Optimization design method for heat accumulation device for grease refining waste heat recovery

Abstract

The application discloses an optimization design method of a heat aggregation device for oil and fat refining waste heat recovery, which comprises the following steps: defining a thermal degradation cumulative index by time-integrating a degradation rate function along an oil and fat micelle motion trajectory according to oil and fat thermal degradation reaction kinetics; establishing a heat exchange channel parameterized geometric model, taking channel geometric parameters as design variables, performing flow and heat transfer numerical solution, and obtaining a velocity field and a temperature field; releasing a plurality of virtual micelles in the velocity field, obtaining each micelle motion trajectory by using a particle tracking method, and calculating the thermal degradation cumulative index and the maximum value of each micelle in combination with local temperature; determining a quality safety threshold according to a target oil product quality standard limit value, taking the maximum value not exceeding the quality safety threshold as a constraint, maximizing channel heat transfer as a target, and optimizing and solving the design variables to obtain channel geometric parameters meeting the oil product quality requirements. The application can consider waste heat recovery performance and oil and fat thermal degradation control.

Description

Technical Field

[0001] This application relates to the field of three-dimensional simulation design technology, specifically to an optimization design method and system for a heat accumulation device used for waste heat recovery in oil refining. Background Technology

[0002] Edible oil refining typically includes processes such as degumming, deacidification, decolorization, and deodorization. The deodorization and physical refining processes require channels to allow indirect heat exchange between the high-temperature finished oil and the oil to be heated, thereby reducing steam consumption and cooling load. The structural design of heat exchange devices is usually based on heat transfer area, average heat transfer coefficient, flow rate, pressure drop, and equipment dimensions, and computational fluid dynamics methods can be used to analyze the velocity and temperature fields within the channels.

[0003] However, the viscosity of oils changes significantly with temperature, and low-velocity zones, reflux zones, or localized stagnation areas easily form on the back side of baffles, at corners, and in gradually expanding regions within complex heat exchange channels. Small amounts of oil in these areas may simultaneously experience higher temperatures and longer residence times, leading to darker oil color, decreased oxidative stability, and increased thermal reaction products. Existing design methods often use average outlet temperature, average residence time, or overall heat transfer to evaluate heat exchange performance, which fails to reflect the actual heating process corresponding to different flow trajectories and does not quantitatively correlate localized thermal degradation risks with channel geometry parameters and oil quality requirements. Therefore, while improving waste heat recovery efficiency, identifying the most unfavorable heating areas within the heat exchange channel and optimizing the channel structure accordingly remains a problem that needs to be solved in the design of waste heat recovery units for oil refining. Summary of the Invention

[0004] The purpose of this application is to provide an optimized design method and system for a heat collection device for waste heat recovery in oil refining, so as to solve the problems mentioned in the background art.

[0005] According to one aspect of this application, an optimized design method for a heat accumulation device for waste heat recovery in oil refining is provided, comprising the following steps: Based on the thermal degradation reaction kinetics of oils, a thermal degradation accumulation index is defined by integrating the degradation rate function over time along the movement trajectory of oil microparticles in the heat exchange channel. The thermal degradation accumulation index characterizes the degree of thermal degradation accumulated by a single oil microparticle throughout the entire process of passing through the heat exchange channel. A parameterized geometric model of the heat exchange channel is established. The channel geometric parameters are used as design variables. The flow and heat transfer are numerically solved on the parameterized geometric model to obtain the velocity field and temperature field inside the channel. Multiple virtual microclusters are released in the velocity field, and the motion trajectory of each microcluster is calculated using a particle tracking method. The local temperature experienced by each microcluster is obtained from the temperature field along each motion trajectory. The thermal degradation cumulative index of each microcluster is calculated based on the local temperature, and the maximum value of the thermal degradation cumulative index of all microclusters is extracted. The quality safety threshold is determined based on the quality standard limit of the target oil. The maximum value of the cumulative thermal degradation index does not exceed the quality safety threshold as a constraint condition. The channel heat transfer is maximized as the optimization objective. The design variables are optimized and solved to obtain the channel geometric parameters that satisfy the constraint condition.

[0006] Preferably, the cumulative thermal degradation index is calculated as follows: the residence time of the micro-particle is discretized into multiple time steps along the motion trajectory; within each time step, the instantaneous degradation rate is calculated according to the Arrhenius equation based on the local temperature at the current position of the micro-particle; the product of the instantaneous degradation rate and the time step length of each time step is gradually accumulated to obtain the cumulative thermal degradation index of the micro-particle; the frequency factor and apparent activation energy in the Arrhenius equation are determined by conducting isothermal heating experiments on the target oil at multiple different temperatures, measuring the relationship between degradation rate and temperature, and performing regression fitting.

[0007] Preferably, the determination of the quality and safety threshold includes: obtaining the allowable increment of degradation products based on the difference between the standard limit of degradation product content in the finished oil and the initial content of degradation products in the semi-finished oil before entering the thermal aggregation device; when the thermal aggregation device contains multiple heat exchange channels, the allowable increment of degradation products is proportionally allocated to each stage according to the relative magnitude of the degradation rate of each stage at its reference temperature; based on the increment of degradation products allocated to each stage, the corresponding quality and safety threshold for each stage is obtained through isothermal degradation experiments or kinetic parameter conversion.

[0008] Preferably, the channel geometric parameters include channel width, channel depth, baffle spacing, baffle notch ratio, and half-angle of the channel inlet and outlet gradually widening section; the baffle notch ratio is the ratio of the flow area of ​​the notch on the baffle to the total cross-sectional area of ​​the channel.

[0009] Preferably, in the numerical solution of flow and heat transfer, the dynamic viscosity of oil is described by an exponential function model that varies with temperature. The viscosity coefficient and viscosity-temperature coefficient in the exponential function model are determined by measuring the viscosity values ​​of the target oil at different temperatures and performing regression fitting.

[0010] Preferably, the plurality of virtual microclusters are released in a uniform grid distribution on the inlet cross section of the heat exchange channel; the number of virtual microclusters is determined by gradually increasing the number of microclusters until the relative change of the maximum value of the thermal degradation cumulative index is less than a preset convergence threshold.

[0011] Preferably, the particle tracking method is a Lagrange particle tracking method, which updates the position coordinates of the micro-particle based on the velocity field value at the current position of the micro-particle in each calculation time step, and stops tracking when the micro-particle reaches the outlet section of the heat exchange channel; the local temperature is obtained by spatial interpolation of the temperature field at the current position of the micro-particle.

[0012] Preferably, the optimization solution includes: generating multiple sets of sample points in the value space of the design variables using the Latin hypercube sampling method; performing the numerical solution of flow and heat transfer, the particle tracking method, and the calculation of the thermal degradation cumulative index on each set of sample points to obtain the maximum values ​​of the channel heat transfer and thermal degradation cumulative index corresponding to each sample point; fitting a response surface model with the multiple sets of sample points and their corresponding calculation results; and performing constraint optimization on the response surface model to solve for the channel geometric parameters that satisfy the constraint conditions.

[0013] Preferably, the channel geometry parameters obtained from the solution on the response surface model are re-executed to verify the flow and heat transfer numerical solution, the particle tracking method, and the calculation of the thermal degradation cumulative index. When the relative error between the maximum value of the verified thermal degradation cumulative index and the predicted value of the response surface model exceeds a preset accuracy threshold, or when the maximum value of the verified thermal degradation cumulative index exceeds the quality and safety threshold, additional sampling points are added in the neighborhood of the channel geometry parameter and the response surface model is updated. Constraint optimization and verification are then re-executed until the maximum value of the verified thermal degradation cumulative index meets the accuracy requirements and does not exceed the quality and safety threshold.

[0014] In another aspect, this application also provides an optimized design system for a heat accumulation device for waste heat recovery in oil refining, comprising: The thermal degradation index definition module is used to define a thermal degradation cumulative index based on the thermal degradation reaction kinetics of oils and fats by integrating the degradation rate function over time along the movement trajectory of oil and fat microparticles in the heat exchange channel. The thermal degradation cumulative index characterizes the degree of thermal degradation accumulated by a single oil and fat microparticle throughout the entire process of passing through the heat exchange channel. The flow and heat transfer solution module is used to establish a parameterized geometric model of the heat exchange channel. Using the channel geometric parameters as design variables, the module performs numerical solutions for flow and heat transfer on the parameterized geometric model to obtain the velocity field and temperature field within the channel. The micro-cluster tracking and index extraction module is used to release multiple virtual micro-clusters in the velocity field, calculate the motion trajectory of each micro-cluster using a particle tracking method, obtain the local temperature experienced by each micro-cluster from the temperature field along each motion trajectory, calculate the thermal degradation cumulative index of each micro-cluster based on the local temperature, and extract the maximum value of the thermal degradation cumulative index among all micro-clusters. The constraint optimization module is used to determine the quality safety threshold based on the quality standard limit of the target oil, with the maximum value of the cumulative thermal degradation index not exceeding the quality safety threshold as a constraint condition, and the maximization of heat transfer in the channel as the optimization objective, to optimize and solve the design variables and obtain the channel geometric parameters that satisfy the constraint conditions.

[0015] This application, based on the kinetics of oil thermal degradation, integrates the local temperature and residence time experienced by oil particles along their actual movement trajectory in the heat exchange channel to form a cumulative thermal degradation index. The maximum index among all particles characterizes the most unfavorable heating state within the channel, enabling the identification of local high-temperature retention risks that are difficult to detect using only average temperature, average residence time, or outlet parameters. Furthermore, based on the quality standards of the target oil, a quality safety threshold is determined. The maximum value of the cumulative thermal degradation index is used as a constraint for channel structure optimization, with maximizing heat transfer as the optimization objective. This ensures that the determination of channel geometry parameters simultaneously considers waste heat recovery performance and oil quality requirements. This method can establish a quantitative relationship between channel structure, flow heat transfer process, and oil thermal degradation during the device design phase. It guides the design results to reduce low-speed retention and high-temperature backflow in areas such as the back side of baffles and channel corners, reducing the risk of quality degradation caused by local overheating. Response surface optimization and numerical verification improve the reliability of the design results, reducing design costs associated with repeated prototype testing. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of an optimized design method for a heat accumulation device for waste heat recovery in oil refining, provided in an embodiment of this application.

[0018] Figure 2 A flowchart for calculating the cumulative thermal degradation index provided in the embodiments of this disclosure.

[0019] Figure 3 This is a schematic diagram of an optimized design system structure for a heat accumulation device used for waste heat recovery in oil refining, provided as an embodiment of this application.

[0020] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This embodiment provides an optimized design method for a heat collection device used for waste heat recovery in oil refining. Edible oil refining processes include multiple steps such as degumming, deacidification, decolorization, and deodorization, with temperatures ranging from 60°C to 260°C for each step. The heat collection device recovers and transfers heat between these steps. Liquid oil flows sequentially through multi-stage heat exchange channels, transferring waste heat from high-temperature steps to lower-temperature steps requiring heating, thus reducing external energy consumption. The deodorization stage reaches oil temperatures of 230 to 260°C, corresponding to the highest temperature stage in the heat collection device and the location with the highest concentration of oil thermal degradation risk. This method focuses on optimizing this high-temperature heat exchange channel, while also being applicable to other stages of channels with temperatures exceeding 180°C. The entire calculation process is executed on a computer, with inputs being channel geometric design parameters and process condition parameters, and output being a channel geometric parameter scheme that meets oil quality and safety constraints. This relies on computational fluid dynamics numerical solution software and mathematical optimization programs.

[0023] The following detailed description, in conjunction with specific embodiments, illustrates the implementation process of the optimized design method for a heat accumulation device used for waste heat recovery in oil refining, as described in this application. It should be noted that these embodiments are merely for explaining this application and not for limiting its scope of protection. Any conventional adjustments or substitutions made by those skilled in the art to the steps without departing from the concept of this application should be included within the scope of protection of this application.

[0024] like Figure 1 As shown in the figure, this application discloses an optimized design method for a heat accumulation device for waste heat recovery in oil refining, including the following method steps: Step S1: Based on the thermal degradation reaction kinetics of oils, define a thermal degradation accumulation index by integrating the degradation rate function over time along the movement trajectory of oil microparticles in the heat exchange channel. The thermal degradation accumulation index characterizes the degree of thermal degradation accumulated by a single oil microparticle throughout the entire process of passing through the heat exchange channel. Step S2: Establish a parameterized geometric model of the heat exchange channel, use the channel geometric parameters as design variables, and perform numerical solutions for flow and heat transfer on the parameterized geometric model to obtain the velocity field and temperature field inside the channel. Step S3: Release multiple virtual micro-clusters in the velocity field, calculate the motion trajectory of each micro-cluster using the particle tracking method, obtain the local temperature experienced by each micro-cluster from the temperature field along each motion trajectory, calculate the thermal degradation cumulative index of each micro-cluster based on the local temperature, and extract the maximum value of the thermal degradation cumulative index among all micro-clusters. Step S4: Determine the quality safety threshold based on the quality standard limit of the target oil, take the maximum value of the cumulative thermal degradation index not exceeding the quality safety threshold as a constraint, take maximizing the heat transfer of the channel as the optimization objective, optimize the design variables, and obtain the channel geometric parameters that satisfy the constraint conditions.

[0025] In some embodiments, for step S1, grease flows continuously from the inlet to the outlet within the heat exchange channel of the heat accumulation device. The velocity and temperature distribution within the channel are affected by the channel geometry and are not uniform: the grease velocity near the wall is lower than in the central area of ​​the channel, and low-velocity zones or even backflow zones are easily formed on the leeward side of the baffles and at channel corners. The actual residence time of grease in these areas in a high-temperature environment can be 3 to 5 times that of grease in the mainstream area. The superposition of high temperature and long residence time accelerates the thermal degradation reaction of the grease, resulting in a much higher trans fatty acid content and color deterioration degree in the local grease compared to the channel average. To quantitatively describe this local thermal degradation phenomenon during the channel structure design stage, this embodiment introduces a cumulative thermal degradation index calculated along the trajectory of grease micro-clusters. Here, "micro-cluster" refers to a sufficiently small grease fluid unit at a certain location within the channel, which can be considered as having uniform temperature and velocity, used to track the temperature history and residence time experienced by grease at different locations within the channel.

[0026] Among them, the thermal degradation reaction of edible oil at high temperatures, with the formation of trans fatty acids as a representative indicator, follows Arrhenius kinetics. For any oil particle within the channel, the degradation rate function is integrally over time along the particle's trajectory from the channel inlet to the outlet, defining a cumulative thermal degradation index. :

[0027] In the formula, The dwell time of the micro-cluster within the channel, in seconds; For this micro-cluster at time The local temperature at the location, in Kelvin (K). The degradation reaction frequency factor is expressed in seconds. -1 ; The apparent activation energy of the degradation reaction is expressed in J / mol. Let be the molar gas constant, taken as 8.314 J / (mol·K). Integrating over time... It is a dimensionless quantity that characterizes the degree of thermal degradation accumulated by the micro-particle within the channel.

[0028] in, and The target oil type was calibrated through isothermal heating experiments. Specifically, the same oil type was heated under multiple sets of different isothermal conditions in the laboratory, and the rate of trans fatty acid formation at each temperature was measured. A linear regression was then performed using the Arrhenius equation, taking the logarithm, and the slope corresponded to... Intercept corresponding Based on this, calculate the reverse. and The numerical value. For example, for soybean oil, Desirable s -1 , Desirable J / mol; the corresponding parameters for rapeseed oil or palm oil are taken from their respective experimental fitted values. The Arrhenius equation is a fundamental model in the field of chemical reaction kinetics. Its principle is that the reaction rate constant increases exponentially with temperature. By taking the natural logarithm of the rate constant from multiple sets of isothermal experimental data and then linearly fitting it with the reciprocal of the temperature, the above two parameters can be obtained.

[0029] The physical meaning of the value is that the greater the cumulative thermal degradation dose of the micro-element during its passage through the heat exchange channel, the higher the degree of thermal degradation experienced by the micro-element. Because Calculations were performed point-by-point along the actual movement trajectory of each micro-cluster. Different micro-clusters had different paths and residence times within the channel. The values ​​also vary. Micro-particles that remain in the high-temperature, low-velocity zone on the leeward side of the baffle... The value is significantly higher than that of microclusters that pass rapidly along the mainstream region. This index is introduced into the constraint system of the channel structure design of the heat accumulation device. Starting from the channel geometry, through the flow field distribution and microcluster trajectory, a quantitative correlation between the channel configuration and product quality is established, so that the quality constraint is moved from the process operation stage to the structural design stage.

[0030] In some embodiments, for step S2, a parametric geometric model is established for the target heat exchange channel in the heat accumulation device. Specifically, the channel geometry is described by five design variables: channel width... (Unit: m) Channel depth (Unit: m) Spacing between adjacent baffles (Unit: m), Baffle notch ratio and the gradually widening section of the passageway entrance and exit half angle (Unit: rad). Baffle notch ratio This is the ratio of the flow area of ​​the notch on the baffle to the total cross-sectional area of ​​the channel, and it is a dimensionless parameter, with a value range of, for example, 0.15 to 0.45. The above five parameters constitute a set of design variables, and each set of values ​​corresponds to a specific channel geometry.

[0031] According to embodiments of this disclosure, a finite volume mesh is divided within the computational domain of a parametric geometric model, and continuity, momentum conservation, and energy conservation equations for incompressible fluids under steady-state conditions are established. The dynamic viscosity of greases changes drastically with temperature at high temperatures; for example, the viscosity difference between 60°C and 250°C can be 5 to 8 times. To reflect this characteristic, the dynamic viscosity of greases is... Described using the Andrade equation:

[0032] In the formula, Viscosity coefficient, unit: Pa·s; Temperature coefficient of viscosity, in K; Temperature, in Kelvin; exponent term It is a dimensionless quantity. and The viscosity values ​​of the target oil were determined by exponential regression fitting at different temperatures. The oil density, specific heat capacity, and thermal conductivity were also determined by temperature-related empirical correlations. The data source was the physical property data table of the corresponding oil in the oil chemistry handbook.

[0033] The channel inlet is set to velocity inlet conditions, with the flow velocity determined by the process flow rate and channel cross-sectional area; the channel outlet is set to pressure outlet conditions; the channel outer wall temperature is taken as the steady-state temperature of the heat storage medium at this stage; and the baffle surface is a non-slip adiabatic wall surface. Optionally, when the Reynolds number in the channel is in the laminar to turbulent transition range, the SSTk-ω turbulence model can be used. For example, when using the SSTk-ω turbulence model, the turbulence intensity in the inlet boundary conditions is... according to Estimate the turbulent viscosity ratio Use values ​​from 1 to 10; reinforce the wall surface, requiring the near-wall mesh to meet certain conditions. The empirical constants in the model are taken from the original values ​​in Menter's literature: , , , , The model can be implemented using mature commercial software, and this embodiment does not limit its implementation.

[0034] The above model was numerically solved using computational fluid dynamics software to obtain the steady-state velocity and temperature fields within the channel. Simultaneously, the total pressure drop and total heat transfer between the channel inlet and outlet were calculated. These velocity and temperature fields served as inputs for calculating the trajectory tracking of micro-clusters and the cumulative thermal degradation index.

[0035] In some embodiments, for step S3, Figure 2 A flowchart for calculating the cumulative thermal degradation index provided in this embodiment of the disclosure is shown below. Figure 2 As shown, in step S201, multiple virtual micro-clusters are released in the velocity field, and the motion trajectory of each micro-cluster is calculated using a particle tracking method.

[0036] Specifically, multiple virtual microclusters are released into the velocity field obtained in the preceding steps. The trajectory of each microcluster is tracked, and the local temperature experienced by each microcluster is obtained from the obtained temperature field along each trajectory. The cumulative thermal degradation index of each microcluster is calculated based on the local temperature. The microclusters are released in a uniform grid distribution on the inlet section of the heat exchange channel. The initial position of each microcluster is the coordinate of the corresponding grid node on the inlet section, and the initial velocity is the interpolation of the velocity field at that position. The number of microclusters... For example, 3,000 to 10,000 can be selected, following the convergence principle: gradually increase the number of microclusters, and when the relative change of the maximum value of the thermal degradation cumulative index is less than the preset convergence threshold (e.g., 1%), the number is considered sufficient and no further increase is made.

[0037] For example, the Lagrange particle tracking method is used to calculate the trajectory of each micro-cluster. At each calculation time step... Inside, update the position coordinates of the micro-cluster based on the velocity field value at its current location:

[0038] In the formula, For the first A micro-group at any moment The location coordinates, in meters; This is the fluid velocity vector at this location, in m / s; The time step is in seconds; tracking stops when the micro-particle reaches the outlet section of the heat exchange channel, and the complete position time series and total residence time of the micro-particle within the channel are recorded. Lagrange particle tracking is a standard method in computational fluid dynamics for tracking the trajectory of discrete phases or fluid elements, updating particle positions step-by-step based on the flow field velocity. In some embodiments, the time step size satisfies the condition that the Corange number is less than 1, i.e. ,in For local grid size, This represents the local velocity magnitude.

[0039] In step S202, along the motion trajectory of each micro-cluster, the local temperature experienced by each micro-cluster is obtained from the obtained temperature field through spatial interpolation. For the location of each micro-cluster at each time step, the local temperature at that location is obtained from the temperature field grid node data using a trilinear interpolation method. Based on the local temperatures experienced by each microparticle, the cumulative thermal degradation index defined in the aforementioned steps is calculated; in the numerical implementation, the time integral is discretized according to the time step:

[0040] In the formula, For the first The total number of time steps a micro-group experiences from entry to exit. .

[0041] For example, suppose a micro-cluster undergoes 120 time steps within the channel, each step... The total residence time was 1.2 s. During the first 80 time steps, the microparticle was in the mainstream region, with local temperatures between 508 and 513 K and a low instantaneous degradation rate. In the last 40 time steps, the microparticle entered the low-velocity region on the leeward side of the baffle, where the local temperature rose to 521 K and the flow velocity decreased to about 1 / 4 of that in the mainstream region, resulting in a significantly increased instantaneous degradation rate. The instantaneous degradation rate at each time step was related to... The product of these factors is gradually accumulated to obtain the value of the micro-cluster. value.

[0042] In step S203, the cumulative thermal degradation index of each micro-group is calculated based on the local temperature, and the maximum value of the cumulative thermal degradation index of all micro-groups is extracted.

[0043] For all The cumulative thermal degradation index of each micro-particle was statistically processed, and the maximum value of the cumulative thermal degradation index of all micro-particles was extracted. :

[0044] The micro-cluster with the most severe thermal degradation in the corresponding channel is located on the most unfavorable trajectory in the entire flow field. Simultaneously, the average value of the cumulative thermal degradation index of all micro-clusters is calculated. And the cumulative thermal degradation index exceeds the quality and safety threshold. Micro-clusters account for the total proportion ; The method for determining this is explained in S4. , and Of the three statistics, As a constraint input for subsequent optimization solutions, and As a supplementary reference indicator.

[0045] In actual production, most greases in the heat exchange channel pass quickly along the main flow zone, resulting in low degradation. However, a small amount of grease (typically 1% to 5% of the total flow) lingers on the leeward side of the baffle plate or in the return flow zone at the channel corners. Due to prolonged exposure to high temperatures, this grease accumulates degradation levels far exceeding the average. Even if this high concentration of degradation products mixes into the entire batch of finished oil, it can still cause the trans fatty acid content of that batch to approach or even exceed the national standard limit, even if it represents a small percentage of the total flow. As a constraint indicator, it reflects the combined effects of temperature and residence time: in the same area with a wall temperature of 250°C, the degradation of grease is minimal when it passes through at high speed, but significant when it remains at a very low speed. Temperature constraint alone cannot distinguish between these two situations.

[0046] In some embodiments, for step S4, the quality safety threshold is... The upper limit of trans fatty acid content in refined oil products is determined by following these steps, based on national standards or internal control standards for the target oil product. Based on the initial content of trans fatty acids in the semi-finished oil before entering the thermal aggregation unit. To obtain the permitted increase in trans fatty acids For example, national standards stipulate that the trans fatty acid content in vegetable oils should not exceed 2% (mass fraction), and the initial content of imported oil should be 0.3%. .

[0047] When the heat collection device includes multi-stage heat exchange channels, the allowable increase in trans fatty acids will be... Distribute the degradation rates of each level to the next level according to their relative magnitudes at their reference temperatures:

[0048] In the formula, For the first The reference temperature for the stage is the temperature of the heat storage medium during steady-state operation of that stage, in K. This represents the total number of stages in the thermal accumulation device. The allocation method distributes the total allowable degradation amount proportionally based on the relative magnitude of the degradation rate at each stage's steady-state temperature; higher-temperature stages receive a smaller share of the allowable incremental degradation.

[0049] The first Allowable increments allocated to each level Convert to the quality and safety threshold of this level Specifically, for the target oil type at this reference temperature level... An isothermal heating experiment was conducted, and the increase in trans fatty acids corresponding to different heating durations was recorded. The increment was taken as exactly the desired value. Heating duration Under isothermal conditions, the integral formula in the aforementioned steps degenerates into... Based on this, calculate In some embodiments, it may also be based on known... , ,Depend on Direct calculation .

[0050] The optimization objective is to maximize the total heat transfer of the channel in the aforementioned steps, and the maximum value of the extracted cumulative thermal degradation index is used as the optimization objective. Not exceeding the determined quality and safety threshold As a core constraint, the following auxiliary constraint is also set: the total pressure drop in the heat exchange channel does not exceed the upper limit of the pressure drop allowed by the pump head. This upper limit is determined by the selection of existing pumps in the production line; the minimum flow velocity at each cross-section within the heat exchange channel shall not be lower than the critical flow velocity required to prevent the deposition of solid impurities in the grease. The critical flow velocity was determined through a sedimentation experiment: grease containing known concentrations of impurities was passed through a transparent straight pipe at different flow rates; the flow rate at which a visible sediment layer appeared at the bottom of the pipe was the critical flow velocity. For example, values ​​can be taken from 0.1 to 0.3 m / s. The design variables are the five channel geometric parameters defined in the preceding steps, with each parameter taking values ​​within a reasonable engineering range, for example... m, m, m, , Between 5° and 30°.

[0051] According to embodiments of this disclosure, multiple sets of sample points, such as 40 to 60 sets, are generated in the value space of the design variables using the Latin hypercube sampling (LHS) method. Latin hypercube sampling divides the value range of each design variable into intervals equal to the number of samples. A value is randomly selected within each interval for each variable, and the random arrangement ensures that the combinations of variable values ​​uniformly cover the design space. For each set of sample points, the aforementioned steps of flow and heat transfer numerical solutions, particle tracking, and thermal degradation accumulation index calculation are performed to obtain the maximum values ​​of the channel heat transfer and thermal degradation accumulation index corresponding to each sample point. Total pressure drop and minimum flow velocity.

[0052] The response surface model is fitted using all sample points and their calculation results. As an example, the radial basis function (RBF) interpolation method is used, with a Gaussian kernel function... As basis functions, To design the Euclidean distance between the point to be predicted and each sampling point in the variable space, The kernel width parameter. The predicted value of the response surface is a linear combination of all basis functions, and the coefficients are determined by constructing and solving a system of linear equations from the sampled data; kernel width parameter. The method used is leave-one-out cross-validation, which involves removing each sampling point one by one, fitting the response surface with the remaining points, predicting the response value of the removed points, and then taking the result where the sum of the squared prediction errors is minimized. value.

[0053] For example, obtained by Latin hypercube sampling Group of sample points ( Using 40 to 60 as the training set, and employing a Gaussian kernel function... Using the basis function, the response surface prediction value is:

[0054] In the formula For the first The design variable vector for each sampling point These are the coefficients to be determined. Coefficient vector. By solving the system of linear equations Confirmed, among which for The kernel matrix, The response value (heat transfer or) at each sampling point Kernel width parameter Leave-one-out cross-validation confirmed that: the first... 1 sample points, using the remaining After fitting the response surface at each point, predict the response value of the removed points. Take the root mean square error of cross-validation for all samples. Minimum Value. When the kernel matrix condition number exceeds At that time, add a small regularization term to the diagonal of the kernel matrix. ( To improve the matrix condition number.

[0055] Constraint optimization is performed on the response surface model using the sequential quadratic programming (SQP) algorithm. Each iteration of SQP approximates the original problem as a quadratic programming subproblem and solves for the optimal step size, gradually approaching the optimal solution that satisfies all constraints. While the response surface model takes milliseconds for a single evaluation, SQP can complete hundreds to thousands of iterations within seconds to obtain the channel geometry parameters that satisfy all constraints.

[0056] The optimized channel geometry parameters were then validated by re-performing the aforementioned steps of numerical solution for flow and heat transfer, particle tracking, and cumulative thermal degradation indices, to obtain accurate channel heat transfer parameters. Total pressure drop and minimum flow rate. The maximum value of the validated cumulative thermal degradation index is compared with the predicted value from the response surface model: if the relative error is within a preset accuracy threshold (e.g., 5%) and the maximum value of the validated cumulative thermal degradation index does not exceed the quality and safety threshold... If the optimization results are accepted, the geometric parameters of the channel set will be output as the structural design scheme of the heat exchange channel of this stage of the heat accumulation device.

[0057] If the relative error exceeds the preset accuracy threshold or the maximum value of the thermal degradation cumulative index exceeds the quality and safety threshold... Then, several sampling points (e.g., 5 to 10) are added to the neighborhood of the channel's geometric parameters. The new samples are added to the training set to update the response surface model. Constraint optimization and verification are then re-executed. This process is repeated until the maximum value of the cumulative thermal degradation index obtained from the verification simultaneously meets the accuracy requirements and quality safety threshold constraints. Optionally, during each round of additional sampling, priority is given to... near Additional sample points are added to the region to improve the local accuracy of the response surface model near the constraint boundary.

[0058] Using the above method, an grease thermal degradation accumulation index is introduced as a quantitative constraint during the heat exchange channel structure design stage of the heat accumulation device, with the maximum value of the cumulative degradation amount in all micro-clusters serving as the constraint boundary. The optimized channel geometry maximizes heat transfer while meeting quality and safety constraints, maintaining waste heat recovery efficiency. The optimization process, constrained by the maximum degradation value, tends to eliminate low-velocity stagnation zones and high-temperature recirculation zones within the channel, improves flow field uniformity, reduces the risk of excessive degradation of local grease due to long-term high-temperature stagnation, and objectively reduces channel pressure drop loss.

[0059] It should be noted that although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart can be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0060] Please see Figure 3 , Figure 3 This application provides an optimized design system for a heat accumulation device used for waste heat recovery in oil refining. The system embodiment is similar to... Figure 1 Corresponding to the illustrated method embodiments, this system can be specifically applied to various electronic devices. The system specifically includes: The thermal degradation index definition module 301 is used to define a thermal degradation cumulative index based on the thermal degradation reaction kinetics of oils and fats by integrating the degradation rate function over time along the movement trajectory of oil and fat microparticles in the heat exchange channel. The thermal degradation cumulative index characterizes the degree of thermal degradation accumulated by a single oil and fat microparticle throughout the entire process of passing through the heat exchange channel. The flow and heat transfer solution module 302 is used to establish a parameterized geometric model of the heat exchange channel, and to perform flow and heat transfer numerical solutions on the parameterized geometric model using the channel geometric parameters as design variables to obtain the velocity field and temperature field in the channel. The micro-cluster tracking and index extraction module 303 is used to release multiple virtual micro-clusters in the velocity field, calculate the motion trajectory of each micro-cluster using a particle tracking method, obtain the local temperature experienced by each micro-cluster from the temperature field along each motion trajectory, calculate the thermal degradation cumulative index of each micro-cluster based on the local temperature, and extract the maximum value of the thermal degradation cumulative index among all micro-clusters. The constraint optimization module 304 is used to determine the quality safety threshold based on the quality standard limit of the target oil, take the maximum value of the cumulative thermal degradation index not exceeding the quality safety threshold as a constraint condition, take maximizing the heat transfer of the channel as the optimization objective, optimize and solve the design variables to obtain the channel geometric parameters that satisfy the constraint conditions.

[0061] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0062] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0063] Based on the same inventive concept, this application also provides an electronic device. The method corresponding to the electronic device can be the method in the foregoing embodiments, and its problem-solving principle is similar to that method. The electronic device provided in this application includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the methods and / or technical solutions of the foregoing embodiments of this application.

[0064] Figure 4The diagram illustrates the structure of an electronic device suitable for implementing the methods and / or technical solutions in the embodiments of this application. The electronic device includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input section 406, an output section 407, a communication section 409, and an input / output (I / O) interface 405 are also connected to the bus 404.

[0065] In particular, the methods and / or embodiments in this application can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowchart. When the computer program is executed by the central processing unit (CPU) 401, it performs the functions defined in the methods of this application.

[0066] Another embodiment of this application provides a computer-readable storage medium having computer program instructions stored thereon, which can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of this application described above.

[0067] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of electronic devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0068] The above description represents the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimized design method for a heat accumulation device for waste heat recovery in oil refining, characterized in that, Includes the following steps: Based on the thermal degradation reaction kinetics of oils, a thermal degradation accumulation index is defined by integrating the degradation rate function over time along the movement trajectory of oil microparticles in the heat exchange channel. The thermal degradation accumulation index characterizes the degree of thermal degradation accumulated by a single oil microparticle throughout the entire process of passing through the heat exchange channel. A parameterized geometric model of the heat exchange channel is established. The channel geometric parameters are used as design variables. The flow and heat transfer are numerically solved on the parameterized geometric model to obtain the velocity field and temperature field inside the channel. Multiple virtual microclusters are released in the velocity field, and the motion trajectory of each microcluster is calculated using a particle tracking method. The local temperature experienced by each microcluster is obtained from the temperature field along each motion trajectory. The thermal degradation cumulative index of each microcluster is calculated based on the local temperature, and the maximum value of the thermal degradation cumulative index of all microclusters is extracted. The quality safety threshold is determined based on the quality standard limit of the target oil. The maximum value of the cumulative thermal degradation index does not exceed the quality safety threshold as a constraint condition. The channel heat transfer is maximized as the optimization objective. The design variables are optimized and solved to obtain the channel geometric parameters that satisfy the constraint condition.

2. The method according to claim 1, characterized in that, The calculation method of the thermal degradation cumulative index is as follows: the residence time of the micro-particle is discretized into multiple time steps along the motion trajectory. In each time step, the instantaneous degradation rate is calculated according to the Arrhenius equation based on the local temperature at the current position of the micro-particle. The product of the instantaneous degradation rate of each time step and the time step length is gradually accumulated to obtain the thermal degradation cumulative index of the micro-particle. The frequency factor and apparent activation energy in the Arrhenius equation are determined by conducting isothermal heating experiments on the target oil at multiple different temperatures, measuring the relationship between degradation rate and temperature, and performing regression fitting.

3. The method according to claim 1, characterized in that, The method for determining the quality and safety threshold includes: obtaining the allowable increment of degradation products based on the difference between the standard limit value of degradation product content in the finished oil and the initial content of degradation products in the semi-finished oil before entering the thermal aggregation device; when the thermal aggregation device contains multiple heat exchange channels, the allowable increment of degradation products is proportionally allocated to each stage according to the relative magnitude of the degradation rate of each stage at its reference temperature; based on the increment of degradation products allocated to each stage, the corresponding quality and safety threshold for each stage is obtained through isothermal degradation experiments or kinetic parameter conversion.

4. The method according to claim 1, characterized in that, The channel geometry parameters include channel width, channel depth, baffle spacing, baffle notch ratio, and half-angle of the channel inlet and outlet gradually widening sections; the baffle notch ratio is the ratio of the flow area of ​​the notch on the baffle to the total cross-sectional area of ​​the channel.

5. The method according to claim 1, characterized in that, In the numerical solution of flow and heat transfer, the dynamic viscosity of oil is described by an exponential function model that varies with temperature. The viscosity coefficient and viscosity-temperature coefficient in the exponential function model are determined by measuring the viscosity values ​​of the target oil at different temperatures and performing regression fitting.

6. The method according to claim 1, characterized in that, The multiple virtual micro-clusters are released in a uniform grid distribution on the inlet section of the heat exchange channel; the number of virtual micro-clusters is determined by gradually increasing the number of micro-clusters until the relative change of the maximum value of the thermal degradation cumulative index is less than a preset convergence threshold.

7. The method according to claim 1, characterized in that, The particle tracking method is the Lagrange particle tracking method, which updates the position coordinates of the micro-particle based on the velocity field value at the current position of the micro-particle in each calculation time step, and stops tracking when the micro-particle reaches the outlet section of the heat exchange channel; the local temperature is obtained by spatial interpolation of the temperature field at the current position of the micro-particle.

8. The method according to claim 1, characterized in that, The optimization solution includes: generating multiple sets of sample points in the value space of the design variables using the Latin hypercube sampling method; performing the numerical solution of flow and heat transfer, the particle tracking method, and the calculation of the thermal degradation cumulative index on each set of sample points to obtain the maximum values ​​of the channel heat transfer and thermal degradation cumulative index corresponding to each sample point; fitting the response surface model with the multiple sets of sample points and their corresponding calculation results; and performing constraint optimization on the response surface model to solve for the channel geometric parameters that satisfy the constraint conditions.

9. The method according to claim 8, characterized in that, For the channel geometry parameters obtained by solving the response surface model, the numerical solution of flow and heat transfer, the particle tracking method, and the calculation of the thermal degradation cumulative index are re-executed for verification. When the relative error between the maximum value of the verified thermal degradation cumulative index and the predicted value of the response surface model exceeds the preset accuracy threshold, or when the maximum value of the verified thermal degradation cumulative index exceeds the quality safety threshold, sampling points are added in the neighborhood of the channel geometry parameter and the response surface model is updated. Constraint optimization and verification are re-executed until the maximum value of the verified thermal degradation cumulative index meets the accuracy requirements and does not exceed the quality safety threshold.

10. An optimized design system for a heat accumulation device for waste heat recovery in oil refining, characterized in that, include: The thermal degradation index definition module is used to define a thermal degradation cumulative index based on the thermal degradation reaction kinetics of oils and fats by integrating the degradation rate function over time along the movement trajectory of oil and fat microparticles in the heat exchange channel. The thermal degradation cumulative index characterizes the degree of thermal degradation accumulated by a single oil and fat microparticle throughout the entire process of passing through the heat exchange channel. The flow and heat transfer solution module is used to establish a parameterized geometric model of the heat exchange channel. Using the channel geometric parameters as design variables, the module performs numerical solutions for flow and heat transfer on the parameterized geometric model to obtain the velocity field and temperature field within the channel. The micro-cluster tracking and index extraction module is used to release multiple virtual micro-clusters in the velocity field, calculate the motion trajectory of each micro-cluster using a particle tracking method, obtain the local temperature experienced by each micro-cluster from the temperature field along each motion trajectory, calculate the thermal degradation cumulative index of each micro-cluster based on the local temperature, and extract the maximum value of the thermal degradation cumulative index among all micro-clusters. The constraint optimization module is used to determine the quality safety threshold based on the quality standard limit of the target oil, with the maximum value of the cumulative thermal degradation index not exceeding the quality safety threshold as a constraint condition, and the maximization of heat transfer in the channel as the optimization objective, to optimize and solve the design variables and obtain the channel geometric parameters that satisfy the constraint conditions.

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