Method for estimating refining efficiency

Abstract

To provide a new method for accurately estimating a desulfurization rate and a denitrification rate in a refining reaction of heavy oil.SOLUTION: A method for estimating at least one of purification efficiency selected from a desulfurization rate and a denitrification rate in a refining reaction of heavy oil by a computer includes the steps of: (1) performing estimation based on a correction formula with a frequency factor of one or more kinds of structural factors contained in the heavy oil, two or more kinds of structural attribute parameters selected from an average total number of rings, an average number of carbon atoms of side chains, and an average degree of aggregation being variables; (2) estimating a molecular composition of generated oil obtained from the heavy oil based on the purification reaction model using the frequency factor obtained in step (1); and (3) estimating the purification efficiency based on the molecular composition of the heavy oil and the molecular composition of the generated oil. The one or more kinds of the structural factor is one or more kinds of single core molecules containing one sulfur atom or nitrogen atom.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for computer-aided estimation of at least one purification efficiency selected from a desulfurization rate and a denitrification rate in a heavy oil purification reaction, an apparatus, a system, a computer, and a method for using the same, and a computer program for causing a computer to execute the apparatus and a recording medium thereof.

Background Art

[0002] Sulfur compounds and nitrogen compounds contained in petroleum cause poisoning of automotive exhaust gas purification catalysts and are sources of SOx and NOx during combustion. Therefore, desulfurization (reduction of sulfur content) and reduction of nitrogen content are major missions of the petroleum refining industry.

[0003] Desulfurization and denitrification of petroleum are applied to a wide range from LPG, gasoline to heavy oil and lubricating oil, and methods adapted to each oil type are adopted. In particular, for atmospheric residue oil, desulfurization and denitrification methods using a residue oil direct desulfurization (RDS) apparatus are the mainstream.

[0004] On the other hand, in the operation of various apparatuses related to petroleum refining, usually, the raw material oil is analyzed based on the overall physical properties such as specific gravity, viscosity, and distillation properties (boiling point), and the operation conditions are determined by referring to the operation results of oil types having past similar data. However, recently, the types of imported crude oils have diversified, and it is not easy to find similar past data. Furthermore, from the aspects of improving operation efficiency and environmental protection, it is no longer sufficient to simply follow past operation results.

[0005] Therefore, rather than grasping petroleum as a whole from the perspective of overall physical properties such as specific gravity, viscosity, and distillation properties, if the chemical structure and existence ratio can be grasped at the level of hydrocarbon molecules constituting petroleum, and the operation conditions can be set based on the knowledge such as the estimated physical property values obtained thereby, it has been considered that more efficient operation based on objectivity can be achieved.

[0006] However, petroleum is a mixture of a vast number of hydrocarbon molecules, and heavy oil in particular has a large molecular weight and contains a great many types of molecules with complex chemical structures. Identifying the chemical structure of each of these molecules and determining their proportions is extremely difficult, and in particular, precise analysis of the molecular structure and abundance of sulfur-containing components present in each type of petroleum has not been practically carried out.

[0007] To date, in analyzing petroleum at the molecular level and determining its chemical structure, techniques have been employed to measure molecular weight with high precision using mass spectrometers employing the Fourier transform ion cyclotron resonance method, a high-resolution mass spectrometer. For example, the methods described in Patent Document 1 or Patent Document 2. In particular, Patent Document 2 describes a method for estimating molecular structure in which molecules constituting petroleum are collided with argon or the like to break down the cross-linking portions of the molecules, decompose them into their core components, determine their chemical structures, and then reconstruct the original molecule by combining them.

[0008] Furthermore, Patent Documents 3 and 4 report a method by the present applicant for estimating the properties of each component in a multi-component mixture based on a multi-component aggregation model (MCAM) that identifies the molecular structure and abundance of each component by utilizing the difference (Δδ) between the average HSP value of the entire liquid phase of the multi-component aggregation model and the HSP value of each component in the non-liquid phase component. MCAM is expected to be established as a tool that can be used to solve various practical operational problems in the petroleum refining field caused by asphaltene aggregation.

[0009] Furthermore, Non-Patent Document 1 reports a method by the applicant for estimating the desulfurization rate by correcting an estimated desulfurization rate, which is calculated based on the abundance of specific sulfur-containing molecules, by referring to the average degree of cohesion of sulfur-containing components. [Prior art documents] [Patent Documents]

[0010] [Patent Document 1] Special Publication No. 2014-500506 [Patent Document 2] Special Publication No. 2014-503816 [Patent Document 3] Japanese Patent Publication No. 2014-218643 [Patent Document 4] Special Publication No. 2020-502495 [Non-patent literature]

[0011] [Non-Patent Document 1] FY2018 Research and Development Project Report on Structural Analysis and Reaction Analysis of Petroleum, which forms the basis of highly efficient petroleum refining technology (Japan Petroleum Energy Technology Center) [Overview of the Initiative] [Problems that the invention aims to solve]

[0012] However, the applicant's research has revealed that, even when applying the conventional methods described above in the refining reaction of heavy oil, there are cases where the estimated desulfurization rate and denitrification rate do not match the measured values, depending on the origin and weight of the petroleum.

[0013] Therefore, as a result of further diligent research, the applicant has found that by applying frequency factors of one or more structural factors, which are estimated based on a correction formula with specific structural attribute parameters as variables, to a refining reaction model for heavy oil, the desulfurization rate and denitrification rate in the refining reaction of heavy oil can be estimated with high accuracy. The present invention is based on this finding.

[0014] Therefore, one objective of the present invention is to provide a new method that can accurately estimate the desulfurization rate and denitrification rate in the refining reaction of heavy oil. [Means for solving the problem]

[0015] To achieve the above object, the present inventors have created the following invention. That is, the gist of the present invention is as follows. A computer-based estimation method for at least one purification efficiency selected from the desulfurization rate and the denitrification rate in the purification reaction of heavy oil according to the present invention is (1) A step of estimating the frequency factor of one or more structural factors contained in the heavy oil based on a correction formula using two or more structural attribute parameters selected from the average total ring number, the average number of carbon atoms in the side chain, and the average degree of aggregation as variables, (2) A step of estimating the molecular composition of the produced oil obtained from the heavy oil based on the purification reaction model using the frequency factor obtained in step (1), and (3) A step of estimating the purification efficiency based on the molecular composition of the heavy oil and the molecular composition of the produced oil including where the one or more structural factors are one or more single-core molecules containing one sulfur atom or one nitrogen atom.

[0016] In another embodiment of the present invention, an apparatus and a system for estimating the desulfurization rate in target petroleum, and their operation methods, as well as a computer program for executing them, a recording medium thereof, and a computer storing the same are also provided.

Advantages of the Invention

[0017] <� According to the present invention, the desulfurization rate and the denitrification rate in the purification reaction of heavy oil can be estimated with high accuracy.

Brief Description of the Drawings

[0018] [Figure 1] It is a flowchart for explaining a computer-based estimation method for the purification efficiency in the purification reaction of heavy oil according to an embodiment of the present invention. [Figure 2] It is a flowchart for obtaining the structural attribute parameters and the frequency factor of a multi-component mixture (heavy oil) in an embodiment of the present invention. [Figure 3]This is a functional block diagram illustrating a device for estimating the refining efficiency in a heavy oil refining reaction according to one embodiment of the present invention. [Figure 4] This diagram shows one or more reaction groups (structure factors) contained in atmospheric residual oil (AR). [Figure 5] This diagram shows the relationship between structural attribute parameters (average degree of aggregation, average total number of rings) for various structure factors in the desulfurization reaction and the frequency factor logA. [Figure 6] This is a correlation diagram showing the estimated and measured values ​​of the frequency factor logA for single-core molecules (S1, S04) in the produced oil. [Figure 7] This diagram shows the relationship between structural attribute parameters (average degree of aggregation, average number of side-chain carbons (C), average total number of rings) for various structure factors in denitrification reactions and the frequency factor logA. [Figure 8] This is a correlation diagram showing the estimated and measured values ​​of the logA frequency factor for single-core molecules (5R, 6R, 09) in the produced oil. [Figure 9] This graph shows the predicted desulfurization and denitrification rates for 16 different ARs (Arbitrages) originating from different crude oils. [Modes for carrying out the invention]

[0019] <Definition> In describing embodiments of the present invention, we will first explain the terms and expressions used herein.

[0020] (1) “Oil” In this specification, "petroleum" refers to a general concept that includes crude oil, as well as the various fractions obtained by distilling crude oil, and fractions obtained by further processing of these fractions using secondary equipment such as reforming or cracking. Alternatively, it may refer to fractions obtained by further separating a certain fraction obtained by distilling crude oil into components such as saturated hydrocarbons and aromatic hydrocarbons. (2) "Ingredients" "Component" refers to "a group of a mixture based on a specific physical or chemical property," that is, "a fraction separated based on a specific physical or chemical property." One method of grouping based on a specific physical or chemical property is to identify the boiling point range in a distillation test and separate those within that temperature range as a single component. In this case, the mixture becomes "an aggregate of fractions." Alternatively, "component" can be considered as each individual component that makes up a multi-component mixture, "an aggregate of molecules that are recognized as belonging to the same molecular species." Here, "identical" can be interpreted as "identical in terms of perfectly identified molecular structure," or "isomers in molecular structure (molecular formulas are the same but structures are different) are considered identical," or, for example, "identical in the structure identified by a method such as JACD, as described later." Furthermore, it can be broadly interpreted as "an aggregate of molecules grouped together based on arbitrarily defined criteria."

[0021] (3) "to constitute" The term "constituting" a multi-component mixture such as petroleum does not necessarily mean that 100% of the components present in the multi-component mixture are included. Depending on how the molecular structures of each component identified by this invention are utilized, the "constituting components" can be appropriately determined according to the level of detail required for identifying the molecular species as components. For example, only molecular species present in a certain amount (proportion) or more in the multi-component mixture may be considered as "constituting components." It is not always necessary to identify the molecular structure of all of the vast number of molecular species, such as those found in petroleum, and molecular species present in trace amounts may be ignored as needed. For example, when polycyclic aromatic resin (PA) is considered as a "multi-component mixture," the presence of paraffinic compounds and olefinic compounds as components constituting PA may be ignored.

[0022] (4) “Fraction” "Fraction" refers to any value indicating the proportion of existence, such as mass fraction, volume fraction, or mole fraction, and is a concept that includes all of these. When calculating the average Hansen solubility index value for the entire liquid phase, volume fraction is preferably used, and it is calculated as a weighted average value weighted by the volume fraction of each component in that liquid phase.

[0023] (5) "Identify molecular structure", "Molecule" "Identifying the molecular structure" encompasses any action that identifies any information about the structure of a molecule in the "components" mentioned above. The degree and method of representation should be appropriately selected according to the purpose and necessity. It is not limited to identifying the structure of the entire molecule; information about the structure of a part of the molecule may also be incorporated. For example, the structure of only the core portion may be identified, while the structures of the side chains and cross-linking portions may be left as molecular formulas without identification.

[0024] In this specification, the molecular structure is preferably identified using "JACD" as described later. A molecule whose structure is identified by "JACD" is a concept that includes all isomers resulting from differences in the attachment positions of the attributes described later. In this specification, "molecule" may be considered a concept that includes all isomers.

[0025] (6) "Identify the proportion of each component" "Identifying the proportion of each component" encompasses any action that involves determining the ratio in which each component constituting the mixture exists. Furthermore, this does not mean that the proportion of every single component constituting the mixture must be identified. It is not necessary to identify the proportion of every component, including those present in amounts too small to be detected by analytical techniques or those that do not need to be identified, before it can be said that "the proportion of each component has been identified." Such trace components may be treated together as "other components." Moreover, these may be excluded from the scope of "each component constituting the mixture" and not included in the denominator when calculating the proportion of other components.

[0026] (7) "All" In this specification, "all" does not necessarily mean "100% of all." For example, where the phrase "all peaks" is used in reference to a mass spectrum, it may be interpreted not only as literally meaning "100% of all peaks," but also as referring to the peaks remaining after excluding, for example, peaks related to molecules that are not necessarily necessary for the purpose of the study in that context, or peaks that are difficult to distinguish.

[0027] (8) "Peak" In mass spectrometry, the horizontal axis of the peak represents the m / z ratio for the molecular or pseudomolecular ions of each component constituting a multi-component mixture. Since the m / z value corresponds to the mass of the molecular or pseudomolecular ion, it generally represents the molecular weight of the molecule attributed to that peak. In this specification, this "m / z peak for molecular or pseudomolecular ions obtained by mass spectrometry" may be referred to as the "peak obtained by mass spectrometry" or simply as the "peak." Furthermore, the height of the peak indicates the relative abundance of the molecule attributed to that peak.

[0028] (9) "Molecular formula" A "molecular formula" is an expression that shows only the types and number of elements that make up a molecule, without specifying its structure. Because the types and number of elements that make up the molecule are known, information such as molecular weight and the DBE value (described later) can be obtained. In the Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (hereinafter also referred to as "FT-ICR MS," and the spectrum obtained by FT-ICR MS is also referred to as the "FT-ICR MS spectrum") primarily used in this invention, the m / z value can be determined to four decimal places. Therefore, by performing a precise mass matching that also takes into account the presence of atomic isotopes, the molecular formula of the molecule assigned to that peak can be determined. Since a molecular formula only represents the types and numbers of elements that make up the molecule, there may be multiple isomers of the molecule corresponding to the determined molecular formula. That is, a single peak may be assigned to multiple isomers with the same molecular formula.

[0029] However, due to the characteristics of FT-ICR MS, even if the molecular formula is the same, the mass may differ from the original molecular ion due to, for example, the addition of hydrogen ions to the molecular ion, and therefore it may appear as a separate peak. Thus, even if they appear as separate peaks in the measurement, if the types and number of elements constituting the molecular formula are the same, they can be considered as having the "same molecular formula." In the phrase "the molecule corresponding to that molecular formula," "that molecular formula" can be understood to mean this "same molecular formula." Also, when referring to "a certain peak," it can be considered a concept that encompasses all the various m / z peaks that were considered to represent the "same molecular formula" in the sense described above.

[0030] (10) "Core", "Single Core", "Double Core", "Heterocore" A "core" is a type of "attribute" described later in the "JACD" section, and specifically refers to the heterocycle or naphthenic ring itself, a heterocycle and naphthenic ring directly bonded rather than bridging, or a heterocycle or naphthenic ring directly bonded to an aromatic ring rather than bridging. Since bridging or side chains are attributes separate from the core, "core" means a component that has no bridging or side chains at all.

[0031] On the other hand, "single-core" refers to a molecule that has only one of the above-mentioned cores. As it is a concept referring to a molecule, it also includes molecules in which side chains are attached to the core. A molecule in which two or more of the above-mentioned cores are cross-linked is called a "multi-core." As "multi-core" also refers to a molecule, it also includes molecules in which side chains are attached to the core. A molecule in which two cores are cross-linked is called a "double-core." For example, the naphthalene molecule shown below is a "single-core" molecule because it consists of one aromatic ring, and not a double-core molecule consisting of two benzene rings.

[0032] [ka] A core containing heteroatoms is also called a "heterocore."

[0033] (11) "DBE value" The "DBE value" refers to a molecular formula that is "C c H h N n O o S s If this is the case, the value is calculated using the following formula (1). DBE = c- h / 2+n / 2 + 1 ···(1) (In the formula, c represents the number of carbon atoms, h represents the number of hydrogen atoms, n represents the number of nitrogen atoms, o represents the number of oxygen atoms, and s represents the number of sulfur atoms.) This value generally indicates the degree of unsaturation in the molecule, particularly the presence of double bonds and rings.

[0034] (12) "JACD (Juxtaposed Attributes for Chemical-structure Description)" "JACD" is a novel representation method for molecular structures that displays the structure of a molecule based on the type and number of attributes. It does not show the position in which one attribute is bound to another.

[0035] In the above, "attribute" refers to the chemical structural components (parts) that make up a molecule. In the case of aromatic compounds, it specifically refers to the aforementioned "core," "crosslinks," and "side chains." This labeling method allows for the identification of the structure of each of the vast number of molecules that make up petroleum, to a necessary and sufficient degree. Let's take the molecule represented by the following chemical formula as an example.

[0036] [ka]

[0037] When this compound is represented in JACD, it is as shown in Table 1 below.

[0038] [Table 1]

[0039] A molecule represented by JACD and whose structure has been identified is a concept that includes all isomers resulting from differences in the attachment positions of attributes.

[0040] (13) "Physical properties" "Physical properties" include any value that expresses the physical or chemical properties, characteristics, or features of a substance, regardless of its name. In this specification, "physical properties" are not limited to these, but include, for example, melting point, Hansen solubility index, Gibbs free energy of formation, ionization potential, polarizability, dielectric constant, vapor pressure, liquid density, API degree, gas viscosity, liquid viscosity, surface tension, boiling point, critical temperature, critical pressure, critical volume, heat of formation, heat capacity, dipole moment, enthalpy, entropy, etc.

[0041] (14) "Equipment related to petroleum" In this specification, "petroleum-related equipment" includes all equipment related to the processing of petroleum, including distillation equipment, extraction equipment, reforming equipment, hydrogenation reactors, desulfurization equipment, and other equipment involving chemical reactions. "Petroleum-related equipment" is also collectively referred to as "petroleum refining equipment." According to a preferred embodiment of the present invention, the petroleum-related equipment is a residual oil direct desulfurization (RDS) unit.

[0042] <Method for estimating the refining rate in the refining reaction of heavy oil> According to one embodiment of the present invention, a computer-based method for estimating the refining rate in a heavy oil refining reaction is shown in Figure 1. (1) A step of estimating the frequency factors of one or more structural factors contained in the heavy oil based on a correction formula in which two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion are used as variables. (2) A step of estimating the molecular composition of the product oil obtained from the heavy oil based on the purification reaction model using the frequency factors obtained in step (1), and (3) A step of estimating the refining efficiency based on the molecular composition of the heavy oil and the molecular composition of the produced oil. It is characterized by containing [something]. The following describes one embodiment of the present invention step by step.

[0043] Step (1): Estimate the frequency factors of one or more structural factors contained in heavy oil based on a correction formula that uses two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion as variables. According to one embodiment of the present invention, as shown in Figure 1, the frequency factors of one or more structure factors contained in heavy oil, i.e., one or more single-core molecules containing one sulfur atom or one nitrogen atom, are estimated based on a correction formula in which two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion are used as variables.

[0044] The heavy oil described above is preferably a fraction having a boiling point higher than the boiling point of light oil (360°C) in accordance with JIS K2254. Specific examples of residual oil from atmospheric distillation include heavy oil (boiling point: approximately 400°C or higher) obtained from the bottom of the distillation column by atmospheric distillation of crude oil, and this heavy oil is referred to as atmospheric residue.

[0045] According to one embodiment of the present invention, the one or more single-core molecules are two or more structure factors selected from the group consisting of a non-aromatic single-core molecule containing one sulfur atom, a polycyclic aromatic single-core molecule containing one sulfur atom, a non-aromatic single-core molecule containing one nitrogen atom, a monocyclic aromatic single-core molecule containing one nitrogen atom, and a polycyclic aromatic single-core molecule containing one nitrogen atom. As shown in the examples described later, it is a surprising fact to those skilled in the art that the frequency factor of the above single-core molecules can be estimated based on a correction formula in which two or more specific structural attribute parameters are variables.

[0046] Furthermore, when the purification efficiency is the desulfurization rate, the one or more single-core molecules used in the present invention are preferably a combination of a non-aromatic single-core molecule containing one sulfur atom and a polycyclic aromatic single-core molecule containing one sulfur atom, more preferably a combination of a dihydrothiophene molecule or a fused thiophene molecule, and even more preferably a combination of a 2,3-dihydrothiophene molecule (hereinafter also referred to as "HDS_S1") and a fused thiophene molecule (hereinafter also referred to as "HDS_S04") formed by the condensation of three benzene rings and a thiophene ring.

[0047] One or more single-core molecules may be fused rings having a ring attached to a heterocyclic structure. The ring attached to the heterocyclic structure may be either an aromatic ring or a naphtha ring, but from the viewpoint of improving the accuracy of estimating the refining rate of heavy oil, an aromatic ring is preferred.

[0048] Furthermore, the number of rings added to the heterocyclic structure is not particularly limited and may be 0 to 7, but from the viewpoint of improving the accuracy of estimating the desulfurization rate of petroleum, it is preferably 1 to 4, more preferably 1 to 3, and even more preferably 1 or 2.

[0049] Furthermore, when the purification efficiency is the denitrification rate, the one or more single-core molecules used in the present invention are preferably a combination of a non-aromatic single-core molecule containing one nitrogen atom, a monocyclic aromatic single-core molecule containing one nitrogen atom, and a polycyclic aromatic single-core molecule containing one nitrogen atom, more preferably a combination of a dihydropyrrole molecule, a pyridine molecule, or a fused pyrrole molecule, and even more preferably a combination of a 2,3-dihydropyrrole molecule (hereinafter also referred to as "HDN_5R"), a pyridine molecule (hereinafter also referred to as "HDN_6R"), and a fused pyrrole molecule (hereinafter also referred to as "HDN_09") formed by the condensation of three benzene rings and a pyrrole ring.

[0050] Furthermore, when the purification efficiency is the desulfurization rate, the structural attribute parameter that becomes the variable in the correction formula for the frequency factor is preferably a combination of the average total number of rings and the average degree of aggregation.

[0051] Furthermore, when the purification efficiency is the denitrification rate, the structural attribute parameters that become variables in the correction formula for the frequency factor are preferably a combination of the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of aggregation.

[0052] According to a preferred embodiment of the present invention, the single core molecule and structural attribute parameters can be obtained using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). An embodiment of obtaining the single core molecule (structure factor) and structural attribute parameters using FT-ICR MS will be described below with reference to Figure 2.

[0053] Step S1 (Mass Spectrometry) (S1) Step S1 is a step in which FT-ICR MS is performed on heavy oil, and for each of the obtained peaks, the molecular formula of the molecule to which that peak belongs is identified, and further, the abundance of that molecule is determined. That is, mass spectrometry is performed on a multi-component mixture (heavy oil), and for all the peaks obtained, the molecular formula of the molecule to which each peak belongs is identified, and further, the abundance of the molecule corresponding to that molecular formula is determined. With FT-ICR MS, highly accurate measurements can be performed by known methods, namely by soft ionizing the sample to form molecular ions or pseudomolecular ions.

[0054] Step S2 (Collision-induced dissociation) (S2) Step S2 is a step in which collision-induced dissociation is performed on a multi-component mixture. Collision-induced dissociation (hereinafter also referred to as "CID") is an operation in which molecules are ionized and then collided with an inert gas such as argon to crosslink and cleave side chains. It is generally preferable to apply collision energy so that the crosslinks and side chains in each component constituting the multi-component mixture are cleaved. By cleaving the crosslinks and side chains, fragment ions for each core are generated. These cores may have aliphatic groups with approximately 0 to 4 carbon atoms as side chains that could not be cleaved by collision-induced dissociation.

[0055] When FT-ICR MS is performed on a multi-component mixture, the molecular formulas of the molecules constituting the mixture can be determined from the m / z of the resulting peaks, but information about the "core" of those molecules cannot be obtained. Therefore, by further performing collision-induced dissociation to cleave the crosslinks and side chains in each molecule constituting the multi-component mixture, it is possible to determine the type of core present in the entire multi-component mixture.

[0056] The conditions for collision-induced dissociation are preferably collision energies that can effectively cleave crosslinks and side chains in the molecule, such as 10 to 50 kcal / mol, and more preferably 20 to 40 kcal / mol. Note that 40 kcal / mol corresponds to 32 eV when the molecular weight is 700.

[0057] Step S3 (Identification of the structure and proportion of each core) Step S3 is a step in which, for each fragment ion generated by collision-induced dissociation in step S2, the structure and abundance of the core constituting each fragment ion are determined by FT-ICR MS.

[0058] (a) First, we will explain how to identify the structure of the core that makes up each fragment ion. Specifically, this method involves comparing the core information obtained in step S2 with the core information listed in a pre-prepared core structure list to identify the structure of each core. Further details are as follows. i. Acquisition of information about the core after collision-induced dissociation In FT-ICR MS of each fragment ion after collision-induced dissociation, even if the core is the same, fragment ions with aliphatic groups having 0 to 4 carbon atoms as side chains will appear as separate peaks because their masses differ depending on the type of side chain. Therefore, by pre-calculating the masses of these various fragments with aliphatic groups having 0 to 4 carbon atoms as side chains in the core, and then comparing and matching the separate peaks that appear, it becomes possible to determine the mass of the core itself.

[0059] Using this method, in step S2, for each peak obtained after collision-induced dissociation, information can be obtained regarding the mass of the core to which that peak belongs, the number of heteroatoms such as O, N, or S atoms present, and the number of aromatic rings present from the DBE value.

[0060] ii. Identification of the core structure after collision-induced dissociation. One method for identifying the core structure after collision-induced dissociation is to first create a "core structure list" containing various core models that are assumed to constitute each component molecule of a multi-component mixture. Then, by comparing the information of the cores stored in this list, such as molecular weight and the type and number of heteroatoms, with the core information obtained above, the most appropriate core model is selected from this list and assigned as the core in question. Using this method, a core can be assigned to all peaks obtained by FT-ICR MS after collision-induced dissociation, making it possible to determine their structure.

[0061] iii. Core Structure List The types of cores to be included in the above core structure list are not particularly limited and can be any type; however, the appropriateness of the selection of cores to be included will directly affect the appropriateness of the structure identification of each core.

[0062] It is preferable to create a "core structure list" in advance, depending on the composition of the multi-component mixture sample itself. For example, based on existing knowledge of heavy oils, a "core structure list for identifying the molecular structure of heavy oils" can be created in advance and used.

[0063] iv. Selection from the core structure list The core structure list may contain multiple structures that have the same molecular weight, DBE value, and type and number of heteroatoms, but different structural formulas. In this case, you can establish rules for selecting which of these structures to prioritize. For example, the following three priorities may be considered: 1. Prioritize compounds consisting solely of aromatic rings. 2. Prioritize materials with a high number of unsaturated bonds. 3. Prioritize those with fewer rings.

[0064] (i) Next, we will explain how to determine the proportion of each core. As mentioned above, in step S2, the m / z ratio, i.e., the proportion of cores with that mass, can be determined from the height of each peak obtained after collision-induced dissociation.

[0065] Step S4 (Estimation of the distribution and proportion of cores for each class) Step S1 involves dividing the molecules assigned to each peak into "classes" based on the "type and number of heteroatoms (including zero) and DBE value" of each molecular formula identified in Step S1, and then estimating the mode of existence and abundance of all molecules belonging to each "class". In other words, Step S1 involves dividing the molecules assigned to each peak into "classes" based on the "type and number of heteroatoms (including zero) and DBE value" of each molecular formula identified in Step S1, and then estimating the mode of existence and abundance of all molecules belonging to each "class".

[0066] Step S4 will be explained in detail below. (a) In step S1, the molecular formula is identified for all peaks, so the type and number of heteroatoms in that molecular formula and the DBE value are determined. Therefore, in this step, based on this "type and number of heteroatoms and DBE value", each molecule assigned to all peaks is incorporated into its respective "class" grouped according to the "type and number of heteroatoms and DBE value".

[0067] "Type and number of heteroatoms" more specifically refers to "the type of heteroatom and the number of heteroatoms of each type." Since heteroatoms are preferably nitrogen atoms, sulfur atoms, and oxygen atoms, "type and number of heteroatoms" can also preferably be said to refer to "the number of nitrogen atoms, sulfur atoms, and oxygen atoms." Therefore, with respect to heteroatoms, those in which "the number of nitrogen atoms, sulfur atoms, and oxygen atoms are all the same" belong to the same "class." In this specification, a group containing one sulfur atom may be referred to as the "S1 class."

[0068] (i) Next, for each class enclosed by the "type and number of heteroatoms and DBE value" described in (a), estimate what kind of single-core or multi-core each molecule belonging to that class is. Also, estimate the proportion of each type of single-core and multi-core molecule. In making these estimations, it is preferable to make several assumptions for the sake of practical computational convenience.

[0069] Here, "multicore" can have various combinations depending on which cores are bridged and bonded together. However, the sum of the DBE values ​​of the multiple cores forming the multicore and the sum of the number of heteroatoms according to the type of heteroatom are all the same for all belonging to that class. However, in this model, the multicore is divided into the single cores that make it up, and the proportion of each single core is estimated. For example, if a double core (single core-bridge-single core) exists, in this model it is divided into single cores and all are considered to exist as single cores. More specifically, a double core (single core (4 rings)-bridge-single core (5 rings)) is a double core with a total of 9 rings, but it is considered to have both a single core (4 rings) and a single core (5 rings).

[0070] (c) As described above, the molecules associated with each peak obtained by FT-ICR MS were regrouped into classes consisting of molecules with the same type and number of heteroatoms and DBE value. Molecules belonging to these classes are either single-core or multi-core. A preferred method for estimating what kind of cores these single-core or multi-core molecules are composed of will be described below.

[0071] If a molecule belonging to that class is single-core, then the single-core having the type, number, and DBE value of heteroatoms corresponding to that class is the one that corresponds. If a molecule belonging to that class is multi-core, then the combination of cores such that the sum of the number of heteroatoms of the same type present in the multiple cores constituting the multi-core, and the sum of the DBE values ​​of these multiple cores, matches the type, number, and DBE value of heteroatoms in that class is the one that corresponds. Since the sum of the number and DBE values ​​corresponding to the type of heteroatoms in the multiple cores must match the type, number, and DBE value of heteroatoms in that class, there are usually not just one, but several possible combinations of the multiple cores constituting a multi-core.

[0072] (e) Next, we estimate the proportion of single-core and multi-core molecules belonging to that class. Preferably, first, it is assumed that the proportion of multicores is the product of the proportions of each of the multiple cores that make up the multicore, and this is used as an estimate.

[0073] Step S5 (Determination of core structure, side chains, and bridges) Step S5 is a step in which, for each molecule whose mode of existence was estimated in Step S4, the structure of the core constituting it is determined, and further, the side chains and crosslinks are determined and assigned.

[0074] (a) For each molecule whose mode of existence was estimated in step S4, "determining the structure of the core that forms them" is carried out by the following operations i to v. i. In the case of a multicore system whose existence mode was estimated in step S4, each of the cores that make up the system is treated separately (uncoupled).

[0075] ii. All cores whose existence mode was estimated to be single-core in step S4, and all cores generated by disabling multicore as described in i above, are regrouped into their respective "classes" based on the same "type and number of heteroatoms and DBE value". Incidentally, the "class" referred to here is a concept relating to the original single-core and multicore cores obtained by disabling multicore, and is different from the "class" relating to molecules described in step S4.

[0076] iii. For all "classes" of "types and number of heteroatoms and DBE values" enclosed in ii above, assign a specific structure to all cores present in that "class".

[0077] (i) The side chains and bridges are further determined by the following operations i to iii. i. As described above, the structure of the core portion of single-core or multi-core structures could be identified. However, assuming only the existence of the core portion does not match the mass indicated by the m / z of the peak obtained by FT-ICR MS for the sample in question. That is, even if the masses based on carbon, hydrogen, and heteroatoms involved in the core portion are added together, there is a difference from the mass indicated by the m / z of the peak obtained by FT-ICR MS.

[0078] Therefore, assuming that the difference in mass originates from the presence of side chains bonded to the core and bridges connecting the cores together, the number of carbon atoms and hydrogen atoms is determined to eliminate the difference, and these are then allocated to the core as side chains and bridges. For example, suppose a certain double core, where core 1 and core 2 are bridged, is assigned to a peak with m / z=n using the procedure described above. In this case, The difference in mass (d) = n - (mass of core 1 + mass of core 2) However, this is due to the presence of side chains and bridges.

[0079] ii. In step i above, the number of carbon atoms and hydrogen atoms to be allocated as side chains and bridges can be determined, but the structure of the side chains and bridges has not yet been determined. Therefore, in order to estimate what structure of side chains and bridges corresponds, the probability of existence of the assumed combination of side chains and bridges can be considered, and rules such as the following can be established and the estimation can be carried out according to these rules. As rules, conditions such as the upper limit of the number of carbon atoms constituting the side chains and bridges and the number of side chains can be predetermined.

[0080] iii. In the above i, if there are no side chains or bridges corresponding to the difference in mass, a structure in which core 1 and core 2 are simply bonded may be applied. (c) The phrase "assigning the side chains and bridges determined above to the core" does not include determining which core and at which position the side chains and bridges are attached.

[0081] (e) In this way, step S5 makes it possible to determine the structure of the cores constituting each single core or double core whose existence mode was estimated in step S4, and further determine the side chains, bridges, and proportions of existence.

[0082] By following steps S1 to S5 described above, the molecular structure of each component (single-core molecule) constituting heavy oil can be identified using JACD, and the average total number of rings, average number of side chain carbons, and other parameters can be determined.

[0083] Step S6 (Obtaining the average cohesion level) Next, the steps for obtaining the average degree of cohesion in one embodiment will be described in the following steps S6-1 to S6-5.

[0084] Step S6-1 (Obtaining melting point and Hansen solubility index values) First, in steps S1 to S5, the melting point and Hansen solubility index (hereinafter also referred to as "HSP value") of each component are obtained from the molecular structure of each component of the multi-component mixture identified using JACD. These physical properties are preferably determined using the Total Petroleum Molecular Database (COMCAT) for the molecular structure of each component of the multi-component mixture identified as described above.

[0085] Comcat is a "JACD-Physical Property Database" that links JACD molecules with their respective physical properties. The database contains approximately 25 million registered molecules and can be used in model system analyses that assume all components of petroleum are composed of molecules included in Comcat.

[0086] The physical properties registered in this database include approximately 200 types of properties, such as melting point, Hansen solubility index, boiling point, critical humidity, critical pressure, critical volume, vapor pressure, liquid density, gas viscosity, liquid viscosity, surface tension, dipole moment, polarizability, ionization potential, heat of formation, enthalpy, entropy, free energy, and heat capacity.

[0087] These physical properties are usually calculated using the group contribution method or the molecular orbital method. The group contribution method is a method for determining the physical properties of a substance by identifying its chemical structure and calculating the properties based on the unique parameter values ​​of the various atomic groups, or "groups," that exist within it. In other words, it is a prerequisite that the "groups" of the substance be identified. Similarly, in the molecular orbital method, it is a prerequisite that the "groups" of the substance be identified first, and that the structure be determined based on that.

[0088] In this invention, as described above, since the various atomic groups present in each component constituting heavy oil are identified, the physical properties of the component can be calculated using the known inherent parameter values ​​of each atomic group. Furthermore, since the abundance ratio of each component is also identified, by considering this abundance ratio, it becomes possible to appropriately estimate the physical properties of the entire heavy oil from the physical properties of each component.

[0089] Next, the Multi-Component Aggregation Model (MCAM) on which the present invention is based will be explained in the following steps S7 to S16.

[0090] Step S6-2 (Separation of liquid and non-liquid phase components) In steps S1 to S6-1 described above, the fraction, melting point, and Hansen solubility index value of each component are obtained, and the desired temperature T is set. Of the components that make up heavy oil, those with a melting point below the desired temperature T are classified as liquid phase components, and those with a melting point at or above the desired temperature T are classified as non-liquid phase components. The desired temperature T is as defined above.

[0091] Step S6-3 (Calculation of the average HSP value for the entire liquid phase) For each component classified as a liquid phase component in step S6-2, a weighted average value is calculated, weighted by the volume fraction of each component in the liquid phase, as the average HSP value for the entire liquid phase. The volume fraction can be calculated by obtaining various physical property information such as density and molecular weight for each component in advance.

[0092] Step S6-4 (Calculation of the difference in HSP values ​​between the entire liquid phase and each non-liquid phase component) In step S6-3, calculate the difference (Δδ) between the average HSP value of the entire liquid phase calculated and the HSP value of each component in the non-liquid phase.

[0093] Step S6-5 (Updating the classification of each component based on Δδ) Each component in the non-liquid phase is reclassified as either a liquid phase component or a non-liquid phase component based on the difference (Δδ) calculated in step S6-4. Components reclassified as liquid phase components are then incorporated from the non-liquid phase components into the liquid phase components, thereby updating the liquid phase and non-liquid phase components. This reclassification update may be performed one component at a time in the non-liquid phase, or it may be performed for multiple components at once.

[0094] Step S6-6 (Calculation of the average HSP value of the entire liquid phase after the update) In step S6-5, the HSP values ​​of each component in the updated liquid phase are weighted by the volume fraction of each component in the updated liquid phase, and this weighted average is calculated as the average HSP value of the entire updated liquid phase.

[0095] Step S6-7 (Repeat steps S6-4 to S6-6) Steps S6-4 to S6-6 are repeated until the final stage in which there are no more non-liquid phase components that are reclassified as liquid phase components in step S6-7.

[0096] Step S6-7 (Calculation of the degree of coagulation of non-liquid phase components) The degree of cohesion D (hereinafter also referred to as the DAgg value) of the non-liquid phase component after renewal at the final stage at the desired temperature is calculated. Here, the degree of cohesion D is a numerical value set by the HSP value, concentration, and temperature.

[0097] Step S6-8 (Classification of non-liquid phase components based on degree of cohesion) In the final stage, each component in the non-liquid phase after renewal is classified into a cohesive phase component based on its degree of cohesiveness D.

[0098] Step S6-9 (Calculation of average cohesion) Calculate the average degree of condensation D of the single-core molecules classified in step S6-8.

[0099] Furthermore, steps S1 to S6 described above may be carried out with reference to the methods described in Patent Documents 3 and 4, and these documents are incorporated into this specification by reference.

[0100] Step S7 (Calculation of Correction Formula) Next, according to one embodiment of the present invention, two or more structural attribute parameters selected from the average total number of rings, average number of carbon atoms in side chains, and average degree of cohesion of the heavy oil obtained in steps S1 to S6, and a frequency factor log 10 Based on the correction formula A (hereinafter, logA), the correction factor for heavy oil is calculated.

[0101] According to a preferred embodiment of the present invention, the correction formula for the frequency factor is: For several pre-selected standard heavy oils, the steps include determining the correlation between two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion, and one or more structural factors in the standard petroleum, and Based on the correlation described above, regression analysis is performed to calculate a correction formula for the frequency factor for one or more structural factors (single-core molecules), with the above two or more structural attribute parameters as variables. It is obtained by a method that includes [a specific method].

[0102] The above-mentioned standard heavy oils are preferably of the same type as the heavy oil subject to estimation in the present invention, and are multiple heavy oils from different origins. The number of standard heavy oils is not particularly limited, but it is preferable to select them so that the corrected R² (coefficient of determination) is 0.6 or higher.

[0103] Data obtained by processing a reference heavy oil using the same refining equipment as the heavy oil being measured can be suitably used in creating correction formulas for frequency factors.

[0104] According to a preferred embodiment of the present invention, when the purification rate is the desulfurization rate, a linear regression can be performed using the average degree of aggregation and the average total number of rings for non-aromatic single-core molecules containing one sulfur atom and polycyclic aromatic single-core molecules containing one sulfur atom, and the regression coefficient can be obtained using the following frequency factor correction formula.

[0105] logA(non-aromatic single-core molecule containing one sulfur atom) = a1 + b1 × (average degree of cohesion) + c1 × (average total number of rings) logA(polycyclic aromatic single-core molecule containing one sulfur atom) = a² + b² × (average degree of cohesion) + c² × (average total number of rings) (a, b, and c are regression coefficients.)

[0106] According to a preferred embodiment of the present invention, when the purification rate is the denitrification rate, linear regression can be performed on non-aromatic single-core molecules containing one sulfur atom and polycyclic aromatic single-core molecules containing one sulfur atom, using the average degree of aggregation, average total number of rings, and average number of side chain carbon atoms, and the regression coefficient can be determined by the following frequency factor correction formula.

[0107] logA(non-aromatic single-core molecule containing 1 nitrogen atom) = a1 + b1 × (average degree of cohesion) + c1 × (average total number of rings) + d1 × (average number of carbon atoms in side chains) logA(monocyclic aromatic single-core molecule containing one nitrogen atom) = a² + b² × (average degree of cohesion) + c² × (average total number of rings) + d² × (average number of carbon atoms in side chains) logA(polycyclic aromatic single-core molecule containing one nitrogen atom) = a³ + b³ × (average degree of cohesion) + c³ × (average total number of rings) + d³ × (average number of carbon atoms in side chains)

[0108] According to a preferred embodiment of the present invention, the structural attribute parameters of the heavy oil to be measured can be substituted into the supplementary formula of the frequency factor to calculate the frequency factor of the heavy oil.

[0109] Step 2: Based on the refining reaction model using the frequency factors obtained in Step (1), estimate the molecular composition of the product oil obtained from heavy oil. According to one embodiment of the present invention, the frequency factor obtained in step (1) can be applied to a pre-prepared purification reaction model to obtain the molecular composition of the resulting oil.

[0110] According to a preferred embodiment of the present invention, the purification reaction model is attribute-based reaction modeling (ARM). This technique is known in the art as a core reaction model developed by Professor Klein et al. of the University of Delaware (see, for example, Korre, SC et al., Catal Today, 3179 (1996); Hagiwara, K. et al., Journal of the Petroleum Institute, 59(5), 219 (2016)), and can define a total of 2,107 reaction paths, comprising 1,233 cores covering 95 mol% or more of the raw heavy oil and the resulting oil, and the desulfurization and denitrification reactions of these 1,233 cores.

[0111] To perform reaction rate analysis in ARM, one only needs to set the activation energy ΔE, which is a reaction rate parameter, and the frequency factor obtained in step (1) for each reaction path. For the activation energy ΔE, the quantitative structure-reactivity relationship (QSRR) equation, which assumes a first-order correlation between ΔE and the standard heat of formation ΔH of the molecule in similar reaction groups, can be used to estimate ΔE from ΔH calculated by the molecular orbital method. In this invention, since the ARM reaction model is constructed entirely as a single core, multi-core processors are used as single cores that have been cut at the cross-linking portion.

[0112] Step (3): Estimate the refining efficiency based on the molecular composition of the heavy oil and the molecular composition of the refined oil. According to one embodiment of the present invention, the refining rate can be estimated from the molecular composition of the product oil obtained in step (2) and the molecular composition of the heavy oil raw material obtained in step (1). By comparing the sulfur or nitrogen content of the heavy oil raw material with the sulfur or nitrogen content of the product oil, a person skilled in the art can calculate the desulfurization rate and denitrification rate.

[0113] The method for estimating the desulfurization rate of target petroleum according to the present invention can be used to set the operating conditions of petroleum-related equipment such as RDS units. Accordingly, according to a preferred embodiment of the present invention, an operating method for petroleum-related equipment is provided, in which the operating conditions are set based on the estimated value of the refining rate estimated by the above method.

[0114] The reaction temperature in an RDS (Refuse Decomposition System) can be set to approximately 300-400°C, for example, when the target petroleum is heavy oil.

[0115] <Device and system for estimating the desulfurization rate of petroleum> Next, with reference to Figure 3, an embodiment of the heavy oil refining efficiency estimation device of the present invention will be described. Figure 3 is a functional block diagram of the heavy oil refining efficiency estimation device 1 of the embodiment. By having a computer execute the program of the present invention, the computer functions as a heavy oil refining efficiency estimation device. Note that Figure 3 omits the illustration of the interface for inputting and outputting information.

[0116] The apparatus 1 includes a frequency factor estimation unit 10, a molecular composition estimation unit 20, and a purification efficiency estimation unit 30. The apparatus 1 may consist of a single CPU, or it may consist of multiple devices connected to each other via a communication line.

[0117] The frequency factor estimation unit 10 includes a component information acquisition unit 11 and a frequency factor correction calculation unit 12.

[0118] The component information acquisition unit 11 can consist of a storage unit in which information on each component obtained from FTICR-MS and information on heavy oil components are stored as a database, and a calculation unit. Based on the information on each component obtained by FTICR-MS and the information stored in the storage unit, the calculation unit can perform analysis of the individual composition information of each component. For example, the calculation unit and storage unit store databases and programs (such as JACD (Juxtaposed Attributes for Chemical-structure Description)) described in Japanese Patent Publication No. 2014-218643 and Japanese Patent Publication No. 2020-502495 by the present applicant, and perform analysis of two or more structural attribute parameter information selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion. Furthermore, the component information acquisition unit 11 may be connected to an FTICR-MS or a heavy oil ionizer, if desired.

[0119] The frequency factor correction calculation unit 12 acquires component parameter information of heavy oil from the component information acquisition unit 11. Based on the component parameter information and a pre-stored frequency factor correction formula, it can estimate the frequency factor of the component of the target heavy oil. The frequency factor correction calculation unit 12 can select the component, component parameter, and correction formula according to whether the refining rate is the desulfurization rate or the denitrification rate, and estimate the frequency factor.

[0120] The molecular composition estimation unit 20 estimates the molecular composition of the produced oil based on the frequency factor information obtained from the frequency factor correction calculation unit 12 and a pre-stored purification reaction model. The purification reaction model may be one that uses the structure attribute-based reaction modeling method described above.

[0121] The refining efficiency estimation unit 30 can estimate the refining efficiency based on the molecular composition information of the produced oil obtained from the molecular composition estimation unit 20 and the molecular composition of the heavy oil to be measured obtained from the constituent component information acquisition unit 11.

[0122] The components of the refining efficiency estimation device of the present invention may be configured as an integral unit, but they may also be configured as separate units as desired. When the refining efficiency of a target petroleum is estimated using such independent components, the refining efficiency estimation device can be provided as a system for estimating refining efficiency.

[0123] Therefore, according to another aspect of the present invention, a system for a refining reaction of heavy oil having at least one refining efficiency selected from desulfurization rate and denitrification rate, (1) A frequency factor estimation unit that estimates the frequency factors of one or more structural factors contained in the heavy oil based on a correction formula in which two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion are variables. (2) A molecular composition estimation unit that estimates the molecular composition of the product oil obtained from the heavy oil based on the purification reaction model using the frequency factors obtained in the frequency factor estimation unit, (3) A refining efficiency estimation unit that estimates the refining efficiency based on the molecular composition of the heavy oil and the molecular composition of the produced oil. Includes, The system is one or more of the above-mentioned structural factors, each being one or more single-core molecules containing one sulfur atom or one nitrogen atom. It will be provided.

[0124] <Computer programs for estimating the rate of leakage, etc.> In this invention, the series of processes—estimating molecular structure using JACD, linking the estimated molecular structure information with physical properties, and estimating the properties of a multi-component mixture using an aggregation model—can be performed by hardware, software, or a combination thereof. When performing the processing by software, a program recording the processing sequence can be installed and executed in the memory of a computer incorporated into dedicated hardware, or the program can be installed and executed on a general-purpose computer capable of performing various processes.

[0125] For example, programs can be pre-recorded on recording media such as hard disks or ROMs. Alternatively, programs can be temporarily or permanently stored (recorded) on removable recording media such as flexible disks, CD-ROMs, MO disks, DVDs, magnetic disks, and semiconductor memory.

[0126] In addition to installing programs from removable storage media as described above, programs can also be downloaded from download sites and transferred wirelessly to the computer, or transferred via a wired connection over a network such as a LAN or the internet. The computer can then receive the programs transferred in this way and install them onto its internal storage media, such as a hard disk.

[0127] The method of the present invention can be suitably implemented in a computer that stores the above-mentioned computer program in its internal memory.

[0128] Furthermore, the various processes described herein may not only be executed sequentially as described, but may also be executed in parallel or individually as needed, depending on the processing capacity of the device performing the process. Also, in this specification, a system is a logical combination of multiple devices, and is not limited to devices in each configuration being located in the same enclosure. [Examples]

[0129] The present invention will be described below with reference to examples, but the present invention is not limited to these examples.

[0130] Test Example 1 (1) About RDS molecular reaction modeling technology This study aimed to construct a quantitative reaction model for analyzing the reaction of RDS using molecular composition data (JACD data) contained in atmospheric residual oil (AR), which is the raw material for RDS, as input data. The following investigations were conducted to achieve this goal.

[0131] Since AR, which is mainly used as a feedstock for RDS, contains tens of thousands of molecules, using its molecular composition directly in molecular reaction modeling would result in an enormous number of parameters to optimize, making it practically impossible. Therefore, this technology employs Attribute Reaction Modeling (ARM), a structure attribute-based reaction modeling method developed by Professor Klein and his colleagues at the University of Delaware. In this technology, as core reaction models, 1,233 types of cores covering more than 95 mol% of RDS feedstock and produced oil, and a total of 2,107 reaction paths for the desulfurization and denitrification reactions of these 1,233 cores are defined.

[0132] To perform a reaction rate analysis, it is necessary to set the reaction rate parameters, namely the activation energy ΔE and the frequency factor A, for each of the 2,107 reaction paths. The activation energy ΔE is estimated from ΔH calculated using the molecular orbital method, using the Quantitative Structure Reactivity Relationship (QSRR) equation, which assumes a first-order correlation between ΔE and the standard heat of formation ΔH of the molecule in similar reaction groups. The frequency factor A is determined by fitting it to match the reaction evaluation test.

[0133] Regarding frequency factor A, it was determined not for each of the 2107 reaction pathways, but for each reaction group (structure factor) as shown in Figure 4.

[0134] (2) Correction of rate parameters (frequency factors) used in RDS molecular reaction modeling techniques Table 2 shows the results obtained by fitting frequency factors for 16 types of reaction groups (ARs) from different crude oil sources to the molecular composition data (JACD data) of DSARs, which are RDS-derived oils, using RDS molecular reaction modeling technology. From the results in Table 2, the frequency factors of each reaction group differ depending on the difference in the crude oil source, i.e., the difference in the molecular composition of the feedstock oil. 10The values ​​of A (hereinafter, logA) were nearly constant for S2 and S1N1, and varied for other reaction groups (S1, S04, 5R, 6R, 09) depending on the crude oil.

[0135] [Table 2]

[0136] Since logA differs for each crude oil, we investigated whether logA could be estimated from the molecular composition. First, we examined four frequency factors related to the desulfurization reaction (S1, S04, S2, S1N1).

[0137] To investigate the correlation between the molecular composition of various ARs and their frequency factors, Figure 5 shows a plot based on the above findings, with the average degree of aggregation of all molecules contained in each of the 16 types of ARs and the average total number of rings of sulfur compounds on the x-axis, and the logA value for each of the four frequency factors on the y-axis. Here, the frequency factors on the y-axis refer to the average value of the logA of the frequency factors obtained at reaction temperatures of 350, 370, and 390°C.

[0138] The frequency factors of S1 and S04 showed a decrease in logA as the average degree of cohesion and average total number of rings increased, indicating a first-order correlation between the two. On the other hand, no correlation was found between the frequency factors of S2 and S1N1.

[0139] Therefore, for S1 and S04, linear regression was performed on logA in various ARs using the average cohesion and average total number of rings, and the regression coefficients (a, b, c) shown below were obtained.

[0140] logA(S1) = a1 + b1 × (average degree of cohesion) + c1 × (average total number of rings) logA(S04)=a2+b2×(average cohesion degree)+c2×(average total number of rings)

[0141] The average degree of cohesion and average total number of rings for 16 types of AR were substituted into the above supplementary formulas to estimate the frequency factors logA for S1 and S04, and their consistency with the frequency factors logA obtained based on measured data of RDS-produced oil was confirmed by regression analysis (Figure 6).

[0142] The coefficients of determination (R²) for S1 and S04 were 0.744 and 0.616, respectively, indicating that the estimated and measured values ​​were generally consistent. Since S2 and S1N1 are nearly constant frequency factors, all the frequency factor data calculated for each crude oil was averaged and used in the molecular reaction simulation.

[0143] Next, we examined three frequency factors (5R, 6R, and 09) related to the denitrification reaction. Similar to the desulfurization reaction, we created a graph plotting the average degree of aggregation of all molecules contained in each AR, the average total number of rings in the nitrogen compound, and the average number of side-chain carbons added to the nitrogen compound on the x-axis, and logA calculated for each of the three frequency factors on the y-axis (Figure 7).

[0144] For the frequency factors 5R, 6R, and 09, logA tended to decrease as the average degree of cohesion, average total number of rings, and average number of side chain carbons increased, indicating a first-order correlation between the two.

[0145] Linear regression was performed on logA for various ARs using the average cohesion, average total number of rings, and average number of side chain carbons, and the regression coefficients (a, b, c, d) shown below were obtained.

[0146] logA(5R) = a1 + b1 × (average degree of cohesion) + c1 × (average total number of rings) + d1 × (average number of carbon atoms in side chains) logA(6R) = a² + b² × (average degree of cohesion) + c² × (average total number of rings) + d² × (average number of carbon atoms in side chains) logA(0⁹) = a³ + b³ × (average degree of cohesion) + c³ × (average total number of rings) + d³ × (average number of carbon atoms in side chains)

[0147] By substituting the average degree of cohesion, average total number of rings, and average number of side chain carbons for the 16 types of AR into the above supplementary formula, the frequency factors logA for 5R, 6R, and 09 were estimated, and their consistency with the frequency factors logA obtained based on measured data of RDS-produced oil was confirmed by regression analysis (Figure 8).

[0148] Based on the above findings, correction formulas were established for two of the ten frequency factors logA for desulfurization reactions (S1, S04) and three for denitrification reactions (5R, 6R, O9). For these five frequency factors logA, the frequency factors logA were estimated using the correction formulas and the average degree of aggregation, average total number of rings, and average number of side chain carbons obtained from JACD data of various ARs, and then used in molecular reaction simulations.

[0149] (3) Prediction of AR desulfurization and denitrification rates by RDS molecular reaction simulation For 16 types of ARs from different crude oil sources whose reactions have been evaluated to date, the frequency factors of desulfurization and denitrification were estimated using the method described above, and RDS molecular reaction simulations were performed using the obtained values ​​under reaction temperatures of 350, 370, and 390°C.

[0150] Based on molecular composition (core) data of RDS-derived oil (DSAR) at various reaction temperatures obtained from RDS molecular reaction simulations, and measured feedstock oil (AR), the desulfurization and denitrification rates for 16 types of AR were calculated at reaction temperatures of 350, 370, and 390°C. Figure 9 shows the calculated predicted desulfurization and denitrification rates plotted on the vertical axis and the measured values ​​on the horizontal axis.

[0151] Compared to the measured values, the predicted desulfurization rate was found to be within ±10%, and the predicted denitrification rate within ±15%. Furthermore, no trends were observed based on the origin of the crude oil. [Explanation of symbols]

[0152] 1. Device for estimating the refining efficiency of heavy oil. 10 Frequency factor estimation part 11 Component information acquisition unit 12 Frequency Factor Correction Calculation Unit 20 Molecular composition estimation section 30. Purification Efficiency Estimation Unit

Claims

1. A computer-based method for estimating at least one refining efficiency selected from the desulfurization rate and the denitrification rate in the refining reaction of heavy oil, (1) A step of estimating the frequency factor logA of one or more structural factors contained in the heavy oil based on a correction formula in which two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion are variables. (2) A step of estimating the molecular composition of the product oil obtained from the heavy oil based on a refining reaction model using the frequency factor logA obtained in step (1), and (3) The process includes the step of estimating the refining efficiency based on the molecular composition of the heavy oil and the molecular composition of the produced oil, The aforementioned one or more structure factors are one or more single-core molecules containing one sulfur atom or one nitrogen atom. When the purification efficiency is the desulfurization rate, the correction formula used to estimate the frequency factor logA is: logA (when the structure factor is a non-aromatic single-core molecule containing one sulfur atom) = a1 + b1 × (average degree of aggregation) + c1 × (average total number of rings) logA (when the structure factor is a polycyclic aromatic single-core molecule containing one sulfur atom) = a² + b² × (average degree of aggregation) + c² × (average total number of rings) (a, b, and c are regression coefficients.) And, When the purification efficiency is the denitrification rate, the correction formula used to estimate the frequency factor logA is: logA (when the structure factor is a non-aromatic single-core molecule containing one nitrogen atom) = a1 + b1 × (average degree of cohesion) + c1 × (average total number of rings) + d1 × (average number of carbon atoms in the side chain) logA (when the structure factor is a monocyclic aromatic single-core molecule containing one nitrogen atom) = a² + b² × (average degree of cohesion) + c² × (average total number of rings) + d² × (average number of carbon atoms in the side chain) logA (when the structure factor is a polycyclic aromatic single-core molecule containing one nitrogen atom) = a³ + b³ × (average degree of cohesion) + c³ × (average total number of rings) + d³ × (average number of carbon atoms in the side chain) (a, b, c, and d are regression coefficients.) The method.

2. The correction formula in step (1) above is For a set of pre-selected standard heavy oils, the steps include determining the correlation between two or more structural attribute parameters selected from the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of cohesion, and one or more structural factors in the standard heavy oils, and The method according to claim 1, obtained by performing a regression analysis based on the correlation, which involves calculating a correction formula for a frequency factor logA with two or more structural attribute parameters as variables for one or more structural factors.

3. The method according to claim 1 or 2, wherein the one or more structural factors are two or more structural factors selected from the group consisting of a non-aromatic single-core molecule containing one sulfur atom, a polycyclic aromatic single-core molecule containing one sulfur atom, a non-aromatic single-core molecule containing one nitrogen atom, a monocyclic aromatic single-core molecule containing one nitrogen atom, and a polycyclic aromatic single-core molecule containing one nitrogen atom.

4. The method according to any one of claims 1 to 3, wherein the one or more structural factors are a non-aromatic single-core molecule containing one sulfur atom and a polycyclic aromatic single-core molecule containing one sulfur atom.

5. The method according to claim 4, wherein the purification efficiency is the desulfurization rate.

6. The method according to claim 4 or 5, wherein the structural attribute parameters that are variables in the correction formula include the average total number of rings and the average degree of cohesion.

7. The method according to any one of claims 1 to 3, wherein the one or more structural factors are a non-aromatic single-core molecule containing one nitrogen atom, a monocyclic aromatic single-core molecule containing one nitrogen atom, and a polycyclic aromatic single-core molecule containing one nitrogen atom.

8. The method according to claim 7, wherein the purification efficiency is the denitrification rate.

9. The method according to claim 7 or 8, wherein the structural attribute parameters that are variables in the correction formula include the average total number of rings, the average number of carbon atoms in the side chains, and the average degree of aggregation.

10. The method according to any one of claims 1 to 9, wherein the refining reaction of the heavy oil is carried out using a residual direct desulfurization (RDS) apparatus.

11. A method for operating a petroleum apparatus, comprising setting operating conditions based on an estimated value of the refining rate obtained by the method according to any one of claims 1 to 10.

12. An apparatus for estimating at least one refining efficiency, selected from desulfurization rate and denitrification rate, in a heavy oil refining reaction, which performs the method according to any one of claims 1 to 10.

13. A system for a heavy oil refining reaction, comprising the method according to any one of claims 1 to 10, wherein the refining efficiency is at least one selected from desulfurization rate and denitrification rate.

14. A computer program for causing the method according to any one of claims 1 to 11, the apparatus according to claim 12, or the system according to claim 13.

15. A recording medium on which the computer program described in claim 14 is recorded.

16. A computer having the computer program described in claim 14 stored in an internal storage device.

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