Method and system for improving compression efficiency of granulation circular mold

Abstract

The invention discloses a granulation circular mould compression efficiency improving method and system, and relates to the technical field of circular mould compression optimizing.Weak coupling areas in a self-adaptive grid model are recognized and marked, time-varying decoupling constraint is applied to the recognized weak coupling areas, and according to material characteristics and the working condition of a compressor, the compression efficiency of a granulation circular mould is improved. Dynamically adjusting the time-varying decoupling constraint of each weak coupling area, applying an external load to the self-adaptive grid model, calculating the stress coefficient of the weak coupling area, if the stress coefficient of the weak coupling area exceeds a coefficient threshold, adjusting the time-varying decoupling constraint of the weak coupling area according to the stress coefficient and the rigidity attenuation rate, and iteratively optimizing the compression process to obtain the self-adaptive grid model. And when the stress coefficient of the weak coupling region is not higher than the coefficient threshold value, the compression process is optimized according to the corrected time-varying decoupling constraint. According to the lifting system, the weak coupling area is identified and optimized, the time-varying decoupling constraint is applied, and dynamic adjustment is carried out, so that unstable factors in the compression process are effectively controlled, and efficient operation of each link is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ring die compression optimization, and in particular to a method and system for improving the compression efficiency of a pelletizing ring die. Background Art

[0002] The lifting system is a key technical system designed to improve the overall efficiency of the granulation and compression process. In the pharmaceutical and chemical industries, ring die compression is the core link in the granule production process. In order to meet the growing production demand and ensure the consistency of product quality, improving the ring die compression efficiency has become an important means to increase productivity, reduce resource consumption and lower costs.

[0003] The existing technology has the following defects:

[0004] 1. Traditional methods lack real-time monitoring and adjustment of the compression process, often failing to promptly detect and correct stress concentration and regional unevenness during the compression process. This results in large fluctuations in parameters such as pressure and temperature during the compression process, which in turn affects compression efficiency and product quality. More seriously, this instability may lead to equipment failure and raw material waste, further increasing production costs.

[0005] 2. During the compression process, there are many complex regional variations in the distribution of material flow and pressure, especially when weakly coupled areas exist. These areas usually exhibit uneven pressure or obstructed material flow, resulting in reduced compression efficiency. However, existing technologies lack accuracy in identifying these weakly coupled areas and fail to effectively dynamically adjust and optimize these areas, resulting in these areas having a negative impact on the overall compression efficiency.

[0006] Based on this, the present invention proposes a method and system for improving the compression efficiency of a pelletizing ring die. By identifying and optimizing the weak coupling area, applying time-varying decoupling constraints and performing dynamic adjustments, the unstable factors in the compression process are effectively controlled, ensuring the efficient operation of each link and avoiding efficiency loss caused by uneven compression. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for improving the compression efficiency of a pelletizing ring die to solve the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for improving the compression efficiency of a pelletizing ring die, the method comprising the following steps:

[0009] Collect operating data during the pelletizing ring die compression process, establish a local framework and perform unit partitioning, define the compression direction and time-varying decoupling constraints for each partition, and build an adaptive grid model;

[0010] Based on the material flow and pressure distribution during the pelletizing ring die compression process, the weak coupling areas in the adaptive grid model are identified and marked;

[0011] Apply time-varying decoupling constraints to the identified weakly coupled regions, and dynamically adjust the time-varying decoupling constraints of each weakly coupled region based on material properties and compressor operating conditions;

[0012] Apply external loads to the adaptive mesh model and calculate the stress coefficients in the weakly coupled region;

[0013] If the stress coefficient of the weak coupling zone exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling zone is adjusted according to the stress coefficient and the stiffness decay rate, and the compression process is iteratively optimized.

[0014] When the stress coefficient in the weak coupling region is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraint.

[0015] In a preferred embodiment, if the stress coefficient of the weak coupling region exceeds a coefficient threshold, adjusting the time-varying decoupling constraint of the weak coupling region according to the stress coefficient and the stiffness decay rate includes the following steps:

[0016] Adjust the time-varying decoupling constraint in the weak coupling zone. The expression is: ΔK(t) = -ε·η(x,y,z,t)·δ·Δt, where ΔK(t) is the stiffness adjustment at time t, η(x,y,z,t) is the stress coefficient at position (x,y,z), ε is the stiffness decay adjustment factor, δ is the stiffness decay rate, and Δt is the time step.

[0017] In a preferred embodiment, applying an external load to the adaptive grid model and calculating the stress coefficient of the weak coupling zone includes the following steps:

[0018] External loads include pressure and temperature changes caused by material flow, temperature changes and equipment working conditions during compression;

[0019] The pressure change is determined by the external force applied by the ring die and the compressor, as well as the fluidity of the material in the ring die. The temperature change is caused by the deformation and friction of the material.

[0020] After applying external loads, the stress coefficient is calculated and the stress distribution is monitored. The stress coefficient is used to measure the stress effect during the compression process.

[0021] In a preferred embodiment, the external load application process is expressed as: Where σ(x,y,z,t) is the stress at position (x,y,z) at time t, P(x,y,z,t) is the external pressure at position (x,y,z) at time t, A(x,y,z) is the contact area at position (x,y,z), and the stress coefficient is expressed as: Where η(x,y,z,t) is the stress coefficient at position (x,y,z), σ max is the maximum stress value.

[0022] In a preferred embodiment, dynamically adjusting the time-varying decoupling constraints of each weakly coupled zone according to the material properties and the operating conditions of the compressor includes the following steps:

[0023] Dynamically adjust the time-varying decoupling constraints of each weakly coupled zone according to the material properties and the working status of the compressor;

[0024] If the material fluidity in a certain weak coupling area is poor or the local compression strength is large, the stiffness decay rate of the weak coupling area will be automatically increased;

[0025] For weak coupling regions with good fluidity, the stiffness decay rate of the weak coupling regions is automatically reduced.

[0026] In a preferred embodiment, a time-varying decoupling constraint is imposed on the identified weakly coupled region, expressed as:

[0027] K(t)=K0·(1-δ·t), where K(t) is the stiffness at time t, K0 is the initial stiffness, δ is the stiffness decay rate, and t is time.

[0028] In a preferred embodiment, based on the material flow and pressure distribution during the pelletizing ring die compression process, identifying and marking the weak coupling area in the adaptive grid model includes the following steps:

[0029] By analyzing the pressure distribution function during the compression process, the pressure field in the compression area is defined, and the pressure distribution is obtained by evaluating the pressure uniformity in the compression area.

[0030] According to the pressure distribution during the compression process, the pressure at each point is calculated, and the pressure inhomogeneous weak coupling area is identified through pressure analysis;

[0031] By describing the flow state of the material in the ring die and identifying the weak coupling area where the flow is obstructed by the flow velocity.

[0032] In a preferred embodiment, the pressure uniformity of the compression area is evaluated using the expression:

[0033] P(x,y,z,t)=∫ Vσ(x′,y′,z′,t′)dV, where P(x,y,z,t) is the pressure at position (x,y,z) at time t, σ(x′,y′,z′,t′) is the stress at position (x,y,z) at time t, V is the volume of the compressed region, and dV is the volume element;

[0034] The flow velocity calculation expression is: Where v(x,y,z,t) is the material flow rate at position (x,y,z) at time t, μ is the dynamic viscosity of the material, is the pressure gradient at position (x, y, z) at time t, ρ is the density of the material, and g is the acceleration due to gravity.

[0035] In a preferred embodiment, establishing a local framework and performing cell partitioning, defining the compression direction and time-varying decoupling constraints of each partition, and constructing an adaptive grid model include the following steps:

[0036] The spatial area of ​​the entire compression process is divided into several independent computing units, each of which is analyzed and optimized separately;

[0037] The number and size of computing units are adjusted according to the physical parameters and the complexity of the compression process. The physical parameters and time-varying decoupling constraints of each unit are dynamically adjusted as the operating state of the compressor changes.

[0038] The present application also provides a pelletizing ring die compression efficiency improvement system, comprising a model building module, a primary adjustment module, a secondary adjustment module, and an optimization module;

[0039] Model building module: collects operating data during the compression process of the pelletizing ring die, establishes a local framework and performs unit partitioning, defines the compression direction and time-varying decoupling constraints of each partition, and constructs an adaptive grid model;

[0040] Primary adjustment module: Based on the material flow and pressure distribution during the compression process of the pelletizing ring die, the module identifies and marks the weak coupling areas in the adaptive grid model, applies time-varying decoupling constraints to the identified weak coupling areas, and dynamically adjusts the time-varying decoupling constraints of each weak coupling area according to the material characteristics and the working conditions of the compressor.

[0041] Secondary adjustment module: applies external loads to the adaptive mesh model and calculates the stress coefficient of the weak coupling zone. If the stress coefficient of the weak coupling zone exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling zone is adjusted according to the stress coefficient and the stiffness decay rate, and the compression process is iteratively optimized.

[0042] Optimization module: When the stress coefficient in the weak coupling region is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraint.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention establishes a local framework and performs unit partitioning, defines the compression direction and time-varying decoupling constraints of each partition, constructs an adaptive grid model, identifies and marks weak coupling areas in the adaptive grid model based on the material flow and pressure distribution during the compression process of the pelletizing ring die, applies time-varying decoupling constraints to the identified weak coupling areas, dynamically adjusts the time-varying decoupling constraints of each weak coupling area according to the material properties and the working conditions of the compressor, applies an external load to the adaptive grid model, and calculates the stress coefficient of the weak coupling area. If the stress coefficient of the weak coupling area exceeds the coefficient threshold, the time-varying decoupling constraints of the weak coupling area are adjusted according to the stress coefficient and the stiffness decay rate, and the compression process is iteratively optimized. When the stress coefficient of the weak coupling area is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraints. The lifting system effectively controls the unstable factors in the compression process by identifying and optimizing the weak coupling area, applying time-varying decoupling constraints and performing dynamic adjustments, thereby ensuring the efficient operation of each link. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 Flowchart of the present invention.

[0047] Figure 2 This is a diagram of the architecture of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] Example: See Figure 1 As shown, this embodiment provides a method for improving the compression efficiency of a pelletizing ring die, and the improvement method includes the following steps:

[0050] Collect operating data during the pelletizing ring die compression process, including compression process duration, pressure, temperature and other parameters. Use data acquisition equipment to obtain information such as raw material characteristics, ring die status, and compressor operating status, providing basic data for subsequent analysis.

[0051] Based on the collected data, a local framework is established and cell partitioning is performed. Based on the characteristics and parameters of each step in the compression process, the compression direction and time-varying decoupling constraints for each partition are defined, and an adaptive mesh model is constructed. The physical parameters and time-varying decoupling constraints of each region are adjusted according to the different operating conditions of the compressor.

[0052] Based on the material flow and pressure distribution during the ring die compression process, the adaptive mesh model identifies potential weak coupling areas. These areas are typically characterized by uneven pressure and impeded material flow, which can lead to reduced compression efficiency. The adaptive mesh model identifies these critical weak coupling areas, paving the way for subsequent optimization.

[0053] Time-varying decoupling constraints are applied to the identified weakly coupled regions. These constraints include initial stiffness (e.g., initial compression strength) and stiffness decay rate (e.g., changes in material flow). Based on the material properties and the compressor's operating conditions, the time-varying decoupling constraints for each weakly coupled region are dynamically adjusted to more precisely control the compression process.

[0054] Under the constraints of time-varying decoupling, external loads (such as pressure and temperature changes) are applied to the adaptive mesh model, and stress coefficients in weakly coupled regions are calculated. This process monitors the stress distribution in various regions during compression to determine whether any areas have stress values ​​exceeding expectations, thereby affecting compression efficiency.

[0055] Determine whether the calculated stress coefficient exceeds the preset coefficient threshold. If the stress coefficient exceeds the coefficient threshold, it indicates that the compression process may be unstable or inefficient. Based on the stress coefficient and stiffness decay rate, adjust the time-varying decoupling constraints in the weak coupling region and iteratively optimize the compression process.

[0056] When the stress coefficient is no higher than the preset coefficient threshold, the compression process enters a stable state. Based on the modified time-varying decoupling constraints, the entire compression process is optimized to ensure optimal efficiency. By dynamically adjusting various parameters, the overall stability and compression efficiency of the system are improved.

[0057] Finally, the system outputs macro-efficiency results for the pelletizing ring die based on the optimized compression process. These results, including optimized compression speed, output, energy efficiency, and other key performance indicators, serve as a reference for production decision-making and ensure high production efficiency under various conditions.

[0058] This application establishes a local framework and performs unit partitioning, and defines the compression direction and time-varying decoupling constraints of each partition to construct an adaptive grid model. Based on the material flow and pressure distribution during the compression process of the pelletizing ring die, the weak coupling area in the adaptive grid model is identified and marked, and time-varying decoupling constraints are applied to the identified weak coupling areas. According to the material properties and the working conditions of the compressor, the time-varying decoupling constraints of each weak coupling area are dynamically adjusted. An external load is applied to the adaptive grid model, and the stress coefficient of the weak coupling area is calculated. If the stress coefficient of the weak coupling area exceeds the coefficient threshold, the time-varying decoupling constraints of the weak coupling area are adjusted according to the stress coefficient and the stiffness decay rate. The compression process is iteratively optimized. When the stress coefficient of the weak coupling area is not higher than the coefficient threshold, the compression process is optimized according to the corrected time-varying decoupling constraints. This lifting system effectively controls the unstable factors in the compression process by identifying and optimizing the weak coupling area, applying time-varying decoupling constraints and making dynamic adjustments, thereby ensuring the efficient operation of each link.

[0059] See also Figure 2 As shown, this embodiment provides a system for improving compression efficiency of a pelletizing ring die, including a model building module, a primary adjustment module, a secondary adjustment module, and an optimization module;

[0060] Model building module: collects operating data during the compression process of the pelletizing ring die, establishes a local framework and performs unit partitioning, defines the compression direction and time-varying decoupling constraints of each partition, builds an adaptive grid model, and sends the adaptive grid model to the primary adjustment module and the secondary adjustment module;

[0061] Primary adjustment module: Based on the material flow and pressure distribution during the compression process of the pelletizing ring die, the module identifies and marks the weak coupling areas in the adaptive grid model, applies time-varying decoupling constraints to the identified weak coupling areas, and dynamically adjusts the time-varying decoupling constraints of each weak coupling area according to the material characteristics and the working conditions of the compressor. The weak coupling areas and the dynamically adjusted time-varying decoupling constraints are sent to the secondary adjustment module.

[0062] Secondary adjustment module: applies external loads to the adaptive mesh model and calculates the stress coefficient of the weak coupling zone. If the stress coefficient of the weak coupling zone exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling zone is adjusted according to the stress coefficient and the stiffness decay rate. The compression process is iteratively optimized and the stress coefficient is sent to the optimization module.

[0063] Optimization module: When the stress coefficient in the weak coupling region is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraint.

[0064] Collect operating data during the pelletizing ring die compression process, including compression process duration, pressure, temperature and other parameters. Use data acquisition equipment to obtain information such as raw material characteristics, ring die status, and compressor operating status, providing basic data for subsequent analysis.

[0065] During the pelletizing ring die compression process, data collection is the primary step in optimizing compression efficiency. First, key operating parameters such as compression duration, pressure, and temperature are collected to provide valuable data support for subsequent analysis and optimization. Compression duration, the time from the start of compression to completion, generally reflects the overall efficiency of the compression process. Pressure and temperature are key physical parameters controlling compression and material flow during the pelletizing process, determining the quality and efficiency of ring die compression. Through precise sensors and monitoring equipment, this data can be collected in real time and converted into information that can be analyzed and processed.

[0066] On this basis, data acquisition equipment is also needed to further capture information on raw material properties, ring die condition, and compressor operating status. These parameters are crucial for analyzing potential issues during the compression process. Raw material properties, including particle size, moisture, density, and fluidity, directly impact material filling, fluidity, and density distribution during compression. Ring die condition, including wear, aperture variation, and surface roughness, significantly impacts compression performance. The compressor's operating status, including power, speed, and load, reflects whether the equipment is operating efficiently and stably.

[0067] After all these parameters are collected, the data is analyzed by building a mathematical model to extract factors that may affect the compression efficiency. For example, when considering pressure and temperature changes, thermodynamic and mechanical models can be used to describe the energy conversion and transfer during the compression process. A commonly used energy balance formula is: E total =P work +P heat +P loss , where E total is the total energy of the system, P work is the mechanical work performed, P heat is the heat generated by compression, P loss Energy loss is usually manifested as friction and heat dissipation. By analyzing these energy forms, the energy efficiency under different conditions and the energy loss during the compression process can be determined. In addition, in order to further improve the accuracy of the model, the state of each link can be analyzed in detail. For example, the wear model of the ring die can be used to consider the impact of wear on the compression efficiency. The wear of the ring die will directly affect the passability of the material, thereby affecting the compression efficiency. Therefore, the wear model of the ring die can be expressed as: M = k·(F load ) α ·t, where M is the wear amount, k is the wear constant, which ranges from 0.9 to 1.1, and F loadis the load force, α is the wear index, typically ranging from 1.1 to 12, and t is time. By monitoring and analyzing wear, the service life of the ring die can be predicted, allowing for proactive maintenance or replacement. Overall, comprehensive data collection during the pelletizing ring die compression process, combined with mathematical models and theoretical analysis such as energy balance, can provide theoretical support for subsequent optimization measures. This collected data not only helps analyze the current system operating status but also provides a data basis for subsequent system adjustments, fault prediction, and efficiency improvements.

[0068] Based on the collected data, a local framework is established and cell partitioning is performed. Based on the characteristics and parameters of each step in the compression process, the compression direction and time-varying decoupling constraints for each partition are defined, and an adaptive mesh model is constructed. The physical parameters and time-varying decoupling constraints of each region are adjusted according to the different operating conditions of the compressor.

[0069] After collecting key data from the compression process, the next step is to establish a local framework based on this data and perform unit partitioning to fine-tune the simulation and optimization of the pelletizing ring die compression process. The construction of the local framework is the foundation of the entire optimization process, providing detailed spatial division and characteristic definition for the subsequent compression process. The division of each partition must consider various physical characteristics of the compression process, including raw material flowability, pressure, temperature distribution, and equipment operating conditions.

[0070] Cell partitioning divides the spatial region of the entire compression process into several independent computational cells, each of which can be analyzed and optimized independently. The number and size of computational cells can be adjusted based on the physical parameters and complexity of the compression process. The physical parameters and time-varying decoupling constraints of each cell are dynamically adjusted as the compressor's operating state changes, achieving efficient control of the compression process.

[0071] When zoning the cells, the compression direction and time-varying decoupling constraints of each segment must be defined and adjusted. The compression direction refers to the direction of material flow within the compression ring die and is determined based on the material's flow characteristics and the compressor's operating mode. Accurate compression direction is crucial for maintaining uniform pressure and material distribution within the die.

[0072] Time-varying decoupling constraints dynamically change over time during the compression process based on factors such as raw material characteristics and equipment status, thereby optimizing the stability and efficiency of the compression process. The introduction of decoupling constraints minimizes the mutual influence of different behaviors during the compression process, thereby improving the system's response speed and stability.

[0073] Building an adaptive mesh model is crucial in this process. This model automatically adjusts the mesh refinement based on the changing physical parameters during compression, enabling more precise control of different regions. Mesh refinement automatically adjusts the computational accuracy of each region based on factors such as material flow and pressure fluctuations, ensuring that critical areas receive adequate attention while reducing the computational burden on less important areas.

[0074] Based on the material flow and pressure distribution during the ring die compression process, the adaptive mesh model identifies potential weak coupling areas. These areas are typically characterized by uneven pressure and impeded material flow, which can lead to reduced compression efficiency. The adaptive mesh model identifies these critical weak coupling areas, paving the way for subsequent optimization.

[0075] During the ring die compression process, precise analysis of material flow and pressure distribution can identify weakly coupled regions within the adaptive mesh model. These regions typically manifest as uneven pressure distribution and obstructed material flow, leading to reduced compression efficiency and even impacting final product quality. Weakly coupled regions typically arise from insufficient material compaction in certain areas within the ring die, or from localized pressure fluctuations that affect the uniformity of the compression process, increasing energy consumption and compression time.

[0076] Analyzing material flow and pressure distribution is a key step in identifying weakly coupled areas. During the compression process, material fluidity is closely related to the pressure field within the ring die. Areas of poor fluidity are often accompanied by lower pressure or uneven pressure distribution. To accurately identify these areas, an adaptive mesh model is required to monitor and analyze material flow and pressure distribution in real time. The advantage of the adaptive mesh model is that it can dynamically adjust the mesh accuracy based on the material flow characteristics and pressure changes during the compression process, ensuring sufficiently detailed calculations in critical areas and accurately identifying weakly coupled areas.

[0077] By analyzing the pressure distribution function during compression, we can define the pressure field of a region and evaluate the pressure uniformity of the compressed region using the following formula:

[0078] P(x,y,z,t)=∫ Vσ(x′,y′,z′,t′)dV, where P(x,y,z,t) is the pressure at position (x,y,z) at time t, σ(x′,y′,z′,t′) is the stress at position (x,y,z) at time t, V is the volume of the compressed region, and dV is the volume element. By calculating this formula, we can determine the pressure distribution within the compressed region, and then determine whether the pressure is uniform and whether there are areas with abnormal pressure fluctuations. Based on the pressure distribution during the compression process, we calculate the pressure at each point, and through pressure analysis, we identify areas with uneven pressure and weak coupling. By calculating the pressure standard deviation of all points in the region, we can then mark areas with a pressure standard deviation greater than the standard deviation threshold as weak coupling regions.

[0079] Areas with poor fluidity are usually caused by differences in material density or excessive friction inside the ring die. To identify these areas, the theory of fluid dynamics can be combined to monitor the material flow rate to determine whether the material flow is smooth. The flow rate can be described by the following formula: Where v(x,y,z,t) is the material flow rate at position (x,y,z) at time t, μ is the dynamic viscosity of the material, is the pressure gradient at position (x, y, z) at time t, ρ is the material density, and g is the acceleration due to gravity. Calculating flow velocity reveals the smoothness of material flow, helping to identify areas of poor fluidity. By describing the flow state of the material in the ring die and using flow velocity to identify areas of flow obstruction, if the flow velocity in a region is less than the velocity threshold, the region is considered a weak coupling zone, and these regions may become weak coupling zones.

[0080] By analyzing pressure distribution and fluidity, combined with the refined division of the adaptive mesh model, we can accurately identify weakly coupled areas during the compression process. These weakly coupled areas are often the key reason for decreased compression efficiency, and therefore require specific attention and improvement during subsequent optimization. For example, by dynamically adjusting time-varying decoupling constraints to optimize material fluidity, or by enhancing the smoothness of the ring die surface to reduce friction, overall compression efficiency can be improved.

[0081] Time-varying decoupling constraints are applied to the identified weakly coupled regions. These constraints include initial stiffness (e.g., initial compression strength) and stiffness decay rate (e.g., changes in material flow). Based on the material properties and the compressor's operating conditions, the time-varying decoupling constraints for each weakly coupled region are dynamically adjusted to more precisely control the compression process.

[0082] After identifying the weak coupling zone, the next key step is to apply time-varying decoupling constraints to optimize the stability and efficiency of the compression process. Time-varying decoupling constraints refer to the dynamic adjustment of the constraints of the compression model based on real-time changes in the compression process, especially for more precise control of the weak coupling zone. The core of this constraint includes two important parameters: initial stiffness and stiffness decay rate. Initial stiffness generally indicates the material's resistance to compression in the early stages, that is, the initial degree of deformation of the material after being subjected to force, while the stiffness decay rate is the rate at which the material's stiffness decays during the compression process, reflecting the degree of change in the material's fluidity.

[0083] Initial stiffness represents the material's compressive resistance at the start of compression and is typically determined by the material's density, friction coefficient, and the initial state of the die. The stiffness decay rate refers to the rate of stiffness reduction caused by changes in the material's fluidity as the compression process progresses. It reflects how the material's flow state and physical properties affect its compressive resistance during compression. During the compression process, these parameters continuously adjust over time and in response to factors such as material fluidity and compressor operating conditions. Dynamically adjusting the time-varying decoupling constraints in the weak coupling zone is crucial. By introducing a time-varying stiffness matrix, these constraints can be adjusted based on the material's real-time characteristics, compressor load, and flow state, thereby avoiding over- or under-compression and ensuring a stable and efficient compression process. The application of the time-varying decoupling constraints can be expressed using the following formula: K(t) = K0·(1-δ·t), where K(t) is the stiffness at time t, K0 is the initial stiffness, δ is the stiffness decay rate, and t is time. This formula describes the stiffness decay process over time, and the stiffness decay rate reflects how the material's flow state affects the change in stiffness during compression. Over time, the stiffness of a material decreases. In particular, during compression, areas with poor fluidity exhibit rapid stiffness decay. This formula dynamically adjusts the stiffness during compression based on time, reflecting how the material's fluidity and state change over time during compression. The inclusion of a stiffness decay rate allows for appropriate reductions in compression as the material softens or becomes more fluid, avoiding over-compression of weakly coupled areas.

[0084] The time-varying decoupling constraints in each weakly coupled zone are dynamically adjusted based on material properties (such as density, moisture, and particle size) and the compressor's operating conditions (such as compressor load, speed, and power). If the material flow in a particular area is poor (material flowability is below the flowability threshold) or the local compression strength is excessive (compression strength is greater than the strength threshold), the system will automatically increase the stiffness decay rate and reduce the compression strength in that area to avoid localized over-compression. For areas with better flowability, the system can correspondingly reduce the stiffness decay rate to improve compression efficiency.

[0085] Furthermore, to ensure that the time-varying decoupling constraint adjustments in the weakly coupled region reflect the compressor's operating status in real time, the stiffness parameters can be dynamically adjusted by monitoring the compressor's load and power fluctuations. For example, if the compressor load is high, the system will reduce the stiffness decay rate in the weakly coupled region to reduce excessive compression strength and avoid system overload. Conversely, when the compressor's operating status is relatively stable, the stiffness decay rate can be appropriately increased to accelerate the compression process.

[0086] Through the dynamic adjustment of this time-varying decoupling constraint, the pressure distribution and material fluidity during the compression process can be optimized in real time, ensuring that over-compression or under-compression does not occur in unstable areas, thereby effectively improving compression efficiency and stability.

[0087] Under the constraints of time-varying decoupling, external loads (such as pressure and temperature changes) are applied to the adaptive mesh model, and stress coefficients in weakly coupled regions are calculated. This process monitors the stress distribution in various regions during compression to determine whether any areas have stress values ​​exceeding expectations, thereby affecting compression efficiency.

[0088] With the time-varying decoupling constraint in place, the next step is to apply external loads, such as pressure and temperature variations, to the adaptive mesh model and calculate the stress coefficients in the weakly coupled regions based on these loads. The goal of this process is to monitor the stress distribution in various regions during compression through stress analysis and determine if any areas experience stress values ​​exceeding expectations. If stress exceeds a preset threshold, compression efficiency can be compromised, leading to increased equipment load and even uneven compression.

[0089] External loads primarily include pressure and temperature changes caused by factors such as material flow, temperature fluctuations, and equipment operating conditions during the compression process. Specifically, pressure changes are determined by the external forces applied by the ring die and compressor, as well as the fluidity of the material within the die. Temperature changes are typically caused by material deformation and friction. By precisely controlling these external loads, we can simulate actual operating conditions during compression, accurately reflecting the stress state within the ring die during compression.

[0090] In the mathematical model, the process of applying external loads can be expressed by the following formula:

[0091] Where σ(x,y,z,t) is the stress at position (x,y,z) at time t, P(x,y,z,t) is the external pressure at position (x,y,z) at time t, and A(x,y,z) is the contact area at position (x,y,z). This formula can be used to calculate the stress distribution at different positions and, therefore, to evaluate the mechanical interaction between the material and the die surface during the compression process.

[0092] Once the external load is applied, the next step is to calculate the stress coefficient and monitor the stress distribution. The stress coefficient is a key parameter that measures the impact of excessive stress on system stability and compression efficiency during compression. The stress coefficient can be defined as: Where η(x,y,z,t) is the stress coefficient at the position (x,y,z), σ(x,y,z,t) is the stress at the position (x,y,z) at time t, and σ max is the maximum stress value in the system. By calculating the stress coefficient, we can determine whether the stress in each region exceeds the expected range of the system. When the stress coefficient exceeds the set threshold, it means that the region is potentially over-compressed or unstable, and corresponding optimization adjustments are required.

[0093] By monitoring the stress coefficients of each region in real time, the system can promptly determine which regions have exceeded a preset threshold and make adjustments accordingly. If the stress coefficients in certain regions are found to be excessively high, this indicates that the compression state in these regions may be unstable, potentially leading to over-compression or uneven compression. Based on the stress distribution, the system dynamically adjusts the time-varying decoupling constraints or external loads to redistribute the pressure and ensure uniformity and efficiency of the compression process.

[0094] For example, when the stress coefficient in a certain area is too high, the system can reduce the pressure in that area or increase the stiffness decay rate to mitigate the compression strength in that area, thereby preventing excessive compression from causing equipment damage or material waste. This feedback mechanism can continuously optimize the compression process, ensuring the overall system operates efficiently while avoiding unstable compression.

[0095] In summary, applying external loads and calculating stress coefficients are key steps in monitoring and optimizing the compression process. This process enables real-time assessment of the stress distribution during compression and timely adjustments to system parameters to improve compression efficiency and ensure system stability.

[0096] Determine whether the calculated stress coefficient exceeds the preset coefficient threshold. If the stress coefficient exceeds the coefficient threshold, it indicates that the compression process may be unstable or inefficient. Based on the stress coefficient and stiffness decay rate, adjust the time-varying decoupling constraints in the weak coupling region and iteratively optimize the compression process.

[0097] Determining whether the calculated stress coefficient exceeds a preset threshold is a crucial step in optimizing the compression process. Exceeding this threshold indicates that the compression process in certain regions may be unstable or inefficient, potentially leading to decreased compression efficiency or even equipment damage. Therefore, adjusting the time-varying decoupling constraints in the weakly coupled region, based on the relationship between the stress coefficient and the stiffness decay rate, is an optimization measure taken to restore system stability and improve compression efficiency.

[0098] When the calculated stress coefficient exceeds a preset threshold, further constraint adjustments are required in the weakly coupled region. The stress coefficient typically represents the ratio of the stress experienced by a local region during compression to the maximum allowable stress. If this value is too high, it indicates excessive compression or stress concentration in that region. In this case, the system needs to make dynamic adjustments based on the relationship between the stress coefficient and the stiffness decay rate.

[0099] The stiffness decay rate refers to the rate at which the material's stiffness decreases over time during compression. It is typically closely related to factors such as the material's fluidity, the compressor's operating conditions, and temperature fluctuations. To reduce local stress and avoid uneven compression, the system needs to appropriately adjust the time-varying decoupling constraints in the weakly coupled region based on the relationship between the current stress coefficient and the decay rate.

[0100] The adjustment of the time-varying decoupling constraint can be described by the following formula:

[0101] ΔK(t) = -ε·η(x,y,z,t)·δ·Δt, where ΔK(t) is the stiffness adjustment at time t, η(x,y,z,t) is the stress coefficient, ε is the stiffness decay adjustment factor, typically 0.9, δ is the stiffness decay rate, and Δt is the time step. This formula allows the system to dynamically adjust the stiffness of the weakly coupled region based on the stress coefficient and the stiffness decay rate, ensuring a smooth and efficient compression process.

[0102] By adjusting the time-varying decoupling constraints in the weakly coupled zones using the above formula, the system iteratively optimizes the entire compression process. In each iteration, the system calculates the appropriate stiffness adjustment in real time based on the current stress coefficient and stiffness decay rate and applies it to the weakly coupled zones. After each iteration, the system updates the stress distribution, recalculates the stress coefficient, and determines whether any regions still exceed the preset stress threshold. If the stress coefficient remains too high, the stiffness decay rate is further adjusted until all regions in the system are in a stable compression state.

[0103] This iterative optimization process not only ensures optimal compression strength in every area during the compression process, but also dynamically adapts to changes in material properties and the operating environment, effectively improving compression efficiency and reducing energy consumption. With each optimization adjustment, the pressure distribution in the weakly coupled area becomes more uniform, continuously improving the stability and efficiency of the compression process.

[0104] In short, by dynamically adjusting the stress coefficient and stiffness decay rate, it is possible to maintain balance across various regions during the compression process, avoiding instability and thus improving the overall compression efficiency and stability of the system. Through repeated iterative optimization, the system can adaptively adjust the constraints, ensuring efficient compression under varying conditions.

[0105] When the stress coefficient is no higher than the preset coefficient threshold, the compression process enters a stable state. Based on the modified time-varying decoupling constraints, the entire compression process is optimized to ensure optimal efficiency. By dynamically adjusting various parameters, the overall stability and compression efficiency of the system are improved.

[0106] Once the stress coefficient during compression falls below the preset threshold, the compression process has reached a stable state. At this point, the system can further optimize the entire compression process based on the modified time-varying decoupling constraints to ensure that the compression process remains at optimal efficiency. Dynamically adjusting various parameters is a key step in ensuring overall system stability and compression efficiency.

[0107] The optimization goal is to ensure uniform stress distribution throughout the compression process while simultaneously improving compression efficiency. By dynamically adjusting various parameters, such as pressure, stiffness decay rate, and material flowability, the system maximizes compression efficiency and minimizes unnecessary energy loss while maintaining stability.

[0108] First, based on the modified time-varying decoupling constraints, the system can dynamically adjust compressor operating parameters, such as pressure and temperature, to account for fluctuations that may occur during the compression process. Specifically, by monitoring the compressor's load and operating status in real time, the system automatically adjusts the compressor's output power, speed, and load to maintain it within the optimal operating range.

[0109] Furthermore, adjusting the stiffness decay rate is a key optimization step. This rate determines the material's deformation characteristics during compression. Excessively high rates can cause the material to flow too quickly, affecting compression effectiveness, while excessively low rates can lead to excessive local pressure, affecting compression uniformity. To maintain a balanced compression process, the system adjusts the stiffness decay rate based on the current material characteristics, compressor status, and real-time stress distribution monitoring.

[0110] During the dynamic optimization process, the stiffness decay rate and pressure can be calculated and adjusted by the following formula: δ(t) = δ0·(1-λ·η(x,y,z,t)), P(x,y,z,t) = P ref·(1-β·η(x,y,z,t)), where δ(t) is the stiffness decay rate at time t, δ0 is the initial decay rate, λ is the stiffness adjustment factor, which is usually set to 1.1, η(x,y,z,t) is the stress coefficient, P(x,y,z,t) is the pressure at time t, and P rcf is the reference pressure, β is the pressure adjustment factor, and its value is usually 0.8 to 1.2.

[0111] After each adjustment, the system continuously monitors the stress distribution in each area during the compression process through a real-time feedback mechanism, ensuring that the stress coefficient remains below a preset threshold and that the compression state in each link remains within the optimal range. Through repeated iterative optimization, the system gradually reaches a balanced state in which all areas can be compressed under efficient and stable conditions.

[0112] For example, during the compression process, if the stress coefficient of a certain area approaches the threshold, the system will automatically adjust the stiffness decay rate or pressure value of the area to reduce the compression strength of the area, avoid excessive compression, and ensure that the entire compression process is more uniform.

[0113] By dynamically adjusting key parameters (such as pressure and stiffness decay rate), the system continuously optimizes the compression process, ensuring that each area is in the optimal compression state. With each iteration, the system's stability and compression efficiency gradually improve, ultimately achieving the optimal compression process and ensuring efficient and stable production performance.

[0114] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0115] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for improving the compression efficiency of a pelletizing ring die, characterized by: The lifting method comprises the following steps: Collect operating data during the pelletizing ring die compression process, establish a local framework and perform unit partitioning, define the compression direction and time-varying decoupling constraints for each partition, and build an adaptive grid model; Based on the material flow and pressure distribution during the pelletizing ring die compression process, the weak coupling areas in the adaptive grid model are identified and marked; Apply time-varying decoupling constraints to the identified weakly coupled regions, and dynamically adjust the time-varying decoupling constraints of each weakly coupled region based on material properties and compressor operating conditions; Apply external loads to the adaptive mesh model and calculate the stress coefficients in the weakly coupled region; If the stress coefficient of the weak coupling zone exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling zone is adjusted according to the stress coefficient and the stiffness decay rate, and the compression process is iteratively optimized. When the stress coefficient in the weak coupling region is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraint.

2. A method for improving compression efficiency of a pelletizing ring die according to claim 1, characterized in that: If the stress coefficient of the weak coupling region exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling region is adjusted according to the stress coefficient and the stiffness decay rate, including the following steps: Adjust the time-varying decoupling constraint in the weak coupling zone. The expression is: ΔK(t) = -ε·η(x,y,z,t)·δ·Δt, where ΔK(t) is the stiffness adjustment at time t, η(x,y,z,t) is the stress coefficient at position (x,y,z), ε is the stiffness decay adjustment factor, δ is the stiffness decay rate, and Δt is the time step.

3. A method for improving compression efficiency of a pelletizing ring die according to claim 2, characterized in that: Applying external loads to the adaptive mesh model and calculating the stress coefficients in the weakly coupled region involves the following steps: External loads include pressure and temperature changes caused by material flow, temperature changes and equipment working conditions during compression; The pressure change is determined by the external force applied by the ring die and the compressor, as well as the fluidity of the material in the ring die. The temperature change is caused by the deformation and friction of the material. After applying external loads, the stress coefficient is calculated and the stress distribution is monitored. The stress coefficient is used to measure the stress effect during the compression process.

4. A method for improving compression efficiency of a pelletizing ring die according to claim 3, characterized in that: The external load application process is expressed as: Where σ(x,y,z,t) is the stress at position (x,y,z) at time t, P(x,y,z,t) is the external pressure at position (x,y,z) at time t, A(x,y,z) is the contact area at position (x,y,z), and the stress coefficient is expressed as: Where η(x,y,z,t) is the stress coefficient at position (x,y,z), σ max is the maximum stress value.

5. A method for improving compression efficiency of a pelletizing ring die according to claim 4, characterized in that: According to the material characteristics and the working conditions of the compressor, the time-varying decoupling constraints of each weak coupling zone are dynamically adjusted, including the following steps: Dynamically adjust the time-varying decoupling constraints of each weakly coupled zone according to the material properties and the working status of the compressor; If the material fluidity in a certain weak coupling area is poor or the local compression strength is large, the stiffness decay rate of the weak coupling area will be automatically increased; For weak coupling regions with good fluidity, the stiffness decay rate of the weak coupling regions is automatically reduced.

6. A method for improving compression efficiency of a pelletizing ring die according to claim 5, characterized in that: A time-varying decoupling constraint is imposed on the identified weakly coupled region, expressed as: K(t)=K0·(1-δ·t), where K(t) is the stiffness at time t, K0 is the initial stiffness, δ is the stiffness decay rate, and t is time.

7. A method for improving compression efficiency of a pelletizing ring die according to claim 6, characterized in that: Based on the material flow and pressure distribution during the pelletizing ring die compression process, the weak coupling areas in the adaptive mesh model are identified and marked, including the following steps: By analyzing the pressure distribution function during the compression process, the pressure field in the compression area is defined, and the pressure distribution is obtained by evaluating the pressure uniformity in the compression area. According to the pressure distribution during the compression process, the pressure at each point is calculated, and the pressure inhomogeneous weak coupling area is identified through pressure analysis; By describing the flow state of the material in the ring die and identifying the weak coupling area where the flow is obstructed by the flow velocity.

8. The method for improving compression efficiency of a pelletizing ring die according to claim 7, characterized in that: Evaluate the pressure uniformity in the compression region using the expression: P(x,y,z,t)=∫ V σ(x′,y′,z′,t′)dV, where P(x,y,z,t) is the pressure at position (x,y,z) at time t, and σ(x ′ ,y ′ ,z ′ ,t ′ ) is the stress at position (x, y, z) at time t, V is the volume of the compression region, and dV is the volume element; The flow velocity calculation expression is: Where v(x,y,z,t) is the material flow rate at position (x,y,z) at time t, μ is the dynamic viscosity of the material, is the pressure gradient at position (x, y, z) at time t, ρ is the density of the material, and g is the acceleration due to gravity.

9. A method for improving compression efficiency of a pelletizing ring die according to claim 8, characterized in that: Establishing a local framework and performing cell partitioning, defining the compression direction and time-varying decoupling constraints for each partition, and building an adaptive grid model include the following steps: The spatial area of ​​the entire compression process is divided into several independent computing units, each of which is analyzed and optimized separately; The number and size of computing units are adjusted according to the physical parameters and the complexity of the compression process. The physical parameters and time-varying decoupling constraints of each unit are dynamically adjusted as the operating state of the compressor changes.

10. A pelletizing ring die compression efficiency improvement system, used to implement the improvement method according to any one of claims 1 to 9, characterized in that: It includes model building module, primary adjustment module, secondary adjustment module and optimization module; Model building module: collects operating data during the compression process of the pelletizing ring die, establishes a local framework and performs unit partitioning, defines the compression direction and time-varying decoupling constraints of each partition, and constructs an adaptive grid model; Primary adjustment module: Based on the material flow and pressure distribution during the compression process of the pelletizing ring die, the module identifies and marks the weak coupling areas in the adaptive grid model, applies time-varying decoupling constraints to the identified weak coupling areas, and dynamically adjusts the time-varying decoupling constraints of each weak coupling area according to the material characteristics and the working conditions of the compressor. Secondary adjustment module: applies external loads to the adaptive mesh model and calculates the stress coefficient of the weak coupling zone. If the stress coefficient of the weak coupling zone exceeds the coefficient threshold, the time-varying decoupling constraint of the weak coupling zone is adjusted according to the stress coefficient and the stiffness decay rate, and the compression process is iteratively optimized. Optimization module: When the stress coefficient in the weak coupling region is not higher than the coefficient threshold, the compression process is optimized according to the modified time-varying decoupling constraint.

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