Oil blending optimization method and system under multiple scenes and storage medium

Abstract

The invention provides an oil product blending optimization method under multiple scenes, an oil product blending optimization system under multiple scenes, and a computer readable storage medium. The multi-scene oil product blending optimization method comprises the following steps: determining an oil product blending formula optimization model according to a first constraint condition and a first optimization objective function; determining a multi-scene optimization algorithm based on the blending rule of the mass / volume fraction and the blending scene constraint rule; and determining an optimization scheme of the oil blending formula optimization model according to a multi-scene optimization algorithm. According to the oil product blending optimization method under the multiple scenes, the characteristics and requirements of different production scenes are comprehensively considered, various requirements for oil products in the multiple scenes can be met, the production efficiency is improved, the cost is reduced, the product quality is improved, and therefore the blending scheme is more flexible and adapts to diversified market requirements.

Description

Technical Field

[0001] The present invention relates to the field of oil blending, and in particular, to an oil blending optimization method under multiple scenarios, an oil blending optimization system under multiple scenarios, and a computer-readable storage medium. Background Art

[0002] In the petroleum industry, oil blending refers to mixing different types and qualities of crude oils and petrochemical products in a certain proportion to obtain a final product that meets market demands. The goal of oil blending is to minimize costs, optimize energy utilization, and ensure the stability of the oil blending production process while meeting product specification requirements. Traditional oil blending methods usually involve manual blending, which is often inaccurate and overly reliant on experience. Manual blending also cannot comprehensively consider the complex physical and chemical changes during the blending process. Moreover, in different markets and application scenarios, there are differences in the requirements for oils. For example, automotive fuels need to have good combustion performance and low emission characteristics, while industrial fuels place more emphasis on energy density and stability.

[0003] Therefore, there is an urgent need in this field for an oil blending optimization technology under multiple scenarios that can adapt to various requirements for oils in multiple scenarios by comprehensively considering the characteristics and requirements of different production scenarios, so as to improve production efficiency, reduce costs, and enhance product quality, thereby making the blending scheme more flexible and adaptable to diverse market demands. Summary of the Invention

[0004] The following presents a brief overview of one or more aspects to provide a basic understanding of these aspects. This overview is not an exhaustive survey of all contemplated aspects, and neither is it intended to identify key or decisive elements of any or all aspects nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that follows.

[0005] The present invention provides an oil blending optimization method under multiple scenarios, an oil blending optimization system under multiple scenarios, and a computer-readable storage medium, which can adapt to various requirements for oils in multiple scenarios by comprehensively considering the characteristics and requirements of different production scenarios, so as to improve production efficiency, reduce costs, and enhance product quality, thereby making the blending scheme more flexible and adaptable to diverse market demands.

[0006] Specifically, the above-mentioned method for optimizing oil product blending in multiple scenarios provided by the first aspect of the present invention includes the steps of: determining an oil product blending formula optimization model according to the first constraint condition and the first optimization objective function; determining a multi-scenario optimization algorithm based on the blending rule of mass / volume fraction and the blending scenario constraint rule; and determining an optimization plan for the oil product blending formula optimization model according to the multi-scenario optimization algorithm.

[0007] Preferably, in an embodiment of the present invention, the first constraint condition includes a refined oil production constraint, a component oil production constraint, and a refined oil property constraint.

[0008] Preferably, in an embodiment of the present invention, the refined oil property constraint includes a refined oil volume formula property constraint and a refined oil mass formula property constraint.

[0009] Preferably, in an embodiment of the present invention, the first optimization objective function includes a price objective function.

[0010] Preferably, in an embodiment of the present invention, the blending scenario constraint rule includes optimization according to the upper and lower limits of production and inventory, optimization according to the upper and lower limits of the formula, and optimization according to the upper and lower limits of flow rate.

[0011] Preferably, in an embodiment of the present invention, the step of determining the multi-scenario optimization algorithm includes the processing of non-linear attributes.

[0012] Preferably, in an embodiment of the present invention, the optimization plan includes refined oil properties, a refined oil optimization formula, and the distribution of component oil production.

[0013] Preferably, in an embodiment of the present invention, it further includes the steps of: determining a second constraint condition and a second optimization objective function for multiple blending batches based on the physical tanks; determining a planned scheduling model according to the second constraint condition and the second optimization objective function; and determining a production scheduling plan for the multiple blending batches according to the planned scheduling model in combination with the optimization plan of the oil product blending formula optimization model.

[0014] Preferably, in an embodiment of the present invention, the second constraint condition includes the blending constraint of each physical tank for each blending batch and the attribute constraint of the blending batch.

[0015] Preferably, in an embodiment of the present invention, the blending constraint of the physical tank for the blending batch is determined by the inventory constraint of each blending batch, the inventory calculation of each blending batch, the minimum blending amount constraint of each blending batch, and the component oil production equality constraint.

[0016] Preferably, in an embodiment of the present invention, the second optimization objective function is set such that the attributes of the slack variables are on the verge while the blending batch satisfies the second constraint condition.

[0017] Preferably, in an embodiment of the present invention, the production scheduling plan includes the production allocation of the component oils for each blending batch, the inventory of the finished product tanks for each blending batch, and the attributes of each blending batch.

[0018] In addition, the above-mentioned oil blending optimization system in multiple scenarios provided by the second aspect of the present invention includes a memory and a processor. A computer instruction is stored on the memory. The processor is connected to the memory and is configured to execute the computer instruction stored on the memory to implement the oil blending optimization method in multiple scenarios provided by any one of the above embodiments.

[0019] In addition, a computer instruction is stored on the above-mentioned computer-readable storage medium provided by the third aspect of the present invention. When the computer instruction is executed by a processor, the oil blending optimization method in multiple scenarios provided by any one of the above embodiments is implemented. Description of the Drawings

[0020] After reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings, the above features and advantages of the present invention can be better understood. In the drawings, the components are not necessarily drawn to scale, and components with similar related characteristics or features may have the same or similar reference numerals.

[0021] Figure 1 Shows a schematic diagram of an oil blending optimization system in multiple scenarios provided by some embodiments of the present invention;

[0022] Figure 2 Shows a flowchart of an oil blending optimization method in multiple scenarios provided by some embodiments of the present invention;

[0023] Figure 3A Shows a schematic diagram of the attribute information of each component oil provided by Embodiment 1 of the present invention;

[0024] Figure 3B Shows a schematic diagram of the upper and lower limit information of the attributes of each refined oil provided by Embodiment 1 of the present invention;

[0025] Figure 3C Shows a schematic diagram of the set production range of each component oil and the tank capacity of each component oil provided by Embodiment 1 of the present invention;

[0026] Figure 4A Shows a schematic diagram of the attributes of the refined oil in the optimization plan provided by Embodiment 1 of the present invention;

[0027] Figure 4B Shows a schematic diagram of the optimized formula of the refined oil in the optimization plan provided by Embodiment 1 of the present invention;

[0028] Figure 4C Shows a schematic diagram of the component oil production allocation in the optimization solution provided according to Embodiment 1 of the present invention;

[0029] Figure 5A Shows a schematic diagram of the formulated ranges of the respective component oils set according to Embodiment 2 of the present invention;

[0030] Figure 5B Shows a schematic diagram of the total amount of the respective component oils to be adjusted as set according to Embodiment 2 of the present invention;

[0031] Figure 6A Shows a schematic diagram of the refined oil properties in the optimization solution provided according to Embodiment 2 of the present invention;

[0032] Figure 6B Shows a schematic diagram of the optimized formula of the refined oil in the optimization solution provided according to Embodiment 2 of the present invention;

[0033] Figure 6C Shows a schematic diagram of the component oil production allocation in the optimization solution provided according to Embodiment 2 of the present invention;

[0034] Figure 7A Shows a schematic diagram of the upper and lower limits of the adjustment flow rates of the respective component oils for the component oil as set according to Embodiment 2 of the present invention;

[0035] Figure 7B Shows a schematic diagram of the total amount of the respective component oils to be adjusted as set according to Embodiment 2 of the present invention;

[0036] Figure 8A Shows a schematic diagram of the refined oil properties in the optimization solution provided according to Embodiment 3 of the present invention;

[0037] Figure 8B Shows a schematic diagram of the optimized formula of the refined oil in the optimization solution provided according to Embodiment 3 of the present invention;

[0038] Figure 8C Shows a schematic diagram of the component oil production allocation in the optimization solution provided according to Embodiment 3 of the present invention;

[0039] Figure 9A Shows a schematic diagram of the tank numbers, initial inventories, and upper capacity limits of the refined oil provided according to Embodiment 4 of the present invention;

[0040] Figure 9B Shows a schematic diagram of the finished product tanks selected for each blending batch provided according to Embodiment 4 of the present invention;

[0041] Figure 9C Shows a schematic diagram of the planned production amounts of the respective component oils provided according to Embodiment 4 of the present invention;

[0042] Figure 10A It shows a schematic diagram of the production allocation of component oils in each blending batch provided according to Embodiment 4 of the present invention;

[0043] Figure 10B It shows a schematic diagram of the finished product tank inventory in each blending batch provided according to Embodiment 4 of the present invention; and

[0044] Figure 10C It shows a schematic diagram of the attributes of each blending batch provided according to Embodiment 4 of the present invention.

[0045] Reference numerals:

[0046] 100: Oil blending optimization system under multiple scenarios;

[0047] 110: Memory;

[0048] 111: Computer-readable storage medium;

[0049] 120: Processor;

[0050] 200: Oil blending optimization method under multiple scenarios; and

[0051] S210 - S230: Steps. Detailed implementation manners

[0052] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the accompanying drawings and specific embodiments are merely exemplary and should not be construed as imposing any limitation on the protection scope of the present invention.

[0053] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0054] In addition, the "upper", "lower", "left", "right", "top", "bottom", "horizontal", and "vertical" used in the following description should be understood as the orientations shown in this section and the relevant drawings. This relative term is only for convenience of description and does not represent that the device described needs to be manufactured or operated in a specific orientation, so it should not be construed as a limitation on the present invention.

[0055] It is understood that although terms such as "first", "second", "third", etc. may be used herein to describe various components, regions, layers, and / or parts, these components, regions, layers, and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers, and / or parts. Therefore, the first component, region, layer, and / or part discussed below may be referred to as the second component, region, layer, and / or part without departing from some embodiments of the present invention.

[0056] As described above, traditional oil blending methods usually involve manual participation in blending. Manual blending is often not accurate enough, and it overly relies on experience. For the complex physical and chemical changes during the blending process, manual blending cannot take everything into consideration. In different markets and application scenarios, there are differences in the requirements for oils. For example, automotive fuels need to have good combustion performance and low emission characteristics, while industrial fuels place more emphasis on energy density and stability.

[0057] The present invention provides an oil blending optimization method in multiple scenarios, an oil blending optimization system in multiple scenarios, and a computer-readable storage medium. By comprehensively considering the characteristics and requirements of different production scenarios, it can adapt to various requirements for oils in multiple scenarios, improve production efficiency, reduce costs, and enhance product quality, thereby making the blending plan more flexible and adaptable to diverse market demands.

[0058] In some non-limiting embodiments, the above-mentioned oil blending optimization method in multiple scenarios provided by the first aspect of the present invention can be implemented via the above-mentioned oil blending optimization system in multiple scenarios provided by the second aspect of the present invention.

[0059] Please refer to Figure 1 , Figure 1 , which shows a schematic diagram of an oil blending optimization system in multiple scenarios provided by some embodiments of the present invention.

[0060] As Figure 1 shown, the oil blending optimization system 100 in multiple scenarios may be configured with a memory 110 and a processor 120. The memory 110 includes but is not limited to the above-mentioned computer-readable storage medium 111 provided by the third aspect of the present invention, on which computer instructions are stored. The processor 120 is connected to the memory 110 and is configured to execute the computer instructions stored on the memory 110 to implement the oil blending optimization method in multiple scenarios provided by the first aspect of the present invention.

[0061] The working principle of the above-mentioned oil blending optimization system in multiple scenarios will be described below with reference to some embodiments of the oil blending optimization methods in multiple scenarios. Those skilled in the art can understand that these embodiments of the oil blending optimization methods in multiple scenarios are only some non-limiting implementation manners provided by the present invention, aiming to clearly show the main concept of the present invention and provide some specific solutions convenient for the public to implement, rather than limiting all functions or all working modes of the oil blending optimization system in multiple scenarios. Similarly, the oil blending optimization system in multiple scenarios is also a non-limiting implementation manner provided by the present invention, and does not limit the execution subject and execution order of each step in these oil blending optimization methods in multiple scenarios.

[0062] Please refer to Figure 2 , Figure 2 which shows a flowchart of an oil blending optimization method provided according to some embodiments of the present invention in multiple scenarios.

[0063] As Figure 2 shown, the oil blending optimization method 200 in multiple scenarios may include step S210: determining an oil blending recipe optimization model according to the first constraint condition and the first optimization objective function.

[0064] The first constraint condition may include refined oil production constraint, component oil production constraint, and refined oil property constraint. The refined oil production constraint may be as shown in formula (1):

[0065]

[0066] where i = 1...n, i represents the i-th component oil, and there are n types of component oils in total; F i k is the production of the i-th component oil used to produce the k-th refined oil; F k is the lower limit of the production of the k-th refined oil.

[0067] The component oil production constraint means that the blending production of a component oil should be equal to the sum of the production of the component oil used for blending all refined oils, and the production of a component oil used to produce the k-th refined oil needs to meet the upper and lower limits of the production of the k-th refined oil.

[0068] That the blending production of a component oil should be equal to the sum of the production of the component oil used for blending all refined oils can be expressed as formula (2):

[0069]

[0070] where F i is the production of the i-th component oil, and there are m types of refined oils in total.

[0071] The output of a component oil used to produce the kth refined oil product needs to satisfy the upper and lower bound constraints of the output of the kth refined oil product, which can be expressed by Equation (3):

[0072] F iLow k ≤F i k ≤F iHigh k (3)

[0073] where F iLow k is the lower limit of the adjustable flow rate of the ith component oil used to produce the kth refined oil product, and F iHigh k is the upper limit of the adjustable flow rate of the ith component oil used to produce the kth refined oil product.

[0074] The refined oil product property constraints can include refined oil product volume formula property constraints and refined oil product quality formula property constraints, which respectively represent the linear upper and lower bound constraints of the relevant properties of the refined oil product in the volume formula blending scenario and the quality formula blending scenario. The refined oil product volume formula property constraints can be as shown in Equation (4):

[0075] ∑ n i＝1 V i k ·P j kLow ≤∑ n i＝1 V i k P i j ≤∑ n i＝1 V i k ·P j kHigh (4)

[0076] The refined oil product quality formula property constraints can be as shown in Equation (5):

[0077] ∑ n i＝1 ρ i V i k ·P j kLow ≤∑ n i＝1 ρ i V i k P i j ≤∑ n i＝1 ρi V i k ·P j kHigh (5)

[0078] Among them, V i k is the volume production of the i-th component oil for blending and producing the k-th refined oil; P i j is the j-th attribute value of the i-th component oil, such as octane number, oxygen content, sulfur content, aromatics, olefins, oxygen content, benzene content, etc.; P j kLow is the lower limit of the j-th attribute value of the k-th refined oil, P j kHigh is the upper limit of the j-th attribute value of the k-th refined oil; ρ i is the density of the i-th component oil.

[0079] The first optimization objective function may include a price objective function. Considering the final economic benefit, the first optimization objective function may be set to maximize the total selling price of the blended refined oil and the unblended component oils, as shown in formula (6):

[0080]

[0081] Among them, q 成品 k is the price of the k-th refined oil, q 组分 i is the price of the i-th component oil for producing the j-th refined oil, F i k is the production of the i-th component oil for producing the k-th refined oil, F 未i is the mass of the unblended component oils.

[0082] According to the above first constraint condition and the first optimization objective function, the oil blending optimization system under multiple scenarios can determine the oil blending recipe optimization model.

[0083] Please continue to refer to Figure 2 , the oil blending optimization method 200 under multiple scenarios may include step S220: determining a multi-scenario optimization algorithm based on the blending rule of mass / volume fraction and the blending scenario constraint rule.

[0084] The blending rule may be a blending rule for increasing the mass / volume fraction of the attributes of the blended refined oil, as shown in formula (7):

[0085] ∑ n i＝1 (V i k ·Pj kLow ·k 1 +ρ i V i k ·P j kLow ·k 2 )≤∑ n i＝1 (ρ i V i k P i j ·k 1 +V i k P i j ·k 2 )≤∑ n i＝1 (ρ i V i k ·P j kHigh ·k 1 +V i k ·P j kHigh ·k 2 ) (7)

[0086] Among them, k 1 is the switching variable of the volume fraction in the calculation of the refined oil properties, and k 2 is the switching variable of the mass fraction in the calculation of the refined oil properties, and its value is only 1 and 0. When k 1 = 1 and k 2 = 0, the property constraints of the blending rule for increasing the mass / volume fraction of the blended refined oil properties will be calculated according to the volume fraction; when k 1 = 0 and k 2 = 1, the property constraints will be calculated according to the mass fraction.

[0087] In addition, in some preferred embodiments, the steps of determining the multi-scenario optimization algorithm may further include the processing of non-linear properties. Through the processing of non-linear properties, the oil blending optimization system under multiple scenarios can transform the properties calculated non-linearly into linearly calculated ones. Specifically, the oil blending optimization system under multiple scenarios can first determine the non-linear model. Taking the octane number TN as an example of the non-linear property, the non-linear model can be as shown in formula (8):

[0088]

[0089] Among them, RON is the Research Octane Number. After the octane number undergoes a non-linear substitution, it participates in the blending calculation. In formula (8), the parameter b = 0.05594 and the parameter m = 102.94.

[0090] After that, the oil product blending optimization system under multiple scenarios can perform non-linear model conversion, as shown in formula (9):

[0091]

[0092] Among them, P k ronLow is the lower limit of the octane number attribute of the kth refined oil product, and P k ronHigh is the upper limit of the octane number attribute of the kth refined oil product, and P i ron is the octane number attribute of the ith component oil. Thus, the octane number attribute is processed non-linearly through formula (8) and formula (9). After that, the octane number attribute after non-linear transformation participates in the blending calculation in the form of linear constraints.

[0093] The blending scenario is determined according to the actual operating conditions. Based on the blending scenario, blending scenario constraint rules can be set. Different blending scenarios apply different blending scenario constraint rules. The blending scenario constraint rules can include optimization according to the upper and lower limits of production and inventory, optimization according to the upper and lower limits of the formula, and optimization according to the upper and lower limits of flow rate.

[0094] The blending scenario constraint rule can be optimization according to the upper and lower limits of production and inventory, so that the process of the component oil entering the component oil tank after production and leaving the tank to participate in blending can ensure being within the safe range of the tank capacity. Optimization according to the upper and lower limits of production and inventory can include inequality constraints on the production of refined oil products and inequality constraints on the production of component oils. The inequality constraint on the production of refined oil products can be as shown in formula (10):

[0095]

[0096] The inequality constraints on the production of component oils can be as shown in formulas (11) to (13):

[0097]

[0098] F i ≤V iHigh -V iIni (12)

[0099]

[0100] Among them, F iLow is the lower limit of the production of the ith component oil, and F iHighis the upper limit of the production of the i-th component oil, V iHigh is the upper limit of the tank volume of the i-th component oil, V iLow is the lower limit of the tank volume of the i-th component oil, V iIni is the initial tank volume of the i-th component oil.

[0101] The blending scenario constraint rules can also be optimized according to the upper and lower limits of the formula, so that each component oil strictly follows the upper and lower limits of the formula of the refined oil when used for blending refined oil. The optimization according to the upper and lower limits of the formula can include the equality constraint of the refined oil production and the inequality constraint of the component oil production. The equality constraint of the refined oil production can be shown as formula (14):

[0102]

[0103] The inequality constraint of the component oil production can be shown as formula (15) and formula (16):

[0104]

[0105] ω iLowk F k ≤F i k ≤ω iHighk F k (16)

[0106] Among them, ω iLowk is the lower limit of the formula for the i-th component oil used to produce the k-th refined oil, ω iHighk is the upper limit of the formula for the i-th component oil used to produce the k-th refined oil.

[0107] The blending scenario constraint rules also include the optimization according to the upper and lower limits of the flow rate, so that each component oil strictly follows the upper and lower limits of the flow rate for producing the refined oil when used for blending refined oil. The optimization according to the upper and lower limits of the flow rate can include the equality constraint of the refined oil production and the inequality constraint of the component oil production. The equality constraint of the refined oil production can be shown as formula (17):

[0108]

[0109] The inequality constraint of the component oil production can be shown as formula (18) and formula (19):

[0110]

[0111] F iLow k ≤F i k ≤F iHigh k (19)

[0112] Such as Figure 2As shown, the oil blending optimization method 200 in multiple scenarios may further include step S230: determining an optimization solution for the oil blending recipe optimization model according to the multi-scenario optimization algorithm.

[0113] The optimization solution of the oil blending recipe optimization model is obtained through the optimization solver set by the multi-scenario optimization algorithm. Here, the optimization solution output by the oil blending recipe optimization model may include refined oil properties, refined oil optimization recipe, and component oil production allocation.

[0114] The following are a number of specific non-limiting preferred embodiments, based on which the oil blending optimization method in multiple scenarios proposed by the present invention will be described in detail.

[0115] In the first preferred embodiment, the oil blending optimization system in multiple scenarios can obtain component oil property information, refined oil property upper and lower limit information, and set the production range of each component oil and the tank capacity of each component oil.

[0116] Please refer to Figures 3A - 3C , Figure 3A which shows a schematic diagram of the component oil property information provided in the first embodiment of the present invention, Figure 3B which shows a schematic diagram of the refined oil property upper and lower limit information provided in the first embodiment of the present invention, Figure 3C which shows a schematic diagram of the set production range of each component oil and the tank capacity of each component oil provided in the first embodiment of the present invention.

[0117] As Figure 3A shown, the component oils may include hydrotreated gasoline, mixed aromatics, MTBE (Methyl Tert-Butyl Ether), pentane oil, topped oil, and raffinate oil. The octane number property of hydrotreated gasoline is 88.5, the sulfur content is 11 ppm, the density is 729 kg / m 3 , the aromatics are 18.5 v%, the olefins are 27 v%, the oxygen content is 0.03 wt%, and the vapor pressure is 65 kPa; the octane number property of mixed aromatics is 110, the sulfur content is 1 ppm, the density is 852 kg / m 3 , the aromatics are 88 v%, the olefins are 2 v%, the oxygen content is 0 wt%, and the vapor pressure is 12 kPa; the octane number property of MTBE is 112, the sulfur content is 10 ppm, the density is 720 kg / m 3 , the aromatics are 0 v%, the olefins are 3 v%, the oxygen content is 15.5 wt%, and the vapor pressure is 58 kPa; the octane number property of pentane oil is 85, the sulfur content is 1 ppm, the density is 626 kg / m 3 , the aromatics are 1.5 v%, the olefins are 2 v%, the oxygen content is 0.01 wt%, and the vapor pressure is 145 kPa; the octane number property of topped oil is 78, the sulfur content is 1 ppm, the density is 635 kg / m3 and the aromatics content is 1 v%, the olefins content is 0.1 v%, the oxygen content is 0.01 wt%, and the vapor pressure is 125 kPa; the octane number property of the raffinate is 68, the sulfur content is 1 ppm, and the density is 673 kg / m 3 and the aromatics content is 0.5 v%, the olefins content is 6 v%, the oxygen content is 0 wt%, and the vapor pressure is 55 kPa.

[0118] As Figure 3B shown, the refined oil can include 92#, X92#, and 95#. The upper limit of the octane number of 92# is 94.5, the upper limit of the sulfur content is 10 ppm, and the upper limit of the density is 775 kg / m 3 and the upper limit of the aromatics content is 34 v%, the upper limit of the olefins content is 13.5 v%, the upper limit of the oxygen content is 2.6 wt%, and the upper limit of the vapor pressure is 65 kPa; the lower limit of the octane number of 92# is 92.2, the lower limit of the sulfur content is 0 ppm, and the lower limit of the density is 725 kg / m 3 and the lower limit of the aromatics content is 0 v%, the lower limit of the olefins content is 0 v%, the lower limit of the oxygen content is 0 wt%, and the lower limit of the vapor pressure is 45 kPa. The upper limit of the octane number of X92# is 94.5, the upper limit of the sulfur content is 10 ppm, and the upper limit of the density is 775 kg / m 3 and the upper limit of the aromatics content is 34 v%, the upper limit of the olefins content is 13.5 v%, the upper limit of the oxygen content is 0.01 wt%, and the upper limit of the vapor pressure is 65 kPa; the lower limit of the octane number of X92# is 90.2, the lower limit of the sulfur content is 0 ppm, and the lower limit of the density is 725 kg / m 3 and the lower limit of the aromatics content is 0 v%, the lower limit of the olefins content is 0 v%, the lower limit of the oxygen content is 0 wt%, and the lower limit of the vapor pressure is 45 kPa. The upper limit of the octane number of 95# is 97.5, the upper limit of the sulfur content is 10 ppm, and the upper limit of the density is 775 kg / m 3 and the upper limit of the aromatics content is 34 v%, the upper limit of the olefins content is 13.5 v%, the upper limit of the oxygen content is 2.6 wt%, and the upper limit of the vapor pressure is 65 kPa; the lower limit of the octane number of 95# is 95, the lower limit of the sulfur content is 0 ppm, and the lower limit of the density is 725 kg / m 3 and the lower limit of the aromatics content is 0 v%, the lower limit of the olefins content is 0 v%, the lower limit of the oxygen content is 0 wt%, and the lower limit of the vapor pressure is 45 kPa.

[0119] As Figure 3CAs shown, the upper limit of the output of hydrogenated gasoline is 52,537.2 t (tons), the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t. The upper limit of the output of mixed aromatics is 20,019.5 t, the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t. The upper limit of the output of MTBE is 2,370.03 t, the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t. The upper limit of the output of pentane oil is 1,462.47 t, the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t. The upper limit of the output of topped oil is 12,737.8 t, the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t. The upper limit of the output of raffinate oil is 14,410.3 t, the lower limit is 0 t, the initial tank volume is 200 t, the upper limit of the tank volume is 80,000 t, and the lower limit of the tank volume is 100 t.

[0120] Please refer to Figures 4A - 4C , Figure 4A which shows a schematic diagram of the properties of refined oil products in the optimization solution provided in the first embodiment of the present invention, Figure 4B which shows a schematic diagram of the optimized formula of refined oil products in the optimization solution provided in the first embodiment of the present invention, Figure 4C which shows a schematic diagram of the output allocation of component oils in the optimization solution provided in the first embodiment of the present invention.

[0121] In the first preferred embodiment, the blending scenario constraint rule of the multi-scenario optimization algorithm can be to optimize according to the upper and lower limits of production and inventory. Thus, the optimization model of the oil blending formula is solved according to the determined multi-scenario optimization algorithm, and the optimization solution in the first preferred embodiment is obtained.

[0122] As Figure 4A shown, in the first preferred embodiment, among the properties of refined oil products in the optimization solution obtained from the optimization model of the oil blending formula, the octane number of 92# is 0, the sulfur content is 0 ppm, the density is 0 kg / m 3 , the aromatics are 0 v%, the olefins are 0 v%, the oxygen content is 0 wt%, and the vapor pressure is 0 kPa; the octane number of 95# is 95, the sulfur content is 13.1 ppm, the density is 744.2 kg / m 3 , the aromatics are 34 v%, the olefins are 13.5 v%, the oxygen content is 1.2 wt%, and the vapor pressure is 59.9 kPa; the octane number of X92# is 94.5, the sulfur content is 11.5 ppm, the density is 732.3 kg / m 3 , the aromatics are 30.9 v%, the olefins are 13.5 v%, the oxygen content is 0.1 wt%, and the vapor pressure is 63.8 kPa.

[0123] As Figure 4B shown, in the first preferred embodiment, in the optimized formula of the refined oil in the optimized solution obtained by the oil blending formula optimization model, the optimized formula of 92# includes 0% hydrogenated gasoline, 0% mixed aromatics, 0% MTBE, 0% pentane oil, 0% topped oil, and 0% raffinate oil. That is, 92# refined oil is not blended during the oil blending process in the first embodiment. The optimized formula of 95# includes 48.48% hydrogenated gasoline, 28.24% mixed aromatics, 7.48% MTBE, 4.73% pentane oil, 11.07% topped oil, and 0% raffinate oil. The optimized formula of X92# includes 47.29% hydrogenated gasoline, 24.76% mixed aromatics, 0% MTBE, 0% pentane oil, 21.06% topped oil, and 6.89% raffinate oil.

[0124] As Figure 4C shown, in the first preferred embodiment, in the distribution of the production volume of component oils in the optimized solution obtained by the oil blending formula optimization model, the blending production volume of hydrogenated gasoline is 36625t, and the single-sale production volume is 16112t; the blending production volume of mixed aromatics is 20120t, and the single-sale production volume is 100t; the blending production volume of MTBE is 2470t, and the single-sale production volume is 100t; the blending production volume of pentane oil is 1562t, and the single-sale production volume is 100t; the blending production volume of topped oil is 12838t, and the single-sale production volume is 100t; the blending production volume of raffinate oil is 3002t, and the single-sale production volume is 11608t.

[0125] In the second preferred embodiment, the oil blending optimization system under multiple scenarios can set the formula range of each component oil, the tank capacity of each component oil, and the total amount of each component oil participating in the blending.

[0126] Please refer to Figure 5A and 5B , Figure 5A which shows a schematic diagram of the formula range of each component oil set according to the second embodiment of the present invention, Figure 5B and which shows a schematic diagram of the total amount of each component oil participating in the blending set according to the second embodiment of the present invention.

[0127] As Figure 5A shown, in the formulas of refined oils 92#, 95#, and X92#, the upper limit of hydrogenated gasoline, mixed aromatics, MTBE, pentane oil, topped oil, and raffinate oil is 100, and the lower limit is 0.

[0128] As Figure 5BAs shown, the total amount of hydrogenated gasoline for blending is 52537.2 t, the total amount of mixed aromatics for blending is 20019.5 t, the total amount of MTBE for blending is 2370.03 t, the total amount of pentane oil for blending is 1462.47 t, the total amount of topped oil for blending is 12737.8 t, and the total amount of raffinate oil for blending is 14410.3 t.

[0129] Please refer to Figures 6A - 6C , Figure 6A shows a schematic diagram of the properties of refined oil products in the optimization solution provided in the second embodiment of the present invention, Figure 6B shows a schematic diagram of the optimized formula of refined oil products in the optimization solution provided in the second embodiment of the present invention, Figure 6C shows a schematic diagram of the production allocation of component oils in the optimization solution provided in the second embodiment of the present invention.

[0130] In the second preferred embodiment, the blending scenario constraint rule of the multi-scenario optimization algorithm can be optimization according to the upper and lower limits of the formula. Thus, the optimization solution in the second preferred embodiment is obtained by solving the refined oil blending formula optimization model according to the determined multi-scenario optimization algorithm.

[0131] As Figure 6A shown, in the second preferred embodiment, among the properties of the refined oil products in the optimization solution obtained by the refined oil blending formula optimization model, the octane number of 92# is 0, the sulfur content is 0 ppm, the density is 0 kg / m 3 , the aromatics are 0 v%, the olefins are 0 v%, the oxygen content is 0 wt%, and the vapor pressure is 0 kPa; the octane number of 95# is 95.0, the sulfur content is 13.1 ppm, the density is 744.2 kg / m 3 , the aromatics are 34 v%, the olefins are 13.5 v%, the oxygen content is 1.2 wt%, and the vapor pressure is 59.9 kPa; the octane number of X92# is 90.2, the sulfur content is 11.5 ppm, the density is 732.3 kg / m 3 , the aromatics are 30.8 v%, the olefins are 13.5 v%, the oxygen content is 0.1 wt%, and the vapor pressure is 63.8 kPa.

[0132] As Figure 6BAs shown, in the second preferred embodiment, in the optimized refined oil formula in the optimization plan obtained by the oil blending formula optimization model, the optimized formula for 92# includes 0% hydrogenated gasoline, 0% mixed aromatics, 0% MTBE, 0% pentane oil, 0% topped oil, and 0% raffinate oil. That is, 92# refined oil is not blended during the oil blending process in the second embodiment. The optimized formula for 95# includes 48.48% hydrogenated gasoline, 28.24% mixed aromatics, 7.48% MTBE, 4.73% pentane oil, 11.07% topped oil, and 0% raffinate oil. The optimized formula for X92# includes 47.29% hydrogenated gasoline, 24.76% mixed aromatics, 0% MTBE, 0% pentane oil, 21.06% topped oil, and 6.89% raffinate oil.

[0133] As Figure 6C shown, in the second preferred embodiment, in the component oil production allocation in the optimization plan obtained by the oil blending formula optimization model, the blending production of hydrogenated gasoline is 36631.93t, and the single-sale production is 16225.27t; the blending production of mixed aromatics is 20019.5t, and the single-sale production is 0t; the blending production of MTBE is 2370.03t, and the single-sale production is 0t; the blending production of pentane oil is 1462.47t, and the single-sale production is 0t; the blending production of topped oil is 12737.8t, and the single-sale production is 0t; the blending production of raffinate oil is 3224.18t, and the single-sale production is 11186.12t.

[0134] In the third preferred embodiment, the oil blending optimization system under multiple scenarios can set the upper and lower limits of the reference flow rates of each component oil for the component oil and the total reference amount of each component oil.

[0135] Please refer to Figure 7A and 7B , Figure 7A which shows a schematic diagram of the set upper and lower limits of the reference flow rates of each component oil for the component oil provided in the second embodiment of the present invention, Figure 7B which shows a schematic diagram of the set total reference amount of each component oil provided in the second embodiment of the present invention.

[0136] As Figure 7AAs shown, the upper limit of the blending flow rate of hydrogenated gasoline for 92#, 95#, and X92# is 52537.2 t, and the lower limit is 0; the upper limit of the blending flow rate of mixed aromatics for 92#, 95#, and X92# is 20019.5 t, and the lower limit is 0; the upper limit of the blending flow rate of MTBE for 92#, 95#, and X92# is 2370.03 t, and the lower limit is 0; the upper limit of the blending flow rate of pentane oil for 92#, 95#, and X92# is 1462.47 t, and the lower limit is 0; the upper limit of the blending flow rate of topped oil for 92#, 95#, and X92# is 12737.8 t, and the lower limit is 0; the upper limit of the blending flow rate of raffinate oil for 92#, 95#, and X92# is 14410.3 t, and the lower limit is 0.

[0137] As Figure 7B shown, the total blending amount of hydrogenated gasoline is 52537.2 t, the total blending amount of mixed aromatics is 20019.5 t, the total blending amount of MTBE is 2370.03 t, the total blending amount of pentane oil is 1462.47 t, the total blending amount of topped oil is 12737.8 t, and the total blending amount of raffinate oil is 14410.3 t.

[0138] Please refer to Figures 8A - 8C , Figure 8A which shows a schematic diagram of the properties of refined oil products in the optimization scheme provided in Embodiment III of the present invention, Figure 8B which shows a schematic diagram of the optimized formula of refined oil products in the optimization scheme provided in Embodiment III of the present invention, Figure 8C which shows a schematic diagram of the component oil production allocation in the optimization scheme provided in Embodiment III of the present invention.

[0139] In the preferred Embodiment III, the blending scenario constraint rule of the multi-scenario optimization algorithm can be optimized according to the upper and lower limits of the flow rate. Thus, according to the determined multi-scenario optimization algorithm, the optimization model of the oil blending formula is solved to obtain the optimization scheme in the preferred Embodiment III.

[0140] As Figure 8A shown, in the preferred Embodiment III, among the properties of the refined oil products in the optimization scheme obtained from the optimization model of the oil blending formula, the octane number of 92# is 0, the sulfur content is 0 ppm, the density is 0 kg / m 3 , the aromatics are 0 v%, the olefins are 0 v%, the oxygen content is 0 wt%, and the vapor pressure is 0 kPa; the octane number of 95# is 95.0, the sulfur content is 13.1 ppm, the density is 744.2 kg / m 3 , the aromatics are 34 v%, the olefins are 13.5 v%, the oxygen content is 1.2 wt%, and the vapor pressure is 59.9 kPa; the octane number of X92# is 90.2, the sulfur content is 11.5 ppm, the density is 732.3 kg / m 3, the aromatic hydrocarbon is 30.8 v%, the olefin is 13.5 v%, the oxygen content is 0.1 wt%, and the vapor pressure is 63.8 kPa.

[0141] As Figure 8B shown, in the third preferred embodiment, in the optimized refined oil formula in the optimized solution obtained by the oil blending formula optimization model, the optimized formula of 92# includes 0% hydrogenated gasoline, 0% mixed aromatics, 0% MTBE, 0% pentane oil, 0% topped oil, and 0% raffinate oil, that is, 92# refined oil is not blended during the oil blending process of the third embodiment. The optimized formula of 95# includes 48.48% hydrogenated gasoline, 28.24% mixed aromatics, 7.48% MTBE, 4.73% pentane oil, 11.07% topped oil, and 0% raffinate oil. The optimized formula of X92# includes 47.29% hydrogenated gasoline, 24.76% mixed aromatics, 0% MTBE, 0% pentane oil, 21.06% topped oil, and 6.89% raffinate oil.

[0142] As Figure 8C shown, in the third preferred embodiment, in the component oil production allocation in the optimized solution obtained by the oil blending formula optimization model, the blending production of hydrogenated gasoline is 36631.93 t, and the single-sale production is 16225.27 t; the blending production of mixed aromatics is 20019.5 t, and the single-sale production is 0 t; the blending production of MTBE is 2370.03 t, and the single-sale production is 0 t; the blending production of pentane oil is 1462.47 t, and the single-sale production is 0 t; the blending production of topped oil is 12737.8 t, and the single-sale production is 0 t; the blending production of raffinate oil is 3224.18 t, and the single-sale production is 11186.12 t.

[0143] Thus, the oil blending optimization method in multiple scenarios provided by the present invention first establishes an oil blending formula optimization model according to the refined oil blending of a refinery enterprise. On this basis, various types of blending scenarios are designed. To ensure the flexibility of blending optimization, multiple blending rules are provided without deviating from the planned requirements, such as the above non-linear model and the adaptation of mass / volume fractions with different attributes. Therefore, the oil blending optimization method in multiple scenarios provided by the present invention overcomes the problems of low solution efficiency and poor generality of existing algorithms in dealing with such problems, greatly improving production efficiency and minimizing costs.

[0144] In addition, the oil blending optimization method in multiple scenarios provided by the present invention can also implement a production plan for multiple blending batches considering physical tanks.

[0145] Specifically, the oil blending optimization system in multiple scenarios can first determine the second constraint conditions and the second optimization objective function for multiple blending batches based on the physical tanks. The second constraint conditions can include the blending constraints of each physical tank for each blending batch and the attribute constraints of the blending batch.

[0146] The blending constraints of each physical tank for each blending batch can be determined by the inventory constraints of each blending batch, the inventory calculation of each blending batch, the minimum blending volume constraint of each blending batch, and the component oil production equation constraint. The inventory constraint of each blending batch can be as shown in formula (20):

[0147]

[0148] The inventory calculation of each blending batch can be as shown in formula (21):

[0149]

[0150] The minimum blending volume constraint of each blending batch can be as shown in formula (22):

[0151]

[0152] Among them, is the inventory of the hth finished product tank in the tth blending batch. When t = 0, is the initial inventory of this finished product tank; V t High is the upper limit of the tank capacity of the finished product tank in the tth blending batch; represents whether the hth finished product tank in the tth blending batch is enabled. When is 1, it means the finished product tank is enabled. When is 0, it means the finished product tank is not enabled; F t i is the part of the tth blending batch used to produce the ith component oil, and F min is the minimum production of each blending batch.

[0153] The second constraint conditions also include the component oil production equation constraint, as shown in formula (23):

[0154]

[0155] In addition, the second constraint conditions can also include the attribute constraints of the blending batch. The attribute constraints of the blending batch can include the slack attribute constraints of each blending batch and the remaining attribute constraints of each blending batch.

[0156] The slack attribute constraints of each blending batch can be as shown in formula (24):

[0157]

[0158] Among them, is the c-th attribute with slack variables of the i-th component oil, is the c-th attribute with slack variables of the t-th blending batch, is the national standard of the c-th attribute with slack variables of the t-th blending batch.

[0159] The constraints on the remaining attributes of each blending batch can be as shown in formula (25):

[0160]

[0161] Among them, is the v-th attribute without slack variables of the i-th component oil, is the national standard of the v-th attribute without slack variables of the t-th blending batch.

[0162] The second optimization objective function can be set to make the attributes of the slack variables on the verge while the blending batch meets the second constraint condition, as shown in formula (26):

[0163]

[0164] After that, the oil product blending optimization system under multiple scenarios can determine the planned scheduling model according to the second constraint condition and the second optimization objective function. Then, combined with the optimization scheme of the oil product blending formula optimization model, the production scheduling scheme for multiple blending batches can be determined according to the planned scheduling model. The production scheduling scheme can include the production allocation of component oils for each blending batch, the inventory of finished product tanks for each blending batch, and the attributes of each blending batch.

[0165] Please refer to Figures 9A - 9C , Figure 9A which shows a schematic diagram of the tank numbers, initial inventories, and capacity upper limits of the refined oils provided in the fourth embodiment of the present invention, Figure 9B which shows a schematic diagram of the selected finished product tanks for each blending batch provided in the fourth embodiment of the present invention, Figure 9C which shows a schematic diagram of the planned production volumes of each component oil provided in the fourth embodiment of the present invention.

[0166] In the preferred fourth embodiment, the oil product blending optimization system under multiple scenarios can obtain the tank numbers, initial inventories, and capacity upper limits of the refined oils, the selected finished product tanks for each blending batch, and the planned production volumes of each component oil.

[0167] Such as Figure 9AAs shown in the figure, the tank number of refined oil 92# can be 921. The initial inventory of the finished product tank with the tank number 921 is 100t, and the upper limit of the capacity is 30000t. The tank number of refined oil 92# can be 922. The initial inventory of the finished product tank with the tank number 922 is 200t, and the upper limit of the capacity is 40000t. The tank number of refined oil 92# can be 923. The initial inventory of the finished product tank with the tank number 923 is 200t, and the upper limit of the capacity is 30000t. The tank number of refined oil 92# can be 924. The initial inventory of the finished product tank with the tank number 924 is 500t, and the upper limit of the capacity is 25000t. The tank number of refined oil 92# can be 925. The initial inventory of the finished product tank with the tank number 925 is 200t, and the upper limit of the capacity is 30000t. The tank number of refined oil 95# can be 951. The initial inventory of the finished product tank with the tank number 951 is 200t, and the upper limit of the capacity is 30000t. The tank number of refined oil 95# can be 952. The initial inventory of the finished product tank with the tank number 952 is 100t, and the upper limit of the capacity is 20000t. The tank number of refined oil 95# can be 953. The initial inventory of the finished product tank with the tank number 953 is 300t, and the upper limit of the capacity is 15000t. The tank number of refined oil X92# can be 321. The initial inventory of the finished product tank with the tank number 321 is 200t, and the upper limit of the capacity is 25000t. The tank number of refined oil X92# can be 322. The initial inventory of the finished product tank with the tank number 322 is 100t, and the upper limit of the capacity is 40000t.

[0168] As Figure 9B shown, the blending batches altogether include batches 1 to 10. The finished product tank selected for batch 1 can be the finished product tank with the tank number 921. The finished product tank selected for batch 2 can be the finished product tank with the tank number 922. The finished product tank selected for batch 3 can be the finished product tank with the tank number 921. The finished product tank selected for batch 4 can be the finished product tank with the tank number 951. The finished product tank selected for batch 5 can be the finished product tank with the tank number 921. The finished product tank selected for batch 6 can be the finished product tank with the tank number 921. The finished product tank selected for batch 7 can be the finished product tank with the tank number 952. The finished product tank selected for batch 8 can be the finished product tank with the tank number 321. The finished product tank selected for batch 9 can be the finished product tank with the tank number 322. The finished product tank selected for batch 10 can be the finished product tank with the tank number 952.

[0169] As Figure 9C shown, the planned output of hydrogenated gasoline is 10000t, the planned output of mixed aromatics is 20000t, the planned output of MTBE is 10000t, the planned output of pentane oil is 20000t, the planned output of topped oil is 10000t, the planned output of raffinate is 20000t, and the total output is 80000t.

[0170] After that, the oil product blending optimization system under multiple scenarios can, considering the physical tanks, determine the production scheduling plan for multiple blending batches in combination with the optimization plan of the oil product blending formula optimization model.

[0171] Please refer to Figures 10A - 10C , Figure 10A which shows a schematic diagram of the production allocation of component oils in each blending batch provided in Embodiment 4 of the present invention, Figure 10B which shows a schematic diagram of the finished product tank inventory in each blending batch provided in Embodiment 4 of the present invention, Figure 10C which shows a schematic diagram of the attributes of each blending batch provided in Embodiment 4 of the present invention.

[0172] As Figure 10AAs shown, the blending batches include Batch 1 to Batch 10. In the total output, the output of hydrogenated gasoline is 10,000 t, the output of mixed aromatics is 20,000 t, the output of MTBE is 10,000 t, the output of pentane oil is 20,000 t, the output of topped oil is 10,000 t, and the output of raffinate oil is 10,000 t. Specifically, in Batch 1, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 228.5 t, the output of MTBE is 157 t, the output of pentane oil is 452.7 t, the output of topped oil is 0 t, and the output of raffinate oil is 161.8 t; in Batch 2, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 3,112.5 t, the output of MTBE is 2,138.9 t, the output of pentane oil is 6,167 t, the output of topped oil is 0 t, and the output of raffinate oil is 2,204.6 t; in Batch 3, the output of hydrogenated gasoline is 479.2 t, the output of mixed aromatics is 220.9 t, the output of MTBE is 0 t, the output of pentane oil is 299.9 t, the output of topped oil is 0 t, and the output of raffinate oil is 0 t; in Batch 4, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 376.1 t, the output of MTBE is 22.1 t, the output of pentane oil is 601.8 t, the output of topped oil is 0 t, and the output of raffinate oil is 0 t; in Batch 5, the output of hydrogenated gasoline is 471.8 t, the output of mixed aromatics is 42.9 t, the output of MTBE is 166.5 t, the output of pentane oil is 317.6 t, the output of topped oil is 0 t, and the output of raffinate oil is 1.2 t; in Batch 6, the output of hydrogenated gasoline is 124.9 t, the output of mixed aromatics is 270.5 t, the output of MTBE is 0 t, the output of pentane oil is 604.6 t, the output of topped oil is 0 t, and the output of raffinate oil is 0 t; in Batch 7, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 376.1 t, the output of MTBE is 22.1 t, the output of pentane oil is 601.8 t, the output of topped oil is 0 t, and the output of raffinate oil is 0 t; in Batch 8, the output of hydrogenated gasoline is 8,924.1 t, the output of mixed aromatics is 1,972.7 t, the output of MTBE is 4,136.4 t, the output of pentane oil is 727.3 t, the output of topped oil is 9,039.5 t, and the output of raffinate oil is 0 t; in Batch 9, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 5,133.2 t, the output of MTBE is 0 t, the output of pentane oil is 5,441.1 t, the output of topped oil is 0 t, and the output of raffinate oil is 3,303.8 t; in Batch 10, the output of hydrogenated gasoline is 0 t, the output of mixed aromatics is 8,266.5 t, the output of MTBE is 3,356.8 t, the output of pentane oil is 4,786.3 t, the output of topped oil is 960.5 t, and the output of raffinate oil is 4,328.6 t.

[0173] As Figure 10BAs shown in the figure, among the finished product tank inventories of each blending batch, the finished product tank with tank number 921 has 1100t in batches 1 and 2, 2100t in batches 3 and 4, 3100t in batch 5, and 4100t in batches 6 to 10; the finished product tank with tank number 922 has 200t in batch 1 and 13823.1t in batches 2 to 10; the finished product tank with tank number 923 has 200t in batches 1 to 10; the finished product tank with tank number 924 has 500t in batches 1 to 10; the finished product tank with tank number 925 has 300t in batches 1 to 10; the finished product tank with tank number 951 has 200t in batches 1 to 3 and 1200t in batches 4 to 10; the finished product tank with tank number 952 has 100t in batches 1 to 6, 1100t in batches 7 to 9, and 22798.8t in batch 10; the finished product tank with tank number 953 has 300t in batches 1 to 10; the finished product tank with tank number 321 has 200t in batches 1 to 7 and 25000t in batches 8 to 10; the finished product tank with tank number 322 has 100t in batches 1 to 8 and 13978.2t in batches 9 and 10.

[0174] As Figure 10C shown, the research octane number of the refined oil blended in batch 1 is 92.2, the saturated vapor pressure is 86.4 kPa, the sulfur content is 2.4 ppm, the density is 700.0 kg / m 3 , the aromatics are 20.9 v%, the olefins are 3.5 v%, and the oxygen content is 2.6 wt%; the research octane number of the refined oil blended in batch 2 is 92.2, the saturated vapor pressure is 86.4 kPa, the sulfur content is 2.4 ppm, the density is 700.0 kg / m 3 , the aromatics are 20.9 v%, the olefins are 3.5 v%, and the oxygen content is 2.6 wt%; the research octane number of the refined oil blended in batch 3 is 92.2, the saturated vapor pressure is 77.3 kPa, the sulfur content is 5.8 ppm, the density is 725.3 kg / m 3 , the aromatics are 28.8 v%, the olefins are 13.5 v%, and the oxygen content is 0.0 wt%; the research octane number of the refined oil blended in batch 4 is 95.0, the saturated vapor pressure is 93.1 kPa, the sulfur content is 1.2 ppm, the density is 713.1 kg / m 3 , the aromatics are 34.0 v%, the olefins are 2.0 v%, and the oxygen content is 0.3 wt%; the research octane number of the refined oil blended in batch 5 is 92.2, the saturated vapor pressure is 87.0 kPa, the sulfur content is 7.2 ppm, the density is 700.0 kg / m 3, aromatics is 13.0 v%, olefins is 13.5 v%, oxygen content is 2.6 wt%; the research octane number of the blended refined oil in batch 6 is 92.2, the saturated vapor pressure is 99.0 kPa, the sulfur content is 2.2 ppm, and the density is 700.0 kg / m 3 , aromatics is 27.0 v%, olefins is 5.0 v%, oxygen content is 0.0 wt%; the research octane number of the blended refined oil in batch 7 is 95.0, the saturated vapor pressure is 93.1 kPa, the sulfur content is 1.2 ppm, and the density is 713.1 kg / m 3 , aromatics is 34.0 v%, olefins is 2.0 v%, oxygen content is 0.3 wt%; the research octane number of the blended refined oil in batch 8 is 90.2, the saturated vapor pressure is 83.8 kPa, the sulfur content is 6.1 ppm, and the density is 700.0 kg / m 3 , aromatics is 14.1 v%, olefins is 10.1 v%, oxygen content is 2.6 wt%; the research octane number of the blended refined oil in batch 9 is 90.2, the saturated vapor pressure is 74.4 kPa, the sulfur content is 1.0 ppm, and the density is 720.8 kg / m 3 , aromatics is 33.3 v%, olefins is 3.9 v%, oxygen content is 0.2 wt%; the research octane number of the blended refined oil in batch 10 is 95.0, the saturated vapor pressure is 62.0 kPa, the sulfur content is 2.4 ppm, and the density is 736.4 kg / m 3 , aromatics is 34.0 v%, olefins is 3.7 v%, oxygen content is 2.6 wt%.

[0175] In this way, by considering the short - cycle scheduling under the physical tank, a feasible production scheduling plan is provided for the case of multiple blending batches.

[0176] Although the above - described methods are illustrated and described as a series of acts for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the acts, because according to one or more embodiments, some acts may occur in a different order and / or concurrently with other acts that are illustrated and described herein or that are not illustrated and described herein but would be understood by those of ordinary skill in the art.

[0177] Those of ordinary skill in the art will understand that information, signals, and data can be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips described throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0178] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0179] The various illustrative logical modules and circuits described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0180] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read from, and write to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0181] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or code. The computer-readable medium includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. The storage media may be any available media that can be accessed by a computer. By way of example and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc where disks typically reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0182] The foregoing description of the disclosure has been provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing oil blending in multiple scenarios, characterized in that: Includes steps: Determine an oil product blending formula optimization model according to the first constraint condition and the first optimization objective function; Determine the multi-scenario optimization algorithm based on the mass / volume fraction blending rules and the blending scenario constraint rules; as well as An optimization scheme for the oil blending formula optimization model is determined based on the multi-scenario optimization algorithm.

2. The oil blending optimization method according to claim 1, characterized in that: The first constraint conditions include finished oil production constraints, component oil production constraints, and finished oil property constraints.

3. The oil blending optimization method according to claim 2, characterized in that: The refined oil attribute constraints include refined oil volume formula attribute constraints and refined oil quality formula attribute constraints.

4. The oil blending optimization method according to claim 1, characterized in that: The first optimization objective function includes a price objective function.

5. The oil blending optimization method according to claim 1, characterized in that: The blending scenario constraint rules include optimization according to upper and lower limits of output and inventory, optimization according to upper and lower limits of recipe, and optimization according to upper and lower limits of flow.

6. The oil blending optimization method according to claim 1, characterized in that: The step of determining a multi-scenario optimization algorithm includes processing of non-linear properties.

7. The oil blending optimization method according to claim 1, characterized in that: The optimization scheme includes finished oil properties, finished oil optimization formula and component oil output allocation.

8. The oil blending optimization method according to claim 1, characterized in that: Also includes the steps: Determine a second constraint condition and a second optimization objective function for a plurality of blending batches based on the physical tank; Determine a planning and scheduling model according to the second constraint condition and the second optimization objective function; as well as In combination with the optimization scheme of the oil blending formula optimization model, the production scheduling scheme of the multiple blending batches is determined according to the planning and scheduling model.

9. The oil blending optimization method according to claim 8, characterized in that: The second constraint condition includes the blending constraint of each of the physical tanks in each of the blending batches and the attribute constraint of the blending batch.

10. The oil blending optimization method according to claim 9, characterized in that: The blending constraints of each physical tank in each blending batch are determined by the inventory constraints of each blending batch, the inventory calculation of each blending batch, the minimum blending quantity constraints of each blending batch and the component oil production equation constraints.

11. The oil blending optimization method according to claim 8, characterized in that: The second optimization objective function is set to make the attribute of the slack variable edge when the blending batch satisfies the second constraint condition.

12. The oil blending optimization method according to claim 8, characterized in that: The production scheduling plan includes the production allocation of the component oils of each blending batch, the finished product tank inventory of each blending batch, and the attributes of each blending batch.

13. A multi-scenario oil blending optimization system, characterized in that: include: a memory having computer instructions stored thereon; as well as A processor is connected to the memory and is configured to execute computer instructions stored in the memory to implement the oil blending optimization method in multiple scenarios as described in any one of claims 1 to 12.

14. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the oil blending optimization method in multiple scenarios according to any one of claims 1 to 12 is implemented.

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