Intelligent dough kneading optimization system of dough kneading machine

Abstract

The invention discloses an intelligent dough kneading optimization system of a dough kneading machine, and relates to the technical field of intelligent control. The system comprises a dough analysis part, a dough kneading machine mechanical control analysis part and a dough kneading machine optimization control part, the dough analysis part is used for establishing a gluten network to form a kinetic model according to the content of gluten protein in flour, and calculating local apparent viscosity distribution and local shear stress distribution of each position in the dough; the dough kneading machine mechanical control analysis part is used for calculating local mechanical energy density distribution and temperature field distribution of the dough kneading machine; and the dough kneading machine optimization control part is used for calculating a dough quality index and calculating a dough kneading angular velocity control quantity and a dough kneading pressure control quantity according to a set optimized dough quality index. According to the method, an operation strategy is adjusted through dynamic feedback, efficient and uniform formation of a gluten network is ensured, and the consistency and stability of dough quality are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to an intelligent dough kneading optimization system for a dough kneading machine. Background Art

[0002] Kneading dough is a key step in dough preparation in the food processing industry, and its quality directly determines the final quality of flour products, including taste, elasticity and structural stability. Traditional dough kneading technology is mostly achieved mechanically, using physical force to evenly mix gluten protein with water, starch and other ingredients to form a gluten network with a certain viscoelasticity. However, with the continuous improvement of consumers' requirements for food quality and the demand for efficient and intelligent production in modern food processing, traditional dough kneading technology has gradually exposed some limitations, and it is in urgent need of technological innovation and optimization.

[0003] In traditional dough kneading technology, the most common equipment includes straight-axis dough kneading machines and double-screw dough kneading machines. These devices apply shear and compression forces to the dough through rotating shafts, kneading knives or other mechanical devices, so that gluten protein molecules interact and cross-link to form a network. In these processes, the kneading efficiency and dough quality usually depend on the adjustment of the following main parameters: The mechanical input of traditional dough kneading machines includes pressure, shear force and angular velocity of rotation. These mechanical forces act directly on the dough, change its rheological properties, and promote the formation of the gluten network. However, the mechanical input is mostly fixed parameter adjustment and cannot be dynamically adjusted according to the real-time state of the dough. During the kneading process, part of the mechanical energy is converted into heat, which increases the temperature of the dough. Appropriate temperature helps protein molecules to form more stable interactions, but too high a temperature may cause the destruction of the protein structure and affect the integrity of the gluten network. In traditional technologies, temperature control is usually limited to the adjustment of the surface of the equipment or the ambient temperature, and there is a lack of dynamic monitoring and regulation of the temperature field distribution inside the dough. The uniform distribution of water during the kneading process is crucial to the formation of the gluten network. Insufficient water content will result in insufficient cross-linking of the network, while excessive water content may dilute the protein concentration and reduce the viscoelasticity of the dough. Traditional dough kneading machines lack sophisticated means for regulating water content, and the amount of water added is mostly determined by experience, making it difficult to achieve dynamic adjustment.

[0004] In recent years, with the technological advancement of food processing equipment, some intelligent dough kneading equipment has begun to be introduced. These devices integrate automated sensors to monitor some state parameters of the dough (such as viscosity and temperature) and adjust the operating parameters through a simple feedback mechanism. For example, some devices can automatically adjust the kneading time according to the initial viscosity of the dough. However, such improvements are mostly limited to the feedback control of a single parameter, fail to achieve multi-parameter joint optimization, and lack a comprehensive understanding of the rheological properties and internal state of the dough. Summary of the invention

[0005] The embodiment of the present application provides an intelligent dough kneading optimization system using a dough kneading machine, which adjusts the operation strategy through dynamic feedback to ensure the efficient and uniform formation of the gluten network and improve the consistency and stability of the dough quality. Compared with traditional technologies, the present invention significantly improves the intelligence level and energy utilization efficiency of the dough kneading process, reduces energy waste, and realizes the standardized production of high-quality dough.

[0006] The embodiment of the present application provides an intelligent dough kneading optimization system for a dough kneading machine, the system comprising: a dough analysis part, a dough kneading machine mechanical control analysis part and a dough kneading machine optimization control part; the dough analysis part is used to estimate the number of gluten protein molecules in the dough kneading machine by gluten protein content in flour, using volume and distribution uniformity assumptions, and calculate the total interaction energy between gluten protein molecules in the dough kneading machine according to the number of gluten protein molecules; based on the total interaction energy, an interaction model between water molecules and proteins in the dough is established; based on the total interaction energy and the interaction model, a gluten network formation kinetic model is established, and combined with the rheological properties of the dough, the local apparent viscosity distribution and the local shear stress distribution at each position in the dough are calculated; the dough kneading machine mechanical control analysis part is used to calculate the local mechanical energy density distribution and temperature field distribution of the dough kneading machine according to the local apparent viscosity distribution and the local shear stress distribution at each position in the dough; the dough kneading machine optimization control part is used to calculate the dough quality index according to the gluten network formation kinetic model, the local mechanical energy density distribution and the temperature field distribution, and calculate the kneading angular velocity control amount and the kneading pressure control amount according to the set optimized dough quality index.

[0007] Furthermore, the gluten content in the flour is set to a value ranging from 8% to 14%; the average molecular weight of all gluten proteins in the dough kneading machine is set to a value ranging from 10 4 g / mol to 10 6 g / mol; using the volume and uniform distribution assumptions, the number of gluten protein molecules in the dough kneading machine is estimated to be N p ; The critical temperature of gluten protein molecules is determined by experimental methods. The critical temperature refers to the temperature point at which the intermolecular bond energy of gluten protein molecules decreases significantly under specific interaction conditions; the charge of gluten protein molecules, the equilibrium distance of intermolecular van der Waals forces of gluten protein molecules and the molecular distance between gluten protein molecules are calculated through the isoelectric point of the protein and the ionic environment under experimental conditions.

[0008] Furthermore, the total interaction energy between gluten protein molecules in the dough kneading machine was calculated by the following formula:

[0009]

[0010] Among them, E p (t) is the total interaction energy between gluten protein molecules at time t; Np is the number of gluten protein molecules; i and j are integer subscript indices; q i is the molecular charge of the i-th gluten protein molecule; ∈ 0 is the dielectric constant of vacuum; q j is the molecular charge of the jth gluten protein molecule; r ij is the molecular distance between the i-th gluten protein molecule and the j-th gluten protein molecule; T(t) is the temperature at time t; T c is the critical temperature; ij is the equilibrium distance of the intermolecular van der Waals force between the i-th gluten protein molecule and the j-th gluten protein molecule.

[0011] Furthermore, the interaction model between water molecules and proteins in dough is expressed by the following formula:

[0012]

[0013] Where W(r, t) is the local moisture content at position r and time t in the dough; D w is the water diffusion coefficient; k h is the hydration rate constant; R g is the gas constant; is the Laplace operator; W sat is the saturated moisture content; P f (t) is the free protein concentration at time t.

[0014] Furthermore, the kinetic model of gluten network formation was established according to the total interaction energy and the interaction model through the following formula:

[0015]

[0016] Where C(r, t) is the gluten network concentration at position r and time t; D c (W(r, t)) is the diffusion coefficient that depends on the water content and is calculated as: kf is the network formation rate constant, which is the set value; is the gradient operator; D 0 is the basic moisture content, no value is set; E d is the diffusion activation energy, is the set value; W opt is the set optimal moisture content; C max is the set maximum gluten network concentration; k B is the Boltzmann constant; f(W(r, t)) is the water content modulation function, and the calculation formula is:

[0017] Furthermore, the local apparent viscosity distribution and local shear stress distribution at each position in the dough are calculated by the following formula in combination with the rheological properties of the dough:

[0018]

[0019] Where η(r, t) is the local apparent viscosity at position r and time t; η 0 is the set base viscosity; C 0 is the baseline network concentration; τ(r, t) is the local shear stress at position r and time t; V mol is the molar volume of all gluten proteins in the dough kneading machine; α is the weighting coefficient, ranging from 0.5 to 0.75; h is the internal space height of the dough kneading machine; F 0 is the initial kneading pressure of the dough kneading machine; ω 0 is the angular velocity of the dough kneading machine.

[0020] Furthermore, the mechanical control analysis part of the dough kneading machine is used to calculate the local mechanical energy density distribution of the dough kneading machine according to the local apparent viscosity distribution and the local shear stress distribution at each position in the dough through the following formula:

[0021]

[0022] Among them, E m (r, t) is the local mechanical energy density at position r and time t; k e The energy transfer coefficient is a set value ranging from 0.3 to 0.6.

[0023] Furthermore, the temperature field distribution is expressed by the following formula:

[0024]

[0025] Where ρ is the dough density; c p k is the specific heat of dough, ranging from 2000 to 3000; T is the thermal conductivity of dough, ranging from 0.2 to 0.6; h is the heat of hydration, ranging from 200 to 500.

[0026] Furthermore, the dough quality index is calculated by the following formula:

[0027]

[0028] Among them, E opt is the set optimized mechanical energy density; Q is the dough quality index.

[0029] Furthermore, the kneading angular velocity control amount and the kneading pressure control amount are calculated by the following formula:

[0030]

[0031] Among them, ω(t) is the kneading angular velocity control value at time t; F(t) is the kneading pressure control value; E ref is the reference mechanical energy, ranging from 300 to 800; η ref is the reference viscosity, the value range is 10 4 to 10 5 ; is the mean of η(r, t).

[0032] The intelligent dough kneading optimization system provided by the present application has the following beneficial effects: the present invention dynamically monitors the dough state during the kneading process, including multiple key parameters such as gluten network concentration, local apparent viscosity, shear stress, temperature field distribution and moisture content. By combining these data, the present invention establishes a dynamic modeling system to reveal the formation mechanism of the gluten network at the molecular level and directly associate it with the actual mechanical input and operating parameters. This real-time analysis and modeling capability not only breaks through the limitation of the traditional kneading equipment's insufficient understanding of the dough state, but also provides an accurate basis for the optimization of operating parameters. For example, by dynamically calculating the total interaction energy and local apparent viscosity distribution between gluten protein molecules, the present invention can capture the key nodes of gluten network formation in real time, thereby increasing mechanical input when the gluten network concentration is low, reducing external force when the concentration is close to saturation, and avoiding excessive processing. This ability of precise monitoring and regulation greatly improves the kneading efficiency and the uniformity of network formation. Secondly, the present invention realizes the dynamic optimization of kneading parameters through an intelligent control system, which greatly improves the efficiency and accuracy of the kneading process. Traditional dough kneading technology usually operates with fixed pressure, angular velocity and kneading time, lacks the ability to respond to real-time status, resulting in energy waste and fluctuations in dough quality. The present invention is based on feedback control of the quality index, which can comprehensively consider factors such as gluten network concentration, mechanical energy density and temperature distribution, and dynamically adjust the angular velocity, pressure, temperature and water input of the dough kneading machine. Through multi-parameter joint optimization, the present invention ensures that the dough kneading machine is always in the best working state during the entire processing process, significantly improves the efficiency and uniformity of gluten network formation, and finally obtains higher quality and more stable dough. This intelligent multi-dimensional control capability is unattainable by traditional dough kneading technology and is also an important technical advantage of the present invention. Secondly, the present invention realizes dynamic optimization of dough kneading parameters through an intelligent control system, which greatly improves the efficiency and accuracy of the dough kneading process. Traditional dough kneading technology usually operates with fixed pressure, angular velocity and kneading time, lacks the ability to respond to real-time status, resulting in energy waste and fluctuations in dough quality. The present invention is based on feedback control of the quality index, which can comprehensively consider factors such as gluten network concentration, mechanical energy density and temperature distribution, and dynamically adjust the angular velocity, pressure, temperature and water input of the dough kneading machine. Through multi-parameter joint optimization, the present invention ensures that the dough kneading machine is always in the best working state during the entire processing process, significantly improving the efficiency and uniformity of gluten network formation, and ultimately obtaining higher quality and more stable dough. This intelligent multi-dimensional control capability is unattainable by traditional dough kneading technology and is also an important technical advantage of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0034] Figure 1 A schematic diagram of the system structure of an intelligent dough kneading optimization system for a dough kneading machine provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0036] Example 1: Reference Figure 1 , an intelligent dough kneading optimization system for a dough kneading machine, the system comprising: a dough analysis part, a dough kneading machine mechanical control analysis part and a dough kneading machine optimization control part; the dough analysis part is used to estimate the number of gluten protein molecules in the dough kneading machine by using the volume and distribution uniformity assumptions through the gluten protein content in the flour, and calculate the total interaction energy between the gluten protein molecules in the dough kneading machine according to the number of gluten protein molecules; according to the total interaction energy, an interaction model between water molecules and proteins in the dough is established; according to the total interaction energy and the interaction model, a gluten network formation dynamics model is established, and the local apparent viscosity distribution and local shear stress distribution at various positions in the dough are calculated in combination with the rheological properties of the dough; the dough kneading machine mechanical control analysis part is used to calculate the local mechanical energy density distribution and temperature field distribution of the dough kneading machine according to the local apparent viscosity distribution and local shear stress distribution at various positions in the dough; the dough kneading machine optimization control part is used to calculate the dough quality index according to the gluten network formation dynamics model, the local mechanical energy density distribution and the temperature field distribution, and calculate the kneading angular velocity control amount and the kneading pressure control amount according to the set optimized dough quality index.

[0037] Specifically, the dough analysis part closely links the physicochemical properties of flour with the mechanical behavior during the kneading process through fine analysis and mathematical modeling at the molecular level, thereby achieving a comprehensive analysis and dynamic prediction of dough characteristics. This process begins with the precise determination of the gluten content in flour. Gluten is a key factor in determining the structural formation and mechanical properties of dough. Its essence is a complex network formed by the interweaving of multiple protein molecules. In this system, the gluten content is converted into the number of protein molecules actually present during the kneading process through the assumption of volume and distribution uniformity. This calculation process uses molecular distribution statistics and volume density theory to ensure that the estimation results can accurately reflect the molecular situation in the actual kneading environment. Based on the number of protein molecules, the system further calculates the total interaction energy between gluten protein molecules. The principle of this step is based on the physicochemical laws of intermolecular interactions, including van der Waals forces, hydrogen bonding, and electrostatic interactions. These forces jointly determine the attraction and repulsion behavior between protein molecules, thereby affecting the aggregation and networking characteristics of proteins during the kneading process. The calculation of the total interaction energy involves not only the simple energy superposition between molecular pairs, but also the spatial distribution of protein molecules in the dough and the dynamic changes during the kneading process. For example, during the kneading process, the input of mechanical energy will cause spatial rearrangement and local deformation of protein molecules, which will have a dynamic impact on the interaction between molecules. To this end, the system introduces a dynamic correction model based on Lagrangian mechanics to ensure that the total interaction energy can reflect the changes in the mechanical behavior of molecules during the kneading process in real time.

[0038] At the same time, the system further analyzes the interaction between gluten protein and water molecules and constructs a mathematical model of water molecule-protein binding. Water molecules play a vital role in dough formation. They bind to protein molecules through hydrogen bonds, thereby promoting the flexibility and networking of protein molecules. The core principle of this model is to simulate how water molecules are distributed during dough kneading and the dynamic changes in their binding strength and mode with protein molecules. Specifically, the binding state of water molecules depends not only on the type and content of protein in flour, but also on the temperature, shear stress and mechanical energy density during dough kneading. Therefore, the model not only needs to reflect the binding behavior of water molecules under static conditions, but also must capture the trend of water molecules redistributing during dough kneading through dynamic simulation. The realization of this dynamic simulation relies on the technical framework of combining molecular dynamics methods and finite element analysis. On this basis, the dough analysis part further establishes a kinetic model for gluten network formation. The key to this model is to convert the interaction behavior at the molecular level into macroscopic rheological properties, such as the distribution of apparent viscosity and shear stress. In principle, the interaction strength between protein molecules directly affects the formation speed and strength of the gluten network, which are the basis of rheological parameters. Therefore, the kinetic model establishes a cross-scale correlation from microscopic interactions to macroscopic physical properties by integrating the interaction energy between gluten protein molecules and water molecules, as well as the input of mechanical energy and the transfer of heat energy during the kneading process. Specifically, apparent viscosity is an important parameter in the formation of the gluten network, which reflects the fluidity of the dough under the action of external forces. The system calculates the local viscosity distribution at different locations by analyzing the interaction energy of gluten protein molecules and the binding behavior of water molecules. This process utilizes the principle of shear thinning effect in non-Newtonian fluid mechanics. The apparent viscosity is not only related to the shear rate, but also closely related to the degree of protein network formation. Similarly, the distribution of shear stress reflects the mechanical equilibrium state inside the dough, which is jointly determined by the local mechanical energy density and rheological properties. Through the kinetic model, the system can predict the viscosity and stress distribution of the dough under different kneading conditions in real time, thereby providing accurate data support for optimization control.

[0039] During the operation of the dough kneading machine, mechanical energy is input into the dough in the form of rotation and pressure. These mechanical energies not only affect the physical form of the dough, but also directly change the internal structure and thermodynamic state of the dough through the transfer and distribution of energy. The primary task of the mechanical control analysis part is to accurately model the distribution of mechanical energy in the dough. This modeling process is based on the basic principles of energy conservation and fluid mechanics, combined with the dynamic effects of shear and tensile stresses on the formation of gluten networks during the kneading process. Specifically, the mechanical energy applied by the dough kneading machine forms a non-uniformly distributed mechanical energy density field inside the dough. The dough at different positions will have different responses to energy absorption and transfer due to differences in apparent viscosity and shear stress. To this end, the mechanical control analysis part calculates its response characteristics to mechanical energy input by dynamically monitoring the viscosity and stress at each position of the dough, thereby obtaining a distribution map of local mechanical energy density. At the same time, this part also needs to analyze the process of converting mechanical energy into heat energy during the kneading process, because temperature changes have an important influence on the formation of gluten networks and dough quality. During the operation of the dough kneading machine, the input of local mechanical energy will lead to the accumulation of friction and deformation energy between molecules, thereby generating heat. The mechanical control analysis part calculates the temperature field distribution in the dough kneading machine through the heat conduction model, simulating the transfer and diffusion of temperature at different locations. This process comprehensively considers the thermal conductivity of the dough, the distribution of mechanical energy density, and the influence of gluten network formation on heat transfer, so as to dynamically predict the temperature change trend in the dough kneading machine. This precise temperature field analysis provides a scientific basis for optimizing the utilization of heat energy in the kneading process, while also ensuring that the dough will not damage its microstructure due to local overheating.

[0040] Another key principle of the mechanical control analysis part is to establish the coupling relationship between the operating parameters of the dough kneading machine and the dough characteristics through the dynamic analysis of the energy and temperature fields. During the kneading process, angular velocity and pressure are the two core variables that determine the form and intensity of mechanical energy input. By calculating the local mechanical energy density distribution and temperature field distribution, this part can reversely infer how the operating parameters of the dough kneading machine under a specific state affect the rheological properties and quality indicators of the dough. The change of angular velocity directly affects the distribution of shear stress during the kneading process, while the adjustment of pressure changes the formation speed and strength of the gluten network. The mechanical control analysis part ensures that the mechanical energy input of the dough kneading machine is optimally matched with the energy absorption capacity of the dough through a comprehensive analysis of the two, thereby maximizing the utilization efficiency of mechanical energy and optimizing the quality of the dough. In addition, the innovation of the mechanical control analysis part is also reflected in its detailed analysis of local energy consumption. Traditional dough kneading technology usually relies on the average distribution of global energy, while ignoring the uneven local energy distribution caused by viscosity, stress and temperature differences in the dough. The system achieves accurate capture of the energy state of the local area through high-resolution mechanical energy density and temperature field modeling. This precise energy analysis can not only optimize the power output of the dough kneading machine, but also avoid overheating or excessive shearing of the dough due to local energy overload by adjusting the operating parameters in real time, thereby achieving intelligent protection of the dough quality.

[0041] The optimization control part is the core link to realize system intelligence and quality optimization. Its principle is to combine the dynamic data of the dough analysis part and the mechanical control analysis part with the target quality index, and dynamically adjust the operating parameters of the dough kneading machine through real-time calculation and feedback control to ensure that the dough quality reaches the preset target. The work of the optimization control part is based on a comprehensive logical framework, that is, through the in-depth analysis of the gluten network formation dynamics model, the local mechanical energy density distribution and the temperature field distribution, the quantitative index of dough quality is constructed, and this is used as the core to drive the optimization algorithm and guide the operation of the dough kneading machine. The first step of optimization control is to define and calculate the dough quality index, which is the basis of the entire optimization process. The dough quality index is a comprehensive parameter that combines the degree of gluten network formation, the rheological properties of the dough, and the influence of the local energy distribution on the overall performance of the dough. This index is not a simple superposition of physical parameters, but is dynamically generated through a high-dimensional function model established in the system. The model fully considers multiple factors such as the interaction energy between gluten protein molecules, the distribution characteristics of water molecules in the dough, and the efficiency of mechanical energy input, and uses machine learning methods such as neural networks to train historical data, so as to achieve accurate evaluation of dough quality. Through such indexed representation, the system transforms complex dough physicochemical properties into quantifiable optimization targets.

[0042] After determining the dough quality index, the core task of the optimization control part is to adjust the operating parameters of the dough kneading machine in real time to bring the current dough quality index as close to the target value as possible. The key to this process is to accurately calculate the control quantities of angular velocity and pressure, because they are the two main variables that directly affect the mechanical energy input and the formation of the gluten network. The optimization control part determines the range of angular velocity and pressure through real-time calculation of the dynamic model and energy distribution, and performs optimization calculations in combination with the target quality index. This process utilizes adaptive control theory and optimal control methods to achieve dynamic adjustment by establishing a feedback loop between the operating parameters of the dough kneading machine and the dough quality index. For example, when the system detects that the local apparent viscosity of the dough is high, it will increase the shear rate by increasing the angular velocity of the dough kneading, thereby accelerating the homogenization process of the gluten network. On the contrary, if the temperature field distribution shows local overheating, thermal damage to the gluten network may be avoided by reducing the pressure or reducing the mechanical energy input. The optimization control part also integrates a prediction model to cope with the dynamic changes that may occur during the kneading process. The physicochemical properties of the dough are not constant and may fluctuate due to the external environment, the unevenness of the mechanical energy input, or other unknown factors. To this end, the optimization control part introduces a prediction mechanism based on time series data, which predicts future changes in dough quality by real-time monitoring of key parameters in the dough kneading process, such as mechanical energy density distribution, temperature field change trend, and dough rheological properties. This prediction mechanism is based on the time series analysis model of the deep learning algorithm, which can capture the risk that dough quality may deviate from the target value in advance and provide forward-looking guidance for the adjustment of the control strategy. In addition, another key function of the optimization control part is the multi-objective optimization capability.

[0043] In actual production, the goal of the dough kneading process is not only to obtain the optimal value of a single quality index, but to find a balance between multiple quality indexes, such as the strength of the gluten network, the flexibility of the dough, and the efficiency of mechanical energy utilization. To this end, the optimization control part constructs a multi-objective optimization model and uses genetic algorithms or particle swarm optimization algorithms to dynamically find the optimal solution set of each quality index and select the solution that best meets the production needs. This multi-objective optimization method not only improves the intelligence of the dough kneading machine, but also significantly enhances the adaptability of the system, enabling it to flexibly respond to changes in different types of flour and different process requirements. It is worth mentioning that the optimization control part fully utilizes the advantages of closed-loop feedback control during execution. Through real-time data feedback from the dough analysis part and the mechanical control analysis part, the optimization control part can continuously adjust the control strategy to deal with any abnormal situation during the dough kneading process. For example, when a large gradient is detected in the local energy distribution, the system will immediately adjust the angular velocity and pressure to balance the input of mechanical energy, thereby preventing local excessive shearing or overheating damage of the dough. Such a closed-loop control mechanism makes the entire system highly robust and responsive in real time, and can maintain a stable operating state in a dynamic environment.

[0044] Example 2: The gluten content in the flour is set to a value ranging from 8% to 14%; the average molecular weight of all gluten proteins in the dough kneading machine is set to a value ranging from 10 4 g / mol to 10 6 g / mol; using the volume and uniform distribution assumptions, the number of gluten protein molecules in the dough kneading machine is estimated to be N p ; The critical temperature of gluten protein molecules is determined by experimental methods. The critical temperature refers to the temperature point at which the intermolecular bond energy of gluten protein molecules decreases significantly under specific interaction conditions; the charge of gluten protein molecules, the equilibrium distance of intermolecular van der Waals forces of gluten protein molecules and the molecular distance between gluten protein molecules are calculated through the isoelectric point of the protein and the ionic environment under experimental conditions.

[0045] Specifically, the gluten protein content in flour is set to range from 8% to 14%, which is based on the typical parameter range of different types of flour in actual production. This ratio determines the mass fraction of gluten protein in the total amount of flour, which directly affects the absolute number of protein molecules during the kneading process. In the actual operation of the dough kneading machine, the level of gluten protein will affect the viscoelasticity and rheological properties of the dough. A low content of gluten protein may result in an insufficiently strong gluten network, while an excessively high content may cause the dough to be too tight and reduce its ductility. By precisely defining this range, the system can adapt to a variety of flour types and ensure that the kneading process has a wide range of applicability. Next, the average molecular weight of gluten protein is set to 10 4 g / mol to 10 6g / mol. Gluten protein is composed of many different molecules, and its molecular weight distribution reflects the structural complexity and aggregation behavior of the protein. The larger the molecular weight, the stronger the structural support provided by a single molecule, but its mobility is poor; the smaller the molecular weight, the easier it is for the molecule to move and participate in network formation, but the strength of a single molecule is insufficient. This range covers the common molecular weight range of gluten protein. Combined with the uniform distribution assumption, the total number of protein molecules in the dough kneading machine, N, can be inferred. p The total number of molecules is the key variable because it is directly related to the density and degree of cross-linking of the gluten network.

[0046] By assuming uniform volume and distribution, the number of gluten protein molecules N was estimated. p . Under the assumption of uniform distribution, the system distributes the total mass of protein in the dough kneading machine within the specified volume, and calculates the molecular number density per unit volume in combination with the molecular weight range. Although this method simplifies the complex protein distribution problem, it can quickly estimate the number of protein molecules in an actual production environment and provide basic data for subsequent analysis. The embodiment further determines the critical temperature of gluten protein by experimental methods. The critical temperature refers to the temperature point at which the intermolecular bond energy of gluten protein molecules significantly decreases under specific interaction conditions. This parameter has a decisive influence on the thermodynamic stability and mechanical properties of the dough. Near the critical temperature, the van der Waals forces and hydrogen bonds between protein molecules will weaken rapidly, resulting in a decrease in the structural strength of the gluten network. Determining the critical temperature can provide direct guidance for the temperature field control of the dough kneading machine, ensuring that network damage caused by overheating is avoided during the kneading process, while using appropriate temperature rise to promote the flexibility enhancement and cross-linking of protein molecules.

[0047] In addition, the embodiment calculates the molecular charge of gluten protein molecules by the isoelectric point of the protein and the ionic environment under experimental conditions. The charge of gluten protein depends on its isoelectric point (i.e., the pH value at which the protein charge is neutralized) and the ion concentration of the surrounding environment. In the actual kneading process, the charge of the protein molecule will affect the strength of the electrostatic interaction between molecules, thereby having an important impact on the formation and stability of the gluten network. High charge density will enhance the repulsive force between protein molecules, thereby reducing the compactness of the network, while low charge density may cause excessive molecular aggregation and reduce the uniformity of the dough. By accurately calculating the charge of protein molecules, the system can optimize and adjust the ionic environment to balance the electrostatic interaction between molecules and ensure the stability and uniformity of network formation. Next, the embodiment calculates the equilibrium distance and molecular spacing of the van der Waals force between gluten protein molecules. The van der Waals force is one of the main attractive forces between gluten protein molecules, and its strength is closely related to the molecular spacing. When the molecular spacing is close to the equilibrium distance, the interaction energy between molecules is the strongest, which is conducive to the stability of the gluten network. However, too small molecular spacing will cause a sharp increase in repulsive force and destroy the network structure, while too large molecular spacing will weaken the attraction and cause the network to be loose. By combining experimental measurements with theoretical calculations, the system can accurately predict the equilibrium distance and actual molecular spacing between protein molecules, and adjust the mechanical input of the dough kneading machine based on these parameters to optimize the gluten network formation process.

[0048] Example 3: The total interaction energy between gluten protein molecules in a dough kneading machine is calculated by the following formula:

[0049]

[0050] Among them, E p (t) is the total interaction energy between gluten protein molecules at time t; N p is the number of gluten protein molecules; i and j are integer subscript indices; q i is the molecular charge of the i-th gluten protein molecule; ∈ 0 is the dielectric constant of vacuum; q j is the molecular charge of the jth gluten protein molecule; r ij is the molecular distance between the i-th gluten protein molecule and the j-th gluten protein molecule; T(t) is the temperature at time t; T c is the critical temperature; ij is the equilibrium distance of the intermolecular van der Waals force between the i-th gluten protein molecule and the j-th gluten protein molecule.

[0051] Specifically, the formula calculates the intermolecular interaction energy of gluten protein pair by pair. It comprehensively describes the N p The electrostatic interaction between gluten protein molecules is caused by It follows the definition of the classical Coulomb force, which describes the attraction or repulsion between charged molecules. This effect is directly affected by the molecular distance r ij The influence of the electrostatic force increases sharply when the distance is reduced. This means that in the operation of the dough kneading machine, by adjusting the mechanical parameters (such as pressure or angular velocity), the electrostatic force between molecules can be indirectly controlled, thereby changing the formation rate and structural compactness of the gluten network. In addition, the van der Waals force part of the formula This reflects the complex balance of weak interactions between molecules. This force is one of the key driving forces for gluten molecules to form a stable network, where the delicate balance between attraction and repulsion determines the optimal value of the molecular distance. ij Close to equilibrium distance σ ij When the intermolecular attraction and repulsion cancel each other out, the most stable bonding state is formed. However, in the actual dough kneading process, due to the unevenness of mechanical input, significant changes in the molecular distance may occur in local areas. At this time, by calculating the dynamic changes of van der Waals forces in real time, it is possible to detect whether the network structure is stable and adjust the operating parameters of the dough kneading machine in time to restore the balance.

[0052] The formula also specifically introduces a temperature correction factor To describe the effect of temperature on the intermolecular interaction energy. Temperature not only affects the speed and range of molecular motion, but also weakens the interaction force by changing the intermolecular bond energy. When the temperature T(t) approaches the critical temperature T c When the intermolecular interaction energy decreases rapidly, the gluten network may lose stability and become loose or even destroyed. This phenomenon is particularly critical in the dough kneading process, because the dough kneading machine converts the mechanical energy input into thermal energy, causing the temperature field distribution to change dynamically. The temperature factor introduced in the formula allows the system to monitor and predict the impact of this change on the behavior of protein molecules in real time, providing a theoretical basis for optimizing the temperature field distribution. In the actual operation of the dough kneading machine, the application of this formula is not limited to simple energy calculation, but also reflects a dynamic feedback control mechanism. By calculating the intermolecular interaction energy E p (t) is calculated in real time, and the system can continuously monitor the formation status of the gluten network. p (t) When it deviates from the ideal range, the system can quickly adjust the mechanical parameters (such as angular velocity or pressure) so that the interaction between molecules can return to stability. This feedback mechanism greatly improves the intelligence of the dough kneading process, allowing the dough kneading machine to adapt to changes in different types of flour, ambient temperature or target quality requirements, thereby always maintaining the optimal operating state.

[0053] Example 4: The interaction model between water molecules and proteins in dough is expressed by the following formula:

[0054]

[0055] Where W(r, t) is the local moisture content at position r and time t in the dough; D w is the water diffusion coefficient; k h is the hydration rate constant; R g is the gas constant; is the Laplace operator; W sat is the saturated moisture content; P f (t) is the free protein concentration at time t.

[0056] Specifically, the basis of this model is the phenomenon of water diffusion. Water diffusion is one of the most basic physical processes in the process of kneading dough, which is expressed by the diffusion coefficient D w and moisture content gradient. During the kneading process, the moisture content of different parts of the dough may vary due to external mechanical energy input and internal structural changes. Water molecules in high moisture concentration areas will diffuse to low concentration areas. This diffusion behavior is described by the Laplace operator, revealing the dynamic change trend of water molecule distribution in space. The diffusion rate is given by D w Decision, D w The value of is affected by factors such as temperature, gluten network density and external pressure. In the dough kneading machine, D can be indirectly controlled by adjusting the temperature field and the distribution of mechanical energy. w , thereby optimizing the water diffusion behavior in the dough and making the gluten network formation more uniform. However, simple diffusion is not enough to explain the complex behavior of water molecules during kneading. Water molecules not only move in the gluten network, but also react with protein molecules to directly participate in the construction of the gluten network. This binding process is driven by thermodynamics and is dynamically regulated by multiple factors. h exp(-E p (t) / R g T(t)) describes the binding rate of water molecules to proteins, where k h is the hydration rate constant, which indicates the basic rate at which water molecules bind to proteins under specific conditions. The magnitude of the binding rate is related to the total interaction energy E between protein molecules. p (t) is directly related to the temperature T(t). Lower interaction energy and higher temperature will accelerate the movement of water molecules, making it easier for them to bind to protein molecules; conversely, higher interaction energy and lower temperature may inhibit this process. This relationship is expressed in detail by an exponential factor, reflecting the dynamic characteristics of hydration during kneading.

[0057] The formula also converts the free protein concentration P f(t) was incorporated into the model, revealing the key role of the dynamic distribution of protein molecules in water binding. Free protein is a protein molecule that does not participate in the cross-linking of the gluten network, and its concentration changes with the kneading process. As the gluten network gradually forms, the concentration of free protein will gradually decrease, but in the early stage of kneading, the concentration of free protein is higher, which provides more binding sites for water molecules. The dynamic change of free protein concentration makes the hydration rate show significant time-varying characteristics during the kneading process. By real-time monitoring of P f (t), the system can determine the progress of gluten network formation and optimize the kneading effect by adjusting mechanical parameters or water input when necessary. In addition, the formula is based on the saturated moisture content W sat Describes the limiting state of water molecule binding. When the water content W(r, t) in the local area is close to W sat When the water content of the dough is less than 10%, the binding rate will slow down significantly, indicating that the area has reached the saturation point of hydration. In this case, further increasing the water input is not only unhelpful, but may also cause the dough to be too thin and soft, affecting the strength and stability of the gluten network. Therefore, the saturated moisture content provides a key boundary condition for the kneading process, and the system can realize intelligent control of water input by monitoring this parameter. By integrating water diffusion, hydration rate, free protein concentration and saturated moisture content, the formula successfully transforms the complex physical and chemical behavior of water molecules in the kneading process into a mathematical model. The model can predict the spatial and temporal distribution of local moisture content of the dough in real time, providing an accurate reference for optimizing mechanical input, adjusting temperature field distribution and controlling water input. Compared with traditional kneading technology, this model has significant advantages. Traditional technology usually relies on experience or simple global control, while this model is based on dynamic data and scientific theory. It can not only accurately predict the behavior of water molecules in the kneading process, but also realize intelligent adjustment through feedback mechanism to ensure the stability and consistency of dough quality.

[0058] Example 5: A gluten network formation kinetic model was established based on the total interaction energy and the interaction model using the following formula:

[0059]

[0060] Where C(r, t) is the gluten network concentration at position r and time t; D c (W(r, t)) is the diffusion coefficient that depends on the water content and is calculated as: kf is the network formation rate constant, which is the set value; is the gradient operator; D 0 is the basic moisture content, no value is set; E d is the diffusion activation energy, is the set value; W opt is the set optimal moisture content; C maxis the set maximum gluten network concentration; k B is the Boltzmann constant; f(W(r, t)) is the water content modulation function, and the calculation formula is:

[0061] Specifically, the gluten network concentration C(r, t) is the core variable of the model, which represents the local concentration change of the gluten network at position r and time t. Describes the diffusion behavior of the network in the dough. The diffusion coefficient D c (W(r, t)) is a function of the moisture content W(r, t), reflecting the effect of moisture content on the diffusion capacity of the gluten network. It can be seen that the diffusion coefficient is not only related to the diffusion activation energy E d It is related to the temperature T(t) and is also significantly regulated by the moisture content. When the moisture content is close to the set optimal value W opt When the moisture content is too high or too low, the diffusion coefficient decreases and the network formation efficiency is also inhibited. The second term of the kinetic model is The main driving force of gluten network formation is described. This part integrates the interaction energy, free protein concentration, moisture content regulation and network concentration saturation effect, revealing the internal mechanism of dynamic formation of gluten network during dough kneading. The interaction energy E p (t) The driving force for the formation of gluten network reflects the thermodynamic properties of molecules. When the interaction energy is high, protein molecules are more likely to attract each other and form a network, while high temperature will weaken this driving force, resulting in a decrease in the rate of network formation.

[0062] Free protein concentration P f (t) is the key variable in network formation, which represents the number of protein molecules that are not involved in cross-linking. As the kneading process progresses, the free protein gradually decreases, which is reflected by the term This indicates that the closer the gluten network concentration is to the maximum concentration C max The smaller the crosslinking, the slower the rate of network formation. This saturation effect is a natural characteristic of network dynamics and prevents excessive crosslinking from causing the dough to become too hard or lose elasticity. Moisture content modulation function The function form shows that when the moisture content W(r, t) deviates from the optimal value W optWhen the moisture content is too low, the network formation rate will drop significantly, while under the ideal moisture content condition, the network formation rate reaches the maximum value. This function fully reflects the dual effect of water on the formation of gluten network: when the moisture content is too low, the protein molecules cannot fully unfold and cross-link; when the moisture content is too high, the structural strength of the gluten network is destroyed. Therefore, by adjusting the moisture distribution in the dough kneading machine, the efficiency and uniformity of network formation can be significantly improved. The model also combines the diffusion term and the dynamic formation term to describe the dynamic distribution of the gluten network in time and space. The diffusion behavior ensures the uniform distribution of the gluten network in the dough, while the dynamic formation behavior drives the local generation and concentration increase of the network. The combination of the two forms a complete gluten network formation kinetic process. By precisely controlling these two processes, the optimal performance of the gluten network can be achieved.

[0063] Example 6: The local apparent viscosity distribution and local shear stress distribution at each position in the dough are calculated by the following formula in combination with the rheological properties of the dough:

[0064]

[0065] Where η(r, t) is the local apparent viscosity at position r and time t; η 0 is the set base viscosity; C 0 is the baseline network concentration; τ(r, t) is the local shear stress at position r and time t; V mol is the molar volume of all gluten proteins in the dough kneading machine; α is the weighting coefficient, ranging from 0.5 to 0.75; h is the internal space height of the dough kneading machine; F 0 is the initial kneading pressure of the dough kneading machine; ω 0 is the angular velocity of the dough kneading machine.

[0066] Specifically, the local apparent viscosity η(r, t) is a parameter that reflects the dough flow resistance, and its distribution is affected by the gluten network concentration C(r, t), the interaction energy E p (t), local moisture content W(r, t) and temperature T(t). In the formula, η 0 Indicates the base viscosity, The term reveals the enhancing effect of gluten network concentration on viscosity. The higher the gluten network concentration, the more significant the increase in apparent viscosity, which is due to the cross-linking between protein molecules, which strengthens the viscoelasticity of the dough. However, this concentration dependence is not linear, but rather exponential, reflecting the nonlinear effect of concentration on viscosity. Compared with the low concentration state, the viscosity-enhancing effect of the high concentration network is more obvious, because when the degree of molecular cross-linking is high, the structure of the protein network produces a stronger resistance to flow. The effect of interaction energy on viscosity is expressed by The intermolecular interaction energy E pThe higher the (t), the stronger the attraction and cross-linking between protein molecules, which will increase the overall viscosity of the dough. The increase in temperature T(t) will weaken this interaction, making the protein molecules easier to slide, thereby reducing the viscosity. This term clearly reflects the dynamic balance between temperature and molecular interaction during the kneading process, which is of great significance for optimizing the temperature field distribution of the dough kneading machine.

[0067] The influence of local moisture content W(r, t) is When the moisture content is close to the optimal value W opt When the moisture content is too low or too high, the viscosity reaches an ideal value, while too low or too high moisture content will lead to a decrease in viscosity. When the moisture content is too low, the fluidity between protein molecules is limited and the network formation is insufficient; when the moisture content is too high, the network is diluted and the viscosity is weakened. This part reveals the important role of water in regulating viscoelasticity during the kneading process. The local shear stress τ(r, t) is the internal stress that the dough is subjected to under the action of external force. Its calculation formula integrates the combined effects of viscosity and external mechanical input. The first term in the formula represents the direct contribution of mechanical force to shear stress, where F 0 is the initial kneading pressure, ω 0 is the angular velocity of the dough kneading machine, and h is the height of the internal space of the dough kneading machine. This term reflects how the mechanical input parameters of the dough kneading machine directly affect the shear stress distribution of the dough. For example, increasing the pressure or angular velocity can increase the shear stress, thereby strengthening the shear deformation of the dough, but excessive mechanical force may cause the destruction of the gluten network, so it is necessary to find a balance between shear stress and network stability. The second term of the formula The contribution of protein molecular interactions and network concentration to shear stress is revealed. The weighting coefficient α ranges from 0.5 to 0.75 and is used to adjust the weight of interaction energy in shear stress. The higher the interaction energy, the stronger the attraction between protein molecules and the more stable the network, which will increase the dough's shear resistance to external forces. At the same time, the increase in gluten network concentration C(r, t) will also enhance shear stress, which is consistent with its enhancing effect on viscosity. Overall, the model describes the dynamic changes of apparent viscosity and shear stress in local areas during the operation of the dough kneading machine. Viscosity and shear stress are the bridge between gluten network formation and mechanical input, and they directly affect the rheological properties and final quality of the dough. By calculating the distribution of η(r, t) and τ(r, t) in real time, the dough kneading machine can dynamically adjust parameters such as pressure, angular velocity and temperature field to optimize the gluten network formation process. Compared with traditional dough kneading technology, this model based on dynamic and rheological properties has significant scientific advantages. It can not only capture the complex spatial and temporal changes in the kneading process, but also achieve intelligent control through feedback mechanisms, thereby ensuring consistent dough quality and efficient production.

[0068] Embodiment 7: The mechanical control analysis part of the dough kneading machine is used to calculate the local mechanical energy density distribution of the dough kneading machine according to the local apparent viscosity distribution and the local shear stress distribution at each position in the dough by the following formula:

[0069]

[0070] Among them, E m (r, t) is the local mechanical energy density at position r and time t; k e The energy transfer coefficient is a set value ranging from 0.3 to 0.6.

[0071] Specifically, the core of the formula is the local mechanical energy density E m The mechanical energy density reflects the energy state stored and transferred inside the dough, and its distribution directly affects the formation of the gluten network and the final quality of the dough. The formula describes the diffusion, input and consumption of mechanical energy through three main terms. The first term Describes the diffusion behavior of mechanical energy in the dough. The energy diffusion is controlled by the energy transfer coefficient k e and local apparent viscosity η(r, t). Energy conductivity k e It represents the efficiency of energy transfer in space, and its value range is 0.3 to 0.6, indicating that the diffusion rate of energy during kneading is limited by the physical properties of the system. At the same time, the apparent viscosity η(r, t) directly affects the energy transfer capacity. In areas with higher viscosity, the diffusion of mechanical energy is hindered, while in areas with lower viscosity, energy is more easily transferred. This part reflects the coupling effect between energy diffusion and the viscoelastic properties of the dough, indicating that the uneven distribution of mechanical input in the dough may lead to local excessive or insufficient energy accumulation. The second term It is the input term of mechanical energy, which directly describes how the external force applied by the dough mixer is converted into local mechanical energy density. Shear stress τ(r, t) is the shear resistance generated inside the dough under the action of external force, and its magnitude is determined by viscosity and mechanical input (such as initial pressure F 0 , angular velocity ω 0 , and the machine space height h). From this item, it can be seen that the size and distribution of external force directly affect the input efficiency of mechanical energy. If the input energy is insufficient, the fluidity and network formation of the dough may be limited; while excessive input may lead to excessive damage to the local structure. Therefore, regulating the shear stress distribution is crucial to optimizing the formation of the gluten network. The third item Describes the mechanical energy dissipation caused by the interaction energy. This term indicates that the interaction between protein molecules and the gluten network concentration will consume the energy of mechanical input. The interaction energy E pThe higher (t), the tighter the network cross-linking, and the greater the mechanical energy consumption. This consumption is an inevitable result of the gluten network formation process, because part of the mechanical input energy is converted into intermolecular binding energy or network structural energy. The weighting coefficient α ranges from 0.3 to 0.6 and is used to adjust the weight of this energy consumption to ensure that the model can adapt to different operating conditions and flour types. By combining the above three items, the formula provides a complete framework that describes how mechanical energy is dynamically distributed in time and space during kneading. The diffusion, input and consumption of mechanical energy jointly determine the energy state of the dough, which directly affects the formation speed and strength of the gluten network. By calculating E in real time m (r, t), the dough kneading machine can dynamically adjust input parameters (such as pressure, angular velocity) to optimize the distribution of mechanical energy. For example, when it is detected that the local mechanical energy density is too low, the energy input can be compensated by increasing the pressure or angular velocity; when the local mechanical energy density is too high, the input parameters can be reduced to avoid excessive shearing or overheating.

[0072] Embodiment 8: The temperature field distribution is expressed by the following formula:

[0073]

[0074] Where ρ is the dough density; c p k is the specific heat of dough, ranging from 2000 to 3000; T is the thermal conductivity of dough, ranging from 0.2 to 0.6; h is the heat of hydration, ranging from 200 to 500.

[0075] Specifically, the temperature change term in the formula It reflects the instantaneous rate of change of temperature T(r, t) at position r and time t. Here, ρ represents the density of the dough, and c p is the specific heat of the dough, which ranges from 2000 to 3000. The specific heat reflects the ease with which the dough changes temperature when absorbing or releasing heat. The higher the specific heat, the smaller the temperature rise of the dough under the same heat input, which is crucial for the thermal stability of the dough and the uniformity of the kneading process. By accurately setting ρ and c p The formula can adapt to the thermal properties of different flour types and ingredients. Describes the heat conduction process in the dough. Thermal conductivity k T Indicates the ability of dough to transfer heat. Its value range is 0.2 to 0.6, which is applicable to the common ingredient ratios and moisture content conditions in dough. The driving force of heat conduction is the temperature gradient. Heat is transferred from hot areas to cold areas to balance the temperature distribution. With this term, the formula reveals how the temperature uniformity inside the kneading machine is affected by the heat transfer characteristics and the distribution of mechanical energy. If the heat cannot be transferred quickly to the cold areas of the dough, it may cause local overheating, which will affect the stability of the gluten network.

[0076] Item 2 Reflects the heat generation caused by mechanical input. This term is calculated by the apparent viscosity η(r, t) and the mechanical input parameters (initial pressure F 0 , angular velocity ω 0 , machine space height h) quantifies the efficiency of converting mechanical energy into thermal energy. During the kneading process, mechanical energy is dissipated as heat through intermolecular friction and deformation. Areas with higher apparent viscosity will generate more heat due to greater flow resistance, and an increase in mechanical input parameters (such as increasing pressure or angular velocity) will also accelerate heat generation. This item clearly reveals the direct impact of the kneading machine's mechanical parameters on the temperature field distribution, and provides a theoretical basis for optimizing mechanical input and preventing local overheating. The third item Describes the heat effect caused by the hydration reaction. Heat of hydration λ h It is the heat released when water molecules in the dough combine with protein molecules, and its value ranges from 200 to 500. The rate of change of moisture content It reflects the dynamic process of the hydration reaction. During the kneading process, water molecules form hydrogen bonds and other weak interactions with protein molecules, releasing hydration heat, thereby providing additional heat to the dough. The size of the hydration heat depends on the water supply rate and the binding capacity of the protein molecules. This item shows that by adjusting the water input rate, the temperature distribution of the dough can be indirectly regulated, thereby optimizing the hydration reaction efficiency. Overall, this formula comprehensively considers the three main heat sources of heat conduction, mechanical input and hydration reaction, and reveals the dynamic mechanism of temperature field distribution during kneading. By calculating the temperature change rate in real time The intelligent control system of the dough kneading machine can monitor and optimize the transfer and distribution of heat. For example, when it detects that the local temperature rises too quickly, the system can control heat generation by reducing mechanical input (such as reducing pressure or angular velocity); or, by adjusting the water input rate to increase the heat dissipation of the hydration reaction, thereby balancing the temperature distribution. Compared with traditional dough kneading technology, this optimization method based on dynamic analysis of the temperature field is more accurate and can significantly improve the energy utilization efficiency and dough quality of the dough kneading machine.

[0077] Example 9: The dough quality index was calculated by the following formula:

[0078]

[0079] Among them, E opt is the set optimized mechanical energy density; Q is the dough quality index.

[0080] Specifically, the numerator part of the formula Describes the contribution of gluten network concentration and mechanical energy density to dough quality. These two items are weighted with weight coefficients of 0.4 and 0.6, respectively, reflecting the different weights of these two factors on dough quality. Gluten network concentration C(r, t) is a direct indicator of gluten network formation. The higher the concentration, the better the strength and elasticity of the dough. However, the contribution of concentration is limited, so C is used max Normalization is performed to avoid evaluation deviation caused by local excessively high concentration. Mechanical energy density E m (r, t) reflects the energy distribution state of the dough under mechanical input, and its contribution is achieved by optimizing the mechanical energy density E opt Normalized. Optimization of mechanical energy density means that energy is used rationally without excess or deficiency. Giving a higher weight (0.6) to mechanical energy density indicates that a rational distribution of mechanical input during kneading has a more significant impact on the overall optimization of dough quality. The integral summation of this part is calculated by traversing the spatial volume V of the entire gluten network. mol , providing a weighted average of the overall properties of the dough in three-dimensional space.

[0081] The denominator of the formula Represents the penalty term for the uniformity of dough property distribution. Gradient term and The inhomogeneity of gluten network concentration, mechanical energy density, and temperature distribution is measured respectively. The larger the distribution gradient, the more dramatic the spatial variation of these physical quantities, indicating that the internal characteristics of the dough are not uniform enough. Uniformity is an important indicator of high-quality dough, so the formula quantifies the distribution inhomogeneity through the penalty term in the denominator. The constant term "1" in the denominator ensures that the denominator is not zero even if the distribution is completely uniform. In fact, the role of the denominator term is to reduce the contribution weight of physical quantities with large gradients to the quality index, indirectly encouraging parameter optimization during the kneading process to achieve a more uniform distribution. In this way, the formula not only focuses on the overall characteristic values ​​of the dough, but also focuses on the spatial consistency of these characteristics, highlighting the importance of uniformity of gluten network formation and uniformity of energy distribution. The final result of the formula, Q, reflects the overall quality level of the dough during the kneading process. A higher Q value indicates that the gluten network is fully formed, the mechanical energy distribution is reasonable, and the temperature field and other key parameters are relatively uniform. On the contrary, a lower Q value indicates that the dough may have problems such as local inhomogeneity, energy waste, or insufficient network formation. This indicator provides a clear optimization target for the intelligent control system of the dough kneading machine. By adjusting parameters such as mechanical input (such as pressure and angular velocity), temperature field distribution, and moisture input, the system can dynamically adjust the operation strategy to maximize the Q value. Compared with traditional dough kneading technology, this formula introduces spatial integral and gradient constraints, and for the first time simultaneously incorporates the overall level and uniformity of dough quality into the evaluation system. This method significantly improves the intelligence level of the dough kneading process. It can not only accurately evaluate the quality of the dough, but also achieve refined control of the dough kneading process through real-time monitoring and optimization of the quality index. This formula provides a core evaluation tool for the intelligent dough optimization system of the dough kneading machine, and also provides important technical support for the development of the food processing industry in the direction of automation and intelligence.

[0082] Embodiment 10: The kneading angular velocity control amount and the kneading pressure control amount are calculated by the following formula:

[0083]

[0084] Among them, ω(t) is the kneading angular velocity control value at time t; F(t) is the kneading pressure control value; E ref is the reference mechanical energy, ranging from 300 to 800; η ref is the reference viscosity, the value range is 10 4 to 10 5 ; is the mean of η(r, t).

[0085] Specifically, the calculation formula of the angular velocity control amount ω(t) is: Here, ω 0 is the initial angular velocity, Expresses the actual quality index Q relative to the target quality index Qtarget When Q target When Q>Q target When the angular velocity is reduced, it avoids excessive mechanical input. This dynamic adjustment ensures that the kneading process operates efficiently within the quality target. The angular velocity is further adjusted, reflecting the mechanical energy density E p (t) The effect on the kneading process. The higher the mechanical energy (such as excessive local or excessive mechanical input), the angular velocity will automatically decrease to avoid structural damage to the gluten network. Reference mechanical energy E ref The range of values ​​(300 to 800) ensures that the sensitivity of the adjustment is adapted to different types of dough and operating environment. The calculation formula of the pressure control amount F(t) is: The core of pressure regulation is to combine the real-time status of gluten network concentration C(r, t), apparent viscosity η(r, t) and quality index Q to ensure that the input of pressure can promote the formation of gluten network without causing excessive compression or destruction. Describes the current gluten network concentration C(r, t) relative to the maximum concentration C max When the network concentration is low, the pressure increases to promote cross-linking; when the concentration approaches the maximum, the pressure automatically decreases to avoid over-action. Adjust the relationship between pressure and apparent viscosity. When the viscosity is low (indicating a soft or thin dough), the pressure is increased to strengthen the gluten network formation; when the viscosity is high, the pressure is reduced to avoid energy waste and network damage. Reference viscosity η ref The value range (10 4 to 10 5 ) Ensure that the adjustment sensitivity is adapted to different dough conditions. Introducing the quality index as the core factor of feedback control to ensure that the ultimate goal of pressure adjustment is to achieve Q to Q target Overall, the adjustment formula of ω(t) and F(t) forms a closed-loop control system through dynamic feedback and multi-factor coupling. In actual operation, the dough kneading machine monitors the quality index Q, gluten network concentration C(r, t), apparent viscosity η(r, t) and mechanical energy density E in real time. p (t), and calculates the adjustment of angular velocity and pressure based on these data. This intelligent regulation enables the dough kneading machine to adapt to the state changes of the dough at different stages. For example, in the initial stage, the angular velocity and pressure are increased to quickly increase the gluten network concentration; in the later stage, the mechanical input is reduced to avoid excessive strengthening or damage of the network structure.

[0086] ​The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. Intelligent dough kneading optimization system for dough kneading machine, characterized in that: The system comprises: a dough analysis part, a dough kneading machine mechanical control analysis part and a dough kneading machine optimization control part; the dough analysis part is used to estimate the number of gluten protein molecules in the dough kneading machine by assuming volume and distribution uniformity through the gluten protein content in the flour, and calculate the total interaction energy between the gluten protein molecules in the dough kneading machine according to the number of gluten protein molecules; an interaction model between water molecules and proteins in the dough is established according to the total interaction energy; a gluten network formation dynamics model is established according to the total interaction energy and the interaction model, and the local apparent viscosity distribution and local shear stress distribution at various positions in the dough are calculated in combination with the rheological properties of the dough; the dough kneading machine mechanical control analysis part is used to calculate the local mechanical energy density distribution and temperature field distribution of the dough kneading machine according to the local apparent viscosity distribution and local shear stress distribution at various positions in the dough; the dough kneading machine optimization control part is used to calculate the dough quality index according to the gluten network formation dynamics model, the local mechanical energy density distribution and the temperature field distribution, and calculate the kneading angular velocity control amount and the kneading pressure control amount according to the set optimized dough quality index.

2. The intelligent dough kneading optimization system for a dough kneading machine according to claim 1, characterized in that: The gluten content in the flour is set to a value ranging from 8% to 14%; the average molecular weight of all gluten proteins in the dough kneading machine is set to a value ranging from 10 4 g / mol to 10 6 g / mol; using the volume and uniform distribution assumptions, the number of gluten protein molecules in the dough kneading machine is estimated to be N p ; The critical temperature of gluten protein molecules is determined by experimental methods. The critical temperature refers to the temperature point at which the intermolecular bond energy of gluten protein molecules decreases significantly under specific interaction conditions; the charge of gluten protein molecules, the equilibrium distance of intermolecular van der Waals forces of gluten protein molecules and the molecular distance between gluten protein molecules are calculated through the isoelectric point of the protein and the ionic environment under experimental conditions.

3. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 2, characterized in that: The total interaction energy between gluten protein molecules in the dough kneading machine is calculated by the following formula: Among them, E p (t) is the total interaction energy between gluten protein molecules at time t; N p is the number of gluten protein molecules; i and j are integer subscript indices; q i is the molecular charge of the i-th gluten protein molecule; ∈0 is the vacuum dielectric constant; q j is the molecular charge of the jth gluten protein molecule; r ij is the molecular distance between the i-th gluten protein molecule and the j-th gluten protein molecule; T(t) is the temperature at time t; T c is the critical temperature; ij is the equilibrium distance of the intermolecular van der Waals force between the i-th gluten protein molecule and the j-th gluten protein molecule.

4. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 3, characterized in that: The interaction model between water molecules and proteins in dough is expressed by the following formula: Where W(r, t) is the local moisture content at position r and time t in the dough; D w is the water diffusion coefficient; k h is the hydration rate constant; R g is the gas constant; is the Laplace operator; W sat is the saturated moisture content; P f (t) is the free protein concentration at time t.

5. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 4, characterized in that: The kinetic model of gluten network formation was established based on the total interaction energy and interaction model using the following formula: Where C(r, t) is the gluten network concentration at position r and time t; D c (W(r, t)) is the diffusion coefficient that depends on the water content and is calculated as: k f is the network formation rate constant, which is the set value; is the gradient operator; D0 is the basic moisture content, and no value is set; E d is the diffusion activation energy, is the set value; W opt is the set optimal moisture content; C max is the set maximum gluten network concentration; k B is the Boltzmann constant; f(W(r, t)) is the water content modulation function, and the calculation formula is:

6. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 5, characterized in that: The local apparent viscosity distribution and local shear stress distribution at each position in the dough are calculated by the following formula combined with the rheological properties of the dough: Wherein, η(r, t) is the local apparent viscosity at position r and time t; η0 is the set reference viscosity; C0 is the reference network concentration; τ(r, t) is the local shear stress at position r and time t; V mol is the molar volume of all gluten proteins in the dough kneading machine; α is the weighting coefficient, ranging from 0.5 to 0.75; h is the internal space height of the dough kneading machine; F0 is the initial kneading pressure of the dough kneading machine; ω0 is the angular velocity of the dough kneading machine.

7. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 6, characterized in that: The mechanical control analysis part of the dough kneading machine is used to calculate the local mechanical energy density distribution of the dough kneading machine according to the local apparent viscosity distribution and the local shear stress distribution at each position in the dough through the following formula: Among them, E m (r, t) is the local mechanical energy density at position r and time t; k e The energy transfer coefficient is a set value ranging from 0.3 to 0.

6.

8. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 6, characterized in that: The temperature field distribution is expressed by the following formula: Where ρ is the dough density; c p k is the specific heat of dough, ranging from 2000 to 3000; T is the thermal conductivity of dough, ranging from 0.2 to 0.6; h is the heat of hydration, ranging from 200 to 500.

9. The intelligent dough kneading optimization system for a dough kneading machine as claimed in claim 8, characterized in that: The dough quality index was calculated using the following formula: Among them, E opt is the set optimized mechanical energy density; Q is the dough quality index.

10. The intelligent dough kneading optimization system for a dough kneading machine according to claim 8, characterized in that: The kneading angular velocity control amount and the kneading pressure control amount are calculated by the following formula: Among them, ω(t) is the kneading angular velocity control value at time t; F(t) is the kneading pressure control value; E ref is the reference mechanical energy, ranging from 300 to 800; η ref is the reference viscosity, the value range is 10 4 to 10 5 ; is the mean of η(r, t).

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