A multi-temperature-zone heating furnace cross-scale design and temperature control optimization method and product

Abstract

The application discloses a multi-temperature-zone heating furnace cross-scale design and temperature control optimization method and product, and belongs to the technical field of thermal field design and control of industrial heating equipment. The application is based on the structural operation coupling relationship between cross-scale objects, first constructs a small-size corresponding pi group system, and obtains a pi group system of a large-size object according to a thermal field equivalent mapping relationship with cross-scale transferability, and then inversely calculates to determine initial values of structural parameters and operation parameters of the large-size object, so that a large-size thermal field model is constructed to perform thermal simulation, and is adjusted in the case of meeting the thermal field equivalent mapping relationship with the thermal field distribution requirement of the large-size object, parameters can be optimized while ensuring that the thermal field simulation is close to the actual situation, and high cost and long period problems caused by multiple rounds of repeated trial production and experimental correction on the large-size multi-temperature-zone heating furnace are avoided.

Description

Technical Field

[0001] This invention relates to the field of industrial heating equipment design and control technology, specifically to a method and product for multi-temperature zone heating furnace cross-scale design and temperature control optimization, applicable to thermal field modeling, parameter mapping, structural design and operation control optimization of multi-temperature zone heating furnaces. Background Technology

[0002] A multi-zone heating furnace (also known as a multi-zone tube furnace) has a cylindrical heating chamber divided into multiple heating zones. Each heating zone supports independent temperature control and can be set to different temperatures, aiming to provide multiple uniform temperature zones with a certain temperature gradient between adjacent zones. It is suitable for placing reaction vessels inside for material preparation.

[0003] The inventors need to prepare a certain material, but the existing multi-zone furnace and its associated reactor are too small, limiting the yield per batch. This necessitates the manufacture or modification of a larger multi-zone furnace and reactor. However, the manufacturing and modification costs of large-size multi-zone furnaces are high, and the experimental calibration cycle is long. Given time constraints, it is unsuitable to complete the design and optimization through multiple rounds of trial production and testing. The inventors also attempted to directly simulate the thermal field of the large-size multi-zone furnace based on the thermal field distribution requirements of the large-size reactor, but found that relying solely on numerical simulation is insufficient to accurately characterize the actual thermal field. This is because large-size multi-zone furnaces involve strong thermal radiation, multi-material coupled heat transfer, and significant boundary heat loss effects during operation, making the thermal field distribution highly sensitive to material properties, boundary conditions, and assembly details.

[0004] Based on the above problems, the purpose of this invention is to provide a method and product for cross-scale design and temperature control optimization of multi-temperature zone heating furnaces based on thermal field similarity constraints. Summary of the Invention

[0005] Therefore, it is necessary to address the problems of high cost, long cycle and inaccurate thermal field simulation in the design and optimization of existing large-size multi-temperature zone heating furnaces, and to provide a method and product for cross-scale design and temperature control optimization of multi-temperature zone heating furnaces based on thermal field similarity constraints.

[0006] This invention is achieved using the following technical solution: In a first aspect, the present invention discloses a method for cross-scale design and temperature control optimization of a multi-temperature zone heating furnace, comprising: The structural and operational parameters of small-sized objects are processed using dimensionless analysis to construct π-group systems corresponding to small-sized objects. Based on the π group system corresponding to the small size, the transformation is carried out according to the acceptable range of the thermal field equivalent mapping relationship to obtain the candidate π group system for the large size. Based on the π-group system of large-size candidates, the initial values ​​of the structural parameters and operating parameters of the large-size object are determined by inversion calculation according to the benchmark data of the large-size object, and the corresponding large-size thermal field model is constructed. Thermal simulations were performed on the large-size thermal field model. Based on the difference between the thermal simulation results and the thermal field distribution requirements of the large-size object, the large-size thermal field model was adjusted while satisfying the thermal field equivalent mapping relationship, so as to obtain a large-size modified model that can provide guidance for the actual processing and control of large-size multi-temperature zone heating furnaces. Among them, the small-sized objects are: small-sized multi-temperature zone heating furnaces and their supporting small-sized reaction vessels; the large-sized objects are: large-sized multi-temperature zone heating furnaces and their supporting large-sized reaction vessels.

[0007] This method for multi-scale design and temperature control optimization of multi-zone heating furnaces implements the methods or processes according to embodiments of this disclosure.

[0008] Secondly, the present invention discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the multi-scale thermal field design and temperature control optimization method for multi-temperature zone heating furnaces disclosed in the first aspect.

[0009] This type of computer program product implements the methods or processes according to embodiments of the present disclosure.

[0010] Compared with the prior art, the present invention has the following beneficial effects: 1. Based on the structural-operational coupling relationship between cross-scale objects, this invention first constructs a π-group system corresponding to the small size, and obtains a π-group system of the large-size object according to the thermal field equivalent mapping relationship with cross-scale transferability. Then, it performs inversion calculation to determine the initial values ​​of the structural and operational parameters of the large-size object, thereby constructing a large-size thermal field model for thermal simulation. The model is adjusted to meet the thermal field distribution requirements of the large-size object while satisfying the thermal field equivalent mapping relationship. This allows for parameter optimization while ensuring that the thermal field simulation is close to reality, avoiding the high cost and long cycle problems caused by repeated trial production and experimental correction of large-size multi-temperature zone heating furnaces.

[0011] 2. This invention also provides initial values ​​for the calculation settings of the thermal simulation of the large-size thermal field model by constructing a highly reliable simulation-measurement closed-loop thermal field model based on a small-size object. Combined with the large-size thermal field model obtained by inversion based on the thermal field equivalent mapping relationship, it can ensure that the thermal field simulation of the large-size thermal field model inherits the thermal field reliability of the small-size modified model and is closer to the actual situation, which helps to reduce the errors caused by parameter sensitivity and boundary condition uncertainty.

[0012] 3. This invention can optimize the structural design and temperature control parameters of a large-size multi-temperature zone heating furnace before it is actually manufactured or modified, providing fast, reasonable and reliable technical guidance for the engineering design, process scale-up and operation control of the multi-temperature zone heating furnace. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace provided in Embodiment 1 of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] First, it should be noted that, for the purposes of this invention, small-sized objects (i.e., small-sized multi-temperature zone heaters and their associated small-sized reactors) are known; for large-sized objects (i.e., large-sized multi-temperature zone heaters and their associated large-sized reactors), the large-sized multi-temperature zone heaters are unknown, while the large-sized reactors are known. Furthermore, the thermal field distribution requirements for both small-sized and large-sized objects are known.

[0019] Example 1

[0020] See Figure 1 It illustrates a flowchart of the multi-scale design and temperature control optimization method for a multi-temperature zone heating furnace provided in Embodiment 1, which can be designed to include the following steps: Step 1: Construct a corresponding small-sized three-dimensional digital model based on the structural parameters of the small-sized object, and construct a small-sized thermal field model based on the operating parameters of the small-sized object.

[0021] Structural and operational parameters are used for the structural design and temperature control system settings of multi-temperature zone heating furnaces.

[0022] The types of structural parameters include: furnace tube inner diameter D. f , outer diameter D of the vessel p Length L of the vessel p The inner diameter D of the i-th temperature zone i The height H of the i-th temperature zone i The center position z of the i-th temperature zone i Effective heating zone height H ∑ (i.e., the sum of the heights of all temperature zones), total envelope outer diameter D c (i.e., the maximum inner diameter of each temperature zone), total envelope height L c (Since the upper and lower end structures of the furnace body have little impact on the overall thermal field similarity, the length of the end structures is ignored, so that the total envelope height and the effective heating zone height are kept the same) etc.; the types of operating parameters include: the input power Q of the i-th temperature zone. i etc.; i takes the values ​​1, 2, 3, ..., representing different temperature zones along the longitudinal direction from top to bottom.

[0023] It should be noted that, in order to distinguish them, the above parameters are marked with a subscript S to represent small objects and a subscript L to represent large objects.

[0024] The structural parameters of small objects can be found in the relevant manufacturer's manual or obtained through actual measurement, while the operating parameters are recommended to be obtained based on actual measurement.

[0025] Therefore, based on the actual structural design and dimensions of small-sized objects, corresponding 3D digital models can be created in 3D modeling software (such as SolidWorks, UG / NX, CATIA, Creo, etc.). It is important to note that necessary simplifications can be made to the structure during modeling, such as ignoring small assembly gaps between components—these simplifications should be reasonable and maintain the relevant geometric dimensions and topological connections to ensure that the core heat transfer characteristics (i.e., simultaneous heat conduction, convection, and radiation) remain unchanged.

[0026] For ease of understanding, this embodiment 1 provides an example of a small-sized object—an 85 furnace with a vertical structure, which is provided with an upper temperature zone, an upper-middle temperature zone, and a lower-middle temperature zone (corresponding to the 1st to 3rd temperature zones respectively) along the axial direction to form a thermal field environment with an axial temperature difference. Its structural parameters are shown in Table 1.

[0027] Table 1. Structural and operational parameters for small-sized objects

[0028] It should be noted that in the small-sized object instance, each temperature zone uses the same resistance heating element and is controlled by an independent temperature control circuit. Therefore, its rated input power is set to the same value (i.e., the input power of each temperature zone is 6.2kW). The temperature difference between different temperature zones is mainly achieved through temperature control strategy and furnace body thermal field distribution.

[0029] Performing thermal simulation on the entire small-sized 3D digital model would be computationally intensive, demanding high-end hardware and requiring a long simulation time. Since multi-temperature zone furnaces and reactors employ cylindrical designs with circumferential symmetry, a portion of the circumferential region of the 3D digital model can be selected as the computational domain to avoid calculating the entire model. It is recommended to select a minimum circumferential region with periodic symmetry—360° / n, where n ≥ 12. This minimum circumferential region can characterize the overall heat transfer characteristics, including the coupling effects of heat conduction, convection, and radiation. Balancing computational accuracy and resource consumption, n = 24 can be chosen, i.e., selecting 1 / 24 of a fan-shaped cylinder (based on the axis) as the computational domain. This minimum circumferential region still contains multiple spatial objects such as the reactor, furnace body, and heating zones, thus requiring separate thermal modeling of each. The overall heat transfer characteristics must also be considered.

[0030] Specifically, thermal modeling actually includes two parts: mesh generation and parameter setting. First, the smallest circumferential region is imported into meshing software (such as ICEM or Fluent meshing) for mesh generation. For locations with significant temperature gradient changes (such as the outer wall of the reactor, near the heating zone, and the inner wall of the furnace), a boundary layer mesh can be used to accurately capture the temperature gradient and flow characteristics, ensuring computational accuracy. Other regions can use polyhedral meshes (such as tetrahedral meshes) for calculations of normal complexity. This constructs a small-scale meshed digital model. Of course, after mesh generation, the mesh quality can be verified to ensure the stability and accuracy of subsequent numerical calculations. If the mesh quality is unsatisfactory, the mesh parameters need to be readjusted.

[0031] Then, import the small-sized meshed digital model into thermal simulation software (Ansys Fluent is recommended), and set the corresponding physical parameters of the model according to the actual physical conditions (e.g., dividing the solid and fluid regions, setting material property parameters, setting interface boundary conditions, setting radiation models, etc.), and set the corresponding heating parameters of the model according to the running parameters of the small-sized object, thereby constructing a small-sized thermal field model.

[0032] Step two involves performing thermal simulation on the small-sized thermal field model and conducting thermal experiments on the small-sized object under the same conditions. The difference between the two results is used to correct the parameters of the small-sized thermal field model to obtain a small-sized corrected model, which can be regarded as a highly reliable simulation-experimental closed-loop thermal field model.

[0033] The specific process of step two is as follows: The calculation is performed in the thermal simulation software (for example, enabling appropriate energy equations to simultaneously consider heat transfer processes such as heat conduction, heat convection and heat radiation; enabling discrete coordinate radiation model when radiative heat transfer is significant, and using a pressure-velocity coupling algorithm suitable for convergence; the heat source in each temperature zone can be applied using an equivalent volume heat source or an equivalent surface heat flow method) and the thermal field simulation results are obtained after the numerical calculation converges.

[0034] Simultaneously, multiple temperature measurement points are arranged in a small-sized object (it is recommended to arrange thermocouples near the outer wall of the reactor or near the heated area that needs to be investigated in detail for temperature measurement), and heating and steady-state heat preservation experiments are carried out under the same or equivalent boundary conditions as the simulation, and temperature data of each temperature measurement point under steady-state conditions are collected.

[0035] After obtaining the thermal simulation and thermal experiment results, the temperature data at the same location are compared, and the average deviation (mean square error is recommended) is calculated. If the average deviation meets the preset accuracy requirements (generally set at 2~3℃, which can be adjusted according to the actual situation), it indicates that the small-size thermal field model can well represent the actual thermal field distribution and can be directly used as a small-size correction model; otherwise, it is necessary to adjust the relevant parameters of the small-size thermal field model (such as material thermal conductivity, boundary heat transfer parameters, radiation parameters, and local mesh density, etc.), and gradually reduce the average deviation between the thermal simulation results and the thermal experiment results through multiple iterations until the preset accuracy requirements are met, thereby obtaining a small-size correction model that can accurately represent the actual thermal field distribution characteristics of small-size objects.

[0036] It is important to note that the small-size correction model actually includes the correct calculation settings parameter values—which can be used as empirical parameter values.

[0037] Step 3: Based on dimensionless analysis, the structural and operational parameters of small-sized objects are processed to construct the π-group system corresponding to the small size.

[0038] Dimensionless analysis (i.e., π–Buckingham method) eliminates the dimensional influence of variables such as structural parameters and operational parameters of small-sized objects by performing dimensionless processing.

[0039] Specifically, the π group system includes the following six types of dimensionless groups: 1. Define the dimensionless group of the radial geometry of the structure as Π1, and its expression is: ; Π1 is used to characterize the radial geometric matching relationship between the heating space and the vessel body.

[0040] 2. Define the dimensionless group for axial position as Π. 2,i Its expression is: ; Π 2,i The normalized relationship between the center position of each temperature zone and the length of the vessel body is described to characterize the similarity of the layout of each temperature zone in the axial direction.

[0041] 3. Define the dimensionless group of heat input intensity as Π. 3,i Its expression is: ; In the formula, k eff The effective thermal conductivity is determined based on the equivalent thermal conductivity of the furnace body structural material; ΔT is the characteristic temperature difference, determined based on the target temperature difference between adjacent temperature zones; V i The effective heating volume for the i-th temperature zone can be based on D. i H i The calculation yielded: .

[0042] Π 3,i The normalized intensity describing the heat input per unit volume relative to the heat dissipation capacity is used to characterize the similarity of heating furnaces of different sizes under heat input conditions.

[0043] It should be noted that during cross-scale mapping, small and large objects will use the same structural materials and maintain a consistent target temperature difference; therefore, k eff ΔT can be considered a constant term when constructing similarity coefficients, and they cancel each other out during the similarity coefficient comparison process, thus Π 3,i In practice, this can be equivalently expressed as the heat input intensity per unit effective heating volume, Q. i / V i .

[0044] 4. Define the dimensionless group Π4 of the heat input gradient in the critical temperature region, and its expression is: ; In the formula, x and y represent the serial numbers corresponding to the critical temperature zones.

[0045] Π4 is in Π 3,i Building upon this foundation, it further describes the relative heat input intensity relationship between key temperature zones, and is used to characterize the similarity of heat input gradients between key temperature zones.

[0046] In the example provided in Embodiment 1, the key temperature zones are the upper-middle temperature zone and the lower-middle temperature zone, then: .

[0047] 5. Define the dimensionless group Π5 for heat loss scales, and its expression is: ; In the formula, A represents the outer surface area of ​​the total envelope structure, which is based on D. c L c calculate: ; V represents the volume enclosed by the overall envelope structure, which is also based on D. c L c calculate: .

[0048] 6. Define the radial geometric dimensionless group of the temperature region as Π. 6,i Its expression is: ; Π 6,i The radial geometric matching relationship between each temperature zone and the overall envelope structure is described.

[0049] For ease of understanding, the subscript S is added to the above six dimensionless groups to represent small-sized objects, and the subscript L is added to represent large-sized objects. Then, based on the above formula and in conjunction with Table 1, the π-group system of small-sized objects can be calculated as shown in Table 2.

[0050] Table 2. π-group hierarchy of small-sized objects

[0051] The above six types of dimensionless groups describe the thermal field distribution characteristics of small-sized objects under specific structural and operating conditions from six aspects: structural radial geometry, axial layout, heat input intensity, key temperature gradient, heat loss scale, and temperature radial set. Therefore, they can serve as the basis for establishing equivalent thermal field mapping relationships between objects of different sizes.

[0052] Step four: Based on the π group system corresponding to the small size, the transformation is performed according to the acceptable range of the thermal field equivalent mapping relationship to obtain the candidate π group system for the large size.

[0053] The equivalent mapping relationship of thermal fields is actually the ratio between the dimensionless groups corresponding to small-sized objects and large-sized objects, which can also be called the similarity coefficient—which includes: 1. Radial geometric similarity coefficient K G Its expression is: .

[0054] 2. Axial position similarity coefficient K A,i Its expression is: .

[0055] 3. Heat input density similarity coefficient K Q,i Its expression is: .

[0056] 4. Key temperature zone gradient similarity coefficient K gradQ Its expression is: .

[0057] 5. Heat loss scale similarity coefficient K A / V Its expression is: .

[0058] 6. Geometric similarity coefficient K of the temperature region D Its expression is: .

[0059] It should be noted that the acceptable range for the first 5 similarity coefficients is [0.9, 1.1], and the 6th similarity coefficient is tentatively set to 1, i.e., K. G ∈[0.9,1.1]、K A,i ∈[0.9,1.1]、K Q,i ∈[0.9,1.1]、K gradQ ∈[0.9,1.1]、 K A / V ∈[0.9,1.1]、K D =1. The range and value of the above similarity coefficient are determined based on the statistical comparison of thermal simulation results and experimental data between small-sized and large-sized objects. Within this range, it can be ensured that objects across scales have good similarity in terms of geometric layout, thermal input conditions, and thermal loss scale. Among them, the closer the similarity coefficient is to 1, the better the cross-scale similarity between objects across sizes (i.e., small-sized objects and large-sized objects) in the corresponding physical characteristics.

[0060] Then, by substituting the values ​​of the π group system corresponding to the small size into the expressions of the above 6 types of similarity coefficients, we can obtain the values ​​of the π group system corresponding to the large size—which serves as a candidate π group system for the large size.

[0061] Specifically, there are: ; ; ; ; ; .

[0062] Step 5: Based on the large-size candidate π group system, the initial values ​​of the structural parameters and operating parameters of the large-size object are determined by inversion calculation according to the benchmark data of the large-size object, and the corresponding large-size thermal field model is constructed.

[0063] It is important to note the specifications and dimensions of large-size reactors (i.e., their outer diameter D). p,L Length L of the vessel p,L The temperature field distribution of a large object is fixed and known, while the temperature field distribution of a large object requires that the height of each temperature zone of the large object also be fixed. Therefore, H i,S These are also considered known. Therefore, these known values ​​serve as the baseline data for large-sized objects.

[0064] Large-size multi-temperature zone furnaces maintain the same structural form and heating method as small-size multi-temperature zone furnaces, with each temperature zone controlled by an independent heating circuit and temperature control circuit. However, compared to small-size multi-temperature zone furnaces, the temperature zone dimensions, system heat load levels, and associated large-size reactors of large-size multi-temperature zone furnaces are all scaled up according to the process capacity requirements. This scale expansion is not a simple geometric enlargement, but a controlled scale-up design that meets the constraints of the large-size candidate π-group system.

[0065] The inversion calculation process is as follows: Firstly, based on (Π) 2,i ) L Based on the baseline data of the large-sized object, the relative layout inside the large-sized object is determined to make its normalized distribution in axial space similar to that of the small-sized object; then, based on (Π1)... L The radial clearance relationship between the furnace tube and the vessel body in large-sized objects is determined to ensure that the relative positional relationship between the heating space and the vessel body is similar to that in small-sized objects; then, based on (Π 3,i ) L Constrain the power configuration of each temperature zone in large-size objects, and according to (Π4). L The power distribution ratio in the critical temperature zone is adjusted to form a critical temperature zone heat input gradient relationship similar to that of small-sized objects; then, based on (Π5)...L Constraining the overall geometric scale of large-sized objects ensures that the scale coupling relationship between surface area and volume of the furnace envelope structure remains similar to that of small-sized objects, thereby guaranteeing the similarity of heat loss boundary conditions after cross-scale scaling; finally, based on (Π 6,i ) L Constrain the inner diameter of the temperature zone of large objects to make them similar to those of small objects.

[0066] Specifically: ①, with (Π1) L For example: due to Substitute After the expression, it can be converted into: ; Because of D p,L D p,S D f,S Since all of these are known, we obtain D. f,L The range of values ​​for .

[0067] ② Similarly, for (Π) 2,i ) L For example, its inversion yields z i,L The range of values ​​for .

[0068] ③ Similarly, for (Π) 3,i ) L (Π4) L In other words, based on its inversion, we will obtain inversion results for two value intervals—that is, there exists Q. i,L Given two intervals of values, it is necessary to find their intersection to ensure that (Π) simultaneously satisfy the condition. 3,i ) L (Π4) L .

[0069] ④ Similarly, for (Π5) L In other words, D can be obtained through inversion. c,L The range of values ​​for .

[0070] ⑤ Regarding (Π6) L Since its value is 1, then we can directly follow D. c,L The range of values ​​for D is converted to obtain the value of D. i,L The range of values ​​for .

[0071] This gives us the range of values ​​for the structural and operational parameters of the large object, and we can then use values ​​from these ranges as the initial values ​​for the corresponding parameters of the large object—it is generally recommended to use the median of these ranges as the initial values ​​for the corresponding parameters of the large object.

[0072] After obtaining the initial values ​​of the corresponding parameters for the large-sized object, a corresponding large-sized thermal field model can be constructed using 3D modeling software and thermal simulation software. Referring to step one, the large-sized thermal field model can also use the smallest circumferential region as the computational domain to reduce the amount of simulation computation and improve computational efficiency.

[0073] Step six: Perform thermal simulation on the large-size thermal field model, and adjust the large-size thermal field model based on the difference between its thermal simulation results and the thermal field distribution requirements of the large-size object, while satisfying the thermal field equivalent mapping relationship, so as to obtain a large-size modified model that can provide guidance for the actual processing and control of large-size multi-temperature zone heating furnaces.

[0074] Similar to step one, after importing the large-scale thermal field model into the thermal simulation software, perform calculation settings and conduct thermal simulation.

[0075] It should be noted that, as mentioned above, since the small-size correction model includes empirical parameter values, it is recommended to directly transfer these values ​​to the large-size thermal field model as the initial values ​​for its calculation settings, thereby reducing the computational setup time. In this way, on the one hand, the large-size thermal field model is not directly established based on experience, but rather obtained through inversion based on the equivalent mapping relationship of the thermal field; on the other hand, the calculation settings of the large-size thermal field model are not randomly set, but rather transfer the empirical parameters of the small-size correction model. Therefore, it can ensure that the thermal field simulation of the large-size thermal field model inherits the thermal field reliability of the small-size correction model and is closer to reality, helping to reduce errors caused by parameter sensitivity and boundary condition uncertainties.

[0076] After obtaining the thermal simulation results of the large-size thermal field model, compare them with the thermal field distribution requirements of the large-size object—compare the temperature data of the two at the same location and calculate the average deviation (mean square error is recommended). If the average deviation meets the preset accuracy requirements (generally set at 2~3℃, which can be adjusted according to the actual situation), the large-size thermal field model can be directly used as the large-size correction model; otherwise, it is necessary to further adjust the large-size thermal field model while maintaining the equivalent mapping relationship of the thermal field—that is, select other values ​​within the value range of the corresponding parameters of the large-size object obtained by inversion, and gradually approach the thermal field distribution requirements of the large-size object through multiple rounds of simulation iterations until the preset accuracy requirements are met, thereby obtaining a large-size correction model containing optimized structural parameters and operating parameters.

[0077] It is important to emphasize that the above large-size correction process should always satisfy the thermal field equivalent mapping relationship to ensure that the large-size correction model inherits the thermal field reliability of the small-size correction model, and will not cause the overall thermal field to be distorted due to simply pursuing local fitting.

[0078] Therefore, large-scale correction models can provide rapid, reasonable, and accurate guidance for the structural design, power configuration, and temperature control of large-scale multi-temperature zone furnaces, thereby enabling efficient cross-scale design and temperature control optimization of large-scale multi-temperature zone furnaces without relying on multiple rounds of large-scale prototype trials and repeated experimental corrections.

[0079] For ease of understanding, this embodiment 1 also provides a large-size correction model—the results of an instance of a large-size object (a vertical structure 170 furnace), see Table 3.

[0080] Table 3 Parameter Range of Large-Size Correction Model

[0081] The final value for 170 furnaces was obtained by adjusting the corresponding parameter range based on thermal simulation.

[0082] Based on Tables 1 and 3, the results of the six similarity coefficients can be calculated, as shown in Table 4.

[0083] Table 4 Similarity coefficient calculation results

[0084] Table 4 shows that the similarity coefficients for the six categories are close to or equal to 1, indicating that small-sized objects and large-sized objects have good cross-scale similarity in terms of geometric layout, heat input conditions, and heat loss scale. Although K A / V The value is slightly greater than 1, mainly due to the difference in boundary heat loss caused by the change in the ratio of surface area to volume of the large-sized furnace body, but it is still within an acceptable range.

[0085] Example 2

[0086] This embodiment 2 discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the multi-scale thermal field design and temperature control optimization method for multi-temperature zone heating furnace disclosed in embodiment 1.

[0087] The computer equipment can be either a mobile terminal or a fixed terminal. Examples of the former include mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (such as in-vehicle navigation terminals); examples of the latter include digital TVs and desktop computers.

[0088] This embodiment 2 also discloses a readable storage medium that stores computer program instructions. When the computer program instructions are read and run by a processor, the steps of the multi-scale thermal field design and temperature control optimization method for multi-temperature zone heating furnace disclosed in embodiment 1 are executed.

[0089] The readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0090] This embodiment 2 also discloses a computer program product, including a computer program. When executed by a processor, this computer program implements the steps of the multi-scale thermal field design and temperature control optimization method for multi-temperature zone heating furnaces disclosed in embodiment 1.

[0091] It should be noted that the computer program used to execute the above can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages. The computer program can be executed entirely on the user's computer, partially on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer through any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN).

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace, characterized in that, It includes: The structural and operational parameters of small-sized objects are processed using dimensionless analysis to construct π-group systems corresponding to small-sized objects. Based on the π group system corresponding to the small size, the transformation is carried out according to the acceptable range of the thermal field equivalent mapping relationship to obtain the candidate π group system for the large size. Based on the π-group system of large-size candidates, the initial values ​​of the structural parameters and operating parameters of the large-size object are determined by inversion calculation according to the benchmark data of the large-size object, and the corresponding large-size thermal field model is constructed. Thermal simulations were performed on the large-size thermal field model. Based on the difference between the thermal simulation results and the thermal field distribution requirements of the large-size object, the large-size thermal field model was adjusted while satisfying the thermal field equivalent mapping relationship, so as to obtain a large-size modified model that can provide guidance for the actual processing and control of large-size multi-temperature zone heating furnaces. Among them, the small-sized objects are: small-sized multi-temperature zone heating furnaces and their supporting small-sized reaction vessels; the large-sized objects are: large-sized multi-temperature zone heating furnaces and their supporting large-sized reaction vessels.

2. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 1, characterized in that, The types of structural parameters include: furnace tube inner diameter D f , outer diameter D of the vessel p Length L of the vessel p The inner diameter D of the i-th temperature zone i The height H of the i-th temperature zone i The center position z of the i-th temperature zone i Effective heating zone height H ∑ Total envelope outer diameter D c Total envelope height L c ; The types of operating parameters include: the input power Q of the i-th temperature zone. i ; Where i takes the values ​​1, 2, 3, ..., representing different temperature zones from top to bottom along the longitudinal direction.

3. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 2, characterized in that, The π group system includes: the dimensionless group of radial geometry Π1 and the dimensionless group of axial position Π 2,i Dimensionless group of heat input intensity Π 3,i 4. Dimensionless group of heat input gradient in key temperature zone; 5. Dimensionless group of heat loss scale; 6. Dimensionless group of radial geometry in temperature zone. 6,i ; in, ; ; ; ; ; ; In the formula, k eff V is the effective thermal conductivity; ΔT is the characteristic temperature difference; V i denoted as the effective heating volume of the i-th temperature zone; x and y represent the sequence numbers corresponding to the critical temperature zones; A represents the outer surface area of ​​the total envelope structure; and V represents the volume enclosed by the total envelope structure.

4. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 3, characterized in that, The equivalent mapping relationship of the thermal field includes: radial geometric similarity coefficient K G axial position similarity coefficient K A,i Heat input density similarity coefficient K Q,i Key temperature zone gradient similarity coefficient K gradQ The similarity coefficient of heat loss scale K A / V Geometric similarity coefficient K for temperature range D ; in, ; ; ; ; ; ; In the formula, (Π1) L 、(Π 2,i ) L 、(Π 3,i ) L (Π4) L (Π5) L 、(Π 6,i ) L This corresponds to the π-group system for large sizes; (Π1) S 、(Π 2,i ) S 、(Π 3,i ) S (Π4) S (Π5) S 、(Π 6,i ) S This is the π-group system corresponding to the small size.

5. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 4, characterized in that, The baseline data for large-size objects include: the outer diameter and length of the large-size reactor, and the height of each temperature zone of the large-size object.

6. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 4 or 5, characterized in that, The inversion calculation process includes: According to (Π) 2,i ) L The relative layout inside the large object is determined based on the baseline data of the large object; then, based on (Π1)... L The radial clearance relationship between the furnace tube and the vessel body in large-sized objects is determined; then, based on (Π 3,i ) L Constrain the power configuration of each temperature zone in large-size objects, and according to (Π4). L Adjust the power distribution ratio in the critical temperature zone; among which, for (Π) 3,i ) L (Π4) L Find the intersection of the inversion results; then, based on (Π5) L Constrain the overall geometric dimensions of large objects; finally, based on (Π) 6,i ) L Constrain the inner diameter of the temperature zone of the large-sized object; obtain the value range of the structural parameters and operating parameters of the large-sized object, and take the values ​​from them as the initial values ​​of the corresponding parameters of the large-sized object.

7. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 1, characterized in that, When performing thermal simulations on large-scale thermal field models, the smallest circumferential region is taken as the computational domain.

8. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 1, characterized in that, It also includes: Before performing thermal simulation on the large-scale thermal field model, a corresponding small-scale three-dimensional digital model is constructed based on the structural parameters of the small-scale object, and a small-scale thermal field model is constructed in combination with the operating parameters of the small-scale object. Thermal simulation is performed on the small-scale thermal field model, and thermal experiments are conducted on the small-scale object under the same conditions. The parameters of the small-scale thermal field model are corrected by comparing the results of the two to obtain a small-scale corrected model. The calculation setting parameter values ​​of the small-scale corrected model are directly transferred to the thermal simulation of the large-scale thermal field model as the initial values ​​of its calculation setting parameters.

9. The method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace according to claim 1 or 8, characterized in that, Methods for obtaining large-size modified models include: After importing the large-size thermal field model into the thermal simulation software, calculation settings are performed and thermal simulation is carried out. After obtaining the thermal simulation results of the large-size thermal field model, it is compared with the thermal field distribution requirements of the large-size object. If the average deviation meets the preset accuracy requirements, the large-size thermal field model is directly used as the large-size correction model. Otherwise, other values ​​are selected within the value range of the corresponding parameters of the large-size object, and the thermal simulation results are gradually made closer to the thermal field distribution requirements of the large-size object through multiple rounds of simulation iterations until the preset accuracy requirements are met, thus obtaining a large-size correction model containing optimized structural parameters and operating parameters.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for cross-scale thermal field design and temperature control optimization of a multi-temperature zone heating furnace as described in any one of claims 1-9.

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