Fischer-Tropsch synthesis slurry bed reactor modeling method and device and computer equipment

Abstract

The invention relates to a Fischer-Tropsch synthesis slurry bed reactor modeling method, a Fischer-Tropsch synthesis slurry bed reactor modeling device and computer equipment. The method comprises the following steps: carrying out lumped division on reactants and products to obtain lumped information; performing reaction prediction according to the lumped information to obtain prediction parameters; forming a slurry bed reactor model according to the prediction parameters; and performing parameter setting according to the slurry bed reactor model to obtain a simulation result. By adopting the method, the Fischer-Tropsch synthesis reaction can be effectively simulated, the solving speed is high, and the calculation result is accurate and reliable, so that the method is widely applied to off-line and on-line real-time simulation and real-time optimization of various Fischer-Tropsch synthesis devices, and has the advantages of wide application range, accurate calculation result, short optimization time, flexible parameter adjustment, full suitability for real-time optimization and the like.

Description

Technical Field

[0001] The present application relates to the field of advanced process control technology, and in particular to a method, device and computer equipment for modeling a Fischer-Tropsch synthesis slurry bed reactor. Background Art

[0002] Fischer-Tropsch Synthesis technology is one of the important directions for clean utilization of coal. Its main reaction is the reaction of CO and H2 to produce hydrocarbons, which has the advantages of cleanliness, environmental protection and excellent combustion performance. With the increasing maturity and development of Fischer-Tropsch synthesis technology, the synthesis of liquid fuels using coal and natural gas as raw materials as a technology to alleviate energy shortages has received more and more attention and research.

[0003] As the core equipment of the Fischer-Tropsch synthesis industrial plant, the performance of the Fischer-Tropsch synthesis reactor has a significant impact on the economic efficiency of the plant. Therefore, it is of great significance to optimize the reactor performance in real time through modeling research of the Fischer-Tropsch reactor.

[0004] Existing models are generally developed for specific devices, and there are great limitations if they are extended to other devices. Axial diffusion is usually considered, and there are second-order derivatives in the model. It is difficult to determine the initial value when solving, and it takes a long time to solve, which cannot meet the needs of real-time optimization. Appropriate simplification of the model greatly improves the solution efficiency while meeting the accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a Fischer-Tropsch synthesis slurry bed reactor modeling method, device, computer equipment, computer-readable storage medium and computer program product with short optimization time and flexible parameter adjustment to address the above technical problems.

[0006] In a first aspect, the present application provides a method for modeling a Fischer-Tropsch synthesis slurry bed reactor. The method comprises:

[0007] Lump and divide the reactants and products to obtain lump information;

[0008] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0009] According to the prediction parameters, a slurry bed reactor model is constructed;

[0010] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0011] In one embodiment, performing reaction prediction according to the aggregate information to obtain prediction parameters includes:

[0012] Analyzing the aggregated information to establish a catalyst deactivation model;

[0013] According to the catalyst deactivation model, catalyst activity parameters are obtained.

[0014] In one embodiment, constructing a slurry bed reactor model according to the predicted parameters includes:

[0015] Wherein, the prediction parameters include catalyst activity parameters, physical property parameters, product parameters and axial temperature parameters;

[0016] According to the predicted parameters, a reaction kinetic equation is formed;

[0017] According to the reaction kinetics equation, a slurry bed reactor model is constructed.

[0018] In one embodiment, forming a reaction kinetics equation according to the predicted parameters comprises:

[0019] According to the predicted parameters, a Fischer-Tropsch synthesis reaction and a water-gas shift reaction are formed;

[0020] Based on the Fischer-Tropsch synthesis reaction shown and the water-gas shift reaction, a reaction kinetic equation is constructed.

[0021] In one embodiment, the step of setting parameters according to the slurry bed reactor model to obtain simulation results includes:

[0022] According to the slurry bed reactor model, simulating and generating kinetic parameters;

[0023] The reactor parameters are adjusted according to the kinetic parameters to obtain simulation results.

[0024] In one embodiment, the method further comprises:

[0025] According to the simulation results, the advanced control system is optimized in real time.

[0026] In a second aspect, the present application also provides a Fischer-Tropsch synthesis slurry bed reactor modeling device. The device comprises:

[0027] An information acquisition module is used to lump reactants and products to obtain lumped information;

[0028] A prediction parameter module, used for performing reaction prediction according to the aggregate information to obtain prediction parameters;

[0029] A model creation module, used to construct a slurry bed reactor model according to the prediction parameters;

[0030] The parameter setting module is used to set parameters according to the slurry bed reactor model to obtain simulation results.

[0031] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] Lump and divide the reactants and products to obtain lump information;

[0033] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0034] According to the prediction parameters, a slurry bed reactor model is constructed;

[0035] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Lump and divide the reactants and products to obtain lump information;

[0038] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0039] According to the prediction parameters, a slurry bed reactor model is constructed;

[0040] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0041] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0042] Lump and divide the reactants and products to obtain lump information;

[0043] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0044] According to the prediction parameters, a slurry bed reactor model is constructed;

[0045] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0046] The above-mentioned Fischer-Tropsch synthesis slurry bed reactor modeling method, device and computer equipment lump reactants and products to obtain lump information; perform reaction prediction based on the lump information to obtain prediction parameters; construct a slurry bed reactor model based on the prediction parameters; perform parameter adjustment based on the slurry bed reactor model to obtain simulation results. This method can effectively simulate the Fischer-Tropsch synthesis reaction, has a fast solution speed, and accurate and reliable calculation results, so it is widely used in offline and online real-time simulation and real-time optimization of various Fischer-Tropsch synthesis devices, and has the advantages of wide application range, accurate calculation results, short optimization time, flexible parameter adjustment, and full applicability to real-time optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A process flow chart of a Fischer-Tropsch synthesis slurry bed reactor modeling method in one embodiment;

[0048] Figure 2 A schematic diagram of a process for modeling a Fischer-Tropsch synthesis slurry bed reactor in one embodiment;

[0049] Figure 3 A schematic diagram of temperature changes in a method for modeling a Fischer-Tropsch synthesis slurry bed reactor in one embodiment;

[0050] Figure 4 It is a structural block diagram of a Fischer-Tropsch synthesis slurry bed reactor modeling device in one embodiment;

[0051] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] like Figure 1 As shown, Figure 1 A schematic diagram of the process flow of a Fischer-Tropsch synthesis slurry bed reactor modeling method in one embodiment.

[0054] The synthesis gas from the fine desulfurization unit is mixed with the circulating gas from the outlet of the first stage of the circulating gas compressor, the hydrogen from the PSA unit, and the decarbonized purified gas from the decarbonization unit. The mixed raw gas enters the circulating heat exchange separator and exchanges heat with the high-temperature oil and gas from the top of the reactor, and then enters the Fischer-Tropsch synthesis reactor. The gas distributor at the bottom of the reactor passes through the slurry bed containing the catalyst in the form of bubbles to carry out the FT synthesis reaction. The light hydrocarbon compounds, carbon dioxide, water and unreacted synthesis gas generated by the reaction are discharged from the top of the reactor in the form of gas phase, and the heavy hydrocarbons generated by the reaction are discharged from the middle of the reactor as heavy wax after passing through the filtration system in the reactor.

[0055] The high-temperature oil and gas from the top of the reactor enters the circulating heat exchange separator and is separated by heat exchange cooling with the synthesis gas from the fine desulfurization unit, the circulating gas from the outlet of the first stage of the circulating gas compressor, the hydrogen from the PSA unit, and the decarbonized purified gas from the decarbonization unit. The separated heavy oil and water mixture passes through the circulating heat exchange separator coalescer for oil-water separation. The gas phase separated by the circulating heat exchange separator enters the oil-gas air cooler for cooling, and the oil and gas are separated by the light oil separator.

[0056] The circulating gas entering the circulating gas compressor is pressurized and then extracted from the first exhaust port and divided into two streams. One stream is mixed with the synthesis gas from the fine desulfurization unit, the hydrogen from the PSA unit, and the decarbonization purified gas from the decarbonization unit, and then enters the reactor for reaction according to the above-mentioned process; the other stream is further pressurized by the second stage of the circulating gas compressor, and a part of it is sent to the back-blowing gas heater and the back-blowing gas buffer tank (26111-V008) as the back-blowing gas for the dewaxing filter inside the reactor. The filter is back-blown according to the control of the dewaxing sequence control program; the other part is sent to the decarbonization unit.

[0057] In one embodiment, Figure 2 As shown, a Fischer-Tropsch synthesis slurry bed reactor modeling method is provided, comprising the following steps:

[0058] Step 202, lumping and dividing the reactants and products to obtain lumped information.

[0059] Specifically, the reaction species of the Fischer-Tropsch synthesis reaction are single but the products are complex, ranging from C1 components to C80+. Based on the lumped kinetic theory, the reactants and products are divided into 11 lumps, i.e., lumped information. The details are as follows:

[0060] Reactants: H2 and CO, 2 lumps in total.

[0061] Products: methane (CH4), petroleum gas (Oilgas), light oil (Loil), heavy oil (Hoil) and wax oil (wax), carbon dioxide (CO2), water (H2O), a total of 7 sets.

[0062] Inert gas: Nitrogen (N2), 1 lump in total.

[0063] Step 204: perform reaction prediction based on the aggregate information to obtain prediction parameters.

[0064] Among them, the prediction parameters include catalyst activity parameters, physical property parameters, product parameters and axial temperature parameters.

[0065] Specifically, performing reaction prediction according to the lumped information to obtain prediction parameters includes: analyzing the lumped information to establish a catalyst deactivation model; and obtaining catalyst activity parameters according to the catalyst deactivation model.

[0066] More specifically, prediction of catalyst activity parameters: In the Fischer-Tropsch synthesis reaction system, both the main and side reactions are affected by the catalyst. The catalyst in the Fischer-Tropsch synthesis gradually deactivates over time and needs to be replaced in time to maintain activity. However, in each catalyst cycle, the overall activity of the catalyst also has obvious differences, which has a great impact on the reaction, so it is necessary to establish a catalyst deactivation model that changes over time.

[0067]

[0068] Among them, k d is the deactivation coefficient, and As is the catalyst activity when the residence time is infinite.

[0069] Prediction of physical property parameters: The flow and interaction of substances in the reactor have a great influence on the three transfers and one reaction. In order to quantitatively describe such an influence, accurate and comprehensive physical property parameters are needed. Physical property parameters include: slurry viscosity, slurry density, slurry surface tension, catalyst density, molar mass of each lumped substance, saturated vapor pressure of each substance, Henry coefficient, mass transfer coefficient and diffusion coefficient. The physical properties will change with the operating conditions of the reactor and will also be different at different axial positions of the reactor. In addition, the molar mass of each lumped substance will change with the change of product distribution under the operating conditions.

[0070] Product parameter prediction: Due to the complexity of FT synthesis products and the wide distribution of carbon chain products, it is difficult to describe them one by one through reactions. ASF (Anderson–Schulz–Flory) distribution is often used to describe the product distribution of Fischer-Tropsch synthesis, and α in the model represents the chain growth factor. Studies have found that the chain growth factors of different carbon numbers are different, and the α value gradually decreases with the increase of carbon number. In general, the distribution of Fischer-Tropsch synthesis products reflects the characteristics of two-stage distribution, and the use of an α ASF distribution model does not conform to the actual situation. Therefore, the double α distribution is used to predict the product distribution. α is affected by operating conditions, positively correlated with pressure and nitrogen-carbon ratio, and negatively correlated with temperature and hydrogen-carbon ratio inlet air velocity. Two α can be obtained by fitting the product analysis data of high carbon number and low carbon number with correlation equations respectively. The correlation equation is as follows:

[0071]

[0072] Among them, b 1 are model parameters; P is pressure, MPa; T is temperature, K; GHSW is the synthesis gas inlet space velocity, h -1 ; R 1 is the hydrogen-carbon molar ratio of synthesis gas; R 1 is the molar ratio of nitrogen to carbon in the synthesis gas. It should be noted that the parameters in the correlation equation should be calibrated regularly using offline analysis data.

[0073] In the process of solving the differential equation, each step forward will recalculate the current product distribution, and calculate the current molar mass of each lumped mass based on the current product distribution. The calculation of the average carbon number of the lumped mass can also be calculated according to the principle of 2 H and n CH2.

[0074] In the process of solving the differential equation, each step forward will recalculate the current product distribution, and calculate the molar mass of each lumped mass based on the current product distribution. The average carbon number of the lumped mass can also be calculated from the molar mass, according to the principle of 2 H and the rest are CH2.

[0075] Prediction of axial temperature parameters: The calculation of heat balance requires a large amount of physical property data, which is a lot of work. In addition, the components in the reaction system are complex and the reactor volume is large, making it difficult to accurately describe the temperature distribution and changes through mechanism.

[0076] When the model is used for online simulation, the axial temperature distribution of the reactor can be achieved by reading the DCS online data and fitting it. When it is necessary to optimize the two temperature control points, the following means can be used: first assume that only the temperature of the lower temperature control point is increased by 1°C, then for the entire reactor, the farther away from the temperature control point, the less the temperature increases. Conversely, if the temperature of the lower temperature control point is reduced by 1°C, then for the entire reactor, the farther away from the temperature control point, the less the temperature decreases. Similarly, if only the upper temperature is changed, the situation is the same. Such temperature changes are similar to normal distributions. The result of the superposition of two normal distributions is the change in the axial temperature of the reactor after adjusting the upper and lower temperatures, such as Figure 3 As shown. By superimposing the original axial temperature distribution, the new axial temperature distribution can be obtained.

[0077] Step 206: construct a slurry bed reactor model based on the predicted parameters.

[0078] Specifically, a reaction kinetics equation is constructed based on the prediction parameters; and a slurry bed reactor model is constructed based on the reaction kinetics equation.

[0079] Wherein, the slurry bed reactor model consists of a mass conservation equation and a momentum conservation equation.

[0080] The forming of a reaction kinetic equation according to the predicted parameters comprises: forming a Fischer-Tropsch synthesis reaction and a water-gas shift reaction according to the predicted parameters; and forming a reaction kinetic equation according to the Fischer-Tropsch synthesis reaction and the water-gas shift reaction.

[0081] Specifically, there are 6 reaction equations, of which the Fischer-Tropsch synthesis reaction includes 5 reactions to generate CH4, petroleum gas, light oil, heavy oil and wax oil, and 1 water-gas shift reaction. The kinetic equations of the Fischer-Tropsch synthesis reaction are all based on the CO conversion kinetics, where CO is converted into hydrocarbons, and then the reaction rate of each product is determined based on the product distribution model, which greatly reduces the number of reaction kinetic parameters. The pre-exponential factor k of the reaction is CO，0 , k WGS , activation energy E CO 、E WGS , and the equilibrium constant K WGS Give an initial value.

[0082]

[0083] tot=nc CH4 m CH4 / mw CH4 +nc Oilgas m Oilgas / mw Oilgas +nc Loiil m Loil / mw Loil +nc Hoil m Hoi +nc wax m wax / mw wax

[0084]

[0085] m CH4 、m Oilgas 、m Loil 、m Hoil 、m wax is the mass fraction of each lumped mass of CH4, LPG, light oil, heavy oil and wax oil calculated according to the ASF distribution; mw CH4 、mw Oilgas 、mw Loil 、mw Hoil 、mw wax is the average molar mass of CH4, LPG, light oil, heavy oil and wax oil; nc CH4 、nc Oilgas 、nc Loil、nc Hoil 、nc wax It is the average carbon number of CH4, LPG, light oil, heavy oil and wax oil.

[0086] More specifically, a slurry bed reactor model is constructed. In the Fischer-Tropsch synthesis slurry bed reactor, there are complex interactions between the gas, liquid and solid phases, and an axial diffusion model is usually used. However, the axial diffusion model has a second-order derivative, which has high requirements for handling when solving, and the solution time is long, and it is not suitable for use in real-time optimization. By degenerating the axial diffusion model into a plug flow model, the catalyst particles and the liquid phase are treated as one phase, that is, the slurry phase, and it is assumed that it flows slowly upward. As the wax oil increases with the reaction, the liquid level will rise. The gas phase is divided into large bubbles and small bubbles. Large bubbles are plug flows with fast flow rates, and small bubbles are plug flows with slow flow rates. After calculation, it was found that the concentration of substances in the slurry phase after degenerating into a plug flow quickly reaches equilibrium, and such an assumption has little effect on the reaction.

[0087] Catalyst particles are affected by various forces in the reactor, and their distribution is uneven. There are more catalysts in the lower part of the reactor and fewer in the upper part of the reactor, and the smaller the particles, the more likely they are to appear in the upper part. This leads to a greater chance of the reactants in the lower part of the reactor contacting the catalyst, and a faster reaction rate. The axial distribution of catalyst particles under the current operating conditions can be calculated using an empirical formula.

[0088] The aggregate substances involved in the reaction have complex interactions in the large bubble phase, small bubble phase and liquid phase. The reactants in the gas phase diffuse into the liquid phase first, and then diffuse to the catalyst surface before the reaction begins. Most of the generated products diffuse from the catalyst surface to the liquid phase first, and then diffuse to the main gas phase and flow out of the reactor. Therefore, in the Fischer-Tropsch synthesis slurry bed reactor, there is material exchange between large bubbles and small bubbles, material exchange between large bubbles and liquid phase, material exchange between small bubbles and liquid phase, and material exchange between liquid phase and solid phase.

[0089] Momentum balance mainly considers static pressure energy, and can obtain the pressure distribution in the axial direction of the reactor.

[0090] Specifically, the catalyst (Catalyst, Cat) particles and the liquid phase are treated as one phase, namely the slurry phase (Slurry, SL), and it is assumed to flow slowly upward (as the reaction proceeds, the wax oil increases and the liquid level rises). The gas phase is divided into large bubbles (Large Bubble, LB) and small bubbles (Small Bubble, SB). Large bubbles are plug flows with fast flow rates, while small bubbles are plug flows with slow flow rates. After calculation, it was found that the concentration of substances in the slurry phase that degenerates into plug flow quickly reaches equilibrium, and this assumption has little effect on the reaction.

[0091] There are interactions between the phases. Since the catalyst particles are small and the reaction occurs on the surface of the catalyst particles, liquid-solid mass transfer is ignored. In addition, it also includes mass transfer between large bubbles and small bubbles, mass transfer between large bubbles and slurry phase, and mass transfer between small bubbles and slurry phase. Therefore, the mass conservation equation is as follows:

[0092] Table 1 Mass conservation equation

[0093]

[0094] The symbols involved are shown in the following formula:

[0095]

[0096] U LB =U G -U SB

[0097]

[0098] σ L =((1.367×10 -4 )T 2 -0.1915T+80.486)

[0099] ρ L =978.05-0.5403T

[0100]

[0101]

[0102] ε L =1-ε G -ε P

[0103] ε P =C p / ρ P

[0104] ε SB =ε G -ε LB

[0105] ε LB =ε G , ε G <f 25 / 4

[0106] ε G >f 25 / 4

[0107]

[0108] Among them, U LB , U SB , U L , U P are the superficial velocities of large bubbles, small bubbles, slurry and catalyst particles in the reactor, in m / s, U L =0.000015; U t is the terminal settling velocity, in m / s; C i，LB , C i，SB , C i，L are the concentrations of each substance in large bubbles, small bubbles and slurry, in mol / m 3 ; ε G , ε LB , ε SB , ε L , ε P are the volume fractions of gas phase, large bubbles, small bubbles, slurry, and catalyst particles in the reactor, respectively; are the equilibrium concentrations of the substances in the large and small bubbles in the liquid phase, in mol / m 3 ;k L a is the volumetric mass transfer coefficient s -1 ; C p is the axial distribution of the catalyst in the reactor, in kg / m 3 ; g is the acceleration due to gravity, unit is m / s 2 ;μ L is the slurry viscosity, in mPa / s; σ L is the surface tension of the slurry, in N / m; ρ L is the slurry density, in kg / m 3 ρ G is the gas phase density, in kg / m 3 ;D s is the solid phase diffusion coefficient, unit: m 2 / s;U t is the particle terminal velocity, in m / s; L is the gas phase cross-flow coefficient, which is 5; l is the liquid level, in m; Re G and Re p are the gas phase and liquid phase Reynolds coefficients respectively; Fr is the Friedman number; M, Ψ l is an intermediate variable in the calculation process; P is the pressure Pa, P V is the saturated vapor pressure Pa.

[0109] The pressure P exerted on the above is calculated by the momentum conservation equation, which is as follows:

[0110] Table 2 Momentum conservation equation

[0111]

[0112] Step 208, performing parameter adjustment according to the slurry bed reactor model to obtain simulation results.

[0113] Specifically, according to the slurry bed reactor model, kinetic parameters are simulated and generated; according to the kinetic parameters, reactor parameters are adjusted to obtain simulation results.

[0114] More specifically, the determination of kinetic parameters requires the collection of a set of offline data, given the initial values ​​of the kinetic parameters and simulated using the Fischer-Tropsch synthesis slurry bed reactor model to obtain the flow and composition of each substance at the reactor outlet. The kinetic parameters are compared with the actual values, and the kinetic parameters are optimized using an optimization algorithm with the minimum absolute value of the relative deviation as the objective function.

[0115] The optimized kinetic parameters are used as the initial values ​​of the reactor parameters, and the reaction energy and activation energy are adjusted online according to the actual operating data to obtain the simulation results. The actual operating data uses multiple sets of data over a period of time.

[0116] In one embodiment, the reactor parameters are updated every 3 to 6 months.

[0117] The method further includes: optimizing the advanced control system in real time according to the simulation results.

[0118] Specifically, the operating variables that can be controlled by the advanced control system are adjusted according to the simulation results to maximize / minimize the objective function, but the operating variables are required not to exceed the upper and lower limits. In the real-time optimization process, a mapping is established between the model variables and the device instrument position numbers, data is acquired in real time, and the instrument deviation is calibrated based on the stable operating condition data over a period of time for optimization calculation, and the optimization results are sent to the advanced control system.

[0119] The objective function can be selected, such as minimizing inert gas (CO and CH4), maximizing output (the sum of liquefied petroleum gas, light oil, heavy oil and wax oil), maximizing economic benefits, etc.; multi-objective optimization can also be performed, which will consider two objectives at the same time and select a compromise solution for optimization, such as not too much inert gas but not too low output.

[0120] In the above-mentioned Fischer-Tropsch synthesis slurry bed reactor modeling method, reactants and products are lumped and divided to obtain lumped information; reaction prediction is performed based on the lumped information to obtain prediction parameters; a slurry bed reactor model is constructed based on the prediction parameters; parameters are adjusted based on the slurry bed reactor model to obtain simulation results. This method can effectively simulate the Fischer-Tropsch synthesis reaction, has a fast solution speed, and accurate and reliable calculation results, so it is widely used in offline and online real-time simulation and real-time optimization of various Fischer-Tropsch synthesis devices, and has the advantages of wide application range, accurate calculation results, short optimization time, flexible parameter adjustment, and full applicability to real-time optimization.

[0121] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] Based on the same inventive concept, the embodiment of the present application also provides a Fischer-Tropsch synthesis slurry bed reactor modeling device for implementing the above-mentioned Fischer-Tropsch synthesis slurry bed reactor modeling method. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more Fischer-Tropsch synthesis slurry bed reactor modeling device embodiments provided below can refer to the limitations of the Fischer-Tropsch synthesis slurry bed reactor modeling method above, and will not be repeated here.

[0123] In one embodiment, Figure 4 As shown, a Fischer-Tropsch synthesis slurry bed reactor modeling device is provided, comprising: an information acquisition module 410, a prediction parameter module 420, a model creation module 430 and a parameter setting module 440, wherein:

[0124] The information acquisition module 410 is used to lump and divide the reactants and products to obtain lumped information.

[0125] The prediction parameter module 420 is used to perform reaction prediction according to the aggregate information to obtain prediction parameters.

[0126] The model creation module 430 is used to construct a slurry bed reactor model according to the prediction parameters.

[0127] The parameter setting module 440 is used to set parameters according to the slurry bed reactor model to obtain simulation results.

[0128] The prediction parameter module 420 is also used to analyze the aggregate information and establish a catalyst deactivation model; and obtain catalyst activity parameters according to the catalyst deactivation model.

[0129] The prediction parameter module 420 is used to construct a reaction kinetics equation according to the prediction parameters; and to construct a slurry bed reactor model according to the reaction kinetics equation.

[0130] The model creation module 430 is also used to construct a Fischer-Tropsch synthesis reaction and a water-gas shift reaction according to the predicted parameters; and to construct a reaction kinetic equation according to the shown Fischer-Tropsch synthesis reaction and the water-gas shift reaction.

[0131] The parameter setting module 440 is also used to simulate and generate kinetic parameters according to the slurry bed reactor model; and to set the reactor parameters according to the kinetic parameters to obtain simulation results.

[0132] The parameter setting module 440 is also used to optimize the advanced control system in real time according to the simulation results.

[0133] Each module in the above-mentioned Fischer-Tropsch synthesis slurry bed reactor modeling device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0134] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store lumped information, prediction parameters, and slurry bed reactor model simulation result data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a Fischer-Tropsch synthesis slurry bed reactor modeling method is implemented.

[0135] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0136] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, any one of the Fischer-Tropsch synthesis slurry bed reactor modeling methods in the above embodiments is implemented.

[0137] Lump and divide the reactants and products to obtain lump information;

[0138] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0139] According to the prediction parameters, a slurry bed reactor model is constructed;

[0140] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0141] In one embodiment, when the processor executes the computer program, the following steps are further implemented: analyzing the aggregate information to establish a catalyst deactivation model;

[0142] According to the catalyst deactivation model, catalyst activity parameters are obtained.

[0143] In one embodiment, when the processor executes the computer program, the following steps are further implemented: constructing a reaction kinetics equation based on the predicted parameters;

[0144] According to the reaction kinetics equation, a slurry bed reactor model is constructed.

[0145] In one embodiment, when the processor executes the computer program, the following steps are further implemented: In one embodiment, when the processor executes the computer program, the following steps are further implemented: according to the predicted parameters, a Fischer-Tropsch synthesis reaction and a water-gas shift reaction are formed;

[0146] Based on the Fischer-Tropsch synthesis reaction shown and the water-gas shift reaction, a reaction kinetic equation is constructed.

[0147] In one embodiment, when the processor executes the computer program, the following steps are further implemented: simulating and generating kinetic parameters according to the slurry bed reactor model;

[0148] The reactor parameters are adjusted according to the kinetic parameters to obtain simulation results.

[0149] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: optimizing the advanced control system in real time according to the simulation results.

[0150] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the Fischer-Tropsch synthesis slurry bed reactor modeling methods in the above embodiments is implemented.

[0151] Lump and divide the reactants and products to obtain lump information;

[0152] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0153] According to the prediction parameters, a slurry bed reactor model is constructed;

[0154] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0155] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: analyzing the aggregate information to establish a catalyst deactivation model;

[0156] According to the catalyst deactivation model, catalyst activity parameters are obtained.

[0157] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: constructing a reaction kinetics equation according to the predicted parameters;

[0158] According to the reaction kinetics equation, a slurry bed reactor model is constructed.

[0159] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: according to the predicted parameters, a Fischer-Tropsch synthesis reaction and a water-gas shift reaction are formed;

[0160] Based on the Fischer-Tropsch synthesis reaction shown and the water-gas shift reaction, a reaction kinetic equation is constructed.

[0161] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: simulating and generating kinetic parameters according to the slurry bed reactor model;

[0162] The reactor parameters are adjusted according to the kinetic parameters to obtain simulation results.

[0163] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: optimizing the advanced control system in real time according to the simulation results.

[0164] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0165] Lump and divide the reactants and products to obtain lump information;

[0166] Perform reaction prediction based on the aggregate information to obtain prediction parameters;

[0167] According to the prediction parameters, a slurry bed reactor model is constructed;

[0168] Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

[0169] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: analyzing the aggregate information to establish a catalyst deactivation model;

[0170] According to the catalyst deactivation model, catalyst activity parameters are obtained.

[0171] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: constructing a reaction kinetics equation according to the predicted parameters;

[0172] According to the reaction kinetics equation, a slurry bed reactor model is constructed.

[0173] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: according to the predicted parameters, a Fischer-Tropsch synthesis reaction and a water-gas shift reaction are formed;

[0174] Based on the Fischer-Tropsch synthesis reaction shown and the water-gas shift reaction, a reaction kinetic equation is constructed.

[0175] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: simulating and generating kinetic parameters according to the slurry bed reactor model;

[0176] The reactor parameters are adjusted according to the kinetic parameters to obtain simulation results.

[0177] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: optimizing the advanced control system in real time according to the simulation results.

[0178] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0180] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A Fischer-Tropsch synthesis slurry bed reactor modeling method, characterized in that: The method comprises: Lump and divide the reactants and products to obtain lump information; Perform reaction prediction based on the aggregate information to obtain prediction parameters; According to the prediction parameters, a slurry bed reactor model is constructed; Parameters are adjusted according to the slurry bed reactor model to obtain simulation results.

2. The method according to claim 1, characterized in that The performing reaction prediction according to the aggregate information to obtain prediction parameters comprises: Analyzing the aggregated information to establish a catalyst deactivation model; According to the catalyst deactivation model, catalyst activity parameters are obtained.

3. The method according to claim 2, characterized in that The slurry bed reactor model is constructed according to the prediction parameters. include: Wherein, the prediction parameters include catalyst activity parameters, physical property parameters, product parameters and axial temperature parameters; According to the predicted parameters, a reaction kinetic equation is formed; According to the reaction kinetics equation, a slurry bed reactor model is constructed.

4. The method according to claim 3, characterized in that The reaction kinetics equation formed according to the prediction parameters includes: According to the predicted parameters, a Fischer-Tropsch synthesis reaction and a water-gas shift reaction are formed; Based on the Fischer-Tropsch synthesis reaction shown and the water-gas shift reaction, a reaction kinetic equation is constructed.

5. The method according to claim 1, characterized in that The parameter setting according to the slurry bed reactor model to obtain simulation results includes: According to the slurry bed reactor model, simulating and generating kinetic parameters; The reactor parameters are adjusted according to the kinetic parameters to obtain simulation results.

6. The method according to claim 1, characterized in that The method further comprises: According to the simulation results, the advanced control system is optimized in real time.

7. A Fischer-Tropsch synthesis slurry bed reactor modeling device, characterized in that: The device comprises: An information acquisition module is used to lump reactants and products to obtain lumped information; A prediction parameter module, used for performing reaction prediction according to the aggregate information to obtain prediction parameters; A model creation module, used to construct a slurry bed reactor model according to the prediction parameters; The parameter setting module is used to set parameters according to the slurry bed reactor model to obtain simulation results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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