Component flow prediction method and device based on quantum chemical calculation and multi-scale modeling coupling

Abstract

The invention provides a component flow prediction method and device based on quantum chemistry calculation and multi-scale modeling coupling. Comprising the following steps: determining a product type of a Fischer-Tropsch synthesis unit, constructing a lumped reaction equation according to the product type, and determining an activation energy barrier of each product and adsorption energy of reactants in an element reaction according to the lumped reaction equation on the basis of a DFT (Discrete Fourier Transform) calculation framework; the method comprises the following steps: acquiring working condition real-time data and offline test analysis data of a Fischer-Tropsch synthesis unit to construct a reactor model data set, constructing a reactor inactivation model, and determining the current activity of a reactor based on the reactor inactivation model; and constructing a Fischer-Tropsch synthesis reactor lumped kinetic model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and predicting the component flow of each product according to the Fischer-Tropsch synthesis reactor lumped kinetic model. The problem that in the prior art, when the component flow of the Fischer-Tropsch synthesis product is predicted, the prediction accuracy is low is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of component flow prediction, in particular to a component flow prediction method and device based on coupling of quantum chemical calculation and multi-scale modeling, a computer readable storage medium and an electronic device. BACKGROUND

[0002] Fischer-Tropsch synthesis (FT) technology, as a key core technology in coal-to-oil, natural gas-to-oil and biomass-to-oil energy conversion processes, plays a crucial role in the field of modern energy chemical industry. Its core principle is to convert synthesis gas (a gas mixture of hydrogen, carbon monoxide and carbon dioxide) into liquid hydrocarbons and various high-value chemicals under the action of catalysts.

[0003] In today's industrial production, simulation technology is used as the core driving force to accurately predict the product flow of Fischer-Tropsch synthesis, which has become a valuable technical means. This prediction method can provide scientific and reliable guidance for on-site industrial operation, help optimize production processes, improve production efficiency and reduce production costs, and thus promote the energy conversion industry to develop in a more efficient and sustainable direction.

[0004] In the field of chemical engineering, when studying Fischer-Tropsch synthesis reactions based on density functional theory (DFT), energy data of various elementary reactions in the Fischer-Tropsch synthesis process on the surface of iron-based catalysts can be accurately obtained through the theory. However, the existing technology has the problem of low prediction accuracy when predicting the component flow of Fischer-Tropsch synthesis products. SUMMARY

[0005] The main purpose of the present application is to provide a component flow prediction method and device based on coupling of quantum chemical calculation and multi-scale modeling, a computer readable storage medium and an electronic device, to at least solve the problem of low prediction accuracy when predicting the component flow of Fischer-Tropsch synthesis products in the existing technology.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling is provided, including: determining the product type of the Fischer-Tropsch synthesis unit, constructing a lumped reaction equation according to the product type, and determining the activation energy barrier of each product in the elementary reaction according to the lumped reaction equation based on the DFT calculation framework, and determining the adsorption energy of the reactant; obtaining the real-time operating condition data and offline laboratory analysis data in the on-site DCS of the Fischer-Tropsch synthesis unit, constructing a reactor model data set according to the real-time operating condition data and the offline laboratory analysis data, and constructing a reactor deactivation model, and determining the current activity of the reactor based on the reactor deactivation model; constructing a lumped kinetic model of the Fischer-Tropsch synthesis reactor according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and predicting the component flow rate of each product of the Fischer-Tropsch synthesis unit according to the lumped kinetic model of the Fischer-Tropsch synthesis reactor, wherein the component flow rate is at least used for the control and optimization of the chemical reaction process.

[0007] Optionally, a lumped kinetic model of a Fischer-Tropsch synthesis reactor is constructed based on the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, including: determining the rate-determining step in the reaction process of the lumped reaction equation based on the activation energy barrier and the adsorption energy, and determining the activation energy of each of the products based on the rate-determining step and the activation energy barrier; and constructing a lumped kinetic model of a Fischer-Tropsch synthesis reactor based on the reactor model data set, the current activity of the reactor, the lumped reaction equation and the activation energy.

[0008] Optionally, constructing a Fischer-Tropsch synthesis reactor lumped kinetic model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy includes: according to a first formula: The lumped kinetic model of the Fischer-Tropsch synthesis reactor is constructed, wherein r i is the component flow rate of product i, A i is the pre-exponential factor of product i, activity is the current activity of the reactor, e is a constant, E a is the activation energy of product i, R is the gas constant, T is the temperature, C1 is the concentration of the first reactant, and C2 is the concentration of the second reactant.

[0009] Optionally, constructing a reactor deactivation model includes: according to the second formula: Construct a reactor deactivation model, where activity is the current activity of the reactor, A s is the minimum activity of the catalyst, e is a constant, k dwherein kcat is the catalyst decay factor, t is the catalyst reaction time.

[0010] Optionally, the reactor model dataset is constructed according to the working condition real-time data and the offline test analysis data, including: identifying dynamic fluctuation characteristics of the working condition real-time data by a sliding window standard deviation method, and screening and processing the working condition real-time data according to the dynamic fluctuation characteristics by a box plot outlier detection technique to obtain preprocessed real-time data; and constructing the reactor model dataset based on the preprocessed real-time data and the offline test analysis data.

[0011] Optionally, the reactor model dataset is constructed based on the preprocessed real-time data and the offline test analysis data, including: performing mean filtering processing on the preprocessed real-time data to obtain filtered real-time data, and performing continuity reconstruction processing on the filtered real-time data by a spline interpolation algorithm to obtain target working condition real-time data; and performing time sequence alignment processing on the target working condition real-time data and the offline test analysis data by a dynamic time warping algorithm to obtain the reactor model dataset.

[0012] Optionally, the activation energy barrier of each product in the elementary reaction is determined according to the lumped reaction equation, and the adsorption energy of the reactant is determined, including: determining the adsorption energy E ads of the reactant according to a third formula: E ads = E complex - (E surface + E adsorbate), wherein E complex is the total energy of the reactant as an adsorbate and the catalyst forming a complex, E surface is the surface state energy of the catalyst without the adsorbate, and E adsorbate is the energy of the adsorbate in a gas or liquid state; determining the difference between the maximum energy of the reactant when the bond is broken and the energy of the reactant when the reactant exists on the surface of the catalyst, and taking the difference as the activation energy barrier of the product.

[0013] According to another aspect of the present application, there is provided a component flow rate prediction apparatus based on coupling quantum chemistry calculation and multi-scale modeling, comprising: a determination unit configured to determine a product type of a Fischer-Tropsch synthesis unit, construct a lumped reaction equation according to the product type, determine an activation energy barrier of each product in a base reaction and an adsorption energy of a reactant based on a DFT calculation framework; a construction unit configured to obtain operating condition real-time data and offline test analysis data in a field DCS of the Fischer-Tropsch synthesis unit, construct a reactor model data set according to the operating condition real-time data and the offline test analysis data, and construct a reactor deactivation model to determine a current activity of the reactor based on the reactor deactivation model; and a prediction unit configured to construct a Fischer-Tropsch synthesis reactor lumped kinetics model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and predict a component flow rate of each of the products of the Fischer-Tropsch synthesis unit according to the Fischer-Tropsch synthesis reactor lumped kinetics model, wherein the component flow rate is used at least for control and optimization of a chemical reaction process.

[0014] According to still another aspect of the present application, there is provided a computer-readable storage medium, the computer-readable storage medium including a stored program, wherein the computer-readable storage medium is caused to perform any one of the component flow rate prediction methods based on coupling quantum chemistry calculation and multi-scale modeling when the program is executed.

[0015] According to yet another aspect of the present application, there is provided an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a program for performing any one of the component flow rate prediction methods based on coupling quantum chemistry calculation and multi-scale modeling.

[0016] The technical scheme of the application determines the product type of the Fischer-Tropsch synthesis unit, constructs a lumped reaction equation according to the product type, determines the activation energy barrier of each product in the elementary reaction and the adsorption energy of the reactant based on the DFT calculation framework, obtains the real-time data and offline test analysis data of the working condition of the Fischer-Tropsch synthesis unit in the field DCS, constructs a reactor model data set according to the real-time data and offline test analysis data of the working condition, and constructs a reactor deactivation model to determine the current activity of the reactor based on the reactor deactivation model. A Fischer-Tropsch synthesis reactor lumped kinetics model is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and the component flow of each product of the Fischer-Tropsch synthesis unit is predicted according to the Fischer-Tropsch synthesis reactor lumped kinetics model, wherein the component flow is at least used for control and optimization of the chemical reaction process. By using the DFT framework, the activation energy barrier of each elementary reaction and the adsorption energy of the reactant on the catalyst surface are deeply explored. By integrating the real-time working condition data in the field DCS of the reactor and the offline test analysis data in the laboratory, a comprehensive reactor model data set is constructed, and a reactor deactivation model is developed to accurately predict the activity change of the catalyst inside the reactor. Based on the lumped kinetics model, the component flow of each product of the Fischer-Tropsch synthesis unit can be accurately predicted, which provides a strong basis for the optimization control of the reactor. The scheme significantly improves the prediction accuracy and control ability of the Fischer-Tropsch synthesis reaction by closely combining micro and macro, and solves the problem of low prediction accuracy in predicting the component flow of the Fischer-Tropsch synthesis product in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the specification explain the application. The use of the same reference numbers in different drawings indicates similar or identical components.

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for performing a component flow prediction method based on coupling of quantum chemical calculation and multi-scale modeling is shown according to an embodiment of the present application;

[0019] Figure 2 A flowchart of a component flow prediction method based on coupling of quantum chemical calculation and multi-scale modeling is shown according to an embodiment of the present application;

[0020] Figure 3 A flowchart of a specific component flow prediction method based on coupling of quantum chemical calculation and multi-scale modeling is shown according to an embodiment of the present application;

[0021] Figure 4 A continuous change curve diagram of activation energy of different carbon numbers is shown according to an embodiment of the present application;

[0022] Figure 5 A schematic diagram of different reactor model catalyst deactivation curves provided by embodiments of the present application is shown;

[0023] Figure 6 A schematic diagram of a comparison of predicted values and actual values of each product of a reactor model provided by embodiments of the present application is shown;

[0024] Figure 7 A structural block diagram of a component flow prediction device based on coupling of quantum chemical calculation and multi-scale modeling provided by embodiments of the present application is shown. DETAILED DESCRIPTION

[0025] It should be noted that the embodiments and features in the present application can be combined with each other without conflict. The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the convenience of description, some nouns or terms related to the embodiments of the present application are described below:

[0029] Quantum calculation: refers to the research field of accurate calculation of electronic structure, energy and reactivity of atoms, molecules and materials based on the principles of quantum mechanics through mathematical modeling and numerical simulation methods. Its core is to solve the Schrödinger equation or its approximate form to analyze the electronic distribution, bonding characteristics and dynamic behavior of chemical systems.

[0030] Adsorption energy: defined as the energy change released / absorbed during the interaction between adsorbate A and carrier B, its physical nature can be characterized as the free energy change of adsorption reaction.

[0031] Reaction energy: in the chemical reaction kinetics system, the elementary reaction energy (E_reaction) is defined as the net change of system free energy during the process of single-step elementary reaction.

[0032] Activation energy barrier: the activation energy barrier of the elementary reaction represents the energy barrier height in the reaction path, which is defined as the energy difference between the transition state and the initial state.

[0033] As introduced in the background art, the prior art has the problem of low prediction accuracy when predicting the component flow of Fischer-Tropsch synthesis products. To solve the problem of low prediction accuracy when predicting the component flow of Fischer-Tropsch synthesis products in the prior art, the embodiments of the present application provide a component flow prediction method, device, computer readable storage medium and electronic equipment based on coupling of quantum chemical calculation and multi-scale modeling.

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0035] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking the case of running on a mobile terminal, Figure 1 is a hardware structure block diagram of a mobile terminal of a component flow prediction method based on coupling of quantum chemical calculation and multi-scale modeling according to the embodiments of the present application. As shown in Figure 1 , the mobile terminal can include one or more (only one is shown in Figure 1 ) processor 102 (the processor 102 can include but is not limited to processing devices such as microprocessor MCU or programmable logic device FPGA) and memory 104 for storing data, wherein the above-mentioned mobile terminal can also include transmission device 106 for communication function and input and output device 108. Those skilled in the art can understand, Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned mobile terminal. For example, the mobile terminal can also include more or less components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0036] The memory 104 can be used to store computer programs, such as software programs of application software and modules, such as a computer program corresponding to the component flow rate prediction method based on coupling of quantum chemical calculation and multi-scale modeling in the embodiments of the present application. The processor 102 can execute various functional applications and data processing, i.e., implement the method described above, by running the computer program stored in the memory 104. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories disposed remotely with respect to the processor 102, which can be connected to the mobile terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The specific examples of the network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0037] In the embodiments, a component flow rate prediction method based on coupling of quantum chemical calculation and multi-scale modeling running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0038] Figure 2 is a flowchart of the component flow rate prediction method based on coupling of quantum chemical calculation and multi-scale modeling according to the embodiments of the present application. As shown in Figure 2 the method includes the following steps:

[0039] In step S201, the product type of the Fischer-Tropsch synthesis unit is determined, the lumped reaction equation is constructed according to the product type, the activation energy barrier of each product in the elementary reaction is determined according to the lumped reaction equation based on the DFT calculation framework, and the adsorption energy of the reactant is determined.

[0040] Specifically, the activation energy barriers of each product in the elementary reactions and the adsorption energy of the reactants are determined based on a DFT (density functional theory) calculation framework. The Fischer-Tropsch synthesis is a chemical process that converts synthesis gas (reactants, such as carbon monoxide and hydrogen) into liquid fuels and chemicals, and the products thereof include, but are not limited to, methane, ethane, propane, butane, pentane, hexane, heptane, octane, nonane, decane, paraffin, alcohol, ketone, ester, carboxylic acid, etc. Through DFT calculation, the adsorption energy of the reactants on the catalyst surface and the activation energy barriers of each product in the reaction path can be obtained, which is the basis for constructing an accurate reaction kinetics model.

[0041] In step S202, the operating condition real-time data and the offline laboratory analysis data in the field DCS of the Fischer-Tropsch synthesis unit are obtained, a reactor model data set is constructed according to the operating condition real-time data and the offline laboratory analysis data, a reactor deactivation model is constructed, and the current activity of the reactor is determined based on the reactor deactivation model.

[0042] The operating condition real-time data include the catalyst addition time, the catalyst addition amount, the reactor inlet gas flow rate, the reactor outlet gas flow rate, the raw material gas flow rate, the circulating gas flow rate, the reactant proportion at the reactor inlet, the product proportion at the reactor inlet, the reactor temperature, the reactor pressure, etc. The offline laboratory analysis data include the carbon number distribution of each product, the proportion of the inlet product and the inert gas, and the proportion of the tail gas at the reactor outlet.

[0043] In step S203, a Fischer-Tropsch synthesis reactor lumped kinetics model is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and the component flow rates of each product of the Fischer-Tropsch synthesis unit are predicted according to the Fischer-Tropsch synthesis reactor lumped kinetics model, wherein the component flow rates are used at least for the control and optimization of the chemical reaction process.

[0044] Through the above steps S201, S202 and S203, the activation energy barriers of each elementary reaction and the adsorption energy of the reactants on the catalyst surface are deeply explored based on the DFT framework, the comprehensive reactor model data set is constructed by integrating the real-time operating condition data in the field DCS of the reactor and the offline laboratory analysis data, and the reactor deactivation model is developed, which can accurately predict the activity change of the catalyst inside the reactor. Based on the lumped kinetics model, the component flow rates of each product of the Fischer-Tropsch synthesis unit can be accurately predicted, which provides a strong basis for the optimization control of the reactor. The present scheme realizes the intercommunication between the micro and macro by converting the quantitative calculation results into macro kinetic parameters, significantly improves the prediction accuracy and control ability of the Fischer-Tropsch synthesis reaction, and solves the problem of low prediction accuracy in predicting the component flow rates of the Fischer-Tropsch synthesis products in the prior art.

[0045] In a specific implementation, the Fischer-Tropsch synthesis reactor lumped kinetics model is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier, and the adsorption energy, including: determining the rate-determining step in the reaction process of the lumped reaction equation according to the activation energy barrier and the adsorption energy, and determining the activation energy of each product according to the rate-determining step and the activation energy barrier; constructing the Fischer-Tropsch synthesis reactor lumped kinetics model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy.

[0046] In the method, the rate-determining step refers to the step with the slowest rate in the reaction process, which plays a decisive role in the overall reaction rate. For example, in Fischer-Tropsch synthesis, the dehydrogenation step of carbon monoxide can be considered as the rate-determining step. By determining the rate-determining step, the reaction rate can be more accurately evaluated, and the reaction conditions can be optimized. The Fischer-Tropsch synthesis reactor lumped kinetics model is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy. The technical solution focuses on the rate-determining step of the reaction and combines the real-time activity state of the reactor to construct a reaction kinetics model that is closer to the actual working condition, effectively improving the accuracy and reliability of the prediction.

[0047] Specifically, the Fischer-Tropsch synthesis reactor lumped kinetics model is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy, including: determining the rate-determining step in the reaction process of the lumped reaction equation according to the first formula: The Fischer-Tropsch synthesis reactor lumped kinetics model is constructed, where r i is the component flow of product i, A i is the pre-exponential factor of product i, activity is the current activity of the reactor, e is a constant, E a is the activation energy of product i, R is the gas constant, T is the temperature, C1 is the concentration of the first reactant, and C2 is the concentration of the second reactant.

[0048] The pre-exponential factor in the method reflects the inherent rate of reactants converting to products under given conditions, while the activation energy determines the trend of reaction rate changing with temperature. Through the above formula, the relationship between reaction rate and temperature, reactant concentration can be quantified, providing theoretical guidance for the operation of the reactor. The technical solution accurately describes the reaction kinetics through mathematical models, realizes the fine control of the Fischer-Tropsch synthesis process, and ensures the stability of product quality and the efficient production of high-value-added products.

[0049] Further, the reactor deactivation model is constructed, including: determining the rate-determining step in the reaction process of the lumped reaction equation according to the second formula: The reactor deactivation model is constructed, where activity is the current activity of the above reactor, A s is the minimum activity of the catalyst, e is a constant, k d is the catalyst attenuation factor, t is the catalyst reaction time.

[0050] Where the activity of the catalyst decreases over time, this process is called catalyst deactivation. This method can predict the change of catalyst activity over time by constructing a deactivation model, which provides a basis for the maintenance of the reactor and the replacement of the catalyst. The technical solution accurately predicts the trend of catalyst activity by establishing a mathematical model of catalyst activity attenuation, provides a scientific basis for the long-term stable operation of the reactor, and avoids the reduction of production efficiency caused by the reduction of catalyst activity.

[0051] Further, the reactor model data set is constructed according to the above working condition real-time data and offline analysis data, including: identifying the dynamic fluctuation characteristics of the above working condition real-time data by the sliding window standard deviation method, and filtering and processing the above working condition real-time data according to the dynamic fluctuation characteristics by the box plot outlier detection technology to obtain preprocessed real-time data; the reactor model data set is constructed based on the preprocessed real-time data and the offline analysis data.

[0052] The working condition real-time data of the method refers to various parameter data generated by the reactor during operation, such as temperature, pressure, flow, etc.; the offline analysis data refers to the detailed information of reactants and products obtained by laboratory analysis. Through preprocessing, noise and outliers in the data can be removed, and the accuracy of the model can be improved. The reactor model data set is constructed based on the preprocessed real-time data and the offline analysis data. The technical solution effectively improves the data quality through data preprocessing technology, ensures the accuracy and reliability of the model construction, and improves the prediction performance of the reactor model.

[0053] Specifically, the reactor model data set is constructed based on the preprocessed real-time data and the offline analysis data, including: performing mean filtering processing on the preprocessed real-time data to obtain filtered real-time data, and performing continuity reconstruction processing on the filtered real-time data by using a spline interpolation algorithm to obtain target working condition real-time data; the dynamic time warping algorithm is used to perform time sequence alignment processing on the target working condition real-time data and the offline analysis data to obtain the reactor model data set.

[0054] The mean filtering of the method is a signal processing technique used to smooth data and reduce random fluctuations; the spline interpolation is used to fill in the gaps in the data and achieve data continuity. The dynamic time warping algorithm is used to process the time difference between different data sets and ensure the synchronization of the data. Through a series of data processing techniques, the technical solution constructs a high-quality reactor model data set, provides a solid data foundation for model construction and optimization, and significantly improves the prediction accuracy and stability of the model.

[0055] More specifically, the activation energy barrier of each product in the elementary reaction is determined according to the above lumped reaction equation, and the adsorption energy of the reactant is determined, including: determining the adsorption energy E_{ads} of the reactant according to the third formula: E_{ads} = E_{complex} - (E_{surface} + E_{adsorbate}), wherein E_{complex} is the total energy of the reactant as an adsorbate and the catalyst forming a complex, E_{surface} is the surface state energy of the catalyst without the adsorbate, and E_{adsorbate} is the energy of the adsorbate in the gas or liquid state; the difference between the maximum energy of the reactant when the bond is broken and the energy of the reactant when it exists on the surface of the catalyst is determined as the activation energy barrier of the product.

[0056] The activation energy barrier in the method is the energy barrier for the conversion of reactants to products, and the adsorption energy reflects the interaction strength between the reactant and the catalyst surface. Through DFT calculation, these key parameters can be obtained, providing accurate data support for the construction of the reaction kinetics model. The technical solution solves the limitations of traditional models in parameter acquisition by accurately calculating the reaction parameters, ensuring the accuracy and applicability of the model, thereby significantly improving the prediction accuracy of the Fischer-Tropsch synthesis process and the optimization level of the reactor design.

[0057] In addition, the embodiment also includes a dynamic adaptive reaction path optimization algorithm for real-time monitoring of the reaction state in the reactor and automatic adjustment of the elementary reaction network to adapt to changes in feed gas composition, temperature, pressure and other working conditions.

[0058] The specific steps are as follows:

[0059] 1. Real-time working condition monitoring: use the DCS system to continuously monitor various parameters in the reactor, including but not limited to gas flow rate, temperature, pressure, and the proportion of CO and H2 in the feed gas.

[0060] 2. Reaction sensitivity analysis: based on the quantitative calculation results obtained in the early stage (such as activation energy barrier and adsorption energy), analyze the sensitivity of each elementary reaction to different working condition parameters, and determine which reaction steps are dominant or limited under certain conditions.

[0061] 3. Path optimization decision: Based on the results of the sensitivity analysis, dynamically adjust the reaction path, preferentially activate those reaction steps that are favorably affected by the current operating conditions, and reduce the frequency of steps that are adversely affected, thereby optimizing the entire reaction process.

[0062] 4. Fine-tuning of reaction kinetics parameters: Taking into account the small changes in reaction rate constants that may be caused by changes in operating conditions, the algorithm can make real-time fine-tuning of the kinetic parameters of each elementary reaction to maintain the accuracy of the reaction kinetics model.

[0063] For example: Suppose that one day the reactor's raw gas supply fluctuates, causing the CO proportion to suddenly rise, and the traditional reactor model may not be able to adjust the control strategy in time under such circumstances, resulting in a decrease in reaction efficiency. Using the dynamic adaptive reaction path optimization algorithm of this embodiment, the model can quickly identify the situation of the rise in CO proportion and automatically adjust the reaction path to increase the probability of occurrence of elementary reactions that are conducive to carbon chain growth, thereby promoting the generation of more efficient heavy hydrocarbon products. Experimental data show that, under the condition of abnormal rise in CO proportion, through dynamic path optimization, the heavy oil (C16H34) yield of the Fischer-Tropsch synthesis reactor increased by about 15%, while the generation of light products (such as methane and C3-C4 gas) was reduced, significantly improving overall economic benefits and product quality.

[0064] This embodiment also includes machine learning-assisted catalyst activity prediction, which collects a large amount of data on catalyst activity under different reaction conditions, including but not limited to time, temperature, pressure, gas composition, etc., and uses machine learning algorithms such as neural networks or support vector machines to train a model that can predict catalyst activity.

[0065] Specifically, the following steps are included:

[0066] 1. Data preprocessing: Standardize and normalize historical operating data to ensure data quality and consistency.

[0067] 2. Feature selection: Select a few key variables that have the greatest impact on catalyst activity from numerous operating condition parameters, such as reaction time, temperature, CO to H2 ratio, etc.

[0068] 3. Model training: Use the selected features and corresponding activity indicators to train a machine learning model. Model selection and optimization can be performed through techniques such as cross-validation and grid search.

[0069] 4. Real-time prediction: Input real-time operating condition data into the trained model to predict the current catalyst activity, thereby more accurately adjusting the parameters of the reactor model and improving reaction efficiency.

[0070] In the prediction of catalyst activity, traditional empirical formulas are often only effective within a certain range. Exceeding the range may produce large errors, especially during the aging or regeneration process of the catalyst. The machine learning-assisted prediction method proposed in this invention can predict the activity changes of catalysts under complex working conditions based on the learning of historical data, with much higher accuracy than traditional formulas. In one case, the research team used deep learning technology to establish a catalyst activity prediction model. Compared with the prediction of traditional formulas, the average absolute error was reduced by 30%. Especially when the catalyst is in the aging stage, the improvement in prediction error is particularly significant. This helps to plan the timing of catalyst regeneration or replacement in advance, avoid production interruptions, and thus ensure the continuity and stability of the entire production process.

[0071] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling of the present application will be described in detail below with reference to specific embodiments.

[0072] This embodiment relates to a specific component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling. It takes the in-depth exploration of the reaction mechanism of Fischer-Tropsch synthesis as the starting point, carries out fine calculations at the microscopic scale, and accurately analyzes key microscopic information such as energy changes and active site action mechanisms during the reaction process. On this basis, the microscopic calculation results are deeply integrated with the macroscopic reactor model to perform high-fidelity simulation analysis of the entire production process of Fischer-Tropsch synthesis, thereby achieving efficient design and performance optimization of the Fischer-Tropsch synthesis reactor. Figure 3 As shown, the following steps are included:

[0073] 1) Construct a lumped reaction equation based on the type of Fischer-Tropsch synthesis product and design the elementary reaction process, taking into account the intermediates involved in the process;

[0074] 2) Based on the DFT calculation framework, we first characterized the energy of the main adsorbed species in the reaction system to determine the reaction thermodynamic parameters. Then, we combined structural optimization with transition state theory to quantitatively analyze the kinetic energy barrier characteristics (activation energy barrier) of each elementary step.

[0075] 3) Obtain the activation energy Ea in macroscopic reaction kinetics by calculating the microscopic activation energy barrier;

[0076] 4) Collecting real-time operating data from the on-site DCS in the Fischer-Tropsch synthesis unit and collating and summarizing laboratory offline test and analysis data, and performing data processing to obtain a reactor model data set;

[0077] 5) Construct a reactor deactivation model and calculate the current activity of the reactor;

[0078] 6) Construct lumped kinetics model of Fischer-Tropsch synthesis reactor and calculate and predict the flow of each product component to guide the on-site operation process.

[0079] This embodiment takes Fe5C2 as the catalyst model, and the Fischer-Tropsch synthesis is lumped as the reaction model of producing methane, natural gas (C 2-4 ), olefin (C 5-20 ), heavy oil (C 10-35 ), wax (C 20+ ) and synthetic water. The lumped reaction equation is as follows:

[0080] CO + 3H2 = CH4 + H2O;

[0081] 3CO + 7H2 = C3H8 + 3H2O;

[0082] 8CO + 17H2 = C8H 18 + 8H2O;

[0083] 16CO + 33H2 = C 16 H 34 + 16H2O;

[0084] 34CO + 69H2 = C 34 H 70 + 34H2O;

[0085] CO + H2O = CO2 + H2;

[0086] First, according to the mechanism of the lumped reaction equation, the participation of free radicals is considered, and the elementary process is simplified as the C-C coupling of the key step. Taking the Fischer-Tropsch synthesis to generate heavy oil (C 16 H 34 ) as an example, the specific elementary reaction list of R1-R17 shown in Table 1 is established.

[0087] Table 1 Fischer-Tropsch synthesis elementary reaction list

[0088]

[0089]

[0090] According to the reaction mechanism established in Table 1, the activation energy barrier of R1-R17 elementary reaction and the adsorption energy of the main adsorption species are calculated by DFT. Taking CO species as an example, the adsorption energy of CO# is equal to the difference between the adsorption energy of CO# and the surface energy and the energy of the surface and carbon monoxide. Taking R3 as an example, the activation energy barrier is equal to the difference between the transition state energy and the initial state energy, that is, the maximum energy when the C-O bond in CO is broken minus the energy when carbon monoxide exists on the surface of the catalyst. The calculation results of all processes are shown in Table 2:

[0091] Table 2 Reaction energy and reaction energy barrier of Fischer-Tropsch synthesis

[0092]

[0093]

[0094] After calculating the activation energy barrier of all calculated reactions and the adsorption energy of main adsorption species, the rate-determining step in the whole reaction process can be found. The activation energy of the reaction can be calculated according to the activation energy barrier of the rate-determining step, and the activation energy of the Fischer-Tropsch synthesis reaction to generate heavy oil (C 16 H 34 ) is taken as an example. After calculating the elementary reactions in the reaction process, the rate-determining step R15 is obtained, and the activation energy barrier of the process is 1.088eV. Therefore, the activation energy of the reaction can be calculated as 108.44Kj / mol. The activation energies corresponding to products with different carbon numbers are shown in Table 2. Figure 4

[0095] The real-time data of the working conditions in the distributed control system (DCS) of the Fischer-Tropsch synthesis unit and the laboratory offline analysis data are collected and summarized. The working condition data includes catalyst addition time, catalyst addition amount, reactor inlet gas flow, reactor outlet gas flow, raw gas flow, circulating gas flow, reactor inlet CO proportion, reactor inlet H2 proportion, reactor inlet CO2 proportion, reactor inlet CH4 proportion, reactor temperature, reactor pressure, etc. The offline analysis data includes carbon number distribution of light oil, heavy oil, heavy wax, petroleum and natural gas, naphtha, inlet C3 + proportion, and inert gas proportion, and CO, H2, CO2, CH4, C3 + proportion of the reactor outlet tail gas.

[0096] For the real-time working condition data collected by DCS, a multi-stage data cleaning framework is constructed. Firstly, the dynamic fluctuation characteristics are identified by the sliding window standard deviation method (window width of 20 data points and step length of 5 points), and a double screening mechanism is implemented in combination with the box plot outlier detection (IQR threshold of 1.5 times the interquartile range). For physically unfeasible working conditions such as temperature mutation rate > 5℃ / min or pressure abnormal sudden drop to negative value, the system automatically marks and removes the corresponding data segment.

[0097] The original online sensor data (covering multiple parameters such as temperature, pressure and gas flow) are processed by overlapping sliding window, and the mean filtering is performed with a 20-minute time window (step length of 1 minute) to effectively suppress high-frequency noise interference. The boundary region data is reconstructed continuously by using cubic spline interpolation algorithm, ensuring smooth transition of the time series signal.

[0098] ​For time series alignment, the data mean in the 30-second window before and after 08:00 (synchronized with the sampling time of the offline laboratory chromatographic analysis benchmark) was taken as the anchor point to construct the standardized data unit. Through the improved dynamic time warping algorithm, the cross-system timestamp offset was corrected, and finally the mixed data set with a unified time reference was established, realizing the spatial alignment and feature fusion of online monitoring data and laboratory analysis results. The catalyst activation parameters are shown in Table 3:

[0099] Table 3 Catalyst activation parameters

[0100] Process parameters Design values Activation temperature 186.2℃ Activation pressure 2.91 kPa Activated gas H2 / CO 960

[0101] The catalyst deactivation model was constructed, and the iron-based catalyst deactivation formula was:

[0102] where A s is the minimum activity of the catalyst, i.e., the minimum activity that the catalyst can retain after an infinite period of time, k d is the catalyst decay factor, and t is the catalyst reaction time. According to the catalyst addition time and catalyst addition amount, the activity of the currently added catalyst can be obtained, and according to the total amount of catalyst in the reactor and the addition time and amount of catalyst of different batches, the average activity of the catalyst in the current reactor can be calculated. According to the field catalyst addition data, the different catalyst deactivation curves in the two reactors are calculated as shown in Figure 5 .

[0103] The Fischer-Tropsch synthesis lumped reactor model was constructed, the model input parameters were the data obtained after cleaning the data set, and according to the lumped reaction equation, the lumped kinetics equation was constructed. Taking olefins (C8H 18 ) as an example, the lumped kinetics formula is as follows:

[0104]

[0105] where, represents the component flow rate, represents the pre-exponential factor, T represents the temperature, R represents the Avogadro constant, and E a represents the activation energy.

[0106] The model output is: methane, natural gas (C3H8), olefins (C8H18), heavy oil (C16H34), wax (C34H70), and synthetic water product flow.

[0107] The reactor input data set is substituted into the reactor model for calculation, and the predicted values of Fischer-Tropsch synthesis products are output. The predicted values are compared with the previous historical data, as shown in Figure 6As shown, orange is the model predicted value, blue is the true value analyzed by offline test data, the overall prediction error is within 8%, which shows that the lumped yield prediction model result is accurate and can provide guidance for actual operation, including the following examples:

[0108] Example 1: The current catalyst addition amount is 8t, the total catalyst amount in the reactor is 70t, and the remaining old catalyst amount is calculated as 62t. According to the historical catalyst addition record, for example, according to the conventional 8t, it is shown that there are 7 times of 8t and 1 time of 6t of old catalyst still existing in the reactor, and the remaining 62t catalyst residence time in the reactor is calculated, according to the residence time into the catalyst deactivation formula, the activity of different catalysts is obtained, then the proportion of each batch of catalyst in the total catalyst is calculated, for example, the current 8t accounts for 11.43% of the total catalyst amount 70t, so the current catalyst activity 1x11.43% = 0.1143 is the latest catalyst activity value, and the activities of the previous 62t catalysts are calculated according to the above method and added up to obtain the catalyst activity value in the current reactor.

[0109] Example 2: Calculate the specific yield of C8H 18 , for example;

[0110] Preceding factor: A C8H18 = 3.8 x 10 -5 , catalyst activity: activity = 0.54, activation energy: Ea = 100.46 Kj / mol = 100460 J / mol, temperature: T = 544 K;

[0111] Gas concentration: C_H2 = 260 mol / m 3 , C_CO = 150 mol / m 3 , C_H2O = 50 mol / m 3 ;

[0112] Reactor height: h = 47.5 m;

[0113] Reactor diameter: d = 9 m;

[0114] Gas constant: R = 8.314 J / (mol·K);

[0115] Calculate Arrhenius term:

[0116] Calculate concentration term:

[0117] Calculate reaction rate:

[0118] Calculate the molar flow rate:

[0119] Calculate the mass flow rate: F C8H18 = Fm x M C8H18 = 13.2 x 114 = 1504.8 g / s = 5.42 t / h.

[0120] The key points of this embodiment are:

[0121] 1. Establishing the calculation of the lumped elementary paths of the Fischer-Tropsch synthesis product by quantitative calculation;

[0122] 2. Converting the quantitative calculation results into macroscopic kinetic parameters to realize the intercommunication between microcosmic and macroscopic;

[0123] 3. Solving the kinetics formula according to the corresponding Arrhenius formula of the product lumping.

[0124] Compared with the traditional reactor model for predicting the product, the advantages of this embodiment are: 1) the traditional reactor model generally regards the catalyst as a "black box", and cannot reveal the distribution of the active sites on the catalyst surface, while this embodiment can realize the explainable catalyst information by characterizing the iron-based catalyst through quantitative calculation; 2) the kinetic parameters of the traditional reactor model are generally obtained directly from the literature, and the reaction mechanism cannot be revealed, and the deviation between the parameter acquisition condition and the actual working condition leads to the failure of extrapolation, while this embodiment can understand the Fischer-Tropsch synthesis reaction mechanism in depth through microcosmic reaction by studying the Fischer-Tropsch synthesis reaction mechanism; 3) the modeling cycle of the traditional reactor model is long, and it is difficult to adapt to the fluctuations of raw material components or changes in product schemes, and the establishment of the reaction kinetics model through lumped reaction in this embodiment is helpful for the rapid modeling of the reactor, and can be adjusted for different products, thereby increasing the flexibility and applicability of the reactor modeling.

[0125] The application embodiment further provides a component flow rate prediction device based on coupling of quantum chemical calculation and multi-scale modeling. It should be noted that the component flow rate prediction device based on coupling of quantum chemical calculation and multi-scale modeling of the application embodiment can be used to execute the component flow rate prediction method based on coupling of quantum chemical calculation and multi-scale modeling provided by the application embodiment. The device is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described here. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the device described in the following embodiments is preferably realized in software, the realization of hardware or a combination of software and hardware is also possible and conceived.

[0126] The component flow rate prediction device based on coupling of quantum chemical calculation and multi-scale modeling provided by the application embodiment is introduced below.

[0127] Figure 7 is a schematic diagram of a component flow rate prediction device based on coupling of quantum chemistry calculation and multi-scale modeling according to an embodiment of the present application. As shown in the figure, the device comprises: Figure 7

[0128] A determination unit 71 is configured to determine the product type of a Fischer-Tropsch synthesis unit, construct a lumped reaction equation according to the product type, determine the activation energy barrier of each product in a basic reaction according to the lumped reaction equation based on a DFT calculation framework, and determine the adsorption energy of a reactant.

[0129] A construction unit 72 is configured to obtain operating condition real-time data and offline test analysis data in a field DCS of the Fischer-Tropsch synthesis unit, construct a reactor model data set according to the operating condition real-time data and offline test analysis data, construct a reactor deactivation model, and determine the current activity of the reactor based on the reactor deactivation model.

[0130] A prediction unit 73 is configured to construct a Fischer-Tropsch synthesis reactor lumped kinetics model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier, and the adsorption energy, and predict the component flow rate of each product of the Fischer-Tropsch synthesis unit according to the Fischer-Tropsch synthesis reactor lumped kinetics model, wherein the component flow rate is used at least for control and optimization of a chemical reaction process.

[0131] ​In the embodiment, the determining unit is configured to determine a product type of a Fischer-Tropsch synthesis unit, construct a lumped reaction equation according to the product type, determine an activation energy barrier of each product in a basic reaction and an adsorption energy of a reactant based on a DFT calculation framework, the constructing unit is configured to acquire working condition real-time data and offline test analysis data in a field DCS of the Fischer-Tropsch synthesis unit, construct a reactor model data set according to the working condition real-time data and the offline test analysis data, and construct a reactor deactivation model to determine a current activity of the reactor based on the reactor deactivation model, and the predicting unit is configured to construct a Fischer-Tropsch synthesis reactor lumped kinetics model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and predict a component flow rate of each product of the Fischer-Tropsch synthesis unit according to the Fischer-Tropsch synthesis reactor lumped kinetics model, where the component flow rate is used at least for control and optimization of a chemical reaction process. Through the DFT framework, the activation energy barrier of each basic reaction and the adsorption energy of the reactant on the catalyst surface are deeply explored, the comprehensive reactor model data set is constructed by integrating the real-time working condition data in the field DCS of the reactor and the offline test analysis data in the laboratory, and the reactor deactivation model is developed to accurately predict the activity change of the catalyst inside the reactor. Based on the lumped kinetics model, the component flow rate of each product of the Fischer-Tropsch synthesis unit can be accurately predicted, which provides a strong basis for the optimized control of the reactor. The embodiment realizes the intercommunication between the micro and macro by converting the quantitative calculation results into macro kinetic parameters, significantly improves the prediction accuracy and control ability of the Fischer-Tropsch synthesis reaction, and solves the problem of low prediction accuracy in predicting the component flow rate of the Fischer-Tropsch synthesis product in the prior art.

[0132] As an optional solution, the predicting unit comprises a first determining module and a first constructing module, the first determining module is configured to determine a rate-determining step in a reaction process of the lumped reaction equation according to the activation energy barrier and the adsorption energy, and determine an activation energy of each product according to the rate-determining step and the activation energy barrier, and the first constructing module is configured to construct the Fischer-Tropsch synthesis reactor lumped kinetics model according to the reactor model data set, the current activity of the reactor, the lumped reaction equation and the activation energy.

[0133] As an optional solution, the first constructing module comprises a constructing submodule, and the constructing submodule is configured to construct the Fischer-Tropsch synthesis reactor lumped kinetics model according to the following first formula: wherein, r is the component flow rate of the product i, A is a pre-exponential factor of the product i, activity is the current activity of the reactor, e is a constant, E is the activation energy of the product i, R is a gas constant, T is a temperature, C1 is a first reactant concentration, and C2 is a second reactant concentration. i wherein, r is the component flow rate of the product i, A is a pre-exponential factor of the product i, activity is the current activity of the reactor, e is a constant, E is the activation energy of the product i, R is a gas constant, T is a temperature, C1 is a first reactant concentration, and C2 is a second reactant concentration. i wherein, r is the component flow rate of the product i, A is a pre-exponential factor of the product i, activity is the current activity of the reactor, e is a constant, E is the activation energy of the product i, R is a gas constant, T is a temperature, C1 is a first reactant concentration, and C2 is a second reactant concentration. a wherein, r is the component flow rate of the product i, A is a pre-exponential factor of the product i, activity is the current activity of the reactor, e is a constant, E is the activation energy of the product i, R is a gas constant, T is a temperature, C1 is a first reactant concentration, and C2 is a second reactant concentration.

[0134] An optional solution, the constructing unit comprises a second constructing module, configured to construct the reactor model according to a second formula: The reactor deactivation model is constructed, wherein, activity is the current activity of the reactor, A s is the minimum activity of the catalyst, e is a constant, k d is the catalyst attenuation factor, t is the catalyst reaction time.

[0135] An optional solution, the constructing unit further comprises a screening processing module and a third constructing module; the screening processing module is configured to identify dynamic fluctuation characteristics of the working condition real-time data by a sliding window standard deviation method, and screen and process the working condition real-time data by a box plot outlier detection technology according to the dynamic fluctuation characteristics, to obtain preprocessed real-time data; the third constructing module is configured to construct the reactor model data set based on the preprocessed real-time data and the offline laboratory analysis data.

[0136] An optional solution, the third constructing module comprises a mean filtering processing submodule and a time sequence alignment processing submodule; the mean filtering processing submodule is configured to perform mean filtering processing on the preprocessed real-time data to obtain filtered real-time data, and perform continuity reconstruction processing on the filtered real-time data by a spline interpolation algorithm to obtain target working condition real-time data; the time sequence alignment processing submodule is configured to perform time sequence alignment processing on the target working condition real-time data and the offline laboratory analysis data by a dynamic time warping algorithm, to obtain the reactor model data set.

[0137] An optional solution, the determining unit comprises a second determining module and a third confirming module; the second determining module is configured to determine the adsorption energy E_{ads} of the reactant according to a third formula: E_{ads} = E_{complex} - (E_{surface} + E_{adsorbate}), wherein, E_{complex} is the total energy of the reactant as an adsorbate forming a complex with the catalyst, E_{surface} is the surface state energy of the catalyst without the adsorbate, and E_{adsorbate} is the energy of the adsorbate in a gaseous or liquid state; the third confirming module is configured to determine the difference between the maximum energy of the reactant when the bond is broken and the energy of the reactant when it exists on the surface of the catalyst, and take the difference as the activation energy barrier of the product.

[0138] The component flow prediction device based on coupling of quantum chemistry calculation and multi-scale modeling comprises a processor and a memory, the determination unit, the construction unit and the prediction unit are all stored in the memory as program units, and the corresponding functions are realized by executing the program units stored in the memory by the processor. The modules are all located in the same processor, or the modules are respectively located in different processors in any combination.

[0139] The processor comprises a core, and the core retrieves the corresponding program unit in the memory. The core can be one or more, and the problem of low prediction accuracy in predicting the component flow of the Fischer-Tropsch synthesis product in the prior art can be solved by adjusting the core parameters.

[0140] The memory can comprise a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory comprises at least one memory chip.

[0141] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium comprises a stored program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute the component flow prediction method based on coupling of quantum chemistry calculation and multi-scale modeling when the program runs.

[0142] The embodiment of the present application provides a processor, and the processor is used for running a program, wherein the component flow prediction method based on coupling of quantum chemistry calculation and multi-scale modeling is executed when the program runs.

[0143] The embodiment of the present application provides an electronic device, and the device comprises a processor, a memory and a program stored in the memory and capable of running on the processor, and the processor executes the program to realize at least the steps of the component flow prediction method based on coupling of quantum chemistry calculation and multi-scale modeling.

[0144] The device in the present application can be a server, a PC, a PAD, a mobile phone or the like.

[0145] The present application also provides a computer program product suitable for executing the program initialized with at least the steps of the component flow prediction method based on coupling of quantum chemistry calculation and multi-scale modeling when executed on a data processing device.

[0146] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0147] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process.Figure 1 one or more processes and / or functions specified in one or more blocks Figure 1 one or more processes and / or functions specified in one or more blocks

[0151] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0152] Memory can include non-persistent memory and / or volatile memory, which can be random access memory (RAM), including forms of RAM with higher latency, such as static random access memory (SRAM), dynamic random access memory (DRAM), and synchronous dynamic random access memory (SDRAM), and / or non-volatile memory, such as read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technology. Memory is an example of computer-readable media.

[0153] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as 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 programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0154] The technical features of the above-described embodiments can be combined in any manner, and in order to make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered within the scope of the present specification.

[0155] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to encompass non-exclusive inclusion, such that processes, methods, articles, or apparatuses that include a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles, or apparatuses. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0156] The above descriptions are only the preferred embodiments of the present application, and are not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling, characterized in that: include: Determining the product type of the Fischer-Tropsch synthesis unit, constructing a lumped reaction equation based on the product type, and determining the activation energy barrier of each product in the elementary reaction based on the lumped reaction equation based on the DFT calculation framework, as well as determining the adsorption energy of the reactant; Acquiring real-time operating data and offline laboratory analysis data from an on-site DCS of the Fischer-Tropsch synthesis unit, constructing a reactor model data set based on the real-time operating data and the offline laboratory analysis data, and constructing a reactor deactivation model, and determining the current activity of the reactor based on the reactor deactivation model; A lumped kinetic model of a Fischer-Tropsch synthesis reactor is constructed based on the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and the component flow rates of each of the products of the Fischer-Tropsch synthesis unit are predicted based on the lumped kinetic model of the Fischer-Tropsch synthesis reactor, wherein the component flow rates are at least used for the control and optimization of the chemical reaction process.

2. The method according to claim 1, characterized in that A lumped kinetic model of a Fischer-Tropsch synthesis reactor is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier, and the adsorption energy, including: Determining the rate-determining step in the reaction process of the lumped reaction equation according to the activation energy barrier and the adsorption energy, and determining the activation energy of each of the products according to the rate-determining step and the activation energy barrier; A lumped kinetic model of a Fischer-Tropsch synthesis reactor is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy.

3. The method according to claim 2, characterized in that A lumped kinetic model of a Fischer-Tropsch synthesis reactor is constructed according to the reactor model data set, the current activity of the reactor, the lumped reaction equation, and the activation energy, including: According to the first formula: The lumped kinetic model of the Fischer-Tropsch synthesis reactor is constructed, wherein r i is the component flow rate of product i, A i is the pre-exponential factor of product i, activity is the current activity of the reactor, e is a constant, E a is the activation energy of product i, R is the gas constant, T is the temperature, C1 is the concentration of the first reactant, and C2 is the concentration of the second reactant.

4. The method according to claim 1, wherein Construct a reactor deactivation model, including: According to the second formula: Construct a reactor deactivation model, where activity is the current activity of the reactor, A s is the minimum activity of the catalyst, e is a constant, k d is the catalyst attenuation factor, and t is the catalyst reaction time.

5. The method according to claim 1, wherein A reactor model data set is constructed based on the real-time operating data and offline laboratory analysis data, including: Identifying dynamic fluctuation characteristics of the real-time data of the working condition by a sliding window standard deviation method, and filtering and processing the real-time data of the working condition by using a box plot outlier detection technology according to the dynamic fluctuation characteristics to obtain preprocessed real-time data; The reactor model data set is constructed based on the pre-processed real-time data and the offline assay analysis data.

6. The method according to claim 5, characterized in that Constructing the reactor model data set based on the pre-processed real-time data and the offline assay analysis data includes: Performing mean filtering on the pre-processed real-time data to obtain filtered real-time data, and performing continuous reconstruction on the filtered real-time data using a spline interpolation algorithm to obtain target working condition real-time data; A dynamic time warping algorithm is used to perform time series alignment processing on the target operating condition real-time data and the offline laboratory analysis data to obtain the reactor model data set.

7. The method according to claim 1, characterized in that Determining the activation energy barrier of each product in the elementary reaction according to the lumped reaction equation, and determining the adsorption energy of the reactant, including: The adsorption energy E_{ads} of the reactant is determined according to the third formula: E_{ads}=E_{complex}-(E_{surface}+E_{adsorbate}), wherein E_{complex} is the total energy of the reactant after forming a complex with the catalyst as an adsorbate, E_{surface} is the surface state energy of the catalyst in the absence of the adsorbate, and E_{adsorbate} is the energy of the adsorbate in the gas phase or liquid state; The difference between the maximum energy of the reactant when the bond is broken and the energy of the reactant when on the catalyst surface is determined, and the difference is used as the activation energy barrier of the product.

8. A component flow prediction device based on the coupling of quantum chemical calculation and multi-scale modeling, characterized in that: include: A determination unit for determining the product type of the Fischer-Tropsch synthesis unit, constructing a lumped reaction equation based on the product type, and determining the activation energy barrier of each product in the elementary reaction based on the lumped reaction equation based on the DFT calculation framework, as well as determining the adsorption energy of the reactant; A construction unit is used to obtain real-time operating data and offline laboratory analysis data from the on-site DCS of the Fischer-Tropsch synthesis unit, construct a reactor model data set based on the real-time operating data and offline laboratory analysis data, and construct a reactor deactivation model, and determine the current activity of the reactor based on the reactor deactivation model; A prediction unit is used to construct a lumped kinetic model of a Fischer-Tropsch synthesis reactor based on the reactor model data set, the current activity of the reactor, the lumped reaction equation, the activation energy barrier and the adsorption energy, and to predict the component flow rates of each of the products of the Fischer-Tropsch synthesis unit based on the lumped kinetic model of the Fischer-Tropsch synthesis reactor, wherein the component flow rates are at least used for the control and optimization of the chemical reaction process.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the component flow prediction method based on the coupling of quantum chemical calculation and multi-scale modeling as described in any one of claims 1 to 7.

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