Method, apparatus, electronic device and storage medium for crude oil blending

Abstract

The present disclosure relates to a crude oil blending method, apparatus, electronic device, and storage medium in the petrochemical industry. The method includes the following steps: determining at least two constituent crude oils and their respective constituent property data, where the constituent property data cover the property items of a predetermined target property data of a target crude oil; determining a combination of constituent crude oils to be verified based on the constituent property data and the target property data using a simulated annealing algorithm; determining the similarity between the combination of constituent crude oils to be verified and the target crude oil; and determining the combination of constituent crude oils to be verified as a blended crude oil combination if the similarity satisfies at least a certain criterion. In this way, the economic efficiency of a refinery can be improved, the calculation load for determining the combination of blended constituent crude oils can be reduced, and at the same time, crude oil blending efficiency can be improved.

Description

[Technical Field]

[0001] The present disclosure relates to the field of petrochemical industry, and in particular to methods, apparatus, electronic devices and storage media for crude oil blending. [Background technology]

[0002] As the economy develops, various industries are increasingly demanding petrochemical products. For example, as environmental protection requirements increase, countries are increasingly demanding cleaner fuels. In the field of marine fuel oil, low-sulfur clean marine fuel oil has become a major concern for the refining industry. Based on these requirements, selecting and blending different crude oils to obtain a blend with properties similar to those of the target crude oil can meet market demands and improve the economic benefits of refineries.

[0003] In the prior art, blending methods used to obtain formulated crude oils with properties similar to those of a target crude oil typically require the exhaustive combination of various crude oils within a range, which lacks a clear purpose and is inefficient. Summary of the Invention [Problem to be solved by the invention]

[0004] To address the shortcomings of the prior art, the present disclosure provides a method, apparatus, electronic device, and storage medium for crude oil blending. [Means for solving the problem]

[0005] A first aspect of the present disclosure provides a method for crude oil blending, the method comprising: Determining at least two component crude oils and component property data for each of the component crude oils, wherein the component property data cover property items of predetermined target property data of the target crude oil, and the target property data characterize the property data of the previous target crude oil; and determining a combination of verification target constituent crude oils by a simulated annealing algorithm based on the constituent property data and the target property data, wherein the combination of verification target constituent crude oils includes verification target constituent crude oils and corresponding verification target constituent proportions of the verification target constituent crude oils; Includes:

[0006] Alternatively, the method comprises: determining a degree of similarity between the combination of the verification constituent crude oils and the target crude oil; and A step of determining the combination of the verification target constituent crude oils as a combination of blended constituent crude oils in accordance with at least the similarity satisfying a certain criterion, wherein a blended crude oil can be formed based on the combination of the blended constituent crude oils; Further includes:

[0007] Furthermore, the step of determining a combination of verification target constituent crude oils by a simulated annealing algorithm based on the constituent property data and the target property data includes: consider the constituent crude oils as verification target constituent crude oils, and randomly generate verification target constituent proportions for each of the verification target constituent crude oils; Normalizing the constituent property data of the verification target constituent crude oil and the target property data, respectively, to obtain normalized constituent property data and normalized target property data; and determining a sum of characteristic item deviations as a target function of the simulated annealing algorithm based on the normalized constituent characteristic data of the verification target constituent crude oil, the normalized target characteristic data, and the randomly determined verification target constituent proportions; Includes:

[0008] Furthermore, the characteristic item deviation characterizes the deviation between weighted normalized constituent characteristic data and the normalized target characteristic data, and the weighted normalized constituent characteristic data is the sum of weighted products of the normalized constituent characteristic data of each of the verification target constituent crude oils and the verification target constituent proportions, which are respectively randomly generated.

[0009] Furthermore, the fact that the similarity at least satisfies a certain standard means that the similarity is equal to or greater than a first target similarity that is set in advance.

[0010] Furthermore, at least the similarity satisfies a certain standard, the similarity is equal to or greater than a predetermined second target similarity, and the verification target composition ratio of each verification target constituent crude oil in the combination of verification target constituent crude oils is within a predetermined composition ratio range; means.

[0011] Alternatively, the step of determining the similarity between the combination of the verification target constituent crude oils and the target crude oil comprises: determining blending characteristic data corresponding to the combination of verification target constituent crude oils based on the verification target constituent crude oils' respective verification target constituent crude oil verification target constituent crude oil ratios in the combination of verification target constituent crude oils and the constituent characteristic data; and determining a similarity based on the blend property data and the target property data, wherein the similarity characterizes a similarity between the combination of the validation constituent crude oils and the target crude oil; Includes:

[0012] Alternatively, the step of determining the similarity based on the blending characteristic data and the target characteristic data includes: determining the target similarity based on the blending characteristic data, the target characteristic data, a predetermined standard deviation of the characteristic items, and a predetermined weight of the characteristic items; Includes:

[0013] Furthermore, the property items include crude oil properties and / or fraction properties of crude oil fractions obtained by true boiling point distillation of crude oil.

[0014] A second aspect of the present disclosure provides an apparatus for crude oil blending, said apparatus comprising: a first determination module configured to determine at least two constituent crude oils and constituent characteristic data for each of the constituent crude oils, the constituent characteristic data covering characteristic items of predetermined target characteristic data of a target crude oil, and the target characteristic data characterizing the characteristic data of the target crude oil; and a second determination module configured to determine a combination of verification target constituent crude oils by a simulated annealing algorithm based on the constituent property data and the target property data, wherein the combination of verification target constituent crude oils includes verification target constituent crude oils and corresponding verification target constituent proportions of the verification target constituent crude oils; Includes.

[0015] Alternatively, the apparatus for blending crude oils comprises: a third determination module configured to determine a degree of similarity between the combination of the verification constituent crude oils and the target crude oil; and a fourth determination module configured to determine the combination of the verification target constituent crude oils as a combination of blended constituent crude oils according to at least the similarity satisfying a certain criterion, wherein a blended crude oil can be formed based on the combination of the blended constituent crude oils; Further includes:

[0016] Furthermore, determining a combination of verification target constituent crude oils by a simulated annealing algorithm based on the constituent property data and the target property data includes: considering the constituent crude oils as verification target constituent crude oils, and randomly generating the verification target constituent proportions for each of the verification target constituent crude oils; Normalizing the constituent property data and the target property data of the verification target constituent crude oil to obtain normalized constituent property data and normalized target property data, respectively; and determining a sum of characteristic item deviations as a target function of the simulated annealing algorithm based on the normalized constituent characteristic data of the verification target constituent crude oil, the normalized target characteristic data, and the verification target constituent proportions determined randomly; Includes:

[0017] A third aspect of the present disclosure is An electronic device, a memory for storing a computer program, the computer program including executable instructions; and a processor configured to execute the executable instructions to perform the method of the first aspect; An electronic device comprising:

[0018] A fourth aspect of the present disclosure is A non-transitory computer-readable storage medium storing a computer program, the computer program comprising executable instructions that, when executed by a processor, cause the processor to perform the method of the first aspect; A computer-readable storage medium is provided.

[0019] A fifth aspect of the present disclosure provides a computer program product comprising executable instructions that, when executed by a processor, cause the processor to perform the method of the first aspect.

[0020] The technical solutions provided by the embodiments of the present disclosure can achieve the following beneficial effects:

[0021] This solution uses a simulated annealing algorithm to determine a combination of constituent crude oils to be verified based on the constituent property data and the target property data, and optionally determines a combination of blended constituent crude oils according to a certain criterion. In this way, the combination of blended constituent crude oils can be determined based on at least two constituent crude oils, and a blended crude oil can be obtained by blending according to the combination of blended crude oils, so that the blended crude oil and the target crude oil have similar properties. In this way, the calculation effort for determining the combination of blended constituent crude oils can be reduced, the efficiency of crude oil blending can be improved, and the combination of blended constituent crude oils can be determined to improve the economic profit of the refinery.

[0022] The drawings illustrate various examples of aspects of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. Those skilled in the art will understand that the specific embodiments shown in the drawings are merely exemplary and are not intended to limit the scope of the present disclosure. It should be recognized that in some examples, an element may be separated into multiple elements, or multiple elements may be combined into a single element. In some examples, an element shown as an internal component of another element may also be implemented as an external component of another element, and vice versa. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a flowchart of a method for crude oil blending according to an exemplary embodiment of the present disclosure. [Figure 2] 2 is a flowchart of further details of the method of FIG. 1 according to an exemplary embodiment of the present disclosure. [Figure 3] 1 is a flowchart of a simulated annealing algorithm according to an exemplary embodiment of the present disclosure. [Figure 4] 10 is a flowchart of yet another method for crude oil blending according to an exemplary embodiment of the present disclosure. [Figure 5]FIG. 1 is a block diagram of an apparatus for crude oil blending according to an exemplary embodiment of the present disclosure. [Figure 6] FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0024] Specific implementations of the present disclosure will now be described in detail in conjunction with the drawings. It should be understood that the specific implementations described herein are intended only to illustrate and describe the present disclosure, and are not intended to limit the scope of the present disclosure.

[0025] As used below, the terms "have," "include," or "comprise," or grammatical variations thereof, are used in a non-exclusive manner. Thus, in addition to the features introduced by these terms, these terms can refer both to a situation in which no other features are present in the entity described in this context, and to a situation in which one or more other features are present. For example, the expressions "A has B," "A includes B," and "A comprises B" can refer to a situation in which no other elements are present in A other than B (i.e., a situation in which A is composed only of B), or to a situation in which one or more other elements (such as element C, elements C and D, or other elements) are present in entity A in addition to B.

[0026] Furthermore, it should be noted that the use of "at least one," "one or more," or similar expressions indicating one or more features or elements is typically only used when the corresponding feature or element is first described. In most cases below, when referring to a corresponding feature or element, the expression "at least one" or "one or more" will not be repeated, even though there may be one or more of the corresponding feature or element.

[0027] Furthermore, terms such as "preferably," "more preferably," "particularly," "more particularly," "particularly," and "more particularly" used below are used in combination with any feature without excluding alternative possibilities. Therefore, features introduced by these terms are optional features and are not intended to limit the scope of the claims. As can be understood by those skilled in the art, the present disclosure can be implemented using alternative features. Similarly, features described by phrases such as "in an embodiment of the present disclosure" are intended to be optional features without excluding alternative embodiments of the present disclosure, without limiting the scope of the present disclosure, and without limiting the possibility of combining the feature thus introduced with other optional or non-optional features of the present disclosure.

[0028] Furthermore, although terms such as "first" and "second" may be used herein to describe various elements, it is understood that these elements should not be limited by these terms. These terms do not indicate an order, but are used only to distinguish one element from another.

[0029] The term "component crude oil" as used in this disclosure refers to a type of crude oil having specific properties, which are selected to be blended with other constituent crude oils to obtain a blended crude oil having properties similar to those of the target crude oil.

[0030] The term "combination of verified constituent crude oils" used in this disclosure refers to a combination of multiple constituent crude oils obtained by a simulated annealing algorithm (see below), and includes multiple verified constituent crude oils and the composition ratios of each verified constituent crude oil. In this disclosure, the "combination of verified constituent crude oils" requires further verification to confirm whether it meets certain criteria. If it meets the criteria, it is considered a "combination of blended constituent crude oils."

[0031] The term "combination of blended crude oils" used in this disclosure refers to a "combination of verified constituent crude oils" that meets the criteria. The "combination of blended crude oils" includes blended constituent crude oils and the blending ratios of each of the blended constituent crude oils. By blending the blended constituent crude oils according to the blending ratios of each of the blended constituent crude oils, a blended crude oil having properties similar to the target crude oil can be obtained.

[0032] The term "blended crude oil" as used in this disclosure refers to a crude oil product blended from constituent crude oils according to the blending proportions of each constituent crude oil, and having predetermined properties or properties sufficiently similar to those of a target crude oil.

[0033] The term "target crude oil" as used in this disclosure refers to a crude oil having target property data that is an ideal target for crude oil blending.

[0034] In the present disclosure, a range of values ​​is represented by (a to b), which means greater than a and less than b. For example, (0 to 1) represents a range of values ​​greater than 0 and less than 1.

[0035] The present disclosure will now be described in conjunction with specific embodiments.

[0036] 1 is a flow chart of a method for crude oil blending according to an exemplary embodiment of the present disclosure. As shown, the method may include the following steps:

[0037] In step S101, at least two constituent crudes and the constituent property data for each constituent crude are determined.

[0038] In some embodiments, the constituent property data must cover property items of preset target property data, and the target property data characterizes property data of the target crude oil.

[0039] In some other embodiments, some of the characteristic items of the target characteristic data are changed according to the demands of different markets.

[0040] In some embodiments, the properties may include crude oil properties and / or fraction properties of crude oil fractions obtained after true boiling point distillation of the crude oil.

[0041] In some embodiments, different target crudes may correspond to different attributes.

[0042] For example, if the target crude oil is a low-sulfur bunker fuel feedstock crude oil, the target property data and the constituent property data may include crude oil properties such as crude oil density, sulfur content, residual carbon, acid number, and vanadium content, as well as fraction properties such as fraction yield, fraction density, fraction sulfur content, and fraction kinematic viscosity. The fractions of the crude oil may include vacuum residue fractions obtained by true boiling point distillation of the crude oil.

[0043] It should be noted that the target crude oil characteristics may include only crude oil characteristics or only fraction characteristics of crude oil fractions obtained after true boiling point distillation of crude oil, but the present disclosure is not limited thereto.

[0044] In some embodiments, a constituent crude library may first be determined. The constituent crude library may include a plurality of crude oil samples. For example, the crude oil samples may include crude oil samples with known properties at a refinery. Next, a predetermined number of crude oil samples are randomly determined from the constituent crude oil library, and these crude oil samples are used as constituent crude oils. The predetermined number may be randomly determined between a predetermined maximum number of constituent crude oils and a predetermined minimum number of constituent crude oils. The predetermined maximum number of preset constituent crude oils and the predetermined minimum number of constituent crude oils may be any predetermined value. For example, the predetermined maximum number of constituent crude oils may be 4, the predetermined minimum number of constituent crude oils may be 2, and the randomly determined predetermined number may be 3.

[0045] In some possible embodiments, the target crude oil property data may be preset or may be data determined by industry-standard acquisition methods. For example, the crude oil density may be determined by a U-shaped vibrating tube method, the sulfur content may be determined by energy dispersive X-ray fluorescence spectroscopy, and the acid number may be determined by potentiometric titration. The present disclosure is not limited thereto.

[0046] Next, the process proceeds to step S102, where a combination of constituent crude oils to be verified is determined by a simulated annealing algorithm according to the constituent property data and the target property data.

[0047] The combination of verified constituent crude oils includes verified constituent crude oils and a verified constituent proportion of each verified constituent crude oil. The verified constituent proportion of each verified constituent crude oil may be the verified constituent proportion by mass of the verified constituent crude oil (also known as the verified constituent proportion by weight of the verified constituent crude oil) or the verified constituent proportion by volume of the verified constituent crude oil. The present disclosure is not limited thereto.

[0048] For example, to obtain the verification target constituent proportions for each of the predetermined number of verification target constituent crude oils, the respective constituent proportions of the predetermined number of constituent crude oils may be optimized through a simulated annealing algorithm. The predetermined number of verification target constituent crude oils and the verification target constituent proportions of each verification target constituent crude oil are combined to obtain a combination of verification target constituent crude oils. After the simulated annealing algorithm is completed, a plurality of combinations of verification target constituent crude oils may be output.

[0049] Next, the method proceeds to step S103, where it is determined whether the combination of constituent crude oils to be verified satisfies a predetermined criterion. If so, the method proceeds to step S104, where the combination of constituent crude oils to be verified is determined as a blend constituent crude oil combination. Similarly, the blend constituent crude oil combination includes blend constituent crude oils and corresponding blend proportions. If the criterion is not met in step S103, the method continues and it is determined whether the next combination of constituent crude oils to be verified satisfies the criterion. The blend constituent crude oil combination is used to blend the blend crude oil.

[0050] Illustratively, the predetermined criterion in step 103 may be a target similarity between the combination of verification-target constituent crude oils and the target crude oil. When the similarity between the combination of verification-target constituent crude oils and the target crude oil reaches the target similarity, the predetermined criterion is deemed to be satisfied. Specifically, the step of determining the similarity includes the following: for the combination of verification-target constituent crude oils, based on the verification-target constituent proportions and the constituent property data of the verification-target constituent crude oils, determining property data of a verification-target blended crude oil obtained by mixing the verification-target constituent crude oils according to the verification-target constituent proportions (i.e., blending property data of the combination of verification-target constituent crude oils), and then comparing the property data of the verification-target constituent crude oils with the target property data of the target crude oil to obtain a similarity therebetween. Accordingly, when the similarity reaches the target similarity, the combination of verification-target constituent crude oils is determined as a blended constituent crude oil combination. Specifically, the verification-target constituent proportions are determined as blended constituent crude oils, and the verification-target constituent crude oils are determined as blended constituent crude oils.

[0051] It should be noted that a blended crude oil can be obtained by blending according to the combination of the blend constituent crude oils. The actual property data of this blended crude oil may be the same as or similar to the target property data. Illustratively, the deviation between the actual property data and the target property data may also be within a preset property deviation range (e.g., the density deviation is 0.01 gcm). -3The deviation of sulfur content (%) does not exceed 0.3, and the deviation of acid value is 0.3 mg KOH.g -1 The metal content deviation does not exceed 50 (μg.g -1 ) and the fraction yield (%) deviation may not exceed 5. The predetermined range of property deviation may be the absolute value of the property deviation or may be a percentage of the property deviation. The present disclosure is not limited thereto.

[0052] According to the above method, the combination of blended constituent crude oils can be determined based on at least two constituent crude oils, and the blended constituent crude oils can be mixed according to the combination of blended constituent crude oils to obtain a blended crude oil. In this way, the efficiency of crude oil blending can be improved, and the economic profit of the refinery can be increased.

[0053] Figure 2 is a flowchart illustrating further details of the method of Figure 1 according to an exemplary embodiment of the present disclosure. Specifically, the steps of Figure 2 are further details of step S102 of Figure 1. As shown in Figure 2, step S102 may include the following steps:

[0054] In step S1021, the constituent crude oils are regarded as verification target constituent crude oils, and the initial verification target constituent ratio of each verification target constituent crude oil is determined.

[0055] Illustratively, a preset number (e.g., represented by N) of constituent crude oils determined in step S101 of Fig. 1 may be set as verification target constituent crude oils. In some possible embodiments, the initial verification target constituent content of each verification target constituent crude oil may be randomly generated within a preset range, and the initial verification target constituent ratio of each verification target constituent crude oil is determined according to the initial verification target constituent content of each verification target constituent crude oil. For example, the initial verification target constituent content r of each verification target constituent crude oil may be set as follows: j is randomly generated, and the initial verification target composition ratio R j is the initial verification target constituent content r j and is determined based on the following formula 1:

[0056]

number

[0057] In the formula, N is the number of constituent crude oils to be verified, and r j is the initial verification target constituent content of the jth verification target constituent crude oil, and R j is the initial composition ratio of the jth verification target crude oil.

[0058] In step S1022, the constituent property data and the target property data of the constituent crude oils to be verified are normalized, respectively, to obtain normalized constituent property data and normalized target property data.

[0059] In some possible embodiments, the constituent property data of the constituent crude oil to be verified is normalized by the following Equation 2 to obtain normalized constituent property data.

[0060]

number

[0061] In the formula, p i,j is the i-th constituent characteristic data of the j-th constituent crude oil to be verified, and P i,j is the ith normalized constituent property data of the jth constituent crude oil, and p i,min is the minimum value of the i-th constituent characteristic data. Illustratively, this minimum value may be the minimum value of the i-th constituent characteristic data among the N constituent crude oils to be verified, and p i,max is the maximum value of the i-th constituent characteristic data. Alternatively, this maximum value may be the maximum value of the i-th constituent characteristic data among the N constituent crude oils to be verified. In another embodiment, p i,min is the minimum value of the i-th constituent property data in the crude oil sample of the constituent crude oil library, and p i,max may be the maximum value of the i-th constituent property data in the crude oil sample of the constituent crude oil library.

[0062] In some possible implementations, the target characteristic data may be normalized using Equation 3 below to obtain normalized target characteristic data.

[0063]

number

[0064] In the formula, p i,obj is the i-th target characteristic data, P i,obj is the i-th normalized target characteristic data, p i,min is the minimum value of the i-th constituent characteristic data. Illustratively, this minimum value may be the minimum value of the i-th constituent characteristic data among the N constituent crude oils to be verified, and p i,max is the maximum value of the i-th constituent characteristic data. Illustratively, this maximum value may be the maximum value of the i-th constituent characteristic data among the N constituent crude oils to be verified. In another embodiment, p i,min is the minimum value of the i-th constituent property data in the crude oil sample of the constituent crude oil library, and p i,max , can also be the maximum value of the i-th constituent property data in the crude oil sample of the constituent crude oil library.

[0065] In step S1023, the normalized constituent characteristic data P of the constituent crude oil to be verified i,j and the normalized target characteristic data P i,obj and the determined initial verification target configuration ratio R j Based on the above, a verification target constituent proportion for each of the verification target constituent crude oils is determined by a simulated annealing algorithm, and then the verification target constituent crude oils and the verification target constituent proportions are combined to form a combination of verification target constituent crude oils.

[0066] The goal of the simulated annealing algorithm is to minimize the sum of the property deviations of multiple properties, which characterize the deviations between the weighted normalized constituent property data and the normalized target property data. The weighted normalized constituent property data is the sum of the weighted products of the normalized constituent property data of each of the verification target constituent crudes and the corresponding verification target constituent proportions.

[0067] 3 is a flowchart of a simulated annealing algorithm according to an exemplary embodiment of the present disclosure. In some possible implementations, the values ​​and value ranges of some key parameters of the simulated annealing algorithm are: initial temperature t0=100°C (value range: 80°C to 200°C), temperature decay coefficient α=0.95 (value range: 0.1 to 1), and end temperature threshold t end = 0.5°C (value range: 0.01°C to 5°C), and the iteration number threshold L at each temperature of the simulated annealing algorithm = 150 (value range: 100 to 200). The meanings of the above initial temperature, temperature decay coefficient, end temperature threshold, and iteration number threshold are described in the related explanation of the simulated annealing algorithm in the related art, so they will not be explained in detail here.

[0068] As shown in Figure 3, the main steps involved in the simulated annealing algorithm are as follows:

[0069] In step S301, the initial temperature t=t0, the temperature decay coefficient α, and the end temperature threshold t end The initial value is determined by determining the iteration count threshold L at each temperature. Then, the total characteristic item deviation ΔP is calculated according to the initial verification target configuration ratio, and this total characteristic item deviation ΔP is used as the target function of the simulated annealing algorithm.

[0070] For example, the sum of the characteristic item deviations can be calculated using the following formula 4.

[0071]

number

[0072] where ΔP is the sum of the characteristic deviations,

number

[0073] As described above, the constituent property data and target property data in the present disclosure are both normalized (as described above with reference to FIG. 2), and constituent property data of different dimensions (e.g., crude oil density, sulfur content, carbon residue, and acid number have different dimensions) can be added and subtracted from each other, making it possible to construct the sum of property item deviations ΔP, i.e., construct the target function for the simulated annealing algorithm, and laying the foundation for implementing the simulated annealing algorithm.

[0074] In step S302, the number of iterations k is initialized as k=1.

[0075] This number of iterations is used in a simulated annealing algorithm to determine new candidate configuration proportions, also called intermediate candidate configuration proportions, at the current temperature.

[0076] In step S303, a new verification target configuration ratio (also called an intermediate verification target configuration ratio) is randomly generated according to the neighborhood function, and a new total characteristic item deviation ΔP new Calculate the increment Δt = ΔP new Calculate -ΔP.

[0077] For example, using the above-mentioned formula 4, the total sum of the new characteristic item deviations ΔP new Calculate the increment Δt = ΔP new Calculate -ΔP.

[0078] In step S304, it is determined whether the increment Δt is less than 0.

[0079] If the increment Δt is less than 0, it indicates that the new sum of characteristic deviations determined based on the new verification target component proportion is smaller, and then the method proceeds to step S311, where the new verification target component proportion is accepted, and ΔP=ΔP new Then, the method proceeds to step S306. If it is determined in step S304 that the increment Δt is not less than 0 (i.e., Δt is equal to or greater than 0), the method proceeds to step S305. In step S305, a new verification target configuration proportion is accepted with a probability of exp(-Δt / t).

[0080] Here, exp is a natural exponential function. This solution method is useful for preventing the verification target constituent ratio from falling into a local optimum when determining the verification target constituent ratio, and for obtaining the final verification target constituent ratio (i.e., the verification target constituent ratio in the verification target constituent crude oil combination).

[0081] In step S306, the current temperature is equal to the end temperature threshold t end It is determined whether the current temperature t is less than the end temperature threshold t end If it is less than the above, in step S307, the new verification target constituent ratio is regarded as the final verification target constituent ratio (i.e., the verification target constituent ratio in the combination of verification target constituent crude oils).

[0082] The current temperature t is the end temperature threshold t end If so, the method proceeds to step S308, where the number of iterations is incremented, i.e. k = k + 1. The method then proceeds to step S309.

[0083] In step S309, it is determined whether the iteration number threshold at the current temperature has been exceeded, ie, whether the number of iterations exceeds the iteration number threshold at the current temperature.

[0084] 3, if the iteration count at the current temperature is less than or equal to the iteration count threshold L, the method returns to step S303 for the next iteration. If not, the method proceeds to step S310.

[0085] In step S310, the current temperature is decreased and the process proceeds to the next iteration.

[0086] Illustratively, the product of the current temperature and a temperature decay coefficient α (eg, 0.95) may be used as the new current temperature, and then the method returns to step S302 for the next iteration.

[0087] According to the above-described solution, the verification target constituent crude oils' verification target constituent proportions are determined by a simulated annealing algorithm, and the verification target constituent crude oils and the verification target constituent proportions are combined to obtain a combination of verification target constituent crude oils. By avoiding falling into a local optimum when determining the verification target constituent proportions, it is possible to more effectively obtain a combination of verification target constituent crude oils having properties close to those of the target crude oil.

[0088] 4 is a flowchart showing further details of the method of FIG. 1 according to an exemplary embodiment of the present disclosure. Specifically, the method steps of FIG. 4 are further detailed versions of step S103 of FIG. 1. As described above, the predetermined criterion in step S103 may be a target similarity between the combination of constituent crude oils to be verified and the target crude oil. If the similarity between the combination of constituent crude oils to be verified and the target crude oil reaches the target similarity, the combination of constituent crude oils to be verified is deemed to satisfy the predetermined criterion. As shown in FIG. 4, specifically, the method may include the following steps:

[0089] In step S1031, blending characteristic data corresponding to the combination of verification target constituent crude oils is obtained based on the verification target constituent ratio of each verification target constituent crude oil in the combination of verification target constituent crude oils and the constituent characteristic data.

[0090] For example, according to the following Equation 5, the blending characteristic data corresponding to the combination of the constituent crude oils to be verified can be obtained according to the constituent proportions to be verified and the constituent characteristic data of each constituent crude oil in the combination of the constituent crude oils to be verified.

[0091]

number

[0092] In the formula, p i,mix is the ith blend property data corresponding to the combination of constituent crude oils to be verified, and p i,j is the i-th constituent characteristic data of the j-th constituent crude oil to be verified, R j is the verification target composition ratio of the jth verification target composition crude oil.

[0093] In step S1032, the similarity is determined based on the blending characteristic data and the target characteristic data.

[0094] The similarity is used to characterize the similarity between the combination of the validation constituent crude oils and the target crude oil.

[0095] In some embodiments, the similarity may be determined based on the blend characteristic data, the target characteristic data, a predetermined standard deviation of the characteristic items, and a predetermined weight of the characteristic items. However, those skilled in the art can understand that the following similarity calculation method is merely a preferred example adopted in the technical solution of the present disclosure. In the prior art, other methods may be used to calculate the similarity. For example, the similarity may be calculated by the percentage of difference between the target characteristic data and the blend characteristic data, or by other means, which will not be listed here.

[0096] According to a preferred embodiment of the present disclosure, the similarity is calculated by the following formulas 6 and 7, i,mix , target characteristic data p i,obj , can be determined based on a preset standard deviation of the characteristic items and a preset weight of the characteristic items.

[0097]

number

number

[0098] where exp is the natural exponential function and μ i is the degree of membership of the i-th blend property data of the combination of constituent crude oils to be verified to the i-th property data of the target crude oil, and c i is the preset weight of the i-th characteristic item, σ i is the predetermined standard deviation of the i-th characteristic item, p i,mix is the ith blend characteristic data corresponding to the combination of constituent crude oils to be verified, p i,obj is the i-th target characteristic data, and m is the number of characteristic items.

[0099] For example, when the target crude oil is a low-sulfur bunker fuel feedstock crude oil, the preset standard deviation and preset weight for each characteristic item are as shown in Table 1 below.

[0100] [Table 1]

[0101] In step S1033, it is determined whether the calculated similarity is equal to or greater than the target similarity.

[0102] In some embodiments, the target similarity is a preset similarity threshold, for example, 0.9, 0.95, or any other value in the range of 0.9 to 1.

[0103] If it is determined in step S1033 that the similarity is less than the target similarity, the method proceeds to step S1034, where the combination of constituent crude oils to be verified may be discarded. If the calculated similarity is equal to or greater than the target similarity, the method proceeds to step S1035, where the combination of constituent crude oils to be verified is determined as the combination of blend constituent crude oils.

[0104] Table 2 below shows a comparison of blend property data with the target property data of the verified constituent crude oil combinations when the target crude oil is a low-sulfur bunker fuel feedstock crude oil.

[0105] [Table 2]

[0106] As shown in Table 3 below, the target similarity between the blending characteristic data and the target characteristic data of the combination of constituent crude oils to be verified is 0.966, and this combination of constituent crude oils to be verified can be determined as a combination of blending constituent crude oils.

[0107] [Table 3]

[0108] The above-mentioned solution can determine the combination of blended constituent crude oils based on at least two constituent crude oils, so as to obtain a blended crude oil having properties similar to that of the target crude oil, thereby meeting market demand and improving the economic profits of the refinery.

[0109] In another embodiment, the blend constituent crude oil combination may be determined from the verification target constituent crude oil combination based on the similarity by the following steps:

[0110] First, it is determined that 1) the similarity is equal to or greater than a second target similarity, and 2) the verification target constituent crude oil proportion of each constituent crude oil in the combination of verification target constituent crude oils is within a predetermined constituent proportion range, and then the combination of verification target constituent crude oils is determined to be a combination of blended constituent crude oils.

[0111] For example, the second target similarity may be smaller than the above-mentioned target similarity. The preset range of constituent proportions represents a range of values ​​of the constituent proportions to be verified for each constituent crude oil, and may be, for example, (0.1 to 0.9).

[0112] The above solution can avoid the problem of increasing errors in evaluating the blending property data of the combination of the constituent crude oils to be verified due to the blending ratio of the constituent crude oils to be verified being too small, and can ensure the stability of the properties of the blended crude oil.

[0113] In some other embodiments, when determining the similarity based on the blending characteristic data and the target characteristic data, the similarity may be determined based on all or some of the above-mentioned characteristic items in order to more comprehensively evaluate the similarity between the combination of the constituent crude oils to be verified and the target crude oil.

[0114] The above solution can further improve the evaluation of the similarity of the combination of constituent crude oils to be verified, improve the accuracy of crude oil blending, and further increase the economic benefits of the refinery.

[0115] In another embodiment, the above-described crude oil blending method may be performed multiple times to obtain multiple combinations of constituent crude oils to be verified. From the multiple combinations of constituent crude oils to be verified, a crude oil blend having the highest similarity and satisfying the following conditions may be obtained: Condition 1: The similarity is equal to or greater than a first target similarity; Condition 2: The similarity is equal to or greater than a second target similarity, and the verification target composition ratio of each constituent crude oil in the combination of verification target constituent crude oils is within a predetermined composition ratio range; A combination of blend constituent crude oils that satisfies any one of the above is selected.

[0116] The above-described solution can determine a combination of multiple verified constituent crude oils, and can determine a combination of blended constituent crude oils from the combination of multiple verified constituent crude oils, thereby further improving the efficiency of crude oil blending.

[0117] 5 is a block diagram of a crude oil blending apparatus according to an exemplary embodiment of the present disclosure. As shown in FIG. 5, the crude oil blending apparatus 500 includes: a first determination module 501 configured to perform step S101 described in FIG. 1; a second determination module 502 configured to perform step S102 described in FIG. 1; a third determination module 503 configured to perform step S103 of FIG. 1; and a fourth determination module 504 configured to perform step S104 described in FIG.

[0118] 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. The electronic device 600 can perform the above-described methods shown in FIGS. 1-4. As shown in FIG. 6, the electronic device 600 can include a processor 601 and a memory 602. The electronic device 600 can also include one or more of a multimedia component 603, an input / output interface 604, and a communication component 605.

[0119] Processor 601 performs the operations of electronic device 600 by executing computer-executable instructions defining the method steps shown in Figures 1-4. A computer program product including the computer-executable instructions may be stored in memory 602. The methods described in Figures 1-4 are defined by the computer-executable instructions included in the computer program product stored in memory 601 and are controlled by processor 601 executing these computer-executable instructions. Memory 602 includes a tangible, non-transitory, machine-readable storage medium. It may also include high-speed random access memory such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid-state memory devices, and may also include non-volatile memory such as one or more disk storage devices (such as internal hard disks and removable disks), magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices (such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), compact disk read-only memory (CD-ROM), digital versatile disk read-only memory (DVD-ROM) disks, or other non-volatile solid-state storage devices.

[0120] In another embodiment, the method described in FIGS. 1-4 above may be implemented in a network-based cloud computing system. In such a network-based cloud computing system, a server communicates with the electronic device 600 via a network. The electronic device 600 stores received data and / or program instructions on the server and can access the data and / or program instructions via the network. The electronic device 600 can send a data request or an online service request to the server via the network. The server can perform the requested service and provide data to the electronic device 600. The server can also send data adapted to enable the electronic device 600 to perform a specific function (e.g., perform a calculation, display specific data on a screen, etc.). Some steps of the above method may be implemented by a server or other computer / processor within the network-based cloud computing system. Some steps of the above method may be implemented locally by a client computer within the network-based cloud computing system. Some steps of the above method may be implemented in combination by one or more devices within the network-based cloud computing system or by a local client computer.

[0121] The multimedia component 603 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component may be configured to output and / or input audio signals. For example, the audio component may include a microphone configured to receive external audio signals. The received audio signals are further stored in the memory 602 or transmitted via the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The input / output interface 604 provides an interface between the processor 601 and other interface modules. The other interface modules may include a keyboard, a mouse, buttons, etc. These buttons may be virtual or physical buttons. The communication component 605 is configured for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as WiFi, Bluetooth, near field communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, or one or more combinations thereof, is not limited herein. Accordingly, the corresponding communication component 605 may include a WiFi module, a Bluetooth module, an NFC module, and the like.

[0122] It should be appreciated that certain features of the present application, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the present disclosure that are, for brevity, described in the context of a single embodiment, may also be provided separately, in any suitable subcombination, or in other described embodiments of the present application, as appropriate. Certain features described in the context of various embodiments should not be considered essential features of those embodiments, unless the embodiment is invalid without those elements.

[0123] While the present disclosure has been described in connection with particular embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art having the benefit of these embodiments. Accordingly, the present disclosure is intended to embrace all such alternatives, modifications, and variations that fall within the spirit and broad scope of the appended claims.

[0124] All publications, patents, and patent applications mentioned herein are incorporated by reference in their entirety to the same extent as if each individual publication, patent, or patent application was specifically and specifically indicated to be incorporated by reference herein. Furthermore, citation or identification of a reference in this disclosure should not be construed as an admission that such reference is available as prior art to the present application. Section headings, when used, should not be construed as necessarily limiting.

Claims

1. A method for crude oil blending, A step of determining at least two component crude oils and their respective component characteristic data, wherein the component characteristic data covers characteristic items of a predetermined target characteristic data of a target crude oil, and the target characteristic data characterizes the characteristic data of the target crude oil; and A method comprising the step of determining a combination of crude oils to be validated by a simulated annealing algorithm based on the aforementioned configuration characteristic data and the aforementioned target characteristic data, wherein the combination of crude oils to be validated includes a crude oil to be validated and a corresponding configuration ratio of the crude oil to be validated.

2. A step of determining the similarity between the combination of crude oils to be verified and the target crude oil; and The method according to claim 1, further comprising the step of determining the combination of constituent crude oils to be verified as a combination of formulated constituent crude oils, in accordance with the fact that at least the similarity meets a certain standard, wherein a formulated crude oil can be formed based on the combination of constituent crude oils.

3. Based on the aforementioned configuration characteristic data and the aforementioned target characteristic data, the simulated annealing algorithm determines the combination of crude oils to be verified. The aforementioned constituent crude oils are considered as the constituent crude oils to be tested, and the respective constituent proportions of the constituent crude oils to be tested are randomly generated; Normalizing the constituent characteristic data and target characteristic data of the crude oil subject to verification, respectively, to obtain normalized constituent characteristic data and normalized target characteristic data; and The method according to claim 1 or 2, further comprising determining the sum of characteristic item deviations as the target function of the simulated annealing algorithm based on the normalized configuration characteristic data, the normalized target characteristic data, and the randomly determined configuration proportion of the crude oil to be verified.

4. The method according to claim 3, wherein the characteristic item deviation characterizes the deviation between the weighted normalized configuration characteristic data and the normalized target characteristic data, and the weighted normalized configuration characteristic data is the sum of the weighted products of each of the normalized configuration characteristic data of the configuration crude oil under verification and the randomly generated configuration proportions under verification.

5. The method according to claim 2, wherein at least the similarity meeting a certain standard means that the similarity is equal to or greater than a predetermined first target similarity.

6. At least the degree of similarity must meet certain criteria. The method according to claim 2, wherein the similarity is equal to or greater than a pre-set second target similarity, and the proportion of each component of the

7. The step of determining the similarity between the combination of crude oils to be verified and the target crude oil is, Based on the respective component ratios of the component crude oils to be verified in the combination of component crude oils to be verified, and the component characteristic data, determine the blending characteristic data corresponding to the combination of component crude oils to be verified; and The method according to any one of claims 2, 5, or 6, comprising determining the similarity based on the blending characteristic data and the target characteristic data, wherein the similarity is characterized by determining the similarity between the combination of crude oils to be verified and the target crude oil.

8. The step of determining the similarity based on the aforementioned formulation characteristic data and the aforementioned target characteristic data is: The method according to claim 7, comprising determining the target similarity based on the blending characteristic data, the target characteristic data, the pre-set standard deviation of the characteristic items, and the pre-set weights of the characteristic items.

9. The method according to claim 1 or 2, wherein the characteristic items include crude oil characteristics and / or fractional characteristics of a crude oil fraction obtained by true boiling point distillation of crude oil.

10. A device for mixing crude oil, A first determination module configured to determine at least two constituent crude oils and their respective constituent characteristic data, wherein the constituent characteristic data is at least partially the same as the characteristic items of a preset target characteristic data of a target crude oil, and the target characteristic data characterizes the characteristic data of the target crude oil; and An apparatus comprising a second determination module configured to determine a combination of crude oils to be verified by a simulated annealing algorithm based on the aforementioned configuration characteristic data and the aforementioned target characteristic data, wherein the combination of crude oils to be verified includes a crude oil to be verified and the corresponding configuration ratio of the crude oil to be verified.

11. A third determination module configured to determine the similarity between the combination of crude oils to be verified and the target crude oil; and Apparatus for crude oil mixing according to claim 10, further comprising a fourth determination module configured to determine the combination of constituent crude oils to be verified as a combination of blended constituent crude oils in accordance with whether the similarity of at least the above-mentioned similarity meets a certain standard, wherein a blended crude oil can be formed based on the combination of blended constituent crude oils.

12. Based on the aforementioned configuration characteristic data and the aforementioned target characteristic data, the simulated annealing algorithm determines the combination of crude oils to be verified. The aforementioned constituent crude oil is considered as the constituent crude oil to be verified, and the respective constituent proportions of the constituent crude oil to be verified are randomly generated; Normalizing the constituent characteristic data and target characteristic data of the crude oil subject to verification, respectively, to obtain normalized constituent characteristic data and normalized target characteristic data; and Apparatus for crude oil mixing according to claim 10 or 11, comprising determining the sum of characteristic item deviations as a target function of the simulated annealing algorithm based on the normalized configuration characteristic data of the configuration crude oil to be verified, the normalized target characteristic data, and the randomly determined configuration ratio to be verified.

13. It is an electronic device, A memory for storing a computer program, wherein the computer program includes executable instructions; and Electronic device comprising a processor configured to execute the executable instructions and carry out the method according to claim 1 or 2.

14. A non-transient computer-readable storage medium for storing computer programs, A computer-readable storage medium wherein the computer program includes executable instructions, and when the executable instructions are executed by the processor, the processor causes the processor to carry out the method according to claim 1 or 2.

15. A computer program product comprising an executable instruction, which, when executed by a processor, causes the processor to perform the method according to claim 1 or 2.

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