Carbon dioxide hydrogenation methanol fixed bed reactor system and its temperature control method

Abstract

The application relates to the technical field of process reaction control, and discloses a fixed bed reactor system for preparing methanol by carbon dioxide hydrogenation and a temperature control method thereof. The system comprises a reactor module and a measurement and control module; the reactor module comprises at least one cooling cavity; the measurement and control module comprises at least one temperature sensor, at least one heat exchange unit, a circulating pump and a control unit; wherein: the inlet of each cooling cavity is connected with an independent liquid inlet pipeline, the outlet of each cooling cavity is connected with an independent liquid outlet pipeline, the liquid outlet pipeline is connected with the liquid inlet pipeline through the heat exchange unit and the circulating pump to form an independent circulating loop; the control unit is in communication connection with each temperature sensor, each heat exchange unit and the circulating pump, is used for receiving real-time temperature data of the cooling cavity and adjusting the working state of the heat exchange unit and the flow of the circulating pump. The beneficial effect lies in overcoming the limitation of single constant temperature control of a traditional reactor, and significantly improving the designability and regulation flexibility of a temperature field of the reactor system.

Description

Technical Field

[0001] This application relates to the field of chemical process reaction technology, and in particular to a fixed-bed reactor system for the production of methanol by carbon dioxide hydrogenation and its temperature control method. Background Technology

[0002] Methanol synthesis reactions are typically carried out in fixed-bed reactors at 50–100 bar and 200–300 °C. Due to the highly exothermic and reversible nature of methanol synthesis, temperature control within the fixed-bed reactor is crucial. Localized heat accumulation forming "hot spots" accelerates catalyst sintering and deactivation, and excessively high or low reaction temperatures lead to decreased methanol selectivity and carbon dioxide conversion, respectively. Therefore, providing a suitable and uniform temperature environment for the catalyst bed is key to ensuring the efficient and stable operation of the carbon dioxide hydrogenation to methanol process. Furthermore, for exothermic and reversible reactions like carbon dioxide hydrogenation to methanol, an ideal reactor should maintain a high temperature in the inlet region to accelerate the reaction, followed by axial staged cooling to shift the reaction equilibrium towards methanol production, thereby increasing methanol yield.

[0003] However, the reactor systems in related technologies still have limitations such as a single temperature control strategy and difficulty in achieving dynamic temperature control that matches the reaction process. Further improvements are needed to enhance the designability and control flexibility of the temperature field in reactor systems. Summary of the Invention

[0004] This application aims to at least partially solve one of the technical problems in related technologies. To this end, this application proposes a fixed-bed reactor system for the hydrogenation of carbon dioxide to methanol and its temperature control method. The main technical solutions adopted in this application include:

[0005] In a first aspect, this application provides a fixed-bed reactor system for the hydrogenation of carbon dioxide to methanol. The system includes a reactor module and a measurement and control module. The reactor module includes at least one cooling chamber. The measurement and control module includes at least one temperature sensor, at least one heat exchange unit, a circulation pump, and a control unit. Each temperature sensor is installed in each cooling chamber to monitor the real-time temperature data of the cooling chamber. Each cooling chamber has an independent inlet pipe connected to its inlet and an independent outlet pipe connected to its outlet. The outlet pipe is connected to the inlet pipe via the heat exchange unit and the circulation pump to form an independent circulation loop. The control unit is communicatively connected to each temperature sensor, each heat exchange unit, and the circulation pump to receive the real-time temperature data of the cooling chamber and adjust the operating status of the heat exchange unit and the flow rate of the circulation pump.

[0006] By configuring reactor modules with independent cooling chambers and measurement and control modules with independent temperature sensing, heat exchange, and circulation units, multiple independent temperature control loops are constructed. Simultaneously, the control unit precisely regulates each loop based on real-time temperature data. This allows the reactor system to set and maintain different target temperatures for different axial regions of the catalyst bed, overcoming the limitations of traditional single isothermal control. This significantly improves the designability and control flexibility of the reactor system's temperature field, providing a suitable and uniform temperature environment for the carbon dioxide hydrogenation to methanol reaction, thus contributing to improved reaction efficiency and product yield.

[0007] Optionally, the reactor module includes a reactor shell, fractal fins, and a catalyst bed; wherein: the fractal fins have cavities extending along their axial direction inside, and the cavities are divided axially to form at least one cooling chamber; the fractal fins are disposed inside the reactor shell, and the gap between the fractal fins and the reactor shell constitutes the catalyst bed.

[0008] By coaxially mounting fractal fins within the reactor shell, and utilizing the complex fractal sub-fin structure on their outer surface to embed and segment the catalyst bed, efficient radial heat transfer is achieved while also regulating the catalyst loading space, simplifying the loading operation. Furthermore, by dividing the internal cavities of the fractal fins into multiple cooling chambers, segmented axial temperature control of the reactor is realized. Ultimately, the reactor module provides both the necessary space and catalyst support for the reaction, and enables precise temperature control, thus providing a stable operating environment for the carbon dioxide hydrogenation to methanol reaction.

[0009] Optionally, the outer surface of the fractal fin is provided with a plurality of fractal sub-fins evenly distributed along the circumference; wherein: the fractal sub-fins adopt at least two levels of self-similar structure, and the number of branches in each level is 2.

[0010] By setting fractal sub-fins evenly distributed circumferentially on the outer surface of the fractal fins, the contact area between the fractal fins and the catalyst bed is increased, thereby improving the efficiency of heat transfer.

[0011] Optionally, the reactor shell includes an upper sealing structure, a cylindrical structure, and a lower sealing structure; the upper sealing structure is provided with a raw material gas inlet and a liquid inlet; the lower sealing structure is provided with a reaction gas outlet, a liquid outlet, and a catalyst discharge port; wherein: the liquid inlet extends into the reactor shell through the liquid inlet and is connected to the inlet of the corresponding cooling chamber; the liquid outlet is connected to the outlet of the corresponding cooling chamber and extends out of the reactor shell through the liquid outlet to connect with the corresponding heat exchange unit.

[0012] By setting up independent feed gas inlets, reactant gas outlets, and catalyst discharge ports, the directional transport of reactants, the smooth export of products, and the convenience of catalyst maintenance are achieved. Furthermore, by connecting the liquid inlet and outlet pipelines to the corresponding cooling chambers, an independent cooling circulation loop is constructed, improving the independence and precision of temperature control in each cooling chamber.

[0013] Optionally, the lower sealing structure is filled with a first filling structure; the upper sealing structure is filled with a second filling structure; wherein: the first filling structure is used to support the catalyst bed and uniformly distribute the gas flow; the second filling structure is used to uniformly distribute the gas flow.

[0014] By filling the lower sealing structure with ceramic balls to form a first filling structure, the uniformity of reactant gas distribution at the bottom of the catalyst bed is improved while providing stable support. By partially filling the upper sealing structure with ceramic balls to form a second filling structure, uniform gas flow is achieved while avoiding unnecessary pressure drop losses. The combined use of these two structures ensures that the feed gas maintains a uniform flow from entering the reactor to contacting the catalyst, preventing uneven reaction efficiency caused by excessively strong or weak local gas flow. It also provides a stable support environment for the catalyst bed, extending the catalyst's lifespan.

[0015] Secondly, this application provides a temperature control method for a fixed-bed reactor system for carbon dioxide hydrogenation to methanol, applied to the aforementioned fixed-bed reactor system for carbon dioxide hydrogenation to methanol. The method includes: modeling and verifying the structural parameters of the reactor modules to obtain a target simulation model of the reactor system; performing multi-condition simulation based on the target simulation model to determine the target temperature data for each cooling chamber; and, during the actual reaction process, adjusting the real-time temperature data of each cooling chamber based on the target temperature data to achieve temperature control of the reactor system.

[0016] By modeling and verifying the actual structural parameters of the reactor system, a target simulation model that accurately reflects the internal state of the reactor was obtained, providing a reliable calculation tool for precise temperature field design. Subsequently, multi-condition simulations and optimizations were performed based on this model, scientifically determining the optimal target temperature for each cooling chamber, overcoming the limitations of traditional empirical temperature control. Finally, in actual operation, through real-time feedback and independent control, independent and precise temperature control of each cooling chamber was achieved, effectively solving the problem of single temperature control strategies and difficulty in achieving dynamic temperature control that matches the reaction process in related technologies. This significantly improved the designability and control flexibility of the reactor system's temperature field, ensuring the efficient and stable operation of the carbon dioxide hydrogenation to methanol reaction.

[0017] Optionally, modeling and verification are performed based on the structural parameters of the reactor module to obtain the target simulation model of the reactor system. This includes: performing three-dimensional modeling and solving based on structural parameters, preset control equations, and preset boundary conditions to obtain the initial simulation model of the reactor system; wherein the preset control equations include the reaction kinetic equations for carbon dioxide hydrogenation, carbon monoxide hydrogenation, and reverse water-gas shift reaction; and performing temperature verification and updating based on the initial simulation model to obtain the target simulation model.

[0018] First, an initial simulation model is constructed using structural parameters, preset control equations, and boundary conditions. This model combines the reactor's physical structure with reaction laws, providing a fundamental computational model for temperature optimization. Next, the initial simulation model is calibrated for temperature and its parameters are updated, gradually optimizing the structural parameters of the fractal fins to ensure the model meets temperature uniformity requirements. The final target simulation model accurately reflects the desired temperature state, providing a reliable digital tool for subsequent operating condition simulation and temperature control.

[0019] Optionally, three-dimensional modeling and solving are performed based on structural parameters, preset control equations, and preset boundary conditions to obtain an initial simulation model of the reactor system. This includes: constructing a three-dimensional model and meshing based on structural parameters to obtain a simplified geometric model of the reactor system; and numerically solving the simplified geometric model using preset control equations and preset boundary conditions to obtain the initial simulation model of the reactor system.

[0020] A simplified geometric model was constructed based on structural parameters and meshed, transforming the complex physical entity into a standard format suitable for numerical computation, thus laying the geometric foundation for subsequent simulations. Subsequently, numerical solutions were obtained by combining accurate reaction kinetic equations and physical boundary conditions, yielding an initial simulation model that can preliminarily reveal the complex multiphysics coupling behavior inside the reactor, providing crucial computational basis for subsequent verification and optimization.

[0021] Optionally, the structural parameters include cylinder parameters and initial fin parameters; temperature verification and updating are performed based on the initial simulation model to obtain the target simulation model, including: performing temperature uniformity verification based on the initial simulation model to obtain the current verification result; if the current verification result indicates that the initial simulation model does not meet the target temperature conditions, then the initial fin parameters are updated based on the current verification result to obtain the updated fin parameters; the updated fin parameters and cylinder parameters are used to perform three-dimensional modeling and solving again to obtain the updated simulation model of the reactor system, and temperature uniformity verification is performed again based on the updated simulation model to obtain the updated verification result; the above update processing steps are repeated until the updated verification result indicates that the updated simulation model meets the target temperature conditions, at which point the updated simulation model is used as the target simulation model.

[0022] By verifying the temperature uniformity of the initial simulation model, the temperature distribution under the current structural parameters was obtained, which can guide the subsequent updating of the initial fin parameters. Subsequently, simulations were performed again based on the updated parameters, achieving closed-loop optimization of structural parameters and temperature performance. Finally, a target simulation model that meets the target temperature conditions was obtained, providing a precise reference standard for determining the target temperature of the actual reaction.

[0023] Optionally, multi-condition simulation processing is performed based on the target simulation model to determine the target temperature data of each cooling chamber, including: performing multi-condition simulation based on the target simulation model to obtain carbon dioxide conversion rate and methanol selectivity data under different candidate conditions; wherein, the candidate conditions include the simulated temperature of the first cooling chamber located at the inlet side of the reactor module, and the simulated temperature difference between the first cooling chamber and the second cooling chamber located at the outlet side of the reactor module; performing surface fitting processing based on the carbon dioxide conversion rate and methanol selectivity data to obtain a three-dimensional performance surface plot; wherein, the three-dimensional performance surface plot includes a first surface plot describing the carbon dioxide conversion rate and a second surface plot describing the methanol selectivity data; performing condition optimization processing based on the three-dimensional performance surface plot to determine the target conditions of the reactor system, and calculating the target temperature data of each cooling chamber based on the target conditions.

[0024] Multi-condition simulations were conducted based on the target simulation model to obtain reaction performance data under different temperature parameters, providing a comprehensive calculation basis for optimizing the operating conditions. Subsequently, intuitive three-dimensional performance surface plots were used to quickly and accurately determine the target operating conditions that balance conversion rate and selectivity. Finally, the target temperature of each cooling chamber was calculated based on the target operating conditions, achieving precise design of the axial temperature gradient. This breaks through the limitations of traditional empirical temperature control, significantly improves the designability and control flexibility of the reactor temperature field, and provides key temperature parameter support for the efficient and stable operation of the reaction. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a structural block diagram of a carbon dioxide hydrogenation to methanol fixed-bed reactor system according to an embodiment of this application;

[0027] Figure 2a This is a schematic diagram of the structure of a reactor module according to an embodiment of this application;

[0028] Figure 2b This is a schematic diagram of the structure of a fractal fin according to an embodiment of this application;

[0029] Figure 2c This is a schematic diagram of the structure of a fractal sub-fin according to an embodiment of this application;

[0030] Figure 3 This is a flowchart of a temperature control method for a fixed-bed reactor system for carbon dioxide hydrogenation to methanol according to an embodiment of this application;

[0031] Figure 4a This is a flowchart of a method for determining a target simulation model according to an embodiment of this application;

[0032] Figure 4b This is a schematic diagram of a simplified geometric model provided according to an embodiment of this application;

[0033] Figure 4c This is a schematic diagram of initial fin parameters provided according to an embodiment of this application;

[0034] Figure 4d This is a temperature distribution cloud map of a radial section provided according to an embodiment of this application;

[0035] Figure 5a This is a flowchart of a method for determining target temperature data according to an embodiment of this application;

[0036] Figure 5b This is a three-dimensional performance surface plot for determining carbon dioxide conversion according to an embodiment of this application;

[0037] Figure 5c This is a three-dimensional performance surface plot for determining methanol selectivity according to one embodiment of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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, 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.

[0039] It should be noted that in related technologies, the most widely used fixed-bed reactors are mostly shell-and-tube structures, removing the heat of reaction by flowing a cooling medium outside or inside the tubes. Typical examples include reactors developed by Lurgi and Linde. The Lurgi reactor packs the catalyst into thousands of vertical tubes, with boiling water used for external cooling. While this structure is advantageous for temperature control, it also limits the amount of catalyst that can be packed into a single tube, and the loading and unloading process is relatively cumbersome. The Linde reactor, on the other hand, uses an internal spiral coil as its cooling structure, with the catalyst packed in the gaps between the coils. While this increases the packing capacity, it makes it difficult to ensure the uniformity of catalyst distribution, and the spiral coil requires high precision in processing, welding, and assembly.

[0040] More importantly, both of the aforementioned mainstream reactor types primarily aim to maintain a relatively constant internal temperature. However, for exothermic and reversible reactions like the hydrogenation of carbon dioxide to methanol, an ideal reactor should be able to maintain a higher temperature in the inlet region to promote the reaction rate and gradually decrease the temperature along the axial direction, thereby shifting the reaction equilibrium towards methanol production and ultimately increasing the methanol yield. Current reactor structures still have significant limitations in this regard. Therefore, a novel reactor structure and temperature control method are needed that can achieve axial gradient temperature control while also possessing simple packing and radial temperature uniformity characteristics, to better match the thermodynamic requirements of the reaction and improve product yield and process economy.

[0041] Based on this, this embodiment provides a fixed-bed reactor system 100 for the hydrogenation of carbon dioxide to methanol, such as... Figure 1 As shown, the system includes a reactor module 110 and a measurement and control module 120.

[0042] The reactor module refers to the core device for the carbon dioxide hydrogenation to methanol chemical reaction, providing physical space for the feed gas reaction, catalyst loading, and heat exchange. The monitoring and control module refers to the auxiliary control unit used to monitor and control the internal temperature state of the reactor module.

[0043] Since the reaction of hydrogenating carbon dioxide to methanol has different temperature requirements along the axial direction, that is, the inlet region needs a higher temperature to accelerate the reaction start-up, and the subsequent region needs to gradually cool down to push the reaction equilibrium to move towards the production of methanol, the reactor module 110 may include at least one cooling chamber 111. These cooling chambers refer to the closed cavity structures arranged along the axial direction inside the reactor module, which can be used to contain and circulate cooling media (such as cooling water or heat transfer oil).

[0044] Specifically, setting the number of cooling chambers to at least one enables basic control of the reactor's internal temperature. For example, a single cooling chamber allows for overall temperature regulation of the catalyst bed. However, setting multiple independent cooling chambers arranged axially allows for differentiated temperature control of different axial regions of the reactor, thus avoiding the problem that a single cooling structure cannot flexibly adapt to axial temperature gradients.

[0045] The measurement and control module 120 includes at least one temperature sensor 121, at least one heat exchange unit 123, a circulating pump 125, and a control unit.

[0046] The heat exchange unit can refer to a functional module that can regulate the temperature of the cooling medium. The circulating pump can refer to a device that provides power for the flow of the cooling medium in the pipeline, enabling the cooling medium to circulate continuously in the loop and ensuring the stable operation of the heat exchange process.

[0047] Specifically, since each cooling chamber needs to independently monitor its temperature and independently adjust its heat exchange effect, the number of temperature sensors and heat exchange units can be the same as the number of cooling chambers. Furthermore, each temperature sensor is installed in each cooling chamber to monitor the real-time temperature data of the cooling chamber. This real-time temperature data can refer to a numerical value that reflects the real-time temperature of the cooling medium within the cooling chamber. For example, this real-time temperature data can be the average temperature within the cooling chamber, or the temperature at the inlet or outlet of the cooling chamber. In actual reaction processes, to ensure measurement consistency and control logic coherence, all temperature sensors are typically uniformly installed at corresponding locations in different cooling chambers, such as the inlet section, central area, or outlet section of the cooling chamber.

[0048] Furthermore, each cooling chamber has an independent inlet pipe 131 at its inlet and an independent outlet pipe 133 at its outlet. The outlet pipe is connected to the inlet pipe via a heat exchange unit and a circulation pump to form an independent circulation loop.

[0049] The inlet pipe refers to the pipe that delivers the cooling medium to the cooling chamber, and the outlet pipe refers to the pipe that removes the cooling medium from the cooling chamber. For example, taking a specific cooling chamber as an example, the circulation loop works as follows: the circulation pump 125 drives the cooling medium (such as heat transfer oil) to flow into the cooling chamber from the inlet pipe 131 corresponding to that cooling chamber. After absorbing or releasing heat, it flows out from the outlet pipe 133 corresponding to that cooling chamber, then flows through the heat exchange unit corresponding to that cooling chamber for temperature regulation (being heated or cooled), and finally is pumped back to the inlet pipe 131 by the circulation pump 125, completing one cycle. Each cooling chamber has such an independent loop.

[0050] The control unit can refer to a controller or computer device with data receiving, logic operation and instruction sending functions, which can be used to receive and process temperature data and send control instructions to the heat exchange unit and the circulating pump.

[0051] Specifically, the control unit can communicate with each temperature sensor, each heat exchange unit, and the circulating pump to receive real-time temperature data of the cooling chamber and adjust the working status of the heat exchange unit and the flow rate of the circulating pump.

[0052] The operating status can refer to the operating mode and operating power of the heat exchange unit. For example, when the real-time temperature of the cooling chamber is higher than the target temperature, the cooling power of the heat exchange unit is increased to reduce the temperature of the cooling medium; when the real-time temperature of the cooling chamber is lower than the target temperature, the cooling power is reduced or the heating function is activated to increase the temperature of the cooling medium, thereby bringing the temperature of the cooling chamber closer to the target value.

[0053] Furthermore, since the flow rate of the cooling medium in the loop affects the heat transfer intensity of the cooling chamber and its response speed to temperature changes, the control unit can also change the flow rate of the cooling medium in the corresponding loop by adjusting the motor frequency of the circulating pump or the valve opening. For example, to meet the needs of rapid response to temperature fluctuations and optimized system energy consumption, the motor frequency of the circulating pump can be increased to increase the flow rate of the cooling medium. A higher flow rate of the cooling medium results in higher heat exchange and heat transfer efficiency within the reactor, faster heat transfer speed, and a shorter time required for the entire system to reach a stable set temperature. It also enhances the response speed of temperature control, thereby enabling timely responses to temperature fluctuations during the reaction process.

[0054] Optionally, the reactor system 100 may also be equipped with an expansion tank 130, which can be a device used to stabilize the pressure of the circulating loop and replenish the cooling medium. Specifically, it can be connected to the section of the circulating loop from the circulating pump to the outlet pipeline to absorb the volume expansion of the cooling medium caused by temperature changes, thus preventing excessive pressure in the entire loop. Simultaneously, it can also be used to replenish the cooling medium lost during the circulation process, ensuring the stable operation of the entire loop.

[0055] In the above embodiments, multiple independent temperature control loops are constructed by setting up a reactor module with an independent cooling chamber and a measurement and control module with independent temperature sensing, heat exchange, and circulation units. Simultaneously, the control unit precisely regulates each loop based on real-time temperature data. This allows the reactor system to set and maintain different target temperatures for different axial regions of the catalyst bed, thus overcoming the limitations of traditional reactors with single isothermal control. Ultimately, this significantly improves the designability and control flexibility of the reactor system's temperature field, providing a suitable and uniform temperature environment for the carbon dioxide hydrogenation to methanol reaction, contributing to improved reaction efficiency and product yield.

[0056] In some implementation methods, please refer to the appendix. Figure 2a The reactor module 110 includes a reactor shell 210, fractal fins 220, and a catalyst bed 230.

[0057] The reactor shell can refer to the closed container structure that forms the outer contour of the reactor, that is, the main structure that provides installation support for each functional component.

[0058] Specifically, the reactor shell includes an upper sealing structure 211, a cylindrical structure 213, and a lower sealing structure 215. The upper sealing structure 211 and the cylindrical structure 213 are connected by flanges to facilitate the installation and maintenance of internal components. The lower sealing structure 215 is fixed to the cylindrical structure 213 by welding to ensure the sealing and pressure-bearing capacity of the connection.

[0059] Among them, the upper sealing structure 211 can refer to the closed structure located at the top of the reactor shell, which is used to seal the upper end of the reactor shell and realize the introduction of raw gas and the introduction of part of the pipeline.

[0060] Specifically, the upper sealing structure 211 is provided with a raw material gas inlet 2111 and a liquid inlet 2113.

[0061] The feed gas inlet 2111 can be used to connect to an external feed gas supply pipeline, that is, to introduce a feed gas composed of carbon dioxide and hydrogen into the reactor shell. The liquid inlet 2113 can be used to provide a passage for the circulation pipeline of the cooling chamber, that is, the liquid inlet can allow multiple independent liquid inlet pipelines to pass through and extend into the reactor shell.

[0062] The cylindrical structure 213 can refer to the cylindrical structure that constitutes the main body of the reactor, that is, the main pressure vessel that provides installation space for the catalyst bed and fractal fins and bears the reaction pressure.

[0063] The lower sealing structure 215 can refer to a closed structure located at the bottom of the reactor shell, used to seal the lower end of the reactor shell and realize the discharge of reaction products, the reflux of cooling medium, and the loading and unloading of catalyst.

[0064] Specifically, the lower sealing structure 215 may be provided with a reaction gas outlet 2151, a liquid outlet 2153 and a catalyst discharge port 2155.

[0065] The reaction gas outlet 2151 is used to discharge the reacted mixture from the reactor shell, specifically to discharge the mixture containing methanol, water, and unreacted gases. A screen can also be installed inside the reaction gas outlet 2151 to prevent catalyst particles from escaping. The liquid outlet 2153 provides a passage for the circulation pipes of the cooling chamber, allowing multiple independent liquid outlet pipes to pass through and extend out of the reactor shell. The catalyst discharge port 2155 is used for loading and unloading the catalyst. Specifically, the catalyst discharge port 2155 is equipped with a detachable discharge flange, which maintains the reactor's seal during normal reaction and can be disassembled and opened when loading or unloading is required.

[0066] Furthermore, the inlet pipe for each cooling chamber extends into the reactor shell through the inlet port 2113 on the upper sealing structure 211, and continues to extend inward, ultimately connecting directly to the inlet of the cooling chamber to deliver the cooling medium to the cooling chamber. Similarly, the outlet pipe connects to the outlet of the corresponding cooling chamber and extends out of the reactor shell through the outlet port to connect to the corresponding heat exchange unit. That is, after the outlet pipe is led out from the cooling chamber outlet, it extends through the outlet port to the outside of the reactor shell, delivering the cooled medium that has completed heat exchange to the heat exchange unit for temperature regulation.

[0067] It should be noted that the inlet and outlet pipes are through holes in the reactor shell for the pipes to pass through. The inlet and outlet pipes, however, are independent pipes corresponding to each cooling chamber. Furthermore, each cooling chamber has independent interfaces (such as an oil inlet and an oil return port) at both its inlet and outlet. Multiple inlet pipes (such as oil inlets) corresponding to the number of cooling chambers extend into the reactor shell through the inlet pipes and connect to the inlet of their respective cooling chambers. Similarly, multiple outlet pipes (such as oil return pipes) connect to the outlets of each cooling chamber and extend through the outlet pipes to connect the cooling chambers to external systems.

[0068] By setting up independent feed gas inlets, reactant gas outlets, and catalyst discharge ports, the directional transport of reactants, the smooth export of products, and the convenience of catalyst maintenance are achieved. Furthermore, by connecting the liquid inlet and outlet pipelines to the corresponding cooling chambers, an independent cooling circulation loop is constructed, improving the independence and precision of temperature control in each cooling chamber.

[0069] Furthermore, the lower sealing structure 215 is filled with a first filling structure; the upper sealing structure 211 is filled with a second filling structure.

[0070] The first filling structure can refer to a chemically inert layer filled within the lower sealing structure. Taking ceramic balls as an example, the first filling structure can be a structure formed by ceramic balls of different diameters arranged in a gradient, which can be used to support the catalyst bed and uniformly distribute the gas flow. Similarly, the second filling structure can refer to a chemically inert layer filled within the upper sealing structure, which is also a structure formed by ceramic balls of different diameters arranged in a gradient. It can also be used to uniformly distribute the gas flow.

[0071] It should be noted that the main component of both the first and second packing structures is chemically inert ceramic spheres, the difference being in their packing density, height, and function. Specifically, the first packing structure within the lower sealing structure needs to bear the entire weight of the catalyst bed, therefore it must be completely filled with ceramic spheres to form a solid support base. The second packing structure within the upper sealing structure primarily serves for airflow distribution, so it can be partially filled, with a filling height approximately half the internal space of the upper sealing structure. For different reactors, this height can be between 200mm and 400mm to avoid excessive airflow resistance due to overfilling.

[0072] During filling, the lower sealing structure can first be laid with larger diameter ceramic balls as a base, and then filled with ceramic balls of decreasing diameter layer by layer to form a gradient structure. The upper sealing structure starts from below the feed gas inlet and is laid in the same way to form a gradient structure. This utilizes the gaps and surface characteristics between the ceramic balls to disperse the airflow, reduce turbulence, and allow the gas to pass through the catalyst bed at a more uniform flow rate and direction, thereby improving reaction efficiency and protecting the catalyst.

[0073] By filling the lower sealing structure with ceramic balls to form a first filling structure, the uniformity of reactant gas distribution at the bottom of the catalyst bed is improved while providing stable support. By partially filling the upper sealing structure with ceramic balls to form a second filling structure, uniform gas flow is achieved while avoiding unnecessary pressure drop losses. The combined use of these two structures ensures that the feed gas maintains a uniform flow from entering the reactor to contacting the catalyst, preventing uneven reaction efficiency caused by excessively strong or weak local gas flow. It also provides a stable support environment for the catalyst bed, extending the catalyst's lifespan.

[0074] Furthermore, the reactor module 110 also includes fractal fins 220 and a catalyst bed 230.

[0075] It is understandable that the gap between the fractal fins and the reactor shell constitutes the catalyst bed, which is a spatial region filled with solid catalyst particles, through which gaseous reactants pass and undergo catalytic reactions, so that the feed gas can contact the catalyst in this region and undergo the chemical reaction of hydrogenation to methanol.

[0076] The fractal fin 220 can refer to a metal structure with a specific fractal geometry, and the fractal fin 220 is disposed inside the reactor shell. Specifically, the fractal fin is coaxially (i.e., parallel to the main axis of the reactor shell) disposed at the center of the internal structure of the reactor shell.

[0077] For example, please refer to Figure 2b The fractal fin 220 has an axially extending cavity 221 inside, where the axial direction refers to the direction consistent with the length of the reactor shell; that is, the cavity is a cylindrical space extending along the length of the reactor. The cavity 221 is further divided by multiple baffles along the same axial direction, forming at least one independent cooling chamber 111. Simultaneously, a heat insulation gap 2211 is provided between adjacent cooling chambers 111 to reduce heat transfer between adjacent cooling chambers and ensure that the temperature of each cooling chamber is independently controllable.

[0078] Furthermore, a reliable sealing structure can be provided at the end of cavity 221, i.e., at the port 2213 connected to the inlet and outlet liquid pipelines. In addition, a reliable sealing structure can also be provided at the wall penetration point 2215 where the inlet and outlet liquid pipelines pass through the reactor shell wall to ensure the sealing of the entire cooling medium circulation loop under high pressure and prevent the cooling medium from leaking or mixing with the reaction gas.

[0079] Furthermore, the outer surface of the fractal fin is also provided with multiple fractal sub-fins evenly distributed along the circumference.

[0080] Here, fractal sub-fins can refer to branch structures extending outward from the fractal fin body. These sub-fins are arranged in a uniform ring on the outer circumference of the fractal fin to increase the contact area with the surrounding catalyst bed.

[0081] Specifically, the fractal sub-fins employ a self-similar structure with at least two levels, and each level has 2 branches.

[0082] It is understandable that a self-similar structure of at least two levels means that after the fractal fin extends from the main body to the first level branch, the first level branch will extend to the second level branch that is similar in shape to itself (if there are multiple levels, it will continue to extend), and the shape of each level branch is similar to the overall structure.

[0083] A branching number of 2 at each level means that each branch will divide into 2 sub-branches. For example, if a first-level branch branches into 2 branches from the main branch, then each first-level branch will further branch into 2 second-level branches.

[0084] For example, taking a fractal fin body with five fractal sub-fins evenly distributed on its outer surface, and the fractal sub-fins adopting a two-level self-similar structure as an example, please refer to... Figure 2cAs shown, each fractal sub-fin extends from the main body and branches out into a first-level branch (two branches) at the first grading point. Subsequently, each first-level branch extends into a second-level branch (two branches) at the second grading point, forming a two-level self-similar branching structure. This structure covers the void area between the fractal fin and the reactor shell, significantly increasing the contact area between the metal fin and the catalyst bed, and creating complex heat transfer channels. By setting fractal sub-fins evenly distributed circumferentially on the outer surface of the fractal fin, the contact area between the fractal fin and the catalyst bed is increased, improving the efficiency of heat transfer.

[0085] In the above embodiments, by coaxially arranging fractal fins within the reactor shell, and utilizing the complex fractal sub-fin structure on their outer surface to embed and segment the catalyst bed, efficient radial heat transfer is achieved while also regulating the catalyst loading space, simplifying the loading operation. Furthermore, by dividing the internal cavities of the fractal fins into multiple cooling chambers, segmented axial temperature control of the reactor is realized. Ultimately, the reactor module possesses both the space and catalyst support required for the reaction, and precise temperature control, thus providing a stable operating environment for the carbon dioxide hydrogenation to methanol reaction.

[0086] Each module in the aforementioned carbon dioxide hydrogenation to methanol fixed-bed reactor system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0087] In this embodiment, a carbon dioxide hydrogenation to methanol fixed-bed reactor system is presented in the form of functional units. These units can refer to actual physical components, ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0088] In some embodiments, this specification also provides a temperature control method for a carbon dioxide hydrogenation to methanol fixed-bed reactor system, applicable to the aforementioned carbon dioxide hydrogenation to methanol fixed-bed reactor system. For example... Figure 3 As shown, the method includes:

[0089] S310. Based on the structural parameters of the reactor module, modeling and verification are performed to obtain the target simulation model of the reactor system.

[0090] Structural parameters can refer to physical data describing the actual construction of the reactor module, such as key dimensions, shape, and material properties. For example, structural parameters may include the dimensions of the cylindrical structure, such as the bottom radius and length, and may also include the initial fin parameters of the fractal fins, such as the fractal angle, fin length, and number of sub-fins.

[0091] Specifically, firstly, a three-dimensional numerical model of the reactor system can be constructed in computational fluid dynamics software based on the structural parameters. Then, the initial simulation model is obtained by solving the preset governing equations and boundary conditions. Subsequently, temperature uniformity is verified based on the initial simulation model, focusing on verifying the temperature distribution across the radial cross-section of the catalyst bed and determining whether the maximum temperature difference meets the preset design requirements (e.g., less than 5K). If the verification fails, it means that the temperature uniformity of the initial simulation model under the current structural parameters is not up to standard. The structural parameters need to be adjusted, and the modeling and solving process needs to be repeated until the obtained simulation model meets the aforementioned temperature uniformity design requirements. This final model that meets the requirements is then determined as the target simulation model.

[0092] The target simulation model can refer to a simulation model that meets the design requirements for radial temperature uniformity of the catalyst bed and can accurately reflect the temperature transfer and flow patterns inside the reactor.

[0093] S320 performs multi-condition simulation processing based on the target simulation model to determine the target temperature data of each cooling chamber.

[0094] Here, "operating condition" can refer to different combinations of operating parameters used by the simulated reactor system in actual operation, including pressure and temperature. For example, taking temperature as an example, the operating condition can be a combination of different temperature parameters, consisting of the simulated temperature of the first cooling chamber on the inlet side and the simulated temperature difference between that cooling chamber and the last cooling chamber on the outlet side during the simulated operation of the reactor system.

[0095] The target temperature data can refer to the operating temperature value set for each independent cooling chamber. At this temperature, the overall performance of the reactor (such as methanol yield) can reach or approach its optimal level.

[0096] Specifically, the categories of operating parameters for the reactor system (such as pressure or temperature) can be defined first, followed by determining the value range of these parameters, i.e., setting the numerical intervals of the operating parameters affecting reactor performance. Then, multiple combinations of specific parameters can be selected within this range as candidate operating conditions. Next, based on the target simulation model, virtual operation calculations are performed on each of these candidate operating conditions to obtain the reactor's performance output data under each condition, such as carbon dioxide conversion rate and methanol selectivity. Afterward, comprehensive analysis and surface fitting are performed based on this performance output data to generate multi-dimensional performance surface plots that visually demonstrate the relationship between performance indicators and specific simulation parameters. Finally, global optimization is performed based on these multi-dimensional performance surface plots to identify the specific candidate operating condition (i.e., the optimal operating condition) that maximizes overall performance. Based on the simulation parameter values ​​corresponding to this optimal operating condition, the specific temperature value required to be maintained for each cooling chamber is calculated, thus obtaining the target temperature data.

[0097] S330. In the actual reaction process, the real-time temperature data of each cooling chamber is adjusted based on the target temperature data to complete the temperature control of the reactor system.

[0098] Specifically, when the reactor system is started for production, the control unit continuously reads the real-time temperature data measured by the temperature sensors in each cooling chamber. Then, the control unit compares the real-time temperature data of each cooling chamber with its corresponding target temperature data, and generates corresponding control commands based on the magnitude and direction of the deviation. These commands are sent to the corresponding heat exchange units and circulating pumps, dynamically adjusting the heating or cooling power of the heat exchange units and the rotation speed of the circulating pumps (thus changing the cooling medium flow rate), so that the real-time temperature of each cooling chamber can quickly and stably reach and maintain near its respective target temperature.

[0099] For example, suppose the target temperature of cooling chamber 1 is 563K, while the real-time temperature data fed back by its temperature sensor is 558K, meaning the current temperature is 5K lower than the target value. After comparison, the control unit determines that the cooling chamber needs to be heated. Therefore, the control unit sends a command to heat exchange unit 1, which is connected to the circulation loop of cooling chamber 1, to increase its heating power. Simultaneously, it may also appropriately increase the speed of the circulation pump driving the cooling medium flow in this loop to accelerate heat transfer. As heating proceeds, the real-time temperature of cooling chamber 1 gradually rises. The control unit continuously monitors this temperature change, and when the real-time temperature approaches 563K, it begins to reduce the heating power, making fine adjustments to ultimately stabilize the temperature at 563K. By performing similar independent closed-loop control on all cooling chambers, a preset optimal temperature gradient can be established and maintained along the entire reactor axis.

[0100] In the above implementation, modeling and verification were performed using the actual structural parameters of the reactor system, resulting in a target simulation model that accurately reflects the internal state of the reactor, providing a reliable computational tool for precise temperature field design. Subsequently, multi-condition simulations and optimizations were conducted based on this model, scientifically determining the optimal target temperature for each cooling chamber, overcoming the limitations of traditional empirical temperature control. Finally, in actual operation, through real-time feedback and independent control, independent and precise temperature control of each cooling chamber was achieved, effectively solving the problem of single temperature control strategies and difficulty in achieving dynamic temperature control that matches the reaction process in related technologies. This significantly improved the designability and control flexibility of the reactor system's temperature field, ensuring the efficient and stable operation of the carbon dioxide hydrogenation to methanol reaction.

[0101] In some implementation methods, please refer to the appendix. Figure 4a Based on the structural parameters of the reactor module, modeling and verification are performed to obtain the target simulation model of the reactor system, including:

[0102] S410. Based on structural parameters, preset control equations, and preset boundary conditions, perform three-dimensional modeling and solving to obtain the initial simulation model of the reactor system.

[0103] The pre-defined control equations can refer to a set of mathematical equations governing the physical and chemical processes within the reactor. Specifically, they can include the reaction kinetics equations for the carbon dioxide hydrogenation reaction, the carbon monoxide hydrogenation reaction, and the reverse water-gas shift reaction.

[0104] For example, the chemical formula for the hydrogenation reaction of carbon dioxide is: The corresponding reaction kinetic equation can be expressed as follows:

[0105]

[0106] In the formula, The reaction rate constant represents the reverse water-gas shift reaction, with units of . ; This represents the CO adsorption constant, in bar. -1 ; Represents the CO2 adsorption constant, in bar. -1 ; This represents the H2O adsorption constant, in bar. -0.5 ; This represents the reaction equilibrium constant for the reverse water-gas shift reaction, with units of bar. -2 ; , , , , These are the fugacity values ​​of H2, CO2, CH3OH, H2O, and CO, respectively, in bar.

[0107] The chemical formula for the hydrogenation reaction of carbon monoxide is: The corresponding reaction kinetic equation can be expressed as follows:

[0108]

[0109] In the formula, The rate constant for the hydrogenation of carbon dioxide is expressed in units of 1000 m³ / s. ; The equilibrium constant for the hydrogenation of carbon dioxide, expressed in bar. -2 .

[0110] The chemical formula for the reverse water-gas shift reaction is: The corresponding reaction kinetic equation can be expressed as follows:

[0111]

[0112] In the formula, This represents the rate constant for the hydrogenation of carbon monoxide, expressed in units of 1000 m³ / s. ; The equilibrium constant representing the hydrogenation reaction of carbon monoxide is a dimensionless number.

[0113] Furthermore, the reaction rate constant and the reaction equilibrium constant in the above equation ( , , , , , , , and It can be calculated using the following formula:

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, R Represents the ideal gas constant; T Represents absolute temperature, measured in Kelvin (K).

[0124] It should be noted that, in addition to the reaction kinetic equations mentioned above, the pre-defined governing equations may also include the mass conservation equation, momentum conservation equation, energy conservation equation, and component transport equation required for the usual reaction (which will not be elaborated here).

[0125] Preset boundary conditions can refer to the constraints that define the physical state of each region of the reactor system in the simulation calculation, which can clarify the operating rules and initial state of different parts in the simulation model.

[0126] For example, the annular void region (i.e., the catalyst bed) between the fractal fins and the cylindrical structure can be defined as the porous media reaction domain. The fractal fins themselves are defined as the solid domain, and their thermal conductivity and specific heat capacity are set according to their actual material (e.g., stainless steel). The contact surface between the porous media reaction domain and the solid domain can be set as a coupling wall to achieve heat transfer. The upper surface of the porous media reaction domain (the feed gas inlet side) is set as a velocity inlet condition. For example, the space velocity can be specified as 7000 h⁻¹ according to the simulated process requirements. -1 (That is, the total flow rate of the feed gas passing through the catalyst per hour is 7000 times the volume of the catalyst bed), the inlet temperature is 563K, and the feed gas composition is... and The molar ratio is 3:1. Its lower end face (reactant gas outlet side) is set to pressure outlet conditions, for example, the outlet pressure is set to 60 bar. The wall surface of the internal cavity (i.e., the cooling chamber) of the fractal fin is set to wall temperature conditions with a specific temperature distribution (e.g., linearly decreasing from 563 K at the inlet to 523 K at the outlet), which is consistent with the target set temperature of the corresponding cooling chamber.

[0127] The initial simulation model can refer to a simulation model that can initially reflect the temperature transfer and flow patterns inside the reactor. It has not yet been verified for temperature uniformity and is only obtained by solving based on the original input initial structural parameters.

[0128] Specifically, the 3D modeling and solving process based on structural parameters, preset control equations, and preset boundary conditions can begin by constructing a 3D model and generating a mesh based on the structural parameters to obtain a simplified geometric model of the reactor system. Then, by combining the preset control equations and preset boundary conditions, the simplified geometric model is numerically solved to obtain the initial simulation model of the reactor system.

[0129] The simplified geometric model refers to a three-dimensional geometric model obtained by reasonably abstracting and simplifying the physical structure of the actual reactor. For ease of operation, this simplified geometric model retains only the key structural features affecting flow and heat transfer, such as the main outline of the cylindrical structure and fractal fins, while ignoring some non-critical details, such as the reactor's small bolts and fillets. For example, the simplified geometric model can be as follows: Figure 4b As shown.

[0130] For example, taking a cylindrical structure with a bottom radius of 0.2m and a length of 2m, and fractal fins with initial parameters of a fractal angle of 30° and a fin length of 0.5m, the cylindrical outline of the cylindrical structure and the three-dimensional geometry of the fractal fins can first be drawn in 3D modeling software according to these structural parameters. Then, the two are combined according to their actual assembly positions to obtain a complete 3D geometric model of the reactor. This 3D geometric model is then imported into mesh generation software or the preprocessing module of computational fluid dynamics software to perform mesh generation. The internal space of the reactor can be divided into a large number of small mesh units (such as tetrahedral or hexahedral meshes) to obtain a simplified geometric model of the reactor system. The mesh generation results of this simplified geometric model can be further referenced... Figure 4b As shown.

[0131] Subsequently, the aforementioned pre-defined governing equations can be assigned to a gridded computational domain in computational fluid dynamics software. Simultaneously, pre-defined boundary conditions are precisely applied to the corresponding geometric boundaries. After setup, the solver is started for iterative calculations. The software solves these governing equations on each grid cell until the solution for the entire flow field converges. The final output is the iteratively converged result, which includes detailed data on the spatial distribution of velocity, pressure, temperature, and the concentrations of each component, thus forming the initial simulation model of the reactor system.

[0132] A simplified geometric model was constructed based on structural parameters and meshed, transforming the complex physical entity into a standard format suitable for numerical computation, thus laying the geometric foundation for subsequent simulations. Subsequently, numerical solutions were obtained by combining accurate reaction kinetic equations and physical boundary conditions, yielding an initial simulation model that can preliminarily reveal the complex multiphysics coupling behavior inside the reactor, providing crucial computational basis for subsequent verification and optimization.

[0133] S420. Based on the initial simulation model, perform temperature verification and update processing to obtain the target simulation model.

[0134] It should be noted that the structural parameters may include cylinder parameters and initial fin parameters. Cylinder parameters can refer to physical dimensional data describing the cylinder structure, such as base radius, length, and thickness. Initial fin parameters can refer to structural data describing the initial state of the fractal fins, such as fractal angle, fin length, fin width, and number of sub-fins. For example, please refer to... Figure 4c As shown, α represents the fractal angle; L0, L1, and L2 represent the lengths of the zero-level, first-level, and second-level fins, respectively, and can be set to L0=L1=L2; W0, W1, and W2 represent the widths of the corresponding fins at each level, and can be set to the ratio W0=2W1=4W2; the number of sub-fins is 5 in this example.

[0135] Further, temperature verification and updating based on the initial simulation model to obtain the target simulation model can include: first, performing temperature uniformity verification based on the initial simulation model to obtain the current verification result; if the current verification result indicates that the initial simulation model does not meet the target temperature conditions, then updating the initial fin parameters based on the current verification result to obtain the updated fin parameters; subsequently, re-modeling and solving the three-dimensional model using the updated fin parameters and cylinder parameters to obtain the updated simulation model of the reactor system, and then performing temperature uniformity verification again based on the updated simulation model to obtain the updated verification result; finally, repeating the above update process until the updated verification result indicates that the updated simulation model meets the target temperature conditions, at which point the updated simulation model is used as the target simulation model.

[0136] It should be noted that in order to ensure that the reactor design can achieve a uniform radial temperature distribution in the catalyst bed, thereby avoiding the formation of local "hot spots", protecting catalyst activity and improving reaction efficiency and selectivity, it is necessary to perform temperature uniformity verification to ensure that the temperature distribution meets the design requirements.

[0137] Specifically, temperature uniformity verification based on the initial simulation model can be performed in a post-simulation processing environment (such as using the post-processing function of simulation software) by capturing radial sections at different axial positions of the catalyst bed. Then, the temperature data of all mesh elements on this section are extracted, the highest and lowest temperatures are identified, and the difference between them is calculated, i.e., the maximum temperature difference of this section. Finally, it is determined whether these maximum temperature differences meet the preset target temperature conditions to obtain the current verification result. This current verification result can refer to the conclusion that the initial simulation model meets the target temperature conditions, i.e., whether it meets or does not.

[0138] For example, suppose the initial simulation model is analyzed, and a cross-section of the catalyst bed is taken at the axial position corresponding to a certain cooling chamber. The calculated temperature at this cross-section is 510K at the highest point and 502K at the lowest point, i.e., the maximum temperature difference is 8K. If the preset target temperature condition is that the maximum temperature difference of all cross-sections is less than 5K, then the current verification result for this cross-section is "not satisfied" with the target temperature condition.

[0139] Subsequently, if the current verification results indicate that the initial simulation model does not meet the target temperature conditions, the initial fin parameters can be updated based on the current verification results to obtain the updated fin parameters.

[0140] The target temperature condition can refer to a quantitative standard for temperature uniformity set for reactor design. Specifically, this standard can be that the maximum temperature difference on any radial cross-section of the catalyst bed must be less than a certain preset threshold, such as 5K.

[0141] Specifically, when an update is required, the initial fin parameters can be adjusted based on the temperature difference exceeding the limit in the current calibration results. This adjustment can be made by increasing the number of sub-fins, adjusting the fractal angle, or changing the fin length, thereby obtaining the updated fin parameters. These updated fin parameters are the geometric data of a new set of fractal fins after adjustment.

[0142] It should be noted that since the excessive cross-sectional temperature difference mainly stems from insufficient radial heat transfer from the fractal fins to the catalyst bed, the heat transfer efficiency can be improved by increasing the heat exchange surface area, thereby reducing the cross-sectional temperature difference. Taking adjusting the number of fractal sub-fins as an example, if the initial fin parameters show 3 sub-fins and the current verification results indicate an excessive temperature difference, then based on the aforementioned consideration of increasing the heat exchange surface area, the number of sub-fins can be increased to 4 or 5, thus obtaining the updated fin parameters. Furthermore, the heat transfer capacity can also be enhanced by adjusting the fractal angle or fin length. Specifically, as the fin length increases, the temperature difference gradually decreases, while as the fractal angle increases, the temperature difference first decreases and then increases; the optimal value needs to be determined through multiple adjustment simulation experiments.

[0143] Next, the updated fin parameters and cylinder parameters can be used to re-model and solve the three-dimensional model to obtain the updated simulation model of the reactor system. Then, the temperature uniformity can be verified again based on the updated simulation model to obtain the updated verification results.

[0144] The updated simulation model is a new simulation model built based on the updated fin parameters, and the updated verification result is the conclusion of whether the updated simulation model meets the target temperature conditions.

[0145] For example, after determining the updated fin parameters, these updated fin parameters can be combined with the original cylinder parameters, and the S410 operation can be repeated to reconstruct the three-dimensional geometric model and perform mesh generation. The same governing equations and boundary conditions can then be set, and numerical solutions can be performed to obtain the updated simulation model. Subsequently, the temperature of the catalyst bed radial section of this model is extracted again to calculate the maximum temperature difference. Taking a target temperature condition of 5K as an example, assuming the maximum temperature difference calculated this time is 5K, the updated verification result is "satisfied" with the target temperature condition. If it is still not satisfied, the parameters need to be further adjusted.

[0146] Finally, repeat the above update process until the updated simulation model meets the target temperature conditions, and then use the updated simulation model as the target simulation model.

[0147] For example, continuing with the example of adjusting the number of fractal fins, please refer to... Figure 4d , Figure 4d The diagram shows the temperature distribution cloud maps of the radial section and the corresponding maximum temperature difference when the number of sub-fins is 3, 4, and 5. Specifically, when the number of sub-fins is 3, the maximum temperature difference is 8K, which does not meet the target temperature condition. After updating the number of sub-fins to 4, the maximum temperature difference decreases to 5K, meeting the target temperature condition. If the number of sub-fins is further updated to 5, the maximum temperature difference can be further reduced to 4K, also meeting the target temperature condition. When the updated verification results (such as the verification results when the number of sub-fins is 4 or 5) indicate that the target temperature condition is met, the corresponding updated simulation model can be determined as the target simulation model.

[0148] By verifying the temperature uniformity of the initial simulation model, the temperature distribution under the current structural parameters was obtained, which can guide the subsequent updating of the initial fin parameters. Subsequently, simulations were performed again based on the updated parameters, achieving closed-loop optimization of structural parameters and temperature performance. Finally, a target simulation model that meets the target temperature conditions was obtained, providing a precise reference standard for determining the target temperature of the actual reaction.

[0149] In the above implementation, an initial simulation model is first constructed using structural parameters, preset control equations, and boundary conditions. This combines the reactor's physical structure with reaction laws, providing a basic computational model for temperature optimization. Next, by performing temperature verification and parameter updates on the initial simulation model, the structural parameters of the fractal fins are gradually optimized to ensure the model meets temperature uniformity requirements. The final target simulation model accurately reflects the desired temperature state, providing a reliable digital tool for subsequent operating condition simulation and temperature control.

[0150] In some implementation methods, please refer to the appendix. Figure 5aMulti-condition simulation processing is performed based on the target simulation model to determine the target temperature data for each cooling chamber, including:

[0151] S510. Perform multi-condition simulation based on the target simulation model to obtain carbon dioxide conversion rate and methanol selectivity data under different candidate conditions.

[0152] Here, candidate operating conditions can refer to a series of different combinations of simulated operating parameters set to explore reactor performance. For example, taking temperature as an example, candidate operating conditions may include the simulated temperature of the first cooling chamber located at the reactor module inlet, denoted as T1; and the simulated temperature difference between the first cooling chamber and the second cooling chamber located at the reactor module outlet, denoted as ΔT. The first cooling chamber is the one closest to the reactor module inlet (the top layer), and the second cooling chamber is the one closest to the reactor module outlet (the bottom layer).

[0153] Specifically, when the operating parameter category of the reactor system is selected as temperature, a reasonable range of values ​​can first be set for the candidate operating conditions. For example, the exploration range of the simulated temperature T1 of the first cooling chamber is set between 523K and 573K, and the exploration range of the simulated temperature difference ΔT is set between 0K and 50K. Then, within this two-dimensional parameter space, multiple representative combinations of (T1, ΔT) values ​​are selected, each combination representing a candidate operating condition to be simulated. Next, using the validated target simulation model, in the computational fluid dynamics software, the temperature conditions corresponding to each candidate operating condition (i.e., the axial temperature distribution determined according to T1 and ΔT) are used as new boundary conditions for independent simulation calculations. After each simulation, detailed data of the reactor system operating stably under that condition is output, including the molar flow rates of each component at the reactor module inlet and outlet.

[0154] It should be noted that the carbon dioxide conversion rate and methanol selectivity data are performance indicators calculated based on the molar flow rates of the reactor inlet and outlet components. Specifically, they are calculated according to the corresponding formulas based on the molar flow rates of the reactor inlet and outlet components at each operating point.

[0155] For example, the carbon dioxide conversion rate can be calculated using the following formula. :

[0156]

[0157] In the formula, Represents the molar flow rate of CO2 at the cavity inlet; The molar flow rate of CH3OH at the cavity outlet; This represents the molar flow rate of CO at the cavity outlet.

[0158] Methanol selectivity data can be calculated using the following formula. :

[0159]

[0160] The above formula can be used to quantitatively evaluate the reactor performance under each set of candidate operating conditions, thereby obtaining a series of corresponding carbon dioxide conversion rate and methanol selectivity data.

[0161] S520: Based on carbon dioxide conversion rate and methanol selectivity data, surface fitting is performed to obtain a three-dimensional performance surface plot.

[0162] The three-dimensional performance surface plot can refer to a visual chart that can intuitively show the performance variation with operating conditions. For example, the three-dimensional performance surface plot can be displayed in a coordinate system with operating conditions (T1 and ΔT) as the base axis and performance indicators as the height axis. Specifically, the three-dimensional performance surface plot can include a first surface plot describing the carbon dioxide conversion rate and a second surface plot describing the methanol selectivity data.

[0163] For example, firstly, the simulated temperature T1 of the first cooling chamber is used as the X-axis coordinate, the simulated temperature difference ΔT as the Y-axis coordinate, and the calculated carbon dioxide conversion rate and methanol selectivity are used as the Z-axis coordinates, respectively. A corresponding three-dimensional scatter plot is then plotted using the calculated carbon dioxide conversion rate and methanol selectivity data. Subsequently, a surface fitting algorithm is used to perform surface fitting and smoothing on these discrete data points, generating a three-dimensional performance surface plot that covers the entire (T1, ΔT) parameter exploration range. The final first surface plot (carbon dioxide conversion rate surface) and second surface plot (methanol selectivity surface) can be plotted as follows: Figure 5b and Figure 5c As shown in the figure, the distribution of the two performance indicators throughout the entire operation window is clearly illustrated.

[0164] S530: Based on the three-dimensional performance surface diagram, the operating condition optimization process is performed to determine the target operating condition of the reactor system, and the target temperature data of each cooling chamber is calculated based on the target operating condition.

[0165] The target operating condition can refer to the combination of temperature parameters that enables the reactor system to achieve a relatively optimal overall performance.

[0166] It should be noted that when performing optimization of operating conditions, it is usually necessary to consider both carbon dioxide conversion rate and methanol selectivity. Considering both reaction economy and product quality, priority is usually given to ensuring that the carbon dioxide conversion rate reaches a high level (entering the conversion plateau region), and then the maximum value of methanol selectivity is sought within this range to determine the overall optimal operating point.

[0167] For example, please continue to refer to Figure 5b and 5c First, in the first surface plot of carbon dioxide conversion ( Figure 5b Observations on the graph show that when T1 is between 563 K and 573 K, and ΔT is between 0 K and 50 K, the carbon dioxide conversion rate gradually plateaus. Then, the determined (T1, ΔT) plateau range can be mapped onto the second surface plot of methanol selectivity. Figure 5c The study searched for the highest methanol selectivity within this region and found that the selectivity reached its maximum when T1 was 563 K and ΔT was 50 K. Therefore, the target operating conditions for the reactor system were determined, namely, the optimal simulated temperature T1_opt = 563 K and the optimal simulated temperature difference ΔT_opt = 50 K.

[0168] After obtaining the target operating conditions, the target temperature data of each cooling chamber can be calculated based on the target operating conditions.

[0169] Specifically, for ease of calculation, all cooling chambers can be numbered sequentially from top to bottom (along the reactor axis) as number 1 to N. Then, based on the optimal simulated temperature T1_opt and optimal simulated temperature difference ΔT_opt under the target operating conditions, linear calculations are performed to obtain the target temperature data for each cooling chamber. Subsequently, the target set temperature T of the cooling chamber numbered X can be calculated using the following formula:

[0170]

[0171] In the formula, N This represents the number of the last cooling chamber, which is the total number of cooling chambers.

[0172] For example, if the total number of cooling chambers N=10, the optimal simulated temperature T1_opt=563K, and the optimal simulated temperature difference ΔT_opt=50K, then based on the above formula, the target temperature data of each cooling chamber numbered 1 to 10 can be calculated as 563K, 557.4K, 551.9K, 546.3K, 540.8K, 535.2K, 529.7K, 524.1K, 518.6K, and 513K respectively.

[0173] Subsequently, during the actual reaction process, after the temperature of all cooling chambers stabilized at the target temperature data calculated above, gas was introduced into the reactor through the feed gas inlet. and The feed gas undergoes a carbon dioxide hydrogenation reaction to produce methanol. The resulting mixed gas is filtered through a screen inside the reactant gas outlet before being discharged from the outlet. Ultimately, compared to a conventional adiabatic reactor under the same reaction conditions, the reactor system in this embodiment achieves a 29.3% increase in methanol yield.

[0174] In the above implementation, multi-condition simulations were performed based on the target simulation model to obtain reaction performance data under different temperature parameters, providing a comprehensive calculation basis for optimizing the operating conditions. Subsequently, intuitive three-dimensional performance surface plots were used to quickly and accurately determine the target operating conditions that balance conversion rate and selectivity. Finally, the target temperature of each cooling chamber was calculated based on the target operating conditions, achieving precise design of the axial temperature gradient. This breaks through the limitations of traditional empirical temperature control, significantly improves the designability and control flexibility of the reactor temperature field, and provides key temperature parameter support for the efficient and stable operation of the reaction.

[0175] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple steps or stages, which are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0176] The systems, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0177] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0178] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0183] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0184] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0185] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. The above descriptions are merely embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0186] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A fixed-bed reactor system for producing methanol by carbon dioxide hydrogenation, characterized in that, The reactor module includes a reactor module and a measurement and control module; the reactor module includes at least one cooling chamber; the measurement and control module includes at least one temperature sensor, at least one heat exchange unit, a circulating pump, and a control unit; the reactor module includes a reactor shell, fractal fins, and a catalyst bed; wherein: The fractal fins are disposed inside the reactor shell, and the gap between the fractal fins and the reactor shell constitutes the catalyst bed. The fractal fins have cavities extending along their axial direction; the cavities are cylindrical spaces extending along the length of the reactor; the cavities are divided by multiple partitions along the same axial direction to form multiple independent cooling chambers; the cooling chambers are closed cavity structures for containing and circulating cooling media; each temperature sensor is respectively installed in each cooling chamber for monitoring the real-time temperature data of the cooling chamber. Each cooling chamber has an inlet connected to an independent liquid inlet pipe, and each cooling chamber has an outlet connected to an independent liquid outlet pipe. The liquid outlet pipe is connected to the liquid inlet pipe through the heat exchange unit and the circulation pump to form an independent circulation loop. The control unit is communicatively connected to each of the temperature sensors, each of the heat exchange units, and the circulating pump. It receives the real-time temperature data of the cooling chamber and adjusts the working state of the heat exchange units and the flow rate of the circulating pump. The control unit continuously reads the real-time temperature data measured by the temperature sensor in each cooling chamber, compares it with the corresponding target temperature data, and generates corresponding control commands to control the heating or cooling power of the corresponding heat exchange unit and the rotation speed of the corresponding circulating pump. This ensures that the real-time temperature of each cooling chamber reaches and is maintained at the corresponding target temperature, thereby establishing and maintaining a preset optimal temperature gradient along the entire reactor axis.

2. The system according to claim 1, characterized in that, The outer surface of the fractal fin is provided with a plurality of fractal sub-fins evenly distributed along the circumference; wherein: the fractal sub-fins adopt at least two levels of self-similar structure, and the number of branches in each level is 2.

3. The system according to claim 1, characterized in that, The reactor shell includes an upper sealing structure, a cylindrical structure, and a lower sealing structure; the upper sealing structure is provided with a raw material gas inlet and a liquid inlet; the lower sealing structure is provided with a reaction gas outlet, a liquid outlet, and a catalyst discharge port; wherein: The liquid inlet pipe extends into the reactor shell through the liquid inlet port and is connected to the inlet of the corresponding cooling chamber; The liquid outlet pipe is connected to the outlet of the corresponding cooling chamber and extends out of the reactor shell through the liquid outlet pipe to connect with the corresponding heat exchange unit.

4. The system according to claim 3, characterized in that, The lower sealing structure is filled with a first filling structure; the upper sealing structure is filled with a second filling structure; wherein: The first filling structure is used to support the catalyst bed and uniformly distribute the gas flow; The second filling structure is used to uniformly distribute the airflow.

5. A temperature control method for a fixed-bed reactor system for carbon dioxide hydrogenation to methanol, characterized in that, The method, applied to the carbon dioxide hydrogenation to methanol fixed-bed reactor system as described in any one of claims 1-4, comprises: Based on the structural parameters of the reactor module, a modeling and verification process is performed to obtain the target simulation model of the reactor system. Multi-condition simulation processing is performed based on the target simulation model to determine the target temperature data of each cooling cavity. In the actual reaction process, the real-time temperature data of each cooling chamber is adjusted based on the target temperature data to achieve temperature control of the reactor system.

6. The method according to claim 5, characterized in that, The modeling and verification process based on the structural parameters of the reactor module yields the target simulation model of the reactor system, including: Based on the structural parameters, preset control equations, and preset boundary conditions, three-dimensional modeling and solving are performed to obtain the initial simulation model of the reactor system; wherein, the preset control equations include the reaction kinetic equations for carbon dioxide hydrogenation reaction, carbon monoxide hydrogenation reaction, and reverse water-gas shift reaction. Temperature verification and update processes are performed based on the initial simulation model to obtain the target simulation model.

7. The method according to claim 6, characterized in that, The process of performing three-dimensional modeling and solving based on the structural parameters, preset control equations, and preset boundary conditions to obtain the initial simulation model of the reactor system includes: Based on the structural parameters, a three-dimensional model is constructed and a mesh is generated to obtain a simplified geometric model of the reactor system. By combining the preset control equations and the preset boundary conditions, the simplified geometric model is numerically solved to obtain the initial simulation model of the reactor system.

8. The method according to claim 6, characterized in that, The structural parameters include cylinder parameters and initial fin parameters; the temperature verification and update process based on the initial simulation model to obtain the target simulation model includes: Temperature uniformity is verified based on the initial simulation model to obtain the current verification result; If the current verification result indicates that the initial simulation model does not meet the target temperature condition, the initial fin parameters are updated based on the current verification result to obtain the updated fin parameters. The updated fin parameters and cylinder parameters are used to perform three-dimensional modeling and solving to obtain the updated simulation model of the reactor system. The temperature uniformity is then verified again based on the updated simulation model to obtain the updated verification results. Repeat the above update process until the updated verification result shows that the updated simulation model meets the target temperature condition, then use the updated simulation model as the target simulation model.

9. The method according to claim 5, characterized in that, The multi-condition simulation processing based on the target simulation model to determine the target temperature data of each cooling cavity includes: Multi-condition simulations are performed based on the target simulation model to obtain carbon dioxide conversion rate and methanol selectivity data under different candidate conditions; wherein, the candidate conditions include the simulated temperature of the first cooling chamber located on the inlet side of the reactor module, and the simulated temperature difference between the first cooling chamber and the second cooling chamber located on the outlet side of the reactor module. A surface fitting process is performed based on the carbon dioxide conversion rate and the methanol selectivity data to obtain a three-dimensional performance surface plot; wherein, the three-dimensional performance surface plot includes a first surface plot describing the carbon dioxide conversion rate and a second surface plot describing the methanol selectivity data; Based on the three-dimensional performance surface plot, the operating condition optimization process is performed to determine the target operating condition of the reactor system, and the target temperature data of each cooling chamber is calculated based on the target operating condition.

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