Molecular-level catalytic reforming simulation optimization method, device and equipment and storage medium

Abstract

The invention relates to the technical field of oil refining simulation, and provides a molecular-level catalytic reforming simulation optimization method, device and equipment and a storage medium, and the method comprises the following steps: obtaining raw material data and an optimization target; performing catalytic reaction simulation and optimization iteration on the catalytic reforming model by taking the raw material data as input until a product output by the catalytic reaction simulation meets an optimization target; generating a key-value pair which takes the optimization target as a key and takes a model parameter set corresponding to a product which is simulated and output by the catalytic reaction when the product meets the optimization target as a value, and writing the key-value pair into a model parameter set space; when a new optimization target is received, matching a target model parameter set most similar to the new optimization target from the model parameter set space; and performing catalytic reaction simulation and optimization iteration by taking the target model parameter set as an initialized model parameter value until a product output by catalytic reaction simulation meets a new optimization target. According to the embodiment of the invention, the optimization efficiency and universality of molecular-level catalytic reforming simulation optimization can be improved.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of oil refining simulation, and in particular to a molecular-level catalytic reforming simulation optimization method, device, equipment and storage medium. BACKGROUND

[0002] Using information technology to simulate and emulate reactions, cutting and other activities in the oil refining process can predict the reaction results without actual testing, and the simulation results can be used to guide the actual oil refining process. Therefore, it is of great significance to simulate and emulate the oil refining process.

[0003] In fact, oil refining process simulation and emulation technology has been widely used in various refining and chemical enterprises. Among them, when optimizing products or processes, establishing a linear programming model based on material balance and solving the model to obtain the optimal solution is also a common technical method. However, the existing technology fully uses various planning algorithms and also considers the optimization needs of different demand scenarios to some extent. However, the model basis used by the existing technology is generally from a mechanism model, which cannot control the model accuracy to the molecular level; or even if the model accuracy can be controlled to the molecular level, the optimization efficiency and scheme universality of the molecular level control also need to be improved. SUMMARY

[0004] The purpose of the embodiments of the present specification is to provide a molecular-level catalytic reforming simulation optimization method, device, equipment and storage medium to improve the optimization efficiency and universality of molecular-level catalytic reforming simulation optimization.

[0005] To achieve the above-mentioned purpose, on the one hand, the embodiments of the present specification provide a molecular-level catalytic reforming simulation optimization method, comprising:

[0006] Obtaining raw material data and optimization targets; the raw material data includes structure-oriented lumped data and molecular mass fraction data of the raw material;

[0007] Performing catalytic reaction simulation and optimization iteration on the catalytic reforming model with the raw material data as input until the products output by the catalytic reaction simulation meet the optimization targets;

[0008] Generating a key-value pair with the optimization target as the key and the model parameter set corresponding to the time when the products output by the catalytic reaction simulation meet the optimization target as the value;

[0009] Writing the key-value pair into a model parameter set space;

[0010] When a new optimization target is received, matching the target model parameter set most similar to the new optimization target from the model parameter set space;

[0011] The target model parameter set is used to initialize the catalytic reforming model, and the catalytic reaction simulation and optimization iteration of the initialized catalytic reforming model are performed until the product output by the catalytic reaction simulation meets the new optimization target.

[0012] In the molecular level catalytic reforming simulation optimization method of the embodiments of the present specification, the optimization target includes the property parameter value of one or more dimensions of the output product.

[0013] In the molecular level catalytic reforming simulation optimization method of the embodiments of the present specification, the optimization target includes the property parameter value of one or more dimensions of the output product.

[0014] In the molecular level catalytic reforming simulation optimization method of the embodiments of the present specification, the model parameter set is a model parameter vector.

[0015] In the molecular level catalytic reforming simulation optimization method of the embodiments of the present specification, matching the target model parameter set most similar to the new optimization target from the model parameter set space includes:

[0016] Calculating the similarity of the new optimization target with the key in each key-value pair in the model parameter set space, respectively;

[0017] Identifying the model parameter set in the key-value pair corresponding to the minimum similarity as the target model parameter set.

[0018] In the molecular level catalytic reforming simulation optimization method of the embodiments of the present specification, matching the target model parameter set most similar to the new optimization target from the model parameter set space includes:

[0019] Calculating the difference between the new optimization target and the key in each key-value pair in the model parameter set space, respectively;

[0020] Identifying the model parameter set in the key-value pair corresponding to the minimum difference as the target model parameter set.

[0021] On the other hand, the embodiments of the present specification also provide a molecular level catalytic reforming simulation optimization device, comprising:

[0022] A data acquisition module is configured to acquire raw material data and an optimization target; the raw material data includes structure-oriented lumped data and molecular mass fraction data of the raw material;

[0023] A first simulation optimization module is configured to perform catalytic reaction simulation and optimization iteration on a catalytic reforming model with the raw material data as input until the product output by the catalytic reaction simulation meets the optimization target;

[0024] The key-value pair generation module is configured to generate a key-value pair with the optimization target as the key and a model parameter set corresponding to a product of the catalytic reaction simulation output satisfying the optimization target as the value;

[0025] The key-value pair writing module is configured to write the key-value pair into a model parameter set space;

[0026] The model parameter set matching module is configured to match a target model parameter set most similar to a new optimization target from the model parameter set space when the new optimization target is received;

[0027] The second simulation optimization module is configured to initialize the catalytic reforming model by using the target model parameter set, and perform catalytic reaction simulation and optimization iteration on the initialized catalytic reforming model until a product of the catalytic reaction simulation output satisfies the new optimization target.

[0028] In another aspect, the embodiments of the present specification also provide a computer device, which comprises a memory, a processor, and a computer program stored in the memory. When the computer program is run by the processor, instructions of the above method are executed.

[0029] In another aspect, the embodiments of the present specification also provide a computer storage medium, which stores a computer program. When the computer program is run by a processor of a computer device, instructions of the above method are executed.

[0030] In another aspect, the embodiments of the present specification also provide a computer program product, which comprises a computer program. When the computer program is run by a processor of a computer device, instructions of the above method are executed.

[0031] From the technical solutions provided by the above embodiments of the present specification, it can be seen that in the embodiments of the present specification, the optimization result of each molecular level catalytic reforming simulation optimization is keyed to the corresponding optimization target, and the model parameter set corresponding to the product output by the catalytic reaction simulation satisfying the optimization target is taken as the value of the key-value pair, and the key-value pair is written into the model parameter set space; each time a new optimization target is received, before optimization, the target model parameter set most similar to the new optimization target is matched from the model parameter set space, and then the catalytic reforming model is initialized using the target model parameter set, and the catalytic reforming model after initialization is subjected to catalytic reaction simulation and optimization iteration until the product output by the catalytic reaction simulation satisfies the new optimization target; since the key corresponding to the target model parameter set is closest to the new optimization target, the catalytic reforming model can be initialized using the target model parameter set, so that the model optimization can start from a relatively optimal model parameter set, rather than starting from zero each time, thereby greatly improving the optimization efficiency of the molecular level catalytic reforming simulation optimization; Furthermore, each time a new optimization target is received, the target model parameter set most similar to the new optimization target can be matched from the model parameter set space, and the catalytic reforming model is initialized using the target model parameter set, so that different initial models can be conveniently provided for different optimization targets, meeting different optimization demand scenarios, and thereby improving the versatility of the molecular level catalytic reforming simulation optimization. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, a brief introduction to the drawings needed to be used in the embodiments or prior art description will be given below. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can also be obtained by those skilled in the art without creative labor. In the drawings:

[0033] Figure 1 A schematic diagram of a molecular level catalytic reforming simulation optimization system in some embodiments of the present specification is shown;

[0034] Figure 2 A flowchart of a molecular level catalytic reforming simulation optimization method in some embodiments of the present specification is shown;

[0035] Figure 3 A flowchart of a method for matching a target model parameter set most similar to a new optimization target from a model parameter set space is shown; Figure 2 A flowchart of a method for matching a target model parameter set most similar to a new optimization target from a model parameter set space is shown;

[0036] Figure 4 A flowchart of a method for matching a target model parameter set most similar to a new optimization target from a model parameter set space is shown; Figure 2 A flowchart of a method for matching a target model parameter set most similar to a new optimization target from a model parameter set space is shown;

[0037] Figure 5 A schematic diagram of a model parameter set space in an example embodiment of the present specification is shown;

[0038] Figure 6 A schematic diagram of a petroleum molecule structure based on structure-oriented lumped representation in an example embodiment of the present specification is shown;

[0039] Figure 7 A structural block diagram of a molecular level catalytic reforming simulation optimization device in some embodiments of the present specification is shown;

[0040] Figure 8 A structural block diagram of a computer device in some embodiments of the present specification is shown.

[0041]

Explanation of reference numerals

[0042] 10, client;

[0043] 20, server;

[0044] 71, data acquisition module;

[0045] 72, first simulation optimization module;

[0046] 73, key-value pair generation module;

[0047] 74, key-value pair writing module;

[0048] 75, model parameter set matching module;

[0049] 76, second simulation optimization module;

[0050] 802, computer device;

[0051] 804, processor;

[0052] 806, memory;

[0053] 808, driving mechanism;

[0054] 810, input / output interface;

[0055] 812, input device;

[0056] 814, output device;

[0057] 816, presentation device;

[0058] 818, graphical user interface;

[0059] 820, network interface;

[0060] 822, communication link;

[0061] 824. Communication bus. Detailed Implementation

[0062] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0063] Figure 1 The diagram illustrates a schematic of a molecular-level catalytic reforming simulation and optimization system in some embodiments of this specification. The system includes a client 10 and a server 20. The client 10 can provide optimization targets and raw material data to the server 20; the server 20 can acquire the raw material data and optimization targets; the raw material data includes structure-guided aggregate data and molecular weight fraction data of the raw materials; using the raw material data as input, the catalytic reforming model is simulated and iterated for optimization until the product output by the catalytic reaction simulation satisfies the optimization target; key-value pairs are generated, with the optimization target as the bond and the model parameter set corresponding to the product output by the catalytic reaction simulation satisfying the optimization target as the value; the key-value pairs are written into the model parameter set space; when a new optimization target is received, the target model parameter set most similar to the new optimization target is matched from the model parameter set space; the catalytic reforming model is initialized using the target model parameter set, and the initialized catalytic reforming model is simulated and iterated for optimization until the product output by the catalytic reaction simulation satisfies the new optimization target. The embodiments of this specification can improve the optimization efficiency and versatility of molecular-level catalytic reforming simulation and optimization.

[0064] In some embodiments of this specification, the client 10 can be a desktop computer, tablet computer, laptop computer, etc. Of course, the client 10 is not limited to the aforementioned physical electronic devices; it can also be software running on such electronic devices. The server 20 can be an electronic device with computing and network interaction functions; it can also be software running on such electronic devices that provides business logic for data processing and network interaction.

[0065] In addition, it should be noted that, Figure 1 The example shown is merely one application environment provided in this specification. In actual applications, there can be multiple clients 10 and multiple servers 20. This specification does not impose any restrictions.

[0066] In the embodiments of this specification, as a refining process, catalytic reforming refers to the process of rearranging the molecular structure of hydrocarbons in gasoline fractions into a new molecular structure under specific conditions.

[0067] This specification provides a molecular-level catalytic reforming simulation and optimization method, which can be applied to the aforementioned server-side implementation. (Refer to...) Figure 2 As shown in some embodiments of this specification, the molecular-level catalytic reforming simulation optimization method may include the following steps:

[0068] Step 201: Obtain raw material data and optimization objectives; the raw material data includes structure-guided aggregate data and molecular weight fraction data of the raw materials.

[0069] Step 202: Using the raw material data as input, perform catalytic reaction simulation and optimization iteration on the catalytic reforming model until the product output by the catalytic reaction simulation meets the optimization objective.

[0070] Step 203: Generate key-value pairs with the optimization objective as the key and the model parameter set corresponding to the product output from the catalytic reaction simulation satisfying the optimization objective as the value.

[0071] Step 204: Write the key-value pairs into the model parameter set space.

[0072] Step 205: When a new optimization objective is received, match the target model parameter set that is most similar to the new optimization objective from the model parameter set space.

[0073] Step 206: Initialize the catalytic reforming model using the target model parameter set, and perform catalytic reaction simulation and optimization iteration on the initialized catalytic reforming model until the product output by the catalytic reaction simulation meets the new optimization objective.

[0074] In the embodiments of this specification, the optimization results of each molecular-level catalytic reforming simulation optimization are key-value pairs, with the corresponding optimization target as the key and the model parameter set corresponding to the product output of the catalytic reaction simulation satisfying the optimization target as the value. These key-value pairs are written into the model parameter set space. Subsequently, each time a new optimization target is received, before optimization, the target model parameter set most similar to the new optimization target is matched from the model parameter set space. Then, the catalytic reforming model is initialized using the target model parameter set, and the initialized catalytic reforming model is subjected to catalytic reaction simulation and optimization iterations until the product output of the catalytic reaction simulation satisfies the new optimization target. Because the target model... The parameter set corresponding to the key is closest to the new optimization objective. By initializing the catalytic reforming model with the target model parameter set, the model optimization can start from a relatively optimal model parameter set instead of starting from zero each time, thus significantly improving the optimization efficiency of molecular-level catalytic reforming simulation optimization. Moreover, when a new optimization objective is received, the target model parameter set that is most similar to the new optimization objective can be matched from the model parameter set space, and the catalytic reforming model can be initialized using the target model parameter set. This allows for the convenient provision of different initial models for different optimization objectives, meeting different optimization needs and improving the versatility of molecular-level catalytic reforming simulation optimization.

[0075] In some embodiments of this specification, the feedstock data refers to data used to characterize the feedstock input for catalytic reforming. This data includes molecular-level structure-oriented lump (SOL) data and molecular weight fraction data. SOL represents petroleum molecules based on specific structural features or functional groups, organizing these groups into a vector. The elements of the vector represent different petroleum molecule structures, and the number of elements indicates the quantity of a specific structural group in the molecule. The molecular weight fraction refers to the ratio of the relative mass of a particular atom in the molecule to the relative mass of the entire molecule. Therefore, the feedstock data, including the structure-oriented lump data and molecular weight fraction data, can, to a certain extent, reflect the catalytic reforming simulation optimization of the embodiments of this application, representing a molecular-level catalytic reforming simulation optimization.

[0076] Specifically, Structure-Oriented Ledger Analysis (SOL) can decompose the complex molecular composition of petroleum and its derivatives into several basic structural units, which represent different types of chemical bonds, functional groups, or molecular fragments. These basic structural units allow for a more accurate description of the chemical composition and properties of molecules. For example, in the analysis of residual oil, the structure-oriented lumped method designed 21 structural units including hydrocarbon structures, heteroatom structures, and heavy metal structures, constructing structure vectors representing 2791 typical molecules across 55 categories that constitute the composition of residual oil molecules. Therefore, although SOL cannot determine the specific arrangement of molecules, it remains significant for predicting the properties of petroleum products and optimizing refining processes.

[0077] For example, Figure 5 The image exemplifies a petroleum molecule represented by SOL in one embodiment of this specification. Figure 5 In this context, the definitions of various functional groups based on SOL representation are as follows:

[0078] A6: A six-carbon aromatic ring, a structural unit that can exist independently;

[0079] A4 and A2: four-carbon and two-carbon aromatic rings, structural increments;

[0080] N6, N5: six-carbon and five-carbon cycloalkanes;

[0081] N4, N3, N2, N1: alicyclic structures with four, three, two, and one carbon atom;

[0082] R: The total number of carbon atoms contained in all alkyl structures attached to the ring structure, or the absence of ring structures.

[0083] The number of carbon atoms in an aliphatic molecule;

[0084] br: The number of branch nodes on a side-chain alkyl group, a straight-chain alkyl group, or an olefin;

[0085] me: The number of methyl groups in an alkyl structure that are directly attached to carbon atoms in an aromatic or aliphatic ring;

[0086] IH: Introduces hydrogen-related structural increments to describe the saturation of molecules (except for aromatic molecules).

[0087] AA: Biphenyl bridging structure between any two unstructured incremental rings (A6, N6 or N5).

[0088] NS, NN, NO: Sulfur, nitrogen, and oxygen atoms located in an aliphatic ring or chain and bonded to two carbon atoms. (Replace -CH) 2- )

[0089] RS, RN, RO: An S atom, -NH- group, or O atom is inserted between a carbon atom and a hydrogen atom.

[0090] AN: In aromatic rings, a nitrogen group is used to replace a carbon atom, such as in pyridine and quinoline;

[0091] Ko: replaces -CH2- or -CH3 to form ketones or aldehydes.

[0092] Ni and V: appear in porphyrin molecules.

[0093] Specifically, in molecular characterization, a 24-dimensional vector array is generated by arranging the number of functional groups in the molecule in the order from A6 to V. For example, the SOL formula for hydrogen sulfide (H2S) is:

[0094] [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0]

[0095] In some embodiments of this specification, the optimization objective refers to, using raw materials as input, simulating molecular-level catalytic reforming to ensure that one or more physical properties of the output product (i.e., the product output after catalytic reforming) meet the expected target. In some embodiments of this specification, the optimization objective may include, but is not limited to, the yield, octane number, and product value of the output product.

[0096] In some embodiments of this specification, obtaining raw material data may refer to receiving raw material data stored in formats such as Excel and CSV, uploaded by a user through a client. Obtaining the optimization target may refer to the normalized physical property parameter values ​​of one or more dimensions of the output product input by the user. For example, in an exemplary embodiment of this specification, the optimization target may be a gasoline yield of 80% from catalytic reforming. For example, in another exemplary embodiment of this specification, the optimization target may be a gasoline yield of 86% from catalytic reforming (i.e., a gasoline yield of not less than 86%) and an octane number of 90 (i.e., a gasoline octane number of not less than 90).

[0097] In some embodiments of this specification, the catalytic reforming model is a pre-built numerical simulation model of a catalytic reforming device (or equipment). In molecular-level catalytic reforming simulation optimization scenarios, the catalytic reforming model may include a reflection simulation model and a cleavage simulation model.

[0098] The simulation model is used to simulate the reforming chemical reaction process in catalytic reforming. The simulation model includes a set of reaction rules, a set of reaction constants, reaction rule configuration items (i.e., whether to enable or disable reaction rules), and constant configuration items (i.e., the constant values ​​of reaction constants). The reaction rules in the reaction rule set are the reforming reaction rules in catalytic reforming (e.g., cycloalkane dehydrogenation, alkane dehydrogenation cyclization, isomerization, and hydrocracking).

[0099] A fractionation simulation model is used to simulate the process of separating intermediate products into different final products during catalytic reforming. The fractionation simulation model includes a set of fractionation rules and a fractionation temperature. The fractionation rules in the set of rules refer to the rules that separate intermediate products into different final products. In some embodiments of this specification, the fractionation rules can be fractionation rules. For example, in an exemplary embodiment of this specification, under conditions of heating, hydrogen pressure, and catalyst addition, a light gasoline fraction (or naphtha) obtained from crude oil distillation is converted into high-octane gasoline (reformed gasoline) rich in aromatics.

[0100] Given that there are already some catalytic reforming models, and the embodiments in this specification do not involve improvements to catalytic reforming models, the catalytic reforming model section will not be described in detail.

[0101] In some embodiments of this specification, catalytic reaction simulation refers to the process of performing simulation calculations using an initialized catalytic reforming model; optimization iteration refers to statistical analysis of the results after each simulation calculation, comparing the specified physical properties of the specified product with the optimization target, and calculating the error. When the error deviation is greater than a preset deviation, catalytic reaction simulation and optimization iteration continue until the error deviation is lower than the preset value, thus completing the molecular-level catalytic reforming simulation optimization for the optimization target.

[0102] In some embodiments of this specification, the model parameter set space stores key-value pairs in a vector space model. That is, key-value pairs where the optimization objective is the key and the model parameter set corresponding to the product output from the catalytic reaction simulation satisfies the optimization objective are represented by vectors. For example, if the optimization objective is the octane number, yield, and output product, corresponding to k1, k2, k3 respectively, the optimized model parameter set is λ1, λ2, ..., λ n (The model parameter set is a model parameter vector), then the key-value pairs can be represented as: (k1,k2,k3:λ1,λ2,…,λ n ).

[0103] Therefore, the dimension of the model parameter set space varies depending on the optimization objective. For example, in Figure 6 In the exemplary embodiment shown, the optimization objectives include three dimensions of indicators: octane number, yield, and output product. Therefore, the model parameter set space is a three-dimensional model parameter set space. Each dimension of the model parameter set space is distributed in the range of 0 to 1, with the corresponding value ranging from 0 to the maximum value of 1 (i.e., normalized values). Figure 6 In this context, each square represents a key-value pair where the key is a specific optimization objective, and the value is the set of model parameters corresponding to the product output of the catalytic reaction simulation that satisfies that optimization objective. For example, for Figure 6The squares marked with 1 / 1 / 1 represent the set of model parameters obtained when the optimization objectives are yield 1, product value 1, and octane number 1. Clearly, corresponding key-value pairs can also be generated when the optimization objective is any one of octane number, product value, and yield, or any two of these objectives. For example, for... Figure 6 The squares marked with 0 / 1 / 0 indicate that the optimization objectives are a yield of 0, a product value of 1, and an octane number of 0. That is, only the product value of the output product is required to be 1, while there are no requirements (or restrictions) on the yield and octane number of the output product.

[0104] In some embodiments of this specification, the model parameter set space can be empty initially. After optimization for each optimization objective is completed, the corresponding optimization result (a key-value pair) can be written into the model parameter set space. Thus, as the number of optimizations increases, the model parameter set space becomes increasingly rich. In other words, the model parameter set space combines a large number of optimization results in the form of key-value pairs, forming a data structure that is easy to search. Subsequently, the most similar model parameter set can be quickly located in the model parameter set space based on a new optimization objective.

[0105] Based on this, whenever a new optimization objective is received, the target model parameter set most similar to the new optimization objective can be matched from the model parameter set space. Then, the catalytic reforming model is initialized using the target model parameter set, and the initialized catalytic reforming model is subjected to catalytic reaction simulation and optimization iteration until the product output by the catalytic reaction simulation satisfies the new optimization objective. In this way, the new optimization objective can start directly from a better model parameter base, without having to start from zero (or default value), thereby improving the optimization efficiency of molecular-level catalytic reforming simulation.

[0106] refer to Figure 3 As shown in some embodiments of this specification, matching the target model parameter set most similar to the new optimization objective from the model parameter set space may include the following steps:

[0107] Step 301: Calculate the similarity between the new optimization objective and the keys in each key-value pair in the model parameter set space.

[0108] In some embodiments of this specification, the new optimization objective (especially a multi-dimensional optimization objective) is represented by a vector, and the key in each key-value pair in the model parameter set space is also represented by a vector. Therefore, the similarity or difference between the new optimization objective and the key in each key-value pair in the model parameter set space can be evaluated by calculating similarity. This is particularly suitable for scenarios where the optimization objective is multi-dimensional.

[0109] In some embodiments of this specification, similarity calculation may employ any suitable similarity algorithm, and this specification does not limit the embodiments thereto.

[0110] Step 302: Identify the model parameter set in the key-value pair corresponding to the one with the lowest similarity as the target model parameter set.

[0111] refer to Figure 4 As shown in some other embodiments of this specification, matching the target model parameter set most similar to the new optimization objective from the model parameter set space may include the following steps:

[0112] Step 401: Calculate the difference between the new optimization objective and the key in each key-value pair in the model parameter set space.

[0113] In some embodiments of this specification, when the new optimization objective specifies only one physical property parameter (e.g., requiring a reformed gasoline yield of 50%), in order to improve efficiency, the difference between the yield of 0.5 and the yield in each key-value pair in the model parameter set space can also be directly calculated.

[0114] Step 402: Identify the model parameter set in the key-value pair corresponding to the one with the smallest difference as the target model parameter set.

[0115] Although the process described above includes multiple operations that occur in a specific order, it should be clearly understood that these processes may include more or fewer operations, which may be executed sequentially or in parallel (e.g., using parallel processors or a multithreaded environment).

[0116] Corresponding to the molecular-level catalytic reforming simulation and optimization method described above, this specification also provides a molecular-level catalytic reforming simulation and optimization device, which can be configured on the aforementioned server. (Refer to...) Figure 7 As shown in some embodiments of this specification, the molecular-level catalytic reforming simulation and optimization apparatus may include:

[0117] Data acquisition module 71 is used to acquire raw material data and optimization targets; the raw material data includes structure-guided aggregate data and molecular weight fraction data of the raw materials;

[0118] The first simulation optimization module 72 is used to simulate and optimize the catalytic reforming model using the raw material data as input, until the product output by the catalytic reaction simulation meets the optimization target.

[0119] The key-value pair generation module 73 is used to generate key-value pairs with the optimization objective as the key and the model parameter set corresponding to the product output of the catalytic reaction simulation satisfying the optimization objective as the value.

[0120] The key-value pair writing module 74 is used to write the key-value pairs into the model parameter set space;

[0121] The model parameter set matching module 75 is used to match the target model parameter set that is most similar to the new optimization objective from the model parameter set space when a new optimization objective is received.

[0122] The second simulation optimization module 76 is used to initialize the catalytic reforming model using the target model parameter set, and to perform catalytic reaction simulation and optimization iteration on the initialized catalytic reforming model until the product output by the catalytic reaction simulation meets the new optimization objective.

[0123] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in the embodiments of this specification are all information and data authorized and agreed upon by the user and fully authorized by all parties. That is, the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0125] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0126] Embodiments of this specification also provide a computer device. For example... Figure 8As shown, in some embodiments of this specification, the computer device 802 may include one or more processors 804, such as one or more central processing units (CPUs) or graphics processing units (GPUs), each of which may implement one or more hardware threads. The computer device 802 may also include any memory 806 for storing information of any kind, such as code, settings, data, etc. In one specific embodiment, a computer program is stored on the memory 806 and can run on the processor 804. When the processor 804 executes the program, it can perform the instructions of the molecular-level catalytic reforming simulation optimization method described in any of the above embodiments. Without limitation, for example, the memory 806 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 802. In one case, when the processor 804 executes associated instructions stored in any memory or combination of memories, the computer device 802 can perform any operation of the associated instructions. The computer device 802 also includes one or more drive mechanisms 808 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0127] Computer device 802 may also include an input / output interface 810 (I / O) for receiving various inputs (via input device 812) and providing various outputs (via output device 814). A specific output mechanism may include a presentation device 816 and an associated graphical user interface 818 (GUI). In other embodiments, the input / output interface 810 (I / O), input device 812, and output device 814 may be omitted, and the device may function solely as a computer device within a network. Computer device 802 may also include one or more network interfaces 820 for exchanging data with other devices via one or more communication links 822. One or more communication buses 824 couple the components described above together.

[0128] Communication link 822 may be implemented in any manner, such as via a local area network (LAN), a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 822 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), computer-readable storage media, and computer program products according to some embodiments of this specification. 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 processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create a mechanism for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processor 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.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processor, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device 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.

[0132] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0133] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0134] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by computer equipment. As defined in this specification, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0135] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented 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.

[0136] The embodiments described in this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processors connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0137] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0138] 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 describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0139] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this specification. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0140] The above description is merely an embodiment of this application and is not intended to limit the scope of 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 principles of this application should be included within the scope of the claims of this application.

Claims

1. A molecular-level catalytic reforming simulation and optimization method, characterized in that, include: Acquire raw material data and optimization objectives; the raw material data includes structure-guided aggregate data and molecular weight fraction data of the raw materials. Using the raw material data as input, the catalytic reforming model is simulated and iterated for catalytic reaction optimization until the product output by the catalytic reaction simulation meets the optimization objective. Generate key-value pairs with the optimization objective as the bond and the model parameter set corresponding to the product output of the catalytic reaction simulation satisfying the optimization objective as the value; Write the key-value pairs into the model parameter set space; When a new optimization objective is received, the target model parameter set that is most similar to the new optimization objective is matched from the model parameter set space; The catalytic reforming model is initialized using the target model parameter set, and the initialized catalytic reforming model is subjected to catalytic reaction simulation and optimization iteration until the product output by the catalytic reaction simulation satisfies the new optimization objective.

2. The molecular-level catalytic reforming simulation and optimization method as described in claim 1, characterized in that, The optimization objective includes the physical property parameter values ​​of one or more dimensions of the output product.

3. The molecular-level catalytic reforming simulation and optimization method as described in claim 1, characterized in that, The optimization objective includes normalized physical property parameter values ​​for one or more dimensions of the output product.

4. The molecular-level catalytic reforming simulation and optimization method as described in claim 1, characterized in that, The model parameter set is a model parameter vector.

5. The molecular-level catalytic reforming simulation and optimization method as described in claim 1, characterized in that, Matching the target model parameter set from the model parameter set space that is most similar to the new optimization objective includes: Calculate the similarity between the new optimization objective and the keys in each key-value pair in the model parameter set space; The model parameter set in the key-value pair corresponding to the one with the lowest similarity is identified as the target model parameter set.

6. The molecular-level catalytic reforming simulation and optimization method as described in claim 1, characterized in that, Matching the target model parameter set from the model parameter set space that is most similar to the new optimization objective includes: Calculate the difference between the new optimization objective and the key in each key-value pair in the model parameter set space; The model parameter set in the key-value pair corresponding to the smallest difference is identified as the target model parameter set.

7. A molecular-level catalytic reforming simulation and optimization device, characterized in that, include: The data acquisition module is used to acquire raw material data and optimization targets; the raw material data includes structure-guided aggregate data and molecular weight fraction data of the raw materials. The first simulation optimization module is used to simulate and optimize the catalytic reforming model using the raw material data as input, until the product output by the catalytic reaction simulation meets the optimization target. The key-value pair generation module is used to generate key-value pairs with the optimization objective as the key and the model parameter set corresponding to the product output of the catalytic reaction simulation satisfying the optimization objective as the value. The key-value pair writing module is used to write the key-value pairs into the model parameter set space; The model parameter set matching module is used to match the target model parameter set that is most similar to the new optimization objective from the model parameter set space when a new optimization objective is received. The second simulation optimization module is used to initialize the catalytic reforming model using the target model parameter set, and to perform catalytic reaction simulation and optimization iteration on the initialized catalytic reforming model until the product output by the catalytic reaction simulation meets the new optimization objective.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor of the computer device, it executes the instructions of the method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when run by the processor of a computer device, executes instructions according to any one of claims 1-6.

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