A method for optimizing cooling process parameters of a fully biodegradable mulch film

Abstract

The present application relates to a kind of full biodegradable mulch cooling process parameter optimization method.The present application determines the adjustable parameter vector of decision variable group in full biodegradable mulch cooling process, and according to the parameter of equipment, the range of decision variable is determined;Optimization target of full biodegradable mulch cooling process is constructed;The optimization target constructed includes the haze target of full biodegradable mulch, the toughness target, the property uniformity target and the generation energy consumption target;Cooling process constraint condition is constructed, including: freezing line height fluctuation constraint, winding temperature constraint, blow film pressure constraint and total cooling time constraint;The adjustable parameter vector of optimal solution is found by using mixed population intelligence optimization, in the premise of meeting the set constraint, according to optimization target positioning Pareto optimal solution set.This application converts complex process debugging into a data-driven intelligent optimization process, realizes the accurate configuration of full biodegradable mulch cooling process parameter.

Description

Technical Field

[0001] This invention relates to the field of biofilm cooling control technology, and in particular to a method for optimizing cooling process parameters of fully biodegradable mulch films. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Biodegradable mulch films, as an environmentally friendly alternative to traditional polyethylene mulch films, can effectively solve the problem of white pollution because they can be decomposed by microorganisms in the natural environment. Biodegradable mulch films are typically manufactured using polylactic acid, polyhydroxyalkanoates, polybutylene succinate, or blends thereof as base materials through blow molding. However, compared to mature polyethylene mulch film materials, bio-based polymers face unique challenges in the blown film processing, especially in the cooling and setting stage. Process control at this stage directly determines the final crystallization morphology, crystal size, internal stress distribution, and thickness uniformity of the film, thus decisively affecting the product's performance.

[0004] Currently, the industry's control over the cooling process of biodegradable mulch films mainly relies on engineers' long-term experience and repeated trial and error. Operators manually adjust parameters such as the wind speed and temperature of the external cooling air ring and the airflow of the internal cooling system (IBC) in an attempt to find an acceptable balance between transparency and toughness. This experience-driven approach has many inherent drawbacks, such as low efficiency and high cost. When developing new products or changing raw material batches, it often requires several days of extensive machine testing, resulting in a huge waste of raw materials and energy. Experience-driven approaches also make it difficult to achieve multi-objective synergistic optimization and to balance the overall quality of the product with production economics.

[0005] Therefore, there is an urgent need for a data-driven, mathematical modeling, and intelligent decision-making approach that moves from engineering experience to achieve precise configuration of the cooling process for biodegradable mulch films. Summary of the Invention

[0006] To solve the above-mentioned technical problems, or at least partially solve them, the present invention provides a method for optimizing the cooling process parameters of a fully biodegradable mulch film.

[0007] This invention provides a method for optimizing cooling process parameters of a fully biodegradable mulch film, comprising:

[0008] Determine the composition of adjustable parameter vectors for decision variables in the fully biodegradable mulch cooling process. The decision variables are determined within a range based on the equipment parameters; the decision variables include: the average wind speed of the external cooling air ring. External cooling air temperature Height of the refrigeration line controlled by a combination of air volume and air temperature Internal cooling system airflow External / internal cooling intensity ratio ;

[0009] The optimization objectives for the cooling process of fully biodegradable mulch film were established. The established optimization objectives include the haze target, toughness target, property uniformity target, and generation energy consumption target of the fully biodegradable mulch film.

[0010] Establish cooling process constraints, including: freezing line height fluctuation constraints, winding temperature constraints, blown film pressure constraints, and total cooling time constraints;

[0011] Hybrid swarm intelligence optimization is used to find the adjustable parameter vector of the optimal solution. Under the premise of satisfying the set constraints, the Pareto optimal solution set is located on the hypersurface where the adjustable parameter vector is located according to the optimization objective. The corresponding cooling process parameters are selected from the Pareto optimal solution set according to the requirements.

[0012] Furthermore, the haze target for fully biodegradable mulch film is expressed as follows:

[0013] ;

[0014] in, The haze value is for fully biodegradable mulch film; The average crystallization temperature of the fully biodegradable mulch film is determined by the cooling rate; the faster the cooling, the higher the crystallization temperature. The lower; Material parameters obtained by fitting DSC data and historical production data of fully biodegradable mulch film; It is the phase transition temperature between the crystalline and molten states of fully biodegradable mulch film materials.

[0015] Furthermore, the resilience target is expressed as:

[0016] ;

[0017] in, The breaking elongation of the fully biodegradable mulch film, The temperature gradient between the inside and outside of the cross-section of the fully biodegradable mulch film. The average crystallization temperature of the fully biodegradable mulch film, determined by the cooling rate. The smaller the value, the lower the internal stress and the better the toughness; Toughness-related parameters were obtained by fitting historical production data of fully biodegradable mulch film materials and cooling processes.

[0018] Furthermore, considering the thickness and longitudinal tensile strength of the fully biodegradable mulch film, the property uniformity is expressed as:

[0019] ;

[0020] in, This is the square of the standard deviation of the transverse thickness of the film; This is the square of the standard deviation of the longitudinal tensile strength of the fully biodegradable mulch film. The standard deviation of the longitudinal tensile strength is related to the standard deviation of the frost line height, which describes the stability of the frost line. Related, modeled as .

[0021] Furthermore, the production energy consumption cost target is expressed as:

[0022] ;

[0023] in, , where are weighting coefficients, representing the energy cost proportions of the fan, refrigeration / heating, and internal cooling systems, respectively; the production energy cost target is a normalized weighted sum model, aiming to reduce all cooling-related energy consumption.

[0024] Furthermore, the constraint on the height fluctuation of the freezing line is expressed as: , The threshold for freezing line height fluctuation; the winding temperature constraint is expressed as: ;in, The winding temperature for fully biodegradable mulch film, The adhesion temperature of the fully biodegradable mulch film material; the blown film pressure constraint is expressed as: ;in, This represents the maximum blown film pressure. The critical pressure value for membrane rupture; total cooling time constraint requirements. It must be less than the minimum allowable traction cycle time on the production line. , is represented as: .

[0025] Furthermore, hybrid swarm intelligence optimization includes:

[0026] Within the domain of the adjustable parameter vector space, N particle populations are randomly generated; the optimal position for each particle is initialized. and speed ; Construct an archive A to store all currently found non-dominated solutions;

[0027] For the current particle population, select K role models based on non-dominated sorting, and then perform clustering based on these role models.

[0028] For any cluster, the particles within the cluster are updated by mimicking the model within the cluster and other random models according to the following formula;

[0029] The particles are updated by combining individual optimal positioning and global optimal experience;

[0030] For some optimized solutions in the current file A, perform a local search for better solutions based on cooling process knowledge, and add the discovered better solutions to the population;

[0031] Evaluate all objectives and constraints of the new population, update the individual optimal position of each particle, and update the archive to preserve all non-dominated solutions;

[0032] If the termination condition is met, output the Pareto optimal solution set in the file; otherwise, iterate.

[0033] Furthermore, for any cluster, the particles within the cluster The update process, which mimics role models within a cluster and incorporates random perturbations from other role models, includes: calculating role models within the cluster by considering teaching factors. and cluster average Differences F represents the teaching factor; any role model is randomly selected from those outside the cluster. Calculate the relationship between the particle and the selected model. Differences ;

[0034] particle By random introduction To fill the gap with the role model in its cluster, the particle By random introduction Introduce random perturbations following other examples:

[0035] ;

[0036] in, This means taking a random value within the range of 0 to 1. This indicates that a random value is selected within the range of -1 to 1.

[0037] Furthermore, particles are updated by combining their individual best position with global best experience, including:

[0038] Update the speed for step t+1 based on the individual's best position and the global best position:

[0039] ;

[0040] Update the particle position in step t+1 using the velocity and position in step t:

[0041] ;

[0042] in, For particles Cluster The best historical position was selected from archive A. Inertial weights control the particle's exploration range. As a learning factor, It is a random number.

[0043] Secondly, the present invention provides a device for optimizing cooling process parameters of fully biodegradable mulch film, comprising: at least one processing unit, wherein the processing unit is connected to a storage unit and a biodegradable mulch film preparation device via a bus unit, the storage unit stores a computer program that can run on a processor, and the processing unit implements the method for optimizing cooling process parameters of fully biodegradable mulch film by running the computer program stored in the storage unit.

[0044] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art:

[0045] This application defines an adjustable parameter vector as the decision variables in the cooling process of fully biodegradable mulch film, and determines the range of these decision variables based on equipment parameters. It constructs an optimization objective for the cooling process, including targets for haze, toughness, property uniformity, and energy consumption. Cooling process constraints are established, including constraints on freezing line height fluctuation, winding temperature, blown film pressure, and total cooling time. A hybrid swarm intelligence optimization approach is used to find the adjustable parameter vector for the optimal solution. Under the premise of satisfying the set constraints, the Pareto optimal solution set is located according to the optimization objective. This invention elevates the cooling process of fully biodegradable mulch film from experience-driven to mathematical modeling and intelligent optimization. The cooling process considers the transparency, mechanical toughness, and film thickness of the fully biodegradable mulch film, achieving multi-objective collaborative optimization and resulting in significant technological progress and economic benefits. It can reduce the overall energy consumption of the cooling system while ensuring product quality, and significantly improve the transverse thickness uniformity and longitudinal mechanical property consistency of the film, reducing the scrap rate. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of a method for optimizing cooling process parameters of fully biodegradable mulch film provided in an embodiment of the present invention;

[0049] Figure 2A flowchart for hybrid swarm intelligence optimization provided in an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the current particle swarm provided for an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the current particle swarm clustering provided in an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the current particle swarm clustering and update provided in an embodiment of the present invention;

[0053] Figure 6 This is a schematic diagram of a device for optimizing the cooling process parameters of a fully biodegradable mulch film, as provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] Example 1

[0057] like Figure 1 As shown, the present invention provides a method for optimizing the cooling process parameters of a fully biodegradable mulch film, comprising:

[0058] S100, determine the decision variables in the fully biodegradable mulch film cooling process, and define the range of these decision variables based on the equipment parameters. Decision variables include: average wind speed of the external cooling air ring. External cooling air temperature Height of the refrigeration line controlled by a combination of air volume and air temperature Internal cooling system airflow External / internal cooling intensity ratio Then all decision variables constitute the adjustable parameter vector of the biofilm cooling process. , , These are the lower limit vector and the upper limit vector, respectively. The elements of the lower limit vector correspond to the lower limits of the average wind speed of the external cooling air ring, the external cooling air temperature, the refrigeration line height controlled by the air volume and air temperature, the air volume of the internal cooling system, and the external / internal cooling intensity ratio. The elements of the upper limit vector correspond to the upper limits of the average wind speed of the external cooling air ring, the external cooling air temperature, the refrigeration line height controlled by the air volume and air temperature, the air volume of the internal cooling system, and the external / internal cooling intensity ratio, respectively.

[0059] The multidimensional control space of the fully biodegradable mulch cooling process, constructed using adjustable parameter vectors, directly determines the physical state of the fully biodegradable mulch during its formation. The average wind speed of the external cooling air ring... External cooling air temperature These constitute the constraints for external heat exchange, controlling the convective heat transfer coefficient and the temperature difference driving force, respectively. (Refrigeration line height) The geometric result of the coupling effect of airflow and air temperature determines where the melt completes the transition from a viscous flow state to a highly elastic state. The airflow of the internal cooling system and the external / internal cooling intensity ratio R introduce adjustments to the internal cooling dimension to correct the uniformity of the wall thickness distribution.

[0060] S200 is the optimization target for constructing a fully biodegradable mulch film cooling process.

[0061] In the specific implementation process, the optimization objectives include the haze target of the fully biodegradable mulch film, aiming to minimize the haze of the fully biodegradable mulch film, that is, maximize its transparency.

[0062] The haze target for fully biodegradable mulch film is expressed as follows:

[0063] ;

[0064] in, The haze value is for fully biodegradable mulch film; The average crystallization temperature of the fully biodegradable mulch film is determined by the cooling rate; the faster the cooling, the higher the crystallization temperature. The lower. Material parameters obtained by fitting DSC data and historical production data of fully biodegradable mulch film. It is the phase transition temperature between the crystalline and molten states of a fully biodegradable mulch film material. A haze target indicates a faster cooling rate. The lower the value, the more inhibited the crystallization, resulting in lower haze and higher transparency.

[0065] The constructed optimization objective includes the toughness objective of the fully biodegradable mulch film. To unify the optimization objective into minimization, the toughness objective takes the reciprocal of the elongation at break. The toughness objective is expressed as:

[0066] ;

[0067] in, The elongation at break (%) of the fully biodegradable mulch film. The temperature gradient between the inside and outside of the cross-section of the fully biodegradable mulch film is controlled by internal cooling and wind circulation. The smaller the value, the lower the internal stress and the better the toughness. Toughness-related parameters were obtained by fitting historical production data of the fully biodegradable mulch film material and cooling process. The toughness target describes the relationship between the average crystallization temperature affected by the cooling process and the temperature gradient between the inside and outside of the cross-section of the fully biodegradable mulch film and its toughness.

[0068] The constructed optimization objective includes the property uniformity objective of the fully biodegradable mulch film, which takes into account the thickness and longitudinal tensile strength of the fully biodegradable mulch film.

[0069] ;

[0070] in, It is the square of the standard deviation of the transverse thickness of the film. This is the square of the standard deviation of the longitudinal tensile strength of the fully biodegradable mulch film. The standard deviation of the longitudinal tensile strength is related to the standard deviation of the frost line height, which describes the stability of the frost line. Related, modeled as .

[0071] In the production process of biodegradable mulch film, the property uniformity objective constructs a two-way coupled penalty mechanism, which combines the following two aspects: the film thickness controlled by the automatic air circulation system in the transverse direction of the biodegradable mulch film, and the uniformity of the longitudinal molecular orientation, i.e., tensile strength, controlled by the high stability of the freezing line. If either aspect experiences a significant fluctuation (e.g., freezing line vibration causing a sharp drop in tensile strength), the property uniformity objective will increase significantly, regardless of how perfectly the thickness is controlled. Therefore, optimizing the property uniformity objective essentially forces the system to find a globally optimal balance between the air circulation response speed and the constant temperature control of the freezing line.

[0072] In addition to the sub-objectives related to fully biodegradable mulch film, this application also considers generating energy consumption targets, the optimization targets including production energy consumption cost targets, expressed as:

[0073] ;

[0074] in, , where are weighting coefficients, representing the energy cost proportions of the fan, refrigeration / heating, and internal cooling systems, respectively. The production energy cost target is a normalized weighted sum model, aiming to reduce all cooling-related energy consumption.

[0075] S300, during the optimization of cooling process constraints, the established cooling process constraints must be followed to ensure the stability of the production process and the quality and safety of the final product. These cooling process constraints include: freezing line height fluctuation constraints, winding temperature constraints, blown film pressure constraints, and total cooling time constraints.

[0076] The constraint on the height fluctuation of the freezing line is expressed as follows: , This is the threshold for fluctuations in the freezing line height. Fluctuations in the actual freezing line height are only controllable when they are strictly suppressed within this threshold; exceeding it will result in product defects.

[0077] The winding temperature constraint is expressed as: ;in, The winding temperature for fully biodegradable mulch film, This refers to the adhesion temperature of fully biodegradable mulch film. In the manufacturing process of fully biodegradable mulch film, winding is a physical process of rolling continuous fully biodegradable mulch film into a roll. When the fully biodegradable mulch film is wound, the layers are in close contact under enormous winding tension. If the winding temperature is too high, the thermal motion of the polymer molecular chains intensifies, causing physical entanglement at the interlayer interfaces, leading to interlayer adhesion. Once adhesion occurs, subsequent slitting and unwinding processes will result in damage to the film surface.

[0078] The blown film pressure constraint is expressed as: ;in, This represents the maximum blown film pressure. This represents the critical pressure value for membrane rupture. As the internal pressure increases, the membrane wall tension increases non-linearly. When the maximum blown membrane pressure reaches the critical pressure value for membrane rupture, which describes the material's yield strength, even a small disturbance can lead to structural instability and rupture.

[0079] Total Cooling Time Constraints: Total Cooling Time It must be less than the minimum allowable traction cycle time on the production line. To ensure production efficiency, it is expressed as: .

[0080] S400 employs a hybrid swarm intelligence optimization approach to find the optimal solution for the adjustable parameter vector. Under the premise of satisfying set constraints, it locates the Pareto optimal solution set on the hypersurface where the adjustable parameter vector lies, according to the optimization objective. For example... Figure 2 As shown, it includes:

[0081] S401, within the domain of the adjustable parameter vector space, randomly generates a population of N particles, represented as: Initialize the individual optimal position for each particle. and speed ; Construct an archive A that stores all currently found non-dominated solutions.

[0082] To endow particles with exploratory capabilities, they are given a velocity attribute. Velocity determines the direction and step size of the particle's next movement during exploration. Initially, the velocity is a small random value simulating thermal motion. (Personal optimal position) It is the short-term memory of a particle; in the initial moment, before iteration has begun, it usually places the particle at its optimal position. Set this to the particle's current position. External file A stores all non-dominated solutions during the iteration process, i.e., optimal solutions on the Pareto front.

[0083] S402, for the current particle population, select K role models based on non-dominated sorting, and perform clustering based on these role models, including:

[0084] For the current particle population Perform a quick nondominated sort;

[0085] From the solutions with the highest frontier level, K role models are selected based on the advantages of sub-objectives set according to the optimization objective, denoted as: ;

[0086] Calculate the distance or similarity between each particle and each role model, and dynamically assign the particles to different clusters based on the distance or similarity between the particles and each role model. Clustering Take role models It serves as the clustering core.

[0087] S403, for any cluster, the particles within the cluster The update process, which mimics role models within a cluster and incorporates random perturbations from other role models, includes: calculating role models within the cluster by considering teaching factors. and cluster average Differences F represents the teaching factor; any role model is randomly selected from those outside the cluster. Calculate the relationship between the particle and the selected model. Differences ;

[0088] particle By random introduction To fill the gap with the role model in its cluster, the particle By random introduction Introduce random perturbations following other examples:

[0089] ;

[0090] in, This means taking a random value within the range of 0 to 1. This indicates that a random value is selected within the range of -1 to 1.

[0091] Each particle within the cluster The following formula is used to mimic the role models within a cluster, and random perturbations are introduced to update the data, referencing random role models:

[0092] ;

[0093] in, This indicates that a random value is selected within the range of 0 to 1. This indicates that a random value is selected within the range of -1 to 1.

[0094] Boundary checks and repairs are performed on the updated particles. If the set cooling process constraints are violated, a penalty term is applied to the optimization objective function value, making it disadvantaged in non-dominated sorting.

[0095] The particle swarm variations involved in S402-S403 are as follows: Figures 3 to 5 As shown,

[0096] S404 updates particles by combining individual best position and global best experience, including:

[0097] Update the speed for step t+1 based on the individual's best position and the global best position:

[0098] ;

[0099] Update the particle position in step t+1 using the velocity and position in step t:

[0100] ;

[0101] in, For particles Cluster The best historical position is selected from archive A. Inertial weights control the particle's exploration range. For learning factors. It is a random number. Controlling particle updates to move closer to the individual's optimal position. This allows particles to move to their historical best position within a cluster during updates, enabling optimal information sharing and collaborative evolution within the cluster.

[0102] S405, for some optimized solutions in the current file A, perform a local search for better solutions based on cooling process knowledge, and add the discovered better solutions to the population.

[0103] Search along the height of the isotherm of the external cooling air temperature and the average wind speed of the external cooling air ring to find a combination of external cooling air temperature and average wind speed with lower energy consumption.

[0104] S406 evaluates all objectives and constraints of the new population, updates the individual optimal position of each particle, updates the archive to preserve all non-dominated solutions, and controls the archive size.

[0105] S407: If the termination condition is met, output the Pareto optimal solution set in the archive. Termination conditions include: maximum number of iterations, convergence of the archive solution set.

[0106] S500 selects the appropriate cooling process parameters from the Pareto optimal solution set according to requirements. It demonstrates the trade-offs between transparency, toughness, energy consumption, and uniformity corresponding to the adjustable parameter vector of the fully biodegradable mulch film cooling process in the Pareto optimal solution set. Based on actual production needs, the final process parameter setpoints are selected from the Pareto optimal solution set.

[0107] This application's hybrid population intelligent optimization effectively guides the population to explore multiple performance extrema simultaneously, maintaining the breadth and diversity of the Pareto frontier. It combines directional guidance within clusters with empirical utilization of PSO (Pareto Optimization Search) to balance exploration and exploitation. Local search and cluster partitioning incorporate an understanding of the physics of the cooling process, significantly improving optimization efficiency and avoiding blind searches. This application transforms complex process debugging into a data-driven intelligent optimization process, achieving precise configuration of cooling process parameters for fully biodegradable mulch films.

[0108] Example 2

[0109] like Figure 6 As shown, this embodiment of the invention provides a device for optimizing cooling process parameters of a fully biodegradable mulch film, comprising: at least one processing unit, wherein the processing unit is connected to a storage unit and a biodegradable mulch film preparation device via a bus unit, the storage unit serving as a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as the software program, computer-executable program, and modules corresponding to a method for optimizing cooling process parameters of a fully biodegradable mulch film in this embodiment of the invention. The processing unit implements the aforementioned method for optimizing cooling process parameters of a fully biodegradable mulch film by running the software program, computer-executable program, and modules stored in the storage unit, including:

[0110] Determine the composition of adjustable parameter vectors for decision variables in the fully biodegradable mulch cooling process. The decision variables are determined within a range based on the equipment parameters; the decision variables include: the average wind speed of the external cooling air ring. External cooling air temperature Height of the refrigeration line controlled by a combination of air volume and air temperature Internal cooling system airflow External / internal cooling intensity ratio ;

[0111] The optimization objectives for the cooling process of fully biodegradable mulch film were established. The established optimization objectives include the haze target, toughness target, property uniformity target, and generation energy consumption target of the fully biodegradable mulch film.

[0112] Establish cooling process constraints, including: freezing line height fluctuation constraints, winding temperature constraints, blown film pressure constraints, and total cooling time constraints;

[0113] Hybrid swarm intelligence optimization is used to find the adjustable parameter vector of the optimal solution. Under the premise of satisfying the set constraints, the Pareto optimal solution set is located on the hypersurface where the adjustable parameter vector is located according to the optimization objective. The corresponding cooling process parameters are selected from the Pareto optimal solution set according to the requirements.

[0114] Of course, the computer program stored in the memory of the fully biodegradable mulch film cooling process parameter optimization device provided in the embodiments of the present invention is not limited to the method operation described above, but can also execute related operations in the fully biodegradable mulch film cooling process parameter optimization method provided in any embodiment of the present invention.

[0115] Example 3

[0116] This invention provides a computer-readable storage medium storing a computer program, characterized in that, when executed, the computer program implements the method for optimizing the cooling process parameters of the fully biodegradable mulch film, comprising:

[0117] Determine the composition of adjustable parameter vectors for decision variables in the fully biodegradable mulch cooling process. The decision variables are determined within a range based on the equipment parameters; the decision variables include: the average wind speed of the external cooling air ring. External cooling air temperature Height of the refrigeration line controlled by a combination of air volume and air temperature Internal cooling system airflow External / internal cooling intensity ratio ;

[0118] The optimization objectives for the cooling process of fully biodegradable mulch film were established. The established optimization objectives include the haze target, toughness target, property uniformity target, and generation energy consumption target of the fully biodegradable mulch film.

[0119] Establish cooling process constraints, including: freezing line height fluctuation constraints, winding temperature constraints, blown film pressure constraints, and total cooling time constraints;

[0120] Hybrid swarm intelligence optimization is used to find the adjustable parameter vector of the optimal solution. Under the premise of satisfying the set constraints, the Pareto optimal solution set is located on the hypersurface where the adjustable parameter vector is located according to the optimization objective. The corresponding cooling process parameters are selected from the Pareto optimal solution set according to the requirements.

[0121] In the embodiments provided by this invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, structures, or units, and may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for optimizing cooling process parameters of a fully biodegradable mulch film, characterized in that, include: Determine the composition of adjustable parameter vectors for decision variables in the fully biodegradable mulch cooling process. And determine the range of decision variables based on the parameters of the equipment; Decision variables include: average airflow speed of the external cooling air ring. External cooling air temperature Height of the refrigeration line controlled by a combination of air volume and air temperature Internal cooling system airflow External / internal cooling intensity ratio ; The optimization objectives for the cooling process of fully biodegradable mulch film were established. The established optimization objectives include the haze target, toughness target, property uniformity target, and generation energy consumption target of the fully biodegradable mulch film. Establish cooling process constraints, including: freezing line height fluctuation constraints, winding temperature constraints, blown film pressure constraints, and total cooling time constraints; Hybrid swarm intelligence optimization is used to find the adjustable parameter vector of the optimal solution. Under the premise of satisfying the set constraints, the Pareto optimal solution set is located on the hypersurface where the adjustable parameter vector is located according to the optimization objective. The corresponding cooling process parameters are selected from the Pareto optimal solution set according to the requirements.

2. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, The haze target for fully biodegradable mulch film is expressed as follows: ； in, The haze value is for fully biodegradable mulch film; The average crystallization temperature of the fully biodegradable mulch film is determined by the cooling rate; the faster the cooling, the higher the crystallization temperature. The lower; Material parameters obtained by fitting DSC data and historical production data of fully biodegradable mulch film; It is the phase transition temperature between the crystalline and molten states of fully biodegradable mulch film materials.

3. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, The resilience target is expressed as: ； in, The breaking elongation of the fully biodegradable mulch film, The temperature gradient between the inside and outside of the cross-section of the fully biodegradable mulch film. The average crystallization temperature of the fully biodegradable mulch film, determined by the cooling rate. The smaller the value, the lower the internal stress and the better the toughness; Toughness-related parameters were obtained by fitting historical production data of fully biodegradable mulch film materials and cooling processes.

4. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, The uniformity of properties, taking into account the thickness and longitudinal tensile strength of the fully biodegradable mulch film, is expressed as: ； in, This is the square of the standard deviation of the transverse thickness of the film; This is the square of the standard deviation of the longitudinal tensile strength of the fully biodegradable mulch film. The standard deviation of the longitudinal tensile strength is related to the standard deviation of the frost line height, which describes the stability of the frost line. Related, modeled as .

5. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, The production energy consumption cost target is expressed as: ； in, , where are weighting coefficients, representing the energy cost proportions of the fan, refrigeration / heating, and internal cooling systems, respectively; the production energy cost target is a normalized weighted sum model, aiming to reduce all cooling-related energy consumption.

6. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, The constraint on the height fluctuation of the freezing line is expressed as: , The threshold for freezing line height fluctuation; the winding temperature constraint is expressed as: ;in, The winding temperature for fully biodegradable mulch film, The adhesion temperature of the fully biodegradable mulch film material; the blown film pressure constraint is expressed as: ;in, This represents the maximum blown film pressure. The critical pressure value for membrane rupture; total cooling time constraint requirements. It must be less than the minimum allowable traction cycle time on the production line. , is represented as: .

7. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 1, characterized in that, Hybrid swarm intelligence optimization includes: Within the domain of the adjustable parameter vector space, N particle populations are randomly generated; the optimal position for each particle is initialized. and speed ; Construct an archive A to store all currently found non-dominated solutions; For the current particle population, select K role models based on non-dominated sorting, and then perform clustering based on these role models. For any cluster, the particles within the cluster Update the data by imitating the role models within the cluster and introducing random perturbations using other role models; The particles are updated by combining individual optimal positioning and global optimal experience; For some optimized solutions in the current file A, perform a local search for better solutions based on cooling process knowledge, and add the discovered better solutions to the population; Evaluate all objectives and constraints of the new population, update the individual optimal position of each particle, and update the archive to preserve all non-dominated solutions; If the termination condition is met, output the Pareto optimal solution set in the file; otherwise, iterate.

8. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 7, characterized in that, For any cluster, the particles within the cluster The update process, which mimics role models within a cluster and incorporates random perturbations from other role models, includes: calculating role models within the cluster by considering teaching factors. and cluster average Differences F represents the teaching factor; any role model is randomly selected from those outside the cluster. Calculate the relationship between the particle and the selected model. Differences ; particle By random introduction To fill the gap with the role model in its cluster, the particle By random introduction Introduce random perturbations following other examples: ； in, This means taking a random value within the range of 0 to 1. This indicates that a random value is selected within the range of -1 to 1.

9. The method for optimizing cooling process parameters of fully biodegradable mulch film according to claim 7, characterized in that, The particles are updated by combining individual optimal positioning and global optimal experience, including: Update the speed for step t+1 based on the individual's best position and the global best position: ； Update the particle position in step t+1 using the velocity and position in step t: ； in, For particles Cluster The best historical position was selected from archive A. Inertial weights control the particle's exploration range. As a learning factor, It is a random number.

10. A device for optimizing cooling process parameters of fully biodegradable mulch film, comprising: At least one processing unit, the processing unit being connected to a storage unit and a biofilm preparation device via a bus unit, the storage unit storing a computer program that can run on a processor, characterized in that the processing unit implements the method for optimizing cooling process parameters of the fully biodegradable mulch film as described in any one of claims 1-9 by running the computer program stored in the storage unit.

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