An optimization method for oil product blending and scheduling applicable to refineries

Abstract

The present invention discloses an oil blending scheduling optimization method applicable to a refinery, comprising: obtaining a market demand plan for finished oil products; using a preset finished oil production plan and a formula optimization model based on the market demand plan for finished oil products to determine a production plan and a preliminary blending formula; constructing a blending task scheduling model based on the production plan and the preliminary blending formula, solving a specific finished oil blending and delivery plan, and obtaining finished oil formula results and task scheduling results; simulating the actual blending process based on the finished oil blending formula results and the task scheduling results, and calculating various property data of the delivery product after blending; judging whether the various property data of the delivery product after blending meet the preset quality indicators: delivering the delivery product that meets the quality indicators; adjusting and optimizing the finished oil blending formula and the blending amount for the delivery product that does not meet the quality indicators until the quality indicators are met. The present invention ensures that the production of refineries is stable, safe and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production scheduling, and in particular to an oil product blending scheduling optimization method applicable to a refinery. Background Art

[0002] Production scheduling is a key link in the comprehensive automation system of process industry, and is the link between production management and production process control. The oil production and blending scheduling process is a typical process in process industry, which is large-scale and complex. It is the last step in the oil refining process and has a vital impact on the quality of oil products.

[0003] A refinery usually has multiple sets of refining production units. After being refined by the production unit, crude oil is divided into many component oils with different distillation ranges, and the properties and quality indicators of each component oil are different. Therefore, how to select different component oils from different units and blend them in what proportion is a complex and difficult task; in addition, there are many types of finished oil after blending, but the oil transportation lines from finished oil to oil storage tanks are limited. How to select the best oil transportation line for each finished oil and send it to the most suitable oil storage tank according to the specified quantity is also a more difficult task. The above are the main problems to be solved in the blending and scheduling of oil products. At present, the blending and scheduling of finished oil in most domestic refineries relies on the dispatchers of the factory. After being fully familiar with and mastering the production process, they manually formulate the blending plan based on their long-term accumulated practical experience. However, this method has many defects. First of all, the formulation of the production plan must require the personnel engaged in the blending and scheduling to be very familiar with the on-site production process, to be familiar with the types of crude oil processed by different production units and the properties of the component oils produced, and to have a relatively accurate estimate of the results of mixing various component oils at different proportions. To meet these requirements, the staff needs to have many years of scheduling experience and a solid background in oil refining knowledge; secondly, the effect of blending and scheduling is not ideal. Since it is all manually formulated, there are certain limitations, such as high blending costs caused by unscientific blending plans and unqualified indicators of blended finished oil.

[0004] At present, research on the scheduling of finished oil products is just in its infancy. Due to the complexity of oil refining production and storage and transportation processes, there are still many areas worth studying and exploring in oil blending scheduling. In terms of oil blending scheduling optimization, the main patent technologies are: (1) a gasoline blending optimization scheduling method based on VERNA-GA (publication number CN 103065204 A), (2) a gasoline blending optimization scheduling method based on pseudo-Golgi membrane calculation (publication number CN 101320440 A), (3) a gasoline pipeline blending scheduling batch automatic switching method (publication number CN 103744402 A). Among them, patent (1) provides a gasoline blending scheduling model with profit and constraint satisfaction as the objective function under the constraints of the blending model and production scheduling, and uses the VERNA-GA method to set parameters and search for the optimal formula; however, this method uses the SQP algorithm for optimization, which has high requirements on the initial value. The initial value directly affects the calculated formula effect, which restricts the effective use of this method. Patent (2) provides a gasoline blending optimization scheduling method based on pseudo-Golgi membrane calculation. According to the information of blending component oil, product oil and market supply and demand, a gasoline blending scheduling model with profit and constraint satisfaction as the goal is formed. The efficient pseudo-Golgi membrane calculation method is used to set and search for the formula, and production is carried out after verification according to the obtained formula; however, this method only solves the formula solution problem in the oil product blending process, and does not consider the product tank and subsequent scheduling problems. Patent (3) discloses a gasoline pipeline blending batch switching method. This method calculates the percentage of the total blending volume that has been completed according to the target total blending volume set by the process scheduling and the current total blending volume, and sets a "total blending volume percentage parameter" to determine whether the batch switching conditions are met. However, this method only considers the oil product blending process of batch operation, and focuses on the control of this process, and does not provide a detailed introduction to the optimization of blending scheduling. Summary of the invention

[0005] The main problems of finished oil blending and scheduling include oil blending formula optimization and finished oil blending and scheduling. The former provides the optimal blending formula ratio for blending products, and the latter provides a specific blending plan. Formula optimization is usually a nonlinear programming problem, and the scheduling problem is usually a mixed integer linear programming problem (MILP), so the finished oil blending and scheduling problem is a large-scale mixed integer nonlinear programming (MINLP) problem. In order to solve the MINLP problem of the oil blending and scheduling process in oil refining enterprises, the present invention provides an oil blending and scheduling optimization method suitable for refineries.

[0006] Under the premise of meeting the product demand plan specified by the user, the present invention comprehensively considers and utilizes the existing blending components, component properties, product tank storage restrictions, blending capacity, component costs, product prices and other constraints, and establishes an oil blending scheduling optimization model to calculate the blending formula, daily output, product tanks used, blending delivery plan, etc. of the finished oil within the scheduling cycle, thereby ensuring the smooth, safe and efficient production of refineries and enterprises, and improving the economic benefits of enterprise production.

[0007] The technical solution adopted by the present invention to overcome the technical problems is:

[0008] A method for optimizing oil product blending and scheduling applicable to a refinery, comprising at least the following steps:

[0009] Obtain market demand plan for refined oil products;

[0010] Based on the market demand plan for refined oil products, a preset refined oil production plan and formulation optimization model is used to determine a production plan and a preliminary blending formula;

[0011] Build a blending task scheduling model based on the production plan and preliminary blending formula, solve the specific finished oil blending and factory delivery plan, and obtain the finished oil formula results and task scheduling results;

[0012] Based on the finished oil blending formula results and task scheduling results, the actual blending process is simulated to calculate the various property data of the products picked up after blending;

[0013] Determine whether the various property data of the picked-up products after blending meet the preset quality indicators: ship the picked-up products that meet the quality indicators; for the picked-up products that do not meet the quality indicators, adjust and optimize the finished oil blending formula and blending quantity until they meet the quality indicators.

[0014] Furthermore, before establishing a finished oil production plan and formula optimization model to determine the production plan and preliminary blending formula, a finished oil quality index calculation model is first established.

[0015] Furthermore, an empirical method combined with data regression method is used to establish a calculation model for refined oil quality indicators.

[0016] Furthermore, the use of a preset refined oil production plan and formula optimization model based on the product market demand plan to determine the production plan and the preliminary blending formula includes:

[0017] According to the market demand plan for refined oil products specified by the user, with the maximum product profit as the objective function, a refined oil production plan and formula optimization model are established to obtain the initial optimized formula and product production plan that meet product specifications, component inventory, and product output;

[0018] Determine whether the refined oil production plan and formula optimization model has a solution: if not, it is necessary to re-formulate the refined oil product market demand plan; if so, determine the production plan and preliminary blending formula.

[0019] Furthermore, the blending task scheduling model is constructed based on the production plan and the preliminary blending formula to solve the specific finished oil blending and factory delivery plan, and the finished oil formula result and task scheduling result are obtained, including:

[0020] Based on the production plan and preliminary blending formula, taking into account the product tank and all operational constraints, and taking the minimum number of blending and pickup operations as the objective function, a blending task scheduling model is constructed to solve the specific finished oil blending and factory pickup plan, and obtain the finished oil blending formula results and task scheduling results.

[0021] Furthermore, based on the finished oil blending formula result and the task scheduling result, the actual blending process is simulated to calculate the various property data of the product after blending, including:

[0022] Based on the finished oil blending formula results and task scheduling results, and taking into account the bottom oil in the tank of each blending product tank, a nonlinear oil blending model is used to simulate blending to calculate the various property data of the product picked up after blending.

[0023] Furthermore, the empirical method combined with the data regression method is used to establish a calculation model for the quality index of refined oil products, which specifically includes:

[0024] V nb =r n / density n / (∑r n / density n )

[0025] VD n =D n ×V nb ×100

[0026] P n =arenes n +olefin n -∑V nb ×arenes n -∑V nb ×olefin n

[0027]

[0028]

[0029] FT_S=∑r n ×S n

[0030] FT_O=∑r n ×O n

[0031] FT_ben=∑ben n ×V nb

[0032] FT_olefin=∑olefin n ×V nb

[0033] FT_arenes=∑arenes n ×V nb

[0034] RVP n =VP n 1.25 ×V nb

[0035] FT_RVP=(∑RVP n ) 0.8

[0036] Among them, n represents the component, r n It indicates the ratio of the mass of component oil n involved in the blending to the total blending mass. n Indicates the density of component oil n, V nb Indicates the volume ratio of component oil n, D n Indicates the weight coefficient of component oil n, VD n Represents the effective weight coefficient of component oil n, P n It represents the difference between the unsaturated hydrocarbon content of component oil n and the unsaturated hydrocarbon content of the product, S n Indicates the sulfur content of component oil n, O n Indicates the oxygen content of component oil n, arenes n Indicates the aromatic content of component oil n, olefin n Indicates the olefin content of component oil n, ben n Indicates the benzene content of component oil n, VP n Indicates the vapor pressure of component oil n, RVP n Indicates the effective vapor pressure of component oil n, RON nIt represents the octane number of component oil n, FTRON represents the octane number of blended gasoline, FT_density represents the density of blended gasoline, FT_S represents the sulfur content of blended gasoline, FT_O represents the oxygen content of blended gasoline, FT_ben represents the benzene content of blended gasoline, FT_olefin represents the olefin content of blended gasoline, FT_arenes represents the aromatic content of blended gasoline, and FT_RVP represents the vapor pressure of blended gasoline.

[0037] Furthermore, the method of using a preset refined oil production plan and formula optimization model based on the refined oil product market demand plan to determine the production plan and preliminary blending formula includes: establishing a refined oil production plan and formula optimization model to determine the production plan and preliminary blending formula;

[0038] The objective function for maximizing product profit is:

[0039]

[0040] Among them, m represents the product, t represents the tth day of the time period, N, M, T represent the set of n, m, t respectively, Ypro represents the product production plan, r nm represents the product blending formula, Ppro represents the sales price of product m, and Pcom represents the unit cost price of component oil n;

[0041] (1) Constraints on the production of blending components:

[0042]

[0043] In the above formula, cinv t,n represents the inventory of product component n at the end of day t, and cpn represents the daily output of component n;

[0044] (2) Product output constraints:

[0045] Bmin≤Ypro≤Bmax

[0046] In the above formula, Bmin is the minimum amount of each blending, and Bmax is the maximum amount of each blending;

[0047] (3) Product demand constraints:

[0048] Ypro t-1,m ≥ProPlan,t∈T,m∈M

[0049] In the above formula, ProPlan represents the market demand plan for refined oil products. The product output on day t must meet the market demand plan on day t+1.

[0050] (4) Product nature constraints:

[0051] density_l≤FT_density≤density_u

[0052] FT_S≤S_u

[0053] FT_O≤O_u

[0054] FT_ben≤ben_u

[0055] FT_olefin≤olefin_u

[0056] FT_arenes≤arenes_u

[0057] RVP_l≤FT_RVP≤RVP_u

[0058] PRON≤FT_RON≤RON_u

[0059] In the above formula, FT_density represents the density of the blended gasoline, density_u represents the upper limit of the density of the blended gasoline, and density_l represents the lower limit of the density of the blended gasoline; FT_S represents the sulfur content of the blended gasoline, and S_u represents the upper limit of the sulfur content of the blended gasoline; FT_O represents the oxygen content of the blended gasoline, and O_u represents the upper limit of the oxygen content of the blended gasoline; FT_ben represents the benzene content of the blended gasoline, and ben_u represents the upper limit of the benzene content of the blended gasoline; FT_olefin represents the content of the blended gasoline. The olefin content of gasoline, olefin_u represents the upper limit of the olefin content of the blended gasoline; FT_arenes represents the aromatic content of the blended gasoline, arenes_u represents the upper limit of the aromatic content of the blended gasoline; FT_RVP represents the vapor pressure of the blended gasoline, RVP_l represents the lower limit of the vapor pressure of the blended gasoline, and RVP_u represents the upper limit of the vapor pressure of the blended gasoline; FT_RON represents the octane number of the blended gasoline, PRON represents the set target octane number, and RON_u represents the upper limit of the octane number of the blended gasoline;

[0060] (5) Component inventory constraints:

[0061]

[0062] cln≤cinv t,n ≤cun,t∈T,n∈N

[0063] In the above formula, cln represents the lower limit of the inventory of the blending component, and cun represents the upper limit of the inventory of the blending component;

[0064] (6) Formulation constraints:

[0065]

[0066] Furthermore, a blending task scheduling model is constructed based on the production plan and the preliminary blending formula to solve the specific finished oil blending and factory delivery plan, and the finished oil formula results and task scheduling results are obtained, including:

[0067] Objective function to minimize the number of blending and picking operations:

[0068]

[0069] In the above formula, j represents the product tank, J represents the set of j; BL tjm PH is the blending operation mark, indicating whether product tank j is blended with product m on day t; tjm is the pick-up operation mark, indicating whether product tank j picks up product m on day t; BL tjm and PH tjm All are variables between 0 and 1;

[0070] (1) Constraints on blending quantity:

[0071] Bmin*BL tjm ≤blend tjm ≤Ta_u*BL tjm

[0072] In the above formula, blend tjm represents the blending amount of product m in product tank j on day t, and Ta_u represents the upper limit of the product tank capacity;

[0073] (2) Pick-up quantity constraints:

[0074] Lmin*PH tjm ≤Lift tjm ≤Ta_u*PH tjm

[0075] In the above formula, Lmin represents the minimum amount of goods to be picked up each time, Lift tjm represents the pickup quantity of product m in product tank j on day t;

[0076] (3) The blending quantity meets the optimized production plan:

[0077]

[0078] (4) Plan for picking up quantity to meet market demand:

[0079]

[0080] (5) Maximum daily blending quantity constraints:

[0081]

[0082] (6) Product tank inventory balance:

[0083]

[0084] In the above formula, Tankc tj represents the tank stock of product tank j on day t;

[0085] (7) Product tank operation allocation:

[0086]

[0087] For each product tank, blending and picking operations cannot be performed simultaneously every day;

[0088] (8) Product tank storage capacity constraints:

[0089] Ta_l≤Tankc tj ≤Ta_u,t∈T,j∈J

[0090] In the above formula, Ta_l represents the lower limit of the tank capacity of product tank j, and Ta_u represents the upper limit of the tank capacity of product tank j.

[0091] Furthermore, for the delivery products that do not meet the quality indicators, the blending formula and blending amount of the finished oil are adjusted and optimized with the minimum change in blending amount as the objective function until the quality indicators are met;

[0092] The objective function of minimizing the change in blending amount is:

[0093]

[0094] In the above formula, nblend tjm Indicates the adjusted amount of component n on day t, blend tjm It represents the amount of component n added on day t calculated previously;

[0095] (1) Constraints on blending quantity:

[0096]

[0097] (2) Component inventory constraints:

[0098] Component Inventory Material Balance:

[0099]

[0100] cln≤cinv t,n ≤cun,t∈T,n∈N

[0101] (3) Product tank tolerance:

[0102]

[0103] Ta_l≤Tankc tj ≤Ta_u,t∈T,j∈J

[0104] (4) Product quality restrictions:

[0105] density_l≤FT_density≤density_u

[0106] FT_S≤S_u

[0107] FT_O≤O_u

[0108] FT_ben≤ben_u

[0109] FT_olefin≤olefin_u

[0110] FT_arenes≤arenes_u

[0111] RVP_l≤FT_RVP≤RVP_u

[0112] PRON≤FT_RON≤RON_u.

[0113] The beneficial effects of the present invention are:

[0114] 1. Aiming at the oil blending and scheduling problem of oil refining enterprises, the present invention proposes a distributed solution strategy, which decomposes a large-scale mixed integer nonlinear programming (MINLP) problem into several sub-problems, thereby avoiding solving complex MINLP problems and making the calculation process simpler.

[0115] 2. The present invention starts from the benefits of oil refining enterprises, takes maximizing the benefits of enterprises as the goal, constructs a mathematical model that is more consistent with actual production, and performs overall optimization of the finished oil blending and scheduling system. The algorithm model has strong executability and can effectively reduce costs and improve the economic benefits of enterprises through optimized operations.

[0116] 3. The present invention can be used to solve large-scale mixed integer nonlinear programming problems with faster calculation speed and high precision. This method is used to solve the optimization scheduling problem of oil blending and achieves ideal results. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] Figure 1 The present invention is a flowchart of an oil product blending scheduling optimization method applicable to a refinery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0118] In order to facilitate those skilled in the art to better understand the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0119] The problem of refined oil blending and scheduling mainly includes two problems: refined oil tank operation task scheduling and refined oil blending formula optimization. Refined oil tank operation task scheduling refers to providing specific factory delivery and blending scheduling plans based on market demand plans and the production plan of blending component oils, on the premise of meeting component inventory and product specifications, including product delivery time, factory tanks, product starting blending time, and blending tanks; refined oil blending formula optimization is to provide a specific optimal blending formula for each blending event. In summary, for the refined oil blending and scheduling problem, on the basis of multi-layer optimization, a step-by-step solution strategy is adopted to decompose the entire refined oil blending and scheduling problem into four sub-problems for solution: determining the production plan and preliminary blending formula; optimizing operation task scheduling; simulating blending; and optimizing the adjustment of blending formulas and blending quantities, such as Figure 1 shown.

[0120] Specifically, the oil product blending scheduling optimization method applicable to a refinery described in this embodiment at least includes the following steps:

[0121] Step S1: Use the empirical method combined with the data regression method to establish a finished oil quality index calculation model. Specifically including:

[0122] V nb =r n / density n / (∑r n / density n )

[0123] VD n =D n ×V nb ×100

[0124] P n =arenes n +olefin n -∑V nb ×arenes n -∑V nb ×olefin n

[0125]

[0126]

[0127] FT_S=∑r n ×S n

[0128] FT_O=∑r n ×O n

[0129] FT_ben=∑ben n ×V nb

[0130] FT_olefin=∑olefin n ×V nb

[0131] FT_arenes=∑arenes n ×V nb

[0132] RVP n =VP n 1.25 ×V nb

[0133] FT_RVP=(∑RVP n ) 0.8

[0134] Among them, n represents the component, r n It indicates the ratio of the mass of component oil n involved in the blending to the total blending mass. n Indicates the density of component oil n, V nb Indicates the volume ratio of component oil n, D n Indicates the weight coefficient of component oil n, VD n Represents the effective weight coefficient of component oil n, P n It represents the difference between the unsaturated hydrocarbon content of component oil n and the unsaturated hydrocarbon content of the product, S n Indicates the sulfur content of component oil n, O n Indicates the oxygen content of component oil n, arenes n Indicates the aromatic content of component oil n, olefin n Indicates the olefin content of component oil n, ben n Indicates the benzene content of component oil n, VP n Indicates the vapor pressure of component oil n, RVP n Indicates the effective vapor pressure of component oil n, RON n It represents the octane number of component oil n, FTRON represents the octane number of blended gasoline, FT_density represents the density of blended gasoline, FT_S represents the sulfur content of blended gasoline, FT_O represents the oxygen content of blended gasoline, FT_ben represents the benzene content of blended gasoline, FT_olefin represents the olefin content of blended gasoline, FT_arenes represents the aromatic content of blended gasoline, and FT_RVP represents the vapor pressure of blended gasoline.

[0135] Step S2: Based on the market demand plan for refined oil products, a preset refined oil production plan and formula optimization model are used to determine the production plan and preliminary blending formula.

[0136] In this embodiment, according to the market demand plan for refined oil products specified by the user, taking the maximum product profit as the objective function, a refined oil production plan and formula optimization model are established, and without considering the product tank and specific operation constraints, an initial optimized formula and product production plan that meet the product specifications, component inventory, and product output are obtained; then, it is determined whether the refined oil production plan and formula optimization model has a solution: if not, it means that there are unreasonable aspects in the market demand plan formulated in advance, and it is necessary to redefine the market demand plan for refined oil products; if so, it means that the model can provide the best formula ratio under the market demand plan, and then the production plan and the preliminary blending formula are determined, and the blending can be performed according to the refined oil production plan and the formula optimization model, and then the next step is executed, and the result of this step is used as the input parameter of the next step.

[0137] The objective function for maximizing product profit is:

[0138]

[0139] Among them, m represents the product, t represents the tth day of the time period, N, M, T represent the set of n, m, t respectively, Ypro represents the product production plan, r nm represents the product blending formula, Ppro represents the sales price of product m, and Pcom represents the unit cost price of component oil n;

[0140] (1) Constraints on the production of blending components:

[0141]

[0142] In the above formula, cinv t,n represents the inventory of product component n at the end of day t, and cpn represents the daily output of component n;

[0143] (2) Product output constraints:

[0144] Bmin≤Ypro≤Bmax

[0145] In the above formula, Bmin is the minimum amount of each blending, and Bmax is the maximum amount of each blending;

[0146] (3) Product demand constraints:

[0147] Ypro t-1,m ≥ProPlan,t∈T,m∈M

[0148] In the above formula, ProPlan represents the market demand plan for refined oil products. The product output on day t must meet the market demand plan on day t+1.

[0149] (4) Product nature constraints:

[0150] density_l≤FT_density≤density_u

[0151] FT_S≤S_u

[0152] FT_O≤O_u

[0153] FT_ben≤ben_u

[0154] FT_olefin≤olefin_u

[0155] FT_arenes≤arenes_u

[0156] RVP_l≤FT_RVP≤RVP_u

[0157] PRON≤FT_RON≤RON_u

[0158] In the above formula, FT_density represents the density of the blended gasoline, density_u represents the upper limit of the density of the blended gasoline, and density_l represents the lower limit of the density of the blended gasoline; FT_S represents the sulfur content of the blended gasoline, and S_u represents the upper limit of the sulfur content of the blended gasoline; FT_O represents the oxygen content of the blended gasoline, and O_u represents the upper limit of the oxygen content of the blended gasoline; FT_ben represents the benzene content of the blended gasoline, and ben_u represents the upper limit of the benzene content of the blended gasoline; FT_olefin represents the content of the blended gasoline. The olefin content of gasoline, olefin_u represents the upper limit of the olefin content of the blended gasoline; FT_arenes represents the aromatic content of the blended gasoline, arenes_u represents the upper limit of the aromatic content of the blended gasoline; FT_RVP represents the vapor pressure of the blended gasoline, RVP_l represents the lower limit of the vapor pressure of the blended gasoline, and RVP_u represents the upper limit of the vapor pressure of the blended gasoline; FT_RON represents the octane number of the blended gasoline, PRON represents the set target octane number, and RON_u represents the upper limit of the octane number of the blended gasoline;

[0159] (5) Component inventory constraints:

[0160]

[0161] cln≤cinv t,n ≤cun,t∈T,n∈N

[0162] In the above formula, cln represents the lower limit of the inventory of the blending component, and cun represents the upper limit of the inventory of the blending component;

[0163] (6) Formulation constraints:

[0164]

[0165] Step S3: construct a blending task scheduling model based on the production plan and the preliminary blending formula, solve the specific finished oil blending and factory delivery plan, and obtain the finished oil formula result and task scheduling result.

[0166] In this embodiment, based on the production plan and the preliminary blending formula, the product tank and all operation constraints are considered, and the minimum number of blending and delivery operations is taken as the objective function. A blending task scheduling model is constructed to solve the specific finished oil blending and delivery plan, and obtain the finished oil blending formula result and task scheduling result. The calculation result is the daily blending and delivery plan. To ensure that this step has a solution, step S2 needs to have a feasible solution.

[0167] Objective function to minimize the number of blending and picking operations:

[0168]

[0169] In the above formula, j represents the product tank, J represents the set of j; BL tjm PH is the blending operation mark, indicating whether product tank j is blended with product m on day t; tjm is the pick-up operation mark, indicating whether product tank j picks up product m on day t; BL tjm and PH tjm All are variables between 0 and 1;

[0170] (1) Constraints on blending quantity:

[0171] Bmin*BL tjm ≤blend tjm ≤Ta_u*BL tjm

[0172] In the above formula, blend tjm represents the blending amount of product m in product tank j on day t, and Ta_u represents the upper limit of the product tank capacity;

[0173] (2) Pick-up quantity constraints:

[0174] Lmin*PH tjm ≤Lift tjm ≤Ta_u*PH tjm

[0175] In the above formula, Lmin represents the minimum amount of goods to be picked up each time, Lift tjmrepresents the pickup quantity of product m in product tank j on day t;

[0176] (3) The blending quantity meets the optimized production plan:

[0177]

[0178] (4) Plan for picking up quantity to meet market demand:

[0179]

[0180] (5) Maximum daily blending quantity constraints:

[0181]

[0182] (6) Product tank inventory balance:

[0183]

[0184] In the above formula, Tankc tj represents the tank stock of product tank j on day t;

[0185] (7) Product tank operation allocation:

[0186]

[0187] For each product tank, blending and picking operations cannot be performed simultaneously every day;

[0188] (8) Product tank storage capacity constraints:

[0189] Ta_l≤Tankc tj ≤Ta_u,t∈T,j∈J

[0190] In the above formula, Ta_l represents the lower limit of the tank capacity of product tank j, and Ta_u represents the upper limit of the tank capacity of product tank j.

[0191] Step S4: Based on the finished oil blending formula result and the task scheduling result, simulate the actual blending process and calculate the property data of the product after blending.

[0192] In this embodiment, based on the finished oil blending formula results and task scheduling results, and taking into account the bottom oil of the tank of each blending product tank, a nonlinear oil blending model is used to perform simulated blending to calculate the various property data of the product after blending. Specifically, in the oil blending calculation, the calculation methods of various properties are nonlinear, so it is said that a nonlinear oil blending model is used. The process of simulated blending is: after the previous calculation, a preliminary proportion formula result of oil blending can be obtained, and taking into account the task scheduling results of the blending tank, to verify the correctness of this result, it is necessary to perform simulated blending, that is, to consider the properties of the bottom oil of the blending tank that actually participates in the blending, on this basis, according to the quality index calculation model, the various property data of the finished oil after blending are obtained. In fact, simulated blending is just to calculate the result again through the quality index calculation model established in step S1.

[0193] Step S5, judging whether the property data of the picked-up products after blending meet the preset quality indicators: the picked-up products that meet the quality indicators are shipped; for the picked-up products that do not meet the quality indicators, the finished oil blending formula and blending amount are adjusted and optimized until they meet the quality indicators.

[0194] In this embodiment, for the delivery products that do not meet the quality indicators, the blending formula and blending amount of the finished oil are adjusted and optimized with the minimum change of the blending amount as the objective function until the quality indicators are met;

[0195] The objective function of minimizing the change in blending amount is:

[0196]

[0197] In the above formula, nblend tjm Indicates the adjusted amount of component n on day t, blend tjm It represents the amount of component n added on day t calculated previously;

[0198] (1) Constraints on blending quantity:

[0199]

[0200] (2) Component inventory constraints:

[0201] Component Inventory Material Balance:

[0202]

[0203] cln≤cinv t,n ≤cun,t∈T,n∈N

[0204] (3) Product tank tolerance:

[0205]

[0206] Ta_l≤Tankc tj ≤Ta_u,t∈T,j∈J

[0207] (4) Product quality restrictions:

[0208] density_l≤FT_density≤density_u

[0209] FT_S≤S_u

[0210] FT_O≤O_u

[0211] FT_ben≤ben_u

[0212] FT_olefin≤olefin_u

[0213] FT_arenes≤arenes_u

[0214] RVP_l≤FT_RVP≤RVP_u

[0215] PRON≤FT_RON≤RON_u.

[0216] The following is an explanation of the gasoline blending and scheduling optimization method applicable to a refinery in this embodiment through the gasoline blending and scheduling process:

[0217] A petrochemical enterprise plans to blend domestic gasoline 93# for automobiles, with a total blending volume of 9000kg. Considering the tank bottom oil, six component oils are used in the blending process: catalytic gasoline, mixed aromatics, light naphtha, raffinate, MTBE, and alkylate. The properties involved in the blending mainly include the density of the blended finished oil, oxygen content, benzene content, sulfur content, aromatic content, olefin content, octane number, and vapor pressure. With the goal of minimizing the excess octane number of the blended gasoline, the optimal blending ratio and the blending amount of each component are solved.

[0218] (1) Once the target refined oil is determined, the quality index data of the corresponding refined oil can be obtained, as shown in Table 1 below.

[0219] Table 1 Quality index data of domestic gasoline 93#

[0220]

[0221] (2) After selecting the tank to be blended, the corresponding tank bottom property data can be obtained. This value can be used to simulate the blending calculation process, as shown in Table 2 below.

[0222] Table 2 Tank bottom oil property data

[0223]

[0224] (3) Select the component oils and blending properties involved in the blending, and the corresponding component oil property data can be obtained, as shown in Table 3 below.

[0225] Table 3 Component oil property data

[0226]

[0227] (4) The above data are used to solve the optimal blending ratio and the blending amount of each component. After the calculation is successful, the results are shown in Tables 4 and 5 below. The blending ratio and blending amount of each component oil, as well as the various property data of the finished oil after blending can be clearly seen.

[0228] Table 4 Blending ratio and amount of each component oil

[0229] Component oil Tank bottom oil Catalytic gasoline Mixed Aromatics Light naphtha Extraction of residual oil MTBE Alkylate Mixing ratio 0.1624 0.1638 0.1846 0.0987 0.1074 0.0891 0.194 Blending amount 1462 1474 1662 887.9 966.1 801.75 1746.3

[0230] Table 5 Properties of finished oil after blending

[0231]

[0232]

[0233] The result data calculated by using the oil blending optimization model are shown in Table 5. Comparing it with the data shown in Table 1, it can be seen that all the calculated property data are within the reasonable range specified by the quality index data, which proves the effectiveness of the above oil blending optimization model.

[0234] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art may make many changes and improvements based on the above description, and these changes and improvements should fall within the protection scope of the present invention.

Claims

1. A method for optimizing oil product blending and scheduling applicable to a refinery, characterized in that: At least the steps include: Obtain market demand plan for refined oil products; Establish a refined oil production plan and formula optimization model, determine the production plan and preliminary blending formula; The objective function for maximizing product profit is: Among them, m represents the product, t represents the tth day of the time period, N, M, T represent the set of n, m, t respectively, Ypro represents the product production plan, r nm represents the product blending formula, Ppro represents the sales price of product m, and Pcom represents the unit cost price of component oil n; (1) Constraints on the production of blending components: In the above formula, cinv t,n represents the inventory of product component n at the end of day t, and cpn represents the daily output of component n; (2) Product output constraints: Bmin≤Ypro≤Bmax In the above formula, Bmin is the minimum amount of each blending, and Bmax is the maximum amount of each blending; (3) Product demand constraints: Ypro t-1,m ≥ProPlan,t∈T,m∈M In the above formula, ProPlan represents the market demand plan for refined oil products. The product output on day t must meet the market demand plan on day t+1. (4) Product nature constraints: density_l≤FT_density≤density_u FT_S≤S_u FT_O≤O_u FT_ben≤ben_u FT_olefin≤olefin_u FT_arenes≤arenes_u RVP_l≤FT_RVP≤RVP_u PRON≤FT_RON≤RON_u In the above formula, FT_density represents the density of the blended gasoline, density_u represents the upper limit of the density of the blended gasoline, and density_l represents the lower limit of the density of the blended gasoline; FT_S represents the sulfur content of the blended gasoline, and S_u represents the upper limit of the sulfur content of the blended gasoline; FT_O represents the oxygen content of the blended gasoline, and O_u represents the upper limit of the oxygen content of the blended gasoline; FT_ben represents the benzene content of the blended gasoline, and ben_u represents the upper limit of the benzene content of the blended gasoline; FT_olefin represents the content of the blended gasoline. The olefin content of gasoline, olefin_u represents the upper limit of the olefin content of the blended gasoline; FT_arenes represents the aromatic content of the blended gasoline, arenes_u represents the upper limit of the aromatic content of the blended gasoline; FT_RVP represents the vapor pressure of the blended gasoline, RVP_l represents the lower limit of the vapor pressure of the blended gasoline, and RVP_u represents the upper limit of the vapor pressure of the blended gasoline; FT_RON represents the octane number of the blended gasoline, PRON represents the set target octane number, and RON_u represents the upper limit of the octane number of the blended gasoline; (5) Component inventory constraints: cln≤cinv t,n ≤cun,t∈T,n∈N In the above formula, cln represents the lower limit of the inventory of the blending component, and cun represents the upper limit of the inventory of the blending component; (6) Formulation constraints: Based on the market demand plan for refined oil products, a preset refined oil production plan and formula optimization model is used to determine the production plan and the preliminary blending formula; specifically, the method includes: according to the market demand plan for refined oil products specified by the user, taking the maximum product profit as the objective function, establishing a refined oil production plan and formula optimization model, obtaining an initial optimized formula and product production plan that meet product specifications, component inventory, and product output; judging whether the refined oil production plan and formula optimization model has a solution: if not, it is necessary to re-formulate the market demand plan for refined oil products; if yes, determining the production plan and the preliminary blending formula; Based on the production plan and preliminary blending formula, taking into account the product tank and all operation constraints, the blending task scheduling model is constructed with the minimum number of blending and delivery operations as the objective function, and the specific finished oil blending and factory delivery plan is solved to obtain the finished oil formula results and task scheduling results, including: Objective function to minimize the number of blending and picking operations: In the above formula, j represents the product tank, J represents the set of j; BL tjm PH is the blending operation mark, indicating whether product tank j is blended with product m on day t; tjm is the pick-up operation mark, indicating whether product tank j picks up product m on day t; BL tjm and PH tjm All are variables between 0 and 1; (1) Constraints on blending quantity: Bmin*BL tjm ≤blend tjm ≤Ta_u*BL tjm In the above formula, blend tjm represents the blending amount of product m in product tank j on day t, and Ta_u represents the upper limit of the product tank capacity; (2) Pick-up quantity constraints: Lmin*PH tjm ≤Lift tjm ≤Ta_u*PH tjm In the above formula, Lmin represents the minimum amount of goods to be picked up each time, Lift tjm represents the pickup quantity of product m in product tank j on day t; (3) The blending quantity meets the optimized production plan: (4) Plan for picking up quantity to meet market demand: (5) Maximum daily blending quantity constraints: (6) Product tank inventory balance: In the above formula, Tankc tj represents the tank stock of product tank j on day t; (7) Product tank operation allocation: For each product tank, blending and picking operations cannot be performed simultaneously every day; (8) Product tank storage capacity constraints: Of the Tank tj ≤Ta_u,t∈T,j∈J In the above formula, Ta_l represents the lower limit of the tank capacity of product tank j, and Ta_u represents the upper limit of the tank capacity of product tank j; Based on the finished oil blending formula results and task scheduling results, and taking into account the bottom oil of each blending product tank, a nonlinear oil blending model is used to simulate the actual blending process and calculate the various property data of the product picked up after blending; Determine whether the various property data of the picked-up products after blending meet the preset quality indicators: ship the picked-up products that meet the quality indicators; for the picked-up products that do not meet the quality indicators, adjust and optimize the finished oil blending formula and blending quantity until they meet the quality indicators.

2. The oil product blending scheduling optimization method applicable to a refinery according to claim 1, characterized in that: Before establishing a finished oil production plan and formula optimization model to determine the production plan and preliminary blending formula, a finished oil quality index calculation model is first established.

3. The oil product blending scheduling optimization method applicable to a refinery according to claim 2, characterized in that: The calculation model of refined oil quality indexes is established by combining empirical method with data regression method.

4. The oil product blending scheduling optimization method applicable to a refinery according to claim 3, characterized in that: The calculation model of refined oil quality index is established by combining empirical method with data regression method, including: V nb =r n / densityn / (∑r n / density n ) VD n =D n ×V nb ×100 P n =developed n +olefin n -∑V nb ×developed n -∑V nb ×olefin n FT_S=∑r n ×S n FT_O=∑r n ×O n FT_in=∑in n ×V nb FT_olefin=∑olefin n ×V nb FT_arenes=∑arenes n ×V nb FT_RVP=(∑RVP n ) 0.8 Among them, n represents the component, r n Indicates the ratio of the mass of component oil n involved in the blending to the total blending mass, density n Indicates the density of component oil n, V nb Indicates the volume ratio of component oil n, D n Indicates the weight coefficient of component oil n, VD n Represents the effective weight coefficient of component oil n, P n It represents the difference between the unsaturated hydrocarbon content of component oil n and the unsaturated hydrocarbon content of the product, S n Indicates the sulfur content of component oil n, O n Indicates the oxygen content of component oil n, arenes n Indicates the aromatic content of component oil n, olefin n Indicates the olefin content of component oil n, ben n Indicates the benzene content of component oil n, VP n Indicates the vapor pressure of component oil n, RVP n Indicates the effective vapor pressure of component oil n, RON n It represents the octane number of component oil n, FTRON represents the octane number of blended gasoline, FT_density represents the density of blended gasoline, FT_S represents the sulfur content of blended gasoline, FT_O represents the oxygen content of blended gasoline, FT_ben represents the benzene content of blended gasoline, FT_olefin represents the olefin content of blended gasoline, FT_arenes represents the aromatic content of blended gasoline, and FT_RVP represents the vapor pressure of blended gasoline.

5. The oil product blending scheduling optimization method applicable to a refinery according to claim 4, characterized in that: For the delivery products that do not meet the quality indicators, the blending formula and blending amount of the finished oil are adjusted and optimized with the minimum change in blending amount as the objective function until the quality indicators are met; The objective function of minimizing the change in blending amount is: In the above formula, nblend tjm Indicates the adjusted amount of component n on day t, blend tjm It represents the amount of component n added on day t calculated previously; (1) Constraints on blending quantity: (2) Component inventory constraints: Component Inventory Material Balance: cln≤cinv t,n ≤cun,t∈T,n∈N (3) Product tank tolerance: Of the Tank tj ≤Ta_u,t∈T,j∈J (4) Product quality restrictions: density_l≤FT_density≤density_uFT_S≤S_u FT_O≤O_u FT_ben≤ben_u FT_olefin≤olefin_u FT_arenes≤arenes_u RVP_l≤FT_RVP≤RVP_u PRON≤FT_RON≤RON_u.

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