Pyrolysis reactor optimization design method and system

Abstract

The invention provides a pyrolysis reactor optimization design method and system, and the method comprises the steps: obtaining initial data, building a digital twinborn model of a pyrolysis reactor, carrying out the initialization setting of the digital twinborn model based on the initial operation condition of the pyrolysis reactor, the method comprises the following steps: carrying out parameter sensitivity analysis on structure parameters and operation parameters of a pyrolysis reactor, determining a most sensitive parameter combination, optimizing the most sensitive parameter combination, obtaining optimized parameters, transforming an actual pyrolysis reactor structure or adjusting the operation parameters of the pyrolysis reactor, and carrying out an actual operation experiment on the pyrolysis reactor. The method comprises the following steps: acquiring an actual experiment result, and comparing the actual experiment result with an optimization result predicted by a digital twinborn model until the actual performance of the pyrolysis reactor reaches an expected optimization target. Through simulation of the digital twinborn model, possible problems of the pyrolysis reactor under different parameter combinations are predicted in advance; preventive measures can be taken before an actual operation experiment, and the risks of equipment damage and production accidents are reduced.

Description

Technical Field

[0001] The present application relates to the field of biomass energy technology, and specifically to a pyrolysis reactor optimization design method and system. Background Art

[0002] Biomass energy has the advantages of low sulfur, zero carbon dioxide emissions and huge storage capacity. It is a renewable and environmentally friendly energy source. Biomass pyrolysis technology can effectively convert low-utilization biomass into high-energy liquid fuel, namely biomass rapid pyrolysis oil, also known as bio-oil. Judging from the energy development situation in recent years, the development prospects of biomass pyrolysis conversion technology are very broad and have great potential to replace fossil energy. The current pyrolysis reactor cannot reveal the causes of various complex phenomena occurring in the reactor. Therefore, how to optimize the design of the pyrolysis reactor is a key issue that needs to be solved. Summary of the invention

[0003] In order to overcome the problem that the prior art cannot reveal the causes of various complex phenomena occurring in the reactor, the present application provides a pyrolysis reactor optimization design method and system, which adopts the following technical solutions:

[0004] In a first aspect, the present application provides a method for optimizing the design of a pyrolysis reactor, comprising:

[0005] Acquiring initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor;

[0006] Build a digital twin model of the pyrolysis reactor;

[0007] Initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor;

[0008] Conduct parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination;

[0009] The most sensitive parameter combination is optimized and the best parameter combination is searched in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters.

[0010] Based on the optimized parameters, the actual pyrolysis reactor structure is modified or the operating parameters of the pyrolysis reactor are adjusted, and actual operation experiments are conducted on the pyrolysis reactor to obtain actual experimental results;

[0011] The actual experimental results are compared with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

[0012] Furthermore, after the digital twin model is initialized based on the initial operating conditions of the pyrolysis reactor, it includes:

[0013] Perform virtual simulation on the digital twin model to obtain virtual simulation results, and compare the virtual simulation results with the actual operating data of the pyrolysis reactor. If there is a large deviation, adjust and correct the digital twin model until the digital twin model reflects the actual operating status of the pyrolysis reactor.

[0014] Furthermore, a parameter sensitivity analysis was performed on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination, including:

[0015] Obtain historical operation data of the pyrolysis reactor, calculate the correlation coefficient between each parameter in the historical operation data and the performance index of the pyrolysis reactor, when the correlation coefficient of a parameter exceeds a preset threshold, the parameter is a sensitive parameter; when the correlation coefficient of a parameter does not meet the preset threshold, the parameter is excluded to obtain an initial sensitive parameter set;

[0016] Determine the variation range of structural parameters and initial sensitive parameter sets;

[0017] Based on the variation range of structural parameters and initial sensitive parameter sets, single factor analysis was performed on the structural parameters and initial sensitive parameter sets;

[0018] Select several parameters that show high sensitivity in single-factor analysis, run different parameter combinations in the digital twin model based on orthogonal experiments, and record the performance indicator data corresponding to each parameter combination;

[0019] Obtain the main effects of each parameter and the interaction effects between parameters based on analysis of variance;

[0020] Based on the sensitivity of each parameter in the univariate sensitivity analysis and the interaction effect between parameters in the multivariate analysis, the parameter combination with high sensitivity in the univariate analysis and significant main effect and interaction effect in the multivariate analysis was determined as the most sensitive parameter combination.

[0021] Furthermore, based on the variation range of the structural parameters and the initial sensitive parameter set, a single factor analysis is performed on the structural parameters and the initial sensitive parameter set, specifically including:

[0022] Within the variation range of the structural parameters and the initial sensitive parameter set, the value of one parameter is changed each time, while keeping other parameters unchanged. The digital twin model is run for simulation to obtain the influence of the changing parameters on the performance indicators of the pyrolysis reactor. The sensitivity is analyzed by calculating the rate of change of the performance indicators relative to the parameter changes, and the parameter-performance indicator change curve is drawn. When the slope of the curve meets the preset threshold, the parameter is a sensitive parameter.

[0023] Furthermore, the most sensitive parameter combination is optimized and the optimal parameter combination is searched in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters, including:

[0024] Based on the most sensitive parameter combination and the variation range of the corresponding parameters, the upper and lower boundaries of each parameter in the optimization process are set to obtain the range of each parameter in the most sensitive parameter combination;

[0025] Based on the range of each parameter in the most sensitive parameter combination, the initial parameter combination is randomly generated as the starting individual of the population;

[0026] For each parameter combination in the population, a digital twin model is used for simulation evaluation. The parameter combination is substituted into the digital twin model, and the digital twin model is run to simulate and obtain the corresponding pyrolysis reactor performance index data. The fitness value corresponding to the parameter combination is calculated according to the preset objective function.

[0027] According to the fitness value of the individual, a preset selection strategy is used to select some individuals from the current population. The individuals with higher fitness have a greater probability of being selected. The selected individuals are crossovered according to the set crossover probability. New parameter combinations are generated by exchanging or fusing their parameter values. The parameters of some individuals are randomly mutated according to the mutation probability, so that the parameters change randomly within a certain range, a new population is generated, and an iteration is completed. When the termination condition is met, the iteration is stopped to obtain the optimal parameter combination, which is used as the optimization parameter.

[0028] Furthermore, the preset objective function is F=w1Y1+w2Y2+...+w n Y n , where Y1, Y2, Y n are the first optimization target, the second optimization target, and the nth optimization target, w1, w2, w n They are the weight coefficients of the first optimization target, the second optimization target, and the nth optimization target respectively.

[0029] In a second aspect, the present application also provides a pyrolysis reactor optimization design system, comprising:

[0030] An initial data acquisition module, used to acquire initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor;

[0031] A digital twin model building module, used to build a digital twin model of the pyrolysis reactor;

[0032] An initialization setting module is used to initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor;

[0033] The most sensitive parameter combination acquisition module is used to perform parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor and determine the most sensitive parameter combination;

[0034] The optimization parameter acquisition module is used to optimize the most sensitive parameter combination and search for the best parameter combination in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters.

[0035] The actual experimental result acquisition module transforms the actual pyrolysis reactor structure or adjusts the operating parameters of the pyrolysis reactor based on the optimization parameters, conducts actual operation experiments on the pyrolysis reactor, and obtains actual experimental results;

[0036] The optimization design module is used to compare the actual experimental results with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

[0037] In a third aspect, the present application provides an electronic device, including:

[0038] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, cause the device to perform the method as described in the first aspect.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first aspect.

[0040] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method described in the first aspect.

[0041] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged together with the processor, or may be stored in whole or in part on a memory not packaged together with the processor.

[0042] This application has the following beneficial effects:

[0043] 1. Through parameter sensitivity analysis and optimization, this application can accurately find the parameter combination that has the greatest impact on the performance of the pyrolysis reactor; for example, in the pyrolysis process, parameters such as temperature, feed rate, and residence time have a key impact on the quality and output of the product; the results of the sensitivity analysis of this application will guide the direction of parameter optimization, help identify parameters that require special attention during the optimization process, and clarify which parameters’ slight changes will cause significant changes in the performance of the pyrolysis reactor, so as to adjust the sensitive parameters in a targeted manner and achieve the optimization of the pyrolysis reactor performance.

[0044] 2. In the process of optimizing the design of the pyrolysis reactor, this application forms a closed-loop feedback mechanism by comparing the actual experimental results with the optimization results predicted by the digital twin model, so that the digital twin model can be continuously corrected and improved according to actual conditions. At the same time, it also ensures that the transformation and adjustment of the pyrolysis reactor is based on effective solutions verified in practice, thereby improving the accuracy and reliability of the optimization.

[0045] 3. This application uses the simulation of the digital twin model to predict in advance the problems that may occur in the pyrolysis reactor under different parameter combinations, such as overheating, blockage, incomplete reaction, etc.; it helps to take preventive measures before the actual operation of the experiment to reduce the risk of equipment damage and production accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0047] Figure 2 This is a flow chart of the optimization design method of the pyrolysis reactor according to an embodiment of the present application;

[0048] Figure 3 A flow chart for determining the most sensitive parameter combination according to an embodiment of the present application;

[0049] Figure 4 A flow chart of obtaining optimization parameters according to an embodiment of the present application;

[0050] Figure 5 This is a system flow chart of an embodiment of the present application;

[0051] Figure 6 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0053] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0055] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0056] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0057] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0058] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0059] It should be noted that the pyrolysis reactor optimization design method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the pyrolysis reactor optimization design system is generally set in the server / terminal device.

[0060] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0061] Continue to refer Figure 2 , the figure shows a flow chart of a pyrolysis reactor optimization design method of the present application, the method comprising the following steps:

[0062] Step 201, obtaining initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor.

[0063] In a possible implementation, the structural data of the pyrolysis reactor includes: obtaining the geometric dimensions of the pyrolysis reactor by laser scanning, including the diameter, height, shape and position of the internal components of the pyrolysis reactor. Obtaining material property information of each part of the reactor, such as the thermal conductivity, specific heat capacity, temperature resistance limit of the reactor wall material, and related characteristics of the internal refractory material or coating.

[0064] In a possible implementation, obtaining the operating parameter data of the pyrolysis reactor includes: installing various sensors on the actually operating pyrolysis reactor, including temperature sensors (distributed at different heights and positions of the reactor), pressure sensors (inlet and outlet and in the bed), flow sensors (material feed, fluidizing gas, pyrolysis products, etc.). The operating parameters of the reactor during operation are collected and recorded for a long time, such as real-time temperature at different positions, pressure changes, material feed rate, fluidizing velocity, pyrolysis gas flow, etc.

[0065] Step 202, constructing a digital twin model of the pyrolysis reactor.

[0066] The geometric structure, material properties, chemical data and operating parameter data of the pyrolysis reactor are obtained. For example, the geometric structure data can be obtained by consulting the design drawings of the pyrolysis reactor, field measurements, etc.; the operating parameter data can be obtained from the existing control system records of the pyrolysis reactor and the historical operation data in the past.

[0067] Based on the geometric structure data of the pyrolysis reactor, a three-dimensional geometric model is constructed to restore the appearance and internal layout of the pyrolysis reactor; based on the material properties, chemical data and operating parameters of the pyrolysis reactor and the three-dimensional geometric model data, the physical process of heat transfer in the pyrolysis reactor is constructed.

[0068] The digital description of the physical process is converted into mathematical equations, and the boundary conditions and initial operating conditions are determined to obtain the digital twin model of the pyrolysis reactor.

[0069] In a possible implementation, the three-dimensional heat conduction mathematical equation in the heat transfer process is: Where T is temperature, t is time, α is the thermal diffusion coefficient, and x, y, x are spatial coordinates.

[0070] Step 203: Initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor.

[0071] In one possible implementation, initializing the digital twin model includes setting parameters such as the initial material feed rate, fluidizing gas flow rate, initial temperature distribution in the reactor (usually room temperature or preheating temperature), and initial concentration distribution of particles, so that the initial state of the digital twin model is consistent with the actual reactor.

[0072] In a possible implementation, after the digital twin model is initialized based on the initial operating conditions of the pyrolysis reactor, the following steps are included:

[0073] Perform virtual simulation on the digital twin model to obtain virtual simulation results, and compare the virtual simulation results with the actual operating data of the pyrolysis reactor. If there is a large deviation, adjust and correct the digital twin model until the digital twin model reflects the actual operating status of the pyrolysis reactor.

[0074] Step 204 , performing parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination.

[0075] In the examples of this application, parameter sensitivity analysis is performed on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination. Please refer to Figure 3, the specific contents include:

[0076] Step 31, obtain the historical operation data of the pyrolysis reactor, calculate the correlation coefficient between each parameter in the historical operation data and the performance index of the pyrolysis reactor, when the correlation coefficient of a parameter exceeds a preset threshold, the parameter is a sensitive parameter; when the correlation coefficient of a parameter does not meet the preset threshold, the parameter is excluded to obtain an initial sensitive parameter set.

[0077] Step 32, determining the variation range of the structural parameters and the initial sensitive parameter set.

[0078] Explanation: For structural parameters, the design specifications and physical limitations of the reactor must be considered. For example, the diameter and height of the reactor cannot exceed its maximum design dimensions, and the limitations of structural parameters due to factors such as material strength and manufacturing process must also be considered. For the initial set of sensitive parameters, the range must be determined in combination with the feasibility and safety of actual operations. For example, the pyrolysis temperature must be within the temperature range that the reactor material can withstand, and consideration must also be given to avoiding the generation of excessive by-products; the feed rate cannot exceed the processing capacity of the reactor, and abnormal fluidization caused by excessive feeding must also be avoided.

[0079] Step 33: Based on the variation range of the structural parameters and the initial sensitive parameter set, a single factor analysis is performed on the structural parameters and the initial sensitive parameter set.

[0080] In the embodiment of the present application, based on the variation range of the structural parameters and the initial sensitive parameter set, a single factor analysis is performed on the structural parameters and the initial sensitive parameter set, specifically including:

[0081] Within the variation range of the structural parameters and the initial sensitive parameter set, the value of one parameter is changed each time, while keeping other parameters unchanged. The digital twin model is run for simulation to obtain the influence of the changing parameters on the performance indicators of the pyrolysis reactor. The sensitivity is analyzed by calculating the rate of change of the performance indicators relative to the parameter changes, and the parameter-performance indicator change curve is drawn. When the slope of the curve meets the preset threshold, the parameter is a sensitive parameter.

[0082] Step 34, select several parameters that show high sensitivity in the single factor analysis, run different parameter combinations in the digital twin model based on orthogonal experiments, and record the performance indicator data corresponding to each group of parameter combinations.

[0083] Explanation: For example, for a combination of pyrolysis temperature of 500 °C, feed rate of 5 kg / h, and fluidizing gas velocity of 0.3 m / s, performance indicator data such as bio-oil yield of 40% and energy conversion efficiency of 70% were recorded.

[0084] Step 35, obtaining the main effect of each parameter and the interaction effect between parameters based on variance analysis.

[0085] Explanation: The variance ratio and significance probability of each parameter are calculated to determine whether the parameter has a significant effect on the performance index. If the variance ratio is less than the set significance level (such as 0.05), it means that the parameter has a significant effect on the performance index. At the same time, by observing the size of the interaction effect, we can understand the degree of influence of the interaction between parameters on the performance index. For example, if it is found that the interaction between pyrolysis temperature and feed rate has a significant effect on bio-oil yield, this means that when these two parameters change at the same time, the effect on bio-oil yield is not a simple superposition, but a complex relationship of synergy or antagonism.

[0086] Step 36, based on the sensitivity of each parameter in the univariate sensitivity analysis and the interaction effect between parameters in the multivariate analysis, the parameter combination with high sensitivity in the univariate analysis and significant main effect and interaction effect in the multivariate analysis is determined as the most sensitive parameter combination.

[0087] Step 205, optimize the most sensitive parameter combination, search for the best parameter combination in the parameter space, and use the digital twin model to quickly evaluate the performance of the pyrolysis reactor under each parameter combination to obtain the optimized parameters.

[0088] In the embodiment of the present application, the most sensitive parameter combination is optimized, and the optimal parameter combination is searched in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters. Please refer to Figure 4 , the specific contents include:

[0089] Step 41, based on the most sensitive parameter combination and the variation range of the corresponding parameters, the upper and lower boundaries of each parameter in the optimization process are set to obtain the range of each parameter in the most sensitive parameter combination.

[0090] Step 42, based on the range of each parameter in the most sensitive parameter combination, randomly generate an initial parameter combination as the starting individual of the population.

[0091] Explanation: For example, in a genetic algorithm, if there are three sensitive parameters (pyrolysis temperature, feed rate, and fluidizing gas flow rate) and the population size is 100, then 100 different combinations of values ​​for these three parameters are randomly generated. Each set of values ​​is within the respective set parameter range. These initial parameter combinations constitute the first generation population, which serves as the starting point for the optimization search.

[0092] Step 43: For each parameter combination in the population, a digital twin model is used to perform simulation evaluation, the parameter combination is substituted into the digital twin model, the digital twin model is run to simulate and obtain the corresponding pyrolysis reactor performance index data, and the fitness value corresponding to the parameter combination is calculated according to the preset objective function.

[0093] In the embodiment of the present application, the preset objective function is F=w1Y1+w2Y2+...+w n Y n , where Y1, Y2, Y n are the first optimization target, the second optimization target, and the nth optimization target, w1, w2, w n They are the weight coefficients of the first optimization target, the second optimization target, and the nth optimization target respectively.

[0094] Step 44, according to the fitness value of the individual, a preset selection strategy is used to select some individuals from the current population, and the individuals with higher fitness have a greater probability of being selected; a crossover operation is performed on the selected individuals according to the set crossover probability, and a new parameter combination is generated by exchanging or fusing their parameter values, and the parameters of some individuals are randomly mutated according to the mutation probability, so that the parameters are randomly changed within a certain range, a new population is generated, and an iteration is completed. When the termination condition is met, the iteration is stopped, the optimal parameter combination is obtained, and the optimal parameter combination is used as the optimization parameter.

[0095] Step 206, based on the optimization parameters, the actual pyrolysis reactor structure is modified or the operating parameters of the pyrolysis reactor are adjusted, and an actual operation experiment is performed on the pyrolysis reactor to obtain actual experimental results.

[0096] Step 207, compare the actual experimental results with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0098] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0099] Continue to refer Figure 5 The pyrolysis reactor optimization design system described in this embodiment includes:

[0100] An initial data acquisition module 501 is used to acquire initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor;

[0101] A digital twin model construction module 502 is used to construct a digital twin model of a pyrolysis reactor;

[0102] An initialization setting module 503 is used to initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor;

[0103] The most sensitive parameter combination acquisition module 504 is used to perform parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination;

[0104] The optimization parameter acquisition module 505 is used to optimize the most sensitive parameter combination and search for the optimal parameter combination in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters;

[0105] The actual experimental result acquisition module 506 is used to modify the actual pyrolysis reactor structure or adjust the operating parameters of the pyrolysis reactor based on the optimization parameters, perform actual operation experiments on the pyrolysis reactor, and obtain actual experimental results;

[0106] The optimization design module 507 is used to compare the actual experimental results with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

[0107] To solve the above technical problems, the present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0108] The computer device 6 includes a memory 6a, a processor 6b, and a network interface 6c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 with components 6a-6c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0109] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0110] The memory 6a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 6a can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 6a can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 6a can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 6a is generally used to store the operating system and various application software installed on the computer device 6, such as the program code of the pyrolysis reactor optimization design method, etc. In addition, the memory 6a can also be used to temporarily store various types of data that have been output or are to be output.

[0111] The processor 6b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 6b is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is used to run the program code or process data stored in the memory 6a, such as running the program code of the pyrolysis reactor optimization design method.

[0112] The network interface 6c may include a wireless network interface or a wired network interface. The network interface 6c is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0113] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a program of a pyrolysis reactor optimization design method, and the pyrolysis reactor optimization design can be executed by at least one processor so that the at least one processor performs the steps of the pyrolysis reactor optimization design method as described above.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0115] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for optimizing the design of a pyrolysis reactor, characterized in that: include: Acquiring initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor; Build a digital twin model of the pyrolysis reactor; Initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor; Conduct parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor to determine the most sensitive parameter combination; The most sensitive parameter combination is optimized and the best parameter combination is searched in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters. Based on the optimized parameters, the actual pyrolysis reactor structure is modified or the operating parameters of the pyrolysis reactor are adjusted, and actual operation experiments are conducted on the pyrolysis reactor to obtain actual experimental results; The actual experimental results are compared with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

2. The pyrolysis reactor optimization design method according to claim 1, characterized in that: After initializing the digital twin model based on the initial operating conditions of the pyrolysis reactor, it includes: Perform virtual simulation on the digital twin model to obtain virtual simulation results, and compare the virtual simulation results with the actual operating data of the pyrolysis reactor. If there is a large deviation, adjust and correct the digital twin model until the digital twin model reflects the actual operating status of the pyrolysis reactor.

3. The pyrolysis reactor optimization design method according to claim 1, characterized in that: The parameter sensitivity analysis of the structural parameters and operating parameters of the pyrolysis reactor was carried out to determine the most sensitive parameter combination, including: Obtain historical operation data of the pyrolysis reactor, calculate the correlation coefficient between each parameter in the historical operation data and the performance index of the pyrolysis reactor, when the correlation coefficient of a parameter exceeds a preset threshold, the parameter is a sensitive parameter; when the correlation coefficient of a parameter does not meet the preset threshold, the parameter is excluded to obtain an initial sensitive parameter set; Determine the variation range of structural parameters and initial sensitive parameter sets; Based on the variation range of structural parameters and initial sensitive parameter sets, single factor analysis was performed on the structural parameters and initial sensitive parameter sets; Select several parameters that show high sensitivity in single-factor analysis, run different parameter combinations in the digital twin model based on orthogonal experiments, and record the performance indicator data corresponding to each parameter combination; Obtain the main effects of each parameter and the interaction effects between parameters based on analysis of variance; Based on the sensitivity of each parameter in the univariate sensitivity analysis and the interaction effect between parameters in the multivariate analysis, the parameter combination with high sensitivity in the univariate analysis and significant main effect and interaction effect in the multivariate analysis was determined as the most sensitive parameter combination.

4. The pyrolysis reactor optimization design method according to claim 3, characterized in that: Based on the variation range of the structural parameters and the initial sensitive parameter set, a single factor analysis is performed on the structural parameters and the initial sensitive parameter set, including: Within the variation range of the structural parameters and the initial sensitive parameter set, the value of one parameter is changed each time, while keeping other parameters unchanged. The digital twin model is run for simulation to obtain the influence of the changing parameters on the performance indicators of the pyrolysis reactor. The sensitivity is analyzed by calculating the rate of change of the performance indicators relative to the parameter changes, and the parameter-performance indicator change curve is drawn. When the slope of the curve meets the preset threshold, the parameter is a sensitive parameter.

5. The pyrolysis reactor optimization design method according to claim 1, characterized in that: The most sensitive parameter combination is optimized and the optimal parameter combination is searched in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters, including: Based on the most sensitive parameter combination and the variation range of the corresponding parameters, the upper and lower boundaries of each parameter in the optimization process are set to obtain the range of each parameter in the most sensitive parameter combination; Based on the range of each parameter in the most sensitive parameter combination, the initial parameter combination is randomly generated as the starting individual of the population; For each parameter combination in the population, a digital twin model is used for simulation evaluation. The parameter combination is substituted into the digital twin model, and the digital twin model is run to simulate and obtain the corresponding pyrolysis reactor performance index data. The fitness value corresponding to the parameter combination is calculated according to the preset objective function. According to the fitness value of the individual, a preset selection strategy is used to select some individuals from the current population. The individuals with higher fitness have a greater probability of being selected. The selected individuals are crossovered according to the set crossover probability. New parameter combinations are generated by exchanging or fusing their parameter values. The parameters of some individuals are randomly mutated according to the mutation probability, so that the parameters change randomly within a certain range, a new population is generated, and an iteration is completed. When the termination condition is met, the iteration is stopped to obtain the optimal parameter combination, which is used as the optimization parameter.

6. The method for optimizing the design of a pyrolysis reactor according to claim 1, characterized in that: The default objective function is F = w1Y1+w2Y2+...+w n Y n , where Y1, Y2, Y n are the first optimization target, the second optimization target, and the nth optimization target, w1, w2, w n They are the weight coefficients of the first optimization target, the second optimization target, and the nth optimization target respectively.

7. A pyrolysis reactor optimization design system, used to implement the pyrolysis reactor optimization design method of claims 1-6, characterized in that: include: An initial data acquisition module, used to acquire initial data, wherein the initial data includes structural data of the pyrolysis reactor and operating parameter data of the pyrolysis reactor; A digital twin model building module, used to build a digital twin model of the pyrolysis reactor; An initialization setting module is used to initialize the digital twin model based on the initial operating conditions of the pyrolysis reactor; The most sensitive parameter combination acquisition module is used to perform parameter sensitivity analysis on the structural parameters and operating parameters of the pyrolysis reactor and determine the most sensitive parameter combination; The optimization parameter acquisition module is used to optimize the most sensitive parameter combination and search for the best parameter combination in the parameter space. The digital twin model is used to quickly evaluate the performance of the pyrolysis reactor under each parameter combination and obtain the optimized parameters. The actual experimental result acquisition module transforms the actual pyrolysis reactor structure or adjusts the operating parameters of the pyrolysis reactor based on the optimization parameters, conducts actual operation experiments on the pyrolysis reactor, and obtains actual experimental results; The optimization design module is used to compare the actual experimental results with the optimization results predicted by the digital twin model to obtain comparative analysis results. When the optimization target is met, the design of the pyrolysis reactor is completed; when the optimization target is not met, parameter sensitivity analysis and parameter optimization design are re-performed until the performance of the actual pyrolysis reactor reaches the expected optimization target.

8. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the pyrolysis reactor optimization design method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the steps of the method for optimizing the design of a pyrolysis reactor as claimed in any one of claims 1 to 6.

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