MTO reaction process simulation system based on molecular modeling and optimization technology

Abstract

The application discloses an MTO reaction process simulation system based on molecular modeling and optimization technology, and relates to the technical field of molecular modeling and digital twinning.The system firstly carries out molecular digitization on raw materials and products in the MTO reaction process, constructs an MTO reaction network, and performs mass balance and heat balance based on reaction path data output by the MTO reaction network, then iteratively optimizes and constructs a reactor model and a regenerator model of an MTO fluidized bed, and finally generates a dynamic simulation scene of MTO catalyst deactivation and regeneration cooperation in a digital twinning environment according to the molecular reaction path data output by the MTO reaction network and the constructed reactor model and regenerator model, and performs regeneration efficiency verification and optimization, so that molecular cascade dynamic simulation and regeneration efficiency precise regulation under the cooperative working condition of the reactor and the regenerator are realized, and the digitalization replication, dynamic optimization and precise prediction of the whole MTO reaction process are realized.

Description

Technical Field

[0001] This invention relates to the field of molecular modeling and digital twin technology, and in particular to a simulation system for MTO reaction processes based on molecular modeling and optimization technology. Background Technology

[0002] Methanol to Olefins (MTO) technology is a core process in coal / natural gas-based chemical routes. It converts methanol into basic chemical feedstocks such as ethylene and propylene using a fluidized bed reactor, offering high production efficiency. The MTO process employs a dual-circulation fluidized bed architecture with both reactor and regenerator, ensuring continuous and efficient reaction while also enabling catalyst recycling. Its core technology system encompasses key aspects such as molecular mechanism modeling, reactor dynamic simulation, and synergistic regulation of catalyst regeneration.

[0003] The complex process of methanol under the action of acidic molecular sieve catalysts was investigated using molecular modeling techniques based on density functional theory (DFT) and molecular dynamics. The mechanism of methanol within the catalyst channels during the MTO process was constructed: methanol first undergoes a condensation reaction at the active sites to generate dimethyl ether, accompanied by water removal. The dimethyl ether further reacts with methanol in a series of reactions including dehydration and cracking, ultimately producing low-carbon olefins (≤4 carbon atoms) such as ethylene and propylene. However, this process inevitably produces aromatic hydrocarbon coking precursors. These substances gradually cover the acidic sites of the catalyst and block the pore structure, causing rapid degradation of catalyst activity. Therefore, the MTO process generally adopts a fluidized bed architecture with a dual-circulation reactor and regenerator, constructing a linked reactor and regenerator system. This ensures both continuous and efficient reaction and the recycling and reuse of deactivated catalysts.

[0004] To further achieve accurate prediction and mechanism in-depth understanding of the molecular level of MTO reaction systems, it is necessary to rely on advanced algorithmic models for quantitative analysis of molecular properties. For example, Chinese invention patent CN115860065A discloses a molecular graph property prediction method based on a graph random neural network with dropout connections. This method first assigns corresponding weights to edges based on the edge characteristics of the molecular graph, performs soft connection transformation on the molecular graph nodes to generate a first adjacency matrix, then generates a second adjacency matrix through masking, and inputs the original node features of the molecular graph and the second adjacency matrix into the graph neural network model to output graph encoding. Subsequently, combined with pre-set labeled and unlabeled data, supervised and unsupervised loss calculations are performed respectively, and corresponding results are obtained. Finally, the results of the two types of loss calculations are integrated to iteratively optimize the parameters of the graph neural network model. The introduction of this type of algorithmic model provides technical support for accurate prediction of molecular properties in MTO reactions and lays the algorithmic foundation for subsequent targeted regulation of reaction pathways and synergistic optimization of catalyst regeneration.

[0005] However, the MTO reaction generates various non-target substances, such as aromatic derivatives and long-chain alkanes, which hinder the diffusion of feedstocks and target products. Furthermore, related substances (such as coking precursors and polycyclic aromatic hydrocarbons) can suppress the main reaction. In actual reactions, due to limited means of monitoring the microscopic state of catalyst particles and the high complexity of multiphase flow field coupling in the reaction system, it is difficult to obtain real-time catalyst activity and reaction efficiency at each step. Moreover, while existing digital twin technology covers data acquisition, model construction, and virtual simulation, reaction control largely relies on empirical parameter adjustments, lacking precise quantification of the dynamic processes related to catalyst activity decay. Coupled with fluctuations in feedstock conditions and dynamic changes in the catalyst, this easily leads to insufficient stability of the reaction process, ultimately resulting in low overall reliability of MTO reaction digital twin simulations. Summary of the Invention

[0006] To address the technical problems existing in the prior art, embodiments of the present invention provide a simulation system for the MTO reaction process based on molecular modeling and optimization technology. The technical solution is as follows:

[0007] A simulation system for the MTO reaction process based on molecular modeling and optimization technology is provided. This system includes the following modules: a molecular reaction network construction module, a fluidized bed mass-heat balance simulation module, and a regeneration-co-simulation scenario generation module. The molecular reaction network construction module constructs the MTO reaction network through the digital characterization of the molecules of raw materials and products during the MTO reaction process, while simultaneously controlling the reaction process parameters. Molecular digital characterization represents the control and optimization of the MTO reaction process by quantifying the reaction characteristics at the molecular level. The fluidized bed mass-heat balance simulation module performs mass and heat balance calculations based on the reaction path data output from the MTO reaction network, and iteratively optimizes and constructs the reactor and regenerator models of the MTO fluidized bed by combining the dynamic prediction results of the MTO reactant concentration and product concentration. The regeneration-co-simulation scenario generation module generates a dynamic simulation scenario of MTO catalyst deactivation and regeneration synergy in a digital twin environment based on the molecular reaction path data output from the MTO reaction network and the constructed reactor and regenerator models, while simultaneously verifying and optimizing the regeneration efficiency.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0009] 1. This invention utilizes a molecular reaction network construction module to digitally characterize the molecules of MTO feedstocks and products, visually presenting the complete molecular reaction pathway from methanol to olefins. Simultaneously, it quantifies molecular-level reaction characteristics to achieve precise control of process parameters. It can accurately identify the formation pathways of non-target products such as aromatic derivatives and long-chain alkanes, clarify the molecular mechanisms by which coking precursors inhibit the main reaction, and solve the problem of understanding the mechanisms by which non-target substances interfere with the main reaction, providing a molecular-level basis for targeted regulation. Through a fluidized bed mass-heat balance simulation module, mass-heat balance calculations are performed based on molecular reaction network data, iteratively constructing reactor and regenerator models, clearly defining the quantitative relationships of material transfer and the dynamic laws of the temperature field. This module can acquire catalyst activity and the efficiency of each reaction step in real time, filling the technical gap in the dynamic quantification of reaction systems. Through a regeneration co-simulation scenario generation module, a catalyst deactivation-regeneration co-simulation scenario is constructed in a digital twin environment, and regeneration efficiency is optimized. This achieves precise quantification of the catalyst activity decay process, offsetting the impact of feedstock fluctuations and catalyst dynamic changes, improving the overall reliability of the MTO reaction digital twin simulation, and ensuring the long-term stability of the MTO reaction process.

[0010] 2. Based on the SU-BEM molecular reaction network and the MTO reaction equation, a methanol-to-olefins (MTO) reaction network including reaction kinetics and coking kinetics models was constructed. The Arrhenius equation was used to obtain the main reaction parameters and equilibrium constants, quantifying the impact of feedstock fluctuations. In the reactor, if the equilibrium constant is lower than the reference value, the temperature step compensation value is calculated using the reaction kinetics model, combined with the deviation and the average feedstock temperature, and the reactor temperature is adjusted stepwise; otherwise, the current temperature step is maintained. When simulating dynamic changes in reaction and regeneration, the output parameters of the coking kinetics model are coupled with an inhibition factor to obtain a product inhibition index, accurately quantifying the degree of inhibition of the coking rate by the product. If this index is higher than the reference value, and the current coking rate and catalyst activity are lower than the reference values, the catalyst residence time compensation value is calculated and adjusted based on the average coking temperature and index deviation; otherwise, the residence time is maintained. This process achieves precise adaptation of reaction parameters and dynamic optimization of the regeneration process, strengthening the synergistic linkage between reaction and regeneration, and ensuring the efficient and stable operation of the entire MTO reaction process.

[0011] 3. First, by analyzing the relationship between the reaction section height and concentration, the concentration-height change rate is calculated. This rate is then compared with a reference range formed from historical data to quantitatively assess the reactor's operating status. If the change rate is below the minimum value of the range, the fluidization rate in the dense phase zone is adjusted based on the lower deviation to balance the mixing of the bubble phase and the emulsion phase, effectively improving the overall apparent reaction rate. If the change rate is within the range, heat balance is directly performed. If the change rate is above the maximum value of the range, the catalyst particle size is adjusted based on the upper deviation to suppress excessive concentration gradients and improve the utilization rate of the upper region of the reactor. Heat balance is then performed again after adjustment. This process achieves precise control of the material distribution in the dense and dilute phase zones of the fluidized bed reactor, providing reliable support for the synergistic optimization of mass and heat in the MTO reaction system and improving the overall stability and efficiency of the reaction.

[0012] 4. First, the product gas distribution index is obtained from the material balance equation to quantify the distribution of olefin products and flue gas in the dense phase bed. Then, the bubble content of the bubble phase is adjusted according to the index deviation to optimize the apparent gas velocity and improve the fluidization state of the reactor. Subsequently, the reaction rate obtained from the mass balance is combined to calculate the heat generation rate per unit volume of bed, accurately characterizing the temperature distribution law within the reactor. Finally, the adjusted gas distribution index and heat generation rate are input into the reactor model for comparison. If the deviation of either index exceeds the preset range, the mass-heat balance iterative optimization is initiated until both indexes meet the target, thus completing the balance calculation. This process achieves deep linkage and closed-loop iteration of mass-heat balance, ensuring fluidization stability through apparent gas velocity adjustment and achieving precise temperature distribution control through heat generation rate quantification, significantly improving the accuracy of the reactor model and providing reliable energy data support for the full-process simulation of MTO reaction.

[0013] 5. First, a reference regeneration catalyst activity recovery rate is set by combining the coking precursor formation path of the MTO reaction network, the deactivated catalyst activity decay data of the reactor model, and historical industrial regeneration operation and maintenance data. Then, regeneration process data is acquired in a digital twin simulation scenario of MTO catalyst deactivation and regeneration coordination. The actual regeneration catalyst activity recovery rate is calculated by quantitative analysis using molecular-level characterization algorithms. This rate is compared with the reference value to obtain the regeneration efficiency deviation, and a preset regeneration operation parameter mapping relationship is input to generate adjustment strategies for parameters such as regeneration temperature, main air volume, and catalyst residence time. The execution of the adjustment strategy is then monitored. The synergistic effect of the reactor and regenerator is quantified by key indicators such as the regeneration flue gas ratio, catalyst micro-reaction activity value, and regeneration carbon content. If any single indicator exceeds the standard, the regeneration efficiency is deemed not to meet the coordination requirements, triggering a control strategy. This involves coordinating the adjustment of the reactor's methanol feed rate and catalyst circulation rate based on the indicator deviation. If all indicators meet the standards, the simulation is complete. In case of anomalies, an early warning is output and the regenerator execution parameters are recorded. This process solves the pain points of traditional regeneration control relying on experience and poor coordination, achieving precise quantification and dynamic control of the regeneration process, and improving the reliability and operational stability of the entire MTO reaction simulation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the structure of the MTO reaction process simulation system based on molecular modeling and optimization technology provided in an embodiment of the present invention;

[0016] Figure 2 The flowchart is provided for the molecular reaction network construction module in the embodiments of the present invention.

[0017] Figure 3 The flowchart is provided for the fluidized bed mass heat balance simulation module in the embodiments of the present invention.

[0018] Figure 4 The flowchart is the corresponding to the regenerative collaborative simulation scenario generation module provided in the embodiments of the present invention;

[0019] Figure 5 A schematic diagram of the MTO trend simulation in the interface diagram of the MTO reaction process simulation system based on molecular modeling and optimization technology provided in the embodiments of the present invention;

[0020] Figure 6 One of the schematic diagrams for MTO optimization simulation calculation provided in an embodiment of the present invention;

[0021] Figure 7 This is a second schematic diagram of the MTO optimization simulation calculation provided in an embodiment of the present invention;

[0022] Figure 8 One of the schematic diagrams for verifying the MTO optimization simulation calculation results provided in the embodiments of the present invention;

[0023] Figure 9 This is the second schematic diagram for verifying the MTO optimization simulation calculation results provided in the embodiments of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0025] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0026] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0027] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0028] This invention provides a simulation system for MTO reaction processes based on molecular modeling and optimization techniques, such as... Figure 1 The schematic diagram of the MTO reaction process simulation system based on molecular modeling and optimization technology shown can include the following modules: molecular reaction network construction module, fluidized bed mass heat balance simulation module, and regeneration synergistic simulation scenario generation module.

[0029] The molecular reaction network construction module serves as the molecular modeling foundation for digital twin simulation of the MTO process. By digitally characterizing the molecules of raw materials (such as methanol) and products (such as ethylene, propylene, and byproducts) during the MTO reaction, an MTO reaction network is constructed. Simultaneously, reaction process parameters are controlled, including the residence time of the catalyst in the reactor and the temperature step. Molecular digital characterization represents the control and optimization of the MTO reaction process by quantifying the reaction characteristics at the molecular level. The MTO reaction network is used to visually present the molecular reaction pathway of methanol molecules converting to olefins via intermediates. The molecular reaction pathway reflects the dynamic transformation state of methanol molecules under the action of catalysts, corresponding to target olefins (ethylene, propylene) and various byproducts (such as alkanes and aromatics).

[0030] The fluidized bed mass and heat balance simulation module is used to perform mass and heat balance calculations based on the reaction path data output by the MTO reaction network. Combined with the dynamic prediction results of MTO reactant concentration on product concentration, iteratively optimizes and constructs the reactor and regenerator models of the MTO fluidized bed. The mass balance is used to quantify the inflow, outflow, and accumulation of each material (raw material, product, catalyst, inert component, etc.) in the MTO reaction network, clarifying the quantitative relationship of mass transfer and transformation. The heat balance is used to quantify the dynamic law of the temperature field distribution corresponding to the energy balance (such as reaction heat, phase change heat, equipment heat dissipation, and external heat exchange) in the MTO reaction network. The reactor model is used to simulate the coupling process of material mixing, mass and heat transfer, and molecular reactions in the fluidized bed in real time, accurately predict the product distribution under different reaction conditions, and provide digital simulation support for the optimization of key conditions such as reaction temperature and pressure. The regenerator model is used to simulate the process of catalyst burning off carbon deposits and restoring activity at high temperatures, quantifying the influence of regeneration temperature, time, and other parameters on the degree of catalyst activity recovery, and providing the reactor with catalyst activity data that conforms to the actual state.

[0031] The regeneration co-simulation scenario generation module is used to integrate multi-source data and build an integrated simulation architecture in a digital twin environment. This module is based on the molecular reaction path data of methanol to low-carbon olefins output by the MTO reaction network, the molecular mechanism data of coking precursor formation and deposition, and the previously iteratively optimized MTO fluidized bed reactor model (including the mass and heat transfer and reaction kinetics logic of the dense phase and dilute phase regions) and regenerator model (including the thermodynamics of coking reaction and the kinetics logic of catalyst activity recovery). It generates a full life cycle co-dynamic simulation scenario of the MTO catalyst from participating in the reaction and gradually deactivating in the reactor, to being transported to the regenerator for coking regeneration, activity recovery, and then returning to the reactor for recycling. At the same time, multi-dimensional verification and iterative optimization of regeneration efficiency are carried out in this scenario, including comparing the deviation between the actual regenerated catalyst activity recovery rate and the reference value, dynamically adjusting key operating parameters such as catalyst residence time, and quantifying the linkage effect between the reactor and regenerator operating conditions. This enables molecular-level digital replication of the entire MTO reaction-regeneration process and precise co-dynamic control of reaction conditions and regeneration process.

[0032] In this embodiment, the three modules work together to achieve precise modeling, dynamic control, and efficient collaboration of the entire MTO reaction process. The beneficial effects can be demonstrated through an application example from a coal-based MTO chemical company: the company's original MTO unit suffered from large fluctuations in product selectivity, unstable catalyst regeneration efficiency, and lagging overall process control. After introducing this digital twin module system, the molecular reaction network construction module first completes the molecular digital characterization of methanol, olefins, and byproducts, visually presenting the formation pathway of aromatic byproducts. Based on this, the catalyst residence time and temperature step size are optimized, significantly improving the overall selectivity of ethylene and propylene. The fluidized bed mass-heat balance simulation module, through precise mass-heat balance calculations, iteratively optimizes the reactor and regenerator models, reducing the temperature fluctuation amplitude in the dense phase region. The regeneration collaborative simulation scenario generation module constructs a dual-reactor collaborative scenario, dynamically adjusting the catalyst residence time in the regenerator to maintain a high and stable catalyst activity recovery rate, shortening the linkage response delay between the reactor and regenerator, and ultimately achieving a significant reduction in the overall energy consumption of the unit.

[0033] Furthermore, the molecular digital characterization of raw materials and products during the MTO reaction process is carried out as follows: Based on the constructed SU-BEM molecular reaction network, a methanol-to-olefins (MTO) reaction network is established through the set MTO reaction equation. The MTO reaction network includes a kinetic model for simulating the dynamic changes in the reaction and regeneration process, including a reaction kinetic model and a coking kinetic model. The MTO reaction network is constructed as follows: Based on the results of the Arrhenius equation, the reaction parameters (usually including reaction rate and concentration terms) and equilibrium constant of the main MTO reaction are obtained to quantify the fluctuation of the reaction raw materials during the main MTO reaction.

[0034] The specific process of controlling reaction process parameters in the reactor model is as follows: If the obtained equilibrium constant is less than the reference equilibrium constant, the equilibrium constant deviation and the average temperature of the reactants in the current MTO main reaction process are input into the reaction kinetic model to obtain the temperature step compensation value. This value is then added to the current reactor temperature step compensation value to obtain the actual temperature step compensation value. The reactor temperature is then gradually adjusted to make the equilibrium constant approach the corresponding reference value. If the obtained equilibrium constant is not less than the reference equilibrium constant, the current reactor temperature step is maintained.

[0035] In this embodiment, by performing mechanistic analysis on the reaction and regeneration processes, the molecular-level reaction pathways and reaction networks of the methanol reaction and air coking processes were obtained. Based on the key molecular properties and reaction process parameters, a molecular-level reaction kinetic model was constructed, which can calculate the olefin composition of the product gas and the amount of flue gas and the coke content of the catalyst under different reaction depths and different coking degrees.

[0036] A hybrid framework of structural unit-bond-electric matrix (SU-BEM) is used to represent all molecules involved in the MTO model. This molecular digital representation method mainly consists of two parts: a bottom layer of bond-electric matrix and a top layer of structural units. The bond-electric matrix can be used to represent each atom in the molecule and its corresponding connection mode. Through this concise mathematical representation method, not only can molecules be represented, but structures containing free radicals and ions can also be handled. Each molecule can be transformed into a computer-recognizable matrix unit. When the molecule undergoes a reaction that leads to structural changes, the values ​​at the corresponding positions in the matrix will change, thus representing changes in the atomic connection mode. Therefore, molecular structural transformation and information storage can be achieved through simple matrix operations.

[0037] like Figure 2 The diagram shows a flowchart of the molecular reaction network construction module provided in this embodiment of the invention. Its design logic is as follows: Based on the constructed MTO reaction network, the reaction parameters and product inhibition index of the main reaction are obtained. Based on the reaction parameters, the reaction process parameters are controlled. It is determined whether the obtained equilibrium constant is greater than the reference value. If so, the current process parameters are maintained; otherwise, the step size compensation for temperature adjustment is performed. At the same time, in the regenerator, the product inhibition index is judged. If the obtained value is greater than the reference value, the residence time of the catalyst is compensated; otherwise, the current residence time is maintained.

[0038] To more intuitively represent the molecular and chemical reactions in the MTO reaction process, structural units were coupled based on the bond-electric matrix. This method decomposes the raw material and product molecules in the MTO reaction into a series of structural groups, and the splicing of these structural groups enables a rapid and intuitive representation of the molecules.

[0039] In the SU-BEM framework, bond-electrical matrices are used for low-level molecular structural description, and the molecular information they represent can be accessed by other top-level modules. Structural units can be directly specified by the user and assembled according to certain rules to represent a complete molecule. The mapping and information transfer between structural units and bond-electrical matrices are achieved through an internal module call. Therefore, the structural unit-bond-electrical matrix coupling framework facilitates molecular structural description, possesses good applicability and user-friendliness, and is convenient for computer information processing.

[0040] To obtain the physical properties of molecules, this invention employs the group contribution method for property calculation. The group contribution method is a simple and reliable property prediction method, and various group partitioning methods and related parameters are currently available. This method assumes that molecules are composed of different types of basic groups that interact with each other, and each group contributes a certain value to macroscopic properties. In the correlation formula of the group contribution method, the interaction parameters are obtained by fitting a large amount of experimental data, after which mathematical models can be developed to predict the macroscopic properties of other substances. Since the group partitioning method in the group contribution method differs from the group partitioning method in the structural units previously introduced, this invention further establishes mapping relationships between these groups and the various structural units within the structural unit-bond-electric matrix framework at different levels; and, according to a certain logical order, compiles the transformation rules for structural units into groups in the group contribution method.

[0041] The methanol-to-olefins (MTO) process occurs on a SAPO-34 catalyst and follows a hydrocarbon pool mechanism. Methanol is first converted to dimethyl ether at the active sites of the catalyst, producing water. The resulting dimethyl ether then dehydrates with methanol to produce low-carbon olefins such as ethylene, propylene, and butene. This olefin formation process is a parallel reaction. The generated olefins further react with hydrogen, and upon saturation, produce alkanes with the corresponding number of carbon atoms. This invention summarizes this reaction process into a reaction network, represented by the average of historical equilibrium constants obtained by controlling the process using historical reaction parameters.

[0042] Furthermore, the dynamic changes in the simulation reaction and regeneration process are analyzed. Specific steps include: outputting the corresponding reaction parameters for the MTO coking reaction based on the coking kinetic model, and obtaining a product inhibition index after coupling with an inhibition factor. The product inhibition index is used to quantify the degree to which the concentration of MTO coking reaction products inhibits the reaction rate during the MTO coking process. The inhibition factor is used to quantify the regulatory weights of reactant concentration, catalyst activity state, and reaction environment parameters on the product inhibition effect during the MTO coking reaction, in order to accurately correct the correlation between the product inhibition index and the reaction rate. If the obtained product inhibition index is greater than the reference product inhibition index, the currently obtained coking rate is judged. The specific process is as follows: If the current coking rate and catalyst activity are lower than the reference coking rate and catalyst activity, the average coking temperature in the corresponding MTO coking reaction process in the regenerator is obtained. The obtained average coking temperature and the deviation of the product inhibition index are input into the coking kinetic model to obtain the compensation value of the catalyst residence time. The residence time of the catalyst is adjusted to accelerate the coking rate and make the catalyst activity approach the corresponding reference value. The catalyst activity is obtained by reading the number of reactant molecules converted by each active site per unit time during the establishment of the reactor model. If the obtained product inhibition index is not greater than the reference product inhibition index, the current MTO coking reaction catalyst residence time is maintained.

[0043] In this embodiment, the reaction parameters of the MTO coking reaction refer to the key variables that affect the reaction process during coking. The core parameters include reaction temperature (bed temperature in the coking zone), reaction pressure (operating pressure in the reactor), oxygen concentration (volume fraction of oxygen in the coking medium), catalyst coke loading (mass of coke deposited on a unit mass of catalyst), and gas-solid contact time (residual time of the reaction gas in the catalyst bed). These parameters directly determine the initial rate and final efficiency of the coking reaction.

[0044] First, the basic coking reaction rate is calculated using the coking kinetic model with the above reaction parameters as input. Then, the inhibition factor is determined (based on the actual values ​​of reactant concentration, catalyst activity, and reaction environment parameters, its regulatory weight on the product inhibition effect is quantified according to a preset weight allocation rule). Finally, the reaction rate data output by the coking kinetic model is coupled with the inhibition factor for calculation (e.g., using the inhibition factor as a correction coefficient to quantify the degree of attenuation of the basic reaction rate). The resulting value is the product inhibition index, which can directly reflect the degree of inhibition of the reaction rate by the concentration of coking reaction products.

[0045] It should be noted that the dynamic model adopts the classical Arrhenius equation, as shown in equations (1) and (2).

[0046]

[0047]

[0048] In the formula, r j K represents the rate of a chemical reaction. The subscript j indicates the chemical reaction, j = 1, 2, 3...J, where J represents the number of types of chemical reactions. srj The reaction rate at the catalyst surface can be calculated using the Arrhenius equation, as shown in equation (2). j and Ea j Let represent the kinetic parameters for chemical reaction type j, where represents the pre-exponential factor and activation energy, respectively; e represents the natural constant; R represents the universal gas constant; and T represents the absolute temperature. The pre-exponential factor and activation energy are the kinetic parameters obtained through regression fitting using industrial data. C m A With C n B This represents the concentration of reactants during the reaction process; A and B represent different substances.

[0049] After determining the kinetic model, the reaction rate expression for each substance is derived based on the molecular transformation relationships in the reaction network. These reaction rate expressions are a series of ordinary differential equations, which can be solved using an ordinary differential equation solver (such as ODE45) to calculate the concentration distribution of each substance in the reactor.

[0050] The MTO process uses SPAO-34 catalyst. Due to the limitation of the catalyst pores, only molecules with fewer than 5 carbon atoms are allowed to pass through the catalyst. Molecules with more than 5 carbon atoms will remain inside the catalyst and react further, eventually forming coke and causing catalyst deactivation. Therefore, the methanol-to-olefins process is usually equipped with a corresponding regenerator to burn off the coke on the catalyst surface through an oxidation combustion reaction. Since the molecular composition of coke is very complex, there is currently no good method to obtain its detailed molecular composition. In this invention, the chemical formula of coke is represented by CHq. The chemical reactions involved in the coking process are shown in formulas (3)-(6).

[0051]

[0052]

[0053]

[0054]

[0055] The charring process also uses the Arrhenius equation as the kinetic model, as shown in formula (2). The reaction rate expressions for these four reactions are shown in formulas (7)-(12).

[0056]

[0057]

[0058]

[0059]

[0060] (11)

[0061] (12)

[0062] In the formula, σ is the inhibition factor, representing the sum of all inhibitory effects. It is usually expressed as the sum of the product of the carbon monoxide concentration and its equilibrium constant, and the product of the oxygen concentration and its equilibrium constant. RGn These are intrinsic reaction rate constants, which are determined only by the reaction itself and temperature, and follow the Arrhenius equation. and The rate constant r represents the effective, weakened rate constant in a real reaction environment. RGn The reaction rate is represented by n, which represents the reaction path, where n = 1, 2, 3, ..., N, and N represents the number of reaction paths. C represents the concentration of the corresponding reactant. ck The concentration of coke is represented by K, which represents the equilibrium constant of the corresponding substance, while the product inhibition index is determined by... and The r represented RG1 r RG2 The difference between the corresponding reaction rate and the reference rate is represented by the summation and averaging of the corresponding reaction rates in the historical charring process.

[0063] The number of active sites on the catalyst surface is limited. During coking, these sites not only adsorb oxygen and coke, but also the reaction products CO and H2O. If CO or H2O molecules are firmly adsorbed on a certain active site, that site can no longer be used for the coke combustion reaction. As the product concentration increases, more active sites are occupied, resulting in fewer sites available for coke and oxygen to utilize, thus reducing the apparent coking reaction rate. Formulas (11) and (12) reflect this inhibitory effect. When there are no products, the effective rate constant is equal to the intrinsic constant. When the product concentration is very high, the denominator becomes large, the effective rate constant becomes very small, and the reaction slows down. The reference product inhibition index is represented by the sum and average of historical product inhibition indices during historical coking reactions.

[0064] like Figure 3The diagram shows the flowchart of the fluidized bed mass-heat balance simulation module provided in this embodiment of the invention. Its design logic is as follows: obtain the concentration height change rate and determine whether it is within the range. If it is greater than the maximum value of the range, adjust the catalyst particle size. If it is less than the minimum value, adjust the fluidization rate. Then perform heat balance calculation. Determine whether it is within the preset deviation range by using the obtained product gas distribution index and heat generation rate. If it is, complete the mass balance and heat balance calculation. Otherwise, perform iterative optimization.

[0065] Furthermore, mass balance is performed within the dense and dilute phase zones of the fluidized bed reactor. Specifically, based on the relationship between the reaction height of the corresponding chemical reaction in the dense phase zone and the concentration of molecular substances in the MTO process, the concentration-height change rate is obtained and compared with a reference concentration-height change rate range. The reaction zone represents the spatial range in which chemical transformation occurs within the reactor in the dense and dilute phase zones. The reaction height of the chemical reaction refers to the effective height range of the catalyst bed in the dense phase zone where the target chemical reaction (such as the main reaction and side reaction of methanol to olefins) actually occurs. The concentration-height change rate reflects the rate of concentration change at the corresponding reaction zone height within the reactor. By observing the magnitude and sign, the reaction height, reaction direction, reaction type, and reaction rate within the reactor are obtained. It represents the instantaneous rate of change of the concentration of a specific substance (such as methanol, ethylene, or byproducts) within the reactor as the axial height of the reactor increases. The reference concentration-height change rate range represents the closed interval formed by the maximum and minimum values ​​of the historical concentration-height change rate, used to evaluate the reactor's operating status during the digital twin process.

[0066] Specifically, a comparison is made with the reference concentration-height change rate range. This includes: if the obtained concentration-height change rate is less than the minimum value of the reference range, the lower deviation of the concentration-height change rate is input into the fluidized bed reactor model to obtain the fluidization rate in the dense phase region. This balances the mixing between the bubble phase and the emulsion phase, thereby increasing the apparent reaction rate within the reactor. The apparent reaction rate quantifies the overall reaction rate of all chemical reactions occurring on the catalyst within the reactor. After adjusting the fluidization rate, a heat balance is performed. If the obtained concentration-height change rate is within the reference range, a heat balance is performed. If the obtained concentration-height change rate is greater than the maximum value of the reference range, the upper deviation of the concentration-height change rate is input into the fluidized bed reactor model to obtain an adjustment value for the catalyst particle size. This alters the catalyst's influence on the apparent reaction rate within the reactor, suppressing excessive concentration gradients and improving the utilization rate of the upper region of the reactor. After adjusting the catalyst particle size, a heat balance is performed.

[0067] The specific process of heat balance calculation is as follows: Based on the results of the material balance equation, the product gas distribution index is obtained, which is used to quantify the distribution ratio of olefin products and flue gas in the dense phase bed; based on the deviation of the product gas distribution index, it is input into the fluidized bed reactor model to obtain the adjustment value of the bubble phase bubble content, which is used to adjust the apparent gas velocity and optimize the fluidization state; based on the results of mass balance calculation, the reaction rate in the reactor is obtained to obtain the heat generation rate per unit volume of bed, which is used to quantify the temperature distribution in the reactor; the product gas distribution index and heat generation rate obtained after adjusting the apparent gas velocity are input into the fluidized bed reactor model for comparison.

[0068] If the deviation of the reacquired product gas distribution index is not within the preset gas distribution index deviation range or the deviation of the heat production rate is not within the preset heat production rate deviation range, then iterative optimization is performed; if both the obtained heat production rate deviation and the reacquired product gas distribution index deviation are within the preset deviation range, then mass balance and energy balance are completed; the preset deviation range includes the preset gas distribution index deviation range and the preset heat production rate deviation range. Iterative optimization means repeating the cycle of mass balance and energy balance. The upper deviation of the concentration-height change rate represents the difference between the obtained concentration-height change rate and the maximum value of the reference concentration-height change rate range, and the lower deviation of the concentration-height change rate represents the difference between the obtained concentration-height change rate and the minimum value of the reference concentration-height change rate range.

[0069] In this embodiment, in the industrial production process, both the reactor and the regenerator in the methanol-to-olefins process are fluidized bed reactors. Therefore, the same model can be used to calculate the transport processes of various substances in the reactor and regenerator. The molecular distribution of the products is usually determined by both the reaction rate and the transport process. Specifically, the kinetic model is used to describe the conversion rate of the reaction system and obtain the molecular composition distribution before and after the reaction.

[0070] The reactor model is mainly used to describe the mass and heat transfer processes between the gas and solid phases in a methanol-to-olefins (MTO) fluidized bed reactor. Both the reactor and regenerator in the MTO process are fluidized bed reactors. A fluidized bed reactor can be divided into two reaction zones: a dense phase zone and a dilute phase zone. Based on the transport principle of the fluidized bed reactor, the dense phase fluidized bed can be further divided into two phases: a bubble phase and a milky phase. During the reaction, methanol mainly exists in the bubble phase and needs to overcome the mass transfer resistance between the gas and solid phases to enter the milky phase. In the milky phase, it contacts the MTO catalyst and undergoes a chemical reaction on the catalyst surface.

[0071] According to the two-phase theory of fluidized beds, the flow states of the bubble phase and the emulsion phase can be assumed to be plug flow and completely mixed flow, respectively. The mass balance equations for each substance are shown in formulas (13) and (14).

[0072] (13)

[0073] (14)

[0074] The expressions on the right-hand side of equations (13) and (14) represent the transport phase and reaction rate terms, respectively. The reaction rate term can be calculated using the kinetic model and the reaction rate expression, v D g_E v D g_B represent the apparent velocity of the fluid in the empty tower in the emulsion phase (E) and the bubble phase (B), respectively; since the two formulas are similar, only one formula is explained (in the formula, _B represents the bubble phase and _E represents the emulsion phase), in formula (13), K represents the rate of change of the concentration of component i in the emulsion phase along the fluidized bed height, reflecting the concentration gradient characteristics with bed height. I The mass transfer coefficient is . C represents the gas phase volume fraction of the emulsion phase. D i_B The equilibrium concentration (C) represents the mass transfer equilibrium concentration between the bubble phase and the emulsion phase. D i_E This represents the equilibrium concentration of mass transfer in the emulsion-bubble phase), R. D i_E The reaction rate term represents the reaction of component i in the emulsion phase, where component i represents the i-th chemical substance participating in the chemical reaction system, i represents any reactant or product in the system, and the superposition D represents the dense phase region. For the mass transfer term, the mass transfer coefficient needs to be calculated. The calculation of the mass transfer coefficient in the gas-solid fluidized reaction system is very complex. The distribution of each substance in the emulsion phase and the bubble phase can be calculated. Then, the average concentration in the dense phase region can be calculated using formula (15).

[0075]

[0076] In the formula, C D i This represents the average concentration of component i throughout the dense phase region. The aforementioned material balance equation is mainly used to calculate the distribution of olefin products and flue gas in the dense phase bed, employing a plug flow model. The generated coke is located on the catalyst, which is primarily present in the emulsion phase. In the fluidized bed model, the emulsion phase is calculated using a fully mixed flow model, where the concentration is uniform throughout the reactor.

[0077] Similarly, the temperature distribution in the dense-phase bed is mainly provided by the catalyst in the emulsion phase, and the heat released or absorbed by the reaction can be calculated using the enthalpy change of the reaction. In this invention, the enthalpy change of the reaction is the sum of the product formation enthalpies minus the reactant formation enthalpies, and the formation enthalpies of each substance can be calculated using the group contribution method. After the mass and heat balance of the dense-phase region are completed, the concentration and temperature distributions of each substance in the dense-phase region can be obtained. A dilute-phase region also exists above the dense-phase region. According to the fluidized bed reactor model, the flow state in the dilute-phase region can be directly assumed to be plug flow. Furthermore, for methanol-to-olefins reactors, the dilute-phase region basically does not react and can theoretically be ignored. In the regenerator, the dilute-phase region mainly undergoes a carbon monoxide post-combustion reaction to produce carbon dioxide. This reaction does not require the presence of a catalyst, and the volume fraction of the catalyst can be estimated using empirical formulas. In addition, the heat balance equation can also be derived based on the plug flow assumption.

[0078] First, the methanol stream is fed into the feed mixer and mixed with the catalyst regenerated from the regenerator. The coke content and catalyst temperature on the catalyst are the initial values ​​for the model. After mixing, the mixture is input into the MTO reactor model, and the mass balance and energy balance equations are executed to calculate the distribution of product gas in the reactor, obtaining the detailed molecular composition of olefins from the top of the reactor. The deactivated catalyst is then input into the regenerator model, with an air stream input from the bottom of the regenerator model. A coking reaction occurs in the regenerator, generating flue gas components such as carbon monoxide and carbon dioxide, and the flue gas composition is calculated from the top of the regenerator. After the coking reaction, the coke content and catalyst temperature on the regenerated catalyst can be calculated using the mass balance and heat balance equations. Next, the calculated coke content and catalyst temperature are compared with the initial values ​​input into the model. If there is an error, iterative calculations are performed until the error is within an acceptable range. In this invention, the iterative error between coke values ​​is set to 10. -4 The iteration error for temperature is 1℃. Furthermore, the model integrates Newton's iteration method and the SQP (Sequential Quadratic Programming) algorithm during iteration, enabling rapid convergence of material and energy balance during the reaction-regeneration process.

[0079] After the entire process simulation system was built, six months of industrial production data were used to fit the kinetic parameters in the model, enabling the model to accurately calculate the product distribution in the unit. Based on this, a corresponding optimization algorithm was developed. The optimization algorithm, based on the SQP algorithm, can calculate the reaction temperature and reactor stock volume that minimize methanol consumption under certain conditions. Users can set the calculation range for temperature and stock volume to more closely reflect actual production. Users can also specify the minimum yields of ethylene and propylene to meet actual industrial needs, thus optimizing the MTO process conditions.

[0080] It's important to understand that the catalyst isn't statically packed; as the gas velocity changes, the catalyst and reactants often move in a fluid state. The reaction section height is primarily reflected in the lower diameter of the reactor. By incorporating the obtained dense phase bed density data into the calculation of the reaction section height, the dynamic reaction height can be accurately calculated. For calculating the coke content on the nascent and regenerated catalysts, two methods exist: UV carbon determination and thermogravimetric analysis. Local optimization and genetic algorithms were incorporated into the model parameter adjustments to enhance the model's predictive ability when dealing with increased data volume, further reducing the overall error.

[0081] The deviation of the product gas distribution index is represented by the difference between the obtained product gas distribution index and the reference product gas distribution index. The reference product gas distribution index is represented by the summation and averaging of historical product indices in the historical heat balance process. The preset gas distribution index deviation range represents the closed interval between the maximum and minimum values ​​of the gas distribution index in the historical heat balance. The heat production rate deviation is represented by the difference between the obtained heat production rate and the preset heat production rate. The preset heat production rate is represented by the summation and averaging of historical heat production rates in the historical heat balance process. The preset heat production rate deviation range represents the closed interval between the maximum and minimum values ​​of the heat production rate deviation in the historical heat balance.

[0082] like Figure 4 The diagram shows the flowchart of the regeneration synergistic simulation scenario generation module provided in this embodiment of the invention. Its design logic is as follows: Regeneration efficiency verification and optimization are performed by pre-setting a reference regeneration catalyst activity recovery rate, obtaining regeneration process data and the regeneration catalyst activity recovery rate, and quantitatively determining the regeneration efficiency deviation. Specifically, the regenerator operation parameters are adjusted based on the deviation of the regeneration catalyst activity recovery rate. Then, the key regeneration indicators are judged. When all key regeneration indicators do not exceed the preset value, the simulation is considered complete. If a single indicator exceeds the preset value, a reaction-regeneration synergistic control strategy is implemented. In addition to these two cases, an abnormal regeneration warning is issued.

[0083] Further understanding is needed regarding the specific process of regeneration efficiency verification and optimization: Based on the formation path of coke precursors in the MTO reaction network and the deactivated catalyst activity decay data output by the reactor model, combined with historical operation and maintenance data of catalyst regeneration in industrial MTO units, the value corresponding to p95 is set as the reference regenerated catalyst activity recovery rate; in the generated dynamic simulation scenario of MTO catalyst deactivation and regeneration synergy, temperature field distribution data, real-time data of main air (oxygen-containing gas) flow rate and oxygen concentration, data of deactivated catalyst feed rate and residence time, data of flue gas component (CO, CO2, H2O) content during regeneration, and molecular structure characterization data of the catalyst discharge sampling point after regeneration are obtained; the obtained regeneration process data are quantitatively analyzed through molecular-level characterization algorithms, and through multi-dimensional data correlation conversion, the regenerated catalyst activity recovery rate that can accurately reflect the performance of the catalyst after regeneration is obtained, and the regeneration efficiency deviation is quantitatively judged, specifically:

[0084] The obtained regenerated catalyst activity recovery rate is compared with the reference regenerated catalyst activity recovery rate to obtain the regeneration efficiency deviation, which is the difference between the obtained regenerated catalyst activity recovery rate and the reference regenerated catalyst activity recovery rate. The regeneration efficiency deviation is input into the pre-constructed regeneration operation parameter mapping relationship to obtain the adjustment strategy for the regenerator operation parameters. The regenerator operation parameters include one or more of regeneration temperature, main air volume, and catalyst residence time, such as adjusting only the regeneration temperature, or a strategy that comprehensively adjusts the regeneration temperature and main air volume. The execution process of the adjustment strategy in the dynamic simulation scenario is monitored to quantify the linkage effect between the reactor model and the regenerator model under coordinated operation. The regeneration operation parameter mapping relationship is obtained by performing decision tree learning on the historical operating data of the MTO unit, analyzing the nonlinear correlation between the historical operating parameters of the MTO unit and the regeneration results, and training to obtain a mapping relationship that can infer the direction and magnitude of parameter adjustment from the deviation.

[0085] The key regeneration indicators acquired through monitoring include the proportion of regenerated flue gas, the catalyst micro-reaction activity value, and the regenerated carbon content. These three indicators construct a three-dimensional evaluation dimension of regeneration effect from the perspectives of flue gas composition, catalyst activity, and residual carbon deposits. If any of the acquired key regeneration indicators exceeds the corresponding preset value, it is determined that the catalyst regeneration efficiency does not meet the requirements of the coordinated operating condition, and the reaction-regeneration coordinated control strategy is triggered. The corresponding preset values ​​include preset regenerated flue gas proportion, preset catalyst micro-reaction activity value, and preset regenerated carbon content, all of which are set through historical regeneration operation and maintenance data of the MTO reaction network and the dual-reactor coordinated operating condition standard. If none of the acquired key regeneration indicators exceed the corresponding preset values, it is determined that the catalyst regeneration efficiency meets the requirements of the coordinated operating condition, and the MTO reaction process simulation is completed. Otherwise, a regeneration anomaly warning is output, and the execution parameters of the current regenerator model are recorded, providing complete data support for subsequent regeneration process parameter optimization, fault tracing, and model iteration. The reaction-regeneration coordinated control strategy is triggered as follows:

[0086] The system displays the specific index deviations and warning levels of the current regeneration operation and reactor coordination imbalance, and automatically performs gradient coordination adjustments on the methanol feed rate and catalyst circulation rate corresponding to the reactor model based on the acquired single regeneration key index deviation. The single regeneration key index deviation represents the difference between any one of the three indicators and its corresponding preset value. After coordination adjustment, if the reacquired regeneration key indicators do not exceed their corresponding preset values, the MTO reaction process simulation is completed.

[0087] In this embodiment, by integrating the molecular mechanism of the MTO reaction, the dual-reactor collaborative simulation model, and the regeneration efficiency quantification system, a collaborative strategy of prior verification in the digital space—completing regeneration parameter optimization and dual-reactor operating condition linkage verification in a digital twin environment—effectively avoids the overall reduction in catalyst activity within the reactor due to insufficient regeneration. This ensures the continuous high efficiency of the methanol conversion process and the stability of the target olefin product yield at the simulation level. This strategy relies on the p95 quantile reference activity recovery rate and multi-dimensional regeneration data, achieving accurate regeneration efficiency determination through molecular-level algorithms. It outputs an optimized scheme using parameter mapping relationships trained by decision trees, and then triggers gradient adjustments to the reactor feed rate and catalyst circulation volume through monitoring key regeneration indicators, forming a closed-loop control. This reduces reliance on human experience and strengthens the collaborative adaptation of the entire reaction-regeneration process, improving the reliability of the MTO digital twin simulation and the stability of process operation.

[0088] like Figure 5The diagram shows the operation interface for temperature simulation of chemical processes using MTO trend simulation provided in this embodiment of the invention. The title is "Temperature Simulation," and the red annotation indicates that the trend is accurate only within the specified operating conditions; beyond these conditions, it has no reference value. It features "Reaction Temperature Simulation" and "Regeneration Temperature Simulation" modes, allowing users to input start temperature (e.g., 500℃), end temperature (e.g., 700℃), and step size (e.g., 50℃) parameters, and then click "Start Simulation" to run the simulation. It supports component screening (e.g., C3H6), batch operations of selecting / deselecting all components, and chart export and download. The horizontal axis of the chart area represents temperature (℃), and the vertical axis represents components (t / h). Different colored curves present the correlation trend between temperature and component flow rates, which can assist in chemical process optimization, anomaly investigation, and training research. It visualizes and interacts with complex models, facilitating efficient exploration of the impact of temperature on processes.

[0089] Figure 6 and Figure 7 These are all schematic diagrams of MTO optimization simulation calculations provided in the embodiments of the present invention. The table at the top lists various process input parameters and corresponding values, such as air feedstock and pure methanol. The left side shows the lower limit of ethylene production, the lower limit of propylene production, etc., marked with "". The input box for constraints has "Calculate" and "Reset" buttons in the middle. After clicking "Calculate", the right side will display the "Calculation Results" area, which includes results such as reactor temperature, reaction catalyst reserves, methanol consumption, and ethylene / propylene / C4 mass flow rates. It can be used in chemical production to simulate and calculate key reaction indicators based on input parameters and constraints, and assist in process analysis and control.

[0090] Figure 8 and Figure 9 These are schematic diagrams verifying the MTO optimization simulation calculation results provided in the embodiments of the present invention, illustrating the connection between the reactor and the regenerator. The left side presents the core equipment such as the reactor and regenerator in a three-dimensional model, with the water vapor flow rate (16.8 t / h) and reactor stock density (84 kg / m³) marked by blue boxes around them. 3 The system includes input parameters such as temperature (480℃), pure methanol feed (240t / h), catalyst circulation rate (29.1t / h), and external nitrogen, air, and pentene feed. The right side is divided into "Product Gas Mass Flow Rate (t / h)" and "Calculation Results" sections. The former lists the flow rates and sums of components such as methanol and ethylene, while the latter covers the coke content of the catalyst to be recycled / regenerated, the ethylene-propylene ratio, methanol consumption, and the molar percentage data of the outlet product and flue gas product. It can intuitively display the material parameters and reaction results of the chemical process and can be used to assist in process analysis and control.

[0091] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function according to the embodiments of the present invention is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. In addition, the character " / " in this document generally indicates that the related objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0092] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0093] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0095] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention.

[0096] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A simulation system for MTO reaction processes based on molecular modeling and optimization technology, characterized in that, It includes the following modules: molecular reaction network construction module, fluidized bed mass-heat balance simulation module, and regeneration synergistic simulation scenario generation module; The molecular reaction network construction module is used to construct an MTO reaction network by digitally characterizing the molecules of raw materials and products in the MTO reaction process, and to control the reaction process parameters. The molecular digital characterization means controlling and optimizing the MTO reaction process by quantifying the reaction characteristics at the molecular level. The MTO reaction network is used to present the molecular reaction pathway of methanol molecules being converted into olefins through intermediates in a visual way. The fluidized bed mass and heat balance simulation module is used to perform mass and heat balance calculations based on the reaction path data output by the MTO reaction network. It also combines the dynamic prediction results of MTO reactant concentration on product concentration to iteratively optimize and construct the reactor model and regenerator model of the MTO fluidized bed. The mass balance calculation is used to quantify the inflow, outflow and accumulation of each material in the MTO reaction network to clarify the quantitative relationship of mass transfer and transformation. The heat balance calculation is used to quantify the dynamic law of the temperature field distribution corresponding to the energy balance in the MTO reaction network. The regeneration synergistic simulation scenario generation module is used to generate a dynamic simulation scenario of MTO catalyst deactivation and regeneration synergy in a digital twin environment based on the molecular reaction path data output by the MTO reaction network and the constructed reactor model and regenerator model, and simultaneously perform regeneration efficiency verification and optimization. The specific process for constructing the MTO reaction network is as follows: Based on the constructed SU-BEM molecular reaction network, a methanol-to-olefins reaction network in the MTO reaction process is established through the set MTO reaction equation. The methanol-to-olefins reaction network includes a kinetic model for simulating the dynamic changes in the reaction and regeneration process, and the kinetic model includes a reaction kinetic model and a coking kinetic model. Based on the results of the Arrhenius equation, the reaction parameters and equilibrium constant of the MTO main reaction are obtained, which can be used to quantify the fluctuation of the reaction feedstock during the MTO main reaction. The specific process for controlling the reaction process parameters in the reactor model is as follows: If the obtained equilibrium constant is less than the reference equilibrium constant, the equilibrium constant deviation and the average temperature of the reactants in the current MTO main reaction process are input into the reaction kinetic model to obtain the temperature step compensation value. This value is then added to the current temperature step compensation value of the reactor to obtain the actual temperature step compensation value. The reactor temperature is then gradually adjusted to make the equilibrium constant approach the corresponding reference value. If the obtained equilibrium constant is not less than the reference equilibrium constant, then maintain the current reactor temperature step size; The specific process for verifying and optimizing regeneration efficiency is as follows: Based on the formation pathway of coke precursors in the MTO reaction network and the deactivated catalyst activity decay data output by the reactor model, combined with the historical operation and maintenance data of catalyst regeneration in industrial MTO units, a reference regenerated catalyst activity recovery rate is set. In the generated dynamic simulation scenario of synergistic deactivation and regeneration of MTO catalyst, regeneration process data is acquired. The acquired regeneration process data is quantitatively analyzed and correlated using a molecular-level characterization algorithm to obtain the regenerated catalyst activity recovery rate and to quantitatively determine the regeneration efficiency deviation.

2. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 1, characterized in that, The simulated reaction and regeneration process involves dynamic changes, and the specific steps include: Based on the char kinetic model, the corresponding reaction parameters of the MTO char reaction are output, and the product inhibition index is obtained after coupling with the inhibition factor. The product inhibition index is used to quantify the degree of inhibition of the reaction rate by the concentration of MTO char reaction products. The inhibition factor is used to quantify the regulatory weight of reactant concentration, catalyst activity state and reaction environment parameters on the product inhibition effect during the MTO char reaction, so as to accurately correct the correlation between the product inhibition index and the reaction rate. If the obtained product inhibition index is greater than the reference product inhibition index, then the currently obtained charring rate is judged. The specific process is as follows: If the current coking rate and catalyst activity are lower than the reference coking rate and catalyst activity, the average coking temperature in the corresponding MTO coking reaction process in the regenerator is obtained. The obtained average coking temperature and the product inhibition index deviation are input into the coking kinetic model to obtain the compensation value of the catalyst residence time. The residence time of the catalyst is adjusted to accelerate the coking rate and make the catalyst activity approach the corresponding reference value. If the obtained product inhibition index is not greater than the reference product inhibition index, then the current MTO coking reaction catalyst residence time is maintained.

3. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 1, characterized in that, The mass balance is performed in the dense phase and dilute phase regions of the fluidized bed reactor, specifically as follows: Based on the relationship between the height of the reaction section and the concentration, the concentration-height change rate is obtained and compared with the reference concentration-height change rate range. The reaction section represents the spatial range in which chemical transformation occurs in the reactor in the dense phase region and the dilute phase region. The concentration-height change rate is used to reflect the rate of concentration change at the height of the reaction section within the reactor. The reference concentration-height change rate range represents a closed interval formed by the maximum and minimum values ​​of the historical concentration-height change rate, which is used to evaluate the reactor's operating status during the digital twin process.

4. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 3, characterized in that, The comparison with the reference concentration-altitude change rate range specifically includes: If the obtained concentration-height change rate is less than the minimum value of the reference concentration-height change rate range, the fluidization rate in the dense phase region is obtained by inputting the deviation of the concentration-height change rate into the fluidized bed reactor model to balance the mixing between the bubble phase and the emulsion phase, thereby improving the apparent reaction rate in the reactor. The apparent reaction rate is used to quantify the overall reaction rate of all chemical reactions occurring on the catalyst in the reactor. After the fluidization rate is adjusted, heat balance is performed. If the obtained concentration-height change rate is within the reference concentration-height change rate range, then a heat balance calculation is performed. If the obtained concentration-height change rate is greater than the maximum value of the reference concentration-height change rate range, the deviation in the concentration-height change rate is input into the fluidized bed reactor model to obtain the adjustment value of the catalyst particle size, so as to change the degree of influence of the catalyst on the apparent reaction rate in the reactor, which is used to suppress excessive concentration gradient and improve the utilization rate of the upper region of the reactor. After the catalyst particle size is adjusted, heat balance is performed.

5. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 3, characterized in that, The heat balance calculation process is as follows: Based on the results of the material balance equation, a product gas distribution index is obtained, which is used to quantify the distribution of olefin products and flue gas in the dense phase bed. Based on the deviation of the product gas distribution index, the value is input into the fluidized bed reactor model to obtain the adjustment value of the bubble phase bubble content, which is used to adjust the apparent gas velocity and optimize the fluidization state. Based on the mass balance results, the reaction rate in the reactor is obtained, and the heat generation rate per unit volume of bed is obtained, which is used to quantify the temperature distribution in the reactor. The product gas distribution index and heat production rate, obtained again after adjusting the apparent gas velocity, are input into the fluidized bed reactor model for comparison.

6. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 5, characterized in that, The comparison process in the fluidized bed reactor model is as follows: If the deviation of the re-acquired product gas distribution index is not within the preset gas distribution index deviation range or the deviation of the heat generation rate is not within the preset heat generation rate deviation range, then iterative optimization is performed. If the deviation of the obtained heat generation rate and the deviation of the re-obtained product gas distribution index are both within the preset deviation range, then the mass balance and energy balance are completed. The preset deviation range includes a preset gas distribution index deviation range and a preset heat generation rate deviation range. The iterative optimization refers to the cycle of re-performing mass balance and energy balance.

7. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 1, characterized in that, The specific process for quantifying the regeneration efficiency deviation is as follows: The obtained regenerated catalyst activity recovery rate is compared with the reference regenerated catalyst activity recovery rate to obtain the regeneration efficiency deviation; The regeneration efficiency deviation is input into a pre-built mapping relationship of regeneration operation parameters to obtain an adjustment strategy for the regenerator operation parameters, which include one or more of regeneration temperature, main air volume and catalyst residence time. Monitor the execution process of adjustment strategies in dynamic simulation scenarios to quantify the linkage effect between the reactor model and the regenerator model under coordinated operating conditions.

8. The MTO reaction process simulation system based on molecular modeling and optimization technology as described in claim 7, characterized in that, The interaction between the quantified reactor model and the regenerator model under coordinated operating conditions is as follows: The key regeneration indicators obtained through monitoring include the proportion of regenerated flue gas, the catalyst micro-reaction activity value, and the regenerated carbon content. If any single key indicator among the acquired regeneration key indicators exceeds the corresponding preset value, it is determined that the catalyst regeneration efficiency does not meet the requirements of the synergistic operating condition, and the reaction-regeneration synergistic regulation strategy is triggered. If the obtained key regeneration indicators do not exceed the corresponding preset values, it is determined that the catalyst regeneration efficiency meets the requirements of the synergistic working condition, and the MTO reaction process simulation is completed. Conversely, if the regeneration anomaly warning is not detected, the execution parameters of the current regenerator model will be recorded. The trigger-regeneration synergistic regulation strategy is specifically as follows: Based on the obtained deviations in key regeneration indicators, the feed rate of methanol feedstock and the catalyst circulation rate corresponding to the reactor model are adjusted in a coordinated manner. If the newly acquired key regeneration indicators do not exceed the corresponding preset values ​​after coordinated adjustment, the MTO reaction process simulation is completed.

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