CN116432795A

CN116432795A - Method, device, equipment and storage medium for determining lubricating oil production and processing information and application

Info

Publication number: CN116432795A
Application number: CN202111661604.7A
Authority: CN
Inventors: 王杭州; 杨诗棋; 纪晔; 王弘历; 边钢月; 刘一心
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2021-12-31
Filing date: 2021-12-31
Publication date: 2023-07-14

Abstract

The invention relates to a method, a device, equipment, a storage medium and an application for determining production and processing information of lubricating oil, wherein the method comprises the following steps: determining the molecular composition of each group of side-stream fractions to be fed into the lubricating oil hydrogenation device according to the distillation range data of the processing raw materials of the lubricating oil hydrogenation device; predicting the molecular composition and the product yield of a product obtained under preset reaction conditions according to the processing amount of the processing raw materials and the molecular composition of each group of side streams; determining physical properties of the product according to the molecular composition of the product; judging whether the physical properties of the product meet the preset physical property index requirements or not: if so, calculating an optimization target according to the product yield of the product, and judging whether the product reaches the maximum value or not: if yes, outputting the production and processing information of the current lubricating oil hydrogenation device; if not, the distillation range data of the processing raw materials are adjusted until the physical properties of the product are judged to meet the preset physical property index requirements and the optimization target reaches the maximum value. The invention can obviously improve the adaptability to the production operation variation of lubricating oil.

Description

Method, device, equipment and storage medium for determining lubricating oil production and processing information and application

Technical Field

The invention relates to the technical field of petroleum processing, in particular to a method, a device, equipment, a storage medium and application for determining lubricating oil generation processing information.

Background

In the petroleum processing industry, in order to increase benefits brought by lubricating oil, the lubricating oil production process needs to be optimized, and at present, two optimization methods commonly adopted in the prior art comprise one for realizing the optimization in the lubricating oil production process and the other for carrying out the optimization in the lubricating oil production process through a simulation method. The optimization in the lubricating oil production process is carried out by a simulation method, usually based on a structure-oriented lumped model, and the physical property prediction of the product material is carried out based on a molecular component represented by the structure-oriented lumped method.

Disclosure of Invention

The inventors found that the first method requires a large amount of analysis and assay work, and the research efficiency is low. In the second mode, the product materials are predicted to have physical properties based on lumped components, and thus the adaptability to large adjustments in production operations is lacking. In order to solve the problems of the prior art, at least one embodiment of the present invention provides a method, an apparatus, a device, a storage medium and an application for determining lubricant generation processing information.

In a first aspect, an embodiment of the present invention provides a method for determining lubricant processing information, including:

determining a distillate cutting scheme and a corresponding distillation cutting model of a preset upstream processing device according to distillation range data of a processing raw material of the lubricating oil hydrogenation device, and determining molecular compositions of each group of side-stream distillates to enter the lubricating oil hydrogenation device through the distillation cutting model; wherein the upstream processing device comprises an atmospheric and vacuum device;

according to the processing amount of the processing raw materials and the molecular composition of each group of side-cut fractions, predicting the molecular composition and the product yield of a product obtained under preset reaction conditions by utilizing a pre-trained lubricating oil hydrogenation reaction product prediction model;

determining physical properties of the product by using a pre-trained physical property calculation model according to the molecular composition of the product;

judging whether the physical properties of the product meet the preset physical property index requirements or not:

when the physical properties of the product meet the preset physical property index requirements, calculating an optimization target according to the product yield of the product, and judging whether the optimization target reaches the maximum value or not:

when the optimization target reaches the maximum value, outputting current production and processing information;

When the optimization target does not reach the maximum value, adjusting the distillation range data of the raw material processed by the lubricating oil hydrogenation device, and re-executing the steps according to the adjusted distillation range data; and (3) re-determining a distillate cutting scheme and a corresponding distillation cutting model of the upstream processing device according to the adjusted distillation range data, and executing the step of determining the molecular composition of each group of side-stream distillate which is to enter the lubricating oil hydrogenation device through the distillation cutting model until the physical properties of the product are judged to meet the preset physical property index requirements, and the optimization target reaches the maximum value.

In a possible implementation manner, when the physical properties of the product do not meet the physical property index requirements, adjusting the distillation range data of the processing raw materials, and re-executing the steps according to the adjusted distillation range data; and (3) re-determining a distillate cutting scheme and a corresponding distillation cutting model according to the adjusted distillation range data, and executing the step of determining the molecular composition of each group of side-stream which is to enter the lubricating oil hydrogenation device through the distillation cutting model until the physical properties of the product are judged to meet the preset physical property index requirements, wherein the optimization target reaches the maximum value.

In a possible implementation manner, the step of predicting the molecular composition and the product yield of the product obtained under the preset reaction conditions by using a pre-trained lube oil hydrogenation reaction product prediction model according to the processing amount of the processing raw material and the molecular composition of each group of side streams includes:

According to the molecular composition of each group of side-cut fractions, predicting a plurality of products obtained under preset reaction conditions by utilizing a pre-trained lube hydrogenation reaction product prediction model;

dividing said plurality of products into a plurality of products according to true boiling point or family composition;

the molecular composition of each product and the product yield are determined, wherein the product yield is the sum of the products of the contents of the various molecules contained in the product and the processing amount of the lubricating oil hydrotreater processing raw material.

In one possible implementation, the step of determining a distillate cut scheme and a corresponding distillation cut model from the distillation range data of the lube oil hydrotreater process feedstock, by which the molecular composition of each set of side cuts to be entered into the lube oil hydrotreater is determined, comprises:

determining the type of single molecules in the processing raw material and the content of each single molecule;

calculating the boiling point of each single molecule in the processing raw material;

determining a distillate cutting scheme and a corresponding distillation cutting model according to the distillation range data of the processing raw material, and performing distillation cutting on the processing raw material based on the distillation cutting model to obtain a plurality of groups of side distillates;

the kind of the single molecule contained in each set of side streams and the content of each single molecule are determined according to the boiling point and the content of each single molecule in the processing raw material.

In a possible implementation manner, the determining a distillate cutting scheme and a corresponding distillation cutting model according to the distillation range data of the lubricating oil hydrotreater processing raw material includes:

obtaining the temperature range covered by the distillation range data of each fraction;

cutting the temperature range by using the distillation range data of each fraction to obtain a temperature interval of each fraction;

constructing a distillation cutting sub-model of the corresponding fraction by utilizing the yield data of each fraction and the content of the corresponding fraction in each temperature interval;

and combining all the distillation cutting sub-models to obtain a distillation cutting model.

In one possible implementation, the lubricating oil hydrogenation reaction product prediction model is trained by the following steps:

establishing a product prediction training model of the lubricating oil hydrogenation reaction; wherein, the product prediction training model of lubricating oil hydrogenation reaction includes: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises at least one reaction rule of alkane thermal cracking, alkane catalytic cracking, aromatic hydrocarbon hydrogenation saturation and naphthene ring opening;

acquiring sample raw material information of a plurality of groups of sample raw materials;

training the reaction rule set by utilizing a plurality of groups of sample raw material information and reaction product molecular component type information to obtain a reaction rule set after training is finished;

Obtaining a reaction rate algorithm after training is completed;

and determining the lubricating oil hydrogenation reaction product prediction model according to the reaction rule set after training and the reaction rate algorithm after training.

In one possible implementation, the reaction rate constant of the reaction rate algorithm is the following expression:

K＝k _st ×k _absor ×Ф _cat

wherein K is a reaction rate constant, K _st Is the surface reaction rate constant, k _absor For the adsorption rate constant, phi _cat Is a catalyst active factor, wherein the catalyst active factor comprises a metal center active site for hydrogenation dehydrogenation reaction and an acid center active site for carbocation reaction.

In one possible implementation, the surface reaction rate constant is obtained by the following reaction rate constant calculation formula:

wherein k is _st K is the reaction rate constant _B The method is characterized in that the method comprises the steps of taking a Boltzmann constant, h as a Planck constant, R as an ideal gas constant, T as a temperature value of an environment where a reaction path is located, exp as an exponential function based on a natural constant, deltaS as entropy change before and after reaction corresponding to a reaction rule corresponding to the reaction path, deltaE as a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, P as a pressure value of the environment where the reaction path is located, and alpha as a pressure influence factor corresponding to the reaction rule corresponding to the reaction path.

In one possible implementation, the sample raw material information of the sample raw material includes: the molecular composition of the sample material and the molecular composition of the actual product to which the sample material corresponds under specific reaction conditions.

In one possible implementation manner, the training the reaction rule set by using multiple sets of the sample raw material information and the reaction product molecular component kind information to obtain a reaction rule set after training is completed, including:

processing the molecular composition of the sample raw material according to a preset reaction rule set to obtain a reaction path corresponding to each molecule in the molecular composition of the sample raw material;

obtaining a first molecular composition of a device product according to a reaction path corresponding to each molecule in the molecular composition of the sample raw material; in the device product, comprising: the sample raw material, intermediate product and predicted product;

determining a first relative deviation according to a first molecular composition of the device product and a second molecular composition of the actual product, and judging whether the first relative deviation meets a first preset condition;

if the first relative deviation accords with a first preset condition, fixing the reaction rule set to obtain a reaction rule set after training is completed;

And if the first relative deviation does not meet the first preset condition, adjusting the reaction rules in the reaction rule set, and re-executing the steps of determining the first relative deviation and judging whether the first relative deviation meets the first preset condition according to the adjusted reaction rule set.

In one possible implementation, the determining the first relative deviation from the first molecular composition of the device product to the second molecular composition of the actual product includes:

obtaining the types of single molecules in the first molecular composition to form a first set;

obtaining the types of single molecules in the second molecular composition to form a second set;

determining whether the second set is a subset of the first set;

if the second set is not a subset of the first set, acquiring a pre-stored relative deviation value which does not meet a first preset condition as the first relative deviation value;

if the second set is a subset of the first set, a first relative deviation is calculated by a first deviation algorithm.

In a possible implementation manner, the first deviation algorithm expression is

Wherein x is ₁ For the first relative deviation, M is the first set, M ₁ M is a collection of species composition of single molecules in the molecular composition of the sample raw material ₂ A set of species composition of single molecules of the molecular composition of the intermediate product, N being the second set, and card representing the setThe number of elements in the composition.

In one possible implementation manner, the training the reaction rate algorithm by using multiple sets of the sample raw material information and the reaction product molecular component type and content information to obtain a reaction rate algorithm after training, including:

according to the reaction rate algorithm, respectively calculating the reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample raw material;

obtaining the predicted content of each molecule in a predicted product corresponding to the sample raw material according to the molecular content of each molecule in the sample raw material and the reaction rate corresponding to the reaction path of the molecule;

calculating a second relative deviation according to the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product, and judging whether the second relative deviation meets a second preset condition or not;

if the second relative deviation accords with a second preset condition, fixing the reaction rate algorithm to obtain a reaction rate algorithm after training is finished;

And if the second relative deviation does not meet the second preset condition, adjusting parameters in the reaction rate algorithm, and re-executing the steps of calculating the second relative deviation and judging whether the second relative deviation meets the second preset condition according to the adjusted reaction rate algorithm.

In one possible implementation, the step of determining the physical properties of the product according to the molecular composition of the product using a pre-trained physical property calculation model includes:

calculating each single molecule physical property of each single molecule in the molecular composition based on a physical property calculation model trained in advance;

based on a preset mixing rule of physical properties of each product, calculating according to the physical properties and the content of each single molecule to obtain the physical properties of the product of the lubricating oil hydrogenation device.

In one possible implementation, the physical property index is at least one of viscosity, viscosity index, pour point, aniline point.

In a possible implementation manner, the step of calculating an optimization target according to the product yield of the product includes:

based on a pre-established optimization target model, calculating the optimization target according to the product yield and the weight of each product, wherein the optimization target model is as follows:

T＝∑(Y _i *W _i )

Wherein T is an optimization target, Y _i Product yield for the ith product, W _i Is the weight of the ith product.

In one possible implementation, the product yield is calculated by the following expression:

Y＝∑(C _j *P)

wherein Y is the product yield, C _j The content of the j-th single molecule contained in the product in all products is that P is the processing amount of the lubricating oil production raw material.

In a possible implementation, the optimization objective is at least one of cumulative benefit and objective physical property parameters.

In one possible implementation, when the optimization objective is to accumulate revenue, the weight is the price of the product.

In one possible implementation, when the optimization objective is a target physical parameter, the weight is an importance score for the product physical parameter.

In one possible implementation, the distillation range data includes at least one of an initial distillation point and a final distillation point;

the distillate cutting scheme comprises a distillation range scheme of each group of side distillates;

the preset reaction conditions comprise at least one of temperature, pressure and space velocity;

the current product production and processing information comprises at least one of distillation range data of the current lubricating oil hydrotreater processing raw material, the molecular composition and product yield of each current product and the current reaction condition.

In a second aspect, an embodiment of the present invention further provides a production processing information determining apparatus, including:

the determining module is used for determining a distillate cutting scheme of a preset upstream processing device and a corresponding distillation cutting model according to the distillation range data of the processing raw material of the lubricating oil hydrogenation device, and determining the molecular composition of each group of side-stream distillates which are to enter the lubricating oil hydrogenation device through the distillation cutting model; wherein the upstream processing device comprises an atmospheric and vacuum device;

the prediction module is used for predicting the molecular composition and the product yield of a product obtained under preset reaction conditions by utilizing a pre-trained lubricating oil hydrogenation reaction product prediction model according to the processing amount of the processing raw materials and the molecular composition of each group of side streams;

the analysis module is used for analyzing the physical properties of the product by utilizing a pre-trained physical property calculation model according to the molecular composition of the product;

the judging module is used for judging whether the physical properties of the product meet the preset physical property index requirements or not:

and when the optimization target does not reach the maximum value, adjusting the distillation range data of the processing raw materials, and sending the data to the determining module.

In a third aspect, an embodiment of the present invention provides an application of the above-mentioned method for determining information about lubricant production and processing in a lubricant production method.

In a fourth aspect, an embodiment of the present invention provides a method for producing a lubricating oil, using the production and processing information determined by the above-described method for determining the production and processing information.

In a fifth aspect, an embodiment of the present invention provides a lubrication oil production and processing information determining apparatus, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;

a memory for storing a computer program;

and the processor is used for realizing the steps of the lubricating oil production and processing information determining method when executing the program stored in the memory.

In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps of the above-described lubricating oil production processing information determining method.

Compared with the prior art, the technical scheme of the invention has the following advantages: according to the embodiment of the invention, the molecular composition of each group of side-stream fractions which are to enter the lubricating oil hydrogenation device is determined according to the distillation range data of the processing raw materials of the lubricating oil hydrogenation device; predicting the molecular composition and the product yield of a product obtained under preset reaction conditions according to the processing amount of the processing raw materials of the lubricating oil hydrogenation device and the molecular composition of each group of side cuts; determining physical properties of the product according to the molecular composition of the product; judging whether the physical properties of the product meet the preset physical property index requirements or not: if so, calculating an optimization target according to the product yield of the product, and judging whether the optimization target reaches the maximum value or not: when the optimization target reaches the maximum value, outputting the production and processing information of the current lubricating oil hydrogenation device; when the optimization target does not reach the maximum value, the distillation range data of the processing raw material of the lubricating oil hydrogenation device is adjusted until the physical properties of the product are judged to meet the preset physical property index requirements and the optimization target reaches the maximum value. The invention establishes a product prediction model based on the molecular components, predicts the physical properties of the product materials based on the molecular components, accurately reflects the conversion rule of the molecular components in the production process, and obviously improves the adaptability of the process to operation variation. According to the invention, the molecular composition of the raw materials is adjusted by adjusting the distillation range data of the raw materials processed by the lubricating oil hydrogenation device, so that the aim of optimizing the production process is fulfilled, and the problem of operation optimization of the lubricating oil hydrogenation device is better solved. The invention fully digs the synergy potential of the raw materials and the processing device, improves the yield of the target product and realizes the maximization of the product benefit.

Drawings

FIG. 1 is a schematic flow chart of a method for determining lubricant production and processing information according to an embodiment of the present invention;

FIG. 2 is a flowchart showing the determination of the molecular composition of each set of side streams in the lubricating oil production process information determining method according to the first embodiment of the present invention;

FIG. 3 is a flowchart showing the operation of training a product prediction model in a method for determining lubricant production process information according to the first embodiment of the present invention;

FIG. 4 is a flowchart showing training of a physical property calculation model in the method for determining lubricating oil production process information according to the first embodiment of the present invention;

FIG. 5 is a schematic diagram of a device for determining lubricant production and processing information according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a lubrication oil production processing information determining apparatus according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

As shown in fig. 1, the embodiment of the invention provides a method for determining production and processing information of lubricating oil. Referring to fig. 1, the lubricating oil production and processing information determination method mainly includes the steps of:

s10, determining a distillate cutting scheme and a corresponding distillation cutting model of a preset upstream processing device according to distillation range data of a processing raw material of the lubricating oil hydrogenation device, and determining molecular compositions of each group of side streams to be fed into the lubricating oil hydrogenation device through the distillation cutting model.

In the embodiment of the invention, the processing raw materials of the lubricating oil hydrogenation device are products output by the upstream processing device, namely, each group of side line fractions which are to enter the lubricating oil hydrogenation device. In general, oil processing and production require distillation of petroleum feedstock by an upstream processing unit (including an atmospheric and vacuum unit) and then feeding each set of side-cut products from the upstream processing unit into an oil hydrogenation unit to obtain the desired product. In order to obtain a target product, it is generally necessary to grasp in advance information about the processing material of the lubricating oil hydrotreater, for example, the distillation range data of the processing material including the initial point and the final point, and the processing amount of the processing material of the lubricating oil hydrotreater.

In this embodiment, first, a distillate cutting scheme (i.e., distillation ranges of different side distillates) and a corresponding distillation cutting model of an upstream processing device are determined according to distillation range data of a processing raw material of a lubricating oil hydrogenation device, and then, the processing raw material (i.e., each group of side distillates) to be entered into the lubricating oil hydrogenation device in each side line is determined through the distillation cutting model, so as to obtain a molecular composition of the processing raw material; wherein the molecular composition of the processing raw material comprises the molecular type of the processing raw material and the content of each molecule.

In this embodiment, determining the distillate cut scheme and the corresponding distillation cut model of the preset upstream processing device according to the distillation range data of the processing feedstock of the lubricating oil hydrogenation device in S10 includes:

In the embodiment of the invention, the yield data of each fraction is determined in advance according to the related parameter information of the preset upstream processing device.

S20, predicting the molecular composition and the product yield of a product obtained under preset reaction conditions by utilizing a pre-trained lubricating oil hydrogenation reaction product prediction model according to the processing amount of the processing raw materials and the molecular composition of each group of side streams.

In this embodiment, the lube oil hydrogenation reaction product prediction model is a product for predicting a catalytic hydrogenation reaction of a lube oil hydrogenation unit processing feedstock at a molecular level under a preset reaction condition. For example, the lube oil hydrogenation reaction product prediction model may be one of a molecular level lube oil hydrogenation plant model, a lube oil hydrogenation model, or a lube oil production process reaction mechanism model. Wherein the lube oil hydrogenation model includes, but is not limited to, the following chemical reactions: the method comprises the steps of finishing reaction, hydrotreating reaction, catalytic dewaxing reaction, isomerization dewaxing reaction, alkane thermal cracking reaction, alkane catalytic cracking reaction, aromatic hydrocarbon hydrogenation saturation reaction and cycloparaffin ring opening reaction, wherein each chemical reaction has a corresponding reaction rule. The reaction rules corresponding to each chemical reaction can be rules of chemical reactions simulated by each molecule in the product prediction model, each reaction rule is obtained by induction and finishing in advance according to different reaction mechanisms, the reaction rules represent reaction processes of the corresponding chemical reactions under the conditions of preset temperature, pressure, airspeed and catalyst, so that the actual production process of the processing raw materials is simulated, and because the molecular composition of the processing raw materials is complex, the reaction paths of all molecules can be quickly generated based on the reaction rules, and the simulation calculation is performed in the model, so that the product of the lubricating oil hydrogenation reaction is obtained.

The predictive model of the lubricating oil hydrogenation reaction product can be a system of ordinary differential equations, wherein each ordinary differential equation in the system of ordinary differential equations is a reaction path corresponding to each single molecule of the lubricating oil hydrogenation raw material, and parameters in each ordinary differential equation are reaction rate constants and influence factors corresponding to a reaction rate constant calculation formula in a reaction rate calculation method corresponding to each reaction path of each single molecule. Each ordinary differential equation is a first order equation, and reflects the time-varying response curve of each molecule.

In this embodiment, the preset reaction conditions may include: at least one parameter condition of temperature, pressure and space velocity. Wherein, the space velocity refers to the gas amount treated by the catalyst per unit volume of unit time under the specified temperature and pressure conditions, and the unit is m ³ /(m ³ Catalyst h), which can be simplified as h ^-1 。

Further, in the present example, the product yield is the sum of the amounts of processing of the various molecules contained in the product, that is, the product yield can be calculated by the following expression:

Y＝∑(C _j *P)

wherein Y is the product yield, C _j And P is the processing amount of the processing raw material of the lubricating oil hydrogenation device, wherein the content of the j-th single molecule contained in the product in all products is the processing amount of the processing raw material of the lubricating oil hydrogenation device.

In this embodiment, the molecular composition of the processed raw materials and products of the lubricating oil hydrogenation apparatus is relatively complex, and in order to improve the processing efficiency of data, the molecular structure of the processed raw materials and products may be determined by one or more of raman spectroscopy, four-stage gas chromatography-mass spectrometer detection, gas chromatography/field ionization-time-of-flight mass spectrometry, gas chromatography, near infrared spectroscopy, sensor method, and nuclear magnetic resonance spectroscopy, and after the molecules in the raw materials are detected by the above methods, single molecules in the molecular composition may be characterized based on a structure-oriented lumped (Structure Oriented Lump, SOL) molecular characterization method. The SOL molecular characterization method can utilize 24 structure increment fragments to characterize the basic structure of the complex hydrocarbon molecules. Any petroleum molecule can be expressed in terms of a specific set of structurally incremental fragments. The 24 structural increment fragments in the SOL molecule characterization method are 24 groups, the groups are a part of characteristic structures of the molecules, and each group generally performs chemical reaction as a whole. The SOL molecular characterization method belongs to the lumped on the molecular scale, reduces the number of molecules in an actual system from millions to thousands, and greatly reduces the simulation complexity. The characterization method can represent not only alkanes, cycloalkanes, up to complex aromatic structures containing 50 to 60 carbon atoms, but also olefins or cycloalkenes as intermediate products or secondary reaction products, and in addition, heteroatom compounds containing sulfur, nitrogen, oxygen, etc. are also considered.

It should be noted that the above method is directed to the case where the product is a single product. In the case where the product is a plurality of products, the method may further include:

according to the molecular composition of the processing raw materials, predicting a plurality of products obtained under preset reaction conditions by utilizing a pre-trained lubricating oil hydrogenation reaction product prediction model;

dividing the plurality of products into a plurality of products according to true boiling point or family composition;

the molecular composition and product yield of each product were determined. Wherein the product yield is the sum of products of the contents of various molecules contained in the product and the processing amount of the processing raw material.

S30, analyzing physical properties of the product by using a physical property calculation model trained in advance according to the molecular composition of the product.

S40, judging whether the physical properties of the product meet the preset physical property index requirements or not:

if yes, go to step S50;

if not, step S70 is performed.

In this embodiment, the predetermined physical property index is at least one of viscosity, viscosity index, pour point, and aniline point. The physical property index requirements of different products are different.

In this embodiment, when the product is a collection of multiple products, the step of determining whether the physical properties of the product are preset physical property index requirements specifically includes:

Judging whether the physical properties of each product in the products meet the preset physical property index requirements corresponding to the physical properties of each product or not:

if yes, judging that the physical properties of the product meet the physical property index requirements;

if not, judging that the physical properties of the product do not meet the physical property index requirements.

S50, calculating an optimization target according to the product yield of the product, and judging whether the optimization target reaches the maximum value or not:

if yes, go to step S60;

if not, step S70 is performed.

In this embodiment, when the product is a plurality of products, the optimization objectives of all the products are calculated according to the following steps:

calculating optimization targets of all products according to product yields and weights of each product based on a pre-established optimization target model, wherein the optimization target model is calculated by the following expression:

T＝∑(Y _i *W _i )

In this embodiment, the optimization objective is at least one of accumulated revenue and a target physical property parameter, where when the optimization objective is accumulated revenue, the weight is a product price; when the optimization target is a target physical parameter, the weight is an importance score of the physical parameter of the product.

In this embodiment, whether the optimization target reaches the maximum value may be determined by a global optimization algorithm of multi-start point random search, or the optimization algorithm further includes an optimization algorithm such as a gradient descent algorithm, a newton method, a conjugate gradient method, and a heuristic optimization method, where the gradient descent algorithm includes: a random gradient descent algorithm or a batch gradient descent algorithm. By the method, whether the optimization targets of all products reach the maximum value can be determined.

S60, outputting current production and processing information of the lubricating oil hydrogenation device.

In this embodiment, the production processing information may include the distillation range data of the processing raw materials of the lubricating oil hydrogenation apparatus, the molecular composition and yield (or product yield) of the current product, and the current reaction conditions provide reference data for the actual lubricating oil production process.

S70, adjusting the distillation range data of the processing raw material of the lubricating oil hydrogenation device, and returning to the step S10. The method comprises the steps of determining molecular composition of a processing raw material through a distillation cutting model, namely, redefining a distillation cutting scheme and a corresponding distillation cutting model of a preset upstream processing device according to adjusted distillation range data, and executing the step of determining molecular composition of the processing raw material through the distillation cutting model until the physical properties of the current product are judged to meet the preset physical property index requirements, and the optimization target reaches the maximum value.

In this embodiment, whether the optimization target reaches the maximum value may be determined by a global optimization algorithm of multi-start point random search, or the optimization algorithm further includes an optimization algorithm such as a gradient descent algorithm, a newton method, a conjugate gradient method, and a heuristic optimization method, where the gradient descent algorithm includes: a random gradient descent algorithm or a batch gradient descent algorithm. By the method, whether the optimization targets of all products reach the maximum value can be determined. For specific implementation manner, reference may be made to the detailed description of the above optimization algorithm in the prior art, which is not repeated here.

In some embodiments, as shown in fig. 2, in step S10, the step of determining a distillate cut scheme and a corresponding distillation cut model from the distillation range data of the lube oil hydrotreater process feedstock, by which the molecular composition of each set of side cuts to be entered into the lube oil hydrotreater is determined, includes:

s11, determining the types of single molecules in the processing raw materials and the content of each single molecule;

s12, calculating the boiling point of each single molecule in the processing raw material;

s13, determining a distillate cutting scheme and a corresponding distillation cutting model according to the distillation range data of the processing raw material, performing distillation cutting on the processing raw material based on the distillation cutting model to obtain a plurality of groups of side streams, and determining the types of single molecules contained in each group of side streams and the content of each single molecule according to the boiling point and the content of each single molecule in the processing raw material.

Wherein the distillation cut model is obtained by:

merging all the distillation cutting sub-models to obtain a distillation cutting model;

In some embodiments, as shown in fig. 3, in step S20, the lube oil hydrogenation reaction product prediction model is trained by:

s21, establishing a product prediction training model of the lubricating oil hydrogenation reaction; wherein, lubricating oil hydrogenation reaction product predicts training model, include: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises at least one reaction rule of a complementary refining reaction, a hydrotreating reaction, a catalytic dewaxing reaction, an isomerization dewaxing reaction, an alkane thermal cracking reaction, an alkane catalytic cracking reaction, an aromatic hydrocarbon hydrogenation saturation reaction and a cycloalkane ring-opening reaction; the reaction rules in the reaction rule set are rules of chemical reactions simulated by each molecule in the product prediction model, each reaction rule is obtained by induction and finishing in advance according to different reaction mechanisms, the reaction rules represent reaction processes of the corresponding chemical reactions under the preset temperature, pressure, airspeed and catalyst conditions, so that the actual production process of the lubricating oil production raw materials is simulated, and because the molecular composition of the lubricating oil production raw materials is complex, the reaction path of each molecule can be quickly generated based on the reaction rules, and the simulation calculation is performed in the model, so that the product of the lubricating oil hydrogenation reaction is obtained.

S22, acquiring sample raw material information of a plurality of groups of sample raw materials; wherein the sample raw material information of the sample raw material includes: the molecular composition of the sample raw material and the molecular composition of the corresponding actual product of the sample raw material under specific reaction conditions;

s23, training the reaction rule set by utilizing a plurality of groups of sample raw material information and reaction product molecular component type information to obtain a reaction rule set after training is finished;

s24, training the reaction rate algorithm by utilizing a plurality of groups of sample raw material information and reaction product molecular component type and content information to obtain a reaction rate algorithm after training is completed;

s25, determining the lubricating oil hydrogenation reaction product prediction model according to the reaction rule set after training and the reaction rate algorithm after training.

In this embodiment, the reaction rate constant expression of the reaction rate algorithm is the following expression:

K＝k _st ×k _absor ×Ф _cat

wherein K is a reaction rate constant, K _st Is the surface reaction rate constant, k _absor For the adsorption rate constant, phi _cat Is a catalyst active factor, wherein the catalyst active factor at least comprises a metal center active site for hydrogenation dehydrogenation reaction and an acid center active site for carbonium ion reaction according to different active sites.

In this example, each reaction rate constant in the above formula, also called a rate constant, is a quantitative expression of chemical reaction rate, and its physical meaning is that its value corresponds to the reaction rate when all the substances participating in the reaction are at a unit concentration (1 mol·l), so is also called a specific rate of the reaction. The different reactions have different reaction rate constants, which are related to reaction temperature, solid surface properties, reaction medium (solvent), catalyst, etc., and even vary with the shape and properties of the reactor, but are independent of concentration.

Wherein the surface reaction rate constant K _st According to the following calculation formula:

wherein K is _st Is the surface reaction rate constant, k _B Is Boltzmann constant, h is Planck constant, R is ideal gas constant, T is temperature value of environment where reaction path is located, exp is exponential function based on natural constant, and deltaS is entropy change before and after reaction corresponding to a reaction rule corresponding to a reaction path, delta E is a reaction energy barrier corresponding to the reaction rule corresponding to the reaction path, P is a pressure value of an environment where the reaction path is located, and alpha is a pressure influence factor corresponding to the reaction rule corresponding to the reaction path.

In the present embodiment, the catalyst activity factor Φ _cat The competitive adsorption capacity of the reactant molecules at different active sites of the catalyst is characterized. The active sites of the lubricating oil hydrogenation catalyst mainly comprise metal centers for hydrogenation and dehydrogenation reactions, desulfurated active sites of the catalyst and acid centers of the catalyst for carbonium ion reactions. Different active sites react differently, and molecules of the same type have the same catalyst active factor phi at the same active site _cat The data may be produced by software simulation or querying the device, as detailed in the prior art.

In some embodiments, in step S23, training the reaction rule set by using multiple sets of the sample raw material information and the reaction product molecular component kind information to obtain a reaction rule set after training, including:

if the first relative deviation does not meet the first preset condition, adjusting the reaction rule in the reaction rule set, and re-executing the steps of determining the first relative deviation and judging whether the first relative deviation meets the first preset condition according to the adjusted reaction rule set, namely processing the molecular composition of the sample raw material according to the adjusted reaction rule set to obtain the first molecular composition of the adjusted device product; and re-determining a first relative deviation according to the adjusted first molecular composition of the device product and the adjusted second molecular composition of the actual product until the first relative deviation meets a first preset condition.

In the embodiment of the invention, the first preset condition is a preset relative deviation range of the first molecular composition of the device product and the second molecular composition of the actual product. The two end values of the preset relative deviation range are empirical values or experimentally obtained values, and the specific determination process may refer to the detailed description in the prior art, and will not be described herein.

Further, determining a first relative deviation from a first molecular composition of the device product and a second molecular composition of the actual product, comprising:

determining whether the second set is a subset of the first set;

if the second set is a subset of the first set, calculating a first relative deviation by the following calculation formula:

wherein x is ₁ For the first relative deviation, M is the first set, M ₁ A molecular composition of the sample material, a kind composition of single molecules in the molecular compositionAggregation, M ₂ And N is the second set, and the card represents the number of elements in the set.

In some embodiments, in step S24, the training is performed on the reaction rate algorithm by using a plurality of sets of the sample raw material information and the reaction product molecular component type and content information, to obtain a reaction rate algorithm after training is completed, including:

if the second relative deviation does not meet the second preset condition, adjusting parameters in the reaction rate algorithm, and re-executing the steps of calculating the second relative deviation and judging whether the second relative deviation meets the second preset condition according to the adjusted reaction rate algorithm, namely calculating the reaction rate of a reaction path corresponding to each molecule in the molecular composition of the sample raw material according to the adjusted reaction rate algorithm to obtain the predicted content of each molecule in the adjusted predicted product; and re-calculating a second relative deviation according to the predicted content of each molecule in the adjusted predicted product and the actual content of each molecule in the actual product until the second relative deviation meets a second preset condition.

In the embodiment of the present invention, the second preset condition is a preset relative deviation range between the predicted content of each molecule in the predicted product and the actual content of each molecule in the actual product. The two end values of the preset relative deviation range are empirical values or experimentally obtained values, and the specific determination process may refer to the detailed description in the prior art, and will not be described herein.

In some embodiments, as shown in fig. 4, in step S30, the step of determining physical properties of the product using a pre-trained physical property calculation model according to a molecular composition of the product includes:

s31, calculating each single molecule physical property of each single molecule in the molecular composition based on a physical property calculation model trained in advance;

s32, calculating the physical properties of each product according to the physical properties and the content of each single molecule based on a preset mixing rule of the physical properties of each product.

In the embodiment of the invention, the preset mixing rule is a mixing rule set according to the physical properties of the predicted lubricating oil product, the preset mixing rule corresponds to the types and the amounts of the used lubricating oil blending raw materials, and the corresponding lubricating oil product is obtained by mixing different lubricating oil blending raw materials.

In some embodiments, calculating individual single molecule physical properties of each of the single molecules in the molecular composition based on a pre-trained physical property calculation model comprises:

inputting the number of groups of each group constituting the single molecule and the contribution value of each group to physical properties into a physical property calculation model trained in advance, and obtaining the physical properties of the single molecule output by the physical property calculation model.

Wherein the contribution value of each group of the single molecule to the physical property is the contribution value of each group to the physical property obtained by training when the physical property calculation model is obtained by training, and the contribution value is stored in a preset storage position and then obtained from the preset storage position.

In some embodiments, in order to quickly obtain the properties of each single molecule, before calculating the properties of each single molecule based on a pre-trained property calculation model, the method further includes:

comparing the number of groups constituting each group of the single molecule with the molecular information of template single molecules with known physical properties prestored in a database; the molecular information includes: the number of groups of each group constituting the template single molecule;

Judging whether the template single molecule which is the same as the single molecule exists or not;

outputting physical properties of the template single molecule as physical properties of the single molecule if the template single molecule identical to the single molecule exists;

and if the template single molecule which is the same as the single molecule does not exist, performing the steps of inputting the number of groups of each group which form the single molecule and the contribution value of each group to physical properties into a pre-trained physical property calculation model.

In some embodiments, the step of training the physical property calculation model comprises:

constructing a single-molecule physical property calculation training model, wherein the physical property calculation training model is as follows:

wherein f is the physical property of the sample single molecule, n _i The number of groups, Δf, being the i-th group _i A is a correlation constant, which is a contribution value of the i-th group to the physical property;

obtaining the number of groups of each group constituting a single molecule of the sample; the physical properties of the sample single molecule are known;

inputting the number of groups of each group contained in the sample single molecule into the physical property calculation model;

obtaining the predicted physical property of the sample single molecule output by the physical property calculation training model;

If the deviation value between the predicted physical property and the known physical property of the sample single molecule is smaller than a preset deviation threshold value, judging that the physical property calculation training model converges, taking the converged physical property calculation training model as a physical property calculation model, acquiring a contribution value corresponding to each group in the converged physical property calculation training model, and storing the contribution value as a contribution value of the group to the physical property;

and if the deviation value between the predicted physical property and the known physical property is greater than or equal to the deviation threshold value, adjusting the contribution value corresponding to each group in the physical property calculation training model, and re-executing the model training until the physical property calculation model converges.

In this embodiment, a training sample set is set in advance. A plurality of sample single molecule information is included in the training sample set. Sample single molecule information including, but not limited to: the number of groups of each group constituting a sample single molecule, and the physical properties of the sample single molecule.

In this embodiment, when the physical property calculation model is obtained, the contribution value of each group to each physical property is stored for each group, so that when the physical property of a single molecule is calculated later, the contribution value of each group in the single molecule to the physical property to be obtained can be obtained, the number of groups of each group of the single molecule and the contribution value of each group to the physical property to be obtained are used as the input of the physical property calculation model, the physical property calculation model uses the number of groups of each group of the single molecule as a model variable, and the contribution value of each group to the physical property to be obtained is used as a model parameter (the adjustable contribution value of each group to the physical property in the alternative physical property calculation training model).

In this embodiment, if there are a plurality of physical properties of the sample single molecules, the predicted physical properties of the sample single molecules outputted from the physical property calculation training model are also a plurality, and at this time, deviation values between each predicted physical property and the corresponding known physical property are calculated, whether the deviation values between all the predicted physical properties and the corresponding known physical properties are smaller than the preset deviation values is determined, if yes, the physical property calculation model is determined to converge, and the contribution value of each physical property corresponding to each group can be obtained from the converged physical property calculation model, and by the above-described scheme, the contribution value of each group to different physical properties can be obtained.

In some embodiments, the method further comprises: and updating the physical property calculation model. That is, after the physical property calculation model is obtained, the number of groups of each group of the new sample single molecule and the corresponding initial contribution value are newly input into the model, and the model training is performed, so that the physical property calculation model is updated.

In some embodiments, the obtaining the number of groups of each group comprising a single molecule of the sample comprises:

determining the number of each level of groups and corresponding groups in all groups of the single molecule of the sample; wherein:

All groups constituting a single molecule are taken as primary groups;

m groups which are simultaneously present and contribute to the same physical property are taken as M-class groups, and the number of the M groups is taken as the class of the M-class groups;

all groups of the sample single molecule comprise a primary group, a secondary group, a … … and an N-level group, wherein N is more than or equal to M, and M is a positive integer more than or equal to 2.

In one embodiment, based on the determined groups of each stage and the number of groups corresponding to each stage in all groups of the sample single molecule, a physical property calculation training model is established as follows:

wherein f is the physical property of the sample single molecule, m _1i The number of groups, Δf, being the i-th group in the primary groups _1i Is the contribution value of the ith group in the primary groups to physical properties, m _2j The number of groups, Δf, being the j-th group in the secondary groups _2j The contribution value of the j-th group in the secondary groups to physical properties; m is m _Nl The number of groups, Δf, being the first group in the N-stage groups _Nl The contribution value of the first group in the N-level groups to physical properties; a is a correlation constant; n is a positive integer greater than or equal to 2.

In the embodiment of the present invention, a plurality of groups that act together with one physical property may be used as the multi-stage groups, and specifically, for example, when N6 and N4 groups are present in different molecules separately, they have a certain influence on the physical property, and when they are present in one molecule, they have a certain fluctuation in the contribution value to the physical property on the basis of the original contribution value to the physical property. The multi-level groups can be divided by molecular bond force among the groups, the groups are divided into a plurality of different levels according to a preset bond force interval, and the levels of the groups can be specifically divided according to the influence of molecular stability on physical properties due to different molecular bond force and different influence on different physical properties.

For example: for boiling point, 24 groups are all primary groups in SOL-based molecular characterization methods; of the 24 groups, one or more of the groups such as N6, N5, N4, N3, me, AA, NN, RN, NO, RO and KO can contribute to boiling point, and the contribution values of the groups to the physical property are inconsistent for different physical properties, but the contribution values of the same group to the same physical property in different molecules are consistent.

The physical properties of the product can be calculated after the physical properties of each single molecule of the product are obtained based on the single molecule physical property calculation model.

For example, when the physical property of the product is calculated as density, based on a preset mixing rule of physical properties of each product, the following calculation formula is established, and the density of the product of the lubricating oil hydrogenation device can be calculated according to each single molecular physical property and content of each single molecule:

density＝∑(D _i ×x _i _volume)；

Wherein density is the density of the mixture, D _i For the density of the ith single molecule, xi_volume is the ith single moleculeThe content is as follows.

The density of each single molecule of each product can be obtained through the following pre-trained density calculation model, the group number of each group of the single molecule of the molecular composition data of the product and the contribution value of each group to physical properties can be input into the density calculation model, and the density of the single molecule can be obtained through calculation:

wherein D is the density of the single molecule, SOL is a single molecule vector converted from the number of GROUPs of each GROUP constituting the single molecule, GROUP ₂₁ GROUP is the n+1-th contribution vector obtained by converting the contribution of the primary GROUP to the density ₂₂ GROUP is an n+2-th contribution vector obtained by converting the contribution of the secondary GROUP to the density _2N The 2N contribution value vector is obtained by converting the contribution value of the N-level group to the density, and e is a fourth preset constant; and N is a positive integer greater than or equal to 2.

The above-mentioned single molecule vector converted from the number of groups of each group constituting a single molecule includes: taking the number of all groups constituting a single molecule as the dimension of a single molecule vector; the number of groups of each group is taken as the element value of the corresponding dimension in the single-molecule vector.

According to the n+1 contribution value vector obtained by converting the contribution value of each primary group of single molecule to the density, the method comprises the following steps: taking the number of primary groups as the dimension of the N+1 contribution value vector; and taking the contribution value of each primary group to the density as the element value of the corresponding dimension in the N+1th contribution value vector. An n+2-th contribution value vector obtained by converting the contribution value of each secondary group of single molecules to the density comprises: taking the number of the secondary groups as the dimension of the N+2 contribution value vector; and taking the contribution value of each secondary group to the density as the element value of the corresponding dimension in the N+2 contribution value vector. In this way, the 2N-th contribution value vector obtained by converting the contribution value of each N-level group of a single molecule to the density comprises: taking the number of N-level groups as the dimension of the 2N contribution value vector; and taking the contribution value of each N-level group to the density as the element value of the corresponding dimension in the 2N-th contribution value vector.

In some embodiments, when the physical property of the product is a pour point, calculating the physical property of the product comprises:

calculating the pour point contribution value of each single molecule according to the density and the molecular weight of each single molecule;

the pour point of the mixture was calculated from the pour point contribution and content of all single molecules in the mixture.

In the embodiment of the invention, the calculation can be performed based on a calculation formula in the prior art. In the embodiment of the present invention, the calculation process of the pour point contribution value may refer to a specific implementation manner in the prior art, and may adopt a calculation formula disclosed in the prior art to calculate the pour point of the mixture according to the pour point contribution values and the content of all single molecules in the mixture, and the specific implementation process may not be specifically limited herein.

In some embodiments, when the physical property of the product is an aniline point, calculating the physical property of the product comprises:

calculating to obtain the aniline point of the single molecule according to the density and boiling point of the single molecule;

the aniline point of the mixture was calculated from the aniline point and the content of all single molecules in the mixture.

In the embodiment of the invention, the calculation can be performed based on a calculation formula in the prior art. In the embodiment of the invention, the calculation process of the aniline point contribution value can refer to a specific implementation mode in the prior art, and a calculation formula disclosed in the prior art can be adopted to calculate the aniline point of the mixture according to the aniline point contribution values and the content of all single molecules in the mixture, and the specific implementation process is not particularly limited.

According to the lubricating oil production processing information determining method, the reaction mechanism model of the lubricating oil production process is established based on the molecular components, the physical properties of the product materials are predicted based on the molecular components, the conversion rule of the molecular components in the production process is accurately reflected, the adaptability of the reaction mechanism model of the lubricating oil production process to operation variation is remarkably improved, and the problem of operation optimization of a lubricating oil production device can be better solved through adjustment of distillation range data.

The lubricating oil production and processing information determining method of the embodiment fully excavates the synergy potential of the raw materials and the processing device by optimizing the distillation range data of the processing raw materials, improves the yield of target products and realizes the maximization of product benefits.

Based on the same inventive concept, referring to fig. 5, an embodiment of the present invention provides a lubricating oil production and processing information determining apparatus, the apparatus including:

a determining module 101, configured to determine a distillate cutting scheme and a corresponding distillation cutting model of a preset upstream processing device according to distillation range data of a processing raw material of the lubricating oil hydrogenation device, and determine molecular compositions of each group of side-line distillates to be entered into the lubricating oil hydrogenation device through the distillation cutting model; wherein the upstream processing device comprises an atmospheric and vacuum device;

A prediction module 102, configured to predict a molecular composition and a product yield of a product obtained under a preset reaction condition by using a pre-trained lube hydrogenation reaction product prediction model according to a processing amount of the processing raw material and a molecular composition of each group of side streams;

an analysis module 103 for analyzing physical properties of the product using a pre-trained physical property calculation model according to the molecular composition of the product;

the judging module 104 is configured to judge whether the physical properties of the product meet a preset physical property index requirement:

when the optimization objective does not reach the maximum value, the distillation range data of the processing raw material is adjusted and sent to the determination module 101.

In this embodiment, the determining module 104 is further configured to:

when the physical properties of the product do not meet the physical property index requirements, the distillation range data of the processing raw materials are adjusted and sent to the determination module 101.

In this embodiment, the prediction module 104 is specifically configured to:

In this embodiment, the determining module 101 is specifically configured to:

In this embodiment, the prediction module 102 is further configured to:

establishing a product prediction training model of the lubricating oil hydrogenation reaction; wherein, lubricating oil hydrogenation reaction product predicts training model, include: a set of reaction rules and a reaction rate algorithm; the reaction rule set comprises at least one reaction rule of alkane thermal cracking, alkane catalytic cracking, aromatic hydrocarbon hydrogenation saturation and naphthene ring opening;

training the reaction rate algorithm by utilizing a plurality of groups of sample raw material information and reaction product molecular component type and content information to obtain a reaction rate algorithm after training is completed;

In this embodiment, in the prediction module 102, the reaction rate constant of the reaction rate algorithm is the following expression:

K＝k _st ×k _absor ×Ф _cat

In this embodiment, the prediction module 102 obtains the surface reaction rate constant by the following reaction rate constant calculation formula:

In this embodiment, in the prediction module 102, sample raw material information of the sample raw material includes: the molecular composition of the sample material and the molecular composition of the actual product to which the sample material corresponds under specific reaction conditions.

In this embodiment, the prediction module 102 is specifically configured to:

determining whether the second set is a subset of the first set;

In this embodiment, in the prediction module 102, the first deviation algorithm expression is

Wherein x is ₁ For the first relative deviation, M is the first set, M ₁ M is a collection of species composition of single molecules in the molecular composition of the sample raw material ₂ And N is the second set, and the card represents the number of elements in the set.

In this embodiment, the prediction module 102 is specifically configured to:

In this embodiment, the analysis module 103 is specifically configured to:

In this embodiment, the determining module 104 is specifically configured to:

based on a pre-established optimization target model, calculating an optimization target according to the product yield and the weight of each lubricant product, wherein the optimization target model is as follows:

T＝∑(Y _i *W _i )

In the present embodiment, in the judgment module 104, the product yield is calculated by the following expression:

Y＝∑(C _j *P)

In this embodiment, in the determining module 104, the optimization target is at least one of an accumulated benefit and a target physical property parameter;

when the optimization target is accumulated income, the weight is the price of the product;

when the optimization target is a target physical parameter, the weight is an importance score of the physical parameter of the product.

In this embodiment, the distillation range data includes at least one of an initial distillation point and an end distillation point;

For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

Based on the same inventive concept, the embodiment of the invention provides an application of the lubricating oil production and processing information determining method in a lubricating oil production method.

Based on the same inventive concept, the embodiment of the invention provides a lubricating oil production method, and the production and processing information determined by the lubricating oil production and processing information determining method is used.

Based on the same inventive concept, as shown in fig. 6, an embodiment of the present invention provides a lubrication oil production processing information determining apparatus, which includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140;

a memory 1130 for storing a computer program;

processor 1110, when executing the program stored in memory 1130, implements the following lubrication oil production processing information determination method:

determining a distillate cutting scheme and a corresponding distillation cutting model according to distillation range data of a raw material processed by the lubricating oil hydrogenation device, and determining molecular compositions of each group of side-line distillates which are to enter the lubricating oil hydrogenation device through the distillation cutting model;

determining a distillate cutting scheme and a corresponding distillation cutting model of a preset upstream processing device according to distillation range data of a processing raw material of the lubricating oil hydrogenation device, and determining molecular compositions of each group of side-stream distillates to enter the lubricating oil hydrogenation device through the distillation cutting model;

Analyzing physical properties of the product by utilizing a pre-trained physical property calculation model according to the molecular composition of the product;

and when the optimization target does not reach the maximum value, adjusting the distillation range data of the processing raw material, and re-executing the steps according to the adjusted distillation range data.

The communication bus 1140 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

The communication interface 1120 is used for communication between the electronic device and other devices described above.

The memory 1130 may include random access memory (Random Access Memory, simply RAM) or may include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. Optionally, the memory 1130 may also be at least one storage device located remotely from the processor 1110.

The processor 1110 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps of the lubricating oil production and processing information determining method in any of the possible implementations described above.

Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

Based on the same inventive concept, embodiments of the present invention also provide a computer program product comprising a computer program which when executed by a processor realizes the steps of the lubricating oil production processing information determining method in any of the possible implementations described above.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.) means from one website, computer, server, or data center. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining lubricant production and processing information, the method comprising:

2. The method according to claim 1, wherein the method further comprises:

and when the physical properties of the product do not meet the physical property index requirements, adjusting the distillation range data of the processing raw materials, and re-executing the steps according to the adjusted distillation range data.

3. The method according to claim 1, wherein the step of predicting the molecular composition and the product yield of the product obtained under the preset reaction conditions using a pre-trained lube oil hydrogenation reaction product prediction model according to the processing amount and the molecular composition of the processing raw material comprises:

4. The method of claim 1, wherein the step of determining a distillate cut schedule and corresponding distillation cut model from the distillation range data of the lube oil hydrotreater process feedstock, by which the molecular composition of each set of side cuts to be entered into the lube oil hydrotreater is determined, comprises:

5. The method of claim 4, wherein determining the distillate cut schedule and corresponding distillate cut model for the upstream process unit based on the distillation range data for the lube oil hydroprocessing unit process feedstock comprises:

6. The method of claim 1, wherein the lube oil hydrogenation reaction product predictive model is trained by:

7. The method of claim 6, wherein the reaction rate constant of the reaction rate algorithm is the following expression:

K＝k _st ×k _absor ×Ф _cat

wherein K is a reaction rate constant, K _st Is the surface reaction rate constant, k _absor For the adsorption rate constant, phi _cat Is a catalyst active factor, wherein the catalyst active factor comprises a metal center active site for hydrogenation dehydrogenation reaction and an acid center for carbocation reactionAn active site.

8. The method according to claim 7, comprising:

the surface reaction rate constant is obtained by the following reaction rate constant calculation formula:

9. The method of claim 6, wherein the sample feedstock information for the sample feedstock comprises: the molecular composition of the sample material and the molecular composition of the actual product to which the sample material corresponds under specific reaction conditions.

10. The method of claim 6, wherein training the reaction rule set using the plurality of sets of sample raw material information and reaction product molecular component type information to obtain a trained reaction rule set comprises:

11. The method of claim 10, wherein determining the first relative deviation from the first molecular composition of the device product and the second molecular composition of the actual product comprises:

determining whether the second set is a subset of the first set;

12. The method of claim 11, wherein the first bias algorithm expression is

Wherein x is ₁ Is saidA first relative deviation, M is the first set, M ₁ M is a collection of species composition of single molecules in the molecular composition of the sample raw material ₂ And N is the second set, and the card represents the number of elements in the set.

13. The method of claim 6, wherein training the reaction rate algorithm using a plurality of sets of the sample raw material information and the reaction product molecular component type and content information to obtain a trained reaction rate algorithm comprises:

14. The method of claim 1, wherein the step of determining physical properties of the product using a pre-trained physical property calculation model based on the molecular composition of the product comprises:

15. The method according to claim 1, wherein the step of calculating an optimization objective from the product yield of the product comprises:

T＝∑(Y _i *W _i )

16. The method of claim 15, wherein the product yield is calculated by the expression:

Y＝∑(C _j *P)

17. The method of claim 15, wherein the optimization objective is at least one of cumulative benefit and objective physical property parameters;

18. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the distillation range data comprises at least one of an initial distillation point and a final distillation point;

19. A lubricating oil production process information determining apparatus, characterized by comprising:

20. Use of the lubricating oil production processing information determining method according to any one of claims 1 to 18 in a lubricating oil production method.

21. A method for producing a lubricating oil, characterized by using the production process information determined by the lubricating oil production process information determination method according to any one of claims 1 to 18.

22. The lubricating oil production and processing information determining device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;

a memory for storing a computer program;

a processor for implementing the steps of the lubricating oil production and processing information determining method according to any one of claims 1 to 18 when executing a program stored on a memory.

23. A computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps of the lubricating oil production process information determination method of any one of claims 1 to 18.