, the circumferential velocity, and the axial velocity obtained above, the velocity field model can be determined.

[0040] In step S130, the velocity gradient tensor is determined according to the velocity field model. In this embodiment, in the cylindrical coordinate system (r, θ, z), the specific form of the velocity gradient tensor is: ┌ ∂vᵣ / ∂r (1 / r)(∂vᵣ / ∂θ) - vθ / r ∂vᵣ / ∂z ┐ L= │ ∂vθ / ∂r (1 / r)(∂vθ / ∂θ) + vᵣ / r ∂vθ / ∂z │ └ ∂vz / ∂r (1 / r)(∂vz / ∂θ) ∂vz / ∂z ┘ Where: vᵣ: radial velocity component (m / s); vθ: circumferential velocity component (m / s); vz: axial velocity component (m / s); r: radial coordinate (m); θ: angular coordinate (rad); z: axial coordinate (m); Where the physical meaning of each component is: L11: radial stretching / compression; L22: circumferential stretching / compression; L33: axial stretching / compression; off-diagonal terms: shear deformation; Suppose at a certain time and at a certain position, according to the velocity field model, the following are calculated: vr = 0.02 m / s (radial velocity); vθ = 0.15 m / s (circumferential velocity); vz = 0.10 m / s (axial velocity); r = 0.03 m (current radius position); calculate the partial derivatives (through finite difference method): ∂vr / ∂r = 0.5 s -1 ;∂vr / ∂θ = 0.1 s -1 ;∂vr / ∂z = 0.05 s -1 ;∂vθ / ∂r = 2.0 s -1 ;∂vθ / ∂θ = 0.3 s -1 ;∂vθ / ∂z = 0.1 s -1;∂vz / ∂r = 0.8 s -1 ;∂vz / ∂θ = 0.2 s -1 ;∂vz / ∂z = 1.5 s -1 . Substitute to get L matrix: [0.5 -1.67 0.05] L = [2.0 5.50 0.10] [0.8 0.20 1.50] The velocity gradient tensor L can be determined by the above calculation process.

[0041] In step S140, the stretch efficiency index is obtained according to the velocity gradient tensor; In this embodiment, the decomposition principle of the velocity gradient tensor L: any flow can be decomposed into two basic motions: deformation motion (changing the shape of fluid element), described by the stretch rate tensor D; rigid rotation (without changing the shape, only changing the direction), described by the vorticity tensor W; The velocity gradient tensor L can be uniquely decomposed into symmetric part and antisymmetric part: L = D + W Where: D = (L + L^T) / 2 (symmetric tensor, stretch rate tensor); W = (L - L^T) / 2 (antisymmetric tensor, vorticity tensor) For example: calculate the transpose matrix L^T of L [0.5 2.0 0.8 ] L^T = [-1.67 5.50 0.20] [0.05 0.10 1.50] Calculate L + L^T [0.5 -1.67 0.05] [0.5 2.0 0.8 ] L + L^T = [2.0 5.50 0.10] + [-1.67 5.50 0.20] [0.8 0.20 1.50] [0.05 0.10 1.50] [1.00 0.33 0.85] = [0.33 11.00 0.30] [0.85 0.30 3.00] Calculate the stretch rate tensor D D = (L + L^T) / 2 [0.50 0.165 0.425] D = [0.165 5.50 0.15 ] [0.425 0.15 1.50 ] Physical meaning of stretch rate tensor D: Diagonal elements (D 11 =0.50, D 22 =5.50, D 33 =1.50): represent stretch / compression rates in three coordinate directions; Off-diagonal elements (D 12 =0.165, D 13 =0.425, D 23 =0.15): represent shear deformation rates; Calculation of vorticity tensor W W = (L - L^T) / 2 [0 -1.835 -0.375] W =[1.835 0 -0.05 ] [0.375 0.05 0 ] Physical meaning of vorticity tensor W: vorticity tensor W is an anti-symmetric tensor, diagonal elements are zero; off-diagonal elements represent rotation rates; do not contribute to mixing, but consume energy.

[0042] For stretch rate tensor D, eigenvalue λ of stretch rate tensor D represents the stretch rate in the principal stretch direction: first eigenvalue λ1: maximum principal stretch rate; second eigenvalue λ2: intermediate principal stretch rate; third eigenvalue λ3: minimum principal stretch rate (may be negative, indicating compression).

[0043] where eigenvalue λ satisfies: det(D - λl) = 0; where I is the unit matrix, det represents the determinant. Expand to a cubic equation in standard form: λ 3 -l1λ 2 +l2λ-I3= 0 Calculation formula of invariant: First invariant (trace): I1= tr(D) = D 11 + D 22 + D 33 Physical meaning: volumetric expansion rate Second invariant: I2 = (1 / 2)[(tr(D)) 2 - tr(D 2 )] = D 11 D 22+ D 22 D 33 + D 33 D11 - D 12 2 - D 23 2 - D 31 2 Physical meaning: area change rate Third invariant (determinant): I3 = det(D) = D 11 (D 22 D 33 - D 23 2 ) - D 12 (D 12 D 33 - D 23 D 13 ) + D 13 (D 12 D 23 - D 22 D 13 ) Variable substitution Let λ = μ + I1 / 3, and the original equation is transformed into: μ 3 +pμ+ q = 0 Where: p = I2- I1 2 / 3;q = 2I1 3 / 27 - I1I2 / 3 + I3; Discriminant calculation: Δ= -(4p 3 + 27q 2 ) / 108 Expression of three real roots When Δ > 0 (symmetric matrix guaranteed): First eigenvalue λ1= I1 / 3 + 2√(-p / 3)cos(θ / 3); Second eigenvalue λ2 = I1 / 3 + 2√(-p / 3)cos((θ+ 2π) / 3); Third eigenvalue λ3= I1 / 3 + 2√(-p / 3)cos((θ+ 4π) / 3); Where: θ= arccos(3q√(-3 / p) / 2p). The eigenvalues satisfy the equation: det(D - λI) = 0; Where I is the identity matrix, and det represents the determinant.

[0044] Expanding gives: |0.50-λ 0.165 0.425 | |0.165 5.50-λ 0.15 | = 0 |0.425 0.15 1.50-λ| Calculating the determinant Expanding the third order determinant to get the characteristic polynomial: -λ 3 + (0.50+5.50+1.50)λ 2 - [(0.50×5.50+0.50×1.50+5.50×1.50) - (0.165 2 +0.425 2 +0.15 2 )]λ+ det(D) = 0 Simplifying gives: -λ 3 + 7.50λ 2 - 11.47λ+ 3.89 = 0 Solving the cubic equation, either using numerical methods (e.g. Newton-Raphson) or analytical methods (Cardan's formula) gives three real roots: First eigenvalue λ1= 5.58 s -1 (maximum eigenvalue); Second eigenvalue λ2 = 1.47 s -1 (intermediate eigenvalue); Third eigenvalue λ3= 0.45 s -1 (minimum eigenvalue).

[0045] Two-dimensional principal tensile strength: Is=√(λ1 2 +λ2 2 ) Parameter explanation: λ1: maximum eigenvalue (strongest tensile direction), λ2: intermediate eigenvalue (second strongest tensile direction).

[0046] Three-dimensional total deformation strength: Id = √(λ1 2 + λ2 2 + λ3 2 ) = √(tr(D 2 )) Definition formula of tensile efficiency index: ψ= Is / Id = √(λ1 2 +λ2 2 ) / √(λ1 2 + λ2 2 + λ32 ) Range of values ​​and physical meaning: ψ ∈ [0, 1]; ψ → 1: pure two-dimensional stretching (ideal); ψ → 0.577: pure shear flow; ψ < 0.5: three-dimensional compression is dominant.

[0047] Maximizing stretching efficiency: maxψ = max{√(λ1) 2 +λ2 2 ) / √(λ1 2 + λ2 2 + λ3 2 )} Constraints: λ1 2 + λ2 2 + λ3 2 = 0 (incompressible fluid); |λᵢ| < λ_max (physical constraint).

[0048] Specifically, the principal tensile strength is defined as the tensile strength within the principal tensile plane (λ1-λ2 plane): Is = √(λ1 2 + λ2 2 ) = √(5.58 2 + 1.47 2 ) = √(31.14 + 2.16) = √33.30 = 5.77 s -1 Is represents the combined tensile strength in the two principal directions that contribute the most to the mixing.

[0049] Calculation of total deformation strength Id: Total deformation strength includes deformation in all three directions: Id = √(λ1 2 + λ2 2 + λ3 2 ) = √(5.58 2 + 1.47 2 + 0.45 2 ) = √(31.14 + 2.16 + 0.20) = √33.50 = 5.79 s -1 Calculation of the tensile efficiency index ψ: ψ = Is / Id = 5.77 / 5.79 = 0.997 Result analysis: ψ = 0.997 ≈ 1.0, which means that almost all the deformation is tensile deformation, and the shear component is very small. In this case, the mixing efficiency is extremely high.

[0050] Through the above calculation process, the tensile efficiency index can be analyzed and determined.

[0051] Through a large number of experiments and theoretical analysis, the optimal value is determined: ψ < 0.5: shear dominated, low mixing efficiency, high energy consumption; 0.6 < ψ < 0.7: tensile dominated, optimal mixing efficiency; ψ > 0.8: excessive stretching, which may lead to degradation of the slurry.

[0052] Generate real-time tensile efficiency index ψ(t), which will be used as the core control target for controlling the screw speed. The embodiments of the present application establish the tensile efficiency index ψ as the core control target, and adjust the motion parameters of the double-shaft eccentric rotor to maintain ψ in the optimal interval of 0.60-0.70.

[0053] In step S150, the parameters of the PID control algorithm are adjusted according to the viscosity of the slurry to determine the specified PID control algorithm.

[0054] In this embodiment, the PID control algorithm can be executed by a PID controller, and the PID control algorithm includes: P (proportional) control: the larger the deviation, the larger the adjustment (fast response); I (integral) control: cumulative historical deviation, eliminate steady-state error (accurate control); D (derivative) control: predict the trend of deviation, adjust in advance (smooth control); Control output = Kp x current deviation e + Ki x deviation integral + Kd x deviation rate of change; High-viscosity slurry (such as rubber, viscosity > 1000 Pa·s): reduce Kp to 1.6 (avoid over-adjustment leading to equipment overload); reduce Ki to 0.4 (prevent cumulative effect from being too strong); keep Kd at 0.08 (moderate prediction); reason: high-viscosity slurry responds slowly, and too fast adjustment will cause instability.

[0055] Low-viscosity slurry (such as paint, viscosity < 100 Pa·s): increase Kp to 2.4 (speed up response speed); increase Ki to 0.6 (enhance steady-state accuracy); increase Kd to 0.12 (improve dynamic performance); reason: low-viscosity slurry responds quickly, and requires more sensitive control.

[0056] For example: Error calculation: e(t) = ψ_target - ψ(t); PID output: u(t) = Kp·e(t) + Ki·∫e(τ)dτ + Kd·de / dt; PID control algorithm rules: If η > 1000 Pa·s (high viscosity slurry): Kp = 1.6 (proportional coefficient, fast response); Ki = 0.4 (integral coefficient, eliminate steady-state error); Kd = 0.08 (differential coefficient, suppress oscillation).

[0057] If 100 < η < 1000 Pa·s (medium viscosity slurry): Kp = 2.0; Ki = 0.5; Kd = 0.10; If η < 100 Pa·s (low viscosity slurry): Kp = 2.4; Ki = 0.6; Kd = 0.12.

[0058] Based on the PID control algorithm described above, parameters are adjusted to suit slurries of different viscosities. When adjusting the PID control algorithm parameters, high-viscosity slurries have a slow response and require a smaller control gain to avoid overshoot; low-viscosity slurries have a fast response and can use a larger control gain to improve the response speed.

[0059] In step S160, the screw speed of the slurry mixing extrusion device is generated according to the stretching efficiency index and the specified PID control algorithm.

[0060] In the embodiments of this application, the real-time tensile efficiency index ψ(t) can be known according to the above embodiments, and a target tensile efficiency index is configured: ψ target For example, ψ target = 0.65 (optimal value); The current deviation e: e = ψ target -ψ current = 0.65 - 0.58 = 0.07; Where: e > 0: insufficient stretching, the rotation speed needs to be adjusted to increase the stretching; e = 0: just at the optimal state; e < 0: excessive stretching or excessive shearing.

[0061] The output of the specified PID control algorithm is u(t) = Kp × current deviation e + Ki × deviation integral + Kd × deviation change rate; The above calculation shows that the current deviation e is 0.07. Substituting this into the formula of the specified PID control algorithm above; The proportional term P = Kp × e = 2.0 × 0.07 = 0.14 Integral term I = Ki x ∫e dt = 0.5 x 0.21 = 0.105 (Assume accumulated deviation is 0.21) Derivative term D = Kd x de / dt = 0.1 x 0.02 = 0.002 (Assume deviation rate of change is 0.02) Control amount u(t) = P + I + D = 0.14 + 0.105 + 0.002 = 0.247 Optimized screw rotation speed ω of slurry mixing extrusion equipment new = Current screw rotation speed ω of slurry mixing extrusion equipment current + Adjustment coefficient α x Control amount u(t) ω new = ω current + α x u(t) Wherein the determination of α (adjustment coefficient), α is too large: adjustment is too sharp, system oscillation; α is too small: adjustment is too slow, response is sluggish; α suitable value: determined through system test, usually 0.1-0.3: ω current is the current rotation speed.

[0062] According to the above example: Basic adjustment amount = α x u(t) = 0.3 x 0.247 = 0.074 rad / s New rotation speed = Current rotation speed + Basic adjustment amount = 3.0 + 0.074 = 3.074 rad / s u(t) is the above specified PID control algorithm.

[0063] On the basis of the new rotation speed calculated above, in order to ensure the safe operation of the equipment, the corresponding safety constraints also need to be configured: Rotation speed limit: 0.5 ≤ ω new ≤ 10.0 rad / s; Rate limit: |dω / dt| ≤ 0.5 rad / s 2 ; Too low rotation speed (<0.5): slurry does not flow, will be blocked Too high rotation speed (10): equipment cannot withstand, may be damaged Too fast change (>0.5 / sec 2 ): mechanical impact is large, affecting service life.

[0064] In this embodiment, according to the stretching efficiency index, the screw speed is adjusted by specifying the PID control algorithm to maintain the stretching efficiency in the optimal range. The precise control based on the physical mechanism is realized, which breaks through the limitations of traditional PID control and significantly improves the mixing efficiency and product quality.

[0065] In an embodiment, the working data includes temperature data, and the method further comprises: dividing the temperature zones of the barrel of the slurry mixing and extruding equipment according to the temperature data, to determine a plurality of temperature zones; obtaining the state of the slurry; determining the temperature of each temperature zone according to the state of the slurry; determining the actual shear heat generation power according to the screw speed; obtaining the reference shear heat generation power; determining the heating compensation power according to the actual shear heat generation power and the reference shear heat generation power; determining the target temperature of each temperature zone and the heating efficiency of each temperature zone according to the heating compensation power, the temperature difference constraint of adjacent temperature zones, and the temperature of each temperature zone.

[0066] In the embodiment of the present application, when the screw speed is adjusted in the above-mentioned embodiment, the heat balance state of the entire equipment will change, and the present embodiment can actively compensate for the change in shear heat generation, by: increasing the screw speed → increasing the shear effect → increasing the conversion of mechanical energy into heat energy, i.e. the external heating power needs to be correspondingly reduced, and the shear heat generation increases by about 15-20 kW for each increase of 1 rad / s of the screw speed.

[0067] To ensure that the slurry is in the optimal processing temperature window, if the temperature is too low, the slurry will not be fully plasticized and the mixing effect will be poor; if the temperature is too high, the slurry will degrade and the molecular chain will break, resulting in a decrease in performance; in the optimal window, the temperature is usually within ±5℃.

[0068] Divide the barrel temperature into zones: the barrel can be divided into 8 independent control zones, and the target temperature of each zone is determined according to the state of the slurry: Optimizing the temperature gradient distribution can avoid thermal stress caused by sudden temperature changes, ensure gradual heating and plasticization of the slurry, and prevent the formation of local hot spots.

[0069] Each temperature zone can be divided according to the corresponding function: The first temperature zone and the second temperature zone Zone 1-2 (solid conveying section), the slurry is in a solid particle state, mainly relying on friction to convey forward.

[0070] Set the temperature T target = melting temperature T melt - 20℃ For example CPVC material: melting temperature: 195°C; Zone 1 setting: 175°C; Zone 2 setting: 180°C.

[0071] The reason for the above temperature setting is the necessity of the preheating effect of the equipment. Directly entering the high-temperature zone with cold slurry will cause thermal shock. Gradual preheating can release the internal stress of the slurry, increase the friction coefficient of the slurry and the screw, increase the conveying efficiency, and avoid premature plasticization. Premature plasticization will cause the slurry to adhere to the screw and form a "bridge" phenomenon, hindering solid conveying, and maintaining a solid state ensures stable volumetric conveying.

[0072] The third and fourth temperature zones Zone 3-4 (compression melting section), the slurry changes from solid to molten state, and the volume decreases sharply.

[0073] Set temperature T target = melting temperature T melt + 5°C Zone 3 setting: 197°C; Zone 4 setting: 200°C: The reason for the above temperature setting is the importance of ensuring that the slurry is completely melted. If unmelted particles become the core of uneven mixing, it will affect the mechanical properties of the final product and may cause extrusion pressure fluctuations.

[0074] The fifth and sixth temperature zones Zone 5-6 (mixing section), the slurry is completely melted and undergoes strong stretching and mixing.

[0075] Set temperature T target = optimum processing temperature T process : This is the most critical temperature control area: The method for determining the optimum processing temperature is as follows: T process = T melt + ΔT rheology + ΔT stability Where: ΔT rheology : temperature correction based on rheological properties (5-15°C); ΔT stability : temperature correction based on thermal stability (-10 to 0°C); The seventh and eighth temperature zones Zone 7-8 (metering section), the slurry is homogenized and ready for extrusion: Set temperature T target = processing temperature T process - 5°C The technical reasons for cooling in the seventh and eighth temperature zones are to stabilize the slurry state, slightly reduce the temperature to increase the slurry viscosity, improve the stability of the extrusion pressure, reduce the outlet expansion effect, enable energy recovery, and use the heat capacity of the slurry itself for buffering, reduce unnecessary heating power, and prepare for subsequent cooling.

[0076] In this embodiment, the temperature field and the velocity field are not independent of each other during the slurry mixing and extrusion process, but are closely coupled through temperature-rotation speed coupling compensation and energy conversion. When the screw rotation speed changes, the shear heat generation changes accordingly, and the external heating power needs to be adjusted to maintain the temperature stable.

[0077] The quantitative calculation process of shear heat generation is as follows: Shear heat generation at the reference rotation speed (ω0 = 3.0 rad / s): Q base =η0 × γ̇0 2 × V Q base is the basic heating power, γ̇0 is the reference shear rate, and η0 is; Shear heat generation at the new rotation speed (ω new ): Q new = η new × γ̇ new 2 × V Since the shear rate γ̇ is proportional to the screw rotation speed ω, when the rotation speed increases by 50%: γ̇ new = 1.5 × γ̇0 Q new ≈ 1.5 2 × Q base = 2.25 × Q base γ̇ new is the new shear rate; Q new is the new heating power Shear heat generation increases by 125%! Compensation formula: New heating power P heatnew = P heatbase - β × (ω - ω base ) 2 ω base : represents a reference rotation speed commonly used in a process, corresponding to a baseline condition; β (shear heat generation coefficient): obtained by historical data regression or heat balance calculation, used to quantify the replacement effect of heat generation on external heating demand, and obtained by experimental calibration; P heat base : reference heating power (empirical or calibrated value at ω = ω base Different compensation factors for different temperature zones because: different channel depth leads to different shear rate, different slurry state leads to different viscosity, different residence time leads to different heating degree.

[0078] Compensation factors for each zone: Zone 1-2: β = 0.5 kW / (rad / s) 2 (solid transport, less shear heating); Zone 3-4: β = 1.0 kW / (rad / s) 2 (starting melting, increased heating); Zone 5-6: β = 1.5 kW / (rad / s) 2 (complete melting, maximum heating); Zone 7-8: β = 0.8 kW / (rad / s) 2 (metering section, reduced heating).

[0079] In addition, the temperature difference between adjacent temperature zones is limited to within 10℃ to avoid thermal stress.

[0080] Reason for limiting the temperature difference between adjacent temperature zones: thermal stress control; thermal stress σ = E·α·ΔT where: E is the elastic modulus (2-3 GPa); α is the thermal expansion coefficient (7-9×10 -5 / ℃); ΔT is the temperature difference.

[0081] When ΔT>10℃, the thermal stress may exceed the material yield strength, preventing condensation and dewing, and a too large temperature difference can cause condensation of volatile components, affecting the uniformity of the slurry composition, and may cause equipment corrosion Cooperative control algorithm: For each temperature zone i: 1. Calculate the ideal temperature T ideal [i]; 2. Check the constraint: |T ideal [i]- T ideal [i-1]| ≤ 10℃; 3. If the constraint is violated: Ta djusted [i] = T ideal [i-1] + sign(ΔT)×10℃; ​4. Backward propagation adjustment: recalculate the set value of the subsequent temperature zone.

[0082] The embodiment determines the target temperature of each temperature zone and the heating efficiency of each temperature zone by shear heat generation, adjacent temperature difference constraint and temperature of each temperature zone, so as to input them into the corresponding PID controller for adjustment, so as to make corresponding compensation according to the actual situation of each temperature zone, fully utilize the shear heat generation of screw rotation, and avoid overheating problem.

[0083] In an embodiment, the working data further includes mass flow data, slurry concentration variation coefficient, total output data, qualified rate data, pressure data and torque data; the geometric parameter includes motor power; the method further includes: determining a minimum unit energy consumption model according to the mass flow data, the motor power and the heating efficiency of each temperature zone; determining a maximum mixing uniformity model according to the slurry concentration variation coefficient; determining a maximum effective productivity according to the total output data and the qualified rate data; obtaining a temperature safety factor, a pressure safety factor and a torque safety factor; determining a maximum safety index model according to the temperature safety factor, the pressure safety factor and the torque safety factor; determining an optimal parameter combination including optimal values of all decision variables by multi-objective optimization algorithm for multi-objective optimization of the minimum unit energy consumption model, the maximum mixing uniformity model, the maximum effective productivity and the maximum safety index model.

[0084] In the embodiment of the application, the optimal operation parameter combination is found by comprehensively considering mixing quality, production efficiency, energy consumption and equipment safety, so as to solve the problem of multiple conflicting objectives, realize global optimization rather than local optimization, and provide flexible production mode selection.

[0085] Minimum unit energy consumption f1: Mathematical expression: f1 = (P moto r + P heat ) / Q Wherein, P motor : motor power (kW), the calculation formula is: P motor = 2π × (M1×ω1 + M2×ω 2) / η trans Wherein, M1, M2: torque of two screws (N·m), ω1, ω2: angular velocity of two screws (rad / s), η trans: Transmission efficiency (dimensionless, ~0.85-0.90), P heat : Total heating power (kW), calculated as: P heat =

[0086] P heat [i]: Heating power of the i-th temperature zone, Q: Mass flow rate (kg / h), calculated as: Q = Vf × ρ × 3600 Vf: Volume flow rate (m 3 / s), ρ: Slurry density (kg / m 3 ).

[0087] Minimize the specific energy consumption f1, i.e. the electrical energy consumed to produce each kg of product, which directly reflects the production cost.

[0088] The smaller the specific energy consumption f1, the better, with the goal of reducing production costs.

[0089] Maximize the mixing uniformity f2: Mathematical expression: f2 = 1 - CV where CV: Coefficient of Variation, calculated as: CV = σ / μ σ: Standard deviation of sample concentration μ: Average value of sample concentration Take n sampling points on the cross-section of the extrudate (usually n=9, 3x3 grid) Measure the concentration of the key component c[i] at each point Calculate the average concentration: μ = (1 / n) × Σ(i=1 to n) c[i] Calculate the standard deviation: σ = √[(1 / n) × Σ(i=1 to n) (c[i] - μ) 2 ] Calculate the coefficient of variation: CV = σ / μ Calculate the uniformity index: f2 = 1 - CV Value range: CV is usually between 0.01-0.20; f2 is between 0.80-0.99; f2=1 indicates complete uniformity (ideal state) f2 < 0.95 means insufficient mixing Maximizing mixing uniformity f2 reflects the uniformity of slurry mixing, which directly affects product quality.

[0090] Maximizing effective productivity f3: Mathematical expression: f3 = Q × η qualified Where Q: total output (kg / h), η qualified : pass rate (dimensionless), the calculation formula is: H qualified = N qualified / N total N qualified : number of qualified products, N total : total product quantity.

[0091] Passing criteria: products must meet the following conditions at the same time to be qualified: Density deviation: |ρ actual - ρ target | / ρ target < 0.02 Viscosity deviation: |η actual -η target | / η target < 0.05 Uniformity index: f2 > 0.95 Maximizing effective productivity f3 is the amount of qualified products produced per unit time, which comprehensively reflects production efficiency and quality.

[0092] Maximizing safety index f4: Mathematical expression: f4 = exp(-ΔT / 5) × exp(-P / 10) × exp(-M / 500) Temperature safety factor: exp(-ΔT / 5); ΔT = max(T[i]) - T safe : difference between maximum temperature and safety temperature (℃); T safe : upper limit of safety temperature of material (such as CPVC 220℃); Denominator 5: temperature characteristic value, indicating that for every 5℃ increase in temperature, safety is reduced by e times; Pressure safety factor: exp(-P / 10); P = max(P[i]): maximum pressure (MPa); Denominator 10: pressure characteristic value, indicating that for every 10MPa increase in pressure, safety is reduced by e times; Torque safety factor: exp(-M / 500); M = max(M1, M 2） Maximum torque (N·m); Denominator 500: Torque characteristic value, indicating that for every 500 N·m increase in torque, safety decreases by a factor of e. f4∈(0, 1] f4=1: Completely safe state (ΔT=0, P=0, M=0); f4 < 0.5: There is a security risk; f4 < 0.2: Dangerous state, needs immediate adjustment.

[0093] Maximize the safety index f4 to comprehensively evaluate the safe operation of the system and avoid equipment damage and safety accidents.

[0094] The decision variable vector X = [ω1, ω2, T1, T2, T] 3, T4, T5, T6, T7, T8, Q] The meaning and scope of each variable: ω1, ω2: angular velocities (rad / s) of the two screws, used to control the slurry conveying speed and shear strength, with a value range of [0.5, 10.0]; where the constraint relationship is: |ω1 - ω2| ≤ 0.5 (to ensure synchronization); T1~T 8: Set temperatures (°C) for 8 temperature zones; T1, T2: Temperature range of the solid conveying section [T] room , T melt -10]; T3, T4: Temperatures of the compression melting zone, range [T] melt -5, T melt +10]; T5, T6: Mixing section temperature, range [T melt , T melt +20]; T7, T8: Metering section temperature, range [T] melt -10, T melt +10]; Adjacent temperature zone constraint: |Ti - Ti+1| ≤ 10℃.

[0095] Q: Target output (kg / h), the output required by the production plan. Value range: [Qmin, Qmax] Qmin = 0.5 × design capacity; Qmax = 1.2 x design capacity.

[0096] Speed constraints include: ωmin ≤ ω1, ω2 ≤ ωmax; ωmin = 0.5 rad / s (minimum stable speed); ωmax = 10.0 rad / s (maximum speed of equipment).

[0097] Temperature constraints: Tmin[i] ≤ T[i] ≤ Tmax[i]; Prevent slurry degradation: T[i] < T degradation - 10℃; Ensure sufficient plasticization: T[i] > T melt - 20℃.

[0098] Pressure constraints include: P[i] ≤ P maxdesign × SF P maxdesign : design maximum pressure (e.g. 30 MPa); SF: safety factor (typically 0.8).

[0099] Torque constraints: M ≤ M rated × 0.9 M rated : motor rated torque; leave 10% margin to prevent overload Density specifications: ρ target × (1 - δρ) ≤ ρ actual ≤ ρ target × (1 + δρ) δρ: allowable deviation (typically 2%) Viscosity specifications: ηtarget × (1 - δη) ≤ ηactual ≤ ηtarget × (1 + δη) δη: allowable deviation (typically 5%) Uniformity requirements: f2 ≥ 0.95; Process constraints include: Temperature gradient constraints: |Ti+1 - Ti| ≤ ΔTmax = 10℃; The purpose is to prevent excessive thermal stress.

[0100] Heating rate constraints include: |dT / dt| ≤ 5℃ / min To prevent thermal shock from rapid heating.

[0101] The residence time constraint includes: tres ≥ tmin tres = V / Q: Actual residence time tmin: Minimum residence time (to ensure adequate mixing).

[0102] The target optimization algorithm of the embodiment is NSGA-III algorithm, specifically: NSGA-III (Non-dominated Sorting Genetic Algorithm-III) is an evolutionary algorithm specifically designed for solving multi-objective optimization problems.

[0103] Pareto dominance relationship, solution x dominates solution y, and only if: for all objectives fi, fi(x) ≤ fi(y) (minimization problem); at least one objective fj exists, such that fj(x) < fj(y) Pareto optimal front: The set of all non-dominated solutions, representing the best trade-off between different objectives.

[0104] The specific implementation steps are as follows: Generate 100 sets of random slurry mixing and extrusion equipment working data, geometric parameters and slurry viscosity parameters, etc.: for i = 1 to 100: X[i] = random_uniform(Xmin, Xmax) Check the constraint condition If the constraint is violated, regenerate Use Latin hypercube sampling to ensure uniform coverage of the parameter space, and add experience solutions (such as current running parameters) as seeds.

[0105] Calculate four objective functions for each individual: run the simulation model or use the proxy model to calculate the four objective functions, specifically: Calculate f1(X)= (P motor + P heat ) / Q; Calculate f2(X)=1-CV; Calculate f3(X) = Q × η qualified ; Calculate f4(X) = exp(-ΔT / 5)×exp(-P / 10)×exp(-M / 500); Non-dominated sorting, stratify the population: Layer 1: Pareto optimal solution (non-dominated) Layer 2: Dominated by Layer 1, but not dominated by other solutions Layer 3: Dominated by Layer 1 and 2, but not dominated by other solutions ... NSGA-III uses reference points to maintain diversity of solutions: Generating reference points: Uniformly distribute reference points in 4-dimensional objective space Generate using Das and Dennis method Number of reference points = C(4+p-1, p) ≈ 35 (p=4 is the partition parameter) Associate solutions to reference points: Calculate distance to all reference points, associate to the nearest one.

[0106] Select next generation population: 1. Prioritize solutions with low rank; 2. Within the same rank, select solutions with less association to reference points; 3. Keep population size as 100.

[0107] Crossover operation: Select parents: Tournament selection; Crossover probability: 0.9; Crossover method: Simulated Binary Crossover (SBX); Offspring = α × parent1 + (1-α) × parent2; Mutation operation: Mutation probability: 1 / n (n is the variable dimension); Mutation method: Polynomial mutation; X_new = X + δ × (Xmax - Xmin) Termination condition: Reach maximum number of generations (e.g. 200 generations), improvement of objective function is less than threshold, Pareto front is stable; Select final scheme from the Pareto optimal solution set: (1) Energy-saving mode X final = argmin(f1) Prioritize solutions with lowest energy consumption, suitable for high energy cost situations.

[0108] (2) Quality mode X final = argmax(f2) Prioritize solutions with best mixing uniformity, suitable for high-end products.

[0109] (3) Efficiency mode X final = argmax(f3) Prioritize solutions with maximum yield, suitable for tight delivery schedules.

[0110] (4) Balanced mode X final = argmin(Σwi × fi normalized ) where: wi: each target weight (e.g. w = [0.25, 0.25, 0.25, 0.25]), fi normalized : normalized target value.

[0111] Through the method of the embodiment, the optimal operation parameter combination can be found by comprehensively considering the mixing quality, production efficiency, energy consumption and equipment safety. The quality stability of the slurry mixing and extrusion equipment is significantly improved, the balance between the optimal stretching efficiency and energy consumption is maintained, the mixing is ensured to be sufficient but not excessive, and the safety and stability of the equipment are ensured.

[0112] In an embodiment, the method further comprises: obtaining historical production data; training the original quality control model through the historical production data to generate a trained quality control model; inputting the optimal parameter combination into the trained quality control model to generate a predicted quality control result, and the quality prediction value includes density, viscosity and uniformity.

[0113] In the embodiments of the present application, based on the historical data and the current process parameters, the product quality in the next 5-10 minutes is predicted to realize predictive quality control. The quality deviation can be found in advance, batch unqualified products can be avoided, the hysteresis of quality detection can be reduced, and a basis for parameter adjustment can be provided. Specifically: According to the optimal parameters X optimal output by the above steps, combined with the historical operation data.

[0114] An input matrix of 60 time steps x 12 features is constructed: The features include: - 4 temperature features (average temperature, temperature gradient, temperature fluctuation, temperature change rate); - 2 speed features (average speed, speed fluctuation); - 2 pressure features (average pressure, pressure gradient); - 1 torque feature; - 1 flow feature; - 1 viscosity feature; - 1 stretching efficiency feature.

[0115] The original quality control model can be a Transformer model, and the architecture is: Input layer: convert the feature matrix to sequence representation; Position encoding: add time position information; Multi-head attention mechanism: capture the relevance between different time steps; Feedforward network: extract high-level feature representation; Output layer: predict three quality indicators: product density, product viscosity, and mixing uniformity index; Model training: train using historical production data, including 10,000 batches of data.

[0116] Train the original quality control model using historical production data to generate a trained quality control model; Then input the optimal parameter combination into the trained quality control model to generate a predicted quality control result, including density, viscosity, and uniformity. The predicted quality control result can be a 5-10 minute quality prediction value. Production efficiency is significantly improved, and through predictive control and optimized scheduling, the overall efficiency of the equipment is effectively improved, reducing abnormal downtime.

[0117] In an embodiment, the method further comprises: Judging whether the quality prediction value deviates from the target range; Generating a first specified strategy when the deviation of density exceeds a first specified range; Adjusting the screw speed, target temperature of each temperature zone, and vacuum degree according to the first specified strategy; Generating a second specified strategy when the deviation of viscosity exceeds a second specified range; Adjusting the screw speed, target temperature of each temperature zone, and slurry ratio according to the second specified strategy; Generating a second specified strategy when the deviation of viscosity exceeds a second specified range; Adjusting the stretching efficiency target, target temperature of each temperature zone, and mixing time according to the third specified strategy.

[0118] In the embodiments of the present application, according to the quality prediction result, it is determined whether the process parameters need to be adjusted, and the adjusted parameters are issued for execution. Closed-loop control is realized to ensure stable product quality and continuously optimize system performance.

[0119] Quality deviation judgment: if |ρ predict - ρ target | / ρ target > 0.02: adjustment is needed (density deviation exceeds 2%); i.e. the first specified range can be 0.02, and the first specified strategy can be: when the density is high, reduce the screw speed by 5%, reduce the temperature by 2°C, and increase the vacuum degree.

[0120] If |η predict - ηtarget | / η target 0.05: need to adjust (viscosity deviation exceeds 5%); that is, the second specified range is 0.05; the first specified strategy can be, when the viscosity is too high: increase the temperature by 3℃, increase the screw speed by 10%, check the slurry ratio.

[0121] If U predict <0.95: need to adjust (insufficient uniformity). The third specified range can be 0.95, and the third specified strategy can be to increase the stretching efficiency target value by 0.05, extend the mixing time, and optimize the temperature distribution.

[0122] In one embodiment, the working data further includes screw angular velocity, additional angular velocity generated by twin-screw interaction, and volume flow rate; the geometric parameters further include barrel eccentricity, inner barrel radius, and outer barrel radius; a velocity field model is established in a cylindrical coordinate system according to the working data and the geometric parameters, and the velocity field model includes radial velocity, circumferential velocity, and axial velocity. A cylindrical coordinate system is established with the axis of the slurry mixing and extruding equipment as the z-axis; A radial velocity component is determined according to the cylindrical coordinate system, the barrel eccentricity, the inner barrel radius, the outer barrel radius, and the screw angular velocity; A circumferential velocity component is determined according to the cylindrical coordinate system and the additional angular velocity; An axial velocity component is determined according to the cylindrical coordinate system, the volume flow rate, and the velocity distribution function.

[0123] In this embodiment, a cylindrical coordinate system (r, θ, z) is established with the axis of the extruder as the z-axis.

[0124] Velocity field equation derivation: Considering the eccentric rotation motion of the twin-screw, the three components of the velocity field are: Radial velocity component vr:

[0125] Wherein: e is the eccentricity (determined by geometric design, typical value 5mm); ω is the screw angular velocity; sinθ indicates that the velocity changes with the angle to form a periodic extrusion; (1-(r-ri) / h(θ)) indicates that the velocity gradually decreases from the rotor surface to the stator surface; Physical meaning: describes the extrusion and expansion motion of the slurry in the radial direction.

[0126] Circumferential velocity component vθ: vθ = ω·r + Δω(r,θ) where Δω is the additional angular velocity due to the twin-screw interaction; Physical meaning: describes the rotational motion and the circumferential transport of the slurry.

[0127] vz = Q / (π(ro 2 -ri 2 ))·f(r) where: Q is the volumetric flow rate; f(r) is the velocity distribution function (determined from rheology); Physical meaning: describes the transport of the slurry in the extrusion direction.

[0128] From the cylindrical coordinate system (r, θ, z), the radial velocity, the circumferential velocity, and the axial velocity obtained above, the velocity field model can be determined.

[0129] In one embodiment, obtaining the stretching efficiency index from the velocity gradient tensor comprises: decomposing the velocity gradient tensor to obtain a stretching rate tensor; determining a first eigenvalue, a second eigenvalue, and a third eigenvalue from the stretching rate tensor; determining a principal stretching strength from the first eigenvalue and the second eigenvalue; determining a total deformation strength from the first eigenvalue, the second eigenvalue, and the third eigenvalue; determining the stretching efficiency index from the principal stretching strength and the total deformation strength.

[0130] In this embodiment, the stretching rate tensor D D = (L + L^T) / 2 [0.50 0.165 0.425] D = [0.165 5.50 0.15 ] (unit: s -1 ) [0.425 0.15 1.50 ] Physical meaning of the stretching rate tensor D: diagonal elements (D11=0.50, D22=5.50, D33=1.50): represent the stretching / compression rates in the three coordinate directions; off-diagonal elements (D12=0.165, D13=0.425, D23=0.15): represent the shear deformation rates; Calculation of the vorticity tensor W W = (L - L^T) / 2 [0 -1.835 -0.375] W = [1.835 0 -0.05 ] (unit: s-1 ) [0.375 0.05 0 ] Physical meaning of vorticity tensor W: Vorticity tensor W is an anti-symmetric tensor, with diagonal elements being zero; Non-diagonal elements represent rotation rate; Does not contribute to mixing, but consumes energy.

[0131] For stretch rate tensor D, eigenvalue λ of stretch rate tensor D represents stretch rate in principal stretch direction: First eigenvalue λ1: maximum principal stretch rate; Second eigenvalue λ2: intermediate principal stretch rate; Third eigenvalue λ3: minimum principal stretch rate (may be negative, indicating compression).

[0132] Where eigenvalue λ satisfies: det(D - λl) = 0 Where I is the identity matrix, and det represents the determinant.

[0133] Expanded into a cubic equation in standard form: λ 3 -l1λ 2 +l2λ-I3= 0 Formula for calculating invariants: First invariant (trace): I1= tr(D) = D 11 + D 22 + D 33 Physical meaning: volumetric expansion rate Second invariant: I2 = (1 / 2)[(tr(D)) 2 - tr(D 2 )] = D 11 D 22 + D 22 D 33 + D 33 D11 - D 12 2 - D 23 2 - D 31 2 Physical meaning: area change rate Third invariant (determinant): I3 = det(D) = D 11 (D 22D 33 - D 23 2 ) - D 12 (D 12 D 33 - D 23 D 13 ) + D 13 (D 12 D 23 - D 22 D 13 ) Variable substitution Let λ = μ + I1 / 3, the original equation is transformed to: μ 3 +pμ+ q = 0 where: p = I2- I1 2 / 3 q = 2I1 3 / 27 - I1I2 / 3 + I3 Step 2: Discriminant calculation Δ= -(4p 3 + 27q 2 ) / 108 Expression of three real roots When Δ > 0 (symmetric matrix guaranteed): First eigenvalue λ1= I1 / 3 + 2√(-p / 3)cos(θ / 3) Second eigenvalue λ2 = I1 / 3 + 2√(-p / 3)cos((θ+ 2π) / 3) Third eigenvalue λ3= I1 / 3 + 2√(-p / 3)cos((θ+ 4π) / 3) where: θ= arccos(3q√(-3 / p) / 2p). Eigenvalues satisfy the equation: det(D - λI) = 0 where I is the identity matrix, and det represents the determinant.

[0134] Expanded as: |0.50-λ 0.165 0.425 | |0.165 5.50-λ 0.15 | = 0 |0.425 0.15 1.50-λ| Calculate the determinant Expand the third-order determinant to get the characteristic polynomial: -λ 3+ (0.50+5.50+1.50)λ 2 - [(0.50×5.50+0.50×1.50+5.50×1.50) - (0.165 2 +0.425 2 +0.15 2 )]λ+ det(D) = 0 Simplify to: -λ 3 + 7.50λ 2 - 11.47λ+ 3.89 = 0 Solve the cubic equation Solve using numerical methods (e.g. Newton-Raphson) or analytical methods (Cardan's formula) to get three real roots: First eigenvalue λ1= 5.58 s -1 (Maximum eigenvalue); Second eigenvalue λ2 = 1.47 s -1 (Middle eigenvalue); Third eigenvalue λ3= 0.45 s -1 (Minimum eigenvalue).

[0135] Two-dimensional principal tensile strength: Is = √(λ1 2 +λ2 2 ) Parameter description: λ1: Maximum eigenvalue (strongest tensile direction) λ2: Middle eigenvalue (second strongest tensile direction) 4.2 Total deformation strength Three-dimensional total deformation strength: Id = √(λ1 2 + λ2 2 + λ3 2 ) = √(tr(D 2 )) Definition formula of tensile efficiency index: ψ= Is / Id = √(λ1 2 +λ2 2 ) / √(λ1 2 + λ2 2 + λ3 2 ) Value range and physical meaning: ψ ∈ [0, 1] ψ → 1: Pure two-dimensional stretching (ideal) ψ → 0.577: Pure shear flow ψ < 0.5: 3D compression dominates.

[0136] Maximize tensile efficiency: maxψ= max{√(λ1 2 +λ2 2 ) / √(λ1 2 + λ2 2 + λ3 2 )} Constraints: λ1 2 + λ2 2 + λ3 2 = 0 (incompressible fluid); |λᵢ| < λ_max (physical limit).

[0137] Specifically, the principal tensile strength is defined as the tensile strength in the principal tensile plane (λ1-λ2 plane): Is = √(λ1 2 + λ2 2 ) = √(5.58 2 + 1.47 2 ) = √(31.14 + 2.16) = √33.30 = 5.77 s -1 Is represents the combined tensile strength in the two principal directions that contribute most to the mixture.

[0138] Calculation of total deformation strength Id: Total deformation strength includes deformation in all three directions: Id = √(λ1 2 + λ2 2 + λ3 2 ) = √(5.58 2 + 1.47 2 + 0.45 2 ) = √(31.14 + 2.16 + 0.20) = √33.50 = 5.79 s -1 Calculation of tensile efficiency index ψ: ψ = Is / Id = 5.77 / 5.79 = 0.997 Result analysis: ψ = 0.997 ≈ 1.0, which means that almost all the deformation is tensile deformation, and the shear component is very small. In this case, the mixing efficiency is extremely high.

[0139] Through the above calculation process, the tensile efficiency index can be analyzed and determined.

[0140] Determination of the optimal value: Through a large number of experiments and theoretical analysis, it is determined that: ψ < 0.5: shear dominated, low mixing efficiency, high energy consumption; 0.6 < ψ < 0.7: tensile dominant, optimal mixing efficiency; ψ > 0.8: excessive stretching, which may lead to slurry degradation.

[0141] Generate real-time tensile efficiency index ψ(t), which will be used as the core control target for controlling the screw speed. The embodiments of the present application establish the tensile efficiency index ψ as the core control target, and adjust the motion parameters of the double-shaft eccentric rotor to maintain ψ in the optimal interval of 0.60-0.70.

[0142] In a second aspect, the embodiments of the present application provide a system, comprising: A first acquisition module for acquiring working data, geometric parameters and slurry viscosity of a slurry mixing extrusion device; A first establishment module for establishing a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model including radial velocity, circumferential velocity and axial velocity; A first determination module for determining a velocity gradient tensor according to the velocity field model; A first obtaining module for obtaining a tensile efficiency index according to the velocity gradient tensor; A second determination module for adjusting parameters of a PID control algorithm according to the slurry viscosity to determine a specified PID control algorithm; A first generation module for generating a screw speed of the slurry mixing extrusion device according to the tensile efficiency index and the specified PID control algorithm.

[0143] The system of the embodiment improves the energy utilization efficiency by fully considering the shear deformation of the tensile deformation replacement part, calculates the tensile efficiency index of the three-dimensional tensile deformation of the slurry in real time, adjusts the screw rotation speed according to the tensile efficiency index and the specified PID algorithm corresponding to the optimized viscosity of the slurry, maintains the tensile efficiency in the optimal range, improves the working efficiency of the slurry mixing and extruding equipment, reduces the energy consumption, and effectively solves the technical problem that the existing equipment mainly relies on shear action to realize mixing and ignores the important role of tensile deformation in dispersion and mixing. The existing equipment only considers the shear action and simplifies the mixing process into one-dimensional shear flow. This simplification ignores the three-dimensional characteristics of the actual flow field and does not consider the key role of tensile deformation in mixing. Only relying on shear action mixing leads to low efficiency of extrusion processing.

[0144] In an embodiment, the working data includes temperature data, and the method further comprises: dividing the temperature zones of the barrel of the slurry mixing and extruding equipment according to the temperature data, and determining a plurality of temperature zones; obtaining the state of the slurry; determining the temperature of each temperature zone according to the state of the slurry; determining the actual shear heat generation power according to the screw rotation speed; obtaining the reference shear heat generation power; determining the heating compensation power according to the actual shear heat generation power and the reference shear heat generation power; determining the target temperature of each temperature zone and the heating efficiency of each temperature zone according to the heating compensation power, the temperature difference constraint of adjacent temperature zones, and the temperature of each temperature zone.

[0145] In an embodiment, the working data further includes mass flow data, slurry concentration variation coefficient, total output data, qualified rate data, pressure data, and torque data; the geometric parameter includes motor power; and the method further comprises: determining a minimum unit energy consumption model according to the mass flow data, the motor power, and the heating efficiency of each temperature zone; determining a maximum mixing uniformity model according to the slurry concentration variation coefficient; determining a maximum effective production rate according to the total output data and the qualified rate data; obtaining a temperature safety factor, a pressure safety factor, and a torque safety factor; determining a maximum safety index model according to the temperature safety factor, the pressure safety factor, and the torque safety factor; determining an optimal parameter combination containing optimal values of all decision variables by multi-objective optimization algorithm for multi-objective optimization of the minimum unit energy consumption model, the maximum mixing uniformity model, the maximum effective production rate, and the maximum safety index model.

[0146] In one implementation, the method further includes: Obtain historical production data; The original quality control model is trained using historical production data to generate a trained quality control model. The optimal parameter combination is input into the trained quality control model to generate predicted quality control results, including density, viscosity, and uniformity.

[0147] In one implementation, the method further includes: Determine whether the predicted quality value deviates from the target range; When the density deviation exceeds a first specified range, a first specified strategy is generated; Adjust the screw speed, target temperature of each temperature zone, and vacuum level according to the first specified strategy; When the viscosity deviation exceeds the second specified range, a second specified strategy is generated; According to the second specified strategy, adjust the screw speed, the target temperature of each temperature zone, and the slurry ratio; When the viscosity deviation exceeds the second specified range, a second specified strategy is generated; According to the third specified strategy, adjust the stretching efficiency target, the target temperature of each temperature zone, and the mixing time.

[0148] In one embodiment, the working data also includes the screw angular velocity, the additional angular velocity generated by the interaction of the twin screws, and the volumetric flow rate; the geometric parameters also include the barrel eccentricity, the barrel inner radius, and the barrel outer radius. Based on the working data and geometric parameters, a velocity field model is established in a cylindrical coordinate system. The velocity field model includes radial velocity, circumferential velocity, and axial velocity. A cylindrical coordinate system is established with the axis of the slurry mixing and extrusion equipment as the z-axis; The radial velocity component is determined based on the cylindrical coordinate system, the barrel eccentricity, the barrel inner radius, the barrel outer radius, and the screw angular velocity. Determine the circumferential velocity component based on the cylindrical coordinate system and the additional angular velocity; The axial velocity component is determined based on the cylindrical coordinate system, volumetric flow rate, and velocity distribution function.

[0149] In one implementation, the tensile efficiency index is obtained based on the velocity gradient tensor, including: The velocity gradient tensor is decomposed to obtain the stretchability tensor; Based on the stretching tensor, determine the first eigenvalue, the second eigenvalue, and the third eigenvalue; The principal tensile strength is determined based on the first and second eigenvalues. The total deformation strength is determined based on the first eigenvalue, the second eigenvalue, and the third eigenvalue. The tensile efficiency index is determined based on the principal tensile strength and the total deformation strength.

[0150] The functions of each module in each device in the embodiments of this application can be found in the corresponding descriptions in the above methods, and will not be repeated here.

[0151] Figure 2 A structural block diagram of an electronic device according to an embodiment of this application is shown. Figure 2 As shown, the electronic device includes a memory 410 and a processor 420. The memory 410 stores instructions that can be executed on the processor 420. When the processor 420 executes the instructions, it implements the slurry extrusion method in the above embodiments. The number of memories 410 and processors 420 can be one or more. This electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0152] The electronic device may also include a communication interface 430 for communicating with external devices and exchanging data. The devices are interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor 420 can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0153] Optionally, in a specific implementation, if the memory 410, processor 420 and communication interface 430 are integrated on a single chip, the memory 410, processor 420 and communication interface 430 can communicate with each other through an internal interface.

[0154] It should be appreciated that the above processor can be a central processing unit (CPU), and can also be other general purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general purpose processor can be a microprocessor or any conventional processor, etc. It is worth noting that the processor can be an advanced RISC machines (ARM) architecture processor.

[0155] The computer readable storage medium (such as the memory 410 described above) provided by the embodiments of the present application stores computer instructions, and the program is executed by the processor to implement the method provided by the embodiments of the present application.

[0156] Optionally, the memory 410 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 410 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 410 can optionally include a memory disposed remotely with respect to the processor 420, and these remote memories can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0157] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0158] In addition, the terms "first", "second", etc. are used herein only to describe different instances, and cannot be construed as indicating or implying relative importance or an indicated number of the technical features. Thus, the features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0159] Any process or method descriptions or descriptions of the flow diagrams described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for performing specific logic functions or steps in the process. And the various embodiments of the application can include additional or fewer steps or processes in addition to or other than those of the specific embodiments described.

[0160] The logic and / or steps represented in the flow diagrams described herein, for example, can be considered as a sequence of executable instructions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from a medium and execute the instructions.

[0161] It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above-described embodiment methods can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer-readable storage medium, and when executed, includes one or a combination of steps of the method embodiments.

[0162] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software function module. The above-mentioned integrated module, if realized in the form of a software function module and sold or used as an independent product, can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0163] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various changes or replacements within the technical range disclosed by the present application, and these should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A slurry extrusion method characterized by, The method comprises: acquiring working data, geometric parameters and slurry viscosity of a slurry mixing and extruding device; establishing a velocity field model in a cylindrical coordinate system according to the working data and the geometric parameters, the velocity field model comprising radial velocity, circumferential velocity and axial velocity; determining a velocity gradient tensor according to the velocity field model; obtaining a stretching efficiency index according to the velocity gradient tensor; adjusting parameters of a PID control algorithm according to the slurry viscosity to determine a specified PID control algorithm; generating a screw rotation speed of the slurry mixing and extruding device according to the stretching efficiency index and the specified PID control algorithm.

2. The method of claim 1, wherein, The working data comprises temperature data, and the method further comprises: dividing a temperature zone of a barrel of the slurry mixing and extruding device according to the temperature data to determine a plurality of temperature zones; acquiring a slurry state; determining a temperature of each of the temperature zones according to the slurry state; determining an actual shear heat generation power according to the screw rotation speed; acquiring a reference shear heat generation power; determining a heating compensation power according to the actual shear heat generation power and the reference shear heat generation power; determining a target temperature of each of the temperature zones and a heating efficiency of each of the temperature zones according to the heating compensation power, a temperature difference constraint between adjacent temperature zones and the temperature of each of the temperature zones.

3. The method of claim 2, wherein, The working data further comprises mass flow data, slurry concentration variation coefficient, total output data, qualified rate data, pressure data and torque data; the geometric parameters comprise motor power; and the method further comprises: determining a minimum unit energy consumption model according to the mass flow data, the motor power and the heating efficiency of each of the temperature zones; determining a maximum mixing uniformity model according to the slurry concentration variation coefficient; determining a maximum effective production rate according to the total output data and the qualified rate data; acquiring a temperature safety factor, a pressure safety factor and a torque safety factor; determining a maximum safety index model according to the temperature safety factor, the pressure safety factor and the torque safety factor; determining an optimal parameter combination by multi-objective optimization algorithm to solve the minimum unit energy consumption model, the maximum mixing uniformity model, the maximum effective production rate and the maximum safety index model, and the optimal parameter combination comprises optimal values of all decision variables.

4. The method of claim 3, wherein, The method further comprises: acquiring historical production data; training an original quality control model by the historical production data to generate a trained quality control model; inputting the optimal parameter combination into the trained quality control model to generate a predicted quality control result, and the quality prediction value comprises density, viscosity and uniformity.

5. The method of claim 4, wherein, The method further comprises: judging whether the quality prediction value deviates from a target range; generating a first specified strategy when the deviation of the density exceeds a first specified range; adjusting the screw rotation speed, the target temperature of each of the temperature zones and the vacuum degree according to the first specified strategy; generating a second specified strategy when the deviation of the viscosity exceeds a second specified range; adjusting the screw rotation speed, the target temperature of each of the temperature zones and the slurry ratio according to the second specified strategy; generating a second specified strategy when the deviation of the viscosity exceeds a second specified range; According to the third specified strategy, the stretching efficiency target, the target temperature of each temperature zone and the mixing time are adjusted.

6. The method of claim 5, wherein, The working data further include screw angular velocity, additional angular velocity generated by twin-screw interaction and volume flow rate; the geometric parameters further include barrel eccentricity, inner radius of the barrel and outer radius of the barrel; the velocity field model is established under the cylindrical coordinate system according to the working data and the geometric parameters, and the velocity field model includes radial velocity, circumferential velocity and axial velocity; A cylindrical coordinate system is established with the axis of the slurry mixing and extruding device as the z-axis; According to the cylindrical coordinate system, the barrel eccentricity, the inner radius of the barrel, the outer radius of the barrel and the screw angular velocity, a radial velocity component is determined; According to the cylindrical coordinate system and the additional angular velocity, a circumferential velocity component is determined; According to the cylindrical coordinate system, the volume flow rate and the velocity distribution function, an axial velocity component is determined.

7. The method of claim 6, wherein, The stretching efficiency index is obtained according to the velocity gradient tensor, including: The velocity gradient tensor is decomposed to obtain a stretching rate tensor; According to the stretching rate tensor, a first eigenvalue, a second eigenvalue and a third eigenvalue are determined; According to the first eigenvalue and the second eigenvalue, a main stretching intensity is determined; According to the first eigenvalue, the second eigenvalue and the third eigenvalue, a total deformation intensity is determined; According to the main stretching intensity and the total deformation intensity, a stretching efficiency index is determined.

8. A system, characterized by including: The first obtaining module is configured to obtain working data, geometric parameters and slurry viscosity of a slurry mixing and extruding device; The first establishing module is configured to establish a velocity field model under a cylindrical coordinate system according to the working data and the geometric parameters, and the velocity field model includes radial velocity, circumferential velocity and axial velocity; The first determining module is configured to determine a velocity gradient tensor according to the velocity field model; The first obtaining module is configured to obtain a stretching efficiency index according to the velocity gradient tensor; The second determining module is configured to determine a specified PID control algorithm by adjusting parameters of a PID control algorithm according to the slurry viscosity; The first generating module is configured to generate screw rotating speed of the slurry mixing and extruding device according to the stretching efficiency index and the specified PID control algorithm.

9. An electronic device, comprising: including: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are executed by the processor to implement the method in any one of claims 1-7.

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