Method for establishing a refinery optimization model

Abstract

The application discloses a method for establishing a refining optimization model, comprising the following steps: establishing a crude oil selection model, a whole-process reverse calculation model, a whole-process forward calculation model, a crude oil distillation and secondary processing device model, a product blending module, a raw material and product module, and a utility module through an EXCEL table and a planning solution tool in the EXCEL table; inputting raw material and product prices in the raw material and product module in the whole-process reverse calculation model, determining the processing crude oil amount or upper and lower limits and a product configuration plan, inputting the prices of utility media in the utility module according to winter and summer conditions, and calculating the optimal crude oil distillation device product yield; inputting the current crude oil price in the crude oil selection model, calculating the optimal crude oil variety and quantity according to the optimal crude oil distillation device product yield; and obtaining the optimal income and the best production scheme by using the whole-process forward calculation model according to the optimal crude oil variety and quantity. The method realizes crude oil selection and production optimization of a refinery, responds to the continuous changes of product market demand and prices, adjusts the production scheme and product structure in time, and thus the maximum economic benefit is obtained.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of refinery production construction optimization, and more particularly, to a method for establishing a refinery optimization model. BACKGROUND

[0002] With the continuous improvement of China's oil refining capacity, the competition in the product oil market is becoming increasingly fierce. How to take the market as the guide, take the benefit as the center, timely capture the market opportunity, how to determine the optimal crude oil processing quantity and variety, how to optimize the device operation and adjust the product structure, reduce the production cost of the oil refining enterprise, and improve the competitiveness of the enterprise have become an important issue faced by the production and operation of the oil refining industry.

[0003] Refinery optimization is to produce the most economical products with the least economic input, to maximize economic benefits, to determine the best variables of raw material selection and production process by using certain technical means, and to create maximum benefits for enterprises.

[0004] At present, the refinery optimization model is mainly divided into three types, namely, the plan optimization model, the device simulation model and the whole process simulation optimization model. The plan optimization model is a linear programming LP model, and the common technology is to use delta-base technology to describe the influence of feed properties and operating conditions on device product yield. Since the technology itself is linear, such model coefficients are only effective within a certain range of feed properties and operating conditions, and when the model is applied to different enterprises with large changes in raw material properties and operating conditions, the accuracy of the model is greatly reduced. The device simulation model is a local optimization of a single device, which belongs to a mechanism model, the model is complex, the calculation is relatively accurate, but a set of device optimal solution is not necessarily the optimal solution in the whole plant range. The whole process simulation optimization model is not mature enough, and it is relatively complex to run, prone to errors, and cannot cover the entire range of the refinery, so it has not been widely used.

[0005] In the actual application process, the above three types of models also have the following problems: first, a large amount of manpower and time is needed to model and calibrate before the model is put into use, and a large amount of device operation and quality data are needed in this process, and the accuracy of the data has a great influence on the accuracy of the model calculation; second, professional software needs to be installed, and the use method is relatively complex, and it is difficult to find the reason when problems occur during the running process; third, professional personnel are needed to operate the model, and general technical personnel cannot do it, so the popularization degree in the oil refining enterprise is not high. SUMMARY

[0006] The purpose of the present application is to provide a method for establishing a refinery optimization model, to realize the selection and production optimization of crude oil in the refinery, to cope with the changing market demand and price of products, to timely adjust the production scheme and product structure, and to obtain the maximum economic benefit.

[0007] To achieve the above object, the present application proposes a method for establishing a refining optimization model, which comprises:

[0008] Step S1: Establishing a crude oil selection model by EXCEL and a programming solver tool therein, which is used for selecting a crude oil variety and quantity with low price and suitable for processing in the refinery according to product yield of a crude oil distillation unit;

[0009] Step S2: Establishing a unit model of the crude oil distillation unit and secondary processing unit by EXCEL and product yield, which is used for simulating unit processing, defining unit processing load, unit raw material and product and calculating raw material and product quantity;

[0010] Step S3: Establishing a product blending module by EXCEL and a blending index calculation method, which is used for simulating gasoline and diesel blending;

[0011] Step S4: Establishing a raw material and product module by EXCEL, which is used for simulating raw material entering the refinery and product leaving the refinery;

[0012] Step S5: Establishing a utility module by EXCEL, which is used for simulating unit variable cost;

[0013] Step S6: Linking material flow directions of the modules by EXCEL calculation formula, performing transmission of raw material, upstream unit product, downstream unit raw material, blending component and product quantity, and establishing a full-process reverse calculation model by a programming solver tool in EXCEL, which is used for simulating a full-process of crude oil processing and optimal product yield calculation of the crude oil distillation unit;

[0014] Step S7: Linking material flow directions of the modules by EXCEL calculation formula, performing transmission of raw material, upstream unit product, downstream unit raw material, blending component and product quantity, and establishing a full-process forward calculation model by a programming solver tool in EXCEL, which is used for simulating a full-process of crude oil processing and optimal production scheme calculation;

[0015] Step S8: Inputting current crude oil price and determining crude oil processing quantity in the crude oil selection model, and inputting current raw material and product price and product configuration plan in the raw material and product module;

[0016] Step S9: Inputting utility medium price in the utility module;

[0017] Step S10: Calculating optimal product yield of the crude oil distillation unit by the full-process reverse calculation model, which provides a basis for crude oil selection;

[0018] Step S11: Based on the optimal product yield of the atmospheric and vacuum distillation unit, calculate the optimal crude oil type and quantity using the crude oil selection model;

[0019] Step S12: Based on the optimal crude oil type and quantity, obtain the optimal processing scheme through the full-process forward calculation model.

[0020] Optionally, step S1 includes:

[0021] Step S13: Enter the crude oil evaluation data into an EXCEL spreadsheet. The crude oil evaluation data includes: crude oil name, price, quantity, yield, and quality indicators.

[0022] Step S14: Establish the calculation relationship:

[0023] R i =Σ(x i r i )

[0024] Where: R i For the corresponding indicator of crude oil; x i The percentage of a single oil type by mass in the total crude oil volume, %; r i For a single oil type, i = 1, 2, 3…n;

[0025] Step S15: Establish the objective equation with the goal of minimizing crude oil cost:

[0026] P min =Σ(x i p i )

[0027] Where: P min Minimum crude oil cost, yuan / ton; x i The percentage of a single oil type by mass in the total crude oil volume, %; p i Price is for a single oil type, in yuan / ton;

[0028] Step S16: Set constraints:

[0029]

[0030] Where: y i The mass yield of each fraction of the blended crude oil is %; Y i The maximum or minimum mass yield of each fraction of the blended crude oil, %; X i Quantity for a single oil type, in tons; C i For the maximum or minimum quantity of a single oil type, in tons; R i For crude oil corresponding indicators; B i This represents the maximum or minimum value of the crude oil index. Where i = 1, 2, 3…n.

[0031] Step S17: With the goal of minimizing crude oil cost, use nonlinear programming in EXCEL to calculate the optimal crude oil type and quantity.

[0032] Optionally, step S17 specifically includes:

[0033] With the objective of minimizing crude oil cost and the quantity of a single crude oil type as the variable, and with crude oil processing volume, the mass yield of each fraction of the blended crude oil, and the quality indicators of the blended crude oil as constraints, the optimal crude oil type and quantity are calculated using nonlinear programming in EXCEL.

[0034] Optionally, step S2 includes:

[0035] Step S18: Enter the relevant information such as the name of each device, raw material and product name, and quantity into the EXCEL spreadsheet;

[0036] Step S19: Calculate the product quantity of each unit using the following formula:

[0037] d i =Σ(D i z i )

[0038] Where: d i D represents the quantity of products produced by the unit, in tons. i The quantity of raw materials for the device is in tons; z i Let be the product yield of the device, %; where i = 1, 2, 3…n.

[0039] Optionally, step S3 includes:

[0040] Step S20: Enter the names and quantities of the blending components for gasoline and diesel, the product names and quantities, and the control parameters into the EXCEL spreadsheet;

[0041] Step S21: Among the quality indicators of gasoline and diesel blending, those indicators showing a linear relationship are used to calculate the corresponding indicators of the blended oil through weighted calculation, as shown in the following formula:

[0042] R i =Σ(x i r i )

[0043] Where: R i For the corresponding indicators of blended oil; x i The percentage of the blended components by mass, %; r i The index is used to determine the composition of the blended components; where i = 1, 2, 3…n.

[0044] Step S22: the index in non-linear change relation, on the basis of weighted calculation result, the relation between weighted calculation result and blending oil index is established by using linear regression in MINITAB software, the corresponding index of blending oil is calculated, and the formula is as follows:

[0045] R i = a x Σ (x i r i ) + b

[0046] Wherein: R i is the corresponding index of blending oil; x i is the mass percentage of blending component, %; r i is the index of blending component; a is the coefficient of formula; b is the constant term of formula; wherein i = 1, 2, 3… n;

[0047] Optionally, the raw material and product modules include the names and prices of raw materials and products, and the raw material and product quantities transferred or calculated from upstream devices.

[0048] Optionally, the step S6 comprises:

[0049] Step S23: the quantity of blending component in the blending module of raw material, upstream device product and downstream device raw material, product, and the quantity of product in the raw material and product module are transferred in the form of equivalence or proportion;

[0050] Step S24: the target equation is established with the maximum benefit of whole process as the target:

[0051] P max = Σ (p i d i ) - Σ (P i D i )

[0052] Wherein: P max is the maximum benefit, yuan; p i is the product price, yuan / ton; d i is the product quantity, ton; P i is the raw material price, yuan / ton; D i is the raw material quantity, ton;

[0053] Step S25: the constraint condition is set:

[0054]

[0055] Wherein: z i is the product yield of atmospheric and vacuum distillation device, %; Z i is the maximum or minimum value of product yield of atmospheric and vacuum distillation device, %; D i is the raw material quantity, ton; c iMaximum or minimum quantity of raw material, ton; d i Quantity of product, ton; C i Maximum or minimum quantity of product quantity, ton; R i Quality index; B i Maximum or minimum value of quality index. Y i Device processing load; E i Maximum or minimum value of device processing load; wherein i = 1, 2, 3…n.

[0056] Step S26: The optimal atmospheric-vacuum distillation unit product yield is calculated by using the nonlinear programming solution in EXCEL in the step S6 full-process reverse calculation model;

[0057] Optionally, the step S26 specifically comprises:

[0058] The maximum full-process benefit is set as the target, the raw material quantity, the atmospheric-vacuum distillation unit product yield, the catalytic device feedstock composition, the quantity of each blending component, the quantity of reforming gasoline, the quantity of S Zorb refined gasoline, the quantity of xylene and the aromatic content are used as variables, the crude oil processing quantity, the device processing load, the product configuration plan and the quality index are used as constraint conditions, and the nonlinear programming solution in EXCEL is used to calculate the optimal atmospheric-vacuum distillation unit product yield.

[0059] Optionally, the step S7 comprises:

[0060] Step S27: The quantity of raw material, upstream device product and downstream device raw material, blending component in the blending module, product in the product module is transmitted in an equivalent or proportional manner;

[0061] Step S28: The target equation is established with the maximum full-process benefit as the target:

[0062] P max =Σ(p i d i )-Σ(P i D i )

[0063] Wherein: P max is the maximum benefit, yuan; p i is the product price, yuan / ton; d i is the product quantity, ton; P i is the raw material price, yuan / ton; D i is the raw material quantity, ton;

[0064] Step S29: The constraint condition is set:

[0065]

[0066] Wherein: Di is the quantity of raw materials, tons; c i is the maximum or minimum quantity of raw materials, tons; d i is the quantity of products, tons; c i is the maximum or minimum quantity of products, tons; R i is a quality index; B i is the maximum or minimum value of the quality index. Y i is the processing load of the device; E i is the maximum or minimum value of the processing load of the device; wherein i = 1, 2, 3…n.

[0067] Step S30: The full-process forward calculation model uses the non-linear programming in EXCEL to obtain the optimal production scheme.

[0068] Optionally, the step S30 specifically comprises:

[0069] Setting the maximum benefit of the full process as the target, the crude oil processing quantity; the feed composition of the catalytic device; the quantity of each blending component; the quantity of reforming gasoline, S Zorb refined gasoline, dimethylbenzene and the aromatic content as variables, using the raw material quantity; the processing load of the device; the product configuration plan; the quality index as the constraint condition, the non-linear programming in EXCEL is used to obtain the optimal production scheme.

[0070] The beneficial effects of the present application are:

[0071] The optimization model of the present application is based on the crude oil evaluation data, the product yield of the device, the main material properties, the production process is the framework, the linear relationship of the main material properties is established by using the linear regression in the MINITAB software, the crude oil selection model, the full-process reverse calculation model and the full-process forward calculation model are established, the raw material quantity, the product yield of the device, the quantity of the blending component and the main material properties are used as the design variables, the crude oil processing quantity, the processing load of the device, the product configuration plan and the quality index are used as the constraint conditions, the benefit of the output minus the input is calculated, the maximum benefit of the full process is taken as the target, the non-linear programming technology in EXCEL is applied to obtain the optimal solution. The optimization model of the present application can be widely applied to the crude oil selection, the crude oil blending, the optimization of the processing scheme, the production and blending optimization of gasoline and diesel oil, the full-process benefit calculation and the like. In addition, the optimization model of the present application has the characteristics of simple learning, convenient operation and fast calculation, which is beneficial to improve the work efficiency.

[0072] The model of the present application has other characteristics and advantages, which will be obvious or will be described in detail in the drawings and subsequent specific embodiments incorporated herein, which are collectively used to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0073] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout the figures, and wherein:

[0074] Figure 1 A flow chart of a method for establishing a refinery optimization model according to the present application is shown. DETAILED DESCRIPTION

[0075] To overcome the problems in the prior art, the present application provides a simple, general technical personnel can operate, in the case of crude oil properties and operating conditions change little, relatively accurate refinery optimization model. The model is based on crude oil evaluation data, device product yield, the main material properties for the framework, using MINITAB software in the linear regression to establish the linear relationship between the main properties, the application of EXCEL in nonlinear programming techniques, with the maximum benefit of the whole process as the objective function, to establish a refinery optimization model, to provide guidance for the production optimization of the refinery.

[0076] The present application will be described in more detail by referring to the attached drawings. Although the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application is more thoroughly and completely conveyed to those skilled in the art, and the scope of the present application is fully conveyed to those skilled in the art.

[0077] Example 1

[0078] Figure 1 A flow chart of a method for establishing a refinery optimization model according to the present application is shown.

[0079] As shown in Figure 1 A method for establishing a refinery optimization model, the method comprising:

[0080] Step S1: establishing a crude oil selection model by EXCEL and the planning solution tool therein, the crude oil selection model being used to select crude oil varieties and quantities with low prices and suitable for processing in the refinery according to the product yield of the crude oil distillation unit;

[0081] Specifically, the crude oil selection model is used to select crude oil varieties and quantities with low prices and suitable for processing in the refinery according to the product yield of the crude oil distillation unit.

[0082] The definition of the crude oil selection model is to collect crude oil evaluation data, single oil price and other information, and to calculate the crude oil varieties and quantities by using the nonlinear programming technology in EXCEL according to the product yield of the crude oil distillation unit.

[0083] The steps of establishing the crude oil selection model specifically include:

[0084] 1) Establishing a basic table

[0085] In the EXCEL table, input the evaluation data of each crude oil, the name, quantity and price of the crude oil, the mass yield of each fraction of the mixed crude oil, and the information of the cost of the crude oil.

[0086] 2) Establishing a calculation formula

[0087] 2.1) Establishing a calculation relationship

[0088] Establish the calculation formula of the quantity, cost, sulfur content and acid value of the crude oil, the yield of naphtha, straight-run gasoline, straight-run diesel, straight-run gas oil, vacuum wax oil and vacuum residue, the aromatic potential content of naphtha, the cetane index of straight-run gasoline, straight-run diesel and straight-run gas oil, the nickel and vanadium content of vacuum wax oil, and the nickel and vanadium content of vacuum residue. Among the above indexes, there is a linear change relationship, and the result is directly obtained by weighted calculation. The formula is as follows:

[0089] R i =Σ(x i r i )

[0090] Wherein: R i is the corresponding index of the crude oil; x i is the mass percentage of a single oil in the total quantity of the crude oil, %; r i is the index of the single oil; wherein i=1, 2, 3…n.

[0091] 2.2) Establishing a target equation

[0092] Taking the minimum cost of the crude oil as the target, the target equation is established as follows:

[0093] P min =Σ(x i p i )

[0094] Wherein: P min is the minimum cost of the crude oil, yuan / ton; x i is the mass percentage of a single oil in the total quantity of the crude oil, %; p i is the price of the single oil, yuan / ton.

[0095] 2.3) Setting a constraint condition

[0096] According to the actual situation, the quantity of the crude oil and the single oil, the mass yield of each fraction of the mixed crude oil, the set-off value of the sulfur content and the acid value of the device, the nickel and vanadium content of the vacuum wax oil, and the nickel and vanadium content of the vacuum residue are taken as the constraint conditions. The specific conditions are as follows:

[0097]

[0098] wherein: y i is the mass yield of each fraction of the mixed crude oil, %; Y i is the maximum or minimum value of the mass yield of each fraction of the mixed crude oil, %; X i is the number of single oils, tons; C i is the maximum or minimum amount of single oils, tons; R i is the corresponding index of the crude oil; B i is the maximum or minimum value of the index of the crude oil. Wherein i = 1, 2, 3…n.

[0099] 3) Extreme value calculation

[0100] The non-linear programming in EXCEL is used for solving, the minimum crude oil cost is set as the target, the number of single oils is set as the variable, and the crude oil and the number of single oils, the mass yield of each fraction of the mixed crude oil, the mass of the mixed crude oil are set as the constraint conditions to perform solving.

[0101] Step S2: a device model of the atmospheric and vacuum distillation and secondary processing device is established through EXCEL and product yield of the device, the device model is used for simulating device processing, and device processing load, device raw material and product are defined and the number of raw material and product is calculated;

[0102] Specifically, the device model is used for simulating device processing, defining device processing load, device raw material and product and calculating the number of raw material and product; the content includes: the name of each device, processing load, the variety and number of raw material, the variety and number of product, and simulating device processing.

[0103] The specific method for establishing the device model is as follows:

[0104] 1) Establishing a basic table

[0105] The name of each device, the name and number of raw material and product and other related information are input in the EXCEL table;

[0106] 2) Calculation of the number of device products

[0107] The number of each product is calculated according to the weighted calculation of the product yield of the device. The formula is as follows:

[0108] d i =Σ(D i z i )

[0109] Wherein: d i is the number of device products, tons; D i is the number of device raw materials, tons; z i is the product yield of the device, %; wherein i = 1, 2, 3…n.

[0110] Step S3: Establishing product blending module by EXCEL and blending index calculation method, which is used for simulating gasoline and diesel blending;

[0111] Specifically, the product blending module is used for simulating gasoline and diesel blending. Its content includes defining gasoline and diesel blending component name, quantity and quality index; product name, quantity and control index. Linear regression in MINITAB software is used to establish S Zorb gasoline aromatic content and octane value; reforming gasoline aromatic content and octane value; diesel cetane value and index calculation formula.

[0112] The specific method for establishing product blending module is as follows:

[0113] 1) Establishing basic table

[0114] In the EXCEL table, input the blending component name, quantity and quality index of gasoline and diesel; product name, quantity and control index.

[0115] 2) Establishing calculation relationship

[0116] In the gasoline and diesel blending quality index, the index with linear change relationship is directly weighted to obtain the result. The formula is as follows:

[0117] R i =Σ(r i x i )

[0118] Wherein: R i is the corresponding index of blended oil; x i is the mass percentage of blending component, %; r i is the quality index of blending component; wherein i=1, 2, 3…n.

[0119] The index with nonlinear change relationship is calculated on the basis of weighted calculation result, and the linear regression in MINITAB software is used to establish the relationship between weighted calculation result and blended oil index, so as to calculate the corresponding index of blended oil. The formula is as follows:

[0120] R i =a×Σ(x i r i )+b

[0121] Wherein: R i is the corresponding index of blended oil; x i is the mass percentage of blending component, %; r i is the index of blending component; a is the coefficient of formula; b is the constant term of formula; wherein i=1, 2, 3…n;

[0122] The linear calculation formula is established by linear regression in MINITAB software. For example:

[0123] (1) The relationship between the octane number of S Zorb gasoline and the aromatic content:

[0124] Y = 0.18 X + 85

[0125] wherein X is the aromatic content of S Zorb gasoline, %; Y is the octane number of S Zorb gasoline.

[0126] (2) The relationship between the octane number of reforming gasoline and the aromatic content:

[0127] Y = 0.5 X + 63

[0128] wherein X is the aromatic content of reforming gasoline, %; Y is the octane number of reforming gasoline.

[0129] (3) The calculation formula of the cetane number and the cetane index of diesel oil:

[0130] Y = 1.016 X - 2.465

[0131] wherein X is the cetane index; Y is the cetane number.

[0132] Step S4: Establishing a raw material and product module by EXCEL, which is used for simulating the raw material entering the plant and the product leaving the plant;

[0133] Specifically, the raw material and product module is used for simulating the raw material entering the plant and the product leaving the plant, and the benefit is calculated by using the price and quantity of the raw material and product. The content includes: defining the name and price of the raw material and product; and transferring or calculating the quantity of the raw material and product from the upstream device.

[0134] Step S5: Establishing a utility module by EXCEL, which is used for simulating the variable cost of the device;

[0135] Specifically, the utility module is used for simulating the processing cost of the device. The content of the module includes: defining the electric power, water, steam, nitrogen, air and other utility media consumed by each device, and calculating the processing cost of each device.

[0136] Step S6: Linking the material flow direction of each module by EXCEL calculation formula, transferring the quantity of the raw material, the product of the upstream device, the raw material of the downstream device, the blending component and the product, and establishing a whole-process reverse calculation model by the planning solution tool in EXCEL, which is used for simulating the whole process of crude oil processing and the optimal CDU product yield calculation;

[0137] Specifically, the full-process reverse calculation model is used to calculate the optimal product yield of the atmospheric and vacuum distillation unit with the goal of maximizing the overall plant process efficiency.

[0138] The definition of the full-process reverse calculation model is: to transfer the quantities of raw materials, upstream unit products, downstream unit raw materials, blending components and products, with the goal of maximizing the overall plant process efficiency, and to calculate the optimal atmospheric and vacuum distillation unit product yield using nonlinear programming technology in EXCEL.

[0139] The specific steps for establishing a full-process reverse engineering model include:

[0140] 1) Quantity transfer

[0141] The quantities of raw materials, upstream unit products, and downstream unit raw materials, blending components in the blending module, and products in the raw materials and product modules are transferred using an equal or proportional method.

[0142] 2) Establish calculation formula

[0143] 2.1) Establish the objective equation

[0144] With the goal of maximizing overall process efficiency, an objective equation is established:

[0145] P max =Σ(p i d i )-Σ(P i D i )

[0146] Where: P max For maximum benefit, yuan; p i Price of the product, in yuan / ton; d i For product quantity, in tons; P i The price is the raw material price, in yuan / ton; D i The quantity of raw materials is in tons.

[0147] 2.2) Set constraints:

[0148] Based on actual conditions, constraints include crude oil processing volume, quantity of purchased raw materials, unit processing load, planned production of gasoline, diesel, naphtha, etc., and product quality indicators. Details are as follows:

[0149]

[0150] Where: z i The product yield of the atmospheric and vacuum distillation unit is %; Z i The maximum or minimum product yield of the atmospheric and vacuum distillation unit, %; D i c represents the quantity of raw materials, in tons. i The maximum or minimum quantity of raw materials, in tons; d iis the product quantity, tons; C i is the maximum or minimum quantity of the product quantity, tons; R i is the quality index; B i is the maximum or minimum value of the quality index. Y i is the device processing load; E i is the maximum or minimum value of the device processing load; wherein i = 1, 2, 3…n.

[0151] 3) Extreme value calculation

[0152] The nonlinear programming in EXCEL is used for solving, the maximum benefit of the whole process is set as the target, the raw material quantity; the product yield of the atmospheric and vacuum distillation unit; the feed composition of the catalytic unit; the quantity of each blending component; the quantity of reforming gasoline, S Zorb refined gasoline, dimethylbenzene and the aromatic content are used as variables, the crude oil processing quantity; the device processing load; the product configuration plan; the quality index are used as constraint conditions, and solving is conducted.

[0153] Step S7: The material flow direction of each module is linked through the EXCEL calculation formula, the transfer of the raw material, the product of the upstream device, the raw material of the downstream device, the blending component and the product quantity is conducted, and the whole process forward calculation model is established through the planning solving tool in EXCEL, and the whole process forward calculation model is used for simulating the whole process of crude oil processing and the optimal production scheme calculation.

[0154] Specifically, the whole process forward calculation model is used for calculating the optimal production scheme with the maximum benefit of the whole plant process as the target.

[0155] The definition of the whole process forward calculation model is that the transfer of the raw material, the product of the upstream device, the raw material of the downstream device, the blending component and the product quantity is conducted, the maximum benefit of the whole plant process is used as the target, and the nonlinear programming technology in EXCEL is used for calculating the optimal production scheme.

[0156] The steps of establishing the whole process forward calculation model specifically include:

[0157] 1) Quantity transfer

[0158] The quantity of the raw material, the product of the upstream device and the raw material of the downstream device, the blending component in the product blending module, the raw material and the product in the product module is transferred in an equivalent or proportional manner.

[0159] 2) Establishing calculation formula

[0160] 2.1) Establishing target equation

[0161] The target equation is established with the maximum benefit of the whole process as the target:

[0162] P max =Σ(p i di ) -∑(P i D i )

[0163] Where: P max is the maximum benefit, yuan; p i is the product price, yuan / ton; d i is the product quantity, ton; P i is the raw material price, yuan / ton; D i is the raw material quantity, ton;

[0164] 2.2) Set constraints:

[0165] According to the actual situation, the crude oil processing amount, the purchased raw material quantity, the device processing load, the gasoline, diesel oil, naphtha product configuration plan, and the product quality index are taken as the constraints. The specific constraints are as follows:

[0166]

[0167] Where: D i is the raw material quantity, ton; c i is the maximum or minimum quantity of the raw material, ton; d i is the product quantity, ton; C i is the maximum or minimum quantity of the product, ton; R i is the quality index; B i is the maximum or minimum value of the quality index. Y i is the device processing load, %; E i is the maximum or minimum value of the device processing load, %; wherein i=1, 2, 3…n.

[0168] 3) Extreme value calculation

[0169] The nonlinear programming in EXCEL is used for solving, the maximum benefit of the whole process is taken as the target, the crude oil processing amount, the catalytic device feed composition, the quantity of each blending component, the quantity of the reforming gasoline, the quantity of the S Zorb refined gasoline, the quantity of dimethylbenzene, and the aromatic content are taken as the variables, the raw material quantity, the device processing load, the product configuration plan, and the quality index are taken as the constraints, and the solving is performed.

[0170] Step S8: input the current crude oil price and determine the crude oil processing amount in the crude oil selection model, and input the current raw material, product price, and product configuration plan in the raw material and product module;

[0171] Step S9: input the utility medium price in the utility module;

[0172] Step S10: measure the optimal atmospheric and vacuum distillation device product yield through the whole process reverse calculation model, and provide the basis for the crude oil selection;

[0173] Step S11: According to the optimal product yield of the atmospheric and vacuum distillation unit, the optimal crude oil variety and quantity are calculated by the crude oil selection model;

[0174] Step S12: According to the optimal crude oil variety and quantity, the optimal processing scheme is obtained by the full-process forward calculation model.

[0175] In this embodiment, the use method of the optimization model is as follows:

[0176] First step: According to the production, crude oil procurement, product configuration plan and price, etc., the optimal product yield of the atmospheric and vacuum distillation unit is calculated by the full-process reverse calculation model.

[0177] Second step: According to the product yield of the atmospheric and vacuum distillation unit calculated in the first step, the optimal crude oil variety and quantity are selected by the crude oil selection model.

[0178] Third step: According to the crude oil variety, quantity and other conditions calculated in the second step, the full-process benefit, processing scheme, product quantity, etc. are calculated by the full-process forward calculation model.

[0179] The use method of the full-process reverse calculation model is as follows:

[0180] (1) The prices of raw materials and products in the current period are input in the raw material and product module.

[0181] (2) The processing crude oil quantity or upper and lower limits and product configuration plan are determined.

[0182] (3) According to the winter and summer seasons, the prices of public media are input in the public engineering module.

[0183] (4) The constraint conditions are confirmed.

[0184] (5) The “Calculate” button in the full-process reverse calculation model is clicked to calculate the optimal product yield of the atmospheric and vacuum distillation unit.

[0185] The use method of the crude oil selection model is as follows:

[0186] (1) The crude oil price in the current period is input in the crude oil selection model.

[0187] (2) The constraint conditions are confirmed.

[0188] (3) The “Calculate” button in the crude oil selection model is clicked to calculate the optimal crude oil variety and quantity.

[0189] The use method of the full-process forward calculation model is as follows:

[0190] (1) The crude oil price in the current period is input in the crude oil selection model.

[0191] (2) The prices of raw materials and products in the current period are input in the raw material and product module.

[0192] (2) Determine the amount of crude oil processing or upper and lower limits and product configuration plan.

[0193] (3) According to the winter and summer season, input the price of public medium in the public engineering module.

[0194] (4) Confirm the constraint condition.

[0195] (5) Click the "calculate" button in the full-process forward calculation model to calculate the optimal benefit.

[0196] (6) Determine the best production plan according to the calculation result.

[0197] Example 2

[0198] The present embodiment proposes a refining optimization model, which is established by the method for establishing a refining optimization model in example 1.

[0199] In the specific implementation process, the refining optimization model is established by using the technical scheme of the present application. The model is used for optimizing gasoline production and calculating the demand of purchased gasoline blending components, so that the number of purchased blending components is reduced from 7000 tons / month to 4000 tons / month. If the contribution of the model is 50%, the benefit is increased by about 1.06 million yuan per month. The production of petroleum coke is optimized by using the calculation result of the refining optimization model to formulate the control index of the sulfur content of the processed crude oil, optimize the production of petroleum coke, and stabilize the sulfur content of the product. If the contribution of the model is 50%, the benefit is increased by about 0.57 million yuan per month. By using the model, the full-plant benefit calculation under different price systems, xylene device start-stop benefit calculation, low gasoline configuration plan production calculation, low freezing diesel production scheme calculation and other optimization calculations are successively completed. The model provides effective guidance for production optimization. As can be seen from many calculation cases, the results of the model are basically consistent with the actual situation.

[0200] The above has described the embodiments of the present application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method of building a refinery optimization model, characterized by, The method comprises: Step S1: establishing a crude oil selection model by EXCEL and a programming solving tool therein, the crude oil selection model being used for selecting crude oil varieties and quantities with low prices and suitable for processing in a refinery according to product yield conditions of a crude oil distillation unit; Step S2: establishing a unit model of the crude oil distillation unit and secondary processing units by EXCEL and product yield, the unit model being used for simulating unit processing, defining unit processing load, unit raw materials and products and calculating raw material and product quantities; Step S3: establishing a product blending module by EXCEL and a blending index calculation method, the product blending module being used for simulating gasoline and diesel blending; Step S4: establishing a raw material and product module by EXCEL, the raw material and product module being used for simulating raw material entering a plant and products leaving the plant; Step S5: establishing a utility module by EXCEL, the utility module being used for simulating unit variable costs; Step S6: linking material flow directions of the modules by EXCEL calculation formulas, performing transmission of raw material, upstream unit products, downstream unit raw materials, blending components and product quantities, and establishing a full-process reverse calculation model by a programming solving tool in EXCEL, the full-process reverse calculation model being used for simulating a full-process of crude oil processing and calculating optimal product yield of the crude oil distillation unit; Step S7: linking material flow directions of the modules by EXCEL calculation formulas, performing transmission of raw material, upstream unit products, downstream unit raw materials, blending components and product quantities, and establishing a full-process forward calculation model by a programming solving tool in EXCEL, the full-process forward calculation model being used for simulating a full-process of crude oil processing and calculating an optimal production scheme; Step S8: inputting current crude oil prices and determining crude oil processing quantities in the crude oil selection model, and inputting current raw material and product prices and product configuration plans in the raw material and product module; Step S9: inputting utility medium prices in the utility module; Step S10: calculating optimal product yield of the crude oil distillation unit by the full-process reverse calculation model, and providing a basis for crude oil selection; Step S11: calculating optimal crude oil varieties and quantities by the crude oil selection model according to the optimal product yield of the crude oil distillation unit; Step S12: obtaining an optimal processing scheme by the full-process forward calculation model according to the optimal crude oil varieties and quantities.

2. The method of building a refinery optimization model of claim 1, wherein, The step S1 comprises: Step S13: inputting crude oil evaluation data in an EXCEL table, the crude oil evaluation data comprising: names, prices, quantities, yields, quality indexes of the crude oils; Step S14: establishing a calculation relationship formula: R i =Σ(x i r i ) wherein: R i is the corresponding index for crude oil; x i is the mass percentage of single oil species in the total amount of crude oil, %; r i is the index of single oil species; wherein i = 1, 2, 3... n; Step S15: establishing a target equation with a minimum crude oil cost as a target: P min =Σ(x i p i ) Where: P min is the minimum crude oil cost, yuan / ton; p i is the single oil species price, yuan / ton; Step S16: setting a constraint condition: where: y i is the mass yield of each fraction of the mixed crude oil, %; Y i is the maximum or minimum value of the mass yield of each fraction of the mixed crude oil, %; X i is the number of single oils, tons; C i is the maximum or minimum amount of single oils, tons; R i is the corresponding index of the crude oil; B i is the maximum or minimum value of the crude oil index, where i = 1, 2, 3... n; Step S17: obtaining optimal crude oil varieties and quantities by using a non-linear programming solving calculation in EXCEL with the minimum crude oil cost as a target.

3. The method of building a refinery optimization model of claim 2, wherein, The step S17 specifically comprises: The crude oil cost is minimized as a target, the number of crude oil varieties is a variable, the crude oil processing amount, the mass yield of each fraction of the mixed crude oil, and the quality index of the mixed crude oil are constraint conditions, and the optimal crude oil variety and quantity are obtained through non-linear programming calculation in EXCEL.

4. The method of building a refinery optimization model of claim 1, wherein, The step S2 comprises: Step S18: inputting the names of each device, raw materials and product names, quantity related information in the EXCEL table; Step S19: calculating the product quantity of each device through the following formula: d i =Σ(D i z i ) wherein: d i is the quantity of device products, tons; D i is the quantity of device raw materials, tons; z i is the yield of device products, %; wherein i = 1, 2, 3... n.

5. The method of building a refinery optimization model of claim 1, wherein, The step S3 comprises: Step S20: inputting the blending component names and quantity of the gasoline and diesel oil, the product names and quantity, and the control index in the EXCEL table; Step S21: in the gasoline and diesel oil blending quality index, the index in a linear change relationship is obtained through weighted calculation, and the formula is as follows: R i =Σ(x i r i ) wherein: R i is the corresponding index of the blending oil; x i is the mass percentage of the blending component, %; r i is the index of the blending component; wherein i = 1, 2, 3... n; Step S22: in the index in a non-linear change relationship, the relationship between the weighted calculation result and the blending oil index is established through linear regression in the MINITAB software on the basis of the weighted calculation result, and the corresponding index of the blending oil is calculated, and the formula is as follows: R i = a x Σ (x i r i ) + b Wherein: a is the coefficient of the formula; b is the constant term of the formula.

6. The method of building a refinery optimization model of claim 1, wherein, The raw material and product module comprises the names and prices of raw materials and products, and the quantity of raw materials and products transferred or calculated from the upstream device.

7. The method of building a refinery optimization model of claim 1, wherein, The step S6 comprises: Step S23: the quantity of raw materials, upstream device products, downstream device raw materials, blending components in the product blending module, and products in the raw material and product module is transferred in an equivalent or proportional manner; Step S24: a target equation is established with the maximum overall process benefit as a target: P max =Σ(p i d i )-Σ(P i D i ) Where: P max is the maximum benefit, yuan; p i is the product price, yuan / ton; d i is the product quantity, tons; P i is the raw material price, yuan / ton; D i is the raw material quantity, tons; Step S25: a constraint condition is set: wherein: z i is the product yield of the atmospheric-vacuum unit; Z i is the maximum or minimum value of the product yield of the atmospheric-vacuum unit; c i is the maximum or minimum amount of feedstock, tons; C i is the maximum or minimum amount of product, tons; R i is the quality index; B i is the maximum or minimum value of the quality index; Y i is the processing load of the unit; E i is the maximum or minimum value of the processing load of the unit; wherein i = 1, 2, 3... n; Step S26: the overall process reverse calculation model in the step S6 is calculated through non-linear programming solving in EXCEL to obtain the optimal product yield of the atmospheric and vacuum distillation device.

8. The method of building a refinery optimization model of claim 7, wherein, The step S26 specifically comprises: The maximum overall process benefit is set as a target, the quantity of raw materials, the product yield of the atmospheric and vacuum distillation device, the feed composition of the catalytic device, the quantity of each blending component, the quantity of reforming gasoline, S Zorb refined gasoline, and dimethylbenzene, and the aromatic content are variables, the crude oil processing amount, the device processing load, the product configuration plan, and the quality index are constraint conditions, and the optimal product yield of the atmospheric and vacuum distillation device is obtained through non-linear programming solving in EXCEL.

9. The method of building a refinery optimization model of claim 1, wherein, The step S7 comprises: Step S27: the quantity of raw materials, upstream device products, and downstream device raw materials, blending components in the product blending module, and products in the raw material and product module is transferred in an equivalent or proportional manner; Step S28: a target equation is established with the maximum overall process benefit as a target: P max =Σ(p i d i )-Σ(P i D i ) Where: P max is the maximum benefit, yuan; p i is the product price, yuan / ton; d i is the product quantity, tons; P i is the raw material price, yuan / ton; D i is the raw material quantity, tons; Step S29: a constraint condition is set: Where: D i is the quantity of raw material, tons; c i is the maximum or minimum quantity of raw material, tons; C i is the maximum or minimum quantity of product, tons; R i is the quality index; B i is the maximum or minimum value of the quality index; Y i is the processing load of the device; E i is the maximum or minimum value of the processing load of the device; where i = 1, 2, 3... n; Step S30: the overall process forward calculation model is calculated through non-linear programming solving in EXCEL to obtain the optimal production scheme.

10. The method of building a refinery optimization model of claim 9, wherein, The step S30 specifically comprises: The maximum benefit of the whole process is set as the target, the crude oil processing capacity, the feed composition of catalytic unit, the quantity of each blending component, the quantity of reforming gasoline, S Zorb refined gasoline and dimethylbenzene and the aromatic content are taken as variables, the quantity of raw materials, the processing load of unit, the product configuration plan and the quality index are taken as constraint conditions, and the optimal production scheme is obtained by using the nonlinear programming solution calculation in EXCEL.

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