Crude oil processing and cutting methods and equipment

Abstract

This invention provides a crude oil processing and cutting method and apparatus. The method includes: determining a prior distribution of cutting temperature; determining a posterior probability distribution of cutting temperature under the prior distribution, with the goal of maximizing product benefits; determining the side-cutting point temperature based on the posterior probability distribution; and processing and cutting the crude oil to be cut based on the side-cutting point temperature. The crude oil processing and cutting method and apparatus provided by this invention, under the prior distribution and with the goal of maximizing product benefits, not only can determine the posterior probability distribution of cutting temperature, i.e., achieve high-precision processing and cutting of the crude oil to be cut based on the side-cutting point temperature determined by the posterior probability distribution, but also can ensure that the product benefits obtained from cutting based on the side-cutting point temperature are maximized.

Description

Technical Field

[0001] This invention relates to the field of crude oil cutting technology, and in particular to a crude oil processing and cutting method and apparatus. Background Technology

[0002] Crude oil fractions, with different carbon numbers, have different uses. C1 to C4 fractions are natural gas and liquefied petroleum gas, commonly used as fuel gas; C5 to C10 fractions are mainly naphtha, primarily used to produce gasoline; C10 to C13 fractions are mainly kerosene, used to produce aviation kerosene; C13 to C20 fractions are diesel, which can be used as vehicle fuel; C20 to C35 fractions are wax oil, used as feedstock for hydrocracking and catalytic cracking to produce low-carbon, high-value-added products; and C35 and above fractions are mainly residue oil, which can be used to produce asphalt products or, after further processing, to produce light distillate oils. Atmospheric and vacuum distillation units, which separate different fractions, directly process crude oil and cut it into various fractions to provide feedstock for subsequent processes.

[0003] Currently, many methods transform the single-process optimization problem into a linear problem by introducing a pendulum structure. However, this method cannot determine the side-cutting point temperature and has inaccurate estimates of distillate oil properties. Summary of the Invention

[0004] This invention provides a crude oil processing and cutting method and apparatus to solve the problem of low precision in crude oil processing and cutting in the prior art.

[0005] This invention provides a crude oil processing and cutting method, comprising:

[0006] Determine the prior distribution of the cutting temperature;

[0007] Under the prior distribution, with the goal of maximizing product benefits, the posterior probability distribution of the cutting temperature is determined;

[0008] The temperature at the side-line cutting point is determined based on the posterior probability distribution.

[0009] Based on the temperature of the side-line cutting point, the crude oil to be cut is processed and cut.

[0010] According to a crude oil processing and cutting method provided by the present invention, the product benefits are determined based on the product price and product yield corresponding to each cutting temperature, and the product yield corresponding to each cutting temperature is determined based on the narrow fraction evaluation data of the crude oil to be cut.

[0011] According to the crude oil processing and cutting method provided by the present invention, the product benefits are determined based on the following formula:

[0012] θ=p i ×x i -δ;

[0013] Where θ represents the product benefit, p i Let x represent the product price corresponding to the i-th cutting temperature. i Let δ represent the product yield corresponding to the i-th cutting temperature, and let δ represent the penalty term.

[0014] According to a crude oil processing cutting method provided by the present invention, determining the posterior probability distribution of the cutting temperature under the prior distribution, with the objective of maximizing product benefits, includes:

[0015] Based on the Markov chain Monte Carlo algorithm, the posterior probability distribution is determined by applying the prior distribution and the formula for determining the product benefit.

[0016] According to a crude oil processing and cutting method provided by the present invention, the narrow fraction evaluation data of the crude oil to be cut is obtained by weighted averaging of crude oil evaluation data in different temperature ranges.

[0017] According to the crude oil processing and cutting method provided by the present invention, the prior distribution of the cutting temperature is as follows:

[0018]

[0019] Where f(t) represents the prior distribution of the cutting temperature, t represents the cutting temperature, μ represents the average value of the cutting temperature, and σ represents the standard deviation of the cutting temperature.

[0020] The present invention also provides a crude oil processing and cutting device, comprising:

[0021] The first determining unit is used to determine the prior distribution of the cutting temperature;

[0022] The second determining unit is used to determine the posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits;

[0023] The third determining unit is used to determine the temperature of the side-line cutting point based on the posterior probability distribution;

[0024] The processing and cutting unit is used to process and cut the crude oil to be cut based on the temperature of the side cutting point.

[0025] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the crude oil processing and cutting methods described above.

[0026] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the crude oil processing and cutting method as described above.

[0027] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the crude oil processing and cutting methods described above.

[0028] The crude oil processing and cutting method and apparatus provided by the present invention, under the premise of prior distribution, aims to maximize product benefits. It can not only determine the posterior probability distribution of the cutting temperature, that is, realize the high-precision processing and cutting of the crude oil to be cut based on the side-line cutting point temperature determined by the posterior probability distribution, but also ensure that the product benefits obtained by cutting based on the side-line cutting point temperature are maximized. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the crude oil processing and cutting method provided by the present invention;

[0031] Figure 2 This is a schematic diagram of the structure of the crude oil processing and cutting device provided by the present invention;

[0032] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] Currently, many methods transform primary crude oil processing optimization problems into linear problems by introducing pendulum structures. However, this method cannot determine the side-cutting point temperature and suffers from inaccurate estimation of distillate oil properties. Therefore, there is an urgent need to develop new methods for optimizing primary crude oil processing, improving the accuracy of distillate oil property estimation, and providing reliable data references for atmospheric and vacuum distillation unit operation.

[0035] In response, the present invention provides a crude oil processing and cutting method. Figure 1 This is a schematic flowchart of the crude oil processing and cutting method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following steps:

[0036] Step 110: Determine the prior distribution of the cutting temperature.

[0037] Here, the prior distribution of cutting temperature refers to the prior distribution of the cutting temperature of the crude oil to be cut. It can be determined based on the Gaussian distribution of the temperature ranges of the atmospheric and vacuum distillation furnace side lines, or it can be a prior distribution set according to actual conditions. This embodiment of the invention does not specifically limit this. Processing cutting refers to separating the crude oil to be cut into multiple components with different boiling point ranges, thereby obtaining various petroleum products. The temperature ranges of the atmospheric and vacuum distillation furnace side lines may include: the cutting point between naphtha and kerosene (130℃-200℃), the cutting point between kerosene and diesel oil (210℃-250℃), the cutting point between diesel oil and light wax oil (320℃-370℃), and the cutting point between wax oil and vacuum residue (500℃-560℃).

[0038] Optionally, the prior distribution of cutting temperature can be understood as the number of temperature intervals that the crude oil to be cut needs to be divided into, and the number of temperature intervals can be determined according to the user's actual needs.

[0039] Step 120: Under the prior distribution, determine the posterior probability distribution of the cutting temperature with the goal of maximizing product benefits.

[0040] Specifically, the posterior probability distribution of the cutting temperature can be understood as the lateral cutting point temperature corresponding to each temperature interval under the prior distribution. Since the prior distribution is used to characterize the number of temperature intervals to be divided in the crude oil to be cut, but cannot determine the lateral cutting point temperature corresponding to each temperature interval, the product benefit can be understood as the distillate product benefit corresponding to the processing and cutting of the crude oil under the prior distribution. The product benefit can be determined based on the product price and the output of each product.

[0041] Different side-cutting point temperatures correspond to different temperature ranges, and different temperature ranges correspond to different distillate products. Consequently, the product yield and product price vary depending on the temperature range, meaning that the product benefits differ depending on the temperature range.

[0042] In this regard, the embodiments of the present invention aim to maximize product benefits by solving the posterior probability distribution corresponding to the maximum product benefits under the prior distribution, so as to ensure optimal product benefits.

[0043] Step 130: Determine the temperature of the side-line cutting point based on the posterior probability distribution.

[0044] Specifically, the posterior probability distribution is used to characterize the temperature of the lateral cutting point in each temperature range corresponding to the prior distribution, and the lateral cutting point temperature can be determined based on the posterior probability distribution.

[0045] Step 140: Based on the temperature of the side cutting point, process and cut the crude oil to be cut.

[0046] Specifically, crude oil processing and cutting refers to a series of technological steps involving the separation, refining, and treatment of crude oil to extract narrow fractions of different components. Crude oil processing and cutting processes can include distillation, cracking, reforming, hydrogenation, and catalytic cracking.

[0047] Since the posterior probability distribution is determined under the prior distribution with the goal of maximizing product benefits, the side-line cutting point temperature determined based on the posterior probability distribution can ensure that the product benefits obtained after cutting are maximized.

[0048] The crude oil processing and cutting method provided in this invention aims to maximize product benefits under a prior distribution. It can not only determine the posterior probability distribution of the cutting temperature, that is, realize high-precision processing and cutting of the crude oil to be cut based on the side-line cutting point temperature determined by the posterior probability distribution, but also ensure that the product benefits obtained by cutting based on the side-line cutting point temperature are maximized.

[0049] Based on the above embodiments, the product benefits are determined based on the product price and product output corresponding to each cutting temperature, and the product output corresponding to each cutting temperature is determined based on the evaluation data of the narrow fraction of the crude oil to be cut.

[0050] Specifically, different cutting temperatures correspond to different distillate products, which in turn lead to different product prices and yields. The product yield at each cutting temperature is determined based on evaluation data of the narrow fraction of the crude oil to be cut.

[0051] Among them, narrow fraction evaluation data refers to the results of physical and chemical property tests and analyses of different narrow fractions (also known as fraction ranges) obtained by fractionation of crude oil during the refining process. Narrow fraction evaluation data usually includes characteristics such as temperature, density, sulfur content, pour point, flash point, vapor pressure, composition, and yield.

[0052] Based on any of the above embodiments, the product benefits are determined using the following formula:

[0053] θ=p i ×x i -δ;

[0054] Where θ represents product benefit, p i Let x represent the product price corresponding to the i-th cutting temperature. iLet δ represent the product yield corresponding to the i-th cutting temperature, and let δ represent the penalty term.

[0055] Based on any of the above embodiments, under the prior distribution, to maximize product benefits, the posterior probability distribution of the cutting temperature is determined, including:

[0056] Based on the Markov chain Monte Carlo algorithm, the posterior probability distribution is determined by applying the prior distribution and the formula for determining product benefits.

[0057] Specifically, Markov chain Monte Carlo is a commonly used method in statistics for obtaining samples from high-dimensional distributions. Its theory involves the transition process of a stationary Markov chain. The main idea is that for a given probability stationary distribution π, the state transition matrix P of an aperiodic Markov chain and the probability distribution π(x) satisfy for all i, j: π(i)P(i,j)=π(j)P(j,i). However, usually, the target stationary distribution π(x) and any Markov chain state transition matrix Q do not satisfy the detailed stationarity condition, i.e.:

[0058] π(i)Q(i,j)≠π(j)Q(j,i)

[0059] To ensure the above equation satisfies the identity condition, a state transition process α(i, j) is introduced into the equation to make the detailed stationarity condition hold, i.e.:

[0060] π(i)Q(i,j)α(i,j)=π(j)Q(j,i)α(j,i)

[0061] Therefore, the Markov chain state transition matrix P corresponding to the distribution π(x) satisfies:

[0062] P(i,j)=Q(i,j)α(i,j)

[0063] That is, the state transition matrix P of the target distribution can be constructed by any Markov chain state transition matrix Q and α, where α is generally called the acceptance rate and takes a value between [0, 1].

[0064] This invention provides a computational flow for the Markov chain Monte Carlo method:

[0065] Select the Markov chain state transition matrix Q, the stationary distribution π(t), the state transition number threshold n1, the number of samples n2, and determine the initial state value t0;

[0066] while i < n1 + n2 - 1:

[0067] From the conditional probability distribution Q(x|t) i Sample t was obtained by sampling from ) * ;

[0068] Take a random number u from [0, 1];

[0069] If u < α(t) i , t * )=π(t * )Q(t * |t i ), then t i+1 =t * Otherwise t i+1 =t i ;

[0070] Finally, the temperature at the side-line cutting point is calculated by analyzing the posterior probability distribution of the cutting parameter t.

[0071] Based on any of the above embodiments, the evaluation data of the narrow fraction of crude oil to be cut is obtained by weighted averaging of crude oil evaluation data in different temperature ranges.

[0072] Specifically, narrow fraction evaluation data can include density, API (American Petroleum Institute) concentration, freezing point, sulfur content, nitrogen content, acid value, metal content, characteristic factor, yield, etc. Yield can be calculated by harmonizing the properties of each narrow fraction within the temperature range; density can be calculated using a volumetric linear harmonization method; sulfur content, nitrogen content, acid value, and metal content can be harmonized using a mass linear weighted calculation method; and freezing point, flash point, and other indicators can be calculated using an exponential linear harmonization method.

[0073] Based on any of the above embodiments, the prior distribution of the cutting temperature is:

[0074]

[0075] Where f(t) represents the prior distribution of cutting temperature, t represents the cutting temperature, μ represents the average cutting temperature, and σ represents the standard deviation of cutting temperature.

[0076] The crude oil processing cutting method provided in this invention uses the Bayesian-Monte Carlo method to estimate the cutting temperature of the atmospheric and vacuum distillation unit side stream. This solves the problems of difficulty in providing a clear cutting temperature and inaccurate estimation of distillate oil properties in previous global resource optimization methods, providing technical support for realizing global resource optimization in refining and real-time optimization of atmospheric and vacuum distillation units. The crude oil processing cutting method provided in this invention has advantages such as high parameter solution accuracy, fast calculation speed, and the ability to achieve fully nonlinear optimization.

[0077] The crude oil processing and cutting apparatus provided by the present invention is described below. The crude oil processing and cutting apparatus described below can be referred to in correspondence with the crude oil processing and cutting method described above.

[0078] Based on any of the above embodiments, the present invention also provides a crude oil processing and cutting device, such as... Figure 2 As shown, the device includes:

[0079] The first determining unit 210 is used to determine the prior distribution of the cutting temperature;

[0080] The second determining unit 220 is used to determine the posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits;

[0081] The third determining unit 230 is used to determine the temperature of the side line cutting point based on the posterior probability distribution;

[0082] The processing and cutting unit 240 is used to process and cut the crude oil to be cut based on the temperature of the side cutting point.

[0083] Based on any of the above embodiments, the product benefits are determined based on the product price and product output corresponding to each cutting temperature, and the product output corresponding to each cutting temperature is determined based on the narrow fraction evaluation data of the crude oil to be cut.

[0084] Based on any of the above embodiments, the product benefits are determined according to the following formula:

[0085] θ=p i ×x i -δ;

[0086] Where θ represents the product benefit, p i Let x represent the product price corresponding to the i-th cutting temperature. i Let δ represent the product yield corresponding to the i-th cutting temperature, and let δ represent the penalty term.

[0087] Based on any of the above embodiments, determining the posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits, includes:

[0088] Based on the Markov chain Monte Carlo algorithm, the posterior probability distribution is determined by applying the prior distribution and the formula for determining the product benefit.

[0089] Based on any of the above embodiments, the narrow fraction evaluation data of the crude oil to be cut is obtained by weighted averaging of crude oil evaluation data in different temperature ranges.

[0090] Based on any of the above embodiments, the prior distribution of the cutting temperature is:

[0091]

[0092] Where f(t) represents the prior distribution of the cutting temperature, t represents the cutting temperature, μ represents the average value of the cutting temperature, and σ represents the standard deviation of the cutting temperature.

[0093] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3 As shown, the electronic device may include a processor 310, a memory 320, a communication interface 330, and a communication bus 340, wherein the processor 310, memory 320, and communication interface 330 communicate with each other through the communication bus 340. The processor 310 can call logical instructions in the memory 320 to execute a crude oil processing and cutting method, which includes: determining a prior distribution of cutting temperature; determining a posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits; determining the side-line cutting point temperature based on the posterior probability distribution; and processing and cutting the crude oil to be cut based on the side-line cutting point temperature.

[0094] Furthermore, the logical instructions in the aforementioned memory 320 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the crude oil processing and cutting method provided by the above methods, the method comprising: determining a prior distribution of cutting temperature; determining a posterior probability distribution of cutting temperature under the prior distribution with the objective of maximizing product benefits; determining a side-line cutting point temperature based on the posterior probability distribution; and processing and cutting the crude oil to be cut based on the side-line cutting point temperature.

[0096] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the crude oil processing and cutting methods provided above, the method comprising: determining a prior distribution of cutting temperature; determining a posterior probability distribution of the cutting temperature under the prior distribution with the objective of maximizing product benefits; determining a side-line cutting point temperature based on the posterior probability distribution; and processing and cutting the crude oil to be cut based on the side-line cutting point temperature.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A crude oil processing and cutting method, characterized in that, include: Determine the prior distribution of the cutting temperature; Under the prior distribution, with the goal of maximizing product benefits, the posterior probability distribution of the cutting temperature is determined; The temperature at the side-line cutting point is determined based on the posterior probability distribution. Based on the temperature of the side-line cutting point, the crude oil to be cut is processed and cut; The product benefits are determined based on the following formula: θ=p i ×x i -d; Where θ represents the product benefit, p i Let x represent the product price corresponding to the i-th cutting temperature. i Let δ represent the product output corresponding to the i-th cutting temperature, and let δ represent the penalty term. The prior distribution of the cutting temperature is as follows: Where f(t) represents the prior distribution of the cutting temperature, t represents the cutting temperature, μ represents the average value of the cutting temperature, and σ represents the standard deviation of the cutting temperature; The posterior probability distribution is determined by selecting the Markov chain state transition matrix Q, the stationary distribution π(t), the state transition number threshold n1, the number of samples n2, and determining the initial state value t0. while i < n1 + n2 - 1: From the conditional probability distribution Q(x|t) i Sample t was obtained by sampling from ) * ; Take a random number u between [0,1]; If u < α(t) i ,t * )=π(t * )Q(t * |t i ), then t i+1 =t * Otherwise t i+1 =t i ; Finally, the temperature at the side-line cutting point is calculated by analyzing the posterior probability distribution of the cutting parameter t.

2. The crude oil processing and cutting method according to claim 1, characterized in that, The product benefits are determined based on the product price and product output corresponding to each cutting temperature, and the product output corresponding to each cutting temperature is determined based on the narrow fraction evaluation data of the crude oil to be cut.

3. The crude oil processing and cutting method according to claim 1, characterized in that, Determining the posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits, includes: Based on the Markov chain Monte Carlo algorithm, the posterior probability distribution is determined by applying the prior distribution and the formula for determining the product benefit.

4. The crude oil processing and cutting method according to claim 2, characterized in that, The evaluation data for the narrow fraction of the crude oil to be cut is obtained by weighted averaging of crude oil evaluation data in different temperature ranges.

5. A crude oil processing and cutting device, characterized in that, include: The first determining unit is used to determine the prior distribution of the cutting temperature; The second determining unit is used to determine the posterior probability distribution of the cutting temperature under the prior distribution, with the goal of maximizing product benefits; The third determining unit is used to determine the temperature of the side-line cutting point based on the posterior probability distribution; The processing and cutting unit is used to process and cut the crude oil to be cut based on the temperature of the side cutting point; The product benefits are determined based on the following formula: θ=p i ×x i -d; Where θ represents the product benefit, p i Let x represent the product price corresponding to the i-th cutting temperature. i Let δ represent the product output corresponding to the i-th cutting temperature, and let δ represent the penalty term. The prior distribution of the cutting temperature is as follows: Where f(t) represents the prior distribution of the cutting temperature, t represents the cutting temperature, μ represents the average value of the cutting temperature, and σ represents the standard deviation of the cutting temperature; The posterior probability distribution is determined by selecting the Markov chain state transition matrix Q, the stationary distribution π(t), the state transition number threshold n1, the number of samples n2, and determining the initial state value t0. while i < n1 + n2 - 1: From the conditional probability distribution Q(x|t) i Sample t was obtained by sampling from ) * ; Take a random number u between [0,1]; If u < α(t) i ,t * )=π(t * )Q(t * |t i ), then t i+1 =t * Otherwise t i+1 =t i ; Finally, the temperature at the side-line cutting point is calculated by analyzing the posterior probability distribution of the cutting parameter t.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the crude oil processing and cutting method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the crude oil processing and cutting method as described in any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the crude oil processing and cutting method as described in any one of claims 1 to 4.

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