Method and device for analyzing properties of mixed crude oil mixed with atmospheric and vacuum side stream heavy distillate oil

Abstract

The invention relates to the technical field of crude oil property analysis, in particular to a property analysis method and device for mixed crude oil mixed with atmospheric and vacuum side stream heavy distillate oil, the mixed crude oil comprises feeding crude oil and heavy distillate oil, and a feeding crude oil property detection model is constructed; constructing a heavy distillate oil property detection model; determining the target crude oil property of the feeding crude oil in the mixed crude oil; determining the target fraction property of the heavy distillate oil in the mixed crude oil; obtaining the flow of the feeding crude oil and the flow of the heavy distillate oil in the mixed crude oil; determining the initial property of the mixed crude oil; and treating the initial properties to obtain the properties of the mixed crude oil. According to the method, a property detection model is established for the properties of the fed crude oil and the fractions, the nonlinear properties are subjected to linearization treatment, and then property superposition is performed according to the flow, so that the key properties of the mixed oil product are obtained, a feasible property detection scheme is provided for an industrial scene of recycling the crude oil from the heavy fraction oil, and the detection accuracy is improved. And the method can better adapt to complex production conditions of refinery enterprises.

Description

Technical Field

[0001] The invention relates to the technical field of crude oil property analysis, in particular to a method and a device for analyzing the properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil. Background Art

[0002] Refining enterprises use atmospheric vacuum units (CDUs) to process crude oil, forming side products of distillate oil products with different boiling points. Enterprises have clear requirements for the properties of crude oil entering the CDU, and often use a variety of crude oils with different properties to blend in order to obtain mixed crude oil that meets the index requirements. Therefore, the detection of mixed crude oil properties is a key link for refining enterprises. Advanced crude oil processing companies have begun to adopt rapid detection methods based on near-infrared spectroscopy. By scanning the mixed crude oil spectrum and modeling (such as partial least squares regression or topology), the key properties of crude oil can be calculated.

[0003] However, in the actual production process, the CDU side-line heavy distillate oil processing unit may not be able to process certain distillate oil products in time due to malfunctions and other reasons. At this time, the company often transfers this part of the distillate oil to the feed crude oil until the processing unit returns to normal.

[0004] For example, a Chinese patent document with publication number CN105911016A discloses a nonlinear modeling method for crude oil spectral properties. This method is aimed at refining and chemical companies. It is based on a near-infrared spectral database and a corresponding crude oil property database. First, baseline correction, interception and vector normalization methods are used to perform spectral preprocessing on the spectral library crude oil and the crude oil to be tested. Then, a single-layer wavelet transform is used for all spectra to extract spectral features. Then, a neural network is trained on the spectral library crude oil. Finally, the crude oil properties are predicted based on the training results and the spectral data of the crude oil to be tested.

[0005] For example, a Chinese patent document with publication number CN116381203A discloses a method for measuring the properties of heavy distillate oil in a crude oil distillation process. First, a sensitivity analysis of the properties to be measured is performed using process simulation software, and auxiliary variables are selected based on the analysis results. Then, a principal component analysis is performed on the auxiliary variables to determine the input variables of the model. Finally, a multivariate regression analysis is performed based on the input variables and output variables to determine the soft measurement model, and then the real-time properties are predicted and estimated based on the model.

[0006] Since heavy distillate oil refining will cause significant changes in the properties of mixed crude oil, rapid property analysis requires the establishment of a crude oil property model based on near-infrared spectroscopy, which requires that the spectrum of the oil product to be tested should be similar to the spectral characteristics used in modeling. In the above-mentioned existing public technologies, the spectra of distillate oil and crude oil are quite different, and the spectral characteristics of crude oil will also change significantly after mixing with distillate oil, which causes the original detection model to have a large deviation when extracting spectral characteristics to measure properties, making it difficult to meet the accuracy requirements for mixed crude oil property detection.

[0007] Therefore, it is necessary to study a new method and device for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, so as to accurately obtain the properties of the mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil. Summary of the invention

[0008] The present invention provides a method and device for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem that the existing mixed crude oil property detection technology has a large deviation in the detection method, thereby leading to insufficient detection accuracy.

[0009] One of the technical solutions of the present invention is achieved by the following measures: a method for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil comprises feed crude oil and heavy distillate oil, and the method for analyzing the properties of the mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil comprises: Construct a model for testing the properties of feed crude oil; Construct a heavy distillate oil property detection model; Based on the feed crude oil property detection model, the target crude oil property of the feed crude oil in the mixed crude oil is determined; Based on the heavy distillate oil property detection model, the target fraction properties of heavy distillate oil in the mixed crude oil are determined; obtaining the flow rate of feed crude oil and the flow rate of heavy distillate oil in the mixed crude oil; Determining initial properties of the blended crude oil based on the flow rate and target crude oil properties of the feed crude oil and the flow rate and target distillate properties of the heavy distillate oil; The initial properties are processed to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0010] The following is a further optimization and / or improvement of one of the above-mentioned technical solutions: The above-mentioned feed crude oil property detection model is constructed, including: obtaining a near infrared spectrum of the initial feed crude oil, and initial crude oil properties of the initial feed crude oil; Based on the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties, a feed crude oil property detection model is constructed.

[0011] The above feed crude oil property detection model is constructed based on the near infrared spectrum of the initial feed crude oil and the initial crude oil properties, including: Performing spectral preprocessing on the near infrared spectrum of the initial feed crude oil to obtain a first near infrared spectrum; Processing the initial crude oil properties to obtain transitional crude oil properties; Based on the first near-infrared spectrum and transition crude oil properties, a feed crude oil property detection model is constructed.

[0012] The above processing of the initial crude oil properties to obtain transitional crude oil properties includes: The nonlinear properties of the initial crude oil properties are linearized to obtain the transitional crude oil properties.

[0013] The feed crude oil property detection model is constructed based on the first near infrared spectrum and the transition crude oil property, including: Based on the first near-infrared spectrum and the properties of transition crude oil, a feed crude oil property detection model was constructed using the partial least squares regression modeling method.

[0014] The above-mentioned near infrared spectrum of the initial feed crude oil is subjected to spectral preprocessing to obtain a first near infrared spectrum, including: The near infrared spectrum of the initial feed crude oil is vector normalized, and a first-order convolution derivative process is performed using a filter fitting method to obtain a first near infrared spectrum.

[0015] The method of obtaining the near infrared spectrum of the initial feed crude oil comprises: An online near-infrared spectrometer is used to collect the near-infrared spectrum of the initial feed crude oil to obtain the near-infrared spectrum of the initial feed crude oil.

[0016] The above-mentioned construction of the heavy distillate oil property detection model includes: obtaining a near infrared spectrum of an initial heavy distillate oil and initial fraction properties of the heavy distillate oil; Based on the near-infrared spectrum of the initial heavy distillate oil and the initial distillate properties, a heavy distillate oil property detection model is constructed.

[0017] The heavy distillate oil property detection model is constructed based on the near infrared spectrum of the initial heavy distillate oil and the initial distillate properties, including: Performing spectral preprocessing on the near infrared spectrum of the initial heavy distillate oil to obtain a second near infrared spectrum; The initial fraction properties are processed to obtain transition fraction properties; Based on the second near-infrared spectrum and the properties of transition fractions, a heavy distillate oil property detection model was constructed.

[0018] The above processing of the initial fraction properties to obtain the transition fraction properties includes: The nonlinear properties in the initial fraction properties are linearized to obtain the transition fraction properties.

[0019] The above-mentioned heavy distillate oil property detection model is constructed based on the second near-infrared spectrum and the properties of the transition fraction, including: Based on the second near-infrared spectrum and the properties of transition fractions, a heavy distillate oil property detection model was constructed using partial least squares regression modeling method.

[0020] The above-mentioned near infrared spectrum of the initial heavy distillate oil is subjected to spectral preprocessing to obtain a second near infrared spectrum, including: The near infrared spectrum of the initial heavy distillate oil is vector normalized and the first-order convolution derivative is performed using the filter fitting method to obtain the second near infrared spectrum.

[0021] The above-mentioned near infrared spectrum of the initial heavy distillate oil is obtained, including: An online near-infrared spectrometer is used to collect the near-infrared spectrum of the initial heavy distillate oil to obtain the near-infrared spectrum of the initial heavy distillate oil.

[0022] The initial properties of the above mixed crude oil are calculated using the following formula: ; in, represents the initial properties of the mixed crude oil, It represents the flow rate of feed crude oil in the mixed crude oil. It represents the flow rate of heavy distillate oil in the mixed crude oil. It represents the target crude oil properties determined by the feed crude oil property detection model. It represents the target fraction properties determined by the heavy distillate oil property detection model.

[0023] The above processing of the initial properties obtains the mixed crude oil properties, including: The initial properties are delinearized to obtain the properties of the mixed crude oil.

[0024] The above method of obtaining the flow rate of feed crude oil and the flow rate of heavy distillate oil in the mixed crude oil includes: A volume flow meter is used to detect the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil, so as to obtain the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0025] The heavy fraction oil in the mixed crude oil includes at least one of the following or a combination thereof: wax oil and residual oil.

[0026] The above-mentioned wax oil includes vacuum wax oil and / or the residual oil includes vacuum residual oil.

[0027] The above-mentioned mixed crude oil properties include API°, acid number, sulfur content and carbon residue.

[0028] The second technical solution of the present invention is achieved by the following measures: a device for analyzing the properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, comprising: Crude oil construction module, used to build a feed crude oil property detection model; The distillate oil construction module is used to build a heavy distillate oil property detection model; A target crude oil determination module is used to determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model; A target fraction determination module, used to determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model; A flow acquisition module, used for acquiring the flow of feed crude oil and the flow of heavy distillate oil in the mixed crude oil; A calculation module for determining the initial properties of the mixed crude oil based on the flow rate and target crude oil properties of the feed crude oil, and the flow rate and target fraction properties of the heavy fraction oil; The processing module is used for processing the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0029] The present invention establishes property detection models for the feed crude oil properties and the fraction properties respectively, linearizes the nonlinear properties, and then superimposes the properties according to the flow rate to obtain the key properties of the mixed oil. This provides a feasible property detection scheme for the industrial scenario of heavy distillate oil recycling crude oil, which can better adapt to the complex production conditions of refining enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Attached Figure 1 A flow chart of a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided in a first embodiment of the present invention; Attached Figure 2 A flow chart of a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided in a second embodiment of the present invention; Attached Figure 3 A flow chart of a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided in a third embodiment of the present invention; Attached Figure 4 A flow chart of a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided in a fourth embodiment of the present invention; Attached Figure 5 A flow chart of a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided in a fifth embodiment of the present invention; Attached Figure 6 The original spectrum of the feed crude oil of a certain refinery in the sixth embodiment of the present invention; Attached Figure 7 This is a principal component analysis diagram of the feed crude oil mixed with 5% of the third-line wax oil distillate in the sixth embodiment of the present invention; Attached Figure 8 This is a spectrum of feed crude oil after vector normalization processing according to the sixth embodiment of the present invention; Attached Fig. 9 This is a spectrum of feed crude oil after Savitzky-Golay convolution smoothing according to the sixth embodiment of the present invention; Attached Fig.10 A seventh embodiment of the present invention provides an apparatus for applying a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 The first embodiment of the present invention provides a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil includes feed crude oil and heavy distillate oil, and the method may include the following steps: S101, constructing a feed crude oil property detection model.

[0033] S102. Construct a heavy distillate oil property detection model.

[0034] S103. Determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model.

[0035] S104. Determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model.

[0036] S105. Obtain the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0037] S106. Determine the initial properties of the mixed crude oil based on the flow rate of the feed crude oil and the target crude oil properties, as well as the flow rate of the heavy distillate oil and the target distillate properties.

[0038] S107. Processing the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0039] According to the method for analyzing the properties of a mixed crude oil mixed with a heavy distillate oil from a normal and low pressure sideline provided by the present invention, a feed crude oil property detection model and a heavy distillate oil property detection model are constructed, and the target crude oil property of the feed crude oil in the mixed crude oil and the target fraction property of the heavy distillate oil in the mixed crude oil are determined according to the two models, so that the initial properties of the mixed crude oil can be determined according to the target crude oil property, the target fraction property, and the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil, and the mixed crude oil property can be accurately obtained after processing the initial properties. It can be understood that the crude oil properties of a single feed crude oil and the crude oil properties of the feed crude oil in the mixed crude oil are different. Similarly, the fraction properties of a single heavy distillate oil and the fraction properties of the heavy distillate oil in the mixed crude oil are also different. Therefore, the target crude oil property is used here to represent the crude oil property of the feed crude oil in the mixed crude oil, and the target fraction property is used to represent the fraction property of the heavy distillate oil in the mixed crude oil. The method for analyzing the properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided by the present invention is aimed at the situation where the feed crude oil of the atmospheric and vacuum unit of a refinery contains recycled heavy distillate oil. By establishing property detection models for the properties of the feed crude oil and the distillate properties, linearizing the nonlinear properties, and then superimposing the properties according to the flow rate, the key properties of the mixed oil are obtained, providing a feasible property detection scheme for the industrial scenario of heavy distillate oil recycling crude oil, which can better adapt to the complex production conditions of refineries. It solves the problem that conventional partial least squares regression or topological modeling is difficult to accurately detect the properties of mixed crude oil with recycled heavy distillate oil.

[0040] In the above embodiment, the step of processing the initial properties to obtain the properties of the mixed crude oil specifically includes: performing inverse linearization processing on the initial properties to obtain the properties of the mixed crude oil.

[0041] In this embodiment, after the initial properties of the mixed crude oil are determined, the properties of the mixed crude oil can be obtained by performing a delinearization process on the initial properties.

[0042] like Figure 2 As shown, the second embodiment of the present invention provides a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil includes feed crude oil and heavy distillate oil, and the method may include the following steps: S201. Obtain a near infrared spectrum of the initial feed crude oil and initial crude oil properties of the initial feed crude oil.

[0043] S202. Construct a feed crude oil property detection model based on the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties.

[0044] S203, obtaining a near infrared spectrum of the initial heavy distillate oil and initial fraction properties of the heavy distillate oil.

[0045] S204, constructing a heavy distillate oil property detection model based on the near infrared spectrum of the initial heavy distillate oil and the initial distillate properties.

[0046] S205. Determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model.

[0047] S206. Determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model.

[0048] S207. Obtain the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0049] S208. Determine the initial properties of the mixed crude oil based on the flow rate of the feed crude oil and the target crude oil properties, as well as the flow rate of the heavy distillate oil and the target distillate properties.

[0050] S209, processing the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0051] Among them, the specific steps of constructing the feed crude oil property detection model are: obtaining the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties of the initial feed crude oil, and constructing the feed crude oil property detection model based on the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties. The initial feed crude oil here refers to a single feed crude oil, not the feed crude oil in the mixed crude oil. The crude oil properties of the single feed crude oil, that is, the initial crude oil properties can be obtained by testing, so that the feed crude oil property detection model can be constructed according to the initial crude oil properties. The specific steps of constructing the heavy distillate oil property detection model are: obtaining the near-infrared spectrum of the initial heavy distillate oil and the initial fraction properties of the heavy distillate oil, and constructing the heavy distillate oil property detection model based on the near-infrared spectrum of the initial heavy distillate oil and the initial fraction properties. The initial heavy distillate oil here refers to a single heavy distillate oil, not the heavy distillate oil in the mixed crude oil. The fraction properties of a single heavy distillate oil, that is, the initial fraction properties can be obtained by testing, so that the heavy distillate oil property detection model can be constructed according to the initial fraction properties.

[0052] In the above embodiment, the step of constructing a feed crude oil property detection model based on the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties specifically includes: spectrally preprocessing the near-infrared spectrum of the initial feed crude oil to obtain a first near-infrared spectrum; processing the initial crude oil properties to obtain transition crude oil properties; and constructing a feed crude oil property detection model based on the first near-infrared spectrum and the transition crude oil properties.

[0053] In this embodiment, when constructing the feed crude oil property detection model through the near infrared spectrum of the initial feed crude oil and the initial crude oil property, the near infrared spectrum of the initial feed crude oil and the initial crude oil property can be processed to obtain the first near infrared spectrum and the transition crude oil property, so that the feed crude oil property detection model can be constructed according to the first near infrared spectrum and the transition crude oil property. By processing the near infrared spectrum of the initial feed crude oil and the initial crude oil property, it can be ensured that the constructed feed crude oil property detection model can accurately determine the crude oil property of the feed crude oil in the mixed crude oil.

[0054] In the above embodiment, the step of constructing a heavy distillate oil property detection model based on the near infrared spectrum of the initial heavy distillate oil and the initial distillate properties specifically includes: spectrally preprocessing the near infrared spectrum of the initial heavy distillate oil to obtain a second near infrared spectrum; processing the initial distillate properties to obtain transition distillate properties; and constructing a heavy distillate oil property detection model based on the second near infrared spectrum and the transition distillate properties.

[0055] In this embodiment, when constructing the heavy distillate oil property detection model through the near infrared spectrum and the initial fraction properties of the initial heavy distillate oil, the near infrared spectrum and the initial fraction properties of the initial heavy distillate oil can be processed to obtain a second near infrared spectrum and transition fraction properties, so that the heavy distillate oil property detection model can be constructed according to the second near infrared spectrum and the transition fraction properties. By processing the near infrared spectrum and the initial fraction properties of the initial heavy distillate oil, it can be ensured that the constructed heavy distillate oil property detection model can accurately determine the fraction properties of the heavy distillate oil in the mixed crude oil.

[0056] In the above embodiment, the step of processing the initial properties to obtain the properties of the mixed crude oil specifically includes: performing inverse linearization processing on the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0057] In this embodiment, after the initial properties of the mixed crude oil are determined, the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil can be obtained by performing a delinearization process on the initial properties.

[0058] In the above embodiment, the step of acquiring the near infrared spectrum of the initial feed crude oil specifically includes: using an online near infrared spectrometer to collect the near infrared spectrum of the initial feed crude oil to obtain the near infrared spectrum of the initial feed crude oil.

[0059] In this embodiment, the near infrared spectrum of the initial feed crude oil can be acquired by an online near infrared spectrometer, which has the advantages of fast analysis speed.

[0060] In the above embodiment, the step of acquiring the near infrared spectrum of the initial heavy distillate oil specifically includes: using an online near infrared spectrometer to collect the near infrared spectrum of the initial heavy distillate oil to obtain the near infrared spectrum of the initial heavy distillate oil.

[0061] In this embodiment, the near infrared spectrum of the initial heavy distillate oil can be acquired by an online near infrared spectrometer, which has the advantages of fast analysis speed.

[0062] like Figure 3 As shown, the third embodiment of the present invention provides a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil includes feed crude oil and heavy distillate oil, and the method may include the following steps: S301. Obtain a near infrared spectrum of the initial feed crude oil and initial crude oil properties of the initial feed crude oil.

[0063] S302, linearizing the nonlinear properties in the initial crude oil properties to obtain transition crude oil properties, vector normalizing the near-infrared spectrum of the initial feed crude oil, and performing first-order convolution derivative processing using a filter fitting method to obtain a first near-infrared spectrum.

[0064] S303. Based on the first near infrared spectrum and the properties of the transition crude oil, a feed crude oil property detection model is constructed using a partial least squares regression modeling method.

[0065] S304, obtaining a near infrared spectrum of the initial heavy distillate oil and initial fraction properties of the heavy distillate oil.

[0066] S305, linearizing the nonlinear properties in the initial fraction properties to obtain transition fraction properties, vector normalizing the near infrared spectrum of the initial heavy fraction oil, and performing first-order convolution derivative processing using a filter fitting method to obtain a second near infrared spectrum.

[0067] S306. Based on the second near-infrared spectrum and the properties of the transition fraction, a heavy fraction oil property detection model is constructed using a partial least squares regression modeling method.

[0068] S307. Determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model.

[0069] S308. Determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model.

[0070] S309, obtaining the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0071] S310, determining the initial properties of the mixed crude oil based on the flow rate of the feed crude oil and the target crude oil properties, as well as the flow rate of the heavy distillate oil and the target distillate properties.

[0072] S311. Processing the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0073] In the above embodiment, the step of processing the initial crude oil properties to obtain the transitional crude oil properties specifically includes: linearizing the nonlinear properties in the initial crude oil properties to obtain the transitional crude oil properties.

[0074] In this embodiment, the nonlinear properties in the initial crude oil properties can be linearized, and the properties that already have linear addition characteristics are not processed, thereby obtaining transitional crude oil properties, so as to facilitate the construction of a feed crude oil property detection model and ensure better quality of the constructed feed crude oil property detection model.

[0075] In the above embodiment, the step of constructing a feed crude oil property detection model based on the first near-infrared spectrum and the transition crude oil properties specifically includes: constructing a feed crude oil property detection model based on the first near-infrared spectrum and the transition crude oil properties using a partial least squares regression modeling method.

[0076] In this embodiment, a feed crude oil property detection model can be constructed based on the first near-infrared spectrum and the transition crude oil properties by using a partial least squares regression modeling method, thereby ensuring the quality of the constructed model.

[0077] In the above embodiment, the step of spectrally preprocessing the near-infrared spectrum of the initial feed crude oil to obtain a first near-infrared spectrum specifically includes: vector normalizing the near-infrared spectrum of the initial feed crude oil, and performing first-order convolution derivative processing using a filter fitting method to obtain the first near-infrared spectrum.

[0078] In this embodiment, the near-infrared spectrum of the initial feed crude oil may be vector normalized and first-order convolution derivative processed using a filter fitting method (Savitzky-Golay) to obtain a first near-infrared spectrum.

[0079] In the above embodiment, the step of constructing a heavy distillate oil property detection model specifically includes: obtaining a near-infrared spectrum of the initial heavy distillate oil and initial distillate properties of the heavy distillate oil; and constructing a heavy distillate oil property detection model based on the near-infrared spectrum of the initial heavy distillate oil and the initial distillate properties.

[0080] In the above embodiment, the step of constructing a heavy distillate oil property detection model based on the near infrared spectrum of the initial heavy distillate oil and the initial distillate properties specifically includes: spectrally preprocessing the near infrared spectrum of the initial heavy distillate oil to obtain a second near infrared spectrum; processing the initial distillate properties to obtain transition distillate properties; and constructing a heavy distillate oil property detection model based on the second near infrared spectrum and the transition distillate properties.

[0081] In the above embodiment, the step of processing the initial fraction properties to obtain the transition fraction properties specifically includes: linearizing the nonlinear properties in the initial fraction properties to obtain the transition fraction properties.

[0082] In this embodiment, the nonlinear properties in the initial fraction properties can be linearized, and the properties that already have linear addition characteristics are not processed, thereby obtaining the transition fraction properties, so as to facilitate the construction of a heavy distillate oil property detection model and ensure that the quality of the constructed heavy distillate oil property detection model is better.

[0083] In the above embodiment, the step of constructing a heavy distillate oil property detection model based on the second near infrared spectrum and the transition fraction properties specifically includes: constructing a heavy distillate oil property detection model based on the second near infrared spectrum and the transition fraction properties by using a partial least squares regression modeling method.

[0084] In this embodiment, a heavy distillate oil property detection model can be constructed based on the second near infrared spectrum and the transition fraction properties by using a partial least squares regression modeling method, thereby ensuring the quality of the constructed model.

[0085] In the above embodiment, the step of performing spectral preprocessing on the near-infrared spectrum of the initial heavy distillate oil to obtain the second near-infrared spectrum specifically includes: performing vector normalization processing on the near-infrared spectrum of the initial heavy distillate oil, and performing first-order convolution derivative processing using a filter fitting method to obtain the second near-infrared spectrum.

[0086] In this embodiment, the near infrared spectrum of the initial heavy distillate oil may be subjected to vector normalization processing, and a first-order convolution derivative processing may be performed using a filter fitting method (Savitzky-Golay) to obtain a second near infrared spectrum.

[0087] like Figure 4 As shown, the fourth embodiment of the present invention provides a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil includes feed crude oil and heavy distillate oil, and the method may include the following steps: S401. Use an online near-infrared spectrometer to collect the near-infrared spectrum of the initial feed crude oil to obtain the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties of the initial feed crude oil.

[0088] S402, linearizing the nonlinear properties in the initial crude oil properties to obtain transition crude oil properties, vector normalizing the near-infrared spectrum of the initial feed crude oil, and performing first-order convolution derivative processing using a filter fitting method to obtain a first near-infrared spectrum.

[0089] S403. Based on the first near infrared spectrum and the properties of the transition crude oil, a feed crude oil property detection model is constructed using a partial least squares regression modeling method.

[0090] S404, obtaining a near infrared spectrum of the initial heavy distillate oil and initial fraction properties of the heavy distillate oil.

[0091] S405, linearizing the nonlinear properties in the initial fraction properties to obtain transition fraction properties, vector normalizing the near infrared spectrum of the initial heavy fraction oil, and performing first-order convolution derivative processing using a filter fitting method to obtain a second near infrared spectrum.

[0092] S406. Based on the second near-infrared spectrum and the properties of the transition fraction, a heavy fraction oil property detection model is constructed using a partial least squares regression modeling method.

[0093] S407. Determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model.

[0094] S408. Determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model.

[0095] S409, obtaining the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0096] S410, determining the initial properties of the mixed crude oil based on the flow rate of the feed crude oil and the target crude oil properties, as well as the flow rate of the heavy distillate oil and the target distillate properties.

[0097] S411. Perform inverse linearization processing on the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0098] In any of the above embodiments, the initial properties of the mixed crude oil are calculated using the following formula: ; in, represents the initial properties of the mixed crude oil, It represents the flow rate of feed crude oil in the mixed crude oil. It represents the flow rate of heavy distillate oil in the mixed crude oil. It represents the target crude oil properties determined by the feed crude oil property detection model. It represents the target fraction properties determined by the heavy distillate oil property detection model.

[0099] In any of the above embodiments, the step of processing the initial properties to obtain the properties of the mixed crude oil specifically includes: performing inverse linearization processing on the initial properties to obtain the properties of the mixed crude oil.

[0100] In these embodiments, after the initial properties of the mixed crude oil are determined, the properties of the mixed crude oil can be obtained by performing a delinearization process on the initial properties.

[0101] In any of the above embodiments, the step of obtaining the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil includes: using a volumetric flowmeter to detect the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil to obtain the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

[0102] In these embodiments, the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil can be detected online by a volume flow meter, thereby ensuring the timeliness of the detection.

[0103] In any of the above embodiments, the heavy fraction oil in the mixed crude oil includes at least one of the following or a combination thereof: wax oil and residual oil.

[0104] In these embodiments, the heavy fraction oil in the mixed crude oil includes at least one of the following or a combination thereof: wax oil and residual oil. Distillates such as wax oil and residual oil are side products formed by processing crude oil using a atmospheric vacuum unit (CDU).

[0105] Further, the wax oil includes vacuum wax oil and / or the residual oil includes vacuum residual oil.

[0106] In any of the above embodiments, the properties of the mixed crude oil include API gravity, acid value, sulfur content and carbon residue.

[0107] In these embodiments, the mixed crude oil properties include API gravity, acid value, sulfur content and residual carbon. That is, the mixed crude oil property analysis method provided by the present invention mixed with atmospheric and vacuum sideline heavy distillate oil can determine the API gravity (API°), acid value, sulfur content and residual carbon of the mixed crude oil. Of course, the mixed crude oil properties are not limited thereto and can also be other properties.

[0108] like Figure 5 As shown, the fifth embodiment of the present invention provides a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, wherein the mixed crude oil includes feed crude oil and heavy distillate oil, and the method may include the following steps: S501, collect near infrared spectrum of feed crude oil , and collect key properties of crude oil for testing .

[0109] S502, collect near infrared spectra of distillate oil , and collect key properties of distillate oil for testing .

[0110] S503, yes and Linearization is performed, and the properties that already have linear addition characteristics are not processed, and the obtained and .

[0111] S504, spectrum and Perform vector normalization and Savitzky-Golay first-order convolution smoothing preprocessing.

[0112] S505, according to the spectrum and Constructing a model for detecting feed crude oil properties , according to the spectrum With nature Constructing a model for detecting distillate oil properties .

[0113] S506, when distillate oil reflux occurs, through the model Computational properties , through the model Computational properties , the flow rate of feed mixed oil and reflux distillate oil is detected by flow meter and .

[0114] S507, calculate the properties of the mixed crude oil according to the properties and flow rate, and obtain the properties ,right Perform inverse linearization to obtain the target properties .

[0115] in, Refers to the transition crude oil properties, refers to the properties of the transition fraction, refers to the target crude oil properties, refers to the target fraction properties, refers to the initial properties of the mixed crude oil, Refers to the properties of mixed crude oil.

[0116] In the sixth embodiment of the present invention, a certain refining enterprise uses a 10 million tons / year CDU (atmospheric and vacuum unit) to process crude oil, and its side products include distillates such as naphtha, gasoline, diesel, wax oil, and residual oil. In order to verify the effectiveness of this method, the method of sample mixing in the enterprise laboratory is used for verification. In this embodiment, 5% CDU minus three-line wax oil is mixed with the CDU feed mixed crude oil of the enterprise for comparative analysis.

[0117] Collect 50 sets of spectra of the company's feed crude oil and their corresponding key properties, as well as the spectra of the three-line wax oil distillate and their key properties. The original spectra of the feed crude oil are as follows: Figure 6 The spectra of feed crude oil and three-line wax oil fraction were preprocessed and the detection models of crude oil properties were established by partial least squares regression modeling method.

[0118] The CDU minus three wax oil sample of the enterprise was mixed into the feed crude oil at a mixing ratio of 5%. The near infrared spectrum of the mixed crude oil was scanned and the principal component analysis of the mixed crude oil and the original feed crude oil was performed. The results are as follows: Figure 7 When the dimension of the spectral data is reduced to two dimensions, the cumulative information contribution rate exceeds 80%, which can represent the main information of the spectrum. Figure 7 The distribution of the original feed crude oil is concentrated, while the distance between the mixed crude oil and the original feed crude oil is far, which is an outlier data. The mixed crude oil is analyzed using the property detection model. The gaps between the model detection results and the test results of API°, acid value, and sulfur content are listed in Table 1.

[0119] Table 1 .

[0120] As can be seen from Table 1, the error between the API° and sulfur content model detection and the test results exceeds the accuracy requirement allowed by the enterprise and cannot meet the accuracy requirement of the feed crude oil property detection of the refining enterprise. Therefore, it is decided to use the method of the present invention to improve the existing property detection.

[0121] The process steps of this embodiment are as follows: (1) Vector normalization and Savitzky-Golay first-order convolution derivative were performed on 50 sets of feed crude oil and three-line wax oil distillate spectra. The results of feed crude oil spectra after two pretreatments are as follows: Figure 8 and Fig. 9 shown.

[0122] (2) Linearize the key properties of the feed crude oil and the distillate oil of the third-line wax oil. Some properties such as sulfur content, acid value and residual carbon can be linearly superimposed, but the API° value as the inverse of the relative density cannot be linearly superimposed. Therefore, it is necessary to convert API° into relative density. The conversion formula is as follows: ; After treatment, the transition crude oil properties are obtained Transition fraction properties of diesel .

[0123] (3) Partial least squares regression was used to analyze the spectrum and transition crude oil properties Modeling and getting the model The same method is used to analyze the spectrum Properties of transition fractions of third-line wax oil Modeling and getting the model .

[0124] (4) Five groups of feed crude oil were selected as test samples, and wax oils similar to the three-line wax oil fraction were mixed into them. The wax oil fraction mixing ratio was 5%. The model and Detect the spectrum of the feed crude oil and obtain the corresponding properties and As shown in Table 2 and Table 3: Table 2 .

[0125] Table 3 .

[0126] (5) According to the mixing ratio of 5% wax oil distillate, the crude oil properties are linearly added, and the superimposed properties are used as the detection properties of the mixed crude oil. The detection properties and the properties obtained by analysis are shown in Table 4.

[0127] Table 4 .

[0128] As can be seen from Table 4, the accuracy of property detection of the present invention is significantly better than that of the original model, both are within the accuracy index, and fully meet the actual production requirements of the refining enterprise.

[0129] like Fig.10 As shown, the seventh embodiment of the second aspect of the present invention provides a device for applying a method for analyzing properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, comprising: A crude oil construction module 1101 is used to construct a feed crude oil property detection model; A distillate oil construction module 1102 is used to construct a heavy distillate oil property detection model; The target crude oil determination module 1103 is used to determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model; The target fraction determination module 1104 is used to determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model; The flow acquisition module 1105 is used to acquire the flow of the feed crude oil and the flow of the heavy distillate oil in the mixed crude oil; A calculation module 1106, for determining the initial properties of the mixed crude oil based on the flow rate of the feed crude oil and the target crude oil properties, and the flow rate of the heavy distillate oil and the target distillate properties; The processing module 1107 is used to process the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0130] The device for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil provided by the present invention is used to implement the method for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil in any one of the embodiments of the first aspect. Since the mixed crude oil property analysis device is used to implement the method for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil in any one of the embodiments of the first aspect. Therefore, the mixed crude oil property analysis device provided by the present invention also has all the beneficial effects of the method for analyzing the properties of a mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil in any one of the embodiments of the first aspect, which will not be repeated here. The present invention (including Figures 1 to 4 ), the properties of the mixed crude oil refer to the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

[0131] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of the specific embodiments of specific inventions. Certain features described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although the features may work as above in certain combinations and even initially claim protection, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of a sub-combination.

[0132] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0133] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0134] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil, characterized in that: The mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil includes feed crude oil and heavy distillate oil. The property analysis method of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil includes: Construct a model for testing the properties of feed crude oil; Construct a heavy distillate oil property detection model; Based on the feed crude oil property detection model, the target crude oil property of the feed crude oil in the mixed crude oil is determined; Based on the heavy distillate oil property detection model, the target fraction properties of heavy distillate oil in the mixed crude oil are determined; obtaining the flow rate of feed crude oil and the flow rate of heavy distillate oil in the mixed crude oil; Determining initial properties of the blended crude oil based on the flow rate and target crude oil properties of the feed crude oil and the flow rate and target distillate properties of the heavy distillate oil; The initial properties are processed to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

2. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, characterized in that: Construct a feed crude oil property detection model, including: obtaining a near infrared spectrum of the initial feed crude oil, and initial crude oil properties of the initial feed crude oil; Based on the near-infrared spectrum of the initial feed crude oil and the initial crude oil properties, a feed crude oil property detection model is constructed.

3. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 2, characterized in that: Based on the near infrared spectrum of the initial feed crude oil and the initial crude oil properties, a feed crude oil property detection model is constructed, including: Performing spectral preprocessing on the near infrared spectrum of the initial feed crude oil to obtain a first near infrared spectrum; Processing the initial crude oil properties to obtain transitional crude oil properties; Based on the first near-infrared spectrum and transition crude oil properties, a feed crude oil property detection model is constructed.

4. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 3, characterized in that: The initial crude oil properties are processed to obtain transitional crude oil properties, including: The nonlinear properties of the initial crude oil properties are linearized to obtain the transitional crude oil properties.

5. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 3 or 4, characterized in that: Based on the first near infrared spectrum and the properties of transition crude oil, a feed crude oil property detection model is constructed, including: Based on the first near-infrared spectrum and the properties of transition crude oil, a feed crude oil property detection model was constructed using the partial least squares regression modeling method.

6. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 3 or 4, characterized in that: The near infrared spectrum of the initial feed crude oil is subjected to spectral preprocessing to obtain a first near infrared spectrum, including: The near infrared spectrum of the initial feed crude oil is vector normalized, and a first-order convolution derivative process is performed using a filter fitting method to obtain a first near infrared spectrum.

7. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 2, 3 or 4, characterized in that: Acquire a near infrared spectrum of the initial feed crude oil, including: An online near-infrared spectrometer is used to collect the near-infrared spectrum of the initial feed crude oil to obtain the near-infrared spectrum of the initial feed crude oil.

8. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, 2, 3 or 4, characterized in that: Construct a heavy distillate oil property detection model, including: obtaining a near infrared spectrum of an initial heavy distillate oil and initial fraction properties of the heavy distillate oil; Based on the near-infrared spectrum of the initial heavy distillate oil and the initial distillate properties, a heavy distillate oil property detection model is constructed.

9. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 8, characterized in that: Based on the near infrared spectrum of the initial heavy distillate oil and the initial distillate properties, a heavy distillate oil property detection model is constructed, including: Performing spectral preprocessing on the near infrared spectrum of the initial heavy distillate oil to obtain a second near infrared spectrum; The initial fraction properties are processed to obtain transition fraction properties; Based on the second near-infrared spectrum and the properties of transition fractions, a heavy distillate oil property detection model was constructed.

10. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 9, characterized in that: The initial fraction properties are processed to obtain transition fraction properties, including: The nonlinear properties in the initial fraction properties are linearized to obtain the transition fraction properties.

11. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 9 or 10, characterized in that: Based on the second near-infrared spectrum and the properties of the transition fraction, a heavy distillate oil property detection model is constructed, including: Based on the second near-infrared spectrum and the properties of transition fractions, a heavy distillate oil property detection model was constructed using partial least squares regression modeling method.

12. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 9 or 10, characterized in that: The near infrared spectrum of the initial heavy distillate oil is subjected to spectral preprocessing to obtain a second near infrared spectrum, including: The near infrared spectrum of the initial heavy distillate oil is vector normalized and the first-order convolution derivative is performed using the filter fitting method to obtain the second near infrared spectrum.

13. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 8, characterized in that: Acquire a near infrared spectrum of the initial heavy distillate, including: An online near-infrared spectrometer is used to collect the near-infrared spectrum of the initial heavy distillate oil to obtain the near-infrared spectrum of the initial heavy distillate oil.

14. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, 2, 3, 4, 9 or 13, characterized in that: The initial properties of the mixed crude oil are calculated using the following formula: ； in, represents the initial properties of the mixed crude oil. It represents the flow rate of feed crude oil in the mixed crude oil. It represents the flow rate of heavy distillate oil in the mixed crude oil. It represents the target crude oil properties determined by the feed crude oil property detection model. It represents the target fraction properties determined by the heavy distillate oil property detection model.

15. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, 2, 3, 4, 9 or 13, characterized in that: The initial properties are processed to obtain the mixed crude oil properties, including: The initial properties are delinearized to obtain the properties of the mixed crude oil.

16. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, 2, 3, 4, 9 or 13, characterized in that: Obtain the flow rate of feed crude oil and heavy distillate oil in the mixed crude oil, including: A volume flow meter is used to detect the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil, so as to obtain the flow rate of the feed crude oil and the flow rate of the heavy distillate oil in the mixed crude oil.

17. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 1, 2, 3, 4, 9 or 13, characterized in that: The heavy fraction oil in the mixed crude oil includes at least one of the following or a combination thereof: wax oil and residual oil.

18. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 17, characterized in that: The wax oil includes vacuum wax oil and / or the residue oil includes vacuum residue oil.

19. The method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to claim 17, characterized in that: Blended crude oil properties include API°, acid number, sulfur content and carbon residue.

20. A device using the method for analyzing properties of mixed crude oil mixed with atmospheric and vacuum sideline heavy distillate oil according to any one of claims 1 to 19, characterized in that: include: Crude oil construction module, used to build a feed crude oil property detection model; The distillate oil construction module is used to build a heavy distillate oil property detection model; A target crude oil determination module is used to determine the target crude oil properties of the feed crude oil in the mixed crude oil based on the feed crude oil property detection model; A target fraction determination module, used to determine the target fraction properties of the heavy fraction oil in the mixed crude oil based on the heavy fraction oil property detection model; A flow acquisition module, used for acquiring the flow of feed crude oil and the flow of heavy distillate oil in the mixed crude oil; A calculation module for determining the initial properties of the mixed crude oil based on the flow rate and target crude oil properties of the feed crude oil, and the flow rate and target fraction properties of the heavy fraction oil; The processing module is used for processing the initial properties to obtain the properties of the mixed crude oil mixed with the atmospheric and vacuum sideline heavy distillate oil.

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