Reaction kinetics calculation method and system based on stream level and medium

Abstract

The invention provides a stream level-based reaction kinetics calculation method, which comprises the following steps of: acquiring a reference temperature, a reference pressure and a reference retention time, and fitting a cumulative probability density function curve of first Weibull distribution based on a distillation curve of feeding to determine parameters of the first Weibull distribution; changing the operating temperature, the operating pressure and / or the operating residence time for multiple times, and respectively obtaining distillation curves after reaction to fit cumulative probability density function curves of a plurality of second Weibull distributions, so as to determine parameters of the plurality of second Weibull distributions; and fitting a parameter transformation model based on differences between the operating temperature, the operating pressure and / or the operating residence time and the reference temperature, the reference pressure and the reference residence time and differences between the parameters of the first Weibull distribution and the parameters of the plurality of second Weibull distributions, and determining binary interaction parameters of the parameter transformation model and reaction kinetic parameters for characterizing change rules of temperature, pressure and residence time.

Description

TECHNICAL FIELD

[0001] The present application relates to the chemical industry, and in particular to a reaction kinetics calculation method based on stream level, a reaction kinetics calculation system based on stream level, and a computer readable storage medium. BACKGROUND

[0002] Catalytic cracking reaction is a method for producing lighter petroleum components such as gasoline, diesel and liquefied gas from heavier petroleum components such as atmospheric residue and vacuum distillate. The catalytic cracking reactor model can simulate the actual catalytic cracking reactor reaction, so as to simulate the product composition according to the provided feedstock and operating conditions. Among them, the catalytic cracking reactor model can change the production conditions by modifying the feedstock and operating conditions.

[0003] For the catalytic cracking reactor, the existing technology for optimizing the product distribution and product properties generally first divides the lumped components, constructs a reaction network, and then establishes a kinetic model. The kinetic equation is as follows:

[0004] wherein, r j is a lumped component. In a general kinetic equation, the number of lumped components r j is between 9 and 50, and for a molecular-level lumped kinetic equation, the number of lumped components r j is about one hundred or even tens of thousands.

[0005] Then, based on the lumped kinetics and the molecular-level kinetics, the parameters of the kinetic equation under the current operating conditions are updated. The updating method includes Monte Carlo simulation, data-driven simulation and memory simulation, and the parameter updating of the kinetic equation is realized by minimizing the deviation between the calculated value and the actual value of the kinetic equation.

[0006] Then, the existing technology uses the updated kinetic parameters to combine different types of optimization and inverse calculation algorithms to maximize the desired product value. The optimization and inverse calculation algorithms include DMO (Dwarf Mongoose Optimization), SQP (Sequential Quadratic Programming), grey wolf algorithm, etc., and the optimization and inverse calculation algorithms maximize the desired product by changing the values of technical parameters such as temperature, pressure, residence time, etc.

[0007] Finally, the existing technology outputs the operating parameters obtained by the optimization and inverse calculation algorithm to the actual device, so as to optimize the production conditions of the current device.

[0008] However, the prior art has the problems of slow updating, slow optimization, high optimization convergence difficulty, and calculation accuracy exceeding the control accuracy of the device operation.

[0009] For example, an existing dynamic calculation software has 23 lumped quantities and more than 50 reactions, and at least three dynamic parameters need to be corrected for updating one dynamic equation. In addition, it is difficult to obtain the correction data for updating the dynamic parameters. The online chromatography is required for product analysis for updating the lumped dynamic parameters, and the measurement time of the online chromatography is long, which is between 1 hour and 2 hours, so it is difficult to provide timely operation information. In addition, the calculation time is further prolonged when the updating algorithm encounters feed fluctuation, and the prolonged calculation time is in the minute level. The online chromatography for updating the molecular dynamics is more complex, and one round can reach more than 4 hours, and large-scale dynamic parameter updating can also take about one hour.

[0010] In addition, the prior art needs to set the optimization algorithm or solve the required calculation according to different reaction devices to obtain the optimal operation parameters under the dynamic parameter updating state. With the expansion of the dynamic model, the optimization speed is gradually slowed down, and due to the slow updating of the dynamic parameters, the calculated dynamic parameters often do not meet the real-time requirements. If the tolerance is set too large, it is difficult to obtain the optimal solution, and if the tolerance is set too small, the calculation speed is slow, so the prior art has the problem of high convergence difficulty in optimization.

[0011] Therefore, the prior art has the problems of high calculation requirement, long time consumption, great difficulty in parameter acquisition, great difficulty in calculation correction, great difficulty in optimization calculation, and high optimization convergence difficulty.

[0012] In order to overcome the above-mentioned defects of the prior art, there is an urgent need in the field for a reaction dynamic calculation method based on the flow level, which can improve the updating efficiency of the dynamic parameters, thereby improving the efficiency of solving the operation conditions of optimization, and quickly solving the optimization scheme of the refinery. SUMMARY

[0013] The following gives a brief overview of one or more aspects to provide a basic understanding of these aspects. This overview is not an extensive overview of all contemplated aspects, and is not intended to identify key or critical elements of all aspects or to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description presented later.

[0014] To overcome the aforementioned deficiencies in the existing technology, this invention provides a reaction kinetics calculation method based on the stream level, a reaction kinetics calculation system based on the stream level, and a computer-readable storage medium, which can improve the efficiency of updating kinetic parameters, thereby improving the efficiency of optimizing and solving operating conditions, and quickly solving refinery optimization schemes.

[0015] Specifically, the above-described reaction kinetics calculation method based on stream level according to the first aspect of the present invention includes the following steps: obtaining a reference temperature, reference pressure, and reference residence time, and fitting a cumulative probability density function curve of a first Weibull distribution to determine the parameters of the first Weibull distribution based on the distillation curve of the feed; repeatedly changing the operating temperature, operating pressure, and / or operating residence time and obtaining the distillation curves after the reaction respectively to fit a cumulative probability density function curve of a plurality of second Weibull distributions, thereby determining the parameters of the plurality of second Weibull distributions; and fitting a parameter transformation model based on the difference between the operating temperature, operating pressure, and / or operating residence time and the reference temperature, reference pressure, and reference residence time, as well as the difference between the parameters of the first Weibull distribution and the parameters of the plurality of second Weibull distributions, to determine the binary interaction parameters of the parameter transformation model and the reaction kinetic parameters used to characterize the changes in temperature, pressure, and residence time.

[0016] Furthermore, in some embodiments of the present invention, the distillation curve is fitted to the cumulative probability density function curve of the Weibull distribution using a nonlinear least squares method.

[0017] Furthermore, in some embodiments of the present invention, the cumulative probability density function of the Weibull distribution is used to characterize the flow stream, and the cumulative probability density function of the Weibull distribution is: , in, It is a random variable used to indicate the virtual components of crude oil. It is a position parameter. It is a range parameter. It is a shape parameter.

[0018] Furthermore, in some embodiments of the present invention, the step of obtaining a reference temperature, reference pressure, and reference residence time, and fitting a cumulative probability density function curve of a first Weibull distribution based on the distillation curve of the feed to determine the parameters of the first Weibull distribution further includes: fitting a cumulative probability density function curve of a third Weibull distribution based on the distillation curve of the discharge to determine the parameters of the third Weibull distribution.

[0019] Furthermore, in some embodiments of the present invention, the parameter transformation model is as follows: , in, express , and , , and It is the difference between the parameters of the first Weibull distribution and the parameters of the second Weibull distribution. It's a temperature parameter. It is a time parameter. It is a pressure parameter. The binary interaction parameters to be fitted are denoted as .

[0020] Furthermore, in some embodiments of the present invention, the temperature parameter The time parameter and the pressure parameters Follow the laws of change: , , , in, , , , , , , , and These are the reaction kinetic parameters. It is the difference between the operating temperature and the reference temperature determined by the temperature parameter. It is the difference between the time parameter determined based on the running dwell time and the reference dwell time. It is the difference between the operating pressure and the reference pressure.

[0021] Furthermore, in some embodiments of the present invention, the method further includes the steps of: determining the parameters of the Weibull distribution at the first boiling point and the parameters of the Weibull distribution at the second boiling point using the parameter transformation model; determining the cumulative probability density function of the Weibull distribution at the first boiling point based on the parameters of the Weibull distribution at the first boiling point, and determining the cumulative probability density function of the Weibull distribution at the second boiling point based on the parameters of the Weibull distribution at the second boiling point; and calculating the difference between the cumulative probability density function of the Weibull distribution at the first boiling point and the cumulative probability density function of the Weibull distribution at the second boiling point by subtracting the previous value from the previous value to achieve the solution set aggregation.

[0022] Furthermore, in some embodiments of the present invention, the method further includes the steps of: acquiring product information and feed information of the current segment in the actual catalytic cracking unit; when the change between the feed information and the feed is within a preset range, updating the binary interaction parameters of the parameter transformation model based on the parameters of the Weibull distribution determined by the product information; when the change between the feed information and the feed exceeds the preset range, determining the parameters of the Weibull distribution of the product stream and the parameters of the Weibull distribution of the feed stream based on the product information and the feed information; and refitting the parameter transformation model based on the parameters of the Weibull distribution of the product stream and the parameters of the Weibull distribution of the feed stream to update the binary interaction parameters and the reaction kinetic parameters.

[0023] Furthermore, the reaction kinetics calculation system based on the stream level provided by the second aspect of the present invention includes a memory and a processor. The memory stores computer instructions. The processor is connected to the memory and configured to execute the computer instructions stored in the memory to implement the reaction kinetics calculation method based on the stream level provided in any of the above embodiments.

[0024] Furthermore, the computer-readable storage medium provided according to the third aspect of the present invention stores computer instructions. When the computer instructions are executed by a processor, they implement the reaction kinetics calculation method based on the stream level provided in any of the above embodiments. Attached Figure Description

[0025] The above-described features and advantages of the present invention will be better understood after reading the following detailed description of embodiments of the present disclosure in conjunction with the accompanying drawings. In the drawings, components are not necessarily drawn to scale, and components having similar related characteristics or features may have the same or similar reference numerals.

[0026] Figure 1 A schematic diagram of a flow-level reaction kinetics calculation system provided according to some embodiments of the present invention is shown; Figure 2 The diagram shows a variety of crude oil distillation curves described using the Weibull distribution; Figure 3 A flowchart of a reaction kinetics calculation method based on stream level according to some embodiments of the present invention is shown; Figure 4 A CDF curve of the Weibull distribution fitted to the inlet material distillation curve provided according to an embodiment of the present invention is shown; Figure 5 The diagram shows a CDF curve of the Weibull distribution fitted to the distillation curve of the outlet material according to an embodiment of the present invention; and Figure 6 A comparison chart of calculated and original values ​​of temperature parameters characterizing temperature change patterns, provided by an embodiment of the present invention, is shown. Detailed Implementation

[0027] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Although the description of the present invention is presented in conjunction with preferred embodiments, this does not mean that the features of the invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with embodiments is to cover other options or modifications that may be derived based on the claims of the present invention. To provide a thorough understanding of the invention, many specific details will be included in the following description. The invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] Furthermore, the terms "upper," "lower," "left," "right," "top," "bottom," "horizontal," and "vertical" used in the following description should be understood as the orientations shown in the relevant paragraphs and accompanying drawings. These relative terms are for illustrative purposes only and do not imply that the described apparatus must be manufactured or operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0030] It is understood that although terms such as "first," "second," and "third" may be used herein to describe various components, regions, layers, and / or parts, these components, regions, layers, and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers, and / or parts. Therefore, the first components, regions, layers, and / or parts discussed below may be referred to as second components, regions, layers, and / or parts without departing from some embodiments of the present invention.

[0031] As mentioned above, existing technologies suffer from problems such as slow updates, slow optimization speed, high difficulty in optimization convergence, and computational precision exceeding the control precision of device operation.

[0032] Taking an existing kinetic calculation software as an example, it lumps 23 kinetic equations and involves over fifty reactions. Updating each kinetic equation requires correcting at least three kinetic parameters. Furthermore, obtaining the corrected data for updating kinetic parameters is difficult. Updating lumped kinetic parameters requires product analysis using online chromatography, which has a long measurement time of 1-2 hours, making it difficult to provide timely operational information. Moreover, if the update algorithm encounters feed fluctuations, the calculation time will be further extended, by minutes. Molecular dynamics is even more complex than lumped kinetics; in its online chromatography updates, a single cycle can take over 4 hours, and large-scale kinetic parameter updates can even reach around one hour.

[0033] Furthermore, existing technologies require specific algorithm settings or inverse calculations based on different reaction devices to obtain the optimal operating parameters under updated kinetic parameters. As the kinetic model expands, the optimization speed gradually slows down. Moreover, due to the aforementioned slow kinetic parameter updates, the calculated kinetic parameters often fail to meet real-time requirements. Regarding algorithm settings, excessively large tolerances make it difficult to find the optimal solution, while excessively small tolerances result in slow computation. Therefore, existing technologies suffer from high convergence difficulty during optimization.

[0034] Finally, when existing technologies apply the calculated results to the adjustment process of actual devices after determining the optimized operating conditions, the accuracy of the calculated results often overflows. That is, the accuracy of the calculated operating conditions exceeds the control accuracy of the device operation, making the optimized operating conditions unapplicable to the actual device.

[0035] Therefore, existing technologies suffer from problems such as high computational requirements, long processing time, difficulty in obtaining parameters, difficulty in computational correction, difficulty in optimization computation, difficulty in optimization convergence, and precision overflow.

[0036] To overcome the aforementioned deficiencies in the existing technology, this invention provides a reaction kinetics calculation method based on the stream level, a reaction kinetics calculation system based on the stream level, and a computer-readable storage medium, which can improve the efficiency of updating kinetic parameters, thereby improving the efficiency of optimizing and solving operating conditions, and quickly solving refinery optimization schemes.

[0037] In some non-limiting embodiments, the reaction kinetics calculation method based on the stream level provided in the first aspect of the present invention can be implemented via the reaction kinetics calculation system based on the stream level provided in the second aspect of the present invention.

[0038] Please refer to Figure 1 , Figure 1A schematic diagram of a flow-level reaction kinetics calculation system provided according to some embodiments of the present invention is shown.

[0039] like Figure 1 As shown, the flow-level reaction kinetics calculation system 100 may be configured with a memory 110 and a processor 120. The memory 110 includes, but is not limited to, the computer-readable storage medium 111 described in the third aspect of the present invention, which stores computer instructions thereon. The processor 120 is connected to the memory 110 and is configured to execute the computer instructions stored in the memory 110 to implement the flow-level reaction kinetics calculation method provided in the first aspect of the present invention.

[0040] The working principle of the above-described reaction kinetics calculation system based on flow-level reaction kinetics will be described below with reference to some embodiments of flow-level reaction kinetics calculation methods. Those skilled in the art will understand that these embodiments of flow-level reaction kinetics calculation methods are merely non-limiting implementations provided by the present invention, intended to clearly demonstrate the main concepts of the invention and provide specific solutions convenient for public implementation, rather than limiting all functions or all operating methods of the flow-level reaction kinetics calculation system. Similarly, the flow-level reaction kinetics calculation system is also only one non-limiting implementation provided by the present invention, and does not constitute a limitation on the executing entity and execution order of each step in these flow-level reaction kinetics calculation methods.

[0041] Catalytic cracking is a process in the processing of petroleum products, often referring to the conversion of heavier petroleum products into lighter ones.

[0042] For petroleum products, their actual components are often in the thousands, making them difficult to describe precisely. To describe petroleum products, they are often divided into different virtual components based on their density and boiling point. These virtual components are used to represent a specific component of the petroleum for calculations. Generally, a boiling point range of approximately 10-20 degrees Celsius is used to generate virtual components. These generated virtual components are called lumped components.

[0043] Existing lumped kinetics techniques use virtual components in calculating petroleum reactions. However, the sheer number of virtual components leads to insufficient computational resources. To reduce computational consumption, existing techniques merge multiple virtual components into larger ones, such as gasoline virtual components, diesel virtual components, etc. But merging virtual components fails to reflect the influence of petroleum molecular structure on the reaction. To express the influence of molecular structure, existing techniques further incorporate the effects of aromatics, alkanes, and cycloalkanes into different gasoline and diesel virtual components; for example, the original diesel component is broken down into diesel aromatics segment virtual components. Molecular-level reactions, on the other hand, represent a more refined molecular lumping that further considers the influence of molecular structure within the virtual components.

[0044] The reaction kinetics calculation method based on stream level provided by this invention uses the Weibull distribution to describe the products in the catalytic cracking reaction process. The Weibull distribution can characterize the products as streams and sum the different virtual components into the distribution within the streams.

[0045] The Weibull distribution exists in various forms, with the three-parameter and four-parameter Weibull distributions being the most common. Due to the flexibility of its parameters, the Weibull distribution can be adapted to a variety of different application scenarios. Compared to the common use of the Weibull distribution to measure the change in failure rate over time, this invention uses a three-parameter Weibull distribution to measure the change in component content in crude oil with boiling point.

[0046] This invention uses the three-parameter form of the probability density function (PDF) curve of the Weibull distribution to characterize the component distribution curve of crude oil. The specific formula for the probability density function of the three-parameter form of the Weibull distribution is as follows: , in, It is a random variable used to indicate the virtual components of crude oil. It is a position parameter. It is a range parameter, this range parameter It can be used to characterize scale, and therefore can also be called a scale parameter. It is a shape parameter. When the shape parameter When the summation is complete, the Weibull distribution is an exponential distribution, called the minimized Weibull distribution. The probability density function of multiple individual Weibull distributions summed with different weights is called a mixture Weibull distribution.

[0047] Integrating the probability density function of the Weibull distribution yields its cumulative probability density function (CDF). The CDF curve of the Weibull distribution can be used to simulate crude oil distillation curves. The specific formula for the three-parameter form of the cumulative probability density function of the Weibull distribution is as follows: .

[0048] like Figure 2 As shown, Figure 2 The diagram shows naphtha distributions with light-end accumulation, gasoline distributions with heavy-end accumulation, diesel distributions with slight overlap on both sides, and vacuum residue distributions with a small amount of heavy end accumulation, all described using the Weibull distribution.

[0049] This indicates that, as the distribution of crude oil components changes with the actual boiling point, the three-parameter Weibull distribution can describe not only the high-content vacancy-type and tail-oil type distillate oils at the rear end, but also the heavy residue type distillate oils with tailing at the rear end, and the ordinary gasoline, kerosene, and diesel type distillate oils with overlapping sides.

[0050] Location parameters of the Weibull distribution It describes the minimum virtual component temperature; the smaller the position parameter, the better. The further forward the overall Weibull distribution curve shifts, the more dry gas is produced in the reaction. The range of the Weibull distribution is variable. This refers to the range in which the reaction products are formed. Range parameter. The larger the value, the wider the range covered by the virtual components of the stream. Shape parameters of the Weibull distribution. It describes the distribution of reaction products and different shape parameters. The different shapes of the resulting streams represent different distribution types of the products.

[0051] Therefore, when using the Weibull distribution to describe the product stream during the catalytic cracking reaction process, this invention can ensure the rationality of the product oil distribution.

[0052] Compared to existing technologies that update kinetic parameters based on lumped or molecular-level lumped data, this invention uses the Weibull distribution for kinetic parameter updates. The Weibull distribution does not consider boiling point or structure; it only considers the changes in the crude oil distillation curve from the perspective of stream dynamics, thus describing the rate relationships of interconversion between products based on stream dynamics.

[0053] Here, crude oil distillation curves may include TBP (True Boiling Point) curves, D86 curves (Enndorf distillation curves), D1160 curves (reduced pressure distillation curves), etc.

[0054] Crude oil distillation profiles are typically plotted using distillation temperature and volume / mass fraction as coordinates, derived from measured distillation range data. The distillation range data consists of a set of monotonic points representing volume / mass fraction versus temperature. Table 1 shows crude oil distillation profiles representing the fractions of petroleum components vaporized at different temperatures.

[0055]

[0056] Table 1

[0057] Using stream-level dynamics, for updating and calculating dynamic parameters, only the parameters of three Weibull distributions and the effects of temperature, pressure, and residence time need to be considered.

[0058] Here, the types of dynamic equations for stream dynamics can include stream model and parameter transformation model.

[0059] The flow stream model can be: , This invention uses the cumulative probability density function of the Weibull distribution to represent the stream, which is a function with a maximum value of 1. Different stream models have different parameters; that is, the parameters of the Weibull distribution representing different streams are different. These parameters can also be called stream characteristic parameters. The transformation of parameters between stream models represents the reaction process.

[0060] The parameter transformation model can be: , in, It refers to , and One of them, namely, , and All can be transformed using this parameter in the model. express; , and This is the difference in characteristic parameters of each stream before and after the reaction, i.e., the difference in parameters of each Weibull distribution before and after the reaction. It is used to calculate this. The intermediate parameters include , and . It is a temperature parameter used to characterize the effect of reaction temperature; It is a time parameter used to characterize the effect of reaction residence time; It is a pressure parameter used to characterize the effect of reaction pressure. represents the binary interaction parameters to be fitted.

[0061] Used to calculate The intermediate parameters follow the same pattern of change in the specific reaction. Taking temperature as an example... For example, the pattern of change is as follows: , in, It is the difference in temperature parameters before and after the reaction. , and 1 is the temperature parameter. The calculation parameters, , and These are constants determined by specific reactions, and are also reaction kinetic parameters in the flow kinetic equation of this invention.

[0062] Since the intermediate parameters follow the same pattern of change, therefore the intermediate parameters and The calculation parameters can be expressed as: , , in, It is the difference in time parameters before and after the reaction. It is the difference in pressure parameters before and after the reaction, and the reaction kinetic parameters. , , , , and It is a constant determined by a specific reaction.

[0063] When the parameters in the kinetic equations are fixed, other parameters can be determined using the flow stream model and parameter transformation model of the aforementioned kinetic equations. For example, given the intermediate parameters and their corresponding calculation parameters, new intermediate parameters can be calculated using the aforementioned kinetic equations, namely, new temperature, pressure, and time parameters. Based on these new temperature, pressure, and time parameters, the flow stream characteristic parameters after the reaction can then be calculated.

[0064] The following will combine Figure 3 The principles and implementation of this invention are described in detail.

[0065] This invention uses the Weibull distribution to update kinetic parameters, while the Weibull distribution only considers changes in the crude oil distillation curve in terms of stream flow. Stream flow level kinetics can be realized using the benchmark-deviation kinetic equation.

[0066] The baseline-deviation kinetic equation sets the basic temperature, pressure, residence time, and the parameters of the Weibull distribution of the product stream before the reaction (i.e., the feed) as the baseline. By changing the temperature, pressure, and residence time, the parameters of the Weibull distribution of the product stream after the reaction are obtained. The changes in each parameter before the reaction (i.e., the baseline) and each parameter after the reaction (including the changed temperature, pressure, residence time parameters, and Weibull distribution parameters) are set as deviations. The changes characterized by the deviations are the kinetics.

[0067] Please refer to Figure 3 , Figure 3 A flowchart of a reaction kinetics calculation method based on stream level provided according to some embodiments of the present invention is shown.

[0068] like Figure 3 As shown, the reaction kinetics calculation system based on the stream level (hereinafter referred to as the reaction kinetics calculation system) first obtains the reference temperature, reference pressure and reference residence time, and then fits the cumulative probability density function curve of the first Weibull distribution based on the distillation curve of the feed to determine the parameters of the first Weibull distribution.

[0069] Next, the reaction kinetics calculation system can change the operating temperature, operating pressure, and / or operating residence time multiple times and obtain the distillation curves after the reaction to fit the cumulative probability density function curves of multiple second Weibull distributions, thereby determining the parameters of multiple second Weibull distributions.

[0070] Then, the reaction kinetics calculation system can fit a parameter transformation model based on the differences between the operating temperature, operating pressure, and / or operating residence time and the reference temperature, reference pressure, and reference residence time, as well as the differences between parameters based on a first Weibull distribution and parameters based on multiple second Weibull distributions, to determine the binary interaction parameters of the parameter transformation model and the reaction kinetic parameters used to characterize the changes in temperature, pressure, and residence time.

[0071] In some embodiments, the reaction kinetics calculation system can use the design values ​​of the current catalytic cracking reactor model as a kinetic reference to generate a base case (i.e., a base case).

[0072] The reaction temperature, pressure, and residence time of the base case can be input by the user or formed based on the acquired data. The reaction temperature, pressure, and residence time of the base case are the reference temperature, reference pressure, and reference residence time.

[0073] Furthermore, the reaction kinetics calculation system can also obtain information such as the distillation curve of the feed for the basic case. The system fits the distillation curve of the feed using a nonlinear least squares method to obtain the cumulative probability density function curve of the first Weibull distribution of the reaction feed, thereby determining the parameters of the first Weibull distribution.

[0074] Preferably, the reaction kinetics calculation system can also obtain the distillation curve of the effluent from the basic case, and fit this distillation curve using a nonlinear least squares method to obtain the cumulative probability density function curve of the Weibull distribution of the reaction products, thereby determining the parameters of the Weibull distribution of the reaction products. The subsequently calculated reaction kinetic parameters are also coefficients of the formulas used to characterize the changes in the feed and effluent Weibull distributions. The parameters of the Weibull distribution of the reaction products obtained using the effluent distillation curve can be used to verify the reaction kinetic parameters.

[0075] To obtain kinetic parameters that characterize the changes, the reaction kinetics calculation system can repeatedly change the operating temperature, operating pressure, and / or residence time of the base case, and transform the distillation curves of the reaction products into a Weibull distribution. Then, the difference between the parameters of the Weibull distribution of the reaction products and the parameters of the Weibull distribution of the base case after each reaction (i.e., the difference in the stream characteristic parameters before and after the reaction) is used as the fitting parameter in the parameter transformation model. The parameters of the parameter transformation model are fitted, and the parameter transformation model is as follows: , The intermediate parameters follow the following variation pattern: , , , in, , , , , , , , and These are reaction kinetic parameters. It is the difference between the operating temperature and the reference temperature. It is the difference between the operating dwell time and the baseline dwell time, determined by time parameters. It is the difference between the operating pressure and the reference pressure.

[0076] Preferably, when the reaction kinetics calculation system changes the operating temperature, operating pressure, and / or operating residence time of the base case multiple times, it only changes one of the operating temperature, operating pressure, and operating residence time.

[0077] Therefore, under multiple operational tests at different temperatures, pressures, and residence times, the reaction kinetics calculation system can determine the kinetic parameters reflecting the changes in temperature, pressure, and residence time, as well as the binary interaction parameters in the parameter transformation model, by fitting the changes in temperature, pressure, and residence time to the changes in parameters of the Weibull distribution. .

[0078] Based on the parameter transformation model determined by the reaction kinetics calculation system, the parameters of the Weibull distribution at any boiling point can be calculated. Thus, the CDF curve of the Weibull distribution at that boiling point can be obtained through the parameters of the Weibull distribution. The CDF curve of the Weibull distribution at that boiling point can characterize the crude oil distillation curve of the reaction product at that boiling point.

[0079] Preferably, the reaction kinetics calculation system is executed synchronously when the catalytic cracking reactor model is established. Figure 3 The reaction kinetics calculation method shown can establish the convection stream kinetic equation by using the binary interaction parameters and reaction kinetic parameters of the determined parameter transformation model.

[0080] Thus, after updating the kinetic parameters, this invention employs the Weibull distribution to consider the changes in the crude oil distillation curve from a flow stream perspective. The crude oil distillation curve is fitted using a nonlinear least squares method to obtain the Weibull distribution CDF curve, thereby achieving crude oil lumping. After updating the kinetic parameters, the parameters of the Weibull distribution at a certain boiling point can be determined based on the parameter transformation model. The CDF curve obtained using these Weibull distribution parameters is numerically equal to the crude oil distillation curve at that boiling point.

[0081] The obtained Weibull CDF curve can be solved by subtracting the previous curve from the previous curve. For example, using CDF(20)-CDF(10) can yield the virtual components of crude oil with a boiling point of 10 degrees.

[0082] Therefore, based on the updated dynamic parameters, the present invention can perform lumped sum and solution lumped sum calculations.

[0083] In other words, the present invention is completely different from the prior art of first performing lumped partitioning and then constructing a reaction network to establish kinetic equations. The present invention processes the reaction at the level of flow information based on the changing law of the flow stream.

[0084] Preferably, after determining the binary interaction parameters of the parameter transformation model of the flow kinetic equation and the reaction kinetic parameters used to characterize the changes in temperature, pressure, and residence time, the present invention can continue to update the binary interaction parameters of the parameter transformation model and the reaction kinetic parameters based on the feed information and product information in the actual device.

[0085] Specifically, the reaction kinetics calculation system can obtain product and feed information for the current stage of an actual catalytic cracking unit.

[0086] When the variation between the feed information and the feed of the base case is within a preset range, i.e., when the variation in the feed information is small, the reaction kinetics calculation system can, based on the parameters of the Weibull distribution determined by the product information, only apply the binary interaction parameters in the parameter transformation model. Update.

[0087] For example, in the parameter transformation model: , Then update the binary interaction parameters according to the following formula. : .

[0088] When the variation between the feed information and the feed of the base case exceeds a preset range, indicating significant fluctuations in the catalytic cracking unit, the reaction kinetics calculation system can determine the parameters of the Weibull distribution of the product stream and the Weibull distribution of the feed stream based on the product and feed information; these are the product stream characteristic parameters and the feed stream characteristic parameters. Then, based on these parameters, the parameter transformation model is refitted to update the binary interaction parameters of the model, as well as the reaction kinetic parameters used to characterize the variations in temperature, pressure, and residence time.

[0089] The following is a specific, non-limiting preferred embodiment, which will be used to further elaborate on the reaction kinetics calculation system based on the stream level proposed in this invention.

[0090] In this embodiment, the reaction kinetics calculation system can conduct multiple running experiments at different temperatures and pressures after obtaining the basic case to obtain the parameters of the Weibull distribution of the reaction products under different influencing factors.

[0091] For example, the reaction kinetics calculation system can obtain the basic product distillation curves as shown in Table 2 under a certain temperature and pressure operation test, including the inlet material distillation curve and the outlet material distillation curve.

[0092]

[0093] Table 2

[0094] Please refer to Figure 4 and Figure 5 , Figure 4 The diagram shows a CDF curve of the Weibull distribution fitted to the inlet material distillation curve according to an embodiment of the present invention. Figure 5 The CDF curve of the Weibull distribution fitted to the distillation curve of the outlet material provided according to an embodiment of the present invention is shown.

[0095] The differences between the parameters of the Weibull distribution fitted to the inlet material distillation curve and the parameters of the Weibull distribution fitted to the outlet material distillation curve are shown in Table 3.

[0096]

[0097] Table 3

[0098] Furthermore, taking the kinetics between temperature change and position parameters as an example, the data used for calculation are shown in Tables 4(a) and 4(b). In Table 4(a), the first row contains data for the base case. The first column of both Tables 4(a) and 4(b) shows the position parameters of the reaction products fitted to a Weibull distribution. The second column shows the difference between the modified temperature and the temperature of the base case. The third column shows the difference between the position parameters of the reaction products fitted to a Weibull distribution and the position parameters of the reaction products of the base case fitted to a Weibull distribution. The fourth column shows the fitting result of fitting the data in the second column (temperature parameters) and the third column (position parameter differences) to a parameter transformation model using the nonlinear least squares method. Based on this fitting result, the kinetic parameters can be obtained. In this example, the reaction kinetics calculation system conducts experiments by changing temperature and pressure to fit the position, range, and shape parameters of the Weibull distribution. In Tables 4(a) and 4(b), the position parameters of the Weibull distribution are used as the dependent variable.

[0099]

[0100] Table 4(a)

[0101] Table 4(b)

[0102] Based on the established temperature variation pattern: The dynamic parameters can be determined. , and 1. Kinetic parameters corresponding to temperature parameters , , Once the kinetic parameters corresponding to the temperature parameters are determined, the parameter transformation model can be determined accordingly.

[0103] like Figure 6 As shown, the horizontal axis represents the temperature difference, and the vertical axis represents the difference in the position parameters of the Weibull distribution. The original value is the difference between the position parameters of the Weibull distribution directly fitted to the reaction products and the position parameters of the base case. The calculated value is the difference between the position parameters of the Weibull distribution of the reaction products calculated by the parameter transformation model at this temperature difference and the position parameters of the base case. Figure 6 It can be seen that the calculated values ​​obtained by this invention are highly similar to the original values. In subsequent practical applications, given the known variation patterns of temperature, pressure, and / or residence time, the parameter transformation model provided by this invention can be used to obtain the parameters of the Weibull distribution fitted to the distillation curve of the reaction products, thereby determining the distillation curve of the reaction products.

[0104] In this embodiment, after determining the kinetic parameters corresponding to the intermediate parameters, the reaction kinetics calculation system can further determine the binary interaction parameters in the parameter transformation model based on the differences between the parameters of the Weibull distribution.

[0105] Furthermore, the reaction kinetics calculation system can continuously update the binary interaction parameters and reaction kinetic parameters based on the actual acquired parameters.

[0106] In this embodiment, those skilled in the art can further optimize the production conditions of the catalytic cracking reactor based on the binary interaction parameters and reaction kinetic parameters determined by the reaction kinetics calculation system of the present invention, according to requirements. Taking high-yield diesel as an example, the diesel fraction is approximately 220~400 degrees Celsius. The reaction kinetics calculation system calculates the parameters of the Weibull distribution at 220 degrees Celsius and 400 degrees Celsius according to the parameter transformation model. The objective function of the optimization algorithm for optimizing the production conditions of the catalytic cracking reactor is set as Weibull(400)-Weibull(220). Here, Weibull(400) is the CDF(400) of the Weibull distribution, and Weibull(220) is the CDF(220) of the Weibull distribution. Weibull(400) and Weibull(220) can be determined using the aforementioned CDF formula and the parameters of the Weibull distribution at 220 degrees Celsius and 400 degrees Celsius obtained by the reaction kinetics calculation system. Then, the optimization results of the production conditions can be quickly obtained based on the optimizer. Those skilled in the art may choose to output the optimization results to the actual device to optimize the current production status of the device.

[0107] Therefore, with the kinetic parameter update method provided by this invention being simpler and faster, the optimization of product distribution and product properties of catalytic cracking reactors achieved by using the reaction kinetic calculation method provided by this invention can improve the optimization speed and convergence efficiency of subsequent optimization algorithms.

[0108] In summary, the reaction kinetics calculation method based on the stream level provided by this invention can simplify the calculation and updating of kinetic parameters, improve the updating efficiency of kinetic parameters, and significantly reduce the computational load and prerequisites required for optimization calculations, thereby improving the efficiency of solving optimization operating conditions and quickly solving refinery optimization schemes.

[0109] Although the methods described above are illustrated and depicted as a series of actions for the sake of simplicity, it should be understood and appreciated that these methods are not limited by the order of the actions, as some actions may occur in a different order and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but which may be understood by those skilled in the art, according to one or more embodiments.

[0110] Those skilled in the art will understand that information, signals, and data can be represented using any of a variety of different techniques and skills. For example, the data, instructions, commands, information, signals, bits, symbols, and chips described throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0111] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0112] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0113] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0114] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0115] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating reaction kinetics based on stream level, characterized in that, Including the following steps: The reference temperature, reference pressure, and reference residence time are obtained, and the cumulative probability density function curve of the first Weibull distribution is fitted based on the distillation curve of the feed to determine the parameters of the first Weibull distribution; By repeatedly changing the operating temperature, operating pressure, and / or operating residence time, and obtaining the distillation curves after the reaction, the cumulative probability density function curves of multiple second Weibull distributions are fitted to determine the parameters of the multiple second Weibull distributions; and A parameter transformation model is fitted based on the differences between operating temperature, operating pressure, and / or operating residence time and reference temperature, reference pressure, and reference residence time, as well as the differences between the parameters of the first Weibull distribution and the parameters of the plurality of second Weibull distributions, to determine the binary interaction parameters of the parameter transformation model and the reaction kinetic parameters used to characterize the changes in temperature, pressure, and residence time.

2. The reaction kinetics calculation method based on stream level as described in claim 1, characterized in that, The distillation curve is fitted to the cumulative probability density function curve of the Weibull distribution using the nonlinear least squares method.

3. The reaction kinetics calculation method based on stream level as described in claim 2, characterized in that, The cumulative probability density function of the Weibull distribution is used to characterize the flow stream. The cumulative probability density function of the Weibull distribution is: ， in, It is a random variable used to indicate the virtual components of crude oil. It is a position parameter. It is a range parameter. It is a shape parameter.

4. The reaction kinetics calculation method based on stream level as described in claim 1, characterized in that, The step of obtaining the reference temperature, reference pressure, and reference residence time, and fitting the cumulative probability density function curve of the first Weibull distribution based on the distillation curve of the feed to determine the parameters of the first Weibull distribution further includes: The cumulative probability density function curve of the third Weibull distribution is fitted based on the distillation curve of the output to determine the parameters of the third Weibull distribution.

5. The reaction kinetics calculation method based on stream level as described in claim 1, characterized in that, The parameter transformation model is as follows: ， in, express , and , , and It is the difference between the parameters of the first Weibull distribution and the parameters of the second Weibull distribution. It's a temperature parameter. It is a time parameter. It is a pressure parameter. The binary interaction parameters to be fitted are denoted as .

6. The reaction kinetics calculation method based on stream level as described in claim 5, characterized in that, The temperature parameter The time parameter and the pressure parameters Follow the laws of change: ， ， ， in, , , , , , , , and These are the reaction kinetic parameters. It is the difference between the operating temperature and the reference temperature determined by the temperature parameter. It is the difference between the time parameter determined based on the running dwell time and the reference dwell time. It is the difference between the operating pressure and the reference pressure.

7. The reaction kinetics calculation method based on stream level as described in claim 1, characterized in that, It also includes the following steps: The parameters of the Weibull distribution at the first boiling point and the parameters of the Weibull distribution at the second boiling point are determined using the parameter transformation model. The cumulative probability density function of the Weibull distribution at the first boiling point is determined based on the parameters of the Weibull distribution at the first boiling point, and the cumulative probability density function of the Weibull distribution at the second boiling point is determined based on the parameters of the Weibull distribution at the second boiling point; and The solution set is achieved by calculating the difference between the cumulative probability density function of the Weibull distribution at the first boiling point and the cumulative probability density function of the Weibull distribution at the second boiling point using the difference method of subtracting the previous value from the previous value.

8. The reaction kinetics calculation method based on stream level as described in claim 1, characterized in that, It also includes the following steps: Obtain actual product and feed information for the current stage of the catalytic cracking unit; When the change between the feed information and the feed is within a preset range, the binary interaction parameters of the parameter transformation model are updated based on the parameters of the Weibull distribution determined by the product information. When the change between the feed information and the feed exceeds a preset range, the parameters of the Weibull distribution of the product stream and the parameters of the Weibull distribution of the feed stream are determined based on the product information and the feed information. as well as The parameter transformation model is refitted based on the parameters of the Weibull distribution of the product stream and the parameters of the Weibull distribution of the feed stream to update the binary interaction parameters and the reaction kinetic parameters.

9. A reaction kinetics calculation system based on stream level, characterized in that, include: Memory, on which computer instructions are stored; as well as A processor, connected to the memory, and configured to execute computer instructions stored in the memory to implement the reaction kinetics calculation method based on the stream level as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, the reaction kinetics calculation method based on the stream level as described in any one of claims 1 to 8 is implemented.

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